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Xiao, L.

Publications and source records attributed to Xiao, L..

At least 37 records · Page 2Linked to original sources

Programmed DNA elimination was present in the last common ancestor of Caenorhabditis nematodes

In most organisms, all cells inherit the same genome, and many mechanisms exist to preserve its integrity across cell divisions. Programmed DNA elimination (PDE), the targeted removal of specific genomic regions from somatic cell lineages during early embryogenesis, is a striking exception. Since its discovery in parasitic nematodes over a century ago, PDE has been observed in diverse eukaryotes, including ciliates, arthropods, and vertebrates. However, the mechanisms, functions, and evolutionary origins of PDE remain poorly understood. Here, we describe the discovery of PDE in three species of the free-living nematode genus Caenorhabditis. Multiple genomic regions are precisely eliminated from somatic cells during early embryogenesis, resulting in chromosome fragmentation and the loss of key germline genes. The sites of elimination are strongly associated with conserved sequence motifs that likely direct DNA breakage. Comparative analyses indicate that PDE was present in the last common ancestor of Caenorhabditis and subsequently lost early during the evolution of many species, including C. elegans. The presence of PDE in the ancestors of one of biologys most important model organisms, together with recent discoveries in other eukaryotic lineages, reveals PDE to be a far more widespread and significant feature of evolution and development than previously recognised.

evolutionary biology↗

Magnesium isoglycyrrhizinate alleviates alcohol-associated liver disease through targeting HSD11B1

While magnesium isoglycyrrhizinate (MgIG) is a clinically approved therapy for alcohol-associated liver disease (ALD), its precise molecular targets and mechanisms remain uncharacterized. This study aimed to define MgIGs hepatoprotective actions in chronic-binge ALD mouse models and ethanol/palmitic acid-exposed AML-12 hepatocytes. Through an integrated strategy encompassing RNA sequencing, molecular docking, and microscale thermophoresis, we discovered that MgIG directly binds to hydroxysteroid 11-beta dehydrogenase 1 (HSD11B1) at residue 187, a finding corroborated by molecular dynamics simulations. In vivo, MgIG markedly attenuated alcohol-induced liver injury, evidenced by ameliorated histological damage, reduced hepatic steatosis, and normalized liver-to-body weight ratios. In vitro, it effectively reduced lipid accumulation, inflammation, and apoptosis. Mechanistically, RNA sequencing identified isopentenyl diphosphate delta isomerase 1 (IDI1) as a key downstream effector. Hepatocyte-specific genetic manipulations confirmed that MgIG modulates the SREBP2-IDI1 axis, thereby suppressing lipogenesis, inflammatory responses, and apoptotic pathways. We reveal HSD11B1 as a novel direct molecular target of MgIG and elucidate its therapeutic mechanism through the HSD11B1-SREBP2-IDI1 signaling axis, which profoundly impacts ALD pathogenesis. These findings not only validate MgIGs clinical utility but also highlight a promising new therapeutic target for ALD.

pharmacology and toxicology↗

Novel Calcin peptides from Scorpion Venom Exhibit Humidity-Dependent Expression and Ryanodine Receptor Activation

Scorpions are venomous animals of considerable pharmacological potential, traditionally used in East Asia to treat cardiovascular and autoimmune diseases. However, geographical variations in the expression of their venom components remain poorly understood. We performed transcriptome profiling on scorpion samples collected from eight distinct climatic and environmental regions in East Asia, we successfully identified two distinct scorpion species: Uroctonus mordax and Mesobuthus martensii. Furthermore, we identified two novel calcins from the venoms of these species, with their expression levels showing a significant correlation to environmental humidity. Functional assays revealed that these calcins differentially potentiate [3H]ryanodine binding to RyR isoforms and induce a persistent subconductance state, suggesting distinct mechanisms of action. Our findings suggest biogeographical variation in venom composition between species and suggest that environmental humidity as a key factor influencing calcin expression. These results expand the structural diversity of the calcin family and provide a mechanistic basis for the medicinal application and ecological adaptation of scorpion venom.

