Olivier Harismendy

9p21 DNA variants associated with coronary artery disease impair interferon-γ signalling response.

Genome-wide association studies have identified single nucleotide polymorphisms (SNPs) in the 9p21 gene desert associated with coronary artery disease (CAD) and type 2 diabetes. Despite evidence for a role of the associated interval in neighbouring gene regulation, the biological underpinnings of these genetic associations with CAD or type 2 diabetes have not yet been explained. Here we identify 33 enhancers in 9p21; the interval is the second densest gene desert for predicted enhancers and six times denser than the whole genome (P < 6.55 × 10−33). The CAD risk alleles of SNPs rs10811656 and rs10757278 are located in one of these enhancers and disrupt a binding site for STAT1. Lymphoblastoid cell lines homozygous for the CAD risk haplotype show no binding of STAT1, and in lymphoblastoid cell lines homozygous for the CAD non-risk haplotype, binding of STAT1 inhibits CDKN2BAS (also known as CDKN2B-AS1) expression, which is reversed by short interfering RNA knockdown of STAT1. Using a new, open-ended approach to detect long-distance interactions, we find that in human vascular endothelial cells the enhancer interval containing the CAD locus physically interacts with the CDKN2A/B locus, the MTAP gene and an interval downstream of IFNA21. In human vascular endothelial cells, interferon-γ activation strongly affects the structure of the chromatin and the transcriptional regulation in the 9p21 locus, including STAT1-binding, long-range enhancer interactions and altered expression of neighbouring genes. Our findings establish a link between CAD genetic susceptibility and the response to inflammatory signalling in a vascular cell type and thus demonstrate the utility of genome-wide association study findings in directing studies to novel genomic loci and biological processes important for disease aetiology.

Microdroplet-based PCR enrichment for large-scale targeted sequencing

Targeted enrichment of specific loci of the human genome is a promising approach to enable sequencing-based studies of genetic variation in large populations. Here we describe an enrichment approach based on microdroplet PCR, which enables 1.5 million amplifications in parallel. We sequenced six samples enriched by microdroplet or traditional singleplex PCR using primers targeting 435 exons of 47 genes. Both methods generated similarly high-quality data: 84% of the uniquely mapping reads fell within the targeted sequences; coverage was uniform across ∼90% of targeted bases; sequence variants were called with >99% accuracy; and reproducibility between samples was high (r2 = 0.9). We scaled the microdroplet PCR to 3,976 amplicons totaling 1.49 Mb of sequence, sequenced the resulting sample with both Illumina GAII and Roche 454, and obtained data with equally high specificity and sensitivity. Our results demonstrate that microdroplet technology is well suited for processing DNA for massively parallel enrichment of specific subsets of the human genome for targeted sequencing.

Genome-wide location of yeast RNA polymerase III transcription machinery

RNA polymerase III (Pol III) transcribes a large set of genes encoding small untranslated RNAs like tRNAs, 5S rRNA, U6 snRNA or RPR1 RNA. To get a global view of class III (Pol III‐transcribed) genes, the distribution of essential components of Pol III, TFIIIC and TFIIIB was mapped across the yeast genome. During active growth, most class III genes and few additional loci were targeted by TFIIIC, TFIIIB and Pol III, indicating that they were transcriptionally active. SNR52, which encodes a snoRNA, was identified as a new class III gene. During the late growth phase, TFIIIC remained bound to most class III genes while the recruitment of Pol III and, to a lesser extent, of TFIIIB was down regulated. This study fixes a reasonable upper bound to the number of class III genes in yeast and points to a global regulation at the level of Pol III and TFIIIB recruitment.

Enrichment of sequencing targets from the human genome by solution hybridization

To exploit fully the potential of current sequencing technologies for population-based studies, one must enrich for loci from the human genome. Here we evaluate the hybridization-based approach by using oligonucleotide capture probes in solution to enrich for approximately 3.9 Mb of sequence target. We demonstrate that the tiling probe frequency is important for generating sequence data with high uniform coverage of targets. We obtained 93% sensitivity to detect SNPs, with a calling accuracy greater than 99%.

