Braveheart, a long noncoding RNA required for cardiovascular lineage commitment.

Long noncoding RNAs (lncRNAs) are often expressed in a development-specific manner, yet little is known about their roles in lineage commitment. Here, we identified Braveheart (Bvht), a heart-associated lncRNA in mouse. Using multiple embryonic stem cell (ESC) differentiation strategies, we show that Bvht is required for progression of nascent mesoderm toward a cardiac fate. We find that Bvht is necessary for activation of a core cardiovascular gene network and functions upstream of mesoderm posterior 1 (MesP1), a master regulator of a common multipotent cardiovascular progenitor. We also show that Bvht interacts with SUZ12, a component of polycomb-repressive complex 2 (PRC2), during cardiomyocyte differentiation, suggesting that Bvht mediates epigenetic regulation of cardiac commitment. Finally, we demonstrate a role for Bvht in maintaining cardiac fate in neonatal cardiomyocytes. Together, our work provides evidence for a long noncoding RNA with critical roles in the establishment of the cardiovascular lineage during mammalian development.

Targeted Degradation of PARP14 Using a Heterobifunctional Small Molecule.

PARP14 is an interferon-stimulated gene that is overexpressed in multiple tumor types, influencing pro-tumor macrophage polarization as well as suppressing the antitumor inflammation response by modulating IFN-γ and IL-4 signaling. PARP14 is a 203 kDa protein that possesses a catalytic domain responsible for the transfer of mono-ADP-ribose to its substrates. PARP14 also contains three macrodomains and a WWE domain which are binding modules for mono-ADP-ribose and poly-ADP-ribose, respectively, in addition to two RNA recognition motifs. Catalytic inhibitors of PARP14 have been shown to reverse IL-4 driven pro-tumor gene expression in macrophages, however it is not clear what roles the non-enzymatic biomolecular recognition motifs play in PARP14-driven immunology and inflammation. To further understand this, we have discovered a heterobifunctional small molecule designed based on a catalytic inhibitor of PARP14 that binds in the enzyme's NAD+ -binding site and recruits cereblon to ubiquitinate it and selectively target it for degradation.

Metformin pharmacogenomics: a genome-wide association study to identify genetic and epigenetic biomarkers involved in metformin anticancer response using human lymphoblastoid cell lines.

Metformin is currently considered as a promising anticancer agent in addition to its anti-diabetic effect. To better individualize metformin therapy and explore novel molecular mechanisms in cancer treatment, we conducted a pharmacogenomic study using 266 lymphoblastoid cell lines (LCLs). Metformin cytotoxicity assay was performed using the MTS assay. Genome-wide association (GWA) analyses were performed in LCLs using 1.3 million SNPs, 485k DNA methylation probes, 54k mRNA expression probe sets, and metformin cytotoxicity (IC50s). Top candidate genes were functionally validated using siRNA screening, followed by MTS assay in breast cancer cell lines. Further study of one top candidate, STUB1, was performed to elucidate the mechanisms by which STUB1 might contribute to metformin action. GWA analyses in LCLs identified 198 mRNA expression probe sets, 12 SNP loci, and 5 DNA methylation loci associated with metformin IC50 with P-values <10−4 or <10−5. Integrated SNP/methylation loci-expression-IC50 analyses found 3 SNP loci or 5 DNA methylation loci associated with metformin IC50 through trans-regulation of expression of 11 or 26 genes with P-value <10−4. Functional validation of top 61 candidate genes in 4 IPA networks indicated down regulation of 14 genes significantly altered metformin sensitivity in two breast cancer cell lines. Mechanistic studies revealed that the E3 ubiquitin ligase, STUB1, could influence metformin response by facilitating proteasome-mediated degradation of cyclin A. GWAS using a genomic data-enriched LCL model system, together with functional and mechanistic studies using cancer cell lines, help us to identify novel genetic and epigenetic biomarkers involved in metformin anticancer response.

Diffuse large B-cell lymphoma patient-derived xenograft models capture the molecular and biological heterogeneity of the disease.

Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous disease defined by transcriptional classifications, specific signaling and survival pathways, and multiple low-frequency genetic alterations. Preclinical model systems that capture the genetic and functional heterogeneity of DLBCL are urgently needed. Here, we generated and characterized a panel of large B-cell lymphoma (LBCL) patient-derived xenograft (PDX) models, including 8 that reflect the immunophenotypic, transcriptional, genetic, and functional heterogeneity of primary DLBCL and 1 that is a plasmablastic lymphoma. All LBCL PDX models were subjected to whole-transcriptome sequencing to classify cell of origin and consensus clustering classification (CCC) subtypes. Mutations and chromosomal rearrangements were evaluated by whole-exome sequencing with an extended bait set. Six of the 8 DLBCL models were activated B-cell (ABC)-type tumors that exhibited ABC-associated mutations such as MYD88, CD79B, CARD11, and PIM1. The remaining 2 DLBCL models were germinal B-cell type, with characteristic alterations of GNA13, CREBBP, and EZH2, and chromosomal translocations involving IgH and either BCL2 or MYC Only 25% of the DLBCL PDX models harbored inactivating TP53 mutations, whereas 75% exhibited copy number alterations of TP53 or its upstream modifier, CDKN2A, consistent with the reported incidence and type of p53 pathway alterations in primary DLBCL. By CCC criteria, 6 of 8 DLBCL PDX models were B-cell receptor (BCR)-type tumors that exhibited selective surface immunoglobulin expression and sensitivity to entospletinib, a recently developed spleen tyrosine kinase inhibitor. In summary, we have established and characterized faithful PDX models of DLBCL and demonstrated their usefulness in functional analyses of proximal BCR pathway inhibition.

Targetable genetic features of primary testicular and primary central nervous system lymphomas.

Primary central nervous system lymphomas (PCNSLs) and primary testicular lymphomas (PTLs) are extranodal large B-cell lymphomas (LBCLs) with inferior responses to current empiric treatment regimens. To identify targetable genetic features of PCNSL and PTL, we characterized their recurrent somatic mutations, chromosomal rearrangements, copy number alterations (CNAs), and associated driver genes, and compared these comprehensive genetic signatures to those of diffuse LBCL and primary mediastinal large B-cell lymphoma (PMBL). These studies identify unique combinations of genetic alterations in discrete LBCL subtypes and subtype-selective bases for targeted therapy. PCNSLs and PTLs frequently exhibit genomic instability, and near-uniform, often biallelic, CDKN2A loss with rare TP53 mutations. PCNSLs and PTLs also use multiple genetic mechanisms to target key genes and pathways and exhibit near-uniform oncogenic Toll-like receptor signaling as a result of MYD88 mutation and/or NFKBIZ amplification, frequent concurrent B-cell receptor pathway activation, and deregulation of BCL6. Of great interest, PCNSLs and PTLs also have frequent 9p24.1/PD-L1/PD-L2 CNAs and additional translocations of these loci, structural bases of immune evasion that are shared with PMBL.

BreaKmer: detection of structural variation in targeted massively parallel sequencing data using kmers.

Genomic structural variation (SV), a common hallmark of cancer, has important predictive and therapeutic implications. However, accurately detecting SV using high-throughput sequencing data remains challenging, especially for 'targeted' resequencing efforts. This is critically important in the clinical setting where targeted resequencing is frequently being applied to rapidly assess clinically actionable mutations in tumor biopsies in a cost-effective manner. We present BreaKmer, a novel approach that uses a 'kmer' strategy to assemble misaligned sequence reads for predicting insertions, deletions, inversions, tandem duplications and translocations at base-pair resolution in targeted resequencing data. Variants are predicted by realigning an assembled consensus sequence created from sequence reads that were abnormally aligned to the reference genome. Using targeted resequencing data from tumor specimens with orthogonally validated SV, non-tumor samples and whole-genome sequencing data, BreaKmer had a 97.4% overall sensitivity for known events and predicted 17 positively validated, novel variants. Relative to four publically available algorithms, BreaKmer detected SV with increased sensitivity and limited calls in non-tumor samples, key features for variant analysis of tumor specimens in both the clinical and research settings.

Global analysis of cell cycle gene expression of the legume symbiont Sinorhizobium meliloti.

