五度易链产业数字化管理平台
500 kN·m联轴器扭矩刚度与疲劳测试系统

500 kN·m联轴器扭矩刚度与疲劳测试系统该500 kN·m联轴器扭矩刚度与疲劳试验系统专为联轴器相关性能测试设计,支持扭矩限制器预磨试验、扭矩标定、扭矩变位能力评估、启动与制动扭矩测试等关键指标的测量。系统可准确计算扭矩力值变化与扭转角度变化之间的比值,以评估联轴器在极限工况下的刚度特性及其连续打滑过程中的疲劳表现。此外,系统还能够实时监测联轴器在运行状态下的位移情况,并绘制时间-扭矩、角度-扭矩以及时间-角度等动态曲线。系统采用高功率精密伺服电机驱动,可带动联轴器、减速装置及扭矩限制器旋转,通过角度传感器和动态扭矩传感器采集旋转角度和扭矩数据,由计算机实时控制与处理测试过程。主要技术参数包括:最大扭矩±500 kN·m,正反向旋转速度不超过5 r/min,最大扭转角度为0~9999.99°。系统支持多种控制方式,包括扭矩控制、角度控制和转速控制,并实现闭环精确调节。配套的HRJ联轴器刚度测试软件具备多阶段扭矩输入能力,可支持12个以上区段的阶梯式加载与卸载操作,适应复杂工况下的测试需求。测试系统预留了机械手自动拧紧接口,螺栓扭矩数据可自动传输至机械手,完成精确的定扭矩拧紧操作。内置的HRJ-Test测控软件采用全数字闭环设计,具备多通道、多功能和易操控特性,支持多种行业标准测试方法。软件界面可实时显示扭矩-角度、扭矩-时间、扭矩-次数及角度-时间等曲线。系统支持灵活的曲线显示方式,用户可根据需要选择一个或多个曲线框,自由切换布局方式,并可调整坐标范围。测试数据可直接在曲线上点击查看,且支持图像保存功能。同时,系统具备远程启停控制能力,可直接连接多家网络平台,实现远程监控与协同。系统还支持试验过程的回放与数据分析功能,包括曲线放大、对比、遍历及多曲线叠加比对等操作。用户可根据需要自由缩放曲线,系统自动调整满度显示。该系统可广泛应用于多种结构件和材料的扭转性能测试,包括传动轴与半轴的扭矩刚度试验、阀门、管材与管件的扭转及疲劳测试,以及碳纤维复合材料(FRP)管材总成的扭转强度评估。

来源:不颓废科技青年发布时间:
ADAS传感器模块中高精度温湿度传感的关键实现路径

ADAS传感器模块中高精度温湿度传感的关键实现路径在高级驾驶辅助系统(ADAS)中,摄像头、毫米波雷达及激光雷达等核心感知设备的性能表现高度依赖于环境温度和湿度的稳定。温度和湿度的异常波动可能引起传感器精度下降、工作寿命缩短,甚至带来潜在的安全风险。因此,在ADAS传感器模块中集成高精度的温湿度传感能力,构建稳定可靠的环境感知体系,成为推动ADAS技术向更高阶演进的重要技术支撑。ADAS传感器模块通常面临极端复杂的工作环境。在车辆运行过程中,传感器需长期适应-40℃至125℃的温度范围,并应对雨雪、雾霾、高湿等多变的湿度条件。温度变化直接影响传感器性能,例如多数汽车摄像头为无主动散热的小型封闭结构,易因热量积聚而导致温度迅速上升,超出图像传感器的额定工作温度范围后,电子控制单元(ECU)可能需要降功率运行或关闭设备。若温度传感存在较大误差,将导致设备误动作,甚至造成不可逆的损坏。湿度的影响同样不容忽视,湿气渗透可能引发电化学迁移、元件腐蚀等问题,而镜头表面的凝露则会干扰光学信号路径,造成信号反射与吸收异常,进而影响ADAS系统的感知能力与判断可靠性。在ADAS传感器模块中实现高精度温湿度传感,首要任务是突破传感元件选型的瓶颈。应根据ADAS的功能特性及功能安全要求,选择高精度、高稳定性的传感产品。传统图像传感器内置的温度检测模块精度通常在±6℃左右,难以满足精确控制需求,因此需要搭配独立式高精度温度传感器,确保测量误差在±1℃以内,并具备快速响应能力,以准确捕捉温度的动态变化。在湿度传感方面,应优先选用符合ISO 26262功能安全标准并通过AEC Q100认证的工业级产品。例如Sensirion SHT4xA系列基于CMOSens®技术,具备高精度湿度测量能力,同时具备良好的抗干扰与抗冷凝性能,适合复杂的车载应用场景。此外,还应关注传感器的长期稳定性,优选年漂移率低于1%RH的产品,以防止元件老化导致精度下降。传感元件的集成布局同样是提升温湿度传感精度的关键环节。ADAS传感器模块内部空间紧凑,各功能单元之间存在信号串扰与热场干扰的风险。因此,需通过科学的布局设计实现干扰隔离。温度传感器应尽可能靠近发热核心,如图像传感器或毫米波雷达的射频模块,以精准获取工作温度,同时与散热结构保持合理距离,避免散热影响测量结果。湿度传感器则应布置在容易受湿气侵入的区域,例如接口或密封缝隙附近,并采用独立气室结构,以防止高速气流干扰测量。软件算法的优化与动态校准则是提升传感精度的重要补充。在环境变化频繁的车辆应用中,单一的静态校准难以覆盖全工况需求。因此,应建立动态校准机制,结合车辆行驶状态、环境参数等信息,实时调整校准系数。针对温度的非线性误差,可采用分段线性拟合算法,对不同温度区间的测量数据进行补偿;而对于湿度传感器中的凝露干扰,可通过内置加热器实现除露,并对比加热前后的数据,以修正误差。此外,机器学习算法的应用有助于实现预测性误差补偿。通过分析长期运行中的数据趋势,系统可提前识别并修正潜在误差,提升传感系统的稳定性。在防护设计与系统验证方面,需构建“主动监测+被动防护”结合的双重策略。通过湿度传感器对湿气侵入进行实时监控,一旦达到临界值,系统可触发预警与防护机制,防止元件受损。同时,传感器模块应具备IP65及以上等级的密封性能,并搭配疏水涂层与干燥剂,以提升整体环境适应性。系统验证阶段需模拟车辆全生命周期的工作场景,包括高低温循环、湿热老化、振动冲击等可靠性测试,确保在极端条件下温湿度传感数据依然可靠,可为ADAS系统提供稳定支撑。高精度的温湿度传感技术不仅能保障ADAS传感器模块的稳定运行,延长设备生命周期,更为系统的功能安全提供坚实基础。随着ADAS向更高阶自动驾驶发展,对传感精度的要求将持续提升,温湿度传感技术也将向集成化、智能化与高可靠性方向演进。未来,随着MEMS工艺的不断优化,温湿度传感元件与核心传感器之间的深度融合将成为可能,推动传感功能的一体化集成,进一步提升系统集成度与测量精度,同时降低功耗与成本。同时,结合预测性维护技术,基于温湿度数据的长期趋势分析,可实现传感器模块健康状态的早期预警,为自动驾驶的安全落地提供技术支持。综上所述,ADAS传感器模块中实现高精度温湿度传感是一项复杂的系统工程,涉及元件选型、布局设计、算法优化、防护策略及测试验证等多个技术环节。唯有构建稳定、精确、适应性强的温湿度传感体系,才能有效抵御环境干扰,提升ADAS系统的感知能力与决策可靠性。随着汽车智能化水平的持续提升,温湿度传感技术也将在推动自动驾驶安全高效落地方面发挥更加关键的作用。

来源:共读科技发布时间:
Dynamic RNA Polymerase compartments organize the transcription of gene clusters

AbstractSpatial organization of transcription machinery is emerging as a key regulator of gene expression, yet how RNA polymerases are organized at gene clusters remains unclear. Here, we show that RNA polymerases II and polymerase III form distinct nuclear foci at the 5S ribosomal DNA (rDNA)–Spliced leader 1 (SL1) cluster in C. elegans. Within this cluster, polymerase II binds to the SL1 gene, while polymerase III associates with 5S rDNA. Both polymerase foci display dynamic but distinct behaviors within the nucleus. The assembly of these polymerase foci is regulated across the cell cycle. ATTF-6, an AT-hook transcription factor, is essential for polymerases II foci formation but dispensable for polymerases III foci. While Pol III foci are largely resistant to temperature changes, Pol II foci are temperature-sensitive, and their dissolution correlates with reduced SL1 expression. Together, these results reveal a spatial and temporal regulation of two RNA polymerases that organize gene cluster transcription.IntroductionIn eukaryotes, transcription is carried out by three major RNA polymerases, each dedicated to distinct classes of genes. RNA polymerase I (Pol I) transcribes ribosomal RNAs (rRNAs), polymerase II (Pol II) produces messenger RNAs (mRNAs) and many long non-coding RNAs, and RNA polymerase III (Pol III) synthesizes transfer RNAs (tRNAs), 5S rRNA, and other small structural RNAs (Cramer 2019; Roeder 2019; Girbig et al. 2022). Beyond diffuse nucleoplasmic distributions, accumulating evidence indicates that transcription is spatially organized into focal compartments or condensates that coordinate efficiency and specificity (Rippe 2022; Rippe and Papantonis 2025).RNA Pol I is highly enriched in the nucleolus, a membraneless compartment specialized for rRNA biogenesis (Grummt 2003). In contrast, Pol II is expressed throughout the nucleoplasm. Pol II transcription can occur within discrete “transcription factories” or nuclear foci (Jackson et al. 1993; Wansink et al. 1993). These foci represent sites where multiple genes, transcription factors, and nascent transcripts are locally concentrated, providing a framework for coordinated transcription and RNA processing. Super-resolution imaging has shown that a substantial fraction of Pol II resides in these foci, and emerging models suggest that liquid–liquid phase separation and related microphase separation processes underlie the formation and dynamics of transcriptional condensates (Banani et al. 2017; Hnisz et al. 2017; Rippe 2022). Proteins involved in transcription, such as Mediator components, transcription factors, and the intrinsically disordered C-terminal domain of Pol II, can form multivalent weak interactions that promote condensate assembly (Hnisz et al. 2017; Boehning et al. 2018; Cho et al. 2018; Palacio and Taatjes 2022). However, the mechanisms governing transcriptional condensate formation and their biological relevance remain actively debated (McSwiggen et al. 2019; Stortz et al. 2024; Rippe and Papantonis 2025).Compared to Pol I and Pol II, the spatial organization of Pol III is less understood. Pol III transcribes non-coding RNAs such as tRNAs and 5S rRNAs, both of which are essential for translation. Studies suggest that its activity may concentrate at specific nuclear foci, analogous to Pol II (Pombo et al. 1999). Yet, the spatial relationship between Pol III, Pol II and other transcriptional machineries, and how these polymerase-specific compartments are regulated, remain largely unknown.Many organisms organize functionally related genes into clusters, including piRNA, histone, and rRNA genomic loci (Blumenthal 1998; Duronio and Marzluff 2017; Pastore et al. 2022). In C. elegans, the 5S rDNA–spliced leader 1 (SL1) cluster offers a unique opportunity to study how Pol II and Pol III transcriptional programs coexist in close proximity. This cluster consists of more than 100 tandem ∼1 kb repeats on chromosome V, each unit harboring one 5S rDNA gene and one SL1 gene (Ellis et al. 1986; Ding et al. 2022). The 5S rRNA is transcribed by Pol III whose largest subunit is RPC-1 in C. elegans, while SL1 is transcribed by Pol II with AMA-1 as its catalytic subunit (Rogalski and Riddle 1988; Bird and Riddle 1989). SL1 encodes a specialized snRNA (small nuclear RNA) required for trans-splicing, a process that appends SL1 to the 5′ ends of more than half of all pre-mRNAs to promote efficient translation initiation (Blumenthal 2012; Yang et al. 2017). Despite its biological importance, the transcriptional regulation and nuclear organization of the 5S rDNA–SL1 cluster remain poorly characterized.Here, we exploit this dual-polymerase gene cluster to investigate how two distinct RNA polymerases form spatially organized transcription compartments in vivo. We show that Pol II and Pol III form separate, dynamic nuclear foci at the 5S rDNA–SL1 cluster, each regulated in a cell cycle–dependent manner. Pol II foci require the transcription factor ATTF-6, are sensitive to temperature, and regulate SL1 expression, whereas Pol III foci are less affected by these conditions. Together, our findings reveal that C. elegans RNA polymerases assemble into distinct, condensate-like compartments at shared gene clusters, uncovering an unexpected layer of spatial organization that coordinates transcription across polymerase systems.ResultsRNA Pol II and Pol III form distinct nuclear foci at the 5S rDNA–SL1 clusterOur recent work showed that ATTF-6, an AT-hook transcription factor, associates with genomic clusters including the piRNA cluster and the 5S rDNA–SL1 cluster in the C. elegans gonad (Wang et al. 2025). The gonad is a syncytial tissue in which germ cells proliferate mitotically at the distal end and then enter meiotic pachytene to produce oocytes, which are fertilized to form embryos (Figure S1A) (Pazdernik and Schedl 2013). ATTF-6 forms a large nuclear focus at the piRNA cluster specifically in the early–mid pachytene region, where homologous chromosomes undergo synapsis (Figures S1B and S1C) (Batista et al. 2008; Das et al. 2008; Wang et al. 2025). Furthermore, ATTF-6 forms a smaller and discrete focus at the 5S rDNA–SL1 cluster throughout pachytene and oogenesis (Figures S1B and S1C) (Wang et al. 2025).To extend these observations to early embryos, we examined the localization of endogenously tagged ATTF-6. We performed 5S rDNA FISH (Fluorescence in situ hybridization) followed by immunofluorescence against ATTF-6::3xFLAG using an anti-FLAG antibody (Figures 1A and 1B). ATTF-6::3xFLAG formed discrete nuclear foci in embryos that colocalized with the 5S rDNA–SL1 cluster indicated by 5S rDNA FISH signals (Figures 1A and 1B). These results indicate that ATTF-6 associates with the cluster not only in germ cells but also in embryonic nuclei.Figure 1.RNA Pol II and Pol III form distinct nuclear foci at the 5S rDNA-SL1 gene cluster.(A) Co-localization of 5S rDNA and ATTF-6 in embryo nuclei detected by DNA-FISH and immunostaining. DNA-FISH was performed using Cy5-labeled probes targeting the 5S rDNA gene cluster, and immunostaining was conducted with an anti-FLAG antibody to detect ATTF-6::3×FLAG. Nuclei were visualized with DAPI. Dashed lines outline the embryo. Confocal images (60× objective) are shown as maximum-intensity projections spanning the top layer of nuclei in the fixed embryo. Scale bar: 10 μm. (B) Enlarged view of three nuclei within the embryo shown in (A). Scale bar: 2 μm. (C) GFP::Pol II (GFP:: AMA-1) and ATTF-6::RFP foci in a live embryo. Dashed lines outline the embryo. Confocal images (60× objective) are shown as maximum-intensity projections spanning the top layer of nuclei in the embryo. Scale bar: 10 μm. (D) Enlarged view of three nuclei within the embryo shown in (C). Scale bar: 2 μm. (E) Intensity profile of GFP::Pol II (GFP::AMA-1) and ATTF-6::RFP signals along the dotted arrow in nucleus No1. in (D). AU, arbitrary unit. (F) GFP::Pol III (GFP::RPC-1) and ATTF-6::RFP foci in a live embryo. Dashed lines outline the embryo. Confocal images (60× objective) are shown as maximum-intensity projections spanning the top layer of nuclei in the embryo. Scale bar: 10 μm. (G) Enlarged view of three nuclei within the embryo shown in (F). Scale bar: 2 μm. (H) Intensity profile of GFP::Pol III (GFP::RPC-1) and ATTF-6::RFP signals along the dotted arrow in nucleus No. 1 in (G). AU, arbitrary units.The 5S rDNA–SL1 cluster is unique in that it contains over 100 tandem copies of the 5S rRNA gene and SL1 snRNA gene (Ellis et al. 1986; Ding et al. 2022). The 5S rRNA, transcribed by Pol III, is an essential component of ribosomes, while SL1, transcribed by Pol II, provides the spliced leader sequence for trans-splicing (Blumenthal 2012; Yang et al. 2017). The presence of two polymerase-dependent transcription units within a highly repetitive genomic region raises the question of whether Pol II and Pol III are locally enriched at this cluster.To address this, we examined the subcellular localization of GFP::AMA-1 (the largest catalytic subunit of Pol II) and GFP::RPC-1 (the largest catalytic subunit of Pol III) expressed from their endogenous genomic loci. Both polymerases were broadly distributed throughout nuclei, but showed modest and noticeable enrichment at the small ATTF-6::RFP foci corresponding to the 5S rDNA–SL1 cluster in the gonad (Figures S1B and S1C). Neither polymerase was enriched at piRNA clusters marked by mCherry::PRDE-1, a piRNA specific transcription factor (Figures S1D and S1E) (Weick et al. 2014; Weng et al. 2019; Wang et al. 2025).In embryos, confocal microscopy revealed that GFP::Pol II formed several nuclear foci, including prominent two that colocalized with ATTF-6::RFP (Figures 1C and 1D), as confirmed by line-scan intensity profiles (Figure 1E). Similarly, GFP::Pol III formed two prominent foci in embryos that overlapped with ATTF-6 (Figures 1F-1H). These data demonstrate that Pol II and Pol III assemble into distinct nuclear foci at the 5S rDNA–SL1 cluster in both the germline and early embryos.Pol II associates with SL1 gene while Pol III associates with 5S rDNATo further validate the enrichment of polymerases at the 5S rDNA–SL1 cluster, we reanalyzed published ChIP-seq (Chromatin Immunoprecipitation followed by high-throughput sequencing) datasets (Ikegami and Lieb 2013). Specifically, we examined ATTF-6, Pol II and Pol III ChIP signals across individual chromosomes including five autosomes and one sex chromosome (chromosome X). Consistent with our previous findings (Wang et al. 2025), genome-wide profiles showed that ATTF-6 binds strongly to the piRNA cluster on Chromosome IV and to the 5S rDNA–SL1 cluster on Chromosome V (Figures 2A and S2). Importantly, the single strongest Pol II and Pol III ChIP peaks across the genome occurred at the 5S rDNA–SL1 locus (Figures 2A, 2B and S2).Figure 2.RNA Pol II and Pol III associate with SL1 and 5S rDNA, respectively.(A) Browser view of ATTF-6, Pol II (AMA-1), and Pol III (RPC-1) ChIP-seq signals on chromosome V. ATTF-6 ChIP-seq signals were normalized to the control IP (Wang et al. 2025)., and AMA-1 and RPC-1 ChIP-signals were normalized to the input (Ikegami and Lieb 2013). The signals represent the average from two biological replicates and are shown in RPKM (Reads Per Kilobase per Million mapped reads). The dashed box highlights the 5S rDNA–SL-1 gene cluster. (B) Browser view zoomed in on the 5S rDNA–SL1 gene cluster. The cluster contains more than 100 tandem repeats of 5S rDNA (yellow arrowhead) and SL1 (green arrowhead) arranged in opposite orientations. (C) An individual example within the 5S rDNA–SL1 gene cluster. The green and yellow arrows indicate the positions and orientations of sls-1.11 and rrn-4.6, respectively. (D) Metagene profiles of ATTF-6, Pol II (AMA-1), and Pol III (RPC-1) ChIP-seq signals around the SL1 snRNA and 5s rRNA genes. The plot is anchored at the 5’ end of the SL1 snRNA genes and spans 500 bp upstream and downstream. Signals (RPKM) were normalized to the control by subtraction and then averaged across two biological replicates. Yellow and green arrows indicate the positions and orientations of the 5S rRNA and SL1 snRNA genes, respectively.A browser view of a single repeat with sls-1.11 and rrn-4.6 showed that ATTF-6 binds to the promoter region shared by the 5S rDNA and SL1 genes, with peak enrichment nearer to the SL gene (Figure 1C). Pol II was sharply enriched at sls-1.11 (SL1 gene), while Pol III was strongly enriched at rrn-4.6 (5S rDNA gene) (Figure 1C). Metagene analysis centered on the SL1 TSS (Transcription Start Site) further supported such organization: ATTF-6 peaked near TSS of SL1, Pol II was highest directly over SL1, and Pol III peaked over the 5S rDNA gene body (Figure 1D). Collectively, ChIP-seq and fluorescence microscopy data demonstrate that Pol II and Pol III each form distinct nuclear foci that correspond to their respective transcriptional targets at the 5S rDNA–SL1 cluster.Pol II and Pol III foci are dynamic and exhibit properties of liquid-like condensatesPrevious studies suggest that Pol II and several transcription factors form liquid-like condensates, potentially through liquid–liquid phase separation (Hnisz et al. 2017; Boehning et al. 2018; Cho et al. 2018; Palacio and Taatjes 2022). We therefore asked whether Pol II and Pol III foci at the 5S rDNA–SL1 cluster display hallmark features of biomolecular condensates. At least three commonly used criteria include: (1) a 2-dimensional rounded or 3-dimensional spherical morphology driven by the surface tension, (2) Dynamic molecular exchange with the surrounding environment, and (3) Although variably observed, sensitivity to aliphatic alcohols such as 1,6-hexanediol which disrupt weak hydrophobic interactions (Brangwynne et al. 2009; Patel et al. 2015; Alberti et al. 2019).We first quantified the morphology of ATTF-6, Pol II, and Pol III foci in embryos using three-dimensional reconstructions. Roundness values (with 1 representing a perfect sphere) were 0.71 for GFP::ATTF-6 foci and 0.70 for GFP::Pol II foci, whereas GFP::Pol III foci were slightly more spherical, with a mean value of 0.76 (Figure 3A). The mean diameter was ∼630 nm for Pol II foci and ∼720 nm for Pol III foci, both of which exceeded the diffraction limit of our fluorescence microscopy (Figure 3A).Figure 3.RNA Pol II and Pol III form dynamic foci in vivo.(A) Table showing foci shape descriptors (top rows), calculated from maximum-intensity projections (60x) for three GFP::ATTF-6, GFP::Pol II, or GFP::Pol III embryos and fluorescence recovery after photobleaching (FRAP) parameters—t1/2 (bottom rows), calculated from models shown in (B) for GFP::ATTF-6, GFP::Pol II, and GFP::Pol III. FRAP parameters for ATTF-6 are labeled as NA (not applicable) because the recovery did not fit the exponential model. (B) Relative intensity of GFP::ATTF-6 (n=4), GFP::Pol II (n=6) and GFP::Pol III (n=4) foci, respectively, after photobleaching in embryos. Intensity was measured from single-plane images every second (sec) before and after photobleaching and normalized to the mean intensity over five timepoints prior to bleaching (time 0). A.U. indicates arbitrary units. Dots represent mean relative intensity, and error bars denote standard deviation. Red and blue curves represent the exponential model of recovery for Pol II or Pol III, respectively. (C) Single-plane time-lapse (100x) images of GFP::ATTF-6 before (pre-bleach) and after photobleaching for the indicated timepoints (seconds). Yellow arrow indicates the photobleached focus; dashed circle indicates the nuclear periphery. Scale bar: 5 µm. (D) Same as in (C) but for GFP::Pol II. Red arrow indicates the photobleached focus. Scale bar: 5 µm. (E) Same as in (C) but for GFP::Pol III. Blue arrow indicates the photobleached focus; dashed circle indicates the nuclear periphery. Scale bar: 5 µm (F-H) Representative maximum intensity projections (60x) of GFP::ATTF-6 (F), GFP::Pol II (G), or GFP::Pol III (H) expressing embryos treated with ptr-2 RNAi and subsequently dissected into either egg buffer (EB) or 5% 1,6-hexanediol. Images depict whole embryos, with each embryo outlined by grey dashed lines (left; scale bar: 10 µm) and the corresponding inset indicated by the white dashed square (right; inset scale bar: 5 µm). Grey dashed circles in each inset outline the nucleus.We next assessed molecular dynamics using FRAP (Fluorescence Recovery After Photobleaching). In brief, ATTF-6, Pol II, or Pol III foci were photobleached and fluorescence recovery was monitored over 30 seconds. Recovery curves for Pol II and Pol III were well described by a single-exponential model, while ATTF-6 recovery could not be fit by this model (Figures 3A-3E). We found that Pol II and Pol III foci displayed much faster and more complete recovery than ATTF-6 foci (Figures 3A-3E). Specifically, the half-time of recovery (t1/2) was 3.41 seconds for Pol II, and 4.35 seconds for Pol III (Figure 3A). Thus, Pol II and Pol III foci exhibit rapid molecular exchange with the surrounding nucleoplasm when compared to ATTF-6.Finally, because 1,6-hexanediol disrupts certain liquid-like condensates by interfering with weak hydrophobic interactions (Alberti et al. 2019; Zheng et al. 2025), we tested the effect of 1,6-hexanediol on ATTF-6, Pol II and Pol III foci. Hexanediol treatment did not dissolve ATTF-6 foci (Figure 3F). In contrast, Both Pol II and Pol III foci were disrupted following treatment (Figures 3G and 3H). Overall, these data indicate that ATTF-6 foci are relatively static, whereas Pol II and Pol III foci display spherical morphology and dynamic exchange consistent with features of biomolecular condensates.Formation of Pol II and Pol III foci is regulated across the cell cycleWe noticed that not all embryonic cells exhibited Pol II or Pol III foci (Figures 1 and 3), indicating that foci formation might depend on the cell cycle. Early C. elegans embryonic divisions consist only of S phase and M phase (Kipreos and van den Heuvel 2019). To monitor these cell-cycle stages, we used fluorescently tagged histone (H2B::mCherry), which appears diffuse within the nucleus during S phase and becomes bright and highly condensed as chromosomes entering mitosis (Figures 4A and 4B).Figure 4:Formation of Pol II and Pol III foci is regulated across the cell cycle.(A) Dynamics of GFP::Pol II foci during cell division in an early embryo. A time-lapse imaging series of an embryo expressing GFP::Pol II and the chromatin marker H2B::mCherry. The circular dashed line outlines the embryo, and the dashed square highlights the dividing cell. The bottom panel shows zoomed-in views of the dividing cell, grouped into S phase and M phase. Arrowheads indicate GFP::Pol II foci in the nucleus of the dividing cell. Confocal images (60× objective) are shown as maximum-intensity projections spanning the top layer of nuclei in the embryo. Scale bars: 10 μm (top panel) and 2 μm (bottom panel). (B) Same analysis as in (A), but with GFP::Pol III.Using this H2B::mCherry marker, we followed the dynamics of Pol II and Pol III foci within a single embryonic cell cycle (∼14 minutes). Pol II foci were consistently observed during S phase (Figure 4A). As the cell entered prophase, Pol II foci diminished in intensity and fully dispersed during mitosis. After division, Pol II foci reappeared rapidly in the S phase of each daughter cell (Figure 4A).Pol III foci displayed a similar dependence on cell-cycle stage but with slightly distinct mitotic behavior. Pol III foci were present during S phase (Figure 4B). Upon entry into mitosis, Pol III signals redistributed onto condensed chromosomes and then became diffuse from metaphase through telophase. As with Pol II, Pol III foci re-formed in the next S phase (Figure 4B). Together, these observations demonstrate that Pol II and Pol III foci are tightly regulated by the cell cycle: both assemble during S phase, disperse during mitosis, and reassemble in the following interphase.ATTF-6 is required for Pol II foci formation but is dispensable for Pol III fociBecause ATTF-6 colocalizes with both Pol II and Pol III at the 5S rDNA–SL1 cluster (Figure 1), we asked whether the formation of ATTF-6 foci and polymerase foci are interdependent. To test whether polymerase activity acts upstream of ATTF-6, we examined ATTF-6 localization following depletion of Pol II or Pol III using RNAi (RNA interference) (Fire et al. 1998; Kamath et al. 2003). In embryos expressing GFP::ATTF-6 and RFP::Pol II from their endogenous loci, RNAi against Pol II efficiently reduced RFP::Pol II signals (Figures 5A and 5B). However, it had no effect on GFP::ATTF-6 foci (Figures 5A and 5B). Likewise, depletion of Pol III did not alter ATTF-6 foci formation (Figures 5C and 5D). These results indicate that neither Pol II nor Pol III is required for assembly of ATTF-6 foci.Figure 5.Pol II nor Pol III are dispensable for assembly of ATTF-6 foci.(A) Fluorescence micrographs of nuclei in live embryos expressing GFP::ATTF-6 and RFP::Pol II. Embryos were dissected from worms treated with control (L4440) or Pol II (ama-1) RNAi. The circular dashed line outlines the embryo. Images are shown as maximum-intensity projections spanning the top nuclear layer. The right panel presents individual enlarged views of three nuclei selected from the embryo. Confocal images were acquired with a 60× objective. Scale bars: 10 μm (embryo) and 2 μm (nuclei). (B) Quantification of GFP::ATTF-6 foci numbers in embryo nuclei. Embryos were dissected from worms treated with control (L4440) or Pol II (ama-1) RNAi. Nuclei from three independent embryos were counted. Error bars represent the standard deviation (SD). Statistical significance was determined using a two-tailed Student’s t-test (ns: p > 0.05). (C-D) Analyses as in (A-B), but using embryos dissected from worms treated with Pol III (rpc-1) RNAi.We next performed the reciprocal test to determine whether ATTF-6 is required for polymerase foci formation. Since attf-6 is an essential gene (Wang et al. 2025), we depleted ATTF-6 protein using the auxin-inducible degron (AID) system (Zhang et al. 2015). Using CRISPR/Cas9 gene editing, we tagged ATTF-6 at its endogenous locus with RFP and AID, enabling both visualization and auxin-dependent degradation. In the absence of auxin, GFP::Pol II formed two prominent nuclear foci that colocalized with ATTF-6::AID::RFP (Figures 6A). Upon auxin treatment, ATTF-6::AID::RFP foci became undetectable (Figures 6A, S3A and S3B). Depletion of ATTF-6 resulted in the loss of large Pol II foci (Figures 6A, S3A and S3B). In contrast, depletion of ATTF-6 had no robust effect on Pol III foci, when compared to the untreated control (Figures 6B, S3A and S3C). These findings demonstrate that ATTF-6 is required for Pol II, but not Pol III, foci formation.Figure 6.SNPC-4 and ATTF-6 are required for Pol II foci, but not Pol III foci formation.(A) Live embryos expressing GFP::Pol II and ATTF-6::AID::RFP, dissected from worms treated with or without 1 mM auxin. The right panel presents individual enlarged views of three nuclei selected from each embryo. Confocal images (60× objective) are shown as maximum-intensity projections spanning the top nuclear layer. Scale bars: 10 μm (embryo) and 2 μm (nuclei). (B) Same auxin treatment conditions as in (A), but using embryos expressing GFP::Pol III and ATTF-6::AID::RFP. (C) Live embryos expressing GFP::Pol II and ATTF-6::AID::RFP, dissected from worms treated with control (L4440), snpc-4, or attf-6 RNAi. Embryos show the merged GFP and RFP signals. The right panels present individual enlarged views of three nuclei selected from each embryo, shown as separate GFP, RFP, and merged channels. Confocal images (60× objective) are shown as maximum-intensity projections spanning the top nuclear layer. Scale bars: 10 μm (embryo) and 2 μm (nuclei). (D) Same RNAi conditions as in (C), but showing embryos expressing GFP::Pol III together with ATTF-6::AID::RFP.To further test this requirement and to identify additional factors involved in polymerase foci assembly, we used RNAi to deplete attf-6 as well as snpc-4, a subunit of Small Nuclear RNA-activating Protein Complex that drives the transcription of snRNA genes (Kasper et al. 2014; Hou et al. 2022). Consistent with AID-mediated depletion, RNAi knockdown of attf-6 caused dispersal of Pol II foci without affecting Pol III foci (Figures 6C, 6D, S3D, S3E). Knockdown of snpc-4 did not disrupt ATTF-6 localization. However, it selectively impaired Pol II foci formation, while Pol III foci remained intact (Figures 6C, 6D, S3D, S3E). Together, these results show that ATTF-6 and SNPC-4 are specifically required for the formation of Pol II foci at the 5S rDNA–SL1 cluster, while Pol III foci assemble independently of these factors.Pol II foci are temperature-sensitive and regulate SL1 expressionTo investigate the functional significance of polymerase foci in regulating SL1 and 5S rRNA expression, we examined how their assembly responds to environmental cues. C. elegans is a poikilothermic organism whose cellular processes adapt to environmental temperature. And biomolecular condensates are often sensitive to thermal perturbation (Fritsch et al. 2021; Alberti et al. 2025). We therefore tested whether elevated temperature affects the formation of ATTF-6, Pol II, or Pol III foci.Embryos were shifted from 20 °C to 32 °C, and imaged after 2 and 3 hours. GFP::ATTF-6 foci remained intact under these mild heat stress conditions (Figures 7A and S4A). In contrast, Pol II foci were highly temperature-sensitive: GFP::Pol II foci dissolved at 32 °C, and most embryonic nuclei completely lost Pol II foci within 3 hours (Figures 7B and S4B). GFP::Pol III foci, however, remained intact at 32 °C (Figures 7C and S4C). These observations demonstrate that only Pol II foci exhibit temperature-dependent disassembly, while ATTF-6 and Pol III foci are temperature-insensitive.Figure 7.Pol II foci are temperature-sensitive and regulate SL1 expression.(A) Representative maximum intensity projections (60x) of GFP::ATTF-6 expressing embryos cultured at 20°C (top) or shifted to 32°C for either 2 hours (middle) or 3 hours (bottom). Whole embryos are shown on the left (scale bar: 10 µm), outlined by grey dashed ovals. The corresponding insets to the right are indicated by the white dashed squares. Insets to the right show single nuclei outlined by grey dashed circles (Inset scale bar: 3 µm). Heating conditions are further described in the Materials and Methods. (B) Same conditions as in (A), but for GFP::Pol II expressing embryos. (C) Same conditions as in (A), but for GFP::Pol III expressing embryos (D) Ethidium bromide staining of total RNAs from wild-type embryos cultured at 20°C, or shifted to 32°C for either 2 hours or 3 hours in M9 buffer, respectively. rRNAs and tRNAs are indicated. (E) Northern blotting of SL1 RNA (top) or 5S rRNA (bottom) from wild-type embryos cultured at 20°C, or shifted to 32°C for either 2 hours or 3 hours, respectively. Relative SL1 abundance upon heating was normalized to 5S rRNA abundance. (F) RT-qPCR analysis of Pol II transcripts (SL1, act-3, and gpd-2) and the Pol III transcript 5S rRNA in embryos. Transcript fold change upon heat stress was calculated relative to 20°C and normalized to 18S rRNA as the internal control. Bar plots show mean +/− SD; dots represent technical replicates. Statistical significance was assessed using Student’s two-sample t-test. * indicates p ≤0.05, ** p ≤0.01, and ns indicates not significant. (G) Working model showing Pol II and Pol III occupy the 5S rDNA–SL1 locus but assemble into distinct nuclear compartments. ATTF-6 promotes Pol II foci formation to drive SL1 transcription while Pol III independently engages nearby 5S rRNA genes.We next investigated whether temperature-dependent changes in polymerase foci correlate with altered SL1 or 5S rRNA expression. Total RNA extracted from embryos incubated at 20 °C or 32 °C was analyzed by denaturing gel electrophoresis. Ethidium bromide staining revealed no detectable change in the abundance of major RNA species including tRNAs, 5S, 5.8S, 18S, and 28S rRNAs (Figure 7D). Northern blotting of the same samples demonstrated that SL1 RNA levels decreased ∼4-fold after 2 h at 32 °C and ∼5-fold after 3 h, when normalized to 5S rRNA signals (Figure 7E). RT–qPCR analysis corroborated these results: SL1 transcripts were significantly reduced under the heat stress, while 5S rRNA levels were unaffected when normalized to 18S rRNA (Figure 7F). Among housekeeping controls, act-3 mRNA showed a modest decrease, while gpd-2 levels were unchanged (Figure 7F). Together, these findings indicate that elevated temperature selectively disrupts Pol II foci and that this disassembly correlates with a marked reduction in SL1 transcription.DiscussionAlthough the spatial organization of transcription machinery is increasingly recognized as a key regulatory layer, a major knowledge gap remains: it is unclear whether different RNA polymerases form distinct compartments when operating within the same gene cluster, and how such organization influences transcriptional output. In this study, we uncover a specialized nuclear microenvironment at the C. elegans 5S rDNA–SL1 cluster in which RNA polymerases II and III form distinct and dynamic compartment. Our findings support a model in which transcription factors including ATTF-6 and SNPC-4 recruit or stabilize Pol II at the SL1 gene cluster to promote efficient SL1 transcription, while Pol III independently assembles on adjacent 5S rRNA genes (Figure 7G). These two polymerases therefore occupy the same genomic locus but form biochemically and functionally distinct nuclear bodies.An emerging theme in gene regulation is that transcription can be organized into spatially restricted structures, ranging from transient hubs of polymerase-transcription factor interactions to fully formed biomolecular condensates generated by phase separation (Banani et al. 2017; Hnisz et al. 2017; Rippe 2022). Our data place Pol II and Pol III foci at the 5S rDNA–SL1 cluster closer to the latter category. Both foci are approximately spherical and display rapid fluorescence recovery after photobleaching. Furthermore, Pol II foci are sensitive to 1,6-hexanediol and thermal perturbation. These features match the operational hallmarks of liquid-like condensates. However, these polymerase foci differ from previously reported Pol II condensates (Cho et al. 2018). They are unusually large (> 600 nm), highly localized to a specific gene cluster, and assemble in a cell-cycle–dependent manner strictly during S phase. This suggests that the polymerase bodies at the 5S rDNA–SL1 locus represent a specialized class of “gene-specific compartment” tuned to the unique regulatory needs of these small non-coding RNA genes.Although Pol II and Pol III foci occupy the same genomic region, their formation is mechanistically separable. Pol III foci assemble independently of ATTF-6 and SNPC-4 and are markedly more stable under heat stress. Pol II foci, in contrast, require ATTF-6 and SNPC-4 and are the only polymerase foci sensitive to elevated temperature. These differences reveal distinct regulatory architectures for the two polymerases. The temperature sensitivity of Pol II condensates is particularly interesting, because this may position the Pol II foci as a temperature-responsive regulatory element, potentially enabling rapid transcriptional adaptation without global repression of rRNA production.Our results extend our previous work showing that ATTF-6 is required for SL1 accumulation but is dispensable for 5S rRNA biogenesis (Wang et al. 2025). We now show that ATTF-6 is required for the assembly of Pol II foci at the 5S rDNA–SL1 locus, providing a mechanistic explanation for its selective effect on SL1 transcription. Pol III foci, by contrast, remain intact without ATTF-6, consistent with the preserved 5S rRNA levels upon ATTF-6 depletion (Figures 6 and 7). Although our analyses show a clear correlation between Pol II foci disassembly and reduced SL1 transcript levels, caveats remain. For example, SL1 and 5S RNA populations may differ in stability. Hence, unchanged 5S abundance under heat stress does not necessarily reflect maintained transcription. Future approaches that directly measure nascent RNA synthesis, such as metabolic labeling, will be important for resolving the transcriptional responses of both loci.Together, our findings support a model in which ATTF-6–dependent Pol II foci drive efficient SL1 transcription, while Pol III independently forms its own foci to produce 5S rRNA (Figure 7G). The two polymerases coexist within a shared genomic compartment yet operate through distinct assembly principles and heat sensitivities. This organizational strategy may allow animals to tightly couple Pol II–driven SL1 production to physiological conditions while preserving robust Pol III–mediated 5S rRNA synthesis.Materials and MethodsMaintenance of C. elegans strainsAll strains were grown at 20°C unless otherwise stated. Wild-type refers to the N2 Bristol strain (Brenner 1974). GFP::AMA-1 and GFP::RPC-1 strains were obtained from the CGC and are referred to as GFP::POL II and GFP::POL III, respectively, for clarity throughout this manuscript. A complete strain list may be found in Supplementary Table S1.CRISPR/Cas9 genome editingCRISPR/Cas9 genome editing in C. elegans was carried out as previously described (Kim et al. 2014). The ATTF-6::RFP and ATTF-6::AID::RFP strains were generated using AID::RFP double-stranded DNA donors in a sun-1p::TIR1 background. The RFP::Pol II and RFP::Pol III strains were produced using RFP double-stranded DNA donors in the GFP::ATTF-6 background. Donor DNA was mixed with a pre-assembled Cas9 ribonucleoprotein complex consisting of Cas9 protein, gRNA, and tracrRNA (IDT). The pRF4 plasmid carrying the dominant rol-6 allele was included as a co-injection marker (Kim et al. 2014). F1 roller progeny were selected and screened by PCR. Homozygous insertion strains were confirmed by Sanger sequencing. DNA donor templates, gRNA sequences, and genotyping primers are listed in Supplementary Table S2.DNA-FISH and immunofluorescenceThe approach was adapted from an established protocol with modifications (Phillips et al. 2009; Adilardi and Dernburg 2022). Briefly, ∼5000 gravid adult worms (ATTF-6::3×FLAG) were bleached and washed with Egg Buffer (EB: 25 mM HEPES-NaOH, pH 7.4, 118 mM NaCl, 48 mM KCl, 2 mM EDTA, 0.5 mM EGTA). Approximately 50 eggs in 20 μL EB were seeded onto a coverslip and fixed by adding 20 μL of 4% formaldehyde in EB to reach a final concentration of 2%, followed by incubation for 2–3 min. After fixation, the eggs were covered with a poly-L-lysine–coated slide. The slide was placed on a metal block pre-cooled with dry ice for 30 min. The coverslip was then carefully flicked off, and the slide was immediately immersed in −20°C methanol for 10 min. Residual liquid was removed from around the eggs, and the samples were washed once with 2× SSCT (0.3 M NaCl, 0.03 M sodium citrate, pH 7.0, 0.1% Tween-20). The eggs were then incubated in 2× SSCT containing 50% formamide at 37°C overnight. For DNA probe hybridization, synthesized Cy5-labeled DNA probes (10 ng/μL; IDT) were diluted in hybridization buffer (HB: 3× SSC, 48% formamide, 10.6% dextran sulfate). The probe mixture was added to the samples, covered with a coverslip, and heated at 80°C for 10 min. Slides were then transferred to a light-protected humid chamber and incubated at 37°C for 6 h. After hybridization, egg samples were washed three times with PBST (PBS + 0.1% Tween-20) and incubated in blocking buffer (PBST containing 0.5% BSA) at room temperature for 1 h. Mouse anti-FLAG primary antibodies (Sigma-Aldrich) were diluted 1:500 in blocking buffer and applied to the samples overnight at 4°C. After three PBST washes, samples were incubated with goat anti-mouse Alexa Fluor 488 secondary antibody (1:500 in blocking buffer) for 2 h at room temperature in the dark. Following incubation, samples were washed three times with PBST. A drop (10 μL) of DAPI-containing mounting medium (Vector Laboratories) was applied to the eggs and incubated for 10 min at room temperature. Samples were then covered with a glass coverslip and sealed with nail polish.RNA interference by feeding of double stranded RNAHT115 RNAi feeding bacteria were streaked from the Ahringer RNAi library as described previously (Kamath et al. 2003; Price et al. 2021). In brief, Nematode Growth Medium (NGM) plates containing 50 µg/mL ampicillin and 5 mM IPTG were seeded with HT115 bacteria expressing double-stranded RNA (dsRNA) targeting the gene of interest. L1 larvae were transferred onto RNAi plates and grown at 25°C for two days. Gravid worms were then dissected, and their embryos were imaged. L4440 RNAi was used as the control for all RNAi experiments.Auxin treatmentThe auxin treatment was performed as previously described (Zhang et al. 2015; Wang et al. 2025). Briefly, gfp::tev::aid::attf-6; sun-1p::TIR1 reporter strains were plated on NGM plates containing 1 mM natural auxin indole-3-acetic acid (IAA) at the L3–L4 stage and incubated overnight at 25°C. Germlines of gravid adult worms were imaged to confirm complete protein depletion. The worms were then dissected, and their embryos were imaged.Granule disruption with hexanediol or heat stressFor 1,6-hexanediol (Sigma-Aldrich) treatment, L4 worms expressing GFP::ATTF-6, GFP::Pol II, or GFP::Pol III, respectively, were plated on ptr-2 RNAi plates to permeabilize the eggshell of embryos for 1,6-hexanediol treatment and fed for approximately 24 hours (Thomas et al. 2023) At adulthood, ptr-2 RNAi treated adults were dissected into either egg buffer (EB) or 5% 1,6-hexanediol in EB on ring slides and imaged.To disrupt granules via heating, GFP::ATTF-6, GFP::Pol II, or GFP::Pol III expressing gravid adults grown at 20°C were bleached to collect embryos. For the control condition (20°C), resuspended embryos were mounted on glass slides with 4% agarose pads and imaged immediately. For heating, embryos were resuspended in ∼ 4 mL M9 in a 15 mL conical tube and transferred to a 32°C incubator (VWR Scientific Model 1545) shaking on a nutating mixer (Fisher Scientific). Embryos were imaged at ambient temperature either 2 hours or 3 hours post-transfer to 32 °C.Northern BlottingTo assay overall ribosomal RNA abundance, 7.5 µg total RNAs were separated on a 8% polyacrylamide/7M urea gel and stained with ethidium bromide (Sigma).To quantify SL1 or 5s rRNA abundance, 15 µg total RNAs were separated on a 8% polyacrylamide/7M urea gel and transferred to Hybond N+ membrane (GE healthcare) at 400 mA for 1 hour with 1x Tris-Borate-EDTA buffer. RNAs were crosslinked to the membrane using ultraviolet light (254 nm; 120 mJ) with a Stratalinker (Stratagene). Membranes were prehybridized with Ultrahyb Ultrasensitive Hybridization Buffer (Invitrogen). Subsequently, 10 µmol of either fluorescein amidite (FAM)-labeled anti-SL1 or anti-5s rRNA probes were hybridized to their respective membranes at 37 °C, rotating overnight. The following day, the membranes were washed three times with wash buffer (0.1% sodium dodecyl sulfate, 0.1% saline-sodium citrate) at 37 °C and visualized using a Sapphire Biomolecular Imager (Azure Biosystems). SL1 and 5S rRNA abundance was quantified using FIJI.Heat-stress conditions and RNA extractionSamples were prepared for RNA extraction as follows. Wild-type gravid adults grown at 20°C were bleached to obtain embryos. For each condition, 600,000 embryos were resuspended with 9 mL M9 in 15 mL conical tubes. For the control condition (20°C), RNA was extracted from resuspended embryos following bleaching. For the heat-stress conditions, RNA was extracted from resuspended embryos transferred to 32°C (VWR Scientific Model 1545) and incubated on a nutating mixer (Fisher Scientific) for either 2 hours (32°C, 2 hours) or 3 hours (32°C, 3 hours). For all three conditions, respectively, samples were pelleted and total RNAs extracted using TriReagent (Sigma-Aldrich).RT-qPCR (Real-time quantitative PCR)100 ng of total RNAs from embryos were reverse transcribed via Multiscribe Reverse Transcriptase (Thermo Fisher Scientific) using gene-specific antisense RT primers containing a universal stem loop. Real-time quantitative PCR was conducted with diluted RT reactions (1/4 for SL1, act-3, and gpd-2; 1/100 for 5S rRNA and 18S rRNA) using the respective gene-specific forward primer and an antisense primer matching the universal stem loop (Pastore et al. 2021). Reactions were conducted in triplicate using PowerUP SYBR Green Master Mix (Thermo Fisher Scientific) and run with the CFX Connect Real-Time PCR System (Bio-Rad). Transcript levels were normalized to the reference gene 18S rRNA, and relative levels upon heating was calculated via the ΔΔCt method. Primer sequences listed in Supplementary Table S2.Analysis of ChIP-seq dataTrimGalore was used to trim adapter sequences and filter out low-quality reads(Kechin et al. 2017). The resulting high-quality reads were aligned to the genome with the BWA-MEM algorithm under default settings (Vasimuddin et al. 2019). Following alignment, BAM files were transformed into BigWig files using DeepTools bamCoverage function(Ramírez et al. 2016). Enrichment peaks from ChIP-seq data were detected with MACS2 employing the narrow-peak option (Zhang et al. 2008; Feng et al. 2012). BigWig signals from two biological replicates were averaged using the DeepTools bigwigMerge function. To compare IP and control signals, experimental BigWig files were subtracted from control BigWig files using bigwigCompare with the parameters (-p max -operation subtract - pseudocount 1 -binSize 5). Further processing and statistical analyses were carried out with custom R and Python pipelines. Visualization of BigWig files was performed using IGV (Robinson et al. 2011). ChIP-seq metagene profiles were generated using the SeqPlots software (Stempor and Ahringer 2016). The subtracted BigWig files from the ChIP-seq pipeline served as input, and BED files containing SL1 snRNA genes were used as references for the plots.MicroscopyUnless otherwise indicated in the figure caption, all images depict live embryos dissected from gravid adults into EB and mounted on glass slides with 4% agarose pads.Most imaging was conducted using the Nikon Ti2 inverted microscope equipped with an X-Light V3 spinning disk confocal unit (CrestOptics) in NIS-Elements AR 5.41.02, using the Plan Apo 60x water objective (Price et al. 2023) with the following exception:To determine fluorescence recovery after photobleaching (FRAP) of ATTF-6, Pol II, and Pol III foci in embryos, spinning disk confocal images were taken using a Nikon TiE inverted microscope equipped with an Andor Revolution WD spinning disk system. Images were captured with a CFI Plan Apo VC 100x/1.4 NA oil immersion objective and Andor iXon Ultra897 camera. All further image processing was conducted with FIJI.Image QuantificationTo quantify Roundness, 3 embryos at the ∼20-30 cell stage were selected per strain. Spinning disk maximum intensity projections (60x) spanning the top layer of embryonic nuclei (9.6 µm; step = 0.6 µm) were used for quantification. First, background fluorescence was removed using a rolling-ball subtraction radius of 140 µm. Foci were selected via the following thresholds: 25-100% of maximum pixel intensity for ATTF-6 and Pol III; 65-100% for Pol II. A more stringent threshold was used for Pol II to isolate the brighter, larger foci at the splicing-leader cluster and to account for the higher background cytoplasmic intensity of GFP::AMA-1. To exclude noises, only particles 0.05-0.6 µm2 were analyzed. Roundness was quantified with the Analyze Particles tool in FIJI. Roundness = . The diameters of ATTF-6, Pol II, and Pol III foci were measured in FIJI by manually drawing a line across the diameter of each focus and measuring the distance, using the same images as those used for roundness quantification.Photobleaching of GFP::ATTF-6, GFP::Pol II, or GFP::Pol III foci was conducted using live embryos dissected into EB. Foci were photobleached using the FRAPPA module in MetaMorph with a 70% 488 nm laser, and a dwell time <1 ms per pixel, and either a single pulse (ATTF-6) or two pulses (Pol II and Pol III) using a rectangular ROI encompassing the foci. Images were acquired every second for 5 seconds prior to and 30 seconds post photobleaching. Images were further analyzed in FIJI.For each focus, the fluorescence intensity at each timepoint (It) was background-corrected with intensity outside the embryo (Ib). The mean pre-bleach intensity (I0) was calculated from 5 frames prior to bleaching, and corrected to Ib. The normalized fluorescent intensity was then computed as: .To determine whether ATTF-6, Pol II, or Pol III exhibited measurable recovery, the maximum post-bleach intensity was compared to the baseline intensity at time zero. Recovery was considered significant if the increase in intensity exceeded twice the standard deviation of the baseline, to avoid fluctuations due to noise. For proteins that met the above criteria, the mean Inorm was calculated at each timepoint, and the mean value at the first post-bleach frame was defined as y0. FRAP recovery was modeled in R with a first-order exponential function: F(t) = y0 + Arec * (1-e−kTime), where Arec is the fluorescence recovery amplitude and k is the recovery rate constant. Curve fitting was performed using the Levenberg–Marquardt algorithm implemented in the nlsLM function from the minpack.lm R package. t1/2 was calculated from the first order exponential model.To quantify the number of foci per nucleus for GFP::ATTF-6, GFP::Pol II, or GFP::Pol III embryos, maximum intensity projections spanning the top single layer of nuclei (6.6 µm, step=0.3 µm) were generated. Circular ROIs were drawn surrounding each nucleus and foci were manually counted using the Multipoint tool in FIJI. The number of points bound by each ROI was calculated in FIJI to calculate foci numbers. Three embryos were quantified per condition.Data AvailabilityATTF-6 ChIP-seq data are available at NCBI under the accession number GSE277641 (Wang et al. 2025). Pol II and Pol III ChIP-seq data are available at NCBI under the accession number GSE42741 (Ikegami and Lieb 2013). Source data have been deposited at Dryad (DOI: 10.5061/dryad.bnzs7h4r8).AcknowledgementsWe thank members in the Tang lab for discussion and critical comments, OSU Neuroscience Imaging Core for instruments (S10OD026842), the Caenorhabditis Genetics Center for providing the C. elegans strains (P40OD010440). Y.H. Wang was supported by Center for RNA Biology graduate fellowship and presidential fellowship at The Ohio State University. This work was supported by the Arthur Burghes professorship, NIH Maximizing Investigators’ Research Award (R35 GM142580), and NSF (MCB 2420329) to W. Tang.Additional informationAuthor contributionsW.T., YH.W. and H.L.H. conceptualized the study. YH.W., H.L.H. and W.T. performed experiments and analyzed the data. W.T. supervised the study and acquired funding. YH.W., H.L.H. and W.T. wrote the manuscript.FundingHHS | NIH | National Institute of General Medical Sciences (NIGMS) (R35 GM142580)Wen TangNational Science Foundation (NSF) (2420329)Wen TangAdditional filesSupplementary table 1.Supplementary table 2.Supplementary figures. Figure S1. RNA Pol II and Pol III foci are formed at the 5S rDNA–SL1 locus in the germline. (A) Schematic of the C. elegans germline and embryos in utero. Colored lines highlight the germline at different developmental stages. Homologous chromosomes undergo synapsis during early-mid pachytene. (B) Fluorescent images of germ nuclei in worms expressing ATTF-6::RFP and GFP::Pol II at early-mid pachytene and late pachytene. Each dotted circle marks a germline nucleus. Confocal images (60× objective) are shown as maximum-intensity projections spanning the germline. Arrows indicate the piRNA gene cluster, and arrowheads mark the 5S rDNA-SL1 gene cluster. Scale bar: 2 μm. (C) Same as (B), but with GFP::Pol III. (D) Fluorescent images of germ nuclei in worms expressing mCherry::PRDE-1 and GFP::Pol II at early-mid pachytene and late pachytene. Each dotted circle marks a germline nucleus. Confocal images (60× objective) are shown as maximum-intensity projections spanning the germline. Arrows indicate the piRNA gene cluster, and arrowheads mark the small GFP::Pol II foci. Scale bar: 2 μm. (E) Same as (D), but with GFP::Pol III. Figure S2. ChIP signals of ATTF-6, RNA Pol II, and RNA Pol III across genome. (A) Browser view of ATTF-6, Pol II (AMA-1), and Pol III (RPC-1) ChIP-seq signals on chromosome I. ATTF-6 ChIP-seq signals were normalized to the control IP, and AMA-1 and RPC-1 signals were normalized to the input by subtracting their corresponding control signals. The signals represent the average from two biological replicates and are shown in RPKM (Reads Per Kilobase per Million mapped reads). (B–E) Browser views for chromosomes II, III, IV, and X. Figure S3. Quantification of ATTF6, Pol II, and pol III foci number in Auxin or RNAi treatment (A) Definition of foci patterns used for quantifying the foci in (B–E), including two clear foci, multiple small foci, and dissolved foci. (B) Quantification of GFP::Pol II and ATTF-6::RFP foci patterns in embryo nuclei based on the definitions in (A). Embryos were dissected from worms treated with or without auxin (1mM IAA). Nuclei from three independent embryos were counted, and the number of nuclei is indicated (n = x). (C) Same analysis as in (B), but examining GFP::Pol III and ATTF-6::RFP foci. (D) Quantification of GFP::Pol II and ATTF-6::RFP foci patterns in embryo nuclei based on the definitions in (A). Embryos were dissected from worms treated with control (L4440), attf-6, and snpc-4 RNAi. Nuclei from three independent embryos were counted, and the number of nuclei is indicated (n = x). (E) Same analysis as in (D), but examining GFP::Pol III and ATTF-6::RFP foci. Figure S4. Number of ATTF6, Pol II, and pol III foci per nucleus (A-C) Violin plots showing the number of foci per nucleus for embryos expressing GFP::ATTF-6 (A), GFP::Pol II (B), or GFP::Pol III (C). Embryos resuspended in M9 buffer were maintained at 20°C or exposed to heat-stress at 32°C. Time (hrs) indicates time post-transfer to a 32°C incubator. Dots represent the median foci number and n represents individual nuclei. Three embryos were quantified per condition. 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Ohio State University, Columbus, United States, Ohio State Biochemistry Program, The Ohio State University, Columbus, United States+These authors contributed equally to this workHannah L Hertz+Department of Biological Chemistry and Pharmacology, The Ohio State University, Columbus, United States, Center for RNA Biology, The Ohio State University, Columbus, United States+These authors contributed equally to this workWen TangDepartment of Biological Chemistry and Pharmacology, The Ohio State University, Columbus, United States, Center for RNA Biology, The Ohio State University, Columbus, United States, Ohio State Biochemistry Program, The Ohio State University, Columbus, United StatesORCID iD: 0000-0001-6684-5258For correspondence: tang.542@osu.eduAuthor NotesCompeting interests: No competing interests declaredVersion historySent for peer review: January 7, 2026Preprint posted: January 8, 2026Reviewed Preprint version 1: March 17, 2026Cite all versionsYou can cite all versions using the DOI https://doi.org/10.7554/eLife.110360. 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来源:e life发布时间:2026-03-17
Effects of knockdown of autophagy pathway genes onlongevity are highly condition dependent

