Biology subjects

Liu, Z.

Publications and source records attributed to Liu, Z..

At least 379 records · Page 21Linked to original sources

bioRxiv2024

A network regularized linear model to infer spatial expression pattern for single cell

Spatial transcriptomics, situated at the intersection of genomics and spatial biology, offers profound insights into the spatial organization of gene expression within tissues. However, its potential has been constrained by either limited resolution or throughput. While single-cell RNA-seq allows for in-depth profiling of cellular gene expression, the crucial spatial information is often sacrificed during sample collection. In a groundbreaking fusion of these two techniques, our research introduces the glmSMA computational algorithm. This innovative approach aims to predict cell locations by integrating scRNA-seq data with spatial-omics reference atlases. The essence of glmSMA lies in formulating cell mapping as a convex problem, strategically minimizing differences between cellular expression profiles and location expression profiles through L1 and Generalized L2 regularization. Our algorithm has undergone rigorous testing across diverse tissues, including mouse brain, drosophila embryo, and human PDAC samples. The compelling results validate glmSMAs efficacy, demonstrating its capability to faithfully recapitulate spatial gene expression and anatomical structures. This marks a significant stride forward in overcoming the limitations of existing spatial transcriptomic techniques.

bioinformatics↗

bioRxiv2024

Exploring the Hypothetical Role of Bacteroides Species in Depression Progression: Insights from Metagenomic Analysis

Depression, a psychiatric disorder with significant morbidity and mortality, has a complex etiology. Recent advances in microbiome research have highlighted the potential role of gut microbiota in depression pathogenesis. This study utilized shotgun metagenomic sequencing to compare the fecal microbiota of 28 depression patients and 26 healthy individuals. Significant differences in gut microbiota composition were observed between the two groups. We generated 350 non-redundant high-quality metagenome-assembled Genomes (MAGs) by binning and conducted comparisons between the depression and control groups. Notably, we found that the MAGs enriched in people with depression mostly belonged to Bactendicating a close link between Bacteroides abundance and the development of depression, suggesting that Bacteroides might be a potential culprit for deroides, ipression. In the depression group, we found that the module of nitric oxide synthesis was remarkably enriched, and all Bacteroides MAGs were annotated to nitric oxide synthase, suggesting that increased levels of Bacteroides may contribute to elevated nitric oxide synthesis. Specifically, the mean relative abundance about the genomes of Bacteroides xylanisolvens, Bacteroides caccae, Bacteroides fragilis, Bacteroides stercoris and Bacteroides ovatus showed strong discriminatory power in distinguishing depressed patients from healthy individuals (AUC=0.834). This research shed light on the potential role of gut microbiota in depression and highlights specific metabolic pathways and microbial markers for further investigation. ImprotanceThis research highlighted significant differences in the composition and function of gut microbiota between individuals with depression and healthy individuals, particularly the enrichment of Bacteroides MAGs in depression patients. The upregulation of the nitric oxide synthesis pathway associated with these MAGs belong to Bacteroides in the gut of depression patients had also been observed. The mean relative abundance of a specific set of Bacteroides MAGs had been identified, which could accurately classify individuals with depression from healthy individuals (AUC=0.834). Our results suggest the importance of exploring microbial markers as potential diagnostic and therapeutic targets in managing depression.

genomics↗

bioRxiv2024

Impaired complement regulation drives chronic lung allograft dysfunction after lung transplantation.

A greater understanding of chronic lung allograft dysfunction (CLAD) pathobiology, the primary cause of mortality after lung transplantation, is needed to improve outcomes. The complement system links innate to adaptive immune responses and is activated early post-lung transplantation to form the C3 convertase, a critical enzyme that cleaves the central complement component C3. We hypothesized that LTx recipients with a genetic predisposition to enhanced complement activation have worse CLAD-free survival mediated through increased adaptive alloimmunity. We interrogated a known functional C3 polymorphism (C3R102G) that increases complement activation through impaired C3 convertase inactivation in two independent LTx recipient cohorts. C3R102G, identified in at least one out of three LTx recipients, was associated with worse CLAD-free survival, particularly in the subset of recipients who developed donor-specific antibodies (DSA). In a mouse orthotopic lung transplantation model, impaired recipient complement regulation resulted in more severe obstructive airway lesions when compared to wildtype controls, despite only moderate differences in graft-infiltrating effector T cells. Impaired complement regulation promoted the intragraft accumulation of memory B cells and antibody-secreting cells, resulting in increased DSA levels. In summary, genetic predisposition to complement activation is associated with B cell activation and worse CLAD-free survival. BRIEF SUMMARYLung transplant recipients genetically predisposed to impaired complement regulation demonstrate worse chronic rejection-free survival. This phenotype is associated with intragraft B cell-activation and donor-specific antibodies.

