HLA-DMA*0103 and HLA-DMB*0104 alleles as novel prognostic factors in rheumatoid arthritis

Evaluation of HLA typing content of next-generation sequencing datasets from family trios and individuals of arab ethnicity

Introduction: HLA typing is a critical tool in both clinical and research applications at the individual and population levels. Benchmarking studies have indicated HLA-HD as the preferred tool for accurate and comprehensive HLA allele calling. The advent of next-generation sequencing (NGS) has revolutionized genetic analysis by providing high-throughput sequencing data. This study aims to evaluate, using the HLA-HD tool, the HLA typing content of whole exome, whole genome, and HLA-targeted panel sequence data from the consanguineous population of Arab ethnicity, which has been underrepresented in prior benchmarking studies. Methods: We utilized sequence data from family trios and individuals, sequenced on one or more of the whole exome, whole genome, and HLA-targeted panel sequencing technologies. The performance and resolution across various HLA genes were evaluated. We incorporated a comparative quality control analysis, assessing the results obtained from HLA-HD by comparing them with those from the HLA-Twin tool to authenticate the accuracy of the findings. Results: Our analysis found that alleles across 29 HLA loci can be successfully and consistently typed from NGS datasets. Clinical-grade whole exome sequencing datasets achieved the highest consistency rate at three-field resolution, followed by targeted HLA panel, research-grade whole exome, and whole genome datasets. Discussion: The study catalogues HLA typing consistency across NGS datasets for a large array of HLA genes and highlights assessments regarding the feasibility of utilizing available NGS datasets in HLA allele studies. These findings underscore the reliability of HLA-HD for HLA typing in underrepresented populations and demonstrate the utility of various NGS technologies in achieving accurate HLA allele calling.

Identification of immunological characterization and Anoikis-related molecular clusters in rheumatoid arthritis

Objective: To investigate the potential association between Anoikis-related genes, which are responsible for preventing abnormal cellular proliferation, and rheumatoid arthritis (RA). Methods: Datasets GSE89408, GSE198520, and GSE97165 were obtained from the GEO with 282 RA patients and 28 healthy controls. We performed differential analysis of all genes and HLA genes. We performed a protein-protein interaction network analysis and identified hub genes based on STRING and cytoscape. Consistent clustering was performed with subgrouping of the disease. SsGSEA were used to calculate immune cell infiltration. Spearman's correlation analysis was employed to identify correlations. Enrichment scores of the GO and KEGG were calculated with the ssGSEA algorithm. The WGCNA and the DGIdb database were used to mine hub genes' interactions with drugs. Results: There were 26 differentially expressed Anoikis-related genes (FDR = 0.05, log2FC = 1) and HLA genes exhibited differential expression (P < 0.05) between the disease and control groups. Protein-protein interaction was observed among differentially expressed genes, and the correlation between PIM2 and RAC2 was found to be the highest; There were significant differences in the degree of immune cell infiltration between most of the immune cell types in the disease group and normal controls (P < 0.05). Anoikis-related genes were highly correlated with HLA genes. Based on the expression of Anoikis-related genes, RA patients were divided into two disease subtypes (cluster1 and cluster2). There were 59 differentially expressed Anoikis-related genes found, which exhibited significant differences in functional enrichment, immune cell infiltration degree, and HLA gene expression (P < 0.05). Cluster2 had significantly higher levels in all aspects than cluster1 did. The co-expression network analysis showed that cluster1 had 51 hub differentially expressed genes and cluster2 had 72 hub differentially expressed genes. Among them, three hub genes of cluster1 were interconnected with 187 drugs, and five hub genes of cluster2 were interconnected with 57 drugs. Conclusion: Our study identified a link between Anoikis-related genes and RA, and two distinct subtypes of RA were determined based on Anoikis-related gene expression. Notably, cluster2 may represent a more severe state of RA.

