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Bai, Z.

Publications and source records attributed to Bai, Z..

At least 55 records · Page 3Linked to original sources
bioRxiv2023

Sterile Soil as Prebiotics Mitigate Intergenerational Loss of Gut Microbial Diversity and Anxiety-Like Behavior Induced by Antibiotics in mice

The decline in gut microbial diversity of modern human is closely associated with the rising prevalence of various diseases. It is imperative to investigate the underlying causes of gut microbial loss and the rescue measures. Although the impact of non-perinatal antibiotic use on gut microbiota has been recognized, its intergenerational effects remain unexplored. Our previous research has highlighted soil in the farm environment as a key prebiotic for gut microbiome health by restoring gut microbial diversity and balance. In this study, we investigated the intergenerational consequences of antibiotic exposure and the therapeutic potential of soil prebiotics. We treated mice with vancomycin and streptomycin for 2 weeks continuously, followed by 4-8 weeks of withdrawal period before breeding. The process was repeated across 3 generations. Half of the mice in each generation received an oral soil prebiotic intervention. We assessed gut microbial diversity, anxiety behavior, microglia reactivity, and gut barrier integrity across generations. The antibiotics exposure led to a decrease in gut microbial diversity over generations, along with aggravated anxiety behavior, microglia abnormalityies, and altered intestinal tight junction protein expression. Notably, the third generation of male mice exhibited impaired reproductive capacity. Oral sterile soil intervention restored gut microbial diversity in adult mice across generations, concomitantly rescuing abnormalities in behavior, microglia activity, and intestinal barrier integrity. In conclusion, this study simulated an important process of the progressive loss gut microbiota diversity in modern human and demonstrated the potential of sterile soil as a prebiotic to reverse this process. The study provides a theoretical and experimental basis for the research and therapeutic interventions targeting multiple modern chronic diseases related to intestinal microorganisms.

microbiology↗
bioRxiv2023

FarmGTEx TWAS-server: an interactive web server for customized TWAS analysis in both human and farm animals

Transcriptome-wide association study (TWAS) is a powerful strategy for elucidating the molecular mechanisms behind the genetic loci of complex phenotypes. However, TWAS analysis is still daunting in many species due to the complication of the TWAS analysis pipeline, including the construction of the gene expression reference panel, gene expression prediction, and the subsequent association analysis in the large cohorts of genome-wide association study (GWAS). Farm animals are major protein sources and biomedical models for humans. To facilitate the translation of genetic findings across species, here we provide an interactive and easy-to-use multi-species TWAS web server for the entire community, called the FarmGTEx TWAS-server (http://twas.farmgtex.org), which is based on the GTEx and FarmGTEx projects. It includes gene expression data from 49, 34, and 23 tissues in 838 humans, 5,457 pigs, and 4,889 cattle, representing 38,180, 21,037, and 17,942 distinct eGenes in prediction models for humans, pigs, and cattle, respectively. It allows users to conduct gene expression prediction for any individuals with genotypes, GWAS summary statistics imputation, customized TWAS, and popular downstream functional annotation. It also provides 479,203, 1,208, and 657 tissue-gene-trait association trios for the research community, representing 1,129 human traits, 41 cattle traits, and 11 pig traits. In summary, the FarmGTEx TWAS-server is a one-stop solution for performing TWAS analysis for researchers without programming skills in both human and farm animal research communities. It will be maintained and updated timely within the FarmGTEx project to facilitate gene mapping and phenotype prediction within and across species.

bioinformatics↗
bioRxiv2023

Single-cell multi-omic topic embedding reveals cell-type-specific and COVID-19 severity-related immune signatures

