Biology subjects

Tian, Y.

Publications and source records attributed to Tian, Y..

At least 145 records · Page 8Linked to original sources

bioRxiv2023

Discrimination of Isomeric Glycans via High-sensitive Engineered Nanopore

Glycan isomers are the basis for forming the spatial structure of glycans, which poses great challenges to glycan structural analysis and hinders the study of the relationship between structure and function. An economical and convenient analytical method with high resolution is still in demand. Here, we designed an engineered -hemolysin nanopore with double-mutants (-HL M113R/T115A) to achieve sensing of glycans with different glycosidic linkages. By extracting three parameters to depict a 3D scatter plot, four glycans with different degrees of polymerization and glycosidic bonds could be distinguished. Through molecular dynamics (MD), we further elucidated the motion trajectories of two different glycosidic bonds in the pore region. In addition, the sensing ability was also applied in the direct identification of glycans in mixture systems. The obtained nanopore data permitted us to achieve a separation accuracy of over 90% independent of the assistance of machine learning algorithms. Designing biological nanopores with high discrimination and sensitivity for glycans with different lineages paves the way for nanopore glycan profiling and potentially sequencing.

bioengineering↗

bioRxiv2023

Comprehensive characterization of the integrin family across 32 cancer types

Integrin genes widely involve in tumorigenesis. Yet, a comprehensive characterization of integrin family and their interactome on the pan-cancer level is lacking. Here, we systematically dissect integrin family in nearly 10000 tumors across 32 cancer types. Globally, integrins represent a frequently altered and misexpressed pathway, with alteration and dysregulation overall being protumorigenic. Expression dysregulation, better than mutational landscape, of integrin family successfully identifies a subgroup of aggressive tumors demonstrating a high level of proliferation and stemness. We identify that several molecular mechanisms jointly regulate integrin expression in a context-dependent manner. For potential clinical usage, we construct a weighted score, integrinScore, to measure integrin signaling patterns in individual tumors. Remarkably, integrinScore consistently correlates with predefined molecular subtypes in multiple cancers, with integrinScore high tumors being more aggressive. Importantly, integrinScore is cancer-dependently and closely associated with proliferation, stemness, tumor microenvironment, metastasis, and immune signatures. IntegrinScore also predicts patients response to immunotherapy. By mining drug databases, we unravel an array of compounds that may serve as integrin signaling modulators. Finally, we build a user-friendly database to facilitate researchers to explore integrin-related knowledge. Collectively, we provide a comprehensive characterization of integrins across cancers, which offers gene- and cancer-specific rationales for developing integrin-targeted therapy.

bioinformatics↗

bioRxiv2023

Model-Based Closed-Loop Control of Thalamic Deep Brain Stimulation

Closed-loop control of deep brain stimulation (DBS) is crucial for effective and automatic treatments of various neurological disorders like Parkinsons disease (PD) and essential tremor (ET). Manual (open-loop) DBS programming solely based on clinical observations relies on neurologists expertise and patients experience. The continuous stimulation in open-loop DBS may decrease battery life and cause side effects. On the contrary, a closed-loop DBS system utilizes a feedback biomarker/signal to track worsening (or improving) patients symptoms and offers several advantages compared to open-loop DBS. Existing closed-loop DBS control systems do not incorporate physiological mechanisms underlying the DBS or symptoms, for example how DBS modulates dynamics of synaptic plasticity. In this work, we proposed a computational framework for development of a model-based DBS controller where a biophysically-reasonable model can describe the relationship between DBS and neural activity, and a polynomial-based approximation can estimate the relationship between the neural and behavioral activity. A controller is utilized in our model in a quasi-real-time manner to find DBS patterns that significantly reduce the worsening of symptoms. These DBS patterns can be tested clinically by predicting the effect of DBS before delivering it to the patient. We applied this framework to the problem of finding optimal DBS frequencies for essential tremor given EMG recordings solely. Building on our recent network model of ventral intermediate nuclei (Vim), the main surgical target of the tremor, in response to DBS, we developed a biophysically-reasonable simulation in which physiological mechanisms underlying Vim-DBS are linked to symptomatic changes in EMG signals. By utilizing a PID controller, we showed that a closed-loop system can track EMG signals and adjusts the stimulation frequency of Vim-DBS so that the power of EMG in [2, 200] Hz reaches a desired target. We demonstrated that our model-based closed-loop control system of Vim-DBS finds an appropriate DBS frequency that aligns well with clinical studies. Our model-based closed-loop system is adaptable to different control targets, highlighting its potential usability for different diseases and personalized systems.

