Biology subjects

Gao, W.

Publications and source records attributed to Gao, W..

At least 55 records · Page 3Linked to original sources

bioRxiv2024

Spatial-DC: a robust deep learning-based method for deconvolution of spatial proteomics

Spatial proteomics studies the spatial distribution of proteins within cells or tissues, providing a new perspective for comprehending cellular processes and disease mechanisms. One important challenge of current spatial proteomics technologies is low resolution, resulting in multiple cells in each spatial proteomics spot. While methods have been proposed to infer the composition of potential cell types in such spots for spatial transcriptomics, the depressed correlation and divergent quantification between transcriptome and proteome limits their capability in spatial proteomics. To enhance the utility of spatial proteomics data, we propose Spatial-DC (Spatial Digital Cytometry), a deep learning-based framework that infers cell-type composition in each spot and reconstructs spatially and cell-type resolved proteomic profiles. We achieve this by utilizing transfer learning and self-supervised learning with graph convolutional networks (GCN), which enables the incorporation of target spatial proteomics with reference single-cell or single-cell-type proteomics data. Through extensive simulations of spatial proteomics data, we demonstrate that Spatial-DC outperforms eight state-of-the-art methods in estimating cell-type composition and provides meaningful reconstructions of proteomic profiles for individual cell types. We apply Spatial-DC to different tissues measured using both multiplexed antibody-based and mass spectrometry (MS)-based spatial proteomics technologies. The results showcase Spatial-DCs superior sensitivity in providing more refined cell-type distribution maps compared to cell-type-specific marker-based distributions and its feasibility in reconstructing spatially and cell-type resolved proteomic profiles from real-world spatial proteomics data. Moreover, we validate Spatial-DC with a newly self-collected pancreatic cancer spatial proteomics data characterized by complex tumor microenvironment (TME) and identify the signaling direction and strength at both spatial and cell-type levels within the TME, suggesting cell-type-specific and spatially-resolved interactions linked to tumor outcomes. Our results highlight Spatial-DC as a versatile tool for the deconvolution of spatial proteomics data across various tissue slices, providing a reliable foundation for downstream analysis.

bioinformatics↗

bioRxiv2024

Immune mechanisms of type 1 diabetes revealed by single-cell transcriptomics, bulk transcriptomics, and experimental validation

BackgroundType 1 diabetes (T1D) is an autoimmune disorder characterized by the destruction of insulin-producing pancreatic {beta} cells. Understanding the immune mechanisms underlying T1D is crucial for developing effective diagnostic and therapeutic strategies. This study aimed to elucidate the immune mechanisms of T1D by integrating single-cell RNA sequencing (scRNA-seq), bulk RNA-seq, and experimental validation. MethodsscRNA-seq data (GSE200695) and bulk RNA-seq data (GSE9006) were obtained from the Gene Expression Omnibus (GEO) database. After data preprocessing, principal component analysis (PCA), and clustering, cell subtypes were annotated using ImmGenData as a reference. Receptor-ligand interactions were analyzed to identify key cell subtypes. Least absolute shrinkage and selection operator (LASSO) regression was performed to identify characteristic genes and construct a diagnostic model. Key genes were further validated using the training and validation sets. Functional enrichment and immune infiltration analyses were conducted for the key genes. In vitro experiments were performed to validate the findings using a high-glucose model in the monocytic cell line THP-1. siRNA-mediated knockdown of TRIB1 was performed to investigate its role in regulating monocyte activation and immune-related pathways under high-glucose conditions. Monocyte activation markers, inflammatory cytokines, apoptosis, and the expression of key genes and immune-related genes were assessed using immunofluorescence staining, ELISA, flow cytometry, qPCR, and Western blot. ResultsMonocytes were identified as the key cell subtype with the most interactions with other cell subtypes. Eleven characteristic genes were selected to construct a diagnostic model, which demonstrated high validation efficiency (AUC > 0.8). Three key genes (ACTG1, REL, and TRIB1) showed consistent expression trends in the training and validation sets. Functional analyses revealed that these genes were involved in immune regulation and PI3K/AKT/mTOR signaling. In vitro experiments confirmed that high glucose induced monocyte activation, as evidenced by increased expression of activation markers (CD86) and pro-inflammatory cytokines (IL-8 and TNF-). High glucose also increased monocyte apoptosis and altered the expression of key genes (ACTG1, REL, and TRIB1) and immune-related genes (CXCL16, TGFBR1, CTLA4, CD48, TMIGD2, and HLA-DPB1). Knockdown of TRIB1 attenuated high glucose-induced monocyte activation, as demonstrated by decreased expression of activation markers and pro-inflammatory cytokines. TRIB1 knockdown also modulated the expression of immune-related genes and PI3K/AKT/mTOR signaling under high-glucose conditions. ConclusionsThis study integrates scRNA-seq, bulk RNA-seq, and experimental validation to unravel the immune mechanisms of T1D. Key genes (ACTG1, REL, and TRIB1) and monocytes were identified as crucial players in T1D pathogenesis. The constructed diagnostic model showed high validation efficiency. In vitro experiments confirmed the role of TRIB1 in regulating monocyte activation and immune-related pathways in a high-glucose model. These findings provide novel insights into the immune mechanisms of T1D and potential diagnostic and therapeutic targets.

