Precipitating and perpetuating factors of rheumatoid arthritis immunopathology – linking the triad of genetic predisposition, environmental risk factors and autoimmunity to disease pathogenesis

Abstract

Rheumatoid arthritis (RA) is considered to occur when genetic and environmental factors interact to trigger immunopathological changes and consequently an inflammatory arthritis. Over the last few decades, epidemiological and genetic studies have identified a large number of risk factors for RA development, the most prominent of which comprise cigarette smoking and the shared epitope alleles. These risks appear to differ substantially between anti-cyclic citrullinated peptide (ACPA)-positive and ACPA-negative disease. In this article, we will summarise the risk factors for RA development that have currently been identified, outlining the specific gene–environment and gene–gene interactions that may occur to precipitate and perpetuate autoimmunity and RA. We will also focus on how this knowledge of risk factors for RA may be implemented in the future to identify individuals at a high risk of disease development in whom preventative strategies may be undertaken.

Background

Rheumatoid arthritis (RA) is considered to occur when genetically predisposed individuals are exposed to specific environmental risk factors. These genetic and environmental risks interact to trigger perturbations in the immune system, with auto-antibody – rheumatoid factor (RF) and anti-cyclic citrullinated peptide antibody (ACPA) – generation in the majority of cases, followed by pro-inflammatory cytokine production and a consequent inflammatory arthritis.

Over the last few decades, epidemiological studies have identified a large number of environmental risk factors for RA. More recently, advances in genomics research have greatly increased our understanding of the genetic architecture underlying RA development, with over 30 risk alleles identified for sero-positive disease in individuals of European ancestry .

There is however a growing appreciation that RA, as opposed to being a single disease entity, is a syndrome comprising several distinct phenotypes . The best-appreciated subdivision is by the presence or absence of immune responses to citrullinated protein antigens, termed ACPA-positive and ACPA-negative RA. Not only do these disease subtypes differ clinically, with ACPA-positive RA having higher rates of erosions and lower remission rates , but they also vary with regard to the genetic and environmental risk factors that contribute to their development .

In this article, we will summarise the genetic and environmental risk factors for RA that have been identified to date. We will outline how these factors may interact to precipitate and perpetuate autoimmunity and RA, focussing on the specific gene–environment interaction between smoking/periodontitis and the shared epitope alleles in the pathogenesis of ACPA-positive RA. Finally, we will describe how knowledge regarding RA risks may be implemented to identify and prevent RA development in high risk individuals in the future.

Genetic risk factors for RA development

Genetic factors dominate an individual’s risk of developing RA, accounting for approximately two-thirds of the overall risk burden for both ACPA-positive and ACPA-negative disease . RA is considered a complex polygenic disease, with multiple alleles contributing towards its development. Although the risk conferred by each individual risk allele is small, if several risk loci are present in the same individual they may be highly influential. To date, the majority of genome-wide associated studies (GWASs) have focussed on ACPA-positive disease; there is limited information regarding the genetic basis for ACPA-negative disease.

Genetic risk factors for ACPA-Positive RA

The majority of genetic risk for sero-positive RA is derived from the major histocompatibility complex, class II, DR beta 1 (HLA-DRB1). This is a group of alleles that encode the HLA class II DRβ-chain, which plays a pivotal role in antigen presentation by influencing the binding and presentation of arthritogenic peptides to auto-reactive CD4 ⁺T cells . Although multiple HLA-DRB1 alleles – particularly DRB1 ^∗0401 and *0404 – are associated with RA, they all share a region of structural similarity termed the shared epitope (SE)

At present over 30 non-MHC risk alleles for ACPA-positive RA have been identified and validated through candidate gene studies and GWAS . The most prominent and well understood of these comprise variants of the PTPN22 and PADI4 genes. The PTPN22 allele, 1858T, encodes the lymphoid-specific tyrosine phosphatase, Lyp, which is a negative regulator of T cell antigen receptor (TCR) signal transduction during T cell activation . The variant associated with RA is a gain-of-function mutation that probably predisposes to autoimmune disease through excessive suppression of TCR signalling during thymic development, resulting in the survival of auto-reactive T cells . PADI4 is a peptidylarginine deiminase enzyme that post-transcriptionally converts arginine residues to citrulline . It may therefore play a significant role in the development of ACPA through influencing protein citrullination.

Other important contributory loci are shown in Table 1 , which summarises published odds ratios (ORs) of validated RA risk alleles from a recent meta-analysis of GWAS RA risk loci in sero-positive patients of European ancestry .

Table 1

Validated risk alleles associated with sero-Positive RA in individuals of European ancestry .

	Locus	SNP ID	Candidate Gene(s)	OR (95% CI)
Established Risk Alleles	1p36	rs3890745	TNFRSF14	0.89 (0.85–0.94)
	1p13	rs2476601	PTPN22	1.94 (1.81–2.08)
	1p13	rs11586238	CD2, CD58	1.13 (1.07–1.19)
	1q23	rs12746613	FCGR2A	1.13 (1.06–1.21)
	1q31	rs10919563	PTPRC	0.88 (0.82–0.94)
	2p16	rs13031237	REL	1.13 (1.07–1.18)
	2q11	rs10865035	AFF3	1.12 (1.07–1.17)
	2q32	rs7574865	STAT4	1.16 (1.10–1.23)
	2q33	rs1980422	CD28	1.12 (1.06–1.18)
	2q33	rs3087243	CTLA4	0.87 (0.83–0.91)
	4q27	rs6822844	IL2, IL21	0.90 (0.84–0.95)
	6p21	rs6910071	HLA-DRB1 (∗0401 tag)	2.88 (2.73–3.03)
	6q21	rs548234	PRDM1	1.10 (1.05–1.16)
	6q23	rs10499194	TNFAIP3	0.91 (0.87–0.96)
	6q23	rs6920220	TNFAIP3	1.22 (1.16–1.29)
	6q23	rs5029937	TNFAIP3	1.40 (1.24–1.58)
	6q25	rs394581	TAGAP	0.91(0.87–0.96)
	8p23	rs2736340	BLK	1.12 (1.07–1.18)
	9p13	rs2812378	CCL21	1.10 (1.05–1.16)
	9q33	rs3761847	TRAF1, C5	1.13 (1.08–1.18)
	10p15	rs2104286	IL2RA	0.92 (0.87–0.97)
	10p15	rs4750316	PRKCQ	0.87 (0.82–0.92)
	11p12	rs540386	TRAF6	0.88 (0.83–0.94)
	12q13	rs1678542	KIF5A, PIP4K2C	0.91 (0.87–0.96)
	20q13	rs4810485	CD40	0.85(0.80–0.90)
	22q12	rs3218253	IL2RB	1.09 (1.03–1.15)
Recently Validated Risk Alleles	2p14	rs934734	SPRED2	1.13 (1.06–1.21)
	5q11	rs6859219	ANKRD55, IL6ST	0.85 (0.78–0.93)
	5q21	rs26232	C5orf30	0.93 (0.88–0.98)
	3p14	rs13315591	PXK	1.13 (1.04–1.23)
	4p15	rs874040	RBPJ	1.18 (1.12–1.24)
	6q27	rs3093023	CCR6	1.11 (1.06–1.16)
	7q32	rs10488631	IRF5	1.25 (1.14–1.37)
	2q11	rs11676922	AFF3	1.15 (1.10–1.20)
	9p13	rs951005	CCL21	0.87 (0.81–0.93)
	10p15	rs706778	IL2RA	1.11 (1.06–1.17)