Biology subjects

Wang, C.

Publications and source records attributed to Wang, C..

At least 613 records · Page 34Linked to original sources

bioRxiv2023

Projectome-defined subtypes and modular intra-hypothalamic subnetworks of peptidergic neurons

The hypothalamus plays a vital role in coordinating essential neuroendocrine, autonomic, and somatomotor responses for survival and reproduction. While previous studies have explored population-level projections of hypothalamic neurons, the specific innervation patterns of individual hypothalamic axons remain unclear. To understand the organization of hypothalamic axon projections, we conducted a comprehensive reconstruction of single-cell projectomes from 7,180 mouse hypothalamic neurons expressing specific neuropeptides. Our analysis identified 31 distinct subtypes based on projectome-defined characteristics, with many exhibiting long-range axon collateral projections to multiple brain regions. Notably, these subtypes selectively targeted specific subdomains within downstream areas, either unilaterally or bilaterally. Furthermore, we observed that individual peptidergic neuronal types encompassed multiple projectome-defined subtypes, explaining their diverse functional roles. Additionally, by examining intra-hypothalamic axon projections, we uncovered six modular subnetworks characterized by enriched intramodular connections and distinct preferences for downstream targets. This modular organization of the intra-hypothalamic network likely contributes to the coordinated organization of hypothalamic outputs. In summary, our comprehensive projectome analysis reveals the organizational principles governing hypothalamic axon projections, providing a framework for understanding the neural circuit mechanisms underlying the diverse and coordinated functions of the hypothalamus.

neuroscience↗

bioRxiv2023

The Haplotype-resolved Autotetraploid Genome Assembly Provides Insights into the genomic evolution and fruit divergence in Wax apple (Syzygium samarangense (BI.) Merr.et Perry)

The wax apple (Syzygium samarangense) is an economically important fruit crop with great potential value to human health because it has rich antioxidant substances. Here, we presented one haplotype-resolved autotetraploid genome assembly of the wax apple with size of 1.59 Gb. Comparative genomic analysis revealed three rounds of whole-genome duplication (WGD) events, including two independent WGDs after WGT-{gamma}. Resequencing analysis of 35 accessions partitioned these individuals into two distinct groups, including 28 landraces and seven cultivated species, and several selectively swept genes possibly contributed to fruit growth, including KRP1-like, IAA17-like, GME-like, and FLACCA-like genes. Transcriptome analysis in three different varieties during flower and fruit development identified key genes related to fruit size, sugar content, and male sterility. We found AP2 also affects the fruit size by regulating the sepal development in wax apples. The expression of sugar transport-related genes (SWEETs and SUTs) was high in ZY, likely contributing to a high level of sugar content. Male sterility in Tub was associated with tapetal abnormalities due to the decreased expression of DYT1, TDF1, and AMS, which affects the early tapetum development. The chromosome-scale genome and large-scale transcriptome data presented in this study offer new valuable resources for biological research on S. samarangense, and sheds new light on fruit size control, sugar metabolism, and male sterility regulatory metabolism in wax apple.

genomics↗

bioRxiv2023

A bioinformatics tool for identifying intratumoral microbes from the ORIEN dataset

Evidence supports significant interactions among microbes, immune cells, and tumor cells in at least 10-20% of human cancers, emphasizing the importance of further investigating these complex relationships. However, the implications and significance of tumor-related microbes remain largely unknown. Studies have demonstrated the critical roles of host microbes in cancer prevention and treatment responses. Understanding interactions between host microbes and cancer can drive cancer diagnosis and microbial therapeutics (bugs as drugs). Computational identification of cancer-specific microbes and their associations is still challenging due to the high dimensionality and high sparsity of intratumoral microbiome data, which requires large datasets containing sufficient event observations to identify relationships, and the interactions within microbial communities, the heterogeneity in microbial composition, and other confounding effects that can lead to spurious associations. To solve these issues, we present a bioinformatics tool, MEGA, to identify the microbes most strongly associated with 12 cancer types. We demonstrate its utility on a dataset from a consortium of 9 cancer centers in the Oncology Research Information Exchange Network (ORIEN). This package has 3 unique features: species-sample relations are represented in a heterogeneous graph and learned by a graph attention network; it incorporates metabolic and phylogenetic information to reflect intricate relationships within microbial communities; and it provides multiple functionalities for association interpretations and visualizations. We analyzed 2704 tumor RNA-seq samples and MEGA interpreted the tissue-resident microbial signatures of each of 12 cancer types. MEGA can effectively identify cancer-associated microbial signatures and refine their interactions with tumors. SIGNIFICANCEStudying the tumor microbiome in high-throughput sequencing data is challenging because of the extremely sparse data matrices, heterogeneity, and high likelihood of contamination. We present a new deep-learning tool, microbial graph attention (MEGA), to refine the organisms that interact with tumors.