bioinformatics↗

bioRxiv2024

STING agonists drive recruitment and intrinsic type I interferon responses in monocytic lineage cells for optimal anti-tumor immunity

The cyclic GMP-AMP synthase-stimulator of interferon genes (cGAS-STING) pathway, a sensor of cytosolic DNA, orchestrates the production of pro-inflammatory cytokines, chemokines, and type I interferons (IFN-I), thereby contributing to spontaneous tumor surveillance. Intratumoral delivery of synthetic STING agonists induces IFN-I dependent tumor regression in preclinical cancer models and is being tested clinically. In this study, we investigate the role of monocytic lineage (MCs) cells in response to STING agonist induced IFN-I signaling. We show that CCR2-deficient mice, lacking inflammatory MCs in the periphery, or Lyz2-Cre-IFNAR1fl/fl mice in which IFN-I signaling in monocytes is reduced, exhibit impaired responses to STING agonist therapy of MC38 and/or B16F10 tumors. STING agonist treatment induced CCR5-dependent migration of MCs carrying tumor antigen from the tumor to the lymph nodes. Single-cell RNA sequencing of CD45+ cells from lymph nodes and tumors of mice in which half the hematopoietic cells lack the interferon alpha/beta receptor 1 (IFNAR1) revealed that STING agonist therapy induces intrinsic IFNAR1-dependent acquisition of an inflammatory monocytic cell phenotype distinct from inflammatory cDC and a reduction in macrophages with a pro-tumor TGF{beta}/angiogenesis transcriptome. IL-18 - IL-18R1 interaction was the top predicted interaction between monocytic lineage cells and CD8+T cells or NK cells. Blocking IL-18 reduced IFN-{gamma} production by CD8 T cells in LNs and decreased the therapeutic efficacy of STING agonist treatment in CCR2+/+ but not in CCR2-/- mice. These findings support a pivotal role for IL-18 producing inflammatory monocytic lineage cells in CD8 T cell control of melanoma following STING agonist treatment.

immunology↗

bioRxiv2024

RING1 dictates GSDMD-mediated inflammatory response and host susceptibility to pathogen infection