molecular biology↗

A role of extracellular vesicle-mediated inter-organ communication in obesity-related arrhythmia

Obesity contributes to the risk of cardiac arrhythmias, but the exact mechanism remains unclear. Here, we show visceral adipose tissue-derived extracellular vesicles (VAT EVs) from individuals with obesity prolong action potential duration (APD) and impair calcium handling in stem cell-derived cardiomyocytes, in addition to activating fibroblasts and macrophages towards a pro-fibrotic/inflammatory state, thereby creating pro-arrhythmic substrate. Adipose-derived EVs target the heart in obese mice, suggesting the potential for direct communication. Transcriptome-wide genetic association (TWAS) and epigenetic studies anchored on genes differentially expressed in cardiomyocytes, fibroblasts, and macrophages after VAT-EV exposure identified genes significantly associated with QT interval and atrial fibrillation. Finally, as a proof-of-principle, we pharmacologically blocked TRPC3 (a VAT-EV-induced ion channel) in cardiomyocytes, restoring the APD towards normality. This molecular genetic evidence supports an EV-mediated direct communication pathway between adipose tissue and the heart in arrhythmogenesis, offering a new paradigm to identify mediators of cardiovascular disease in obesity. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=192 SRC="FIGDIR/small/676027v1_ufig1.gif" ALT="Figure 1"> View larger version (74K): org.highwire.dtl.DTLVardef@114b230org.highwire.dtl.DTLVardef@1be7926org.highwire.dtl.DTLVardef@3d6b0org.highwire.dtl.DTLVardef@3ef15_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

scYeast: a Biological-knowledge-guided Foundation Model on Yeast Single-Cell Transcriptomics

Though large-scale pre-trained models are vital for foundational cell modeling, most focus on human or mouse systems, neglecting model organisms like yeast (Saccharomyces cerevisiae) and often failing to use existing biological prior knowledge effectively. To address this, we present scYeast, the first foundational cell model tailored for yeast that seamlessly embeds biological priors. scYeast employs a novel asymmetric parallel architecture to infuse transcriptional regulatory information directly into the Transformers attention mechanism, leveraging established biological knowledge during training. Pre-trained on large-scale yeast single-cell transcriptomics data, scYeast demonstrates strong generalization and biological interpretability. It excels in zero-shot tasks, such as inferring regulatory relationships and identifying critical cell states. After fine-tuning, scYeast performs exceptionally across a diverse set of tasks from cell type classification to predicting growth doubling time and gene perturbation response. Additionally, using transfer learning, scYeast can be adapted to other omics datasets, such as proteomics, thus broadening its utility in big data analysis. Overall, scYeast is a powerful tool for yeast single-cell biology research and sets a new standard for integrating foundational models with biological prior knowledge, dramatically accelerating the pace of discovery in yeast synthetic and systems biology and providing a replicable framework for other organisms.

bioinformatics↗

Heuristic Multi-site Optimization for Protein Sequence Design using Masked Protein Language Models

