Biology subjects

Zhang, S.

Publications and source records attributed to Zhang, S..

At least 379 records · Page 21Linked to original sources

bioRxiv2025

Altered Interpersonal Neural Synchronization during Social Interaction After Shared Excluded Experiences in Depressed Adolescents

BackgroundMajor depressive disorder (MDD) is common in adolescents, and the special development stage, during which adolescents brain and neuroendocrine system develop intensively, makes it subtly difficult to develop prevention and treatment strategies for depressed adolescents compared with depressed adults. Meanwhile, public psychosocial stressors significantly influence adolescents mental health and social interaction, rendering it essential to explore how a shared psychosocial stressor, i.e., shared excluded experiences, influences social interaction in depressed adolescents. MethodsWe designed a 4-player cyberball game to probe adolescents responses to shared excluded experiences and explore the underlying interpersonal neural synchronization (INS) with functional near-infrared spectroscopy (fNIRS). ResultsWe found that shared excluded experiences could enhance adolescents social interaction preferences but decreased INS in each pair of excluded adolescents, which indicates a reduced willingness to interact with others after the exclusion. However, no significantly different behavioral responses to the shared excluded experiences were found in depressed adolescents compared to adolescents as healthy controls (HC). Further analyses revealed that adolescents with MDD experienced more negative feelings than HC after exclusion. Of note, adolescents with MDD demonstrated stronger INS than HC, indicating the potential empathic stress in depressed adolescents. In addition, there existed altered brain-behavioral association patterns in responses to shared excluded experiences in depressed adolescents. ConclusionsIn summary, our study gives us deeper insights into how a shared psychosocial stressor impacts the INS in depressed adolescents, and it might be demonstrated that INS could be more sensitive than behavioral responses to detect social interaction deficits in depressed adolescents.

neuroscience↗

bioRxiv2025

Structure of a transcribing Pol II-DSIF-SPT6-U1 snRNP complex

RNA polymerase II (Pol II) facilitates co-transcriptional splicing by recruiting the U1 small nuclear ribonucleoprotein particle (U1 snRNP) to the nascent transcripts. Here, we report the cryo-electron microscopy structure of a transcribing Pol II-U1 snRNP complex with elongation factors DSIF and SPT6. Furthermore, our biochemical analysis revealed that the phosphorylated Pol II carboxyl-terminal domain and SPT6 interact directly with U1 snRNP proteins, facilitating its recruitment to the elongation complex. This multivalent interaction allows efficient spliceosome assembly and ensures transcription processivity.

molecular biology↗

bioRxiv2025

Silicon regulation of pectin methyl-esterification and cellulose microfibril organization modulates FERONIA-mediated cell wall integrity signaling for rice adaption to salt stress

The beneficial element silicon (Si) plays a crucial role in mitigating salt stress in plants and can activate intracellular signal transduction pathways that reprogram the transcriptome of salt-stressed plants. However, the mechanisms by which plants perceive Si nutrition, induce the expression of Si-responsive genes, and regulate the activity of Si-responsive proteins remain inadequately understood. In this study, we found that Si enhances cell wall integrity by increasing the degree of pectin methyl-esterification and changing the cellulose microfibril arrangement and functions as a regulatory switch for the activities of cell wall integrity sensors (FER homologs OsFLR1 and OsFLR2) during rice plant adaptation to high salinity. Furthermore, the inhibition of OsFLR1 and OsFLR2 through FER-specific inhibitors could negate the beneficial effects of Si and affect the uptake and accumulation of Si in rice seedlings. In conclusion, these findings suggest that Si signaling initiation involves FER-mediated cell wall integrity signaling pathways during salt stress adaptation in rice.

plant biology↗

bioRxiv2025

Longitudinal analysis of retinal cell state transitions in RB1-deficient retinal organoids reveals the nascent cone precursors are the earliest cell-origin of human retinoblastoma

