GNLY Gene Polymorphism: A Potential Role in Understanding Psoriasis Pathogenesis

GNLY as A Novel Cis-eQTL and Cis-pQTL Mediated Susceptibility Gene in Suppressing Prostatitis. Mendelian Randomization Study

BACKGROUND

Prostatitis is characterized by high prevalence, low cure rates, and frequent recurrences, and remains one of the most clinically challenging problems. Hence, in this article, we first integrated Mendelian randomization (MR) with expression quantitative trait loci (eQTL) and protein quantitative trait loci (pQTL) data to identify novel therapeutic targets and their potential metabolic mechanisms for prostatitis.

METHODS

Prostatitis-related genetic data, eQTLs, pQTLs, and 1400 metabolites were downloaded from online databases. MR, or summary data-based MR (SMR) analyses were applied to assess the potential causal relationships between exposures and predicted outcomes. Sensitivity analysis was conducted using pleiotropy, heterogeneity, and leave-one-out analysis to evaluate the robustness of our results.

RESULTS

Based on our results, we first identified and validated GNLY as a novel cis-eQTL and cis-pQTL-mediated susceptibility gene for reducing prostatitis risk in five independent datasets (one discovery dataset and four validation datasets) (all p <0.05). Meanwhile, we also found that the GNLY eQTL could increase the metabolite of sphingomyelin level (d18:0/20:0, d16:0/22:0) risks (p <0.05), and the metabolite of sphingomyelin level (d18:0/20:0, d16:0/22:0) could reduce the risk of prostatitis (p <0.05). According to the above-mentioned relationships, we finally revealed the potential metabolic mechanism of GNLY eQTL in suppressing prostatitis via regulating the metabolite of sphingomyelin level (d18:0/20:0, d16:0/22:0).

CONCLUSIONS

We successfully identified GNLY as a novel cis-eQTL and cis-pQTL-mediated susceptibility gene in suppressing prostatitis and its potential metabolic mechanism via regulating sphingomyelin (d18:0/20:0, d16:0/22:0) levels, providing a novel therapeutic target and paving the way for future GNLY-related studies in prostatitis.

Combining Single-Cell RNA Sequencing and Mendelian Randomization to Explore Novel Drug Targets for Parkinson's Disease

Neuroinflammation is a key pathological factor of PD, and T cells play a central role in neuroinflammatory progression. However, the causal effect of T cell-related genes on the risk of PD is still unclear. We explored single-cell RNA sequencing (scRNA-Seq) datasets of the peripheral blood T cells of PD patients and healthy controls, and screened the differentially expressed genes (DEGs) in the cytotoxic CD4 + T cells relative to the other T cell subsets. Pseudo-time series analysis, cell-cell communication analysis, and metabolic pathway analysis was performed for the cytotoxic CD4 + T cells. The DEGs were also functionally annotated through GO and KEGG pathway enrichment analyses. The MR approach was used to establish causal effects of the DEGs (exposure) on PD risk (outcome), and explore new drug targets for PD. The findings of MR analysis were further validated by Steiger filtering, bidirectional MR, Bayesian colocalization analysis, and phenotype scanning, and the GWAS data from an independent PD case-control cohort was used for external validation of the results. Finally, differences in gene expression between PD patients and healthy controls were further validated in scRNA-Seq and bulk transcriptome sequencing data. We found that increased expression of IL-32, GNLY, MT2A, and ARPC2 was significantly associated with a higher risk of PD. In contrast, the increase in ARRB2 was closely related to a lower risk of PD. IL32, GNLY, MT2A, ARRB2, and ARPC2 are the causal genes and potential drug targets of PD. Cytotoxic CD4 + T cells are likely the key effectors of PD-related neuroinflammation. These findings provide new insights into the pathogenesis and treatment options for PD, and further research and clinical trials based on the five potential drug targets and neuroinflammation are necessary.

Potential Functions and Causal Associations of GNLY in Primary Open-Angle Glaucoma: Integration of Blood-Derived Proteome, Transcriptome, and Experimental Verification

Purpose

Genome-wide association studies (GWAS) have identified multiple genetic loci associated with primary open-angle glaucoma (POAG). However, the mechanisms by which these loci contribute to POAG progression remain unclear. This study aimed to identify potential causative genes involved in the development of POAG.