A role of inflammaging in aortic aneurysm: new insights from bioinformatics analysis

Introduction

Aortic aneurysms (AA) are prevalent worldwide with a notable absence of drug therapies. Thus, identifying potential drug targets is of utmost importance. AA often presents in the elderly, coupled with consistently raised serum inflammatory markers. Given that ageing and inflammation are pivotal processes linked to the evolution of AA, we have identified key genes involved in the inflammaging process of AA development through various bioinformatics methods, thereby providing potential molecular targets for further investigation.

Methods

The transcriptome data of AA was procured from the datasets GSE140947, GSE7084, and GSE47472, sourced from the NCBI GEO database, whilst gene data of ageing and inflammation were obtained from the GeneCards Database. To identify key genes, differentially expressed analysis using the "Limma" package and WGCNA were implemented. Protein-protein intersection (PPI) analysis and machine learning (ML) algorithms were employed for the screening of potential biomarkers, followed by an assessment of the diagnostic value. Following the acquisition of the hub inflammaging and AA-related differentially expressed genes (IADEGs), the TFs-mRNAs-miRNAs regulatory network was established. The CIBERSORT algorithm was utilized to investigate immune cell infiltration in AA. The correlation of hub IADEGs with infiltrating immunocytes was also evaluated. Lastly, wet laboratory experiments were carried out to confirm the expression of hub IADEGs.

Results

342 and 715 AA-related DEGs (ADEGs) recognized from GSE140947 and GSE7084 datasets were procured by intersecting the results of "Limma" and WGCNA analyses. After 83 IADEGs were obtained, PPI analysis and ML algorithms pinpointed 7 and 5 hub IADEGs candidates respectively, and 6 of them demonstrated a high diagnostic value. Immune cell infiltration outcomes unveiled immune dysregulation in AA. In the wet laboratory experiments, 3 hub IADEGs, including BLNK, HLA-DRA, and HLA-DQB1, finally exhibited an expression trend in line with the bioinformatics analysis result.

Discussion

Our research identified three genes - BLNK, HLA-DRA, and HLA-DQB1- that play a significant role in promoting the development of AA through inflammaging, providing novel insights into the future understanding and therapeutic intervention of AA.

Identification of Immune-Related Risk Genes in Osteoarthritis Based on Bioinformatics Analysis and Machine Learning

In this research, we aimed to perform a comprehensive bioinformatic analysis of immune cell infiltration in osteoarthritic cartilage and synovium and identify potential risk genes. Datasets were downloaded from the Gene Expression Omnibus database. We integrated the datasets, removed the batch effects and analyzed immune cell infiltration along with differentially expressed genes (DEGs). Weighted gene co-expression network analysis (WGCNA) was used to identify the positively correlated gene modules. LASSO (least absolute shrinkage and selection operator)-cox regression analysis was performed to screen the characteristic genes. The intersection of the DEGs, characteristic genes and module genes was identified as the risk genes. The WGCNA analysis demonstrates that the blue module was highly correlated and statistically significant as well as enriched in immune-related signaling pathways and biological functions in the KEGG and GO enrichment. LASSO-cox regression analysis screened 11 characteristic genes from the hub genes of the blue module. After the DEG, characteristic gene and immune-related gene datasets were intersected, three genes, PTGS1, HLA-DMB and GPR137B, were identified as the risk genes in this research. In this research, we identified three risk genes related to the immune system in osteoarthritis and provide a feasible approach to drug development in the future.