The advent of single-cell multi-omics sequencing technology makes it possible for re-searchers to leverage multiple modalities for individual cells and explore cell heterogeneity. However, the high dimensional, discrete, and sparse nature of the data make the downstream analysis particularly challenging. Most of the existing computational methods for single-cell data analysis are either limited to single modality or lack flexibility and interpretability. In this study, we propose an interpretable deep learning method called multi-omic embedded topic model (moETM) to effectively perform integrative analysis of high-dimensional single-cell multimodal data. moETM integrates multiple omics data via a product-of-experts in the encoder for efficient variational inference and then employs multiple linear decoders to learn the multi-omic signatures of the gene regulatory programs. Through comprehensive experiments on public single-cell transcriptome and chromatin accessibility data (i.e., scRNA+scATAC), as well as scRNA and proteomic data (i.e., CITE-seq), moETM demonstrates superior performance compared with six state-of-the-art single-cell data analysis methods on seven publicly available datasets. By applying moETM to the scRNA+scATAC data in human bone marrow mononuclear cells (BMMCs), we identified sequence motifs corresponding to the transcription factors that regulate immune gene signatures. Applying moETM analysis to CITE-seq data from the COVID-19 patients revealed not only known immune cell-type-specific signatures but also composite multi-omic biomarkers of critical conditions due to COVID-19, thus providing insights from both biological and clinical perspectives.

bioinformatics↗
bioRxiv2023

Learning functional conservation between pig and human to decipher evolutionary mechanisms underlying gene expression and complex trait

The assessment of genomic conservation between human and pig at the functional level can help understand and improve the potential of pig as a human biomedical model. To address this, we developed a Deep learning-based approach to learn the Genomic Conservation at the Functional level (DeepGCF) between species by integrating 386 and 374 epigenome and transcriptome profiles from human and pig, respectively. DeepGCF demonstrated a better prediction performance compared to the previous functional conservation prediction method. In addition, we showed that the resulting DeepGCF score captures the functional conservation by examining DeepGCF on chromatin states, sequence ontologies, and regulatory variants. Regions with higher DeepGCF score play a more important role in regulatory activities and show heritability enrichment in human complex traits and diseases. Our DeepGCF approach shows a promising application on the comparison of cross-species functional conservation, and the model framework can be easily adapted to other species. By expanding the model to integrate the functional profiles of multiple species, including human, mouse, pig, cattle, and other livestock animals in the future, the functional conservation information will provide additional insight into the genetic and evolutionary mechanisms behind complex traits and diseases.

genetics↗
bioRxiv2022

Dysfunctional analysis of the pre-training model on nucleotide sequences and the evaluation of different k-mer embeddings

Pre-training has attracted much attention in recent years. Although significant performance improvements have been achieved in many downstream tasks using pre-training, the mechanism of how a pre-training method works for downstream tasks is not fully illustrated. In this work, focusing on nucleotide sequences, we decompose a pre-training model of Bidirectional Encoder Representations from Transformers (BERT) into embedding and encoding modules to illustrate what a pre-trained model learns from pre-training data. Through dysfunctional analysis on both data and model level, we demonstrate that the context-consistent k-mer representation is the primary product that a typical BERT model learns in the embedding layer. Surprisingly, single usage of the k-mer embedding pre-trained on the random data can achieve comparable performance to that of the k-mer embedding pre-trained on actual biological sequences. We further compare the learned k-mer embeddings with other commonly used k-mer representations in downstream tasks of sequence-based functional predictions and propose a novel solution to accelerate the pre-training process. Contactyaozhong@ims.u-tokyo.ac.jp or imoto@hgc.jp Supplementary informationThe source code and relevant data are available at https://github.com/yaozhong/bert_investigation.

bioinformatics↗
bioRxiv2022

DiffDomain enables identification of structurally reorganized topologically associating domains