AbstractAutophagy is proposed to protect against aging by clearing damaged cellular constituents. In line with this several life-extending interventions in model organisms show some degree of autophagy dependence. In C. elegans, inhibiting autophagy can shorten, lengthen or have no effect on lifespan. Differences between published findings likely reflect variability in experimental conditions. Here we investigate the condition dependence of effects on lifespan of RNA-mediated interference (RNAi) knockdown of autophagy pathway components. Effects on several interventions causing a strong Age (increased lifespan) phenotype were examined, including mutation of daf-2 (insulin/IGF-1 receptor). Factors varied included daf-2 mutant allele class, atg gene, temperature and presence of 5-fluoro-2’-deoxyuridine (FUDR). Effects on lifespan of atg RNAi proved to be highly condition dependent. Notably, for most atg genes tested lifespan was not usually reduced more in the long-lived mutant than in the wild-type control. Greater suppression was seen at 20°C for certain atg genes with daf-2(e1368) but not daf-2(e1370). At 25°C, little reduction in lifespan was seen. However, atg-18 knockdown behaved differently, suppressing daf-2 Age under all conditions, suggesting possible pleiotropic action. FUDR at a high concentration caused knockdown of several atg genes to increase lifespan. Thus, depending on experimental conditions, atg knockdown can increase, decrease or have no effect on daf-2 Age. The lack of suppression of Age by atg RNAi under most conditions questions the importance of autophagy in daf-2 Age. Moreover, condition dependence of effects creates a risk of possible condition selection bias.IntroductionA long-standing theory is that senescence (aging) is largely a consequence of the accumulation of random molecular damage caused by, among other things, reactive oxygen species (Beckman and Ames, 1998; Harman, 1956; Murphy, 2023; Shore and Ruvkun, 2013; Szilard, 1959). This view predicts that mechanisms of somatic maintenance, particularly those that prevent accumulation of damaged cellular constituents, will decelerate the aging process. One somatic maintenance function viewed as a potential longevity-assurance process is autophagy (specifically macroautophagy), which effects lysosome-dependent degradation of cellular constituents, including damaged matter (Aman et al., 2021).The possible anti-aging role of autophagy has been extensively tested in the short-lived nematode Caenorhabditis elegans, and supporting evidence found with respect to several life-extending interventions, including reduced insulin/IGF-1 signaling (IIS), reduced germline signaling, and dietary restriction (Hansen et al., 2018). These studies were principally performed in the 2000s; however, during the same period, falsification tests of the molecular damage theory, particularly that relating to oxidative damage, led to some uncertainty about its validity (Gems and Doonan, 2009; Perez et al., 2009; Shields et al., 2021). Meanwhile, an alternative theoretical framework emerged, based on the evolutionary theory of aging (Arnold and Rose, 2023; Williams, 1957), arguing that senescence is largely the consequence of genetically-determined, programmatic mechanisms (Blagosklonny, 2006; de Magalhães and Church, 2005; Gems, 2022; Maklakov and Chapman, 2019), and very much so in C. elegans (Gems and de la Guardia, 2013; Gems et al., 2021; Pires da Silva et al., 2024).One form of programmatic aging involves costly programs: genetically-determined processes that degrade somatic tissues as a by-product of wider, fitness-promoting processes (Gems and Kern, 2024; Gems et al., 2021). In some cases this involves biomass repurposing, where biomass of one tissue is broken down by autophagic processes to release molecular constituents to support functions in another. This occurs to a high degree in semelparous organisms in the process of reproductive death (suicidal reproductive effort), as in many monocarpic plants, and semelparous fish such as Pacific salmon (Gems et al., 2021).Several lines of evidence support the hypothesis that reproductive death occurs in C. elegans hermaphrodites (Gems et al., 2021; Kern et al., 2023). This includes a putative costly program in which intestinal biomass is broken down and repurposed to support production of yolk, that is then vented to support larval growth, leading to intestinal atrophy (a senescent pathology) (Ezcurra et al., 2018; Kern et al., 2021; Sornda et al., 2019). Notably, such biomass repurposing is supported by autophagy, as evidenced by deceleration of intestinal atrophy and yolk pool formation when autophagy is inhibited (Benedetto and Gems, 2019; Ezcurra et al., 2018). Thus, in this particular context autophagy appears to be promoting rather than preventing senescence.These developments warrant a careful re-examination of the evidence that autophagy is protective against aging in C. elegans. In this study we re-examine the question of whether the longevity of daf-2 insulin/IGF-1 receptor mutants is autophagy dependent. Here the principal form of past evidence involves epistasis tests, where effects on lifespan of reduction of function of genes encoding proteins effecting autophagy is compared in the wild type (N2) and daf-2 mutants. Findings from 8 previous studies involving 46 epistasis experiments are summarised in Table S1. Although it is widely believed that autophagy is essential for daf-2 mutant longevity (Meléndez et al., 2003), scrutiny of the results of past tests raises doubts about the strength of this claim.Careful consideration of these prior studies identifies six distinct limitations, as follows. (1) A life-shortening effect of inhibition of autophagy does not necessarily indicate its role in the normal aging process, or in daf-2 mutant longevity. (2) If autophagy is inhibited during development as well as adulthood, a life-shortening effect could result from disruption of normal development. (3) The claim that daf-2 longevity is autophagy dependent requires evidence that the life-shortening effect in daf-2 is significantly greater than in wild type, e.g. using Cox proportional hazard analysis, and this is rarely performed. (4) If effects of reducing autophagy are condition dependent, this introduces a potential bias: a risk that investigators might unwittingly select conditions where the data generated supports a role of autophagy in longevity - what may be referred to as condition selection bias. Potential determinative conditions that have varied across studies include choice of autophagy-determining gene to inhibit, of daf-2 mutant allele, and ambient temperature. (5) 5-fluoro-2’-deoxyuridine (FUDR) is sometimes used to facilitate C. elegans lifespan assays by preventing progeny hatching; this could potentially affect test outcomes, as shown in other contexts (Aitlhadj and Sturzenbaum, 2010; Anderson et al., 2016; Burnaevskiy et al., 2018; Van Raamsdonk and Hekimi, 2011; Zhao et al., 2019). (6) A final, straightforward issue is whether a given finding proves to be reproducible in subsequent reports under, seemingly, the same conditions.Of 46 prior tests (Table S1) only 4 present clear evidence that reducing autophagy shortens lifespan more in daf-2 than in the wild type. Regarding one of these four instances, the effect of bec-1 RNAi on daf-2(e1370) at 15°C (Meléndez et al., 2003), a subsequent study did not replicate this finding (Hars et al., 2007). In two other cases the weaker daf-2(mu150) allele was used (Hansen et al., 2008; Patel et al., 2008). In two further studies where large reductions in daf-2 lifespan were observed (Chang et al., 2017; Minnerly et al., 2017) knockdown of atg-18 was used. In a 2009 study where effects of knockdown of 14 different autophagy genes was tested, adult-limited atg-18 RNAi was one of only 2/14 that significantly reduced lifespan in the wild type (Hashimoto et al., 2009), suggesting possible atg-18 idiosyncrasy. The 2009 study includes more than half of all of the prior tests (28/46); strikingly, in only 1/14 genes (atg-4.1) did adult-limited RNAi significantly reduce daf-2(e1370) lifespan, while for 3/14 genes (atg-9, bec-1 and unc-51) it increased daf-2 lifespan (Hashimoto et al., 2009). One possible reason for the lack of observed life-shortening effects given autophagy knockdown is its use of FUDR at a high concentration.In this study we assess the condition-dependence of the effects of RNAi knockdown of autophagy genes on C. elegans longevity. To this end we have tested the effects of RNAi of six genes in the autophagy pathway on longevity in two different daf-2 mutants, and a glp-1 germlineless mutant, at two temperatures. We have also assessed effects of FUDR and prevention of bacterial infection. Our results provide a robust foundation of evidence relating to possible autophagy dependence of daf-2 and glp-1 longevity. They suggest a weak and highly-condition dependent contribution of autophagy to daf-2 and glp-1 Age. This implies that prevention of damage accumulation by autophagy is, at most, a minor determinant of daf-2 longevity.ResultsEffects of atg RNAi on daf-2 Age vary with daf-2 alleleWe first tested whether effects on lifespan of inhibiting autophagy depend upon daf-2 allele severity. Two daf-2 mutants were examined. daf-2(e1368) is a class 1 (less pleiotropic) mutant where adults show normal behavior at both 20°C and 25°C, while daf-2(e1370) is a class 2 (more pleiotropic) mutant where at 25°C adults become paralyzed and cease feeding (Gems et al., 1998). Knockdown of autophagy was performed by RNAi from the L4 (late larval) stage, to exclude possible confounding, life-shortening disruption of normal development. RNAi was performed for six genes at several stages of the autophagy process: initiation (atg-13), membrane nucleation (atg-9, bec-1), phagophore formation (atg-2, atg-18) and elongation (atg-4.1) (Figure 1A). 5/6 genes were those examined in our previous study (Ezcurra et al., 2018), with bec-1 added because it was the subject of several earlier studies (Table S1).Figure 1.Effects of atg RNAi on longevity are highly variable and condition dependent.(A) The autophagy pathway, and genes tested in this study. (B-D) Effects at 20°C. (B) Effects on N2 (wild type). (C) Effects on daf-2(e1368). (D) Effects on daf-2(e1370). (F, G) Effects at 25°C. (F) Effects on N2. (G) Effects on daf-2(e1368). (H) Effects of bec-1 RNAi on daf-2(e1370) at 15°C. (B-G summed data, N = 2; for individual trials and statistical comparisons, see Table S2, S5.A methodological note: for tests of effects of a given intervention on C. elegans lifespan an often-applied standard is to include 3 biological replicates. This is true of several recent studies where the effect of knockdown of a single atg gene on daf-2 longevity was studied (Minnerly et al., 2017; Wilhelm et al., 2017; Yang et al., 2024). However, given that the present condition dependence study effectively performs this test in 18 different ways, involving RNAi of 6 atg genes, 2 daf-2 mutants and 2 temperatures, N = 2 biological replicates were judged to be sufficient to draw robust conclusions; similarly, an earlier study of RNAi 14 atg genes under two conditions used 2-3 biological replicates (Hashimoto et al., 2009); for an overview of N sizes in previous studies, see Table S1.Trials were performed at 20°C, and used gfp RNAi as a negative control. In wild-type C. elegans (N2), statistically significant reductions were sometimes seen: atg-9, atg-13 and, particularly, atg-18 RNAi shortened lifespan in both trials (summed data, N = 2, means: −13.7%, p < 0.0001, −12.4%, p = 0.0002, −31%, p < 0.0001, respectively; log rank test) (Figure 1B, Table S2; for all raw mortality data, see Supplemental Dataset 1). For survival curves comparing RNAi effects of individual atg genes on the three genotypes, see Figure 2.Figure 2.Degree of suppression of daf-2 longevity by atg gene RNAi differs greatly between daf-2 alleles and atg RNAi treatments (20°C), cf. Figure 1B-D (same data).Summed data, N = 2; for individual trials and statistical comparisons, see Table S2. All trials were performed in parallel, i.e. all lifespans are directly comparable.RNAi of all six atg genes consistently reduced lifespan in the daf-2(e1368) mutant, with effects ranging from −10.5% (p = 0.0009) for atg-4.1 to −50.4% (p < 0.0001) for atg-18 (Figure 1C, Table S2). To assess whether atg RNAi reduced lifespan more in daf-2 mutants than in the wild type, Cox proportional hazard (CPH) analysis was used. This showed a significantly greater effect in daf-2(e1368) for 4/6 genes (exceptions: atg-4.1, bec-1) (Table S2). These findings are in line with the earlier observation that bec-1 and vps-34 RNAi shortened the lifespan of the daf-2(mu150) class 1 mutant but not of N2 at 20°C (Hansen et al., 2008).To test whether atg RNAi is able to fully suppress daf-2(e1368) Age, the lifespans of N2 and daf-2 subjected to atg RNAi were compared. Under all six atg RNAi conditions, daf-2(e1368) still significantly increased mean lifespan, from +15.7% (p = 0.0031) for atg-13 to +60.9% (p < 0.0001) for atg-4.1 (summed data, 20°C, Table S2). This could imply either that daf-2(e1368) Age is not fully autophagy dependent, or that it is but autophagy is not fully suppressed by the RNAi treatment.In daf-2(e1370), atg RNAi had far more modest effects on lifespan than in daf-2(e1368). Summed data showed significant reductions in lifespan resulting from RNAi of atg-9, atg-13 and atg-18 only, with the latter again causing the largest reduction: −8.3% (p = 0.012), −17.8% (p = 0.0034), and −29.2% (p < 0.0001), respectively (Figure 1D, Table S2). However, effects were in no instance significantly greater than in N2 (CPH analysis, Table S2), thus failing to provide evidence that e1370 longevity is mediated by autophagy. Taken together, these results could imply that at 20°C autophagy contributes to longevity in weaker but not stronger daf-2 mutants, possibly in class 1 but not class 2 mutants.RNAi effects varied between atg genes, with atg-2 and atg-4.1 RNAi effects weaker, and atg-18 RNAi effects generally stronger than the rest. One possible cause of this variability is differences between the plasmid vectors used for RNAi in terms of how efficiently they destroy their target mRNA. To investigate this possibility, RNAi of each atg gene was performed in N2 animals, and transcript levels measured by RT–qPCR and analyzed using the ΔΔCt method, normalized to a gfp RNAi control, with fold changes calculated as 2^−ΔΔCt (n = 4 independent trials). Effects of RNAi on mRNA level varied greatly, from no detected reduction in atg-2 to an 86% reduction in atg-18 (Fig. S1A; Table S3; Supplementary dataset 2). Pairwise comparisons of ΔΔCt values showed that, atg-2 aside, there were no significant differences between RNAi treatment effects on mRNA levels, apart from a greater effect of atg-18 RNAi when compared to either atg-4.1 and atg-9 RNAi (Fig. S1B).Comparing effects of RNAi on mRNA levels and lifespan, the only clear correspondence between the former and the latter involved atg-2 and atg-4.1 RNAi, which in N2 had no significant effect on either (Figure 1B, Fig. S1A; Table S2). However for the remaining 4 genes, there was no clear correspondence, though atg-18 mRNA levels appear to be the lowest, in line with its greater effect on lifespan (Figure 1B-D, Fig. S1A; Table S2, Table S3). These results imply that RNAi efficacy and perhaps also gene-specific issues contribute to variation in atg RNAi effects on lifespan (discussed further below). While mRNA levels after RNAi under the various other conditions tested were not assayed, reduced IIS (including daf-2(e1370)) intensifies the RNAi response (Wang and Ruvkun, 2004), thus lack of effect on lifespan in daf-2(e1370) is very unlikely to reflect suppression of mRNA knockdown.Effect of atg RNAi on daf-2 Age is temperature dependentResults of a previous study performed at 25°C appear to show no greater reduction in lifespan in daf-2(e1370) compared to daf-2(+) after autophagy knockdown, even when using the atg-18(gk378) deletion mutation (Toth et al., 2008) (Table S1), suggesting possible temperature dependence of atg RNAi effects. To explore this further, in parallel to tests at 20°C, we also compared effects of atg RNAi on lifespan in N2 and daf-2(e1368) at 25°C. Effects on daf-2(e1370) were not tested, partly because this mutant ceases feeding at 25°C (Gems et al., 1998) which would be expected to interfere with RNAi by feeding.At 25°C, atg RNAi did not shorten N2 lifespan for any of the six genes tested, not even atg-18 (summed data; Figure 1E, Table S2), i.e. culture at 25°C suppressed the life-shortening effect of atg RNAi in N2. Also notable is that the increases in lifespan with atg-2 and atg-13 RNAi at 25°C, described in our earlier study (Ezcurra et al., 2018), were not reproduced (discussed below).At 25°C only atg-18 RNAi significantly reduced daf-2(e1368) mean lifespan, by 15.5% (p < 0.0001) (summed data, Figure 1F, Table S2), a reduction that was significantly greater than in N2 (p = 0.0012, CPH, summed data only; Table S2). For survival curves comparing RNAi effects of individual atg genes on the two genotypes, see Fig. S2. In one case, atg-9, RNAi slightly increased daf-2 lifespan (+9.8%, p = 0.011, summed data).The initial tests showing that bec-1 RNAi reduces daf-2(e1370) lifespan were performed at 15°C (Meléndez et al., 2003); moreover, a greater N2 life-shortening effect of bec-1 RNAi at 15°C than 20°C has been reported (Chen et al., 2019). Taken together with the weaker RNAi effects at 25°C observed here, this suggested that stronger effects might be seen at 15°C. To explore this we compared effects of bec-1 RNAi from L4 on N2 and daf-2(e1370) at 15°C and 20°C (N = 2). However, no suppression of daf-2(e1370) Age by bec-1 RNAi was seen at either temperature (Figure 1G; Table S5), consistent with findings of an earlier study performed at 15°C (Hars et al., 2007) (Table S1).FUDR can alter the effect of atg RNAi on lifespanSince the 1980s FUDR, a thymidylate synthase inhibitor and anti-cancer drug, has sometimes been added to C. elegans survival trials to prevent progeny production (Gandhi et al., 1980; Mitchell et al., 1979). Notably, two previous reports that observed increases in C. elegans lifespan given atg RNAi employed FUDR. Our own study saw increases in N2 lifespan after atg-2 and atg-13 RNAi from L4 with 15 µM FUDR (Ezcurra et al., 2018). Another study that saw increases in N2 lifespan given adult-limited atg-7, atg-9, bec-1 and unc-51 RNAi used FUDR at a higher concentration, 800 μM (Hashimoto et al., 2009) (E. Nishida, personal communication).To test for FUDR-dependent effects, we compared the impact of atg gene RNAi (atg-2, atg-4.1, atg-9, atg-13, atg-18 and bec-1) on N2 lifespan at 20°C with 0, 15 or 800 µM FUDR (N = 2). With 0 or 15 µM FUDR, only shortening of lifespan was seen, and FUDR did not significantly alter the effects of RNAi (CPH, Figure 3A,B, Table S6).Figure 3.FUDR but not infection alters outcome on atg gene RNAi (20°C).(A-C) Effects of 0 μM, 15 μM and 800 µM FUDR. (D) No alteration by kanamycin of atg-13 RNAi effect on N2 lifespan. Summed data, N = 2; for individual trials and statistical comparisons, see Table S6, S7.Addition of 800 µM FUDR increased the lifespan of the gfp RNAi negative control by 61.7% (p < 0.0001, Table S6), perhaps due to prevention of bacterial proliferation, which can otherwise shorten C. elegans lifespan (Garigan et al., 2002; Gems and Riddle, 2000). In the presence of 800 µM FUDR, atg-9 and atg-13 RNAi increased lifespan, by +12.5% (p = 0.014) and +8.7% (p = 0.031), respectively (summed data; Figure 3C, Table S6). Moreover, the life-shortening effect of bec-1 RNAi, seen with 0 or 15 µM FUDR, was absent. Again, atg-18 RNAi robustly reduced lifespan under all three conditions (Figure 3C, Table S6). The results using 800 µM FUDR are broadly in line with those of Hashimoto et al. (2009), where atg-9 and atg-13 RNAi increased lifespan and atg-18 was one of only 2/14 genes tested where adult-limited RNAi decreased N2 lifespan. This suggests that the increases in lifespan after atg RNAi reported in that study could have reflected its use of 800 µM FUDR (Hashimoto et al., 2009).atg RNAi shortens lifespan in the absence of bacterial infectionUnder standard laboratory culture conditions, C. elegans lifespan is limited by infection by the E. coli food source, such that prevention of bacterial proliferation substantially increases lifespan (Garigan et al., 2002; Gems and Riddle, 2000). The preceding results could imply that 800 µM FUDR suppresses life-shortening effects of atg RNAi by preventing bacterial infection. Xenophagy is generally protective against infection in C. elegans (Balla et al., 2019; Jia et al., 2009). Thus, reduction in lifespan given atg gene knockdown could reflect increased susceptibility to infection.To probe this hypothesis, we compared effects on N2 lifespan of atg-13 RNAi at 20°C in the absence or presence of the antibiotic kanamycin (Kan), to suppress bacterial infection. In the absence of Kan, atg-13 RNAi caused a slight reduction in lifespan in these trials that did not reach statistical significance (Figure 3D, Table S7), in contrast to other trials in this study (Table S2, S6). Application of Kan extended C. elegans lifespan (+27.1%, p < 0.0001, summed data, Table S7), as previously observed (Garigan et al., 2002), and in its presence atg-13 RNAi resulted in the same slight reduction in lifespan (Figure 3D, Table S7). These results suggest that life-shortening effects of atg RNAi are not solely attributable to increased susceptibility to E. coli infection. Moreover, they do not indicate marked condition dependency in atg RNAi effects on lifespan with respect to E. coli proliferative status.atg-18 RNAi robustly suppresses glp-1(e2141) AgeWe next explored more widely the reproducibility and condition dependence of the requirement for autophagy of C. elegans longevity. Prevention of germline development by laser surgery or mutation increases lifespan in C. elegans hermaphrodites (Arantes-Oliveira et al., 2002; Hsin and Kenyon, 1999; Pires da Silva et al., 2024). Prior tests for possible autophagy dependence of such longevity have largely used the temperature-sensitive glp-1(e2141) germline proliferation mutant, which is fertile at 15°C but sterile and with greatly reduced germline development at 25°C. A key study reported strong and reproducible suppression of glp-1 Age by RNAi of five autophagy-related genes, including atg-18 and bec-1 (Lapierre et al., 2011) (previous findings summarized in Table S8).We first tested the effect of atg RNAi on glp-1 longevity with animals raised from L1 until L4 stage at 25°C, and maintained at 20°C thereafter, similar to previous studies (Table S8). Again, effects of atg-2, atg-4.1, atg-9, atg-13, atg-18 and bec-1 RNA were tested. In the RNAi control glp-1 increased mean lifespan by +50.9% (p < 0.0001, summed data, Table S9). glp-1 lifespan was significantly reduced by atg-2, atg-4.1, atg-9, atg-18 and bec-1 RNAi (but not atg-13 RNAi), and effects were greater in glp-1 than N2 in all cases except atg-4.1 (Figure 4A, B, Figure 5, Table S9). However, suppression was only robust (of a large magnitude) for atg-2 and atg-18 RNAi (Figure 4B, Figure 5). Under all six atg RNAi conditions, glp-1 still significantly increased mean lifespan, from +4.12% (p = 0.0003) for atg-18 to +43.8% (p < 0.0001) for atg-4.1 (summed data, 20°C, Table S9). This could imply either that glp-1 Age is not fully autophagy dependent, or that it is but autophagy is not fully suppressed by the RNAi treatment.Figure 4.Degree of suppression of glp-1(e2141) longevity by atg gene RNAi differs greatly between genes.(A, B) 20°C. (A) Effects on N2. (B) Effects on glp-1. (C, D) 25°C. (C) Effects on N2. (D) Effects on glp-1. Summed data, N = 2-5; for individual trials and statistical comparisons, see Table S9. (E) Overview of effects of suppression of Age by RNAi of genes specifying autophagy. Dark blue, reduction of daf-2 or glp-1 Age that is significantly greater than in N2. Light blue, reduction of daf-2 or glp-1 Age that is not significantly greater than in N2. R, robust suppression, i.e. strong but incomplete suppression of longevity, defined here as <30% increase in mean lifespan in daf-2 or glp-1 relative to N2, with RNAi, and as evident from Figure 2, Figure 5, and Fig. S2.Figure 5.Degree of suppression of glp-1(e2141) longevity by atg gene RNAi differs greatly between genes (20°C), cf. Figure 4A,B (same data).Summed data, N = 2-5; for individual trials and statistical comparisons, see Table S9.In tests with daf-2(e1368) life-shortening effects of atg RNAi were largely absent at 25°C (apart from atg-18) (Figure 1F, Table S2). We therefore wondered whether temperature might also influence the outcome of atg RNAi treatment in glp-1 mutants. To test this we examined RNAi effects on lifespan at 25°C. Under these conditions, glp-1 lifespan was significantly reduced by only atg-2, atg-4.1, and atg-18 RNAi (Figure 4C, D, Fig. S3, Table S9), and effects were significantly greater in glp-1 than N2 only with atg-2 and atg-18 RNAi (CPH analysis, Table S9). In summary, of the six genes tested only atg-2 and atg-18 RNAi robustly suppressed glp-1 Age, and RNAi effects were weaker at 25°C.For an overview of the effects of RNAi on daf-2 and glp-1, see Figure 4E. In 11 of the 30 conditions tested RNAi knockdown of autophagy caused a greater reduction in lifespan in the long-lived mutant. In 7/30 the RNAi effect was robust, i.e. the mutant longevity was largely suppressed. This suppression was highly condition dependent, differing according to gene knocked down, temperature, daf-2 allele used, and between daf-2 and glp-1. Notably, only atg-18 RNAi robustly suppressed longevity in both daf-2 and glp-1 mutants.Why are effects of atg-18 RNAi stronger than those of other atg genes?The particularly strong life-shortening effects of atg-18 RNAi could reflect either a greater reduction in autophagy, or the presence in addition to its effects on autophagy of pleiotropic effects not directly related to autophagy. Given that longevity due to either daf-2 mutation or germline loss are wholly dependent on the FOXO transcription factor DAF-16 (Hsin and Kenyon, 1999; Kenyon et al., 1993), we wondered whether atg-18 RNAi might inhibit DAF-16. To probe this two approaches were taken. First, we used a constitutive dauer formation assay. High population density and food depletion causes C. elegans larvae to form developmentally arrested dauer larvae (Cassada and Russell, 1975). daf-2 mutants undergo constitutive dauer arrest (the Daf-c phenotype) in a temperature-sensitive manner, and this is fully suppressed by daf-16(−) (Riddle et al., 1981). However, using a sensitive assay (daf-2(m41), 22°C, giving a mix of dauer and non dauers), we detected no reduction in the number of dauers formed given atg gene RNAi (atg-2, atg-13 and atg-18) (Fig. S4A).Second, we tested a GFP reporter for a gene whose expression is elevated in daf-2 mutants in a daf-16-dependent manner (ftn-1), using strains previously constructed (Ackerman and Gems, 2012). GFP levels were compared in daf-2(m577) and daf-16(mgDf50); daf-2 backgrounds. As expected, GFP levels were higher in daf-2 than in daf-16; daf-2 (Fig. S4B). RNAi did not suppress the daf-2-induced increase of ftn-1::gfp expression (Fig. S4B). These findings argue against a pleiotropic effect of atg-18 on DAF-16 function.Inhibiting autophagy does not reduce vitellogenin accumulationFinally, we further investigated the hypothesis that autophagy promotes biomass repurposing in C. elegans. Inhibition of yolk synthesis or of autophagy delays intestinal atrophy and yolk pool accumulation, suggesting that intestinal biomass is repurposed for yolk synthesis (Benedetto and Gems, 2019; Ezcurra et al., 2018; Sornda et al., 2019). In principle, this could involve repurposing into yolk protein (vitellogenin) or yolk lipid. To test the former possibility, wild-type hermaphrodites or fog-2(q71) (feminization of germline) mutant females were subjected to atg RNAi. The fog-2 mutant, which lacks self-sperm and so lays no eggs, was used to avoid possible effects of atg RNAi on fertility, reduction of which can increase vitellogenin levels within nematodes (Sornda et al., 2019).For none of the six atg genes tested did RNAi detectably reduce yolk protein levels, either in N2 hermaphrodites or fog-2 females (Figure 6). This implies that autophagic machinery, including that in the intestine, does not enhance yolk protein production. Thus, if intestinal biomass repurposing occurs, then it likely supports yolk secretion or yolk lipid production.Figure 6.Absence of effect of atg RNAi on vitellogenin accumulation.Fold change of yolk proteins YP170, YP115 and YP88 in N2 and fog-2 normalized to gfp day 1 (N = 3). In no case is vitellogenin level significantly different to that in the gfp RNAi control at any time point (two-way ANOVA, Table S10). For raw data for vitellogenin levels see Supplementary Dataset 3.DiscussionOverall, the effects of reducing atg gene function described here are ambiguous with respect to the role of autophagy in daf-2 or glp-1 longevity, neither strongly supporting or excluding it. However, they are consistent with a role of autophagy in the longevity of weaker, class 1 daf-2 alleles at lower temperatures (Figure 1C,D,F,G, Table S2). Class 1 allele-limited suppression of daf-2 Age has been seen previously, for example in epistasis tests with daf-12 (encoding a dafachronic acid receptor) (Gems et al., 1998; Larsen et al., 1995) and skn-1 (Nrf2-like transcription factor) (Tullet et al., 2008). This could reflect a role of autophagy in longevity assurance limited to conditions of mild IIS reduction, or sensitivity to differential effects of daf-2 on distinct downstream signaling outputs, such as phosphatidylinositol 3-kinase and Ras signaling (Patel et al., 2008).atg RNAi suppresses daf-2(e1368) Age at 20°C but not 25°C (Figure 1C,F, Table S2). Given that for hypomorphic daf-2 alleles (such as e1368 and e1370) many mutant traits, including Age, show some degree of temperature sensitivity (Gems et al., 1998; Riddle and Albert, 1997), the absence of suppression at 25°C may reflect the increased severity of the daf-2(e1368) mutant phenotype at this higher temperature. However, life-shortening effects of atg RNAi on N2 are also reduced at 25°C, suggesting that additional mechanisms may also mediate such temperature sensitivity.Resolving discrepancies between past studies of autophagy dependence of daf-2 AgeThe increasing non-reproducibility of many experimental findings, that has been referred to as the reproducibility crisis, is a problem that particularly afflicts biological and biomedical research (Baker, 2016; Lithgow et al., 2017; Prinz et al., 2011; Ritchie, 2020; Voelkl et al., 2020). A strength of C. elegans as an experimental model is the relative ease with which such discrepancies can be resolved, at least in principle. This is thanks to the use of standardized culture conditions across the C. elegans research community, and of nematode strains based on the same isogenic wild-type strain (N2), plus the relatively low cost and short duration of experiments.Regarding lifespan assays in particular, possible reasons for discrepant findings include clearly identifiable differences in experimental design, such as use or not of FUDR. Less obvious causes include cryptic variation in genetic background (Zhao et al., 2019), or subtle differences in culture conditions (e.g. due to batch variation in Bacto Peptone, a constituent of nematode growth medium) (Petrascheck, 2014).Our findings potentially resolve several discrepancies between earlier studies relating to the possible role of autophagy in daf-2 mutant longevity. Previous studies found that inhibiting autophagy either suppressed or enhanced daf-2 Age, or had no effect (Table S1). One study showing suppression used the weak class 1 allele daf-2(mu150) (Hansen et al., 2008). This is consistent with our observation that, atg-18 aside, atg RNAi can suppress Age in a class 1 but not a class 2 allele (Figure 1C,D Table S2).Does bec-1 RNAi suppress daf-2(e1370) Age? The initial test suggesting this was performed at 15°C, and employed RNAi by injection of the mothers of assayed individuals (Meléndez et al., 2003). In a subsequent study at 15°C, in which both mothers and adult progeny were exposed to bec-1 RNAi by feeding, daf-2(e1370) lifespan appeared not to be shortened (Hars et al., 2007). Several further trials under different conditions did not observe a life-shortening effect either (Hashimoto et al., 2009; Toth et al., 2008). In the present study, we saw no shortening of daf-2(e1370) lifespan in summed data with adult-limited bec-1 RNAi by feeding, at either 15°C or 20°C (Figure 1D,G, Fig. S3, Table S2, S5). Taken together with earlier evidence, our observations suggest that suppression of daf-2(e1370) Age by bec-1 RNAi is not a readily reproducible finding.Several studies reported lifespan extension following atg RNAi (Ezcurra et al., 2018; Hashimoto et al., 2009). Here we were able to reproduce this effect for several atg genes by applying high dose FUDR (800 µM), thus recapitulating findings by Hashimoto et al (2009), and potentially accounting for the life span increases seen in that study; it was only subsequent to that study that evidence emerged of the capacity of FUDR to alter effects of interventions that impact lifespan (Aitlhadj and Sturzenbaum, 2010; Anderson et al., 2016; Burnaevskiy et al., 2018; Van Raamsdonk and Hekimi, 2011; Zhao et al., 2019).Regarding issues with replicating published findings, in an earlier study we observed increases in N2 lifespan given atg-2 and atg-13 RNAi (15 µM FUDR present) (Ezcurra et al., 2018). However, this finding proved not to be robust to replication (Figure 3B, Table S6), perhaps reflecting inherent effect variability, condition dependence or a combination of the two. This again illustrates the value, as with the effects of bec-1 RNAi on daf-2(e1370) Age, of repeated verification of effects of interventions on C. elegans lifespan.As with daf-2, our tests with the glp-1 germline proliferation mutant did not clearly support the view that mutant longevity is autophagy dependent. However, consistent with an earlier study (Lapierre et al., 2011), we observed that atg-18 and bec-1 RNAi reduce glp-1 lifespan more than in wild-type at 20°C.Why does atg-18 RNAi more strongly suppress daf-2 Age?Of the six autophagy-determining genes tested here, RNAi of atg-18 showed a greater capacity to suppress both daf-2 and glp-1 Age than the other five. atg-18 appears to have replaced bec-1 as the gene of choice for autophagy-related epistasis studies in C. elegans (Chang et al., 2017; Minnerly et al., 2017). Notably, atg-18 was one of only 2/14 autophagy genes tested where RNAi during development caused a high level (>50%) of larval growth arrest or lethality (Hashimoto et al., 2009). The more marked effects of atg-18 RNAi could reflect either greater inhibition of autophagy, or pleiotropy in which processes other than autophagy are altered.Regarding pleiotropy, several proteins in the canonical autophagy pathway have recently been found to participate in other processes. For example, ATG8 (LGG-1 in C. elegans) functions in trafficking of single-membrane organelles (Nieto-Torres et al., 2021), and C. elegans ATG-16.2 contributes to neuronal exopher formation via its WD40 domain (Yang et al., 2024). In an as yet unexplained instance of atg gene idiosyncrasy, mutation of atg-16.2, atg-18 or bec-1 retards the cell cycle in C. elegans germline cells, while that of atg-7 does not (Ames et al., 2017).Concerning possible atg-18 pleiotropy, several studies suggest this possibility. ATG-18 is a predicted WIPI (WD repeat protein interacting with phosphoinositides) family member. In humans there are four WIPI proteins, WIPI1 - WIPI4; C. elegans atg-18 is more closely related to WIPI1/WIPI2, while epg-6 more closely resembles WIPI3/WIPI4 (Lu et al., 2011). Notably, deletion mutations of atg-18 and epg-6 appear to cause similar reductions in levels of autophagy, yet only atg-18 shortens lifespan (Takacs et al., 2019).Next, a C. elegans studies of rescue of longevity and fat storage in daf-2(e1370); atg-18(gk378) double mutants by tissue-specific expression of atg-18(+) revealed a major role of atg-18 in food-sensing chemosensory neurons, potentially reflecting a vesicle trafficking role in neurosecretion (possibly of neuropeptides) (Jia et al., 2019; Minnerly et al., 2017). Whether such a role is unique to atg-18, or shared by other atg genes remains unclear.Third, recent evidence suggests that ATG-18 activates HLH-30/TFEB (transcription factor EB). HLH-30 is a master transcriptional regulator of autophagy and lysosomal biogenesis, that becomes nuclear localized in daf-2(e1370) and glp-1(e2141) mutants, and whose inhibition reduces longevity in both contexts (Lapierre et al., 2013; Lin et al., 2018; Wong et al., 2023). Notably, among genes up-regulated upon over-expression of atg-18, ones with HLH-30 elements in their promoters are over-represented, and similarly in genes down-regulated in an atg-18(gk378) mutant. Also, heat shock-induced nuclear localization of HLH-30 is suppressed by atg-18 RNAi, as is longevity resulting from atg-18 over-expression (Schmauck-Medina et al., 2026).Inhibition of HLH-30 by atg-18 RNAi could potentially explain its unusually strong life-shortening effects. If correct, this could reflect greater suppression of autophagy, but also, in principle, other effects of HLH-30 inhibition. HLH-30 affects a variety of traits, including adult reproductive diapause (Gerisch et al., 2020), proteostasis (Shalash et al., 2025), sex differences in immunity (Sohn et al., 2025), and resistance to various insults, including infectious pathogens (El-Houjeiri et al., 2019; Visvikis et al., 2014; Wani et al., 2021), starvation (Harvald et al., 2017; O’Rourke and Ruvkun, 2013; Settembre et al., 2013), and oxidative and heat stress (Lin et al., 2018).While in at least some of these cases autophagy likely contributes to hlh-30-mediated effects, given that this transcription factor also promotes expression of many genes unrelated to autophagy (Chen et al., 2017; Lin et al., 2018; Shalash et al., 2025; Sohn et al., 2025; Visvikis et al., 2014), other functions may also be involved. For example, in response to infection HLH-30 can activate expression of signaling (including IIS), autophagy-related and immunity-related genes, and both of latter can contribute to infection resistance (Chen et al., 2017; Sohn et al., 2025; Visvikis et al., 2014). In conclusion, if the more severe effects of atg-18 RNAi on lifespan reported here are attributable to reduced HLH-30 activity, this could well reflect greater reduction of autophagy. However, it remains possible that other functions controlled by HLH-30 also play a role, e.g. relating to immunity (Chen et al., 2017; Visvikis et al., 2014) and protein quality control (Shalash et al., 2025). Also notable is that HLH-30 regulates gene expression combinatorially with DAF-16 (Lin et al., 2018), mutation of which fully suppresses daf-2(rf) longevity (Kenyon et al., 1993).Autophagy in biomass repurposing during programmatic agingAutophagic processes play a major role in tissue degeneration related to biomass repurposing in semelparous organisms (that reproduce once and then die), particularly plants (Avila-Ospina et al., 2014; Gems et al., 2021). Previous studies support the hypothesis that intestinal biomass is repurposed for synthesis of yolk that, subsequent to egg laying, is vented to support larval development (Kern et al., 2021), and that autophagy facilitates this biomass conversion (Ezcurra et al., 2018; Sornda et al., 2019). If late-life mortality is promoted by intestinal atrophy, then preventing it should extend lifespan. Consistent with this, intestinal atrophy and yolk production are suppressed in daf-2 mutants (Depina et al., 2011; Ezcurra et al., 2018), and blocking yolk production both retards intestinal atrophy and modestly increases lifespan (Ezcurra et al., 2018; Murphy et al., 2003; Sornda et al., 2019). This could imply that inhibiting autophagy, by retarding intestinal atrophy could extend lifespan under some conditions, and several earlier studies observed life extension after atg RNAi (Ezcurra et al., 2018; Hashimoto et al., 2009; Wilhelm et al., 2017).The results of the present study are in line with the view that intestinal atrophy, though a salient feature of C. elegans senescence, by itself contributes only weakly to late-life mortality. Consistent with this, inhibition of daf-2 using auxin-induced degradation of DAF-2 protein can strongly increasing lifespan even when initiated only at advanced ages, long after intestinal atrophy has occurred, and without any detectable reversal of major senescent pathologies (Molière et al., 2024; Venz et al., 2021). One possibility is that atg RNAi has antagonistic effects on late-life mortality: modestly reducing it due to suppression of intestinal atrophy (as seen when vitellogenin synthesis is inhibited) (Ezcurra et al., 2018; Murphy et al., 2003; Sornda et al., 2019), but also increasing it, due to disruption of essential cellular functions.The gut-to-yolk biomass repurposing hypothesis drew particularly on the observation that blocking yolk protein (vitellogenin) synthesis or autophagy reduced intestinal atrophy and pseudocoelomic yolk pool size (Ezcurra et al., 2018; Sornda et al., 2019); given that yolk pools contain yolk protein and lipid (Ezcurra et al., 2018; Garigan et al., 2002; Herndon et al., 2002; Palikaras et al., 2017), such putative biomass repurposing could increase production of yolk protein, yolk lipid or both. Here we tested whether atg RNAi reduces yolk protein levels, and found that it did not (Figure 6). If the hypothesis is correct, then this would support the view that promotion by autophagy of repurposing of intestinal biomass into yolk involves yolk lipid rather than yolk protein, or overall yolk secretion. In principle, this could involve mass export of stored lipid, either by means of lipophagy, or perhaps secretory autophagy (Ponpuak et al., 2015). As an aside, we note that there is evidence that vitellogenin levels can affect autophagy (Seah et al., 2016).Concluding remarksThis study reassesses the claim that daf-2 Age is autophagy dependent. While the results do not entirely prove or disprove this claim, they clearly show that evidence of dependence varies greatly with context, and suggest that autophagy only contributes substantially to daf-2 Age given a weak reduction in IIS.One limitation in establishing the role of autophagy in daf-2 mutant longevity is the difficulty of reliably measuring its level of activity. While several assays using fluorescent reporters to identify putative autophagosomes suggest possible increases in autophagy levels (Chang et al., 2017; Hansen et al., 2008; Meléndez et al., 2003), daf-2 mutants also show marked reductions in both protein synthesis rate (Depuydt et al., 2013) and turnover rate (Dhondt et al., 2016; Visscher et al., 2016), including reduced ATG-18 turnover rate (Visscher et al., 2016). This is more consistent with a reduction rather than an increase in autophagy, and in line with observed suppression of intestinal atrophy in daf-2 mutants (Ezcurra et al., 2018).One issue raised by condition dependence of a given effect is that it creates a risk of condition selection bias, where choice of experimental conditions can bias the outcome of an experiment. Where condition dependence has been identified, condition selection bias may be avoided by selection of multiple conditions to test a given hypothesis. In the present case, this could entail comparing effects on daf-2(e1368) and daf-2(e1370) at 20°C, ideally without FUDR use, and RNAi tests with multiple atg genes, at least until the reason for the idiosyncratic effects of atg-18 RNAi are understood. General questions relating to how to deal with condition dependency have been discussed previously (Munafò and Davey Smith, 2018; Voelkl et al., 2020).A second, wider issue is that condition dependence risks generating a situation where different research articles make conflicting claims, supporting different views held by different groups of researchers. For science to progress effectively it is necessary for research communities to resolve discrepancies between published findings, though the work required can be tedious, a task akin to washing the dishes in a communal household. It requires identifying and flagging findings that are either condition dependent or, seemingly, unreproducible and, where possible, distinguishing the two. For C. elegans at least, such a tidying process is highly feasible, as is use of methodologies devised to reduce such discrepancies (Driscoll et al., 2025; Lucanic et al., 2017; Petrascheck and Miller, 2017), which is a particular virtue of this model organism.Materials and MethodsCulture methods and strainsC. elegans maintenance was performed using standard protocols (Brenner, 1974). Unless otherwise stated, all strains were on nematode growth media (NGM, containing Bacto Peptone) with plates seeded with E. coli OP50 to provide a food source. An N2 hermaphrodite stock recently obtained from the Caenorhabditis Genetics Center was used as wild type (N2H) (Zhao et al., 2019). Genotypes of most mutants used are as described in Wormbase (www.wormbase.org). Strains used included CB4027 glp-1(e2141), GA633 daf-2(m577); wuIs177 [Pftn-1::gfp lin-15(+)], GA643 daf-16(mgDf50); daf-2(m577); wuIs177 [Pftn-1::gfp lin-15(+)]], GA1930 daf-2(e1370), GA1945 daf-2(m41), GA1960 daf-2(e1368), and JK574 fog-2(q71) V.Constitutive dauer larva formation assaydaf-2(m41) animals were maintained on RNAi feeding strains for at least two generations prior to analysis. For the dauer larva formation assay, performed at 22°C, 10 L4-stage hermaphrodites were transferred onto RNAi plates and allowed to lay eggs for approximately 6 h, after which adults were removed. Dauers were scored at 166 h.Epifluorescence microscopyNematodes were anaesthetized with 10 μl 2 mM levamisole on 2% agar pads prior to imaging. For imaging, we used either a Zeiss Axioskop 2 plus microscope with Hamamatsu ORCA-ER digital camera C4742-95 and Volocity 6.3 software (Macintosh version) for image acquisition; or an ApoTome.2 Zeiss microscope with a Hamamatsu digital camera C13440 ORCA-Flash4.0 V3 and Zen software for image acquisition.RNA-mediated interference (RNAi)RNAi by feeding was performed using RNAi plasmids transformed into E. coli OP50(xu363) as previously described (Xiao et al., 2015). Bacterial transformants were selected on LB agar plates containing 10 µg/ml tetracycline and 25 µg/ml carbenicillin. Inserts of all RNAi feeding clones were confirmed by sequencing. Origins of plasmids in RNAi feeding strains: atg-13, bec-1: Ahringer library (Kamath et al., 2003); atg-2, atg-4.1, atg-9: Vidal library (Rual et al., 2004). As an atg-18 RNAi feeding clone was not available in local RNAi libraries, a custom atg-18 RNAi plasmid was generated by cloning the target sequence into the L4440 vector using primers gcctccacttcctgttgaag and gagactcttttcgtcggca.For RNAi induction, bacteria were grown for 16 h at 37°C with shaking (200 rpm) in 5 ml LB supplemented with 25 µg/ml carbenicillin. Overnight cultures were diluted 1:100 into fresh LB containing 25 µg/ml carbenicillin and grown for a further 4 h or until reaching an OD₆₀₀ of ∼0.4. Expression of double-stranded RNA was induced by addition of 1 mM IPTG, and cultures were incubated for an additional 1 h. Following induction, cultures were allowed to cool to room temperature, concentrated five-fold by centrifugation, and seeded onto NGM plates supplemented with 25 µg/ml carbenicillin and 1 mM IPTG. Seeded plates were allowed to dry at room temperature before use.To minimize IPTG degradation, RNAi plates were stored in foil-lined boxes. Typically, unseeded plates were stored at 4°C for up to one month, while seeded plates were used within two weeks. RNAi treatment was initiated from the L4 larval stage unless otherwise stated.Survival analysisNematodes were maintained at a density of 25-30 per plate, and transferred daily during the egg laying period, and every 6-7 days thereafter. The L4 stage was defined as day 0. Mortality was scored every 1-2 days, with worms scored as alive if they showed any movement, either spontaneously or in response to gentle touch with a worm pick.RNA extraction, cDNA synthesis and RT-qPCRApproximately 100–200 day 4 adult animals per treatment group were lysed in 250 µl of TRIzol™ reagent by vortexing for 10 min at 4°C, followed by a 10 min incubation on ice; this cycle was repeated three times. RNA was purified using the RNeasy Mini Kit (QIAGEN, Cat. No. 74104) according to the manufacturer’s instructions, including on-column DNase digestion using the RNase-Free DNase Set (QIAGEN, Cat. No. 79256). cDNA was synthesized from 100 ng of total RNA using the SuperScript™ First-Strand Synthesis System (Thermo Fisher Scientific, Cat. No. 10684803).PCR primers were designed using Primer-BLAST with the following parameters: maximum primer length of 20 nucleotides, PCR product size of 70–200 bp, and exon–exon spanning where possible. Primer efficiency and R² values were determined from standard curves to ensure suitability for quantitative analysis and comparable amplification efficiencies between primer pairs. Primers were used at a final concentration of 2 µM.Quantitative PCR was performed using Fast SYBR™ Green chemistry on a QuantStudio™ 6 Flex Real-Time PCR System (Applied Biosystems) with 384-well plates and a total reaction volume of 10 µl. Cycling conditions were 95°C for 20 s, followed by 40 cycles of 95°C for 1 s and 65°C for 20 s, with fluorescence data acquisition during the annealing/extension step. Melt curve analysis was performed following amplification to confirm product specificity. A full list of primers used in this study is provided in Table S11. mRNA levels were normalized to mRNA levels from two housekeeping genes, cdc-42 and pmp-3 (Hoogewijs et al., 2008).Electrophoresis of C. elegans yolk proteinN2 and fog-2(q71) worms were synchronized by performing an egg lay and allowing nematodes to grow at 20°C until they reached the L4 stage. L4 worms were then transferred to fresh seeded RNAi plates and maintained under standard culture conditions at 20°C. Five worms were harvested at day 1, day 4 and day 7 into microcentrifuge Eppendorf tubes containing 10 μl M9 buffer. Before running the gel, 10 μl of 2x Laemmli sample buffer was added. Samples were incubated at 70°C and vortexed periodically for 15 min. Samples were then incubated at 95°C and vortexed periodically for 5 min, and were centrifuged at 6,000 rpm for 15 min.10 μl of sample was loaded into wells of Invitrogen NuPAGE Bis-Tris protein gels. 5 μl of CozyHi pre-stained protein ladder was loaded at the left side of the gels. The running buffer used was 5% XT MOPS (Bio-Rad). The gels were then run at 150V for 2 hr. Gels were removed from cassette and placed in 100 ml of Coomassie staining solution overnight. Gels were then washed with distilled water three times, then placed into destaining solution and soaked for 40 min. Gels were then washed with distilled water and stored at 4°C until imaged. Gels were imaged and saved as 8 bit grayscale TIF files. Images of gels were analysed using Fiji software. Bands of interest (e.g. myosin, YP170, YP115, YP88) were selected manually based on their molecular weight, and intensity of the bands was measured and exported to Microsoft Excel for further analysis.Data analysis and statisticsData were plotted using ePrism 9.0 (GraphPad Software, Boston, MA, USA) or Jupyter Notebook using Python with the matplotlib, pandas and NumPy libraries. Statistical tests were performed on raw data using either Prism 9 or JMP Pro 15 (JMP Statistical Discovery LLC, Cary, NC, USA) unless otherwise stated. The specific tests and post hoc corrections performed are described in the figure legends. To detect alterations in lifespan, the log rank test was used. To compare the magnitude of changes in lifespan, Cox Proportional Hazard (CPH) analysis was used. RT-qPCR data was analyzed using the ΔΔCt method and t-tests. To compare yolk protein levels, a two-way ANOVA was used. For lifespan trials, no statistical methods were used to predetermine sample size. The experiments were not randomized. The investigators were not blinded to allocation during experiments and outcome assessment.Data availabilityThe datasets used and/or analyzed during the current study are available with this article, and also from the corresponding author Prof. David Gems (david.gems@ucl.ac.uk) on request.AcknowledgementsWe thank Georg Fuellen (Rostock University), Kailiang Jia (Florida Atlantic University), Alicia Meléndez (Queens College-CUNY), Eisuke Nishida (RIKEN), and John Labbadia, Om Patange and Hongyuan Wang (UCL) for useful discussion, and/or comments on the manuscript, and Minh Tran Dang, Anna Girtle, Changtai Li, Gadea Meecham-Garcia and Suzie Mishima for minor research contributions. Some strains were provided by the Caenorhabditis Genetics Center, which is funded by NIH Office of Research Infrastructure Programs (P40 OD010440).Additional informationFundingThis work was supported by a Wellcome Trust Investigator Award (215574/Z/19/Z) to D.G..FundingWellcome Trust (WT)https://doi.org/10.35802/215574David GemsAdditional filesSupplementary informationSupplementary tables 2-7, 9-11Supplementary dataset 1Supplementary dataset 2Supplementary dataset 3ReferencesAckerman D.Gems D2012Insulin/IGF-1 and hypoxia signaling act in concert to regulate iron homeostasis in C. elegansPLoS Genet 8:e1002498Google ScholarAitlhadj L.Sturzenbaum S.R2010The use of FUdR can cause prolonged longevity in mutant nematodesMech Ageing Dev 131:364–5Google ScholarAman Y.Schmauck-Medina T.Hansen M.Morimoto R.I.Simon A.K.Bjedov I.Palikaras K.Simonsen A.Johansen T.Tavernarakis N.Rubinsztein D.C.Partridge L.Kroemer G.Labbadia J.Fang E.F2021Autophagy in healthy aging and diseaseNat Aging 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Healthy Ageing, and Research Department of Genetics, Evolution and Environment, University College London, London, United KingdomORCID iD: 0000-0002-2107-6368*These authors contributed equally.Hannah Chapman*Institute of Healthy Ageing, and Research Department of Genetics, Evolution and Environment, University College London, London, United Kingdom*These authors contributed equally.Xiaoya WeiInstitute of Healthy Ageing, and Research Department of Genetics, Evolution and Environment, University College London, London, United KingdomXiaoyu SunInstitute of Healthy Ageing, and Research Department of Genetics, Evolution and Environment, University College London, London, United KingdomIsadora RawlinsonInstitute of Healthy Ageing, and Research Department of Genetics, Evolution and Environment, University College London, London, United KingdomDavid GemsInstitute of Healthy Ageing, and Research Department of Genetics, Evolution and Environment, University College London, London, United KingdomORCID iD: 0000-0002-6653-4676For correspondence: david.gems@ucl.ac.ukAuthor NotesCompeting interests: No competing interests declaredVersion historyPreprint posted: January 15, 2026Sent for peer review: January 20, 2026Reviewed Preprint version 1: March 17, 2026Cite all versionsYou can cite all versions using the DOI https://doi.org/10.7554/eLife.110620. This DOI represents all versions, and will always resolve to the latest one.Copyright© 2026, Hsiung et al.This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.Metricsviews0downloads0citations0Views, downloads and citations are aggregated across all versions of this paper published by eLife.

