Biology subjects

Ni, Y.

Publications and source records attributed to Ni, Y..

At least 37 records · Page 2Linked to original sources

bioRxiv2024

Breast tumor microbiome regulates anti-tumor immunity and T cell-associated metabolites

BackgroundBreast cancer, the most common cancer type among women, was recently found to contain a specific tumor microbiome, but its impact on host biology remains unclear. CD8+ tumor-infiltrating lymphocytes (TILs) are pivotal effectors of anti-tumor immunity that influence cancer prognosis and response to therapy. This study aims to elucidate interactions between CD8+ TILs and the breast tumor microbiome and metabolites, as well as how the breast tumor microbiome may affect the tumor metabolome. MethodsWe investigated the interplay among CD8+ TILs, the tumor microbiome, and the metabolome in a cohort of 46 breast cancer patients with mixed subtypes (Cohort A). We characterized the tumor metabolome by mass spectrometry and CD8+ TILs by immunohistochemistry. Microbiome composition and T cell gene transcript levels were obtained from data from our previous study, which utilized 16S rRNA gene sequencing and a targeted mRNA expression panel. To examine interactions between intratumoral Staphylococcus and specific breast cancer subtypes, we analyzed RNA sequencing data from an independent cohort of 370 breast cancer patients (Cohort B). We explored the functions of the tumor microbiome using mouse models of triple-negative breast cancer (TNBC). ResultsIn tumors from Cohort A, the relative abundance of Staphylococcus positively correlated with the expression of T cell activation genes. The abundances of multiple metabolites exhibited significant correlations with CD8+ TILs, of which NADH, {gamma}-glutamyltryptophan, and {gamma}-glutamylglutamate displayed differential abundance in Staphylococcus-positive versus Staphylococcus-negative breast tumors. In a larger breast cancer cohort (Cohort B), we observed positive correlations between tumoral Staphylococcus and CD8+ TIL activity exclusively in TNBC. Preclinical experiments demonstrated that intratumoral administration of S. aureus, the predominant species of Staphylococcus in human breast tumors, resulted in a depletion of total NAD metabolites, and reduced the growth of TNBC tumors by activating CD8+ TILs. ConclusionsWe identified specific metabolites and microbial taxa associated with CD8+ TILs, delineated interactions between the breast tumor microbiome and metabolome, and demonstrated that intratumoral Staphylococcus influences anti-tumor immunity and TIL-associated metabolites. These findings highlight the role of low-biomass microbes in tumor tissues and provide potential biomarkers and therapeutic agents for breast cancer immunotherapy that merit further investigation.

cancer biology↗

bioRxiv2024

LLPS REDIFINE allows the biophysical characterization of multicomponent condensates without tags or labels

Liquid-liquid phase separation (LLPS) phenomenon plays a vital role in multiple cell biology processes, providing a mechanism to concentrate biomolecules and promote cellular reactions locally. Despite its significance in biology, there is a lack of conventional techniques suitable for studying biphasic samples in their biologically relevant form. Here, we present a label-free and non-invasive approach to characterize protein, RNA and water in biomolecular condensates termed LLPS REstricted DIFusion of INvisible speciEs (REDIFINE). Relying on diffusion NMR measurements, REDIFINE exploits the exchange dynamics between the condensed and dispersed phases to allow the determination of not only diffusion constants in both phases but also the fractions of the species, the average radius of the condensed droplets and the exchange rate between the phases. We can also access the concentration of proteins in both phases. Observing proteins, RNAs, water, and even small molecules, REDIFINE analysis allows a rapid biophysical characterization of multicomponent condensates which is important to understand their functional roles. In comparing multiple systems, REDIFINE reveals that folded RNA-binding proteins form smaller and more dynamic droplets compared to the disordered ones. In addition, REDIFINE proved to be valuable beyond LLPS for the determination of binding constants in soluble protein-RNA without the need for titration.

biophysics↗

bioRxiv2024

Oxytocin salvages context-specific hyperaltruistic preference through moral framing