bioinformatics↗

bioRxiv2023

SeMOE allows for quantitative glycan perception and exhibits anti-cancer potentiality

Metabolic oligosaccharide engineering (MOE) is a classical chemical approach to perturb, profile and perceive glycans in physiological systems, but probes upon bioorthogonal reaction require accessibility and background signal readout makes it challenging to achieve absolute glycan quantification. Here we develop SeMOE, a selenosugar-based metabolic oligosaccharide engineering strategy that combines elemental analysis and MOE to enable the absolute quantification and mass spectrometric imaging of glycome in a concise procedure. We demonstrate that SeMOE probes allow for perception, absolute quantification and visualization of glycans in diverse biological contexts. We demonstrate that chemical reporters on conventional MOE can be integrated into a bifunctional SeMOE probe to provide multimodality signal readouts. We further show the anti-cancer potentiality of SeMOE probes. SeMOE thus provides a convenient and simplified method to "see more" of the glyco-world.

bioengineering↗

bioRxiv2023

Genome-scale analysis of interactions between genetic perturbations and natural variation

Interactions between genetic perturbations and segregating loci can cause perturbations to show different phenotypic effects across genetically distinct individuals. To study these interactions on a genome scale in many individuals, we used combinatorial DNA barcode sequencing to measure the fitness effects of 7,700 CRISPRi perturbations targeting 1,712 distinct genes in 169 yeast cross progeny (or segregants). We identified 460 genes whose perturbation has different effects across segregants. Several factors caused perturbations to show variable effects, including baseline segregant fitness, the mean effect of a perturbation across segregants, and interacting loci. We mapped 234 interacting loci and found four hub loci that interact with many different perturbations. Perturbations that interact with a given hub exhibit similar epistatic relationships with the hub and show enrichment for cellular processes that may mediate these interactions. These results suggest that an individuals response to perturbations is shaped by a network of perturbation-locus interactions that cannot be measured by approaches that examine perturbations or natural variation alone.

genetics↗

bioRxiv2023

The D84G mutation in STIM1 causes nuclear envelope dysfunction and myopathy in mice

Stromal interaction molecule 1 (STIM1) is a Ca2+ sensor located in the sarcoplasmic reticulum (SR) of skeletal muscle where it is best known for its role in store operated Ca2+ entry (SOCE). Genetic syndromes resulting from STIM1 mutations are recognized as a cause of muscle weakness and atrophy. Here, we focus on a gain of function mutation that occurs in humans and mice (STIM1+/D84G mice) where muscles exhibit constitutive SOCE. Unexpectedly, this constitutive SOCE did not affect global Ca2+ transients, SR Ca2+ content or excitation contraction coupling (ECC) and was therefore unlikely to underlie the reduced muscle mass and weakness observed in these mice. Instead, we demonstrate that the presence of D84G STIM1 in the nuclear envelope of STIM1+/D84G muscle disrupts nuclear-cytosolic coupling causing severe derangement in nuclear architecture, DNA damage, and altered lamina A associated gene expression. Functionally, we found D84G STIM1 reduced the transfer of Ca2+ from the cytosol to the nucleus in myoblasts resulting in a reduction of [Ca2+]N. Taken together, we propose a novel role for STIM1 in the nuclear envelope that links Ca2+ signaling to nuclear stability in skeletal muscle.