来源:e life发布时间:2026-03-17
ARID5B mutations cause a neurodevelopmental syndrome with neuroinflammation episodes

AbstractGenetic disorders affecting the epigenetic machinery constitute a major group of neurodevelopmental conditions. Pathogenic variants in several ARID transcription factors—particularly ARID1A, ARID1B, and ARID2—cause Coffin–Siris syndromes, all characterized by intellectual disability (ID). These genes encode core subunits of the BRG1/BRM-associated factor (BAF) chromatin remodeling complex. In contrast, ARID family members that function in other regulatory complexes have remained largely unexplored in neurodevelopmental disease.Here, we identify 29 individuals carrying heterozygous ARID5B variants, of which 24 (83%) introduce premature termination codons in the exceptionally long final exon, one affects the exon 9 splice donor site, and four are missense variants in conserved domains within the N-terminal half of the protein. Using a CRISPR–Cas9 knock-in mouse model harboring the p.Q522Ter variant, together with in vitro assays, we investigated the functional consequences of C-terminal ARID5B truncations.All affected individuals presented with global developmental delay or ID—most commonly mild—and frequent speech and language impairment. Recurrent features included kidney malformations, behavioral difficulties, and recurrent infections of the respiratory and urinary tracts. Two individuals experienced central nervous system inflammation, and two infants presented with persistent pulmonary hypertension. Remarkably, 19 of 29 variants (66%) cluster within the first quarter of exon 10, are de novo, and escape nonsense-mediated mRNA decay (NMD), which we confirmed for two variants affecting seven individuals. Variants outside this region were inherited. Heterozygous mice exhibited developmental and behavioral abnormalities, while homozygous mutations was perinatally lethal. Truncations and a small deletion within a predicted nuclear localization signal (NLS) caused cytosolic mislocalization of ARID5B, whereas the isolated C-terminal half retained nuclear localization, suggesting an independent distal NLS.Collectively, these findings define ARID5B-related neurodevelopmental disorder as a distinct clinical entity and reveal how disruption of specific ARID5B domains impacts protein localization, mammalian development, immune and neurobehavioral function.IntroductionEpigenetic diseases that are caused by genetic variants have emerged as a major category of neurodevelopmental disorders (NDD). The AT-Rich Interaction Domain 5B (ARID5B [OMIM: 608538]), known as modulator recognition factor 2 (MRF2) or DESRT, belongs to the ARID transcription factor family which have key roles in gene regulation via chromatin remodeling and epigenetic modification1. Variants in several members of the ARID family have been associated with neurodevelopmental disorders, in particular ARID1A (Coffin-Siris Syndrome 2 (CSS2, [OMIM #614607]), ARID1B (Coffin-Siris Syndrome 1 (CSS1, [OMIM #135900])2,3 and ARID2, another member of the BRG1-associated complex (BAF) chromatin remodeling complex, leading to a Coffin-Siris-like syndrome4. ARID family members that are not members of the BAF complex have been less frequently associated with NDD. How-ever, via meta-analyses of a genomic study on intellectual disability (ID)5, we could associate a loss of function variant (LoF) in ARID5B with NDD. Notably, this variant would escape nonsense-mediated mRNA decay (NMD) as a consequence of the premature termination co-don (PTCs) being in the proximal part of the exceptionally long last exon, exon 10 of AR-ID5B (2.16 kb, 98th percentile for exon length based on NCBI human exon sizes)6,7.Through gene matching, we initially identified three additional individuals with de novo ARID5B variants presenting with intellectual disability, speech and global developmental delay, learning difficulties and two with kidney defects. These individuals did not resemble the set of phenotypes typical of Coffin-Siris(-like) syndromes and lacked any disease-causing variants in known neurodevelopmental disorders. Moreover, all three truncating variants clustered in the first quarter of the long exon 10, similar to the reported variant, which prompted us to systematically characterize the clinical manifestations associated with ARID5B disruption.Here, we present a cohort of 29 probands with developmental delay and rare ARID5B variants identified via the Matchmaker Exchange platform8. The individuals have divergent geographical and ethnic backgrounds and females and males are nearly equally distributed. Remarkably, the majority of individuals have truncating variants with premature codons in the long exon 10 that affect both TV1 and TV2 transcript variants while escaping NMD. The others four variants are missense variants in conserved missense intolerant domains in the N-terminal half of the protein.Materials And MethodsCohort selection criteriaInclusion criteria were individuals with a predicted deleterious (premature termination codon or missense) variant in ARID5B and a neurodevelopmental disorder (NDD), in the absence of known NDD causing variants. The criteria for a NDD were either if patients presented with global developmental delay (all but one), or when speech was delayed, or when intellectual disability was diagnosed (ID). All the cases were isolated, except for three sisters, who inherited the variant from an affected mother. All variants were identified through exome or genome sequencing.Data and sample collectionsInformed consent for genetic studies as well as the blood sampling and publication of photographs included here was obtained from parents or legal guardians. Written, signed forms were accompanied for consent to use the donated cells and images of the participating individuals or their parents. Lymphoblastoid cell lines (LCLs) were provided by Care4Rare Canada. Peripheral blood mononuclear cells (PBMCs) controls were generated following blood sampling and from cohort individuals provided by M.D. and R.J. and subsequently processed at the Centre hospitalier universitaire Sainte-Justine (CHUSJ) Institutional Mother-Child Biobank, following standard protocols.MiceC57BL/6NJ mice were maintained in an established facility at the Centre de recherche Azrieli du CHUSJ (CRA-CHUSJ). Mouse husbandry and colony maintenance were performed according to the animal protocol approved (2022-3860) by the “Comité Institutionnel des Bonnes Pratiques Animales en Recherche” (CIBPAR). This committee is following the guidelines of the Canadian Council on Animal Care (CCAC).Generation of Arid5bemQ522* miceThe human variant NM_032199.3 c.1564C>T, p.Gln522Ter, was introduced into the mouse genome using CRISPR/Cas9. The mice were generated by the McGill Integrated Core for Animal Modeling (MICAM; McGill University, Montreal, Quebec, Canada). Briefly, the sgRNA (Synthego), Cas9 protein (IDT, catalog 1081058) and ssODN (ultramer, IDT) were microinjected into the pronucleus of C57BL/6N mouse zygotes with respective concentrations of 50:50:30 ng/μL. Embryos were subsequently implanted in CD-1 pseudopregnant surrogate mothers according to standard procedures approved by the McGill University Animal Care Committee (UACC). Oligonucleotides used were mArid5b-gRNA: 5’-AAGGCCAATGAAACTGACCA-3’ and ssODN:5’-TGGGAGCTGAATCTTTTTCAGGAGGCAAGGGAGGGCTTGGAAGCAGAGGGGCAA GGCCCTTATCTCCCATCTCCTCGGCCTCTTTCTCGCTGTTGGAAggTTaaTCAGTTT CATTGGCCTTTTCTGGGTCTGCTCTGGACAC-3’. After weaning, the mice were transferred to the CRA-CHUSJ. Mice were backcrossed for at least 3 generations to C57BL/6NJ (Jackson). The presence of the variant was confirmed by Sanger sequencing. Mouse husbandry and experiments at CRA-CHUSJ were done according to the approved animal user 732-NAGANO protocol no. 2021-3228 by the coordinator of the CIBPAR, in accordance with the CCAC guidelines.Staining quantificationImageJ software9 was used to quantify pictures of fluorescence semi-automated and blinded. For each data point in Figure 5B, 2 wells of a 12-wells plate were used per condition and for each well, 2 images were generated and quantified. For the quantification in Figure 5B, channels from the overlay ImageJ.tif files for DAPI and FLAG were split. The option Process>Binary>Make binary was used and ‘Analyze Particles’ to determine the region of interest (ROI). For the settings, a range of 20 to 1000000 pixels for FLAG and from 10 to 1000000 for DAPI was chosen and holes were included. This way, we verified that almost solely separated single nuclei were selected using the DAPI channel and FLAG staining images generated ROIs that corresponded to single cell expression, regardless of whether the staining was cytosolic and/or nuclear, this way selecting transfected cells. Next, the nuclear ROI was overlayed on the 16-bit FLAG-channel layer to quantify total FLAG staining per cell. Next, the DAPI ROI overlay was again used to fill up the nuclear ROI within the FLAG-16 bit image, masking the nuclear FLAG-staining but leaving the cytosolic expression. Then, we used the FLAG ROI again to quantify single cellular cytosolic staining. Nuclear staining per individual cell was determined as the difference between total FLAG-staining and cytosolic staining. Finally, each image output nuclear/cytosolic ratios for each cell were calculated per cell, generating several hundreds of measurements per image, then averaged per well and used for statistics and graphical presentation. This was repeated for three separate experiments and the averages of each experiment are plotted and used for statistical analyses. For the quantification in Figure 5G, one image per (n) per condition was taken and measurements were based on the size of the FLAG area per cell (imageJ, ROI per cell), see Rscript. Each data point is the mean of ROI surfaces of all individual cells in one coverslip/12-wells well. The wells were from three separate plates.See supplemental information for additional, more standardized experimental procedures and statistics.ResultsDe novo truncating ARID5B variants cause neurodevelopmental phenotypesAssessing the potential deleterious effects of variants in the ARID family, other than those in the previously established critical neurodevelopmental genes ARID1A and ARID1B, we initially were compelled by a loss of function (LoF) variants in ARID5B in a genomic study on individuals with intellectual disabilities5. We next collected information on three additional individuals with developmental delay (DD) and ARID5B variants (not selecting for a specific type of variant) with exome or whole genome sequencing. Remarkably, all four variants caused premature termination codons (PTCs) at the beginning of the long final exon 10, leading to the truncation of at least half of the protein while predicted to escape nonsense-mediated mRNA decay. This prompted our hypothesis that truncating variants at the beginning of exon 10 can cause neurodevelopmental perturbations and motivated us to collect a larger cohort of de novo ARID5B variants, for which we also aimed to collect a more complete clinical profile.With the help of Matchmaker Exchange platform tools8,10 we have assembled a cohort of in total 29 probands with neurodevelopmental phenotypes and ARID5B variants of which 24 (83%) are loss of function variants that have PTCs in exon 10 and one that affect the exon 9/10 splice site. Four others are missense variants in conserved protein domains in the N-terminal half of the protein. (Fig. 3B,C). Nineteen (68%) variants lead to gained stops or frame shifts in the first quarter of the long last exon 10, all of those that were assessed (14 individuals) were de novo variants. However, five out of six exon 10 truncating variants that were more dispersed over the other three quarters of exon 10 were inherited from three symptomatic mothers (including three sisters) and one non-symptomatic mother. Inheritance of a sixth C-terminal truncating variant could not be assessed.A detailed summary of overlapping dysmorphisms and images can be found in Figure 1. In Figure 2 a detailed summary of frequencies of other overlapping phenotypes can be found. Both the number of individuals and the percentages are noted, the latter are calculated by excluding individuals of whom a particular phenotype was not assessed at the time of writing. Detailed per patient information including phenotypes that occur only once can be found in Supplementary table 1.Figure 1.Dysmorphic features.(A) Facial dysmorphic features. Individual 9, with variant c.1379delA, p.Lys460SerfsTer19, has a broad nasal tip, protruding cupped ears, gingival cleft, micrognathia. Individual 10, with c.1398+1del, affecting the exon 9-10 splice site, has a prominent forehead, deeply groved philtrum, broad nose, and protuberant ear helices. Individual 11, with variant c.1419del, p.Glu474AsnfsTer5, has a high forehead, hypertelorism, telecanthus, high arched palate, ptosis, small mouth, long philtrum, malar hypoplasia, and hypomimia. Individual 12, with variant c.1420del, p.Glu474AsnfsTer5, has left ptosis, hypertelorism, long philtrum, a broad nasal tip, micro- and retrognathia. Individual 17, with variant c.1489dupA, p.Ile497AsnfsTer31, has mild ptosis, frontal bossing, telecanthus, a short nasal bridge, long philtrum. Individual 21 (c.1587_1588del, p.Ala530ArgfsTer38) has a wide forehead and a broad nasal ridge. Individual 23 (c.1804C>T, p.Gln602Ter) has almond-shaped eyes, slightly upslanting palpebral fissures, a hyperteloric appearance to eyes with telecanthus, and a bulbous nose with a slightly bifid nasal tip. Individual 29 has a C-terminal variant (c.3435dupT, p.Ala1146CysfsTer15) inherited from his mother (not shown here), who both having a triangular-shaped face with ptosis, hypertelorism or telecanthus and a thin upper lip. (B) Hand and foot features. Individual 9 had bilateral mild radial deviation of wrists, bilateral mildly overlapping toes. Individual 10 camptodactyly of distal interphalangeal (DIP) joints, small hypothenar eminence contour. Individual 11, presented with the proximal phalanx of his fifth finger short and the base lower, as indicated by the position of the proximal interphalangeal crease. Individual 12 presented with clinodactyly of his fifth finger, and his palmar creases are hardly visible, small hypothenar eminence contour. Individual 21 presented long flat feet and increased sandal gaps. Individual 29 presented with the position of the base of the small finger and the proximal interphalangeal joint are aberrant with a deviation of the hands with slender fingers, as seen for other individuals. (C) Individual 10 presented with circumferential skin folds on upper (and lower) extremities, and prominent umbilicus (not shown). Individual 17 presented with mild laxity of wrists, elbows, knees, and fingers, and out-toeing of his feet (suspect vertical talus) with folded skin and an umbilical hernia. (D) Individual 21 presented with abnormal teeth and dentition. (E) Individual 23 presented a lobulated, slightly bifid uvula. His congenital pes planovalgus of both feet, right single palmar crease, and narrowing bilateral distal palms are not shown. (F) Overall frequencies of recurring (more than one) dysmorphisms, with calculated percentages that indicate the frequencies as a percentage of addressed individuals. Only explicitly known features that were not adressed per indivdual are subtracted, for example when individuals are too young to asses certain phenotypes, or when complete assesment was not yet performed by the time of writing. Abbreviation: PRS = Pierre Robin Sequence.Figure 2.Phenotype frequencies.Frequencies of recurring phenotype features, except dysmorphism features, with calculated percentages that indicate the frequencies as a percentage of addressed individuals. Only explicitly known features that were not addressed per individual are subtracted, for example when individuals are too young to asses certain phenotypes, or when complete phenotype assessment was not (yet) performed by the time of writing. General categories are used to calculated frequencies of related features. Specific features with their recurrence (between parentheses) are written out next to the calculated percentage of each bar. Abbreviations: ID = Intellectual disability [OMIM: 156200], GGD = Global developmental delay, SD = Speech disorder/delay, ADHD = Attention Deficit&Hyperactivity Disorder, IQ = Intelligence Quotient, UTI = Urinary Tract Infection, CNS = Central Nervous System, SWH = Septal Wall Hypertrophy, ASD = Atrial Septal Defect, PDA=Patent Ductus Arteriosus [OMIM: 607411], VSD=Ventral Septal Defect [OMIM: 614431], PFO=Patent Foramen Ovale, WHO = World Health Organization, IUGR = Intrauterine Growth Restriction, EIF = Echogenic Intracardiac Focus, PRS = Pierre Robin Sequence.Of the 29 probands, 14 females and 15 males with the age of their last follow up ranging from 0 to 29 years, all presented with a neurodevelopmental disorder. All individuals had global developmental delay (GDD), with the exception of one individual who was not assessed but suspected to have mild ID. Ten individuals were officially diagnosed with intellectual disability (ID [OMIM: 156200]), mostly mild (seven individuals), eight others likely have ID, but were not formally diagnosed at the time of writing, and all others were suspected to have cognitive challenges. Behavioral issues were frequent (18 individuals), including autism spectrum disorder (ASD [OMIM: 209850]) (8 individuals) and other behavioral issues (14 individuals), particularly attention-deficit/hyperactivity disorder (ADHD [OMIM: 143465]). Speech was affected in 21 individuals, including one individual with regressive language externalization leading to speech apraxia.Frequent systemic manifestations that characterize the disease are kidney abnormalities (11 individuals) including hydronephrosis (five individuals), megaureter (two individuals), pyelectasis and pyelic hypotony. Hypotonia (10 individuals) and feeding difficulties (nine individuals) were common, with three individuals presenting with oropharyngeal dysphagia. Visual impairments (seven individuals), including myopia (OMIM: 160700) (6 individuals) and in addition three individuals with strabismus (OMIM: 185100). Ear/auditory issues (seven individuals) included sensorineural hearing loss (2 individuals) and tympanostomy tube placement (two individuals).Immune dysfunction was noted in nine individuals, mostly with recurrent infections (seven individuals), two individuals presented with rare forms of CNS inflammation and another individual was hospitalized for a prolonged fever with unknown cause. Individual 27 (8-year-old girl) developed autoimmune cerebellitis and was unresponsive to therapy, while individual 23 (7-year-old boy) had recurrent acute disseminated encephalomyelitis (ADEM), but responded to therapy. Both were treated with corticosteroids and intravenous immunoglobulin (IVIG) and experienced seizures related to neuroinflammation. Two infants presented with persistent pulmonary hypertension, which was the predominant cause of death in individual 19 at six months of age. Finally, out of the four individuals that had missense variants, two presented with macrocephaly and another one with (mild) microcephaly with wide ventricles and corpus callosum atrophy that appeared after the age of 10 months. Macrocephaly was not reported for truncating variants, while individual 15, a two-year-old girl with a truncating variant, presented with a head circumference <0.4th centile. Finally, intrauterine growth retardation was reported 4 times, while post-natal growth was normal, with two individuals having a tall stature, and most individuals with truncations at the beginning of exon 10 being on the tall side.A very common craniofacial feature was the shape of the nose, generally shorter and broader, a bit bulbous, often with a lower nasal bridge (13 individuals) (Figure 1A,B). 10 individuals had an uncommon head shape, with a more prominent forehead and narrow face, frequently with micro/retrognathia (8 individuals). Five individuals presented with hypertelorism, ptosis and, nine individuals presented with thin upper lip, long philtrum, or narrow mouth. Skeletal abnormalities included toe/foot anomalies (7 individuals), radially deviated wrists (2 individuals), long and slender fingers or clino-comptodactyly (6 individuals) and abnormal palmar creases (6 individuals).Premature termination codons cluster in the first quarter of exon 10 and escape NMDNineteen out of 29 variants (66%) cluster in exon 9 (only frameshift) and early exon 10 (frameshift and nonsense), with all PTCs being in the first quarter of exon 10 and one variant that affects the exon 9 / exon 10 splice donor site (Fig. 3B). Since PTCs in such exceptionally long exon 10 (2.16 kb, >95th percentile for length)6 are known to escape NMD, we quantified and sequenced the exon 9 – exon 10 border at the mRNA level to confirm the absence or strongly reduction of NMD in two lymphoblast cell lines (LCLs) and primary peripheral blood mononuclear (PBMCs) of two patients generated from blood collected at different ages. The variant affecting the donor of the LCLs was five times recurring in our cohort (p.Ile497AsnfsTer31). We indeed found no signs of NMD (Fig. 3E, F). Unlike our cohort, gnomAD LoF variants are dispersed, with only 3/23 (13%) clustering in the first quarter of exon 10, compared to 19/24 (79%) in our cohort (Fig. 3B)11. Moreover, all tested probands (5 out of 6, of which three sisters) were inherited, suggesting that the approximate location of the PTC could be relevant for the phenotypic outcomes. Less truncated, longer ARID5B protein could lead to milder phenotypes if they contain additional translated C-terminal domains that are still functional. One particular protein domain between amino acid 610-650, that is intolerant to missense variants, is lost in all truncations that cluster at the beginning of exon 10 (Fig 3B,C) but still encoded in all inherited truncations that affect the latter three quarters of exon 10. Moreover, the regional clustering of pathogenic LoF variants mirrors that seen in other epigenetic regulators (e.g., KAT6B [OMIM: 612990], ASXL1 [OMIM: 612990], ASXL3 [OMIM: 615115]).Figure 3.Schematic representation of ARID5B variant types and their consequences(A) The two main isoforms generated from the curated transcript variants 1 and 2. The shorter isoform uses an alternative exon, here named 4b (light blue). Further, the two known domains, the BAH and the DNA binding BRIGHT domain are shown in yellow and dark blue, respectively. Sox9 has been shown to associate with the latter 2/3 of the protein (shown in orange), downstream of the BRIGHT domain. Variant p.(Asn434LysfsTer45) in this cohort has been included in the OMIXCARE cohort as well39. (B) Linear representation of isoform I, with the locations of the variants described in the current cohort on top and variants retrieved from gnomAD V4 or genomic studies below the protein bar. Diamonds indicate truncating variants. Light blue diamonds are predicted to cause NMD of only the long transcript variant 1. Red diamonds indicate variants that are predicted to cause NMD of both transcript variants. Dark blue variants, mainly found in the cohort, are both proven and predicted truncating variants associated with ID. The yellow diamonds represent truncating variants predicted to skip NMD, but with unknown phenotypes (retrieved from gnomAD V4). Green dots represent missense variants associated with neurological perturbations (four from the cohort, two from ASD studies2,14. (C) Isoform-I is represented with on top aligned the missense tolerance rate (MTR). The MTR indicates the running average P-value of synonymous versus observed missense variant. A lower value indicates a lower missense tolerance40. At the bottom, the homology between mouse and human ARID5B is represented with each non-conserved amino acid represented as a gap. Critical regions like the BRIGHT domain show a low missense tolerance and high conservation. Three mouse-to-human conserved domains are indicated with blue boxes. (D) Zebrafish-to-mouse-to-Human conservation of the loci with cohort missense variants (4X) and the ARID5B C-terminus. In (E), to confirm the expression of stable mutant RNA transcripts (escaping NMD), Sanger sequencing was performed on cDNA generated from RNA purified from patient cell lines, following DNAse treatment, and using primers that generate an amplicon that crosses the exon 9-10 junction (further avoiding amplification of DNA instead of RNA). The expression of mutated RNA with similar Sanger sequencing peak depth as wild type RNA. In (F), we validated this by quantifying the RNA levels and comparing normalized exon 9/exon10 RNA levels between controls and cell lines from patients with ARID5B variants, observing no effect on average RNA levels. Three PBMC control cell lines (blue dots) and three LCL control cell lines (yellow dots) were compared with two LCL clones generated at two different ages from individual 14 (c.1489dupA, p.Ile497AsnfsTer31; brown dots), and individual 23 (c.1804C>T, p.Gln602Ter; light blue dot). The line indicates average expression.The four missense variants in our cohort resided in highly conserved, missense-intolerant regions and were not found in gnomAD V4; p.Ile54Met (BAH domain, but unknown target), pArg186Trp (domain of unknown function), p.Ser266Asn (domain of unknown function) and p.Arg335Ser (BRIGHT/ARID, DNA-binding domain). Especially the latter is in a strongly missense intolerant domain, and loss of a positively charged arginine at the end of the first alpha-helix in the the ARID5B DNA-binding domainis likely to affect binding to the negatively charged DNA12. All were conserved from mouse to human, and all but p.Ile54Met from mouse to zebrafish (Fig. 3C,D). Notably, in gnomAD V411, the p.Ser266Gly substitution can be found 12 times.Eight individuals presented with ASD, with only four of those being truncating at the beginning of exon 10, while three out of four missense variants, among which those residing in the BAH and BRIGH domain, were associated with ASD. We also noted two ASD-associated missense variants (p.Ala702Pro, p.Thr906Ala) in genomic studies13,14. Eventhough p.Ala702Pro was absent in gnomAD, p.Thr906Ala was identified four times in gnomAD V4. Another ASD-associated variant (e.g., p.lIe213HisfsTer16) affected only transcript variant 1 (TV1) (Fig. 3B).Arid5b(Q522*/+) mice exhibit developmental and behavior phenotypesTo further establish deleterious developmental consequences of truncating ARID5B variants, we generated knock-in mice that are carriers of the truncating variant of individual 20, a gained stop at the beginning of exon 10. Following repeated backcrossing and Sanger sequencing validation of the mutation and the ends of the donor DNA arms (Fig. 4A), we first tested the viability of mice heterozygous and homozygous for the variant. We did not observe any effect on the one-year survival rate of heterozygous mice, but homozygous mice generally died shortly after birth (Fig. 4B). Of note, with manual supplementation of food, some mice were able to survive slightly longer. Since homozygous mice remained very small and vulnerable, we decided not to proceed with their characterization for ethical reasons.Figure 4.Mouse development and behavior are affected in Arid5bemQ522* mice(A) Sanger sequencing-based validation of genotype showing the four induced DNA mutations leading to a gained stop at Q522. (B) Kaplan-Meier survival plot over one year. The majority homozygous Arid5bQ522* mice (green line) die in the first postnatal days. (C) Body weights of Arid5bQ522* mice across development. Independent ages (P1, P6, P60) were analyzed using one-tailed t-tests to assess a reduction in body weight, comparing wild type with heterozygous mice. Repeated measurements at P8 and P21, obtained from the same cohort of mice, were analyzed using a linear mixed-effects model to account for repeated measures. Body weights at P60 were corrected for sex to account for naturally lighter female weights. Heterozygous Arid5bQ522* mice have significantly reduced weight at P6 (P = 0.0084, df = 13), P8 (P = 0.0265, df = 22.10), and P21 (P = 0.00076, df = 22.10), but not at P60 (df = 21), P1 was inconclusive (P = 0.129, df = 10). (D) Open field test time spent in the center of the open field box as a percentage of total time spent in the open field box per mouse (wild type mice yellow dots/left box; Arid5bQ522* mice orange dots/right box). A two-tailed t-test showed a significant increase in time spent in the center (P = 0.0223, df = 42). One outlier was removed. (E) The total distance covered during the total time spent in the open field box per mouse (wild type mice yellow dots/left box; Arid5bQ522* mice orange dots/right box). A two-tailed t-test was inconclusive (P = 0.1852). (F) Three-chamber sociability test. Left side (“Sociability”): difference in time spent exploring an object versus a mouse for wild-type (yellow) and Arid5bQ522*mice (orange) mice. Right side (“Social Novelty”): difference in time spent exploring a familiar versus a novel mouse. Expected social behavior was confirmed using one-tailed t-tests to asses increased time spent with a mouse vs object or novel mouse vs familiar mouse. (G) Total social interaction time (summed across the sociability and social novelty experiments) in wild-type (WT, yellow) and Arid5bQ522* (orange) mice. A one-tailed (H₁: reduced social interaction in heterozygous mice) t-test was not applicable, since the mean interaction time was increased in Arid5bQ522* mice. A two-tailed t-test comparing total social interaction time between genotypes was inconclusive (WT = 132.17 ± 56.58 s, n = 21; Q522* = 173.64 ± 79.78 s, n = 21; t(36.1) = –1.94, P = 0.0598). Three outliers were identified and removed using Grubbs’ test — two in the wild-type group and one in the Arid5bQ522* group. Statistical significance is indicated as follows: P < 0.05 (*), P < 0.01 (**), and p < 0.001 (***).Even though heterozygous mice showed no signs of reduced one-year-viability, their lower weight throughout development suggested a developmental defect that seems to recover into adulthood (Fig. 4C). In addition, heterozygous mice showed different behavior in the open field test, being more frequently in the center than wild type mice (Fig. 4D, E). Similar patterns of behavior are seen in other mouse models of ID or ASD, like Fragile X syndrome (Fmr1 disruption), Bosch-Boonstra-Schaaf Optic Atrophy Syndrome (Nr2f1 disruption), AUTS2-related syndrome (Auts2 disruption)15–17. However, we did not observe sociability or social novelty defects, as heterozygous mice showed a normal preference for a mouse over an object and for a novel mouse over a familiar mouse in the 3-chamber sociability test(Fig. 4F). Also the overall time that Arid5b(Q522*/+) mice spent on social exploration did not significantly change (Fig. 4G).Truncated ARID5B delocalizes into the cytoplasmTo investigate the protein consequences of truncating variants in exon 10, we constructed open reading frames (ORFs) encoding variously tagged ARID5B variants (see Methods). As expected, overexpression of ARID5B in HEK293T cells resulted in near exclusive nuclear localization under standard growth medium conditions, regardless of the tag added (Fig. 5A). However, introducing the nonsense variant of individual 20 (c.1489dupA, p.Gln522Ter) resulted in near complete delocalization of the truncated protein in the cytoplasm (Fig. 5A). Surface plots overlaying FLAG and DAPI staining further showed the strong extra-nuclear localization together with the nuclear absence of the p.Gln522Ter variant in more detail (Fig. S2A).Figure 5.Terminating variants affect cellular localization with divergent effect sizes.