An intriguing advancement in recent moral decision-making research suggests that people are more willing to sacrifice monetary gains to spare others from suffering than to spare themselves, yielding the hyperaltruistic tendency. Other studies, however, indicate an opposite egoistic bias in that subjects are less willing to harm themselves for the benefits of others than for their own benefits. These results highlight the delicate inner workings of moral decision and call for a mechanistic account of hyperaltruistic preference. We investigated the boundary conditions of hyperaltruism by presenting subjects with trade-off choices combing monetary gains and painful electric shocks, or, choices combing monetary losses and shocks. We first showed in study 1 that switching the decision context from gains to losses effectively eliminated the hyperaltruistic preference and the decision context effect was associated with the altered relationship between subjects instrumental harm (IH) trait attitudes and their relative pain sensitivities. In the pre-registered study 2, we tested whether oxytocin, a neuropeptide linked to parochial altruism, might restore the context-dependent hyperaltruistic preference. We found that oxytocin increased subjects reported levels of framing the task as harming (vs. helping) others, which mediated the correlation between IH and relative pain sensitivities. Thus, the loss decision context and oxytocin diminished and restored the mediation effect of subjective harm framing, respectively. Our results help to elucidate the psychological processes underpinning the contextual specificity of hyperaltruism and carry implications in promoting prosocial interactions in our society.

neuroscience↗

bioRxiv2024

Causal Associations of Circulating Helicobacter pylori Antibodies with 5 Skin Disorders: A Mendelian Randomization Analysis

Helicobacter pylori (H. pylori) infection, a prevalent bacterial ailment affecting the stomach, has been linked to diverse gastrointestinal disorders. Nevertheless, the correlation between H. pylori infection and skin disorders has not been definitively established. We use Mendelian randomization analysis to explore causal relationships with seven pylori antibody levels and five skin diseases, such as rosacea, psoriasis, alopecia areata, pruritus and urticaria. The primary analytical method employed was inverse variance weighted (IVW), and a sensitivity analysis was executed to evaluate the robustness of the primary findings. The results revealed a positive causal relationship between H. pylori catalase antibody levels and AA (IVW: OR = 1.32, 95% CI = 1.07 to 1.61, P = 0.008), as well as a positive causal relationship between H. pylori UreA antibody levels and pruritus (IVW: OR = 1.17, 95% CI = 1.05 to 1.31, P = 0.006). These effects were robust to sensitivity analyses. Our results suggested that H. pylori infection was a risk factor for AA and pruritus. This finding may have implications for the prevention and treatment of these skin disorders.

genomics↗

bioRxiv2024

Genetic burden of dysregulated cytoskeletal organisation in the pathogenesis of pulmonary fibrosis

BackgroundRare genetic variants contribute to pulmonary fibrosis (PF) risk and outcome, with known variants highlighting the importance of impaired telomere maintenance and surfactant biology. However, much of the disrupted genetic architecture of PF remains unexplained. This study aimed to identify genes with rare pathogenic coding variants that represented a burden at the exon level associated with the pathogenesis of PF. MethodsPF case cohorts included the PROFILE study of incident idiopathic pulmonary fibrosis (IPF) and PF-classified participants from the Genomics England 100K (GE100KGP) study. Whole genome sequencing data were used to test the burden of rare protein altering variants (PAVs; CADD score >20) defined by minor allele frequency <0.1% in the summary level gnomAD reference database (v3.1.2). A summary exon-level pathogenicity score was derived by standardising PAV predictions from functional annotation tools (AlphaMissense, REVEL, ClinPred, CADD, PolyPhen-2, SIFT) averaged across exons. SKAT-O based kernel regression associated exon-level pathogenicity features with disease progression and survival. Single cell lung transcriptomic datasets were harmonised to evaluate expression patterns and gene ontology characterised enriched molecular functions. ResultsAcross 507 PROFILE cases and 451 GE100KGP cases, each compared against 76,156 reference participants, 77 genes showed overlapping significant rare PAV burden, comprising 206 concordant PAVs. Of these, 15 genes had exons with pathogenicity scores that were associated with worse clinical outcomes in IPF, including FAT4 exon-10 and DNAH7 exon-43. Dysregulated gene expression of COL6A3 and FAT4 was observed in IPF lung fibroblasts, while DNAH7, DNAH12 and PCM1 showed high expression in IPF epithelial cells. The top enriched molecular functions were related to cytoskeletal motor activity. ConclusionsRare PAV burden highlighted that pathogenicity within specific exons was associated with PF risk and worse clinical outcomes, identifying genes with dysregulated expression in disease lung cells. The genetic architecture implicates disrupted cytoskeletal organisation in the pathogenesis of PF.

