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Chronic kidney disease (CKD), defined by low estimated glomerular filtration rate (eGFR), contributes to global morbidity and mortality. Here we conduct a transethnic Genome-Wide Association Study of eGFR in 280,722 participants of the Million Veteran Program (MVP), with replication in 765,289 participants from the Chronic Kidney Disease Genetics (CKDGen) Consortium. We identify 82 previously unreported variants, confirm 54 loci, and report interesting findings including association of the sickle cell allele of betaglobin among non-Hispanic blacks. Our transcriptome-wide association study of kidney function in healthy kidney tissue identifies 36 previously unreported and nine known genes, and maps gene expression to renal cell types. In a Phenome-Wide Association Study in 192,868 MVP participants using a weighted genetic score we detect associations with CKD stages and complications and kidney stones. This investigation reinterprets the genetic architecture of kidney function to identify the gene, tissue, and anatomical context of renal homeostasis and the clinical consequences of dysregulation.
OBJECTIVE - In the randomised scleroderma: Cyclophosphamide Or Transplantation (SCOT trial) (NCT00114530), myeloablation, followed by haematopoietic stem cell transplantation (HSCT), led to improved clinical outcomes compared with monthly cyclophosphamide (CYC) treatment in systemic sclerosis (SSc). Herein, the study aimed to determine global molecular changes at the whole blood transcript and serum protein levels ensuing from HSCT in comparison to intravenous monthly CYC in 62 participants enrolled in the SCOT study.
METHODS - Global transcript studies were performed at pretreatment baseline, 8 months and 26 months postrandomisation using Illumina HT-12 arrays. Levels of 102 proteins were measured in the concomitantly collected serum samples.
RESULTS - At the baseline visit, interferon (IFN) and neutrophil transcript modules were upregulated and the cytotoxic/NK module was downregulated in SSc compared with unaffected controls. A paired comparison of the 26 months to the baseline samples revealed a significant decrease of the IFN and neutrophil modules and an increase in the cytotoxic/NK module in the HSCT arm while there was no significant change in the CYC control arm. Also, a composite score of correlating serum proteins with IFN and neutrophil transcript modules, as well as a multilevel analysis showed significant changes in SSc molecular signatures after HSCT while similar changes were not observed in the CYC arm. Lastly, a decline in the IFN and neutrophil modules was associated with an improvement in pulmonary forced vital capacity and an increase in the cytotoxic/NK module correlated with improvement in skin score.
CONCLUSION - HSCT contrary to conventional treatment leads to a significant 'correction' in disease-related molecular signatures.
© Author(s) (or their employer(s)) 2019. No commercial re-use. See rights and permissions. Published by BMJ.
SUMMARY - Single cell RNA sequencing is a revolutionary technique to characterize inter-cellular transcriptomics heterogeneity. However, the data are noise-prone because gene expression is often driven by both technical artifacts and genuine biological variations. Proper disentanglement of these two effects is critical to prevent spurious results. While several tools exist to detect and remove low-quality cells in one single cell RNA-seq dataset, there is lack of approach to examining consistency between sample sets and detecting systematic biases, batch effects and outliers. We present scRNABatchQC, an R package to compare multiple sample sets simultaneously over numerous technical and biological features, which gives valuable hints to distinguish technical artifact from biological variations. scRNABatchQC helps identify and systematically characterize sources of variability in single cell transcriptome data. The examination of consistency across datasets allows visual detection of biases and outliers.
AVAILABILITY AND IMPLEMENTATION - scRNABatchQC is freely available at https://github.com/liuqivandy/scRNABatchQC as an R package.
SUPPLEMENTARY INFORMATION - Supplementary data are available at Bioinformatics online.
© The Author(s) 2019. Published by Oxford University Press.
The genetic architecture of psychiatric disorders is characterized by a large number of small-effect variants located primarily in non-coding regions, suggesting that the underlying causal effects may influence disease risk by modulating gene expression. We provide comprehensive analyses using transcriptome data from an unprecedented collection of tissues to gain pathophysiological insights into the role of the brain, neuroendocrine factors (adrenal gland) and gastrointestinal systems (colon) in psychiatric disorders. In each tissue, we perform PrediXcan analysis and identify trait-associated genes for schizophrenia (n associations = 499; n unique genes = 275), bipolar disorder (n associations = 17; n unique genes = 13), attention deficit hyperactivity disorder (n associations = 19; n unique genes = 12) and broad depression (n associations = 41; n unique genes = 31). Importantly, both PrediXcan and summary-data-based Mendelian randomization/heterogeneity in dependent instruments analyses suggest potentially causal genes in non-brain tissues, showing the utility of these tissues for mapping psychiatric disease genetic predisposition. Our analyses further highlight the importance of joint tissue approaches as 76% of the genes were detected only in difficult-to-acquire tissues.