(A) Overexpression with both C- and N-terminally, HA- or FLAG-tagged ARID5B isoforms in HEK293T cells show a strong preference for nuclear localization. The N-terminally HA or FLAG-tagged ARID5B, containing the variant of individual 10 (p.Glu522Ter), strongly locate in the cytosol instead. (B) Ratio of nuclear versus cytosolic ARID5B of (FLAG-tagged) wild-type, p.Glu522Ter, and the most C-terminal variant in our cohort detected for individual 22 and his mother (p.Ala1146CysfsTer15). The dots represent averages per experiment (see Methods). Two-tailed, two-sample t-tests (equal variance) compared each mutant to WT, with Bonferroni correction (α = 0.05). WT vs A1146C fsTer15: t(4) = 2.34, adjusted p = 0.176 (#), Cohen’s d ≈ 1.87 (ns). WT vs Q522*: t(4) = 7.39, adjusted p = 0.00412, Cohen’s d ≈ 6.49 (**). In (C), FLAG-tagged predicted isoform III cellular localization showing nuclear localization. (D) Analysis of ARID5B sequence using various structural prediction tools. The graph shows predicted nuclear localization sites (NLSs) generated by NLStradamus representing 3 Hidden Markov Modeled NLS states18. The elevated/peak signals predict three regions that could regulate nuclear localization. The most C-terminal elevated signals co-localize with predicted NLS and nuclear export signals (NES) predicted by other tools, here shown by name of the tool and amino acid sequence. Furthermore, the most abundant kinase signal transduction target sites that Motif Scan/Prosite19 predicted were Casein kinase 2 (CK2) sites, that are scattered and marked in red, and cAMP phospho-sites, located within predicted NLSs. This was in line with both PKA and CK2 involved in regulating pathways wherein ARID5B is active30,41. The nuclear export site is shown as black/yellow stripes. The truncating variants of individuals 16 and 20 are shown too, truncated proteins of 521 and 967 aa long, respectively. (E) Overview of mutated ARID5B proteins tested. In brown variants from cohort individuals, in blue a variant from the gnomAD database, in chartreuse green, a variant that was previously associated with ASD14, finally, in black are the designed variants. (F) Microscope images of the cellular localization of various variants in different growth conditions to assess cellular localization and stability of the phenotype under divergent conditions. Overnight transfected cells were then cultured for 24 hours without serum, after which serum was added 3 hours before staining (+S24h), or no serum was added (SF), or kept in serum-rich medium during the whole experiment (+S). (G) Quantification of the expression area of ARID5B variants using a one-way ANOVA (F(11, 57) = 20.78, P = 4.37e-16, one-tailed, alpha = 0.05). Post hoc t-tests (one-tailed; increase of surface area for defect nuclear localization) comparing each variant to the control (ARID5B-IsoI) were performed and Bonferroni-corrected. Significant increases were observed for Q522* (P-adj. = 1.14 × 10⁻7), Q474* (P-adj. = 5.68 × 10⁻5), Y968* (p adj = 0.00125), del1018-1026 (P-adj. = 0.036), and G634Afs34 (P-adj. = 1.54 × 10⁻7). Error bars indicate SD. The grey-blue bar represents wild-type ARID5B, the orange bars are variants identified in our cohort, and the green bars are in-house designed variants. The light yellow bar one of the missense variants found in the genomic studies on ASD. Finally, the red bar represents a truncating gnomAD_V3 variant. P-adj. < 0.05 (*), Padj. < 0.01 (**), and P-adj. < 0.001 (***), indicate adjusted (P-adj.) afer post-hoc Bonferroni adjustments. For conciseness of the figure we did not include the exact P-value in this figure.C-terminal truncating variant shows only a very minor delocalization phenotypeWe set up a highly-sensitive, semi-automated blinded quantification of the cellular localization and assessed p.Gln522Ter and the most-C-terminal variant, p.Ala1146CysfsTer15. Whereas p.Gln522Ter showed, as expected, a strong cytosolic delocalization, p.Ala1146CysfsTer15 only showed a very minor but reproducible, cytosolic localization defect (Fig. 5B). However, we want to note that the typical localization patterns of p.Ala1146CysfsTer15 is similar to those of wild-type ARID5B, regardless of total protein expressed per cell (Fig. S2B), a small portion of cells showed cytosolic localization more similar to p.Gln522Ter (Fig. S2C). This could explain the small effect on overall cellular localization of p.Ala1146CysfsTer15 observed in Figure 5B.The long exon 10 harbors 3 ATG sites that have predicted functional Kozak-sequences that have the potential to produce alternative isoforms (Fig. S3A). In mouse embryonic brain lysates, proteins of the expected sizes of these alternative exons react with anti-ARID5B antibodies (Fig. S3B). In addition, we have found evidence for alternative isoform expression from C-terminally tagged, overexpressed ARID5B (Fig. S3C). To investigate if such isoforms would localize in the nucleus, which would suggest the use of a C-terminal nuclear localization signal (NLS), we overexpressed this predicted alternative isoform III and assessed its localization. Indeed, this C-terminal part of ARID5B alone, encoded solely by exon 10, also resided in the nucleus (Fig. 5C). A detailed description of potential alternative isoforms can be found in the supplemental information and figure Fig. S3.Various tools predict a complex nuclear export/import domain in exon 10To predict protein sequences that could play a role in ARID5B cellular localization, we combined the output of several motif-predicting tools (Fig. 5D). NLStradamus homo Sapiens predicted two significant nuclear localization (NLS) sites in the N-terminal half of the protein (Fig. 5D)18. However, a broader almost significant C-terminal domain was indicated by NLS-Mapper, Prosite and MotifScan, with two NLSs flanking a nuclear export signal (NES) in the region covering amino acids 999-1030 of ARID5B isoform-I, potentially forming a complex NLS/NES/NLS (NNN) domain (Fig. 5D)19–21. Notably, one of the missense variants in our cohort (p.Ser266Asn) resides on the flank of the predicted N-terminal NLS (Fig. 5D).Considering the truncating variants, most truncations lead to the complete loss of the C-terminal predicted NNN, including p.Tyr968Ter, and thus we hypothesized that cellular localization for variants that lost the NNN would be similarly affected (Fig. 5D).Cohort and designed ARID5B variants suggest a dominant C-terminal nuclear localization domainWe generated nine more ARID5B variants tagged with a FLAG peptide to investigate conse-quences on cellular localization (Fig. 3E). First, we confirmed that all overexpressed truncat-ed variants result in proteins of the expected size (Fig. S2D). In addition to p.Gln522Ter and the more C-terminal p.A1146CfsTer15, we also included shorter and intermediate truncated variants from our cohort (c.1423C>T, p.Gln475Ter and c.2904C>A, p.Tyr968Ter) and the NLS-located missense (p.Ser266Asn). Further, we added several designed variants aiming to explore the mechanism of cytosolic localization in more detail, including one with a small de-letion (p.del1018-1026) in the middle of the NNN domain and additional missense in the primary NLS signal (p.Ser264/Ser266Asn) that affects a potential regulatory serine in the N-terminal NLS domain near the variant of individual 3. We also added one C-terminal truncat-ing variant from gnomAD V3 (p.Glu634AlafsTer29) and one missense variant derived from the genomic ASD study, p.Ala702Pro14. As expected, truncating variants from the cohort (p.Gln474Ter, p.Tyr968Ter) and gnomAD truncating (p.Gly634AlafsTer29) variants that completely lose the NNN domain located in the cytosol (Fig. 5F). We further quantified this by measuring the (increased) average surface expression per cell (Fig. 5G). Of note, we could not find any changes in expression patterns caused by stress or altered growth conditions fol-lowing temporarily depleting cells of serum (Fig. 5E).The p.Ser266Asn cohort variant located in the N-terminal NLS did not lead to more FLAG-tagged protein outside the nucleus. Additional mutation of p.Ser264Leu (p.Ser264Leu/Ser266Asn) did not change this finding (Fig. 5F,G). Strikingly, a small deletion in the middle of the NNN (p.del1018-1026) had a surprisingly strong effect, completely delocalizing ARID5B in the cytosol (Fig. 5F,G). Other designed variants with altered charges of the N-terminal NLS (p.Lys255Asp/Lys256Asp) and one variant with two serines mutated in the first NLS of the NNN domain (p.Ser1002/1015Asp) did not change the cellular localization (Fig. 5F,G). Neither did the ASD-associated p.Ala702Pro variant affect the nuclear localization of ARID5B, all having a punctate nuclear signal that is typical for wild type ARID5B (Fig. S2E). In summary, our data reveals a conserved C-terminal ARID5B protein domain that can regulate ARID5B cellular localization, explaining cellular delocalization of truncated ARID5B variants.DiscussionWe have gathered evidence that truncating ARID5B variants, especially those that cluster within exon 9 and at the beginning of the exceptionally long last exon 10 of ARID5B, escape NMD, cause a neurodevelopmental disorder with GDD, ID and speech delay. Additional features included kidney defects and recurrent infections. Two individuals had episodes of CNS inflammation and two infants had persistent pulmonary hypertension, being the main cause of death of a 6 months old boy. Facial dysmorphic features most frequently include a short, bulbous nose, noted for half of the individuals, often accompanied by a shorter/lower nasal bridge. Several individuals presented with micrognathia, ptosis and some with a prominent forehead.While none of the variants that cluster in the first quarter of exon 10 were inherited (14/19 were tested), three out of four of the truncating variants outside the cluster were inherited and the fourth one could not be addressed, which suggest that these variants could have unique deleterious effects for some phenotypical aspects, possibly with milder ID. While one mother was not diagnosed, two others were affected.Truncations that lead to the loss of fewer domains may lead to reduced loss of function compared to variants that loos the complete exon 10 coding coding RNA, particularly if the additional domain(s) are still functional. Since LoF variants that lead to complete NMD are so rare, as discussed above, we opt that the lack of losing a conserved domain of unknown function, spanning amino acid 610 and 650 of ARID5B isoform one, can play a role. This domain may still be intact in all inherited truncating variants, while being lost in variants that cluster at the beginning of exon 10.Another possible mutation type specific phenotype could be macrocephaly, with which two individuals with missense variants presented but none of the individuals with truncating variants. Also, four out of eight individuals with ASD had variants that did not cluster in exon 10, while three out of four individuals with a missense variant had ASD. Several missense and one LoF of TV1 were identified in individuals with ASD. We did not find clear other patterns in genotype-phenotype relationships.null of the missense variants were identified in gnomAD V4, and the affected residues are conserved between mouse and human (p.Ile54Met, individual 1), or between zebrafish, mouse and human (p.Arg186Trp, p.Ser266Asn, p.Arg335Ser, indivduals 2-4). All were present in missense intolerant regions, and three were confirmed to be de novo, suggesting (neuro)developmental importance. However, p.Ser266Asn can be found substituted into a glycine (12X) in gnomAD V4 and had unique phenotypes (no ASD, mild microcephaly) compared to the other missense variants. The other three individuals with missense variants and ASD, have variants residing in known critical domains like the BAH and ARID domain, and two of them presented with macrocephaly (BAH and ARID domain variants).ARID5B is predicted to be haploinsufficient considering the gnomAD V4 database, having a loss-of-function (LoF) observed/expected upper bound fraction (LOEUF) of 0.21, with the current threshold for V4 being 0.611. However, ARID5B variants that are predicted to lead to the complete loss of function (LoF) ARID5B via NMD of both ARID5B transcript variants (TV1 and TV2) are much more sparse than LoF in general. To our knowledge, out of >800.000 individuals in gnomAD V4, and theoretically dozens more that could have been identified through gene matching, we could only find two individuals (both in gnomAD V4) that carried variants that are predicted to lead to NMD of both ARID5B TV1 and TV2. In addition, variants in the exceptionally long final exon, exon 10, generally escape NMD7.Therefore, the translational consequences can be particularly divergent for ARID5B, with LoF variants in a large part of the coding regions escape mRNA decay, or otherwise, only affecting TV1, not TV2, leading in both cases to only a partial loss of endogenous ARID5B protein functions and/or abnormal protein variants.Both complete (TV1+TV2) LoF variants were identified in gnomAD, while in our cohort, and in many large genomic ASD and ID studies, no additional similar variants were identified. This extremely low frequency could emphasize a strong deleterious effect compared to other ARID5B variants, perhaps generally too strong to be compatible with survival. The presence of variants in gnomAD is sometimes interpreted as meaning that they are unlikely to cause neurodevelopmental disorders, but at very low frequencies, one should be cautious interpreting gnomAD in such a way22–it should be kept in mind that these individuals for instance may have a mosaic variants, or are affected by these variants. We conclude that only complete genotype and phenotype characterization of heterozygous individuals with LoF variants can determine if such ultra-rare variants indeed lead to limited or variably penetrant phenotypes. For now, we expect that complete ARID5B LoF (affecting both transcript variants) will generally be as deleterious, or more deleterious as the truncating variants described here.A few other potentially deleterious ARID5B variants have been previously described. One individual with a nonsense variant affecting only TV1 (p.Gln84Ter) presented with a neurodevelopmental disorder, with ID, in combination with seizures, pituitary defects, a severe short stature23, which are mostly phenotypes we have not observed with other ARID5B variants. Several other variants, both LoF affecting only TV1, and missense variants in the C-terminal half of ARID5B, have been associated with autism spectrum disorder (ASD)13,13,14 and several more can be found in gnomAD V4.Several immune and inflammatory roles have been ascribed to ARID5B. For instance, in adaptive natural killer (NK) cells, ARID5B mRNA levels correlate with the expression of electron transport chain genes, oxidative metabolism and Interferon gamma levels24.ARID5B’s role as an immune mediator has been supported in aged monocytes, in which increased ARID5B expression correlates with atherosclerosis. In these aged monocytes, ARID5B is upregulated by immune activation and acts upstream as an activator of lipid metabolism and inflammatory genes. Moreover, the knockdown of ARID5B also reduced their migration, while increased expression correlated with increased mobility25,26. ARID5B transcription itself is under the influence of ARIEL (ARID5B-inducing enhancer-associated long noncoding RNA/XLOC_005968) which is also up-regulated in TAL1-positive types of T-ALL and acts as an enhancer RNA by facilitating ARID5B enhancer-transcription complexes27. A SNP in intron 4 of ARID5B, wherein two alternative exons reside28, has been linked to autoimmune diseases, specifically rheumatoid arthritis (RA [OMIM: 180300]) and Graves disease (GD [OMIM: 275000])29. In our cohort, two individuals had severe neuroinflammatory episodes (individuals 23 and 26) and a third individual (20) showed regressive loss of speech externalization from the age of two and was hospitalized for several weeks due to a prolonged fever with unknown cause around the age of 10. Given the rarity of cerebellitis and encephalomyelitis in pediatrics, the often precarious clinical situation that may arise, and given the numerous associations between ARID5B and immune system function, we think that it is critical to emphasize that truncating ARID5B variants are likely causally related to the recurrent CNS inflammatory episodes and other immune perturbations experienced by this subset of patients.Furthermore, we discovered that the long exon 10 ORF has an independent nuclear localization domain that is lost for most variants following truncation, except the two most C-terminal variants. These former variants localize in the cytosol, and not in the nucleus of HEK293T cells, in which ARID5B is normally found with a typical punctate nuclear localization (Fig 5, Fig. S2). We observed this effect particularly for variants that have translation termination codons upstream of a predicted, complex nuclear localization sequence (NNN). The NNN is very likely involved in ARID5B nuclear localization, in particular because we could drastically diminish ARID5B nuclear localization by a small, designed deletion in the center of the broad NNN domain. Moreover, the predicted isoform-III, which lacks two predicted NLSs that are present in the former half of the main ARID5B isoform, localized in the nucleus, while the loss of the last 42 amino acids (p.A1146CfsTer15, individual 29) of the C-terminus did not substantially affect cellular localization (Fig. 5B,F,G). Still, it remains uncertain if the removal of the center of the NNN is directly involved, or that it affects protein localization indirectly by changing the (local) protein conformation. Furthermore, since serine phosphorylation often plays a role in nuclear localization, for example of IKAROS, an ARID5B coregulator30,31, we substituted two serines in the NNN-domain but that did not affect the cellular localization of ARID5B.Escaping ARID5B NMD may affect its function in at least three ways as compared to complete LoF. Firstly, the loss of domains encoded by exon 10 may cause only a partial LoF, with the truncated protein still executing some of its functions. Considering the genotype-phenotype relationship, we would like to emphasize that truncating variants that cluster in the first quarter of exon 10 lose all three distinguishable conserved protein domains (or regions) encoded by exon 10 (Fig. 3), while the most C-terminal variants may not lose the function of at least two of these domains. Secondly, the truncated protein may have gained functions harmful to neurodevelopment. Thirdly, transcripts that bypass NMD may still express the alternative isoform(s) encoded by exon 10, and their functions may therefore not be lost, reducing the deleterious effect of LoF variants. Investigating the protein localization of ARID5B, isoform expression and truncated protein expression in other cell types and our mice, would surely help address these questions, but are currently challenged by a lack of antibodies that effectively bind to the N-terminal half of ARID5B.Three separate mouse models with deletions of exons encoding portions of the BRIGHT domain showed strain-dependent sub-lethality frequencies, growth retardation and were lean32–34. Arid5b mutant mice only deviate postnatally in weight, as adults when they become smaller than wild-type mice33. Both homo- and heterozygous Arid5b mutant mice are resistant to high-fat diet-induced weight gains. Such resistance to diet-driven weight gains has also been observed in mice lacking ARID5B solely in fat cells by Fabp4-Cre induced deletion of exon 634, which seems to be the consequence of triglyceride and lipid metabolism being downstream of ARID5B34,35. Even though, in humans, a SNP in an ARID5B motif has been associated with obesity, and mice lacking ARID5B are lean (or at least are protected against diet-induced weight gains)34–36, we did not observe clear trends in weight or growth defects in our cohort, with the individuals falling within a broad spectrum of weight and height. However, 4 individuals presented with IUGR, while later in life, individuals may catch up or even have a taller than average stature. In our mice, their reduced birth weight is at least partially compensated for into adolescence as well. Heterozygous mice seem to catch up, while homozygous mice are too vulnerable to make it through postnatal phases without support.This discrepancy can be due to several factors. Firstly, the human SNP may affect the binding of other transcription factors, not only ARID5B function. Furthermore, mice that lack ARID5B completely likely have unique phenotypes that could differ from variants that are truncating and escape NMD and/or have gain-of-functions. Therefore, comparing between truncating variants, NMD-causing ‘gene-trap’ mice, or overexpression models should be done cautiously.We also found subtle changes in the open field test, with, perhaps unexpectedly, the mice spending more time in the center of the arena in the open field test, a phenotype also observed for other ASD/ID NDD mouse models, for example Fragile X syndrome (Fmr1 disruption), Bosch-Boonstra-Schaaf Optic Atrophy Syndrome (Nr2f1 disruption), AUTS2-related syndrome (Auts2 disruption)10,16,17, and in other mouse models that have established cognitive limitations37. An overall lack of awareness, eventual in combination with a lower overall drive to explore could prompt spending more time in the center of an open field, likely even in the absence of an anxiolytic phenotype. Further characterization of our newly generated Arid5bemQ522* mice, and comparison with gene-trap models can reveal truncation specific deregulated mechanisms that could further pinpoint disease mechanisms specific for truncating variants that locate at the beginning of exon 10, like Arid5bemQ522*. Challenging the immune system of these mice is a logical future approach to assess their immunological vulnerabilities.ARID5B also plays a role during chondrogenesis by promoting the production of collagen type II in a manner dependent on SOX9 and involving the activation of SIRT138. This suggests that ARID5B contributes to extracellular matrix (ECM) formation and may support the maintenance of tissue integrity. Although homozygous mice are smaller than their heterozygous counterparts, subtle alterations in ECM formation or phenotypes manifesting only under specific challenges remain to be investigated. Such mechanisms could help explain the variably expressed systemic manifestations—such as hydronephrosis or immunological abnormalities—which may arise not only from direct immune cell perturbations but also indirectly through impaired barrier function and altered cell adhesion secondary to collagen dysregulation.In conclusion, we have shown that truncating variants, including frameshift variants in exon 9, leading to stop-gains that cluster in the first quarter of the disproportionally long exon 10 of ARID5B, cause a neurodevelopment syndrome with a spectrum of phenotypes, including kidney issues, hypotonia and feeding problems, most frequently mild ID and with DD, speech and language delay/disorder and behavioral issues. A broad or bulbous nose, often with a short and low nasal bridge was observed. Other distinctive features included ptosis, radial hand deviations and foot/toe abnormalities. There is also a possible susceptibility to episodes of recurrent infections, often without a known cause, of the urinary tract, upper airways and inner ear, and two rare cases of CNS neuroinflammation, with one individual poorly responding to therapy being treated palliatively at the time of writing. Two infants presented with persistent pulmonary hypertension, leading to the death of individual 19 at six months of age. Truncating mutations escape NMD and showed cellular localization defects that we attribute to the loss or impairment of a C-terminal nuclear localization signal/nuclear export domain. Mice with truncating mutations in ARID5B show developmental and behavioral abnormalities. Missense variants in critical domains like the BRIGHT domain and truncating variants in the distal part of exon 10 very likely affect neurodevelopment, but their consequences may require individual assessment due to variable deleteriousness and partially unique phenotypes.Data availabilityThe published article includes all datasets generated or analyzed during this study. Raw imaging data, constructs, and plasmid maps will be provided upon request. R code for statistics in figures 4C, 4G, and 5G, is available at https://github.com/Hjvanheesbeen/ARID5B_Cohort_Statistics.AcknowledgementsPart of this work was performed under the Care4Rare Canada Consortium funded by Genome Canada and the Ontario Genomics Institute (OGI-147), the Canadian Institutes of Health Research, Ontario Research Fund, Genome Alberta, Genome British Columbia, Genome Quebec, and Children’s Hospital of Eastern Ontario Foundation.Genetic testing of individuals with the c.1419del (p.Asn434LysfsTer45) and the c.1365dupA (p.Glu456ArgfsTer31) was performed within the framework of the GAD (Génétique des Anomalies du Développement) collection and approved by the appropriate institutional review board of Dijon University Hospital (DC2011-1332). The work was supported by grants from Dijon University Hospital, the ISITE-BFC (PIA ANR), the European Union through the FEDER programs (PERSONALISE), and the Burgundy-Franche-Compté regional council (INTEGRA). The sequencing platform at the CNRGH was supported by the France Génomique National infrastructure, funded as part of the “Investissements d’Avenir” program, managed by the Agence Nationale pour la Recherche (contract ANR-10-INBS-09). The whole genome sequencing performed at the CNRGH was funded by the Laboratory of Excellence GENMED (Medical Genomics) Grant No. ANR-10-LABX-0013, managed by the National Research Agency (ANR) as part of the Investment for the Future program.The CRISPR-Cas9 images (thumbnail) (image “Cas9 gene”) was reproduced from the original work by the author (2024) under the Creative Commons Attribution-ShareAlike 4.0 International License. No modifications were made. The inner ear illustration (thumbnail) was provided by MedicalGraphics.de and is used under a Creative Commons Attribution-NoDerivatives 4.0 International License (CC BY-ND 4.0). No modifications were made to the original image. The drawing of the lungs Medical illustration (thumbnail) by Patrick J. Lynch and C. Carl Jaffe, Yale University School of Medicine, licensed under CC BY 2.5.Megaureter illustration courtesy (thumbnail) of the Children’s Hospital of Philadelphia, used under public domain. The image “Cartoon brain illustration” (thumbnail) was obtained from the public domain collection on Rawpixeland is free to use without restriction under the CC0 Public Domain Dedication.Additional informationEthics DeclarationThe study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of CHU Sainte-Justine (protocol code 015-853:4072 and date of approval 26 May 2015). Written informed consent from the participants’ legal guardian/next of kin was provided in accordance with the national legislation and the institutional requirements for the provided images to be published and donated blood to be used for scientific purposes.Author ContributionsH.v.H wrote the manuscript and designed the experiments. H.v.H, N.R, A.G, A.P, and J.C. performed the experiments. H.v.H., N.R. and A.P. analyzed the experiments, P.C. & H.v.H. supervised the project, H.v.H. and P.C. recruited the cohort, and K.B. edited the manuscript. All other authors contributed by reviewing the manuscript and providing clinical information and/or exome analyses.Additional filesSupplemental InformationTable S1References1.Deák GCook AG2022Missense Variants Reveal Functional Insights Into the Human ARID Family of Gene RegulatorsJournal of Molecular Biology 434:167529https://doi.org/10.1016/J.JMB.2022.167529Google Scholar2.Kosho TOkamoto NCollaborators CSSI2014Genotype-phenotype correlation of Coffin-Siris syndrome caused by mutations in SMARCB1, SMARCA4, SMARCE1, and AR-ID1AAmerican Journal of Medical Genetics Part C: Seminars in Medical Genetics 166:262–275https://doi.org/10.1002/ajmg.c.31407Google Scholar3.Vals MAÕiglane-Shlik ENõukas Met al.2014Coffin–Siris Syndrome with obesity, macro-cephaly, hepatomegaly and hyperinsulinism caused by a mutation in the ARID1B geneEur J Hum Genet 22:1327–1329https://doi.org/10.1038/ejhg.2014.25Google Scholar4.Schrier Vergano SA2024ARID2, a milder cause of Coffin-Siris Syndrome? 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VitobelloUniversité Bourgogne Europe, CHU Dijon Bourgogne, Laboratoire de Génomique Médicale, FHU-TRANSLAD, Centre de recherche Translationnelle en Médecine moléculaire – Inserm UMR1231 équipe GAD, Dijon, FranceORCID iD: 0000-0003-3717-8374Ange-Line BruelUniversité de Bourgogne, UMR1231 GAD, Inserm - Université de Bourgogne and Unité Fonctionnelle Innovation en Diagnostic génomique des maladies rares, FHU-TRANSLAD, CHU Dijon Bourgogne, Dijon, FranceAnne-Sophie Denommé-PichonUniversité de Bourgogne, UMR1231 GAD, Inserm - Université de Bourgogne and Unité Fonctionnelle Innovation en Diagnostic génomique des maladies rares, FHU-TRANSLAD, CHU Dijon Bourgogne, Dijon, FranceORCID iD: 0000-0002-8986-8222Estelle ColinCHU Angers, Service de Génétique médicale and UMR CNRS 6214-INSERM 1083, Université d’Angers, Angers, FranceBertrand IsidorNantes Université, CHU de Nantes, Service de Génétique médicale, CNRS, INSERM, l’institut du thorax, Nantes, FranceSophie NambotUniversité de Bourgogne, Centre de Référence Maladies Rares “Anomalies du développement et syndromes malformatifs”, Centre de Génétique, FHUTRANSLAD et Institut GIMI, CHU Dijon Bourgogne, Dijon, FranceDesiree DeMilleARUP Institute for Clinical and Experimental Pathology, Salt Lake City, United StatesPinar Bayrak-ToydemirARUP Institute for Clinical and Experimental Pathology, Salt Lake City, United StatesNicola LongoDivision of Medical Genetics, University of Utah, Salt Lake City, United StatesBoris KerenGenetic Department, Pitié-Salpêtrière Hospital, AP-HP.Sorbonne University, Paris, FranceAlexandra AfenjarGenetic Department, Armand-Trousseau Hospital, AP-HP.Sorbonne University, Paris, FranceJolien S Klein Wassink-RuiterDepartment of Genetics, University of Groningen, University Medical Center Groningen, Groningen, NetherlandsIngrid PC KrapelsDepartment of Clinical Genetics, Maastricht University Medical Center, Maastricht, NetherlandsHannah TitheradgeUniversity of Birmingham, Birmingham, United Kingdom, Clinical Genetics, Birmingham Women’s and Children’s NHS Foundation Trust, Birmingham, United KingdomGavin RyanWest Midlands Regional Genetics Laboratory, Birmingham women’s and children’s NHS hospital trust, Birmingham, United KingdomMatias WagnerInstitute of Human Genetics, School of Medicine and Health, Technical University of Munich, Munich, GermanyJill A RosenfeldBaylor Genetics Laboratories, Baylor College of Medicine, Houston, United StatesORCID iD: 0000-0001-5664-7987David R WittGenetics Department, Kaiser Permanente, San Jose, United StatesAnirudh SaronwalaMedical Genetics and Genomics, Kaiser Permanente San Jose-Santa Clara Medical Center, San Jose, United StatesYaping YangAiLIfe Diagnostics, Pearland, United StatesORCID iD: 0000-0001-5608-8628Annick Rein-RothschildInstitute of Rare Diseases, Safra Children’s Hospital, Sheba Medical Center, Tel Hashomer, IsraelOrtal BarelGenomics Unit, The Center for Cancer Research, Sheba Medical Center, Tel Hashomer, IsraelReena JethvaCenter for Genetic and Genomic Medicine, Hackensack Meridian Health, Joseph M. Sanzari Children’s Hospital at HUMC, New Jersey, United StatesSaskia B WortmannUniversity Children’s Hospital Salzburg, Salzburg, AustriaKatharina DiepoldClinic of Pediatrics and Neuropediatrics, Northeim, GermanyKevin RostasyDepartment of Paediatric Neurology, Witten/Herdecke University, Children’s Hospital Datteln, Datteln, GermanyLola K ClarksonGreenwood Genetic Center, Columbia, United StatesKathryn T DrazbaGreenwood Genetic Center, Columbia, United StatesRaymond J LouieGreenwood Genetic Center, Greenwood, United StatesHimanshu GoelHunter Genetics, The University of Newcastle, Newcastle, AustraliaOuti KuisminDepartment of Clinical Genetics, Research Unit of Clinical Medicine, Medical Research Center Oulu, Oulu University Hospital and University of Oulu, Oulu, FinlandPekka NokelainenDepartment of Pediatric Neurology, Tampere University Hospital, Tampere, FinlandJianling JiDepartment of Pathology and Laboratory Medicine, Children’s Hospital of Los Angeles, and University of Southern California Keck School of Medicine, Los Angeles, United StatesAshley MillsDepartment of Pathology and Laboratory Medicine, Children’s Hospital of Los Angeles, and University of Southern California Keck School of Medicine, Los Angeles, United StatesMatthew A DeardorffDepartment of Pathology and Laboratory Medicine, Children’s Hospital of Los Angeles, and University of Southern California Keck School of Medicine, Los Angeles, United StatesMaría Palomares-BraloDepartment of Genetics, INGEMM, Institute of Medical and Molecular Genetics, Hospital Universitario La Paz, Madrid, SpainMaría-Ángeles Gómez-CanoDepartment of Genetics, INGEMM, Institute of Medical and Molecular Genetics, Hospital Universitario La Paz, Madrid, SpainAlberto Fernández-JaénNeuropediatric Department, Hospital Universitario Quirónsalud, Universidad Europea de Madrid, Pozuelo de Alarcón, SpainORCID iD: 0000-0003-3306-9832Peter J HulickMark R. Neaman Center for Personalized Medicine, Endeavor Health, Evanston, United StatesMaureen JacobInstitute of Human Genetics, School of Medicine and Health, Technical University of Munich, Munich, GermanyBenjamin CogneNantes Université, CHU de Nantes, Service de Génétique médicale, Nantes, FranceKandamurugu ManickamDivision of Genetic and Genomic Medicine, Nationwide Children’s Hospital/OSU College of Medicine, Columbus, United StatesORCID iD: 0000-0001-6269-7710Xueqi WangChildren’s Hospital of Eastern Ontario Research Institute, Ottawa, CanadaGail GrahamChildren’s Hospital of Eastern Ontario, Department of Genetics, Ottawa, CanadaBert CallewaertCenter for Medical Genetics, Ghent University Hospital and Department of Biomolecular Medicine, Ghent University, Ghent, BelgiumORCID iD: 0000-0002-9743-4205Mercedes ZoetemanGenomics Institute, MultiCare Health System, Tacoma, United StatesMichael L RaffGenomics Institute, MultiCare Health System, Tacoma, United StatesMarion Aubert MuccaService de génétique médicale, CHU de Toulouse, Toulouse, FranceMédéric JeanneDepartment of Genetics, Eurofins Biomnis, Lyon, FranceGrace RainesUniversity of Alabama at Birmingham, Department of Genetics, Birmingham, United StatesAmy CrunkGeneDx, LLC, Gaithersburg, United StatesSureni V MullegamaGeneDx, LLC, Gaithersburg, United StatesTaila HartleyChildren’s Hospital of Eastern Ontario Research Institute, University of Ottawa, Ottawa, CanadaKristin KernohanChildren’s Hospital of Eastern Ontario, Newborn Screening Ontario, Ottawa, CanadaKym BoycottChildren’s Hospital of Eastern Ontario Research Institute, University of Ottawa, Ottawa, CanadaPhilippe M CampeauCHU Sainte-Justine Research Center, Montreal, CanadaORCID iD: 0000-0001-9713-7107For correspondence: p.campeau@umontreal.caAuthor NotesCompeting interests: Baylor Genetics (BG) is a diagnostic laboratory partially owned by Baylor College of Medicine. Several authors are located at BG, as indicated. AC and SVM are employees of and may own stock in GeneDx, LLC.Version historySent for peer review: January 12, 2026Preprint posted: January 19, 2026Reviewed Preprint version 1: March 17, 2026Cite all versionsYou can cite all versions using the DOI https://doi.org/10.7554/eLife.109921. This DOI represents all versions, and will always resolve to the latest one.Copyright© 2026, van Heesbeen et al.This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.Metricsviews0downloads0citations0Views, downloads and citations are aggregated across all versions of this paper published by eLife.