genetics↗

bioRxiv2024

Reducing competition between msd and genomic DNA significantly improved the editing efficiency of the retron editing system

A retron is a distinct system encoding reverse transcriptase and a unique single-stranded DNA/RNA hybrid called multicopy single-stranded DNA (msDNA). The ability of msDNA to serve as a homologous recombination donor for gene editing has attracted great interest. However, the mechanism by which msDNA expression affects editing efficiency remains unclear. In this study, we show that an increase in msd number increased msDNA yield but was not necessarily accompanied by an increase in editing efficiency. Mechanistic studies indicate that msd and genomic regions competed for msDNA during recombination. As the number of msd increased, the amount of msDNA allocated to the genomic targets decreased, resulting in a decrease in editing efficiency. Finally, we reduced msd editing by expressing msDNA corresponding to the plasmid replication leading strand sequence, thus constructing a retron-based gene editing system that achieved 100% editing efficiency in the shortest time reported to date. The above results reveal a completely different features between retron-based gene editing system and oligonucleotide-mediated gene editing system and will provide theoretical guidance for the design and application of the retron system.

synthetic biology↗

bioRxiv2024

Trajectory-centric Framework TrajAtlas reveals multi-scale differentiation heterogeneity among cells, genes, and gene module in osteogenesis

Osteoblast differentiation is crucial for bone formation and maintaining skeletal integrity. Although it is now understood that this process exhibits significant heterogeneity across developmental stages and tissue microenvironments, the underlying mechanisms remain largely unexplored. In the present study, we introduce TrajAtlas, a comprehensive framework that addresses this gap in knowledge. TrajAtlas comprises four modules: a reference atlas (Differentiation Atlas), a differentiation model (Differentiation Model), a tool for differential pseudotime analysis (TrajDiff), and a method for pseudotemporal gene module detection (TRAVMap). By leveraging single-cell technologies, TrajAtlas offers a systematic approach to exploring the multi-scale heterogeneity among cells, genes, and gene modules within population-level trajectories across diverse tissues and age groups. We systematically investigate the impact of age and injury on osteogenesis, providing new insights into osteoporosis and bone regeneration. In conclusion, our comprehensive framework offers novel insights into osteogenesis and provides a valuable resource for understanding the complexities of bone formation. Author SummaryOsteoblasts, the cells responsible for bone formation, can originate from various cellular sources. However, its unclear how different progenitor cells differentiate into osteoblasts, and how this process is influenced by factors such as age and tissue location. This knowledge gap stems from the lack of comprehensive databases and tools to decipher the differentiation process. In this study, we introduce TrajAtlas, a comprehensive framework designed to bridge this gap. To explore the cellular origins of osteoblasts, we constructed an atlas centered on osteogenesis. To answer how progenitor cells differentiate to osteoblasts, we developed a model that reveals the dynamic regulatory landscape during this process. To elucidate the influence of age and tissue location on differentiation, we built a tool for differential analysis. Furthermore, to identify conserved patterns of differentiation, we developed an approach to detect pseudotemporal gene modules. We validated the effectiveness of this framework by applying it to more datasets, unveiling novel cell states associated with injury. Notably, this framework focuses on dynamic processes, with the potential for broader applications in studying cell differentiation and complementing cell-centric analyses.

bioinformatics↗

bioRxiv2024

Single-cell landscapes of long non-coding RNAs in early vascular endothelial development and hemogenic specification

Understanding the molecular regulation of arterial and hemogenic specification during early embryonic vascular development is crucial for guiding vascular and hematopoietic regeneration. Accumulating evidence emphasizes the role of long non-coding RNAs (lncRNAs) in cell fate decision. However, the dynamic expression and the potential roles of lncRNAs in early vascular development are still unknown. Here, we first constructed a single-cell landscape of lncRNA expression based on the deeply sequenced tag-based single-cell transcriptome data of early embryonic vascular endothelial cells (VECs). We revealed the contribution of lncRNAs to VEC heterogeneity and identified 295 lncRNAs with specific expression in eight VEC populations. Furthermore, we identified a series of lncRNAs potentially involved in regulating the two waves of arterial specification and hemogenic specification. We uncovered a transient downregulation of H19 in the hemogenic endothelial population during endothelial-to-hematopoietic transition. Additionally, we constructed a transcription factor regulatory network composed of 287 regulons for early VEC development. We further revealed differential activation patterns of regulons and modules in the eight VEC populations, and predicted potential lncRNA-regulon regulatory network. Moreover, unsupervised analysis of the lncRNA expression profile revealed novel VEC subpopulations strongly associated with the maturation of VECs, suggesting the prominent roles of lncRNAs in endothelial maturation. In summary, our study fills the gap in understanding of lncRNA regulatory networks in early vascular development and provides insights into the fields of vascular and hematopoietic regeneration research.

