Identification of potential drug targets for rheumatoid arthritis from genetic insights: a Mendelian randomization study

Targeting RAC1 might be a potential therapeutic strategy for diabetic kidney disease: a Mendelian randomization study

PURPOSE

This study aimed to ascertain the causal association between Ras-related C3 botulinum toxin substrate 1 (RAC1) and the incidence and progression of diabetic kidney disease (DKD) through Mendelian randomization analysis.

METHODS

RAC1 expression, evaluated using expression quantitative trait loci data from the eQTLGen Consortium, was served as the exposure variable. Outcomes encompassed the risk of DKD, end-stage renal disease (ESRD), albuminuria assessed by the urinary albumin-to-creatinine ratio (ACR), and estimated glomerular filtration rate (eGFR) among individuals with diabetes. Causal associations were computed using the inverse variance weighted (IVW), weighted median, and MR-PRESSO models. Additionally, we conducted analyses for heterogeneity, horizontal pleiotropy, and sensitivity.

RESULTS

This study revealed a causal association between the genetic activation of RAC1 and an elevated risk of DKD among individuals with diabetes [IVW, odds ratio (OR) = 1.28, 95% confidence intervals (CI) 1.08-1.51, P = 0.004]. Furthermore, increased expression of RAC1 was linked to a higher risk of ESRD (IVW, OR = 1.20, 95% CI 1.02-1.43, P = 0.032). Excessive RAC1 expression was causally associated with elevated ACR (IVW, β = 0.052, 95% CI 0.003-0.100, P = 0.036). However, the analysis regarding RAC1 and eGFR showed significant heterogeneity and pleiotropy, with no discernible causal relationship.

CONCLUSIONS

These findings suggested a positive correlation between the genetic activation of RAC1 and the incidence of DKD, the risk of ESRD, and exacerbated albuminuria among individuals with diabetes. Targeting RAC1 might potentially serve as a therapeutic strategy for DKD.

Integrative genetics and multiomics analysis reveal mechanisms and therapeutic targets in vitiligo highlighting JAK STAT pathway regulation of CTSS

Vitiligo is a complex autoimmune disease characterized by the loss of melanocytes, leading to skin depigmentation. Despite advances in understanding its genetic and molecular basis, the precise mechanisms driving vitiligo remain elusive. Integrating multiple layers of omics data can provide a comprehensive view of disease pathogenesis and identify potential therapeutic targets. The study aims to delineate the genetic and molecular mechanisms of vitiligo pathogenesis using an integrative multiomics strategy. We focus on exploring the regulatory influence of the JAK/STAT pathway on Cathepsin S, a potential therapeutic target in vitiligo. Our GWAS-meta analysis pinpointed five druggable genes: ERBB3, RHOH, CDK10, MC1R, and NDUFAF3, and underwent drug target exploration and molecular docking. SMR analysis linked CTSS, CTSH, STX8, KIR2DL3, and GRHPR to vitiligo through pQTL and eQTL associations. Microarray and single-cell RNA-seq data showed differential expression of CTSS and STAT1/3 in vitiligo patients' blood and skin lesions. Our study offers novel perspectives on vitiligo's genetic and molecular basis, highlighting the JAK/STAT pathway's role in regulating CTSS for antigen processing in melanocytes. Further research is needed to confirm these results and assess the therapeutic potential of CTSS and related genes.

Sarcopenia and immune-mediated inflammatory diseases: Evaluating causality and exploring therapeutic targets for sarcopenia through Mendelian randomization

BACKGROUND

An increasing body of evidence has revealed the association between immune-mediated inflammatory diseases (IMIDs) and sarcopenia. However, a genetically direct causality between IMIDs and sarcopenia remains elusive.

METHODS

To investigate the relationship between IMIDs and sarcopenia-related traits and identify potential therapeutic targets, a Mendelian randomization (MR) was performed. We collected publicly available genome-wide association studies (GWAS) data for seven common IMIDs, including systemic lupus erythematosus (SLE), inflammatory bowel disease (IBD), Crohn's disease (CD), ulcerative colitis (UC), psoriasis (PSO), ankylosing spondylitis (AS), and rheumatoid arthritis (RA). Additionally, summary-level GWAS data for sarcopenia-related traits, including appendicular lean mass (ALM), left-hand grip strength, and right-hand grip strength were collected. To search for therapeutic targets, we used two types of genetic instruments to proxy the exposure of druggable genes, including genetic variants within or nearby drug targets and expression quantitative trait loci (eQTLs) of drug targets. Two-sample MR and summary-data-based MR (SMR) were used to calculate effect estimates, and sensitivity analyses were implemented for robustness. Drug tractability, gene enrichment analysis, and protein-protein interaction (PPI) analysis were used to validate the biological and clinical significance of the selected drug targets.

RESULTS

The two-sample MR analysis indicated the existence of casual associations between IMIDs and sarcopenia-related traits in the overall and sex-stratified populations. In particular, PSO had causal effects on decreased ALM, which showed significance in all six MR analysis tests with directional consistency in the overall population. Grounded in this robust association, HLA-DRB5, HLA-DRB1, and AGER were identified as potential therapeutic targets for ALM decline by drug target MR and further confirmed by SMR analysis. These genes were associated with therapeutic agents currently undergoing evaluations in clinical trials. Gene enrichment and PPI analysis indicated a strong association of these genes with immune functions.

CONCLUSIONS

This MR study contributes novel genetic evidence supporting the causal link between IMIDs and sarcopenia, with a particular emphasis on the association between PSO and decreased ALM. Additionally, AGER, HLA-DRB1, and HLA-DRB5 emerge as potential therapeutic targets for ALM decline.

