Biology subjects

Jiang, M.-Z.

Publications and source records attributed to Jiang, M.-Z..

4 recordsLinked to original sources

bioRxiv2025

Global Mapping of Combinatorial Chromatin Regulatory Events Using Hi-Plex CUT&Tag

Epigenetic modulators, transcription factors, and chromatin-associated proteins collaboratively regulate essential genomic functions, such as transcription, repression, and DNA damage repair. However, the intricate cross-talk or the interaction between these chromatin regulatory factors (CRFs) remains poorly understood, largely due to limitations in experimental methods, which traditionally interrogate the genomic locations of only one CRF. Although the latest methods can study multiple CRFs simultaneously, they cannot examine the interaction between these factors. Inferring biological interactions often requires integrating data from multiple experiments, which can be labor-intensive and imprecise since the measurement is not from the same DNA molecule. To address these challenges, we developed Hi-Plex CUT&Tag, an advanced technology that enables the robust detection of co-localization events across over 600 pairs of CRFs using 36 barcoded monoclonal antibodies (mAbs), while minimizing background signals and cross-contamination. Hi-Plex CUT&Tag facilitates comprehensive pairwise analysis of epigenetic modifiers, including histone post-translational modifications (PTMs), writers, and transcription factors (TFs). Each Tn5 tagmentation fragment provides detailed insights, capturing the identities of two co-localized events, the underlying genomic sequences, and the molecular distance between them. For the first time, numerous novel bivalent events, epigenetic-context-dependent transcriptional regulation, and specific chromatin mark combinations with significant impacts on gene regulation can be detected in a single experiment. Furthermore, single-cell Hi-Plex CUT&Tag enables the analysis of synergistic interactions between CRF pairs at the single-cell level, providing unprecedented resolution for studying chromatin dynamics.

genetics↗

bioRxiv2025

Berberrubine Inhibits Helicobacter pylori by Inducing Oxidative Stress

Helicobacter pylori is a prevalent pathogen that colonizes the pylorus and contributes to the pathogenesis of gastric cancer. The increasing antibiotic resistance in H. pylori presents a substantial challenge for effective eradication, making the development of more effective therapeutic strategies imperative. In this study, we screened 37 pure compounds (purity >95%), 3 ethanol extracts, and 6 aqueous extracts from 6 different medicinal herbs for their potential inhibitory effects against H. pylori in vitro. Among the tested compounds, 15 exhibited inhibitory activity, with berberrubine demonstrating the strongest effect, characterized by a minimum inhibitory concentration (MIC) of 0.011 mg/ml. The comparative transcriptome analysis revealed a significant upregulation of the oxidative stress response related genes, such as fumC and sodB, in H. pylori treated with berberrubine. Further in vitro assays confirmed that berberrubine exerted antibacterial activity by stimulating oxidative stress, as evidenced by increased reactive oxygen species (ROS) production and impaired cell membrane integrity. Additionally, similar inhibitory tests were applied to Escherichia coli, a common gut opportunistic pathogen. The results showed that berberrubine inhibited E. coli through the same mechanism observed for H. pylori. In conclusion, berberrubine is a potent antibacterial agent against gastrointestinal pathogens, including H. pylori and E. coli, making it a promising candidate for further development as an antimicrobial therapeutic ImportanceThis study identified berberrubine as a potent antibacterial agent against gastrointestinal pathogens, including Helicobacter pylori and Escherichia coli by inducing cellular oxidative stress. Through increased reactive oxygen species (ROS) production and disruption of bacterial cell membrane integrity, berberrubine demonstrated strong efficacy in inhibiting drug-resistant pathogens. These findings highlight its potential as a promising therapeutic candidate for eradicating drug-resistant gastrointestinal pathogens.

microbiology↗

bioRxiv2023

Whole Genome Sequencing Based Analysis of Inflammation Biomarkers in the Trans-Omics for Precision Medicine (TOPMed) Consortium