All cancers develop from malignant transformation of normal cells, while it is challenging to identify these "cells-of-origin" since it is impossible to witness the dynamic changes in human cancers. Retinoblastoma (Rb), an intraocular malignant cancer, is a typical model for study the molecular and cellular mechanisms of cancers. Although the maturing cone precursors (CPs) are proposed as the cellular origin of human Rb, it is unknow whether other retinal cells are also sensitive to RB1 inactivation. Here we developed RB1-deficient human retinal organoids (ROs) models from RB1-/- or RB1+/- human induced pluripotent stem cells (hiPSCs). RB1-/- hiPSCs developed into Rb tumors which recapitulated the features of human native Rb and had the ability to form consecutive orthotopic xenografts in mice. Importantly, RB1 loss induced the overproliferation of ATOH7+ neurogenic retinal progenitor cells (nRPCs), which disrupted the retinal development by generating proliferative, nascent retinal cells including retinal ganglion cells and CPs. scRNA-seq analysis verified the ATOH7+/RXR{gamma}+ nascent CPs survived and finally drove Rb development. In contrast, monoallelic RB1 inactivation with low pRB expression did not induce nascent CPs proliferation, but only induced nRPCs overproliferation which caused retinocytoma-like phenotype. Finally, a potential therapeutic agent for Rb was identified from multi-omics data. Our findings firstly indicate that nRPCs are the most sensitive cells to RB1 loss inducing nascent retinal cells abnormal proliferation, and ATOH7+ nascent CPs are the earliest cellular origin of human Rb, facilitating drug development for Rb.

cell biology↗

bioRxiv2025

Single-cell multiomics reveals disrupted glial gene regulatory programs in Alzheimer's disease via interpretable machine learning

Recent development of single-cell technology across multiple omics platforms has provided new ways to obtain holistic views of cells to study disease pathobiology. Alzheimers disease (AD) is the most common form of dementia worldwide, yet the detailed understanding of its cellular and molecular mechanisms remains limited. In this study, we analyzed paired single-cell transcriptomic (scRNA-seq) and chromatin accessibility (scATAC-seq) data from the Seattle Alzheimers Disease Brain Cell Atlas (SEA-AD) Consortium to investigate the molecular mechanisms of AD at a cell-subpopulation-specific resolution focusing on glial cells. We benchmarked various multi-omics integration methods using diverse metrics and built an analytic workflow that enabled effective batch correction and cross-modality alignment, creating a unified cell state space. Through integrative analysis of 26 human brain samples, we uncovered AD-associated gene expression and pathway changes in glial subpopulations and highlighted important transcriptomic and epigenomic signatures via functional inference and interpretable machine learning paradigms, discovering the profound involvement of the Solute Carrier proteins (SLC) family genes in multiple glial cell types. We also identified glial cell-specific regulatory programs mediated by key transcription factors such as JUN and FOSL2 in astrocytes, the Zinc Finger (ZNF) family genes in microglia, and the SOX family of transcription factors in oligodendrocytes. Our study provides a comprehensive workflow and a high-resolution view of how glial regulatory programs are disrupted in AD. Our findings offer novel insights into disease-related changes in gene regulation and suggest potential targets for further research and therapy.

genomics↗

bioRxiv2025

Structural variants contribute to the gut microbiome, blood metabolic traits and their causal relationships