Combining single-cell analysis and molecular docking techniques to construct a prognostic model for colon adenocarcinoma and uncovering inhibin subunit βb as a novel therapeutic target

Background

Colon adenocarcinoma (COAD) is a malignancy with a high mortality rate and complex biological characteristics and heterogeneity, which poses challenges for clinical treatment. Anoikis is a type of programmed cell death that occurs when cells lose their attachment to the extracellular matrix (ECM), and it plays a crucial role in tumor metastasis. However, the specific biological link between anoikis and COAD, as well as its mechanisms in tumor progression, remains unclear, making it a potential new direction for therapeutic strategy research.

Methods

We employed transcriptomic data and clinical information from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) to pinpoint differentially expressed anoikis-related genes (ARGs) in COAD. Using Cox proportional hazards models and Lasso regression analysis, we developed a prognostic signature derived from these ARGs. We also investigated the roles and interactions of these genes in the tumor microenvironment by analyzing single-cell RNA sequencing data. Additionally, we employed molecular docking techniques to evaluate the potential of inhibin subunit beta B (INHBB) as therapeutic targets and to assess the binding affinity of candidate drugs. Finally, we used gene knockout techniques to silence the key gene INHBB and explored its biological functions in vitro.

Results

In our study, by analyzing the expression differences of ARGs, we successfully classified patients with COAD. Kaplan-Meier survival analysis demonstrated that patients with elevated risk scores experienced poorer prognosis, a finding that was confirmed in both the training and validation cohorts. Additionally, immune infiltration analysis revealed a notable increase in immune cell presence within the tumor microenvironment of high-risk patients. Molecular docking identified potential drug candidates with high binding affinity to INHBB, including risperidone. Furthermore, in vitro experiments with INHBB showed that downregulation of its expression in COAD cell lines significantly reduced cellular viability and migration capacity.

Conclusion

In summary, our research, based on the expression characteristics of ARGs, provides new insights into the precise classification, prognosis assessment, and identification of potential therapeutic targets in COAD. It also validates the key role of INHBB in the progression of COAD, establishing the foundation for future personalized treatment strategies.

Causal Inference and Annotation of Phosphoproteomics Data in Multi-omics Cancer Studies

Protein phosphorylation plays a crucial role in regulating diverse biological processes. Perturbations in protein phosphorylation are closely associated with downstream pathway dysfunctions, while alterations in protein expression could serve as sensitive indicators of pathological status. However, there are currently few methods that can accurately identify the regulatory links between protein phosphorylation and expression, given issues like reverse causation and confounders. Here, we present Phoslink, a causal inference model to infer causal effects between protein phosphorylation and expression, integrating prior evidence and multi-omics data. We demonstrated the feasibility and advantages of our method under various simulation scenarios. Phoslink exhibited more robust estimates and lower FDR than commonly used Pearson and Spearman correlations, with better performance than canonical IV selection methods for Mendelian randomization. Applying this approach, we identified 345 causal links involving 109 phosphosites and 310 proteins in 79 lung adenocarcinoma (LUAD) samples. Based on these links, we constructed a causal regulatory network and identified 26 key regulatory phosphosites as regulators strongly associated with LUAD. Notably, 16 of these regulators were exclusively identified through phosphosite-protein causal regulatory relationships, highlighting the significance of causal inference. We explored potentially druggable phosphoproteins and provided critical clues for drug repurposing in LUAD. We also identified significant mediation between protein phosphorylation and LUAD through protein expression. In summary, our study introduces a new approach for causal inference in phosphoproteomics studies. Phoslink demonstrates its utility in potential drug target identification thereby accelerating the clinical translation of cancer proteomics and phosphoproteomic data.

Unveiling therapeutic targets for spinal stenosis from genetic insights: a Mendelian randomization analysis

Spinal stenosis is a commonly chronic spinal degenerative disease, which is a major cause of pain and dysfunction in the elderly. Mendelian randomization (MR) has been widely applied to repurpose licensed drugs and identify novel therapeutic targets. Consequently, we intended to identify new therapeutic targets for spinal stenosis and to analyze their possible mechanisms and potential side effects.We conducted the Mendelian randomization analysis to identify potential drug targets for the management of spinal stenosis. Cis-expressed quantitative trait loci (cis-eQTL) data as genetic instrumental variables were acquired from the eQTLGen consortium. The summary statistics for single nucleotide polymorphism (SNP) associations of spinal stenosis were obtained from the FinnGen study(20,807 cases and 294,770 controls). Co-localization analysis was performed to determine whether there was shared causal variation between the SNPs associated with spinal stenosis as well as the eQTL. Multiple external validations were performed to reinforce the reliability and stability of the findings utilizing the cis-eQTL from the GTEx portal, the Ferkingstad et al. pQTL dataset, and the Sun et al. pQTL dataset. The viability of the identified drug targets for future clinical applications was elucidated through the phenome-wide association study and drug candidate prediction. Three drug targets (BMP6, DLK1, and GFPT1) exhibited significant causal associations with spinal stenosis in the eQTLGen cohort by MR analysis, which was strongly supported by the results of the co-localization analysis. The causal association of DLK1 and GFPT1 with spinal stenosis remained remarkable with multiple external validations. Multivariate MR and phenome-wide association study analysis indicated that both targets were not associated with other traits. In addition, phenome-wide association study analysis and drug prediction analysis demonstrated the potential of these two targets for future clinical applications. In this study, DLK1 and GFPT1 were identified as promising novel therapeutic targets for spinal stenosis, providing initial genetic insights for drug development in spinal stenosis.

