Transcriptome Study of 2 Black Cohorts Reveals cis Long Noncoding RNAs Associated With Hypertension-Related mRNAs.

BACKGROUND: Long noncoding RNAs (lncRNAs) have emerged as critical regulators of the expression of genes involved in cardiovascular diseases. This project aims to identify circulating lncRNAs associated with protein-coding mRNAs differentially expressed between hypertensive and normotensive individuals and establish their link with hypertension. METHODS AND RESULTS: The analyses were conducted in 3 main steps: (1) an unbiased whole blood transcriptome-wide analysis was conducted to identify and replicate protein-coding genes differentially expressed by hypertension status in 497 and 179 Black individuals from the GENE-FORECAST (Genomics, Environmental Factors and the Social Determinants of Cardiovascular Disease in African-Americans Study) and MH-GRID (Minority Health Genomics and Translational Research Bio-Repository Database) studies, respectively. Subsequently, (2) proximal lncRNAs, termed cis lncRNA quantitative trait loci, associated with each mRNA were identified in the GENE-FORECAST study and replicated in the MH-GRID study. Finally, (3) the lncRNA quantitative trait loci were used as predictors in a random forest model to predict hypertension in both data sets. A total of 129 mRNAs were significantly differentially expressed between normotensive and hypertensive individuals in both data sets. The lncRNA-mRNA association analysis revealed 249 cis lncRNA quantitative trait loci associated with 102 mRNAs, including VAMP2 (vesicle-associated membrane protein 2), mitogen-activated protein kinase kinase 3, CCAAT enhancer binding protein beta, and lymphocyte antigen 6 complex, locus E. The 249 lncRNA quantitative trait loci predicted hypertension with an area under the curve of 0.79 and 0.71 in GENE-FORECAST and MH-GRID studies, respectively. CONCLUSIONS: This study leveraged a significant sample of Black individuals, a population facing a disproportionate burden of hypertension. The analyses unveiled a total of 271 lncRNA-mRNA relationships involving mRNAs that play critical roles in vascular pathways relevant to blood pressure regulation. The compelling findings, consistent across 2 independent data sets, establish a reliable foundation for designing in vitro/in vivo experiments.

Leveraging the transcriptome to further our understanding of GWAS findings: eQTLs associated with genes related to LDL and LDL subclasses, in a cohort of African Americans.

Background: GWAS discoveries often pose a significant challenge in terms of understanding their underlying mechanisms. Further research, such as an integration with expression quantitative trait locus (eQTL) analyses, are required to decipher the mechanisms connecting GWAS variants to phenotypes. An eQTL analysis was conducted on genes associated with low-density lipoprotein (LDL) cholesterol and its subclasses, with the aim of pinpointing genetic variants previously implicated in GWAS studies focused on lipid-related traits. Notably, the study cohort consisted of African Americans, a population characterized by a heightened prevalence of hypercholesterolemia. Methods: A comprehensive differential expression (DE) analysis was undertaken, with a dataset of 17,948 protein-coding mRNA transcripts extracted from the whole-blood transcriptomes of 416 samples to identify mRNA transcripts associated with LDL, with further granularity delineated between small LDL and large LDL subclasses. Subsequently, eQTL analysis was conducted with a subset of 242 samples for which whole-genome sequencing data were available to identify single-nucleotide polymorphisms (SNPs) associated with the LDL-related mRNA transcripts. Lastly, plausible functional connections were established between the identified eQTLs and genetic variants reported in the GWAS catalogue. Results: DE analysis revealed 1,048, 284, and 94 mRNA transcripts that exhibited differential expression in response to LDL, small LDL, and large LDL, respectively. The eQTL analysis identified a total of 9,950 significant SNP-mRNA associations involving 6,955 SNPs including a subset 101 SNPs previously documented in GWAS of LDL and LDL-related traits. Conclusion: Through comprehensive differential expression analysis, we identified numerous mRNA transcripts responsive to LDL, small LDL, and large LDL. Subsequent eQTL analysis revealed a rich landscape of eQTL-mRNA associations, including a subset of eQTL reported in GWAS studies of LDL and related traits. The study serves as a testament to the important role of integrative genomics in unraveling the enigmatic GWAS relationships between genetic variants and the complex fabric of human traits and diseases.

Microbial and metabolic succession on common building materials under high humidity conditions.

Despite considerable efforts to characterize the microbial ecology of the built environment, the metabolic mechanisms underpinning microbial colonization and successional dynamics remain unclear, particularly at high moisture conditions. Here, we applied bacterial/viral particle counting, qPCR, amplicon sequencing of the genes encoding 16S and ITS rRNA, and metabolomics to longitudinally characterize the ecological dynamics of four common building materials maintained at high humidity. We varied the natural inoculum provided to each material and wet half of the samples to simulate a potable water leak. Wetted materials had higher growth rates and lower alpha diversity compared to non-wetted materials, and wetting described the majority of the variance in bacterial, fungal, and metabolite structure. Inoculation location was weakly associated with bacterial and fungal beta diversity. Material type influenced bacterial and viral particle abundance and bacterial and metabolic (but not fungal) diversity. Metabolites indicative of microbial activity were identified, and they too differed by material.