Characterization of chromatin accessibility patterns in different mouse cell types using machine learning methods at single-cell resolution

Chromatin accessibility is a generic property of the eukaryotic genome, which refers to the degree of physical compaction of chromatin. Recent studies have shown that chromatin accessibility is cell type dependent, indicating chromatin heterogeneity across cell lines and tissues. The identification of markers used to distinguish cell types at the chromosome level is important to understand cell function and classify cell types. In the present study, we investigated transcriptionally active chromosome segments identified by sci-ATAC-seq at single-cell resolution, including 69,015 cells belonging to 77 different cell types. Each cell was represented by existence status on 20,783 genes that were obtained from 436,206 active chromosome segments. The gene features were deeply analyzed by Boruta, resulting in 3897 genes, which were ranked in a list by Monte Carlo feature selection. Such list was further analyzed by incremental feature selection (IFS) method, yielding essential genes, classification rules and an efficient random forest (RF) classifier. To improve the performance of the optimal RF classifier, its features were further processed by autoencoder, light gradient boosting machine and IFS method. The final RF classifier with MCC of 0.838 was constructed. Some marker genes such as H2-Dmb2, which are specifically expressed in antigen-presenting cells (e.g., dendritic cells or macrophages), and Tenm2, which are specifically expressed in T cells, were identified in this study. Our analysis revealed numerous potential epigenetic modification patterns that are unique to particular cell types, thereby advancing knowledge of the critical functions of chromatin accessibility in cell processes.

Single cell sequencing analysis identifies genetics-modulated ORMDL3+ cholangiocytes having higher metabolic effects on primary biliary cholangitis

BACKGROUND

Primary biliary cholangitis (PBC) is a classical autoimmune disease, which is highly influenced by genetic determinants. Many genome-wide association studies (GWAS) have reported that numerous genetic loci were significantly associated with PBC susceptibility. However, the effects of genetic determinants on liver cells and its immune microenvironment for PBC remain unclear.

RESULTS

We constructed a powerful computational framework to integrate GWAS summary statistics with scRNA-seq data to uncover genetics-modulated liver cell subpopulations for PBC. Based on our multi-omics integrative analysis, 29 risk genes including ORMDL3, GSNK2B, and DDAH2 were significantly associated with PBC susceptibility. By combining GWAS summary statistics with scRNA-seq data, we found that cholangiocytes exhibited a notable enrichment by PBC-related genetic association signals (Permuted P < 0.05). The risk gene of ORMDL3 showed the highest expression proportion in cholangiocytes than other liver cells (22.38%). The ORMDL3⁺ cholangiocytes have prominently higher metabolism activity score than ORMDL3^- cholangiocytes (P = 1.38 × 10^-15). Compared with ORMDL3^- cholangiocytes, there were 77 significantly differentially expressed genes among ORMDL3⁺ cholangiocytes (FDR < 0.05), and these significant genes were associated with autoimmune diseases-related functional terms or pathways. The ORMDL3⁺ cholangiocytes exhibited relatively high communications with macrophage and monocyte. Compared with ORMDL3^- cholangiocytes, the VEGF signaling pathway is specific for ORMDL3⁺ cholangiocytes to interact with other cell populations.

CONCLUSIONS

To the best of our knowledge, this is the first study to integrate genetic information with single cell sequencing data for parsing genetics-influenced liver cells for PBC risk. We identified that ORMDL3⁺ cholangiocytes with higher metabolism activity play important immune-modulatory roles in the etiology of PBC.

The DNA methylation Profile of Undifferentiated Arthritis Patients Anticipates their Subsequent Differentiation to Rheumatoid Arthritis

OBJECTIVE

Undifferentiated arthritis (UA) is the term used to cover all the cases of arthritis that do not fit a specific diagnosis. A significant percentage of UA patients progress to rheumatoid arthritis (RA), others to a different definite rheumatic disease, and the rest undergo spontaneous remission. Therapeutic intervention in patients with UA can delay or halt disease progression and its long-term consequences. It is therefore of inherent interest to identify those UA patients with a high probability of progressing to RA who would benefit from early appropriate therapy. We hypothesized that alterations in the DNA methylation profiles of immune cells may inform on the genetically- or environmentally-determined status of patients and potentially discriminate between disease subtypes.

METHODS

In this study, we performed DNA methylation profiling of a UA patient cohort, in which progression into RA occurs for a significant proportion of the patients.

