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Type 1 diabetes (T1D) is an autoimmune disease caused by the destruction of insulin-producing pancreatic islet beta cells. Despite significant advancements, the precise pathogenesis of the disease remains unknown. This work integrated data from expression quantitative trait locus (eQTL) studies with Genome wide association study (GWAS) summary data of T1D and single-cell transcriptome data to investigate the potential pathogenic mechanisms of the CTSH gene involved in T1D in exocrine pancreas. Using the summary data–based Mendelian randomization (SMR) approach, we obtained four potential causative genes associated with T1D: BTN3A2 , PGAP3 , SMARCE1 and CTSH. To further investigate these genes'roles in T1D development, we validated them using a scRNA-seq dataset from pancreatic tissues of both T1D patients and healthy controls. The analysis showed a significantly high expression of the CTSH gene in T1D acinar cells, whereas the other three genes showed no significant changes in the scRNA-seq data. Moreover, single-cell WGCNA analysis revealed the strongest positive correlation between the module containing CTSH and T1D. In addition, we found cellular ligand–receptor interactions between the acinar cells and different cell types, especially ductal cells. Finally, based on functional enrichment analysis, we hypothesized that the CTSH gene in the exocrine pancreas enhances the antiviral response, leading to the overexpression of pro-inflammatory cytokines and the development of an inflammatory microenvironment. This process promotes β cells injury and ultimately the development of T1D. Our findings offer insights into the underlying pathogenic mechanisms of T1D. [ABSTRACT FROM AUTHOR]

Acute myeloid leukemia (AML) is a cancer of the myeloid line of blood cells, and generally considered to be caused by environment and genetic factors. In this study, we combined a genome-wide haplotype association study (GWHAS) and gene prioritization strategy to mine AML-related genetic affect factors and understand its pathogenesis. A total of 175 AML patients were downloaded from the public GEO database (GSE32462) and 218 matched Caucasian controls were from the HapMap Project. We first identified the linkage disequilibrium (LD) blocks and performed a GWHAS to scan AML-related haplotypes. Then we mapped these haplotypes to the corresponding genes as candidate. And finally, we prioritized all the AML candidate genes based on the similarity with 38 known AML susceptibility genes. The results showed that 1754 haplotypes were significant associated with AML (P<1E-5) and mapped to 591 candidate genes. After prioritizing all 591 AML candidate genes, we obtained four genes ranking at the front as AML risk genes: RUNX1, JAK1, PDGFRA, and FGFR2. Among them, RUNX1, JAK1 and PDGFRA had been confirmed as AML risk genes. In particular, we found that the gene FGFR2 was a novel AML susceptibility gene with a haplotype TT (rs7090018 and rs2912759) showed significant association with AML (P-value = 7.07E-06). In a word, our findings might provide a new perspective to understand the pathogenesis of AML.

Although evidence indicates that drug target genes share some common evolutionary features, there have been few studies analyzing evolutionary features of drug targets from an overall level. Therefore, we conducted an analysis which aimed to investigate the evolutionary characteristics of drug target genes. We compared the evolutionary conservation between human drug target genes and non-target genes by combining both the evolutionary features and network topological properties in human protein-protein interaction network. The evolution rate, conservation score and the percentage of orthologous genes of 21 species were included in our study. Meanwhile, four topological features including the average shortest path length, betweenness centrality, clustering coefficient and degree were considered for comparison analysis. Then we got four results as following: compared with non-drug target genes, 1) drug target genes had lower evolutionary rates; 2) drug target genes had higher conservation scores; 3) drug target genes had higher percentages of orthologous genes and 4) drug target genes had a tighter network structure including higher degrees, betweenness centrality, clustering coefficients and lower average shortest path lengths. These results demonstrate that drug target genes are more evolutionarily conserved than non-drug target genes. We hope that our study will provide valuable information for other researchers who are interested in evolutionary conservation of drug targets.

Rheumatoid arthritis (RA) is an autoimmune disease that has a complex genetic basis. Therefore, it is important to explore the genetic background of RA. The extensive recent application of polymorphic genetic markers, especially single nucleotide polymorphisms, has presented us with a large quantity of genetic data. In this study, we developed the Database of Rheumatoid Arthritis-related Polymorphisms (RADB), to integrate all the RA-related genetic polymorphisms and provide a useful resource for researchers. We manually extracted the RA-related polymorphisms from 686 published reports, including RA susceptibility loci, polymorphisms associated with particular clinical features of RA, polymorphisms associated with drug response in RA and polymorphisms associated with a higher risk of cardiovascular disease in RA. Currently, RADB V1.0 contains 3235 polymorphisms that are associated with 636 genes and refer to 68 countries. The detailed information extracted from the literature includes basic information about the articles (e.g., PubMed ID, title and abstract), population information (e.g., country, geographic area and sample size) and polymorphism information (e.g., polymorphism name, gene, genotype, odds ratio and 95% confidence interval, P-value and risk allele). Meanwhile, useful annotations, such as hyperlinks to dbSNP, GenBank, UCSC, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway, are included. In addition, a tool for meta-analysis was developed to summarize the results of multiple studies. The database is freely available at http://www.bioapp.org/RADB. Database URL: http://www.bioapp.org/RADB., (© The Author(s) 2014. Published by Oxford University Press.)