Novel rheumatoid arthritis susceptibility locus at 22q12 identified in an extended UK genome-wide association study

Identification of TACSTD2 as novel therapeutic targets for cisplatin-induced acute kidney injury by multi-omics data integration

Cisplatin-induced acute kidney injury (CP-AKI) is a common complication in cancer patients. Although ferroptosis is believed to contribute to the progression of CP-AKI, its mechanisms remain incompletely understood. In this study, after initially processed individual omics datasets, we integrated multi-omics data to construct a ferroptosis network in the kidney, resulting in the identification of the key driver TACSTD2. In vitro and in vivo results showed that TACSTD2 was notably upregulated in cisplatin-treated kidneys and BUMPT cells. Overexpression of TACSTD2 accelerated ferroptosis, while its gene disruption decelerated ferroptosis, likely mediated by its potential downstream targets HMGB1, IRF6, and LCN2. Drug prediction and molecular docking were further used to propose that drugs targeting TACSTD2 may have therapeutic potential in CP-AKI, such as parthenolide, progesterone, premarin, estradiol and rosiglitazone. Our findings suggest a significant association between ferroptosis and the development of CP-AKI, with TACSTD2 playing a crucial role in modulating ferroptosis, which provides novel perspectives on the pathogenesis and treatment of CP-AKI.

PASTRY: achieving balanced power for detecting risk and protective minor alleles in meta-analysis of association studies with overlapping subjects

BACKGROUND

Meta-analysis is a statistical method that combines the results of multiple studies to increase statistical power. When multiple studies participating in a meta-analysis utilize the same public dataset as controls, the summary statistics from these studies become correlated. To solve this challenge, Lin and Sullivan proposed a method to provide an optimal test statistic adjusted for the correlation. This method quickly became the standard practice. However, we identified an unexpected power asymmetry phenomenon in this standard framework. This can lead to unbalanced power for detecting protective minor alleles and risk minor alleles.

RESULTS

We found that the power asymmetry of the current framework is mainly due to the errors in approximating the correlation term. We then developed a meta-analysis method based on an accurate correlation estimator, called PASTRY (A method to avoid Power ASymmeTRY). PASTRY outperformed the standard method on both simulated and real datasets in terms of the power symmetry.

CONCLUSIONS

Our findings suggest that PASTRY can help to alleviate the power asymmetry problem. PASTRY is available at https://github.com/hanlab-SNU/PASTRY .

Identifying shared genetic loci and common risk genes of rheumatoid arthritis associated with three autoimmune diseases based on large-scale cross-trait genome-wide association studies

Introduction

Substantial links between autoimmune diseases have been shown by an increasing number of studies, and one hypothesis for this comorbidity is that there is a common genetic cause.

Methods

In this paper, a large-scale cross-trait Genome-wide Association Studies (GWAS) was conducted to investigate the genetic overlap among rheumatoid arthritis, multiple sclerosis, inflammatory bowel disease and type 1 diabetes.

Results and discussion

Through the local genetic correlation analysis, 2 regions with locally significant genetic associations between rheumatoid arthritis and multiple sclerosis, and 4 regions with locally significant genetic associations between rheumatoid arthritis and type 1 diabetes were discovered. By cross-trait meta-analysis, 58 independent loci associated with rheumatoid arthritis and multiple sclerosis, 86 independent loci associated with rheumatoid arthritis and inflammatory bowel disease, and 107 independent loci associated with rheumatoid arthritis and type 1 diabetes were identified with genome-wide significance. In addition, 82 common risk genes were found through genetic identification. Based on gene set enrichment analysis, it was found that shared genes are enriched in exposed dermal system, calf, musculoskeletal, subcutaneous fat, thyroid and other tissues, and are also significantly enriched in 35 biological pathways. To verify the association between diseases, Mendelian randomized analysis was performed, which shows possible causal associations between rheumatoid arthritis and multiple sclerosis, and between rheumatoid arthritis and type 1 diabetes. The common genetic structure of rheumatoid arthritis, multiple sclerosis, inflammatory bowel disease and type 1 diabetes was explored by these studies, and it is believed that this important discovery will lead to new ideas for clinical treatment.

Innate and adaptive immune abnormalities underlying autoimmune diseases: the genetic connections

With the exception of an extremely small number of cases caused by single gene mutations, most autoimmune diseases result from the complex interplay between environmental and genetic factors. In a nutshell, etiology of the common autoimmune disorders is unknown in spite of progress elucidating certain effector cells and molecules responsible for pathologies associated with inflammatory and tissue damage. In recent years, population genetics approaches have greatly enriched our knowledge regarding genetic susceptibility of autoimmunity, providing us with a window of opportunities to comprehensively re-examine autoimmunity-associated genes and possible pathways. In this review, we aim to discuss etiology and pathogenesis of common autoimmune disorders from the perspective of human genetics. An overview of the genetic basis of autoimmunity is followed by 3 chapters detailing susceptibility genes involved in innate immunity, adaptive immunity and inflammatory cell death processes respectively. With such attempts, we hope to expand the scope of thinking and bring attention to lesser appreciated molecules and pathways as important contributors of autoimmunity beyond the 'usual suspects' of a limited subset of validated therapeutic targets.

Platelet FcγRIIA in immunity and thrombosis: Adaptive immunothrombosis

Sepsis and autoimmune diseases remain major causes of morbidity and mortality. The last decade has seen a new appreciation of platelets in host defense, in both immunity and thrombosis. Platelets are first responders in the blood to microbes or non-microbial antigens. The role of platelets in physiologic immunity is counterbalanced by their role in pathology, for example, microvascular thrombosis. Platelets encounter microbes and antigens via both innate and adaptive immune processes; platelets also help to shape the subsequent adaptive response. FcγRIIA is a receptor for immune complexes opsonized by IgG or pentraxins, and expressed in humans by platelets, granulocytes, monocytes and macrophages. With consideration of the roles of IgG and Fc receptors, the host response to microbes and autoantigens can be called adaptive immunothrombosis. Here we review newer developments involving platelet FcγRIIA in humans and humanized mice in immunity and thrombosis, with special attention to heparin-induced thrombocytopenia, systemic lupus erythematosus, and bacterial sepsis. Human genetic diversity in platelet receptors and the utility of humanized mouse models are highlighted.

Integrative genomics analysis of various omics data and networks identify risk genes and variants vulnerable to childhood-onset asthma

BACKGROUND

Childhood-onset asthma is highly affected by genetic components. In recent years, many genome-wide association studies (GWAS) have reported a large group of genetic variants and susceptible genes associated with asthma-related phenotypes including childhood-onset asthma. However, the regulatory mechanisms of these genetic variants for childhood-onset asthma susceptibility remain largely unknown.

