Transcriptome-wide association study of the plasma proteome reveals cis and trans regulatory mechanisms underlying complex traits.

Regulation of transcription and translation are mechanisms through which genetic variants affect complex traits. Expression quantitative trait locus (eQTL) studies have been more successful at identifying cis-eQTL (within 1 Mb of the transcription start site) than trans-eQTL. Here, we tested the cis component of gene expression for association with observed plasma protein levels to identify cis- and trans-acting genes that regulate protein levels. We used transcriptome prediction models from 49 Genotype-Tissue Expression (GTEx) Project tissues to predict the cis component of gene expression and tested the predicted expression of every gene in every tissue for association with the observed abundance of 3,622 plasma proteins measured in 3,301 individuals from the INTERVAL study. We tested significant results for replication in 971 individuals from the Trans-omics for Precision Medicine (TOPMed) Multi-Ethnic Study of Atherosclerosis (MESA). We found 1,168 and 1,210 cis- and trans-acting associations that replicated in TOPMed (FDR < 0.05) with a median expected true positive rate (π1) across tissues of 0.806 and 0.390, respectively. The target proteins of trans-acting genes were enriched for transcription factor binding sites and autoimmune diseases in the GWAS catalog. Furthermore, we found a higher correlation between predicted expression and protein levels of the same underlying gene (R = 0.17) than observed expression (R = 0.10, p = 7.50 × 10-11). This indicates the cis-acting genetically regulated (heritable) component of gene expression is more consistent across tissues than total observed expression (genetics + environment) and is useful in uncovering the function of SNPs associated with complex traits.

Polygenic transcriptome risk scores for COPD and lung function improve cross-ethnic portability of prediction in the NHLBI TOPMed program.

While polygenic risk scores (PRSs) enable early identification of genetic risk for chronic obstructive pulmonary disease (COPD), predictive performance is limited when the discovery and target populations are not well matched. Hypothesizing that the biological mechanisms of disease are shared across ancestry groups, we introduce a PrediXcan-derived polygenic transcriptome risk score (PTRS) to improve cross-ethnic portability of risk prediction. We constructed the PTRS using summary statistics from application of PrediXcan on large-scale GWASs of lung function (forced expiratory volume in 1 s [FEV1] and its ratio to forced vital capacity [FEV1/FVC]) in the UK Biobank. We examined prediction performance and cross-ethnic portability of PTRS through smoking-stratified analyses both on 29,381 multi-ethnic participants from TOPMed population/family-based cohorts and on 11,771 multi-ethnic participants from TOPMed COPD-enriched studies. Analyses were carried out for two dichotomous COPD traits (moderate-to-severe and severe COPD) and two quantitative lung function traits (FEV1 and FEV1/FVC). While the proposed PTRS showed weaker associations with disease than PRS for European ancestry, the PTRS showed stronger association with COPD than PRS for African Americans (e.g., odds ratio [OR] = 1.24 [95% confidence interval [CI]: 1.08-1.43] for PTRS versus 1.10 [0.96-1.26] for PRS among heavy smokers with ≥ 40 pack-years of smoking) for moderate-to-severe COPD. Cross-ethnic portability of the PTRS was significantly higher than the PRS (paired t test p < 2.2 × 10-16 with portability gains ranging from 5% to 28%) for both dichotomous COPD traits and across all smoking strata. Our study demonstrates the value of PTRS for improved cross-ethnic portability compared to PRS in predicting COPD risk.

Genetically regulated expression underlies cellular sensitivity to chemotherapy in diverse populations.