Research on genetic variants affecting the gut microbiome and blood metabolites has mainly focused on single nucleotide variants (SNPs), with structural variants (SVs) remaining largely unexamined. In this study, we utilized an advanced structural variant detection pipeline on whole-genome sequencing data from 2,002 individuals with an average sequencing depth of 42 folds, identifying 138,859 high-confidence SVs, 49.57% of which were previously unreported. We performed genome-wide association studies on 22,519 common SVs with 616 gut microbial features and 121 blood metabolic traits, uncovering 30 significant links between SVs and microbial taxa/pathways and 48 connections between SVs and blood metabolites. Notable results include 19 associations reaching study-wide significance, such as an Asian-specific 6.5k deletion at the TENT2 gene, which is strongly associated with the abundance of Coprobacillus sp. 29_1 ({beta}=0.158, p=2.5x10-9), and a deletion covering HBA1/HBA2/HBM/HBQ118 linked to nine blood metabolic traits, most significantly with mean corpuscular volume (MCV; {beta}=-0.570, p=1.22x10-91). Most SVs showed a greater impact than nearby SNPs in their associations with the microbiome/metabolites. Analysis of rare SVs also identified multiple genes associated with the microbiome/metabolites. Additionally, Mendelian randomization using SVs demonstrated causal relationships, including effects from the microbiome to metabolites, such as Clostridium ramosums impact on serum uric acid and hyperuricemia, and from metabolites to the microbiome, like the influence of mean corpuscular hemoglobin concentration on MF0064: triacylglycerol degradation. This study provides a detailed SV catalog for the Chinese population and highlights the significant role of SVs in the gut microbiome, blood metabolites, and their causal relationships. HighlightsO_LIProvides a catalog of 138,859 high-confidence SVs and 49.57% of which were novel C_LIO_LIIdentify 30 genome-wide significant "SVs--microbial taxa/pathways" and 48 "SVs--blood metabolites" associations, respectively C_LIO_LIAn Asian-specific 6.5k deletion at TENT2 gene associated with the species Coprobacillus sp. 29_1 C_LIO_LIA deletion spanning HBA1/HBA2/HBM/HBQ118 associated with 9 blood metabolic traits C_LIO_LIThe majority of SVs demonstrated a more pronounced impact than neighboring SNPs in the associations with microbiome/metabolites C_LIO_LISVs help to reveal the host-microbe causal relationships such as Clostridium ramosums causal effects on serum uric acid and hyperuricemia C_LI O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=155 SRC="FIGDIR/small/643427v2_ufig1.gif" ALT="Figure 1"> View larger version (36K): org.highwire.dtl.DTLVardef@e660d0org.highwire.dtl.DTLVardef@ddfe4dorg.highwire.dtl.DTLVardef@62a704org.highwire.dtl.DTLVardef@c31bb5_HPS_FORMAT_FIGEXP M_FIG Graphic abstract Based on a multi-omics cohort of 2,002 individuals comprising high-depth whole genome and metagenomic sequencing data, blood metabolites, and detailed questionnaires, we identified 138,859 high-confidence SVs, uncovering 36 significant associations between SVs and microbial taxa/pathways, as well as 48 associations between SVs and blood metabolites. Mendelian randomization using SVs demonstrated causal relationships, such as Clostridium ramosums causal effect on serum uric acid and hyperuricemia. This study provides a detailed SV catalog for the Chinese population and highlights the significant role of SVs in the gut microbiome, blood metabolites, and their causal relationships. C_FIG

genomics↗

bioRxiv2025

BioBatchNet: A Dual-Encoder Framework for Robust Batch Effect Correction in Imaging Mass Cytometry

MotivationImaging Mass Cytometry (IMC) is a cutting-edge technology for analysing spatially resolved protein expression at the single-cell level. However, its downstream analyses are often hindered by batch effects, which introduce systematic biases and obscure true biological variations. Existing correction methods, largely developed for scRNA-seq data, struggle to achieve precise control, leading to either over-correction by removing critical biological information, or under-correction by leaving residual batch effects. Moreover, these methods face challenges in adapting to IMC data due to differences in data characteristics. Furthermore, IMC data often feature imbalanced and overlapping cell populations, complicating clustering and downstream analysis. These challenges underscore the need for a robust and controllable batch effect correction approach tailored to IMC data. ResultsWe present BioBatchNet, a dual-encoder framework utilising adversarial training to explicitly disentangle batch-specific and biological signals. BioBatchNet enables controllable batch effect correction, effectively balancing correction with the preservation of biological variation. We evaluated BioBatchNet on three IMC datasets, where it outperformed seven benchmarking methods with robustness in both correction and biological signal conservation. Additionally, we developed a Constrained Pairwise Clustering (CPC) method, which employs constrained pairs to improve clustering performance, even in datasets with imbalanced and overlapping cell populations. To validate its generalisability, BioBatchNet was also applied to four scRNA-seq datasets, where it delivered competitive performance compared to eight typical methods. These results demonstrate BioBatchNets generalisability and robustness in correcting batch effects across diverse single-cell datasets and underscore its potential for large-scale biological analyses.

bioinformatics↗

bioRxiv2025

Extrinsic Noise or Intrinsic Coupling: Dissecting Correlated Fluctuations in Gene Transcription