METHODS

In the current investigation, we conducted a two-stage designed Sherlock-based integrative genomics analysis to explore the cis- and/or trans-regulatory effects of genome-wide SNPs on gene expression as well as childhood-onset asthma risk through incorporating a large-scale GWAS data (N = 314,633) and two independent expression quantitative trait loci (eQTL) datasets (N = 1890). Furthermore, we applied various bioinformatics analyses, including MAGMA gene-based analysis, pathway enrichment analysis, drug/disease-based enrichment analysis, computer-based permutation analysis, PPI network analysis, gene co-expression analysis and differential gene expression analysis, to prioritize susceptible genes associated with childhood-onset asthma.

RESULTS

Based on comprehensive genomics analyses, we found 31 genes with multiple eSNPs to be convincing candidates for childhood-onset asthma risk; such as, PSMB9 (cis-rs4148882 and cis-rs2071534) and TAP2 (cis-rs9267798, cis-rs4148882, cis-rs241456, and trans-10,447,456). These 31 genes were functionally interacted with each other in our PPI network analysis. Our pathway enrichment analysis showed that numerous KEGG pathways including antigen processing and presentation, type I diabetes mellitus, and asthma were significantly enriched to involve in childhood-onset asthma risk. The co-expression patterns among 31 genes were remarkably altered according to asthma status, and 25 of 31 genes (25/31 = 80.65%) showed significantly or suggestively differential expression between asthma group and control group.

CONCLUSIONS

We provide strong evidence to highlight 31 candidate genes for childhood-onset asthma risk, and offer a new insight into the genetic pathogenesis of childhood-onset asthma.

Association of BLK and BANK1 Polymorphisms and Interactions With Rheumatoid Arthritis in a Latin-American Population

Introduction

BLK has been identified as a risk factor to rheumatoid arthritis (RA) primarily in Asian or European-derived populations. However, this finding has not been evaluated in other populations such as Latin-Americans, except for Colombians. On the other hand, BANK1 single nucleotide variants (SNVs) have been scarcely studied in RA patients.

Objective

The aim of this study was to determine whether the BLK rs2736340T/C, rs13277113A/G, and BANK1 rs10516487G/A (R61H) and rs3733197G/A (A383T) polymorphisms are risk factors to RA in a sample of patients from Central Mexico.

Materials and Methods

We studied 957 women; 487 controls and 470 patients with RA by means of a TaqMan® SNP genotyping assay with fluorescent probes for the BLK rs13277113A/G, rs2736340T/C and BANK1 10516487G/A (R61H) and rs3733197G/A (A383T) variants.

Result

The BLK rs2736340T/C and rs13277113A/G variants were associated with risk for RA: C vs T; OR 1.39, p = 0.001, and G vs A; OR 1.37, p = 0.004, respectively. In addition, there was also an association between BANK1 R61H and RA: A vs G; OR 1.49, p = 0.003, but no with BANK1 A383T. We also identified an interaction significant between genotypes of BLK rs2736340T/C-BANK1 rs10516487G/A and RA: OR 1.65, p = 0.0001.

Conclusions

Our data suggest that both BLK and BANK1 confer susceptibility to RA in Mexican patients. The individual association of BANK1 rs1054857G/A with RA had not been previously reported in a particular population (except for pooled patients from several countries), therefore, our study presents the first evidence of association between this BANK1 variant and RA.

Regulatory network mediated by RBP-J/NFATc1-miR182 controls inflammatory bone resorption

Bone resorption is a severe consequence of inflammatory diseases associated with osteolysis, such as rheumatoid arthritis (RA), often leading to disability in patients. In physiological conditions, the differentiation of bone-resorbing osteoclasts is delicately regulated by the balance between osteoclastogenic and anti-osteoclastogenic mechanisms. Inflammation has complex impact on osteoclastogenesis and bone destruction, and the underlying mechanisms of which, especially feedback inhibition, are underexplored. Here, we identify a novel regulatory network mediated by RBP-J/NFATc1-miR182 in TNF-induced osteoclastogenesis and inflammatory bone resorption. This network includes negative regulator RBP-J and positive regulators, NFATc1 and miR182, of osteoclast differentiation. In this network, miR182 is a direct target of both RBP-J and NFATc1. RBP-J represses, while NFATc1 activates miR182 expression through binding to specific open chromatin regions in the miR182 promoter. Inhibition of miR182 by RBP-J servers as a critical mechanism that limits TNF-induced osteoclast differentiation and inflammatory bone resorption. Inflammation, such as that which occurs in RA, shifts the expression levels of the components in this network mediated by RBP-J/NFATc1-miR182-FoxO3/PKR (previously identified miR182 targets) towards more osteoclastogenic, rather than healthy conditions. Treatment with TNF inhibitors in RA patients reverses the expression changes of the network components and osteoclastogenic potential. Thus, this network controls the balance between activating and repressive signals that determine the extent of osteoclastogenesis. These findings collectively highlight the biological significance and translational implication of this newly identified intrinsic regulatory network in inflammatory osteoclastogenesis and osteolysis.

The evolution in our understanding of the genetics of rheumatoid arthritis and the impact on novel drug discovery

Introduction: Rheumatoid arthritis (RA) is an autoimmune disease that is characterized by chronic inflammation of the joints and affects 1% of the population. Polymorphisms of genes that encode proteins that primarily participate in inflammation may influence RA occurrence or become useful biomarkers for certain types of anti-rheumatic treatment.Areas covered: The authors summarize the recent progress in our understanding of the genetics of RA. In the last few years, multiple variants of genes that are associated with RA risk have been identified. The development of new technologies and the detection of new potential therapeutic targets that contribute to novel drug discovery are also described.Expert opinion: There is still the need to search for new genes which may be a potential target for RA therapy. The challenge is to develop appropriate strategies for achieving insight into the molecular pathways involved in RA pathogenesis. Understanding the genetics, immunogenetics, epigenetics and immunology of RA could help to identify new targets for RA therapy. The development of new technologies has enabled the detection of a number of new genes, particularly genes associated with proinflammatory cytokines and chemokines, B- and T-cell activation pathways, signal transducers and transcriptional activators, which might be potential therapeutic targets in RA.

'RA and the microbiome: do host genetic factors provide the link?

