Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases.

Inflammatory bowel diseases, which include Crohn's disease and ulcerative colitis, affect several million individuals worldwide. Crohn's disease and ulcerative colitis are complex diseases that are heterogeneous at the clinical, immunological, molecular, genetic, and microbial levels. Individual contributing factors have been the focus of extensive research. As part of the Integrative Human Microbiome Project (HMP2 or iHMP), we followed 132 subjects for one year each to generate integrated longitudinal molecular profiles of host and microbial activity during disease (up to 24 time points each; in total 2,965 stool, biopsy, and blood specimens). Here we present the results, which provide a comprehensive view of functional dysbiosis in the gut microbiome during inflammatory bowel disease activity. We demonstrate a characteristic increase in facultative anaerobes at the expense of obligate anaerobes, as well as molecular disruptions in microbial transcription (for example, among clostridia), metabolite pools (acylcarnitines, bile acids, and short-chain fatty acids), and levels of antibodies in host serum. Periods of disease activity were also marked by increases in temporal variability, with characteristic taxonomic, functional, and biochemical shifts. Finally, integrative analysis identified microbial, biochemical, and host factors central to this dysregulation. The study's infrastructure resources, results, and data, which are available through the Inflammatory Bowel Disease Multi'omics Database ( http://ibdmdb.org ), provide the most comprehensive description to date of host and microbial activities in inflammatory bowel diseases

A Robust and Universal Metaproteomics Workflow for Research Studies and Routine Diagnostics Within 24 h Using Phenol Extraction, FASP Digest, and the MetaProteomeAnalyzer

The investigation of microbial proteins by mass spectrometry (metaproteomics) is a key technology for simultaneously assessing the taxonomic composition and the functionality of microbial communities in medical, environmental, and biotechnological applications. We present an improved metaproteomics workflow using an updated sample preparation and a new version of the MetaProteomeAnalyzer software for data analysis. High resolution by multidimensional separation (GeLC, MudPIT) was sacrificed to aim at fast analysis of a broad range of different samples in less than 24 h. The improved workflow generated at least two times as many protein identifications than our previous workflow, and a drastic increase of taxonomic and functional annotations. Improvements of all aspects of the workflow, particularly the speed, are first steps toward potential routine clinical diagnostics (i.e., fecal samples) and analysis of technical and environmental samples. The MetaProteomeAnalyzer is provided to the scientific community as a central remote server solution at www.mpa.ovgu.de.Peer Reviewe

Potential of fecal microbiota for early-stage detection of colorectal cancer

Several bacterial species have been implicated in the development of colorectal carcinoma (CRC), but CRC-associated changes of fecal microbiota and their potential for cancer screening remain to be explored. Here, we used metagenomic sequencing of fecal samples to identify taxonomic markers that distinguished CRC patients from tumor-free controls in a study population of 156 participants. Accuracy of metagenomic CRC detection was similar to the standard fecal occult blood test (FOBT) and when both approaches were combined, sensitivity improved > 45% relative to the FOBT, while maintaining its specificity. Accuracy of metagenomic CRC detection did not differ significantly between early- and late-stage cancer and could be validated in independent patient and control populations (N = 335) from different countries. CRC-associated changes in the fecal microbiome at least partially reflected microbial community composition at the tumor itself, indicating that observed gene pool differences may reveal tumor-related host-microbe interactions. Indeed, we deduced a metabolic shift from fiber degradation in controls to utilization of host carbohydrates and amino acids in CRC patients, accompanied by an increase of lipopolysaccharide metabolism

Integrating taxonomic, functional, and strain-level profiling of diverse microbial communities with bioBakery 3

17openInternationalBothCulture-independent analyses of microbial communities have progressed dramatically in the last decade, particularly due to advances in methods for biological profiling via shotgun metagenomics. Opportunities for improvement continue to accelerate, with greater access to multi-omics, microbial reference genomes, and strain-level diversity. To leverage these, we present bioBakery 3, a set of integrated, improved methods for taxonomic, strain-level, functional, and phylogenetic profiling of metagenomes newly developed to build on the largest set of reference sequences now available. Compared to current alternatives, MetaPhlAn 3 increases the accuracy of taxonomic profiling, and HUMAnN 3 improves that of functional potential and activity. These methods detected novel disease-microbiome links in applications to CRC (1262 metagenomes) and IBD (1635 metagenomes and 817 metatranscriptomes). Strain-level profiling of an additional 4077 metagenomes with StrainPhlAn 3 and PanPhlAn 3 unraveled the phylogenetic and functional structure of the common gut microbe Ruminococcus bromii, previously described by only 15 isolate genomes. With open-source implementations and cloud-deployable reproducible workflows, the bioBakery 3 platform can help researchers deepen the resolution, scale, and accuracy of multi-omic profiling for microbial community studies.openBeghini, Francesco; McIver, Lauren J; Blanco-Míguez, Aitor; Dubois, Leonard; Asnicar, Francesco; Maharjan, Sagun; Mailyan, Ana; Manghi, Paolo; Scholz, Matthias; Thomas, Andrew Maltez; Valles-Colomer, Mireia; Weingart, George; Zhang, Yancong; Zolfo, Moreno; Huttenhower, Curtis; Franzosa, Eric A.; Segata, NicolaBeghini, F.; Mciver, L.J.; Blanco-Míguez, A.; Dubois, L.; Asnicar, F.; Maharjan, S.; Mailyan, A.; Manghi, P.; Scholz, M.; Thomas, A.M.; Valles-Colomer, M.; Weingart, G.; Zhang, Y.; Zolfo, M.; Huttenhower, C.; Franzosa, E.A.; Segata, N

