Response to interferon-beta treatment in multiple sclerosis patients: a genome-wide association study

Zfp697 is an RNA-binding protein that regulates skeletal muscle inflammation and remodeling

Skeletal muscle atrophy is a morbidity and mortality risk factor that happens with disuse, chronic disease, and aging. The tissue remodeling that happens during recovery from atrophy or injury involves changes in different cell types such as muscle fibers, and satellite and immune cells. Here, we show that the previously uncharacterized gene and protein Zfp697 is a damage-induced regulator of muscle remodeling. Zfp697/ZNF697 expression is transiently elevated during recovery from muscle atrophy or injury in mice and humans. Sustained Zfp697 expression in mouse muscle leads to a gene expression signature of chemokine secretion, immune cell recruitment, and extracellular matrix remodeling. Notably, although Zfp697 is expressed in several cell types in skeletal muscle, myofiber-specific Zfp697 genetic ablation in mice is sufficient to hinder the inflammatory and regenerative response to muscle injury, compromising functional recovery. We show that Zfp697 is an essential mediator of the interferon gamma response in muscle cells and that it functions primarily as an RNA-interacting protein, with a very high number of miRNA targets. This work identifies Zfp697 as an integrator of cell-cell communication necessary for tissue remodeling and regeneration.

Interdependency of NINJ2 gene expression and polymorphism with susceptibility and response to interferon beta in patients with multiple sclerosis

OBJECTIVE

Multiple sclerosis (MS) is a multifactorial inflammatory and autoimmune condition that lead to chronic neurodegeneration and central nervous system (CNS) demyelination that mainly affects young adults. The incidence and prevalence rate of MS considerably vary in ethnicities and geographic regions and affecting women more than men. Interferon-β (IFN-β) is the first-line disease management for MS, while the majority of affected members does not respond to the IFN-β. Numerous recent studies shown a significant relationship between genetic variations and responsiveness to the IFN-β. Therefore, determining the genetic differences in the drug response could help determine precise treatment strategies.

METHODS

The genotyping of the rs7298096 polymorphism (SNP) and NINJ2 gene expression were assessed in 99 responders and 106 non-responder patients with IFN-β treated RRMS.

RESULTS

The distribution of rs7298096 SNP was significantly different in the responders and non-responder patients and the NINJ2 gene expression considerably increased in the non-responder patients compare to the responders. The NINJ2 gene expression level in the AA genotype of the non-responder group was higher than to the other genotypes of both groups.

CONCLUSION

Our results showed that the NINJ2 gene expression level and rs7298096 genotype possibly affect the response to the IFN-β in patients with RRMS.

Zfp697 is an RNA-binding protein that regulates skeletal muscle inflammation and regeneration

Muscular atrophy is a mortality risk factor that happens with disuse, chronic disease, and aging. Recovery from atrophy requires changes in several cell types including muscle fibers, and satellite and immune cells. Here we show that Zfp697/ZNF697 is a damage-induced regulator of muscle regeneration, during which its expression is transiently elevated. Conversely, sustained Zfp697 expression in mouse muscle leads to a gene expression signature of chemokine secretion, immune cell recruitment, and extracellular matrix remodeling. Myofiber-specific Zfp697 ablation hinders the inflammatory and regenerative response to muscle injury, compromising functional recovery. We uncover Zfp697 as an essential interferon gamma mediator in muscle cells, interacting primarily with ncRNAs such as the pro-regenerative miR-206. In sum, we identify Zfp697 as an integrator of cell-cell communication necessary for tissue regeneration.

Omics approaches to understanding the efficacy and safety of disease-modifying treatments in multiple sclerosis

From the perspective of precision medicine, the challenge for the future is to improve the accuracy of diagnosis, prognosis, and prediction of therapeutic responses through the identification of biomarkers. In this framework, the omics sciences (genomics, transcriptomics, proteomics, and metabolomics) and their combined use represent innovative approaches for the exploration of the complexity and heterogeneity of multiple sclerosis (MS). This review examines the evidence currently available on the application of omics sciences to MS, analyses the methods, their limitations, the samples used, and their characteristics, with a particular focus on biomarkers associated with the disease state, exposure to disease-modifying treatments (DMTs), and drug efficacies and safety profiles.