In α-proteobacteria, strict regulation of cell cycle progression is necessary for the specific cellular differentiation required for adaptation to diverse environmental niches. The symbiotic lifestyle of Sinorhizobium meliloti requires a drastic cellular differentiation that includes genome amplification. To achieve polyploidy, the S. meliloti cell cycle program must be altered to uncouple DNA replication from cell division. In the α-proteobacterium Caulobacter crescentus, cell cycle-regulated transcription plays an important role in the control of cell cycle progression but this has not been demonstrated in other α-proteobacteria. Here we describe a robust method for synchronizing cell growth that enabled global analysis of S. meliloti cell cycle-regulated gene expression. This analysis identified 462 genes with cell cycle-regulated transcripts, including several key cell cycle regulators, and genes involved in motility, attachment, and cell division. Only 28% of the 462 S. meliloti cell cycle-regulated genes were also transcriptionally cell cycle-regulated in C. crescentus. Furthermore, CtrA- and DnaA-binding motif analysis revealed little overlap between the cell cycle-dependent regulons of CtrA and DnaA in S. meliloti and C. crescentus. The predicted S. meliloti cell cycle regulon of CtrA, but not that of DnaA, was strongly conserved in more closely related α-proteobacteria with similar ecological niches as S. meliloti, suggesting that the CtrA cell cycle regulatory network may control functions of central importance to the specific lifestyles of α-proteobacteria.

Host plant peptides elicit a transcriptional response to control the Sinorhizobium meliloti cell cycle during symbiosis.

The α-proteobacterium Sinorhizobium meliloti establishes a chronic intracellular infection during the symbiosis with its legume hosts. Within specialized host cells, S. meliloti differentiates into highly polyploid, enlarged nitrogen-fixing bacteroids. This differentiation is driven by host cells through the production of defensin-like peptides called "nodule-specific cysteine-rich" (NCR) peptides. Recent research has shown that synthesized NCR peptides exhibit antimicrobial activity at high concentrations but cause bacterial endoreduplication at sublethal concentrations. We leveraged synchronized S. meliloti populations to determine how treatment with a sublethal NCR peptide affects the cell cycle and physiology of bacteria at the molecular level. We found that at sublethal levels a representative NCR peptide specifically blocks cell division and antagonizes Z-ring function. Gene-expression profiling revealed that the cell division block was produced, in part, through the substantial transcriptional response elicited by sublethal NCR treatment that affected ∼15% of the genome. Expression of critical cell-cycle regulators, including ctrA, and cell division genes, including genes required for Z-ring function, were greatly attenuated in NCR-treated cells. In addition, our experiments identified important symbiosis functions and stress responses that are induced by sublethal levels of NCR peptides and other antimicrobial peptides. Several of these stress-response pathways also are found in related α-proteobacterial pathogens and might be used by S. meliloti to sense host cues during infection. Our data suggest a model in which, in addition to provoking stress responses, NCR peptides target intracellular regulatory pathways to drive S. meliloti endoreduplication and differentiation during symbiosis.

Multiplexed DNA repair assays for multiple lesions and multiple doses via transcription inhibition and transcriptional mutagenesis.

The capacity to repair different types of DNA damage varies among individuals, making them more or less susceptible to the detrimental health consequences of damage exposures. Current methods for measuring DNA repair capacity (DRC) are relatively labor intensive, often indirect, and usually limited to a single repair pathway. Here, we describe a fluorescence-based multiplex flow-cytometric host cell reactivation assay (FM-HCR) that measures the ability of human cells to repair plasmid reporters, each bearing a different type of DNA damage or different doses of the same type of DNA damage. FM-HCR simultaneously measures repair capacity in any four of the following pathways: nucleotide excision repair, mismatch repair, base excision repair, nonhomologous end joining, homologous recombination, and methylguanine methyltransferase. We show that FM-HCR can measure interindividual DRC differences in a panel of 24 cell lines derived from genetically diverse, apparently healthy individuals, and we show that FM-HCR may be used to identify inhibitors or enhancers of DRC. We further develop a next-generation sequencing-based HCR assay (HCR-Seq) that detects rare transcriptional mutagenesis events due to lesion bypass by RNA polymerase, providing an added dimension to DRC measurements. FM-HCR and HCR-Seq provide powerful tools for exploring relationships among global DRC, disease susceptibility, and optimal treatment.

Diverse cell stresses induce unique patterns of tRNA up- and down-regulation: tRNA-seq for quantifying changes in tRNA copy number.