physiology↗

bioRxiv2023

scGPT: Towards Building a Foundation Model for Single-Cell Multi-omics Using Generative AI

Generative pre-trained models have achieved remarkable success in various domains such as natural language processing and computer vision. Specifically, the combination of large-scale diverse datasets and pre-trained transformers has emerged as a promising approach for developing foundation models. Drawing parallels between linguistic constructs and cellular biology -- where texts comprise words, similarly, cells are defined by genes -- our study probes the applicability of foundation models to advance cellular biology and genetics research. Utilizing the burgeoning single-cell sequencing data, we have pioneered the construction of a foundation model for single-cell biology, scGPT, which is based on generative pre-trained transformer across a repository of over 33 million cells. Our findings illustrate that scGPT, a generative pre-trained transformer, effectively distills critical biological insights concerning genes and cells. Through the further adaptation of transfer learning, scGPT can be optimized to achieve superior performance across diverse downstream applications. This includes tasks such as cell-type annotation, multi-batch integration, multi-omic integration, genetic perturbation prediction, and gene network inference. The scGPT codebase is publicly available at https://github.com/bowang-lab/scGPT.

bioinformatics↗

bioRxiv2023

Superior colliculus cell types bidirectionally modulate choice activity in frontal cortex

Action selection occurs through competition between potential choice options. Neural correlates of choice competition are observed across frontal cortex and downstream superior colliculus (SC) during decision-making, yet how these regions interact to mediate choice competition remains unresolved. Here we report that cell types within SC can bidirectionally modulate choice competition and drive choice activity in frontal cortex. In the mouse, topographically matched regions of frontal cortex and SC formed a descending motor pathway for directional licking and a re-entrant loop via the thalamus. During decision-making, distinct neuronal populations in both frontal cortex and SC encoded opposing lick directions and exhibited push-pull dynamics. SC GABAergic neurons encoded ipsilateral choice and glutamatergic neurons encoded contralateral choice, and activating or suppressing these cell types could bidirectionally drive push-pull choice activity in frontal cortex. These results thus identify SC as a major locus to modulate choice competition within the broader action selection network.

neuroscience↗

bioRxiv2023

OpenABC Enables Flexible, Simplified, and Efficient GPU Accelerated Simulations of Biomolecular Condensates

Biomolecular condensates are important structures in various cellular processes but are challenging to study using traditional experimental techniques. In silico simulations with residue-level coarse-grained models strike a balance between computational efficiency and chemical accuracy. They could offer valuable insights by connecting the emergent properties of these complex systems with molecular sequences. However, existing coarse-grained models often lack easy-to-follow tutorials and are implemented in software that is not optimal for condensate simulations. To address these issues, we introduce OpenABC, a software package that greatly simplifies the setup and execution of coarse-grained condensate simulations with multiple force fields using Python scripting. OpenABC seamlessly integrates with the OpenMM molecular dynamics engine, enabling efficient simulations with performances on a single GPU that rival the speed achieved by hundreds of CPUs. We also provide tools that convert coarse-grained configurations to all-atom structures for atomistic simulations. We anticipate that Open-ABC will significantly facilitate the adoption of in silico simulations by a broader community to investigate the structural and dynamical properties of condensates. Open-ABC is available at https://github.com/ZhangGroup-MITChemistry/OpenABC

biophysics↗

bioRxiv2023

A novel simian adenovirus vectored COVID-19 vaccine elicits effective mucosal and systemic immunity in mice by intranasal and intramuscular vaccination regimens