Rheumatoid arthritis (RA) is a chronic autoimmune disease, characterised by painful synovium inflammation, bony erosions, immune activation and the circulation of autoantibodies. Despite recent advances in therapeutics enabling disease suppression, there is a considerable demand for alternative therapeutic strategies as well as optimising those available at present. The relatively low concordance rate between monozygotic twins, 20–30% contrasts with heritability estimates of ∼65%, indicating a substantive role of other risk factors in RA pathogenesis. There is established evidence that RA has an infective component to its aetiology. More recently, differences in the commensal microbiota in RA compared to controls have been identified. Studies have shown that the gut, oral and lung microbiota is different in new onset treatment naïve, and established RA patients, compared to controls. Key taxonomic associations are an increase in abundance of Porphyromonas gingivalis and Prevotella copri in RA patients, compared to healthy controls. Host genetics may provide the link between disease and the microbiome. Genetic influence may be mediated by the host immune system; a differential response to RA associated taxa is suggested. The gut microbiome contains elements which are as much as 30% heritable. A better understanding of the influence of host genetics will shed light onto the role of the microbiome in RA. Here we review the role of the microbiome in RA through the lens of host genetics, and consider future research areas addressing microbiome study design and bioinformatics approaches.

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Rheumatoid arthritis (RA) affects 1% of the population and is highly debilitating.

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RA is ~65% heritable, yet the concordance rate between monozygotic twins is just 20–30%, indicating a substantive role of other risk factors.

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Studies have shown that the gut, oral and lung microbiome is different in treatment naïve and established RA patients, compared to controls.

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Current findings suggest an important influence of host genetics on the microbiome, which may contribute to RA via the host immune system.

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Associations of the microbiome with RA described thus far are confounded by host genetics, and future studies need to take account of this.

Integrative omics analysis identifies macrophage migration inhibitory factor signaling pathways underlying human hepatic fibrogenesis and fibrosis

The genetic basis underlying liver fibrosis remains largely unknown. We conducted a study to identify genetic alleles and underlying pathways associated with hepatic fibrogenesis and fibrosis at the genome-wide level in 121 human livers. By accepting a liberal significance level of P<1e-4, we identified 73 and 71 candidate loci respectively affecting the variability in alpha-smooth muscle actin (α-SMA) levels (fibrogenesis) and total collagen content (fibrosis). The top genetic loci associated with the two markers were BAZA1 and NOL10 for α-SMA expression and FAM46A for total collagen content (P<1e-6). We further investigated the relationship between the candidate loci and the nearby gene transcription levels (cis-expression quantitative trait loci) in the same liver samples. We found that 44 candidate loci for α-SMA expression and 44 for total collagen content were also associated with the transcription of the nearby genes (P<0.05). Pathway analyses of these genes indicated that macrophage migration inhibitory factor (MIF) related pathway is significantly associated with fibrogenesis and fibrosis, though different genes were enriched for each marker. The association between the single nucleotide polymorphisms, MIF and α-SMA showed that decreased MIF expression is correlated with increased α-SMA expression, suggesting that variations in MIF locus might affect the susceptibility of fibrogenesis through controlling MIF gene expression. In summary, our study identified candidate alleles and pathways underlying both fibrogenesis and fibrosis in human livers. Our bioinformatics analyses suggested MIF pathway as a strong candidate involved in liver fibrosis, thus further investigation for the role of the MIF pathway in liver fibrosis is warranted. The study was reviewed and approved by the Institutional Review Board (IRB) of Wayne State University (approval No. 201842) on May 17, 2018.

TIM family gene polymorphism and susceptibility to rheumatoid arthritis: Systematic review and meta-analysis

BACKGROUND

TIM-family proteins are expressed on different immune cells such as dendritic cells, macrophages, type 1 and 2 T helper (Th) cells. Therefore, they have the ability to contribute to the various intracellular signals and immune responses, importantly the regulation of Th1 and Th17 cell differentiation, which plays a remarked role in fight against inflammatory and autoimmune diseases. Association of TIM family gene polymorphisms with rheumatoid arthritis (RA) has been frequently investigated. The findings however are not entirely consistent. Therefore, we carried out the present meta-analysis to examine the association between RA and the following TIM family gene polymorphisms: rs41297579, rs1036199, rs10515746, and rs7700944.

METHODS

A systematic search of Scopus, PubMed, and Web of Science databases was conducted through December 2018. Combined odds ratios (OR) with their corresponding 95% confidence intervals (CI) were calculated under different possible genetic models.

RESULTS

A total of eight case-control studies were included in the present meta-analysis. The results demonstrated significant association of RA with TIM-3 rs1036199 polymorphism under dominant (OR, 1.93, 95% CI, 1.43-2.61) and allelic models (OR, 1.74, 95% CI, 1.31-2.30). None of the other examined polymorphisms indicated significant association with RA.

CONCLUSIONS

The present meta-analysis revealed that the TIM-3 rs1036199 polymorphism might confer susceptibility to RA. Further studies are required to reassert our findings.

Comparative study for haplotype block partitioning methods - Evidence from chromosome 6 of the North American Rheumatoid Arthritis Consortium (NARAC) dataset

Haplotype-based methods compete with “one-SNP-at-a-time” approaches on being preferred for association studies. Chromosome 6 contains most of the known genetic biomarkers for rheumatoid arthritis (RA) disease. Therefore, chromosome 6 serves as a benchmark for the haplotype methods testing. The aim of this study is to test the North American Rheumatoid Arthritis Consortium (NARAC) dataset to find out if haplotype block methods or single-locus approaches alone can sufficiently provide the significant single nucleotide polymorphisms (SNPs) associated with RA. In addition, could we be satisfied with only one method of the haplotype block methods for partitioning chromosome 6 of the NARAC dataset? In the NARAC dataset, chromosome 6 comprises 35,574 SNPs for 2,062 individuals (868 cases, 1,194 controls). Individual SNP approach and three haplotype block methods were applied to the NARAC dataset to identify the RA biomarkers. We employed three haplotype partitioning methods which are confidence interval test (CIT), four gamete test (FGT), and solid spine of linkage disequilibrium (SSLD). P-values after stringent Bonferroni correction for multiple testing were measured to assess the strength of association between the genetic variants and RA susceptibility. Moreover, the block size (in base pairs (bp) and number of SNPs included), number of blocks, percentage of uncovered SNPs by the block method, percentage of significant blocks from the total number of blocks, number of significant haplotypes and SNPs were used to compare among the three haplotype block methods. Individual SNP, CIT, FGT, and SSLD methods detected 432, 1,086, 1,099, and 1,322 associated SNPs, respectively. Each method identified significant SNPs that were not detected by any other method (Individual SNP: 12, FGT: 37, CIT: 55, and SSLD: 189 SNPs). 916 SNPs were discovered by all the three haplotype block methods. 367 SNPs were discovered by the haplotype block methods and the individual SNP approach. The P-values of these 367 SNPs were lower than those of the SNPs uniquely detected by only one method. The 367 SNPs detected by all the methods represent promising candidates for RA susceptibility. They should be further investigated for the European population. A hybrid technique including the four methods should be applied to detect the significant SNPs associated with RA for chromosome 6 of the NARAC dataset. Moreover, SSLD method may be preferred for its favored benefits in case of selecting only one method.