Understanding stochastic gene transcription requires distinguishing between intrinsic molecular randomness and extrinsic environmental variability. Traditionally, these components are identified as the independent and correlated fluctuations between identical gene copies within the same cell. However, intrinsic gene-gene interactions can introduce additional correlations, challenging this standard approach. Here, we develop a new noise decomposition framework based on a theoretical model of stochastic transcription for an intrinsically coupled gene pair under fluctuating environments. By analytically deriving correlated fluctuations of nascent RNA, we disentangle intrinsic coupling from extrinsic noise based on their distinct relationships with the mean and variance of gene activation probabilities across varying environments. Applying this framework to single-cell transcription data from Drosophila embryos, we uncover previously unidentified couplings between sister alleles and between alternative promoters of an endogenous gene. Our findings offer a versatile approach for deciphering intrinsic gene-gene interactions in stochastic transcription within fluctuating environments.

biophysics↗

bioRxiv2025

Deep Learning-Based High-Resolution Time Inference for Deciphering Dynamic Gene Regulation from Fixed Embryos

Embryo development is driven by the spatiotemporal dynamics of complex gene regulatory networks. Uncovering these dynamics requires simultaneous tracking of multiple fluctuating molecular species over time, which exceeds the capabilities of traditional live-imaging approaches. Fixed-embryo imaging offers the necessary sensitivity and capacity but lacks temporal resolution. Here, we present a multi-scale ensemble deep learning approach to precisely infer absolute developmental time with 1-minute resolution from nuclear morphology in fixed Drosophila embryo images. Applying this approach to quantitative imaging of fixed wild-type embryos, we resolve the spatiotemporal regulation of the endogenous segmentation gene Kruppel (Kr) by multiple transcription factors (TFs) during early development without genetic modification. Integrating a time-resolved theoretical model of single-molecule mRNA statistics, we further uncover the unsteady-state bursty kinetics of the endogenous segmentation gene, hunchback (hb), driven by dynamic TF binding. Our method provides a versatile framework for deciphering complex gene network dynamics in genetically unmodified organisms.

biophysics↗

bioRxiv2025

A multiplexed, target-based phenotypic screening platform using CRISPR interference in Mycobacterium abscessus

The rise of difficult-to-treat Mycobacterium abscessus infections presents a growing clinical challenge due to the immense arsenal of intrinsic, inducible and acquired antibiotic resistance mechanisms that render many existing antibiotics ineffective against this pathogen. Moreover, the limited success in discovery of novel compounds that inhibit novel pathways underscores the need for innovative drug discovery strategies. Here, we report a strategic advancement in PROSPECT (PRimary screening Of Strains to Prioritize Expanded Chemistry and Targets), which is an antimicrobial discovery strategy that measures chemical-genetic interactions between small molecules and a pool of bacterial mutants, each depleted of a different essential protein target, to identify whole-cell active compounds with high sensitivity. Applying this modified strategy to M. abscessus, in contrast to previously described versions of PROSPECT which utilized protein degradation or promoter replacement strategies for generating engineered hypomorphic strains, here we leveraged CRISPR interference (CRISPRi) to more efficiently generate mutants each depleted of a different essential gene involved in cell wall synthesis or located at the bacterial surface. We applied this platform to perform a pooled PROSPECT pilot screen of a library of 809 compounds using CRISPRi guides as mutant barcodes. We identified a range of active hits, including compounds targeting InhA, a well-known mycobacterial target but under-explored in the M. abscessus space. The unexpected susceptibility to isoniazid, traditionally considered to be ineffective in M. abscessus, suggested a complex interplay of several intrinsic resistance mechanisms. While further complementary efforts will be needed to change the landscape of therapeutic options for M. abscessus, we propose that PROSPECT with CRISPRi engineering provides an increasingly accessible, high-throughput target-based phenotypic screening platform and thus represents an important step towards accelerating early-stage drug discovery.

microbiology↗

bioRxiv2025

C9orf50 is a Targetable Spliceosome Regulator for Cancer Immunotherapy

Cancer cells harbor cell-intrinsic programs capable of sculpting immunogenic landscapes, yet molecular brakes that enforce immune evasion remain poorly defined. Here, we identify C9ORF50, a previously uncharacterized intrinsically disordered protein, as a liquid-liquid phase separation-driven regulator of RNA processing and actionable immunotherapy target. Genetic ablation of C9ORF50 induces selective intron retention in spliceosome component transcripts, leading to cytoplasmic accumulation of immunogenic double-stranded RNA (dsRNA). This dsRNA activates type I interferon pathway that in turn orchestrates a chemokine secretory program critical for T cell recruitment. The resultant enhancement of tumor immunogenicity and reprogramming of tumor immune microenvironment drives robust antitumor immunity, effectively converting immunologically "cold" tumors to "hot" phenotypes. Therapeutic targeting of C9ORF50 via RNA interference achieved robust tumor suppression in syngeneic models, suggesting its translational relevance. Notably, C9ORF50 exhibits tumor-predominant expression, with minimal baseline levels detected in healthy tissues or immune cell populations, underscoring its potential safety as a therapeutic target. Our study not only identifies C9ORF50 as a novel LLPS-dependent regulator of RNA splicing but also establishes it as a promising and potentially safe therapeutic target for cancer immunotherapy.