CD4+ and CD25+ T-cell response to short-time interferon-beta therapy on IL10, IL23A and FOXP3 genes in multiple sclerosis patients

AIM OF THE STUDY

Interferon-beta (IFN-β), multiple sclerosis (MS) drug for years, does not have therapeutic effects on each patient. Yet, a considerable portion has experienced no therapeutic response to IFN-β. Therefore, it is necessary to determine disease-specific biomarkers that affect drug response. Here, we aimed to determine the effects of interleukin 10 (IL10) and 23 (IL23A), as well as forkhead box P3 (FOXP3) genes on MS after IFN-β therapy.

MATERIALS AND METHODS

Peripheral blood mononuclear cells (PBMCs) of 42 MS patients were isolated to obtain CD4+ and CD25+ T cells. Both cell types were characterised by flow cytometry. To determine optimum drug concentration of IFN-β, cytotoxicity assays were assessed on each cell type for 4, 16, 24 and 48 hours respectively. Then, cells were cultured in the presence of 500 IU/mL of IFN-β. cDNA synthesis was performed after mRNA extraction. RT-PCR was performed to measure gene expressions of IL10, IL23A and FOXP3. Results were evaluated statistically.

RESULTS

It was found that the cytotoxic effect of IFN-β was more efficient as the exposure time was expanded regardless of drug concentration. Moreover, CD25+ T lymphocytes were more resistant to IFN-β. IL23A was down-regulated, whereas FOXP3 was up-regulated at 48 hours in CD4+ T cells. For CD25+ T cells, the graded increase in FOXP3 was obtained while IL10 expression was gradually decreased throughout the drug intake.

CONCLUSION

Although a considerable change in expression was obtained, the long-term IFN-β effect on both genes and cells should be determined by follow-up at least a year.

Pharmacogenetic Predictors of Response to Interferon Beta Therapy in Multiple Sclerosis

First-line therapy with interferon beta (IFN-β), involved in gene expression modulation in immune response, is widely used for multiple sclerosis. However, 30-50% of patients do not respond optimally. Variants in CBLB, CTSS, GRIA3, OAS1 and TNFRSF10A genes have been proposed to contribute to the variation in the individual response. The purpose of this study was to evaluate the influence of gene polymorphisms on the IFN-β response in relapsing-remitting multiple sclerosis (RRMS) patients. CBLB (rs12487066), GRIA3 (rs12557782), CTSS (rs1136774), OAS1 (rs10774671) and TNFRSF10A (rs20576) polymorphisms were analysed by Taqman in 137 RRMS patients. Response to IFN-β and change in the Expanded Disability Status Scale (EDSS) after 24 months were evaluated using multivariable logistic regression analysis. Carriers of at least one copy of the C allele of CTSS-rs1136774 had a better response to IFN-β (p = 0.0423; OR = 2.94; CI95% = 1.03, 8.40). Carriers of TT genotype of TNFRSF10A-rs20576 had a higher probability of maintaining their EDSS stable after 24 months of IFN-β treatment (p = 0.0251; OR = 5.71; CI95% = 1.39, 31.75). No influence of CBLB (rs12487066), OAS1 (rs10774671) and GRIA3 (rs12557782) gene polymorphisms in the variation of the individual response to IFN-β was shown. Our results suggest that the TNFRSF10A-rs20576 and CTSS-rs1136774 gene polymorphisms influence the response to IFN-β after 24 months, while the CBLB (rs12487066), OAS1 (rs10774671) or GRIA3 (rs12557782) gene polymorphisms had no effect on the variation of the individual response to IFN-β.