neuroscience↗

bioRxiv2023

Comparison of chronic aerosol inhalation with combustible cigarette and e-cigarette on the psychiatric behaviors and neuroimmune profile in mice

With the worldwide use of electronic cigarettes (e-cigarettes) as a substitution for tobacco, the effects of e-cigarette vapor exposure on human health have been investigated. However, the comparison of long-term effects of aerosol inhalation with combustible cigarette and e-cigarette on the psychiatric behaviors has not been fully revealed. The present study examines the distinct effects of combustible cigarette and e-cigarette on anxiety, depression, cognition, and social behaviors. Here we found that the combustible cigarette induced the higher level of anxiety after long-term inhalation compared to the e-cigarettes with or without the tobacco flavor. Since the mechanism of action on the psychiatric behaviors entails the alterations on the neuroimmune-sensors and principal regulators such as glial cells, we further profiled the alterations of microglia and astrocytes by chronic inhalation of combustible tobacco cigarette, specifically the negative correlations between the IBA-1 level in the locus coeruleus (LC) and the latency to nest in VLT; as well as the GFAP level in LC and the open arm time in EPM were observed. Our current data provided an insight into the less impact of e-cigarettes on the anxiety-like behaviors and neuroimmune activation compared to combustible tobacco cigarettes which is not related to the flavor in e-cigarette, and the modulation on the neuroimmune signals in LC could be a therapeutic target for smoking-related anxiety.

animal behavior and cognition↗

bioRxiv2023

Uncovering network mechanism underlying thalamic Deep Brain Stimulation using a novel firing rate model

Thalamic ventral intermediate nucleus (Vim) is the primary surgical target of deep brain stimulation (DBS) for reducing symptoms of essential tremor. High-frequency Vim-DBS ([≥]100Hz) has been clinically effective, generating two experimentally-observed features in Vim spiking activity: 1) a large transient excitatory response (lasting <1s), followed by 2) a suppressed steady-state consisting of oscillations. Yet, mechanisms underlying these observations have not been fully understood by previous studies. In this work, we developed a network rate model and a novel parameter optimization method that accurately fit in-vivo single-unit recordings of Vim in human patients with essential tremor receiving a wide range of DBS frequencies (5[~]200Hz). Our model incorporates both the DBS-induced synaptic plasticity of Vim neurons, and the recurrent connections among excitatory and inhibitory neurons in Vim-network. We hypothesized that besides inducing synaptic depression, the therapeutic mechanism of high-frequency Vim-DBS could be to engage more inhibitory neurons in stabilizing the underlying circuits. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=86 SRC="FIGDIR/small/570924v2_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@1e78cbcorg.highwire.dtl.DTLVardef@c8bde1org.highwire.dtl.DTLVardef@12a3bb3org.highwire.dtl.DTLVardef@1b5a0dd_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2023

pan-MHC and cross-Species Prediction of T Cell Receptor-Antigen Binding

Profiling the binding of T cell receptors (TCRs) of T cells to antigenic peptides presented by MHC proteins is one of the most important unsolved problems in modern immunology. Experimental methods to probe TCR-antigen interactions are slow, labor-intensive, costly, and yield moderate throughput. To address this problem, we developed pMTnet-omni, an Artificial Intelligence (AI) system based on hybrid protein sequence and structure information, to predict the pairing of TCRs of {beta} T cells with peptide-MHC complexes (pMHCs). pMTnet-omni is capable of handling peptides presented by both class I and II pMHCs, and capable of handling both human and mouse TCR-pMHC pairs, through information sharing enabled this hybrid design. pMTnet-omni achieves a high overall Area Under the Curve of Receiver Operator Characteristics (AUROC) of 0.888, which surpasses competing tools by a large margin. We showed that pMTnet-omni can distinguish binding affinity of TCRs with similar sequences. Across a range of datasets from various biological contexts, pMTnet-omni characterized the longitudinal evolution and spatial heterogeneity of TCR-pMHC interactions and their functional impact. We successfully developed a biomarker based on pMTnet-omni for predicting immune-related adverse events of immune checkpoint inhibitor (ICI) treatment in a cohort of 57 ICI-treated patients. pMTnet-omni represents a major advance towards developing a clinically usable AI system for TCR-pMHC pairing prediction that can aid the design and implementation of TCR-based immunotherapeutics.

