Lara M. Mangravite

Evolutionary conservation predicts function of variants of the human organic cation transporter, OCT1

The organic cation transporter, OCT1, is a major hepatic transporter that mediates the uptake of many organic cations from the blood into the liver where the compounds may be metabolized or secreted into the bile. Because OCT1 interacts with a variety of structurally diverse organic cations, including clinically used drugs as well as toxic substances (e.g., N-methylpyridinium, MPP+), it is an important determinant of systemic exposure to many xenobiotics. To understand the genetic basis of extensive interindividual differences in xenobiotic disposition, we functionally characterized 15 protein-altering variants of the human liver organic cation transporter, OCT1, in Xenopus oocytes. All variants that reduced or eliminated function (OCT1-R61C, OCT1-P341L, OCT1-G220V, OCT1-G401S, and OCT1-G465R) altered evolutionarily conserved amino acid residues. In general, variants with decreased function had amino acid substitutions that resulted in more radical chemical changes (higher Grantham values) and were less evolutionarily favorable (lower blosum62 values) than variants that maintained function. A variant with increased function (OCT1-S14F) changed an amino acid residue such that the human protein matched the consensus of the OCT1 mammalian orthologs. Our results indicate that changes at evolutionarily conserved positions of OCT1 are strong predictors of decreased function and suggest that a combination of evolutionary conservation and chemical change might be a stronger predictor of function.

Genome-Wide Association of Lipid-Lowering Response to Statins in Combined Study Populations

Background Statins effectively lower total and plasma LDL-cholesterol, but the magnitude of decrease varies among individuals. To identify single nucleotide polymorphisms (SNPs) contributing to this variation, we performed a combined analysis of genome-wide association (GWA) results from three trials of statin efficacy. Methods and Principal Findings Bayesian and standard frequentist association analyses were performed on untreated and statin-mediated changes in LDL-cholesterol, total cholesterol, HDL-cholesterol, and triglyceride on a total of 3932 subjects using data from three studies: Cholesterol and Pharmacogenetics (40 mg/day simvastatin, 6 weeks), Pravastatin/Inflammation CRP Evaluation (40 mg/day pravastatin, 24 weeks), and Treating to New Targets (10 mg/day atorvastatin, 8 weeks). Genotype imputation was used to maximize genomic coverage and to combine information across studies. Phenotypes were normalized within each study to account for systematic differences among studies, and fixed-effects combined analysis of the combined sample were performed to detect consistent effects across studies. Two SNP associations were assessed as having posterior probability greater than 50%, indicating that they were more likely than not to be genuinely associated with statin-mediated lipid response. SNP rs8014194, located within the CLMN gene on chromosome 14, was strongly associated with statin-mediated change in total cholesterol with an 84% probability by Bayesian analysis, and a p-value exceeding conventional levels of genome-wide significance by frequentist analysis (P = 1.8×10−8). This SNP was less significantly associated with change in LDL-cholesterol (posterior probability = 0.16, P = 4.0×10−6). Bayesian analysis also assigned a 51% probability that rs4420638, located in APOC1 and near APOE, was associated with change in LDL-cholesterol. Conclusions and Significance Using combined GWA analysis from three clinical trials involving nearly 4,000 individuals treated with simvastatin, pravastatin, or atorvastatin, we have identified SNPs that may be associated with variation in the magnitude of statin-mediated reduction in total and LDL-cholesterol, including one in the CLMN gene for which statistical evidence for association exceeds conventional levels of genome-wide significance. Trial Registration PRINCE and TNT are not registered. CAP is registered at Clinicaltrials.gov NCT00451828

Variation in the 3-Hydroxyl-3-Methylglutaryl Coenzyme A Reductase Gene Is Associated With Racial Differences in Low-Density Lipoprotein Cholesterol Response to Simvastatin Treatment

