Detection of Genetic Overlap Between Rheumatoid Arthritis and Systemic Lupus Erythematosus Using GWAS Summary Statistics

Examination of the shared genetic architecture between multiple sclerosis and systemic lupus erythematosus facilitates discovery of novel lupus risk loci

Systemic Lupus Erythematosus (SLE) is an autoimmune disease with heterogeneous manifestations, including neurological and psychiatric symptoms. Genetic association studies in SLE have been hampered by insufficient sample size and limited power compared to many other diseases. Multiple Sclerosis (MS) is a chronic relapsing autoimmune disease of the central nervous system (CNS) that also manifests neurological and immunological features. Here, we identify a method of leveraging large-scale genome wide association studies (GWAS) in MS to identify novel genetic risk loci in SLE. Statistical genetic comparison methods including linkage disequilibrium score regression (LDSC) and cross-phenotype association analysis (CPASSOC) to identify genetic overlap in disease pathophysiology, traditional 2-sample and novel PPI-based mendelian randomization to identify causal associations and Bayesian colocalization were applied to association studies conducted in MS to facilitate discovery in the smaller, more limited datasets available for SLE. Pathway analysis using SNP-to-gene mapping identified biological networks composed of molecular pathways with causal implications for CNS disease in SLE specifically, as well as pathways likely causal of both pathologies, providing key insights for therapeutic selection.

Comprehensive Analysis of lncRNA and mRNA Expression Profile of Macrophage RAW264.7 Stimulated by Antimicrobial Peptide BSN-37

BACKGROUND

BSN-37, a novel antimicrobial peptide (AMP) containing 37 amino acid residues isolated from the bovine spleen, has not only antibacterial activity but also immunomodulatory activity. Recent evidence shows that long non-coding RNAs (lncRNAs) play an important role in regulating the activation and function of immune cells. The purpose of this experiment was to investigate the lncRNA and mRNA expression profile of mouse macrophages RAW264.7 stimulated by bovine antimicrobial peptide BSN-37.

METHODS

The whole gene expression microarray was used to detect the differentially expressed lncRNA and mRNA between antimicrobial peptide BSN-37 activated RAW264.7 cells and normal RAW264.7 cells. KEGG pathway analysis and GO function annotation analysis of differentially expressed lncRNAs and mRNA were carried out. Eight kinds of lncRNAs and nine kinds of mRNA with large differences were selected for qRT-PCR verification, respectively.

RESULTS

In the current study, we found that 1294 lncRNAs and 260 mRNAs were differentially expressed between antibacterial peptide BSN-37 treatment and control groups. Among them, Bcl2l12, Rab44, C1s, Cd101 and other genes were associated with immune responses and were all significantly up-regulated. Mest and Prkcz are related to cell growth, and other genes are related to glucose metabolism and lipid metabolism. In addition, some immune-related terms were also found in the GO and KEGG analyses. At the same time, real-time quantitative PCR was used to verify selected lncRNA and mRNA with differential expression. The results of qRT-PCR verification were consistent with the sequencing results, indicating that our data were reliable.

CONCLUSION

This study provides the lncRNA and mRNA expression profiles of RAW264.7 macrophages stimulated by antimicrobial peptide BSN-37 and helps to provide a reference value for subsequent studies on lncRNA regulation of antimicrobial peptide BSN-37 immune function.

Genome- and transcriptome-wide association studies show that pulmonary embolism is associated with bone-forming proteins

BACKGROUND

Pulmonary embolism (PE) is a leading cause of death in stroke patients and a severe health burden worldwide. There is a pressing need to understand the mechanisms by which it occurs and to identify at-risk patients efficiently and accurately.

OBJECTIVES

The aim of this paper was to analyze the genetic correlation between PE and human plasma proteins through genome-wide association study (GWAS) with transcriptome-wide association study (TWAS), in combination with mRNA expression profiling at three levels: DNA, RNA, and protein.

METHODS

First, based on data from GWAS in European populations, we performed a linkage disequilibrium score regression (LDSC) analysis of plasma proteins and PE in 3,283 individuals and additionally analyzed the genetic association between PE and fracture. Then, we performed a TWAS on PE GWAS data using skeletal muscle and blood for gene expression references. Finally, we validated the genetic correlation between PE and human plasma proteins by co-matching the genes encoding the identified proteins and those identified using TWAS with the differentially expressed genes obtained from mRNA expression profiling of PE (Figure1).

RESULTS

We identified five plasma proteins associated with PE, including hydroxycarboxylic acid receptor 2, defensin 118, and bone morphogenetic protein (BMP) 7, as well as a relationship between PE and fracture. Comparison of genes encoding these proteins with genes obtained from TWAS and then with differentially expressed genes obtained from PE mRNA expression profiling revealed that PE was highly correlated with the BMP family of genes.