Emerging evidence points to roles for tRNA modifications and tRNA abundance in cellular stress responses. While isolated instances of stress-induced tRNA degradation have been reported, we sought to assess the effects of stress on tRNA levels at a systems level. To this end, we developed a next-generation sequencing method that exploits the paucity of ribonucleoside modifications at the 3'-end of tRNAs to quantify changes in all cellular tRNA molecules. Application of this tRNA-seq method to Saccharomyces cerevisiae identified all 76 expressed unique tRNA species out of 295 coded in the yeast genome, including all isoacceptor variants, with highly precise relative (fold-change) quantification of tRNAs. In studies of stress-induced changes in tRNA levels, we found that oxidation (H2O2) and alkylation (methylmethane sulfonate, MMS) stresses induced nearly identical patterns of up- and down-regulation for 58 tRNAs. However, 18 tRNAs showed opposing changes for the stresses, which parallels our observation of signature reprogramming of tRNA modifications caused by H2O2 and MMS. Further, stress-induced degradation was limited to only a small proportion of a few tRNA species. With tRNA-seq applicable to any organism, these results suggest that translational control of stress response involves a contribution from tRNA abundance.

Discovery of genetic biomarkers contributing to variation in drug response of cytidine analogues using human lymphoblastoid cell lines.

BACKGROUND: Two cytidine analogues, gemcitabine and cytosine arabinoside (AraC), are widely used in the treatment of a variety of cancers with a large individual variation in response. To identify potential genetic biomarkers associated with response to these two drugs, we used a human lymphoblastoid cell line (LCL) model system with extensive genomic data, including 1.3 million SNPs and 54,000 basal expression probesets to perform genome-wide association studies (GWAS) with gemcitabine and AraC IC50 values. RESULTS: We identified 11 and 27 SNP loci significantly associated with gemcitabine and AraC IC50 values, respectively. Eleven candidate genes were functionally validated using siRNA knockdown approach in multiple cancer cell lines. We also characterized the potential mechanisms of genes by determining their influence on the activity of 10 cancer-related signaling pathways using reporter gene assays. Most SNPs regulated gene expression in a trans manner, except 7 SNPs in the PIGB gene that were significantly associated with both the expression of PIGB and gemcitabine cytotoxicity. CONCLUSION: These results suggest that genetic variation might contribute to drug response via either cis- or trans- regulation of gene expression. GWAS analysis followed by functional pharmacogenomics studies might help identify novel biomarkers contributing to variation in response to these two drugs and enhance our understanding of underlying mechanisms of drug action.

FKBP5 genetic variation: association with selective serotonin reuptake inhibitor treatment outcomes in major depressive disorder.

OBJECTIVES: FKBP51 (51 kDa immunophilin) acts as a modulator of the glucocorticoid receptor and a negative regulator of the Akt pathway. Genetic variation in FKBP5 plays a role in antidepressant response. The aim of this study was to comprehensively assess the role of genetic variation in FKBP5, identified by both Sanger and Next Generation DNA resequencing, as well as genome-wide single nucleotide polymorphisms (SNPs) associated with FKBP5 expression in the response to the selective serotonin reuptake inhibitor (SSRI) treatment of major depressive disorder. METHODS: We identified 657 SNPs in FKBP5 by Next Generation sequencing of 96 DNA samples from white patients, and 149 SNPs were selected for the genotyping together with 235 SNPs that were trans-associated with variation in FKBP5 expression in lymphoblastoid cells. A total of 529 DNA samples from the Mayo Clinic PGRN-SSRI Pharmacogenomic trial for which genome-wide SNPs had already been obtained were genotyped for these 384 SNPs, and associations with treatment outcomes were determined. The most significant SNPs were genotyped using 96 DNA samples from white non-Hispanic patients of the NIMH-supported Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study to attempt replication, followed by functional genomic studies. RESULTS: Genotype-phenotype association analysis indicated that rs352428 was associated with both 8-week treatment response in the Mayo study (odds ratio=0.49; P=0.003) and 6-week response in the STAR*D replication study (odds ratio=0.74; P=0.05). The electrophoresis mobility shift assay and the reporter gene assay confirmed the possible role of this SNP in transcription regulation. CONCLUSION: This comprehensive FKBP5 sequence study provides insight into the role of common genetic polymorphisms that might influence SSRI treatment outcomes in major depressive disorder patients.