immunology↗

bioRxiv2024

scMoE: single-cell Multi-Modal Multi-Task Learning via Sparse Mixture-of-Experts

Recent advances in measuring high-dimensional modalities, including protein levels and DNA accessibility, at the single-cell level have prompted the need for frameworks capable of handling multi-modal data while simultaneously addressing multiple tasks. Despite these advancements, much of the work in the single-cell domain remains limited, often focusing on either a single-modal or single-task perspective. A few recent studies have ventured into multimodal, multi-task learning, but we identified a [circled1] Optimization Conflict issue, leading to suboptimal results when integrating additional modalities, which is undesirable. Furthermore, there is a [circled2] Costly Interpretability challenge, as current approaches predominantly rely on costly post-hoc methods like SHAP. Motivated by these challenges, we introduce scMoE1, a novel framework that, for the first time, applies Sparse Mixture-of-Experts (SMoE) within the single-cell domain. This is achieved by incorporating an SMoE layer into a transformer block with a cross-attention module. Thanks to its design, scMoE inherently possesses mechanistic interpretability, a critical aspect for understanding underlying mechanisms when handling biological data. Furthermore, from a post-hoc perspective, we enhance interpretability by extending the concept of activation vectors (CAVs). Extensive experiments on simulated datasets, such as Dyngen, and real-world multi-modal single-cell datasets, including {DBiT-seq, Patch-seq, ATAC-seq}, demonstrate the effectiveness of scMoE. Source code of scMoE is available at: https://github.com/UNITES-Lab/scMoE.

bioinformatics↗

bioRxiv2024

An integrative approach to prioritize candidate causal genes for complex traits in cattle

Genome-wide association studies (GWAS) have identified many quantitative trait loci (QTL) associated with complex traits, predominantly in non-coding regions, posing challenges in pinpointing the causal variants and their target genes. Three types of evidence can help identify the gene through which QTL act: (1) proximity to the most significant GWAS variant, (2) correlation of gene expression with the trait, and (3) the genes physiological role in the trait. However, there is still uncertainty in the success of these methods in identifying the correct genes. Here we test the ability of these methods in a comparatively simple series of traits associated with the concentration of polar lipids in milk. We conducted single-trait GWAS for [~]14 million imputed variants and 56 individual milk polar lipid (PL) phenotypes in 336 cows. A meta-analysis of multi-trait GWAS identified 10,063 significant SNPs at FDR [≤] 10% (P [≤] 7.15E-5). Transcriptome data from blood ([~]12.5K genes, 143 cows) and mammary tissue ([~]12.2K genes, 169 cows) were analysed using the genetic score omics regression (GSOR) method. This method links observed gene expression to genetically predicted phenotypes and was used to find associations between gene expression and 56 PL phenotypes. GSOR identified 2,186 genes in blood and 1,404 in mammary tissue associated with at least one PL phenotype (FDR [≤] 1%). We partitioned the genome into non-overlapping windows of 100 Kb to test for overlap between GSOR-identified genes and GWAS signals. We found a significant overlap between these two datasets, indicating GSOR significant genes were more likely to be located within 100 Kb windows that have GWAS signals compared to those without (P = 0.01; odds ratio = 1.47). These windows included 70 significant genes expressed in mammary tissue and 95 in blood. Compared to all expressed genes in each tissue, these genes were enriched for lipid metabolism gene ontology (GO). That is, 7 of the 70 significant mammary transcriptome genes (P < 0.01; odds ratio = 3.98) and 5 of the 95 significant blood genes (P < 0.10; odds ratio = 2.24) were involved in lipid metabolism GO. The candidate causal genes include DGAT1, ACSM5, SERINC5, ABHD3, CYP2U1, PIGL, ARV1, SMPD5, and NPC2, with some overlap between the two tissues. The overlap between GWAS, GSOR, and GO analyses suggests that together these methods can identify genes mediating QTL, though their power remains limited, as reflected by modest odds ratios. Larger sample sizes would enhance the power of these analyses, but issues like linkage disequilibrium would remain.

