Genetic Architecture of Gene Expression Traits Across Diverse Populations

For many complex traits, gene regulation is likely to play a crucial mechanistic role. How the genetic architectures of complex traits vary between populations and subsequent effects on genetic prediction are not well understood, in part due to the historical paucity of GWAS in populations of non-European ancestry. We used data from the MESA (Multi-Ethnic Study of Atherosclerosis) cohort to characterize the genetic architecture of gene expression within and between diverse populations. Genotype and monocyte gene expression were available in individuals with African American (AFA, n = 233), Hispanic (HIS, n = 352), and European (CAU, n = 578) ancestry. We performed expression quantitative trait loci (eQTL) mapping in each population and show genetic correlation of gene expression depends on shared ancestry proportions. Using elastic net modeling with cross validation to optimize genotypic predictors of gene expression in each population, we show the genetic architecture of gene expression for most predictable genes is sparse. We found the best predicted gene in each population, TACSTD2 in AFA and CHURC1 in CAU and HIS, had similar prediction performance across populations with R2 \u3e 0.8 in each population. However, we identified a subset of genes that are well-predicted in one population, but poorly predicted in another. We show these differences in predictive performance are due to allele frequency differences between populations. Using genotype weights trained in MESA to predict gene expression in independent populations showed that a training set with ancestry similar to the test set is better at predicting gene expression in test populations, demonstrating an urgent need for diverse population sampling in genomics. Our predictive models and performance statistics in diverse cohorts are made publicly available for use in transcriptome mapping methods at https://github.com/WheelerLab/DivPop

Modulation of Host Learning in Aedes aegypti Mosquitoes

How mosquitoes determine which individuals to bite has important epidemiological consequences. This choice is not random; most mosquitoes specialize in one or a few vertebrate host species, and some individuals in a host population are preferred over others. Mosquitoes will also blood feed from other hosts when their preferred is no longer abundant, but the mechanisms mediating these shifts between hosts, and preferences for certain individuals within a host species, remain unclear. Here, we show that olfactory learning may contribute to Aedes aegypti mosquito biting preferences and host shifts. Training and testing to scents of humans and other host species showed that mosquitoes can aversively learn the scent of specific humans and single odorants and learn to avoid the scent of rats (but not chickens). Using pharmacological interventions, RNAi, and CRISPR gene editing, we found that modification of the dopamine-1 receptor suppressed their learning abilities. We further show through combined electrophysiological and behavioral recordings from tethered flying mosquitoes that these odors evoke changes in both behavior and antennal lobe (AL) neuronal responses and that dopamine strongly modulates odor-evoked responses in AL neurons. Not only do these results provide direct experimental evidence that olfactory learning in mosquitoes can play an epidemiological role, but collectively, they also provide neuroanatomical and functional demonstration of the role of dopamine in mediating this learning-induced plasticity, for the first time in a disease vector insect

Erratum to: A multi-stage genome-wide association study of uterine fibroids in African Americans

The article "A multi-stage genome-wide association study of uterine fibroids in African Americans", written by Jacklyn N. Hellwege, was originally published Online First without open access. After publication in volume 136, issue 10, page 1363-1373 the author decided to opt for Open Choice and to make the article an open access publication. Therefore, the copyright of the article has been changed t