An interdependence between GAPVD1 gene polymorphism, expression level and response to interferon beta in patients with multiple sclerosis

Interferon-β (IFN-β) is among the first drugs used for reducing the symptoms of multiple sclerosis (MS). Many studies show that the genetic predisposition of patients might modulate their response to IFN-β treatment. In this study GAPVD1 gene expression and the genotyping of rs2291858 variant were analysed in 100 responder and 100 non-responder patients with MS treated using IFN-β. Moreover, rs2291858 genotyping was performed for 200 patients with MS and 200 healthy controls. GAPVD1 expression was significantly increased in the responder patients than in non-responders and the distribution of rs2291858 polymorphism was significantly different between them. The GAPVD1 expression level in AA genotype of the responder group was higher than that in other genotypes of these two groups. The results show that the GAPVD1 expression level and rs2291858 genotype probably affect the response to IFN- β in patients with MS.

Drug response in association with pharmacogenomics and pharmacomicrobiomics: towards a better personalized medicine

Researchers have long been presented with the challenge imposed by the role of genetic heterogeneity in drug response. For many years, Pharmacogenomics and pharmacomicrobiomics has been investigating the influence of an individual's genetic background to drug response and disposition. More recently, the human gut microbiome has proven to play a crucial role in the way patients respond to different therapeutic drugs and it has been shown that by understanding the composition of the human microbiome, we can improve the drug efficacy and effectively identify drug targets. However, our knowledge on the effect of host genetics on specific gut microbes related to variation in drug metabolizing enzymes, the drug remains limited and therefore limits the application of joint host-microbiome genome-wide association studies. In this paper, we provide a historical overview of the complex interactions between the host, human microbiome and drugs. While discussing applications, challenges and opportunities of these studies, we draw attention to the critical need for inclusion of diverse populations and the development of an innovative and combined pharmacogenomics and pharmacomicrobiomics approach, that may provide an important basis in personalized medicine.

Pharmacogenomics and Pharmacogenetics: In Silico Prediction of Drug Effects in Treatments for Novel Coronavirus SARS-CoV2 Disease

The latest developments in precision medicine allow the modulation of therapeutic approaches in different pathologies on the basis of the specific molecular characterization of the patient. This review of the literature coupled with in silico analysis was to provide a selected screening of interactions between single-nucleotide polymorphisms (SNPs) and drugs (repurposed, investigational, and biological agents) showing efficacy and toxicityin counteracting Covid-19 infection. In silico analysis of genetic variants related to each drug was performed on such databases as PharmGKB, Ensembl Genome Browser, www.drugs.com, and SNPedia, with an extensive literature review of papers (to May 10, 2020) on Covid-19 treatments using Medline, Embase, International Pharmaceutical Abstracts, PharmGKB, and Google Scholar. The clinical relevance of SNPs, known as both drug targets and markers, considering genetic variations with known drug responses, and the therapeutic consequences are discussed. In the context of clinical treatment of Covid-19, including infection prevention, control measures, and supportive care, this review highlights the importance of a personalized approach in the final selection of therapy, which is probably essential in the management of the Covid-19 pandemic.

Effect of genetic polymorphisms on therapeutic response in multiple sclerosis relapsing-remitting patients treated with interferon-beta

Treatment with interferon beta (IFNβ) is one of the first-line treatments for multiple sclerosis. In clinical practice, however, many patients present suboptimal response to IFNβ, with the proportion of non-responders ranging from 20 to 50%. This variable response can be affected by genetic factors, such as polymorphisms in the genes involved in the disease state, pharmacodynamics, metabolism or in the action mechanism of IFNβ, which can affect the efficacy of this drug. This review assesses the impact of pharmacogenetics studies on response to IFNβ treatment among patients diagnosed with relapsing-remitting multiple sclerosis (RRMS). The results suggest that the detection of polymorphisms in several genes (CD46, CD58, FHIT, IRF5, GAPVD1, GPC5, GRBRB3, MxA, PELI3 and ZNF697) could be used in the future as predictive markers of response to IFNβ treatment in patients diagnosed with RRMS. However, few studies have been carried out and they have been performed on small sample sizes, which makes it difficult to generalize the role of these genes in IFNβ treatment. Studies on large sample sizes with longer term follow-up are therefore required to confirm these results.

