The effect of ESR1 and ESR2 gene polymorphisms on the outcome of rheumatoid arthritis treatment with leflunomide

High Throughput Genetic Characterisation of Caucasian Patients Affected by Multi-Drug Resistant Rheumatoid or Psoriatic Arthritis

Rheumatoid and psoriatic arthritis (RA and PsA) are inflammatory rheumatic disorders characterised by a multifactorial etiology. To date, the genetic contributions to the disease onset, severity and drug response are not clearly defined, and despite the development of novel targeted therapies, ~10% of patients still display poor treatment responses. We characterised a selected cohort of eleven non-responder patients aiming to define the genetic contribution to drug resistance. An accurate clinical examination of the patients was coupled with several high-throughput genetic testing, including HLA typing, SNPs-array and Whole Exome Sequencing (WES). The analyses revealed that all the subjects carry very rare HLA phenotypes which contain HLA alleles associated with RA development (e.g., HLA-DRB1*04, DRB1*10:01 and DRB1*01). Additionally, six patients also carry PsA risk alleles (e.g., HLA-B*27:02 and B*38:01). WES analysis and SNPs-array revealed 23 damaging variants with 18 novel “drug-resistance” RA/PsA candidate genes. Eight patients carry likely pathogenic variants within common genes (CYP21A2, DVL1, PRKDC, ORAI1, UGT2B17, MSR1). Furthermore, “private” damaging variants were identified within 12 additional genes (WNT10A, ABCB7, SERPING1, GNRHR, NCAPD3, CLCF1, HACE1, NCAPD2, ESR1, SAMHD1, CYP27A1, CCDC88C). This multistep approach highlighted novel RA/PsA candidate genes and genotype-phenotype correlations potentially useful for clinicians in selecting the best therapeutic strategy.

TLR-8, TNF-α, and ESR-1α Gene Polymorphism Susceptibility in Onset of Arthritis

Arthritis is a genetic disorder characterized by bones and joint degradation assisted by severe pain and inflammation. It is evident by the studies that 0 candidate genes variations play vital role in its development and progression. Therefore, we investigated the genetic variation of TLR-8, TNF, and ESR-1α genes in the Pakistani population. A case-control study comprising 300 RA, 316 OA, and 412 control subjects was conducted. PCR-RFLP and direct sequencing methods were used for determining genetic variations. Analysis was performed by using PLINK and MEGA 6.0 software. Allelic and genetic frequencies of polymorphisms identified on rs3764879 (TLR-8), rs3764880 (TLR-8), rs5744080 (TLR-8), rs1800629 (TNF), rs2228480 (ESR-1α), and rs1451501590 (ESR-1α) were significantly varied among RA, OA, and controls. Novel functional mutations SCV000844945 and SCV000844946 on TLR-8 as well as a non-functional SCV000804801 and functional variation SCV000804802 on ESR-1α were also identified and reported for the first time in the studied population. Multiple site analyses indicated that polymorphisms on TLR-8 and ESR-1α genes were significant risk factors in disease onset to the next generation. In conclusion, TLR-08 and ESR-1α were significant in the onset of arthritis whereas the TNF was not found as a significant risk factor in the onset of RA and OA.

Toward Overcoming Treatment Failure in Rheumatoid Arthritis

Rheumatoid arthritis (RA) is an autoimmune disorder characterized by inflammation and bone erosion. The exact mechanism of RA is still unknown, but various immune cytokines, signaling pathways and effector cells are involved. Disease-modifying antirheumatic drugs (DMARDs) are commonly used in RA treatment and classified into different categories. Nevertheless, RA treatment is based on a "trial-and-error" approach, and a substantial proportion of patients show failed therapy for each DMARD. Over the past decades, great efforts have been made to overcome treatment failure, including identification of biomarkers, exploration of the reasons for loss of efficacy, development of sequential or combinational DMARDs strategies and approval of new DMARDs. Here, we summarize these efforts, which would provide valuable insights for accurate RA clinical medication. While gratifying, researchers realize that these efforts are still far from enough to recommend specific DMARDs for individual patients. Precision medicine is an emerging medical model that proposes a highly individualized and tailored approach for disease management. In this review, we also discuss the potential of precision medicine for overcoming RA treatment failure, with the introduction of various cutting-edge technologies and big data.

