Mendelian randomization analysis revealed potential causal factors for systemic lupus erythematosus

Integrated analyses of Mendelian randomization, eQTL, and single-cell transcriptome identify CCN3 as a potential biomarker in aortic dissection

Plasma secretory proteins are associated with various diseases, including aortic dissection (AD). However, current research on the correlation between AD and plasma protein levels is scarce or lacks specificity. This study aimed to explore plasma secretory proteins as potential biomarkers for AD. Through genome-wide association studies, expression quantitative trait locus (eQTL) analysis, and human plasma protein profiling, we identified DBNL, NPC2, SUMF2, and TFPI as high-risk genes and CCN3, PRKCSH, TEX264, and TGFBR3 as low-risk genes for AD. Further cell localization and differential expression analysis of these eight genes were conducted using single-cell data. We also examined their expression in three Gene Expression Omnibus datasets, measured their mRNA levels in AD versus normal tissues using qPCR, and assessed their protein levels in patients' blood versus healthy individuals using enzyme-linked immunosorbent assay. Our findings suggest that CCN3, consistently downregulated in both mRNA and plasma levels during AD, may have a protective role. Initial enrichment analyses of differentially expressed CCN3 cells suggested their involvement in focal adhesion, actin cytoskeleton regulation, and the PI3K-Akt signaling pathway.

Study on the causality of cathepsin on autoimmune diseases and cancer: evidence from mendelian randomization analysis

The genetic causality between cathepsin levels and autoimmune diseases (ADs) bidirectionally was investigated and the associated cancer risk was explored with Mendelian randomization. Mendelian randomization analyses were used to explore causal associations between cathepsin and 14 ADs. The final results came from a meta-analysis of two datasets to get a robust result. Furthermore, the potential carcinogenic effects of reduced cathepsin levels were explored. Sensitivity analyses were used to evaluate the robustness of the results. Based on the Mendelian randomization analysis, it was found that lower levels of specific cathepsins were associated with reduced risk of ADs. Reduced cathepsin E levels were linked to decreased susceptibility to psoriasis and a potential reduction in breast cancer risk. Reduced cathepsins G and L2 showed an inhibitory effect on psoriasis without increasing cancer risk. These results emphasized the genetic causal connection between cathepsin and ADs. Targeting cathepsins may be beneficial in treating ADs, but potential oncogenic effects must be considered to provide a basis for safer therapeutic strategies.

Exploring Pathogenic Genes in Frozen Shoulder through weighted gene co-expression network analysis and Mendelian Randomization

Background: Frozen shoulder (FS) is characterized by the thickening and fibrosis of the joint capsule, leading to joint contracture and a reduction in joint volume. The precise etiology responsible for these pathological changes remains elusive. Therefore, the primary aim of this study was to explore the potential involvement of pathogenic genes in FS and analyze their underlying roles in the disease progression. Methods: Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were employed to investigate co-expressed genes potentially associated with FS. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were conducted to elucidate the potential roles of these co-expressed genes. Subsequently, Mendelian randomization (MR) analysis was performed using expression quantitative trait loci datasets for the co-expressed genes, combined with summary statistics from the genome-wide association study of FS, aiming to identify key genes causally associated with FS. The identified key genes were further validated through reverse transcription-quantitative PCR (RT-qPCR). Additionally, a nomogram model and receiver operating characteristic (ROC) curves were established to assess the diagnostic value of the hub genes. Furthermore, the infiltration of immune cells was evaluated using the CIBERSORT algorithm and the relationship between key genes and immune-infiltrating cells was analyzed. Results: 295 overlapping co-expressed genes were identified by intersecting the differentially expressed genes with the hub genes obtained from associated modules identified through WGCNA. Utilizing MR analysis, four key genes, namely ADAMTS1, NR4A2, PARD6G and SMKR1, were found to exhibit positive causal relationships with FS, which were subsequently validated through RT-qPCR analysis. Moreover, the diagnostic value of these four key genes was demonstrated through the development of a nomogram model and the construction of ROC curves. Notably, a causal relationship between ADAMTS1 and immune cell infiltration in FS was observed. Conclusion: Our study suggested genetic predisposition to higher expression levels of ADAMTS1, NR4A2, PARD6G and SMKR1, was associated with an increased risk of FS. Further investigations elucidating the functional roles of these genes will enhance our understanding of the pathogenesis of FS and may facilitate the development of targeted treatment strategies.