cancer biology↗

bioRxiv2025

Topoisomerase IIIb protects from tumorigenesis and immune dysregulation

Topoisomerase III-beta (Top3b) reduces nucleic acid torsional stress and intertwining generated during RNA and DNA metabolism while protecting the genome from pathological R-loops, which otherwise result in DNA breakage and genome instability. By studying Top3b knockout mice (Top3b-KO), we find that the loss of Top3b accelerates the development of spontaneous lymphoid tumors arising in spleens and lymph nodes, the organs with prominent Top3b expression. Aging Top3b-KO mice also display splenomegaly and systemic immune alterations including neutrophilia and lymphopenia suggestive of chronic inflammation. At the molecular level, Top3b deficiency causes genome-wide R-loop accumulation in splenocytes as measured by CUT&Tag sequencing. Increased R-loops is associated with genomic DNA breaks and activation of immune signaling pathways including the IL-6 signaling, interleukin-7 signaling and cGAS-STING. Moreover, knocking-out Top3b promotes the rapid development of syngeneic EL4 T-cell lymphomas. In conclusion, our work implies that, in addition to its role in preserving the nervous system, Top3b protects from tumorigenesis and immune dysregulations. SignificanceTopoisomerase III-beta (TOP3B) is the only dual RNA-DNA topoisomerase. Its inactivation has previously been shown to be critical for neurodevelopmental disorders in humans and R-loop suppression in cell line models. Here, we demonstrate that loss of Top3b accelerates spontaneous lymphomagenesis and promotes syngeneic lymphoma growth in association with innate immune defects and R-loop accumulation in Top3b-KO mice. These findings provide evidence for a previously unappreciated role of Top3b as a tumor suppressor gene and immune regulator.

cancer biology↗

bioRxiv2025

The Pesticide Chlorpyrifos Increases the Risk of Parkinson's Disease

Background and PurposePesticides have been associated with an increased risk of Parkinsons disease (PD), but it is unclear which specific pesticides contribute to this association and whether it is causal. Since chlorpyrifos (CPF) exposure has been implicated as a risk factor for PD, we investigated its association to incident PD and if this association is biologically plausible using human, rodent, and zebrafish (ZF) studies. MethodsThe association of CPF with PD was assessed using the UCLA PEG study (829 PD and 824 control subjects), and proximity-based exposure estimates from living or working near agricultural CPF use. For the mammalian studies, 6 months old male C57BL/6 mice were divided into two groups, CPF and controls, for open field, rotarod, and wire hang behavioral testing. Mice were then exposed to CPF in an inhalation chamber (0.65-2.9 mg/m3/day) for 6 hrs./day 5 days/wk., whereas control mice were exposed to vehicle alone. Behavioral tests were performed before and 2.5 months after CPF exposure following a 3-day washout. Mice were then perfused for immunohistochemical analysis. For the mechanistic studies, ZF embryos were treated with CPF (250 nM) 24 hours post fertilization for 5-7 days. Behavioral testing was performed using the Viewpoint Imaging System. Neuronal loss and microglial activation were determined using immunohistochemistry. Neuronal autophagic flux was determined using autophagy modulators in GFP-LC3 transgenic ZF and Western blots. ResultsLong-term residential CPF exposure was linked to an increased risk of developing PD with an odds ratio of 2.68 (CI 1.58-4.55). Mice exposed to aerosolized CPF developed motor impairment and a significant loss of dopaminergic neurons in the substantia nigra and activation of microglia. TH positive neurons in the substantia nigra (SN) had significantly higher levels of phosphoserine 129 (pS129) -synuclein (-syn), a marker for pathological phosphorylated -syn, and ubiquitin. In contrast, neither pS129 -syn or ubiquitin accumulated in TH neurons in the VTA after CPF exposure. Consistent with the mice data, CPF exposure resulted in impairment of locomotor activity and selective loss of aminergic neurons in ZF. We also found an increase in neuronal apoptosis and microglial activation. Importantly, dopamine neuron loss was found to be at least partially dependent on {gamma}1-synuclein (closest functional homologue to human -syn) as neuronal loss did not occur in {gamma}1-synuclein knockout ZF. Using an in vivo ZF assay, we found impaired autophagic flux and an increase in lysosomal labelling within the zebrafish brain. CPF exposure also led to elevated {gamma}1-synuclein and p62 (autophagic cargo protein) levels consistent with impaired degradation. Furthermore, induction of autophagy was protective, supporting the hypothesis that impaired autophagic flux is at least partially responsible for neuron loss following CPF exposure. ConclusionsCPF exposure is associated with an increased risk of developing PD and this association is likely causal since PD-like pathology was recapitulated in animal models. Furthermore, impaired autophagic flux appears to underly this toxicity, a pathway implicated in the pathogenesis of PD.