Protein sequence design for tailored functional properties is a fundamental task in protein engineering, with critical applications in drug discovery and therapeutic development. Efficient navigation of the combinatorial vastness of protein sequence space to identify functional variants remains a formidable challenge. Conventional approaches, which predominantly rely on template-based local search or single-residue mutagenesis, are constrained by their susceptibility to local optima and their potential risk of destabilizing native structural stability. In this study, we introduce ProtHMSO, a heuristic multi-site optimization framework leveraging masked protein language models (ProtLMs) for context-aware sequence exploration. ProtHMSO mimics natural evolutionary mechanisms by employing ProtLM-derived substitution probabilities to guide heuristic searches for synergistic mutations, thereby constraining combinatorial search spaces through evolutionary and biophysical priors. Given a starting sequence and a predictive fitness model, ProtHMSO heuristically generates high-fitness multi-site variants under evolutionary constraints. Besides, ProtHMSO can also be as a modular plugin that enhances the convergence efficiency of genetic algorithms (GAs) and Monte Carlo tree search (MCTS) by integrating evolutionary priors into exploration strategies. Benchmark experiments demonstrate that protein sequences generated by ProtHMSO exhibit superior functional performance and closer alignment with natural sequence distribution, compared with state-of-the-art methods. These advancements highlight that ProtHMSO has strong potential and compatibility to accelerate functional protein discovery, offering a robust framework for efficient and context-aware exploration of protein sequence space. Author summaryTo address the challenge of efficiently discovering functional new proteins in protein engineering due to the vast sequence space, and to overcome the limitations of traditional evolutionary algorithms that rely on blind random mutagenesis, resulting in inefficiency and prone to structural destabilization, we proposed a heuristic multi-site optimization framework, ProtHMSO. Its core concept is to leverage the powerful contextual prediction capabilities of masked protein language models (such as ESM-2) to guide sequence mutagenesis. By predicting amino acid substitutions at specific sites that are consistent with evolutionary laws and biophysical priors, ProtHMSO narrows the exploration scope from the vast combinatorial space to a small number of high-potential candidate sequences, achieving intelligent and efficient optimization of protein sequences. Furthermore, ProtHMSO is not just a standalone algorithm, but also a plug-and-play "enhancement module". By embedding it into a genetic algorithm (GA) and a Monte Carlo tree search (MCTS), it replaces the random mutation operator in the former with its intelligent mutation and guides the tree expansion process in the latter. This enables these classic optimization algorithms to break free from the blindness of exploration and achieve faster convergence and better results, demonstrating the wide applicability and great potential of this framework in improving the performance of tools in the entire field of computational protein design.

bioinformatics↗

C-Nucleosides Stabilize RNA by Reducing Nucleophilicity at 2'-OH

Nucleotides with carbon substitution for heteroatoms are common in biological and therapeutic RNAs. Important examples include the C-nucleosides pseudouridine and N1-methyl-pseudouridine; these modifications were reported to slow degradation of large RNAs, but the mechanism is unknown. We measured kinetics of spontaneous and enzymatic cleavage at a single bond of synthetically modified RNAs, and find that carbon substitution markedly reduces strand cleavage rates in RNA by both mechanisms. Studies of nucleophilic acylation reactions of RNAs and of small alcohols of varied pKa suggest that reduced inductive effects resulting from carbon substitution for electronegative atoms results in both higher pKa and lower nucleophilicity. The results provide insight into native transcriptome modifications as well as RNA therapies. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=157 SRC="FIGDIR/small/666442v2_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@73c194org.highwire.dtl.DTLVardef@1aa0af7org.highwire.dtl.DTLVardef@b5f8a0org.highwire.dtl.DTLVardef@d601fa_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOTOC GraphicC_FLOATNO C_FIG SynopsisThis study reveals mechanisms by which modifications found in native and therapeutic RNAs enhance RNA stability. Carbon substitution for nitrogen or oxygen results in reduced 2'-OH nucleophilicity.

biophysics↗

AlphaGEM Enables Precise Genome-Scale Metabolic Modelling by Integrating Protein Structure Alignment with deep-learning-based Dark Metabolism Mining

Constructing high-quality genome-scale metabolic models (GEMs) for less-studied species remains challenging. To address this, we developed AlphaGEM, a versatile toolbox leveraging proteome-scale structural alignment and deep-learning-based predictions for efficient genomic mining to generate GEMs ready for applications. Our findings show that the structural alignment or protein-language-model-based prediction (i.e., PLMSearch), could identify more homologous protein relationships than sequence-blast-based alignment, contributing to the accurate profiling of metabolism from target organisms. Additionally, AlphaGEM encompasses an ensemble procedure empowered by multiple deep learning toolboxes to effectively mine the dark metabolic functions encoded by nonhomologous proteins, significantly expanding species-specific metabolic networks. We demonstrate AlphaGEMs accuracy by building GEMs for eukaryotes (e.g., S. pombe, C. albicans) and prokaryotes (e.g., K. pneumoniae, B. subtilis), achieving predictions comparable to manually curated models while outperforming existing tools. AlphaGEM also successfully reconstructs GEMs for M. musculus and C. griseus, showcasing its great potential for uncovering dark metabolism in complex mammals. Lastly, we demonstrate that AlphaGEM could facilitate the automatic GEMs reconstruction for 332 distinct yeast species with high prediction fidelity. In conclusion, AlphaGEM provides unprecedented opportunities for the precise, rapid construction of GEMs across diverse domains, which sets a solid foundation for universal functional analysis of non-model organisms having genome sequences available.

