Biology subjects

Zheng, W.

Publications and source records attributed to Zheng, W..

At least 127 records · Page 7Linked to original sources

bioRxiv2024

Unraveling principles of thermodynamics for genome-scale metabolic networks using graph neural networks

The fundamental laws of thermodynamics determine the feasibility of all natural processes including metabolism. Although several algorithms have been developed to predict the most important thermodynamic parameter, the standard Gibbs free energy, for metabolic reactions and metabolites, their application to genome-scale metabolic networks (GEMs) with thousands of metabolites and reactions is still limited. Here, we develop a graph neural network (GNN)- based model dGbyG for predicting Gibbs energy for metabolites and metabolic reactions which outperforms all existing methods in accuracy, versatility, robustness, and generalization ability. By applying dGbyG to the human GEM, Recon3D, we identify a critical subset of reactions with substantial negative values of the standard Gibbs free energy change, which we name thermodynamic driver reactions. These reactions exhibit distinctive network topological characteristics akin to driver nodes defined in control theory and remarkable heterogeneity in enzyme abundance, implying evolutionary constraints on the selection of thermodynamic parameters of metabolic networks. We also develop a thermodynamics-based flux balance analysis (TFBA) approach to integrate reaction thermodynamics with GEMs to curate these models. Our work not only transcends the augmentation of accessible thermodynamic data to facilitate an enriched understanding of metabolism, but also enables refinement of metabolic reconstructions from a thermodynamic perspective, thereby underscoring the critical role of thermodynamics in the accurate modeling of biological systems.

systems biology↗

bioRxiv2024

Role of histone variants H2BC1 and H2AZ.2 in H2AK119ub nucleosome organization and Polycomb gene silencing

Ubiquitination of histone H2A at lysine 119 residue (H2AK119ub) plays critical roles in a wide range of physiological processes, including Polycomb gene silencing 1,2, replication 3-5, DNA damage repair 6-10, X inactivation 11,12, and heterochromatin organization 13,14. However, the underlying mechanism and structural basis of H2AK119ub remains largely elusive. In this study, we report that H2AK119ub nucleosomes have a unique composition, containing histone variants H2BC1 and H2AZ.2, and importantly, this composition is required for H2AK119ub and Polycomb gene silencing. Using the UAB domain of RSF1, we purified H2AK119ub nucleosomes to a sufficient amount and purity. Mass spectrometry analyses revealed that H2AK119ub nucleosomes contain the histone variants H2BC1 and H2AZ.2. A cryo-EM study resolved the structure of native H2AK119ub nucleosomes to a 2.6A resolution, confirming H2BC1 in one subgroup of H2AK119ub nucleosomes. Tandem GST-UAB pulldown, Flag-H2AZ.2, and HA-H2BC1 immunoprecipitation revealed that H2AK119ub nucleosomes could be separated into distinct subgroups, suggesting their composition heterogeneity and potential dynamic organization. Knockout or knockdown of H2BC1 or H2AZ.2 reduced cellular H2AK119ub levels, establishing H2BC1 and H2AZ.2 as critical determinants of H2AK119ub. Furthermore, genomic binding profiles of H2BC1 and H2AZ.2 overlapped significantly with H2AK119ub binding, with the most significant overlapping in the gene body and intergenic regions. Finally, assays in developing embryos reveal an interaction of H2AZ.2, H2BC1, and RING1A in vivo. Thus, this study revealed, for the first time, that the H2AK119ub nucleosome has a unique composition, and this composition is required for H2AK119ub and Polycomb gene silencing.

molecular biology↗

bioRxiv2024

An arms race under RIG-I loss: 5'ppp-RNA and its alternative recognition receptor MDA5