Integrating metatranscriptomes and metagenomes for deconvolution of composition and expression in human gut and artificial communities

Over the last 15 years the human microbiome has received increasing attention.During this time, many studies have contributed to shed light on the complex network of interactions both between the microorganisms and their host, and within microbial communities themselves. While traditionally aiming at assessing composition, recent studies have broadened this scope to multi-dimensional aspects, using multi-omics approaches.By integrating information about genomes, transcripts, proteins and metabolites, a holistic understanding of the microbiome is now within reach.However progressive, these studies generally suffer from a lack of closure, as interpretation and integration of this data is all but straightforward. In the particular case of metatranscriptomes, species abundance and gene expression are coupled into a single readout.Consequently, normalization of this data is a crucial but poorly understood and unresolved problem. Here I present different approaches to normalise metatranscriptomes and highlight procedural concerns when obtaining this type of data. Results show that better normalization strategies are necessary when integrating multi-omics data and that controlled pilot experiments are required for a better understanding of the intricate dynamics and interactions between members of these communities. This work further exposes concerns about the interpretation of functional aspects of microbial populations, primarily driven by the many uncontrolled sources of variation herein discussed. As these new data types become more widespread, methods will certainly evolve towards better standardization and controlled procedures. This will help the microbiome field to move beyond its descriptive state into one able to provide a more detailed and mechanistic understanding

Host phenotype classification from human microbiome data is mainly driven by the presence of microbial taxa

Machine learning-based classification approaches are widely used to predict host phenotypes from microbiome data. Classifiers are typically employed by considering operational taxonomic units or relative abundance profiles as input features. Such types of data are intrinsically sparse, which opens the opportunity to make predictions from the presence/absence rather than the relative abundance of microbial taxa. This also poses the question whether it is the presence rather than the abundance of particular taxa to be relevant for discrimination purposes, an aspect that has been so far overlooked in the literature. In this paper, we aim at filling this gap by performing a meta-analysis on 4,128 publicly available metagenomes associated with multiple case-control studies. At species-level taxonomic resolution, we show that it is the presence rather than the relative abundance of specific microbial taxa to be important when building classification models. Such findings are robust to the choice of the classifier and confirmed by statistical tests applied to identifying differentially abundant/present taxa. Results are further confirmed at coarser taxonomic resolutions and validated on 4,026 additional 16S rRNA samples coming from 30 public case-control studies

Dispersal-competition tradeoff in microbiomes in the quest for land colonization

Ancestor microbes started colonizing inland habitats approximately 2.7 to 3.5 billion years ago. With some exceptions, the key physiological adaptations of microbiomes associated with marine-to-land transitions have remained elusive. This is essentially caused by the lack of suitable systems that depict changes in microbiomes across sufficiently large time scales. Here, we investigate the adaptive routes taken by microbiomes along a contemporary gradient of land formation. Using functional trait-based metagenomics, we show that a switch from a microbial 'dispersal' to a 'competition' response modus best characterizes the microbial trait changes during this eco-evolutionary trajectory. The 'dispersal' modus prevails in microbiomes at the boundary sites between land and sea. It encompasses traits conferring cell chemosensory and motile behaviors, thus allowing the local microbes to exploit short-lived nutritional patches in high-diffusion microhabitats. A systematic transition towards the 'competition' modus occurs progressively as the soil matures, which is likely due to forces of viscosity or strain that favor traits for competition and chemical defense. Concomitantly, progressive increases in the abundances of genes encoding antibiotic resistance and complex organic substrate degradation were found. Our findings constitute a novel perspective on the ecology and evolution of microbiome traits, tracking back one of the most seminal transitions in the evolutionary history of life