RESULTS

We find differential DNA methylation in UA patients compared to healthy controls. Most importantly, our analysis identifies a DNA methylation signature characteristic of those UA cases that differentiate to RA. We demonstrate that the methylome of peripheral mononuclear cells can be used to anticipate the evolution of UA to RA, and that this methylome is associated with a number of inflammatory pathways and transcription factors. Finally, we design a machine-learning strategy for DNA methylation-based classification that predicts the differentiation of UA patients towards RA.

CONCLUSION

DNA methylation profiling provides a good predictor of UA-to-RA progression to anticipate targeted treatments and improve clinical management.

Meta-analysis of global and high throughput public gene array data for robust vascular gene expression discovery in chronic rhinosinusitis: Implications in controlled release

BACKGROUND

Chronic inflammation is known to cause alterations in vascular homeostasis that directly affects blood vessel morphogenesis, angiogenesis, and tissue permeability. These phenomena have been investigated and exploited for targeted drug delivery applications in the context of cancers and other disease processes. Vascular pathophysiology and its associated genes and signaling pathways, however, have not been systematically investigated in patients with chronic rhinosinusitis (CRS). Understanding the interplay between key vascular signaling pathways and top biomarkers associated with CRS may facilitate the development of new targeted delivery strategies and treatment paradigms. Herein, we report findings from a gene meta-analysis to identify key vascular pathways and top genes involved in CRS.

METHODS

Proprietary software (Illumina BaseSpace Correlation Engine) and open-access data sets were used to perform a gene meta-analysis to systematically determine significant differences between key vascular biomarkers and vascular signaling pathways expressed in sinonasal tissue biopsies of controls and patients with CRS.

RESULTS

Thirteen studies were initially identified, and then reduced to five after applying exclusion principle algorithms. Genes associated with vasculature development and blood vessel morphogenesis signaling pathways were identified to be overexpressed among the top 15 signaling pathways. Out of many significantly upregulated genes, the levels of pro angiogenic genes such as early growth response (EGR3), platelet endothelial cell adhesion molecule (PECAM1) and L-selectin (SELL) were particularly significant in patients with CRS compared to controls.

DISCUSSION

Key vascular biomarkers and signaling pathways were significantly overexpressed in patients with CRS compared to controls, suggesting a contribution of vascular dysfunction in CRS pathophysiology. Vascular dysregulation and permeability may afford opportunities to develop drug delivery systems to improve efficacy and reduce toxicity of CRS treatment.

Identification of novel genetic loci for osteoporosis and/or rheumatoid arthritis using cFDR approach

There are co-morbidity between osteoporosis (OP) and rheumatoid arthritis (RA). Some genetic risk factors have been identified for these two phenotypes respectively in previous research; however, they accounted for only a small portion of the underlying total genetic variances. Here, we sought to identify additional common genetic loci associated with OP and/or RA. The conditional false discovery rate (cFDR) approach allows detection of additional genetic factors (those respective ones as well as common pleiotropic ones) for the two associated phenotypes. We collected and analyzed summary statistics provided by large, multi-center GWAS studies of FNK (femoral neck) BMD (a major risk factor for osteoporosis) (n = 53,236) and RA (n = 80,799). The conditional quantile-quantile (Q-Q) plots can assess the enrichment of SNPs related to FNK BMD and RA, respectively. Furthermore, we identified shared loci between FNK BMD and RA using conjunction cFDR (ccFDR). We found strong enrichment of p-values in FNK BMD when conditional Q-Q was done on RA and vice versa. We identified 30 novel OP-RA associated pleiotropic loci that have not been reported in previous OP or RA GWAS, 18 of which located in the MHC (major histocompatibility complex) region previously reported to play an important role in immune system and bone health. We identified some specific novel polygenic factors for OP and RA respectively, and identified 30 novel OP-RA associated pleiotropic loci. These discovery findings may offer novel pathobiological insights, and suggest new targets and pathways for drug development in OP and RA patients.