The incessant arms race between viruses and hosts has led to numerous evolutionary innovations that shape lifes evolution. During this process, the interactions between viral receptors and viruses have garnered significant interest since viral receptors are cell surface proteins exploited by viruses to initiate infection. Our study sheds light on the arms race between the MDA5 receptor and 5ppp-RNA virus in a lower vertebrate fish, M. miiuy. Firstly, the frequent and independent loss events of RIG-I in vertebrates prompted us to search for alternative immune substitutes, with homology-dependent genetic compensation response (HDGCR) being the main pathway. Our further analysis suggested that MDA5 of M. miiuy and G. gallus, the homolog of RIG-I, can replace RIG-I in recognizing 5ppp-RNA virus, which may lead to redundancy of RIG-I and loss from the species genome during evolution. Secondly, as an adversarial strategy, 5ppp-RNA SCRV can utilize the m6A methylation mechanism to degrade MDA5 and weaken its antiviral immune ability, thus promoting its own replication and immune evasion. In summary, our study provides a snapshot into the interaction and coevolution between vertebrate and virus, offering valuable perspectives on the ecological and evolutionary factors that contribute to the diversity of the immune system.

immunology↗

bioRxiv2024

MobiChIP: a compatible library construction method of single-cell ChIP-seq based droplets

In order to illustrate the epigenetic heterogeneity, versatile tools of single-cell ChIP-seq (scChIP-seq) are necessary to meet the convenience and accuracy. Here, we develop MobiChIP, a compatible ChIP-seq library construction method based current sequencing platform with single cell level. As a novel capture strategy, MobiChIP is efficient to capture the fragments from tagmented nuclei of numerous species and execute the mixing of samples from different tissues or species. Especially, this strategy enables the flexible sequencing manipulation and sufficient nucleosome amplification without customized sequencing primers. MobiChIP reveals the landscape of chromatin regulation regions with active(H3K27ac) and repressive(H3K27me3) histone modification markers in peripheral blood mononuclear cells (PBMCs), and accurately unveiled the epigenetic repression of hox gene cluster in PBMCs than ATAC-seq. Meanwhile, we complete the bioinformatics pipeline to integrates the scChIP-seq data and scRNA-seq to illustrate the cellular epigenetic and genetic heterogeneity. One-Sentence SummaryA high-throughput single-cell ChIP-seq based droplet reveals the integration of scRNA-seq data and scChIP-seq data.

molecular biology↗

bioRxiv2023

The ICF2 gene Zbtb24 specifically regulates the differentiation of B1 cells via promoting heme synthesis

Loss-of-function mutations of ZBTB24 cause the Immunodeficiency, Centromeric Instability and Facial Anomalies syndrome 2 (ICF2). ICF2 is a rare autosomal recessive disorder with immunological defects in serum antibodies and circulating memory B cells, indicating an essential role of ZBTB24 in the terminal differentiation of B cells. Here we generated B-cell specific Zbtb24-deficient mice and systemically investigated its role in B cell development and function both in vivo and in vitro. Zbtb24 is dispensable for B cell development & maintenance in naive mice. Surprisingly, B-cell specific deletion of Zbtb24 does not evidently compromise germinal center reactions and the resulting primary & secondary antibody responses induced by T-cell dependent antigens, but significantly inhibits T-cell independent antigen-elicited antibody productions in vivo. At the cellular level, Zbtb24-deficiency specifically impedes the plasma cell differentiation of B1 cells without impairing their survival, activation and proliferation in vitro. Mechanistically, Zbtb24-ablation attenuates heme biosynthesis partially through mTORC1 in B1 cells, and addition of exogenous hemin abrogates the differentiation defects of Zbtb24-null B1 cells. Our study suggests that the defected B1 functions may contribute to recurrent infections in ICF2 patients, and discloses a B1-specific role of Zbtb24 in regulating plasma cell differentiation and antibody production, which is relevant for barrier defenses against invading pathogens.

immunology↗

bioRxiv2023

scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis

Various Foundation Models (FMs) have been built based on the pre-training and fine-tuning framework to analyze single-cell data with different degrees of success. In this manuscript, we propose a method named scELMo (Single-cell Embedding from Language Models), to analyze single-cell data that utilizes Large Language Models (LLMs) as a generator for both the description of metadata information and the embeddings for such descriptions. We combine the embeddings from LLMs with the raw data under the zero-shot learning framework to further extend its function by using the fine-tuning framework to handle different tasks. We demonstrate that scELMo is capable of cell clustering, batch effect correction, and cell-type annotation without training a new model. Moreover, the fine-tuning framework of scELMo can help with more challenging tasks including in-silico treatment analysis or modeling perturbation. scELMo has a lighter structure and lower requirements for resources. Our method also outperforms recent large-scale FMs (such as scGPT [1], Geneformer [2]) and other LLM-based single-cell data analysis pipelines (such as GenePT [3] and GPTCelltype [4]) based on our evaluations, suggesting a promising path for developing domain-specific FMs.

