Wellcome Trust Sanger Institute

About: Wellcome Trust Sanger Institute is a nonprofit organization based out in Cambridge, United Kingdom. It is known for research contribution in the topics: Population & Genome. The organization has 4009 authors who have published 9671 publications receiving 1224479 citations.

Evolution and transmission of drug-resistant tuberculosis in a Russian population

Abstract: The molecular mechanisms determining the transmissibility and prevalence of drug-resistant tuberculosis in a population were investigated through whole-genome sequencing of 1,000 prospectively obtained patient isolates from Russia. Two-thirds belonged to the Beijing lineage, which was dominated by two homogeneous clades. Multidrug-resistant (MDR) genotypes were found in 48% of isolates overall and in 87% of the major clades. The most common rpoB mutation was associated with fitness-compensatory mutations in rpoA or rpoC, and a new intragenic compensatory substitution was identified. The proportion of MDR cases with extensively drug-resistant (XDR) tuberculosis was 16% overall, with 65% of MDR isolates harboring eis mutations, selected by kanamycin therapy, which may drive the expansion of strains with enhanced virulence. The combination of drug resistance and compensatory mutations displayed by the major clades confers clinical resistance without compromising fitness and transmissibility, showing that, in addition to weaknesses in the tuberculosis control program, biological factors drive the persistence and spread of MDR and XDR tuberculosis in Russia and beyond.

Assessing the gene space in draft genomes

Abstract: Genome sequencing projects have been initiated for a wide range of eukaryotes. A few projects have reached completion, but most exist as draft assemblies. As one of the main reasons to sequence a genome is to obtain its catalog of genes, an important question is how complete or completable the catalog is in unfinished genomes. To answer this question, we have identified a set of core eukaryotic genes (CEGs), that are extremely highly conserved and which we believe are present in low copy numbers in higher eukaryotes. From an analysis of a phylogenetically diverse set of eukaryotic genome assemblies, we found that the proportion of CEGs mapped in draft genomes provides a useful metric for describing the gene space, and complements the commonly used N50 length and x-fold coverage values.

Machine Learning Prediction of Cancer Cell Sensitivity to Drugs Based on Genomic and Chemical Properties

Abstract: Predicting the response of a specific cancer to a therapy is a major goal in modern oncology that should ultimately lead to a personalised treatment. High-throughput screenings of potentially active compounds against a panel of genomically heterogeneous cancer cell lines have unveiled multiple relationships between genomic alterations and drug responses. Various computational approaches have been proposed to predict sensitivity based on genomic features, while others have used the chemical properties of the drugs to ascertain their effect. In an effort to integrate these complementary approaches, we developed machine learning models to predict the response of cancer cell lines to drug treatment, quantified through IC50 values, based on both the genomic features of the cell lines and the chemical properties of the considered drugs. Models predicted IC50 values in a 8-fold cross-validation and an independent blind test with coefficient of determination R2 of 0.72 and 0.64 respectively. Furthermore, models were able to predict with comparable accuracy (R2 of 0.61) IC50s of cell lines from a tissue not used in the training stage. Our in silico models can be used to optimise the experimental design of drug-cell screenings by estimating a large proportion of missing IC50 values rather than experimentally measuring them. The implications of our results go beyond virtual drug screening design: potentially thousands of drugs could be probed in silico to systematically test their potential efficacy as anti-tumour agents based on their structure, thus providing a computational framework to identify new drug repositioning opportunities as well as ultimately be useful for personalized medicine by linking the genomic traits of patients to drug sensitivity.