Biology subjects

Caporaso, J. G.

Publications and source records attributed to Caporaso, J. G..

11 recordsLinked to original sources

bioRxiv2026

Bacteroides fragilis modulates gut microbiome community composition, frontal cortex gene expression, and fecal and frontal cortex metabolites in a mouse model of Alzheimers disease pathologies

Alzheimers disease (AD) is a neurodegenerative disease and the leading cause of dementia among elderly. Gut microbiome alterations precede pathogenesis and may affect disease outcomes. We evaluated the role of Bacteroides fragilis in triple transgenic mice modeling AD pathologies (3xTg-AD) and wild-type controls (WT). Subsets of 3xTg-AD and WT mice were longitudinally treated with B. fragilis or sterile vehicle control for five consecutive days at 8 weeks of age and then monthly up to 52-56 weeks. Fecal samples were collected fortnightly from 8 through 52-56 weeks of age. Mice were sacrificed at 8 (baseline), 24 (amyloid-{beta} plaques modeled), and 52-56 (amyloid-{beta} plaques and neurofibrillary tangles modeled) weeks of age. Expression of genes involved in neuroinflammation and neurotransmission were quantified using reverse transcription quantitative polymerase chain reaction (RT-qPCR). Fecal bacterial microbiota were assessed by sequencing the V4 region of the 16S rRNA gene, and microbiome sequence data were analyzed using QIIME 2. Frontal cortex and fecal metabolomes were evaluated using LC-MS/MS. We observed that B. fragilis colonized the gut microbiota of 3xTg-AD mice earlier and more consistently than WT mice. 3xTg-AD mice treated with B. fragilis demonstrate lower gene expression of GFAP, SLC1A3, and FOXO3 in the frontal cortex. Consistent with this finding, treatment with B. fragilis restores levels of amino acid derivatives and neurotransmitters in 3xTg-AD mice to resemble levels in WT mice. These results highlight the role of the gut microbiome in AD-associated neuroinflammation and neurotransmission and the need for future studies to elucidate the mechanisms underlying these changes. ImportancePrevious studies in animals modeling Alzheimers disease pathologies and humans living with Alzheimers disease demonstrate shifts in the gut microbial community composition prior to and concomitant with pathological onset. The bacterial genus, Bacteroides, is commonly found differentially abundant in these studies but its effects on disease outcomes are poorly understood. In this study, we explore the effects of chronic exposure to Bacteroides fragilis in triple transgenic mice modeling Alzheimers disease pathologies and healthy, wild-type controls. We observed changes in microbial community composition in mice modeling Alzheimers disease when treated with B. fragilis, and associated changes in neuroinflammation, biomarkers of neurotransmission, and the brain metabolome. Taken together, these results suggest that Bacteroides fragilis exerts neuromodulatory effects that may be beneficial in Alzheimers disease.

microbiology↗

bioRxiv2025

MOSHPIT: accessible, reproducible metagenome data science on the QIIME 2 framework

Metagenome sequencing has revolutionized functional microbiome analysis across diverse ecosystems, but is fraught with technical hurdles. We introduce MOSHPIT (https://moshpit.readthedocs.io), software built on the QIIME 2 framework (Q2F) that integrates best-in-class CAMI2-validated metagenome tools with robust provenance tracking and multiple user interfaces, enabling streamlined, reproducible metagenome analysis for all expertise levels. By building on Q2F, MOSHPIT enhances scalability, interoperability, and reproducibility in complex workflows, democratizing and accelerating discovery at the frontiers of metagenomics.

bioinformatics↗

bioRxiv2020

Soluble corn fiber reduces ovalbumin-induced sinonasal inflammation via the gut microbiota-airway axis