Association between copper intake and essential hypertension: dual evidence from Mendelian randomization analysis and the NHANES database

Background

Although previous studies have identified an association between trace elements and essential hypertension, the specific trace elements involved and the mechanisms of their association remain unclear. This study aimed to elucidate the relationship between various human trace elements and essential hypertension, thereby addressing existing gaps in the research.

Methods

This study employed two-sample, multivariate, and inverse Mendelian randomization (MR) analyses to investigate the causal relationship between 15 human trace elements as exposure factors and essential hypertension as the outcome. The analysis revealed a statistically significant association between copper intake and essential hypertension. Further validation was conducted using logistic regression models based on data from the National Health and Nutrition Examination Survey (NHANES).

Results

Eighteen trace elements were initially identified through searches in the GWAS database and PubMed. After screening, 15 trace elements were selected as potential exposure factors. MR analysis, utilizing the 2021 genome-wide dataset for essential hypertension, identified copper as a risk factor, showing a positive association with hypertension. Subsequent logistic regression analyses based on NHANES data further confirmed a significant association between dietary copper intake and the risk of essential hypertension, except for the 0.80-1.08 mg/d group in model 3 (p < 0.05). Restricted cubic spline (RCS) analysis indicated a nonlinear relationship between copper intake and the risk of developing essential hypertension.

Conclusion

This study demonstrates a significant association between copper intake and the development of essential hypertension. The findings suggest that higher copper intake is linked to an increased risk of hypertension, underscoring the need to monitor copper intake levels in the prevention and management of this condition.

Modeling of horizontal pleiotropy identifies possible causal gene expression in systemic lupus erythematosus

Background

Systemic lupus erythematosus (SLE) is a chronic autoimmune condition with complex causes involving genetic and environmental factors. While genome-wide association studies (GWASs) have identified genetic loci associated with SLE, the functional genomic elements responsible for disease development remain largely unknown. Mendelian Randomization (MR) is an instrumental variable approach to causal inference based on data from observational studies, where genetic variants are employed as instrumental variables (IVs).

Methods

This study utilized a two-step strategy to identify causal genes for SLE. In the first step, the classical MR method was employed, assuming the absence of horizontal pleiotropy, to estimate the causal effect of gene expression on SLE. In the second step, advanced probabilistic MR methods (PMR-Egger, MRAID, and MR-MtRobin) were applied to the genes identified in the first step, considering horizontal pleiotropy, to filter out false positives. PMR-Egger and MRAID analyses utilized whole blood expression quantitative trait loci (eQTL) and SLE GWAS summary data, while MR-MtRobin analysis used an independent eQTL dataset from multiple immune cell types along with the same SLE GWAS data.

Results

The initial MR analysis identified 142 genes, including 43 outside of chromosome 6. Subsequently, applying the advanced MR methods reduced the number of genes with significant causal effects on SLE to 66. PMR-Egger, MRAID, and MR-MtRobin, respectively, identified 13, 7, and 16 non-chromosome 6 genes with significant causal effects. All methods identified expression of PHRF1 gene as causal for SLE. A comprehensive literature review was conducted to enhance understanding of the functional roles and mechanisms of the identified genes in SLE development.