immunology↗

bioRxiv2023

The Lateral Ganglionic Eminence Does Not Generate Cortical Oligodendrocytes

The emergence of myelinating oligodendrocytes represents a pivotal developmental milestone in vertebrates, given their capacity to ensheath axons and facilitate the swift conduction of action potentials. It is widely accepted that cortical oligodendrocyte progenitor cells (OPCs) arise from medial ganglionic eminence (MGE), lateral/caudal ganglionic eminence (LGE/CGE) and cortical radial glial cells (RGCs). Here, we used two different fate mapping strategies to challenge the established notion that the LGE generates cortical OPCs. Furthermore, we used a Cre/loxP-dependent exclusion strategy to reveal that the LGE/CGE-derived OPCs are minimal. Additionally, we showed that specifically eliminating MGE-derived OPCs leads to a significant reduction of cortical OPCs. Together, our findings indicate that the contribution of OPCs from LGE/CGE is minimal, contrary to previous beliefs. These findings provide a new view of the developmental origins of cortical OPCs and a valuable foundation for future research on both normal development and oligodendrocyte-related disease.

developmental biology↗

bioRxiv2023

Evolution-guided large language model is a predictor of virus mutation trends

The increasing frequency of emerging viral infections necessitates a rapid human response, highlighting the cost-effectiveness of computational methods. However, existing computational approaches are limited by their input forms or incomplete functionalities, preventing a unified prediction of diverse viral variation drivers and hindering in-depth applications. To address this issue, we propose a unified evolution-driven framework for predicting virus variation drivers, named E2VD, which is guided by virus evolutionary traits priors. With evolution-inspired design, E2VD comprehensively and significantly outperforms state-of-the-art methods across various virus variation drivers prediction tasks. Moreover, E2VD effectively captures the fundamental patterns of virus evolution. It not only distinguishes different types of mutations but also accurately identifies rare beneficial mutations that are critical for virus to survival, while maintains generalization capabilities on different viral lineages. Importantly, with predicted biological drivers, E2VD perceives virus evolutionary trends, in which potential high-risk mutation sites are accurately recommended. Overall, E2VD represents a unified, structure-free, and interpretable approach for analyzing and predicting viral evolutionary fitness, providing an ideal alternative to costly wet-lab measurements to accelerate responses to emerging viral infections.

bioinformatics↗

bioRxiv2023

Mitochondrial One-Carbon Metabolism is Required for TGF-β-Induced Glycine Synthesis and Collagen Protein Production

A hallmark of Idiopathic Pulmonary Fibrosis is the TGF-{beta}-dependent activation of lung fibroblasts, leading to excessive deposition of collagen proteins and progressive scarring. We have previously shown that synthesis of collagen by lung fibroblasts requires de novo synthesis of glycine, the most abundant amino acid in collagen protein. TGF-{beta} upregulates the expression of the enzymes of the de novo serine/glycine synthesis pathway in lung fibroblasts through mTORC1 and ATF4- dependent transcriptional programs. SHMT2, the final enzyme of the de novo serine/glycine synthesis pathway, transfers a one-carbon unit from serine to tetrahydrofolate (THF), producing glycine and 5,10-methylene-THF (meTHF). meTHF is converted back to THF in the mitochondrial one-carbon (1C) pathway through the sequential actions of MTHFD2 (which converts meTHF to 10-formyl-THF), and either MTHFD1L, which produces formate, or ALDH1L2, which produces CO2. It is unknown how the mitochondrial 1C pathway contributes to glycine biosynthesis or collagen protein production in fibroblasts, or fibrosis in vivo. Here, we demonstrate that TGF-{beta} induces the expression of MTHFD2, MTHFD1L, and ALDH1L2 in human lung fibroblasts. MTHFD2 expression was required for TGF-{beta}-induced cellular glycine accumulation and collagen protein production. Combined knockdown of both MTHFD1L and ALDH1L2 also inhibited glycine accumulation and collagen protein production downstream of TGF-{beta}; however knockdown of either protein alone had no inhibitory effect, suggesting that lung fibroblasts can utilize either enzyme to regenerate THF. Pharmacologic inhibition of MTHFD2 recapitulated the effects of MTHFD2 knockdown in lung fibroblasts and ameliorated fibrotic responses after intratracheal bleomycin instillation in vivo. Our results provide insight into the metabolic requirements of lung fibroblasts and provide support for continued development of MTHFD2 inhibitors for the treatment of IPF and other fibrotic diseases.