Most cancer chemotherapeutic agents are ineffective in a subset of patients; thus, it is important to consider the role of genetic variation in drug response. Lymphoblastoid cell lines (LCLs) in 1000 Genomes Project populations of diverse ancestries are a useful model for determining how genetic factors impact the variation in cytotoxicity. In our study, LCLs from three 1000 Genomes Project populations of diverse ancestries were previously treated with increasing concentrations of eight chemotherapeutic drugs, and cell growth inhibition was measured at each dose with half-maximal inhibitory concentration (IC50) or area under the dose-response curve (AUC) as our phenotype for each drug. We conducted both genome-wide association studies (GWAS) and transcriptome-wide association studies (TWAS) within and across ancestral populations. We identified four unique loci in GWAS and three genes in TWAS to be significantly associated with the chemotherapy-induced cytotoxicity within and across ancestral populations. In the etoposide TWAS, increased STARD5 predicted expression associated with decreased etoposide IC50 (P = 8.5 × 10-8). Functional studies in A549, a lung cancer cell line, revealed that knockdown of STARD5 expression resulted in the decreased sensitivity to etoposide following exposure for 72 (P = 0.033) and 96 h (P = 0.0001). By identifying loci and genes associated with cytotoxicity across ancestral populations, we strive to understand the genetic factors impacting the effectiveness of chemotherapy drugs and to contribute to the development of future cancer treatment.

Modulation of SF3B1 in the pre-mRNA spliceosome induces a RIG-I-dependent type I IFN response.

Nucleic acid-sensing pathways play critical roles in innate immune activation through the production of type I interferon (IFN-I) and proinflammatory cytokines. These factors are required for effective antitumor immune responses. Pharmacological modulators of the pre-mRNA spliceosome splicing factor 3b subunit 1 (SF3B1) are under clinical investigation as cancer cytotoxic agents. However, potential roles of these agents in aberrant RNA generation and subsequent RNA-sensing pathway activation have not been studied. In this study, we observed that SF3B1 pharmacological modulation using pladienolide B (Plad B) induces production of aberrant RNA species and robust IFN-I responses via engagement of the dsRNA sensor retinoic acid-inducible gene I (RIG-I) and downstream interferon regulatory factor 3. We found that Plad B synergized with canonical RIG-I agonism to induce the IFN-I response. In addition, Plad B induced NF-κB responses and secretion of proinflammatory cytokines and chemokines. Finally, we showed that cancer cells bearing the hotspot SF3B1K700E mutation, which leads to global aberrant splicing, had enhanced IFN-I response to canonical RIG-I agonism. Together, these results demonstrate that pharmacological modulation of SF3B1 in cancer cells can induce an enhanced IFN-I response dependent on RIG-I expression. The study suggests that spliceosome modulation may not only induce direct cancer cell cytotoxicity but also initiate an innate immune response via activation of RNA-sensing pathways.

Integrating predicted transcriptome from multiple tissues improves association detection.

Integration of genome-wide association studies (GWAS) and expression quantitative trait loci (eQTL) studies is needed to improve our understanding of the biological mechanisms underlying GWAS hits, and our ability to identify therapeutic targets. Gene-level association methods such as PrediXcan can prioritize candidate targets. However, limited eQTL sample sizes and absence of relevant developmental and disease context restrict our ability to detect associations. Here we propose an efficient statistical method (MultiXcan) that leverages the substantial sharing of eQTLs across tissues and contexts to improve our ability to identify potential target genes. MultiXcan integrates evidence across multiple panels using multivariate regression, which naturally takes into account the correlation structure. We apply our method to simulated and real traits from the UK Biobank and show that, in realistic settings, we can detect a larger set of significantly associated genes than using each panel separately. To improve applicability, we developed a summary result-based extension called S-MultiXcan, which we show yields highly concordant results with the individual level version when LD is well matched. Our multivariate model-based approach allowed us to use the individual level results as a gold standard to calibrate and develop a robust implementation of the summary-based extension. Results from our analysis as well as software and necessary resources to apply our method are publicly available.

Fine-mapping and QTL tissue-sharing information improves the reliability of causal gene identification.