Elucidating the environmental risk factors for rheumatic diseases: An umbrella review of meta-analyses

AIMS

Although rheumatic diseases constitute a leading cause of disability, the environmental risk factors for these diseases are not clarified. In the present study, we aim to systematically appraise the epidemiological credibility of the environmental risk factors for rheumatic diseases.

METHODS

We systematically searched PubMed to capture meta-analyses of observational studies on environmental risk factors for the most prevalent rheumatic diseases. For each association, we estimated the summary effect size estimate, the 95% confidence and prediction intervals, and the I² metric. We further examined the presence of small-study effects and excess significance bias.

RESULTS

Overall, we identified 30 eligible papers describing 42 associations. Thirty-three associations were statistically significant at P < 0.05, whereas 13 of them were statistically significant at P < 1 × 10^-6 . Thirty-two associations had large or very large between-study heterogeneity. In 12 associations, evidence of small-study effects and/or excess significance bias was found. Six risk factors (nine associations) presented convincing or highly suggestive evidence of association: smoking and pack-years of smoking for rheumatoid arthritis; BMI (per 5 kg/m² increase) for gout and hip osteoarthritis; alcohol consumption for gout; BMI (overweight vs lean, obese vs lean), knee injury and participation in heavy work for knee osteoarthritis.

CONCLUSION

Our umbrella review indicated that a narrow range of risk factors has been examined for rheumatic diseases. Current evidence strongly supports that smoking, obesity, alcohol consumption, knee injury, and work activities are associated with risk for at least one rheumatic disease.

The potential of PTPN22 as a therapeutic target for rheumatoid arthritis

INTRODUCTION

PTPN22 encodes a lymphoid-specific tyrosine phosphatase (LYP) that is a master regulator of the immune response. This gene is a major susceptibility factor for a wide range of autoimmune conditions, including rheumatoid arthritis (RA) for which it represents the strongest non-HLA contributor to disease risk. A missense PTPN22 allele (R620W) affecting the protein-protein interaction of LYP with other relevant players was described as the functional variant of the association. This review will focus on the role of PTPN22 in the pathogenic mechanisms underlying RA predisposition and discuss the possibility of developing LYP-based treatment strategies with a potential application in clinical practice. Areas covered: This review covers the literature showing how PTPN22 is implicated in signalling pathways involved in the autoimmune and autoinflammatory processes underlying RA. Insights obtained from studies aimed at developing novel selective LYP suppressors for treating RA are summarized. Expert opinion: Targeting key risk factors during the early steps of the disease may represent a good strategy to accomplish complete disease remission. As cumulating evidences suggest that PTPN22 R620W is a gain-of-function variant, a growing interest in developing LYP inhibitors has arisen. The potential efficacy and possible application of such compounds are discussed.

Transcriptomics and machine learning predict diagnosis and severity of growth hormone deficiency

BACKGROUND

The effect of gene expression data on diagnosis remains limited. Here, we show how diagnosis and classification of growth hormone deficiency (GHD) can be achieved from a single blood sample using a combination of transcriptomics and random forest analysis.

METHODS

Prepubertal treatment-naive children with GHD (n = 98) were enrolled from the PREDICT study, and controls (n = 26) were acquired from online data sets. Whole blood gene expression was correlated with peak growth hormone (GH) using rank regression and a random forest algorithm tested for prediction of the presence of GHD and in classification of GHD as severe (peak GH <4 μg/l) and nonsevere (peak ≥4 μg/l). Performance was assessed using area under the receiver operating characteristic curve (AUC-ROC).

RESULTS

Rank regression identified 347 probe sets in which gene expression correlated with peak GH concentrations (r = ± 0.28, P < 0.01). These 347 probe sets yielded an AUC-ROC of 0.95 for prediction of GHD status versus controls and an AUC-ROC of 0.93 for prediction of GHD severity.

CONCLUSION

This study demonstrates highly accurate diagnosis and disease classification for GHD using a combination of transcriptomics and random forest analysis.

TRIAL REGISTRATION

NCT00256126 and NCT00699855.

FUNDING

Merck and the National Institute for Health Research (CL-2012-06-005).

Genetic variants within the TNFRSF1B gene and susceptibility to rheumatoid arthritis and response to anti-TNF drugs: a multicenter study

BACKGROUND

Recent research suggests that genetic variants in the tumor necrosis factor receptor 2 (TNFRSF1B) gene may have an impact on susceptibility to rheumatoid arthritis (RA) and drug response. The present population-based case-control study was carried out to evaluate whether 5 tagging single-nucleotide polymorphisms (SNPs) within the TNFRSF1B gene are associated with the risk of RA and response to antitumor necrosis factor (TNF) drugs.

METHODS

The study population included 1412 RA patients and 1225 healthy controls. A subset of 596 anti-TNF-naive RA patients was selected to assess the association of TNFRSF1B SNPs and drug response according to the EULAR response criteria.

RESULTS

We found that carriers of the TNFRSF1Brs3397C allele had a significantly increased risk of developing RA (P=0.0006). Importantly, this association remained significant after correction for multiple testing. We also confirmed the lack of association of the TNFRSF1Brs1061622 SNP with the risk of RA in the single-SNP analysis (P=0.89), but also through well-powered meta-analyses (PDOM=0.67 and PREC=0.37, respectively). In addition, our study showed that carriers of the TNFRSF1Brs3397C/C, TNFRSF1Brs1061622G/G, and TNFRSF1Brs1061631A/A genotypes had an increased risk of having a worse response to anti-TNF drugs at the level of P less than 0.05 (P=0.014, 0.0085 and 0.028, respectively). We also observed that, according to a log-additive model, carriers of the TNFRSF1Brs3397C or TNFRSF1Brs1061622G alleles showed an increased risk of having worse response to anti-TNF medications (P=0.018 and 0.0059). However, the association of the TNFRSF1Brs1061622 SNP only reached marginal significance after correction for multiple testing according to a log-additive model (P=0.0059) and it was not confirmed through a meta-analysis (PDOM=0.12).

