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Integrated Multi-Omics of the Human Gut Microbiome in a Case Study Of ARTICLES PUBLISHED: 10 OCTOBER 2016 | VOLUME: 2 | ARTICLE NUMBER: 16180 OPEN Integrated multi-omics of the human gut microbiome in a case study of familial type 1 diabetes Anna Heintz-Buschart1*,PatrickMay1,CédricC.Laczny1,LauraA.Lebrun1, Camille Bellora2, Abhimanyu Krishna1, Linda Wampach1, Jochen G. Schneider1,3,4, Angela Hogan2, Carine de Beaufort1,5 and Paul Wilmes1* 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. etagenomic studies of faecal samples have provided deep Furthermore, the microbiota of siblings have been found to be insights into the structure and functional potential of closer to each other than to their parents’22. In the present study, Mmicrobial communities within the human gastrointestinal healthy family members were used as a stringent control group in tract1,2, revealing their inter-individual variability3 and apparent type 1 diabetes mellitus (T1DM), a disease supposedly influenced intra-individual stability4,5. Case–control studies have associated by an interplay of genetics and environment. microbial traits in individuals with patterns of diseases, thereby The worldwide increase in the incidence of T1DM (ref. 24), implicating gastrointestinal microbial communities in a range especially in individuals with a low genetic risk25, suggests that of human disorders6–10. Metagenomics is able to resolve environmental factors may trigger the autoimmune destruction differences in the functional potential of microbial communities of insulin-producing beta cells in the endocrine pancreas that and has revealed a surprisingly stable set of core functions, causes T1DM. Gastrointestinal agents have been implicated in even though gastrointestinal community structures display this process due to the immune-modulatory role of the gastrointes- great inter-individual variation2. tinal tract26,27 and the potential involvement of the pancreatic ducts These deep insights notwithstanding, metagenomics offers little in the inflammation that culminates in beta-cell destruction28. information on which microbial traits are actually contributed to Case–control studies of the microbial community structure human physiology. More specifically, previous studies analysing during29–32, immediately after33,34 and before31,35 seroconversion, functional ‘omes’, such as the metatranscriptomes or the metapro- have tried but failed to identify generalizable disease-causing or teomes, in gastrointestinal samples9,11–17 have found that functional biomarker organism signatures. The present study does not focus genes predicted from metagenomes are not necessarily on the aetiology, but examines a relatively small cohort of individ- expressed9,11–13. Furthermore, as the functional omes display uals living with T1DM to determine whether there are effects higher variability and sensitivity to perturbation9,11–13, these vari- that are apparent in the structure and function of the ations may better reflect disease-related changes in host–micro- gastrointestinal microbiome linked to T1DM. biome interactions14,15. For this reason, the integration of In this Article, we expand our recently developed methodologies metagenomic, metatranscriptomic and metaproteomic data is for integrated multi-omic analyses of microbial consortia36,37 to expected to yield important insights into the human microbiome18, systematically explore the variability of the different omes and as it has for environmental microbial consortia19,20. their relationships in the context of a case study of familial Families play an important role in the formation of the micro- T1DM. Our study allowed us to link the expression of disease- biota of individuals3,21,22. More specifically, greater similarities associated microbial functions to distinct taxa, which demonstrates have been observed between the microbiota of family members the necessity for integrated multi-omic analyses of microbiota in the and cohabiting partners than between unrelated individuals23. context of human disease research. 1Luxembourg Centre for Systems Biomedicine, 7 avenue des Hauts-Fourneaux, 4362 Esch-sur-Alzette, Luxembourg. 2Integrated BioBank of Luxembourg, 6 rue Nicolas Ernest Barblé, 1210 Luxembourg, Luxembourg. 