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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.
Intimal stiffening has been linked with increased vascular permeability and leukocyte transmigration, hallmarks of atherosclerosis. However, recent evidence indicates age-related intimal stiffening is not uniform but rather characterized by increased point-to-point heterogeneity in subendothelial matrix stiffness, the impact of which is much less understood. To investigate the impact of spatially heterogeneous matrix rigidity on endothelial monolayer integrity, we develop a micropillar model to introduce closely-spaced, step-changes in substrate rigidity and compare endothelial monolayer phenotype to rigidity-matched, uniformly stiff and compliant substrates. We found equivalent disruption of adherens junctions within monolayers on step-rigidity and uniformly stiff substrates relative to uniformly compliant substrates. Similarly, monolayers cultured on step-rigidity substrates exhibited equivalent percentages of leukocyte transmigration to monolayers on rigidity-matched, uniformly stiff substrates. Adherens junction tension and focal adhesion density, but not size, increased within monolayers on step-rigidity and uniformly stiff substrates compared to more compliant substrates suggesting that elevated tension is disrupting adherens junction integrity. Leukocyte transmigration frequency and time, focal adhesion size, and focal adhesion density did not differ between stiff and compliant sub-regions of step-rigidity substrates. Overall, our results suggest that endothelial monolayers exposed to mechanically heterogeneous substrates adopt the phenotype associated with the stiffer matrix, indicating that spatial heterogeneities in intimal stiffness observed with age could disrupt endothelial barrier integrity and contribute to atherogenesis.
CHIP (carboxyl terminus of heat shock 70-interacting protein) has long been recognized as an active member of the cellular protein quality control system given the ability of CHIP to function as both a co-chaperone and ubiquitin ligase. We discovered a genetic disease, now known as spinocerebellar autosomal recessive 16 (SCAR16), resulting from a coding mutation that caused a loss of CHIP ubiquitin ligase function. The initial mutation describing SCAR16 was a missense mutation in the ubiquitin ligase domain of CHIP (p.T246M). Using multiple biophysical and cellular approaches, we demonstrated that T246M mutation results in structural disorganization and misfolding of the CHIP U-box domain, promoting oligomerization, and increased proteasome-dependent turnover. CHIP-T246M has no ligase activity, but maintains interactions with chaperones and chaperone-related functions. To establish preclinical models of SCAR16, we engineered T246M at the endogenous locus in both mice and rats. Animals homozygous for T246M had both cognitive and motor cerebellar dysfunction distinct from those observed in the CHIP null animal model, as well as deficits in learning and memory, reflective of the cognitive deficits reported in SCAR16 patients. We conclude that the T246M mutation is not equivalent to the total loss of CHIP, supporting the concept that disease-causing CHIP mutations have different biophysical and functional repercussions on CHIP function that may directly correlate to the spectrum of clinical phenotypes observed in SCAR16 patients. Our findings both further expand our basic understanding of CHIP biology and provide meaningful mechanistic insight underlying the molecular drivers of SCAR16 disease pathology, which may be used to inform the development of novel therapeutics for this devastating disease.
We apply integrative approaches to expression quantitative loci (eQTLs) from 44 tissues from the Genotype-Tissue Expression project and genome-wide association study data. About 60% of known trait-associated loci are in linkage disequilibrium with a cis-eQTL, over half of which were not found in previous large-scale whole blood studies. Applying polygenic analyses to metabolic, cardiovascular, anthropometric, autoimmune, and neurodegenerative traits, we find that eQTLs are significantly enriched for trait associations in relevant pathogenic tissues and explain a substantial proportion of the heritability (40-80%). For most traits, tissue-shared eQTLs underlie a greater proportion of trait associations, although tissue-specific eQTLs have a greater contribution to some traits, such as blood pressure. By integrating information from biological pathways with eQTL target genes and applying a gene-based approach, we validate previously implicated causal genes and pathways, and propose new variant and gene associations for several complex traits, which we replicate in the UK BioBank and BioVU.
Metastasis is the most lethal aspect of cancer, yet current therapeutic strategies do not target its key rate-limiting steps. We have previously shown that the entry of cancer cells into the blood stream, or intravasation, is highly dependent upon in vivo cancer cell motility, making it an attractive therapeutic target. To systemically identify genes required for tumor cell motility in an in vivo tumor microenvironment, we established a novel quantitative in vivo screening platform based on intravital imaging of human cancer metastasis in ex ovo avian embryos. Utilizing this platform to screen a genome-wide shRNA library, we identified a panel of novel genes whose function is required for productive cancer cell motility in vivo, and whose expression is closely associated with metastatic risk in human cancers. The RNAi-mediated inhibition of these gene targets resulted in a nearly total (>99.5%) block of spontaneous cancer metastasis in vivo.