The integration of transcriptomic studies and genome-wide association studies (GWAS) via imputed expression has seen extensive application in recent years, enabling the functional characterization and causal gene prioritization of GWAS loci. However, the techniques for imputing transcriptomic traits from DNA variation remain underdeveloped. Furthermore, associations found when linking eQTL studies to complex traits through methods like PrediXcan can lead to false positives due to linkage disequilibrium between distinct causal variants. Therefore, the best prediction performance models may not necessarily lead to more reliable causal gene discovery. With the goal of improving discoveries without increasing false positives, we develop and compare multiple transcriptomic imputation approaches using the most recent GTEx release of expression and splicing data on 17,382 RNA-sequencing samples from 948 post-mortem donors in 54 tissues. We find that informing prediction models with posterior causal probability from fine-mapping (dap-g) and borrowing information across tissues (mashr) can lead to better performance in terms of number and proportion of significant associations that are colocalized and the proportion of silver standard genes identified as indicated by precision-recall and receiver operating characteristic curves. All prediction models are made publicly available at predictdb.org.

Clinical and genetic risk factors for radiation-associated ototoxicity: A report from the Childhood Cancer Survivor Study and the St. Jude Lifetime Cohort.

BACKGROUND: Cranial radiation therapy (CRT) is associated with ototoxicity, which manifests as hearing loss and tinnitus. The authors sought to identify clinical determinants and genetic risk factors for ototoxicity among adult survivors of pediatric cancer treated with CRT. METHODS: Logistic regression evaluated associations of tinnitus (n = 1991) and hearing loss (n = 2198) with nongenetic risk factors and comorbidities among CRT-treated survivors in the Childhood Cancer Survivor Study. Genome-wide association studies (GWASs) of CRT-related tinnitus and hearing loss were also performed. RESULTS: Males were more likely to report CRT-related tinnitus (9.4% vs 5.4%; P = 5.1 × 10-4 ) and hearing loss (14.0% vs 10.7%; P = .02) than females. Survivors with tinnitus or hearing loss were more likely to experience persistent dizziness or vertigo (tinnitus: P < 2 × 10-16 ; hearing loss: P = 6.4 × 10-9 ), take antidepressants (tinnitus: P = .02; hearing loss: P = .01), and report poorer overall health (tinnitus: P = 1.5 × 10-6 ; hearing loss: P = 1.7 × 10-6 ) in comparison with controls. GWAS of CRT-related tinnitus revealed a genome-wide significant signal in chromosome 1 led by rs203248 (P = 1.5 × 10-9 ), whereas GWAS of CRT-related hearing loss identified rs332013 (P = 5.8 × 10-7 ) in chromosome 8 and rs67522722 (P = 7.8 × 10-7 ) in chromosome 6 as nearly genome-wide significant. A replication analysis identified rs67522722, intronic to ATXN1, as being significantly associated with CRT-related hearing loss (P = .03) and de novo hearing loss (P = 3.6 × 10-4 ). CONCLUSIONS: CRT-associated ototoxicity was associated with sex, several neuro-otological symptoms, increased antidepressant use, and poorer self-reported health. GWAS of CRT-related hearing loss identified rs67522722, which was supported in an independent cohort of survivors. LAY SUMMARY: Hearing loss and subjective tinnitus (the perception of noise or ringing in the ear) are long-term side effects of cancer treatment and are common in children treated with radiation to the brain. These toxicities can affect childhood development and potentially contribute to serious learning and behavioral difficulties. This study's data indicate that males are at greater risk for hearing loss and tinnitus than females after radiation therapy to the brain. Those who develop these toxicities are more likely to use antidepressants and report poorer overall health. Health care providers can improve the management of survivors by informing patients and/or their parents of these risks.

Light-activated protein interaction with high spatial subcellular confinement.

Methods to acutely manipulate protein interactions at the subcellular level are powerful tools in cell biology. Several blue-light-dependent optical dimerization tools have been developed. In these systems one protein component of the dimer (the bait) is directed to a specific subcellular location, while the other component (the prey) is fused to the protein of interest. Upon illumination, binding of the prey to the bait results in its subcellular redistribution. Here, we compared and quantified the extent of light-dependent dimer occurrence in small, subcellular volumes controlled by three such tools: Cry2/CIB1, iLID, and Magnets. We show that both the location of the photoreceptor protein(s) in the dimer pair and its (their) switch-off kinetics determine the subcellular volume where dimer formation occurs and the amount of protein recruited in the illuminated volume. Efficient spatial confinement of dimer to the area of illumination is achieved when the photosensitive component of the dimerization pair is tethered to the membrane of intracellular compartments and when on and off kinetics are extremely fast, as achieved with iLID or Magnets. Magnets and the iLID variants with the fastest switch-off kinetics induce and maintain protein dimerization in the smallest volume, although this comes at the expense of the total amount of dimer. These findings highlight the distinct features of different optical dimerization systems and will be useful guides in the choice of tools for specific applications.