Topologically associating domains (TADs) are critical structural units in three-dimensional genome organization of mammalian genome. Dynamic reorganizations of TADs between health and disease states are associated with transcription and other essential genome functions. However, computational methods that can identify reorganized TADs are still in the early stages of development. Here, we present DiffDomain, an algorithm leveraging high-dimensional random matrix theory to identify structurally reorganized TADs using chromatin contact maps. Method comparison using multiple real Hi-C datasets reveals that DiffDomain outperforms alternative methods for FPRs, TPRs, and identifying a new subtype of reorganized TADs. The robustness of DiffDomain and its biological applications are demonstrated by applying on Hi-C data from different cell types and disease states. Identified reorganized TADs are associated with structural variations and changes in CTCF binding sites and other epigenomic changes. By applying to a single-cell Hi-C data from mouse neuronal development, DiffDomain can identify reorganized TADs between cell types with reasonable reproducibility using pseudo-bulk Hi-C data from as few as 100 cells per condition. Moreover, DiffDomain reveals that TADs have differential cell-to-population variability and heterogeneous cell-to-cell variability. Therefore, DiffDomain is a statistically sound method for better comparative analysis of TADs using both Hi-C and single-cell Hi-C data.

bioinformatics↗
bioRxiv2022

A compendium of genetic regulatory effects across pig tissues

The Farm animal Genotype-Tissue Expression (FarmGTEx, https://www.farmgtex.org/) project has been established to develop a comprehensive public resource of genetic regulatory variants in domestic animal species, which is essential for linking genetic polymorphisms to variation in phenotypes, helping fundamental biology discovery and exploitation in animal breeding and human biomedicine. Here we present results from the pilot phase of PigGTEx (http://piggtex.farmgtex.org/), where we processed 9,530 RNA-sequencing and 1,602 whole-genome sequencing samples from pigs. We build a pig genotype imputation panel, characterize the transcriptional landscape across over 100 tissues, and associate millions of genetic variants with five types of transcriptomic phenotypes in 34 tissues. We study interactions between genotype and breed/cell type, evaluate tissue specificity of regulatory effects, and elucidate the molecular mechanisms of their action using multi-omics data. Leveraging this resource, we decipher regulatory mechanisms underlying about 80% of the genetic associations for 207 pig complex phenotypes, and demonstrate the similarity of pigs to humans in gene expression and the genetic regulation behind complex phenotypes, corroborating the importance of pigs as a human biomedical model.

genomics↗
bioRxiv2022

R-loop editing by DNA cytosine deaminase APOBEC3B determines the activity of estrogen receptor enhancers

Estrogen receptor (ER) activation results in the formation of DNA double strand breaks (DSB), which promote genomic instability and tumour heterogeneity in ER-positive breast cancers. The single-stranded DNA (ssDNA) cytosine deaminase APOBEC3B (A3B) regulates ER activity by inducing DSB at ER enhancers. To delineate how A3B recognises its substrates and unveil the underlying mechanism leading to the formation of ER-induced DSB, we sampled A3B-mediated deamination sites using whole genome sequencing in a human breast cancer cell model lacking base excision repair function. Our genome-wide analysis revealed that C>U conversions carried out by A3B in R-loop structures are processed into DSB in the vicinity of ER promoters or enhancers. A mechanism which required both the processing of A3B-editing sites and R-loops by distinct DNA damage repair mechanisms. In addition, using BioID-enabled mass-spectroscopy proteomics, we identified TDRD3 as a key A3B-binding partner directing the activity of A3B to ER-induced R-loops. This study suggests a function for A3B in sustaining tumour evolution as an adaptive response at the transcriptional and epigenetic level and supports A3B as a promising target to control ER activity in cancer.

cancer biology↗
bioRxiv2022

Microfluidic immuno-serology assay revealed a limited diversity of protection against COVID-19 in patients with altered immunity