Little is known about the in vivo impacts of targeted therapy on melanoma cell abundance and protein expression. Here, 21 antibodies were added to an established melanoma mass cytometry panel to measure 32 cellular features, distinguish malignant cells, and characterize dabrafenib and trametinib responses in BRAF melanoma. Tumor cells were biopsied before neoadjuvant therapy and compared to cells surgically resected from the same site after 4 weeks of therapy. Approximately 50,000 cells per tumor were characterized by mass cytometry and computational tools t-SNE/viSNE, FlowSOM, and MEM. The resulting single-cell view of melanoma treatment response revealed initially heterogeneous melanoma tumors were consistently cleared of Nestin-expressing melanoma cells. Melanoma cell subsets that persisted to week 4 were heterogeneous but expressed SOX2 or SOX10 proteins and specifically lacked surface expression of MHC I proteins by MEM analysis. Traditional histology imaging of tissue microarrays from the same tumors confirmed mass cytometry results, including persistence of NES- SOX10+ S100β+ melanoma cells. This quantitative single-cell view of melanoma treatment response revealed protein features of malignant cells that are not eliminated by targeted therapy.
© 2018 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
Alveolar type II (AT2) epithelial cells are uniquely specialized to produce surfactant in the lung and act as progenitor cells in the process of repair after lung injury. AT2 cell injury has been implicated in several lung diseases, including idiopathic pulmonary fibrosis and bronchopulmonary dysplasia. The inability to maintain primary AT2 cells in culture has been a significant barrier in the investigation of pulmonary biology. We have addressed this knowledge gap by developing a three-dimensional (3D) organotypic coculture using primary human fetal AT2 cells and pulmonary fibroblasts. Grown on top of matrix-embedded fibroblasts, the primary human AT2 cells establish a monolayer and have direct contact with the underlying pulmonary fibroblasts. Unlike conventional two-dimensional (2D) culture, the structural and functional phenotype of the AT2 cells in our 3D organotypic culture was preserved over 7 days of culture, as evidenced by the presence of lamellar bodies and by production of surfactant proteins B and C. Importantly, the AT2 cells in 3D cocultures maintained the ability to replicate, with approximately 60% of AT2 cells staining positive for the proliferation marker Ki67, whereas no such proliferation is evident in 2D cultures of the same primary AT2 cells. This organotypic culture system enables interrogation of AT2 epithelial biology by providing a reductionist in vitro model in which to investigate the response of AT2 epithelial cells and AT2 cell-fibroblast interactions during lung injury and repair.
OBJECTIVE - Hepatorenal Syndrome (HRS) is a devastating form of acute kidney injury (AKI) in advanced liver disease patients with high morbidity and mortality, but phenotyping algorithms have not yet been developed using large electronic health record (EHR) databases. We evaluated and compared multiple phenotyping methods to achieve an accurate algorithm for HRS identification.
MATERIALS AND METHODS - A national retrospective cohort of patients with cirrhosis and AKI admitted to 124 Veterans Affairs hospitals was assembled from electronic health record data collected from 2005 to 2013. AKI was defined by the Kidney Disease: Improving Global Outcomes criteria. Five hundred and four hospitalizations were selected for manual chart review and served as the gold standard. Electronic Health Record based predictors were identified using structured and free text clinical data, subjected through NLP from the clinical Text Analysis Knowledge Extraction System. We explored several dimension reduction techniques for the NLP data, including newer high-throughput phenotyping and word embedding methods, and ascertained their effectiveness in identifying the phenotype without structured predictor variables. With the combined structured and NLP variables, we analyzed five phenotyping algorithms: penalized logistic regression, naïve Bayes, support vector machines, random forest, and gradient boosting. Calibration and discrimination metrics were calculated using 100 bootstrap iterations. In the final model, we report odds ratios and 95% confidence intervals.
RESULTS - The area under the receiver operating characteristic curve (AUC) for the different models ranged from 0.73 to 0.93; with penalized logistic regression having the best discriminatory performance. Calibration for logistic regression was modest, but gradient boosting and support vector machines were superior. NLP identified 6985 variables; a priori variable selection performed similarly to dimensionality reduction using high-throughput phenotyping and semantic similarity informed clustering (AUC of 0.81 - 0.82).
CONCLUSION - This study demonstrated improved phenotyping of a challenging AKI etiology, HRS, over ICD-9 coding. We also compared performance among multiple approaches to EHR-derived phenotyping, and found similar results between methods. Lastly, we showed that automated NLP dimension reduction is viable for acute illness.
Copyright © 2018 Elsevier Inc. All rights reserved.
Many patients with type 1 diabetes (T1D) have residual β cells producing small amounts of C-peptide long after disease onset but develop an inadequate glucagon response to hypoglycemia following T1D diagnosis. The features of these residual β cells and α cells in the islet endocrine compartment are largely unknown, due to the difficulty of comprehensive investigation. By studying the T1D pancreas and isolated islets, we show that remnant β cells appeared to maintain several aspects of regulated insulin secretion. However, the function of T1D α cells was markedly reduced, and these cells had alterations in transcription factors constituting α and β cell identity. In the native pancreas and after placing the T1D islets into a non-autoimmune, normoglycemic in vivo environment, there was no evidence of α-to-β cell conversion. These results suggest an explanation for the disordered T1D counterregulatory glucagon response to hypoglycemia.
Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.