The failure of COVID-19 vaccines to prevent SARS-CoV-2 infection and transmission, a possibly critical reason was the lack of protective mucosal immunity in respiratory tract. Here, we evaluated the effects of mucosal and systemic immunity from a novel simian adenovirus vectored COVID-19 vaccine (Sad23L- nCoV-S) in mice in comparison with Ad5-nCoV-S by intranasal (IN) drip and intramuscular (IM) injection vaccinations. As good as the well-known Ad5-nCoV-S vaccine, a single-dose IN inoculation of 1x109 PFU Sad23L-nCoV-S vaccine induced a similar level of IgG S-binding antibody (S-BAb) and neutralizing antibody (NAb) and higher IgA in serum, while IN route raised significantly higher IgG and IgA S- BAb and NAb in bronchoalveolar lavage (BAL), and specific IFN-{gamma} secreting T cell response in lung compared with IM route, but lower T cell response in spleen. By prime-boost vaccination regimens with different combination of IN and IM inoculations of Sad23L-nCoV-S vaccine, the IN involved vaccinations stimulated higher protective mucosal or local immunity in BAL and lung, while the IM involved immunizations induced higher systemic immunity in serum and spleen. A long-term sustained systemic and mucosal NAb and T cell immunity to SARS-CoV-2 was maintained at high levels over 32 weeks by prime-boost vaccination regimens with IN and IM routes. In conclusion, priming or boosting immunization with IN inoculation of Sad23L-nCoV-S vaccine could induced effective mucosal immunity and in combination of IM route could additionally achieve systemic immunity, which provided an important reference for vaccination regimens against respiratory virus infection. IMPORTANCEThe essential goal of vaccination is to generate potent and long-term protection against diseases. Several factors including type of vector, delivery route, boosting regimen influence the outcome of prime-boost immunization approaches. The immunization regimen by constructing a novel simian adenovirus vectored COVID-19 vaccine and employing combination of intranasal and intramuscular inoculations, could elicit mucosal neutralizing antibodies against five mutant strains in the respiratory tract, and strong systemic immunity. Immune protection could last for more than 32 weeks. Vectored vaccine construction and immunization regimens have positively impacted respiratory disease prevention.

immunology↗

bioRxiv2023

Assisting and Accelerating NMR Assignment with Restrainted Structure Prediction

NMR experiments can detect in situ structures and dynamic interactions, but the NMR assignment process requires expertise and is time-consuming, thereby limiting its applicability. Deep learning algorithms have been employed to aid in experimental data analysis. In this work, we developed a RASP model which can enhance structure prediction with restraints. Based on the Evoformer and structure module architecture of AlphaFold, this model can predict structure based on sequence and a flexible number of input restraints. Moreover, it can evaluate the consistency between the predicted structure and the imposed restraints. Based on this model, we constructed an iterative NMR NOESY peak assignment pipeline named FAAST, to accelerate assignment process of NOESY restraints and obtaining high quality structure ensemble. The RASP model and FAAST pipeline not only allow for the leveraging of experimental restraints to improve model prediction, but can also facilitate and expedite experimental data analysis with their integrated capabilities.

bioinformatics↗

bioRxiv2023

A Weighted Two-stage Sequence Alignment Framework to Identify DNA Motifs from ChIP-exo Data

Identifying precise transcription factor binding sites (TFBS) or regulatory DNA motifs plays a fundamental role in researching transcriptional regulatory mechanisms in cells and in helping construct regulatory networks. Current algorithms developed for motif searching focus on the analysis of ChIP-enriched peaks but are not able to integrate the ChIP signal in nucleotide resolution. We present a weighted two-stage alignment tool (TESA). Our framework implements an analysis workflow from experimental datasets to TFBS prediction results. It employs a binomial distribution model and graph searching model with ChIP-exonuclease (ChIP-exo) reads depth and sequence data. TESA can effectively measure the possibility for each position to be an actual TFBS in a given promoter sequence and predict statistically significant TFBS sequence segments. The algorithm substantially improves prediction accuracy and extends the scope of applicability of existing approaches. We apply the framework to a collection of 20 ChIP-exo datasets of E. coli from proChIPdb and evaluate the prediction performance through comparison with three existing programs. The performance evaluation against the compared programs indicates that TESA is more accurate for identifying regulatory motifs in prokaryotic genomes.