bioinformatics↗

Neural Timescale of Adolescents Major Depressive Disorder

Adolescent major depressive disorder (MDD) is characterized by heterogeneous symptomatology and complex neurodevelopmental underpinnings. Here, we investigated whether cortical intrinsic neural timescales (INT)--a measure of temporal stability in neural activity--are altered in adolescents with MDD and whether these alterations relate to clinical symptoms, suicidality, early-life adversity, and underlying neurobiological mechanisms. Using resting-state fMRI in adolescents with MDD and healthy controls (HCs), we found widespread reductions in INT in frontal, parietal, and sensorimotor regions, with a notable prolongation in the left temporoparietal junction. Disrupted timescales significantly impaired network modularity and clustering, and SVR-based machine learning revealed that altered INT patterns predicted individual depression and anxiety severity. INT abnormalities were further associated with suicidal ideation and childhood trauma, particularly emotional and physical neglect. Biophysical modeling linked INT variations to local recurrent excitation and external input strength, differing between HCs and MDD. Spatial correlations with PET-derived neurotransmitter receptor maps demonstrated that INT alterations colocalize with serotonergic, dopaminergic, and cholinergic systems. Transcriptomic enrichment analysis revealed associations with genes involved in mitochondrial function, synaptic signaling, and metabolic regulation. Together, these findings identify neural timescale disruption as a core pathophysiological feature of adolescent MDD, bridging macroscale dynamics with microcircuit and molecular architecture. INT may serve as a promising biomarker and mechanistic target for precision psychiatry in youth depression.

neuroscience↗

Neurotrophic Factor-α1/carboxypeptidase E regulates critical protein networks to rescue neurodegeneration, defective synaptogenesis and impaired autophagy in Alzheimer's Disease mice

BackgroundThe global aging population is increasingly inflicted with Alzheimers disease (AD), but a cure is still unavailable. Neurotrophic Factor-1/carboxypeptidase E (NF-1/CPE) gene therapy has been shown to prevent and reverse memory loss and pathology AD mouse models However, the mechanisms of action of NF-1/CPE are not fully understood. We investigated if a non-enzymatic form of NF-1/CPE-E342Q is efficient in reversing AD pathology and carried out a proteomic study to uncover the mechanisms of action of NF-1/CPE in AD mice. MethodsAAV-human NF-1/CPE and a non-enzymatic form, NF-1/CPE -E342Q were delivered into hippocampus of 3xTg-AD mice and effects on cognitive function, neurodegeneration, synaptogenesis and autophagy were investigated. A quantitative proteomic analysis of hippocampus of 3xTg-AD mice with and without AAV-NF-1/CPE treatment was carried out. ResultsHippocampal delivery of AAV-NF-1/CPE-E342Q prevented memory loss, neurodegeneration and increase in activated microglia in 3xTg-AD mice, indicating its action is independent of its enzymatic activity. Quantitative proteomic analysis of hippocampus of 3xTg-AD mice that underwent NF-1/CPE gene therapy revealed differential expression of >2000 proteins involving many metabolic pathways. Of these, two new proteins down-regulated by NF-1/CPE: Nexin4 (SNX4) and Trim28 which increase A{beta} production and tau levels, respectively were identified. Western blot analysis verified that they were reduced in AAV-NF-1/CPE treated 3xTg-AD mice compared to untreated mice. Our proteomic analysis indicated synaptic organization as top signaling pathway altered as a response to CPE expression. Synaptic markers PSD95 and Synapsin1 were decreased in 3xTg-AD mice and were restored with AAV-NF-1/CPE treatment. Proteomic analysis hypothesized involvement of autophagic signaling pathway. Indeed, multiple proteins known to be markers of autophagy were down-regulated in 3xTg-AD mice, accounting for impaired autophagy. Expression of these proteins were upregulated in 3xTg-AD mice with NF-1/CPE gene therapy, thereby reversing autophagic impairment. ConclusionsThis study uncovered vast actions of NF-1/CPE in restoring expression of networks of critical proteins including those necessary for maintaining neuronal survival, synaptogenesis and autophagy, while down-regulating many proteins that promote tau and A{beta} accumulation to reverse memory loss and AD pathology in 3xTg-AD mice. AAV-NF-1/CPE gene therapy uniquely targets many metabolic levels, offering a promising holistic approach for AD treatment. Graphic abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/657876v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@10e468forg.highwire.dtl.DTLVardef@3a78adorg.highwire.dtl.DTLVardef@c8d62forg.highwire.dtl.DTLVardef@165d118_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