Meta-Analysis of Polymyositis and Dermatomyositis Microarray Data Reveals Novel Genetic Biomarkers

Polymyositis (PM) and dermatomyositis (DM) are both classified as idiopathic inflammatory myopathies. They share a few common characteristics such as inflammation and muscle weakness. Previous studies have indicated that these diseases present aspects of an auto-immune disorder; however, their exact pathogenesis is still unclear. In this study, three gene expression datasets (PM: 7, DM: 50, Control: 13) available in public databases were used to conduct meta-analysis. We then conducted expression quantitative trait loci analysis to detect the variant sites that may contribute to the pathogenesis of PM and DM. Six-hundred differentially expressed genes were identified in the meta-analysis (false discovery rate (FDR) < 0.01), among which 317 genes were up-regulated and 283 were down-regulated in the disease group compared with those in the healthy control group. The up-regulated genes were significantly enriched in interferon-signaling pathways in protein secretion, and/or in unfolded-protein response. We detected 10 single nucleotide polymorphisms (SNPs) which could potentially play key roles in driving the PM and DM. Along with previously reported genes, we identified 4 novel genes and 10 SNP-variant regions which could be used as candidates for potential drug targets or biomarkers for PM and DM.

Pharmacogenetics: A strategy for personalized medicine for autoimmune diseases

For many years, a considerable number of patients with autoimmune diseases (ADs) have suffered from a lack of drug response and drug-related toxicity. Despite the emergence of new therapeutic options such as biological agents, patients continue to struggle with these problems. Unfortunately, new challenges, including the paradoxical effects of biological drugs, have complicated the situation. In recent decades, efforts have been made to predict drug response as well as drug-related side effects. Thanks to the many advances in genetics, evaluation of markers to predict drug response/toxicity before the initiation of treatment may be an avenue toward personalizing treatments. Implementing pharmacogenetics and pharmacogenomics in the clinic could improve clinical care; however, obstacles remain to effective personalized medicine for ADs. The present study attempted to clarify the concept of pharmacogenetics/pharmacogenomics for ADs. After an overview on the pathogenesis of the most common types of treatments, this paper focuses on pharmacogenetic studies related to the selected ADs. Bridging the gap between pharmacogenetics and personalized medicine is also discussed. Moreover, the advantages, disadvantages and recommendations related to making personalized medicine practical for ADs have been addressed.

Multiple sclerosis: disease modifying therapy and the human leukocyte antigen

OBJECTIVE

To investigate the potential relationship between the human leukocyte antigen (HLA) type (class I and II) and the response to several disease-modifying therapies (DMTs) in patients with multiple sclerosis (MS).

METHODS

We analyzed clinical data of 87 patients with MS at the beginning and end of each type of DMT including the disease duration, Expanded Disability Status Scale and Multiple Sclerosis Severity Score (MSSS). Genotyping of HLA-DRB1, HLA-DPB1, HLA-DQB1, HLA-A, HLA-B and HLA-C alleles were identified using high-resolution techniques. Statistical correlation between the HLA type and response to DMTs was done using the initial and final MSSS.

RESULTS

Statistical relationships (p < 0.05) were found for only 15 of 245 alleles tested. There was a reduction in the MSSS for patients treated with corticosteroids (DRB1*15:01, DPB1*04:01, DQB1*02:01 and DQB1*03:01), azathioprine (DRB1*03:01, DPB1*04:01, DQB1*03:02, DQB1*06:02, HLA-C*07:02), interferon β-1a 22 mcg (DRB1*11:04, DQB1*03:01 and DQB1*03:02), interferon β-1a 30 mcg (DPB1*02:01, HLA-C*05:01) and interferon β-1b (DQB1*02:01).

CONCLUSION

These findings suggest a few relationships between the HLA and response to DMTs in the disability for some types of HLA class I and II alleles in a specific subset of MS patients.

