Investigation of genetically regulated gene expression and response to treatment in rheumatoid arthritis highlights an association between IL18RAP expression and treatment response

Expression quantitative trait loci analysis in rheumatoid arthritis identifies tissue specific variants associated with severity and outcome

OBJECTIVE

Genome-wide association studies have successfully identified more than 100 loci associated with susceptibility to rheumatoid arthritis (RA). However, our understanding of the functional effects of genetic variants in causing RA and their effects on disease severity and response to treatment remains limited.

METHODS

In this study, we conducted expression quantitative trait locus (eQTL) analysis to dissect the link between genetic variants and gene expression comparing the disease tissue against blood using RNA-Sequencing of synovial biopsies (n=85) and blood samples (n=51) from treatment-naïve patients with RA from the Pathobiology of Early Arthritis Cohort.

RESULTS

This identified 898 eQTL genes in synovium and genes loci in blood, with 232 genes in common to both synovium and blood, although notably many eQTL were tissue specific. Examining the HLA region, we uncovered a specific eQTL at HLA-DPB2 with the critical triad of single-nucleotide polymorphisms (SNPs) rs3128921 driving synovial HLA-DPB2 expression, and both rs3128921 and HLA-DPB2 gene expression correlating with clinical severity and increasing probability of the lympho-myeloid pathotype.

CONCLUSIONS

This analysis highlights the need to explore functional consequences of genetic associations in disease tissue. HLA-DPB2 SNP rs3128921 could potentially be used to stratify patients to more aggressive treatment immediately at diagnosis.

Speos: an ensemble graph representation learning framework to predict core gene candidates for complex diseases

Understanding phenotype-to-genotype relationships is a grand challenge of 21st century biology with translational implications. The recently proposed "omnigenic" model postulates that effects of genetic variation on traits are mediated by core-genes and -proteins whose activities mechanistically influence the phenotype, whereas peripheral genes encode a regulatory network that indirectly affects phenotypes via core gene products. Here, we develop a positive-unlabeled graph representation-learning ensemble-approach based on a nested cross-validation to predict core-like genes for diverse diseases using Mendelian disorder genes for training. Employing mouse knockout phenotypes for external validations, we demonstrate that core-like genes display several key properties of core genes: Mouse knockouts of genes corresponding to our most confident predictions give rise to relevant mouse phenotypes at rates on par with the Mendelian disorder genes, and all candidates exhibit core gene properties like transcriptional deregulation in disease and loss-of-function intolerance. Moreover, as predicted for core genes, our candidates are enriched for drug targets and druggable proteins. In contrast to Mendelian disorder genes the new core-like genes are enriched for druggable yet untargeted gene products, which are therefore attractive targets for drug development. Interpretation of the underlying deep learning model suggests plausible explanations for our core gene predictions in form of molecular mechanisms and physical interactions. Our results demonstrate the potential of graph representation learning for the interpretation of biological complexity and pave the way for studying core gene properties and future drug development.

Prognostic and immunological roles of IL18RAP in human cancers

Across several cancers, IL18 receptor accessory protein (IL18RAP) is abnormally expressed, and this abnormality is related to tumor immunity and heterogeneous clinical outcomes. In this study, based on bioinformatics analysis, we discovered that IL18RAP is related to the human tumor microenvironment and promotes various immune cells infiltration. Additionally, the multiple immunofluorescence staining revealed that with the increased expression of IL18RAP, the number of infiltrated M1 macrophages increased. This finding was confirmed by coculture migration analysis using three human cancer cell lines (MDA-MB-231, U251, and HepG2) with IL18RAP knockdown. We discovered a positive link between IL18RAP and the majority of immunostimulators, immunoinhibitors, major histocompatibility complex (MHC) molecules, chemokines, and chemokine receptor genes using Spearman correlation analysis. Additionally, functional IL18RAP's gene set enrichment analysis (GSEA) revealed that it is related to a variety of immunological processes, such as positive regulation of interferon gamma production and positive regulation of NK cell-mediated immunity. Moreover, we used single-cell RNA sequencing analysis to detect that IL18RAP was mainly expressed in immune cells, and HALLMARK analysis confirmed that the INF-γ gene set expression was upregulated in CD8Tex cells. In addition, in human and mouse cancer cohorts, we found that the level of IL18RAP can predict the immunotherapy response. In short, our study showed that IL18RAP is a new tumor biomarker and may become a potential immunotherapeutic target in cancer.

Composite immune marker scores associated with severe mental disorders and illness course

Background

Low-grade inflammation has been implicated in the pathophysiology of severe mental disorders (SMDs) and a link between immune activation and clinical characteristics is suggested. However, few studies have investigated how patterns across immune markers are related to diagnosis and illness course.

Methods

A total of 948 participants with a diagnosis of schizophrenia (SCZ, N = 602) or bipolar (BD, N = 346) spectrum disorder, and 814 healthy controls (HC) were included. Twenty-five immune markers comprising cell adhesion molecules (CAMs), interleukin (IL)-18-system factors, defensins, chemokines and other markers, related to neuroinflammation, blood-brain barrier (BBB) function, inflammasome activation and immune cell orchestration were analyzed. Eight immune principal component (PC) scores were constructed by PC Analysis (PCA) and applied in general linear models with diagnosis and illness course characteristics.

