Biology subjects

Divaris, K.

Publications and source records attributed to Divaris, K..

7 recordsLinked to original sources

bioRxiv2025

Deep-learning-based interpolation of longitudinal microbiome data powers biologically informative discovery

The human microbiome is a foundational and dynamic foundation for several health-related functions and disease processes. Advances in microbiome sequencing have enabled the characterization of microbial communities in several niches. Longitudinal microbiome studies further strive to discover clinically informative microbial community trajectories. However, these data are fraught with dropout events, high noise, and irregular sampling that limit and prevent the use of many available longitudinal analysis tools. To address these challenges, we introduce Bidirectional GRU-ODE-Bayes (BGOB), a deep learning framework developed for longitudinal microbiome interpolation. BGOB combines bidirectional information flow and ODE-based continuous modeling to jointly interpolate and smoothen trends across individual participants, providing uniform, denoised time intervals across patients. BGOB enables vastly improved performance in differential abundance testing and time-to-event analysis, and makes possible longitudinal analyses requiring uniformity, such as lead-lag detection and temporal clustering. After interpolation, previously low-powered datasets are able to broadly recapitulate known microbiology and elucidate interacting microbial communities. We highlight several associations between microbial taxa and disease, including novel species associated with Early Childhood Caries and disruption of key healthy gut microbiota in Inflammatory Bowel Disease. The BGOB package is publicly available at https://github.com/Rachel-Lyu/BGOB_n_test.

microbiology↗

bioRxiv2024

LP-Micro Offers Interpretable Disease Outcome Prediction by Leveraging Microbial Biomarkers and Their Time-Varying Effects

Information generated from longitudinally-sampled microbial data has the potential to illuminate important aspects of development and progression for many human conditions and diseases. Identifying microbial biomarkers and their time-varying effects can not only advance our understanding of pathogenetic mechanisms, but also facilitate early diagnosis and guide optimal timing of interventions. However, longitudinal predictive modeling of highly noisy and dynamic microbial data (e.g., metagenomics) poses analytical challenges. To overcome these challenges, we introduce a robust and interpretable machine-learning-based longitudinal microbiome analysis framework, LP-Micro, that encompasses: (i) longitudinal microbial feature screening via a polynomial group lasso, (ii) disease outcome prediction implemented via machine learning methods (e.g., XGBoost, deep neural networks), and (iii) interpretable association testing between time points, microbial features, and disease outcomes via permutation feature importance. We demonstrate in simulations that LP-Micro can not only identify incident disease-related microbiome taxa but also offers improved prediction accuracy compared to existing approaches. Applications of LP-Micro in two longitudinal microbiome studies with clinical outcomes of childhood dental disease and weight loss following bariatric surgery yield consistently high prediction accuracy. The identified critical early predictive time points are informative and aligned with clinical expectations.

bioinformatics↗

bioRxiv2023

BZINB model-based pathway analysis and module identification facilitates integration of microbiome and metabolome data

Integration of multi-omics data is a challenging but necessary step to advance our understanding of the biology underlying human health and disease processes. To date, investigations seeking to integrate multi-omics (e.g., microbiome and metabolome) employ simple correlation-based network analyses; however, these methods are not always well-suited for microbiome analyses because they do not accommodate the excess zeros typically present in these data. In this paper, we introduce a bivariate zero-inflated negative binomial (BZINB) model-based network and module analysis method that addresses this limitation and improves microbiome-metabolome correlation-based model fitting by accommodating excess zeros. We use real and simulated data based on a multi-omics study of childhood oral health (ZOE 2.0; investigating early childhood dental disease, ECC) and find that the accuracy of the BZINB model-based correlation method is superior compared to Spearmans rank and Pearson correlations in terms of approximating the underlying relationships between microbial taxa and metabolites. The new method, BZINB-iMMPath facilitates the construction of metabolite-species and species-species correlation networks using BZINB and identifies modules of (i.e., correlated) species by combining BZINB and similarity-based clustering. Perturbations in correlation networks and modules can be efficiently tested between groups (i.e., healthy and diseased study participants). Upon application of the new method in the ZOE 2.0 study microbiome-metabolome data, we identify that several biologically-relevant correlations of ECC-associated microbial taxa with carbohydrate metabolites differ between healthy and dental caries-affected participants. In sum, we find that the BZINB model is a useful alternative to Spearman or Pearson correlations for estimating the underlying correlation of zero-inflated bivariate count data and thus is suitable for integrative analyses of multi-omics data such as those encountered in microbiome and metabolome studies.

bioinformatics↗

bioRxiv2021

Distribution-based comprehensive evaluation ofmethods for differential expression analysis inmetatranscriptomics

