Congruent microbiome signatures in fibrosis-prone autoimmune diseases: IgG4-related disease and systemic sclerosis

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Abstract

Background

Immunoglobulin G4-related disease (IgG4-RD) and systemic sclerosis (SSc) are rare autoimmune diseases characterized by the presence of CD4+ cytotoxic T cells in the blood as well as inflammation and fibrosis in various organs, but they have no established etiologies. Similar to other autoimmune diseases, the gut microbiome might encode disease-triggering or disease-sustaining factors.

Methods

The gut microbiomes from IgG4-RD and SSc patients as well as healthy individuals with no recent antibiotic treatment were studied by metagenomic sequencing of stool DNA. De novo assembly-based taxonomic and functional characterization, followed by association and accessory gene set enrichment analysis, were applied to describe microbiome changes associated with both diseases.

Results

Microbiomes of IgG4-RD and SSc patients distinctly separated from those of healthy controls: numerous opportunistic pathogenic Clostridium and typically oral Streptococcus species were significantly overabundant, while Alistipes, Bacteroides, and butyrate-producing species were depleted in the two diseases compared to healthy controls. Accessory gene content analysis in these species revealed an enrichment of Th17-activating Eggerthella lenta strains in IgG4-RD and SSc and a preferential colonization of a homocysteine-producing strain of Clostridium bolteae in SSc. Overabundance of the classical mevalonate pathway, hydroxyproline dehydratase, and fibronectin-binding protein in disease microbiomes reflects potential functional differences in host immune recognition and extracellular matrix utilization associated with fibrosis. Strikingly, the majority of species that were differentially abundant in IgG4-RD and SSc compared to controls showed the same directionality in both diseases. Compared with multiple sclerosis and rheumatoid arthritis, the gut microbiomes of IgG4-RD and SSc showed similar signatures; in contrast, the most differentially abundant taxa were not the facultative anaerobes consistently identified in inflammatory bowel diseases, suggesting the microbial signatures of IgG4-RD and SSc do not result from mucosal inflammation and decreased anaerobism.

Conclusions

These results provide an initial characterization of gut microbiome ecology in fibrosis-prone IgG4-RD and SSc and reveal microbial functions that offer insights into the pathophysiology of these rare diseases.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13073-021-00853-7.

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Most cited references 65

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Fast and accurate short read alignment with Burrows–Wheeler transform

Heng Li, Richard Durbin (2009)

Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: rd@sanger.ac.uk

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Prodigal: prokaryotic gene recognition and translation initiation site identification

Doug Hyatt, Gwo-Liang Chen, Philip LoCascio … (2010)

Background The quality of automated gene prediction in microbial organisms has improved steadily over the past decade, but there is still room for improvement. Increasing the number of correct identifications, both of genes and of the translation initiation sites for each gene, and reducing the overall number of false positives, are all desirable goals. Results With our years of experience in manually curating genomes for the Joint Genome Institute, we developed a new gene prediction algorithm called Prodigal (PROkaryotic DYnamic programming Gene-finding ALgorithm). With Prodigal, we focused specifically on the three goals of improved gene structure prediction, improved translation initiation site recognition, and reduced false positives. We compared the results of Prodigal to existing gene-finding methods to demonstrate that it met each of these objectives. Conclusion We built a fast, lightweight, open source gene prediction program called Prodigal http://compbio.ornl.gov/prodigal/. Prodigal achieved good results compared to existing methods, and we believe it will be a valuable asset to automated microbial annotation pipelines.

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CD-HIT: accelerated for clustering the next-generation sequencing data

Limin Fu, Beifang Niu, Zhengwei Zhu … (2012)

Summary: CD-HIT is a widely used program for clustering biological sequences to reduce sequence redundancy and improve the performance of other sequence analyses. In response to the rapid increase in the amount of sequencing data produced by the next-generation sequencing technologies, we have developed a new CD-HIT program accelerated with a novel parallelization strategy and some other techniques to allow efficient clustering of such datasets. Our tests demonstrated very good speedup derived from the parallelization for up to ∼24 cores and a quasi-linear speedup for up to ∼8 cores. The enhanced CD-HIT is capable of handling very large datasets in much shorter time than previous versions. Availability: http://cd-hit.org. Contact: liwz@sdsc.edu Supplementary information: Supplementary data are available at Bioinformatics online.

