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

Heintz‐Buschart, Anna; Laczny, Cédric Christian; Lebrun, Laura; Bellora, Camille; Krishna, Abhimanyu; Wampach, Linda; Schneider, Jochen G.; Hogan, Angela Duff; Beaufort, Carine de; Wilmes, Paul

doi:10.1038/nmicrobiol.2016.227

Cited by 14 publications

(15 citation statements)

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“…A previous study, published in 2016, sets the stage for larger-scale metagenomics studies by demonstrating, on a smaller-scale, how specific gastrointestinal microbial communities and host-microbe phenotypic relationships are associated with onset of human disorders (18). Although the role of exocrine pancreas function in T1D onset remain unclear, they showed that exocrine pancreas enzymes, including amylase, carboxypeptidase CPA1 and CUZD1are less abundant in T1D patients (18). These initial results are promising and provides the field with an initial insight into how the T1D metaproteome differs in functionality compared to controls.…”

Section: Gut Proteomementioning

confidence: 99%

“…A plethora of studies have demonstrated that the human microbiome has the potential to affect the pathogenesis of immune diseases, particularly autoimmune diseases in which the immune system fails to distinguish self from non-self proteins and attacks self-tissues. Examples of such diseases include multiple sclerosis (8), rheumatoid arthritis, systemic lupus erythematosus, anti-phospholipid syndrome (10), Crohn's disease (11)(12)(13), ulcerative colitis (14), inflammatory bowel diseases (15,16), coeliac disease (17), and Type 1 Diabetes (T1D) (18,19).…”

Section: Introductionmentioning

confidence: 99%

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The Role of Gut Microbiota and Environmental Factors in Type 1 Diabetes Pathogenesis

et al. 2020

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Section: Gut Proteomementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Role of Gut Microbiota and Environmental Factors in Type 1 Diabetes Pathogenesis

et al. 2020

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“…However, the fate of these microbial consortia in the face of perturbations is often not understood nor predictable (Muller, 2019). Longitudinal approaches are necessary to understand microbial community dynamics, as they may offer valuable insights into temporal trends and consequences of environmental forcings, when used in tandem with host-derived (Heintz-Buschart et al, 2016;Lloyd-Price et al, 2019;Mars et al, 2020) or environmental (Law et al, 2016;Herold et al, 2020) data. Longitudinal studies can be conducted using diachronic or synchronic approaches (Costa Junior et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Challenges, Strategies, and Perspectives for Reference-Independent Longitudinal Multi-Omic Microbiome Studies

et al. 2021

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In recent years, multi-omic studies have enabled resolving community structure and interrogating community function of microbial communities. Simultaneous generation of metagenomic, metatranscriptomic, metaproteomic, and (meta) metabolomic data is more feasible than ever before, thus enabling in-depth assessment of community structure, function, and phenotype, thus resulting in a multitude of multi-omic microbiome datasets and the development of innovative methods to integrate and interrogate those multi-omic datasets. Specifically, the application of reference-independent approaches provides opportunities in identifying novel organisms and functions. At present, most of these large-scale multi-omic datasets stem from spatial sampling (e.g., water/soil microbiomes at several depths, microbiomes in/on different parts of the human anatomy) or case-control studies (e.g., cohorts of human microbiomes). We believe that longitudinal multi-omic microbiome datasets are the logical next step in microbiome studies due to their characteristic advantages in providing a better understanding of community dynamics, including: observation of trends, inference of causality, and ultimately, prediction of community behavior. Furthermore, the acquisition of complementary host-derived omics, environmental measurements, and suitable metadata will further enhance the aforementioned advantages of longitudinal data, which will serve as the basis to resolve drivers of community structure and function to understand the biotic and abiotic factors governing communities and specific populations. Carefully setup future experiments hold great potential to further unveil ecological mechanisms to evolution, microbe-microbe interactions, or microbe-host interactions. In this article, we discuss the challenges, emerging strategies, and best-practices applicable to longitudinal microbiome studies ranging from sampling, biomolecular extraction, systematic multi-omic measurements, reference-independent data integration, modeling, and validation.

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“…Core Operational Taxonomic Units (OTUs), based on 16S rRNA gene amplicon libraries have been reported as being present in diverse anaerobic communities (Rivière et al, 2009;Huse et al, 2012;Rui et al, 2015;Calusinska et al, 2018). Core microbial communities can be more precisely identified by the combination of read-based and binning metagenome techniques (Nolla-Ardèvol et al, 2015a;Campanaro et al, 2016Campanaro et al, , 2018aHeintz-Buschart et al, 2016;Stolze et al, 2016;Treu et al, 2016;Zhu et al, 2018). Metagenomics studies also allow for the extrapolation to ecological functional networks (Schlüter et al, 2008;Wirth et al, 2012;Campanaro et al, 2016;Stolze et al, 2016), although this is of limited information, because the abundances of genes coding for enzymes do not necessarily reflect biological activity (De Vrieze et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Characterization of Core Microbiomes and Functional Profiles of Mesophilic Anaerobic Digesters Fed With Chlorella vulgaris Green Microalgae and Maize Silage

et al. 2019

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Microalgal biomass is an alternative feedstock for biogas production although its C/N ratio is usually lower than optimal, therefore co-fermentation is recommended. Biogas production from photoautotrophically grown Chlorella vulgaris (C. vulgaris) biomass (240 mL CH 4 g oTS −1 ) and co-fermentation with maize silage (330 mL CH 4 g oTS −1 ) has been studied in semi continuous laboratory biogas fermenters. Maize silage control yielded 310 mL CH 4 g oTS −1 . The microbial community and the read-based functional profiles, derived from these data, were examined during the process by using next-generation metagenome Ion Torrent sequencing technology. The read-based core microbiome consisted of 92 genera from which 60 abundant taxa were directly associated with the microbial methane producing food chain. The data-set was also analyzed in a genome-based approach. Sixty-five bins were assembled, 52 of them belonged in the core biogas producing genera identified by the read-based metagenomes. The read-based and genome-based approaches complemented and verified each other. The functional profiles indicated a variety of glycoside hydrolases. Substantial rearrangements of the methanogen functions have also been observed. Co-fermentation of algal biomass and plant biomass can be carried out for an extended period of time without process failure. The microbial members of the inoculum are well-conserved, feedstock composition changes caused mostly relative abundance alterations in the core microbiome.

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

Abstract: © 2 0 1 6 M a c m i l l a n P u b l i s h e r s L i m i t e d , p a r t o f S p r i n g e r N a t u r e . A l l r i g h t s r e s e r v e d .

Cited by 14 publications

References 0 publications

The Role of Gut Microbiota and Environmental Factors in Type 1 Diabetes Pathogenesis

The Role of Gut Microbiota and Environmental Factors in Type 1 Diabetes Pathogenesis

Challenges, Strategies, and Perspectives for Reference-Independent Longitudinal Multi-Omic Microbiome Studies

Characterization of Core Microbiomes and Functional Profiles of Mesophilic Anaerobic Digesters Fed With Chlorella vulgaris Green Microalgae and Maize Silage

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