RING1 is an E3 ligase component of the polycomb repressive complex 1 (PRC1) with known roles in chromatin regulation and cellular processes such as apoptosis and autophagy. However, its involvement in inflammation and pyroptosis needs to be better characterized. Here, we found that human RING1, but not RING2, promoted K48-linked ubiquitination of Gasdermin D (GSDMD) and acted as a negative regulator of pyroptosis and bacterial infection. Ring1 knockout mice increased the bacterial loads and mortality rate by treatment of S. typhimurium. Upon infection by M. tuberculosis (Mtb) H37Rv strain, RING1 deletion initially reduced bacterial loads but later increased lung inflammation and impaired immune defense responses in mice. Also, Ring1 knockout exacerbated acute sepsis induced by lipopolysaccharide (LPS). Mechanistically, RING1 directly interacts with GSDMD and ubiquitinates the K51 and K168 sites of GSDMD for K48-linked proteasomal degradation, thereby inhibiting pyroptosis. Inhibition of RING1 E3 ligase activity by mutation or small molecular compound increased GSDMD level and cell death during pyroptosis. Our findings reveal that RING1 dictates GSDMD-mediated inflammatory response and host susceptibility to pathogen infection, highlighting RING1 as a potential therapeutic target for combating infectious diseases. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=115 SRC="FIGDIR/small/622030v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@e6d4a6org.highwire.dtl.DTLVardef@92f137org.highwire.dtl.DTLVardef@11a69c4org.highwire.dtl.DTLVardef@115dc0b_HPS_FORMAT_FIGEXP M_FIG Graphic Abstract C_FIG HighlightsO_LIE3 ubiquitin ligase RING1 targets GSDMD for proteasomal degradation. C_LIO_LIMice lacking RING1 are more vulnerable to S. typhimurium infection and LPS- induced sepsis. C_LIO_LIIn the early stages of M. tuberculosis (Mtb) infection, Ring1-/- mice exhibit efficient bacterial clearance. C_LIO_LIChronic inflammation in Ring1-/- mice impairs the ability to control Mtb in the late stages of infection. C_LI

cell biology↗

bioRxiv2024

Cell extract-based in vitro DNA replication enables sustainable cell-free gene expression

Cell extract-based cell-free gene expression (CFE) systems break cellular boundaries and create emerging platforms for synthetic biology applications. However, these systems lack intrinsic DNA replication capability, thereby limiting sustained transcription and translation in a cell-free environment. Here, we introduce a LoopReX system based on E. coli CFE by integration of the DNA replication machinery, enabling iterative in vitro DNA replication and sustainable cell-free gene expression. We establish LoopReX by integrating phi29 DNA polymerase into the crude lysate-based E. coli CFE system, allowing for coupled DNA replication and protein expression. We elucidate the synergetic mechanisms between T7 RNA polymerase and phi29 DNA polymerase in facilitating in vitro DNA replication. Additionally, we optimize LoopReX reaction conditions by machine learning strategies. Finally, we demonstrate that the enhanced LoopReX system achieves highly efficient DNA replication and protein production in an iterative, scalable, and sustainable manner. We anticipate that LoopReX will significantly advance the application of CFE in synthetic biology.

synthetic biology↗

bioRxiv2024

Organ-specific Sympathetic Innervation Defines Visceral Functions

The autonomic nervous system orchestrates the brain and body functions through the sympathetic and parasympathetic pathways. However, our understanding of the autonomic system, especially the sympathetic system, at the cellular and molecular levels is severely limited. Here, we show unique topological representations of individual visceral organs in the major abdominal sympathetic ganglion complex. Using multi-modal transcriptomic analyses, we identified distinct sympathetic populations that are both molecularly and spatially separable in the celiac-superior mesenteric ganglia (CG-SMG). Notably, individual CG-SMG populations exhibit selective and mutually exclusive axonal projections to visceral organs, targeting either the gastrointestinal (GI) tract or secretory areas including the pancreas and bile tract. This combinatorial innervation pattern suggests functional segregation between different CG-SMG populations. Indeed, our neural perturbation experiments demonstrated that one class of neurons selectively regulates GI food transit. Another class of neurons controls digestion and glucagon secretion independent of gut motility. These results reveal the molecularly diverse sympathetic system and suggest modular regulations of visceral organ functions through distinct sympathetic populations.

neuroscience↗

bioRxiv2024

Precision-dependent modulation of social attention

Social attention, guided by cues like gaze direction, is crucial for effective social interactions. However, how dynamic environmental context modulates this process remains unclear. Integrating a hierarchical Bayesian model with fMRI, this study investigated how individuals adjusted attention based on the predictions about cue validity (CV). Thirty-three participants performed a modified Posner location-cueing task with varying CV. Behaviorally, individuals allocation of social attention was finely tuned to the precision (inverse variance) of CV predictions, with the predictions being updated by precision-weighted prediction errors (PEs) about the occurrence of target locations. Neuroimaging results revealed that the interaction between allocation of social attention and CV influenced activity in regions involved in spatial attention and/or social perception, such as the temporoparietal junction (TPJ), frontal eye field (FEF), superior temporal sulcus (STS), and inferior parietal sulcus (IPS). Precision-weighted PEs about target locations specifically modulated activity in the TPJ, STS, and primary visual cortex (V1), underscoring their roles in refining attentional predictions. Dynamic causal modeling (DCM) further demonstrated that enhanced absolute precision-weighted PEs about target locations strengthened the effective connectivity from V1 and STS to TPJ, emphasizing their roles in conveying residual error signals from low-level sensory areas to high-level critical attention areas. These findings elucidated how the precision of contextual predictions dynamically modulated social attention, offering insights into the computational and neurocognitive mechanisms of context-dependent social attention.