Lack of association between CYB5A gene rs1790834 polymorphism and the response to leflunomide in women with rheumatoid arthritis

Aim

Leflunomide is a disease-modifying antirheumatic drug used in therapy for rheumatoid arthritis (RA). Previous studies indicated that oestrogens and androgens may affect the response to leflunomide in RA patients. The synthesis of androgens is regulated by cytochrome CYB5A. The aim of this study was to examine the association between the CYB5A gene rs1790834 polymorphism and the response to leflunomide in women with RA.

Methods

The study included 111 women diagnosed with RA. Leflunomide was administered in monotherapy at a dose of 20 mg/day. All patients underwent a monthly evaluation for 12 months after the initiation of treatment with leflunomide.

Results

After 12 months of therapy, the changes in individual disease activity parameters, such as: DAS28, ESR, CRP and VAS, were not statistically significantly different between rs1790834 genotypes in the Kruskal–Wallis test.

Conclusions

The results of our study suggest lack of statistically significant association between the CYB5A gene rs1790834 polymorphism and the response to leflunomide in women with RA.

Computational Functional Genomics-Based AmpliSeq™ Panel for Next-Generation Sequencing of Key Genes of Pain

The genetic background of pain is becoming increasingly well understood, which opens up possibilities for predicting the individual risk of persistent pain and the use of tailored therapies adapted to the variant pattern of the patient's pain-relevant genes. The individual variant pattern of pain-relevant genes is accessible via next-generation sequencing, although the analysis of all "pain genes" would be expensive. Here, we report on the development of a cost-effective next generation sequencing-based pain-genotyping assay comprising the development of a customized AmpliSeq™ panel and bioinformatics approaches that condensate the genetic information of pain by identifying the most representative genes. The panel includes 29 key genes that have been shown to cover 70% of the biological functions exerted by a list of 540 so-called "pain genes" derived from transgenic mice experiments. These were supplemented by 43 additional genes that had been independently proposed as relevant for persistent pain. The functional genomics covered by the resulting 72 genes is particularly represented by mitogen-activated protein kinase of extracellular signal-regulated kinase and cytokine production and secretion. The present genotyping assay was established in 61 subjects of Caucasian ethnicity and investigates the functional role of the selected genes in the context of the known genetic architecture of pain without seeking functional associations for pain. The assay identified a total of 691 genetic variants, of which many have reports for a clinical relevance for pain or in another context. The assay is applicable for small to large-scale experimental setups at contemporary genotyping costs.

Biomarkers to Personalize the Treatment of Rheumatoid Arthritis: Focus on Autoantibodies and Pharmacogenetics

Rheumatoid arthritis (RA) is a chronic inflammatory disease that is very complex and heterogeneous. If not adequately treated, RA patients are likely to manifest excess of morbidity and disability with an important impact on the quality of life. Pharmacological treatment is based on the administration of the disease-modifying antirheumatic drugs (DMARDs), subdivided into conventional synthetic (csDMARDs), targeted synthetic (tsDMARDs), and biological (bDMARDs). bDMARDs are now frequently administered in patients, both as alternative treatment and together with csDMARDs. Unfortunately, there is a therapeutic response variability both to old and new drugs. Therefore, to identify pre-therapeutic and on-treatment predictors of response is a priority. This review aims to summarize recent advances in understanding the causes of the variability in treatment response in RA, with particular attention to predictive potential of autoantibodies and DMARD pharmacogenetics. In recent years, several biomarkers have been proposed to personalize the therapy. Unfortunately, a magic bullet does not exist, as many factors concur to disease susceptibility and treatment outcomes, acting around the patient’s congenital background. Models integrating demographic, clinical, biochemical, and genetic data are needed to enhance the predictive capacity of specific factors singularly considered to optimize RA treatment in light of multidisciplinary patient management.

Integrating Network Pharmacology with Molecular Docking to Unravel the Active Compounds and Potential Mechanism of Simiao Pill Treating Rheumatoid Arthritis

Objective

To explore the main components and unravel the potential mechanism of simiao pill (SM) on rheumatoid arthritis (RA) based on network pharmacological analysis and molecular docking.

Methods

Related compounds were obtained from TCMSP and BATMAN-TCM database. Oral bioavailability and drug-likeness were then screened by using absorption, distribution, metabolism, and excretion (ADME) criteria. Additionally, target genes related to RA were acquired from GeneCards and OMIM database. Correlations about SM-RA, compounds-targets, and pathways-targets-compounds were visualized through Cytoscape 3.7.1. The protein-protein interaction (PPI) network was constructed by STRING. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed via R packages. Molecular docking analysis was constructed by the Molecular Operating Environment (MOE).