Conclusions

The findings from the three MR methods exhibited overlapping genes with causal effects on SLE, demonstrating consistent results. However, each method also uncovered unique genes due to different modelling assumptions and technical factors, highlighting the complementary nature of the approaches. Importantly, MRAID demonstrated a reduced percentage of causal genes from the Major Histocompatibility complex (MHC) region on chromosome 6, indicating its potential in minimizing false positive findings. This study contributes to unraveling the mechanisms underlying SLE by employing advanced probabilistic MR methods to identify causal genes, thereby enhancing our understanding of SLE pathogenesis.

Spatial proteomics landscape and immune signature analysis of renal sample of lupus nephritis based on laser-captured microsection

OBJECTIVE

We aimed to reveal a spatial proteomic and immune signature of kidney function regions in lupus nephritis (LN).

MATERIAL AND METHODS

The laser capture microdissection (LCM) was used to isolate the glomerulus, tubules, and interstitial of the kidney from paraffin samples. The data-independent acquisition (DIA) method was used to collect proteomics data. The bioinformatic analysis was performed.

RESULTS

A total of 49,658 peptides and 4056 proteins were quantitated. Our results first showed that a high proportion of activated NK cells, naive B cells, and neutrophils in the glomerulus, activated NK cells in interstitial, and resting NK cells were accumulated in tubules in LN. The immune-related function analysis of differential expression proteins in different regions indicated that the glomerulus and interstitial were major sites of immune disturbance and regulation connected with immune response activation. Furthermore, we identified 7, 8, and 9 hub genes in LN's glomerulus, renal interstitial, and tubules. These hub genes were significantly correlated with the infiltration of immune cell subsets. We screened out ALB, CTSB, LCN2, A2M, CDC42, VIM, LTF, and CD14, which show higher performance as candidate biomarkers after correlation analysis with clinical indexes. The function within three regions of the kidney was analyzed. The differential expression proteins (DEGs) between interstitial and glomerulus were significantly enriched in the immune-related biological processes, and myeloid leukocyte-mediated immunity and cellular response to hormone stimulus. The DEGs between tubules and glomerulus were significantly enriched in cell activation and leukocyte-mediated immunity. While the DEGs between tubules and interstitial were enriched in response to lipid, antigen processing, and presentation of peptide antigen response to oxygen-containing compound, the results indicated a different function within kidney regions.

CONCLUSIONS

Collectively, we revealed spatial proteomics and immune signature of LN kidney regions by combined using LCM and DIA.

A review of genetic risk in systemic lupus erythematosus

INTRODUCTION

Systemic Lupus Erythematosus (SLE) is a complex multisystem autoimmune disease with a wide range of signs and symptoms in affected individuals. The utilization of genome-wide association study (GWAS) technology has led to an explosion in the number of genetic risk factors mapped for autoimmune diseases, including SLE.

AREAS COVERED

In this review, we summarize the more recent genetic risk loci mapped in SLE, which bring the total number of loci mapped to approximately 200. We review prioritization analyses of the associated variants and experimental validation of the putative causal variants. This includes the implementation of new bioinformatic techniques to align genomic and functional data and the use of transcriptomics with single-cell RNA-sequencing, CRISPR genome editing, and Massive Parallel Reporter Assays to analyze non-coding regulatory genetics.

EXPERT OPINION

Despite progress in identifying more genetic risk loci and variant-gene pairs for SLE, understanding its pathogenesis and applying findings clinically remains challenging. The polygenic risk score (PRS) has been used as an application of SLE genetics, but with limited performance in non-EUR populations. In the next few years, advancements in proteomics, post-translational modification estimation, and whole-genome sequencing will enhance disease understanding.