neuroscience↗

bioRxiv2024

Inhibition of Soluble Epoxide Hydrolase Ameliorates Cerebral Blood Flow Autoregulation and Cognition in Alzheimer's Disease and Diabetes-Related Dementia Rat Models

Alzheimers Disease and Alzheimers Disease-related dementias (AD/ADRD) pose major global healthcare challenges, with diabetes mellitus (DM) being a key risk factor. Both AD and DM-related ADRD are characterized by reduced cerebral blood flow, although the exact mechanisms remain unclear. We previously identified compromised cerebral hemodynamics as early signs in TgF344-AD and type 2 DM-ADRD (T2DN) rat models. Genome-wide studies have linked AD/ADRD to SNPs in soluble epoxide hydrolase (sEH). This study explored the effects of sEH inhibition with TPPU on cerebral vascular function and cognition in AD and DM-ADRD models. Chronic TPPU treatment improved cognition in both AD and DM-ADRD rats without affecting body weight. In DM-ADRD rats, TPPU reduced plasma glucose and HbA1C levels. Transcriptomic analysis of primary cerebral vascular smooth muscle cells from AD rats treated with TPPU revealed enhanced pathways related to cell contraction, alongside decreased oxidative stress and inflammation. Both AD and DM-ADRD rats exhibited impaired myogenic responses and autoregulation in the cerebral circulation, which were normalized with chronic sEH inhibition. Additionally, TPPU improved acetylcholine-induced vasodilation in the middle cerebral arteries (MCA) of DM-ADRD rats. Acute TPPU administration unexpectedly caused vasoconstriction in the MCA of DM-ADRD rats at lower doses. In contrast, higher doses or longer durations were required to induce effective vasodilation at physiological perfusion pressure in both control and ADRD rats. Additionally, TPPU decreased reactive oxygen species production in cerebral vessels of AD and DM-ADRD rats. These findings provide novel evidence that chronic sEH inhibition can reverse cerebrovascular dysfunction and cognitive impairments in AD/ADRD, offering a promising avenue for therapeutic development.

molecular biology↗

bioRxiv2024

SARS-CoV-2 nsp16 is regulated by host E3 ubiquitin ligases, UBR5 and MARCHF7

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19), remains a global public health threat with considerable economic consequences. The non-structural protein 16 (nsp16), in complex with nsp10, facilitates the final viral mRNA capping step through its 2'-O-methylase activity, helping the virus to evade host immunity and prevent mRNA degradation. However, nsp16 regulation by host factors remains poorly understood. While various E3 ubiquitin ligases interact with SARS-CoV-2 proteins, their roles in targeting nsp16 for degradation are unclear. In this study, we demonstrate that nsp16 undergoes ubiquitination and proteasomal degradation mediated by the host E3 ligases UBR5 and MARCHF7. UBR5 induces K48-linked ubiquitination, whereas MARCHF7 promotes K27-linked ubiquitination, independently suppressing SARS-CoV-2 replication in cell cultures and in mice. Notably, UBR5 and MARCHF7 also degrade nsp16 variants from different viral strains, exhibiting broad-spectrum antiviral activity. Our findings reveal novel antiviral mechanisms of the ubiquitin-proteasome system (UPS) and highlight their potential therapeutic targets against COVID-19.

microbiology↗

bioRxiv2024

A new regulator of sporulation sheds light on spore morphogenesis and ballistospory in mushroom-forming fungi