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 https://github.com/WheelerLab/DivPop.

Imputed gene associations identify replicable trans-acting genes enriched in transcription pathways and complex traits.

Regulation of gene expression is an important mechanism through which genetic variation can affect complex traits. A substantial portion of gene expression variation can be explained by both local (cis) and distal (trans) genetic variation. Much progress has been made in uncovering cis-acting expression quantitative trait loci (cis-eQTL), but trans-eQTL have been more difficult to identify and replicate. Here we take advantage of our ability to predict the cis component of gene expression coupled with gene mapping methods such as PrediXcan to identify high confidence candidate trans-acting genes and their targets. That is, we correlate the cis component of gene expression with observed expression of genes in different chromosomes. Leveraging the shared cis-acting regulation across tissues, we combine the evidence of association across all available Genotype-Tissue Expression Project tissues and find 2,356 trans-acting/target gene pairs with high mappability scores. Reassuringly, trans-acting genes are enriched in transcription and nucleic acid binding pathways and target genes are enriched in known transcription factor binding sites. Interestingly, trans-acting genes are more significantly associated with selected complex traits and diseases than target or background genes, consistent with percolating trans effects. Our scripts and summary statistics are publicly available for future studies of trans-acting gene regulation.

Chronic circadian misalignment results in reduced longevity and large-scale changes in gene expression in Drosophila.

BACKGROUND: Circadian clocks are found in nearly all organisms, from bacteria to mammals, and ensure that behavioral and physiological processes occur at optimal times of day and in the correct temporal order. It is becoming increasingly clear that chronic circadian misalignment (CCM), such as occurs in shift workers or as a result of aberrant sleeping and eating schedules common to modern society, has profound metabolic and cognitive consequences, but the proximate mechanisms connecting CCM with reduced organismal health are unknown. Furthermore, it has been difficult to disentangle whether the health effects are directly induced by misalignment or are secondary to the alterations in sleep and activity levels that commonly occur with CCM. Here, we investigated the consequences of CCM in the powerful model system of the fruit fly, Drosophila melanogaster. We subjected flies to daily 4-h phase delays in the light-dark schedule and used the Drosophila Activity Monitoring (DAM) system to continuously track locomotor activity and sleep while simultaneously monitoring fly lifespan. RESULTS: Consistent with previous results, we find that exposing flies to CCM leads to a ~ 15% reduction in median lifespan in both male and female flies. Importantly, we demonstrate that the reduced longevity occurs independent of changes in overall sleep or activity. To uncover potential molecular mechanisms of CCM-induced reduction in lifespan, we conducted whole body RNA-sequencing to assess differences in gene transcription between control and misaligned flies. CCM caused progressive, large-scale changes in gene expression characterized by upregulation of genes involved in response to toxic substances, aging and oxidative stress, and downregulation of genes involved in regulation of development and differentiation, gene expression and biosynthesis. CONCLUSIONS: Many of these gene expression changes mimic those that occur during natural aging, consistent with the idea that CCM results in premature organismal decline, however, we found that genes involved in lipid metabolism are overrepresented among those that are differentially regulated by CCM and aging. This category of genes is also among the earliest to exhibit CCM-induced changes in expression, thus highlighting altered lipid metabolism as a potentially important mediator of the negative health consequences of CCM.

Integration of genetic and functional genomics data to uncover chemotherapeutic induced cytotoxicity.