Vitamin D regulates olfactory function via dual transcriptional and mTOR-dependent translational control of synaptic proteins

Vitamin D (VitD) deficiency, affecting over 1 billion people worldwide, is associated with neurological dysfunction, but its cell-type-specific neural mechanisms remain unclear. Using a dietary mouse model, we show that VitD bidirectionally regulates olfactory acuity: deficiency impairs odor discrimination, while supplementation enhances sensitivity. Single-nucleus and spatial transcriptomics pinpoint selective vitamin D receptor (VDR) expression in olfactory bulb (OB) tufted cells, where it drives synaptic protein expression. Genetic VDR knockdown replicates deficiency-associated olfactory deficits, establishing VDR as essential for synaptic and translational regulation. Notably, we identify mTOR-mediated protein synthesis as a critical convergence point--pharmacological mTOR inhibition (rapamycin) rescues synaptic protein deficits and behavioral impairments in VitD-deficient mice. These findings delineate a noncanonical VDR-mTOR-translational axis--complementing conventional transcriptional regulation--through which VitD serves as a nutrient-sensitive neuromodulator that integrates dietary status with synaptic functions and sensory processing. Our study expands the physiological role of VitD beyond traditional endocrine signaling and reveals mechanistic insights that may inform novel therapeutic strategies for neurological and psychiatric conditions associated with VitD deficiency.

neuroscience↗

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↗

Rewiring of 3D chromatin topology orchestrates transcriptional reprogramming in muscle fiber-type specification and transformation

The composition of muscle fibers, characterized by distinct contractile and metabolic properties, significantly influences meat quality and glucose homeostasis. However, the mechanisms by which three-dimensional (3D) genome topology integrates with epigenetic states to regulate muscle fiber specification and transformation remain poorly understood. Here, we present an integrative analysis of the transcriptome, epigenome, and 3D genome architecture in the slow-twitch glycolytic extensor digitorum longus (EDL) and fast-twitch oxidative soleus (SOL) muscles. Global remodeling of enhancer-promoter (E-P) interactions emerged as a central driver of transcriptional reprogramming associated with muscle contraction and glucose metabolism. We identified tissue-specific super-enhancers (SEs) that regulate muscle fiber-type specification through cooperation of chromatin looping and transcription factors such as KLF5. Notably, the SE-driven activation of STARD7 facilitated the transformation of glycolytic fibers into oxidative fibers by mitigating reactive oxygen species levels and suppressing ERK MAPK signaling. This study elucidates the principles of 3D genome organization in the epigenetic regulation of muscle fiber specification and transformation, providing a foundation for novel therapeutic strategies targeting metabolic disorders and enhancing meat quality. O_FIG O_LINKSMALLFIG WIDTH=142 HEIGHT=200 SRC="FIGDIR/small/643571v1_ufig1.gif" ALT="Figure 1"> View larger version (40K): org.highwire.dtl.DTLVardef@990eecorg.highwire.dtl.DTLVardef@a2b491org.highwire.dtl.DTLVardef@1fcd142org.highwire.dtl.DTLVardef@10e4d47_HPS_FORMAT_FIGEXP M_FIG Schematic overview of the study. C_FIG

developmental biology↗

Decoding yeast transcriptional regulation via a data- and mechanism-driven distributed large-scale network model