来源:e life发布时间:2026-03-17
No evidence for disassortative mating based on HLA in a small-scale, endogamous population

AbstractStudies dating back several decades have suggested that humans prefer potential mates with dissimilar HLA genotypes. Evidence for actualized disassortative mating based on the human-specific MHC remains inconclusive. For instance, cosmopolitan populations have often exhibited the opposite trend whereby assortative mating at the MHC is observed, indicating that social stratification may overwhelm potential biological mate preferences. However, small-scale, endogamous populations–whose social structures more closely resemble those throughout most of human evolution–have been largely overlooked. Here, we assess HLA dissimilarity among Himba pastoralists from Namibia, where socially accepted concurrency allows individuals to maintain both arranged marital and self-selected (“love match”) partnerships. This provides a rare opportunity to directly test HLA similarity across contrasting partnership types (arranged vs chosen) within the same social system (n = 249 observed partnerships). We find no difference in HLA dissimilarity (neither at the genotype nor protein divergence level) between partnership types, nor in their fitness benefits to potential offspring as assessed via computationally predicted pathogen binding affinities. The effects of the partnership types likewise do not differ from a random, background distribution of 18,487 possible unrelated pairings. Finally, we detect extensive haplotype sharing across the HLA region, suggesting that episodes of fluctuating positive selection may be a stronger force maintaining HLA polymorphism than disassortative mating, even in an evolutionarily relevant social context.1. IntroductionMHC dissimilarity is predicted to play a critical role in mate choice strategies, as choosing a dissimilar mate enhances offspring immune function by increasing their heterozygosity and therefore broadening pathogen resistance. Individuals who are heterozygous are codominant for MHC allele expression. Sexually mediated balancing selection has therefore been proposed as a major force maintaining extreme allelic diversity in this region (1–3). Meta-analysis of 27 non-human vertebrate species has indeed shown a small, but significant preference for MHC dissimilar mates (4). Earlier experimental work at the human specific MHC (i.e., HLA) demonstrated both male and female preference for the odour of HLA dissimilar mates (5). Initial studies about realized mate choice in two ethnically and genetically homogeneous populations (Hutterites and Mormons) likewise reported greater HLA dissimilarity among married partners than expected under random mating (6, 7). However, meta-analyses of the cumulative literature in the decades since have found no significant mate preferences for HLA dissimilarity (8, 9). In fact, when ethnically heterogeneous, large-scale study populations are considered, the opposite pattern emerges: assortative mating is more common, where partners are more similar at their HLA than random pairings within the population (8). Cosmopolitan populations in the Western Hemisphere tend to reflect migration from multiple continental and ancestral sources and in such populations, married pairs often share more similar ancestry proportions than random pairings (10–12). Such ancestry-assortative mating can arise either from active preference for partners of similar ancestry proportions within an admixed population, or from socially-structured mating between groups that contain ethnic, linguistic, or socioeconomic diversity (13).One way to test this idea is to directly distinguish between HLA and overall genome-wide relatedness. In comparisons across six European and one Israeli population, both the Dutch sample and a joint analysis of five Northern European populations did show significantly greater HLA dissimilarity among married pairs than in randomly permuted pairs, independent of genome-wide similarity (14). These results suggest that preference for HLA-dissimilar mates can sometimes be detected in cosmopolitan populations with sufficiently large sample sizes. However, another recent meta-analysis of studies that successfully controlled for genome-wide relatedness (including the seven estimates from the aforementioned study) still found no overall evidence for HLA-based disassortative mating (9).Notably lacking in studies that control for genome-wide similarity are any non-European, small-scale societies. A more evolutionarily relevant test of HLA-based mate preferences would therefore examine smaller-scale, endogamous societies–social structures that more closely reflect those in which human mating behaviors evolved and where ancestry-assortative mating is less likely. Moreover, in heterogeneous cosmopolitan populations, mating with almost any unrelated individual may already yield offspring with dissimilar HLA haplotypes beyond a threshold that maximizes fitness, potentially obscuring subtle selection for HLA dissimilarity. In contrast, in small, endogamous populations where background relatedness is higher, evolved preferences for HLA-dissimilar mates may be more pronounced (9, 15).We directly test for evidence of mate selection based on HLA dissimilarity, independent of genome-wide similarity, in a group of Himba agropastoralists in northern Namibia–the first study of its kind in a non-European, small-scale society, and with a substantially larger sample than the only previously analyzed small-scale population controlling for genome-wide similarity (HapMap CEU individuals from the Mormon community n=28 (7), n=24 (15)). Several features of the Himba marital system make them particularly well-suited for this investigation. Marriages may be either arranged (via parental choice) or self-selected (“love matches”) (16)) and concurrent partnerships are both common and socially acceptable (17–19). If intrinsic preferences for HLA-dissimilar mates exist, we would expect to observe them among love matches rather than arranged marriages. Moreover, arranged marriages often fulfill social obligations, potentially allowing love matches to reflect more active mate choice (17–19)). The Himba are also genetically well-suited for studying ancestral mate preferences. While they are endogamous, they exhibit greater overall genetic diversity than the previously studied out-of-Africa isolates, like the Hutterites and Mormons, making them more relevant for inferences about the majority of human evolution. Finally, admixture from neighboring populations is minimal (mean Western African ancestry >95% across k = 4,5 runs), and the Himba have served as a reference “Western African” population in our group’s admixture analyses (20), suggesting that ancestry-assortative mating is unlikely to confound results.We use paired genetic HLA and genome-wide genetic data along with partnership records to test whether unions resulting from partner choice exhibit greater HLA dissimilarity–at both the genotype and protein sequence level–than arranged marriages. Additionally, we assess the predicted pathogen-binding capacity of hypothetical offspring from arranged and chosen partnerships to evaluate potential fitness benefits of parental HLA allele combinations.2. Methods(a) Study PopulationThe Himba are an agro-pastoralist group residing in Northwestern Namibia and Southern Angola, who have a total population size of around 30,000-40,000 individuals (21). The groups remain semi-nomadic but increasingly have access to formal education and the market economy. The present study was conducted in the community of Omuhonga as part of the Kunene Rural Health and Demography Project, which has been working in the community since 2010. There are about 1000 residents of Omuhonga, living in extended family compounds. Marriages are arranged by parents or other kin, although “love match” marriages are common, particularly after the first marriage (16). Extra-marital relationships are also common for both men and women (17).(b) Data GenerationDNA was collected with informed consent from individuals via saliva using the prepIT-L2P kit and protocol. DNA was used to generate HLA genotype calls for 366 individuals. HLA class I and II loci were targeted for DNA sequencing using a well-established biotinylated DNA probe-based capture method (22). HLA alleles were determined from the sequence capture using the consensus calls obtained from two algorithms: NGSengine® 2.10.0 (GenDX, Utrecht, the Netherlands) and HLA Explore™ (Omixon Biocomputing Ltd. Budapest, Hungary). Alleles were then converted into their P-group designations from https://hla.alleles.org as of 10/7/2024 (2-field designation), which groups alleles whose nucleotide sequences encode identical protein sequence in the peptide binding domains (exon 2 and 3 for HLA class I and exon 2 for HLA class II alleles). Genotype array data using one of two arrays, Illumina H3Africa or MEGAex, were previously generated for 360 of these individuals [dbGaP: phs001995.v3.p1]. One individual was newly genotyped using H3Africa. Five individuals with HLA capture data did not have corresponding SNP array data and were not included in the partnership analyses requiring genome-wide relatedness metrics. These SNP array datasets previously underwent quality control measures and were merged as previously described (18).There were 219 adults (82 males, 137 females) having both genotype array and HLA genotype data who were in at least one documented partnership: arranged marriage, love match marriage, self-reported informal partnership, or a discovered informal partnership. Marriages and self-reported informal partnerships were recorded during demographic interviews conducted between 2010 and 2017. Additional informal partnerships were discovered due to shared biological offspring as determined by a list of parent offspring trios generated via KING 2.1.3 (23), using the identical by descent (IBD) segment flag. There were 47 arranged marriages, 47 love match marriage pairs, and 155 informal pairs. Of the informal pairs, 48 were discovered only through shared biological offspring. These pairings may have been excluded during the interviews because they occurred many years in the past and our interviews only asked about current partnerships. The 47 love match marriages were combined with 203 informal partnerships and designated as chosen partnerships to compare with the 47 arranged partnerships in our analyses.(c) Genotype array data quality controlIndividuals were genotyped using one of two arrays, H3Africa or MEGAex. These datasets previously underwent quality control measures and were filtered using PLINK v1.90b3v(24) for missingness greater than 5%, a MAF less than or equal to 1%, and a Hardy–Weinberg equilibrium exact test with a p-value below 0.0001 as described previously (18) and documented in dbGaP accession phs001995.v3.p1. The merged array dataset containing 376,657 SNPs was used for analyses.(d) Creating a set unrelated sets of individualsTraditional pedigree inference methods that use expected IBD sharing perform poorly in endogamous populations with elevated background sharing and pedigree reticulations. We used PONDEROSA (25), recently developed in our lab, to accurately infer close relationships in our sample. PONDEROSA uses a machine learning approach to learn the population distribution of IBD summary statistics to more accurately classify close relationships. Based on the inferred relations we excluded any pairs who were 2nd degree or closer relatives from our background distributions in our linear models (Statistical Methods eq 2-3). Third degree relative pairs were permitted in this background distribution to reflect the stated cultural preference for cousin marriages in arranged unions (16). We also used this program to maximize a set of unrelated individuals related at 4th degree or less (n=102 individuals) for allele and haplotype frequency based analyses.(e) Hardy-Weinberg AnalysisTo test whether the HLA genotypes complied with Hardy-Weinberg equilibrium, we used the Asymptotic Statistical Test with Ambiguity (ASTA) from the HWETests package (26). ASTA is designed to evaluate deviations from Hardy-Weinberg equilibrium for highly polymorphic loci while accounting for ambiguity in genotype calls. Using ASTA, we ran the full_algorithm() function with default parameters on the set of 102 unrelated individuals (maximum relation of 4th degree).(f) Identity by Descent (IBD) SharingGenotype data was phased using SHAPEIT v2.r837(27) with --duohmm -W 5 using an ancestry-matched recombination map (28), constructed based on linkage disequilibrium patterns in the 1000 Genomes Project (29) Yorubans, matching the predominant West African ancestry present in the Himba. We called IBD segments with phasedIBD (30) using default parameters with phase correction turned off. We determined phasedIBD calls to be slightly more accurate than germline 1.5.3 (31) and GERMLINE2 (32) by comparing the total cM of IBD shared between parent offspring pairs (Table S1). We expected parents and offspring to share the full length of the recombination map in IBD1 and share an additional background amount of IBD2 estimated to be the average cM of IBD shared between unrelated (> 3rd degree relations) individuals in the population. The phasedIBD calls without phase correction most closely matched this expectation. The phasedIBD calls with phase correction option turned on were the least accurate. A minimum threshold of 3cM was used for all runs.(g) HLA similarity scoreWe assessed HLA similarity in terms of the number of alleles shared across the eight typed HLA genes for each pair (minimum of 0 if no alleles are shared at any gene, maximum of 16 if both alleles at each gene are shared). A shared allele in a homozygous state in one individual only counted as one shared allele between the individuals. Importantly, due to the many alleles at each HLA gene, two individuals with heterozygous genotypes at a locus do not necessarily imply shared alleles.(h) Average log odds of homozygous offspringOdds of homozygous offspring for each locus were determined according to standard mendelian transmission probabilities, accounting for the multi-allelic states of each gene. Table S2 shows the possible states for this variable considering only one gene. Importantly, for a given number of alleles shared at a locus, there are multiple possibilities for the odds of homozygous offspring depending on the genotype states of the individuals in the pair. We log transformed these odds to normalize the distribution and then averaged across the 8 loci. We included a +0.1 boundary correction in the numerator and denominator of the odds calculations to allow for numerators and denominators of 0 (eq 1). (i) Peptide Binding Groove Protein DivergenceWe used a script available from a previous publication (33) to assess the mean Grantham amino acid distance (34) of the peptide binding groove between HLA molecule allotypes, comparing the 4 pairwise combinations of allotypes between two individuals. Only peptide binding groove sequences (exons 2 and 3 for HLA class I and exon 2 for HLA class II) were compared because all alleles were grouped according to their P group (identical peptide binding grooves). We used the fasta files provided for the HLA class I, DRB1, and DQB1 alleles and the Grantham distance matrix available in a previously published package (https://granthamdist.sourceforge.io/). For the remaining HLA class II alleles we used the IPD-MHC (https://www.ebi.ac.uk/ipd/mhc/alignment) multiple sequence alignment tool to align alleles and extract exon 2 amino acid sequences.(j) Pathogen ProteinsWe modified a previously published list of representative human pathogen proteins (33) that was curated from the Gideon database (35) based on global distributions, potential for high mortality, and historical relevance. After personal correspondence with an infectious disease expert, Dr. Ashley Hazel, who has extensive experience working with the Himba community we removed 6 pathogens unlikely to be ecologically relevant in Northern Namibia: Entamoeba histolytica (amebiasis), Trichinella spiralis (roundworm), Schitosoma mansoni (schistosomiasis), HIV, Hepatitis C, and Yersinia pestis (plague). We added 4 sexually transmitted diseases known to be highly prevalent in the Himba (HSV1, HSV2, Chlamydia trachomatis (chlamydia), and Neisseria gonorrhoeae (gonorrhea)) for a total of 25 pathogens (36). We used the NCBI accession numbers available in the previously published study (33) to gather amino acid sequences for the antigenic proteins of the 21 pathogens overlapping that publication. For the 4 additional pathogens we used IEDB https://www.iedb.org/ to find the pathogen proteins that are MHC ligands and then identified these proteins’ amino acid sequences using NCBI. The complete list of 298 proteins and their accession numbers are available in Appendix 1.(k) Pathogen Binding PredictionWe used NetMHCpan (v4.1) (37) to predict peptide binding affinity for the allotypes of HLA class I (HLA-A, B, and C) and used NetMHCIIpan (v4.3) (38) for the allotypes of HLA class II (HLA-DP, DQ, and DR). These algorithms are trained using experimental binding affinity and eluted ligand data to predict binding affinity. Binding prediction was performed on all possible amino acid 9mers to match the 9-residue binding groove for HLA class I genes and all possible amino acid 15mers for HLA class II molecules. The HLA class II molecules DPα1 and DPβ1, DQα1 and DQβ1, and DRα1 and DRβ1 can often combine to form the DP, DQ, or DR epitopes, respectively (39, 40). DRα1 has limited polymorphism, and we did not include DRβ3/4/5 in this analysis so only DRβ1 binding was assessed for DR. All possible combined DP and DQ molecules were cross-referenced for their binding affinities. The predicted binding affinity scores between every possible peptide and every MHC molecule were ranked as a percentage by the algorithms based on comparison with a large set of naturally occurring peptides. We used the default rank threshold for strong binding peptides (top 0.5% for HLA class I genes, and top 1% for HLA class II genes). One of the rare alleles (DPB1*61:01N) is a null allele, i.e., does not present peptides, and thus pairs including the individual with this allele were excluded for the DP binding analysis.(l) HLA haplotype resolutionThe HLA allele calls from target sequence capture are unphased. We resolved HLA haplotypes by imputing closely matched HLA allele calls from the paired, phased SNP data across the 5Mb region encompassing the typed HLA genes. Although HLA imputation based on sparse genotype data is not accurate at detailed resolutions, we were still able to resolve the haplotypes by comparing imputed alleles to the known alleles from the HLA targeted sequencing. We trained HIBAG HLA imputation classifier models (41) on the Himba SNP data across the whole 5Mb region with paired HLA allele calls from the target capture (n=361 individuals). The models had “out of bag” accuracies of 97.96%, 96.97%, 98.60%, 98.75%, 98.73%, 98.75%, 97.66% across A, B, C, DPB1, DQA1, DQB1, and DRB1, respectively. The program does not impute DPA1 genotypes. We then extracted each phased SNP-based haplotype at the HLA region and created two pseudo-homozygous genotypes for each individual (i.e., ID1_hap1|ID1_hap1 and ID1_hap2|ID1_hap2). We ran these pseudo-homozygous genotypes through our curated HIBAG model to predict the HLA allele associated with each pseudo-homozygous genotype. We ensured that the predicted alleles across the two pseudo-homozygous genotypes matched one of the known target sequenced HLA allele calls for at least the first field. In this way, we filled in the allele calls across the two phased haplotypes. We were able to resolve 698/722 haplotypes with this method. The linkage disequilibrium patterns determined via asymmetric conditional linkage disequilibrium closely correspond to previously identified patterns (42) with high LD between HLA-B and C and also among HLA-DRB1, DQA1, and DQB1, demonstrating the accuracy of our haplotype resolution (Figure S1).(m) Calculating IBD sharing ratesTo calculate IBD sharing rates at each base pair genome-wide, we used a line sweep algorithm (Figure S2). In this method, the start of an IBD segment is marked +1, and the base pair directly following the last base pair in the IBD segment, is marked -1. A ‘line’ sweeps across the chromosome-length array summing the tally at the current base pair with the tally carried over from the previous base pair to generate IBD coverage scores for each base pair. In this way, a base pair located within a larger set of IBD fragment markings receives a larger coverage value during the line sweep step. The IBD sharing rate for each base pair is then computed by dividing the IBD coverage score by the total number of possible haplotypes in the dataset, the number of pairs multiplied by 2. This procedure reduces the complexity of the IBD sharing calculation problem, allowing for efficient computation of the IBD sharing rate at each base pair, genome-wide. IBD sharing rates were binned and averaged in 1000 base pair chunks.(n) Statistical ModelsWe constructed a dyadic data model (43) to assess the effect of partnership type on IBD sharing (eq 2) and on our HLA metrics (eq 3): HLA similarity score, mean log odds of homozygous offspring, peptide binding groove divergence, and mean number of predicted pathogen peptides bound by hypothetical offspring genotypes. Each partnership type (arranged, chosen) was encoded as a binary variable, and random effects were included for each female and each male to account for individual-level variation. IBD sharing was included as a covariate in equation 3 to determine whether differences in HLA metrics between partnership types remained significant after conditioning on overall genetic relatedness.The intercepts of both models represent a baseline derived from all 18,487 possible opposite sex pairings that are not in known partnerships, are no closer than 3rd degree relatives, and also constrained to those that have a permissible age differential based on the range observed in the known partnership data. This baseline provides a background distribution for the expected similarity of partners in the population if there is random mating, but with avoidance of close kin. We allowed 3rd degree relatives to exist in our background distribution of partners as Himba individuals report a cultural preference for cousin marriages in arranged unions (16). However, surprisingly, no individuals in actual arranged partnerships were related at 3rd degree or closer, but 1 “love” marriage and 2 informal partnerships discovered through pedigree analysis were found to be 3rd degree relatives.This background distribution allows us to test whether each pair type deviates from the baseline expectation of similarity while providing additional information to estimate the random effects. The random effects of each female and male capture the unique contributions of each individual to their dyadic HLA metrics/IBD sharing across all pairings (both real and random) (43). This approach allows us to identify whether individuals’ real partnerships (arranged and chosen) differ systematically in their similarity scores when compared to their average similarity with potential random partners in the population. Pairwise contrasts in the effect size estimates of the two different partnership types (arranged vs chosen) were assessed by calculating the Z-statistic, defined as the contrast estimate divided by its standard error. P-values for these Z-statistics were derived from a two-tailed normal distribution. Statistical significance for the main effects and pairwise contrasts was determined using a p-value threshold of 0.05.3. ResultsTo help contextualize our findings, we first compared HLA diversity in the Himba, which has not been previously characterized, with other publicly available populations, curated to represent a diverse world-wide sample (44) and all available sub-Saharan African groups (45). With HLA allele calls from 366 individuals, we assessed the diversity of HLA alleles based on their P group designation, that is, alleles were grouped according to identical peptide binding domains to indicate differences in pathogen binding function between alleles, across three HLA class I (A, B, C) and five HLA class II genes (DPA1, DPB1, DQA1, DQB1, DRB1) (Figure S3). In order to make unbiased comparisons of allelic diversity based on sample size, we subset our sample to the maximum set of unrelated Himba individuals (n=102, maximum 4th degree relations) (Figure S4). Despite endogamy and a recent population bottleneck (46), the allelic diversity at each gene falls along the linear prediction based on sample size constructed from the publicly available data, with expected or somewhat greater diversity than expected given the Himba sample size (Figure 1). We ensured genotype frequencies at each HLA gene did not deviate from Hardy Weinberg equilibrium (Table S3) as this could indicate genotyping error or population substructure (47).Figure 1.Allelic diversity in the Himba follows or somewhat exceeds linear predictions based on sample size from publicly available cohorts.Allelic diversity for the 8 genotyped HLA genes was measured in the set of 102 unrelated Himba. African cohorts are labeled in orange, non-African cohorts in blue, and the Himba with a green diamond. Linear predictions of the number of unique alleles based on sample size were constructed from the available datasets, excluding the Himba. Ribbons indicate the 95% confidence interval for the linear prediction of the number of unique alleles based on sample size.Next, we assessed whether there was an effect of partnership type (chosen or arranged) on overall genetic relatedness. If mate choice is, in part, driven by minimizing overall genetic relatedness to reduce fitness consequences associated with increased homozygosity in offspring (46, 48, 49), chosen partnerships could be more genetically dissimilar overall than arranged partnerships. This could produce an associated, but spurious pattern of HLA dissimilarity in chosen partnerships as compared to arranged partners and the background distribution. Separately, preferences for cross-cousin marriage and strong ties between allied families could lead to greater sharing of IBD segments among arranged marriage partners than a background distribution would predict, leading to associated, but spurious patterns of greater HLA similarity in arranged partners as compared to chosen partners and the background distribution.We assessed overall relatedness via the total number of centimorgans shared in genome-wide IBD segments between the individuals in the partnership (see eq 2 in Methods). There was no effect of arranged partnership or chosen partnership on genome-wide IBD sharing, relative to a background population distribution created from random, minimally related (maximum 3rd degree relations), heterosexual pairings with realistic age gaps (Figure 2A) (p = 0.84, 0.82, for arranged and chosen partnership effects, respectively) nor a significant difference between the effects of arranged and chosen partnerships in a pairwise contrast of effect size estimates (p = 0.93). 3rd degree relatives were allowed in the background distribution to reflect the stated cultural preference for cousin marriages in arranged unions (16).Figure 2.No effect of partnership types on genome-wide IBD sharing.A) We consider a linear model where the intercept is the expected genome-wide IBD sharing based on a background distribution of unrelated female - male pairs from the population. We plot the effect size estimates of arranged partnerships and chosen partnerships on IBD sharing and find that the 95% confidence intervals of the effects contain zero, indicating there is no significant effect of arranged or chosen partnerships on IBD sharing. B) The distributions of the IBD sharing (cM) for arranged and chosen partnerships clearly overlap. Means of each distribution are noted with a dashed line.(a) HLA similarity does not differ among partnership typesWe next addressed our main question: do couples who have chosen to be together have lower HLA similarity than arranged partnerships? That is, is mate choice driven by minimizing relatedness at the HLA region rather than genome-wide relatedness? We assessed HLA genotype similarity in two ways, first by constructing a HLA similarity score, the number of shared alleles between a pair of individuals across all 8 genes (i.e. minimum score of 0, maximum score of 16) and also by computing the average log odds of homozygous offspring across all 8 genes (see Methods). These two metrics are highly correlated (Pearson r = 0.96), but average log odds of homozygous offspring provides a finer measurement (Figure S5). Using linear mixed models for dyadic data that correct for overall IBD sharing and account for the unique influence of each female and male to their similarity scores across all pairings (43) (see eq 3 in Methods), we found no effect of arranged or chosen partnership on overall HLA similarity (p = 0.7, 0.61, respectively) (Figure 3A) or on average log odds of HLA homozygosity in potential offspring (p = 0.79, 0.80, respectively) (Figure 3C). In a pairwise contrast, there were no differences between the effects of chosen and arranged partnerships on HLA similarity score (p = 0.56) or on average log odds of HLA homozygosity in potential offspring (p = 0.72). We also assessed whether there was a difference in HLA peptide binding groove protein divergence (exon 2 and 3 for HLA class I genes, exon 2 for HLA class 2 genes) by calculating the grantham distance between the amino acid sequences (33, 34) coded by partners’ alleles. Again, we found no significant effects of arranged or chosen partnerships on mean amino acid sequence divergence between the molecules coded by partners’ HLA genotypes (p = 0.74, 0.70, for arranged and chosen partnership effects, respectively, Figure S6) and no significant difference in their effects from a pairwise contrast (p = 0.64)Figure 3.No effect of partnership types on HLA similarity or average log odds of offspring HLA homozygosity.We again consider linear models where the intercept is the expected outcome based on a background distribution of random, unrelated, opposite sex pairs from the population and control for genome-wide IBD sharing. We apply the model to the HLA similarity score (A) and to the average log odds of HLA homozygous offspring (C) across all 8 genes. We plot the effect size estimates of arranged partnerships and chosen partnerships on HLA similarity score (A) and average log odds of offspring homozygosity (C) and find the 95% confidence intervals contain zero, indicating no significant effect of arranged or chosen partnerships on HLA similarity or average log odds of offspring homozygosity. The distributions of HLA similarity scores (C) and average log odds of offspring HLA homozygosity (D) clearly overlap between arranged and chosen partnerships. Means of each distribution are noted with a dashed line.Figure 4.Elevated IBD sharing at the HLA region is consistent with recent, fluctuating positive selection.Panels A and B depict IBD sharing rates across chromosome 6 (rates were averaged within 1000 base pair bins). The dashed line depicts the genome-wide median IBD sharing rate, excluding chromosome 6. Panel A depicts IBD sharing rates for the pairs that make up the background distribution for the partnership analyses (random heterosexual pairings, with relevant age gaps, related at no closer than 3rd degree; n=352 individuals). Panel B depicts the IBD sharing rates for the unrelated individuals included in the allele frequency estimations (related at no closer than 4th degree) with the common haplotypes shown in Figure S9 (n=71 individuals). The HLA region is designated in grey. The pink shaded region depicts +2 and -2 standard deviations from the genome-wide (excluding chromosome 6) median IBD sharing rate, 3.1% and 2.8% for panels A and B, respectively.(b) Pathogen binding repertoires of potential offspring do not differ among partnership typesIncreased pathogen binding ability of offspring is theorized to be the alleged evolutionary driver of disassortative mating at the MHC region (50, 51). Therefore, even if similarity scores, as assessed by allelic or protein similarity do not differ among pair types, individuals might seek out complementary HLA genotypes in mates that increase the pathogen binding repertoire of potential offspring. Using the same linear mixed model (Methods eq 3) as above, we looked for effects of arranged and chosen partnerships on the average computationally-predicted pathogen binding repertoires in hypothetical offspring (i.e., the average number of unique pathogen peptides predicted to be bound by each offspring HLA genotype). We used a curated list of 25 pathogens, taking a subset of those listed in a previous study of evolutionarily significant human pathogens (33) that have plausible transmission in the Himba, and included additional sexually transmitted pathogens known to be prevalent in the Himba (52–54) (personal correspondence with Dr. Ashley Hazel) (see Appendix Table 1). At each locus, the hypothetical offspring genotypes, in expected proportions, were cross-referenced to respective binding affinities to our list of antigenic peptides producing a mean number of unique pathogen peptides that strongly bind per HLA genotype. Many of the HLA class II DPA/B or DQA/B gene products can form both cis and trans heterodimers, comprising DP and DQ epitopes, respectively (39, 40). Thus, an individual heterozygous for both DPA1 and DPB1 may form 4 unique DPα1/DPβ1 molecules. We assessed the mean number of bound antigenic peptides for all possible offspring DP and DQ epitope combinations, again based on expected genotype proportions.Across all 8 HLA genes tested, there was no effect of arranged or chosen partnerships on the mean number of pathogen peptides bound by offspring allotypes that differed from the background distribution of random partners (Figure S7, Table S4). Likewise, we found no differences in the effect estimates of arranged and chosen partnerships in pairwise contrasts (Table S5). HLA class I molecules generally bind peptides derived from intracellular pathogens (e.g. viruses) whereas HLA class II molecules generally bind those derived from extracellular pathogens (e.g. bacteria and parasites) (55). Thus, we also performed this analysis including only intracellular pathogens for HLA-A, B, and C genotypes and only extracellular pathogens for HLA-DP, DQ, and DR genotypes. We still found no significant effects of arranged or chosen partnerships in mean pathogen peptides bound and no significant differences of effects in pairwise contrasts (Figure S8, Tables S6-7).(c) Extensive haplotype sharing could indicate recent episodes of fluctuating positive selectionWe find no evidence that disassortative mating is driving maintenance of polymorphism at the HLA. Other forms of selection, besides sexually mediated balancing selection, could therefore play a larger role in maintaining polymorphism at this locus. We assessed haplotype sharing by visualizing the SNP-based haplotypes (from genotyping array) across the entire 5Mb region surrounding the 8 genotyped HLA genes. The SNP-based haplotypes along this much larger 5Mb region were grouped by their HLA allele haplotypes (typed via short-read sequencing) (Figure S9). Although the 7 HLA genes used to create allele-based haplotypes comprise only ∼1% of the base pairs in this 5Mb region, we see virtually identical SNP haplotypes across unrelated individuals (maximum relatedness of 4th degree) who share frequent HLA allele-based haplotypes (n=92 haplotypes with >4 occurrences in the 102 unrelated individuals) (Figure S9-10). This finding indicates that the entire 5Mb HLA region was inherited without recombination in these unrelated individuals, consistent with recent positive selection on these haplotypes (56, 57).Unlike our previous study of sub-Saharan African populations, where in 6 of the 7 populations the majority of haplotypes were observed only once (45), 70% of haplotypes were observed at least twice in our set of unrelated individuals, with the most frequent haplotype (A*30:02 - C*03:04 - B*15:10 - DRB1*03:01 - DQA1*05:01 - DQB1*02:01 - DPB1*01:01) observed 23 times in the unrelated set. Nineteen of the 29 haplotypes occurring more than once in the unrelated set of individuals were observed in at least one of the 7 sub-Saharan African populations from our previous study (Figure S11). We assessed whether there was a relationship between haplotype frequency and number of unique peptides computationally predicted to be bound by each haplotype (58), but found no significant relationship (p = 0.91, Figure S12) and likewise found no significant relationship with number of peptides bound when we analyzed each pathogen independently (Figure S13).Consistent with the SNP-based haplotype visualization, we found increased IBD sharing at the HLA region relative to genome-wide levels. This pattern implies recent (within 200 generations) positive selection as opposed to overdominance (i.e., heterozygote advantage) (56, 57). The mean rate of IBD sharing across the HLA region between random pairs of unrelated individuals from the background distribution used in our pair analyses (n=352 individuals, n=18,487 pairs, related at a maximum of 3rd degree) is 5.3%, 1.97 standard deviations above the genome-wide median of 3.1%. The rate of IBD sharing across the HLA region in the subset of unrelated individuals from our allele frequency analyses (related at a maximum of 4th degree) who have common HLA-based haplotypes (n=71 individuals, haplotypes shown in figure S9) is even higher at 10.4%, 6.4 standard deviations above the genome-wide median of 2.8%.4. DiscussionThis study contributes an important new data point to the growing literature on HLA and mate choice. Unlike studies based on cosmopolitan datasets—where apparent HLA similarity in couples may arise spuriously from preferences for ethnically, and therefore genetically, similar partners—our study examines a small-scale population in which such confounding is minimized. Small-scale societies like the Himba more closely approximate evolutionarily relevant social contexts, characterized by high endogamy, which may make any preferences for HLA dissimilarity more relevant that in societies where any unrelated individual is sufficiently dissimilar at their HLA. To date, only one other relatively small-scale population (European Americans from the Mormon community (7, 15)) has been examined with comparable genome-wide relatedness measures.Cultural norms like arranged marriage and cross-cousin marriage preferences are predicted to promote similarity across the HLA, while tolerance of extramarital partnerships and “love match” marriages could enhance the possibility of seeking out an HLA-dissimilar partner. Therefore, the unique socioecology of Himba marriage patterns sets up a strong test of the dissimilarity hypothesis–contrasting HLA similarity in arranged and chosen partnerships.Surprisingly, we find that chosen partnerships are neither less similar at their HLA than random pairs constructed from the population background distribution, nor do they differ in their HLA similarity from arranged partnerships. We also find no evidence that chosen partners are selected to directly maximize an offspring pathogen binding repertoire. Unlike studies of Hutterites (6) and Mormons (7) that do show evidence for greater HLA dissimilarity in married pairs, background genetic diversity in an African population like the Himba might be high enough that any unrelated individual is sufficiently dissimilar at the HLA and there may be no fitness benefit to maximizing dissimilarity. It has been suggested that offspring may not benefit from maximal diversity at the HLA; T cells that bind self peptide-MHC complexes too strongly undergo pruning in the thymus. Possessing many different MHC alleles could deplete the T cell repertoire required for appropriate immune response (59, 60). This process of negative selection for extremely autoreactive T-cells has been hypothesized to inhibit the expansion of the MHC genomic region to include additional copy numbers (61). Supporting this optimality hypothesis, decreased parasite load in intermediate versus maximally diverse MHC individuals has been observed in voles (60) and stickleback fish (62) and our data suggest that this may also be relevant for humans.We find extensive IBD sharing at the HLA region consistent with recent, fluctuating positive selection which may contribute to polymorphism maintenance at this region, rather than heterozygote advantage (56, 57). Despite association with many diseases (63) and consistently showing up as a target of natural selection (58, 61), the selective forces shaping HLA diversity are not well understood (61). We suggest that innate drive for disassortative mating based on HLA genotype is unlikely to be a major evolutionary force in maintaining diversity in evolutionarily relevant social contexts. Future work should focus on assessing the contribution of other selective regimes (e.g., negative frequency dependent, fluctuating selection, heterozygote advantage, divergent allele advantage, etc.) to the maintenance of polymorphism at the HLA region.Assortative mating for sociocultural traits may be a more important driver of mate choice for groups like the Himba rather than maximizing HLA diversity. Our previous work has shown that resource scarcity affects women’s partner preferences (64, 65), and that relationship success is correlated with similarity in mate value (66). Concurrent partnerships, rather than increasing the chance of finding a partner with a dissimilar HLA genotype, may be more important for garnering resource security for women, and distributing reproductive opportunities for men who are otherwise limited by polygyny and a later age at marriage (17, 67).Data availabilityGenotype and HLA allele call data for the individuals included in this study have been deposited at dbGap as phs001995.v3.p1. All original code and data used to conduct analyses are deposited in the following github repository https://github.com/gillianmeeks/Himba_HLA_mating and on dryad https://doi.org/10.5061/dryad.02v6wwqg2. Partnership type assignments are not available due to the sensitive nature of the data.AcknowledgementsWe thank the Himba communities in which we have worked; without their support, this study would not have been possible. We thank Dr. Mark Grote and Cole Williams for assistance with statistics and data analysis. We thank Dr. Ashley Hazel for guidance in selecting ecologically relevant pathogens. BMH and GLM were supported by the National Institutes of Health (NIH) grant R35GM133531. BS and SP were supported by the National Science Foundation grant BCS-1534682. BMH, GLM, TDJF, NFP, and PJN were supported by NIH award R01AI151549. The authors acknowledge the High Performance Computing Core Facility at the University of California, Davis for providing computational resources that have contributed to the research results reported in this paper. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the National Science Foundation. The funders had no role in the decision to publish or prepare the manuscript.Additional informationEthicsEthical approval for this study was granted by the University of California, Los Angeles (IRB-10-000238), the State University of New York, Stony Brook (IRB-636415-12), and was approved by the Namibian Ministry of Home Affairs and the University of Namibia Office of Academic Affairs and Research. Chief Basekama Ngombe provided permission to work in the community and local approval of the study. Community leaders were actively involved in discussions regarding what genetic data could be used for, who would have access to it, and whether there was a for-profit element involved (there was not). Individual informed consent, and for minors’ parental assent, was obtained orally from all participants, given low rates of literacy in the community. Care was taken to protect participants’ privacy, for example via a double-blind procedure for DNA collection (68). These data were collected as part of the longitudinal Kunene Rural Health and Demography Project, which has been working in the community since 2010.Author ContributionsBS, PN, and BMH conceived of the study. GLM conducted analyses with help from KH and CB. GLM and BS wrote the manuscript with input from PN, SP, and BMH. KK, GH, NFP, TDJF, and NP generated HLA sequence data under the supervision of PN. Sample and ethnographic data collection were conducted by BS and SP. BMH and BS supervised the research.Use of AI-assistanceAI was not used in the preparation of this article.FundingNIH (R35GM133531)Brenna HennNIH (R01AI151549)Paul J NormanNSF (BCS-1534682)Brooke ScelzaAdditional filesAppendix Table 1. Spreadsheet listing pathogen peptide NCBI accessions, related to methods.Supplementary Figures and TablesReferences1.Winternitz J. C.et al.2013Sexual selection explains more functional variation in the mammalian major histocompatibility complex than parasitismProc. Biol. 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Sci 2:1–36Google ScholarMeeks GScelza BKichula KBerevoescu CHardy KFarias THarrison GPollock NFont-Porterias NPrall SNorman PHenn B2025No evidence for disassortative mating based on HLA genotype in a natural fertility populationDryad Digital Repository https://doi.org/10.5061/dryad.02v6wwqg2Scelza BAHenn BMNorman PJGopalan S2025Capturing the genetic diversity of the Himba populationdbGap ID phs001995.v3.p1https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001995.v3.p1Scelza BAHenn BMNorman PJGopalan S2025Capturing the genetic diversity of the Himba populationdbGap ID phs001995.v3.p1https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001995.v3.p1Article and author informationAuthor informationGillian L Meeks*Graduate Program in Integrative Genetics and Genomics, University of California, Davis, Davis, United StatesORCID iD: 0000-0002-8436-6514*These authors contributed equallyBrooke Scelza*Department of Anthropology, University of California, Los Angeles, Los Angeles, United StatesORCID iD: 0000-0001-5875-8875For correspondence: bscelza@gmail.com*These authors contributed equallyKatherine. M KichulaDepartment of Biomedical Informatics, University of Colorado School of Medicine, Aurora, United StatesORCID iD: 0000-0002-9817-3043Catrinel BerevoescuUndergraduate Program in Genetics and Genomics, University of California, Davis, Davis, United StatesKristin HardyCenter for Population Biology, University of California, Davis, Davis, United StatesTiciana DJ FariasDepartment of Biomedical Informatics, University of Colorado School of Medicine, Aurora, United StatesORCID iD: 0000-0002-3606-2428Genelle F HarrisonDepartment of Biomedical Informatics, University of Colorado School of Medicine, Aurora, United StatesORCID iD: 0000-0003-3386-8159Nicholas R PollockDepartment of Biomedical Informatics, University of Colorado School of Medicine, Aurora, United StatesORCID iD: 0000-0003-0114-528XNeus Font-PorteriasDepartment of Biomedical Informatics, University of Colorado School of Medicine, Aurora, United States, UC Davis Genome Center, University of California, Davis, Davis, United StatesORCID iD: 0000-0002-1357-9096Sean PrallDepartment of Anthropology, University of California, Los Angeles, Los Angeles, United StatesORCID iD: 0000-0001-5719-6460Paul J Norman†Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, United States, Department of Immunology and Microbiology, University of Colorado School of Medicine, Aurora, United StatesORCID iD: 0000-0001-8370-7703For correspondence: paul.norman@cuanschutz.edu†These authorsalso contributed equallyBrenna M Henn†‡Center for Population Biology, University of California, Davis, Davis, United States, Department of Anthropology, University of California, Davis, Davis, United States, UC Davis Genome Center, University of California, Davis, Davis, United StatesORCID iD: 0000-0003-4998-287XFor correspondence: bmhenn@ucdavis.edu†These authorsalso contributed equally‡Lead contactAuthor NotesCompeting interests: No competing interests declaredVersion historyPreprint posted: November 25, 2025Sent for peer review: November 28, 2025Reviewed Preprint version 1: March 17, 2026Cite all versionsYou can cite all versions using the DOI https://doi.org/10.7554/eLife.110118. This DOI represents all versions, and will always resolve to the latest one.Copyright© 2026, Meeks et al.This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.Metricsviews0downloads0citations0Views, downloads and citations are aggregated across all versions of this paper published by eLife.

来源:e life发布时间:2026-03-17
Alternative splicing ofregulates plant development under heat stress