Identification of Potential Biomarkers and Therapeutic Targets for Periodontitis

BACKGROUND

Periodontitis is a chronic and multifactorial inflammatory disease. However, existing medications often lack sufficient therapeutic effects. The aim is to identify potential biomarkers and efficient therapeutic targets using Mendelian randomisation (MR) and single-cell analysis.

METHODS

MR analysis was conducted based on the cis-expression quantitative trait loci (cis-eQTLs) extracted from the eQTLGen Consortium and genome-wide association study (GWAS) data of periodontitis sourced from the Gene Lifestyle Interactions in Dental Endpoints (GLIDE) consortium (17,353 cases, 28,210 controls). Subsequently, colocalisation analysis was employed to detect whether genes and periodontitis shared the same casual variant. Finally, enrichment analysis, protein-protein interaction (PPI) networks, drug prediction, phenome-wide association study (PheWAS), molecular docking, and single-cell analysis were conducted to validate the significance of target genes.

RESULTS

Fourteen drug targets were significant related with periodontitis in MR analysis. Following the colocalisation and summary-data-based MR (SMR) analysis, 3 targets (S100A12, S100A9, and S100A8) were classified into tier 1 with strong evidence, 6 therapeutic targets (ADAM12, ADHFE1, BLK, HEBP1, SERPINE2, and TEK) were classified into tier 2 with moderate evidence, and 5 therapeutic targets (LY86, MMEL1, S100B, SPP1, and TRIB3) were classified into tier 3 with convincing evidence. PheWAS analysis showed that only TEK and SPP1 in tier 2 may induce side effects, including cardiometabolic and oncological issues. Molecular docking demonstrated strong binding between drugs and their respective protein targets. In the single-cell analysis, 5 target genes (HEBP1, LY86, S100A8, S100A9, and S100A12) exhibited enrichment in monocytes, while BLK and LY86 were primarily enriched in B cells.

CONCLUSION

The study identified 14 potential therapeutic targets for periodontitis. Among these, 3 therapeutic targets (S100A12, S100A9, and S100A8) demonstrated robust and well-supported results. Drugs designed to target these genes have a higher possibility of success in clinical trials, which are hopeful for prioritising periodontitis drug development.

Proteome-wide Mendelian randomization provides novel insights into the pathogenesis and druggable targets of osteoporosis

Background

With the aging population, the prevalence and impact of osteoporosis are expected to rise, and existing anti-osteoporosis agents have limitations due to adverse events. This study aims to discover novel drug targets for osteoporosis.

Methods

The protein data were obtained from the latest proteome-wide association studies (PWAS) including 54, 219 participants. The osteoporosis data were extracted from a GWAS meta-analysis, characterized by heel bone mineral density (HBMD) comprising 426,824 individuals. Mendelian randomization (MR) was the primary approach used to establish genetic causality between specific traits. Summary-data-based MR (SMR), colocalization analysis, heterogeneity test, and external validation were applied to ensure the findings were reliable. The underlying mechanisms behind these causal associations were investigated by additional analyses. Finally, the druggability of the identified proteins was assessed.

Results

After Bonferroni correction, a total of 84 proteins were found to have a genetic association with osteoporosis. With strong colocalization evidence, proteins such as ACHE, HS6ST1, LRIG1, and LRRC37A2 were found to negatively influence HBMD, whereas CELSR2, CPE, FN1, FOXO1, and FSHB exhibited a positive association with HBMD. No significant heterogeneity was found. Additionally, CELSR2, FN1, FSHB, HS6ST1, LRIG1, and LRRC37A2 were replicated in the external validation. The effect of FSHB on HBMD was more pronounced in females compared to males. Interestingly, ACHE, LRIG1, FN1, and FOXO1 were observed to partially act on HBMD through BMI. Phewas analysis indicated that CPE and FOXO1 did not have genetic associations with any phenotypes other than osteoporosis. FN1 was highlighted as the most significant protein by protein-protein interaction network analysis.

Conclusion

In conclusion, this study offers valuable insights into the role of specific proteins in the development of osteoporosis, and underscores potential therapeutic targets. Future studies should emphasize exploring these causal relationships and elucidating their underlying mechanisms.

Exploration of protein and genetic targets causing atrioventricular block: mendelian-randomization analyses based on eQTL data and pQTL data

BACKGROUND

Atrioventricular block (AVB) is a heterogeneous group of arrhythmias. AVB can lead to sudden arrest of the heart and subsequent syncope or sudden cardiac death. Few scholars have investigated the underlying molecular mechanisms of AVB. Finding molecular markers can facilitate understanding of AVB and exploration of therapeutic targets.

METHODS

Two-sample Mendelian randomization (MR) analysis was undertaken with inverse variance weighted (IVW) model and Wald ratio as the primary approach. Reverse MR analysis was undertaken to identify the associated protein targets and gene targets. Expression quantitative trait loci (eQTL) data from the eQTLGen database and protein quantitative trait loci (pQTL) data from three previous large-scale proteomic studies on plasma were retrieved as exposure data. Genome-wide association study (GWAS) summary data (586 cases and 379,215 controls) for AVB were retrieved from the UK Biobank database. Colocalization analyses were undertaken to identify the effect of filtered markers on outcome data. Databases (DrugBank, Therapeutic Target, PubChem) were used to identify drugs that interacted with targets.

RESULTS

We discovered that 692 genes and 42 proteins showed a significant correlation with the AVB phenotype. Proteins (cadherin-5, sTie-1, Notch 1) and genes (DNAJC30, ABO) were putative molecules to AVB. Drug-interaction analyses revealed anticancer drugs such as tyrosine-kinase inhibitors and TIMD3 inhibitors could cause AVB. Other substances (e.g. toxins, neurological drugs) could also cause AVB.