Background— Use of 3-hydroxyl-3-methylglutaryl-3 coenzyme A reductase (HMGCR) inhibitors, or statins, reduces cardiovascular disease risk by lowering plasma low-density lipoprotein cholesterol (LDL-C) concentrations. However, LDL-C response is variable and influenced by many factors, including racial ancestry, with attenuated response in blacks compared with whites. We hypothesized that single nucleotide polymorphisms in the gene encoding HMGCR, a rate-limiting enzyme in cholesterol synthesis and the direct enzymatic target of statins, contribute to variation in statin response. Methods and Results— Genomic resequencing of HMGCR in 24 blacks and 23 whites identified 79 single nucleotide polymorphisms. Eleven single nucleotide polymorphisms were selected to tag common linkage disequilibrium clusters. These single nucleotide polymorphisms and the common haplotypes inferred from them were tested for association with plasma LDL-C and LDL-C response to simvastatin treatment (40 mg/d for 6 weeks) in 326 blacks and 596 whites. Black carriers of H7 and/or H2 had significantly lower baseline LDL-C (P=0.0006) and significantly attenuated LDL-C response compared with black participants who did not carry either haplotype as measured by absolute response (−1.23±0.04 mmol/L, n=209, versus −1.45±0.06 mmol/L, n=117; P=0.0008) and percent response (−36.9±1.0% versus −40.6±1.3%; P=0.02), but no haplotype effect was observed in whites. Percent LDL-C response was lowest in carriers of both H2 and H7, all but one of whom were black (−28.2±4.9%, n=12 H2+H7 carriers, versus −41.5±0.5%, n=650 H2/H7 noncarriers; P=0.001). LDL-C responses in H7 and/or H2 noncarriers were indistinguishable between blacks and whites. Conclusions— HMGCR gene polymorphisms are associated with reduced plasma LDL-C and LDL-C response to simvastatin, and these effects are most evident in blacks.

A statin-dependent QTL for GATM expression is associated with statin-induced myopathy.

Statins are prescribed widely to lower plasma low-density lipoprotein (LDL) concentrations and cardiovascular disease risk and have been shown to have beneficial effects in a broad range of patients. However, statins are associated with an increased risk, albeit small, of clinical myopathy and type 2 diabetes. Despite evidence for substantial genetic influence on LDL concentrations, pharmacogenomic trials have failed to identify genetic variations with large effects on either statin efficacy or toxicity, and have produced little information regarding mechanisms that modulate statin response. Here we identify a downstream target of statin treatment by screening for the effects of in vitro statin exposure on genetic associations with gene expression levels in lymphoblastoid cell lines derived from 480 participants of a clinical trial of simvastatin treatment. This analysis identified six expression quantitative trait loci (eQTLs) that interacted with simvastatin exposure, including rs9806699, a cis-eQTL for the gene glycine amidinotransferase (GATM) that encodes the rate-limiting enzyme in creatine synthesis. We found this locus to be associated with incidence of statin-induced myotoxicity in two separate populations (meta-analysis odds ratio = 0.60). Furthermore, we found that GATM knockdown in hepatocyte-derived cell lines attenuated transcriptional response to sterol depletion, demonstrating that GATM may act as a functional link between statin-mediated lowering of cholesterol and susceptibility to statin-induced myopathy.

Gene expression elucidates functional impact of polygenic risk for schizophrenia.

Over 100 genetic loci harbor schizophrenia-associated variants, yet how these variants confer liability is uncertain. The CommonMind Consortium sequenced RNA from dorsolateral prefrontal cortex of people with schizophrenia (N = 258) and control subjects (N = 279), creating a resource of gene expression and its genetic regulation. Using this resource, ∼20% of schizophrenia loci have variants that could contribute to altered gene expression and liability. In five loci, only a single gene was involved: FURIN, TSNARE1, CNTN4, CLCN3 or SNAP91. Altering expression of FURIN, TSNARE1 or CNTN4 changed neurodevelopment in zebrafish; knockdown of FURIN in human neural progenitor cells yielded abnormal migration. Of 693 genes showing significant case-versus-control differential expression, their fold changes were ≤ 1.33, and an independent cohort yielded similar results. Gene co-expression implicates a network relevant for schizophrenia. Our findings show that schizophrenia is polygenic and highlight the utility of this resource for mechanistic interpretations of genetic liability for brain diseases.