Methods

We utilized multi-dimensional high-throughput data, integrating proteome-wide association study(PWAS), transcriptome-wide association study (TWAS), and summary data-based Mendelian randomization (SMR) analysis. This approach enabled the identification of genes influencing POAG risk by affecting gene expression and protein concentrations in the bloodstream. The key gene was validated through enzyme-linked immunosorbent assay (ELISA) analysis.

Results

PWAS identified 86 genes associated with altered blood protein levels in POAG patients. Of these, eight genes (SFTPD, CSK, COL18A1, TCN2, GZMK, RAB2A, TEK, and GNLY) were identified as likely causative for POAG (P SMR < 0.05). TWAS revealed that GNLY was significantly associated with POAG at the gene expression level. GNLY-interacting genes were found to play roles in immune dysregulation, inflammation, and apoptosis. Clinical and cell-based validation confirmed reduced GNLY expression in POAG groups.

Conclusion

This study reveals GNLY as a significant potential therapeutic target for managing primary open-angle glaucoma.

Multi-omic analysis revealed the immunological patterns and diagnostic value of exhausted T cell-derived PTTG1 in patients with psoriasis

BACKGROUND

Psoriasis, characterized by chronic inflammation, is a persistent skin condition that is notoriously challenging to manage and prone to relapse. Despite significant advancements in its treatment, many adverse reactions still occur. Therefore, exploring the mechanisms behind the occurrence and development of psoriasis is extremely important.

METHODS

The weighted correlation network analysis (WGCNA) algorithm was used to identify phenotype-related genes in patients with psoriasis. We recruited clinical samples of patients with psoriasis, and used single-cell RNA sequencing (scRNA-seq) to visualize divergent genes and metabolisms of varied cells for the psoriasis. Various machine-learning methods were used to identify core genes, and molecular docking was used to analyze the stability of leptomycin B targeting pituitary tumor transforming 1 (PTTG1). Immunofluorescence (IHC) analysis, multiplex immunofluorescence (mIF) analysis, and quantitative reverse transcription polymerase chain reaction (qRT-PCR) were used to validate the results.

RESULTS

Our results identified 1391 genes associated with the phenotype in patients with psoriasis and highlighted the significant alterations in T-cell functionality observed in the disease by WGCNA. There were nine distinct cellular clusters in psoriasis analyzed with the aid of scRNA-seq data. Each subtype of cell exhibited distinct genetic profiles, functional roles, signaling mechanisms, and metabolic characteristics. Machine-learning methods further demonstrated the potential diagnostic value of T cell-derived PTTG1 and its relationship with T-cell exhaustion in psoriasis. Lastly, the leptomycin B was scrutinized and verified had high stability targeting PTTG1.

CONCLUSIONS

This study elucidates the biological basis of psoriasis. At the same time, it was discovered that PTTG1 derived from exhausted T cells serves as a diagnostic biomarker for psoriasis. Leptomycin B could be a potential drug for targeted treatment of psoriasis on PTTG1.

A study of granulysin and pentraxin 3 genetic polymorphisms and their contribution to acne susceptibility

This study aims to examine the genetic polymorphisms of the granulysin (GNLY) and pentraxin 3 (PTX3) genes and their association with acne in Egypt. Acne vulgaris is classified as a disorder of the pilosebaceous unit. Clinical, histological, and immunological findings indicate that inflammation is involved in every stage of acne development. GNLY and PTX3 are both involved in the body's immune system and may play a role in the pathophysiology of acne. This case-control study included 180 participants who have acne and 180 healthy controls. Real-time PCR was used to genotype GNLY rs7908 and PTX3 rs2305619 polymorphisms. Genotype occurrence and allelic spreading for both single nucleotide polymorphisms (SNP) are in Hardy-Weinberg equilibrium. Regarding rs7908, no statistical difference was observed in the genotype and allele distributions between acne patients and controls. On the other hand, rs2305619 showed a statistical difference in the genotype and allele distributions between acne patients and controls, with a marked prevalence of the GG group and G allele in acne patients. Our study revealed a significant link between the PTX3 rs2305619 and acne susceptibility in Egypt, with the AG + GG genotype strongly predicting acne. In contrast, the GNYL rs7908 polymorphism was not associated with acne. These results highlight a genetic component to acne and suggest that PTX3 rs2305619 could be a key marker for understanding acne susceptibility.

Screening of four lysosome-related genes in sepsis based on RNA sequencing technology

PURPOSE

Screening of lysosome-related genes in sepsis patients to provide direction for lysosome-targeted therapy.