The immune response to SARS-CoV-2 for patients with altered immunity such as hematologic malignancies and autoimmune disease may differ substantially from that in general population. These patients remain at high risk despite wide-spread adoption of vaccination. It is critical to examine the differences at the systems level between the general population and the patients with altered immunity in terms of immunologic and serological responses to COVID-19 infection and vaccination. Here, we developed a novel microfluidic chip for high-plex immuno-serological assay to simultaneously measure up to 50 plasma or serum samples for up to 50 soluble markers including 35 plasma proteins, 11 anti-spike/RBD IgG antibodies spanning all major variants, and controls. Our assay demonstrated the quintuplicate test in a single run with high throughput, low sample volume input, high reproducibility and high accuracy. It was applied to the measurement of 1,012 blood samples including in-depth analysis of sera from 127 patients and 21 healthy donors over multiple time points, either with acute COVID infection or vaccination. The protein association matrix analysis revealed distinct immune mediator protein modules that exhibited a reduced degree of diversity in protein-protein cooperation in patients with hematologic malignancies and patients with autoimmune disorders receiving B cell depletion therapy. Serological analysis identified that COVID infected patients with hematologic malignancies display impaired anti-RBD antibody response despite high level of anti-spike IgG, which could be associated with limited clonotype diversity and functional deficiency in B cells and was further confirmed by single-cell BCR and transcriptome sequencing. These findings underscore the importance to individualize immunization strategy for these high-risk patients and provide an informative tool to monitor their responses at the systems level.

bioengineering↗
bioRxiv2022

Effect of compound Tinglizi Decoction on non-classical pathway of pyroptosis in rats with chronic obstructive pulmonary disease related pulmonary hypertension

ObjectiveTo observe the effect of compound Tinglizi Decoction on non-classical pathway of cell death in rats with pulmonary hypertension, and to explore its mechanism of action in the treatment of COPD-related pulmonary hypertension. Methods60 male SD rats were randomly divided into Western group, normal group, model group, high-dose group, medium dose group and low-dose group, with 10 rats per group. We detected the protein expression levels of caspase-11, GSDMD and IL-6 by Western blot, detected serum TNF- by ELISA, and observed the morphology of lung tissue by hematoxylin and eosin (HE) staining. ResultsIn the expression of caspase11, GSDMD, IL-6 mRNA and protein in lung tissue, the model group was significantly higher than the normal group (P < 0.01), and the high-dose group, middledose group, low-dose group and western medicine group were significantly lower than the model group (P < 0.01). The levels of serum TNF- and serum creatinine in the model group were significantly higher than those in the normal group (P < 0.01); and those in the high-dose group, middle-dose group, low-dose group and western medicine group were significantly lower than those in the model group (P < 0.05). ConclusionThe mechanism of compound Tinglizi Decoction in the intervention of COPD-related pulmonary hypertension rats is related to the inhibition of non-classical pathway of cell death.

cell biology↗
bioRxiv2022

Cell- and non-cell-autonomous ARF3 coordinates meristem proliferation and organ patterning in Arabidopsis

In cell-cell communication, non-cell-autonomous transcription factors play vital roles in controlling plant stem cell fate. We previously reported that AUXIN RESPONSE FACTOR 3 (ARF3), a member of the ARF family with critical roles in floral meristem maintenance and determinacy, has a distinct accumulation pattern that differs from the expression domain of its encoding gene in the shoot apical meristem (SAM). However, the biological meaning of this difference is obscure. Here, we demonstrate that ARF3 expression is mainly activated at the periphery of the SAM by auxin, where ARF3 cell-autonomously regulates the expression of meristem-organ boundary-specific genes, such as CUP-SHAPED COTYLEDON1-3 (CUC1-3), BLADE ON PETIOLE1-2 (BOP1-2) and TARGETS UNDER ETTIN CONTROL3 (TEC3) to determine organ patterning. We also show that ARF3 is translocated into the organizing center, where it represses cytokinin activity and WUSCHEL expression to regulate meristem activity non-cell-autonomously. Therefore, ARF3 acts as a molecular link that mediates the interaction of auxin and cytokinin signaling in the SAM while coordinating the balance between meristem maintenance and organogenesis. Our findings reveal an ARF3-mediated coordination mechanism through cell-cell communication in dynamic SAM maintenance.

developmental biology↗
bioRxiv2021

Identification of Bacteriophages Using Deep Representation Model with Pre-training