genomics↗

bioRxiv2024

DIA-BERT: pre-trained end-to-end transformer models for enhanced DIA proteomics data analysis

Data-independent acquisition mass spectrometry (DIA-MS) plays an increasingly important role in quantitative proteomics. Here, we introduce DIA-BERT, a software tool that leverages a transformer-based pre-trained artificial intelligence (AI) model for the analysis of DIA proteomics data. Over 276 million of high-quality peptide precursors extracted from existing DIA-MS files were used for training the identification model, while 34 million peptide precursors from synthetic DIA-MS files were used for training the quantification model. Compared to DIA-NN, DIA-BERT led to on average 54% more protein identifications and 37% more peptide precursors in five different human cancer (cervical cancer, pancreatic adenocarcinoma, myosarcoma, gallbladder cancer, and gastric carcinoma) sample sets with a high degree of quantitative accuracy. This study highlights the potential of utilizing pre-trained models and synthetic datasets to advance DIA proteomics analysis.

systems biology↗

bioRxiv2024

Distinct Binding Modes of Inter-Spike Cross-Linking Suggest a Supplementary Mechanism for SARS-CoV-2 Antibody Neutralization

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its Omicron subvariants drastically amplifies transmissibility, infectivity, and immune escape, mainly due to their resistance to most neutralizing antibodies. Thus, exploring the mechanisms underlying antibody evasion is crucial. Although the full-length native form of antibody, immunoglobulin G (IgG), offers valuable insights into the neutralization, structural investigations primarily focus on the fragment of antigen-binding (Fab). Here, we employ single-particle cryo-electron microscopy (cryo-EM) to characterize a W328-6H2 antibody, in its native IgG form complexed with severe acute respiratory syndrome (SARS), severe acute respiratory syndrome coronavirus 2 wild-type (WT) and Omicron variant BA.1 spike protein (S). Three high-resolution structures reveal that the full-length IgG forms a centered head-to-head dimer of trimer when bound fully stoichiometrically with both SARS and WT S, while adopting a distinct offset configuration with Omicron BA.1 S. Combined with functional assays, our results suggest that, beyond the binding affinity between the RBD epitope and Fab, the higher-order architectures of S trimer and full-length IgG play an additional role in neutralization, elucidate a potential molecular basis of Omicron immune escape, and expand our understanding on antibody evasion mechanisms.

immunology↗

bioRxiv2024

Accurate prediction of CRISPR editing outcomes in somatic cell lines and zygote with few-shot learning

The CRISPR-Cas system has revolutionized gene editing, while its outcome prediction remains unsatisfactory, especially in new cell states due to their distinct DNA repair preferences. In this study, we introduce inDecay, a flexible system for predicting CRISPR editing outcomes from target sequence, returning probabilities of nearly the full spectrum of indel events. Uniquely, inDecay utilizes informative and parameter-efficient features for each indel event and incorporates cell-type-specific repair preferences through a multi-stage design. While both inDecay and existing methods achieve accurate results for prediction within cell lines, only inDecay with transfer learning can retain the high performance for cross-cell line prediction. We then applied inDecay to mouse embryo editing using our newly generated data and observed remarkable accuracy by including as few as 30 fine-tuning embryonic samples. Notably, inDecay is the first software to predict embryonic editing. Therefore, our few-shot learning-supported system may accelerate guide RNA prioritization in mouse model generation, mammal embryonic gene editing, and cellular therapeutics.

bioinformatics↗

bioRxiv2024

Assessing Conformation Validity and Rationality of Deep Learning-Generated 3D Molecules

Recent advancements in artificial intelligence (AI) have revolutionized the field of 3D molecule generation. However, the lack of effective evaluation methods for 3D conformations limits further improvements. Current techniques, in order to achieve the necessary speed for evaluating large numbers of AI-generated molecules, often rely on empirical geometric metrics that do not adequately capture various conformational anomalies, or on molecular mechanics (MM) energy metrics that exhibit low accuracy and lack atomic or torsional details. To address this gap, we propose a two-stage approach that achieves both high speed and quantum mechanical (QM) level accuracy. The first stage, termed the validity test, employs an AI-derived force field to identify atoms with elevated energy resulting from implausible neighboring environments. The second stage, known as the rationality test, utilizes a deep learning network trained on data with density functional theory (DFT) accuracy to detect rotatable bonds with high torsional energies. To demonstrate the functionality of our evaluation system, we applied our approach to five recently reported 3D molecule generation AI models across 102 targets in Directory of Useful Decoys-Enhanced (DUD-E) dataset. To facilitate accessibility for the academic community, our method is available as an open-source package.