Results

Three PC scores were significantly associated with a SCZ and/or BD diagnosis (HC reference), with largest, however small, effect sizes of scores based on CAMs, BBB markers and defensins (p < 0.001, partial η² = 0.02–0.03). Number of psychotic episodes per year in SCZ was associated with a PC score based on IL-18 system markers and the potential neuroprotective cytokine A proliferation-inducing ligand (p = 0.006, partial η² = 0.071).

Conclusion

Analyses of composite immune markers scores identified specific patterns suggesting CAMs-mediated BBB dysregulation pathways associated with SMDs and interrelated pro-inflammatory and neuronal integrity processes associated with severity of illness course. This suggests a complex pattern of immune pathways involved in SMDs and SCZ illness course.

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Composite score of VCAM-1, ICAM-1, NCAD and IL-18BP associated with SCZ and BD.

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Composite score of MadCAM-1 and BD-1 associated with SCZ and BD.

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Composite score of S100B, furin, HNP1-3 and BD-1 associated with BD.

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Composite score of APRIL and IL-18R markers associated with psychotic episode rate.

Network analysis of synovial RNA sequencing identifies gene-gene interactions predictive of response in rheumatoid arthritis

BACKGROUND

To determine whether gene-gene interaction network analysis of RNA sequencing (RNA-Seq) of synovial biopsies in early rheumatoid arthritis (RA) can inform our understanding of RA pathogenesis and yield improved treatment response prediction models.

METHODS

We utilized four well curated pathway repositories obtaining 10,537 experimentally evaluated gene-gene interactions. We extracted specific gene-gene interaction networks in synovial RNA-Seq to characterize histologically defined pathotypes in early RA and leverage these synovial specific gene-gene networks to predict response to methotrexate-based disease-modifying anti-rheumatic drug (DMARD) therapy in the Pathobiology of Early Arthritis Cohort (PEAC). Differential interactions identified within each network were statistically evaluated through robust linear regression models. Ability to predict response to DMARD treatment was evaluated by receiver operating characteristic (ROC) curve analysis.

RESULTS

Analysis comparing different histological pathotypes showed a coherent molecular signature matching the histological changes and highlighting novel pathotype-specific gene interactions and mechanisms. Analysis of responders vs non-responders revealed higher expression of apoptosis regulating gene-gene interactions in patients with good response to conventional synthetic DMARD. Detailed analysis of interactions between pairs of network-linked genes identified the SOCS2/STAT2 ratio as predictive of treatment success, improving ROC area under curve (AUC) from 0.62 to 0.78. We identified a key role for angiogenesis, observing significant statistical interactions between NOS3 (eNOS) and both CAMK1 and eNOS activator AKT3 when comparing responders and non-responders. The ratio of CAMKD2/NOS3 enhanced a prediction model of response improving ROC AUC from 0.63 to 0.73.

CONCLUSIONS

We demonstrate a novel, powerful method which harnesses gene interaction networks for leveraging biologically relevant gene-gene interactions leading to improved models for predicting treatment response.

Associations of carotid intima media thickness with gene expression in whole blood and genetically predicted gene expression across 48 tissues

Carotid intima media thickness (cIMT) is a biomarker of subclinical atherosclerosis and a predictor of future cardiovascular events. Identifying associations between gene expression levels and cIMT may provide insight to atherosclerosis etiology. Here, we use two approaches to identify associations between mRNA levels and cIMT: differential gene expression analysis in whole blood and S-PrediXcan. We used microarrays to measure genome-wide whole blood mRNA levels of 5647 European individuals from four studies. We examined the association of mRNA levels with cIMT adjusted for various potential confounders. Significant associations were tested for replication in three studies totaling 3943 participants. Next, we applied S-PrediXcan to summary statistics from a cIMT genome-wide association study (GWAS) of 71 128 individuals to estimate the association between genetically determined mRNA levels and cIMT and replicated these analyses using S-PrediXcan on an independent GWAS on cIMT that included 22 179 individuals from the UK Biobank. mRNA levels of TNFAIP3, CEBPD and METRNL were inversely associated with cIMT, but these associations were not significant in the replication analysis. S-PrediXcan identified associations between cIMT and genetically determined mRNA levels for 36 genes, of which six were significant in the replication analysis, including TLN2, which had not been previously reported for cIMT. There was weak correlation between our results using differential gene expression analysis and S-PrediXcan. Differential expression analysis and S-PrediXcan represent complementary approaches for the discovery of associations between phenotypes and gene expression. Using these approaches, we prioritize TNFAIP3, CEBPD, METRNL and TLN2 as new candidate genes whose differential expression might modulate cIMT.

Immune cell-specific smoking-related expression characteristics are revealed by re-analysis of transcriptomes from the CEDAR cohort

Introduction

Smoking is known to affect whole-blood expression and methylation profiles. Although whole-genome methylation studies indicated that effects observed in blood may be driven by changes within leukocyte subtypes, these phenomena have not been explored using expression profiling.