Sporulation is the most widespread means of reproduction and dispersal in fungi. In the Basidiomycota, sexual spores are produced on specialised cells known as basidia, from which they are discharged forcibly by a powered process called ballistospory, the highest known acceleration in nature. However, the genetics of sporulation, in particular postmeiotic events related to spore morphogenesis and ballistospory, remain poorly known. Here, we explore the genetics of these processes, based on a new, highly conserved transcription factor, Sporulation-Related Regulator 1 (SRR1), and its putative downstream regulatory network. Reverse genetics of Srr1 in the model mushroom Coprinopsis cinerea and commercially produced oyster mushroom indicated a conserved role of Srr1 in sporulation across Agaricomycetes. RNA-Seq analysis and motif-based inference of a hypothetical SRR1 gene regulatory network allowed delimiting putative targets regulated by SRR1 in a direct and indirect manner. Using this network and comparative genomics, we identified genes associated with ballistospory, including a putative SRR1-target chitinase, which was found to be required for normal spore production and morphology. Overall, our study offers new insights into the genetic mechanisms governing postmeiotic spore morphogenesis and ballistospory in the Agaricomycetes.

microbiology↗

bioRxiv2024

SARS-CoV-2 B Epitope-Guided Neoantigen NanoVaccines Enhance Tumor-Specific CD4/CD8 T Cell Immunity Through B Cell Antigen Presentation

Current neoantigen cancer vaccines activate T cell immunity through dendritic cell /macrophage-mediated antigen presentation. It is unclear whether incorporating B cell-mediated antigen presentation into current neoantigen vaccines could enhance CD4/CD8 T cell immunity to improve their anticancer efficacy. We developed a SARS-CoV-2 B cell epitope-guided neoantigen peptide/mRNA cancer nanovaccines (BSARSTNeoAgVax) to improve anticancer efficacy by enhancing tumor-specific CD4/CD8 T cell antitumor immunity through B cell-mediated antigen presentation. BSARSTNeoAgVax crosslinked with B cell receptor, promoted SARS-CoV-2 B cell-mediated antigen presentation to tumor-specific CD4 T cells, increased tumor-specific follicular/non-follicular CD4 T cells, and enhanced B cell-dependent tumor-specific CD8 T cell immunity. BSARSTNeoAgVax achieved superior efficacy in melanoma, pancreatic, and breast cancer models compared to the current neoantigen vaccines. Our study provides a universal platform, SARS-CoV-2 B epitope-guided neoantigen nanovaccines, to improve anticancer efficacy against various cancer types by enhancing CD4/CD8 T cell antitumor immunity through viral-specific B cell-mediated antigen presentation.

immunology↗

bioRxiv2024

Droplet-AI-empowered high-throughput screening of cell-free gene expression

Cell-free gene expression (CFE) systems enable transcription and translation using crude cellular extracts, offering a versatile platform for synthetic biology by eliminating the need to maintain living cells. This allows direct manipulation of molecular components and the focused synthesis of specific products. However, the optimization of CFE systems is constrained by cumbersome composition, high costs, and limited yields due to numerous additional components required to maintain biocatalytic efficiency. While optimizing such complicated systems is daunting for existing high-throughput screening means, we introduce DropAI, a droplet-based, AI-driven screening strategy designed to optimize CFE systems with high throughput and economic efficiency. DropAI employs microfluidics to generate picoliter reactors and utilizes a fluorescent color-based coding-decoding system to address and screen a vast array of additive combinations. The in-droplet screening is complemented by in silico optimization, where experimental results train a machine-learning model to estimate the contribution of the components and predict high-yield combinations, which are then validated in vitro. Applying DropAI to an Escherichia coli-based CFE system, we simplified a set of 12 additives to only 3 essential components. Through further optimization, we achieved a 2.1-fold cost reduction and a 1.9-fold increase in yield for the expression of superfolder green fluorescent protein (sfGFP). This optimized formulation was further validated across 12 different proteins. Notably, the established E. coli model is successfully adapted to a Bacillus subtilis-based system through transfer learning, leading to doubled yield through prediction. DropAI thus offers a generalizable and scalable method for optimizing CFE systems, enhancing their potential for biochemical engineering and biomanufacturing applications.

bioengineering↗

bioRxiv2024

Informing grassland ecosystem modeling with in-situ and remote sensing observations