developmental biology↗

bioRxiv2024

Association of Body Index with Fecal Microbiome in Children Cohorts with Ethnic-Geographic Factor Interaction: Accurately Using a Bayesian Zero-inflated Negative Binomial Regression Model

The exponential growth of High-Throughput Sequencing (HTS) data on the microbial communities presents researchers with an unparalleled opportunity to delve deeper into the association of microorganisms with host phenotype. However, this growth also poses a challenge, as microbial data is complex, sparse, discrete, and prone to zero-inflation. Moreover, current methods for integrating microbiome data and other covariates are severely lacking. Hence, we propose a Bayesian zero-inflated negative binomial (ZINB) regression model that is capable of identifying differentially abundant taxa with distinct phenotypes and quantifying the effects of covariates on these taxa. Our model exhibits excellent performance when tested on simulated data. Upon successfully applying our model to a real multi-ethnic cohort study, we discovered that the prevailing understanding of microbial count data from previous research was overly dogmatic, because only a subset of taxa demonstrated zero inflation in real data. Moreover, we have discovered that dispersion parameters significantly influence the accuracy of model results, and increasing sample size can alleviate this issue. In all, we have presented an innovative integrative Bayesian regression model and a comprehensive pipeline for conducting a multi-ethnic cohort study of children, which facilitates bacterial differential abundance analysis and quantification of microbiome-covariate effects. This approach can be applied to general microbiome studies. IMPORTANCEMicrobiome are closely associated with physical indicators of the body, such as height, weight, age and BMI, which can be used as measures of human health. How to accurately identify which taxa in the microbiome are closely related to indicators of physical development is valuable as microbial markers of local child growth. Complex biological systems can be effectively modeled with ZINB model which is a Bayesian Generalized Linear Model. However, the potential of the ZINB model in the microbiome field has not yet been fully utilized in practice. Microbial count data are more complex than other scenarios, and our model captures this complexity. Our study is the first to discuss the effects of zero inflation and the degree of overdispersion in microbiome data on the results of model solutions. Finally, our work successfully applied it to a real multi-ethnic cohort study.

bioinformatics↗

bioRxiv2024

Giraffe: a tool for comprehensive processing andvisualization of multiple long-read sequencing data

Third-generation sequencing techniques have become increasingly popular due to their ability to generate long, high-quality reads. Utilizing datasets from various samples and multiple sequencing platforms for comparative and comprehensive analysis is essential for exploring biological mechanisms and establishing benchmark baselines. However, current tools for long reads primarily focus on quality control (QC) and read processing for individual samples, complicating the profiling and comparison of multiple datasets. The lack of tools for data comparison and visualization presents challenges for researchers with limited bioinformatics experience. Furthermore, developing a comprehensive long-read QC method that facilitates comparative analysis and visualization across multiple samples and platforms is necessary to establish benchmark baselines for selecting appropriate sequencing platforms. We introduce Giraffe, a Python3-based command line tool designed for comparative analysis and visualization across multiple samples and platforms. Giraffe enables the assessment of read quality, sequencing bias, and genomic regional methylation proportions for both DNA and direct RNA sequencing reads. Its usability has been demonstrated in various scenarios, including comparisons of different biological processing methods (whole genome amplification vs. shotgun), sequencing platforms (Oxford Nanopore Technology vs. Pacific Biosciences), tissues (kidney marrow with and without blood), and biological replicates (kidney marrows). Additionally, our findings indicate that Oxford Nanopore duplex reads outperform PacBio HiFi reads in homopolymer identification and GC evenness while maintaining comparable overall read quality.