CONCLUSION

Our results suggest that the TNFRSF1Brs3397 variant may play a role in modulating the risk of RA, but does not provide strong evidence of an impact of TNFRSF1B variants in determining response to anti-TNF drugs.

FOLD: a method to optimize power in meta-analysis of genetic association studies with overlapping subjects

Motivation

In genetic association studies, meta-analyses are widely used to increase the statistical power by aggregating information from multiple studies. In meta-analyses, participating studies often share the same individuals due to the shared use of publicly available control data or accidental recruiting of the same subjects. As such overlapping can inflate false positive rate, overlapping subjects are traditionally split in the studies prior to meta-analysis, which requires access to genotype data and is not always possible. Fortunately, recently developed meta-analysis methods can systematically account for overlapping subjects at the summary statistics level.

Results

We identify and report a phenomenon that these methods for overlapping subjects can yield low power. For instance, in our simulation involving a meta-analysis of five studies that share 20% of individuals, whereas the traditional splitting method achieved 80% power, none of the new methods exceeded 32% power. We found that this low power resulted from the unaccounted differences between shared and unshared individuals in terms of their contributions towards the final statistic. Here, we propose an optimal summary-statistic-based method termed as FOLD that increases the power of meta-analysis involving studies with overlapping subjects.

Availability and implementation

Our method is available at http://software.buhmhan.com/FOLD.

Supplementary information

Supplementary data are available at Bioinformatics online.

The TNF Receptor Superfamily in Co-stimulating and Co-inhibitory Responses

Cytokines related to tumor necrosis factor (TNF) provide a communication network essential for coordinating multiple cell types into an effective host defense system against pathogens and malignant cells. The pathways controlled by the TNF superfamily differentiate both innate and adaptive immune cells and modulate stromal cells into microenvironments conducive to host defenses. Members of the TNF receptor superfamily activate diverse cellular functions from the production of type 1 interferons to the modulation of survival of antigen-activated T cells. Here, we focus attention on the subset of TNF superfamily receptors encoded in the immune response locus in chromosomal region 1p36. Recent studies have revealed that these receptors use diverse mechanisms to either co-stimulate or restrict immune responses. Translation of the fundamental mechanisms of TNF superfamily is leading to the design of therapeutics that can alter pathogenic processes in several autoimmune diseases or promote immunity to tumors.

Are programmed cell death 1 gene polymorphisms correlated with susceptibility to rheumatoid arthritis?: A meta-analysis

BACKGROUND

Several studies investigated the relationship between programmed cell death 1 (PDCD1) gene polymorphisms and rheumatoid arthritis (RA) risk, but the results were controversial. To explore whether PDCD1 gene polymorphisms have an effect on RA risk, we conducted this meta-analysis to investigate the relationships between PDCD1 polymorphisms (rs36084323 [PD-1.1 G/A], rs11568821 [PD-1.3 G/A] and rs2227981 [PD-1.5 C/T]) and RA risk under 4 genetic models.

METHODS

PubMed, EMBASE, Web of Science, Cochrane Library China National Knowledge Infrastructure (CNKI), and Chinese Biomedical Literature Database (CBLM) were systematically searched for all eligible case-control studies. The last search was updated on September 10, 2016. Studies were accessed using Newcastle-Ottawa Scale case control study (NOS), and the combined effect size was calculated using STATA software, version 12.0. The pooled odds ratio (OR) with 95% confidence interval (CI) was calculated to assess the association. Heterogeneity analysis and subgroup analysis were also performed. Sensitivity analysis and publication bias were also performed if necessary.

RESULTS

This meta-analysis included 6 studies. The result demonstrated null association between rs36084323 (PD-1.1 G/A) polymorphism and RA susceptibility in all 4 genetic models. With regard to rs11568821 (PD-1.3 G/A), statistically significant association with RA risk was observed under allele model in Caucasians (allele model A vs G, OR = 1.19, 95% CI = 1.03-1.41). There was no significant association between rs2227981 (PD-1.5 C/T) polymorphism and RA risk.

CONCLUSION

The present study suggests that mutant A allele in rs11568821 (PD-1.3 G/A) might increase the susceptibility to RA in Caucasians.

Genetics of rheumatoid arthritis susceptibility, severity, and treatment response

A decade after the first genome-wide association study in rheumatoid arthritis (RA), a plethora of genetic association studies have been published on RA and its clinical or serological subtypes. We review the major milestones in the study of the genetic architecture of RA susceptibility, severity, and response to treatment. We set the scientific context necessary for non-geneticists to understand the potential clinical applications of human genetics and its significance for a stratified approach to the management of RA in the future.

Associations between PTPN22 and TLR9 polymorphisms and systemic lupus erythematosus: a comprehensive meta-analysis

Previous studies have explored the relationship of PTPN22 and TLR9 polymorphisms with systemic lupus erythematosus (SLE). In consideration of the population stratification, conflicting results and updating data, we conducted a comprehensive meta-analysis, which consists of a total of 17 research articles (9120 cases and 11,724 controls) for PTPN22 and 20 articles (including up to 2808 cases and 3386 controls) for TLR9. Significant association was verified between PTPN22 rs2476601 and SLE in the overall population (OR = 1.511 per T allele, 95% CI 1.338-1.706, P = 2.931 × 10^-11) and under dominant model of T allele (TT+CT vs. CC: OR = 1.531, 95% CI 1.346-1.742, P = 9.17 × 10^-11). Analysis after stratification by ethnicity indicated that PTPN22 rs2476601 was related to SLE in Americans (OR = 2.566, 95% CI 1.796-3.665, P = 2.219 × 10^-7), Europeans (OR = 1.399, 95% CI 1.261-1.552, P = 2.153 × 10^-10), and Africans (OR = 4.14, 95% CI 1.753-9.775, P = 1.0 × 10^-3). We did not observe any association between TLR9 polymorphisms (rs187084, rs352140, rs5743836 and rs352139) and SLE under any model, after excluding the data that were inconsistent with Hardy-Weinberg equilibrium (HWE). In summary, PTPN22 rs2476601 was significantly interrelated with SLE and contributed to susceptibility and development of SLE in Americans, Europeans and Africans in this analysis, while their relationship needs to be validated in Africans by future research.

Prediction of gene expression with cis-SNPs using mixed models and regularization methods

Background

It has been shown that gene expression in human tissues is heritable, thus predicting gene expression using only SNPs becomes possible. The prediction of gene expression can offer important implications on the genetic architecture of individual functional associated SNPs and further interpretations of the molecular basis underlying human diseases.