CONCLUSIONS

We identified the proteins (cadherin-5, sTie-1, Notch 1) and gene (DNAJC30, ABO) targets associated with AVB pathogenesis. Anticancer drugs (tyrosine-kinase inhibitors, TIMD3 inhibitors), toxins, or neurological drugs could also cause AVB.

Mendelian Randomization Reveals Serum Copper as a Micronutrient is a Risk Factor for Erectile Dysfunction

The cause and effect between serum micronutrients and erectile dysfunction (ED) is not clear. The purpose of this study was to evaluate the causal relationship between micronutrients and ED by Mendelian randomization (MR) analysis. We used the published genome-wide association study (GWAS) data for two-sample MR analysis. This study utilized inverse variance weighted (IVW), MR egger, weighted median, simple mode, and weighted mode method to assess the causal relationship between serum micronutrients and ED. IVW is considered the standard method for MR analysis. We used Cochran's Q tests to evaluate the heterogeneity. To investigate horizontal pleiotropy, this study employed the MR Egger method. Additionally, leave-one-out analysis was used to evaluate the influence of individual genetic loci on the results. The results showed that there was a causal relationship between serum copper and ED, and it was positively correlated with ED (OR = 1.115, CI = 1.00-1.24, p = 0.014). There was no significant correlation between other micronutrients and ED. Sensitivity analysis results indicated that our findings exhibit no heterogeneity or pleiotropy, thereby strengthening our conclusions. Serum copper is a risk factor for ED, which provides a new idea for the diagnosis and treatment of ED in the future. However, further experiments are needed to elucidate the underlying mechanisms.

CXCR1 and CXCR2 are potential neutrophil extracellular trap-related treatment targets in ulcerative colitis: insights from Mendelian randomization, colocalization and transcriptomic analysis

Objectives

There is already substantial evidence indicating that neutrophil extracellular trap (NET) formation contributes to the inflammatory cascade in ulcerative colitis (UC). However, the precise regulatory mechanisms governing this process remain elusive. This study aimed to determine the role of NET-related genes in UC and reveal possible mechanisms.

Methods

Employing a two-sample MR methodology, we investigated the correlations between NET-associated genes (NRGs) and UC with summary data derived from a genome-wide association study (12,366 cases vs. 33,609 controls) and FinnGen (8,279 cases vs. 261,098 controls). The main analysis employed the inverse variance weighted method, supplemented by the MR-Egger method and weighted median method. Sensitivity analysis was conducted to rule out the interference of heterogeneity and pleiotropy among utilized instrument variables. The colocalization analysis was used to determine whether the identified NRGs and UC shared casual variants. Cross-tissue expression analysis was performed to characterize the expression patterns of target NRGs, while multi-gene correlation analysis and GSEA analysis were conducted to explore the mechanisms by which target NRGs promote UC and NET formation. Immunohistochemistry was used to validate the protein expression of target NRGs in the colon tissue of UC patients.

Results

After the validation of two datasets, seven NRGs were associated with the risk of UC. The higher expression of ITGB2 was associated with increased UC risk, while the expression of CXCR1, CXCR2, IRAK4, MAPK3, SIGLEC14, and SLC22A4 were inversely associated with UC risk. Colocalization analysis supported the correlation between CXCR1/2 and UC risk. Expression analysis indicated that CXCR1/2 were down-regulated in peripheral blood, but up-regulated in colon tissue. GSEA analysis and correlation analysis indicated that CXCR1/2 promoted UC and NET formation through neutrophil chemotaxis and PAD4-mediated pathways, separately. Immunohistochemical results confirmed the high expression of CXCR1/2 in colon tissues of UC patients.

Conclusions

Our study identified CXCR1/2 as candidate targets in UC among all NRGs through multi-method argumentation, providing new insights of the regulation mechanisms of NET formation in the pathogenesis of UC.

Genetic insights into drug targets for sporadic Creutzfeldt-Jakob disease: Integrative multi-omics analysis

OBJECTIVE

Sporadic Creutzfeldt-Jakob disease (sCJD) is a fatal rapidly progressive neurodegenerative disorder with no effective therapeutic interventions. We aimed to identify potential genetically-supported drug targets for sCJD by integrating multi-omics data.

METHODS

Multi-omics-wide association studies, Mendelian randomization, and colocalization analyses were employed to explore potential therapeutic targets using expression, single-cell expression, DNA methylation, and protein quantitative trait locus data from blood and brain tissues. Outcome data was from a case-control genome-wide association study, which included 4110 sCJD patients and 13,569 controls. Further investigations encompassed druggability, potential side effects, and associated biological pathways of the identified targets.

RESULTS

Integrative multi-omics analysis identified 23 potential therapeutic targets for sCJD, with five targets (STX6, XYLT2, PDIA4, FUCA2, KIAA1614) having higher levels of evidence. One target (XYLT2) shows promise for repurposing, two targets (XYLT2, PDIA4) are druggable, and three (STX6, KIAA1614, and FUCA2) targets represent potential future breakthrough points. The expression level of STX6 and XYLT2 in neurons and oligodendrocytes was closely associated with an increased risk of sCJD. Brain regions with high expression of STX6 or causal links to sCJD were often those areas commonly affected by sCJD.

CONCLUSIONS

Our study identified five potential therapeutic targets for sCJD. Further investigations are warranted to elucidate the mechanisms underlying the new targets for developing disease therapies or initiate clinical trials.