Identifying genetic variants associated with chemotherapeutic induced toxicity is an important step towards personalized treatment of cancer patients. However, annotating and interpreting the associated genetic variants remains challenging because each associated variant is a surrogate for many other variants in the same region. The issue is further complicated when investigating patterns of associated variants with multiple drugs. In this study, we used biological knowledge to annotate and compare genetic variants associated with cellular sensitivity to mechanistically distinct chemotherapeutic drugs, including platinating agents (cisplatin, carboplatin), capecitabine, cytarabine, and paclitaxel. The most significantly associated SNPs from genome wide association studies of cellular sensitivity to each drug in lymphoblastoid cell lines derived from populations of European (CEU) and African (YRI) descent were analyzed for their enrichment in biological pathways and processes. We annotated genetic variants using higher-level biological annotations in efforts to group variants into more interpretable biological modules. Using the higher-level annotations, we observed distinct biological modules associated with cell line populations as well as classes of chemotherapeutic drugs. We also integrated genetic variants and gene expression variables to build predictive models for chemotherapeutic drug cytotoxicity and prioritized the network models based on the enrichment of DNA regulatory data. Several biological annotations, often encompassing different SNPs, were replicated in independent datasets. By using biological knowledge and DNA regulatory information, we propose a novel approach for jointly analyzing genetic variants associated with multiple chemotherapeutic drugs.

Cancer pharmacogenomics: strategies and challenges.

Genetic variation influences the response of an individual to drug treatments. Understanding this variation has the potential to make therapy safer and more effective by determining selection and dosing of drugs for an individual patient. In the context of cancer, tumours may have specific disease-defining mutations, but a patient's germline genetic variation will also affect drug response (both efficacy and toxicity), and here we focus on how to study this variation. Advances in sequencing technologies, statistical genetics analysis methods and clinical trial designs have shown promise for the discovery of variants associated with drug response. We discuss the application of germline genetics analysis methods to cancer pharmacogenomics with a focus on the special considerations for study design.

Survey of the Heritability and Sparse Architecture of Gene Expression Traits across Human Tissues.

Understanding the genetic architecture of gene expression traits is key to elucidating the underlying mechanisms of complex traits. Here, for the first time, we perform a systematic survey of the heritability and the distribution of effect sizes across all representative tissues in the human body. We find that local h2 can be relatively well characterized with 59% of expressed genes showing significant h2 (FDR < 0.1) in the DGN whole blood cohort. However, current sample sizes (n ≤ 922) do not allow us to compute distal h2. Bayesian Sparse Linear Mixed Model (BSLMM) analysis provides strong evidence that the genetic contribution to local expression traits is dominated by a handful of genetic variants rather than by the collective contribution of a large number of variants each of modest size. In other words, the local architecture of gene expression traits is sparse rather than polygenic across all 40 tissues (from DGN and GTEx) examined. This result is confirmed by the sparsity of optimal performing gene expression predictors via elastic net modeling. To further explore the tissue context specificity, we decompose the expression traits into cross-tissue and tissue-specific components using a novel Orthogonal Tissue Decomposition (OTD) approach. Through a series of simulations we show that the cross-tissue and tissue-specific components are identifiable via OTD. Heritability and sparsity estimates of these derived expression phenotypes show similar characteristics to the original traits. Consistent properties relative to prior GTEx multi-tissue analysis results suggest that these traits reflect the expected biology. Finally, we apply this knowledge to develop prediction models of gene expression traits for all tissues. The prediction models, heritability, and prediction performance R2 for original and decomposed expression phenotypes are made publicly available (https://github.com/hakyimlab/PrediXcan).

Genetic Variants Contributing to Colistin Cytotoxicity: Identification of TGIF1 and HOXD10 Using a Population Genomics Approach.