Methods

We compared three types of methods for predicting gene expression using only cis-SNPs, including the polygenic model, i.e. linear mixed model (LMM), two sparse models, i.e. Lasso and elastic net (ENET), and the hybrid of LMM and sparse model, i.e. Bayesian sparse linear mixed model (BSLMM). The three kinds of prediction methods have very different assumptions of underlying genetic architectures. These methods were evaluated using simulations under various scenarios, and were applied to the Geuvadis gene expression data.

Results

The simulations showed that these four prediction methods (i.e. Lasso, ENET, LMM and BSLMM) behaved best when their respective modeling assumptions were satisfied, but BSLMM had a robust performance across a range of scenarios. According to R² of these models in the Geuvadis data, the four methods performed quite similarly. We did not observe any clustering or enrichment of predictive genes (defined as genes with R² ≥ 0.05) across the chromosomes, and also did not see there was any clear relationship between the proportion of the predictive genes and the proportion of genes in each chromosome. However, an interesting finding in the Geuvadis data was that highly predictive genes (e.g. R² ≥ 0.30) may have sparse genetic architectures since Lasso, ENET and BSLMM outperformed LMM for these genes; and this observation was validated in another gene expression data. We further showed that the predictive genes were enriched in approximately independent LD blocks.

Conclusions

Gene expression can be predicted with only cis-SNPs using well-developed prediction models and these predictive genes were enriched in some approximately independent LD blocks. The prediction of gene expression can shed some light on the functional interpretation for identified SNPs in GWASs.

Update on the genetic architecture of rheumatoid arthritis

Human genetic studies into rheumatoid arthritis (RA) have uncovered more than 100 genetic loci associated with susceptibility to RA and have refined the RA-association model for HLA variants. The majority of RA-risk variants are highly shared across multiple ancestral populations and are located in noncoding elements that might have allele-specific regulatory effects in relevant tissues. Emerging multi-omics data, high-density genotype data and bioinformatic approaches are enabling researchers to use RA-risk variants to identify functionally relevant cell types and biological pathways that are involved in impaired immune processes and disease phenotypes. This Review summarizes reported RA-risk loci and the latest insights from human genetic studies into RA pathogenesis, including how genetic data has helped to identify currently available drugs that could be repurposed for patients with RA and the role of genetics in guiding the development of new drugs.

Rheumatoid Arthritis and Coronary Artery Disease: Genetic Analyses Do Not Support a Causal Relation

OBJECTIVE

Inflammatory diseases, specifically rheumatoid arthritis (RA), are assumed to increase the risk of coronary artery disease (CAD). More recently, multiple single-nucleotide polymorphisms (SNP) associated with RA risk were identified. If causal mechanisms affecting risks of RA and CAD are overlapping, risk alleles for RA might also increase the risk of CAD.

METHODS

Sixty-one SNP associating with RA in genome-wide significant analyses were tested for association with CAD in CARDIoGRAM (Coronary ARtery DIsease Genome wide Replication and Meta-analysis), a metaanalysis including genome-wide association data (22,233 CAD cases, 64,762 controls). In parallel, a set of SNP being associated with low-density lipoprotein cholesterol (LDL-C) was tested as a positive control.

RESULTS

Twenty-nine RA-associated SNP displayed a directionality-consistent association with CAD (OR range 1.002-1.073), whereas 32 RA-associated SNP were not associated with CAD (OR range 0.96-0.99 per RA risk-increasing allele). The proportion (48%) of directionality-consistent associated SNP equaled the proportion expected by chance (50%, p = 0.09). Of only 5 RA-associated SNP showing p values for CAD < 0.05, 4 loci (C5orf30, IL-6R, PTPN22, and RAD51B) showed directionality-consistent effects on CAD, and 1 (rs10774624, locus SH2B3) reached study-wide significance (p = 7.29E-06). By contrast, and as a proof of concept, 46 (74%) out of 62 LDL-C-associated SNP displayed a directionality-consistent association with CAD, a proportion that was significantly different from 50% (p = 5.9E-05).

CONCLUSION

We found no evidence that RA-associated SNP as a group are associated with CAD. Even though we were not able to study potential effects of all genetic variants individually, shared nongenetic factors may more plausibly explain the observed coincidence of the 2 conditions.

SF3A1 and pancreatic cancer: new evidence for the association of the spliceosome and cancer

A two-stage case-control study was conducted to examine the association between six candidate U2-depedent spliceosome genes (SRSF1, SRSF2, SF3A1, SF3B1, SF1 and PRPF40B) and pancreatic cancer (PC). Subjects with one or two T alleles at rs2074733 in SF3A1 had a lower risk of PC compared to those with two C alleles in combined two populations (OR: 0.59, 95% confidence interval: 0.48–0.73, False discovery rate (FDR)-P = 1.5E-05). Moreover, the presence of the higher-risk genotype at rs2074733 plus smoking or drinking had synergic effects on PC risk. These findings illustrate that RNA splicing-related genes appear to be associated with the occurrence of PC, and show synergic interactions with smoking and drinking in the additive model. In the future, our novel findings should be further confirmed by functional studies and independent large-scale population studies.

Rheumatoid Arthritis: The Stride from Research to Clinical Practice

Over 70 different genetic variants with a significant association with rheumatoid arthritis (RA) have been discovered. Anti-citrullination protein antibodies (ACPA)-positive RA variants are more well-defined than their ACPA-negative counterparts. The human leukocyte antigen, HLA-DRB1 locus remains the prime suspect in anti-citrullination protein antibodies (ACPA)—positive RA. Different HLA-DRB1 alleles are linked to RA susceptibility across different ethnicities. With evolving techniques, like genome-wide association studies (GWAS) and single nucleotide polymorphism (SNP) arrays, more non-HLA susceptibility loci have been identified for both types of RA. However, the functional significance of only a handful of these variants is known. Their roles include increasing susceptibility to RA or in determining the speed at which the disease progresses. Additionally, a couple of variations are associated with protection from RA. Defining such clear-cut biological functions can aid in the clinical diagnosis and treatment of RA. Recent research has focused on the implication of microRNAs, with miR-146a widely studied. In addition to disease susceptibility, genetic variations that influence the efficacy and toxicity of anti-RA agents have also been identified. Polymorphisms in the MTHFR gene influence the effectiveness of methotrexate, the first line of therapy in RA. Larger studies are, however, needed to identify potential biomarkers for early disease identification and monitoring disease progression.