bioinformatics↗

bioRxiv2024

Deciphering the Transcription Factor Landscape in Neuroendocrine Prostate Cancer Progression: A Novel Approach to Understand NE Transdifferentiation

Background and ObjectiveProstate cancer (PCa) is a leading cause of cancer mortality in men, with neuroendocrine prostate cancer (NEPC) representing a particularly resistant subtype. The role of transcription factors (TFs) in the progression from prostatic adenocarcinoma (PRAD) to NEPC is poorly understood. This study aims to identify and analyze lineage-specific TF profiles in PRAD and NEPC and illustrate their dynamic shifts during NE transdifferentiation. MethodsA novel algorithmic approach was developed to evaluate the weighted expression of TFs within patient samples, enabling a nuanced understanding of TF landscapes in PCa progression and TF dynamic shifts during NE transdifferentiation. Resultsunveiled TF profiles for PRAD and NEPC, identifying 126 shared TFs, 46 adenocarcinoma-TFs, and 56 NEPC-TFs. Enrichment analysis across multiple clinical cohorts confirmed the lineage specificity and clinical relevance of these lineage-TFs signatures. Functional analysis revealed that lineage-TFs are implicated in pathways critical to cell development, differentiation, and lineage determination. Novel lineage-TF candidates were identified, offering potential targets for therapeutic intervention. Furthermore, our longitudinal study on NE transdifferentiation highlighted dynamic TF expression shifts and delineated a three-phase hypothesis for the process comprised of de-differentiation, dormancy, and re-differentiation. and proposing novel insights into the mechanisms of PCa progression. ConclusionThe lineage-specific TF profiles in PRAD and NEPC reveal a dynamic shift in the TF landscape during PCa progression, highlighting three distinct phases of NE transdifferentiation.

cancer biology↗

bioRxiv2024

Dissociable memory modulation mechanisms facilitate fear amnesia at different timescales

Memory reactivation renders consolidated memory fragile and thereby opens the window for memory updates, such as memory reconsolidation. However, whether memory retrieval facilitates update mechanisms other than memory reconsolidation remains unclear. We tested this hypothesis in three experiments with healthy human participants. First, we demonstrate that memory retrieval-extinction protocol prevents the return of fear expression shortly after extinction training and this short-term effect is memory reactivation dependent (Study 1, N = 57 adults). Furthermore, across different timescales, the memory retrieval-extinction paradigm triggers distinct types of fear amnesia in terms of cue-specificity and cognitive control dependence, suggesting that the short-term fear amnesia might be caused by different mechanisms from the cue-specific amnesia at a longer and separable timescale (Study 2, N = 79 adults). Finally, using continuous theta-burst stimulation (Study 3, N = 75 adults), we directly manipulated brain activity in the dorsolateral prefrontal cortex, and found that both memory reactivation and intact prefrontal cortex function were necessary for the short-term fear amnesia after the retrieval-extinction protocol. The differences in temporal scale, cue-specificity, and cognitive control ability dependence between the short- and long-term amnesia suggest that memory retrieval and extinction training trigger distinct underlying memory update mechanisms. These findings suggest the potential involvement of coordinated memory modulation processes upon memory retrieval and may inform clinical approaches for addressing persistent maladaptive memories.

neuroscience↗

bioRxiv2024

The Kv2.2 channel mediates the inhibition of Prostaglandin E2 on glucose-stimulated insulin secretion in pancreatic β/-cells

Prostaglandin E2 (PGE2) is an endogenous inhibitor of glucose-stimulated insulin secretion (GSIS) and plays an important role in pancreatic {beta}-cell dysfunction in type 2 diabetes mellitus (T2DM). This study aimed to explore the underlying mechanism by which PGE2 inhibits GSIS. Our results showed that PGE2 inhibited Kv2.2 channels via increasing PKA activity in HEK293T cells overexpressed with Kv2.2 channels. Point mutation analysis demonstrated that S448 residue was responsible for the PKA-dependent modulation of Kv2.2. Furthermore, the inhibitory effect of PGE2 on Kv2.2 was blocked by EP2/4 receptor antagonists, while mimicked by EP2/4 receptor agonists. The immune fluorescence results showed that EP1-EP4 receptors are expressed in both mouse and human {beta}-cells. In INS-1(832/13) {beta}-cells, PGE2 inhibited voltage-gated potassium currents and electrical activity through EP2/4 receptors and Kv2.2 channels. Knockdown of Kv2.2 reduced the action potential firing frequency and alleviated the inhibition of PGE2 on GSIS in INS-1(832/13) {beta}-cells. PGE2 impaired glucose tolerance in wild-type mice but did not alter glucose tolerance in Kv2.2 knockout mice. Knockout of Kv2.2 reduced electrical activity, GSIS and abrogated the inhibition of PGE2 on GSIS in mouse islets. In conclusion, we have demonstrated that PGE2 inhibits GSIS in pancreatic {beta}-cells through the EP2/4-Kv2.2 signaling pathway. The findings highlight the significant role of Kv2.2 channels in the regulation of {beta}-cell repetitive firing and insulin secretion, and contribute to the understanding of the molecular basis of {beta}-cell dysfunction in diabetes.