Inflammation biomarkers can provide valuable insight into the role of inflammatory processes in many diseases and conditions. Sequencing based analyses of such biomarkers can also serve as an exemplar of the genetic architecture of quantitative traits. To evaluate the biological insight, which can be provided by a multi-ancestry, whole-genome based association study, we performed a comprehensive analysis of 21 inflammation biomarkers from up to 38,465 individuals with whole-genome sequencing from the Trans-Omics for Precision Medicine (TOPMed) program. We identified 22 distinct single-variant associations across 6 traits - E-selectin, intercellular adhesion molecule 1, interleukin-6, lipoprotein-associated phospholipase A2 activity and mass, and P-selectin - that remained significant after conditioning on previously identified associations for these inflammatory biomarkers. We further expanded upon known biomarker associations by pairing the single-variant analysis with a rare variant set-based analysis that further identified 19 significant rare variant set-based associations with 5 traits. These signals were distinct from both significant single variant association signals within TOPMed and genetic signals observed in prior studies, demonstrating the complementary value of performing both single and rare variant analyses when analyzing quantitative traits. We also confirm several previously reported signals from semi-quantitative proteomics platforms. Many of these signals demonstrate the extensive allelic heterogeneity and ancestry-differentiated variant-trait associations common for inflammation biomarkers, a characteristic we hypothesize will be increasingly observed with well-powered, large-scale analyses of complex traits.

genetics↗

bioRxiv2022

Uncovering Cross-Cohort Molecular Features with Multi-Omics Integration Analysis

Integrative approaches that simultaneously model multi-omics data have gained increasing popularity because they provide holistic system biology views of multiple or all components in a biological system of interest. Canonical correlation analysis (CCA) is a correlation-based integrative method. It was initially designed to extract latent features shared between two assays by finding the linear combinations of features - referred to as canonical vectors (CVs) - within each assay that achieve maximal across-assay correlation. Sparse multiple CCA (SMCCA), a widely-used derivative of CCA, allows more than two assays but can result in non-orthogonal CVs when applied to high-dimensional data. Here, we incorporated a variation of the Gram-Schmidt (GS) algorithm with SMCCA to improve orthogonality among CVs. Applying our SMCCA-GS method to proteomics and methylomics data from the Multi-Ethnic Study of Atherosclerosis (MESA) and Jackson Heart Study (JHS), we identified strong associations between blood cell counts and protein abundance. This finding suggests that adjustment of blood cell composition should be considered in protein-based association studies. Importantly, CVs obtained from two independent cohorts demonstrate transferability across the cohorts. For example, proteomic CVs learned from JHS explain similar amounts of blood cell count phenotypic variance in MESA, explaining 39.0% ~ 50.0% variation in JHS and 38.9% ~ 49.1% in MESA, similar transferability was observed for other omics-CV-trait pairs. This suggests that biologically meaningful and cohort-agnostic variation is captured by CVs. We further developed Sparse Supervised Multiple CCA (SSMCCA) to allow supervised integration analysis for more than two assays. We anticipate that applying our SMCCA-GS and SSMCCA on various cohorts would help identify cohort-agnostic biologically meaningful relationships between multi-omics data and phenotypic traits. Author SummaryComprehensive understanding of human complex traits may benefit from incorporation of molecular features from multiple biological layers such as genome, epigenome, transcriptome, proteome, and metabolome. CCA is a correlation-based method for multi-omics data which reduces the dimension of each omic assay to several orthogonal components - commonly referred to as canonical vectors (CVs). The widely-used SMCCA method allows effective dimension reduction and integration of multi-omics data, but suffers from potentially highly correlated CVs when applied to high-dimensional omics data. Here, we improve the statistical independence among the CVs by adopting a variation of the GS algorithm. We applied our SMCCA-GS method to proteomic and methylomic data from two cohort studies, MESA and JHS. Our results reveal a pronounced effect of blood cell counts on protein abundance, strongly suggesting blood cell composition adjustment in protein-based association studies may be necessary. Finally, we present SSMCCA which allows supervised CCA analysis for the association between one phenotype of interest and more than two assays. We anticipate that SMCCA-GS would help reveal meaningful system-level factors from biological processes involving features from multiple assays; and SSMCCA would further empower interrogation of these factors for phenotypic traits related to health and diseases.

genomics↗