Accurate detection and genotyping of SNPs utilizing population sequencing data

Next-generation sequencing technologies have made it possible to sequence targeted regions of the human genome in hundreds of individuals. Deep sequencing represents a powerful approach for the discovery of the complete spectrum of DNA sequence variants in functionally important genomic intervals. Current methods for single nucleotide polymorphism (SNP) detection are designed to detect SNPs from single individual sequence data sets. Here, we describe a novel method SNIP-Seq (single nucleotide polymorphism identification from population sequence data) that leverages sequence data from a population of individuals to detect SNPs and assign genotypes to individuals. To evaluate our method, we utilized sequence data from a 200-kilobase (kb) region on chromosome 9p21 of the human genome. This region was sequenced in 48 individuals (five sequenced in duplicate) using the Illumina GA platform. Using this data set, we demonstrate that our method is highly accurate for detecting variants and can filter out false SNPs that are attributable to sequencing errors. The concordance of sequencing-based genotype assignments between duplicate samples was 98.8%. The 200-kb region was independently sequenced to a high depth of coverage using two sequence pools containing the 48 individuals. Many of the novel SNPs identified by SNIP-Seq from the individual sequencing were validated by the pooled sequencing data and were subsequently confirmed by Sanger sequencing. We estimate that SNIP-Seq achieves a low false-positive rate of approximately 2%, improving upon the higher false-positive rate for existing methods that do not utilize population sequence data. Collectively, these results suggest that analysis of population sequencing data is a powerful approach for the accurate detection of SNPs and the assignment of genotypes to individual samples.

A covering method for detecting genetic associations between rare variants and common phenotypes.

Genome wide association (GWA) studies, which test for association between common genetic markers and a disease phenotype, have shown varying degrees of success. While many factors could potentially confound GWA studies, we focus on the possibility that multiple, rare variants (RVs) may act in concert to influence disease etiology. Here, we describe an algorithm for RV analysis, RareCover. The algorithm combines a disparate collection of RVs with low effect and modest penetrance. Further, it does not require the rare variants be adjacent in location. Extensive simulations over a range of assumed penetrance and population attributable risk (PAR) values illustrate the power of our approach over other published methods, including the collapsing and weighted-collapsing strategies. To showcase the method, we apply RareCover to re-sequencing data from a cohort of 289 individuals at the extremes of Body Mass Index distribution (NCT00263042). Individual samples were re-sequenced at two genes, FAAH and MGLL, known to be involved in endocannabinoid metabolism (187Kbp for 148 obese and 150 controls). The RareCover analysis identifies exactly one significantly associated region in each gene, each about 5 Kbp in the upstream regulatory regions. The data suggests that the RVs help disrupt the expression of the two genes, leading to lowered metabolism of the corresponding cannabinoids. Overall, our results point to the power of including RVs in measuring genetic associations.

Biomarkers of endocannabinoid system activation in severe obesity.

Background Obesity is a worldwide epidemic, and severe obesity is a risk factor for many diseases, including diabetes, heart disease, stroke, and some cancers. Endocannabinoid system (ECS) signaling in the brain and peripheral tissues is activated in obesity and plays a role in the regulation of body weight. The main research question here was whether quantitative measurement of plasma endocannabinoids, anandamide, and related N-acylethanolamines (NAEs), combined with genotyping for mutations in fatty acid amide hydrolase (FAAH) would identify circulating biomarkers of ECS activation in severe obesity. Methodology/Principal Findings Plasma samples were obtained from 96 severely obese subjects with body mass index (BMI) of ≥40 kg/m2, and 48 normal weight subjects with BMI of ≤26 kg/m2. Triple-quadrupole mass spectroscopy methods were used to measure plasma ECS analogs. Subjects were genotyped for human FAAH gene mutations. The principal analysis focused on the FAAH 385 C→A (P129T) mutation by comparing plasma ECS metabolite levels in the FAAH 385 minor A allele carriers versus wild-type C/C carriers in both groups. The main finding was significantly elevated mean plasma levels of anandamide (15.1±1.4 pmol/ml) and related NAEs in study subjects that carried the FAAH 385 A mutant alleles versus normal subjects (13.3±1.0 pmol/ml) with wild-type FAAH genotype (p = 0.04), and significance was maintained after controlling for BMI. Conclusions/Significance Significantly increased levels of the endocannabinoid anandamide and related NAEs were found in carriers of the FAAH 385 A mutant alleles compared with wild-type FAAH controls. This evidence supports endocannabinoid system activation due to the effect of FAAH 385 mutant A genotype on plasma AEA and related NAE analogs. This is the first study to document that FAAH 385 A mutant alleles have a direct effect on elevated plasma levels of anandamide and related NAEs in humans. These biomarkers may indicate risk for severe obesity and may suggest novel ECS obesity treatment strategies.