Historical grassland aboveground plant productivity (ANPP) was simulated by the DayCent-UV ecosystem model across the midwestern and western conterminous United States. For this study we developed a novel method for informing the DayCent-UV model and validating its plant productivity estimates for grasslands of the midwestern and western conterminous USA by utilizing a wide range of data sources at multiple scales, from field observations to remotely sensed satellite data. The model phenology was informed by the MODIS MCD12Q2 product, which showed good agreement with in-situ observations of growing season commencement and duration across different grassland ecosystems, and with observed historical trends. Model results from each simulated grid cell were compared to a remote-sensing ANPP modified version offered by the Analysis Rangeland Platform (RAP). This modified RAP ANPP calculation incorporated total annual precipitation, instead of mean annual temperature, as the control factor for the fraction of carbon allocated to roots. Strong temporal correlations were obtained between RAP and DayCent-UV, especially across the Great Plains. Good agreement was also found when the model results were compared with ANPP observations at the site and county level. The data produced by this study will serve as a valuable resource for validation or calibration of various models that aim to capture accurate productivity dynamics across diverse grassland ecosystems. Plain Language SummaryThis research used a computer model called DayCent-UV to simulate daily grassland growth across the central and western regions of the contiguous United States. To improve the agreement between the simulations and real-world conditions, we incorporated data from local field measurements and satellite imagery. This data helped determine the start and end dates of the growing season at each location. The simulated annual growth showed good agreement with satellite estimates from the Rangeland Analysis Platform (RAP), another computer application that monitors rangeland vegetation, and with local observations based on harvesting and weighing vegetation, particularly across the Great Plains. These results are valuable for validating and refining other computer models that aim to accurately simulate plant growth in grassland ecosystems; the predictions of these models are crucial for understanding the balance of carbon between plants, soils, and the atmosphere as the climate changes. Key PointsO_LIThe DayCent-UV model was used to simulate historical aboveground net primary productivity (ANPP) for different grassland ecosystems across the midwestern and western United States. C_LIO_LIMODIS MCD12Q2 was used to provide the phenology for the model. C_LIO_LIThe Rangeland Analysis Platform (RAP) fraction of biomass production allocated to roots calculation was modified, resulting in a stronger agreement between its ANPP estimates and those from the DayCent-UV model. C_LIO_LISite- and county-level ANPP observations were used to validate the model. C_LI

ecology↗

bioRxiv2024

TDC-2: Multimodal Foundation for Therapeutic Science

Drug discovery AI datasets and benchmarks have not traditionally included single-cell analysis biomarkers. While benchmarking efforts in single-cell analysis have recently released collections of single-cell tasks, they have yet to comprehensively release datasets, models, and benchmarks that integrate a broad range of therapeutic discovery tasks with cell-type-specific biomarkers. Therapeutics Commons (TDC-2) presents datasets, tools, models, and benchmarks integrating cell-type-specific contextual features with ML tasks across therapeutics. We present four tasks for contextual learning at single-cell resolution: drug-target nomination, genetic perturbation response prediction, chemical perturbation response prediction, and protein-peptide interaction prediction. We introduce datasets, models, and benchmarks for these four tasks. Finally, we detail the advancements and challenges in machine learning and biology that drove the implementation of TDC-2 and how they are reflected in its architecture, datasets and benchmarks, and foundation model tooling.

bioinformatics↗

bioRxiv2024

Systematic identification of secondary bile acid production genes in global microbiome

Microbial metabolism of bile acids (BAs) is crucial for maintaining homeostasis in vertebrate hosts and environments. Although certain organisms involved in BA metabolism have been identified, a global, comprehensive elucidation of the microbes, metabolic enzymes, and BA remains incomplete. To bridge this gap, we employed hidden Markov models to systematically search in a large-scale and high-quality search library comprising 28,813 RefSeq multi-kingdom microbial complete genomes, which enabled us to construct a secondary bile acid (SBA)-production gene catalog. This catalog greatly expanded the distribution of SBA-production genes across 11 phyla, encompassing bacteria, archaea and fungi, and extended to 14 habitats spanning hosts and environmental contexts. Furthermore, we highlighted the associations between SBAs and gastrointestinal and hepatic disorders, including inflammatory bowel disease, colorectal cancer and nonalcoholic fatty liver disease, further elucidating disease-specific alterations in SBA-production genes. Additionally, we proposed the pig as a particularly suitable animal model for investigating SBA production in humans, given its closely aligned SBA-production gene composition. This gene catalog provides a comprehensive and reliable foundation on microbial BA metabolism for future studies, offering new insights into the microbial contributions to health and disease.