RBP-J-Regulated miR-182 Promotes TNF-α-Induced Osteoclastogenesis

Increased osteoclastogenesis is responsible for osteolysis, which is a severe consequence of inflammatory diseases associated with bone destruction, such as rheumatoid arthritis and periodontitis. The mechanisms that limit osteoclastogenesis under inflammatory conditions are largely unknown. We previously identified transcription factor RBP-J as a key negative regulator that restrains TNF-α-induced osteoclastogenesis and inflammatory bone resorption. In this study, we tested whether RBP-J suppresses inflammatory osteoclastogenesis by regulating the expression of microRNAs (miRNAs) important for this process. Using high-throughput sequencing of miRNAs, we obtained the first, to our knowledge, genome-wide profile of miRNA expression induced by TNF-α in mouse bone marrow-derived macrophages/osteoclast precursors during inflammatory osteoclastogenesis. Furthermore, we identified miR-182 as a novel miRNA that promotes inflammatory osteoclastogenesis driven by TNF-α and whose expression is suppressed by RBP-J. Downregulation of miR-182 dramatically suppressed the enhanced osteoclastogenesis program induced by TNF-α in RBP-J-deficient cells. Complementary loss- and gain-of-function approaches showed that miR-182 is a positive regulator of osteoclastogenic transcription factors NFATc1 and B lymphocyte-induced maturation protein-1. Moreover, we identified that direct miR-182 targets, Foxo3 and Maml1, play important inhibitory roles in TNF-α-mediated osteoclastogenesis. Thus, RBP-J-regulated miR-182 promotes TNF-α-induced osteoclastogenesis via inhibition of Foxo3 and Maml1. Suppression of miR-182 by RBP-J serves as an important mechanism that restrains TNF-α-induced osteoclastogenesis. Our results provide a novel miRNA-mediated mechanism by which RBP-J inhibits osteoclastogenesis and suggest that targeting of the newly described RBP-J-miR-182-Foxo3/Maml1 axis may represent an effective therapeutic approach to suppress inflammatory osteoclastogenesis and bone resorption.

Genetic data: The new challenge of personalized medicine, insights for rheumatoid arthritis patients

Rapid advances in genotyping technology, analytical methods, and the establishment of large cohorts for population genetic studies have resulted in a large new body of information about the genetic basis of human rheumatoid arthritis (RA). Improved understanding of the root pathogenesis of the disease holds the promise of improved diagnostic and prognostic tools based upon this information. In this review, we summarize the nature of new genetic findings in human RA, including susceptibility loci and gene-gene and gene-environment interactions, as well as genetic loci associated with sub-groups of patients and those associated with response to therapy. Possible uses of these data are discussed, such as prediction of disease risk as well as personalized therapy and prediction of therapeutic response and risk of adverse events. While these applications are largely not refined to the point of clinical utility in RA, it seems likely that multi-parameter datasets including genetic, clinical, and biomarker data will be employed in the future care of RA patients.

Meta-analysis reveals PTPN22 1858C/T polymorphism confers susceptibility to rheumatoid arthritis in Caucasian but not in Asian population

The PTPN22 1858C/T polymorphism is associated with rheumatoid arthritis (RA). However, reports from the Asian populations are conflicting in nature and lacks consensus. The aim of our study was to evaluate the association between the PTPN22 1858C/T polymorphism and RA in Asian and Caucasian subjects by carrying out a meta-analysis of Asian and Caucasian data. A total of 27 205 RA cases and 27 677 controls were considered in the present meta-analysis involving eight Asian and 35 Caucasian studies. The pooled odds ratios (ORs) were performed for the allele, dominant, and recessive genetic model. No statistically significant association was found between the PTPN22 1858C/T polymorphism and risk of RA in Asian population (allele genetic model: OR = 1.217, 95% confidence interval (CI) = 0.99-1.496, p value 0.061; dominant genetic model: OR = 1.238, 95% CI = 0.982-1.562, p value 0.071; recessive genetic model: OR = 1.964, 95% CI = 0.678-5.693, p value 0.213). A significant association with risk of RA in Caucasian population suggesting that T-- allele does confer susceptibility to RA in this subgroup was observed (allele genetic model: OR = 1.638, 95% CI = 1.574-1.705, p value < 0.0001; dominant genetic model: OR = 1.67, 95% CI = 1.598-1.745, p value < 0.0001; recessive genetic model: OR = 2.65, 95% CI = 2.273-3.089, p value < 0.0001). The PTPN22 1858C/T polymorphism is not associated with RA risk in Asian populations. However, our meta-analysis confirms that the PTPN22 1858C/T polymorphism is associated with RA susceptibility in Caucasians.

The TNF-family cytokine TL1A: from lymphocyte costimulator to disease co-conspirator

Originally described in 2002 as a T cell-costimulatory cytokine, the tumor necrosis factor family member TNF-like factor 1A (TL1A), encoded by the TNFSF15 gene, has since been found to affect multiple cell lineages through its receptor, death receptor 3 (DR3, encoded by TNFRSF25) with distinct cell-type effects. Genetic deficiency or blockade of TL1A-DR3 has defined a number of disease states that depend on this cytokine-receptor pair, whereas excess TL1A leads to allergic gastrointestinal inflammation through stimulation of group 2 innate lymphoid cells. Noncoding variants in the TL1A locus are associated with susceptibility to inflammatory bowel disease and leprosy, predicting that the level of TL1A expression may influence host defense and the development of autoimmune and inflammatory diseases.

Genetic and genomic markers of anti-TNF treatment response in rheumatoid arthritis

Despite the success of anti-TNF drugs in the treatment of rheumatoid arthritis, a significant rate of nonresponse remains. Current clinical factors confer little power for predicting response and, in current practice, an unsatisfactory ‘trial and error’ approach governs therapeutic decisions. Candidate gene and unbiased genome-wide investigations have sought to identify genetic biomarkers that predict who will respond to anti-TNF drugs before the drug is administered. To date, few studies have yielded robust associations; herein, we discuss currently identified associations and the issues that need to be addressed in future investigations including insufficient power and an inadequate measure of disease activity. The potential for alternative predictors of anti-TNF therapy response from transcriptomic and epigenetic data will also be explored.

Unmet needs in autoimmunity and potential new tools

The term "autoimmunity" refers to a pathological condition in which the immunological tolerance of self-antigens is broken through, cross-reactive T cells are activated, and autoantibodies are produced by B cells. The intricate interplay among those aberrantly activated immune cells as well as inflammatory cytokines secreted by them contributes to the development of proinflammatory cascade which eventually leads to the occurrence of autoimmune diseases (AIDs) and organ damage. Autoimmune diseases occupy a broad spectrum of human diseases with more than 70 different disorders and afflict approximately 5-8 % of the world's population. AIDs can be categorized into organ-specific and systemic. Although the exact mechanism of AIDs remains elusive, it is generally believed that both genetic polymorphism and environmental exposure are involved in the development of AIDs. Aberrant epigenetic marks are also identified in patients with AIDs. In addition, dysregulation of innate immune system and molecular mimicry are indicated to play important roles in the initiation and maintenance of autoreactive inflammation. Based on the progress made in elucidating molecular mechanisms underlying AIDs, novel biomarkers for prediction, early diagnosis, prognosis and treatment response, and therapeutic strategies are proposed, which represents a promising future in the battle against AIDs. However, challenges remain regarding the clinical application of these potential new tools.

