Reverse Microbiomics: A New Reverse Dysbiosis Analysis Strategy and Its Usage in Prediction of Autoantigens and Virulent Factors in Dysbiotic Gut Microbiomes From Rheumatoid Arthritis Patients

Haihe Wang; Edison Ong; John Y Kao; Duxin Sun; Yongqun He

doi:10.3389/fmicb.2021.633732

Reverse Microbiomics: A New Reverse Dysbiosis Analysis Strategy and Its Usage in Prediction of Autoantigens and Virulent Factors in Dysbiotic Gut Microbiomes From Rheumatoid Arthritis Patients

Front Microbiol. 2021 Feb 25:12:633732. doi: 10.3389/fmicb.2021.633732. eCollection 2021.

Authors

Haihe Wang^{1

2}, Edison Ong³, John Y Kao⁴, Duxin Sun⁵, Yongqun He^{2

6

7}

Affiliations

¹ Department of Pathogen Biology, Harbin Medical University (Daqing), Daqing, China.
² Unit for Laboratory Animal Medicine, University of Michigan, Ann Arbor, MI, United States.
³ Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, United States.
⁴ Department of Internal Medicine, Division of Gastroenterology and Hepatology, University of Michigan Medical School, Ann Arbor, MI, United States.
⁵ Department of Pharmaceutical Sciences, College of Pharmacy, University of Michigan, Ann Arbor, MI, United States.
⁶ Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI, United States.
⁷ Center of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, United States.

Abstract

Alterations in the gut microbiome have been associated with various human diseases. Most existing gut microbiome studies stopped at the stage of identifying microbial alterations between diseased or healthy conditions. As inspired by reverse vaccinology (RV), we developed a new strategy called Reverse Microbiomics (RM) that turns this process around: based on the identified microbial alternations, reverse-predicting the molecular mechanisms underlying the disease and microbial alternations. Our RM methodology starts by identifying significantly altered microbiota profiles, performing bioinformatics analysis on the proteomes of the microbiota identified, and finally predicting potential virulence or protective factors relevant to a microbiome-associated disease. As a use case study, this reverse methodology was applied to study the molecular pathogenesis of rheumatoid arthritis (RA), a common autoimmune and inflammatory disease. Those bacteria differentially associated with RA were first identified and annotated from published data and then modeled and classified using the Ontology of Host-Microbiome Interactions (OHMI). Our study identified 14 species increased and 9 species depleted in the gut microbiota of RA patients. Vaxign was used to comparatively analyze 15 genome sequences of the two pairs of species: Gram-negative Prevotella copri (increased) and Prevotella histicola (depleted), as well as Gram-positive Bifidobacterium dentium (increased) and Bifidobacterium bifidum (depleted). In total, 21 auto-antigens were predicted to be related to RA, and five of them were previously reported to be associated with RA with experimental evidence. Furthermore, we identified 94 potential adhesive virulence factors including 24 microbial ABC transporters. While eukaryotic ABC transporters are key RA diagnosis markers and drug targets, we identified, for the first-time, RA-associated microbial ABC transporters and provided a novel hypothesis of RA pathogenesis. Our study showed that RM, by broadening the scope of RV, is a novel and effective strategy to study from bacterial level to molecular level factors and gain further insight into how these factors possibly contribute to the development of microbial alterations under specific diseases.

Keywords: bioinformatics; gut microbiota; microbiome; ontology; reverse microbiomics; reverse vaccinology; rheumatoid arthritis.