Gut microbiome perturbations induced by bacterial infection affect arsenic biotransformation.

Exposure to arsenic affects large human populations worldwide and has been associated with a long list of human diseases, including skin, bladder, lung, and liver cancers, diabetes, and cardiovascular disorders. In addition, there are large individual differences in susceptibility to arsenic-induced diseases, which are frequently associated with different patterns of arsenic metabolism. Several underlying mechanisms, such as genetic polymorphisms and epigenetics, have been proposed, as these factors closely impact the individuals' capacity to metabolize arsenic. In this context, the role of the gut microbiome in directly metabolizing arsenic and triggering systemic responses in diverse organs raises the possibility that perturbations of the gut microbial communities affect the spectrum of metabolized arsenic species and subsequent toxicological effects. In this study, we used an animal model with an altered gut microbiome induced by bacterial infection, 16S rRNA gene sequencing, and inductively coupled plasma mass spectrometry-based arsenic speciation to examine the effect of gut microbiome perturbations on the biotransformation of arsenic. Metagenomics sequencing revealed that bacterial infection significantly perturbed the gut microbiome composition in C57BL/6 mice, which in turn resulted in altered spectra of arsenic metabolites in urine, with inorganic arsenic species and methylated and thiolated arsenic being perturbed. These data clearly illustrated that gut microbiome phenotypes significantly affected arsenic metabolic reactions, including reduction, methylation, and thiolation. These findings improve our understanding of how infectious diseases and environmental exposure interact and may also provide novel insight regarding the gut microbiome composition as a new risk factor of individual susceptibility to environmental chemicals.

Serine hydroxymethyltransferase 1 and 2: gene sequence variation and functional genomic characterization.

Serine hydroxymethyltransferase (SHMT) catalyzes the transfer of a ß-carbon from serine to tetrahydrofolate to form glycine and 5,10-methylene-tetrahydrofolate. This reaction plays an important role in neurotransmitter synthesis and metabolism. We set out to resequence SHMT1 and SHMT2, followed by functional genomic studies. We identified 87 and 60 polymorphisms in SHMT1 and SHMT2, respectively. We observed no significant functional effect of the 13 non-synonymous single-nucleotide polymorphism (SNPs) in these genes, either on catalytic activity or protein quantity. We imputed additional variants across the two genes using '1000 Genomes' data, and identified 14 variants that were significantly associated (p<1.0E-10) with SHMT1 messenger RNA expression in lymphoblastoid cell lines. Many of these SNPs were also significantly correlated with basal SHMT1 protein expression in 268 human liver biopsy samples. Reporter gene assays suggested that the SHMT1 promoter SNP, rs669340, contributed to this variation. Finally, SHMT1 and SHMT2 expression were significantly correlated with those of other Folate and Methionine Cycle genes at both the messenger RNA and protein levels. These experiments represent a comprehensive study of SHMT1 and SHMT2 gene sequence variation and its functional implications. In addition, we obtained preliminary indications that these genes may be co-regulated with other Folate and Methionine Cycle genes.

Shared genomic segment analysis: the power to find rare disease variants.

Shared genomic segment (SGS) analysis uses dense single nucleotide polymorphism genotyping in high-risk (HR) pedigrees to identify regions of sharing between cases. Here, we illustrate the power of SGS to identify dominant rare risk variants. Using simulated pedigrees, we consider 12 disease models based on disease prevalence, minor allele frequency and penetrance to represent disease loci that explain 0.2-99.8% of total disease risk. Pedigrees were required to contain ≥ 15 meioses between all cases and to be HR based on significant excess of disease (P < 0.001 or P < 0.00001). Across these scenarios, the power for a single pedigree ranged widely. Nonetheless, fewer than 10 pedigrees were sufficient for excellent power in the majority of models. Power increased with the risk attributable to the disease locus, penetrance and the excess of disease in the pedigree. Sharing allowing for one sporadic case was uniformly more powerful than sharing using all cases. Furthermore, an SGS analysis using a large attenuated familial adenomatous polyposis pedigree identified a 1.96 Mb region containing the known causal APC gene with genome-wide significance. SGS is a powerful method for detecting rare variants and a valuable complement to genome-wide association studies and linkage analysis.