Gene-Based Genome-Wide Association Analysis in European and Asian Populations Identified Novel Genes for Rheumatoid Arthritis

Objective

Rheumatoid arthritis (RA) is a complex autoimmune disease. Using a gene-based association research strategy, the present study aims to detect unknown susceptibility to RA and to address the ethnic differences in genetic susceptibility to RA between European and Asian populations.

Methods

Gene-based association analyses were performed with KGG 2.5 by using publicly available large RA datasets (14,361 RA cases and 43,923 controls of European subjects, 4,873 RA cases and 17,642 controls of Asian Subjects). For the newly identified RA-associated genes, gene set enrichment analyses and protein-protein interactions analyses were carried out with DAVID and STRING version 10.0, respectively. Differential expression verification was conducted using 4 GEO datasets. The expression levels of three selected ‘highly verified’ genes were measured by ELISA among our in-house RA cases and controls.

Results

A total of 221 RA-associated genes were newly identified by gene-based association study, including 71‘overlapped’, 76 ‘European-specific’ and 74 ‘Asian-specific’ genes. Among them, 105 genes had significant differential expressions between RA patients and health controls at least in one dataset, especially for 20 genes including 11 ‘overlapped’ (ABCF1, FLOT1, HLA-F, IER3, TUBB, ZKSCAN4, BTN3A3, HSP90AB1, CUTA, BRD2, HLA-DMA), 5 ‘European-specific’ (PHTF1, RPS18, BAK1, TNFRSF14, SUOX) and 4 ‘Asian-specific’ (RNASET2, HFE, BTN2A2, MAPK13) genes whose differential expressions were significant at least in three datasets. The protein expressions of two selected genes FLOT1 (P value = 1.70E-02) and HLA-DMA (P value = 4.70E-02) in plasma were significantly different in our in-house samples.

Conclusion

Our study identified 221 novel RA-associated genes and especially highlighted the importance of 20 candidate genes on RA. The results addressed ethnic genetic background differences for RA susceptibility between European and Asian populations and detected a long list of overlapped or ethnic specific RA genes. The study not only greatly increases our understanding of genetic susceptibility to RA, but also provides important insights into the ethno-genetic homogeneity and heterogeneity of RA in both ethnicities.

Identifying genes related with rheumatoid arthritis via system biology analysis

Rheumatoid arthritis (RA) is a chronic, inflammatory joint disease that mainly attacks synovial joints. However, the underlying systematic relationship among different genes and biological processes involved in the pathogenesis are still unclear. By analyzing and comparing the transcriptional profiles from RA, OA (osteoarthritis) patients as well as ND (normal donors) with bioinformatics methods, we tend to uncover the potential molecular networks and critical genes which play important roles in RA and OA development. Initially, hierarchical clustering was performed to classify the overall transcriptional profiles. Differentially expressed genes (DEGs) between ND and RA and OA patients were identified. Furthermore, PPI networks were constructed, functional modules were extracted, and functional annotation was also applied. Our functional analysis identifies 22 biological processes and 2 KEGG pathways enriched in the commonly-regulated gene set. However, we found that number of set of genes differentially expressed genes only between RA and ND reaches up to 244, indicating this gene set may specifically accounts for processing to disease of RA. Additionally, 142 biological processes and 19 KEGG pathways are over-represented by these 244 genes. Meanwhile, although another 21 genes were differentially expressed only in OA and ND, no biological process nor pathway is over-represented by them.