The complex transcriptional regulatory relationships among genes influence gene expression levels and play crucial roles in determining cellular phenotypes. Here, we present a novel distributed large-scale transcriptional regulatory neural network model (DLTRNM) by integrating prior knowledge into the reconstruction of pre-trained machine learning models followed with fine-tuning. Taken S. cerevisiae as the example, the transcriptional regulatory relationships, carefully compiled and documented, are utilized to define interactions between TFs and their TGs within DLTRNM. Subsequently, DLTRNM is pre-trained on pan-transcriptomic data and fine-tuned with time-series data, enabling it to accurately predict dynamic regulatory correlations between TFs and TGs, such as simulating TG responses by tuning the expression of TFs. In addition, DLTRNM can assist in identifying potential key TFs for a subset of genes, thus simplifying the complex and interrelated transcriptional regulatory networks (TRN). Based on the key TFs, it can also refine previously reported transcriptional regulatory subnetworks and highlight the core regulatory networks. DLTRNM offers a powerful tool for studying transcriptional regulation with reduced computational demands and enhanced interpretability. Thus, this study represents a significant step forward in systems biology for understanding the complicated transcriptional regulation within cells.

bioinformatics↗

Super-omics and PCAS reveal rheumatoid arthritis as a tumor-like disease

Rheumatoid arthritis (RA) is a debilitating systemic autoimmune disorder that significantly impairs quality of life. To elucidate the molecular alterations in RA progression, we performed comprehensive super-omics analyses on synovial tissues from patients with joint trauma, arthritis, and RA. These analyses included transcriptomics, proteomics, metabolomics, microbiomics, and fourteen PTMs (phosphorylation, acetylation, lactylation, O-GlcNAc glycosylation, arginine monomethylation, lysine monomethylation, lysine dimethylation, lysine trimethylation, succinylation, malonylation, glutarylation, tyrosine nitration, N-glycosylation, and O-glycosylation). Additionally, we developed a protein-centered association study (PCAS) method to integrate these complex datasets. Using this approach, we identified key proteins, such as STK17B, and its interactors, which may be crucial in RA pathogenesis. Tumor-like features, including aberrant angiogenesis and epithelial-mesenchymal transition, were observed in RA, alongside the potential involvement of oncogenes and tumor suppressor genes. Finally, we constructed a molecular interaction blueprint of RA, providing a comprehensive framework for advancing RA pathogenesis, diagnosis, and therapy.

immunology↗

EPSD 2.0: An Updated Database of Protein Phosphorylation Sites across Eukaryotic Species

As one of the most crucial post-translational modifications (PTMs), protein phosphorylation regulates a broad range of biological processes in eukaryotes. Biocuration, integration and annotation of reported phosphorylation events will deliver a valuable resource for the community. Here, we present an updated database, the eukaryotic phosphorylation site database 2.0 (EPSD 2.0), which includes 2,769,163 experimentally identified phosphorylation sites (p-sites) in 362,707 phosphoproteins from 223 eukaryotes. From the literature, 873,718 new p-sites identified through high-throughput phosphoproteomic research were first collected, and 1,078,888 original phosphopeptides together with primary references were reserved. Then, this dataset was merged into EPSD 1.0, comprising 1,616,804 p-sites within 209,326 proteins across 68 eukaryotic organisms [1]. We also integrated 362,190 additional known p-sites from 10 public databases. After redundancy clearance, we manually re-checked each p-site and annotated 88,074 functional events for 32,762 p-sites, covering 58 types of downstream effects on phosphoproteins, and regulatory impacts on 107 biological processes. In addition, phosphoproteins and p-sites in 8 model organisms were meticulously annotated utilizing information supplied by 100 external platforms encompassing 15 areas. These areas included kinase/phosphatase, transcription regulators, three-dimensional structures, physicochemical characteristics, genomic variations, functional descriptions, protein domains, molecular interactions, drug-target associations, disease-related data, orthologs, transcript expression levels, proteomics, subcellular localization, and regulatory pathways. We expect that EPSD 2.0 will become a useful database supporting comprehensive studies on phosphorylation in eukaryotes. The EPSD 2.0 database is freely accessible online at https://epsd.biocuckoo.cn/.