physiology↗

bioRxiv2024

Transcranial Focused Ultrasound Remotely Modulates Extrastriate Visual Cortex with Subregion Specificity

Low-intensity transcranial focused ultrasound (tFUS) has emerged as a powerful neuromodulation tool characterized by its deep penetration and precise spatial targeting to influence neural activity. Our study directed low-intensity tFUS stimulation onto a region of prefrontal cortex (the frontal eye field, or FEF) of a rhesus macaque to examine its impact on a remote site, the extrastriate visual cortex (area V4). This pair of cortical regions form a top-down modulatory circuit that has been studied extensively with electrical microstimulation. To measure the impact of tFUS stimulation, we recorded local field potentials (LFPs) and multi-unit spiking activities from a multi-electrode array implanted in the visual cortex. To deliver tFUS stimulation, we leveraged a customized 128-element random array ultrasound transducer with improved spatial targeting. We observed that tFUS stimulation in FEF produced modulation of V4 neuronal activity, either through enhancement or suppression, dependent on the pulse repetition frequency of the tFUS stimulation. Electronically steering the transcranial ultrasound focus through the targeted FEF cortical region produced changes in the level of modulation, indicating that the tFUS stimulation was spatially targeted within FEF. Modulation of V4 activity was confined to specific frequency bands, and this modulation was dependent on the presence or absence of a visual stimulus during tFUS stimulation. A control study targeting the insula produced no effect, emphasizing the region-specific nature of tFUS neuromodulation. Our findings shed light on the capacity of tFUS to modulate specific neural pathways and provide a comprehensive understanding of its potential applications for neuromodulation within brain networks.

neuroscience↗

bioRxiv2023

Social Rank Alters the Synchrony of Attribute Integration in Altruistic Decisions

Social rank, which represents the hierarchical dominance structure in societies, forms the backdrop against which most social decisions are made. Effective social decision-making demands the flexible integration of multifaceted information, including the relative weights and timings of different attributes. However, there is limited understanding of the potential impact of attribute timing on social preferences and, crucially, whether attribute weights and timings are susceptible to the influence of social rank. Here, through a two-stage social decision task, we manipulated subjects social rank before they engaged in altruistic decisions. By interrogating behavioral data through the lens of the time-varying drift diffusion process, we found that the behavioral patterns are better explained by a model that considers both attribute timing and weight. Interestingly, varying social ranks only modulate attribute timings in the decision process, leaving attribute weights unaffected. Furthermore, we demonstrated that individuals with more prosocial tendencies exhibited higher sensitivities of attribute timing in response to the changes of social rank and these results were replicated in a separate and larger cohort of participants. Our results underscore the intricate interplay between social rank and social attribute integration and introduce a new dimension to the computational mechanisms that underlie social decision-making. Author SummarySocial rank characterizes the background where most social decisions take place, however, the cognitive and computational mechanisms underlying how social rank influences altruistic decisions remain elusive. Here, combining a two-stage social decision task and time-varying DDM, we reveal that social rank specifically modulates attribute timings in the decision process, leaving attribute weights unaffected. Our results underscore the intricate interplay between social rank and social attribute integration during social decision-making.