Integration of Mendelian randomisation and systems biology models to identify novel blood-based biomarkers for stroke

Stroke is the second largest cause of mortality in the world. Genome-wide association studies (GWAS) have identified some genetic variants associated with stroke risk, but their putative functional causal genes are unknown. Hence, we aimed to identify putative functional causal gene biomarkers of stroke risk. We used a summary-based Mendelian randomisation (SMR) approach to identify the pleiotropic associations of genetically regulated traits (i.e., gene expression and DNA methylation) with stroke risk. Using SMR approach, we integrated cis-expression quantitative loci (cis-eQTLs) and cis-methylation quantitative loci (cis-mQTLs) data with GWAS summary statistics of stroke. We also utilised heterogeneity in dependent instruments (HEIDI) test to distinguish pleiotropy from linkage from the observed associations identified through SMR analysis. Our integrative SMR analyses and HEIDI test revealed 45 candidate biomarker genes (FDR < 0.05; P_HEIDI>0.01) that were pleiotropically or potentially causally associated with stroke risk. Of those candidate biomarker genes, 10 genes (HTRA1, PMF1, FBN2, C9orf84, COL4A1, BAG4, NEK6, SH2B3, SH3PXD2A, ACAD10) were differentially expressed in genome-wide blood transcriptomics data from stroke and healthy individuals (FDR<0.05). Functional enrichment analysis of the identified candidate biomarker genes revealed gene ontologies and pathways involved in stroke, including "cell aging", "metal ion binding" and "oxidative damage". Based on the evidence of genetically regulated expression of genes through SMR and directly measured expression of genes in blood, our integrative analysis suggests ten genes as blood biomarkers of stroke risk. Furthermore, our study provides a better understanding of the influence of DNA methylation on the expression of genes linked to stroke risk.

Exploring genes for immunoglobulin A nephropathy: a summary data-based mendelian randomization and FUMA analysis

BACKGROUND

Immunoglobulin A nephropathy (IgAN) is a complex autoimmune disease, and the exact pathogenesis remains to be elucidated. This study aimed to explore genes underlying the pathogenesis of IgAN.

METHODS

We conducted the summary data-based Mendelian randomization (SMR) analysis and performed functional mapping and annotation using FUMA to explore genetic loci that are potentially involved in the pathogenies of IgAN. Both analyses used summarized data of a recent genome-wide association study (GWAS) on IgANs, which included 477,784 Europeans (15,587 cases and 462,197 controls) and 175,359 East Asians (71 cases and 175,288 controls). We performed SMR analysis using Consortium for the Architecture of Gene Expression (CAGE) expression quantitative trait loci (eQTL) data and replicated the analysis using Genotype-Tissue Expression (GTEx) eQTL data.

RESULTS

Using the CAGE eQTL data, our SMR analysis identified 32 probes tagging 25 unique genes whose expression were pleiotropically associated with IgAN, with the top three probes being ILMN_2150787 (tagging HLA-C, P_SMR= 2.10 × 10^-18), ILMN_1682717 (tagging IER3, P_SMR= 1.07 × 10^-16) and ILMN_1661439 (tagging FLOT1, P_SMR=1.16 × 10^-14). Using GTEx eQTL data, our SMR analysis identified 24 probes tagging 24 unique genes whose expressions were pleiotropically associated with IgAN, with the top three probes being ENSG00000271581.1 (tagging XXbac-BPG248L24.12, P_SMR= 1.44 × 10^-10), ENSG00000186470.9 (tagging BTN3A2, P_SMR= 2.28 × 10^-10), and ENSG00000224389.4 (tagging C4B, P_SMR= 1.23 × 10 ^-9). FUMA analysis identified 3 independent, significant and lead SNPs, 2 genomic risk loci and 39 genes that are potentially involved in the pathogenesis of IgAN.

CONCLUSION

We identified many genetic variants/loci that are potentially involved in the pathogenesis of IgAN. More studies are needed to elucidate the exact mechanisms of the identified genetic variants/loci in the etiology of IgAN.