Gemcitabine metabolic pathway genetic polymorphisms and response in patients with non-small cell lung cancer.

BACKGROUND AND OBJECTIVE: Gemcitabine is widely used to treat non-small cell lung cancer (NSCLC). The aim of this study was to assess the pharmacogenomic effects of the entire gemcitabine metabolic pathway, we genotyped single nucleotide polymorphisms (SNPs) within the 17 pathway genes using DNA samples from patients with NSCLC treated with gemcitabine to determine the effect of genetic variants within gemcitabine pathway genes on overall survival (OS) of patients with NSCLC after treatment of gemcitabine. METHODS: Eight of the 17 pathway genes were resequenced with DNA samples from Coriell lymphoblastoid cell lines (LCLs) using Sanger sequencing for all exons, exon-intron junctions, and 5'-, 3'-UTRs. A total of 107 tagging SNPs were selected on the basis of the resequencing data for the eight genes and on HapMap data for the remaining nine genes, followed by successful genotyping of 394 NSCLC patient DNA samples. Association of SNPs/haplotypes with OS was performed using the Cox regression model, followed by functional studies performed with LCLs and NSCLC cell lines. RESULTS: Five SNPs in four genes (CDA, NT5C2, RRM1, and SLC29A1) showed associations with OS of those patients with NSCLC, as well as nine haplotypes in four genes (RRM1, RRM2, SLC28A3, and SLC29A1) with a P value of less than 0.05. Genotype imputation using the LCLs was performed for a region of 200 kb surrounding those SNPs, followed by association studies with gemcitabine cytotoxicity. Functional studies demonstrated that downregulation of SLC29A1, NT5C2, and RRM1 in NSCLC cell lines altered cell susceptibility to gemcitabine. CONCLUSION: These studies help in identifying biomarkers to predict gemcitabine response in NSCLC, a step toward the individualized chemotherapy of lung cancer.

Merging pharmacometabolomics with pharmacogenomics using '1000 Genomes' single-nucleotide polymorphism imputation: selective serotonin reuptake inhibitor response pharmacogenomics.

OBJECTIVE: We set out to test the hypothesis that pharmacometabolomic data could be efficiently merged with pharmacogenomic data by single-nucleotide polymorphism (SNP) imputation of metabolomic-derived pathway data on a 'scaffolding' of genome-wide association (GWAS) SNP data to broaden and accelerate 'pharmacometabolomics-informed pharmacogenomic' studies by eliminating the need for initial genotyping and by making broader SNP association testing possible. METHODS: We previously genotyped 131 tag SNPs for six genes encoding enzymes in the glycine synthesis and degradation pathway using DNA from 529 depressed patients treated with citalopram/escitalopram to pursue a glycine metabolomics 'signal' associated with selective serotonine reuptake inhibitor response. We identified a significant SNP in the glycine dehydrogenase gene. Subsequently, GWAS SNP data were generated for the same patients. In this study, we compared SNP imputation within 200 kb of these same six genes with the results of the previous tag SNP strategy as a rapid strategy for merging pharmacometabolomic and pharmacogenomic data. RESULTS: Imputed genotype data provided greater coverage and higher resolution than did tag SNP genotyping, with a higher average genotype concordance between genotyped and imputed SNP data for '1000 Genomes' (96.4%) than HapMap 2 (93.2%) imputation. Many low P-value SNPs with novel locations within genes were observed for imputed compared with tag SNPs, thus altering the focus for subsequent functional genomic studies. CONCLUSION: These results indicate that the use of GWAS data to impute SNPs for genes in pathways identified by other 'omics' approaches makes it possible to rapidly and cost efficiently identify SNP markers to 'broaden' and accelerate pharmacogenomic studies.

Automated construction and testing of multi-locus gene-gene associations.