MotivationBacteriophages/Phages are the viruses that infect and replicate within bacteria and archaea, and rich in human body. To investigate the relationship between phages and microbial communities, the identification of phages from metagenome sequences is the first step. Currently, there are two main methods for identifying phages: database-based (alignment-based) methods and alignment-free methods. Database-based methods typically use a large number of sequences as references; alignment-free methods usually learn the features of the sequences with machine learning and deep learning models. ResultsWe propose INHERIT which uses a deep representation learning model to integrate both database-based and alignment-free methods, combining the strengths of both. Pre-training is used as an alternative way of acquiring knowledge representations from existing databases, while the BERT-style deep learning framework retains the advantage of alignment-free methods. We compare INHERIT with four existing methods on a third-party benchmark dataset. Our experiments show that INHERIT achieves a better performance with the F1-score of 0.9932. In addition, we find that pre-training two species separately helps the non-alignment deep learning model make more accurate predictions. AvailabilityThe codes of INHERIT are now available in: https://github.com/Celestial-Bai/INHERIT. Contactyaozhong@ims.u-tokyo.ac.jp and imoto@hgc.jp Supplementary informationSupplementary data are available at BioRxiv online.

bioinformatics↗
bioRxiv2021

Proteomic analysis of coarse and fine skin tissues of Liaoning cashmere goat

Proteomics is the study of all proteins expressed by a cell or even an organism. However, knowledge of proteins that regulate the fineness of cashmere is limited. Liaoning Cashmere goat (LCG) is a valuable genetic resource of China. The skin samples of Liaoning cashmere goats during the growing period were collected performed Tandem Mass Tag (TMT) method and identified 117 differentially expressed proteins in CT_LCG (course type) and FT_LCG (fine type). To verify protein genes differentially expressed in LCG, we performed PRM validation on three candidate proteins (ALB, SDC1 and ITGB4) in CT-LCG and FT-LCG. Furthermore, primary metabolic process and lysosome are most enriched in the GO and KEGG pathways, respectively. In addition, we also derived a protein-protein interaction (PPI) regulatory network from the perspective of bioinformatics. This study sought to elucidate the molecular mechanism of differential proteins regulating cashmere fineness of Liaoning cashmere goats by using TMT quantitative proteomics analysis. Differentially expressed proteins ALB and SDC1 may regulate cashmere fineness, ITGB4 can be further studied as a promising protein. They can be used as key genes to lay a foundation for the study of cashmere fineness of Liaoning cashmere goats.

genetics↗
bioRxiv2021

Single-cell antigen-specific activation landscape of CAR T infusion product identifies determinants of CD19 positive relapse in patients with ALL

Chimeric antigen receptor-modified (CAR) T cells targeting CD19 have mediated dramatic responses in relapsed/refractory acute lymphoblastic leukemia (ALL), yet a notable number of patients have CD19-positive relapse within one year of treatment. It remains unclear if the long-term response is associated with the characteristics of CAR T cells in infusion products, hindering the identification of biomarkers to predict therapeutic outcomes prior to treatment. Herein we present 101,326 single cell transcriptomes and surface protein landscape from the CAR T infusion products of 12 pediatric ALL patients upon CAR antigen-specific stimulation in comparison with TCR-mediated activation and controls. We observed substantial heterogeneity in the antigen-specific activation states, among which a deficiency of Th2 function was associated with CD19-positive relapsed patients (median remission 9.6 months) compared with very durable responders (remission>54 months). Proteomic profiles also revealed that the frequency of early memory T cell subsets, rather than activation or co-inhibitory signatures could distinguish CD19-positive relapse. Additionally, a deficit of type 1 helper and cytotoxic effector function and an enrichment for terminally differentiated CD8+ T cells exhibiting low cytokine polyfunctionality was associated with initial non-responders. By contrast, the single-cell transcriptomic data of unstimulated or TCR-activated CAR T cells failed to predict clinical responses. In aggregate, our results dissect the landscape of CAR-specific activation states in infusion products that can identify patients who do not develop a durable response to the therapy, and unveil the molecular mechanisms that may inform strategies to boost specific T cell function to maintain long term remission.