3 Department of Internal Medicine II, Saarland University Medical Center, 66421 Homburg, Germany. 4Centre Hospitalier Emile Mayrisch, Rue Emile Mayrisch, 4240 Esch-sur-Alzette, Luxembourg. 5Clinique Pédiatrique – Centre Hospitalier de Luxembourg, 4 rue Nicolas Ernest Barblé, 1210 Luxembourg. *e-mail: [email protected]; [email protected] NATURE MICROBIOLOGY 2, 16180 (2016) | DOI: 10.1038/nmicrobiol.2016.180 | www.nature.com/naturemicrobiology 1 © 2016 Macmillan Publishers Limited, part of Springer Nature. All rights reserved. ARTICLES NATURE MICROBIOLOGY a M01 M02 M03 M04 Female with T1DM 2.1 2.4 3.1 4.6 4.1 1.1 1.2 1.3 Male with T1DM Female without T1DM 2.3 2.5 2.2 3.5 3.4 3.3 4.5 4.4 4.3 4.2 1.4 Male without T1DM 1.5 b #! Reads Variant calling !" Protein profile Whole human genome Individualized protein sequences Mass spectra Human proteins Functional profile Metaproteome Protein identification Microbial proteins Strain-resolved gene predictions Reads Taxonomic profile Functional profile Metatranscriptome Variant calling Functional Genome annotations reconstructions Assembly Alignment Gene predictions Reads Taxonomic profile Functional profile Metagenome Binning Detected mOTU marker genes mOTU marker genes Functional categories Figure 1 | Overview of the cohort and framework for integrated analyses. a, Pedigrees of participant families. Individuals who donated samples are represented by coloured symbols; these colours are reflected in all subsequent figures. Individuals with T1DM are represented by filled symbols. Samples of the two individuals denoted in grey letters and represented by symbols with dashed lines (M01.5 and M03.1) were only subjected to metagenomic analyses. b, Schematic representation of the per-sample analysis workflow. Resulting data sets framed with coloured boxes were used for cross-sample comparisons. Study cohort and microbial community structures groups than unrelated individuals, and samples did not cluster accord- The study cohort consisted of 20 individuals from four families of at ing to T1DM status (Supplementary Fig. 2). Similar to earlier ana- least two generations presenting at least two cases of T1DM (Fig. 1a). lyses38, 43 ± 14% of the detected microorganisms in each sample T1DM was in all cases diagnosed for more than five years and belonged to mOTUs without a sequenced isolate genome. The relative successfully controlled by insulin replacement therapy, and the abundance of single mOTUs not assigned to any phylum reached up to auto-antibody status of the healthy individuals did not indicate 9% (Supplementary Section 2). These results indicate that analytical immediate risk of developing T1DM (for detailed descriptions see approaches purely based on isolate reference genomes would not be Supplementary Section 1 and Supplementary Table 1). able to capture the taxonomic and functional diversity in these To obtain overviews of the microbial community structures in 53 microbial communities. well-preserved faecal samples collected over a period of two to four months, we calculated relative abundances of metagenomic operational Integrated multi-omics taxonomic units (mOTUs)38 from 3.9 ± 0.1 Gbp (mean ± standard Given the considerable proportion of taxa not represented by deviation (s.d.)) of metagenomic data per sample (Supplementary sequenced isolate genomes in the mOTU analyses, we devised a refer- Section 2, Supplementary Fig. 1 and Supplementary Table 2). In ence genome-independent workflow for the integration of metage- most cases, mOTU-based taxonomic profiles were stable over time nomic, metatranscriptomic and metaproteomic data (Fig. 1b). A within individuals. Given that family members share environment, total of 8.0 ± 1.0 Gbp (mean ± s.d.) of metatranscriptomic sequen- nutrition and genetic background, families formed more distinct cing data and 185,000 ± 13,000 peptide mass spectra were obtained 2 NATURE MICROBIOLOGY 2, 16180 (2016) | DOI: 10.1038/nmicrobiol.2016.180 | www.nature.com/naturemicrobiology © 2016 Macmillan Publishers Limited, part of Springer Nature. All rights reserved. NATURE MICROBIOLOGY ARTICLES G18 a G8 b 700 d 1 × 100 600 Essential genes O50 G6 500 G17 400 1 × 10−1 P26.2 300 G10 L26.1 200 Gene count 100 1 × 10−2 L2.2.1.2.1.1 O22O23 0 010203040 P19
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