HLA-DMA polymorphisms differentially affect MHC class II peptide loading

During the adaptive immune response, MHCII proteins display antigenic peptides on the cell surface of APCs for CD4(+) T cell surveillance. HLA-DM, a nonclassical MHCII protein, acts as a peptide exchange catalyst for MHCII, editing the peptide repertoire. Although they map to the same gene locus, MHCII proteins exhibit a high degree of polymorphism, whereas only low variability has been observed for HLA-DM. As HLA-DM activity directly favors immunodominant peptide presentation, polymorphisms in HLA-DM (DMA or DMB chain) might well be a contributing risk factor for autoimmunity and immune disorders. Our systematic comparison of DMA*0103/DMB*0101 (DMA-G155A and DMA-R184H) with DMA*0101/DMB*0101 in terms of catalyzed peptide exchange and dissociation, as well as direct interaction with several HLA-DR/peptide complexes, reveals an attenuated catalytic activity of DMA*0103/DMB*0101. The G155A substitution dominates the catalytic behavior of DMA*0103/DMB*0101 by decreasing peptide release velocity. Preloaded peptide-MHCII complexes exhibit ∼2-fold increase in half-life in the presence of DMA*0103/DMB*0101 when compared with DMA*0101/DMB*0101. We show that this effect leads to a greater persistence of autoimmunity-related Ags in the presence of high-affinity competitor peptide. Our study therefore reveals that HLA-DM polymorphic residues have a considerable impact on HLA-DM catalytic activity.

HLA-DM constrains epitope selection in the human CD4 T cell response to vaccinia virus by favoring the presentation of peptides with longer HLA-DM-mediated half-lives

HLA-DM (DM) is a nonclassical MHC class II (MHC II) protein that acts as a peptide editor to mediate the exchange of peptides loaded onto MHC II during Ag presentation. Although the ability of DM to promote peptide exchange in vitro and in vivo is well established, the role of DM in epitope selection is still unclear, especially in human response to infectious disease. In this study, we addressed this question in the context of the human CD4 T cell response to vaccinia virus. We measured the IC(50), intrinsic dissociation t(1/2), and DM-mediated dissociation t(1/2) for a large set of peptides derived from the major core protein A10L and other known vaccinia epitopes bound to HLA-DR1 and compared these properties to the presence and magnitude of peptide-specific CD4(+) T cell responses. We found that MHC II-peptide complex kinetic stability in the presence of DM distinguishes T cell epitopes from nonrecognized peptides in A10L peptides and also in a set of predicted tight binders from the entire vaccinia genome. Taken together, these analyses demonstrate that DM-mediated dissociation t(1/2) is a strong and independent factor governing peptide immunogenicity by favoring the presentation of peptides with greater kinetic stability in the presence of DM.

HLA Immune Function Genes in Autism

The human leukocyte antigen (HLA) genes on chromosome 6 are instrumental in many innate and adaptive immune responses. The HLA genes/haplotypes can also be involved in immune dysfunction and autoimmune diseases. It is now becoming apparent that many of the non-antigen-presenting HLA genes make significant contributions to autoimmune diseases. Interestingly, it has been reported that autism subjects often have associations with HLA genes/haplotypes, suggesting an underlying dysregulation of the immune system mediated by HLA genes. Genetic studies have only succeeded in identifying autism-causing genes in a small number of subjects suggesting that the genome has not been adequately interrogated. Close examination of the HLA region in autism has been relatively ignored, largely due to extraordinary genetic complexity. It is our proposition that genetic polymorphisms in the HLA region, especially in the non-antigen-presenting regions, may be important in the etiology of autism in certain subjects.

Prediction of disease severity in patients with early rheumatoid arthritis by gene expression profiling

In order to test the ability of peripheral blood gene expression profiles to predict future disease severity in patients with early rheumatoid arthritis (RA), a group of 17 patients (1 ± 0.2 years disease duration) was evaluated at baseline for gene expression profiles. Disease status was evaluated after a mean of 5 years using an index combining pain, global and recoded MHAQ scores. Unsupervised and supervised algorithms identified "predictor genes" whose combined expression levels correlated with follow-up disease severity scores. Unsupervised clustering algorithms separated patients into two branches. The only significant difference between these two groups was the disease severity score; demographic variables and medication usage were not different. Supervised T-Test analysis identified 19 "predictor genes" of future disease severity. Results were validated in an independent cohort of subjects of established RA with using Support Vector Machines and K-Nearest-Neighbor Classification. Our study demonstrates that peripheral blood gene expression profiles may be a useful tool to predict future disease severity in patients with early and established RA.

