TRUSST is reference database of human transcriptional regulatory interactions.TRRUST v2 is manually curated expanded reference database of human and mouse transcriptional regulatory interactions.

Key TFs that regulate hub genes predicted by TRRUST database.

BackgroundGout is a type of chronic inflammatory disease linked to the accumulation of monosodium urate crystals, leading to arthritis. Studies have shown that patients with gout are more likely to develop atherosclerosis, but the specific mechanisms involved remain unknown. The purpose of the research was to explore the key molecules and potential mechanisms between gout and atherosclerosis.MethodsGene expression profiles for gout as well as atherosclerosis were obtained from the Gene Expression Omnibus (GEO) database, then differential analysis was utilized to identify common differentially expressed genes (DEGs) between the two diseases. The analysis of functional enrichment was conducted to investigate the biological processes that the DEGs might be involved in. The Cytoscape software was utilized to develop a protein–protein interaction (PPI) network as well as identify hub genes, while LASSO analysis was employed to select key genes. The TRRUST database was utilized to forecast transcription factors (TFs), and the miRTarBase database was utilized to forecast miRNAs.ResultsFour key genes, CCL3, TNF, CCR2, and CCR5, were identified. The receiver operating characteristic (ROC) curves showed that the areas under ROC curve (AUC) for these four key genes in both gout and atherosclerosis were greater than 0.9. The analysis of functional enrichment revealed that the DEGs were primarily involved in “regulation of T-cell activation”, “chemokine signaling pathway”, and other biological processes. The TRRUST prediction results indicated that RELA and NFKB1 are common regulatory transcription factors for CCR2, CCR5, CCL3, and TNF. The miRTarBase prediction results showed that hsa-miR-203a-3p is a common regulatory miRNA for TNF and CCR5.ConclusionThis study preliminarily explored the potential key molecules and mechanisms between gout and atherosclerosis. These findings provide new insights for further research into identifying potential biomarkers and clinical treatment strategies for these two diseases.

Recent evidence suggests that individuals with polycystic ovary syndrome (PCOS) have an increased risk of developing mental health disorders and comorbidities linked to nervous system dysfunction. Interestingly, patients with schizophrenia (SCZ) often exhibit PCOS symptoms, indicating a possible connection between the two conditions. However, the underlying molecular links between these diseases remain poorly understood. We employed a comprehensive in-silico approach, utilizing publicly available datasets to investigate shared biomarkers candidates and key regulators involved in the development of PCOS and SCZ. We retrieved the datasets from the NCBI GEO database and differentially expressed genes (DEGs) were identified for each dataset. Common DEGs (cDEGs) were determined, and transcription factors (TFs) and miRNA targeting cDEGs were examined using the mirDIP portal and TRRUST database, respectively. We also assessed the TF-miRNA interactions by TransmiR database and constructed a regulatory network including TFs-microRNAs-cDEGs. Our analysis identified a total of 15 cDEGs that are regulated by 15 TFs and 8 mRNAs. Among our findings, we prioritized RELA as a potential TF regulator for both diseases, demonstrating synergistic interaction with four cDEGs (EGR1, CXCL8, IL1RN, IL1B) and seven microRNAs (hsa-miR-580, hsa-miR-5695, hsa-miR-936, hsa-miR-3675, hsa-miR-634, hsa-miR-603, hsa-miR-222) that target these genes. Our data highlights potential common biomarkers for PCOS and SCZ, presenting a novel regulatory network that elucidates the molecular mechanisms underlying both conditions. This emphasizes the importance of further research to explore new translational approaches, which may ultimately lead to improved diagnostic and therapeutic strategies for affected individuals.

The Global Subnational Trust Database (SUB-TRUST) is a dataset in which subnational estimates of social (interpersonal) and political (institutional) trust from 153 countries across the world have been created and harmonized without interpolation. The data are based on nationally representative surveys from over 22 cross-national and national surveys spanning the years 1980 to 2024 (N > 6 million respondents). SUB-TRUST will mainly be useful for researchers and practitioners seeking to understand the variation in trust across subnational entities, countries, institutions, and over time, and the consequences of this variation.

BackgroundOsteoporosis is one of the most common systemic metabolic bone diseases, especially in postmenopausal women. Circular RNA (circRNA) has been implicated in various human diseases. However, the potential role of circRNAs in postmenopausal osteoporosis (PMOP) remains largely unknown. The study aims to identify potential biomarkers and further understand the mechanism of PMOP by constructing a circRNA-associated ceRNA network.MethodsThe PMOP-related datasets GSE161361, GSE64433, and GSE56116 were downloaded from the Gene Expression Omnibus (GEO) database and were used to obtain differentially expressed genes (DEGs). Gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were applied to determine possible relevant functions of differentially expressed messenger RNAs (mRNAs). The TRRUST database was used to predict differential transcription factor (TF)-mRNA regulatory pairs. Afterwards, combined CircBank and miRTarBase, circRNA-miRNA as well as miRNA-TF pairs were constructed. Then, a circRNA-miRNA-TF-mRNA network was established. Next, the correlation of mRNAs, TFs, and PMOP was verified by the Comparative Toxicogenomics Database. And expression levels of key genes, including circRNAs, miRNAs, TFs, and mRNAs in the ceRNA network were further validated by quantitative real-time PCR (qRT-PCR). Furthermore, to screen out signaling pathways related to key mRNAs of the ceRNA network, Gene Set Enrichment Analysis (GSEA) was performed.ResultsA total of 1201 DE mRNAs, 44 DE miRNAs, and 1613 DE circRNAs associated with PMOP were obtained. GO function annotation showed DE mRNAs were mainly related to inflammatory responses. KEGG analysis revealed DE mRNAs were mainly enriched in osteoclast differentiation, rheumatoid arthritis, hematopoietic cell lineage, and cytokine-cytokine receptor interaction pathways. We first identified 26 TFs and their target mRNAs. Combining DE miRNAs, miRNA-TF/mRNA pairs were obtained. Combining DE circRNAs, we constructed the ceRNA network contained 6 circRNAs, 4 miRNAs, 4 TFs, and 12 mRNAs. The expression levels of most genes detected by qRT-PCR were generally consistent with the microarray results. Combined with the qRT-PCR validation results, we eventually identified the ceRNA network that contained 4 circRNAs, 3 miRNAs, 3 TFs, and 9 mRNAs. The GSEA revealed that 9 mRNAs participate in many important signaling pathways, such as “olfactory transduction”, “T cell receptor signaling pathway”, and “neuroactive ligand-receptor interaction”. These pathways have been reported to the occurrence and development of PMOP. To sum up, key mRNAs in the ceRNA network may participate in the development of osteoporosis by regulating related signal pathways.ConclusionsA circRNA-associated ceRNA network containing TFs was established for PMOP. The study may help further explore the molecular mechanisms and may serve as potential biomarkers or therapeutic targets for PMOP.