neuroscience↗

bioRxiv2025

Integrating Literature Evidence to Enhance CRISPR-based Target Screening for Hand-foot Syndrome

The experimental high-throughput screening (HTS) methods, exemplified by CRISPR- based screening techniques, have revolutionized target identification in drug discovery. However, such screens frequently yield extensive, often unrelated target lists necessitating costly and time-intensive experimental evaluation and validation. To address this challenge, we propose a dual-filter strategy that integrates literature-mined targets with CRISPR/Cas9 screening outputs, systematically prioritizing the most credible candidates and thereby reducing the experimental validation burden and increasing success rate. To validate this strategy, we applied it with hand-foot syndrome (HFS), a clinically challenging side effect induced by fluoropyrimidine treatment. We identified ATF4 as a key regulator of 5-fluorouracil (5-FU) toxicity in the skin and revealed forskolin as a potential therapeutic agent of HFS through the strategy. Mechanistically, forskolin triggers MEK/ERK-dependent ATF4 induction, subsequently driving 5-FU detoxification via the ATF4-mediated eIF2/I{kappa}B signaling pathway. Our findings demonstrate that this dual-filter strategy could notably accelerate drug discovery by reducing experimental validation burden after target screening.

pharmacology and toxicology↗

bioRxiv2025

Benchmarking AlphaFold3-like Methods for Protein-Peptide Complex Prediction

Protein-short peptide interactions are central mechanisms regulating signal transduction, chaperone functions, and drug targeting design. However, the precise prediction of their complex structures has long been constrained by the limited accuracy of traditional methods. The breakthrough of AlphaFold2 resolved the challenge of predicting protein monomer structures. Its derivative method, AlphaFold2-multimer, surpassed traditional docking approaches in predicting protein complexes, yet its accuracy remains suboptimal. AlphaFold3 and its replication models, such as Protenix, Chai-1, and Boltz-1, have further improved prediction accuracy for protein-protein interactions. To evaluate the performance of these methods in protein-short peptide structure prediction, this study conducted predictions and comprehensive comparisons on two benchmark datasets of protein-short peptide complexes. One dataset did not intentionally exclude structures from the training set of these methods, while the other did. For the former, the results demonstrate that the new-generation structural method significantly increased the success rate under stringent criteria (Fnat[≥] 0.8) to 70-80%, compared to AlphaFold2-multimer (53%), with Protenix achieving the highest accuracy at 80.8%. For the second dataset, AlphaFold2-multimer showed consistent performance, but the success rates of the new-generation methods all dropped significantly (to 40-56%). Our analysis suggests that the main challenge for the performance decline comes from the accurate prediction of binding sites. For all datasets, A multi-method combination strategy enabled a higher-quality prediction success rate compared to the individual models, highlighting synergistic advantages among methods. Notably, inter-model consistency analysis provided an efficient metric for selecting optimal predictions.

bioinformatics↗

bioRxiv2025

Quantitative phosphoproteomic analysis of testes from Iqcn-deficient mice highlights the significance of calmodulin signaling in spermiogenesis