Genomic investigations of unexplained acute hepatitis in children

Since its first identification in Scotland, over 1,000 cases of unexplained paediatric hepatitis in children have been reported worldwide, including 278 cases in the UK1. Here we report an investigation of 38 cases, 66 age-matched immunocompetent controls and 21 immunocompromised comparator participants, using a combination of genomic, transcriptomic, proteomic and immunohistochemical methods. We detected high levels of adeno-associated virus 2 (AAV2) DNA in the liver, blood, plasma or stool from 27 of 28 cases. We found low levels of adenovirus (HAdV) and human herpesvirus 6B (HHV-6B) in 23 of 31 and 16 of 23, respectively, of the cases tested. By contrast, AAV2 was infrequently detected and at low titre in the blood or the liver from control children with HAdV, even when profoundly immunosuppressed. AAV2, HAdV and HHV-6 phylogeny excluded the emergence of novel strains in cases. Histological analyses of explanted livers showed enrichment for T cells and B lineage cells. Proteomic comparison of liver tissue from cases and healthy controls identified increased expression of HLA class 2, immunoglobulin variable regions and complement proteins. HAdV and AAV2 proteins were not detected in the livers. Instead, we identified AAV2 DNA complexes reflecting both HAdV-mediated and HHV-6B-mediated replication. We hypothesize that high levels of abnormal AAV2 replication products aided by HAdV and, in severe cases, HHV-6B may have triggered immune-mediated hepatic disease in genetically and immunologically predisposed children

Genetic architecture of gene expression traits across diverse populations

For many complex traits, gene regulation is likely to play a crucial mechanistic role. How the genetic architectures of complex traits vary between populations and subsequent effects on genetic prediction are not well understood, in part due to the historical paucity of GWAS in populations of non-European ancestry. We used data from the MESA (Multi-Ethnic Study of Atherosclerosis) cohort to characterize the genetic architecture of gene expression within and between diverse populations. Genotype and monocyte gene expression were available in individuals with African American (AFA, n = 233), Hispanic (HIS, n = 352), and European (CAU, n = 578) ancestry. We performed expression quantitative trait loci (eQTL) mapping in each population and show genetic correlation of gene expression depends on shared ancestry proportions. Using elastic net modeling with cross validation to optimize genotypic predictors of gene expression in each population, we show the genetic architecture of gene expression for most predictable genes is sparse. We found the best predicted gene in each population, TACSTD2 in AFA and CHURC1 in CAU and HIS, had similar prediction performance across populations with R2>0.8 in each population. However, we identified a subset of genes that are well-predicted in one population, but poorly predicted in another. We show these differences in predictive performance are due to allele frequency differences between populations. Using genotype weights trained in MESA to predict gene expression in independent populations showed that a training set with ancestry similar to the test set is better at predicting gene expression in test populations, demonstrating an urgent need for diverse population sampling in genomics. Our predictive models and performance statistics in diverse cohorts are made publicly available for use in transcriptome mapping methods at&nbsp;https://github.com/WheelerLab/DivPop.</p

Endothelial extracellular vesicles contain protective proteins and rescue ischemia-reperfusion injury in a human heart-on-chip

Extracellular vesicles (EVs) derived from various stem cell sources induce cardioprotective effects during ischemia-reperfusion injury (IRI). These have been attributed mainly to the antiapoptotic, proangiogenic, microRNA (miRNA) cargo within the stem cell-derived EVs. However, the mechanisms of EV-mediated endothelial signaling to cardiomyocytes, as well as their therapeutic potential toward ischemic myocardial injury, are not clear. EV content beyond miRNA that may contribute to cardioprotection has not been fully illuminated. This study characterized the protein cargo of human vascular endothelial EVs (EEVs) to identify lead cardioactive proteins and assessed the effect of EEVs on human laminar cardiac tissues (hlCTs) exposed to IRI. We mapped the protein content of human vascular EEVs and identified proteins that were previously associated with cellular metabolism, redox state, and calcium handling, among other processes. Analysis of the protein landscape of human cardiomyocytes revealed corresponding modifications induced by EEV treatment. To assess their human-specific cardioprotection in vitro, we developed a human heart-on-a-chip IRI assay using human stem cell-derived, engineered cardiac tissues. We found that EEVs alleviated cardiac cell death as well as the loss in contractile capacity during and after simulated IRI in an uptake- and dose-dependent manner. Moreover, we found that EEVs increased the respiratory capacity of normoxic cardiomyocytes. These results suggest that vascular EEVs rescue hlCTs exposed to IRI possibly by supplementing injured myocytes with cargo that supports multiple metabolic and salvage pathways and therefore may serve as a multitargeted therapy for IRI