neuroscience↗

bioRxiv2023

Probabilistic Graphical Modeling under Heterogeneity

Probabilistic graphical models are powerful and widely used tools to quantify, visualize and interpret dependencies in complex biological systems such as highthroughput genomics and proteomics. However, most existing graphical modeling methods assume homogeneity within and across samples which restricts their broad applicability to cases where sample-specific heterogeneity exists e.g. tumor heterogeneity. We propose a flexible Bayesian approach called Graphical Regression (GraphR) which (a) allows direct incorporation of intrinsic factors of sample heterogeneity at different scales through a regression-based formulation, (b) enables sparse network estimation at a sample-specific level, (c) allows identification and uncertainty quantification of potential effects of heterogeneity on network structures, and (d) is computationally efficient through the use of variational Bayes algorithms. We illustrate the comparative efficiency of GraphR against existing methods in terms of graph structure recovery and computational cost across multiple realistic simulation settings. We use GraphR to analyze four diverse multi-omics and spatial transcriptomics datasets to study inter- and intra-sample molecular networks and delineate biological discoveries that otherwise cannot be revealed by existing approaches. We have developed a GraphR R-package along with an accompanying Shiny App that provides comprehensive analysis and dynamic visualization functions.

genomics↗

bioRxiv2023

OBMeta: a comprehensive web server to analyze and validate gut microbial features and biomarkers for obesity-associated metabolic diseases

Gut dysbiosis is closely associated with obesity and related metabolic diseases including type 2 diabetes (T2D) and non-alcoholic fatty liver disease (NAFLD). The gut microbial features and biomarkers have been increasingly investigated in recent studies, which require further validation due to the limited sample size and various confounding factors that may affect microbial compositions. So far, it lacks a comprehensive bioinformatics pipeline providing automated statistical analysis and integrating independent studies for cross validation simultaneously. OBMeta aims to streamline the standard metagenomics data analysis from diversity analysis, comparative analysis, functional analysis, to co-abundance network analysis. In addition, a curated database has been established with a total of 88 public research projects, covering three different phenotypes (Obesity, T2D, and NAFLD) and more than five different intervention strategies (exercise, diet, probiotics, medication, and surgery). With OBMeta, users can not only analyze their own research projects, but also search and match public datasets of interest for cross-project validation. Moreover, OBMeta provides cross-phenotype and cross-intervention-based advanced validation that maximally supports preliminary findings from an individual study. To summarize, OBMeta is a comprehensive web server to analyze and validate gut microbial features and biomarkers for obesity-associated metabolic diseases. OBMeta is freely available at: http://obmeta.met-bioinformatics.cn/. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=82 SRC="FIGDIR/small/552363v1_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@1b58a9borg.highwire.dtl.DTLVardef@19efa47org.highwire.dtl.DTLVardef@418597org.highwire.dtl.DTLVardef@1a9bc3c_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2023

Alterations in the preneoplastic breast microenvironment of BRCA1/2 mutation carriers revealed by spatial transcriptomics

Breast cancer is the most common cancer in females, affecting one in every eight women and accounting for the majority of cancer-related deaths in women worldwide. Germline mutations in the BRCA1 and BRCA2 genes are significant risk factors for specific subtypes of breast cancer. BRCA1 mutations are associated with basal-like breast cancers, whereas BRCA2 mutations are associated with luminal-like disease. There are currently few chemoprevention strategies available for BRCA1/2 mutation carriers, and irreversible prophylactic mastectomy is the primary option. Designing chemo-preventive strategies requires an in-depth understanding of the physiological processes underlying tumor initiation. Here, we employ spatial transcriptomics to investigate defects in mammary epithelial cell differentiation accompanied by distinct microenvironmental alterations in preneoplastic breast tissues from BRCA1/2 mutation carriers and normal breast tissues from non-carrier controls. We uncovered spatially defined receptor-ligand interactions in these tissues for the investigation of autocrine and paracrine signaling. We discovered that {beta}1-integrin-mediated autocrine signaling in BRCA2-deficient mammary epithelial cells differs from BRCA1-deficient mammary epithelial cells. In addition, we found that the epithelial-to-stromal paracrine signaling in the breast tissues of BRCA1/2 mutation carriers is greater than in control tissues. More integrin-ligand pairs were differentially correlated in BRCA1/2-mutant breast tissues than non-carrier breast tissues with more integrin receptor-expressing stromal cells. These results reveal alterations in the communication between mammary epithelial cells and the microenvironment in BRCA1 and BRCA2 mutation carriers, laying the foundation for designing innovative breast cancer chemo-prevention strategies for high-risk patients.

cancer biology↗