Pharmacogenomic discovery using cell-based models.

Quantitative variation in response to drugs in human populations is multifactorial; genetic factors probably contribute to a significant extent. Identification of the genetic contribution to drug response typically comes from clinical observations and use of classic genetic tools. These clinical studies are limited by our inability to control environmental factors in vivo and the difficulty of manipulating the in vivo system to evaluate biological changes. Recent progress in dissecting genetic contribution to natural variation in drug response through the use of cell lines has been made and is the focus of this review. A general overview of current cell-based models used in pharmacogenomic discovery and validation is included. Discussion includes the current approach to translate findings generated from these cell-based models into the clinical arena and the use of cell lines for functional studies. Specific emphasis is given to recent advances emerging from cell line panels, including the International HapMap Project and the NCI60 cell panel. These panels provide a key resource of publicly available genotypic, expression, and phenotypic data while allowing researchers to generate their own data related to drug treatment to identify genetic variation of interest. Interindividual and interpopulation differences can be evaluated because human lymphoblastoid cell lines are available from major world populations of European, African, Chinese, and Japanese ancestry. The primary focus is recent progress in the pharmacogenomic discovery area through ex vivo models.

The PsychENCODE project

Recent research on disparate psychiatric disorders has implicated rare variants in genes involved in global gene regulation and chromatin modification, as well as many common variants located primarily in regulatory regions of the genome. Understanding precisely how these variants contribute to disease will require a deeper appreciation for the mechanisms of gene regulation in the developing and adult human brain. The PsychENCODE project aims to produce a public resource of multidimensional genomic data using tissue- and cell type–specific samples from approximately 1,000 phenotypically well-characterized, high-quality healthy and disease-affected human post-mortem brains, as well as functionally characterize disease-associated regulatory elements and variants in model systems. We are beginning with a focus on autism spectrum disorder, bipolar disorder and schizophrenia, and expect that this knowledge will apply to a wide variety of psychiatric disorders. This paper outlines the motivation and design of PsychENCODE.

Characterizing the expression of CYP3A4 and efflux transporters (P-gp, MRP1, and MRP2) in CYP3A4-transfected Caco-2 cells after induction with sodium butyrate and the phorbol ester 12-O-tetradecanoylphorbol-13-acetate.

AbstractPurpose. To examine the changes in expression levels of CYP3A4 and efflux transporters in CYP3A4-transfected Caco-2 (colon carcinoma) cells in the presence of the inducers sodium butyrate (NaB) and 12-O-tetradecanoylphorbol-13-acetate (TPA). To characterize the transport of [3H]-digoxin and the metabolism of midazolam in the cells under different inducing conditions. Methods. CYP3A4-Caco-2 cells were seeded onto cell culture inserts and were grown for 13-14 days. Transport and metabolism studies were performed on cells induced with NaB and/or TPA for 24 h. The expression and localization of P-gp, MRP1, MRP2, and CYP3A4 were examined by Western blot and confocal microscopy. Results. In the presence of both inducers, CYP3A4 protein levels were increased 40-fold over uninduced cells, MRP2 expression was decreased by 90%, and P-gp and MRP1 expression were unchanged. Midazolam 1-OH formation exhibited a rank order correlation with increased CYP3A4 protein, whereas [3H]-digoxin transport (a measure of P-gp activity) was unchanged with induction. P-gp and MRP2 were found on the apical membrane, whereas MRP1 was found peri-nuclear within the cell. CYP3A4 displayed a punctate pattern of expression consistent with endoplasmic reticulum localization and exhibited preferential polarization towards the apical side of the cell. Conclusions. The present study characterized CYP3A4-Caco-2 cell monolayers when induced for 24 h in the presence of both NaB and TPA. These conditions provide intact cells with significant CYP3A4 and P-gp expression suitable for the concurrent study of transport and metabolism.