HLA-DM negatively regulates HLA-DR4-restricted collagen pathogenic peptide presentation and T cell recognition

Rheumatoid arthritis, an autoimmune disease, is significantly associated with the HLA class II allele HLA-DR4. While the etiology of rheumatoid arthritis remains unknown, type II collagen (CII) is a candidate autoantigen. An immunodominant pathogenic epitope from this autoantigen, CII(261-273), which binds to HLA-DR4 and activates CD4+ T cells, has been identified. The non-classical class II antigen, HLA-DM, is also a key component of class II antigen presentation pathways influencing peptide presentation by HLA-DR molecules expressed on professional antigen-presenting cells (APC). Here, we investigated whether the HLA-DR4-restricted presentation of the pathogenic CII(261-273) epitope was regulated by HLA-DM expression in APC. We show that APC lacking HLA-DM efficiently display the CII(261-273) peptide/epitope to activate CD4+ T cells, and that presentation of this peptide is modulated dependent on the level of HLA-DM expression in APC. Mechanistic studies demonstrated that the CII(261-273) peptide is internalized by APC and edited by HLA-DM molecules in the recycling pathway, inhibiting peptide presentation and T cell recognition. These findings suggest that HLA-DM expression in APC controls class II-mediated CII(261-273) peptide/epitope presentation and regulates CD4+ T cell responses to this self epitope, thus potentially influencing CII-dependent autoimmunity.

Genetics of rheumatoid arthritis

Rheumatoid arthritis (RA) is a heterogeneous autoimmune disorder of unknown cause with variable clinical expression. About 70% of patients are women. Genetic factors play an important role and likely account for about 60% of disease susceptibility and expression. The association with the HLA-DRB1 gene is the best understood, although several non-HLA loci have been linked to RA, including the 18q21 region of the TNFRSR11A gene, which encodes the receptor activator of nuclear factor kappaB, important in bone resorption in RA. Genetic factors are also important in the treatment of RA because the activity of enzymes relevant in the metabolism of drugs such as methotrexate and azathioprine, including methylenetetrahydrofolate reductase and thiopurine methyltransferase, are in part genetically determined.

Pharmacological management of transient synovitis

Synovitis is a painful and, occasionally, disabling disease. Patients with synovitis, especially new onset synovitis, should be referred to a rheumatologist urgently so that they can be assed and treated as quickly as possible. Clinical assessment and investigations are required to help differentiate between transient (< 3 months) and persistent (> 3 months) synovitis. This differentiation is important, as persistent synovitis can lead to joint damage and disability. Septic arthritis is a rheumatological emergency requiring immediate assessment and specific treatment. The earlier synovitis is treated, the more effective treatment is likely to be. If treated very early, there is potential to prevent the move from transient to persistent synovitis. Transient synovitis can be treated with painkillers, NSAIDs and/or corticosteroids, depending on severity. Persistent synovitis may also require disease-modifying drugs. Clinical indicators of persistence include symptom duration at first visit, early morning stiffness for > 1 h, arthritis in more than three joints, bilateral compression pain in metatarsophalangeal joints, rheumatoid factor positivity, anti-cyclic citrullinated peptide antibody positivity, erosions on hand or feet X-rays and a family history of rheumatoid arthritis. Disease-modifying drugs need to be considered early to achieve clinical remission before damage and disability occur. Despite emerging new treatments for synovitis, especially persistent synovitis, full clinical remission is still not achieved in most patients, and more research into disease processes and targeted therapies is required.