AUTHOR=Gao Lu , Cheng Zijian , Zhu Fudong , Bi Chunsheng , Shi Qiongling , Chen Xiaoyan 

TITLE=The Oral Microbiome and Its Role in Systemic Autoimmune Diseases: A Systematic Review of Big Data Analysis

JOURNAL=Frontiers in Big Data

VOLUME=Volume 5 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/big-data/articles/10.3389/fdata.2022.927520

DOI=10.3389/fdata.2022.927520

ISSN=2624-909X

ABSTRACT=Introduction: Despite decades of research, systemic autoimmune diseases (SADs) continue to be a major global health concern and the aetiology of these diseases is still not clear. To date, with the development of high-throughput techniques, increasing evidence indicated a key role of oral microbiome in the pathogenesis of SADs and the alterations of oral microbiome may contribute to the disease emergence or evolution. This review is to present the latest knowledge on the relationship between the oral microbiome and SADs, focusing on the multi-omics data generated from a large set of samples.
Methodology: By searching the PubMed and Embase databases, studies that investigated the oral microbiome of SADs, including systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), and Sjogren’s syndrome (SS), were systematically reviewed according to the PRISMA guidelines. 
Results: One thousand and thirty-eight studies were found, and 25 studies were included: three referred to SLE, 12 referred to RA, nine referred to SS, and one to both SLE and SS. The 16S rRNA sequencing was the most frequent technique used. HOMD was the most common database align to and QIIME was the most popular pipeline for downstream analysis. Alterations of bacterial composition and population have been found in the oral samples of SAD patients, compared with the healthy controls. Results regarding candidate pathogens were not always in accordance but Selenomonas and Veillonella were found significantly increased in the three SADs and Streptococcus was significantly decreased in the SADs compared with controls. 
Conclusion: Large amount of sequencing data was collected from SAD patients and controls in this systematic review. Oral microbial dysbiosis had been identified in these SADs, although the dysbiosis features were different among studies. There was a lack of standardized study methodology for each study, from the inclusion criteria, sample type, sequencing platform, referred database, to downstream analysis pipeline and cutoff. Besides the genomics, transcriptomics, proteomics and metabolomics technology should be used to investigate the oral microbiome of SADs patients and also the at-risk individuals of disease development, which may provide us with a better understanding of the aetiology of SADs and promote the development of the novel therapies.