Prioritizing drug targets in systemic lupus erythematosus from a genetic perspective: a druggable genome-wide Mendelian randomization study

OBJECTIVES

Systemic lupus erythematosus (SLE) is a heterogeneous autoimmune disease with an unsatisfactory state of treatment. We aim to explore novel targets for SLE from a genetic standpoint.

METHODS

Cis-expression quantitative trait loci (eQTLs) for whole blood from 31,684 samples provided by the eQTLGen Consortium as well as two large SLE cohorts were utilized for screening and validating genes causally associated with SLE. Colocalization analysis was employed to further investigate whether changes in the expression of risk genes, as indicated by GWAS signals, influence the occurrence and development of SLE. Targets identified for drug development were evaluated for potential side effects using a phenome-wide association study (PheWAS). Based on the multiple databases, we explored the interactions between drugs and genes for drug prediction and the assessment of current medications.

RESULTS

The analysis comprised 5427 druggable genes in total. The two-sample Mendelian randomization (MR) in the discovery phase identified 20 genes causally associated with SLE and validated 8 genes in the replication phase. Colocalization analysis ultimately identified five genes (BLK, HIST1H3H, HSPA1A, IL12A, NEU1) with PPH4 > 0.8. PheWAS further indicated that drugs acting on BLK and IL12A are less likely to have potential side effects, while HSPA1A and NEU1 were associated with other traits. Four genes (BLK, HSPA1A, IL12A, NEU1) have been targeted for drug development in autoimmune diseases and other conditions.

CONCLUSIONS

.This study identified five genes as therapeutic targets for SLE. Repurposing and developing drugs targeting these genes is anticipated to improve the existing treatment state for SLE. Key Points • We identified five gene targets of priority for the treatment of SLE, with BLK and IL12A indicating fewer side effects. • Among the existing drugs that target these candidate genes, Ustekinumab, Ebdarokimab, and Briakinumab (targeting the IL12 gene) and CD24FC (targeting HSPA1A) may potentially be repurposed for the treatment of SLE.

Potential drug targets for osteoporosis identified: A Mendelian randomization study

Background

Osteoporosis is a prevalent global health condition, primarily affecting the aging population, and several therapies for osteoporosis have been widely used. However, available drugs for osteoporosis are far from satisfactory because they cannot alleviate disease progression. This study aimed to explore potential drug targets for osteoporosis through Mendelian randomization analysis.

Methods

Using cis-expression quantitative trait loci (cis-eQTL) data of druggable genes and two genome-wide association studies (GWAS) datasets related to osteoporosis (UK Biobank and FinnGen cohorts), we employed mendelian randomization (MR) analysis to identify the druggable genes with causal relationships with osteoporosis. Subsequently, a series of follow-up analyses were conducted, such as colocalization analysis, cell-type specificity analysis, and correlation analysis with risk factors. The association between potential drug targets and osteoporosis was validated by qRT-PCR.

Results

Six druggable genes with causal relationships with osteoporosis were identified and successfully replicated, including ACPP, DNASE1L3, IL32, PPOX, ST6GAL1, and TGM3. Cell-type specificity analysis revealed that PPOX and ST6GAL1 were expressed in all cell types in the bone samples, while IL32, ACPP, DNASE1L3, and TGM3 were expressed in specific cell types. The GWAS data showed there were seven risk factors for osteoporosis, including vitamin D deficiency, COPD, physical activity, BMI, MMP-9, ALP and PTH. Furthermore, ACPP was associated with vitamin D deficiency and COPD; DNASE1L3 was linked to physical activity; IL32 correlated with BMI and MMP-9; and ST6GAL1 was related to ALP, physical activity, and MMP-9. Among these risk factors, only MMP-9 had a high genetic correlation with osteoporosis. The results of qRT-PCR demonstrated that IL32 was upregulated while ST6GAL1 was downregulated in peripheral blood of osteoporosis patients.

Conclusion

Our findings suggested that those six druggable genes offer potential drug targets for osteoporosis and require further clinical investigation, especially IL32 and ST6GAL1.

Unveiling potential drug targets for lung squamous cell carcinoma through the integration of druggable genome and genome-wide association data

Background: Lung squamous cell carcinoma (LSCC) is a major subtype of lung cancer with poor prognosis and low survival rate. Compared with lung adenocarcinoma, yet no FDA-approved targeted-therapy has been found for lung squamous cell carcinoma. Methods: To identify potential drug targets for LSCC, Summary-data-based Mendelian randomization (SMR) analysis was used to examine the potential association between 4,543 druggable genes and LSCC, followed by colocalization analysis and HEIDI tests to confirm the robustness of the result. Phenome-wide association study (PheWAS) explored potential side effects of candidate drug targets. Enrichment analysis and protein-protein interaction networks revealed the function and significance of therapeutic targets. Single-cell expression analysis was used to examine cell types with enrichment expression of druggable genes in LSCC tissue. Drug prediction included screening potential drug candidates and evaluating their interactions with targets through molecular docking. Results: This research has identified ten significant drug targets for LSCC through a comprehensive SMR analysis. These targets included (COPA, PKD2L1, CCR1, C2, CYP21A2, and NCSTN as risk factors, and CCNA2, C4A, APOM, and LPAR2 as protective factors). PheWAS demonstrated that C2, CCNA2, LPAR2, and NCSTN exhibited associations with other phenotypes at the genetic level. Then, we found four potentially effective drugs with the Dsigdb database. Subsequently, molecular docking indicated that favorable binding interactions between drug candidates and potential target molecules. In the druggability evaluation, five out of ten drug target genes have been used in drug development (APOM, C4A, CCNA2, COPA, and PKD2L1). Six out of ten druggable genes showed significant expression in LSCC tissues (COPA, PKD2L1, CCR1, C2, NCSTN, LPAR2). Besides, Single-cell expression analysis revealed that C2 and CCNA2 were primarily enriched in macrophages, while COPA and NCSTN were enriched in both macrophages and epithelial cells. Conclusion: Our research revealed ten potential druggable genes for LSCC treatment, which might help to advance the precise and efficient therapeutic approaches of LSCC.

