Biology subjects

Cunningham, K. Y.

Publications and source records attributed to Cunningham, K. Y..

4 recordsLinked to original sources

bioRxiv2023

Gut Microbiome Wellness Index 2 for Enhanced Health Status Prediction from Gut Microbiome Taxonomic Profiles

Recent advancements in human gut microbiome research have revealed its crucial role in shaping innovative predictive healthcare applications. We introduce Gut Microbiome Wellness Index 2 (GMWI2), an advanced iteration of our original GMWI prototype, designed as a robust, disease-agnostic health status indicator based on gut microbiome taxonomic profiles. Our analysis involved pooling existing 8069 stool shotgun metagenome data across a global demographic landscape to effectively capture biological signals linking gut taxonomies to health. GMWI2 achieves a cross-validation balanced accuracy of 80% in distinguishing healthy (no disease) from non-healthy (diseased) individuals and surpasses 90% accuracy for samples with higher confidence (i.e., outside the "reject option"). The enhanced classification accuracy of GMWI2 outperforms both the original GMWI model and traditional species-level -diversity indices, suggesting a more reliable tool for differentiating between healthy and non-healthy phenotypes using gut microbiome data. Furthermore, by reevaluating and reinterpreting previously published data, GMWI2 provides fresh insights into the established understanding of how diet, antibiotic exposure, and fecal microbiota transplantation influence gut health. Looking ahead, GMWI2 represents a timely pivotal tool for evaluating health based on an individuals unique gut microbial composition, paving the way for the early screening of adverse gut health shifts. GMWI2 is offered as an open-source command-line tool, ensuring it is both accessible to and adaptable for researchers interested in the translational applications of human gut microbiome science.

bioinformatics↗

bioRxiv2022

GMHI-webtool: a user-friendly browser application for assessing health through metagenomic gut microbiome profiling

SummaryWe recently introduced the Gut Microbiome Health Index (GMHI), a stool-based indicator for monitoring health given the state of ones gut microbiome. GMHI depends on health-prevalent and health-scarce species determined and validated using a pooled dataset of 5,026 stool shotgun metagenomic samples from 43 independent studies. Encouragingly, GMHI has already been utilized in various studies focusing on identifying differences in the gut microbiome between cases and controls. However, current computational barriers and logistical issues prevent researchers from computing, interpreting, and contextualizing GMHI, thereby limiting its further widespread utilization. Herein, we introduce the GMHI-webtool, a user-friendly browser application that computes GMHI, health-prevalent/scarce species, -diversities, and taxonomic distributions of the gut microbiome from stool samples. Users of our interactive online tool can visualize their results and compare side-by-side with those from our pooled reference dataset, as well as export data in .csv format and high-resolution figures. Availability and implementationGMHI-webtool is freely available here: https://gmhi-webtool.github.io/. Source code: https://github.com/danielchang2002/GMHI-webtool.

bioinformatics↗

bioRxiv2020

Single-Cell Mass Cytometry on Peripheral Blood Identifies Immune Cell Subsets Associated with Primary Biliary Cholangitis

The relationship between Primary Biliary Cholangitis (PBC), a chronic cholestatic autoimmune liver disease, and the peripheral immune system remains to be fully understood. Herein, we performed the first mass cytometry (CyTOF)-based, immunophenotyping analysis of the peripheral immune system in PBC at single-cell resolution. CyTOF was performed on peripheral blood mononuclear cells (PBMCs) from PBC patients (n=33) and age-/sex-matched healthy controls (n=33) to obtain immune cell abundance and marker expression profiles. Hiearchical clustering methods were applied to identify immune cell types and subsets significantly associated with PBC. Subsets of gamma-delta T cells (CD3+TCRgd+), CD8+ T cells (CD3+CD8+CD161+PD1+), and memory B cells (CD3-CD19+CD20+CD24+CD27+) were found to have lower abundance in PBC than in control. In contrast, higher abundance of subsets of monocytes and naive B cells were observed in PBC compared to control. Furthermore, several naive B cell (CD3-CD19+CD20+CD24-CD27-) subsets were significantly higher in PBC patients with cirrhosis (indicative of late-stage disease) than in those without cirrhosis. Alternatively, subsets of CD8+CD161+ T cells and memory B cells were lower in abundance in cirrhotic relative to non-cirrhotic PBC patients. Future immunophenotyping investigations could lead to better understanding of PBC pathogenesis and progression, and also to the discovery of novel biomarkers and treatment strategies.

bioinformatics↗

bioRxiv2020

A Novel Index for Predicting Health Status Using Species-level Gut Microbiome Profiling

The development of a biologically-interpretable and robust metric that provides clear insight into the general health status (i.e. healthy or non-healthy) of ones gut microbiome remains an important target in human microbiome research. We introduce the Gut Microbiome Health Index (GMHI), a mathematical formula that determines the degree to which a gut microbiome profile reflects good or adverse health. GMHI was formulated based on microbial species specific to healthy gut ecosystems. These species were identified through a multi-study, integrative analysis on 4,347 human stool metagenomes from 34 published studies across healthy and 12 different disease or abnormal bodyweight conditions. When demonstrated on our population-scale meta-dataset, GMHI is the most robust and consistent predictor of general health compared to -diversity indices commonly considered as markers for gut health. Validation of GMHI on 679 samples from 9 additional studies resulted in remarkable reproducibility in distinguishing healthy and non-healthy groups. Our findings suggest that gut taxonomic signatures can indeed serve as robust predictors of general health, and highlight the importance of how data sharing efforts can provide broadly-applicable novel discoveries.

bioinformatics↗