Biology subjects

Roach, J.

Publications and source records attributed to Roach, J..

8 recordsLinked to original sources

bioRxiv2026

Validated Semantic Scoring Improves Filtering Generic Concepts from Knowledge-Graphs for Drug Repurposing Queries

When an ontology-based biomedical knowledge graph (a KG, containing information about genes, pathways, phenotypes, and molecular interactions) is analyzed using a computational reasoning system, the system can return unwanted generic concepts. Such generic concepts, in the context of a query about potential drug treatments for a disease, might include 'pharmaceutical preparations' or 'antibiotics'. Answers can be filtered based on hand-curated block-lists or ontology Information Content (IC), but to our knowledge, (1) neither filtering method has been validated against expert judgments of generality, and (2) IC is not available for the majority of nodes in the representative KG used in this work. In this study, three domain experts rated representative lists of KG concepts for generality. We built a semantic precision score (SPS) from a concept's name and description alone, using a biomedical language model (BioLORD). For the purpose of filtering generic answers from biomedical query results, we compared (using queries for 200 diseases) the SPS against a curated concept block-list and two IC thresholds. IC agreed moderately with the experts (Spearman's {rho} = 0.59), but correlations were negligible or negative within IC bands. The SPS was well-correlated with the experts ({rho} = 0.76) globally and within SPS bands, and remained informative on concepts without IC ({rho} = 0.59). Hard filtering of query results using the SPS removed 1,502 of 33,161 query answers, nearly three times the block-list's 513, while removing fewer of the query-specific DrugCentral indications. Hard filtering by IC removed either few answers or many approved drugs. We additionally compared the filtering methods' performance on an independent evaluation set of queries for 200 diseases. The SPS filter removed 5,135 of 91,203 answers compared with the block-list's 1,419 and lost none of 1,296 indications compared with 17. Further, SPS filtering removed approved drugs no more often from the half of DrugCentral not used in training than from the half used in training. Semantic meaning captures expert judgments of specificity more faithfully than ontology position, and semantic scoring can replace IC and manually maintained block-lists for filtering drug-repurposing results.

bioinformatics↗

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

The impact of transplant location on the gut microbiome and resistome in patients undergoing hematopoietic stem cell transplantation at home versus in the hospital

ObjectivesHome-based hematopoietic stem cell transplantation (HCT) is a novel approach that has the potential to improve outcomes, however, the impact of transplant location on the gut microbiome remains uncharacterized. We hypothesized that patients randomized to undergo home HCT would have higher gut taxonomic diversity and lower antimicrobial resistance (AMR) gene abundance compared to those undergoing standard hospital HCT. MethodsWe identified 28 patients enrolled in Phase II randomized trials of home (n=16) v. hospital (n=12) HCT at Duke and performed shotgun metagenomic sequencing of stools to compare taxonomic and AMR gene composition between groups. We performed a secondary analysis of patients from each group transplanted at an outpatient infusion clinic with those who underwent standard inpatient HCT ("outpatient" v. "inpatient"). ResultsNo significant differences in duration of hospitalization were found in those randomized to home v. hospital HCT. Taxonomic and AMR gene - and {beta}-diversity were comparable. In contrast, secondary analyses demonstrated that patients from both home and hospital groups transplanted at an outpatient infusion clinic spent significantly less time in the hospital and demonstrated higher taxonomic -diversity and differential {beta}-diversity compared to standard inpatient HCT, although AMR gene -diversity did not differ, and comparisons were confounded by both differences in transplant type and use of antibiotics. ConclusionsRandomization by transplant location did not impact the gut microbiota to the same extent as the duration of hospitalization, although secondary analyses were heavily confounded. Even when taxonomic differences were observed, AMR genes were similar between groups.

microbiology↗

bioRxiv2024

Gut Microbial Impact on Colitis and Colitis-Associated Carcinogenesis in a Primary Sclerosing Cholangitis-IBD Model

Background and AimsPrimary sclerosing cholangitis (PSC) associated inflammatory bowel diseases (IBD) increase colorectal dysplasia and malignancy risk. Current mouse models do not adequately replicate human PSC-IBD, limiting mechanistic understanding and therapeutic development. This study uses Mdr2/Il10 double knockout (DKO) mice to examine microbiota roles in mediating colitis, colitis-associated colorectal dysplasia and hepatobiliary inflammation/fibrosis. GoalDevelop and phenotype a chronic spontaneous PSC-IBD mouse model, emphasizing colitis, colonic dysplasia, hepatobiliary inflammation/ fibrosis and the functional roles of resident microbiota. MethodsWe utilized germ-free (GF) and specific-pathogen-free Mdr2/Il10 DKO, Il10-/- and Mdr2-/- mice to model PSC-IBD. We monitored colonic dysplasia progression, colitis kinetics and severity by lipocalin-2, cytokine measurement, and tissue evaluations of colon and liver. We manipulated the microbiome to assess its functional effects. ResultsDKO mice exhibited age- and region-specific accelerated colitis and spontaneous colonic dysplasia progressing to high-grade invasive adenocarcinomas. Despite aggressive colonic inflammation, DKO mice showed reduced hepatic fibrosis, increased hepatic reparative macrophages, and matrix metalloproteinase activity compared to Mdr2-/- mice. GF DKO had heightened liver inflammation and mortality with absent colitis and colonic dysplasia, reversed with microbial reconstitution from DKO mice. Changes in DKO primary/secondary bile acid profiles mirrored those in PSC-IBD. ConclusionThe Mdr2/Il10 DKO model mirrors key factors in PSC-IBD patients in terms of inflammation and carcinogenesis. We found an important role for the dysbiotic microbiota in DKO mice for disease onset and progression. Targeting microbiota and bile acid metabolism may provide promising strategies for developing effective PSC-IBD therapies.

microbiology↗

bioRxiv2023

Persistent biofluid small molecule alterations induced by Trypanosoma cruzi infection are not restored by antiparasitic treatment

Table of contents graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=105 SRC="FIGDIR/small/543565v1_figu1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@10c3a0aorg.highwire.dtl.DTLVardef@1b69d6dorg.highwire.dtl.DTLVardef@199867org.highwire.dtl.DTLVardef@64b20b_HPS_FORMAT_FIGEXP M_FIG C_FIG Chagas Disease (CD), caused by Trypanosoma cruzi (T. cruzi) protozoa, is a complicated parasitic illness with inadequate medical measures for diagnosing infection and monitoring treatment success. To address this gap, we analyzed changes in the metabolome of T. cruzi-infected mice via liquid chromatography tandem mass spectrometry analysis of clinically-accessible biofluids: saliva, urine, and plasma. Urine was the most indicative of infection status, across mouse and parasite genotypes. Metabolites perturbed by infection in the urine include kynurenate, acylcarnitines, and threonylcarbamoyladenosine. Based on these results, we sought to implement urine as a tool for assessment of CD treatment success. Strikingly, it was found that mice with parasite clearance following benznidazole antiparasitic treatment had comparable overall urine metabolome to mice that failed to clear parasites. These results match with clinical trial data in which benznidazole treatment did not improve patient outcomes in late-stage disease. Overall, this study provides insights into new small molecule-based CD diagnostic methods and a new approach to assess functional treatment response.

microbiology↗

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↗