Calmodulin (CaM) plays a crucial role in sperm function. Studies have reported that proteins containing IQ motif interact with CaM, subsequently engaging with downstream target proteins known as calmodulin-binding proteins (CaMBPs). It is reported that the loss of interaction between IQCN and CaM is mainly manifested as decreased motility, leading to fertilization failure and male infertility. However, no relevant reports have been published detailing which CaMBPs exist and the mechanisms by which they are regulated. In this study, we conducted a comprehensive quantitative proteomic and phosphoproteomic analysis of mouse testes from wild-type (WT) and Iqcn knockout (Iqcn-/-) mice. The results indicated that Iqcn deficiency substantially rewires the downstream phosphorylation signaling pathway, while not causing equivalent changes in protein levels. Among the total 577 differentially regulated phosphorylated sites in our results, most of them (494/577) belong to calmodulin-binding proteins (CaMBPs). Gene ontology analysis of these differentially phosphorylated CaMBPs showed enrichment in male gamete generation, actin cytoskeleton organization and microtubule cytoskeleton organization process, demonstrating IQCN regulates sperm function by interacting with CaM, which in turn affects the phosphorylation level of CaMBPs. Further kinase substrate network analysis revealed that most kinases with substrates phosphorylation sites up-regulated were tyrosine kinases, and the inhibition assay showed that FGFR4 and SYK tyrosine kinases are important for sperm motility and progressive motility. In summary, this study reveals the interaction between IQ motif-containing protein IQCN and CaM, which regulates the phosphorylation of downstream CaMBPs and is involved in the related processes of spermiogenesis and sperm function.

developmental biology↗

bioRxiv2025

A Hyperbolic Discrete Diffusion 3D RNA Inverse Folding Model for functional RNA design

Generative design of functional RNAs presents revolutionary opportunities for diverse RNA-based biotechnologies and biomedical applications. To this end, RNA inverse folding is a promising strategy for generatively designing new RNA sequences that can fold into desired topological structures. However, three-dimensional (3D) RNA inverse folding remains highly challenging due to limited availability of experimentally derived 3D structural data and unique characteristics of RNA 3D structures. In this study, we propose RIdiffusion, a hyperbolic denoising diffusion generative RNA inverse folding model, for 3D RNA design tasks. By embedding geometric features of RNA 3D structures and topological properties into hyperbolic space, RIdiffusion efficiently recovers the distribution of nucleotides for targeted RNA 3D structures based on limited training samples using a discrete diffusion model. We perform extensive evaluations on RIdiffusion using different datasets and strict data-splitting strategies and the results demonstrate that RIdiffusion consistently outperforms baseline generative models for RNA inverse folding. This study introduces RIdiffusion as a powerful tool for the generative design of functional RNAs, even in structure-data-scarce scenarios. By leveraging geometric deep learning, RIdiffusion enhances performance and holds promise for diverse downstream applications.

bioinformatics↗

bioRxiv2025

Dickkopf1 is a Novel Endogenous Ligand for priming NLRP3 Inflammasome in Macrophages via TLR4

Dickkopf1(DKK1) is a quintessential Wnt antagonist and immunomodulator in various inflammatory diseases. The underlying molecular mechanisms of DKK1-mediated immunomodulation remain elusive. Here, we identified TLR4 as a new receptor for DKK1 to activate NF{kappa}B pathway-mediated gene expressions and pyroptosis via NLRP3 inflammasome in human and mouse macrophages. DKK1 employed TLR4 to initiate NF{kappa}B signaling cascade via MyD88. MyD88-TAK1-NF{kappa}B pathway activation by DKK1 increased HIF1, NF{kappa}B, and NLRP3 protein expression levels, leading to pyroptosis. Unlike LPS, DKK1 did not induce IRAK4 phosphorylation, while the interaction between MyD88 and IRAK4 was maintained for downstream signaling activation. DKK1 did not induce IRF3 phosphorylation in the nucleus and failed to induce IFN{beta} gene expression, indicating differential signaling from LPS. DKK1 primed macrophages via TLR4-MyD88, resulting in NLRP3 inflammasome-mediated pyroptosis via Caspase-1 and Gasdermin D maturation with various NLRP3 inflammasome activators, including Nigericin. Our results demonstrated that DKK1 is a novel endogenous priming ligand that augments differential NF{kappa}B pathway from LPS and NLRP3 inflammasome activation via TLR4 in mouse and human macrophages.

immunology↗