AbstractThe Phytochrome-Interacting Factor 4 (PIF4) is a key player in the integration of multiple internal and external stimuli to optimize different aspects of plant development. While both the DNA encoding this transcription factor and its protein are known to be under tight control, no regulation at the RNA level has been previously reported. Our genomic analysis revealed that the exon/intron structure of the basic Helix-Loop-Helix (bHLH) DNA binding domain of PIF4 is conserved and pointed to skipping of an exon in this region specifically in response to heat stress. We then showed that this alternative splicing event downregulates PIF4 function under heat, which in etiolated seedlings induces photomorphogenic-related traits. Our results disclose a role for PIFs in plant responses to heat and reveal a new regulatory layer for the control of PIF4 function, underscoring the critical role of posttranscriptional regulatory processes in the molecular integration of environmental cues.IntroductionPhytochrome Interacting Factors (PIFs) belong to the basic Helix-Loop-Helix (bHLH) family of transcription factors. The bHLH protein domain consists of two segments: the basic region, required for DNA binding, and the helix-loop-helix, responsible for hetero and homodimerization (Toledo-Ortiz et al., 2003). PIFs are also characterized by the presence of a protein domain that interacts with photoactivated phytochromes (Favero, 2020). This interaction promotes the degradation of PIFs in the light and is crucial for their role as major regulators of light-regulated biological processes (Leivar and Monte, 2014). In etiolated seedlings, which germinate and develop in subterranean darkness, PIFs are active and repress photomorphogenic features, such as chlorophyll biosynthesis, cotyledon expansion, and repression of hypocotyl elongation (Leivar et al., 2008; Shin et al., 2009).Besides their well-known function in adjusting seedling development to light, PIFs, particularly PIF4, have over the past years also been implicated in the regulation of different biological processes such as immunity (Gangappa et al., 2017), morphological adaptations to high ambient temperatures (Koini et al., 2009), stomatal development (Casson et al., 2009), leaf senescence (Sakuraba et al., 2014), freezing tolerance (Lee and Thomashow, 2012), salt tolerance (Wang et al., 2025), anthocyanin biosynthesis (Liu et al., 2015), or fatty acid biosynthesis (Liao et al., 2025). PIF4 has thus emerged as a key integrator of multiple external and internal signals to optimize plant development (Choi and Oh, 2016; Lucyshyn and Wigge, 2009).Several studies have described the molecular mechanisms that control PIF4 gene expression, protein levels, and activity (Leivar and Quail, 2011; Paik et al., 2017; Pham et al., 2018), but its posttranscriptional regulation has never been characterized. Here we show that alternative splicing, a posttranscriptional process generating multiple mRNAs from the same gene, produces two different PIF4 transcripts specifically in response to heat stress.Temperature deviations from the optimal range significantly impact plant development and survival. Increases in temperature are classified as either high ambient temperature or excessively hot temperatures. High ambient temperature is typically 5-6ºC above the optimum temperature (22ºC for Arabidopsis thaliana), while excessively hot temperatures exceed this range (Li et al., 2018). These distinct temperature ranges activate independent signaling pathways, leading to different physiological outcomes. Warm temperatures induce thermomorphogenesis, which generally promotes growth and development in a PIF4-dependent manner (Quint et al., 2016). Conversely, excessively hot temperatures trigger stress-responsive pathways aimed at adjusting growth and physiology to mitigate the negative effects of heat (Kan et al., 2023). To date, the role of PIFs in temperature signaling has centered on thermomorphogenesis, with only a few studies having explored the role of PIF proteins in heat stress responses (Li et al., 2021; Yang et al., 2022). Intriguingly, our results reveal that heat stress induces photomorphogenic features in etiolated seedlings and that this developmental response is mediated by an alternative splice form of PIF4.ResultsPIF4 is alternatively spliced in response to heat stressOur analysis of the exon and intron positioning in all 15 members of the XV subfamily of Arabidopsis thaliana bHLH transcription factors that includes PIFs (Toledo-Ortiz et al., 2003) showed that, with the sole exception of the less conserved member HFR1, the intron/exon distribution of the bHLH domain is maintained. Its 150-bp long sequence is distributed among three exons, following an invariable proportion: 22%, 44% and 34% (Figure 1A). In addition, the middle exon containing the largest section of the bHLH domain is always 66-bp long and surrounded by phase 0 introns, which preserve codon identities and therefore the reading frame (Figure 1A). Hence, alternative splicing of this exon will produce protein isoforms differing only in the bHLH region (Supplemental Figure 1). Given the genomic particularities of the bHLH middle exon in these genes, we investigated its splicing regulation by quantifying its PSI (Percent Spliced In; percentage of transcripts that include the exon) in publicly available Arabidopsis thaliana RNA-seq samples covering several environmental conditions and tissues at different developmental stages (Supplemental Table 1). This analysis revealed skipping of the exon exclusively in two genes: PIF4 and PIF6 (Figure 1B and Supplemental Table 1). In the case of PIF6, although exon skipping occurs in nearly all tissues and conditions tested, this alternative splicing event has only been shown to be functional in seeds and embryos (Penfield et al., 2010), where PIF6 is highly expressed (Supplemental Figure 2). Strikingly, we found that the PIF4 gene undergoes alternative splicing in this particular exon, exon 5, exclusively when plants are under heat stress (Figure 1B and Supplemental Figure 3). Given that the protein arising from this exon skipping event will lack a portion of the bHLH domain (Supplemental Figure 1), we expect heat-induced alternative splicing to reduce the amounts of functional PIF4.Figure 1.Alternative splicing regulation of the bHLH central exon in the XV subfamily of Arabidopsis thaliana bHLH transcription factors.(A) Exon and intron gene distribution (left) and phylogeny (right; adapted from Leivar and Quail, 2011) of all members of the XV subfamily of Arabidopsis bHLH transcription factors. The bHLH subdomains are shown in blue. For all genes with the exception of HFR1, percentages indicate the proportion of the bHLH domain encoded in each exon. (B) PSI (Percent Spliced In) of the bHLH central exon using publicly available RNA-seq data covering several Arabidopsis thaliana tissues and environmental conditions (Supplemental Table 1). Blue dots indicate PSI<90, the generally considered cutoff for exon skipping. n.d., not detected.Heat stress induces photomorphogenesis in the darkTo study the impact of the exon skipping event in the bHLH domain of PIF4, we applied heat stress (37 ºC) to 3-day-old etiolated seedlings, a developmental stage at which PIFs are known to be functional in repressing photomorphogenesis (Leivar et al., 2008; Shin et al., 2009). First, we confirmed that the heat-induced PIF4 alternative splicing event occurs in etiolated seedlings as well (Figure 2A), also showing that it does not occur in response to light and is sustained along time (Figure 2A and 2B). Remarkably, heat treatment of dark-grown seedlings partially induces photomorphogenesis — cotyledons open and hypocotyl elongation is repressed (Figure 2C and 2D). These morphological changes, although less pronounced, are characteristic of seedlings lacking PIF activity, as is the case with etiolated seedlings transferred to light or dark-grown quadruple pif1pif3pif4pif5 (pifq) mutants (Figure 2B and Supplemental Figure 4) (Leivar et al., 2009, Leivar et al., 2008; Shin et al., 2009). Moreover, we quantified protochlorophyllide (Pchlide), the phototoxic chlorophyll precursor that, when overaccumulated in etiolated seedings, leads to photobleaching upon light exposure. This analysis revealed higher levels of Pchlide and increased photobleaching in wild-type (WT) etiolated seedlings exposed to heat for 24 hours prior to light exposure, a trend also partially phenocopying pifq mutants (Figure 2E, 2F and Supplemental Figure 5) (Leivar et al., 2009). These phenotypes are consistent with heat-induced alternative splicing reducing the amounts of functional PIF4. Analysis of PIF1, PIF3, PIF4 and PIF5 expression levels under 37ºC demonstrated that none of these genes were transcriptionally downregulated by heat stress (Figure 2G and Supplemental Figure 6), discarding a strong reduction in PIF transcript levels as the cause of the observed phenotypes. Heat-stressed etiolated seedlings are phenotypically more similar to higher order pif mutants than to pif4 single mutants (Supplemental Figure 7 and 8) (Leivar et al., 2008; Shin et al., 2009), suggesting that the PIF4-S splice form generated by exon skipping may act as a dominant negative rather than being merely inactive. Previous studies have reported that different PIF4 protein isoforms can exert dominant negative effects, inhibiting the activity of other PIFs, and that alternative splicing can produce dominant negative transcription factor isoforms (Gangappa et al., 2017; Kim et al., 2020; Nicolas et al., 2015; Seo et al., 2011). However, direct evidence is needed to confirm that the PIF4 short isoform functions as a dominant negative, reducing the activity of the long isoform and other PIFs. Nevertheless, because heat-induced phenotypes are milder than those observed in pifq seedlings (Figure 2C, 2E and 2F), a substantial fraction of PIFs likely remains functional at 37 ºC. This is consistent with heat-induced alternative splicing affecting only around 50% of transcribed PIF4 mRNAs (Figure 2A and 2B).Figure 2.Impact of heat treatment in etiolated seedlings.(A-B) PSI (Percent Spliced In) quantification (left) from RT-PCR (right) of the PIF4 alternatively-spliced exon in seedlings grown in continuous dark for 3 days (d) and then transferred to 37 ºC (red) or white light (WL; yellow), or kept at 22 ºC for 3 (A) or 9 (B) additional hours (h) in darkness (D; gray). Data represent means ± SEM of biological triplicates. -RT, no reverse transcriptase. (C) Representative image of 3-day-old wild-type (WT) and pifq seedlings subjected or not to heat stress. Scale bar, 5 mm. (D) Boxplot representations of the cotyledon aperture (left) and hypocotyl length (right) of at least 35 WT seedlings grown in darkness for 3 days and then transferred to 37 ºC in darkness (red) or kept in the dark at 22 ºC (gray) for the indicated time in hours. Asterisks indicate statistically differences between medians (Mann Whitney test). Fluorescence of protochlorophyllide (Pchlide; 635 nm) (E; n=4) and percentage of photobleaching (F; n=3) in heat-stressed (red) or unstressed (gray) etiolated WT and pifq seedlings. Asterisks indicate statistically differences between averages (t-test). (G) RT-qPCR analysis of PIF4 transcript levels in WT seedlings grown as in (B). Values were normalized to PP2A and expression levels are expressed relative to the initial time point set at one. Data represent means ± SEM of biological triplicates, and different letters denote statistically significant differences under each condition (Tukey test; P<0.05). n.d., not detected. (A, D, E, F) *P<0.05, **P<0.01 and ***P<0.001.Heat-induced photomorphogenesis depends on PIF4 alternative splicingTo confirm the implication of PIF4 alternative splicing in the physiological changes undergone by heat-stressed etiolated seedlings, we quantified the morphological and chlorophyll-related phenotypes of transgenic plants expressing predominantly the long PIF4 splice form in the pif4-101 mutant background, (PIF4p::PIF4-L; Figure 3A and Supplemental Figure 9). Importantly, both the heat-induced Pchlide accumulation and cotyledon opening were strongly reduced in these lines, while the repression of hypocotyl elongation was maintained (Figure 3B, 3C and Supplemental Figure 10). In agreement with the reduction of Pchlide in PIF4-L plants, a non-significant but correlating trend in their photobleaching phenotypes was also observed (Figure 3B). Importantly, PIF4-L.1 expresses the long isoform at levels similar to those of WT plants (Supplemental Figure 9A), ruling out the possibility that the suppression of heat-induced phenotypes (cotyledon opening and Pchlide accumulation) is due to elevated PIF4 expression levels. In addition, consistent with the comparable alternative splicing levels observed in heat-stressed WT and pif4 seedlings (Figure 3A and Supplemental Figure 9B), the skipped exon is located upstream of the pif4-101 mutation (Supplemental Figure 9C), and the phenotypes are also comparable (Figure 3). Similar results were obtained with the other commonly used pif4 mutant (pif4-2; Leivar et al., 2008), which harbors a similar insertion site (Supplemental Figure 9D). Overall, our results substantiate a role for PIF4 alternative splicing in controlling heat-induced developmental responses in etiolated seedlings. Next, we quantified the transcriptional response to heat stress in etiolated seedlings with different levels of the long PIF4 splice form (Figure 3A). Interestingly, we found a strong enrichment of heat-regulated genes among those reported as PIFq-regulated or PIFq-bound (Pfeiffer et al., 2014) (Figure 3D). Furthermore, heat-induced transcriptional changes in pif4 mutants, the genetic background of PIF4-L seedlings, were significantly attenuated in these transgenic lines, yet the response remained far from abolished (Figure 3E). This result could be explained by some heat-induced transcriptional changes being fully PIF-independent, as shown in Figure 3D, and others being PIF-dependent but unaffected due to the considerable fraction of PIFs still functional under heat stress. Either scenario would also explain the partial reversion of the heat-induced phenotypic responses observed in PIF4-L lines.Figure 3.Enhancing expression of the PIF4 long isoform at 37ºC reduces the impact of heat stress in etiolated seedlings.(A) PSI (Percent Spliced In) quantification of the PIF4 alternatively-spliced exon in wild-type (WT), pif4 and PIF4-L seedlings grown in continuous dark for 3 days (d) and then transferred to 37 ºC for 3 additional hours (h) in darkness. Data represent means ± SEM of biological triplicates. (B) Protochlorophyllide (Pchlide; 635 nm) fluorescence (left; n=4) and percentage of photobleaching (right; n=3) in 3-day-old etiolated WT, pif4 and PIF4-L seedlings transferred to 37 ºC (red) or kept at 22 ºC (gray) for 24 additional hours (h) in the dark. For photobleaching quantification, seedlings were subsequently exposed to white light (WL) for 3 days. Data represent means ± SEM of biological replicates (t-test). a.u., arbitrary units. (C) Boxplot representations of the cotyledon aperture (left) and hypocotyl length (right) of at least 35 WT, pif4 and PIF4-L seedlings grown as in (B). Mann Whitney test was used to define statistically differences. (D) Venn diagram showing overlap among heat-regulated genes in WT seedlings defined in this study and PIF-regulated and PIF-bound genes defined previously (Pfeiffer et al., 2014) (two-sided Fisher’s exact test). (E) Heat responsiveness (fold change; FC) in WT, pif4 and PIF4-L for heat-regulated genes in pif4 seedlings (n=3). Different letters denote statistically significant differences between genotypes by Dunn’s test (P<0.05). (A-C) Asterisks indicate statistically significant differences from pif4 (*P<0.05, **P<0.01 and ***P<0.001; n.s., not significant), and n the number of biological replicates.To confirm the role of PIF4 alternative splicing in regulating heat-induced responses, we generated transgenic plants expressing the short isoform of PIF4 under the control of its endogenous promoter (PIF4p::PIF4-S) and evaluated their morphology in the dark under control temperature conditions. These transgenic lines (PIF4-S) showed higher PIF4 expression levels than the corresponding WT control (Figure 4A), and in all three lines, the short isoform was the predominantly expressed variant (Figure 4B). We then conducted a phenotypic analysis of seedlings grown in the dark for 3 and 4 days at 21ºC. Interestingly, our results showed that enhanced production of the short isoform consistently promoted cotyledon opening, while changes in the hypocotyl length were not always detectable (Figure 4C). Thus, the cotyledon phenotype of these plants resembles that of WT plants exposed to heat stress (Figure 2C and 2D), linking the production of this isoform with heat-induced morphological adaptations. Notably, cotyledon opening in these transgenic plants at 21ºC is less pronounced than in heat-stressed plants (37ºC), indicating that the production of this isoform is not the unique mechanism underlying heat-induced cotyledon opening.Figure 4.Enhancing expression of the PIF4 short isoform promotes cotyledon opening in the dark.(A) RT-qPCR analysis of PIF4 transcript levels in wild-type (WT), pif4 and PIF4-S seedlings grown for 4 days in darkness. Values were normalized to PP2A and expression levels are expressed relative to the WT. Data represent means ± SEM of technical triplicates. (B) RT-PCR of the alternatively-spliced exon of PIF4 in seedlings grown as in (A). -RT, no reverse transcriptase. (C) Boxplot representations of the cotyledon aperture (top) and hypocotyl length (bottom) of at least 28 WT, pif4 and PIF4-S seedlings grown for 3 or 4 days (d) in the dark (D). Asterisks indicate statistically differences from WT at each day (Mann Whitney test; *P<0.05, **P<0.01 and ***P<0.001; n.s., not significant).DiscussionOur study reveals, for the first time, that cotyledon opening is a developmental response of etiolated seedlings exposed to heat stress. Heat stress also exerts a repressive effect on hypocotyl elongation in etiolated seedlings, a phenomenon previously reported but not extensively studied (Hong and Vierling, 2000; Karayekov et al., 2013; Larkindale et al., 2005; Martín and Duque, 2022). Karayekov et al. linked this inhibitory effect on hypocotyls to altered functioning of light signaling components such as CONSTITUTIVE PHOTOMORPHOGENIC 1 (COP1), ELONGATED HYPOCOTYL5 (HY5) and circadian clock components. Notably, they also proposed a physiological rationale for this response: dark-germinated seedlings approaching the soil surface may become more susceptible to heat shock episodes due to their proximity to sunlight. They hypothesized that the molecular mechanisms triggered by these episodes would prime seedlings for imminent light exposure. Given that the first exposure to sunlight represents a critical phase for etiolated seedlings, requiring rapid adaptation to ensure survival, we agree that the early activation of photomorphogenesis-associated traits could be advantageous. Our finding that heat promotes cotyledon opening, another hallmark of photomorphogenesis (Arsovski et al., 2012), supports the hypothesis that, in etiolated seedlings, heat may function as a signal to induce photomorphogenesis. In addition, we show that heat enhances Pchlide accumulation in dark-grown seedlings. Although the conversion of Pchlide to chlorophyllide is light-dependent, Arabidopsis seedlings accumulate Pchlide in the dark to expedite this process upon light exposure (Sperling et al., 1997). However, the amount of Pchlide must be tightly balanced with the availability of the enzyme that catalyzes its conversion to prevent the generation of reactive oxygen species and subsequent cellular damage upon illumination (Mochizuki et al., 2010; Reinbothe et al., 1996). This raises the question of whether heat-induced Pchlide accumulation in etiolated seedlings is an adaptative mechanism to accelerate chlorophyll production and optimize the transition to autotrophic development, or whether it is a side effect of prematurely activating light signaling in the dark, an outcome that, as suggested by increased photobleaching, would negatively impact seedling survival.Our data also indicate that the induction of these photomorphogenic traits in etiolated seedlings depends, at least in part, on a heat-specific regulatory event: the alternative splicing of PIF4. This represents a novel finding, as PIF4, despite being one of the most studied proteins in Arabidopsis, has been reported to be regulated only at the transcriptional and post-translational levels (Balcerowicz, 2020; Favero, 2020). Notably, our phenotypic analyses of seedlings with altered patterns of PIF4 alternative splicing (PIF4-L and PIF4-S) suggest that the heat-induced isoform plays a role specifically in controlling cotyledon-related phenotypes. This implies that the mechanisms reported by Karayekov to control hypocotyl elongation under heat stress may operate in parallel with the alternative isoform of PIF4. Because our transcriptomic experiment was conducted using whole seedlings, we were unable to assess organ-specific effects in detail. We hypothesize that this alternative splicing event may be organ-specific or, alternatively, that the protein encoded by the heat-induced PIF4 isoform may be preferentially active in cotyledons due to specific protein interactors and/or molecular targets. Further research into organ-specific dynamics is needed to elucidate why this alternative splicing event appears to predominantly affect cotyledon development. These findings would provide valuable insights into the organ-specific roles of PIF proteins, an emerging area of research (Dong et al., 2019; Sun et al., 2016; Zhang et al., 2021).Our study focuses on the role of PIF4 under heat stress, a condition in which its function remains poorly understood. In fact, the few previous reports linking PIF function to heat stress have yielded contrasting results, with PIFs acting as either positive (Li et al., 2021) or negative (Yang et al., 2022) regulators of the response. Here, we significantly expand the current understanding of this transcription factor by demonstrating its involvement in heat stress and and further reinforcing its role as a key integrator of diverse environmental signals in the regulation of plant development (Paik et al., 2017). Importantly, modulating alternative splicing to alter isoform abundance is emerging as a promising strategy for developing stress-resilient plants (Alhabsi et al., 2025). In this context, investigating the role of the PIF4 alternative isoform in heat-stressed adult plants, along with molecular strategies that specifically target the splicing sites involved in its regulation, could reveal a novel molecular target and offer an alternative genetic approach to enhance plant stress tolerance.MethodsPlant materialsThe Arabidopsis thaliana pif1-1pif3-3pif4-2pif5-3 (pifq) and pif4-2 mutant was obtained from the Nottingham Arabidopsis Stock Centre (NASC). PIF4-L transgenic plants expressing the PIF4 coding region driven by the endogenous promoter, together with its respective pif4 mutant control (pif4-101; Lorrain et al., 2008) were kindly provided by U. Pedmale (Cold Spring Harbor Laboratory, USA). PIF4-S transgenic plants were generated by PCR amplification and cloning a 1227-bp fragment containing the coding sequence region of the short splice variant under the control of a 2505-bp fragment upstream the PIF4 start codon corresponding to the PIF4 promoter, in the eGFP-tagged version of the binary pBA002 vector using the XbaI/AatI restriction sites (5’-GACGTTTCTAGAATGGAACACCAAGGTTGGAG-3’and 5’-GTGACGTCCGAGTGGTCCAAACGAGAAC-3’). The pPIF4 promoter was insertved into the promoterless pBA002 via HindIII/XbaI restriction sites (5’-TGTGAAGCTTCCAAAGTAATAAAAGTTGCCACAAC-3’and 5’-GACGTTTCTAGAGTCAGATCTCTGGAGACATTTC-3’). The resulting constructs were introduced into Agrobacterium tumefaciens strain EHA105 and subsequently used for agroinfiltration-mediated transformation of Col-0 seedlings (Clough and Bent, 1998).Phenotypical and photobleaching analysisSterile seeds were sown on MS medium containing 1X Murashige and Skoog (MS) salts (Duchefa Biochemie), 2.5 mM MES (pH 5.7), 0.5 mM myo-inositol, and 0.8% agar (w/v). After stratification for 4 days at 4 ºC in darkness, seeds were subjected to a 3-hour light pulse to induce germination and then transferred to continuous darkness for 69 hours at 22 ºC. Maintaining the absence of light, seedlings were then either kept at 22 ºC for control conditions or transferred to 37 ºC for heat stress. Hypocotyl and cotyledon measurements of at least 30 seedlings and two biological replicates were carried out using the National Institutes of Health ImageJ software as described before (Sentandreu et al., 2011). Pictures were taken before and after exposure to stress as indicated in each figure. Photobleaching experiments were adapted from previous studies (Leivar et al., 2009), with 3-day-old etiolated seedlings being grown under control conditions or at 37 ºC for 24 hours and then transferred to continuous white light for 3 additional days (100 μmol·m−2·s−1). At this point, the percentage of seedlings that failed to become green were scored in three biological replicates.Protochlorophyllide quantificationApproximately 30 sterile seeds, sown on MS medium and stratified for 4 days at 4 ºC in the dark, were subjected to a 3-hour light pulse before being transferred to continuous darkness for 69 hours at 22 ºC. Seedlings were then either kept under these conditions (control) or transferred to 37 ºC in the dark to induce heat stress. Whole seedlings were collected in the dark 24 hours later, flash-frozen in liquid N2,and ground before extraction with 0.75 mL ice-cold 9:1 acetone:0.1 M NH4OH, as described previously (Terry and Kacprzak, 2019). The resulting mixture was vortexed for 1 minute and then centrifuged at 14,000 rpm at 4 ºC for 5 minutes. After supernatant recovery, the protochlorophyllide (Pchlide) content was determined as the peak value (635 nm) of the fluorescence emission spectrum between 600-700 nm, measured with a bandwidth of 5 nm after excitation at 440 nm and using a Synergy Neo2 microplate reader (Biotek). Pchlide data is shown as the average of Pchlide per seedling of four biological replicates.Gene expression and PSI quantification from RNA extractionTotal RNA was extracted from Arabidopsis thaliana seedlings using the InnuPREP Plant RNA kit (Analytik Jena BioSolutions) and 1 µg treated with DNase I to remove genomic DNA. cDNA synthesis using the oligo dT primer and the enzyme SuperScript III reverse transcriptase (Invitrogen) was conducted in the presence of RNase Out (Invitrogen). The cDNA was then used to quantify either gene expression or exon skipping of PIF4’s fifth exon. In both cases, three biological replicates were analyzed for each condition and/or genotype tested. Gene expression was measured by Reverse Transcription-quantitative PCR (RT-qPCR) using a QuantStudioTM 7 Flex Real-Time PCR System 384-well format and the Absolute SYBR Green ROX Mix (Thermo Scientific) on 2.5 µL of cDNA (diluted 1:10) per 10 µL of reaction volume, containing 300 nM of each gene-specific primer (see below). The PP2A gene was used for normalization (Shin et al., 2007). Exon skipping of the fifth exon of PIF4 was quantified from RT-PCRs using primers spanning the two adjacent exons. These primer sequences were obtained from PastDB (Plant alternative splicing and transcription Data Base; www.pastdb.crg.eu; Martín et al., 2021). The resulting bands were quantified using the National Institutes of Health ImageJ software.RNA sequencingRNA was extracted from 3-day-old WT, pif4-101, PIF4-L.1 and PIF4-L.2 etiolated seedlings grown for 3 hours at 37 ºC or 22 ºC in complete darkness. Oligo dT, non-strand specific libraries from triplicate biological replicates were built and sequenced using NextSeq500 at the Gulbenkian Institute for Molecular Medicine (GIMM). An average of 15 million 75-nucleotide single-end reads were generated per sample. Raw sequencing data was submitted to the Sequence Read Archive (accession number GSE200247).Gene expression quantification from RNA sequencing dataQuantification of Arabidopsis thaliana transcript expression from our RNA-seq experiment (GSE200247) and public sequencing data (Dataset S1) was performed using vast-tools v2.5.1 and v2.2.2 (Martín et al., 2021; Tapial et al., 2017), respectively. For each Arabidopsis transcript, this tool provides the number of mapped reads per million mapped reads divided by the number of uniquely mappable positions of the transcript (cRPKM; corrected-for-mappability reads per kbp of mappable sequence per million mapped reads) (Labbé et al., 2012). To identify genes differentially expressed between different temperatures, we used vast-tools compare_expr using the option -norm to perform a quantile normalization of cRPKM values between samples. Next, we filtered out the genes that were not expressed at cRPKM > 5 and had read counts > 50 across all the replicates of at least one of the samples compared. Finally, differentially-expressed genes were defined as those with a fold change of at least 2 between each of the individual replicates from each genotype. See https://github.com/vastgroup/vast-tools for details.PSI quantification from RNA sequencing dataWe employed vast-tools v2.2.2 to quantify alternative splicing from public sequencing data (Martín et al., 2021; Tapial et al., 2017). This tool quantifies exon skipping (ES), intron retention (IR) and alternative donor (ALTD) and acceptor (ALTA) site choices. For all these types of events, vast-tools estimates the Percent Spliced In (PSI) of the alternative sequence using only exon-exon (or exon-intron for IR) junction reads and provides information about the read coverage See https://github.com/vastgroup/vast-tools for details. Data shown in Figure 1 and Supplemental Table 1 indicate the PSI quantification of specific alternative splicing events in the subfamily XV of the bHLH transcription factors (see below) using a wide array of samples (Supplemental Table 1).Data availabilityRNA-seq data have been deposited in Gene Expression Omnibus (GEO) (GSE200247).AcknowledgementsWe thank U. Pedmale for kindly providing pif4-101 mutants and PIF4pro:PIF4-3xFlag transgenic lines, and V. Nunes for excellent plant care at the Gulbenkian Institute for Molecular Medicine (GIMM) Plant Facility. This work was funded by Fundação para a Ciência e a Tecnologia (FCT) through grants PTDC/BIA-FBT/31018/2017, PTDC/BIA-BID/30608/2017 and PTDC/ASP-PLA/2550/2021 as well as by the Spanish Ministry of Science and Innovation trough grant PID2021-125223NA-I00 (MCIN/AEI/10.13039/501100011033/FEDER). Funding from the research unit GREEN-it “Bioresources for Sustainability” (ID/04551/2025, UID/PRR/04551/2025) and the Generalitat de Catalunya (AGAUR, GRE2021, ref. SGR00873) is also acknowledged. G.M. was supported by an EMBO Long-Term Fellowship (ALTF 1576-2016), a Marie Skłodowska-Curie Individual Postdoctoral Fellowship (EU project 750469) and a Ramón y Cajal fellow from the Spanish Ministry of Science and Innovation (RYC2021-032539-I). T.L was supported by a Marie Skłodowska-Curie Individual Postdoctoral Fellowship (EU project 706274).Additional informationAuthor contributionsM.N.-G., B.A., D.S., T.L. and G.M performed the experiments and analyzed the data. All authors discussed the results. G.M. conceived the project and designed research. G.M. and P.D. wrote the manuscript.FundingFCT - Fundação para a Ciência e a Tecnologia (PTDC/BIA-BID/30608/2017)Guiomar MartínFCT - Fundação para a Ciência e a Tecnologia (PTDC/BIA-FBT/31018/2017)Paula DuqueFCT - Fundação para a Ciência e a Tecnologia (PTDC/ASP-PLA/2550/2021)Paula DuqueFCT - Fundação para a Ciência e a Tecnologia (ID/04551/2025,UID/PRR/04551/2025)Paula DuqueSpanish Ministry of Science and Innovation (PID2021-125223NA-I00)Guiomar MartínEMBO (ALTF 1576-2016)Guiomar MartínSpanish Ministry of Science and Innovation (RYC2021-032539-I)Guiomar MartínMSCA-IF European Commission (750469)Guiomar MartínMSCA-IF European Commission (706274)Tom LaloumAdditional filesSupplementary file. Supplemental Figures 1-10 and Supplemental Tables 1-2.Supplemental Table 1. List of publicly available RNA sequencing samples used. Samples available at the Short Read Archive (SRA) were used to quantify the PSI (Percent Spliced In) and expression values shown in Figure 1, Supplemental Figure 2 and Supplemental Figure 3. Columns indicate our sample classification based on their growth conditions and tissue source, which are summarized in the sample description column, as well as the original SRA code number and name. 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This DOI represents all versions, and will always resolve to the latest one.Copyright© 2026, Niño-González et al.This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.Metricsviews0downloads0citations0Views, downloads and citations are aggregated across all versions of this paper published by eLife.

来源:e life发布时间:2026-03-17
Nerve Injury-Induced Protein 2 preserves lysosomal membrane integrity to suppress ferroptosis