Genetic and epigenetic alterations of cyclic AMP response element modulator in rheumatoid arthritis

BACKGROUND

Genetic and epigenetic factors are strongly associated with the autoimmune disease rheumatoid arthritis (RA). Cyclic AMP response element modulator (CREM), a gene related to immune system regulation, has been implicated in various immune-mediated inflammatory processes, although it remains unknown whether CREM is involved in RA.

METHODS

This study enrolled 278 RA patients and 262 controls. Three variants [rs12765063, rs17499247, rs1213386] were identified through linkage disequilibrium and expression quantitative trait locus analysis, and CREM transcript abundance was determined by quantitative real-time polymerase chain reaction. The identified variants were genotyped using the TaqMan Allelic Discrimination assay, and CREM promoter methylation was assessed by bisulphite sequencing. Differences between groups and correlations between variables were assessed with Student's t-tests and Pearson's correlation coefficients. Associations between phenotypes and genotypes were evaluated with logistic regression.

RESULTS

Rheumatoid arthritis patients exhibited increased CREM expression (p < .0001), which was decreased by methotrexate (p = .0223) and biologics (p = .0001), but could not be attributed to CREM variants. Interestingly, rs17499247 displayed a significant association with serositis (p = .0377), and rs1213386 increased the risk of lymphadenopathy (p = .0398). Furthermore, seven CpG sites showed decreased methylation in RA (p = .0477~ p < .0001).

CONCLUSIONS

Collectively, our results indicate that CREM hypomethylation and CREM upregulation occur in RA and that CREM variants are involved in the development of serositis and lymphadenopathy in RA. This study highlights the novel roles of CREM in RA pathophysiology.

Machine learning approaches for the genomic prediction of rheumatoid arthritis and systemic lupus erythematosus

BACKGROUND

Rheumatoid arthritis (RA) and systemic lupus erythematous (SLE) are autoimmune rheumatic diseases that share a complex genetic background and common clinical features. This study's purpose was to construct machine learning (ML) models for the genomic prediction of RA and SLE.

METHODS

A total of 2,094 patients with RA and 2,190 patients with SLE were enrolled from the Taichung Veterans General Hospital cohort of the Taiwan Precision Medicine Initiative. Genome-wide single nucleotide polymorphism (SNP) data were obtained using Taiwan Biobank version 2 array. The ML methods used were logistic regression (LR), random forest (RF), support vector machine (SVM), gradient tree boosting (GTB), and extreme gradient boosting (XGB). SHapley Additive exPlanation (SHAP) values were calculated to clarify the contribution of each SNPs. Human leukocyte antigen (HLA) imputation was performed using the HLA Genotype Imputation with Attribute Bagging package.

RESULTS

Compared with LR (area under the curve [AUC] = 0.8247), the RF approach (AUC = 0.9844), SVM (AUC = 0.9828), GTB (AUC = 0.9932), and XGB (AUC = 0.9919) exhibited significantly better prediction performance. The top 20 genes by feature importance and SHAP values included HLA class II alleles. We found that imputed HLA-DQA1*05:01, DQB1*0201 and DRB1*0301 were associated with SLE; HLA-DQA1*03:03, DQB1*0401, DRB1*0405 were more frequently observed in patients with RA.

CONCLUSIONS

We established ML methods for genomic prediction of RA and SLE. Genetic variations at HLA-DQA1, HLA-DQB1, and HLA-DRB1 were crucial for differentiating RA from SLE. Future studies are required to verify our results and explore their mechanistic explanation.

Beyond GWAS: from simple associations to functional insights

Each human, when born, has slightly different DNA sequences, which make each of us unique. The variations in DNA sequences are called genetic variants. The primary aim of genome-wide association study (GWAS) is to detect associations between genetic variants and human phenotypes. Since GWAS focuses on germ-line variants, there is no reverse causation. Therefore, GWAS is one of the few tools that can assess the causality of human diseases. In the past 10 years, many large-scale GWAS have been conducted. Although the primary outputs of GWAS are just a series of statistics, its downstream analyses provided many insights beyond simple associations: the causal mechanisms for autoimmune diseases and shared etiology between diseases. Moreover, GWAS downstream analyses generated scores potentially helpful in predicting clinical outcomes of each patient. This review focuses on GWAS for autoimmune diseases and introduces significant achievements of its downstream analyses. We also provide future directions that potentially overcome current limitations. We restrict our discussion to common autoimmune diseases (e.g., rheumatoid arthritis) since rare Mendelian diseases possess distinct genetic etiologies and are not tested by GWAS.