Dataset Information

Metagenomic predictions: from microbiome to complex health and environmental phenotypes in humans and cattle.

ABSTRACT: Mammals have a large cohort of endo- and ecto- symbiotic microorganisms (the microbiome) that potentially influence host phenotypes. There have been numerous exploratory studies of these symbiotic organisms in humans and other animals, often with the aim of relating the microbiome to a complex phenotype such as body mass index (BMI) or disease state. Here, we describe an efficient methodology for predicting complex traits from quantitative microbiome profiles. The method was demonstrated by predicting inflammatory bowel disease (IBD) status and BMI from human microbiome data, and enteric greenhouse gas production from dairy cattle rumen microbiome profiles. The method uses unassembled massively parallel sequencing (MPS) data to form metagenomic relationship matrices (analogous to genomic relationship matrices used in genomic predictions) to predict IBD, BMI and methane production phenotypes with useful accuracies (r = 0.423, 0.422 and 0.466 respectively). Our results show that microbiome profiles derived from MPS can be used to predict complex phenotypes of the host. Although the number of biological replicates used here limits the accuracy that can be achieved, preliminary results suggest this approach may surpass current prediction accuracies that are based on the host genome. This is especially likely for traits that are largely influenced by the gut microbiota, for example digestive tract disorders or metabolic functions such as enteric methane production in cattle.

SUBMITTER: Ross EM

PROVIDER: S-EPMC3762846 | biostudies-other | 2013

REPOSITORIES: biostudies-other

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Metagenomic predictions: from microbiome to complex health and environmental phenotypes in humans and cattle.

Ross Elizabeth M EM Moate Peter J PJ Marett Leah C LC Cocks Ben G BG Hayes Ben J BJ

PloS one 20130904 9

Mammals have a large cohort of endo- and ecto- symbiotic microorganisms (the microbiome) that potentially influence host phenotypes. There have been numerous exploratory studies of these symbiotic organisms in humans and other animals, often with the aim of relating the microbiome to a complex phenotype such as body mass index (BMI) or disease state. Here, we describe an efficient methodology for predicting complex traits from quantitative microbiome profiles. The method was demonstrated by pred ...[more]

PMID: 24023808

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Project description:BackgroundThe mechanisms underlying the clinical outcome disparity during human infection with Giardia duodenalis are still unclear. In recent years, evidence has pointed to the roles of host factors as well as parasite's genetic heterogeneity as major contributing factors in the development of symptomatic human giardiasis. However, it remains contested as to how only a small fraction of individuals infected with G. duodenalis develop clinical gastrointestinal manifestations, whereas the majority of infected individuals remain asymptomatic. Here, we demonstrate that diversity in the fecal microbiome correlates with the clinical outcome of human giardiasis.MethodsThe genetic heterogeneity of G. duodenalis clinical isolates from human subjects with asymptomatic and symptomatic giardiasis was determined using a multilocus analysis approach. We also assessed the genetic proximity of G. duodenalis isolates by constructing phylogenetic trees using the maximum likelihood. Total genomic DNA (gDNA) from fecal specimens was utilized to construct DNA libraries, followed by performing paired-end sequencing using the HiSeq X platform. The Kraken2-generated, filtered FASTQ files were assigned to microbial metabolic pathways and functions using HUMAnN 3.04 and the UniRef90 diamond annotated full reference database (version 201901b). Results from HUMAnN for each sample were evaluated for differences among the biological groups using the Kruskal-Wallis non-parametric test with a post hoc Dunn test.ResultsWe found that a total of 8/11 (72.73%) human subjects were infected with assemblage A (sub-assemblage AII) of G. duodenalis, whereas 3/11 (27.27%) human subjects in the current study were infected with assemblage B of the parasite. We also found that the parasite's genetic diversity was not associated with the clinical outcome of the infection. Further phylogenetic analysis based on the tpi and gdh loci indicated that those clinical isolates belonging to assemblage A of G. duodenalis subjects clustered compactly together in a monophyletic clade despite being isolated from human subjects with asymptomatic and symptomatic human giardiasis. Using a metagenomic shotgun sequencing approach, we observed that infected individuals with asymptomatic and symptomatic giardiasis represented distinctive microbial diversity profiles, and that both were distinguishable from the profiles of healthy volunteers.ConclusionsThese findings identify a potential association between host microbiome disparity with the development of clinical disease during human giardiasis, and may provide insights into the mechanisms by which the parasite induces pathological changes in the gut. These observations may also lead to the development of novel selective therapeutic targets for preventing human enteric microbial infections.

Project description:Metagenomic next-generation sequencing (mNGS) allows the monitoring of microbiota composition of murine colonies employed for scientific purposes in a single test by assessing the composition of gut microbiome and the detection of pathogens from fecal pellets. In this study, we tested the potential use of mNGS for monitoring both microbiota composition and the presence of pathogens through Environmental Health Monitoring, by using exhaust dust collection filters derived from individually ventilated cages (IVC) systems.mNGS analysis was performed on nucleic acids isolated from filters collecting air from the exhaust of: (1) cages with mice housed in a non-pathogen free facility; (2) animal-free cages with clean chow and bedding from the same facility; (3) cages housing mice from a specific-pathogen free (SPF) facility. mNGS results revealed correspondence between microbiome composition from fecal pellets and filter, including pathogenic bacteria (Helicobacter hepaticus, Helicobacter typhlonius, Chlamydia muridarum, Rodentibacter pneumotropicus, Citrobacter rodentium), intestinal protozoa (Tritrichomonas muris, Spironucleus muris) nematoda (Aspiculuris tetraptera) and eukaryotic parasites (Myocoptes musculinus), present in the colony. Entamoeba muris and Syphacia obvelata were detected in fecal pellets but not in filter. The animal free exhaust dust filter, exposed to clean cages (no mice) placed in the IVC after removal of all mice, exhibited the presence of the same pathogens due to contaminated connecting pipes, confirming the sensitivity of the approach. Conversely, the filter from SPF colony revealed the absence of pathogens.The current use of exhaust dust collection filters in health surveillance requires multiple molecular tests to identify specific pathogens and does not provide information on the colony microbiome. This work provides the proof-of-principle that assaying exhaust dust collection filters by mNGS for microbiota monitoring of laboratory mice is feasible. In its daily application, results suggest the usefulness of the test in SPF facilities, where pathogenic micro-organisms are expected to be absent. mNGS analysis of exhaust dust collection filters allows the analysis of multiple cages, reducing the number of tests required for pathogen detection and corresponding costs, and avoiding the use of sentinel mice.

Dataset Information

Metagenomic predictions: from microbiome to complex health and environmental phenotypes in humans and cattle.

Publications

Metagenomic predictions: from microbiome to complex health and environmental phenotypes in humans and cattle.

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