Biology subjects

liang, y.

Publications and source records attributed to liang, y..

4 recordsLinked to original sources

bioRxiv2026

A Phospho-Switch for Cell Fate Control

Cell fate control is thought to be complex, likely involving both cell intrinsic and extrinsic factors, and remains ill-defined at the molecular level. Here we show a phospho-switch controls cell fate both in vitro and in vivo. We first show that SALL4 is phosphorylated at multiple sites, but only T903 as the primary one as SALL4T903A lost BAF interaction and [~]90% activity in reprogramming. Mechanistically, we demonstrate that SALL4pT903 is sensitive to BMP4 signaling through DUSP9 axis. Finally, we show by tetraploid embryo complementation that mESCs harboring SALL4T903A can sustain embryo development but with severe defects including cranial hypoplasia and flattened skull vault after birth. A genome wide search identifies 608 transcription factors harboring the same HTG motif sandwiched by two zinc fingers, suggesting that this phospho-switch may be a conserved mechanism to control cell fate. In briefA phosphorylation switch, pT903, in SALL4 governs cell fate by modulating BAF complex interaction in response to BMP4-DUSP9 signaling, as its dephosphorylation abrogates reprogramming and causes cranial defects in mice, suggesting that such switches may broadly regulate cell fate machinery. HighlightsO_LIA T903 phospho-switch in SALL4 gates BAF interaction to orchestrate gene activation and reprogramming C_LIO_LIBMP4-DUSP9 signaling converges on SALL4pT903 dephosphorylation to couple extracellular cues with cell fate determination C_LIO_LIDisrupting the SALL4-T903 phospho-switch causes severe postnatal developmental defects in mice. C_LIO_LIThe HTGE motif may function as a central switch on key transcription factors for sensing upstream signals C_LI

developmental biology↗

bioRxiv2025

A comprehensive RNA virome from molluscan transcriptomes reveals extensive diversity and modular genome evolution

Mollusca is the second-largest animal phylum and an important marine food resource for humans. While DNA viruses that threaten molluscan aquaculture have received much attention, molluscan RNA viromes remain poorly explored. Here, based on 223 molluscan metatranscriptomes covering eight animal classes, we identified 80 species-level RNA viruses spanning three viral phyla and nine viral families. Phylogenetic result combined structural modeling found a major increase in the number of Pisuviricota-related lineages. Extensive modular evolution in viral genomes was observed including gene rearrangements and co-evolution of capsid and RdRP genes. Host prediction linked 78% of the RNA viruses to a diverse range of eukaryotes, including vertebrates, invertebrates, and algae. These findings expand the known diversity of RNA viruses in molluscs and shed light on their phylogenetic relationships, highlighting molluscs as valuable models for RNA virus evolution. Graphical abstractRNA virus diversity within 223 molluscan transcriptome samples were analyzed based on RdRP identification. Viruses belonging to the phylum Pisoviricota were the most abundant; co-evolution between RdRP and capsid proteins of molluscan Picornavirales was revealed, and a complex virus-host interaction network was constructed. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=153 SRC="FIGDIR/small/673676v1_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@1ce8fc0org.highwire.dtl.DTLVardef@3f9068org.highwire.dtl.DTLVardef@121cc2forg.highwire.dtl.DTLVardef@1e431a0_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIComprehensive metatranscriptomic analysis found extensive RNA viral diversity in molluscs. C_LIO_LIPisuviricota dominate the molluscan RNA virome, substantially broadening known viral lineages. C_LIO_LIModular genome evolution involving gene rearrangements and conserved structural features characterize molluscan RNA viruses. C_LIO_LIMolluscs potentially serve as major ecological reservoirs for diverse RNA viruses infecting various eukaryotic hosts. C_LI

bioinformatics↗

bioRxiv2025

Investigating the Mechanisms and Potential Therapeutic Targets of Vestibular Migraine

BackgroundVestibular Migraine (VM) is a complex neurological disorder with recurrent headaches and various vestibular symptoms. Although it severely affects patients quality of life, its causes and pathophysiology are still unclear, and effective treatments are scarce. The lack of data emphasizes the need for bioinformatics to find key genes and pathways in VM, which could help develop new diagnostic and treatment methods. MethodThe GSE109558 dataset was acquired from the Gene Expression Omnibus (GEO) database. To identify VM - related differentially expressed genes, screening was carried out through limma and Weighted Correlation Network Analysis (WGCNA).The functional analysis of VM - related differentially expressed genes was conducted using three bioinformatics approaches: Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and Gene Set Enrichment Analysis (GSEA). Feature selection was further refined using lasso regression and random forest. Also, CIBERSORT was utilized to analyze the infiltration of immune cells, and Spearmans correlation analysis was employed to explore the correlations between diagnostic differentially expressed genes and immune cells.Finally, the Comparative Toxicogenomics Database (CTD) was utilized to search for corresponding drugs, and molecular docking was performed to explore potential therapeutic targets. ResultSix key feature genes (CABIN1, IFIT3, HEATR1, ARHGDIA, RAB11FIP4, and ZNF444) were identified as potential diagnostic markers for VM. Among these, CABIN1 demonstrated the highest diagnostic potential based on ROC curve performance, highlighting its promise as a diagnostic biomarker.Functional annotation of DEGs revealed their enrichment in biological processes related to inflammation, calcium ion channel regulation, and other pathways likely involved in VM pathophysiology. Through the CTD, drugs like Acetaminophen, bisphenol A, and Phenylephrine were identified. Molecular docking simulation was used to explore their potential therapeutic mechanisms for VM. ConclusionThis study offers important insights into the molecular mechanisms of VM and identifies six key feature genes, with CABIN1 standing out as a potential diagnostic marker.These findings pave the way for further research to validate the diagnostic and therapeutic implications of these genes and pathways.

genetics↗

bioRxiv2022

Imaging the Neural Substrate of Trigeminal Neuralgia Pain Using Deep Learning

Trigeminal neuralgia (TN) is a severe and disabling facial pain condition and is characterized by intermittent, severe, electric shock-like pain in one (or more) trigeminal subdivisions. This pain can be triggered by an innocuous stimulus or can be spontaneous. Presently available therapies for TN include both surgical and pharmacological management; however, the lack of a known etiology for TN contributes to the unpredictable response to treatment and the variability in long-term clinical outcomes. Given this, a range of peripheral and central mechanisms underlying TN pain remain to be understood. We acquired functional magnetic resonance imaging (fMRI) data from TN patients who (1) rested comfortably in the scanner during a resting state session and (2) rated their pain levels in real time using a calibrated tracking ball-controlled scale in a pain tracking session. Following data acquisition, the data was analyzed using the conventional correlation analysis and two artificial intelligence (AI)-inspired deep learning methods: convolutional neural network (CNN) and graph convolutional neural network (GCNN). Each of the three methods yielded a set of brain regions related to the generation and perception of pain in TN. There were six regions that were identified by all three methods, including the superior temporal cortex, the insula, the fusiform, the precentral gyrus, the superior frontal gyrus, and the supramarginal gyrus. Additionally, 17 regions, including dorsal anterior cingulate cortex(dACC) and the thalamus, were identified by at least two of the three methods. Collectively, these 23 regions represent signature centers of TN pain and provide target areas for future studies relating to central mechanisms of TN.

neuroscience↗