Material and methods

This study reanalyzed data from the Correlated Expression and Disease Association Research (CEDAR) patient cohort recruited by Momozawa et al. (E-MTAB-6667). Data from gene expression profiling of immunomagnetically sorted CD4⁺, CD8⁺, CD14⁺, CD15⁺, and CD19⁺ cells were processed. Differential expression analyses were conducted in each immune cell type, followed by gene ontology analysis and supplementary investigations.

Results

Ninety-four differentially expressed genes were found (CD8⁺ n = 58, CD14⁺ n = 20, CD4⁺ n = 14, CD19⁺ n = 2). Two key smoking-related genes were overexpressed in specific cell types: LRRN3 (CD4⁺, CD8⁺) and MMP25 (CD8⁺, CD14⁺). In CD4⁺ cells smoking was associated with reduced expression of the NK cell receptor KLRB1, suggesting CD4⁺ subpopulation shifts and differences in interferon signaling (reduced IRF1 and IL18RAP in smokers). Key results and their integration with an immune protein-protein interaction network revealed that smoking influences integrins in CD8⁺ cells (ITGB7, ITGAL, ITGAM, ITGB2). C-type lectin CLEC4A was reduced in CD8⁺ cells and CLEC10A was increased in CD14⁺ cells from smokers; moreover, CLEC5A (CD8⁺), CLEC7A (CD8⁺) and CLEC9A (CD19⁺) were related to smoking in supplementary analyses. CD14⁺ cells from smokers exhibited overexpression of LDLR and the formyl peptide receptor FPR3.

Conclusions

Smoking specifically alters vital immune regulation genes in lymphocyte subtypes, especially CD4⁺, CD8⁺ and CD14⁺ cells.

Which Biologic Therapies to Treat Active Rheumatoid Arthritis and When?

Biological disease-modifying anti-arthritis drugs (bDMARD) have transformed rheumatoid arthritis (RA) treatment and allowed many patients to reach clinical remission. With the huge growth in the development of different bDMARDs, there is now a need to decide on which treatment should be prescribed to achieve optimal patient outcomes. Decisions are made by weighing up the comparative efficacy of each agent against risks, namely the risk of bacterial infections. The most powerful tools for investigating the comparative efficacy of bDMARDs are head-to-head trials that directly compare one therapy to another; however, very few trials of this type exist. Furthermore, the heterogeneity of RA calls for consideration of the comparative efficacy of therapies on an individual basis. Many studies have found associations between specific biomarkers and response to different bDMARDs to enable stratification of patient groups, although many results have not been reproducible in different cohorts. Combining predictors to create models of treatment response may be the ultimate key to finding reliable biomarkers with enough predictive power to enable a personalised medicine approach to treating RA in the clinic.

Novel approaches to develop biomarkers predicting treatment responses to TNF-blockers

Introduction: Chronic inflammatory diseases (CIDs) cause significant morbidity and are a considerable burden for the patients in terms of pain, impaired function, and diminished quality of life. Important progress in CID treatment has been obtained with biological therapies, such as tumor-necrosis-factor blockers. However, more than a third of the patients fail to respond to these inhibitors and are exposed to the side effects of treatment, without the benefits. Therefore, there is a strong interest in developing tools to predict response of patients to biologics. Areas covered: The authors searched PubMed for recent studies on biomarkers for disease assessment and prediction of therapeutic responses, focusing on the effect of TNF blockers on immune responses in spondyloarthritis (SpA), and other CID, in particular rheumatoid arthritis and inflammatory bowel disease. Conclusions will be drawn about the possible development of predictive biomarkers for response to treatment. Expert opinion: No validated biomarker is currently available to predict treatment response in CID. New insight could be generated through the development of new bioinformatic modeling approaches to combine multidimensional biomarkers that explain the different genetic, immunological and environmental determinants of therapeutic responses.

Genotype-Based Gene Expression in Colon Tissue-Prediction Accuracy and Relationship with the Prognosis of Colorectal Cancer Patients

Colorectal cancer (CRC) survival has environmental and inherited components. The expression of specific genes can be inferred based on individual genotypes—so called expression quantitative trait loci. In this study, we used the PrediXcan method to predict gene expression in normal colon tissue using individual genotype data from 91 CRC patients and examined the correlation ρ between predicted and measured gene expression levels. Out of 5434 predicted genes, 58% showed a negative ρ value and only 16% presented a ρ higher than 0.10. We subsequently investigated the association between genotype-based gene expression in colon tissue for genes with ρ > 0.10 and survival of 4436 CRC patients. We identified an inverse association between the predicted expression of ARID3B and CRC-specific survival for patients with a body mass index greater than or equal to 30 kg/m² (HR (hazard ratio) = 0.66 for an expression higher vs. lower than the median, p = 0.005). This association was validated using genotype and clinical data from the UK Biobank (HR = 0.74, p = 0.04). In addition to the identification of ARID3B expression in normal colon tissue as a candidate prognostic biomarker for obese CRC patients, our study illustrates the challenges of genotype-based prediction of gene expression, and the advantage of reassessing the prediction accuracy in a subset of the study population using measured gene expression data.