METHODS

Peripheral blood samples were obtained from 22 patients diagnosed with sepsis and 10 normal controls for the purpose of RNA sequencing and subsequent analysis of differential gene expression. Concurrently, lysosome-related genes were acquired from the Gene Ontology database. The intersecting genes between the differential genes and lysosome-related genes were then subjected to PPI, GO and KEGG analyses. Core genes were identified through survival analysis, and their expression trends in different groups were determined using meta-analysis. Single-cell RNA sequencing was used to clarify the cellular localization of core genes.

RESULTS

The intersection of 1328 sepsis-differential genes with 878 lysosome-related genes yielded 76 genes. PPI analysis showed that intersecting genes were mainly involved in Cellular process, Response to stimulus, Immune system process, Signal transduction, Lysosome. GO and KEGG analysis showed that intersecting genes were mainly involved in leukocyte mediated immunity, cell activation involved in immune response, lytic vacuole, lysosome. Survival analysis screened four genes positively correlated with sepsis prognosis, namely GNLY, GZMB, PRF1 and RASGRP1. The meta-analysis revealed that the expression levels of these four genes were significantly higher in the normal control group compared to the sepsis group, which aligns with the findings from RNA sequencing data. Furthermore, single-cell RNA sequencing demonstrated that T cells and NK cells exhibited high expression levels of GNLY, GZMB, PRF1, and RASGRP1.

CONCLUSION

GNLY, GZMB, PRF1, and RASGRP1, which are lysosome-related genes, are closely linked to the prognosis of sepsis and could potentially serve as novel research targets for sepsis, offering valuable insights for the development of lysosome-targeted therapy. The clinical trial registration number is ChiCTR1900021261, and the registration date is February 4, 2019.

Shared biomarkers and immune cell infiltration signatures in ulcerative colitis and nonalcoholic steatohepatitis

The coexistence of ulcerative colitis (UC) and nonalcoholic steatohepatitis (NASH) involves a intricate interplay, though the precise pathophysiological mechanisms remain elusive. To shed light on this, our study endeavors to unravel the shared gene signatures and molecular mechanisms by employing quantitative bioinformatics analysis on a publicly available RNA-sequencing database. Gene expression profiles of UC (GSE87466) and NASH (GSE89632) were retrieved from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were analyzed using R software. After identifying common DEGs, functional enrichment analysis, protein-protein interaction (PPI) network analysis and module construction were performed to obtain candidate hub genes. GSE47908 for UC and GSE159676 for NASH were selected to validate the obtained candidate genes. A total of 119 common DEGs were found in NASH and UC patients. Functional and pathway analyses emphasized that viral infection, inflammation and immune response were enriched in these two diseases. After module construction and validation, CD2, CD8A, GNLY, IFI44, NKG7 and OAS2 were identified as hub genes. 6 hub genes and their combined prediction scores were found with an impressive accuracy and sensitivity. Functional estimation, gene set enrichment analysis and immune infiltration signature identification showed notable associations of the six hub genes with T cells, natural killer cells and type I interferon levels. In addition, we constructed UC combined with NASH mice model successfully with significantly higher expression of hub genes in both liver and colonic tissues than those in control group. Our study elucidates 6 hub genes of UC and NASH, which may participate in immune, inflammatory and antiviral effects. These findings provide some potential biochemical markers for further exploration of UC coexistence with NASH.

Screening of Sepsis Biomarkers Based on Bioinformatics Data Analysis

Methods

Gene expression profiles of GSE13904, GSE26378, GSE26440, GSE65682, and GSE69528 were obtained from the National Center for Biotechnology Information (NCBI). The differentially expressed genes (DEGs) were searched using limma software package. Gene Ontology (GO) functional analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and protein-protein interaction (PPI) network analysis were performed to elucidate molecular mechanisms of DEGs and screen hub genes.

Results

A total of 108 DEGs were identified in the study, of which 67 were upregulated and 41 were downregulated. 15 superlative diagnostic biomarkers (CCL5, CCR7, CD2, CD27, CD274, CD3D, GNLY, GZMA, GZMH, GZMK, IL2RB, IL7R, ITK, KLRB1, and PRF1) for sepsis were identified by bioinformatics analysis.

Conclusion

15 hub genes (CCL5, CCR7, CD2, CD27, CD274, CD3D, GNLY, GZMA, GZMH, GZMK, IL2RB, IL7R, ITK, KLRB1, and PRF1) have been elucidated in this study, and these biomarkers may be helpful in the diagnosis and therapy of patients with sepsis.