bioinformatics↗

bioRxiv2023

Nucleolar structure connects with global nuclear organization

The nucleolus is a multi-functional nuclear body. To tease out the roles of nucleolar structure without resorting to multi-action drugs, we knocked down RNA polymerase I subunit RPA194 in HeLa cells by siRNA. Loss of RPA194 resulted in nucleolar structural segregation and effects on both nucleolus-proximal and distal nuclear components. The perinucleolar compartment was disrupted, centromere-nucleolus interactions were significantly reduced, and the intranuclear locations of specific genomic loci were altered. Moreover, Cajal bodies, distal from nucleoli, underwent morphological and compositional changes. To distinguish whether these global reorganizations are the results of nucleolar structural disruption or inhibition of ribosome synthesis, the pre-ribosomal RNA processing factor, UTP4, was also knocked down, which did not lead to nucleolar segregation, nor the intranuclear effects seen with RPA195A knockdown, demonstrating that they do not arise from a cessation of ribosome synthesis. These findings point to a commutative system that links nucleolar structure to the maintenance and spatial organization of certain nuclear bodies and genomic loci.

cell biology↗

bioRxiv2023

Latent transcriptional programs reveal histology-encoded tumor features spanning tissue origins

Precision medicine in cancer treatment depends on deciphering tumor phenotypes to reveal the underlying biological processes. Molecular profiles, including transcriptomics, provide an information-rich tumor view, but their high-dimensional features and assay costs can be prohibitive for clinical translation at scale. Recent studies have suggested jointly leveraging histology and genomics as a strategy for developing practical clinical biomarkers. Here, we use machine learning techniques to identify de novo latent transcriptional processes in squamous cell carcinomas (SCCs) and to accurately predict their activity levels directly from tumor histology images. In contrast to analyses focusing on pre-specified, individual genes or sample groups, our latent space analysis reveals sets of genes associated with both histologically detectable features and clinically relevant processes, including immune response, collagen remodeling, and fibrosis. The results demonstrate an approach for discovering clinically interpretable histological features that indicate complex, potentially treatment-informing biological processes.

cancer biology↗

bioRxiv2023

Elevated ENO2 Disrupts VSMCs Homeostasis Facilitating the Development of Aortic Dissection

OBJECTIVEAortic dissection (AD) is a dangerous cardiovascular disease. However, its regulatory mechanism remains poorly understood. Autophagy is an important pathway for maintaining cellular homeostasis. Recent studies have shown that glycolysis and autophagy are essential for the development of AD. Elevated glycolysis can promote cells autophagy, but the relationship between the two is unclear. Enolase 2 (ENO2) is a glycolytic enzyme. Here, we investigated whether ENO2 can regulate autophagy in vascular smooth muscle cells (VSMCs) involved in the progression of AD. APPROACH AND RESULTSThe levels of ENO2 and autophagy-related proteins in aortic tissues from AD patients were identified by qRT-PCR, Western blot and IHC. We found that autophagosome clearance was impaired in aortic tissues and positively correlated with elevated ENO2 expression. Furthermore, aortic application of smooth muscle-specific adeno-associated virus (AAV) inhibiting ENO2 expression attenuated the development of AD in mice. in vitro experiments, downregulation of ENO2 partially restored PDGF-BB-induced impairment of autophagic flux, as evidenced by reduced expression of Beclin-1, SQSTM1, LC3BII/I and increased levels of LAMP2 in human aortic vascular smooth muscle cells (HAVSMCs). Similarly, increased levels of ENO2 exacerbated the autophagic dysfunction in HAVSMCs. Mechanistically, ENO2 may block autophagic flux through the ENO2-GAP43-ATF4 pathway and disrupt cellular homeostasis. CONCLUSIONSOur data reveal a perturbing role of ENO2 in the homeostasis of VSMCs, suggesting ENO2 as a potential target for intervention in AD.