bioinformatics↗

p300/CBP-catalyzed acetylation safeguards minor zygotic genome activation via activating DUX

Zygotic genome activation (ZGA), the first transcriptional event at the onset of life, is crucial for early embryonic development. However, the mechanisms that initiate and regulate ZGA in mammals remain poorly understood. In this study, we demonstrate that p300/CBP-catalyzed acetylation plays a vital role in regulating ZGA. Inhibition of p300/CBP acetyltransferase activity results in impaired minor ZGA and a subsequent 2-cell arrest. By profiling the dynamic landscape of p300 during the ZGA stage, we elucidate the distinct patterns of p300 establishment in promoter and enhancer regions. Moreover, p300 recruits RNA polymerase (Pol II) to pre-configure in the promoter regions of ZGA genes, and orchestrates the gradual establishment of enhancer activity during the early 2-cell stage to activate ZGA. Furthermore, p300 enhances minor ZGA by promoting the expression of DUX. Restoring DUX levels, which are diminished due to p300/CBP inhibition, rescues ZGA and embryonic development by re-establishing Pol II enrichment on minor ZGA genes. Overall, our findings elucidate the mechanism by which p300/CBP regulates ZGA through acetylation and activation of the key transcription factor DUX. HighlightsO_LIp300/CBP plays a stage-specific role during preimplantation development phase. C_LIO_LIp300 displays distinctive dynamic changes in promoter and enhancer regions during ZGA stage. C_LIO_LIp300/CBP-catalyzed acetylation facilitates the pre-configuration and elongation of Pol II at ZGA gene regions. C_LIO_LIDUX recruits p300 and Pol II to activate minor ZGA genes independently of p300s catalytic activity. C_LI

developmental biology↗

Efficiently Constructing Complete Genomes with CycloneSEQ to Fill Gaps in Bacterial Draft Assemblies

BackgroundCurrent microbial sequencing relies on short-read platforms like Illumina and DNBSEQ, favored for their low cost and high accuracy. However, these methods often produce fragmented draft genomes, hindering comprehensive bacterial function analysis. CycloneSEQ, a novel long-read sequencing platform developed by BGI-Research, its sequencing performance and assembly improvements has been evaluated. FindingsUsing CycloneSEQ long-read sequencing, the type strain produced long reads with an average length of 11.6 kbp and an average quality score of 14.4. After hybrid assembly with short reads data, the assembled genome exhibited an error rate of only 0.04 mismatches and 0.08 indels per 100 kbp compared to the reference genome. This method was validated across 9 diverse species, successfully assembling complete circular genomes. Hybrid assembly significantly enhances genome completeness by using long reads to fill gaps and accurately assemble multi-copy rRNA genes, which unable be achieved by short reads solely. Through data subsampling, we found that over 500 Mbp of short-read data combined with 100 Mbp of long-read data can result in a high-quality circular assembly. Additionally, using CycloneSEQ long reads effectively improves the assembly of circular complete genomes from mixed microbial communities. ConclusionsCycloneSEQs read length is sufficient for circular bacterial genomes, but its base quality needs improvement. Integrating DNBSEQ short reads improved accuracy, resulting in complete and accurate assemblies. This efficient approach can be widely applied in microbial sequencing.

genomics↗