bioinformatics↗

bioRxiv2024

Geographic-specific Diversification of Vaginal Microbiota in China

The vaginal microbiota is pivotal to womens reproductive health, with significant geographic and ethnic heterogeneity suggesting evolutionary adaptations to environmental, climatic, and lifestyle factors. In this multi-centric study, we analyzed vaginal microbiome samples from women across five provinces in China, each characterized by distinct climatic, ethnic, and lifestyle traits. Our results indicate that the vaginal microbiota among Chinese women is characterized by low alpha diversity, with Lactobacillus crispatus being the dominant species in Community State Type (CST) I, especially in the northern regions. While most participants exhibited stable CSTs throughout their menstrual cycles, fluctuations were observed in some individuals. We also detected low-abundance bacterial species such as Fannyhesea vaginae and Sneathia vaginalis. Comparative genomic analysis of high-quality metagenome-assembled genomes of L. crispatus revealed minimal single nucleotide polymorphism (SNP) variations within individuals but substantial diversity between individuals, delineating a distinct separation between vaginal-associated populations (VAP) and gut-associated populations (GAP). These findings highlight the intricate dynamics of the vaginal microbiome, which are markedly influenced by geographic and individual determinants. This study offers an in-depth phylogenetic and genomic analysis of the vaginal microbiota among Chinese women, underscoring the imperative for further research into the impact of regional and individual variations on microbial community composition. Such knowledge could facilitate the development of personalized vaginal health management strategies that accommodate the unique microbiome profiles specific to different geographical regions. ImportanceThis study provides a groundbreaking analysis of the vaginal microbiota among Chinese women, offering insights into the population structure and evolutionary dynamics of this critical component of womens reproductive health. The research reveals that Lactobacillus crispatus, a key species in vaginal health, has a recent evolutionary emergence and exhibits significant geographic variation in prevalence, being more common in northern China. This discovery, along with the identification of distinct vaginal-associated and gut-associated populations, underscores the importance of understanding the vaginal microbiotas complexity. The findings have profound implications for developing personalized strategies to manage vaginal health, tailored to regional microbiome characteristics. This work not only advances our understanding of the vaginal microbiotas role in health and disease but also sets the stage for future research into the factors that shape microbial community structures across diverse populations.

microbiology↗

bioRxiv2024

HumanTestisDB: A Comprehensive Atlas of Testicular Transcriptomics and Cellular Interactions

Advances in single-cell technology have enabled the detailed mapping of testicular cell transcriptomes, which is essential for understanding spermatogenesis. However, the fragmented nature of age-specific data from various literature sources has hindered comprehensive analysis. To overcome this, the Human Testis Database (HumanTestisDB) was developed, consolidating multiple human testicular sequencing datasets to address this limitation. Through extensive investigation, 38 unique cell types were identified, providing a detailed perspective on cellular variety. Furthermore, the database systematically categorizes samples into eight developmental stages, offering a structured framework to comprehend the temporal dynamics of testicular development. Each stage features comprehensive maps of cell-cell interactions, elucidating the complex communication network inside the testicular microenvironment at particular developmental stages. Moreover, by facilitating comparisons of interactions among various cell types at different stages, the database permits examining alterations that transpire during critical transitions in spermatogenesis. HumanTestisDB, available at https://shalab.njmu.edu.cn/humantestisdb, offers vital insights into testicular transcriptomics and interactions, serving as an essential resource for advancing research in reproductive biology.

cell biology↗

bioRxiv2024

Positive impact of Type-A and -C response regulator genes on growth and panicle architecture in rice

