TABLE 1.

Possible mechanism of microbial influence on RA development.

Study	Link to RA	Methodology	Findings
Maresz et al., 2013; Gully et al., 2014; Sandal et al., 2016; Courbon et al., 2019	PPAD citrullination	Rodent arthritis model	Infection with P. gingivalis aggravated arthritic symptoms
Konig et al., 2016	Citrullination via A. actinomycetemcomitans	Mass spectrometry	Neutrophilic hypercitrullination induced by pore forming toxin Lt×A.
Moutsopoulos et al., 2012	Pro-inflammatory cytokine production leading to osteoclastic activation	In-vitro cytokine production	Increased Th17 and IL17 production on exposure to P. gingivalis
Sato et al., 2017; Tsukasaki et al., 2018	Pro-inflammatory cytokine production leading to osteoclastic activation	Rodent model	Increased Th17 and IL17 production on exposure to P. gingivalis
Lundberg et al., 2008	Molecular mimicry	ELISA, mass spectrometry	Cross-reactivity between human α-enolase and P. gingivalis α-enolase
Khandpur et al., 2013	Citrullination via NETosis	Neutrophils isolated via sedimentation and quantification via fluorescence microscopy	Increased NETosis in RA patients. NETosis correlated with ACPA presence.
Totaro et al., 2013	Bacterial translocation	PCR	Presence of P. gingivalis DNA in synovial tissue
Taurog et al., 1994	Toll like receptor activation	Rodent model	Arthritis development was dependant on LTR activation by Lactobacillus bifidus
Chen et al., 2016	Disease prediction model based on random forest plots.	16S rRNA sequencing in humans. Rodent model used to investigate causality	Collinsella, Eggerthella, and Faecalibacterium identified as predictor organisms of RA. Collinsella increase disease severity in rodent models
Kim et al., 2018	Osteoclastic activity inhibited by butyrate	Rodent model	Butyrate inhibited HDAC2 in osteoclast and HDAC8 in T cells. Control of Th17/Treg balance.