genomics↗

bioRxiv2024

Three-dimensional chromatin mapping of sensory neurons reveals that cohesin-dependent genomic domains are required for axonal regeneration

The in vivo three-dimensional genomic architecture of adult mature neurons at homeostasis and after medically relevant perturbations such as axonal injury remains elusive. Here we address this knowledge gap by mapping the three-dimensional chromatin architecture and gene expression programme at homeostasis and after sciatic nerve injury in wild-type and cohesin-deficient mouse sensory dorsal root ganglia neurons via combinatorial Hi-C and RNA-seq. We find that cohesin is required for the full induction of the regenerative transcriptional program, by organising 3D genomic domains required for the activation of regenerative genes. Importantly, loss of cohesin results in disruption of chromatin architecture at regenerative genes and severely impaired nerve regeneration. Together, these data provide an original three-dimensional chromatin map of adult sensory neurons in vivo and demonstrate a role for cohesin-dependent chromatin interactions in neuronal regeneration.

neuroscience↗

bioRxiv2024

Molecular Mechanism of Huanglian Jiedu Decoction in Treatment of Alzheimer's Disease based on Network Pharmacology

PurposeThe aim of this study was to investigate the pharmacological mechanism of Huanglian Jiedu Decoction (HLJDD) in treating Alzheimers disease through network pharmacology. HLJDD is a classic Chinese medicine prescription that is recommended in the Chinese Alzheimers disease diagnosis and treatment guidelines. However, the mechanism of HLJDD treatment for Alzheimers disease (AD) remains unclear because of its complicated components. MethodsThe related ingredients and targets of HLJDD in treating AD were screened by Traditional Chinese Medicine Systems Pharmacology Database and Analysis platform (TCMSP), TTD, OMIM, GeneCards, and DrugBank. The data of the protein-protein interaction (PPI) network was constructed using STRING. The Metascape database was adopted for Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. AutoDockTools was used for molecular docking verification. ResultsIn the treatment of AD, HLJDD demonstrated strong efficacy with its core active components including were quercetin, {beta}-sitosterol, stigmasterol, targeting key proteins such as AKT1, TNF, and IL6. Molecular docking tests confirmed the significant binding affinity between these components and the aforementioned targets. The biological pathway of HLJDD in treating AD primarily involves the modulation of IL-17, TNF, and other inflammatory cytokines to regulate their impact on nerve functions. ConclusionsHLJDD may treat AD by inhibiting neuroinflammation through a comprehensive, multi-component, multi-target, and multi-pathway method.

pharmacology and toxicology↗

bioRxiv2024

Towards anchoring evolutionary fitness for protein stability with virtual chemical environment recovery

Predicting the protein stability changes upon mutations is one of the effective ways to improve the efficiency of protein engineering. Here, we propose a dual-view ensemble learning-based framework, DVE-stability, for mutation-induced protein stability change prediction from single sequence. DVE-stability integrates the global and local dependencies of mutations to capture the intramolecular interactions from two views through ensemble learning, in which a structural microenvironment simulation module is designed to indirectly introduce the information of structural microenvironment at the sequence level. DVE-stability achieved state-of-the-art prediction performance on 7 single-point mutation benchmark datasets, and comprehensively surpassed other methods on 5 of them. Furthermore, DVE-stability outperformed other methods comprehensively through zero-shot inference on multiple-point mutation prediction task, demonstrating superior model generalizability to capture the epistasis of multiple-point mutations. More importantly, DVE-stability exhibited superior generalization performance in predicting rare beneficial mutations that are crucial for practical protein directed evolution scenarios. In addition, DVE-stability identified important intramolecular interactions via attention scores, demonstrating interpretable. Overall, DVE-stability provides a flexible and efficient tool for mutation-induced protein stability change prediction in an interpretable ensemble learning manner.

bioinformatics↗