cell biology↗

bioRxiv2023

Dynamic Regulation of Innate Lymphoid Cell Development During Ontogeny

Innate lymphoid cells (ILCs) are crucial for maintaining tissue homeostasis. The dynamic composition of ILC subsets during ontogeny has been observed for over a decade, yet the underlying mechanisms remain incompletely understood. Here, we combined differentiation assay and scRNA-seq analysis to compare the fetal and adult ILC development, and assessed their contribution to the dynamic change of ILC subsets. We revealed the preference for LTi cell differentiation in fetal hematopoietic progenitors, contrasted with a non-LTi differentiation bias in adult stages. Our analyses elucidated that the two stages shared a conserved differentiation trajectory and regulatory programs but used different controlling logic. Through computational prediction and dose-response experiments, we proposed a model where a transition from fetal ROR{gamma}t-dominant states to adult Notch-GATA3-dominant states orchestrates these changes. This study suggests that ILC development can be modulated to accommodate the varying demands for specific ILC subsets within the body at different developmental stages. HighlightO_LIThe ratio of LTi cells to non-LTi ILCs decreases from fetal to adult stage C_LIO_LIModified fate determination of ILC progenitors contributes to this decrease of LTi cell ratio during ontogeny C_LIO_LIA conserved trajectory exists from early LPs to Innate Lymphoid Cell Precursors (ILCPs) during both fetal and adult ILC development C_LIO_LIEnhanced activity of Notch-GATA3 signaling in ILC precursors accounts for the transition of their differentiation from LTi cells in the fetal to non-LTi ILCs in adults. C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=181 SRC="FIGDIR/small/565141v2_ufig1.gif" ALT="Figure 1"> View larger version (85K): org.highwire.dtl.DTLVardef@11a031eorg.highwire.dtl.DTLVardef@c2d652org.highwire.dtl.DTLVardef@184cdfeorg.highwire.dtl.DTLVardef@1efe7ae_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2023

A diet-dependent host metabolite shapes the gut microbiota to protect from autoimmunity

Diet can protect from autoimmune disease; however, whether diet acts via the host and/or microbiome remains unclear. Here, we use a ketogenic diet (KD) as a model to dissect these complex interactions. A KD rescued the experimental autoimmune encephalomyelitis (EAE) mouse model of multiple sclerosis in a microbiota-dependent fashion. Dietary supplementation with a single KD-dependent host metabolite ({beta}-hydroxybutyrate, {beta}HB) rescued EAE whereas transgenic mice unable to produce {beta}HB in the intestine developed more severe disease. Transplantation of the {beta}HB-shaped gut microbiota was protective. Lactobacillus sequence variants were associated with decreased T helper 17 (Th17) cell activation in vitro. Finally, we isolated a L. murinus strain that protected from EAE, which was phenocopied by the Lactobacillus metabolite indole lactic acid. Thus, diet alters the immunomodulatory potential of the gut microbiota by shifting host metabolism, emphasizing the utility of taking a more integrative approach to study diet-host-microbiome interactions.

microbiology↗

bioRxiv2023

MiceVAPORDot: A novel automated approach for high-throughput behavioral characterization during E-cigarette exposure in mice

The prosperity of electronic nicotine delivery systems (ENDS) or e-cigarette use has been regarded to lead an increasing risk of nicotine addiction, especially among youth. Understanding and evaluating the behaviors induced by ENDS are fundamental to the study of neuropsychiatric effects of e-cigarettes. However, little is known regarding the behavioral features during e-cigarette exposure in mice. Current behavioral assessments for nicotine addiction are based on nicotine withdrawal-induced anxiety which can be only performed after ENDS exposures. Here we developed MiceVAPORDot, a novel high-throughput tool for automated in situ behavioral characterization during e-cigarette exposure. The integration of a deep learning-based animal pose tracking method by MiceVAPORDot allows precise characterization on behavioral phenotypes of e-vapor exposed mice, which were unable revealed by traditional evaluation methodology such as conditioned place preference and elevated plus maze tests. The behavioral fingerprints recognized by MiceVAPORDot can be used for high-throughput screening on incentive nature of e-cigarette flavors as well as medications for smoking cessation.