An epidemiological perspective of personalized medicine: the Estonian experience

The Estonian Biobank and several other biobanks established over a decade ago are now starting to yield valuable longitudinal follow-up data for large numbers of individuals. These samples have been used in hundreds of different genome-wide association studies, resulting in the identification of reliable disease-associated variants. The focus of genomic research has started to shift from identifying genetic and nongenetic risk factors associated with common complex diseases to understanding the underlying mechanisms of the diseases and suggesting novel targets for therapy. However, translation of findings from genomic research into medical practice is still lagging, mainly due to insufficient evidence of clinical validity and utility. In this review, we examine the different elements required for the implementation of personalized medicine based on genomic information. First, biobanks and genome centres are required and have been established for the high-throughput genomic screening of large numbers of samples. Secondly, the combination of susceptibility alleles into polygenic risk scores has improved risk prediction of cardiovascular disease, breast cancer and several other diseases. Finally, national health information systems are being developed internationally, to combine data from electronic medical records from different sources, and also to gradually incorporate genomic information. We focus on the experience in Estonia, one of several countries with national goals towards more personalized health care based on genomic information, where the unique combination of elements required to accomplish this goal are already in place.

Genetic variants are major determinants of CSF antibody levels in multiple sclerosis

Immunological hallmarks of multiple sclerosis include the production of antibodies in the central nervous system, expressed as presence of oligoclonal bands and/or an increased immunoglobulin G index-the level of immunoglobulin G in the cerebrospinal fluid compared to serum. However, the underlying differences between oligoclonal band-positive and -negative patients with multiple sclerosis and reasons for variability in immunoglobulin G index are not known. To identify genetic factors influencing the variation in the antibody levels in the cerebrospinal fluid in multiple sclerosis, we have performed a genome-wide association screen in patients collected from nine countries for two traits, presence or absence of oligoclonal bands (n = 3026) and immunoglobulin G index levels (n = 938), followed by a replication in 3891 additional patients. We replicate previously suggested association signals for oligoclonal band status in the major histocompatibility complex region for the rs9271640*A-rs6457617*G haplotype, correlated with HLA-DRB1*1501, and rs34083746*G, correlated with HLA-DQA1*0301 (P comparing two haplotypes = 8.88 × 10(-16)). Furthermore, we identify a novel association signal of rs9807334, near the ELAC1/SMAD4 genes, for oligoclonal band status (P = 8.45 × 10(-7)). The previously reported association of the immunoglobulin heavy chain locus with immunoglobulin G index reaches strong evidence for association in this data set (P = 3.79 × 10(-37)). We identify two novel associations in the major histocompatibility complex region with immunoglobulin G index: the rs9271640*A-rs6457617*G haplotype (P = 1.59 × 10(-22)), shared with oligoclonal band status, and an additional independent effect of rs6457617*G (P = 3.68 × 10(-6)). Variants identified in this study account for up to 2-fold differences in the odds of being oligoclonal band positive and 7.75% of the variation in immunoglobulin G index. Both traits are associated with clinical features of disease such as female gender, age at onset and severity. This is the largest study population so far investigated for the genetic influence on antibody levels in the cerebrospinal fluid in multiple sclerosis, including 6950 patients. We confirm that genetic factors underlie these antibody levels and identify both the major histocompatibility complex and immunoglobulin heavy chain region as major determinants.

Innovative human-specific investigational approaches to autoimmune disease

An organ-on-chip disease model approach, including “pre-clinical trial-on-chip” is introduced for understanding of human autoimmune disease pathophysiology and drug development.

RBP-J imposes a requirement for ITAM-mediated costimulation of osteoclastogenesis

Osteoclastogenesis requires activation of RANK signaling as well as costimulatory signals from immunoreceptor tyrosine-based activation motif-containing (ITAM-containing) receptors/adaptors, predominantly tyrosine kinase-binding proteins DAP12 and FcRγ, in osteoclast precursors. It is not well understood how costimulatory signals are regulated and integrated with RANK signaling. Here, we found that osteopetrotic bone phenotypes in mice lacking DAP12 or DAP12 and FcRγ are mediated by the transcription factor RBP-J, as deletion of Rbpj in these mice substantially rescued the defects of bone remodeling. Using a TNF-α-induced model of inflammatory bone resorption, we determined that RBP-J deficiency enables TNF-α to induce osteoclast formation and bone resorption in DAP12-deficient animals. Thus, RBP-J imposes a requirement for ITAM-mediated costimulation of RANKL or TNF-α-induced osteoclastogenesis. Mechanistically, RBP-J suppressed induction of key osteoclastogenic factors NFATc1, BLIMP1, and c-FOS by inhibiting ITAM-mediated expression and function of PLCγ2 and activation of downstream calcium-CaMKK/PYK2 signaling. Moreover, RBP-J suppressed Plcg2 expression and downstream calcium oscillations indirectly by a TGF-β/PLCγ2/calcium axis. Together, our findings indicate that RBP-J suppresses ITAM-mediated costimulation, thereby limiting crosstalk between ITAM and RANK/TNFR signaling and allowing fine tuning of osteoclastogenesis during bone homeostasis and under inflammatory conditions. Furthermore, these data suggest that environmental cues that regulate RBP-J expression/function potentially modulate the requirement for costimulatory signaling for osteoclast differentiation and bone remodeling.

The genetics of rheumatoid arthritis: risk and protection in different stages of the evolution of RA

There is now a general consensus that RA has a spectrum of disease stages that can begin many years before the onset of clinical symptoms. It is widely thought that understanding the complex interplay between genetics and environment, and their role in pathogenesis, is essential in gaining further insight into the mechanisms that drive disease development and progression. More than 100 genetic susceptibility loci have now been identified for RA through studies that have focused on patients with established RA compared with healthy controls. Studying the early preclinical phases of disease will provide valuable insights into the biological events that precede disease and could potentially identify biomarkers to predict disease onset and future therapeutic targets. In this review we will cover recent advances in the knowledge of genetic and environmental risk factors and speculate on how these factors may influence the transition from one stage of disease to another.