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Multiomic Strategies Reveal Diversity and Important Functional Aspects of Human Gut Microbiome.

ABSTRACT: It is well accepted that dysbiosis of microbiota is associated with disease; however, the biological mechanisms that promote susceptibility or resilience to disease remain elusive. One of the major limitations of previous microbiome studies has been the lack of complementary metatranscriptomic (functional) data to complement the interpretation of metagenomics (bacterial abundance). The purpose of this study was twofold, first to evaluate the bacterial diversity and differential gene expression of gut microbiota using complementary shotgun metagenomics (MG) and metatranscriptomics (MT) from same fecal sample. Second, to compare sequence data using different Illumina platforms and with different sequencing parameters as new sequencers are introduced, and to determine if the data are comparable on different platforms. In this study, we perform ultradeep metatranscriptomic shotgun sequencing for a sample that we previously analyzed with metagenomics shotgun sequencing. We performed sequencing analysis using different Illumina platforms, with different sequencing and analysis parameters. Our results suggest that use of different Illumina platform did not lead to detectable bias in the sequencing data. The analysis of the sample using MG and MT approach shows that some species genes are highly represented in the MT than in the MG, indicating that some species are highly metabolically active. Our analysis also shows that ~52% of the genes in the metagenome are in the metatranscriptome and therefore are robustly expressed. The functions of the low and rare abundance bacterial species remain poorly understood. Our observations indicate that among the low abundant species analyzed in this study some were found to be more metabolically active compared to others, and can contribute distinct profiles of biological functions that may modulate the host-microbiota and bacteria-bacteria interactions.

SUBMITTER: Ranjan R

PROVIDER: S-EPMC6280293 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Multiomic Strategies Reveal Diversity and Important Functional Aspects of Human Gut Microbiome.

Ranjan Ravi R Rani Asha A Finn Patricia W PW Perkins David L DL

BioMed research international 20181114

It is well accepted that dysbiosis of microbiota is associated with disease; however, the biological mechanisms that promote susceptibility or resilience to disease remain elusive. One of the major limitations of previous microbiome studies has been the lack of complementary metatranscriptomic (functional) data to complement the interpretation of metagenomics (bacterial abundance). The purpose of this study was twofold, first to evaluate the bacterial diversity and differential gene expression o ...[more]

PMID: 30584534

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Project description:BackgroundHuman gut microbial functions are often associated with various diseases and host physiologies. Aging, a less explored factor, is also suspected to affect or be affected by microbiome alterations. By combining functional feature selection with supervised classification, we aim to facilitate identification of age-related functional characteristics in metagenomes from several human gut microbiome studies (MetaHIT, MicroAge, MicroObes, Kurokawa et al.'s and Gill et al.'s dataset).ResultsWe apply two feature selection methods, term frequency-inverse document frequency (TF-iDF) and minimum-redundancy maximum-relevancy (mRMR), to identify functional signatures that differentiate metagenomes by age. After features are reduced, we use a support vector machine (SVM) to predict host age of new metagenomes. Functional features are from protein families (Pfams), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, KEGG ontologies and the Gene Ontology (GO) database. Initial investigations demonstrate that ordination of the functional principal components shows great overlap between different age groups. However, when feature selection is applied, mRMR tightens the ordination cluster for each age group, and TF-iDF offers better linear separation. Both TF-iDF and mRMR were used in conjunction with a SVM classifier and achieved areas under receiver operating characteristic curves (AUCs) 10 to 15% above chance to classify individuals above/below mid-ages (about 38 to 43 years old) using Pfams. Better performance around mid-ages is also observed when using other functional categories and age-balanced dataset. We also identified some age-related Pfams that improved age discrimination at age 65 with another feature selection method called LEfSe, on an age-balanced dataset. The selected functional characteristics identify a broad range of age-relevant metabolisms, such as reduced vitamin B12 synthesis, reduced activity of reductases, increased DNA damage, occurrences of stress responses and immune system compromise, and upregulated glycosyltransferases in the aging population.ConclusionsFeature selection can yield biologically meaningful results when used in conjunction with classification, and makes age classification of new human gut metagenomes feasible. While we demonstrate the promise of this approach, the data-dependent prediction performance could be further improved. We hypothesize that while the Qin et al. dataset is the most comprehensive to date, even deeper sampling is needed to better characterize and predict the microbiomes' functional content.

Project description:Background: While CRISPR-Cas systems have been identified in bacteria from a wide variety of ecological niches, there are no studies to describe CRISPR-Cas elements in Bacteroides species, the most prevalent anaerobic bacteria in the lower intestinal tract. Microbes of the genus Bacteroides make up ~25% of the total gut microbiome. Bacteroides fragilis comprises only 2% of the total Bacteroides in the gut, yet causes of >70% of Bacteroides infections. The factors causing it to transition from benign resident of the gut microbiome to virulent pathogen are not well understood, but a combination of horizontal gene transfer (HGT) of virulence genes and differential transcription of endogenous genes are clearly involved. The CRISPR-Cas system is a multi-functional system described in prokaryotes that may be involved in control both of HGT and of gene regulation. Results: Clustered regularly interspaced short palindromic repeats (CRISPR) elements in all strains of B. fragilis (n = 109) with publically available genomes were identified. Three different CRISPR-Cas types, corresponding most closely to Type IB, Type IIIB, and Type IIC, were identified. Thirty-five strains had two CRISPR-Cas types, and three strains included all three CRISPR-Cas types in their respective genomes. The cas1 gene in the Type IIIB system encoded a reverse-transcriptase/Cas1 fusion protein rarely found in prokaryotes. We identified a short CRISPR (3 DR) with no associated cas genes present in most of the isolates; these CRISPRs were found immediately upstream of a hipA/hipB operon and we speculate that this element may be involved in regulation of this operon related to formation of persister cells during antimicrobial exposure. Also, blood isolates of B. fragilis did not have Type IIC CRISPR-Cas systems and had atypical Type IIIB CRISPR-Cas systems that were lacking adjacent cas genes. Conclusions: This is the first systematic report of CRISPR-Cas systems in a wide range of B. fragilis strains from a variety of sources. There are four apparent CRISPR-Cas systems in B. fragilis-three systems have adjacent cas genes. Understanding CRISPR/Cas function in B. fragilis will elucidate their role in gene expression, DNA repair and ability to survive exposure to antibiotics. Also, based on their unique CRISPR-Cas arrays, their phylogenetic clustering and their virulence potential, we are proposing that blood isolates of B. fragilis be viewed a separate subgroup.

Dataset Information

Multiomic Strategies Reveal Diversity and Important Functional Aspects of Human Gut Microbiome.

Publications

Multiomic Strategies Reveal Diversity and Important Functional Aspects of Human Gut Microbiome.

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