Asthma is a chronic airway inflammatory disease that affects approximately 300 million people worldwide, causing a substantial healthcare burden. Although there is a large degree of heterogeneity in the inflammatory response of asthmatics, a subset of patients are characterized by type-2 inflammation, which is in part mediated by TH2 cells in both the upper and lower airways. Asthma prevalence is increased in low-socioeconomic-status populations, where disparities in health behavior exist, including a shift toward a western diet characterized by low dietary fiber. Gut microbes metabolize fiber into short chain fatty acids that can reduce type-2 inflammation in peripheral organs, such as the airways. We hypothesized that soluble fiber can reduce ovalbumin (OVA)-induced upper airway inflammation in the context of the unified airway hypothesis, in mice maintained on ingredient-matched western (WD) and control diets (CD) through production of short chain fatty acids. Our results show that soluble fiber reduces IL-4 and IL-13 gene expression (p<0.05, Mann Whitney) in the sinonasal cavity of CD-fed mice, but this effect was lost in WD-fed mice. This loss of protection in WD-fed mice parallels compositional changes of the cecal and fecal microbiota. Mice fed a soluble fiber supplement while being maintained on a WD had altered microbial communities characterized by lower abundance of fiber fermentering bacteria. This work can be used to develop effective microbiome-based therapeutics as a low-cost method to reduce asthma morbidity. IMPORTANCEPrevious research has supported that western-style diets, typically high-fat and low-fiber, are associated with changes in the gut microbiome and increased inflammation. Western diets are accessible and prominent in low-socioeconomic-status populations, where asthma rates are highest; however, there has yet to be a low-cost asthma therapeutic. For the first time, we investigated whether supplementation with a physiologically relevant quantity of soluble corn fiber can reduce allergic airway inflammation. Our study supports that soluble corn fiber supplementation is associated with compositional shifts of the gut microbiota and reduced airway inflammation, promoting the use of fiber as a low-cost microbiome modifying therapy to reduce asthma-associated inflammation. However, soluble corn fiber in conjunction with a western diet resulted in an alternate gut microbiome composition and loss of protection against allergic airway inflammation. These findings further support the importance of the gut microbiota in host health.

microbiology↗

bioRxiv2020

Environmental Exposures Influence Nasal Microbiome Composition in a Longitudinal Study of Division I Collegiate Athletes

BackgroundThe anterior nares host a complex microbial community that contributes to upper airway health. Although the bacterial composition of the nasal passages have been well characterized in healthy and diseased cohorts, the role of prolonged environmental exposures and exercise in shaping the nasal microbiome in healthy adults is poorly understood. In this study, we longitudinally sampled female collegiate Division I athletes from two teams experiencing a similar athletic season and exercise regimen but vastly different environmental exposures (Swim/Dive and Basketball). Using 16S rRNA gene sequencing, we evaluated the longitudinal dynamics of the nasal microbiome pre-, during-, and at the end of the athletic season. ResultsThe nasal microbiota of the Swim/Dive and Basketball teams were distinct from each other at each time point sampled, driven by either low abundance (Jaccard, PERMANOVA p<0.05) or high-abundance changes in composition (Bray-Curtis, PERMANOVA p<0.05). The rate of change of microbial communities were greater in the Swim/Dive team compared to the Basketball team characterized by an increase in Staphylococcus in Swim/Dive and a decrease in Corynebacterium in both teams over time. ConclusionsThis is the first study that has evaluated the nasal microbiome in athletes. We obtained longitudinal nasal swabs from two gender-matched teams with similar age distributions (18-22 years old) over a 6 month period. Differences in the microbiota between teams and over time indicate that chlorine exposure, and potentially athletic training, induced changes in the nasal microbiome.

microbiology↗

bioRxiv2019

Microbiotyping the sinonasal microbiome

This study offers a novel description of the sinonasal microbiome, through an unsupervised machine learning approach combining dimensionality reduction and clustering. We apply our method to the International Sinonasal Microbiome Study (ISMS) dataset of 410 sinus swab samples. We propose three main sinonasal microbiotypes or states: the first is Corynebacterium-dominated, the second is Staphylococcus-dominated, and the third dominated by the other core genera of the sinonasal microbiome (Streptococcus, Haemophilus, Moraxella, and Pseudomonas). The prevalence of the three microbiotypes studied did not differ between healthy and diseased sinuses, but differences in their distribution were evident based on geography. We also describe a potential reciprocal relationship between Corynebacterium species and Staphylococcus aureus, suggesting that a certain microbial equilibrium between various players is reached in the sinuses. We validate our approach by applying it to a separate 16S rRNA gene sequence dataset of 97 sinus swabs from a different patient cohort. Sinonasal microbiotyping may prove useful in reducing the complexity of describing sinonasal microbiota. It may drive future studies aimed at modeling microbial interactions in the sinuses and in doing so may facilitate the development of a tailored patient-specific approach to the treatment of sinus disease in the future.