AbstractNerve injury-induced protein 1 (NINJ1), a cell adhesion molecule, is oligomerized during lytic cell death and mediates plasma membrane rupture to release large intracellular molecules that propagate the inflammatory response. We and others previously showed that NINJ2, a close relative of NINJ1, does not promote plasma membrane rupture to spread inflammation. Here, we identify that NINJ2 is necessary for the lysosome membrane integrity to protect cells from ferroptosis. Specifically, we found that NINJ2 localizes to lysosomes and interacts with LAMP1, an anchor glycoprotein of the lysosome membranes and a sensor of stressed lysosomes. We also found that loss of NINJ2 exacerbates lysosomal membrane permeabilization (LMP), which allows for selective leakage of lysosomal contents, such as labile iron, into the cytosol. Accordingly, loss of NINJ2 elevates cellular labile iron accumulation and decreases expression of ferritins, the primary intracellular iron storage protein complexes. Mechanistically, we found that loss of NINJ2 promotes ferritin FTH degradation in lysosomes, which can be reversed by knockdown of LAMP1. Moreover, we found that loss of NINJ2 sensitizes cells to ferroptosis induced by RSL3 and Erastin, consistent with a recent study that loss of Ninj2 predisposes mice to chronic inflammation. Together, these findings uncover a previously unrecognized activity of NINJ2 from lysosome homeostasis to ferroptosis, which can be explored as a cancer therapeutic strategy especially considering that NINJ2 and ferritins are found to be overexpressed and positively associated with iron-addicted cancers.IntroductionNinjurin 2 (Nerve Injury-Induced Protein 2; NINJ2), along with NINJ1, belongs to the Ninjurin family of homophilic cell-surface adhesion molecules [1, 2]. These proteins were originally identified as being upregulated in Schwann cells and dorsal root ganglion neurons following peripheral nerve injury. Subsequent studies showed that NINJ1 and NINJ2 promote neurite outgrowth and facilitate interactions between Schwann cells and regenerating axons, thereby contributing to effective nerve repair [1-3]. Structural analysis indicated that NINJ1 and NINJ2 share approximately 52%–55% amino acid sequence identity and 67% sequence similarity. They are a two-pass transmembrane protein composed of an extracellular N-terminal domain, two conserved hydrophobic transmembrane domains, and a C-terminal extracellular domain [4]. Despite high sequence homology, recent studies showed that NINJ1 and NINJ2 exert divergent functions potentially due to their structural variations. For instance, NINJ1 mediates plasma membrane rupture by polymerizing into straight, amphipathic filaments that disrupt membrane integrity [5-7]. In contrast, NINJ2 assembles into curved filaments that are unable to permeabilize the membrane and does not promote plasma membrane rupture [7]. These data indicated that NINJ1 and NINJ2 exhibit both overlapping and distinct functions across diverse biological processes.Recent studies have shown that NINJ2 is a multifaced protein with profound biological functions. NINJ2 is found to be highly expressed in immune-related tissues, including bone marrow, lymph nodes, spleen, and thymus [8, 9], suggesting a role in immune regulation. Indeed, studies have shown that NINJ2 acts as a pro-inflammatory mediator by physically interacting with TLR4, activating the NF-κB pathway to promote the expression of inflammatory markers like IL-6 and TNF-α [10-12]. Additionally, we found that loss of NINJ2 leads to activation of NLRP3 inflammasome and subsequently, promotes pyroptosis, a pro-inflammatory programmed cell death that leads to the dysregulated release of cytokines like IL-1β and IL-18 [9]. In line with this, we also found that loss of NINJ2 results in chronic inflammation in mice [9]. Moreover, we and others have found that mice deficient in Ninj2 are prone to metabolic abnormalities, including pancreatic islet hyperplasia, elevated circulating cholesterol levels and insulin resistance [8, 9, 13]. Furthermore, we recently identified NINJ2 as a transcriptional target of the tumor suppressor p53 and that NINJ2, in turn, represses p53 mRNA translation, implicating its role in tumorigenesis [14]. The reciprocal regulation between NINJ2 and p53 suggests that NINJ2 may promote or inhibit tumor growth depending on the p53 status. Indeed, while we reported that mice deficient in Ninj2 are prone to spontaneous tumors [9], NINJ2 over-expression was also found to promote tumor growth [15]. Finally, NINJ2 gene polymorphisms are found to be associated with increased risk of ischemic stroke, coronary artery disease, multiple sclerosis (MS), and Alzheimer’s disease [16-19], suggesting a role in vascular and neuroinflammatory pathologies. Together, these data indicate that NINJ2 participates in a wide range of physiological and pathological processes, yet the molecular mechanisms underlying its multifunctional roles remain poorly understood.Lysosomes are central regulators of cellular homeostasis, coordinating protein degradation, autophagy, and metabolic signaling [20]. Lysosomal membrane permeabilization (LMP) is defined by the formation of ultrastructurally-undetectable, tiny pores at the lysosomal membrane and thereby causes selective releases of lysosomal contents into the cytosol [21, 22]. Recently, it was found that low-grade LMP does not always cause cell death but can be repaired by ESCRT complex [23]. It was also found that LMP triggers the activation of NLRP3 inflammasome and subsequently, elicits immune response [24, 25]. We previously reported that NINJ2 deficiency leads to NLRP3 inflammasome activation [9]. However, it was not certain whether this activation was linked to lysosomal damage. To address this, we examined the role of NINJ2 in regulating lysosomal morphology and function. We found that NINJ2 localizes to lysosomes and loss of NINJ2 exacerbates LMP. We also found that the LMP mediated by NINJ2-deficiency results in increased levels of labile iron along with decreased expression of ferritin. We further demonstrated that NINJ2-deficiency promotes degradation of Ferritin and subsequently, enhances ferroptosis, a regulated form of cell death driven by iron-dependent lipid peroxidation. Together, these findings reveal a previously unrecognized function of NINJ2 in linking lysosomal homeostasis to ferroptosis.ResultsNinj2 protein localizes to Lysosomes and interacts with LAMP1To determine whether NINJ2 is involved in modulating lysosomal activity, we first examined whether NINJ2 localizes to lysosomes. To this end, MCF7 cells expressing Flag-tagged NINJ2 were stained with LysoTracker to label lysosomes and with an anti-Flag antibody to detect NINJ2. Confocal microscopy revealed substantial co-localization of NINJ2 with LysoTracker-positive vesicles, indicating that some NINJ2 proteins localize to lysosomes (Fig. 1A). To further test this, MCF7 cells expressing Flag-tagged NINJ2 were co-stained with antibodies against NINJ2 and lysosome-associated membrane protein 1 (LAMP1), a well-established lysosomal marker [26-28]. Consistent with the data from LysoTracker staining (Fig. 1A), we found that NINJ2 was co-localized with LAMP1 in lysosomes (Fig. 1B), confirming that NINJ2 localizes to lysosomes. Next, we examined whether NINJ2 physically interacts with LAMP1 by performing reciprocal immunoprecipitation assays using 293T cells expressing Flag-tagged NINJ2. We found that endogenous LAMP1 was detectable in NINJ2-immunocomplex when immunoprecipitated with anti-Flag antibody (Fig. 1C). Conversely, Flag-tagged NINJ2 was readily detected in LAMP1-immunocomplexes when immunoprecipitated with anti-LAMP1 antibody (Fig. 1D). To verify that NINJ2 interacts with LAMP1, we performed proximity ligation assay (PLA) [29], which detects protein-protein interactions at a subcellular level. Briefly, MCF7 cells expressing Flag-tagged NINJ2 were incubated with anti-Flag and/or anti-LAMP1 antibodies. PLA signals were visualized as discrete fluorescent puncta. We observed robust PLA signals in cells stained with both antibodies, indicating close interaction between NINJ2 and LAMP1 (Fig. 1E). In contrast, no PLA signals were detected in control conditions in which cells were incubated without any primary antibody or with only one antibody (Fig. 1E).Figure 1.NINJ2 protein localizes to lysosomes and interacts with LAMP1.(A) MCF7 cells were transiently transfected with a plasmid expressing Flag-tagged NINJ2, followed by immunostaining with, DAPI, lysoDye and Flag. The arrow indicates costaining of NINJ2 and LysoDye. (B) MCF7 cells were transiently transfected with a plasmid expressing Flag-tagged NINJ2, followed by immunostaining with anti-Flag and anti-LAMP1. (C-D) 293T cells were transiently transfected with Flag-tagged NINJ2 plasmid for 24 hours, followed by immunoprecipitation with IgG, anti-Flag (C) or anti-LAMP1 (D). The immunocomplex were examined by western blot analysis with Flag or LAMP1 antibody. (E) MCF7 cells were transfected with Flag-tagged NINJ2 antibody, followed by PLA assay as described in “Material and Methods”. The positive PLA signal is shown in red puncta.Loss of NINJ2 leads to enhanced lysosomal membrane permeability along with increased expression of LAMP1The localization of NINJ2 in lysosomes prompted us to determine whether NINJ2 regulates lysosomal function. To this end, isogenic control and NINJ2-KO MCF7 cells were mock-treated or treated with L-leucyl-L-leucine methyl ester (LLOMe), a well-established inducer of lysosomal membrane permeabilization (LMP) [30, 31], followed by immunostaining with Galectin-3 and LAMP1. We would like to note that in response to lysosomal damage, Galectin-3 rapidly translocalizes to lysosomal membranes where it participate in lysosomal repair and removal [32, 33]. Thus, colocalization of Galectin-3 with LAMP1 serves as a key indicator of LMP. We found that in the absence of LLOMe, little colocalization of Galectin-3 and LAMP1 was observed in isogenic control and NINJ2-KO MCF7 cells (Fig. 2A, top two panels), indicating minimal lysosomal membrane damage (Fig. 2A, compare top two “MERGE” panels). Upon treatment with LLOMe, isogenic control cells exhibited moderate co-staining of Galectin-3 and LAMP1 (Fig. 2A, Iso. Ctrl+ LLOMe panel). Strikingly, the recruitment of Galectin-3 to LAMP1-positive lysosomes was markedly enhanced in NINJ2-KO MCF7 cells, suggesting that NINJ2 deficiency exacerbates LLOMe-induced lysosomal membrane permeabilization (Fig. 2A, NINJ2-KO+LLOMe panel). In addition to elevated LMP mediated by NINJ2-deficiency, we observed that LAMP1 staining was enhanced in NINJ2-KO MCF7 cells compared to isogenic controls regardless of LLOMe treatment (Fig. 2A, LAMP1 panels), suggesting that NINJ2-deficiency alters LAMP1 expression. To verify this, isogenic control and NINJ2-KO Molt4 cells were mock-treated or treated with LLOMe or glucose oxidase (GO), another LMP inducer [34], followed by measurement of LAMP1 expression. We found that loss of NINJ2 resulted in a marked increase in LAMP1 expression regardless of LLOMe or GO treatment (Fig. 2B-C, LAMP1 panel, compare lanes 1, 3, and 5 with 2, 4, and 6, respectively). In contrast, LLOMe and GO did not significantly alter LAMP1 expression in these cells. Similarly, loss of NINJ2 led to increased LAMP1 expression in MCF7 cells regardless of LLOMe or GO treatment (Fig. 2D-E). Consistent with this, we also showed that the level of LAMP1 transcripts were increased by loss of NINJ2 regardless of LLOME treatment in both Molt4 and MCF7 cells (Supplemental Figure 1). Together, these data suggest that loss of NINJ2 leads to low-grade lysosomal membrane damage accompanied by elevated LAMP1 expression, which in turn exacerbates LMP.Figure 2.Loss of NINJ2 leads to enhanced LAMP1 expression and lysosomal membrane permeability(A) Isogenic control and NINJ2-KO MCF7 cells were mock-treated or treated with LLOMe (1mM) for 5 hours, followed by immunostaining with antibodies against Galectin 3 and LAMP1. Scale bar: 20µM. (B-C) Isogenic control and NINJ2-KO Molt4 cells were mock-treated or treated with LLOMe (B) or GO (C) for 5 hours. The cell lysates were subjected to western blot analysis using antibodies against NINJ2, LAMP1 and actin. (D-E) Isogenic control and NINJ2-KO MCF7 cells were mock-treated or treated with LLOMe (D) or GO (E) for 5 hours, followed by western blot analysis to detect expression of LAMP1 and actin.Loss of NINJ2 elevates intracellular labile iron level and inhibits ferritin expressionLMP is known to release lysosomal contents, such as redox-active Fe2+, from lysosomes into the cytosol, thereby expanding the labile iron pool and promoting lipid peroxidation [35]. Thus, we measured the level of labile iron levels in isogenic control and NINJ2-KO Molt4 and MCF7 cells treated with Ferric Ammonium Citrate (FAC), an iron source. We found that the level of intracellular labile iron was markedly increased by loss of NINJ2 in both Molt4 and MCF7 cells (Fig. 3A-B). To verify that NINJ2-deficiency alters iron homeostasis, we measured the expression of ferritin heavy chain (FTH) and ferritin light chain (FTL) in isogenic control and NINJ2-KO Molt4 cells treated with or without FAC. FTH and FTL are oligomerized to form ferritins, the primary intracellular iron storage protein complexes [36, 37]. FTH exerts ferroxidase activity by catalyzing Fe2+to Fe3+, whereas FTL helps promote iron nucleation and mineralization for long-term storage. In response to FAC treatment, both FTL and FTH protein were elevated as expected (Fig. 3C-D, FTL and FTH panels, compare lane 1 with 3).Figure 3.Loss of NINJ2 leads to increased level of labile iron and reduced expression of ferritin(A-B) Isogenic control and NINJ2-KO Molt4 (A) and MCF7 (B) cells were treated with FAC (30mg/ml) for 24 hours. Cell lysates were collected and the level of labile iron was measured using QuantiChrom™ assay kit. (C-D) Isogenic control and NINJ2-KO Molt4 cells were mock-treated or treated with FAC (30mg/ml) for 16 hours, followed by western blot analysis to measure the level of NINJ2 (C), FTL (C), FTH (D) and actin (C-D). (E-F) Isogenic control and NINJ2-KO MCF7 cells were mock-treated or treated with FAC (30mg/ml) for 16 hours, followed by western blot analysis to measure the level of FTL(E), FTH (F) and actin (E-F).Importantly, loss of NINJ2 markedly decreased expression of FTL and FTH (Fig. 3C-D, compare lane 1 and 3 with lane 2 and 4, respectively), suggesting that NINJ2 is required for FTH and FTL expression. Additionally, we found that loss of NINJ2 markedly reduced FTH and FTL expression in MCF7 cells regardless of FAC treatment (Fig. 3E-F, compare lane 1 and 3 with lane 2 and 4, respectively).Loss of NINJ2 promotes ferritin degradationTo uncover the mechanism by which NINJ2 regulates Ferritin expression, we first examined the level of FTH transcripts and found it not to be altered by loss of NINJ2 (Fig. 4A). Next, the half-life of FTH protein was measured in Isogenic control and NINJ2-KO MCF7 cells treated with cycloheximide for various times. We found that the half-life of FTH protein decreased from 23.1h in isogenic control cells to 11.95h in NINJ2-KO cells (Fig. 4B-C), indicating that NINJ2 is required for maintaining FTH protein stability. Since FTH is known to be degraded primarily in lysosomes via NCOA4 [38, 39], we then asked whether LAMP1 plays a role in FTH expression decreased by loss of NINJ2. To this end, two siRNA against LAMP1 were designed and transiently transfected into isogenic control and NINJ2-KO MCF7 cells. As expected, the level of LAMP1 protein was diminished upon siRNA transfection (Fig. 4D, LAMP1). Importantly, knockdown of LAMP1 led to increased expression of FTH and FTL in both isogenic control and NINJ2-KO MCF7 cells (Fig. 4D). Together, these data indicated that loss of NINJ2 promotes Ferritin turnover in lysosomes, which can be reverted by knockdown of LAMP1.Figure 4.Loss of NINJ2 promotes ferritin degradation(A) The level of FTH and HPRT transcripts was measured in isogenic control and NINJ2-KO Molt4 cells. (B) Isogenic control and NINJ2-KO MCF7 cells were treated with cycloheximide (50 μg/mL from 3 to 15 hours. The cell lysates were collected and subjected to western blot analysis using FTH and actin antibodies. (C) The level of FTH and actin protein in (B) were quantitated and the relative protein half-life of FTH was calculated by GraphPad Prism software. (D) Isogenic control and NINJ2-KO MCF7 cells were transiently transfected with scrambled siRNA or siRNAs against LAMP1 for 3 days, followed by western blot analysis with antibodies against LAMP1, FTH, FTL, and actin.Loss of NINJ2 promotes ferroptosisRecent studies have shown that LMP releases reactive iron into the cytosol and promotes lipid peroxidation, thereby serving as a critical initiating event in ferroptosis, an iron-dependent form of programmed cell death [35, 40]. Since loss of NINJ2 increases the level of labile iron (Fig. 3A-B), we thus examined whether NINJ2 modulates ferroptosis. As FTH serves as a protector against ferroptosis, its expression was first examined in isogenic control and NINJ2-KO cells treated with or without RSL3 and Erastin, both of which are ferroptosis inducers [41-43]. Indeed, we found that the level of FTH was decreased by NINJ2-KO regardless of RSL3 or Erastin treatment in both Molt4 and MCF7 cells (Fig. 5A-B), suggesting that NINJ2-deficiency promotes ferroptosis. To further test this, isogenic control and NINJ2-KO Molt4 cells were treated with various doses of RSL3 or Erastin, and cell viability was assessed. We found that loss of NINJ2 significantly sensitized Molt4 cells to RSL3 treatment, reducing the IC50 from 0.31 μM in isogenic control cells to 0.16 μM in NINJ2-KO cells (Fig. 5C). Similarly, NINJ2 deficiency enhanced the sensitivity of Molt4 cells to Erastin, as evidenced by decreased IC50 in NINJ2-KO cells (Fig. 5D). To validate these findings, isogenic control and NINJ2-KO MCF7 cells were treated with RSL3 or Erastin. Consistently, we found that loss of NINJ2 promoted ferroptosis in MCF7 cells, as indicated by a marked reduction in their IC50 values (Fig. 5E-F). To further verify this, colony formation assay was performed. We found that under mock treatment conditions, NINJ2-KO suppressed colony formation, consistent with previous reports [14]. Importantly, RSL3 and Erastin markedly reduced colony formation in MCF7 cells, which was further inhibited by NINJ2-KO (Fig. 5G-H).Figure 5.Loss of NINJ2 promotes ferroptosis(A) Isogenic control and NINJ2-KO Molt4 cells were treated with RSL3 (1.25 µM) or Erastin (2.5 µM) for 8 hours, and the level of Ninj2, FTH, and actin were measured by western blot analysis. (B) Isogenic control and NINJ2-KO MCF7 cells were treated with RSL3 (1.25 µM) for 8 hours, followed by western blot analysis to detect FTH, and actin. (C-D) Isogenic control and NINJ2-KO Molt4 cells were treated with RSL3 (C) or Erastin (D) from 0-7.29 µM for 48 hours. The relative cell viability was measured using CellTiter-Glo Viability Assay kit. The relative Cell viability is calculated as a percentage of untreated (control) cells. Data points represent the mean ± SD from four representative experiments. Curves were fitted using nonlinear regression using GraphPad Prism Software. IC50 values represent the drug concentration required to achieve 50% inhibition of maximal proliferation capacity. (E-F) Isogenic control and NINJ2-KO MCF7 cells were treated with RSL3 (E) or Erastin (F) from 0-7.29 µM for 48 hours, followed by cell viability assay. The IC50 was calculated with nonlinear regression analysis using GraphPad Prism Software. (G-H) Colony formation was performed with isogenic control and NINJ2-KO MCF7 cells were treated with or without RSL3 or Erastin for 24 hours. The drugs were then withdrawn to allow colonies to grow for 3 weeks.NINJ2, FTH1 and FTL are overexpressed in hepatocellular and breast carcinomas and are positively associated among one anotherPrevious reports have shown that Ferritins promotes tumorigenesis through regulating iron homeostasis and oxidative stress as well as suppressing ferroptosis. To determine whether the NINJ2-Ferrrtins axis contributes to cancer development, we searched the TCGA database for the association between NINJ2 and ferritin in several types of human cancers. Interestingly, we found that NINJ2, FTH, and FTL were all up-regulated in hepatocellular carcinoma (Fig. 6A-C) and breast cancer (Fig. 6D-F), both of which are iron-addicted malignancies. Further, we found that NINJ2 expression was positively associated with FTH and FTL in both hepatocellular carcinoma (Fig. 6G-H) and breast carcinoma (Fig. 6I-J). Together, these findings suggest that NINJ2 may function in concert with ferritin to promote tumor cell growth especially in iron-addicted tumors, which may represent a therapeutic vulnerability for these types of cancers.Figure 6.NINJ2 and Ferritins are overexpressed in hepatocellular and breast carcinomas and are positively associated with one another(A-C) Boxplot shows the relative expression of NINJ2 (A), FTH (B), and FTL (C) in normal and hepatocellular carcinomas. The analysis was performed using UACLAN database. (D-F) Boxplot shows the relative expression of NINJ2 (A), FTH (B), and FTL (C) in normal and breast carcinomas. The analysis was performed using UACLAN database. (G) NINJ2 expression is positively associated with FTH (G) in hepatocellular carcinomas. The analysis was performed using the GEPIA2 database (http://gepia2.cancer-pku.cn/#correlation). Statistical analysis suggests a strong correlation between NINJ2 and FTH expression in hepatocellular carcinomas (Pearson’s r =0.54). (H) NINJ2 expression is positively associated with FTL (H) in hepatocellular carcinomas (Pearson’s r =0.54). (I-J) NINJ2 expression is positively associated with FTH (I, Pearson’s r =0.54) and FTL (J, Pearson’s r =0.67) in breast carcinomas.DiscussionNINJ2 is a multifaceted protein and participates in various biological and pathological processes. However, the underlying mechanisms underlying these processes are not fully understood. Here, we identify NINJ2 as a critical regulator of lysosomal integrity and ferroptosis. Specifically, we demonstrate that NINJ2 localizes to lysosomes and interacts with LAMP1, and that loss of NINJ2 enhances LMP. We also found that loss of NINJ2 increases the level of cytosolic labile iron by promoting lysosomal ferritin degradation. Consequently, NINJ2 deficiency sensitizes cells to ferroptosis. Finally, we showed that NINJ2 and ferritin are co-overexpressed and positively correlated in several iron-addicted cancers, including hepatocellular carcinoma and breast cancer, suggesting that the NINJ2-FTH axis may be targeted for these types of cancer. These data let us speculate that loss of NINJ2 leads to low-grade lysosomal membrane damage, leading to leakage of labile iron into the cytosol. The released iron increases oxidative stress and promotes ferritin degradation in lysosomes, which further elevates cytosolic iron levels, creating a feed-forward loop that sensitizes cells to ferroptosis. Thus, our study indicated a critical role of NINJ2 in maintaining lysosomal membrane integrity and iron homeostasis.Another interesting observation is that NINJ2 is required for Ferritin expression. Our data indicated the NINJ2-deficiency leads to reduced expression of Ferritin (Fig. 3C-F). Moreover, we found that reduced Ferritin expression by loss of NINJ2 is due to enhanced lysosomal degradation (Fig. 4B-C). In support of this, we showed that knockdown of LAMP1 reverses the inhibition of ferritin expression observed in NINJ2-knockout cells (Fig. 4D). Although not yet experimentally confirmed, we tentatively propose that the enhanced degradation of ferritin in NINJ2-KO cells results from increased labile iron in these cells owing to the low-grade LMP. On the other hand, the NINJ2-Ferritin axis may be explored as viable strategies for cancer management. In support of this notion, we found that NINJ2 is positively associated with Ferritins in iron-addicted cancers, such as HCC and Breast cancer (Fig. 6). Notably, most iron-addicted cancers rely on intact lysosomal function to shield excess iron and evade oxidative stress. Thus, targeting NINJ2 may unmask a convergent vulnerability, rendering tumor cells more susceptible to ferroptosis. Notably, we have developed a small peptide derived from N-terminal extracellular domain that can enhance pyroptosis [44]. It would be interesting to determine whether this peptide can enhance ferroptosis.Recent studies have shown that lysosomal leakage does not always lead to cell death since minor lysosomal membrane damage can be repaired via the ESCRT complexes [23, 45]. Interestingly, low-grade lysosomal leakage can lead to limited release of lysosomal contents, such as iron and acid hydrolases, thereby influencing diverse cellular processes such as inflammatory responses [46]. The finding that loss of NINJ2 leads to low-grade LMP (Fig. 2), is consistent with previous observation that loss of NINJ2 promotes pyroptosis and inflammation [9]. How does loss of NINJ2 lead to LMP? One possibility is that loss of NINJ2 may alter membrane fluidity through lipid remodeling. Indeed, we showed previously that loss of NINJ2 alters lipid metabolism and NINJ2-deficiency leads to marked increase in ceramide level [9].Notably, ceramide is a critical lipid mediator that destabilizes lysosomal membranes, acting as a potent trigger for LMP and subsequent cell death. Another possibility is through forming a complex with LAMP1 (Fig. 1B-E). We postulate that the NINJ2-LAMP1 complex acts as a protective scaffold on the lysosome surface and thereby prevents the leakage of lysosomal content. Interestingly, we also observed that NINJ2-KO leads to increased LAMP1 expression at both transcript and protein levels (Fig. 2 B-E and Supplemental Figure 1), which may be a compensatory protective response. Indeed, elevated LAMP1 levels have been found to stabilize damaged lysosomal membranes and facilitate lysosomal repair, thereby preventing cells from death induced by membrane rupture [47]. LAMP1 expression is known to be updated by TFEB (Transcription Factor EB), a master regulator of the lysosomal-autophagy system [48]. Thus, further studies are needed to elucidate whether NINJ2 play a role in TFEB-mediated LAMP1 transcription and how NINJ2 modulates lysosomal integrity.Overall, our data expand the functional landscape of NINJ2 and establish it as a potential therapeutic node at the intersection of lysosomal integrity, iron metabolism, and ferroptosis.Materials and MethodsReagentsAnti-FTH (Cat #4393S), anti-LAMP1 (Cat #3243S and Cat #9091S), and anti-GAPDH (Cat #2118L) were purchased from Cell Signaling Technology. Anti-actin (Cat #sc-8432) and anti-Galectin 3(Cat #sc-32790) were purchased from Santa Cruz Technology. Anti-Flag (Cat# 80801-2-RR) and anti-FTL (Cat #10727-1-AP) were purchased from Proteintech. Anti-NINJ2 was custom-made as previously described [49] and affinity purified. Goat anti-Rabbit IgG (FITC) (Cat #ab6717) and Goat Anti-Mouse IgG (FITC) were purchased from abcam. Goat anti-Mouse IgG (Alexa 555) and Goat anti-Rabbit IgG (Alexa 555) were purchased from Life technology. Proteinase inhibitor cocktail (Cat #78429), Trizol Reagent (Cat #15596026), RNAiMAX (Cat #13778), and RevertAid First Strand cDNA Synthesis Kit (Cat #K1621) were purchased from Life Technologies. The WesternBright Sirius HRP substrate (Cat #K12043-D20) was purchased from Advansta. JetPRIME transfection reagent (Cat #101000046) was purchased from Polyplus. Protein A/G magnetic beads (Cat # HY-K0202), RSL3 (Cat #HY-100218A), and Erastin (Cat # HY-15763) were purchased from MedChemExpress. QuantiChrom™ Iron Assay Kit (Cat # DIFE-250) was purchased from BioAssay SystemsCell cultureMCF7, Molt 4 and 293T cells were purchased from the American Type Culture Collection (ATCC). Since all cell lines from ATCC have been thoroughly tested and authenticated, we did not authenticate the cell lines used in this study. MCF7 and 293T were cultured in Dulbecco’s modified Eagle’s medium (Gibco, Cat #12100061) supplemented with 10% fetal bovine serum (Gibco, Cat # 10437-028) and Penicillin-Streptomycin (Gibco, Cat #15140122). Molt4 cells were cultured in RPMI 1640 medium (Cat# 11875093) supplemented with 10% fetal bovine serum and Penicillin-Streptomycin. Isogenic control and NINJ2-KO MCF7 and Molt4 cells were previously generated All the cells were used below passage 25 or within 2 months after thawing.Immunofluorescence mirocoscopyMCF7 cells expressing 3 x Flag-tagged NINJ2 were fixed with 3.7% formaldehyde in phosphate buffered saline (PBS), permeabilized with 0.2% Triton X-100 in PBS, and blocked with 2% bovine serum albumin in PBS. The cells were then stained with primary antibodies, followed by fluorophore-conjugated secondary antibodies. The cells were mounted with ProLong Gold with DAPI and observed with Leica SP8 confocal microscope with a 40x oil immersion objective or x63 oil immersion objective. The dilutions of the primary antibodies were 1:100 for anti-LAMP1, 1:100 for anti-Ninj2, and 1:100 for anti-Gal3. The dilutions for secondary antibody were 1:500 for Alexa 555-conjugated and 1:1000 for FITC-conjugated.Lysosomal stainingCells were incubated with CytoFix Red Lysosomal stain (AAT, Cat# 23210) for 30 minutes at 37°C. The cells were fixed, permeabilized, and blocked, and then stained with primary antibodies followed by fluorophore-conjugated secondary antibodies as described above. The dilution for CytoFix Red was 1:500.Proximity ligation assay (PLA assay)PLA assay was carried out by using DuoLink PLA assay kit (Millipore Sigma, Cat# DUO92008, DUO92001, and DUO92005) according to the manufacturer’s protocol. Briefly, cells were fixed with 3.7% formaldehyde in PBS, permeabilized with 0.2% Triton X-100 in PBS, and blocked using the blocking reagent provided in the kit. The cells were then incubated with primary antibodies at 4 °C overnight (∼20 h). On the following day, cells were incubated with the PLUS and MINUS probes at 37 °C for 1 h, followed by incubation with DNA ligase at 37 °C for 30 min and subsequent incubation with DNA polymerase together with the fluorescent probe at 37 °C for 100 min. The cells were mounted with ProLong Gold with DAPI and observed with Leica SP8 confocal microscope as described above.Transient transfectionSiRNA transfection was performed with RNAiMAX according to user’s manual. The sequence for scrambled siRNA was 5’-GCA GUG UCU CCA CGU ACU A-3’. The sequences for LAMP1 siRNA were 5’-CAG CAA UGU UUA UGG UGA AUU-3’, and 5’-CCA AAG AAA UCA AGA CUG UUU-3’.Western Blot and Immunoprecipitation (IP) analysesWestern blot analysis was performed as previously described [50]. Briefly, whole-cell lysates were resolved on 8-13% SDS–polyacrylamide gels and transferred to nitrocellulose membranes. Membranes were incubated with primary and secondary antibodies, followed by detection using enhanced chemiluminescence and visualization with VisionWorks LS software (version 8.0; Analytik Jena, Jena, Germany). For IP analysis, Cells were lysed in an IP lysis buffer (1% NP-40, 50 mM Tris-HCl (pH 8.0), 150 mM NaCl, and 1 mM EDTA) supplemented with proteinase inhibitor cocktail. The cell lysates with incubated with 1 µg of primary antibody and protein A/G magnetic beads at 4 °C overnight and the immunocomplex were subjected to western blot analyses to detect protein-protein association.Cell viability4×10^3 Cells were seeded per well in 96-well plates (100 µl per well) in quadruplicate and allowed to adhere overnight. Next day, cells were treated with varying concentrations of RSL3 or Erastin (0-7.29 µM) for 48 hours. Cell viability was assessed by adding 100 µl of CellTiter-Glo reagent (Promega) to each well and incubated for 10-minute at room temperature. Following incubation, luminescence was measured using a SpectraMAX Gemini Microplate Reader (Molecular Devices, Silicon Valley, CA, USA). The viability in the control group was set as 100% and the relative cell viability was calculated as a percentage of treatment group vs control group. The IC50 was calculated using GraphPad Prism 10.Colony formation assay2×10^3 Cells were seeded per well in a 6-well plate in triplicate. At 48 hours, cells were treated with various amounts of RSL3 or Erastin for 24 hours and the drug was withdrawn. Cells were then cultured in regular medium for 2 weeks to allow colonies to form. The colonies were then fixed with methanol/glacial acetic acid (7:1) and stained with 0.1% of crystal violet at room temperature.Total RNA isolation and RT-PCR analysisTotal RNA was isolated with Trizol reagent as described according to the user’s manual (Life Technologies). 3 µg of total RNA was used to synthesize cDNA by using RevertAid First Strand cDNA Synthesis Kit, followed by PCR analysis. The program used for amplification was (i) 94 °C for 5 min, (ii) 94 °C for 45 s, (iii) 58 °C for 45 s, (iv) 72 °C for 30 s, and (v) 72 °C for 10 min. From steps 2 to 4, the cycle was repeated 28–35 times depending on the targets or 22 times for actin and GAPDH. The primers for LAMP1 were a forward primer, 5′-AGG ACA TAC ACT CAC TCT C-3′, and a reverse primer, 5′-GTG CCA CTA ACA CAT CTG-3′. The primers for FTH were a forward primer, 5′-CGA TGA TGT GGC TTT GAA GA-3’, and a reverse primer, 5′-AAT GGG GGT CAT TTT TGT CA-3′. The primers for HPRT1 were a forward primer, 5′-TAT GGC GAC CCG CAG CCC T-3′, and a reverse primer, 5′-CAT CTC GAG CAA GAC GTT CAG-3′. The primers for Actin were a forward primer, 5′-CTG AAG TAC CCC ATC GAG CAC GGC A-3′, and reverse primer, 5′-GGA TAG CAC AGC CTG GAT AGC AAC G-3′.Labile iron measurementsCells were treated with ferric ammonium citrate (300 µg/ml) for 16 hours. After treatment, 1×105 cells were collected and lysed in 200 µl of RIPA buffer. Cell lysates were then subjected to labile iron assay using the QuantiChrom Iron Assay Kit according to manufacturer’s instructions.Protein half-life measurementCells were mock-treated or treated with cycloheximide (50 µg/ml) from 0-15 hours. Cell lysates collected at each timepoint were subjected to western blot analysis to detect FTH and actin. Band intensities at the different time points were quantified using the VisionWorks LS software, normalized to actin and plotted in graph as relative percentage of remaining protein.Statistical analysisStudent’s t test was used for statistical analysis. p<0.05 is considered as significant.Supplementary Figure 1.Data availabilityAll study data are included within the articleAcknowledgementsThis work was supported in part by National Institutes of Health R01 grants (CA272753), a UC Davis Cancer Center Core Support Grant (CA093373).Additional informationAuthor ContributionsJ.Z. and X.C. designed the research. J.Z., M.B., K.N., and Y.S. performed the research. J.Z., M.B., K.N., and Y.S.and X.C. analyzed the data. J.Z. and X.C. wrote the paper. All authors have read and agreed to the published version of the manuscript.FundingHHS | National Institutes of Health (NIH) (CA272753)Xinbin ChenHHS | National Institutes of Health (NIH) (CA093373)Xinbin ChenReferences1.Araki T.Milbrandt J.2000Ninjurin2, a novel homophilic adhesion molecule, is expressed in mature sensory and enteric neurons and promotes neurite outgrowthJournal of Neuroscience 20:187–195Google Scholar2.Araki T.Milbrandt J.1996Ninjurin, a novel adhesion molecule, is induced by nerve injury and promotes axonal growthNeuron 17:353–361Google Scholar3.Tomita Y.et al.2019Ninjurin 1 mediates peripheral nerve regeneration through Schwann cell maturation of NG2-positive cellsBiochemical and Biophysical Research Communications 519:462–468Google Scholar4.Araki T.et al.1997Mechanism of homophilic binding mediated by ninjurin, a novel widely expressed adhesion moleculeJournal of Biological Chemistry 272:21373–21380Google Scholar5.Kayagaki N.et al.2021NINJ1 mediates plasma membrane rupture during lytic cell deathNature 591:131Google Scholar6.Mossemann J.et al.2023Ninjurin-1 Mediated Plasma Membrane Rupture Contributes to Hepatic Ischemia-Reperfusion InjuryAmerican Journal of Transplantation 23:S917–S917Google Scholar7.Sahoo B.et al.2025How NINJ1 mediates plasma membrane rupture and why NINJ2 cannotCell 188:292–302Google Scholar8.Liu J.Hu W.W.Feng Z.H.2024The Unrecognized Role of Ninjurin 2 in Inflammation, Metabolism, and PyroptosisAmerican Journal of Pathology 194:637–640Google Scholar9.Zhang J.et al.2024Ninjurin 2 Modulates Tumorigenesis, In fl ammation, and Metabolism via PyroptosisAmerican Journal of Pathology 194:849–860Google Scholar10.Wang J.J.et al.2017-A novel regulator of endothelial inflammation and activationCellular Signalling 35:231–241Google Scholar11.Liu S.et al.2025SLAMF8 and NINJ2 promote neuroinflammation and oxidative stress through TLR4 NF kappa B pathway in Alzheimer’s diseaseScientific Reports 15Google Scholar12.Peroni S.et al.2018Involvement of NINJ2 in inflammatory activity in MSMultiple Sclerosis Journal 24:827–828Google Scholar13.Peng H.X.et al.2023deficiency inhibits preadipocyte differentiation and promotes insulin resistance through regulating insulin signalingObesity 31:123–138Google Scholar14.Zhang J.et al.2024Ninjurin 2, a Cell Adhesion Molecule and a Target of p53, Modulates Wild-Type p53 in Growth Suppression and Mutant p53 in Growth PromotionCancers 16Google Scholar15.Zhou L.N.et al.2019Ninjurin2 overexpression promotes glioma cell growthAging (Albany NY) 11:11136–11147Google Scholar16.Wan X.H.et al.2011polymorphism is associated with ischemic stroke in Chinese Han populationJournal of the Neurological Sciences 308:67–71Google Scholar17.Noroozi R.et al.2019A single nucleotide polymorphism within is associated with risk of multiple sclerosisMetabolic Brain Disease 34:1415–1419Google Scholar18.Cheng Y.M.et al.2021rs118050317 gene polymorphism and endometrial cancer riskCancer Cell International 21Google Scholar19.Wang P.Y.et al.2021Functional rare variant in a / binding site in gene increases the risk of coronary artery diseaseAging-Us 13:25393–25407Google Scholar20.Lamming D.W.Bar-Peled L.2019Lysosome: The metabolic signaling hubTraffic 20:27–38Google Scholar21.Wang F.Gomez-Sintes R.Boya P.2018Lysosomal membrane permeabilization and cell deathTraffic 19:918–931Google Scholar22.Serrano-Puebla A.Boya P.2016Lysosomal membrane permeabilization in cell death: new evidence and implications for health and diseaseAnn N Y Acad Sci 1371:30–44Google Scholar23.Radulovic M.et al.2018ESCRT-mediated lysosome repair precedes lysophagy and promotes cell survivalEMBO J 37Google Scholar24.Katsnelson M.A.et al.2016NLRP3 inflammasome signaling is activated by low-level lysosome disruption but inhibited by extensive lysosome disruption: roles for K<SUP>+</SUP> efflux and Ca<SUP>2+</SUP> influxAmerican Journal of Physiology-Cell Physiology 311:C83–C100Google Scholar25.Heid M.E.et al.2013Mitochondrial reactive oxygen species induces NLRP3-dependent lysosomal damage and inflammasome activationJ Immunol 191:5230–8Google Scholar26.Eskelinen E.L.2006Roles of LAMP-1 and LAMP-2 in lysosome biogenesis and autophagyMol Aspects Med 27:495–502Google Scholar27.Carlsson S.R.Fukuda M.1989Structure of human lysosomal membrane glycoprotein 1. 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This DOI represents all versions, and will always resolve to the latest one.Copyright© 2026, Zhang et al.This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.Metricsviews0downloads0citations0Views, downloads and citations are aggregated across all versions of this paper published by eLife.