Results

A total of 72 potential compounds and 77 associated targets of SM were identified. The compounds-targets network analysis indicated that the 6 compounds, including quercetin, kaempferol, baicalein, wogonin, beta-sitosterol, and eugenol, were linked to ≥10 target genes, and the 10 target genes (PTGS1, ESR1, AR, PGR, CHRM3, PPARG, CHRM2, BCL2, CASP3, and RELA) were core target genes in the network. Enrichment analysis indicated that PI3K-Akt, TNF, and IL-17 signaling pathway may be a critical signaling pathway in the network pharmacology. Molecular docking showed that quercetin, kaempferol, baicalein, and wogonin have good binding activity with IL6, VEGFA, EGFR, and NFKBIA targets.

Conclusion

The integrative investigation based on bioinformatics/network topology strategy may elaborate on the multicomponent synergy mechanisms of SM against RA and provide the way out to develop new combination medicines for RA.

An exploratory analysis on the influence of genetic variants on weight gain among etonogestrel contraceptive implant users

OBJECTIVE

To identify genetic variants associated with weight gain related to etonogestrel contraceptive implant use.

STUDY DESIGN

We conducted a retrospective analysis from a parent pharmacogenomic study of healthy, reproductive-aged women using etonogestrel implants. We reviewed medical records to calculate objective weight changes from implant insertion to study enrollment and asked participants about subjective weight gain (yes/no) during contraceptive implant use. We used genotyping data (99 genetic variants) from the parent study to conduct backward-stepwise generalized linear modeling to identify genetic variants associated with objective weight changes.

RESULTS

Among 276 ethnically diverse participants, median body-mass index (BMI) was 25.8 kg/m² (range 18.5-48.1). We found a median weight change of +3.2 kg (range -27.6 to +26.5) from implant insertion to study enrollment. Report of subjective weight gain had minimal agreement with measured weight gain during implant use (Cohen's kappa = 0.21). Our final generalized linear model contained two variables associated with objective weight change that met conservative statistical significance (p < 5.0 × 10^-4). Participants with two copies (homozygous) of the ESR1 rs9340799 variant on average gained 14.1 kg more than all other participants (p = 1.4 × 10^-4). Higher enrollment BMI was also associated with objective weight gain (β = 0.54, p = 9.4 × 10^-12).

CONCLUSION

Genetic variants in the estrogen receptor 1 (ESR1) do not have known associations with obesity or metabolic syndrome, but there is physiologic plausibility for a progestin-mediated genetic association between ESR1 and weight gain. Additional genetic research is needed to substantiate our findings and elucidate further advances in individualized counseling on the risk of weight gain with exogenous steroid hormones.

IMPLICATIONS

Genetic variation in the estrogen receptor 1 gene may account for variability in weight gain among etonogestrel contraceptive implant users. If these findings can be replicated with other progestin-containing medications, we may be able to better individualize contraceptive counseling.

The Potential Role of Genomic Medicine in the Therapeutic Management of Rheumatoid Arthritis

During the last decade, important advances have occurred regarding understanding of the pathogenesis and treatment of rheumatoid arthritis (RA). Nevertheless, response to treatment is not universal, and choosing among different therapies is currently based on a trial and error approach. The specific patient’s genetic background influences the response to therapy for many drugs: In this sense, genomic studies on RA have produced promising insights that could help us find an effective therapy for each patient. On the other hand, despite the great knowledge generated regarding the genetics of RA, most of the investigations performed to date have focused on identifying common variants associated with RA, which cannot explain the complete heritability of the disease. In this regard, rare variants could also contribute to this missing heritability as well as act as biomarkers that help in choosing the right therapy. In the present article, different aspects of genetics in the pathogenesis and treatment of RA are reviewed, from large-scale genomic studies to specific rare variant analyses. We also discuss the shared genetic architecture existing among autoimmune diseases and its implications for RA therapy, such as drug repositioning.

Development of an AmpliSeqTM Panel for Next-Generation Sequencing of a Set of Genetic Predictors of Persisting Pain

Background: Many gene variants modulate the individual perception of pain and possibly also its persistence. The limited selection of single functional variants is increasingly being replaced by analyses of the full coding and regulatory sequences of pain-relevant genes accessible by means of next generation sequencing (NGS).