animal behavior and cognition↗

bioRxiv2023

An artificial intelligence method for phenotyping of OCT scans using unsupervised and self-supervised deep learning

Artificial intelligence (AI) has been increasingly used to analyze optical coherence tomography (OCT) images to better understand physiology and genetic architecture of ophthalmic diseases. However, to date, research has been limited by the inability to transfer OCT phenotypes from one dataset to another. In this work, we propose a new AI method for phenotyping and clustering of OCT-derived retinal layer thicknesses using unsupervised and self-supervised methods in a large clinical dataset using glaucoma as a model disease and subsequently transfer our phenotypes to a large biobank. The model includes a deep learning model, manifold learning, and a Gaussian mixture model. We also propose a correlation analysis for the performance evaluation of our model based on Pearson correlation coefficients. Our model was able to identify clinically meaningful OCT phenotypes and successfully transfer phenotypes from one dataset to another. Overall, our results will contribute to stronger research methodologies for future research in OCT imaging biomarkers, augment testing of OCT phenotypes in multiple datasets, and ultimately improve our understanding of pathophysiology and genetic architecture of ocular diseases.

bioinformatics↗

bioRxiv2023

Oligodendroglial precursors orchestrate immune network instigating early demyelination in experimental autoimmune encephalomyelitis

The immunomodulatory cellular network that triggers early inflammation and demyelination, the key steps in multiple sclerosis (MS) pathogenesis remains poorly characterized. Here, we demonstrate that overactivation of Wnt pathway promotes pathological transformation of oligodendrocyte precursor cells (OPCs) to replicate pathological OPCs in human MS. In mouse experimental autoimmune encephalomyelitis (EAE), pathological OPCs attract CD4+ T-helper 1 (Th1) cells into the spinal cord and brain through CC-chemokine ligand 4 (CCL4), whilst OPCs cooperate with Th1 cells inducing transformation of cytotoxic macrophages that execute early demyelination. Simultaneously, Th1 cells and cytotoxic macrophages upregulate Wnt signaling and CCL4 expression in OPCs, thus exerting positive feedback onto the OPC-immune cascade and establishing a vicious cycle propagating EAE pathogenesis. Breaking this cascade by targeting CCL4 reduces immune cell infiltration, alleviates demyelination, and attenuates EAE severity. Our findings demonstrate a closely coordinated network of OPCs and immune cells therefore providing an alternative insight into MS pathophysiology.

neuroscience↗

bioRxiv2023

Development of properly-polarized trophoblast stem cell-derived organoids to model early human pregnancy

The development of human trophoblast stem cells (hTSC) and stem cell-derived trophoblast organoids has enabled investigation of placental physiology and disease and early maternal-fetal interactions during a stage of human pregnancy that previously had been severely restricted. A key shortcoming in existing trophoblast organoid methodologies is the non-physiologic position of the syncytiotrophoblast (STB) within the inner portion of the organoid, which neither recapitulates placental villous morphology in vivo nor allows for facile modeling of STB exposure to the endometrium or the contents of the intervillous space. Here we have successfully established properly-polarized human trophoblast stem cell (hTSC)-sourced organoids with STB forming on the surface of the organoid. These organoids can also be induced to give rise to the extravillous trophoblast (EVT) lineage with HLA-G+ migratory cells that invade into an extracellular matrix-based hydrogel. Compared to previous hTSC organoid methods, organoids created by this method more closely mimic the architecture of the developing human placenta and provide a novel platform to study normal and abnormal human placental development and to model exposures to pharmaceuticals, pathogens and environmental insults. MotivationHuman placental organoids have been generated to mimic physiological cell-cell interactions. However, those published models derived from human trophoblast stem cells (hTSCs) or placental villi display a non-physiologic "inside-out" morphology. In vivo, the placental villi have an outer layer of syncytialized cells that are in direct contact with maternal blood, acting as a conduit for gas and nutrient exchange, and an inner layer of progenitor, single cytotrophoblast cells that fuse to create the syncytiotrophoblast layer. Existing "inside-out" models put the cytotrophoblast cells in contact with culture media and substrate, making physiologic interactions between syncytiotrophoblast and other cells/tissues and normal and pathogenic exposures coming from maternal blood difficult to model. The goal of this study was to develop an hTSC-derived 3-D human trophoblast organoid model that positions the syncytiotrophoblast layer on the outside of the multicellular organoid. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=69 SRC="FIGDIR/small/560327v1_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@7f1e7forg.highwire.dtl.DTLVardef@1ab41d9org.highwire.dtl.DTLVardef@7046d5org.highwire.dtl.DTLVardef@13a7542_HPS_FORMAT_FIGEXP M_FIG C_FIG

developmental biology↗

bioRxiv2023

GeneCompass: Deciphering Universal Gene Regulatory Mechanisms with Knowledge-Informed Cross-Species Foundation Model