bioinformatics↗

bioRxiv2023

Golgi-associated retrograde protein (GARP) complex recruits retromer to trans-Golgi network for FgKex2 and FgSnc1 recycling, necessary for development and pathogenicity of Fusarium graminearum

In eukaryotic cells, the retromer complex plays a crucial role in orchestrating the sorting and retrograde transport of cargo proteins from endosomes to the trans-Golgi network (TGN). Despite its significance, the molecular details of this intracellular trafficking process remain unclear. Here, we identified a Golgi-associated retrograde protein (GARP) complex as a mediator of vesicular transport, facilitating the recruitment of the retromer complex to TGN to exert its functions. The GARP complex is mainly localized to the TGN, where it interacts with the retromer complex. This interaction is evolutionarily conserved across different species. Furthermore, we identified FgKex2 and Fgsnc1 as cargo proteins in the GARP/retromer-mediated recycling pathway. Loss of GARP or retromer results in a complete mis-sorting of FgKex2 and FgSnc1 to the vacuolar degradation pathway, which affects the growth, development, toxisome biogenesis, and pathogenicity of F. graminearum. In summary, we assert that GARP facilitates the recruitment of retromer from endosomes to the TGN, orchestrating the recycling of FgKex2 and FgSnc1. This is integral to sustaining continuous growth, development and contributes significantly to the pathogenicity of F. graminearum.

microbiology↗

bioRxiv2023

Rapid and prolonged response of oligodendrocyte lineage cells in standard acute cuprizone demyelination model revealed by in situ hybridization

Dietary administration of a copper chelator, cuprizone (CPZ), has long been reported to induce intense and reproducible demyelination of several brain structures such as the corpus callosum (CC) in mice, followed by spontaneous remyelination after drug withdrawal. Despite the widespread use of CPZ as an animal model for demyelinating diseases such as multiple sclerosis (MS), the mechanism by which it induces demyelination and then allows robust remyelination is still unclear. An intensive mapping of the oligodendrocyte (OL) lineage cell dynamics during the de-and remyelination course would be of particular importance for a deeper understanding of this model. Here, using a panel of OL lineage cell markers as in situ hybridization (ISH) probes, including Pdgfra, Plp, Mbp, Mog, Enpp6, combined with immunofluorescence staining of CC1, SOX10, we provide a detailed dynamic profile of OL lineage cells during the entire course of the model from 3.5 days, 1, 2, 3, 4,5 weeks of CPZ treatment, i.e. the demyelination period, as well as after 1, 2, 3, 4 weeks of recovery (drug withdrawal) from 5 weeks of CPZ treatment, i.e. the remyelination period. The result showed an unexpected early death of mature OLs and response of OL progenitor cells (OPCs) in vivo upon CPZ challenge, and a prolonged upregulation of myelin-forming OLs compared to the intact control even 4 weeks after CPZ withdrawal. These data may point to the need to optimize the timing windows for the introduction of pro-remyelination therapies in demyelinating diseases such as MS, and may serve as a basic reference system for future studies of the effects of any intervention on demyelination and remyelination using the CPZ model.

neuroscience↗

bioRxiv2023

LightRoseTTA: High-efficient and Accurate Protein Structure Prediction Using an Ultra-Lightweight Deep Graph Model

