Integrated multi-omics of the human gut microbiome in a case study of familial type 1 diabetes

Abstract

The gastrointestinal microbiome is a complex ecosystem with functions that shape human health. Studying the relationship between taxonomic alterations and functional repercussions linked to disease remains challenging. Here, we present an integrative approach to resolve the taxonomic and functional attributes of gastrointestinal microbiota at the metagenomic, metatranscriptomic and metaproteomic levels. We apply our methods to samples from four families with multiple cases of type 1 diabetes mellitus (T1DM). Analysis of intra-and inter-individual variation demonstrates that family membership has a pronounced effect on the structural and functional composition of the gastrointestinal microbiome. In the context of T1DM, consistent taxonomic differences were absent across families, but certain human exocrine pancreatic proteins were found at lower levels. The associated microbial functional signatures were linked to metabolic traits in distinct taxa. The methodologies and results provide a foundation for future large-scale integrated multi-omic analyses of the gastrointestinal microbiome in the context of host-microbe interactions in human health and disease.

IMP: a pipeline for reproducible reference-independent integrated metagenomic and metatranscriptomic analyses

“…High-quality assemblies yield higher quality taxonomic information and gene annotations while longer contigs (≥1 kb) are a prerequisite for unsupervised population-level genome reconstruction [14, 19, 56] and subsequent multi-omics data integration [39, 43, 44]. Throughout all the different comparative analyses which we performed, IMP performed more consistently across all the different datasets when compared to existing methods, thereby emphasizing the overall stability and broad range of applicability of the method (section “Assembly quality: multi-omic iterative co-assembly”).…”

“…These include studies of the human gut microbiome [28, 39], aquatic microbial communities from the Amazon river [27], soil microbial communities [40, 41], production-scale biogas plants [29], hydrothermal vents [42], and microbial communities from biological wastewater treatment plants [43, 44]. These studies employed differing ways for analyzing the data, including reference-based approaches [27, 28, 42], MG assembly-based approaches [29, 40], MT assembly-based approaches [42], and integrated analyses of the meta-omic data [39, 42–44]. Although these studies clearly demonstrate the power of multi-omic analyses by providing deep insights into community structure and function, standardized and reproducible computational workflows for integrating and analyzing the multi-omic data have so far been unavailable.…”

Visualizing balances of compositional data: A new alternative to balance dendrograms

“…These data measure the abundance of microbe taxa in the feces of diabetics and their non-diabetic relatives 30 , making it a true relative data set. Since these data contain many zeros that disrupt the log-ratio transformations, the zeros are first replaced through imputation by the package.…”

“…30 . The supplement of this manuscript contains code to pre-process these data and reproduce the analysis.…”

Microbiote intestinal, la clé pour une meilleure alimentation ?

“…Il existe plusieurs outils pour assembler, détecter et annoter les gènes à partir des lectures issues d'un séquençage à haut débit [16][17][18][19]. Le goulot d'étrangle-ment de l'analyse se situe au niveau des bases de données publiques disponibles qui souffrent d'un manque de représentativité des microorganismes anaérobies (majoritaires dans le microbiote intestinal) et des gènes non annotés fonctionnellement [8,20,38] (➜). L'utilisation de bases de données d'annotation potentielle de protéines telles que les bases CAZy (Carbohydrate-active enzymes) ou KEGG (Kyoto encyclopedia of genes and genomes) [16,21,22] permettent d'inférer les fonctions des enzymes impliquées respectivement dans la dégradation des sucres complexes ou des voies métaboliques.…”