Methods: An NGS panel was created for a set of 77 human genes selected following different lines of evidence supporting their role in persisting pain. To address the role of these candidate genes, we established a sequencing assay based on a custom AmpliSeq^TM panel to assess the exomic sequences in 72 subjects of Caucasian ethnicity. To identify the systems biology of the genes, the biological functions associated with these genes were assessed by means of a computational over-representation analysis.

Results: Sequencing generated a median of 2.85 ⋅ 10⁶ reads per run with a mean depth close to 200 reads, mean read length of 205 called bases and an average chip loading of 71%. A total of 3,185 genetic variants were called. A computational functional genomics analysis indicated that the proposed NGS gene panel covers biological processes identified previously as characterizing the functional genomics of persisting pain.

Conclusion: Results of the NGS assay suggested that the produced nucleotide sequences are comparable to those earned with the classical Sanger sequencing technique. The assay is applicable for small to large-scale experimental setups to target the accessing of information about any nucleotide within the addressed genes in a study cohort.

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.

Pharmacogenetics of treatment response in psoriatic arthritis

TNF-blocking agents, non-biological disease-modifying anti-rheumatic drugs (nbDMARDs) and non-steroidal anti-inflammatory drugs (NSAIDs) are commonly prescribed treatments in psoriatic arthritis. A large proportion of patients do not respond to these medications, although unfortunately clinically useful biomarkers that predict future response are currently lacking. Several candidate gene polymorphisms have been associated with responses to biologic therapies and nbDMARDs; however, replication and validation of these variants in large prospective psoriatic arthritis cohorts are required before translating these to clinical practice. In this review, we discuss the advances made in pharmacogenetics of treatment response in psoriatic arthritis to date, with focus on biologic therapies approved for use, nbDMARDs and NSAIDs, as well as outline emerging methodologies to obtain data that will help inform a future precision medicine approach in this condition.

The effect of gene polymorphisms on patient responses to rheumatoid arthritis therapy

INTRODUCTION

Rheumatoid arthritis (RA) is a systemic disease leading to joint destruction. The therapy of RA is mainly based on disease-modifying anti-rheumatic drugs (DMARDs) and biological drugs. The response to treatment is different among patients. Therefore, we have searched for factors that may predict the efficacy and toxicity during therapy in individual patients.

AREAS COVERED

This review presents the role of genetic polymorphisms as predictors of the efficacy and toxicity during the therapy of RA patients with DMARDs (methotrexate, leflunomide, sulfasalazine) and biological drugs (anti-TNF-alpha antagonists, Tocilizumab, Rituximab).

EXPERT OPINION

Despite studies having shown an association between genetic polymorphisms and response to therapy in RA patients, the majority of these findings are still inconclusive and inconsistent. We are still far from applying pharmacogenetic tests in routine clinical practice that can predict the outcome of treatment. Several factors, such as small sample size with low statistical power, variability in the outcome definitions and the heterogeneity of the cohorts, limited number of tested single nucleotide polymorphisms (SNPs), small effect for the selected variant, and a lack of consideration of epigenetic factors, may contribute to the inconsistency observed and may lead to limited success in personalizing therapy.

Using gene expression signatures to identify novel treatment strategies in gulf war illness

Background

Gulf War Illness (GWI) is a complex multi-symptom disorder that affects up to one in three veterans of this 1991 conflict and for which no effective treatment has been found. Discovering novel treatment strategies for such a complex chronic illness is extremely expensive, carries a high probability of failure and a lengthy cycle time. Repurposing Food and Drug Administration approved drugs offers a cost-effective solution with a significantly abbreviated timeline.

Methods

Here, we explore drug re-purposing opportunities in GWI by combining systems biology and bioinformatics techniques with pharmacogenomic information to find overlapping elements in gene expression linking GWI to successfully treated diseases. Gene modules were defined based on cellular function and their activation estimated from the differential expression of each module’s constituent genes. These gene modules were then cross-referenced with drug atlas and pharmacogenomic databases to identify agents currently used successfully for treatment in other diseases. To explore the clinical use of these drugs in illnesses similar to GWI we compared gene expression patterns in modules that were significantly expressed in GWI with expression patterns in those same modules in other illnesses.