High throughput sequencing revealed enhanced cell cycle signaling in SLE patients

The multi-system involvement and high heterogeneity of systemic lupus erythematosus (SLE) pose great challenges to its diagnosis and treatment. The purpose of the current study is to identify genes and pathways involved in the pathogenesis of SLE. High throughput sequencing was performed on the PBMCs from SLE patients. We conducted differential gene analysis, gene ontology (GO) analysis, kyoto encyclopedia of genes and genomes (KEGG) analysis, and quantitative real-time PCR (qRT-PCR) verification. Protein-protein interaction (PPI) analysis, alternative splicing analysis, and disease correlation analysis were conducted on some key pathogenic genes as well. Furthermore, si-CDC6 was used for transfection and cell proliferation was monitored using a cell counting kit-8 (CCK-8) assay. We identified 2495 differential genes (1494 upregulated and 1001 downregulated) in SLE patients compared with healthy controls. The significantly upregulated genes were enriched in the biological process-related GO terms of the cell cycle, response to stress, and chromosome organization. KEGG enrichment analysis revealed 7 significantly upregulated pathways including SLE, alcoholism, viral carcinogenesis, cell cycle, proteasome, malaria, and transcriptional misregulation in cancer. We successfully verified some differential genes on the SLE pathway and the cell cycle pathway. CDC6, a key gene in the cell cycle pathway, had remarkably higher MXE alternative splicing events in SLE patients than that in controls, which may explain its significant upregulation in SLE patients. We found that CDC6 participates in the pathogenesis of many proliferation-related diseases and its levels are positively correlated with the severity of SLE. Knockdown of CDC6 suppressed the proliferation of Hela cells and PBMCs from SLE patients in vitro. We identified SLE-related genes and their alternative splicing events. The cell cycle pathway and the cell cycle-related biological processes are over-activated in SLE patients. We revealed a higher incidence of MXE events of CDC6, which may lead to its high expression in SLE patients. Upregulated cell cycle signaling and CDC6 may be related to the hyperproliferation and pathogenesis of SLE.

Mendelian randomization study on the causal effects of systemic lupus erythematosus on major depressive disorder

The vast majority of epidemiological studies suggested a link between systemic lupus erythematosus (SLE) and major depressive disorder (MDD). However, the causality for SLE on the risk of MDD remained unknown due to confounding factors or reverse causality. Herein, we investigated the causality between SLE and MDD in those of European ancestry by a Mendelian randomization (MR) approach. Summary genetic data of cases with SLE/MDD were derived from independent largest public genome-wide association study. Forty-six single nucleotide polymorphisms associated with SLE were used as instrumental variables. The main causal inference was carried out using the MRE-IVW method. Additional, reverse-direction MR and multivariable MR analyses were further performed. Result indicated that SLE was causally associated with a lower risk of MDD (using the MRE-IVW method, odds ratio [OR] = 0.983, 95% confidence interval [CI] = 0.974-0.991, p = 1.18 × 10^-4). Complementary analysis found no heterogeneity or horizontal pleiotropy. Multivariate MR analysis yielded consistent results (OR =  0.981; 95% CI = 0.969-0.993; p = 2.75 × 10^-3). Reverse-direction MR analysis suggested non-causal relationship of MDD on the risk of SLE (using the IVW method, OR = 0.846, 95% CI = 0.345-2.072; p = 0.714). Thus, this is the first study providing evidence of potential causal links between SLE and MDD and further related research is needed.

A network causal relationship between type-1 diabetes mellitus, 25-hydroxyvitamin D level and systemic lupus erythematosus: Mendelian randomization study

BACKGROUND

Observational studies have suggested a relationship between type-1 diabetes mellitus (T1DM) and systemic lupus erythematosus (SLE). In both autoimmunities, 25-hydroxyvitamin D (25-OHD) deficiency is common. However, the causality between T1DM, 25-OHD level and SLE remains largely unknown.

METHODS

Independent genetic variants associated with T1DM, 25-OHD level, and SLE from the largest genome-wide association studies were used to conduct two-sample bidirectional Mendelian randomization (BIMR) and two-step Mendelian randomization (MR) analysis to estimate causal relationship between T1DM, 25-OHD level and SLE, and further multivariable Mendelian randomization (MVMR) was used to verify direct causality of T1DM and 25-OHD level on SLE. A series of sensitivity analysis as validation of primary MR results were performed.