molecular biology↗

bioRxiv2023

Structural basis for functional properties of cytochrome c oxidase

Cytochrome c oxidase (CcO) is an essential enzyme in mitochondrial and bacterial respiration. It catalyzes the four-electron reduction of molecular oxygen to water and harnesses the chemical energy to translocate four protons across biological membranes, thereby establishing the proton gradient required for ATP synthesis1. The full turnover of the CcO reaction involves an oxidative phase, in which the reduced enzyme (R) is oxidized by molecular oxygen to the metastable oxidized OH state, and a reductive phase, in which OH is reduced back to the R state. During each of the two phases, two protons are translocated across the membranes2. However, if OH is allowed to relax to the resting oxidized state (O), a redox equivalent to OH, its subsequent reduction to R is incapable of driving proton translocation2,3. How the O state structurally differs from OH remains an enigma in modern bioenergetics. Here, with resonance Raman spectroscopy and serial femtosecond X-ray crystallography (SFX)4, we show that the heme a3 iron and CuB in the active site of the O state, like those in the OH state5,6, are coordinated by a hydroxide ion and a water molecule, respectively. However, Y244, a residue covalently linked to one of the three CuB ligands and critical for the oxygen reduction chemistry, is in the neutral protonated form, which distinguishes O from OH, where Y244 is in the deprotonated tyrosinate form. These structural characteristics of O provide new insights into the proton translocation mechanism of CcO.

biophysics↗

bioRxiv2023

Lactococcus G423 Ameliorates the Growth Performance of Broilers by Modulation of Gut Microbiota- Metabolites

This study aimed to explore whether Lactococcus G423 could ameliorate growth performance of broilers by modulation of gut microbiota-metabolites based on the 16S ribosomal RNA (rRNA) and liquid chromatography-mass spectrometry (LC-MS). A total of 640 one-day-old AA broilers were randomly divided into 4 groups (Control (CON), Lac_L, Lac_H, and ABX). Average daily gain (ADG), average daily feed intake (ADFI), and feed conversion ratio (FCR) were calculated on the 42nd day. The ileum content was harvested and immediately frozen in liquid nitrogen for 16S rRNA and LC-MS analyses. Then, the results of 16S rRNA analysis were confirmed by quantitative polymerase chain reaction (qPCR). Compared with the CON group, ADG significantly increased in the Lac_H group (P<0.05), and survival rate significantly decreased in the Lac_H, Lac_H, and ABX groups (all P<0.05). A significant difference in microbial diversity was found among the four groups. Compared with the CON group, the abundance rates of Firmicutes and Lactobacillus in the Lac_H group were significantly risen (P<0.05). The global and overview maps and membrane transport in the Lac_L, Lac_H, and ABX groups significantly changed versus those in the CON group (P<0.05). The results of LC-MS demonstrated that Lactococcus could significantly improve the levels of some metabolites (6-hydroxy-5-methoxyindole glucuronide, 9,10-DiHOME, carbamazepine-O-quinone, N-Acetyl-L-phenylalanine, and kynurenine), and these metabolites were involved in 5 metabolic pathways. Among them, the pathways of linoleic acid metabolism, phenylalanine metabolism, and pentose and glucuronate interconversions significantly changed (P<0.05). Lactococcus improved wight and survival rate of broilers through the gut microbiota, regulating the pathways of amino acid metabolism, lipid metabolism, bile acid metabolism, and carbohydrate metabolism. However, antibiotics may negatively influence the gut microbiota. IMPORTANCEImprovements in the growth rate of broiler chickens can be achieved through dietary manipulation of the naturally occurring bacterial populations while mitigating the withdrawal of antibiotic growth promoters. Lactococcus is industrially crucial lactic acid bacteria, can be incorporated into the diets of chickens to improve their growth performance. This study investigated the key mechanisms behind this progression and pinpointed Lactococcus improved wight and survival rate of broilers through the gut microbiota, regulating the pathways of amino acid metabolism, lipid metabolism, bile acid metabolism, and carbohydrate metabolism.