microbiology↗

bioRxiv2019

The international sinonasal microbiome study (ISMS): a multi centre, international characterization of sinonasal bacterial ecology

The sinonasal microbiome remains poorly defined, with our current knowledge based on a few cohort studies whose findings are inconsistent. Furthermore, the variability of the sinus microbiome across geographical divides remains unexplored. We characterise the sinonasal microbiome and its geographical variations in both health and disease using 16S rRNA gene sequencing of 410 individuals from across the world. Although the sinus microbial ecology is highly variable between individuals, we identify a core microbiome comprised of Corynebacterium, Staphylococcus, Streptococcus, Haemophilus, and Moraxella species in both healthy and chronic rhinosinusitis (CRS) cohorts. Corynebacterium (mean relative abundance = 44.02%) and Staphylococcus (mean relative abundance = 27.34%) appear particularly dominant in the majority of patients sampled. There was a significant variation in microbial diversity between countries (p = 0.001). Amongst patients suffering from CRS with nasal polyps, a significant depletion of Corynebacterium (40.29% vs 50.43%; p = 0.02) and over-representation of Streptococcus (7.21% vs 2.73%; p = 0.032) was identified. The delineation of the sinonasal microbiome and standardised methodology described within our study will enable further characterisation and translational application of the sinus microbiota.

microbiology↗

bioRxiv2019

Rapidly processed stool swabs approximate stool microbiota profiles

Studies of the intestinal microbiome commonly utilize stool samples to measure microbial composition in the distal gut. However, collection of stool can be difficult from some subjects under certain experimental conditions. In this study we validate the use of swabs of fecal matter to approximate measurements of microbiota in stool using 16S rRNA gene Illumina amplicon sequencing, and evaluate the effects of shipping time at ambient temperatures on accuracy. Results indicate that swab samples reliably replicate stool microbiota bacterial composition, alpha diversity, and beta diversity when swabs are processed quickly (< 2 days), but sample quality quickly degrades after this period, accompanied by increased abundances of Enterobacteriaceae. Fresh swabs appear to be a viable alternative to stool sampling when standard collection methods are challenging, but extended exposure to ambient temperatures prior to processing threatens sample integrity.

microbiology↗

bioRxiv2018

Species-level microbial sequence classification is improved by source-environment information

Popular naive Bayes taxonomic classifiers for amplicon sequences assume that all species in the reference database are equally likely to be observed. We demonstrate that classification accuracy degrades linearly with the degree to which that assumption is violated, and in practice it is always violated. By incorporating environment-specific taxonomic abundance information, we demonstrate that species-level resolution is attainable.

bioinformatics↗

bioRxiv2018

q2-sample-classifier: machine-learning tools for microbiome classification and regression

Microbiome studies often aim to predict outcomes or differentiate samples based on their microbial compositions, tasks that can be efficiently performed by supervised learning methods. Here we present a benchmark comparison of supervised learning classifiers and regressors implemented in scikit-learn, a Python-based machine-learning library. We additionally present q2-sample-classifier, a plugin for the QIIME 2 microbiome bioinformatics framework, that facilitates application of the scikit-learn classifiers to microbiome data. Random forest, extra trees, and gradient boosting models demonstrate the highest performance for both supervised classification and regression of microbiome data. Automated feature selection and hyperparameter tuning enhance performance of most methods but may not be necessary under all circumstances. The q2-sample-classifier plugin makes these methods more accessible and interpretable to a broad audience of microbiologists, clinicians, and others who wish to utilize supervised learning methods for predicting sample characteristics based on microbiome composition. The q2-sample-classifier source code is available at https://github.com/qiime2/q2-sample-classifier. It is released under a BSD-3-Clause license, and is freely available including for commercial use.