Pharmacogenomics of Multiple Sclerosis: A Systematic Review

Background: Over the past two decades, various novel disease-modifying drugs for multiple sclerosis (MS) have been approved. However, there is high variability in the patient response to the available medications, which is hypothesized to be partly attributed to genetics. Objectives: To conduct a systematic review of the current literature on the pharmacogenomics of MS therapy. Methods: A systematic literature search was conducted using PubMed/MEDLINE database searching for articles investigating a role of genetic variation in response to disease-modifying MS treatments, published in the English language up to October 9th, 2018. PRISMA guidelines for systematic reviews were applied. Studies were included if they investigated response or nonresponse to MS treatment defined as relapse rate, by expanded disability status scale score or based on magnetic resonance imaging. The following data were extracted: first author's last name, year of publication, PMID number, sample size, ethnicity of patients, method, genes, and polymorphisms tested, outcome, significant associations with corresponding P-values and confidence intervals, response criteria, and duration of the follow-up period. Results: Overall, 48 articles published up to October 2018, evaluating response to interferon-beta, glatiramer acetate, mitoxantrone, and natalizumab, met our inclusion criteria and were included in this review. Among those, we identified 42 (87.5%) candidate gene studies and 6 (12.5%) genome-wide association studies. Existing pharmacogenomic evidence is mainly based on the results of individual studies, or on results of multiple studies, which often lack consistency. In recent years, hypothesis-free approaches identified novel candidate genes that remain to be validated. Various study designs, including the definition of clinical response, duration of the follow-up period, and methodology as well as moderate sample sizes, likely contributed to discordances between studies. However, some of the significant associations were identified in the same genes, or in the genes involved in the same biological pathways. Conclusions: At the moment, there is no available clinically actionable pharmacogenomic biomarker that would enable more personalized treatment of MS. More large-scale studies with uniform design are needed to identify novel and validate existing pharmacogenomics findings. Furthermore, studies investigating associations between rare variants and treatment response in MS patients, using next-generation sequencing technologies are warranted.

The Challenge to Search for New Nervous System Disease Biomarker Candidates: the Opportunity to Use the Proteogenomics Approach

Alzheimer's disease, Parkinson's disease, prion diseases, schizophrenia, and multiple sclerosis are the most common nervous system diseases, affecting millions of people worldwide. The current scientific literature associates these pathological conditions to abnormal expression levels of certain proteins, which in turn improved the knowledge concerning normal and affected brains. However, there is no available cure or preventive therapy for any of these disorders. Proteogenomics is a recent approach defined as the data integration of both nucleotide high-throughput sequencing and protein mass spectrometry technologies. In the last years, proteogenomics studies in distinct diseases have emerged as a strategy for the identification of uncharacterized proteoforms, which are all the different protein forms derived from a single gene. For many of these diseases, at least one protein used as biomarker presents more than one proteoform, which fosters the analysis of publicly available data focusing proteoforms. Given this context, we describe the most important biomarkers for each neurodegenerative disease and how genomics, transcriptomics, and proteomics separately contributed to unveil them. Finally, we present a selection of proteogenomics studies in which the combination of nucleotide and proteome high-throughput data, from cell lines or brain tissue samples, is used to uncover proteoforms not previously described. We believe that this new approach may improve our knowledge about nervous system diseases and brain function and an opportunity to identify new biomarker candidates.

Pharmacogenetic Biomarkers to Predict Treatment Response in Multiple Sclerosis: Current and Future Perspectives

Disease-modifying therapies (DMTs) have significantly advanced the treatment of relapsing multiple sclerosis (MS), decreasing the frequency of relapses, disability, and magnetic resonance imaging lesion formation. However, patients' responses to and tolerability of DMTs vary considerably, creating an unmet need for biomarkers to identify likely responders and/or those who may have treatment-limiting adverse reactions. Most studies in MS have focused on the identification of pharmacogenetic markers, using either the candidate-gene approach, which requires prior knowledge of the genetic marker and its role in the target disease, or genome-wide association, which examines multiple genetic variants, typically single nucleotide polymorphisms (SNPs). Both approaches have implicated numerous alleles and SNPs in response to selected MS DMTs. None have been validated for use in clinical practice. This review covers pharmacogenetic markers in clinical practice in other diseases and then reviews the current status of MS DMT markers (interferon β, glatiramer acetate, and mitoxantrone). For a complex disease such as MS, multiple biomarkers may need to be evaluated simultaneously to identify potential responders. Efforts to identify relevant biomarkers are underway and will need to be expanded to all MS DMTs. These will require extensive validation in large patient groups before they can be used in clinical practice.

