Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program, bioRxiv, 2019-03-07

Summary paragraphThe Trans-Omics for Precision Medicine (TOPMed) program seeks to elucidate the genetic architecture and disease biology of heart, lung, blood, and sleep disorders, with the ultimate goal of improving diagnosis, treatment, and prevention. The initial phases of the program focus on whole genome sequencing of individuals with rich phenotypic data and diverse backgrounds. Here, we describe TOPMed goals and design as well as resources and early insights from the sequence data. The resources include a variant browser, a genotype imputation panel, and sharing of genomic and phenotypic data via dbGaP. In 53,581 TOPMed samples, >400 million single-nucleotide and insertiondeletion variants were detected by alignment with the reference genome. Additional novel variants are detectable through assembly of unmapped reads and customized analysis in highly variable loci. Among the >400 million variants detected, 97% have frequency <1% and 46% are singletons. These rare variants provide insights into mutational processes and recent human evolutionary history. The nearly complete catalog of genetic variation in TOPMed studies provides unique opportunities for exploring the contributions of rare and non-coding sequence variants to phenotypic variation. Furthermore, combining TOPMed haplotypes with modern imputation methods improves the power and extends the reach of nearly all genome-wide association studies to include variants down to ~0.01% in frequency.

biorxiv genomics 100-200-users 2019

Absence of entourage Terpenoids commonly found in Cannabis sativa do not modulate the functional activity of Δ9-THC at human CB1and CB2 receptors, bioRxiv, 2019-03-06

AbstractIntroductionCompounds present in Cannabis sativa such as phytocannabinoids and terpenoids, may act in concert to elicit therapeutic effects. Cannabinoids such as Δ9-tetrahydrocannabinol (Δ9-THC) directly activate cannabinoid receptor 1 (CB1) and cannabinoid receptor 2 (CB2), however, it is not known if terpenoids present in Cannabis also affect cannabinoid receptor signalling. Therefore, we examined 6 common terpenoids alone, and in combination with cannabinoid receptor agonists, on CB1 and CB2 signalling in vitro.Materials and MethodsPotassium channel activity in AtT20 FlpIn cells transfected with human CB1 or CB2 receptors was measured in real-time using FLIPR® membrane potential dye in a FlexStation 3 plate reader. Terpenoids were tested individually and in combination for periods up to 30 minutes. Endogenous somatostatin receptors served as a control for direct effects of drugs on potassium channels.Resultsα-Pinene, β-pinene, β-caryophyllene, linalool, limonene and β-myrcene (up to 30-100 µM) did not change membrane potential in AtT20 cells expressing CB1 or CB2, or affect the response to a maximally effective concentration of the synthetic cannabinoid CP55,940. The presence of individual or a combination of terpenoids did not affect the hyperpolarization produced by Δ9-THC (10µM) (CB1 control, 59±7%; with terpenoids (10 µM each) 55±4%; CB2 Δ9-THC 16±5%, with terpenoids (10 µM each) 17±4%). To investigate possible effect on desensitization of CB1 responses, all six terpenoids were added together with Δ9-THC and signalling measured continuously over 30 min. Terpenoids did not affect desensitization, after 30 minutes the control hyperpolarization recovered by 63±6%, in the presence of the terpenoids recovery was 61±5%.DiscussionNone of the six of the most common terpenoids in Cannabis directly activated CB1 or CB2, or modulated the signalling of the phytocannabinoid agonist Δ9-THC. These results suggest that if a phytocannabinoid-terpenoid entourage effect exists, it is not at the CB1 or CB2 receptor level. It remains possible that terpenoids activate CB1 and CB2 signalling pathways that do not involve potassium channels, however, it seems more likely that they may act at different molecular target(s) in the neuronal circuits important for the behavioural effect of Cannabis.

biorxiv pharmacology-and-toxicology 0-100-users 2019

Clustering co-abundant genes identifies components of the gut microbiome that are reproducibly associated with colorectal cancer and inflammatory bowel disease, bioRxiv, 2019-03-06

AbstractBackgroundWhole-genome “shotgun” (WGS) metagenomic sequencing is an increasingly widely used tool for analyzing the metagenomic content of microbiome samples. While WGS data contains gene-level information, it can be challenging to analyze the millions of microbial genes which are typically found in microbiome experiments. To mitigate the ultrahigh dimensionality challenge of gene-level metagenomics, it has been proposed to cluster genes by co-abundance to form Co-Abundant Gene groups (CAGs). However, exhaustive co-abundance clustering of millions of microbial genes across thousands of biological samples has previously been intractable purely due to the computational challenge of performing trillions of pairwise comparisons.ResultsHere we present a novel computational approach to the analysis of WGS datasets in which microbial gene groups are the fundamental unit of analysis. We use the Approximate Nearest Neighbor heuristic for near-exhaustive average linkage clustering to group millions of genes by co-abundance. This results in thousands of high-quality CAGs representing complete and partial microbial genomes. We applied this method to publicly available WGS microbiome surveys and found that the resulting microbial CAGs associated with inflammatory bowel disease (IBD) and colorectal cancer (CRC) were highly reproducible and could be validated independently using multiple independent cohorts.ConclusionsThis powerful approach to gene-level metagenomics provides a powerful path forward for identifying the biological links between the microbiome and human health. By proposing a new computational approach for handling high dimensional metagenomics data, we identified specific microbial gene groups that are associated with disease that can be used to identify strains of interest for further preclinical and mechanistic experimentation.

biorxiv bioinformatics 100-200-users 2019

 

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