bioinformatics↗

bioRxiv2017

Lack of Evidence that Ursodeoxycholic Acid’s Effects on the Gut Microbiome Influence Colorectal Adenoma Risk

ObjectiveWe previously reported that Ursodeoxycholic acid (UDCA), a therapeutic bile acid, reduces risk for advanced colorectal adenoma in men but not women. Interactions between the gut microbiome and fecal bile acid composition as a factor in colon cancer neoplasia have been postulated but evidence is limited to small cohorts and animal studies.\n\nDesignUsing banked stool samples collected as part of a phase III randomized clinical trial of UDCA for the prevention of colorectal neoplasia, we compared change in the microbiome composition after 3 years intervention in a subset of participants randomized to 8-10 mg/kg of body weight UDCA (n=198) to placebo (n=203). UDCA effects on the microbiome, sex and adenoma outcome were investigated.\n\nResultsStudy participants randomized to UDCA experienced compositional changes in their microbiome that were statistically more similar to other individuals in the UDCA arm than to those in the placebo arm. This change reflected an UDCA-associated shift in microbial community distance metrics (P <0.001), independent of sex, with no evidence of UDCA effect on microbial richness (P > 0.05). These UDCA-associated shifts in microbial community distance metrics from baseline to end-of-study were not associated with risk of any or advanced adenoma (all P> 0.05) in men or women.\n\nConclusionDespite a large sampling of randomized clinical trial participants, daily UDCA use only modestly influenced the relative abundance of microbial species in stool with no evidence for effects of UDCA on stool microbial community composition as a modifier of colorectal adenoma risk.\n\nSUMMARYO_ST_ABSWhat is already known about this subject?C_ST_ABSO_LIUrsodeoxycholic acid (UDCA) is a therapeutic bile acid used in the treatment of primary biliary cirrhosis (PBC) and investigated for anti-cancer activity in the colon\nC_LIO_LIIn humans, UDCA is produced in the colon from the conjugation of primary bile acids by intestinal bacteria\nC_LIO_LIIntestinal bacteria play a critical role in human intestinal health and disease including a hypothesized role in the development of colorectal cancer.\nC_LIO_LIUDCA was found to reduce the risk of more advanced colorectal adenoma with effects present in men but not women.\nC_LIO_LITherapeutic UDCA was recently shown to reduce the extent of bacterial dysbiosis in patients with PBC\nC_LI\n\nWhat are the new findings?O_LIAmong a population of patients with colorectal adenoma, low dose oral UDCA taken daily produced modest changes in fecal bacterial composition\nC_LIO_LIUDCA associated changes in the gut microbiome were similar in men and women.\nC_LIO_LIUDCA associated changes in the gut micobiome were not associated with risk of any or advanced colorectal adenoma in the patient population.\nC_LI\n\nHow might it impact on clinical practice in the foreseeable future?O_LIThese findings confirm effects of oral UDCA on the microbiome that may be beneficial for patients with PBC.\nC_LIO_LIThese findings suggest that the anti-cancer effects of UDCA for colorectal adenoma prevention are not due to major effects of UDCA on the gut microbiome.\nC_LI

cancer biology↗

bioRxiv2017

q2-longitudinal: a QIIME 2 plugin for longitudinal and paired-sample analyses of microbiome data

Studies of host-associated and environmental microbiomes often incorporate longitudinal sampling or paired samples in their experimental design. Longitudinal sampling provides valuable information about temporal trends and subject/population heterogeneity, offering advantages over cross-sectional and pre/post study designs. To support the needs of microbiome researchers performing longitudinal studies, we developed q2-longitudinal, a software plugin for the QIIME 2 microbiome analysis platform (https://qiime2.org). The q2-longitudinal plugin incorporates multiple methods for analysis of longitudinal and paired-sample data, including paired differences and distances, linear mixed effects models, microbial interdependence test, first differencing, and volatility analyses. The q2-longitudinal package (https://github.com/qiime2/q2-longitudinal) is open source software released under a BSD-3-Clause license and is freely available, including for commercial use.

bioinformatics↗