Cytokinin signal transduction occurs through a "two-component system." Type-A and -C response regulators (RRs) are groups of proteins of similar structures constituting significant components of cytokinin signal transduction. In rice, 13 (Type-A) and two (Type-C) RRs have been identified to date; however, their functions remain partially known. In this study, we examined the expression patterns of Type-A and Type-C RRs in rice using RNA-Seq and confirmed their functions by constructing mutants of the 15 genes with CRISPR/Cas9. Almost all Type-A RRs played positive roles in the development of secondary branches and secondary spikelets, particularly RR2 and RR4. Notably, rr1 rr2 and rr8 rr12 rr13 higher-order mutants displayed small panicle sizes and decreased plant height. Additionally, both Type-C RRs played positive roles in regulating heading date. RNA-seq revealed several genes with significantly altered expression in the rr2 and rr4 mutants, with almost half of the differentially expressed genes (DEGs) overlapping between the two mutants. Many of the DEGs were associated with the cytokinin and abscisic acid pathways. Our findings provide new insights into the functions of Type-A and -C RRs in rice growth and may serve as a foundation for future studies focusing on cytokinin signaling.

plant biology↗

bioRxiv2024

Unveiling a unique macrophage population in exocrine glands sustained by ILC2-derived GM-CSF

Granulocyte-macrophage colony-stimulating factor (GM-CSF) has a non-redundant role in the emergence and maintenance of alveolar macrophages (AMs). However, its role in developmental and steady-state myelopoiesis outside the lung is largely unexplored. Scanning through developing tissues using a Fate-map and reporter of GM-CSF mouse strain, we discovered that GM-CSF was produced by type 2 innate lymphoid cells (ILC2s) in the submandibular and sublingual salivary gland (SG) during postnatal development. GM-CSF producing ILC2s foster the development of a hitherto undescribed phagocyte subset, which we named adenophages. Detailed analysis focusing on phenotypic and transcriptional profiling revealed that adenophages display shared aspects of both, macrophages and dendritic cells (DCs). We found them to be homogenously distributed across the SG, but always in close proximity to GM-CSF producing ILC2s and myoepithelial cells. Importantly, adenophages were present throughout all analyzed exocrine glands such as lacrimal glands and mammary glands, and were also identified in human SG sections, indicating a conserved role in exocrine glands across species.

immunology↗

bioRxiv2024

CELF2 promotes tau exon 10 inclusion via hinge domain-mediated nuclear condensation

Alternative splicing is a fundamental process that contributes to the functional diversity and complexity of proteins. The regulation of each alternative splicing event involves the coordinated action of multiple RNA-binding proteins, creating a diverse array of alternatively spliced products. Dysregulation of alternative splicing is associated with various diseases, including neurodegeneration. Here we demonstrate that CELF2, a splicing regulator and a GWAS-identified risk factor for Alzheimers disease, binds to mRNAs associated with neurodegenerative diseases, with a specific interaction observed in the intron adjacent to exon 10 on Tau mRNA. Loss of CELF2 in the mouse brain results in a decreased inclusion of Tau exon 10, leading to a reduced 4R:3R ratio. Further exploration shows that the hinge domain of CELF2 possesses an intrinsically disordered region (IDR), which mediates CELF2 condensation and function. The functionality of IDR in regulating CELF2 function is underscored by its substitutability with IDRs from FUS and TAF15. Using TurboID we identified proteins that interact with CELF2 through its IDR. We revealed that CELF2 co-condensate with NOVA2 and SFPQ, which coordinate with CELF2 to regulate the alternative splicing of Tau exon 10. A negatively charged residue within the IDR (D388), which is conserved among CELF proteins, is critical for CELF2 condensate formation, interactions with NOVA2 and SFPQ, and function in regulating tau exon 10 splicing. Our data allow us to propose that CELF2 regulates Tau alternative splicing by forming condensates through its IDR with other splicing factors, and that the composition of the proteins within the condensates determines the outcomes of alternative splicing events.

molecular biology↗

bioRxiv2024

Colchicine Inhibits Smooth Muscle Cell Phenotypic Switch and Aortic Dissection in Mice

Aortic dissection is a serious cardiovascular disease characterized by intimal tearing and vascular delamination. Smooth muscle cell phenotypic transformation, extracellular matrix degradation and vascular inflammation are the main pathogenic mechanisms of aortic dissection. Colchicine is an alkaloid derived from the plant Colchicum autumnale L. and was the first FDA-approved anti-inflammatory drug in the cardiovascular field. In this study, we investigated the protective effects of colchicine in aortic dissection in mice and found that colchicine resisted aortic dissection by inhibiting vascular inflammation and smooth muscle cell phenotypic transformation, and that colchicine reversed smooth muscle cell phenotypic transformation by targeting the transcriptional activator myocardin. This study provides new directions for the development of drug therapies for aortic dissection.