Maf1 Is Involved in Coupling Carbon Metabolism to RNA Polymerase III Transcription

ABSTRACT RNA polymerase III (Pol III) produces essential components of the biosynthetic machinery, and therefore its activity is tightly coupled with cell growth and metabolism. In the yeast Saccharomyces cerevisiae, Maf1 is the only known global and direct Pol III transcription repressor which mediates numerous stress signals. Here we demonstrate that transcription regulation by Maf1 is not limited to stress but is important for the switch between fermentation and respiration. Under respiratory conditions, Maf1 is activated by dephosphorylation and imported into the nucleus. The transition from a nonfermentable carbon source to that of glucose induces Maf1 phosphorylation and its relocation to the cytoplasm. The absence of Maf1-mediated control of tRNA synthesis impairs cell viability in nonfermentable carbon sources. The respiratory phenotype of maf1-Δ allowed genetic suppression studies to dissect the mechanism of Maf1 action on the Pol III transcription apparatus. Moreover, in cells grown in a nonfermentable carbon source, Maf1 regulates the levels of different tRNAs to various extents. The differences in regulation may contribute to the physiological role of Maf1.

Sensitive gene fusion detection using ambiguously mapping RNA-Seq read pairs

MOTIVATION Paired-end whole transcriptome sequencing provides evidence for fusion transcripts. However, due to the repetitiveness of the transcriptome, many reads have multiple high-quality mappings. Previous methods to find gene fusions either ignored these reads or required additional longer single reads. This can obscure up to 30% of fusions and unnecessarily discards much of the data. RESULTS We present a method for using paired-end reads to find fusion transcripts without requiring unique mappings or additional single read sequencing. Using simulated data and data from tumors and cell lines, we show that our method can find fusions with ambiguously mapping read pairs without generating numerous spurious fusions from the many mapping locations. AVAILABILITY A C++ and Python implementation of the method demonstrated in this article is available at http://exon.ucsd.edu/ShortFuse. CONTACT mckinsel@ucsd.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Use of Liquid Biopsies in Clinical Oncology: Pilot Experience in 168 Patients

Purpose: There is a growing interest in using circulating tumor DNA (ctDNA) testing in patients with cancer. Experimental Design: A total of 168 patients with diverse cancers were analyzed. Patients had digital next-generation sequencing (54 cancer-related gene panel including amplifications in ERBB2, EGFR, and MET) performed on their plasma. Type of genomic alterations, potential actionability, concordance with tissue testing, and patient outcome were examined. Results: Fifty-eight percent of patients (98/168) had ≥1 ctDNA alteration(s). Of the 98 patients with alterations, 71.4% had ≥ 1 alteration potentially actionable by an FDA-approved drug. The median time interval between the tissue biopsy and the blood draw was 2.7 months for patients with ≥ 1 alteration in common compared with 14.4 months (P = 0.006) for the patients in whom no common alterations were identified in the tissue and plasma. Overall concordance rates for tissue and ctDNA were 70.3% for TP53 and EGFR, 88.1% for PIK3CA, and 93.1% for ERBB2 alterations. There was a significant correlation between the cases with ≥ 1 alteration with ctDNA ≥ 5% and shorter survival (median = 4.03 months vs. not reached at median follow-up of 6.1 months; P < 0.001). Finally, 5 of the 12 evaluable patients (42%) matched to a treatment targeting an alteration(s) detected in their ctDNA test achieved stable disease ≥ 6 months/partial remission compared with 2 of 28 patients (7.1%) for the unmatched patients, P = 0.02. Conclusions: Our initial study demonstrates that ctDNA tests provide information complementary to that in tissue biopsies and may be useful in determining prognosis and treatment. Clin Cancer Res; 22(22); 5497–505. ©2016 AACR.