microbiology↗

bioRxiv2023

Genome-scale enzymatic reaction prediction by variational graph autoencoders

BackgroundEnzymatic reaction networks are crucial to explore the mechanistic function of metabolites and proteins in biological systems and understanding the etiology of diseases and potential target for drug discovery. The increasing number of metabolic reactions allows the development of deep learning-based methods to discover new enzymatic reactions, which will expand the landscape of existing enzymatic reaction networks to investigate the disrupted metabolisms in diseases. ResultsIn this study, we propose the MPI-VGAE framework to predict metabolite-protein interactions (MPI) in a genome-scale heterogeneous enzymatic reaction network across ten organisms with thousands of enzymatic reactions. We improved the Variational Graph Autoencoders (VGAE) model to incorporate both molecular features of metabolites and proteins as well as neighboring features to achieve the best predictive performance of MPI. The MPI-VGAE framework showed robust performance in the reconstruction of hundreds of metabolic pathways and five functional enzymatic reaction networks. The MPI-VGAE framework was also applied to a homogenous metabolic reaction network and achieved as high performance as other state-of-art methods. Furthermore, the MPI-VGAE framework could be implemented to reconstruct the disease-specific MPI network based on hundreds of disrupted metabolites and proteins in Alzheimers disease and colorectal cancer, respectively. A substantial number of new potential enzymatic reactions were predicted and validated by molecular docking. These results highlight the potential of the MPI-VGAE framework for the discovery of novel disease-related enzymatic reactions and drug targets in real-world applications. Data availability and implementationThe MPI-VGAE framework and datasets are publicly accessible on GitHub https://github.com/mmetalab/mpi-vgae. Author BiographiesCheng Wang received his Ph.D. in Chemistry from The Ohio State Univesity, USA. He is currently a Assistant Professor in School of Public Health at Shandong University, China. His research interests include bioinformatics, machine learning-based approach with applications to biomedical networks. Chuang Yuan is a research assistant at Shandong University. He obtained the MS degree in Biology at the University of Science and Technology of China. His research interests include biochemistry & molecular biology, cell biology, biomedicine, bioinformatics, and computational biology. Yahui Wang is a PhD student in Department of Chemistry at Washington University in St. Louis. Her research interests include biochemistry, mass spectrometry-based metabolomics, and cancer metabolism. Ranran Chen is a master graduate student in School of Public Health at University of Shandong, China. Yuying Shi is a master graduate student in School of Public Health at University of Shandong, China. Gary J. Patti is the Michael and Tana Powell Professor at Washington University in St. Louis, where he holds appointments in the Department of Chemisrty and the Department of Medicine. He is also the Senior Director of the Center for Metabolomics and Isotope Tracing at Washington University. His research interests include metabolomics, bioinformatics, high-throughput mass spectrometry, environmental health, cancer, and aging. Leyi Wei received his Ph.D. in Computer Science from Xiamen University, China. He is currently a Professor in School of Software at Shandong University, China. His research interests include machine learning and its applications to bioinformatics. Qingzhen Hou received his Ph.D. in the Centre for Integrative Bioinformatics VU (IBIVU) from Vrije Universiteit Amsterdam, the Netherlands. Since 2020, He has serveved as the head of Bioinformatics Center in National Institute of Health Data Science of China and Assistant Professor in School of Public Health, Shandong University, China. His areas of research are bioinformatics and computational biophysics. Key pointsO_LIGenome-scale heterogeneous networks of metabolite-protein interaction (MPI) based on thousands of enzymatic reactions across ten organisms were constructed semi-automatically. C_LIO_LIAn enzymatic reaction prediction method called Metabolite-Protein Interaction Variational Graph Autoencoders (MPI-VGAE) was developed and optimized to achieve higher performance compared with existing machine learning methods by using both molecular features of metabolites and proteins. C_LIO_LIMPI-VGAE is broadly useful for applications involving the reconstruction of metabolic pathways, functional enzymatic reaction networks, and homogenous networks (e.g., metabolic reaction networks). C_LIO_LIBy implementing MPI-VGAE to Alzheimers disease and colorectal cancer, we obtained several novel disease-related protein-metabolite reactions with biological meanings. Moreover, we further investigated the reasonable binding details of protein-metabolite interactions using molecular docking approaches which provided useful information for disease mechanism and drug design. C_LI

bioinformatics↗