The immunomodulatory of interleukin-33 in rheumatoid arthritis: A systematic review

Rheumatoid arthritis (RA) is a systemic chronic autoimmune disease that primarily affects the joints and surrounding soft tissues, characterized by chronic inflammation and proliferation of the synovium. Various immune cells are involved in the pathophysiology of RA. The complex interplay of factors such as chronic inflammation, genetic susceptibility, dysregulation of serum antibody levels, among others, contribute to the complexity of the disease mechanism, disease activity, and treatment of RA. Recently, the cytokine storm leading to increased disease activity in RA has gained significant attention. Interleukin-33 (IL-33), a member of the IL-1 family, plays a crucial role in inflammation and immune regulation. ST2 (suppression of tumorigenicity 2 receptor), the receptor for IL-33, is widely expressed on the surface of various immune cells. When IL-33 binds to its receptor ST2, it activates downstream signaling pathways to exert immunoregulatory effects. In RA, IL-33 regulates the progression of the disease by modulating immune cells such as circulating monocytes, tissue-resident macrophages, synovial fibroblasts, mast cells, dendritic cells, neutrophils, T cells, B cells, endothelial cells, and others. We have summarized and analyzed these findings to elucidate the pathways through which IL-33 regulates RA. Furthermore, IL-33 has been detected in the synovium, serum, and synovial fluid of RA patients. Due to inconsistent research results, we conducted a meta-analysis on the association between serum IL-33, synovial fluid IL-33, and the risk of developing RA in patients. The pooled SMD was 1.29 (95% CI: 1.15-1.44), indicating that IL-33 promotes the onset and pathophysiological progression of RA. Therefore, IL-33 may serve as a biomarker for predicting the risk of developing RA and treatment outcomes. As existing drugs for RA still cannot address drug resistance in some patients, new therapeutic approaches are needed to alleviate the significant burden on RA patients and healthcare systems. In light of this, we analyzed the potential of targeting the IL-33/ST2-related signaling pathway to modulate immune cells associated with RA and alleviate inflammation. We also reviewed IL-33 and RA susceptibility-related single nucleotide polymorphisms, suggesting potential involvement of IL-33 and macrophage-related drug-resistant genes in RA resistance therapy. Our review elucidates the role of IL-33 in the pathophysiology of RA, offering new insights for the treatment of RA.

Integrating Mendelian randomization and single-cell RNA sequencing to identify therapeutic targets of baicalin for type 2 diabetes mellitus

Background

Alternative and complementary therapies play an imperative role in the clinical management of Type 2 diabetes mellitus (T2DM), and exploring and utilizing natural products from a genetic perspective may yield novel insights into the mechanisms and interventions of the disorder.

Methods

To identify the therapeutic target of baicalin for T2DM, we conducted a Mendelian randomization study. Druggable targets of baicalin were obtained by integrating multiple databases, and target-associated cis-expression quantitative trait loci (cis-eQTL) originated from the eQTLGen consortium. Summary statistics for T2DM were derived from two independent genome-wide association studies available through the DIAGRAM Consortium (74,124 cases vs. 824,006 controls) and the FinnGen R9 repository (9,978 cases vs. 12,348 controls). Network construction and enrichment analysis were applied to the therapeutic targets of baicalin. Colocalization analysis was utilized to assess the potential for the therapeutic targets and T2DM to share causative genetic variations. Molecular docking was performed to validate the potency of baicalin. Single-cell RNA sequencing was employed to seek evidence of therapeutic targets' involvement in islet function.

Results

Eight baicalin-related targets proved to be significant in the discovery and validation cohorts. Genetic evidence indicated the expression of ANPEP, BECN1, HNF1A, and ST6GAL1 increased the risk of T2DM, and the expression of PGF, RXRA, SREBF1, and USP7 decreased the risk of T2DM. In particular, SREBF1 has significant interaction properties with other therapeutic targets and is supported by strong colocalization. Baicalin had favorable combination activity with eight therapeutic targets. The expression patterns of the therapeutic targets were characterized in cellular clusters of pancreatic tissues that exhibited a pseudo-temporal dependence on islet cell formation and development.

Conclusion

This study identified eight potential targets of baicalin for treating T2DM from a genetic perspective, contributing an innovative analytical framework for the development of natural products. We have offered fresh insights into the connections between therapeutic targets and islet cells. Further, fundamental experiments and clinical research are warranted to delve deeper into the molecular mechanisms of T2DM.

Unveiling the Potential of Migrasomes: A Machine-Learning-Driven Signature for Diagnosing Acute Myocardial Infarction

BACKGROUND

Recent studies have demonstrated that the migrasome, a newly functional extracellular vesicle, is potentially significant in the occurrence, progression, and diagnosis of cardiovascular diseases. Nonetheless, its diagnostic significance and biological mechanism in acute myocardial infarction (AMI) have yet to be fully explored.

METHODS

To remedy this gap, we employed an integrative machine learning (ML) framework composed of 113 ML combinations within five independent AMI cohorts to establish a predictive migrasome-related signature (MS). To further elucidate the biological mechanism underlying MS, we implemented single-cell RNA sequencing (scRNA-seq) of cardiac Cd45+ cells from AMI-induced mice. Ultimately, we conducted mendelian randomization (MR) and molecular docking to unveil the therapeutic effectiveness of MS.