Colistin sulfate (polymixin E) is an antibiotic prescribed with increasing frequency for severe Gram-negative bacterial infections. As nephrotoxicity is a common side effect, the discovery of pharmacogenomic markers associated with toxicity would benefit the utility of this drug. Our objective was to identify genetic markers of colistin cytotoxicity that were also associated with expression of key proteins using an unbiased, whole genome approach and further evaluate the functional significance in renal cell lines. To this end, we employed International HapMap lymphoblastoid cell lines (LCLs) of Yoruban ancestry with known genetic information to perform a genome-wide association study (GWAS) with cellular sensitivity to colistin. Further association studies revealed that single nucleotide polymorphisms (SNPs) associated with gene expression and protein expression were significantly enriched in SNPs associated with cytotoxicity (p ≤ 0.001 for gene and p = 0.015 for protein expression). The most highly associated SNP, chr18:3417240 (p = 6.49 × 10-8), was nominally a cis-expression quantitative trait locus (eQTL) of the gene TGIF1 (transforming growth factor ß (TGFß)-induced factor-1; p = 0.021) and was associated with expression of the protein HOXD10 (homeobox protein D10; p = 7.17 × 10-5). To demonstrate functional relevance in a murine colistin nephrotoxicity model, HOXD10 immunohistochemistry revealed upregulated protein expression independent of mRNA expression in response to colistin administration. Knockdown of TGIF1 resulted in decreased protein expression of HOXD10 and increased resistance to colistin cytotoxicity. Furthermore, knockdown of HOXD10 in renal cells also resulted in increased resistance to colistin cytotoxicity, supporting the physiological relevance of the initial genomic associations.

Poly-omic prediction of complex traits: OmicKriging.

High-confidence prediction of complex traits such as disease risk or drug response is an ultimate goal of personalized medicine. Although genome-wide association studies have discovered thousands of well-replicated polymorphisms associated with a broad spectrum of complex traits, the combined predictive power of these associations for any given trait is generally too low to be of clinical relevance. We propose a novel systems approach to complex trait prediction, which leverages and integrates similarity in genetic, transcriptomic, or other omics-level data. We translate the omic similarity into phenotypic similarity using a method called Kriging, commonly used in geostatistics and machine learning. Our method called OmicKriging emphasizes the use of a wide variety of systems-level data, such as those increasingly made available by comprehensive surveys of the genome, transcriptome, and epigenome, for complex trait prediction. Furthermore, our OmicKriging framework allows easy integration of prior information on the function of subsets of omics-level data from heterogeneous sources without the sometimes heavy computational burden of Bayesian approaches. Using seven disease datasets from the Wellcome Trust Case Control Consortium (WTCCC), we show that OmicKriging allows simple integration of sparse and highly polygenic components yielding comparable performance at a fraction of the computing time of a recently published Bayesian sparse linear mixed model method. Using a cellular growth phenotype, we show that integrating mRNA and microRNA expression data substantially increases performance over either dataset alone. Using clinical statin response, we show improved prediction over existing methods. We provide an R package to implement OmicKriging (http://www.scandb.org/newinterface/tools/OmicKriging.html).

Genetic and epigenetic variants contributing to clofarabine cytotoxicity.

2-chloro-2-fluoro-deoxy-9-D-arabinofuranosyladenine (Clofarabine), a purine nucleoside analog, is used in the treatment of hematologic malignancies and as induction therapy for stem cell transplantation. The discovery of pharmacogenomic markers associated with chemotherapeutic efficacy and toxicity would greatly benefit the utility of this drug. Our objective was to identify genetic and epigenetic variants associated with clofarabine toxicity using an unbiased, whole genome approach. To this end, we employed International HapMap lymphoblastoid cell lines (190 LCLs) of European (CEU) or African (YRI) ancestry with known genetic information to evaluate cellular sensitivity to clofarabine. We measured modified cytosine levels to ascertain the contribution of genetic and epigenetic factors influencing clofarabine-mediated cytotoxicity. Association studies revealed 182 single nucleotide polymorphisms (SNPs) and 143 modified cytosines associated with cytotoxicity in both populations at the threshold P ≤ 0.0001. Correlation between cytotoxicity and baseline gene expression revealed 234 genes at P ≤ 3.98 × 10(-6). Six genes were implicated as: (i) their expression was directly correlated to cytotoxicity, (ii) they had a targeting SNP associated with cytotoxicity, and (iii) they had local modified cytosines associated with gene expression and cytotoxicity. We identified a set of three SNPs and three CpG sites targeting these six genes explaining 43.1% of the observed variation in phenotype. siRNA knockdown of the top three genes (SETBP1, BAG3, KLHL6) in LCLs revealed altered susceptibility to clofarabine, confirming relevance. As clofarabine's toxicity profile includes acute kidney injury, we examined the effect of siRNA knockdown in HEK293 cells. siSETBP1 led to a significant change in HEK293 cell susceptibility to clofarabine.