Integrative modeling of eQTLs and cis-regulatory elements suggests mechanisms underlying cell type specificity of eQTLs.

Genetic variants in cis-regulatory elements or trans-acting regulators frequently influence the quantity and spatiotemporal distribution of gene transcription. Recent interest in expression quantitative trait locus (eQTL) mapping has paralleled the adoption of genome-wide association studies (GWAS) for the analysis of complex traits and disease in humans. Under the hypothesis that many GWAS associations tag non-coding SNPs with small effects, and that these SNPs exert phenotypic control by modifying gene expression, it has become common to interpret GWAS associations using eQTL data. To fully exploit the mechanistic interpretability of eQTL-GWAS comparisons, an improved understanding of the genetic architecture and causal mechanisms of cell type specificity of eQTLs is required. We address this need by performing an eQTL analysis in three parts: first we identified eQTLs from eleven studies on seven cell types; then we integrated eQTL data with cis-regulatory element (CRE) data from the ENCODE project; finally we built a set of classifiers to predict the cell type specificity of eQTLs. The cell type specificity of eQTLs is associated with eQTL SNP overlap with hundreds of cell type specific CRE classes, including enhancer, promoter, and repressive chromatin marks, regions of open chromatin, and many classes of DNA binding proteins. These associations provide insight into the molecular mechanisms generating the cell type specificity of eQTLs and the mode of regulation of corresponding eQTLs. Using a random forest classifier with cell specific CRE-SNP overlap as features, we demonstrate the feasibility of predicting the cell type specificity of eQTLs. We then demonstrate that CREs from a trait-associated cell type can be used to annotate GWAS associations in the absence of eQTL data for that cell type. We anticipate that such integrative, predictive modeling of cell specificity will improve our ability to understand the mechanistic basis of human complex phenotypic variation.

Systematic Analysis of Challenge-Driven Improvements in Molecular Prognostic Models for Breast Cancer