RESULTS

MS demonstrated robust predictive performance and superior generalization, driven by the optimal combination of Stepglm and Lasso, on the expression of nine migrasome genes (BMP1, ITGB1, NDST1, TSPAN1, TSPAN18, TSPAN2, TSPAN4, TSPAN7, TSPAN9, and WNT8A). Notably, ITGB1 was found to be predominantly expressed in cardiac macrophages in AMI-induced mice, mechanically regulating macrophage transformation between anti-inflammatory and pro-inflammatory. Furthermore, we showed a positive causality between genetic predisposition towards ITGB1 expression and AMI risk, positioning it as a causative gene. Finally, we showed that ginsenoside Rh1, which interacts closely with ITGB1, could represent a novel therapeutic approach for repressing ITGB1.

CONCLUSIONS

Our MS has implications in forecasting and curving AMI to inform future diagnostic and therapeutic strategies for AMI.

PTGES2 and RNASET2 identified as novel potential biomarkers and therapeutic targets for basal cell carcinoma: insights from proteome-wide mendelian randomization, colocalization, and MR-PheWAS analyses

Introduction

Basal cell carcinoma (BCC) is the most common skin cancer, lacking reliable biomarkers or therapeutic targets for effective treatment. Genome-wide association studies (GWAS) can aid in identifying drug targets, repurposing existing drugs, predicting clinical trial side effects, and reclassifying patients in clinical utility. Hence, the present study investigates the association between plasma proteins and skin cancer to identify effective biomarkers and therapeutic targets for BCC.

Methods

Proteome-wide mendelian randomization was performed using inverse-variance-weight and Wald Ratio methods, leveraging 1 Mb cis protein quantitative trait loci (cis-pQTLs) in the UK Biobank Pharma Proteomics Project (UKB-PPP) and the deCODE Health Study, to determine the causal relationship between plasma proteins and skin cancer and its subtypes in the FinnGen R10 study and the SAIGE database of Lee lab. Significant association with skin cancer and its subtypes was defined as a false discovery rate (FDR) < 0.05. pQTL to GWAS colocalization analysis was executed using a Bayesian model to evaluate five exclusive hypotheses. Strong colocalization evidence was defined as a posterior probability for shared causal variants (PP.H4) of ≥0.85. Mendelian randomization-Phenome-wide association studies (MR-PheWAS) were used to evaluate potential biomarkers and therapeutic targets for skin cancer and its subtypes within a phenome-wide human disease category.

Results

PTGES2, RNASET2, SF3B4, STX8, ENO2, and HS3ST3B1 (besides RNASET2, five other plasma proteins were previously unknown in expression quantitative trait loci (eQTL) and methylation quantitative trait loci (mQTL)) were significantly associated with BCC after FDR correction in the UKB-PPP and deCODE studies. Reverse MR showed no association between BCC and these proteins. PTGES2 and RNASET2 exhibited strong evidence of colocalization with BCC based on a posterior probability PP.H4 >0.92. Furthermore, MR-PheWAS analysis showed that BCC was the most significant phenotype associated with PTGES2 and RNASET2 among 2,408 phenotypes in the FinnGen R10 study. Therefore, PTGES2 and RNASET2 are highlighted as effective biomarkers and therapeutic targets for BCC within the phenome-wide human disease category.

Conclusion

The study identifies PTGES2 and RNASET2 plasma proteins as novel, reliable biomarkers and therapeutic targets for BCC, suggesting more effective clinical application strategies for patients.

Leveraging a neutrophil-derived PCD signature to predict and stratify patients with acute myocardial infarction: from AI prediction to biological interpretation

BACKGROUND

Programmed cell death (PCD) has recently been implicated in modulating the removal of neutrophils recruited in acute myocardial infarction (AMI). Nonetheless, the clinical significance and biological mechanism of neutrophil-related PCD remain unexplored.

METHODS

We employed an integrative machine learning-based computational framework to generate a predictive neutrophil-derived PCD signature (NPCDS) within five independent microarray cohorts from the peripheral blood of AMI patients. Non-negative matrix factorization was leveraged to develop an NPCDS-based AMI subtype. To elucidate the biological mechanism underlying NPCDS, we implemented single-cell transcriptomics on Cd45+ cells isolated from the murine heart of experimental AMI. We finally conducted a Mendelian randomization (MR) study and molecular docking to investigate the therapeutic value of NPCDS on AMI.

RESULTS

We reported the robust and superior performance of NPCDS in AMI prediction, which contributed to an optimal combination of random forest and stepwise regression fitted on nine neutrophil-related PCD genes (MDM2, PTK2B, MYH9, IVNS1ABP, MAPK14, GNS, MYD88, TLR2, CFLAR). Two divergent NPCDS-based subtypes of AMI were revealed, in which subtype 1 was characterized as inflammation-activated with more vibrant neutrophil activities, whereas subtype 2 demonstrated the opposite. Mechanically, we unveiled the expression dynamics of NPCDS to regulate neutrophil transformation from a pro-inflammatory phase to an anti-inflammatory phase in AMI. We uncovered a significant causal association between genetic predisposition towards MDM2 expression and the risk of AMI. We also found that lidoflazine, isotetrandrine, and cepharanthine could stably target MDM2.

CONCLUSION

Altogether, NPCDS offers significant implications for prediction, stratification, and therapeutic management for AMI.

Identifying potential drug targets for varicose veins through integration of GWAS and eQTL summary data

Background

Varicose veins (VV) are a common chronic venous disease that is influenced by multiple factors. It affects the quality of life of patients and imposes a huge economic burden on the healthcare system. This study aimed to use integrated analysis methods, including Mendelian randomization analysis, to identify potential pathogenic genes and drug targets for VV treatment.