molecular biology↗

bioRxiv2024

Embryonic Mast Cells Arise from Cpa3-expressing Precursors Independent of Granulocyte-Monocyte Progenitors

Most of the mast cells (MCs) in connective tissues, such as skin, are long-lived embryonic-derived immune cells that play important roles in host defense and various immunological diseases, including allergies. Their embryonic origin and ontogeny remain to be fully studied since several overlapping waves of embryonic hematopoiesis have been shown to give rise to these cells. Here, combining single-cell RNA sequencing and new genetic fate mapping models, we identified a Cpa3-expressing population sequentially appearing in the yolk sac, fetal liver, and peripheral tissues which gives rise to dermal MCs during embryonic days 11.5 to 14.5. Using in vitro differentiation and in vivo transplant methods, we identified a Ter119-F4/80- CD45+CD117+CD16/32+CD135-CD115-Ly6C-CD34lo population as potential fetal liver MC precursors. Fate mapping with Cpa3CreERT2and Zbtb16CreERT2 models, as well as the granulocyte-monocyte progenitor (GMP) models Ms4a3Cre and ElaneCre, demonstrated that MCs arise from Cpa3+ progenitors rather than Ms4a3+ or Elane+ GMPs. A corresponding population with a similar developmental trajectory was also identified in human early yolk sac and fetal liver, suggesting a conserved MC developmental program across species. These findings delineate the detailed developmental path of MCs in embryos, permitting future functional studies in immunity and development. HighlightsO_LICpa3-expressing cells in the yolk sac and fetal liver contain mast cell precursors C_LIO_LICpa3CreERT2 labels embryonic mast cells and their precursors C_LIO_LIEmbryonic mast cells arise through Cpa3+ mast cell precursors, but not Elane+/Ms4a3+ GMPs C_LIO_LIThe seeding of MCPs to embryonic skin slows down after E14.5 C_LI

immunology↗

bioRxiv2024

Complex regulatory interactions at GDF5 shape joint morphology and osteoarthritis disease risk

Our ability to pinpoint causal variants using GWAS is dependent on understanding the dynamic epigenomic and epistatic context of each associated locus. Being the best studied skeletal locus, GDF5 associates with many diseases and has a complex cis-regulatory architecture. We interrogate GDF5 regulatory interactions and model disease variants in vitro and in vivo. For all regulatory regions we see that local epigenetic activation/repression impacts patterns of joint-specific expression and disease risk. By modeling the most cited risk variant in mice we found that it had no impact on expression, joint morphology, or disease. Yet, we identified significant epistatic expression interactions between this risk variant and others lying within regulatory regions subject to repression or activation. These findings are important lessons on how regulatory interactions and local epistasis work in the etiology of disease risk, and that assessment of individual variants of high GWAS significance need not alone be considered causal. TeaserGenetic interactions at the most studied skeletal disease locus reveal hidden complexities in pinpointing causal mutations.

genetics↗

bioRxiv2024

Defective Neurogenesis in Lowe Syndrome is Caused by Mitochondria Loss and Cilia-related Sonic Hedgehog Defects

Human brain development requires tight coordination of metabolic and signaling pathways. Lowe syndrome (LS) is a recessive X-linked disorder characterized by proximal tubular renal disease, congenital cataracts, glaucoma, and neurodevelopmental delays. While LS results from mutations in the OCRL gene, which encodes an inositol polyphosphate 5-phosphatase, the cellular mechanisms driving neuronal dysfunction remain poorly understood. In this study, using patient-derived iPSC neurons, an OCRL knockout mouse model, and an independent zebrafish OCRL-deficient model, we identified mitochondrial dysfunction as a conserved phenotype of OCRL loss across species. Collectively, our findings showed that OCRL deficiency leads to reduced mitochondrial activity, decreased mtDNA levels, reduced mitochondrial content (TOM20), and increased oxidative stress. We further showed that OCRL-deficient neural cells exhibited an altered balance of neuronal versus astrocytic differentiation, rather than a defect in neurogenesis. Additionally, we observed impaired Sonic Hedgehog (Shh) signaling and ciliary homeostasis. Thus, we propose that mitochondrial dysfunction-induced oxidative stress acts as a central mediator linking OCRL loss to altered cell fate and disrupted Shh signaling, providing a unifying framework for these phenotypes.

cell biology↗