来源:e life发布时间:2026-03-17
Microglia: Replacing the immune system of the brain

Julie RebejacSoyon Hong UK Dementia Research Institute, University College London, United Kingdom; The brain is the most complex organ in the human body and is estimated to contain over 3,000 cell types, including its resident immune cells, the microglia. Microglia are macrophages that colonise the central nervous system during embryonic development and persist throughout life (Ginhoux et al., 2010). Unlike most immune cells, they are not replenished from the bloodstream; instead, they maintain their population through local self-renewal. Microglia are essential for brain function and homeostasis. During development, they preserve tissue integrity and refine neural circuits by eliminating weak synapses (Lawrence et al., 2024; Schafer et al., 2012). In adults, they continuously survey their environment, respond to neuronal activity, and contribute to synaptic plasticity (Badimon et al., 2020). However, during aging or disease, microglia can become dysregulated, leading to synaptic loss and neuroinflammation (Hong et al., 2016). Distinguishing between these protective and pathogenic states is therefore critical for understanding brain health and disease. In some neurological disorders, the distinction between these two states is clear. Loss-of-function mutations impair microglial responses to damage (e.g. the gene Trem2, Wang et al., 2022), whereas other mutations can induce a toxic accumulation of lipids that ultimately leads to neuronal loss (e.g. Hexb in Sandhoff disease; Mader et al., 2025). These findings have prompted interest in strategies to replace native microglia with engineered cells for research and therapeutic studies. However, technical limitations have hindered progress as primary microglia are difficult to manipulate outside the body, and other cell alternatives, such as induced pluripotent stem cells, require substantial financial investment. Now, in eLife, Mariko Bennett, Chris Bennett and colleagues – including Kelsey Nemec as first author – report a novel replacement strategy to overcome these obstacles and create alternative microglia surrogates (Figure 1; Nemec et al., 2026). The researchers used a method known as the estrogen-regulated homeobox B8 (Hoxb8) system to generate unlimited numbers of hematopoietic progenitors; these are immature cells from the bone marrow that are capable of differentiating into multiple lineages. Overexpressing Hoxb8 by adding estrogen maintained the cells in an undifferentiated and expandable state, whereas removing estrogen induced differentiation into macrophages. Figure 1 Download asset Open asset Microglia replacement strategy using genetically modified immature progenitor cells. Progenitor cells (pink) can be isolated from the bone marrow of mice (left) and genetically modified in vitro by adding the Estrogen-regulated-Hoxb8 virus (brown) to obtain an immature state where they continuously expand as progenitors (second from left). When estrogen is removed from the culture medium and CSF1 (growth factor) is added (second from right), the progenitor cells differentiate to become macrophages (red). When transplanted into mice (right), these macrophages turn into microglia-like cells (red branched shapes), allowing researchers to study how genes in microglia can alter the brain environment. Created in BioRender.com. After transplantation into mice, the macrophages dispersed throughout the brain and adopted microglia-like features. They expressed genetic markers typical for microglia, including genes known to be involved in microglial function – Tmem119, P2yr12, and Hexb. However, the macrophages lacked Sall1, a transcription factor critical for establishing microglial identity (Buttgereit et al., 2016). These findings indicate that the engineered cells approximate – but do not fully recapitulate – the molecular identity of microglia found in the brain. Nevertheless, this approach would be faster, more cost-effective, and more experimentally tractable than current replacement strategies. Nemec et al. leveraged this method to model Aicardi-Goutières syndrome, a rare genetic disorder caused by mutations in genes required for nucleic acid sensing and type I interferon production. Excessive interferon signalling in the brain drives pathological neuroinflammation, and microglia are thought to play an essential role as disease mediators (Hofer et al., 2024). To model this condition, Nemec et al. genetically engineered cells to lack ADAR1, a gene often mutated in patients with Aicardi-Goutières syndrome. Without this gene, which helps prevent aberrant activation of innate immune pathways, most cells failed to differentiate into mature macrophages. Those that did differentiate were highly inflammatory, characterised by elevated interferon-stimulated gene expression and heightened immune activation. Transplantation of these cells into mice proved largely lethal, and the few surviving animals showed poor microglial engraftment. In contrast, progenitor cells carrying a patient-derived mutation in Adar1 that only impairs the catalytic domain of this protein could be transplanted efficiently into mice and induced the production of an interferon-stimulated gene. These findings suggest that mutations in microglia are sufficient to initiate neuroinflammation in mice. The approach used by Nemec et al. to replace microglia offers several experimental advantages. The progenitor cells used are amenable to genetic manipulation before differentiation and transplantation, and the timing of maturation can be precisely controlled. This flexibility makes it possible to investigate how specific mutations influence microglial function and brain health. Beyond its research applications, this strategy offers an exciting avenue for microglial replacement as a potential therapy. Previous studies in mouse models of other genetic disorders suggest that microglial engraftment can improve neurological outcomes (Mader et al., 2025). Yet translation to humans faces major challenges. Current protocols rely on microglia-depleted or immunodeficient mice, conditions difficult to replicate safely. In addition, transplanted cells may never fully match primary microglia. It remains to be seen how closely replacement cells must mirror native microglia to be safe, if transplantation has any long-term effects on neural circuits and behaviour, and if similar findings can be seen using human models of microglia (Mancuso et al., 2019; Weerakkody et al., 2025). Despite these uncertainties, Nemec et al. – who are based at the University of Pennsylvania, the Children’s Hospital of Philadelphia, and other institutions in the United States – provide a versatile tool for dissecting microglial biology and modelling neuroinflammatory diseases. By enabling controlled replacement of immune cells in the brain, this approach opens new avenues for understanding the role of microglia and exploring potential therapeutic interventions. Add a comment + Open annotations. The current annotation count on this page is being calculated. References Badimon A Strasburger HJ Ayata P Chen X Nair A Ikegami A Hwang P Chan AT Graves SM Uweru JO Ledderose C Kutlu MG Wheeler MA Kahan A Ishikawa M Wang YC Loh YHE Jiang JX Surmeier DJ Robson SC Junger WG Sebra R Calipari ES Kenny PJ Eyo UB Colonna M Quintana FJ Wake H Gradinaru V Schaefer A (2020) Negative feedback control of neuronal activity by microglia Nature 586:417–423. https://doi.org/10.1038/s41586-020-2777-8 PubMed Google Scholar Buttgereit A Lelios I Yu X Vrohlings M Krakoski NR Gautier EL Nishinakamura R Becher B Greter M (2016) Sall1 is a transcriptional regulator defining microglia identity and function Nature Immunology 17:1397–1406. https://doi.org/10.1038/ni.3585 PubMed Google Scholar Ginhoux F Greter M Leboeuf M Nandi S See P Gokhan S Mehler MF Conway SJ Ng LG Stanley ER Samokhvalov IM Merad M (2010) Fate mapping analysis reveals that adult microglia derive from primitive macrophages Science 330:841–845. https://doi.org/10.1126/science.1194637 PubMed Google 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provides insight into Aicardi–Goutières Syndrome neuropathology eLife 14:RP102900. https://doi.org/10.7554/eLife.102900 Google Scholar Schafer DP Lehrman EK Kautzman AG Koyama R Mardinly AR Yamasaki R Ransohoff RM Greenberg ME Barres BA Stevens B (2012) Microglia sculpt postnatal neural circuits in an activity and complement-dependent manner Neuron 74:691–705. https://doi.org/10.1016/j.neuron.2012.03.026 PubMed Google Scholar Wang S Sudan R Peng V Zhou Y Du S Yuede CM Lei T Hou J Cai Z Cella M Nguyen K Poliani PL Beatty WL Chen Y Cao S Lin K Rodrigues C Ellebedy AH Gilfillan S Brown GD Holtzman DM Brioschi S Colonna M (2022) TREM2 drives microglia response to amyloid-β via SYK-dependent and -independent pathways Cell 185:4153–4169. https://doi.org/10.1016/j.cell.2022.09.033 Google Scholar Weerakkody TN Sabelström H Andrews SV Chadarevian JP Chin MY Tatarakis D Propson NE Kim DJ Theolis R Parico GCG Misker H Kung JE Bandyopadhyay A Colmenares YR Jackson TN Qerqez AN Balasundar S Davis 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Publication history Version of Record published: March 11, 2026 Copyright © 2026, Rebejac and HongThis article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited. Metrics 154 views 15 downloads 0 citations Views, downloads and citations are aggregated across all versions of this paper published by eLife. Download links A two-part list of links to download the article, or parts of the article, in various formats. Downloads (link to download the article as PDF) Article PDF Open citations (links to open the citations from this article in various online reference manager services) Mendeley Cite this article (links to download the citations from this article in formats compatible with various reference manager tools) Julie Rebejac Soyon Hong (2026) Microglia: Replacing the immune system of the brain eLife 15:e110753. https://doi.org/10.7554/eLife.110753 Download BibTeX Download .RIS Categories and tags Insight Immunology and Inflammation Neuroscience microglia microglia replacement leukodystrophies transplantation Research organism Mouse

来源:e life发布时间:2026-03-11
Scientific Publishing: Rethinking how research is reviewed and published

Timothy E Behrens Yamini DalalDiane M Harper eLife, United Kingdom; In 2023, eLife moved to a new model of publishing in which all articles that are peer reviewed are published on the eLife website, along with feedback from the reviewers. Moreover, every article now includes an eLife Assessment, written by the editor and reviewers, that summarises the significance of the findings reported in the article and the strength of the evidence. Since eLife only reviews articles that are available on a preprint server, this new approach to publishing combines the speed and openness of preprints with the scrutiny of expert peer review. The aim of this approach – which is an example of the publish-review-curate (PRC) model – is twofold. First, to move to a system in which research articles are judged on the basis of their scientific content, not on the name or impact factor of the journal in which they are published. This means that all readers – including panels and committees charged with evaluating scientists for jobs, tenure and promotion – can engage easily and meaningfully with the strengths and weaknesses of the research being reported. Second, to move to a system where authors do not have to cascade down a hierarchy of journals until they find favourable reviewers. Instead, if an article is peer reviewed by eLife, the authors know that it will be published, and they do not have to worry about going through the whole process again at a different journal. Authors can also decide when to publish their work as a regular journal article (the Version of Record) to mark the end of the review and publication process. That said, in our experience, almost all authors revise their articles in response to the comments from the reviewers. In the three years since we changed eLife’s approach to publishing, we have learnt a great deal. We should, for example, have explained sooner how and why we select papers for peer review. We also underestimated the commercial forces that the scientific community as a whole will need to overcome to enact any meaningful change in publishing. Overall, however, the most important thing we have learnt is that our new approach to publishing works. Authors, reviewers and editors routinely tell us that they have had a more constructive experience with the new approach. Earlier this month we announced that Wellcome has invested £2.4m over three years in eLife Pathways, our initiative to build open publishing infrastructure for the global research community. Today, we are releasing submission and review data for the period from February 2023 until the end of 2025. The story told by the data is complex, but in our view remarkable. Two major changes took place during this period: we introduced our version of the PRC approach and, at the end of 2024, Clarivate – the company responsible for the impact factor – announced that our new approach would mean that we would lose our impact factor. To our knowledge, it is unprecedented for a major international journal to lose its impact factor for reasons other than editorial misbehaviour, such as mass self-citation. Such journals tend to suffer submission and editorial board collapse. What would happen if a journal lost its impact factor for its values, rather than its behaviour? Would we too collapse? If so, can any journal ever innovate without the permission of the powerful commercial sector? To cut a long story short, the loss of our impact factor did lead to a drop in submissions, but this fall was smaller than the falls seen at other journals that have lost their impact factor. There are countries that still rely heavily on the impact factor for scientific evaluation, and submissions from these countries were particularly affected. Moreover, like all journals, eLife has always received a number of low quality submissions. We now receive fewer of these, which means that we are currently reviewing a higher proportion of submissions (around 35% in 2025, compared with 27% in 2024). Most exciting, we have a substantial author base who remain with us, who are choosing eLife not because of our impact factor, but because of our values and our review process. Overall, we reviewed and published an average of 84 articles per month in 2025, compared with 143 in 2024. Critically, this number has been fairly stable for a year. Moreover, we can measure the quality of the articles we publish in a quantitative manner because, during the review process, we ask reviewers to choose terms that assess the significance of the findings and the strength of evidence in the article. We also did this for the last year of our old model. We have compared the distributions of the terms chosen by reviewers for that year, the period before the Clarivate decision, and the period after, and found them to be remarkably similar. Having proven that the PRC approach works, we will now focus our energy on making it the best system possible for communicating science and the discussion around science. This means that we will stop accepting new submissions to our legacy peer-review process, but we will continue to bring new developments to PRC. Although every eLife article contains an eLife Assessment that summarizes what the reviewers thought about the article, there is a lot more we could do to enhance the discussion around articles and help readers engage with our reviews. Modern AI tools offer an opportunity to do this, and we are partnering with a company called qed science to develop new approaches. We are also going to reintroduce the option for reviewers to be named so that they can receive public recognition for their work. Similarly, we want to explore new ways for authors to communicate their scientific message. We are particularly excited by a recent addition whereby authors can embed explainer videos in any, or every, figure in an article, giving the reader the experience of being at a seminar, whilst also being able to read the details of the article. (Please see our author guide for more details, and this article for examples of explainer videos.) In this new world there will also be opportunities to broaden the types of studies we can publish. For example, we recently introduced a new article type specifically for Replication Studies. One reason other journals may be reluctant to promote such articles is because they want to protect their impact factor! Scientific publishing must evolve for the modern era. It must promote science but also provide a framework for honest discourse around science. The last three years at eLife suggest such a future is possible, while maintaining high standards of peer review and editorial judgment. It shows that even when commercial forces need to be confronted, a substantial community of authors subscribe to this future, and in doing so they benefit from a more constructive experience. Add a comment + Open annotations. The current annotation count on this page is being calculated. Article and author information Author details Timothy E Behrens Timothy E Behrens is the Editor-in-Chief of eLife For correspondence editorial@elifesciences.org Competing interests No competing interests declared "This ORCID iD identifies the author of this article:" 0000-0003-0048-1177 Yamini Dalal Yamini Dalal is a Deputy Editor of eLife Competing interests No competing interests declared "This ORCID iD identifies the author of this article:" 0000-0002-7655-6182 Diane M Harper Diane M Harper is a Deputy Editor of eLife Competing interests No competing interests declared "This ORCID iD identifies the author of this article:" 0000-0001-7648-883X Publication history Version of Record published: March 17, 2026 Copyright © 2026, Behrens et al.This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited. Metrics 4 views 0 citations Views, downloads and citations are aggregated across all versions of this paper published by eLife. Download links A two-part list of links to download the article, or parts of the article, in various formats. Downloads (link to download the article as PDF) Article PDF Open citations (links to open the citations from this article in various online reference manager services) Mendeley Cite this article (links to download the citations from this article in formats compatible with various reference manager tools) Timothy E Behrens Yamini Dalal Diane M Harper (2026) Scientific Publishing: Rethinking how research is reviewed and published eLife 15:e110392. https://doi.org/10.7554/eLife.110392 Download BibTeX Download .RIS Categories and tags Editorial scientific publishing peer review preprints research assessment publish-review-curate

来源:e life发布时间:2026-03-17
内存短缺导致VPS等服务器产品涨价 DMIT等提供商对洛杉矶服务器涨价

#服务器 内存短缺导致 VPS 等服务器产品也开始涨价,DMIT 和 ByteVirt 均宣布位于美国洛杉矶的 CN2GIA 服务器涨价。其中 DMIT 暂停销售 AN5 系列并通过 AN4 系列进行过渡,暂时仍然维持 9.99 美元的起售价 (PRO 和 EB 系列),ByteVirt 则是所有 CN2GIA 产品涨价 10%。查看全文:https://ourl.co/112132由人工智能热潮引起的全球内存短缺问题正在影响多个行业,当然受影响的也不只是内存产品,人工智能基础设施需求暴增导致机械硬盘 / 固态硬盘和其他零部件价格也在上涨。 现在这种情况已经传导到常规的 VPS 服务器行业,包括搬瓦工完全停止提供促销产品,DMIT 和 ByteVirt 则是对美国洛杉矶区域的现有产品进行涨价。 DMIT 在调价公告里表示受到全球硬件成本持续上涨的压力影响,当前的硬件成本已经超出原有定价的覆盖范围,所以 DMIT 直接停售部分低价机器并在后续扩容来提供价格更高的服务器。 ByteVirt 则是对所有洛杉矶 CN2GIA 线路的产品整体涨价 10%,即便是老用户也需要涨价 5%,从这里也可以看出来后续越是以前低价的促销服务器,涨价的可能性也越高。 下面是 DMIT 的涨价和产品调整概览: 暂时停止销售 DMIT LAX AN5 系列产品 后续计划在 LAX 区域增加并扩容 AS3 系列硬件 DMIT LAX AN4 系列现作为过渡产品 AN4 Pro 和 AN4 EB 系列暂时维持 AN5 系列初始定价 (均为 9.99 美元 / 月起步) DMIT LAX AN4 系列主要采用 AMD EPYC 9004 系列处理器 (AN5 系列采用 9005 处理器),LAX 区域的 Pro 系列机型采用 CN2GIA 线路进行回程优化,EB 系列则采用 CMIN2 和 AS9929 回程,线路质量上 Pro 系列要比 EB 系列更好更稳定。 相关推荐: DMIT LAX AN4 Pro (CN2GIA、1TB 流量,9.99 美元 / 月) DMIT LAX EB.TINY (CMIN2&AS9929、1.5TB 流量,9.99 美元 / 月) ByteVirt 主要是直接涨价: 从 2026 年 3 月 31 日起,所有 LA CN2 GIA 产品标准售价上涨 10%,若是现有老用户则后续续费价格上涨 5%,调整后的价格将从下个账单周期生效,当前周期内的价格不受影响。 ByteVirt 表示,涨价原因主要是运营成本持续上涨,为保障服务质量与产品稳定运行,公司经过评估后决定对 LA CN2 GIA 产品的价格进行调整。

来源:蓝点网发布时间:2026-03-12
OPPO Find N6发布:钛合金边框三配色主打轻薄质感

【ZOL中关村在线原创新闻】今晚发布会上,OPPO Find N6的外观设计成为最先亮相的环节。刘作虎介绍,新机采用钛合金边框,整机厚度为8.93mm。配色方面,Find N6提供了三款选择:原钛被定位为“既不张扬也不沉闷”的保险色,深黑依然是经典耐看的百搭款,而金橙则采用了黄金镀膜工艺来实现独特的金色质感。镜头模组的设计细节也在现场得到展示。一道优美的曲线将镜头模组与后盖完美衔接,摸上去过渡自然、看上去也不突兀。全新的柔砂工艺让边框和后盖在视觉和触感上都呈现出浑然一体的感觉,官方称其“温润舒适”。此外,Find N6这次大幅缩窄了屏幕边框,极窄边框的设计让视野更加开阔。屏幕方面,Find N6搭载了行业最大尺寸的内屏,官方强调屏幕性能全部拉满,支持通透流畅的色彩还原。暗光环境下支持1nit超低亮度显示,并获得金标认证护眼屏。【中关村在线观点】Find N6在工业设计上延续了OPPO一贯的工艺追求,钛合金边框的引入和黄金镀膜工艺的尝试,让折叠屏在质感层面又向前迈了一步。本文属于原创文章,如若转载,请注明来源:OPPO Find N6发布:钛合金边框三配色主打轻薄质感https://mobile.zol.com.cn/1149/11499187.html https://mobile.zol.com.cn/1149/11499187.html mobile.zol.com.cn true 中关村在线 https://mobile.zol.com.cn/1149/11499187.html report 825 【ZOL中关村在线原创新闻】今晚发布会上,OPPO Find N6的外观设计成为最先亮相的环节。刘作虎介绍,新机采用钛合金边框,整机厚度为8.93mm。配色方面,Find N6提供了三款选择:原钛被定位为“既不张扬也不沉闷”的保险色,深黑依然是经典耐看的百搭款,而金橙则采用了黄...

来源:中关村在线发布时间:2026-03-17
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