RESULTS

Consistent with the results of BIMR, there was strong evidence for a direct causal effect of T1DM on the risk of SLE (ORMVMR-IVW = 1.249, 95% CI = 1.148-1.360, PMVMR-IVW = 1.25×10-5), and 25-OHD level was negatively associated with the risk of SLE (ORMVMR-IVW = 0.305, 95% CI = 0.109-0.857, PMVMR-IVW = 0.031). We also observed a negative causal effect of T1DM on 25-OHD level (ORBIMR-IVW = 0.995, 95% CI = 0.991-0.999, PBIMR-IVW = 0.030) while the causal effect of 25-OHD level on the risk of T1DM did not exist (PBIMR-IVW = 0.106). In BIMR analysis, there was no evidence for causal effects of SLE on the risk of T1DM and 25-OHD level (PBIMR-IVW > 0.05, respectively).

CONCLUSION

Our MR analysis suggested that there was a network causal relationship between T1DM, 25-OHD level and SLE. T1DM and 25-OHD level both have causal associations with the risk of SLE, and 25-OHD level could be a mediator in the causality of T1DM and SLE.

Cysteine cathepsins: A long and winding road towards clinics

Biomedical research often focuses on properties that differentiate between diseased and healthy tissue; one of the current focuses is elevated expression and altered localisation of proteases. Among these proteases, dysregulation of cysteine cathepsins can frequently be observed in inflammation-associated diseases, which tips the functional balance from normal physiological to pathological manifestations. Their overexpression and secretion regularly exhibit a strong correlation with the development and progression of such diseases, making them attractive pharmacological targets. But beyond their mostly detrimental role in inflammation-associated diseases, cysteine cathepsins are physiologically highly important enzymes involved in various biological processes crucial for maintaining homeostasis and responding to different stimuli. Consequently, several challenges have emerged during the efforts made to translate basic research data into clinical applications. In this review, we present both physiological and pathological roles of cysteine cathepsins and discuss the clinical potential of cysteine cathepsin-targeting strategies for disease management and diagnosis.

A transcriptome-wide association study provides new insights into the etiology of osteoarthritis

Background

Osteoarthritis (OA) is a common clinical disease caused by a variety of factors, including genetic variants. Although genome-wide association studies (GWAS) have been performed to elucidate the genetic basis of OA, some loci of risk located in noncoding regions of the genome have been neglected. Therefore, we integrated multiple data types to detect the genetic component of gene expression in OA patients through transcriptome-wide association studies (TWAS) and summary-data-based Mendelian randomization (SMR) analysis.

Methods

TWAS was performed by integrating the larger GWAS summary-data for OA (n=30,727 cases, n=297,191 controls) and 2 expression weight sets (muscle-skeletal tissue and whole blood). Colocalization analysis, conditional analysis, and fine-mapping analysis were also conducted. A broad description of the identified associations was obtained. In addition, a causal relationship between certain risk genes and OA was identified with SMR.

Results

New significant genome-wide associations were found, including on chromosome 1q36.12 (rs1555024, P=4.24E-07) near the ASAP3 and TCEA3 genes, on chromosome 17q24.2 (rs2521348, P=1.01E-06) near the ABCA9 gene, on chromosome 20q11.22 (rs224331, P=8.17E-09) near the UQCC1 and MYH7B genes, and on chromosome 21q21.3 (rs2832155, P=5.39E-08) near the RWDD2B gene. In addition, SMR results exhibited that upregulated UQCC1 and downregulated ASAP3 were associated with OA development and both had a significant causal relationship with OA.

Conclusions

We revealed some novel OA-associated genes and risk loci by integrating multiple data types and analysis methods, thus providing new clues for the study of genetic mechanisms of OA.