An open challenge to model breast cancer prognosis revealed that collaboration and transparency enhanced the power of prognostic models. DREAMing of Biomedicine’s Future Although they no longer live in the lab, scientific editors still enjoy doing experiments. The simultaneous publication of two unusual papers offered Science Translational Medicine’s editors the chance to conduct an investigation into peer-review processes for competition-based crowdsourcing studies designed to address problems in biomedicine. In a Report by Margolin et al. (which was peer-reviewed in the traditional way), organizers of the Sage Bionetworks/DREAM Breast Cancer Prognosis Challenge (BCC) describe the contest’s conception, execution, and insights derived from its outcome. In the companion Research Article, Cheng et al. outline the development of the prognostic computational model that won the Challenge. In this experiment in scientific publishing, the rigor of the Challenge design and scoring process formed the basis for a new style of publication peer review. DREAM—Dialogue for Reverse Engineering Assessments and Methods—conducts a variety of computational Challenges with the goal of catalyzing the “interaction between theory and experiment, specifically in the area of cellular network inference and quantitative model building in systems biology.” Previous Challenges involved, for example, modeling of protein-protein interactions for binding domains and peptides and the specificity of transcription factor binding. In the BCC—which was a step in the translational direction—participants competed to create an algorithm that could predict, more accurately than current benchmarks, the prognosis of breast cancer patients from clinical information (age, tumor size, histological grade), genome-scale tumor mRNA expression data, and DNA copy number data. Participants were given Web access to such data for 1981 women diagnosed with breast cancer and used it to train computational models that were then submitted to a common, open-access computational platform as re-runnable source code. The predictive value of each model was assessed in real-time by calculating a concordance index (CI) of predicted death risks compared to overall survival in a held-out data set, and CIs were posted on a public leaderboard. The winner of the Challenge was ultimately determined when a select group of top models were validated in a new breast cancer data set. The winning model, described by Cheng et al., was based on sets of genes (signatures)—called attractor metagenes—that the same research group had previously shown to be associated, in various ways, with multiple cancer types. Starting with these gene sets and some other clinical and molecular features, the team modeled various feature combinations, selecting ones that improved performance of their prognostic model until they ultimately fashioned the winning algorithm. Before the BCC was initiated, Challenge organizers approached Science Translational Medicine about the possibility of publishing a Research Article that described the winning model. The Challenge prize would be a scholarly publication—a form of “academic currency.” The editors pondered whether winning the Challenge, with its built-in transparency and check on model reproducibility, would be sufficient evidence in support of the model’s validity to substitute for traditional peer review. Because the specific conditions of a Challenge are critical in determining the meaningfulness of the outcome, the editors felt it was not. Thus, they chose to arrange for peer-reviewers, chosen by the editors, to be embedded within the challenge process, as members of the organizing team—a so-called Challenge-assisted review. The editors also helped to develop criteria for determining the winning model, and if the criteria were not met, there would have been no winner—and no publication. Last, the manuscript was subjected to advisory peer review after it was submitted to the journal. So what new knowledge was gained about reviewing an article in which the result is an active piece of software? Reviewing such a model required that referees have access to the data and platform used for the Challenge and have the ability to re-run each participant’s code; in the context of the BCC, this requirement was easily achievable, because Challenge-partner Sage Bionetworks had created a platform (Synapse) with this goal in mind. In fact, both the training and validation datasets for the BCC are available to readers via links into Synapse (for a six month period of time). In general, this requirement should not be an obstacle, as there are code-hosting sites such as GitHub and TopCoder.com that can accommodate data sharing. Mechanisms for confidentiality would need to be built into any computational platform to be used for peer review. Finally, because different conventions are used in divergent scientific fields, communicating the science to an interdisciplinary audience is not a trivial endeavor. The architecture of the Challenge itself is critical in determining the real-world importance of the result. The question to be investigated must be framed so as to capture a significant outcome. In the BCC, participants’ models had to score better than a set of 60 different prognostic models developed by a team of expert programmers during a Challenge precompetition as well as a previously described first-generation 70-gene risk predictor. Thus, the result may or may not be superior to existing gene expression profiling tests used in clinical practice. This remains to be tested. It also remains to be seen whether prize-based crowdsourcing contests can make varied and practical contributions in the clinic. Indeed, DREAM and Sage Bionetworks have immediate plans to collaborate on new clinically relevant Challenges. But there is no doubt that the approach has value in solving big-data problems. For example, in a recent contest, non-immunologists generated a method for annotating the complex genome sequence of the antibody repertoire when the contest organizers translated the problem into generic language. In the BCC, the Challenge winners used a mathematical approach to identify biological modules that might, with continued investigation, teach us something about cancer biology. These examples support the notion that harnessing the expertise of contestants outside of traditional biological disciplines may be a powerful way to accelerate the translation of biomedical science to the clinic. Although molecular prognostics in breast cancer are among the most successful examples of translating genomic analysis to clinical applications, optimal approaches to breast cancer clinical risk prediction remain controversial. The Sage Bionetworks–DREAM Breast Cancer Prognosis Challenge (BCC) is a crowdsourced research study for breast cancer prognostic modeling using genome-scale data. The BCC provided a community of data analysts with a common platform for data access and blinded evaluation of model accuracy in predicting breast cancer survival on the basis of gene expression data, copy number data, and clinical covariates. This approach offered the opportunity to assess whether a crowdsourced community Challenge would generate models of breast cancer prognosis commensurate with or exceeding current best-in-class approaches. The BCC comprised multiple rounds of blinded evaluations on held-out portions of data on 1981 patients, resulting in more than 1400 models submitted as open source code. Participants then retrained their models on the full data set of 1981 samples and submitted up to five models for validation in a newly generated data set of 184 breast cancer patients. Analysis of the BCC results suggests that the best-performing modeling strategy outperformed previously reported methods in blinded evaluations; model performance was consistent across several independent evaluations; and aggregating community-developed models achieved performance on par with the best-performing individual models.