Results

We found 19 functional modules with significantly altered gene expression patterns in GWI. Within these modules, 45 genes were documented drug targets. Illnesses with highly correlated gene expression patterns overlapping considerably with GWI were found in 18 of the disease conditions studied. Brain, muscular and autoimmune disorders composed the bulk of these.

Conclusion

Of the associated drugs, immunosuppressants currently used in treating rheumatoid arthritis, and hormone based therapies were identified as the best available candidates for treating GWI symptoms.

Electronic supplementary material

The online version of this article (doi:10.1186/s12920-015-0111-3) contains supplementary material, which is available to authorized users.

Genetic polymorphisms affect efficacy and adverse drug reactions of DMARDs in rheumatoid arthritis

Disease-modifying antirheumatic drugs (DMARDs) and biological agents are critical in preventing the severe complications of rheumatoid arthritis (RA). However, the outcome of treatment with these drugs in RA patients is quite variable and unpredictable. Drug-metabolizing enzymes (dihydrofolate reductase, cytochrome P450 enzymes, N-acetyltransferases, etc.), drug transporters (ATP-binding cassette transporters), and drug targets (tumor necrosis factor-α receptors) are coded for by variant alleles. These gene polymorphisms may influence the pharmacokinetics, pharmacodynamics, and side effects of medicines. The cause for differences in efficacy and adverse drug reactions may be genetic variation in drug metabolism among individuals. Polymorphisms in drug transporter genes may change the distribution and excretion of medicines, and the sensitivity of the targets to drugs is strongly influenced by genetic variations. In this article, we review the genetic polymorphisms that affect the efficacy of DMARDs or the occurrence of adverse drug reactions associated with DMARDs in RA.

Individualization of leflunomide dosing in rheumatoid arthritis patients

Leflunomide is largely considered to be a second-line treatment option for rheumatoid arthritis (RA). Those who fail to respond, tend to progress to treatment with expensive biological agents, which can also be associated with serious toxicities. Optimizing leflunomide treatment to meet the needs of individuals would hence be beneficial in terms of patient outcomes and health care expenditure. In this respect, therapeutic drug monitoring (TDM) may be useful, as plasma concentrations of leflunomide's active metabolite, teriflunomide, correlate with response to treatment, but are highly variable between patients. A number of pharmacogenetic markers have also been identified that influence response and toxicity. Incorporation of these findings into clinical practice could facilitate more efficient use of leflunomide.

Lack of association between CAG repeat polymorphism in the androgen receptor gene and the outcome of rheumatoid arthritis treatment with leflunomide

Purpose

Leflunomide (LEF) is a disease-modifying antirheumatic drug used for treating rheumatoid arthritis (RA) and the action of which may be modified by sex hormones. The aim of this study was to examine the association between CAG repeat polymorphism in the androgen receptor (AR) gene and the response to treatment with LEF in women with RA.

Methods

We studied 114 women diagnosed with RA and treated with LEF (20 mg daily). Follow-up was 12 months. CAG repeat polymorphism was determined using polymerase chain reaction (PCR) and subsequent fragment analysis by capillary electrophoresis.

Results

Analysis revealed no statistically significant associations between CAG repeat polymorphism in the AR gene and improvement of disease activity parameters: erythrocyte sedimentation rate, serum C-reactive protein, patient’s global assessment of disease activity on a visual analog scale (VAS), disease activity score of 28 joints (DAS28), and swollen and tender joint count.

Conclusion

Our results suggest no correlation between CAG repeat polymorphism in the AR gene and response to treatment with LEF in women with RA.

Pharmacogenetics: implications for therapy in rheumatic diseases

DMARDs not only improve the joint pain and swelling associated with rheumatoid arthritis (RA), but also slow down the joint damage associated with the disease. The efficacy of biologic therapies, introduced in the past decade for the treatment of RA, has been unequivocally established. Similarly, in addition to traditional drugs such as hydroxychloroquine, new biologic agents such as rituximab have been introduced for systemic lupus erythematosus in recent years. However, considerable variability occurs in the responses of patients to these therapies. Pharmacogenetics, the study of variations in genes encoding drug transporters, drug-metabolizing enzymes and drug targets, and their translation to differential responses to drugs, is a rapidly progressing field in rheumatology. Pharmacogenetic applications, particularly to the old vanguard DMARD, methotrexate, and the newer, more expensive biologic agents, might make personalized therapy in rheumatic diseases possible. The pharmacogenetics of commonly used DMARDs and of biologic therapies are described in this Review.