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 Pathogenesis, Molecular Mechanisms, and Therapeutic Potential of the Interferon Pathway in Systemic Lupus Erythematosus and Other Autoimmune Diseases

Therapeutic success in treating patients with systemic lupus erythematosus (SLE) is limited by the multivariate disease etiology, multi-organ presentation, systemic involvement, and complex immunopathogenesis. Agents targeting B-cell differentiation and survival are not efficacious for all patients, indicating a need to target other inflammatory mediators. One such target is the type I interferon pathway. Type I interferons upregulate interferon gene signatures and mediate critical antiviral responses. Dysregulated type I interferon signaling is detectable in many patients with SLE and other autoimmune diseases, and the extent of this dysregulation is associated with disease severity, making type I interferons therapeutically tangible targets. The recent approval of the type I interferon-blocking antibody, anifrolumab, by the US Food and Drug Administration for the treatment of patients with SLE demonstrates the value of targeting this pathway. Nevertheless, the interferon pathway has pleiotropic biology, with multiple cellular targets and signaling components that are incompletely understood. Deconvoluting the complexity of the type I interferon pathway and its intersection with lupus disease pathology will be valuable for further development of targeted SLE therapeutics. This review summarizes the immune mediators of the interferon pathway, its association with disease pathogenesis, and therapeutic modalities targeting the dysregulated interferon pathway.

Mendelian randomization integrating GWAS and eQTL data revealed genes pleiotropically associated with major depressive disorder

Previous genome-wide association studies (GWAS) have identified potential genetic variants associated with the risk of major depressive disorder (MDD), but the underlying biological interpretation remains largely unknown. We aimed to prioritize genes that were pleiotropically or potentially causally associated with MDD. We applied the summary data-based Mendelian randomization (SMR) method integrating GWAS and gene expression quantitative trait loci (eQTL) data in 13 brain regions to identify genes that were pleiotropically associated with MDD. In addition, we repeated the analysis by using the meta-analyzed version of the eQTL summary data in the brain (brain-eMeta). We identified multiple significant genes across different brain regions that may be involved in the pathogenesis of MDD. The prime-specific gene BTN3A2 (corresponding probe: ENSG00000186470.9) was the top hit showing pleiotropic association with MDD in 9 of the 13 brain regions and in brain-eMeta, after correction for multiple testing. Many of the identified genes are located in the human major histocompatibility complex (MHC) region on chromosome 6 and are mainly involved in the immune response. Our SMR analysis indicated that multiple genes showed pleiotropic association with MDD across the brain regions. These findings provided important leads to a better understanding of the mechanism of MDD and revealed potential therapeutic targets for the prevention and effective treatment of MDD.

A catalog of GWAS fine-mapping efforts in autoimmune disease

Genome-wide association studies (GWASs) have enabled unbiased identification of genetic loci contributing to common complex diseases. Because GWAS loci often harbor many variants and genes, it remains a major challenge to move from GWASs’ statistical associations to the identification of causal variants and genes that underlie these association signals. Researchers have applied many statistical and functional fine-mapping strategies to prioritize genetic variants and genes as potential candidates. There is no gold standard in fine-mapping approaches, but consistent results across different approaches can improve confidence in the fine-mapping findings. Here, we combined text mining with a systematic review and formed a catalog of 85 studies with evidence of fine mapping for at least one autoimmune GWAS locus. Across all fine-mapping studies, we compiled 230 GWAS loci with allelic heterogeneity estimates and predictions of causal variants and trait-relevant genes. These 230 loci included 455 combinations of locus-by-disease association signals with 15 autoimmune diseases. Using these estimates, we assessed the probability of mediating disease risk associations across genes in GWAS loci and identified robust signals of causal disease biology. We predict that this comprehensive catalog of GWAS fine-mapping efforts in autoimmune disease will greatly help distill the plethora of information in the field and inform therapeutic strategies.