Accurately predicting protein structure, from amino acid sequences to three-dimensional structures, is of great significance in biological research. To tackle this issue, a representative deep big model, RoseTTAFold, has been proposed with promising success. Here, we report an ultra-lightweight deep graph network, named LightRoseTTA, to achieve accurate and high-efficient prediction for proteins. Notably, three highlights are possessed by our LightRoseTTA: (i) high-accurate structure prediction for proteins, being competitive with RoseTTAFold on multiple popular datasets including CASP14 and CAMEO; (ii) high-efficient training and inference with an ultra-lightweight model, costing only one week on one single general NVIDIA 3090 GPU for model-training (vs 30 days on 8 high-speed NVIDIA V100 GPUs for RoseTTAFold) and containing only 1.4M parameters (vs 130M in RoseTTAFold); (iii) low dependency on multi-sequence alignments (MSA, widely-used homologous information), achieving the best performance on three MSA-insufficient datasets: Orphan, De novo, and Orphan25. Besides, our LightRoseTTA is transferable from general proteins to antibody data, as verified in our experiments. We visualize some case studies to demonstrate the high-quality prediction, and provide some insights on how the structure predictions facilitate the understanding of biological functions. We further make a discussion on the time and resource costs of LightRoseTTA and RoseTTAFold, and demonstrate the feasibility of lightweight models for protein structure prediction, which may be crucial in the resource-limited research for universities and academy institutions. We release our code and model to speed biological research.

bioinformatics↗

bioRxiv2023

Defining metabolic flexibility in hair follicle stem cell induced squamous cell carcinoma

Among the numerous changes associated with the transformation to cancer, cellular metabolism is one of the first discovered and most prominent[1, 2]. However, despite the knowledge that nearly every cancer is associated with the strong upregulation of various metabolic pathways, there has yet to be much clinical progress on the treatment of cancer by targeting a single metabolic enzyme directly[3-6]. We previously showed that inhibition of glycolysis through lactate dehydrogenase (LDHA) deletion in cancer cells of origin had no effect on the initiation or progression of cutaneous squamous cell carcinoma[7], suggesting that these cancers are metabolically flexible enough to produce the necessary metabolites required for sustained growth in the absence of glycolysis. Here we focused on glutaminolysis, another metabolic pathway frequently implicated as important for tumorigenesis in correlative studies. We genetically blocked glutaminolysis through glutaminase (GLS) deletion in cancer cells of origin, and found that this had little effect on tumorigenesis, similar to what we previously showed for blocking glycolysis. Tumors with genetic deletion of glutaminolysis instead upregulated lactate consumption and utilization for the TCA cycle, providing further evidence of metabolic flexibility. We also found that the metabolic flexibility observed upon inhibition of glycolysis or glutaminolysis is due to post-transcriptional changes in the levels of plasma membrane lactate and glutamine transporters. To define the limits of metabolic flexibility in cancer initiating hair follicle stem cells, we genetically blocked both glycolysis and glutaminolysis simultaneously and found that frank carcinoma was not compatible with abrogation of both of these carbon utilization pathways. These data point towards metabolic flexibility mediated by regulation of nutrient consumption, and suggest that treatment of cancer through metabolic manipulation will require multiple interventions on distinct pathways.

cancer biology↗

bioRxiv2023

Cell context-specific Synthetic lethality Prediction and Mechanism Analysis

Synthetic lethality (SL) holds significant promise as a targeted cancer therapy by selectively eliminating tumor cells while sparing normal cells. However, the discovery of SL gene pairs has encountered tremendous challenges, including high costs and low efficiency of experimental methods. Current computational approaches only provide limited insights because of overlooking the crucial aspects of cellular context dependency and mechanistic understanding of SL pairs. To overcome these challenges, we have developed SLWise, a deep-learning model capable of predicting SL interactions in diverse cellular backgrounds. We evaluated SLWise using a real world ground truth standard. The evaluation demonstrated that SLWise outperformed benchmark models in SL prediction. Additionally, we proposed a novel analysis scheme called SLAD-CE (Synthetic Lethal Associated Gene Detection and Cell Damage Evaluation) for the identification of abnormal essential genes induced by SL gene pairs and tracking the extent of cell damage. Leveraging the cell-line-specific input feature L1000 and employing Gene Set Enrichment Analysis (GSEA), SLAD-CE provides valuable insights into the underlying mechanisms of SLWise-predicted gene pairs. The combined utilization of SLWise and SLAD-CE offers an approach for predicting and analyzing SL interactions in specific cellular contexts. Our findings highlight the potential of SLWise and SLAD-CE in advancing SL-based therapies by improving prediction accuracy and enhancing mechanistic understanding, ultimately contributing to the development of effective precision treatments for cancer.

cancer biology↗

bioRxiv2023

Tissue-specific transfer-learning enables retasking of a general comprehensive model to a specific domain