Novel therapeutic regulators of uterine contractility are needed to manage preterm labor, induce labor and control postpartum hemorrhage. Therefore, we previously developed a high-throughput assay for large-scale screening of small molecular compounds to regulate calcium-mobilization in primary mouse uterine myometrial cells. The goal of this study was to select the optimal myometrial cells for our high-throughput drug discovery assay, as well as determine the similarity or differences of myometrial cells to vascular smooth muscle cells (VSMCs)-the most common off-target of current myometrial therapeutics. Molecular and pharmacological assays were used to compare myometrial cells from four sources: primary cells isolated from term pregnant human and murine myometrium, immortalized pregnant human myometrial (PHM-1) cells and immortalized non-pregnant human myometrial (hTERT-HM) cells. In addition, myometrial cells were compared to vascular SMCs. We found that the transcriptome profiles of hTERT-HM and PHM1 cells were most similar (r = 0.93 and 0.90, respectively) to human primary myometrial cells. Comparative transcriptome profiling of primary human myometrial transcriptome and VSMCs revealed 498 upregulated (p ≤ 0.01, log2FC≥1) genes, of which 142 can serve as uterine-selective druggable targets. In the high-throughput Ca-assay, PHM1 cells had the most similar response to primary human myometrial cells in OT-induced Ca-release (E = 195% and 143%, EC = 30 nM and 120 nM, respectively), while all sources of myometrial cells showed excellent and similar robustness and reproducibility (Z' = 0.52 to 0.77). After testing a panel of 61 compounds, we found that the stimulatory and inhibitory responses of hTERT-HM cells were highly-correlated (r = 0.94 and 0.95, respectively) to human primary cells. Moreover, ten compounds were identified that displayed uterine-selectivity (≥5-fold E or EC compared to VSMCs). Collectively, this study found that hTERT-HM cells exhibited the most similarity to primary human myometrial cells and, therefore, is an optimal substitute for large-scale screening to identify novel therapeutic regulators of myometrial contractility. Moreover, VSMCs can serve as an important counter-screening tool to assess uterine-selectivity of targets and drugs given the similarity observed in the transcriptome and response to compounds.
Copyright © 2019 Elsevier Ltd. All rights reserved.
INTRODUCTION - Metabolic stress (e.g., gestational diabetes mellitus (GDM) and obesity) and infections are common during pregnancy, impacting fetal development and the health of offspring. Such antenatal stresses can differentially impact male and female offspring. We sought to determine how metabolic stress and maternal immune activation (MIA), either alone or in combination, alters inflammatory gene expression within the placenta and whether the effects exhibited sexual dimorphism.
METHODS - Female C57BL/6 J mice were fed a normal diet or a high fat diet for 6 weeks prior to mating, with the latter diet inducing a GDM phenotype during pregnancy. Dams within each diet group at gestational day (GD) 12.5 received either an intraperitoneal injection of the viral mimic, polyinosinic:polycytidylic acid (poly(I:C)) or saline. Three hours post injection; placentae were collected and analyzed for changes in the expression of 248 unique immune genes.
RESULTS - Placental immune gene expression was significantly altered by GDM, MIA and the combination of the two (GDM+MIA). mRNA expression was generally lower in placentae of mice exposed to GDM alone compared with the other experimental groups, while mice exposed to MIA exhibited the highest transcript levels. Notably, fetal/placental sex influenced the responses of many immune genes to both metabolic and inflammatory stress.
DISCUSSION - GDM and MIA provoke inflammatory responses within the placenta and such effects exhibit sexual dimorphism. The combination of these stressors impacts the placenta differently than either condition alone. These findings may help explain sexual dimorphism observed in adverse pregnancy outcomes in human offspring exposed to similar stressors.
Copyright © 2019. Published by Elsevier Ltd.
Transcriptomic imputation approaches combine eQTL reference panels with large-scale genotype data in order to test associations between disease and gene expression. These genic associations could elucidate signals in complex genome-wide association study (GWAS) loci and may disentangle the role of different tissues in disease development. We used the largest eQTL reference panel for the dorso-lateral prefrontal cortex (DLPFC) to create a set of gene expression predictors and demonstrate their utility. We applied DLPFC and 12 GTEx-brain predictors to 40,299 schizophrenia cases and 65,264 matched controls for a large transcriptomic imputation study of schizophrenia. We identified 413 genic associations across 13 brain regions. Stepwise conditioning identified 67 non-MHC genes, of which 14 did not fall within previous GWAS loci. We identified 36 significantly enriched pathways, including hexosaminidase-A deficiency, and multiple porphyric disorder pathways. We investigated developmental expression patterns among the 67 non-MHC genes and identified specific groups of pre- and postnatal expression.