A pharmacogenetic signature of high response to Copaxone in late-phase clinical-trial cohorts of multiple sclerosis

Background

Copaxone is an efficacious and safe therapy that has demonstrated clinical benefit for over two decades in patients with relapsing forms of multiple sclerosis (MS). On an individual level, patients show variability in their response to Copaxone, with some achieving significantly higher response levels. The involvement of genes (e.g., HLA-DRB1*1501) with high inter-individual variability in Copaxone’s mechanism of action (MoA) suggests the potential contribution of genetics to treatment response. This study aimed to identify genetic variants associated with Copaxone response in patient cohorts from late-phase clinical trials.

Methods

Single nucleotide polymorphisms (SNPs) associated with high and low levels of response to Copaxone were identified using genome-wide SNP data in a discovery cohort of 580 patients from two phase III clinical trials of Copaxone. Multivariable Bayesian modeling on the resulting SNPs in an expanded discovery cohort with 1171 patients identified a multi-SNP signature of Copaxone response. This signature was examined in 941 Copaxone-treated MS patients from seven independent late-phase trials of Copaxone and assessed for specificity to Copaxone in 310 Avonex-treated and 311 placebo-treated patients, also from late-phase trials.

Results

A four-SNP signature consisting of rs80191572 (in UVRAG), rs28724893 (in HLA-DQB2), rs1789084 (in MBP), and rs139890339 (in ZAK(CDCA7)) was identified as significantly associated with Copaxone response. Copaxone-treated signature-positive patients had a greater reduction in annualized relapse rate (ARR) compared to signature-negative patients in both discovery and independent cohorts, an effect not observed in Avonex-treated patients. Additionally, signature-positive placebo-treated cohorts did not show a reduction in ARR, demonstrating the predictive as opposed to prognostic nature of the signature. A 10% subset of patients, delineated by the signature, showed marked improvements across multiple clinical parameters, including ARR, MRI measures, and higher proportion with no evidence of disease activity (NEDA).

Conclusions

This study is the largest pharmacogenetic study in MS reported to date. Gene regions underlying the four-SNP signature have been linked with pathways associated with either Copaxone’s MoA or the pathophysiology of MS. The pronounced association of the four-SNP signature with clinical improvements in a ~10% subset of the MS patient population demonstrates the complex interplay of immune mechanisms and the individualized nature of response to Copaxone.

Electronic supplementary material

The online version of this article (doi:10.1186/s13073-017-0436-y) contains supplementary material, which is available to authorized users.

Neuroinflammation - using big data to inform clinical practice

Neuroinflammation is emerging as a central process in many neurological conditions, either as a causative factor or as a secondary response to nervous system insult. Understanding the causes and consequences of neuroinflammation could, therefore, provide insight that is needed to improve therapeutic interventions across many diseases. However, the complexity of the pathways involved necessitates the use of high-throughput approaches to extensively interrogate the process, and appropriate strategies to translate the data generated into clinical benefit. Use of 'big data' aims to generate, integrate and analyse large, heterogeneous datasets to provide in-depth insights into complex processes, and has the potential to unravel the complexities of neuroinflammation. Limitations in data analysis approaches currently prevent the full potential of big data being reached, but some aspects of big data are already yielding results. The implementation of 'omics' analyses in particular is becoming routine practice in biomedical research, and neuroimaging is producing large sets of complex data. In this Review, we evaluate the impact of the drive to collect and analyse big data on our understanding of neuroinflammation in disease. We describe the breadth of big data that are leading to an evolution in our understanding of this field, exemplify how these data are beginning to be of use in a clinical setting, and consider possible future directions.