Machine learning (ML) has proven successful in biological data analysis. However, may require massive training data. To allow broader use of ML in the full spectrum of biology and medicine, including sample-sparse domains, re-directing established models to specific tasks by add-on training via a moderate sample may be promising. Transfer learning (TL), a technique migrating pre-trained models to new tasks, fits in this requirement. Here, by TL, we retasked Enformer, a comprehensive model trained by massive data, tailored to breast cancers using breast-specific data. Its performance has been validated through statistical accuracy of predictions, annotation of genetic variants, and mapping of variants associated with breast cancer. By allowing the flexibility of adding dedicated training data, our TL protocol unlocks future discovery within specific domains with moderate add-on samples by standing on the shoulders of giant models.

bioinformatics↗

bioRxiv2023

Sparse Modeling of Genomic Landscape Identifies Pathogenic Processes and Therapeutic Targets in Metastatic Breast Cancer

Breast cancer is a heterogeneous disease and ranks as one of the most lethal and frequently detected disease in the world. It poses significant challenges for precision therapy. To better decipher the patterns of heterogeneous nature in human genome and converge them into common functionals, mutational signatures are introduced to define the types of DNA damage, repair and replicative mechanisms that shape the genomic landscape of each cancer patient. In this study, we developed a deep learning (DL) model, MetaWise 2.0, based on pruning technology that improved model generalization with deep sparsity. We applied it to patient samples from multiple sequencing studies, and identified statistically significant mutational signatures associated with metastatic progression using Shapley additive explanations (SHAP). We also employed gene cumulative contribution abundance analysis to link the mutational signatures with relevant genes, which could unearth the shared molecular mechanisms behind tumorigenesis and metastasis of each patient and lead to novel therapeutic target identification. Our study illustrates that MetaWise 2.0 is an effective DL tool for discovering clinically meaningful mutational signatures in metastatic breast cancer (MBC) and relating them directly to relevant biological functions and gene targets. These findings could facilitate the development of novel therapeutic strategies and improve the clinical outcomes for individual patients.

bioinformatics↗

bioRxiv2023

Using graph-based model to identify cell specific synthetic lethal effects

Synthetic lethal (SL) pairs are pairs of genes whose simultaneous loss-of-function results in cell death, while a damaging mutation of either gene alone does not affect the cells survival. This makes SL pairs attractive targets for precision cancer therapies, as targeting the unimpaired gene of the SL pair can selectively kill cancer cells that already harbor the impaired gene. Limited by the difficulty of finding true SL pairs, especially on specific cell types, the identification of SL targets still relies on expensive, time-consuming experimental approaches. In this work, we utilized various cell-line specific omics data to design a deep learning model for predicting SL pairs on particular cell-lines. By incorporating multiple types of cell-specific omics data with a self-attention module, we represent gene relationships as graphs. Our approach demonstrates the potential to facilitate the discovery of cell-specific SL targets for cancer therapeutics, providing a tool to unearth mechanisms underlying the origin of SL in cancer biology. Our approach allows for prediction of SL pairs in a cell-specific manner and enhances cancer precision medicine. The code and data of our approach can be found at https://github.com/promethiume/SLwise HighlightsO_LIFew computational methods can systematically predict SL pairs at a cell-specific level, and their performance may not generalize well to clinical scenarios due to the heterogeneity of cancer types. C_LIO_LIThe SLWise utilizes various cell-line specific omics data to design a deep learning model with a graph-based representation and self-attention mechanism. C_LIO_LIThis approach allows for the prediction of SL pairs in a cell-specific manner, providing valuable insights on effectively identifying the cell-type specific SL targets for personalized treatment strategies. C_LI

bioinformatics↗

bioRxiv2023

Large pilin subunits provide distinct structural and mechanical properties for the Myxococcus xanthus type IV pilus