Relating human genetic variation to variation in drug responses.

Although sequencing a single human genome was a monumental effort a decade ago, more than 1000 genomes have now been sequenced. The task ahead lies in transforming this information into personalized treatment strategies that are tailored to the unique genetics of each individual. One important aspect of personalized medicine is patient-to-patient variation in drug response. Pharmacogenomics addresses this issue by seeking to identify genetic contributors to human variation in drug efficacy and toxicity. Here, we present a summary of the current status of this field, which has evolved from studies of single candidate genes to comprehensive genome-wide analyses. Additionally, we discuss the major challenges in translating this knowledge into a systems-level understanding of drug physiology, with the ultimate goal of developing more effective personalized clinical treatment strategies.

Comprehensive genetic analysis of cytarabine sensitivity in a cell-based model identifies polymorphisms associated with outcome in AML patients.

A whole-genome approach was used to investigate the genetic determinants of cytarabine-induced cytotoxicity. We performed a meta-analysis of genome-wide association studies involving 523 lymphoblastoid cell lines (LCLs) from individuals of European, African, Asian, and African American ancestry. Several of the highest-ranked single-nucleotide polymorphisms (SNPs) were within the mutated in colorectal cancers (MCC) gene. MCC expression was induced by cytarabine treatment from 1.7- to 26.6-fold in LCLs. A total of 33 SNPs ranked at the top of the meta-analysis (P < 10(-5)) were successfully tested in a clinical trial of patients randomized to receive low-dose or high-dose cytarabine plus daunorubicin and etoposide; of these, 18 showed association (P < .05) with either cytarabine 50% inhibitory concentration in leukemia cells or clinical response parameters (minimal residual disease, overall survival (OS), and treatment-related mortality). This count (n = 18) was significantly greater than expected by chance (P = .016). For rs1203633, LCLs with AA genotype were more sensitive to cytarabine-induced cytotoxicity (P = 1.31 × 10(-6)) and AA (vs GA or GG) genotype was associated with poorer OS (P = .015), likely as a result of greater treatment-related mortality (P = .0037) in patients with acute myeloid leukemia (AML). This multicenter AML02 study trial was registered at www.clinicaltrials.gov as #NCT00136084.

Comprehensive multiphase NMR: a promising technology to study plants in their native state.

Nuclear magnetic resonance (NMR) spectroscopy is arguably one the most powerful tools to study the interactions and molecular structure within plants. Traditionally, however, NMR has developed as two separate fields, one dealing with liquids and the other dealing with solids. Plants in their native state contain components that are soluble, swollen, and true solids. Here, a new form of NMR spectroscopy, developed in 2012, termed comprehensive multiphase (CMP)-NMR is applied for plant analysis. The technology composes all aspects of solution, gel, and solid-state NMR into a single NMR probe such that all components in all phases in native unaltered samples can be studied and differentiated in situ. The technology is evaluated using wild-type Arabidopsis thaliana and the cellulose-deficient mutant ectopic lignification1 (eli1) as examples. Using CMP-NMR to study intact samples eliminated the bias introduced by extraction methods and enabled the acquisition of a more complete structural and metabolic profile; thus, CMP-NMR revealed molecular differences between wild type (WT) and eli1 that could be overlooked by conventional methods. Methanol, fatty acids and/or lipids, glutamine, phenylalanine, starch, and nucleic acids were more abundant in eli1 than in WT. Pentaglycine was present in A. thaliana seedlings and more abundant in eli1 than in WT.