Exploiting tumor aneuploidy as a biomarker and therapeutic target in patients treated with immune checkpoint blockade

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Genome-resolved metagenomics of an autotrophic thiocyanate-remediating microbial bioreactor consortium

Industrial thiocyanate (SCN−) waste streams from gold mining and coal coking have polluted environments worldwide. Modern SCN− bioremediation involves use of complex engineered heterotrophic microbiomes; little attention has been given to the ability of a simple environmental autotrophic microbiome to biodegrade SCN−. Here we present results from a bioreactor experiment inoculated with SCN− -loaded mine tailings, incubated autotrophically, and subjected to a range of environmentally relevant conditions. Genome-resolved metagenomics revealed that SCN− hydrolase-encoding, sulphur-oxidizing autotrophic bacteria mediated SCN− degradation. These microbes supported metabolically-dependent non-SCN--degrading sulphur-oxidizing autotrophs and non-sulphur oxidizing heterotrophs, and “niche” microbiomes developed spatially (planktonic versus sessile) and temporally (across changing environmental parameters). Bioreactor microbiome structures changed significantly with increasing temperature, shifting from Thiobacilli to a novel SCN− hydrolase-encoding gammaproteobacteria. Transformation of carbonyl sulphide (COS), a key intermediate in global biogeochemical sulphur cycling, was mediated by plasmid-hosted CS2 and COS hydrolase genes associated with Thiobacillus, revealing a potential for horizontal transfer of this function. Our work shows that simple native autotrophic microbiomes from mine tailings can be employed for SCN− bioremediation, thus improving the recycling of ore processing waters and reducing the hydrological footprint of mining

ReQTL: Identifying correlations between expressed SNVs and gene expression using RNA-sequencing data

© 2019 The Author(s). Published by Oxford University Press. Motivation: By testing for associations between DNA genotypes and gene expression levels, expression quantitative trait locus (eQTL) analyses have been instrumental in understanding how thousands of single nucleotide variants (SNVs) may affect gene expression. As compared to DNA genotypes, RNA genetic variation represents a phenotypic trait that reflects the actual allele content of the studied system. RNA genetic variation at expressed SNV loci can be estimated using the proportion of alleles bearing the variant nucleotide (variant allele fraction, VAFRNA). VAFRNA is a continuous measure which allows for precise allele quantitation in loci where the RNA alleles do not scale with the genotype count. We describe a method to correlate VAFRNA with gene expression and assess its ability to identify genetically regulated expression solely from RNA-sequencing (RNA-seq) datasets. Results: We introduce ReQTL, an eQTL modification which substitutes the DNA allele count for the variant allele fraction at expressed SNV loci in the transcriptome (VAFRNA). We exemplify the method on sets of RNA-seq data from human tissues obtained though the Genotype-Tissue Expression (GTEx) project and demonstrate that ReQTL analyses are computationally feasible and can identify a subset of expressed eQTL loci