Understanding the function of the human microbiome is important; however, the development of statistical methods specifically for the microbial gene expression (i.e., metatranscriptomics) is in its infancy. Many currently employed differential expression analysis methods have been designed for different data types and have not been evaluated in metatranscriptomics settings. To address this gap, we undertook a comprehensive evaluation and benchmarking of ten differential analysis methods for metatranscriptomics data. We used a combination of real and simulated data to evaluate performance (i.e., model fit, type I error, false discovery rate, and sensitivity) of the methods: log-normal (LN), logistic-beta (LB), MAST, DESeq2, metagenomeSeq, ANCOM-BC, LEfSe, ALDEx2, Kruskal-Wallis, and two-part Kruskal-Wallis. The simulation was informed by supragingival biofilm microbiome data from 300 preschool-age children enrolled in a study of early childhood caries (ECC), whereas validations were sought in two additional datasets from an ECC study and an inflammatory bowel disease (IBD) study. The LB test showed the highest sensitivity in both small and large samples and reasonably controlled type I error. Contrarily, MAST was hampered by inflated type I error. Upon application of the LN and LB tests in the ECC study, we found that genes C8PHV7 and C8PEV7, harbored by the lactate-producing Campylobacter gracilis, had the strongest association with childhood dental diseases. This comprehensive model evaluation offer practical guidance for selection of appropriate methods for rigorous analyses of differential expression in metatranscriptomics. Selection of an optimal method increases the possibility of detecting true signals while minimizing the chance of claiming false ones.

bioinformatics↗

bioRxiv2021

Improved Metabolite Prediction Using Microbiome Data-Based Elastic Net Models

Microbiome data are becoming increasingly available in large health cohorts yet metabolomics data are still scant. While many studies generate microbiome data, they lack matched metabolomics data or have considerable missing proportions of metabolites. Since metabolomics is key to understanding microbial and general biological activities, the possibility of imputing individual metabolites or inferring metabolomics pathways from microbial taxonomy or metagenomics is intriguing. Importantly, current metabolomics profiling methods such as the HMP Unified Metabolic Analysis Network (HUMAnN) have unknown accuracy and are limited in their ability to predict individual metabolites. To address this gap, we developed a novel metabolite prediction method, and we present its application and evaluation in an oral microbiome study. We developed ENVIM based on the Elastic Net Model (ENM) to predict metabolites using micorbiome data. ENVIM introduces an extra step to ENM to consider variable importance scores and thus achieve better prediction power. We investigate the metabolite prediction performance of ENVIM using metagenomic and metatranscriptomic data in a supragingival biofilm multi-omics dataset of 297 children ages 3-5 who were participants of a community-based study of early childhood oral health (ZOE 2.0) in North Carolina, United States. We further validate ENVIM in two additional publicly available multi-omics datasets generated from studies of gut health and vagina health. We select gene-family sets based on variable importance scores and modify the existing ENM strategy used in the MelonnPan prediction software to accommodate the unique features of microbiome and metabolome data. We evaluate metagenomic and metatranscriptomic predictors and compare the prediction performance of ENVIM to the standard ENM employed in MelonnPan. The newly-developed ENVIM method showed superior metabolite predictive accuracy than MelonnPan using metatranscriptomics data only, metagenomics data only, or both of these two. Both methods perform better prediction using gut or vagina microbiome data than using oral microbiome data for the samples corresponding metabolites. The top predictable compounds have been reported in all these three datasets from three different body sites. Enrichment of prediction some contributing species has been detected.

microbiology↗

bioRxiv2021

Single-cell atlas of human oral mucosa reveals a stromal-neutrophil axis regulating tissue immunity in health and inflammatory disease

The oral mucosa remains an understudied barrier tissue rich in exposure to antigens, commensals and pathogens. Moreover, it is the tissue where one of the most prevalent human microbe-triggered inflammatory diseases, periodontitis, occurs. To understand this complex environment at the cellular level, we assemble herein a human single-cell transcriptome atlas of oral mucosal tissues in health and periodontitis. Our work reveals transcriptional diversity of stromal and immune cell populations, predicts intercellular communication and uncovers an altered immune responsiveness of stromal cells participating in tissue homeostasis and disease at the gingival mucosa. In health, we define unique populations of CXCL1,2,8-expressing epithelial cells and fibroblasts mediating immune homeostasis primarily through the recruitment of neutrophils. In disease, we further observe stromal, particularly fibroblast hyper-responsiveness linked to recruitment of leukocytes and neutrophil populations. Ultimately, a stromal-neutrophil axis emerges as a key regulator of mucosal immunity. Pursuant to these findings, most Mendelian forms of periodontitis were shown to be linked to genetic mutations in neutrophil and select fibroblast-expressed genes. Moreover, we document previously unappreciated expression of known pattern- and damage-recognition receptors on stromal cell populations in the setting of periodontitis, suggesting avenues for triggering stromal responses. This comprehensive atlas offers an important reference for in-depth understanding of oral mucosal homeostasis and inflammation and reveals unique stromal-immune interactions implicated in tissue immunity.

systems biology↗

bioRxiv2021

Fibrin is a critical regulator of neutrophil effector function at mucosal barrier sites

Tissue-specific cues are critical for homeostasis at mucosal barriers. Here, we document that the clotting factor fibrin is a critical regulator of neutrophil function at mucosal barriers. We demonstrate that fibrin engages neutrophils through the M{beta}2 integrin receptor and activates effector functions, including the production of reactive oxygen species and NET formation. These immune-protective neutrophil functions become tissue damaging in the context of impaired plasmin-mediated fibrinolysis. Indeed, the accumulation of fibrin due to Mendelian genetic defects in plasmin leads to severe oral mucosal immunopathology in mice and humans. Concordantly, genetic polymorphisms in the human PLG gene, encoding plasminogen, are associated with common forms of the oral mucosal disease periodontitis. Our work uncovers fibrin as a critical regulator of neutrophil effector function within the mucosal tissue microenvironment and suggests fibrin-neutrophil engagement as a pathogenic instigator and therapeutic target in common mucosal disease.

immunology↗