BACKGROUND AND AIMS - Cardiovascular disease (CVD) is the leading cause of death in chronic kidney disease (CKD) patients, however, the underlying mechanisms that link CKD and CVD are not fully understood and limited treatment options exist in this high-risk population. microRNAs (miRNA) are critical regulators of gene expression for many biological processes in atherosclerosis, including endothelial dysfunction and inflammation. We hypothesized that renal injury-induced endothelial miRNAs promote atherosclerosis. Here, we demonstrate that dual inhibition of endothelial miRNAs inhibits atherosclerosis in the setting of renal injury.
METHODS - Aortic endothelial miRNAs were analyzed in apolipoprotein E-deficient (Apoe) mice with renal damage (5/6 nephrectomy, 5/6Nx) by real-time PCR. Endothelial miR-92a-3p and miR-489-3p were inhibited by locked-nucleic acid (LNA) miRNA inhibitors complexed to HDL.
RESULTS - Renal injury significantly increased endothelial miR-92a-3p levels in Apoe;5/6Nx mice. Dual inhibition of miR-92a-3p and miR-489-3p in Apoe;5/6Nx with a single injection of HDL + LNA inhibitors significantly reduced atherosclerotic lesion area by 28.6% compared to HDL + LNA scramble (LNA-Scr) controls. To examine the impact of dual LNA treatment on aortic endothelial gene expression, total RNA sequencing was completed, and multiple putative target genes and pathways were identified to be significantly altered, including the STAT3 immune response pathway. Among the differentially expressed genes, Tgfb2 and Fam220a were identified as putative targets of miR-489-3p and miR-92a-3p, respectively. Both Tgfb2 and Fam220a were significantly increased in aortic endothelium after miRNA inhibition in vivo compared to HDL + LNA-Scr controls. Furthermore, Tgfb2 and Fam220a were validated with gene reporter assays as direct targets of miR-489-3p and miR-92a-3p, respectively. In human coronary artery endothelial cells, over-expression and inhibition of miR-92a-3p decreased and increased FAM220A expression, respectively. Moreover, miR-92a-3p overexpression increased STAT3 phosphorylation, likely through direct regulation of FAM220A, a negative regulator of STAT3 phosphorylation.
CONCLUSIONS - These results support endothelial miRNAs as therapeutic targets and dual miRNA inhibition as viable strategy to reduce CKD-associated atherosclerosis.
Copyright © 2019. Published by Elsevier B.V.
Interpretation of genetic association results is difficult because signals often lack biological context. To generate hypotheses of the functional genetic etiology of complex cardiometabolic traits, we estimated the genetically determined component of gene expression from common variants using PrediXcan (1) and determined genes with differential predicted expression by trait. PrediXcan imputes tissue-specific expression levels from genetic variation using variant-level effect on gene expression in transcriptome data. To explore the value of imputed genetically regulated gene expression (GReX) models across different ancestral populations, we evaluated imputed expression levels for predictive accuracy genome-wide in RNA sequence data in samples drawn from European-ancestry and African-ancestry populations and identified substantial predictive power using European-derived models in a non-European target population. We then tested the association of GReX on 15 cardiometabolic traits including blood lipid levels, body mass index, height, blood pressure, fasting glucose and insulin, RR interval, fibrinogen level, factor VII level and white blood cell and platelet counts in 15 755 individuals across three ancestry groups, resulting in 20 novel gene-phenotype associations reaching experiment-wide significance across ancestries. In addition, we identified 18 significant novel gene-phenotype associations in our ancestry-specific analyses. Top associations were assessed for additional support via query of S-PrediXcan (2) results derived from publicly available genome-wide association studies summary data. Collectively, these findings illustrate the utility of transcriptome-based imputation models for discovery of cardiometabolic effect genes in a diverse dataset.
© The Author(s) 2019. Published by Oxford University Press.
In this trans-ethnic multi-omic study, we reinterpret the genetic architecture of blood pressure to identify genes, tissues, phenomes and medication contexts of blood pressure homeostasis. We discovered 208 novel common blood pressure SNPs and 53 rare variants in genome-wide association studies of systolic, diastolic and pulse pressure in up to 776,078 participants from the Million Veteran Program (MVP) and collaborating studies, with analysis of the blood pressure clinical phenome in MVP. Our transcriptome-wide association study detected 4,043 blood pressure associations with genetically predicted gene expression of 840 genes in 45 tissues, and mouse renal single-cell RNA sequencing identified upregulated blood pressure genes in kidney tubule cells.