UNLABELLED: It has been argued that the missing heritability in common diseases may be in part due to rare variants and gene-gene effects. Haplotype analyses provide more power for rare variants and joint analyses across genes can address multi-gene effects. Currently, methods are lacking to perform joint multi-locus association analyses across more than one gene/region. Here, we present a haplotype-mining gene-gene analysis method, which considers multi-locus data for two genes/regions simultaneously. This approach extends our single region haplotype-mining algorithm, hapConstructor, to two genes/regions. It allows construction of multi-locus SNP sets at both genes and tests joint gene-gene effects and interactions between single variants or haplotype combinations. A Monte Carlo framework is used to provide statistical significance assessment of the joint and interaction statistics, thus the method can also be used with related individuals. This tool provides a flexible data-mining approach to identifying gene-gene effects that otherwise is currently unavailable. AVAILABILITY: http://bioinformatics.med.utah.edu/Genie/hapConstructor.html.

Human liver methionine cycle: MAT1A and GNMT gene resequencing, functional genomics, and hepatic genotype-phenotype correlation.

The "methionine cycle" plays a critical role in the regulation of concentrations of (S)-adenosylmethionine (AdoMet), the major biological methyl donor. We set out to study sequence variation in genes encoding the enzyme that synthesizes AdoMet in liver, methionine adenosyltransferase 1A (MAT1A) and the major hepatic AdoMet using enzyme, glycine N-methyltransferase (GNMT), as well as functional implications of that variation. We resequenced MAT1A and GNMT using DNA from 288 subjects of three ethnicities, followed by functional genomic and genotype-phenotype correlation studies performed with 268 hepatic biopsy samples. We identified 44 and 42 polymorphisms in MAT1A and GNMT, respectively. Quantitative Western blot analyses for the human liver samples showed large individual variation in MAT1A and GNMT protein expression. Genotype-phenotype correlation identified two genotyped single-nucleotide polymorphisms (SNPs), reference SNP (rs) 9471976 (corrected p = 3.9 × 10(-10)) and rs11752813 (corrected p = 1.8 × 10(-5)), and 42 imputed SNPs surrounding GNMT that were significantly associated with hepatic GNMT protein levels (corrected p values < 0.01). Reporter gene studies showed that variant alleles for both genotyped SNPs resulted in decreased transcriptional activity. Correlation analyses among hepatic protein levels for methionine cycle enzymes showed significant correlations between GNMT and MAT1A (p = 1.5 × 10(-3)) and between GNMT and betaine homocysteine methyltransferase (p = 1.6 × 10(-7)). Our discovery of SNPs that are highly associated with hepatic GNMT protein expression as well as the "coordinate regulation" of methionine cycle enzyme protein levels provide novel insight into the regulation of this important human liver biochemical pathway.

Genetic association with overall survival of taxane-treated lung cancer patients - a genome-wide association study in human lymphoblastoid cell lines followed by a clinical association study.

BACKGROUND: Taxane is one of the first line treatments of lung cancer. In order to identify novel single nucleotide polymorphisms (SNPs) that might contribute to taxane response, we performed a genome-wide association study (GWAS) for two taxanes, paclitaxel and docetaxel, using 276 lymphoblastoid cell lines (LCLs), followed by genotyping of top candidate SNPs in 874 lung cancer patient samples treated with paclitaxel. METHODS: GWAS was performed using 1.3 million SNPs and taxane cytotoxicity IC50 values for 276 LCLs. The association of selected SNPs with overall survival in 76 small or 798 non-small cell lung cancer (SCLC, NSCLC) patients were analyzed by Cox regression model, followed by integrated SNP-microRNA-expression association analysis in LCLs and siRNA screening of candidate genes in SCLC (H196) and NSCLC (A549) cell lines. RESULTS: 147 and 180 SNPs were associated with paclitaxel or docetaxel IC50s with p-values <10-4 in the LCLs, respectively. Genotyping of 153 candidate SNPs in 874 lung cancer patient samples identified 8 SNPs (p-value < 0.05) associated with either SCLC or NSCLC patient overall survival. Knockdown of PIP4K2A, CCT5, CMBL, EXO1, KMO and OPN3, genes within 200 kb up-/downstream of the 3 SNPs that were associated with SCLC overall survival (rs1778335, rs2662411 and rs7519667), significantly desensitized H196 to paclitaxel. SNPs rs2662411 and rs1778335 were associated with mRNA expression of CMBL or PIP4K2A through microRNA (miRNA) hsa-miR-584 or hsa-miR-1468. CONCLUSIONS: GWAS in an LCL model system, joined with clinical translational and functional studies, might help us identify genetic variations associated with overall survival of lung cancer patients treated paclitaxel.