immunology↗
bioRxiv2021

Spatial Epigenome Sequencing at Tissue Scale and Cellular Level

Spatial biology is emerging as a new frontier of biomedical research in development and disease, but currently limited to transcriptome and a panel of proteins. Here we present spatial epigenome profiling for three histone modifications (H3K27me3, H3K4me3, H3K27ac) via next-generation sequencing by combining in-tissue CUT&Tag chemistry and microfluidic deterministic barcoding. Spatial chromatin states in mouse embryos or olfactory bulbs revealed tissue type-specific epigenetic regulations, in concordance with ENCODE reference data, but providing spatially resolved genome-wide profiles at tissue scale. Using fluorescence imaging to identify the tissue pixels (20m) each containing one nucleus allowed us to extract single-cell epigenomes in situ. Spatial chromatin state profiling in tissue may enable unprecedented opportunities to study epigenetic regulation, cell function and fate decision in normal physiology and pathogenesis.

genomics↗
bioRxiv2021

Soil causes gut microbiota to flourish and total serum IgE levels to decrease in mice

Traditional farm environments induce protection from allergic diseases. In this study, farm environmental factors were classified into three categories, environmental microbes, soil, and organic matter. To explore the impact of soil and environmental microorganisms on gut microbiota and immune function, mice were fed sterilized soil, soil microbes (in lieu of environmental microbes), or non-sterilized soil. Metagenomic sequencing results showed the intake of sterile soil i.e. inhaling a small amount of soil microbes in the air increased gut microbial diversity and the abundance of type III secretion system (T3SS) genes, and decreased serum immune globulin E (IgE) levels induced by 2-4-dinitrofluorobenzene(DNFB). The intake of soil microbes increased the abundance of genes involved in the metabolism of short chain fatty acids and amino acid biosynthesis. Meanwhile, the intake of soil increased gut microbial diversity, the abundance of T3SS genes and related infectious elements, and genes associated with the metabolism of short chain fatty acids and amino acid biosynthesis, and decreased serum IgE levels. Therefore, soil may be useful as a potential "prebiotic" promoting the reproduction and growth of some intestinal microorganisms that harbor bacterial secretion system genes, especially those of T3SS, whose abundance was positively and significantly correlated with innate immune function of mice.

ecology↗
bioRxiv2021

Discovering hidden brain network responses to naturalistic stimuli via tensor component analysis of multi-subject fMRI data

The study of brain network interactions during naturalistic stimuli facilitates a deeper understanding of human brain function. Intersubject correlation (ISC) analysis of functional magnetic resonance imaging (fMRI) data is a widely used method that can measure neural responses to naturalistic stimuli that are consistent across subjects. However, interdependent correlation values in ISC artificially inflated the degrees of freedom, which hinders the investigation of individual differences. Besides, the existing ISC model mainly focus on similarities between subjects but fails to distinguish neural responses to different stimuli features. To estimate large-scale brain networks evoked with naturalistic stimuli, we propose a novel analytic framework to characterize shared spatio-temporal patterns across subjects in a purely data-driven manner. In the framework, a third-order tensor is constructed from the timeseries extracted from all brain regions from a given parcellation, for all participants, with modes of the tensor corresponding to spatial distribution, time series and participants. Tensor component analysis (TCA) will then reveal spatially and temporally shared components, i.e., naturalistic stimuli evoked networks, their temporal courses of activity and subject loadings of each component. To enhance the reproducibility of the estimation with TCA, a novel spectral clustering method, tensor spectral clustering, was proposed and applied to evaluate the stability of TCA algorithm. We demonstrate the effectiveness of the proposed framework via simulations and real fMRI data collected during a motor task with a traditional fMRI study design. We also apply the proposed framework to fMRI data collected during passive movie watching to illustrate how reproducible brain networks are identified evoked by naturalistic movie viewing.

neuroscience↗