IntroductionType 2 diabetes mellitus (T2DM) is a major cause of atherosclerosis (AS). However, definitive evidence regarding the common molecular mechanisms underlying these two diseases are lacking. This study aimed to investigate the mechanisms underlying the association between T2DM and AS.MethodsThe gene expression profiles of T2DM (GSE159984) and AS (GSE100927) were obtained from the Gene Expression Omnibus, after which overlapping differentially expressed gene identification, bioinformatics enrichment analyses, protein–protein interaction network construction, and core genes identification were performed. We confirmed the discriminatory capacity of core genes using receiver operating curve analysis. We further identified transcription factors using TRRUST database to build a transcription factor–mRNA regulatory network. Finally, the immune infiltration and the correlation between core genes and differential infiltrating immune cells were analyzed.ResultsA total of 27 overlapping differentially expressed genes were identified under the two-stress conditions. Functional analyses revealed that immune responses and transcriptional regulation may be involved in the potential pathogenesis. After protein–protein interaction network deconstruction, external datasets, and qRT-PCR experimental validation, four core genes (IL1B, C1QA, CCR5, and MSR1) were identified. ROC analysis further showed the reliable value of these core genes. Four common differential infiltrating immune cells (B cells, CD4+ T cells, regulatory T cells, and M2 macrophages) between T2DM and AS datasets were selected based on immune cell infiltration. A significant correlation between core genes and common differential immune cells. Additionally, five transcription factors (RELA, NFκB1, JUN, YY1, and SPI1) regulating the transcription of core genes were mined using upstream gene regulator analysis.DiscussionIn this study, common target genes and co-immune infiltration landscapes were identified between T2DM and AS. The relationship among five transcription factors, four core genes, and four immune cells profiles may be crucial to understanding T2DM complicated with AS pathogenesis and therapeutic direction.

The Office of the District of Columbia Auditor (ODCA) created this database based on documentation provided by the Department of Housing and Community Development (DHCD) and the Office of the Chief Financial Officer (OCFO), as well as ODCA analysis of HPTF expenditures. The database provides detail on all projects that received HPTF funds with loan and/or grant agreements that referenced HPTF funds. The database does not indicate whether any units were actually produced or maintained as affordable. Our data does not reflect whether projects participate in other local and federal housing programs that may impact the number and type of reserved units (i.e. the Local Rent Supplement Program).

Toxicants with the potential to bioaccumulate in humans and animals have long been a cause for concern, particularly due to their association with multiple diseases and organ injuries. Per- and polyfluoro alkyl substances (PFAS) and polycyclic aromatic hydrocarbons (PAH) are two such classes of chemicals that bioaccumulate and have been associated with steatosis in the liver. Although PFAS and PAH are classified as chemicals of concern, their molecular mechanisms of toxicity remain to be explored in detail. In this study, we aimed to identify potential mechanisms by which an acute exposure to PFAS and PAH chemicals can induce lipid accumulation and whether the responses depend on chemical class, dose, and sex. To this end, we analyzed mechanisms beginning with the binding of the chemical to a molecular initiating event (MIE) and the consequent transcriptomic alterations. We collated potential MIEs using predictions from our previously developed ToxProfiler tool and from published steatosis adverse outcome pathways. Most of the MIEs are transcription factors, and we collected their target genes by mining the TRRUST database. To analyze the effects of PFAS and PAH on the steatosis mechanisms, we performed a computational MIE-target gene analysis on high-throughput transcriptomic measurements of liver tissue from male and female rats exposed to either a PFAS or PAH. The results showed peroxisome proliferator-activated receptor (PPAR)-α targets to be the most dysregulated, with most of the genes being upregulated. Furthermore, PFAS exposure disrupted several lipid metabolism genes, including upregulation of fatty acid oxidation genes (Acadm, Acox1, Cpt2, Cyp4a1-3) and downregulation of lipid transport genes (Apoa1, Apoa5, Pltp). We also identified multiple genes with sex-specific behavior. Notably, the rate-limiting genes of gluconeogenesis (Pck1) and bile acid synthesis (Cyp7a1) were specifically downregulated in male rats compared to female rats, while the rate-limiting gene of lipid synthesis (Scd) showed a PFAS-specific upregulation. The results suggest that the PPAR signaling pathway plays a major role in PFAS-induced lipid accumulation in rats. Together, these results show that PFAS exposure induces a sex-specific multi-factorial mechanism involving rate-limiting genes of gluconeogenesis and bile acid synthesis that could lead to activation of an adverse outcome pathway for steatosis.