Type IV pili (T4P) are ubiquitous bacterial cell surface filaments important for surface motility, adhesion to biotic and abiotic surfaces, DNA uptake, biofilm formation, and virulence. T4P are built from thousands of copies of the major pilin subunit and tipped by a complex composed of minor pilins and in some systems also the PilY1 adhesin. While the major pilins of structurally characterized T4P have lengths of up to 161 residues, the major pilin PilA of Myxococcus xanthus is unusually large with 208 residues. All major pilins have a highly conserved N-terminal domain and a highly variable C-terminal domain, and the additional residues in the M. xanthus PilA are due to a larger C-terminal domain. We solved the structure of the M. xanthus T4P (T4PMx) at a resolution of 3.0 [A] using cryo-electron microscopy (cryo-EM). The T4PMx follows the structural blueprint observed in other T4P with the pilus core comprised of the extensively interacting N-terminal 1-helices while the globular domains decorate the T4P surface. The atomic model of PilA built into this map shows that the large C-terminal domain has much more extensive intersubunit contacts than major pilins in other T4P. As expected from these greater contacts, the bending and axial stiffness of the T4PMx is significantly higher than that of other T4P and supports T4P-dependent motility on surfaces of different stiffnesses. Notably, T4PMx variants with interrupted intersubunit interfaces had decreased bending stiffness and strongly reduced motility on all surfaces. These observations support an evolutionary scenario whereby the large major pilin enables the formation of a rigid T4P that expands the environmental conditions in which the T4P system functions.

microbiology↗

bioRxiv2023

Phosphorylation modulates estrogen receptor disorder by altering long-range hydrophobic interactions

Protein intrinsic disorder is coupled to a range of biological phenomena, from gene regulation to cancer progression. Phosphorylation of the estrogen receptor (ER) at Ser118 through its disordered N-terminal domain (NTD) activates its transcriptional function, but it is challenging to rationalize how this modification regulates ER activity. Using biophysical approaches of small-angle X-ray scattering and nuclear magnetic resonance spectroscopy, we demonstrate that Ser118 phosphorylation triggers long-range conformational changes in ER-NTD, particularly between two hydrophobic clusters of residual structures. Alanine substitution of hydrophobic amino acids near Ser118 produces similar conformational alterations and rescues impaired ER activity caused by a phosphorylation-deficient mutant. These findings establish a direct link between phosphorylation-induced conformational changes and the activation function of this disordered protein as a promising avenue to block ER transcriptional activation.

biophysics↗

bioRxiv2023

Opposing implications of co-evolutionary lineages and traits of gut microbiome on human health status

Little is known about the co-evolutionary history of the human gut microbe and its relevance to host physiology. Here, we constructed a gut prokaryotic genomic database of wild primates (pSGBs) and compared it with the human gut prokaryotic database (hSGBs) to define shared co-evolutionary clusters (SCEC-hSGBs) and co-evolutionary traits of hSGBs. We analyzed the evolutionary trends of specific functions like carbohydrate-active enzymes and antibiotic resistance in hSGBs and uncovered host-jumping events and genome reduction tendencies in SCEC-hSGBs. Intriguingly, the SCEC-hSGBs and the super enrichers of the traits (SUEN-hSGBs), which are putatively partially derived from carnivores, showed opposite implications for host health status. Specifically, SUEN-hSGBs are enriched in various diseases, showing a negative correlation with gut biodiversity and disproportionate contributions to the known health-negative marker taxa and metabolite. Our study provides insight into the origin and adaptability of human gut microbes and references for developing probiotics and microbiome-based host health prediction.

microbiology↗

bioRxiv2023

Sex-biased regulatory changes in the placenta of native highlanders contribute to adaptive fetal development

Compared with lowlander migrants, native Tibetans have a higher reproductive success at high altitude though the underlying mechanism remains unclear. Here, we compared the transcriptome and histology of full-term placentas between native Tibetans and Han migrants. We found that the placental trophoblast shows the largest expression divergence between Tibetans and Han, and Tibetans show decreased immune response and endoplasmic reticulum stress. Remarkably, we detected a sex-biased expression divergence, where the male-infant placentas show a greater between-population difference than the female-infant placentas. The umbilical cord plays a key role in the sex-biased expression divergence, which is associated with the higher birth weight of the male newborns of Tibetans. We also identified adaptive histological changes in the male-infant placentas of Tibetans, including larger umbilical artery wall and umbilical artery intima and media, and fewer syncytial knots. These findings provide valuable insights into the sex-biased adaptation of human populations, with significant implications for medical and genetic studies of human reproduction.

evolutionary biology↗