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Author and article information

Contributors

Ramnik J. Xavier:

ORCID: http://orcid.org/0000-0002-5630-5167

xavier@molbio.mgh.harvard.edu

Journal

Journal ID (nlm-ta): Genome Med

Journal ID (iso-abbrev): Genome Med

Title: Genome Medicine

Publisher: BioMed Central (London )

ISSN (Electronic): 1756-994X

Publication date (Electronic): 28 February 2021

Publication date PMC-release: 28 February 2021

Publication date Collection: 2021

Volume: 13

Electronic Location Identifier: 35

Affiliations

[1 ]GRID grid.66859.34, Broad Institute of MIT and Harvard, ; Cambridge, MA USA

[2 ]GRID grid.5373.2, ISNI 0000000108389418, Department of Computer Science, , Aalto University, ; 02150 Espoo, Finland

[3 ]GRID grid.418424.f, ISNI 0000 0004 0439 2056, Novartis Institute for Biomedical Research, ; Cambridge, MA USA

[4 ]GRID grid.32224.35, ISNI 0000 0004 0386 9924, Division of Rheumatology, Allergy, and Immunology, , Massachusetts General Hospital, ; Boston, MA USA

[5 ]GRID grid.32224.35, ISNI 0000 0004 0386 9924, Clinical Epidemiology Program and Rheumatology Unit, , Massachusetts General Hospital and Harvard Medical School, ; Boston, MA USA

[6 ]GRID grid.461656.6, ISNI 0000 0004 0489 3491, Ragon Institute of MGH, MIT and Harvard, ; Cambridge, MA USA

[7 ]GRID grid.32224.35, ISNI 0000 0004 0386 9924, Division of Gastroenterology, , Massachusetts General Hospital and Harvard Medical School, ; Boston, MA USA

[8 ]GRID grid.32224.35, ISNI 0000 0004 0386 9924, Center for Cancer Risk Assessment, , Massachusetts General Hospital and Harvard Medical School, ; Boston, MA USA

[9 ]GRID grid.214458.e, ISNI 0000000086837370, University of Michigan Scleroderma Program, ; Ann Arbor, MI USA

[10 ]GRID grid.32224.35, ISNI 0000 0004 0386 9924, Center for Computational and Integrative Biology, , Massachusetts General Hospital and Harvard Medical School, ; Boston, MA USA

[11 ]GRID grid.32224.35, ISNI 0000 0004 0386 9924, Department of Molecular Biology, , Massachusetts General Hospital and Harvard Medical School, ; Boston, MA USA

[12 ]GRID grid.116068.8, ISNI 0000 0001 2341 2786, Center for Microbiome Informatics and Therapeutics, MIT, ; Cambridge, MA USA

Author information

Ramnik J. Xavier http://orcid.org/0000-0002-5630-5167

Article

Publisher ID: 853

DOI: 10.1186/s13073-021-00853-7

PMC ID: 7919092

PubMed ID: 33648559

SO-VID: eb60d721-f284-4389-9705-983699d05721

License:

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

History

Date received : 24 July 2020

Date accepted : 11 February 2021

Funding

Funded by: National Institutes of Health (US)

Award ID: U19 AI110495

Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;

Award ID: UM1 AI144295

Award ID: UM1-AI-110557

Award ID: K24 AR063120

Custom metadata

ScienceOpen disciplines: Molecular medicine

Keywords: gut microbiome,igg4-rd,systemic sclerosis,autoimmunity

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ScienceOpen disciplines: Molecular medicine

Keywords: gut microbiome, igg4-rd, systemic sclerosis, autoimmunity

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Congruent microbiome signatures in fibrosis-prone autoimmune diseases: IgG4-related disease and systemic sclerosis

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Abstract

Background

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Supplementary Information

Related collections

Bacterial extracellular vesicles

Most cited references 65

Fast and accurate short read alignment with Burrows–Wheeler transform

Prodigal: prokaryotic gene recognition and translation initiation site identification

CD-HIT: accelerated for clustering the next-generation sequencing data

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Cited by 18

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