Methods

This study conducted Summary-data-based Mendelian Randomization (SMR) analysis and colocalization analysis on data collected from genome-wide association studies and cis-expression quantitative trait loci databases. Only genes with PP.H4 > 0.7 in colocalization were chosen from the significant SMR results. After the above analysis, we screened 12 genes and performed Mendelian Randomization (MR) analysis on them. After sensitivity analysis, we identified four genes with potential causal relationships with VV. Finally, we used transcriptome-wide association studies and The Drug-Gene Interaction Database data to identify and screen the remaining genes and identified four drug targets for the treatment of VV.

Results

We identified four genes significantly associated with VV, namely, KRTAP5-AS1 [Odds ratio (OR) = 1.08, 95% Confidence interval (CI): 1.05-1.11, p = 1.42e-10] and PLEKHA5 (OR = 1.13, 95% CI: 1.06-1.20, p = 6.90e-5), CBWD1 (OR = 1.05, 95% CI: 1.01-1.11, p = 1.42e-2) and CRIM1 (OR = 0.87, 95% CI: 0.81-0.95, p = 3.67e-3). Increased expression of three genes, namely, KRTAP5-AS1, PLEKHA5, and CBWD1, was associated with increased risk of the disease, and increased expression of CRIM1 was associated with decreased risk of the disease. These four genes could be targeted for VV therapy.

Conclusion

We identified four potential causal proteins for varicose veins with MR. A comprehensive analysis indicated that KRTAP5-AS1, PLEKHA5, CBWD1, and CRIM1 might be potential drug targets for varicose veins.

Unveiling potential drug targets for hyperparathyroidism through genetic insights via Mendelian randomization and colocalization analyses

Hyperparathyroidism (HPT) manifests as a complex condition with a substantial disease burden. While advances have been made in surgical interventions and non-surgical pharmacotherapy for the management of hyperparathyroidism, radical options to halt underlying disease progression remain lacking. Identifying putative genetic drivers and exploring novel drug targets that can impede HPT progression remain critical unmet needs. A Mendelian randomization (MR) analysis was performed to uncover putative therapeutic targets implicated in hyperparathyroidism pathology. Cis-expression quantitative trait loci (cis-eQTL) data serving as genetic instrumental variables were obtained from the eQTLGen Consortium and Genotype-Tissue Expression (GTEx) portal. Hyperparathyroidism summary statistics for single nucleotide polymorphism (SNP) associations were sourced from the FinnGen study (5590 cases; 361,988 controls). Colocalization analysis was performed to determine the probability of shared causal variants underlying SNP-hyperparathyroidism and SNP-eQTL links. Five drug targets (CMKLR1, FSTL1, IGSF11, PIK3C3 and SLC40A1) showed significant causation with hyperparathyroidism in both eQTLGen and GTEx cohorts by MR analysis. Specifically, phosphatidylinositol 3-kinase catalytic subunit type 3 (PIK3C3) and solute carrier family 40 member 1 (SLC40A1) showed strong evidence of colocalization with HPT. Multivariable MR and Phenome-Wide Association Study analyses indicated these two targets were not associated with other traits. Additionally, drug prediction analysis implies the potential of these two targets for future clinical applications. This study identifies PIK3C3 and SLC40A1 as potential genetically proxied druggable genes and promising therapeutic targets for hyperparathyroidism. Targeting PIK3C3 and SLC40A1 may offer effective novel pharmacotherapies for impeding hyperparathyroidism progression and reducing disease risk. These findings provide preliminary genetic insight into underlying drivers amenable to therapeutic manipulation, though further investigation is imperative to validate translational potential from preclinical models through clinical applications.

Insight into telomere regulation: road to discovery and intervention in plasma drug-protein targets

BACKGROUND

Telomere length is a critical metric linked to aging, health, and disease. Currently, the exploration of target proteins related to telomere length is usually limited to the context of aging and specific diseases, which limits the discovery of more relevant drug targets. This study integrated large-scale plasma cis-pQTLs data and telomere length GWAS datasets. We used Mendelian randomization(MR) to identify drug target proteins for telomere length, providing essential clues for future precision therapy and targeted drug development.

METHODS

Using plasma cis-pQTLs data from a previous GWAS study (3,606 Pqtls associated with 2,656 proteins) and a GWAS dataset of telomere length (sample size: 472,174; GWAS ID: ieu-b-4879) from UK Biobank, using MR, external validation, and reverse causality testing, we identified essential drug target proteins for telomere length. We also performed co-localization, Phenome-wide association studies and enrichment analysis, protein-protein interaction network construction, search for existing intervening drugs, and potential drug/compound prediction for these critical targets to strengthen and expand our findings.

RESULTS

After Bonferron correction (p < 0.05/734), RPN1 (OR: 0.96; 95%CI: (0.95, 0.97)), GDI2 (OR: 0.94; 95%CI: (0.92, 0.96)), NT5C (OR: 0.97; 95%CI: (0.95, 0.98)) had a significant negative causal association with telomere length; TYRO3 (OR: 1.11; 95%CI: (1.09, 1.15)) had a significant positive causal association with telomere length. GDI2 shared the same genetic variants with telomere length (coloc.abf-PPH 4 > 0.8).

CONCLUSION

Genetically determined plasma RPN1, GDI2, NT5C, and TYRO3 have significant causal effects on telomere length and can potentially be drug targets. Further exploration of the role and mechanism of these proteins/genes in regulating telomere length is needed.