Deciphering the universal gene regulatory mechanisms in diverse organisms holds great potential to advance our knowledge of fundamental life process and facilitate research on clinical applications. However, the traditional research paradigm primarily focuses on individual model organisms, resulting in limited collection and integration of complex features on various cell types across species. Recent breakthroughs in single-cell sequencing and advancements in deep learning techniques present an unprecedented opportunity to tackle this challenge. In this study, we developed GeneCompass, the first knowledge-informed, cross-species foundation model pre-trained on an extensive dataset of over 120 million single-cell transcriptomes from human and mouse. During pre-training, GeneCompass effectively integrates four types of biological prior knowledge to enhance the understanding of gene regulatory mechanisms in a self-supervised manner. Fine-tuning towards multiple downstream tasks, GeneCompass outperforms competing state-of-the-art models in multiple tasks on single species and unlocks new realms of cross-species biological investigation. Overall, GeneCompass marks a milestone in advancing knowledge of universal gene regulatory mechanisms and accelerating the discovery of key cell fate regulators and candidate targets for drug development.

bioinformatics↗

bioRxiv2023

CellPolaris: Decoding Cell Fate through Generalization Transfer Learning of Gene Regulatory Networks

Cell fate changes are determined by gene regulatory network (GRN), a sophisticated system regulating gene expression in precise spatial and temporal patterns. However, existing methods for reconstructing GRNs suffer from inherent limitations, leading to compromised accuracy and application generalizability. In this study, we introduce CellPolaris, a computational system that leverages transfer learning algorithms to generate high-quality, cell-type-specific GRNs. Diverging from conventional GRN inference models, which heavily rely on integrating epigenomic data with transcriptomic information or adopt causal strategies through gene co-expression networks, CellPolaris employs high-confidence GRN sources for model training, relying exclusively on transcriptomic data to generate previously unknown cell-type-specific GRNs. Applications of CellPolaris demonstrate remarkable efficacy in predicting master regulatory factors and simulating in-silico perturbations of transcription factors during cell fate transition, attaining state-of-the-art performance in accurately predicting candidate key factors and outcomes in cell reprogramming and spermatogenesis with validated datasets. It is worth noting that, with a transfer learning framework, CellPolaris can perform GRN based predictions in all cell types even across species. Together, CellPolaris represents a significant advancement in deciphering the mechanisms of cell fate regulation, thereby enhancing the precision and efficiency of cell fate manipulation at high resolution.

bioinformatics↗

bioRxiv2023

Gut microbial genomes with paired isolates from China signify probiotic and cardiometabolic effects

The gut microbiome displays significant genetic differences between populations while systematic characterization of the genomic landscape of the gut microbiome in Asia populations remains limited. Here, we present the Chinese gut microbial reference (CGMR) set, comprising 101,060 high quality metagenomic assembled genomes (MAGs) of 3,707 non-redundant species paired with 1,376 live isolates from a national wide collection of 3,234 fecal samples across China. This improved reference set contains 987 novel species compared with existing resources worldwide. By associating MAGs with geographic and phenotypic characteristics, we observed regional-specific coexisting MAGs and MAGs with probiotic and cardiometabolic functionalities. We further conducted mice experiments to confirm the probiotic effects of two Faecalibacterium intestinalis isolates in alleviating constipation, the cardiometabolic influences of three Bacteroides fragilis_A isolates in obesity, and the functional potential of isolates from the two new species belonging to the genera Parabacteroides and Lactobacillus in host lipids metabolism. Our study not only expands the current microbial genomes with paired isolates but also demonstrates their probiotic and cardiometabolic effects on hosts, contributing to the mechanistic understanding of host-microbe interactions and the translation of microbiome-based personalized therapies.

microbiology↗