138 research outputs found

    Surveillance-embedded genomic outbreak resolution of methicillin-susceptible Staphylococcus aureus in a neonatal intensive care unit

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    We observed an increase in methicillin-susceptible Staphylococcus aureus (MSSA) infections at a Dutch neonatal intensive care unit. Weekly neonatal MSSA carriage surveillance and cross-sectional screenings of health care workers (HCWs) were available for outbreak tracing. Traditional clustering of MSSA isolates by spa typing and Multiple-Locus Variable number tandem repeat Analysis (MLVA) suggested that nosocomial transmission had contributed to the infections. We investigated whether whole-genome sequencing (WGS) of MSSA surveillance would provide additional evidence for transmission. MSSA isolates from neonatal infections, carriage surveillance, and HCWs were subjected to WGS and bioinformatic analysis for identification and localization of high-quality single nucleotide polymorphisms, and in-depth analysis of subsets of isolates. By measuring the genetic diversity in background surveillance, we defined transmission-level relatedness and identified isolates that had been unjustly assigned to clusters based on MLVA, while spa typing was concordant but of insufficient resolution. Detailing particular subsets of isolates provided evidence that HCWs were involved in multiple outbreaks, yet it alleviated concerns about one particular HCW. The improved resolution and accuracy of genomic outbreak analyses substantially altered the view on outbreaks, along with apposite measures. Therefore, inclusion of the circulating background population has the potential to overcome current issues in genomic outbreak inference

    Whole-genome sequencing reveals host factors underlying critical COVID-19

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    Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

    Inferring metabolic states in uncharacterized environments using gene-expression measurements

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    Contains fulltext : 118653.pdf (publisher's version ) (Open Access)The large size of metabolic networks entails an overwhelming multiplicity in the possible steady-state flux distributions that are compatible with stoichiometric constraints. This space of possibilities is largest in the frequent situation where the nutrients available to the cells are unknown. These two factors: network size and lack of knowledge of nutrient availability, challenge the identification of the actual metabolic state of living cells among the myriad possibilities. Here we address this challenge by developing a method that integrates gene-expression measurements with genome-scale models of metabolism as a means of inferring metabolic states. Our method explores the space of alternative flux distributions that maximize the agreement between gene expression and metabolic fluxes, and thereby identifies reactions that are likely to be active in the culture from which the gene-expression measurements were taken. These active reactions are used to build environment-specific metabolic models and to predict actual metabolic states. We applied our method to model the metabolic states of Saccharomyces cerevisiae growing in rich media supplemented with either glucose or ethanol as the main energy source. The resulting models comprise about 50% of the reactions in the original model, and predict environment-specific essential genes with high sensitivity. By minimizing the sum of fluxes while forcing our predicted active reactions to carry flux, we predicted the metabolic states of these yeast cultures that are in large agreement with what is known about yeast physiology. Most notably, our method predicts the Crabtree effect in yeast cells growing in excess glucose, a long-known phenomenon that could not have been predicted by traditional constraint-based modeling approaches. Our method is of immediate practical relevance for medical and industrial applications, such as the identification of novel drug targets, and the development of biotechnological processes that use complex, largely uncharacterized media, such as biofuel production

    Genomes in flux: the evolution of archaeal and proteobacterial gene content.

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    Contains fulltext : 186579.pdf (publisher's version ) (Open Access)In the course of evolution, genomes are shaped by processes like gene loss, gene duplication, horizontal gene transfer, and gene genesis (the de novo origin of genes). Here we reconstruct the gene content of ancestral Archaea and Proteobacteria and quantify the processes connecting them to their present day representatives based on the distribution of genes in completely sequenced genomes. We estimate that the ancestor of the Proteobacteria contained around 2500 genes, and the ancestor of the Archaea around 2050 genes. Although it is necessary to invoke horizontal gene transfer to explain the content of present day genomes, gene loss, gene genesis, and simple vertical inheritance are quantitatively the most dominant processes in shaping the genome. Together they result in a turnover of gene content such that even the lineage leading from the ancestor of the Proteobacteria to the relatively large genome of Escherichia coli has lost at least 950 genes. Gene loss, unlike the other processes, correlates fairly well with time. This clock-like behavior suggests that gene loss is under negative selection, while the processes that add genes are under positive selection

    Exploration of the omics evidence landscape: adding qualitative labels to predicted protein-protein interactions

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    Contains fulltext : 34770.pdf ( ) (Open Access)ABSTRACT: BACKGROUND: In the post-genomic era various functional genomics, proteomics and computational techniques have been developed to elucidate the protein interaction network. While some of these techniques are specific for a certain type of interaction, most predict a mixture of interactions. Qualitative labels are essential for the molecular biologist to experimentally verify predicted interactions. RESULTS: Of the individual protein-protein interaction prediction methods, some can predict physical interactions without producing other types of interactions. None of the methods can specifically predict metabolic interactions. We have constructed an 'omics evidence landscape' that combines all sources of evidence for protein interactions from various types of omics data for Saccharomyces cerevisiae. We explore this evidence landscape to identify areas with either only metabolic or only physical interactions, allowing us to specifically predict the nature of new interactions in these areas. We combine the datasets in ways that examine the whole evidence landscape and not only the highest scoring protein pairs in both datasets and find specific predictions. CONCLUSION: The combination of evidence types in the form of the evidence landscape allows for qualitative labels to be inferred and placed on the predicted protein interaction network of S. cerevisiae. These qualitative labels will help in the biological interpretation of gene networks and will direct experimental verification of the predicted interactions

    Gene co-regulation is highly conserved in the evolution of eukaryotes and prokaryotes

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    Contains fulltext : 58411.pdf (publisher's version ) (Open Access

    Structure based hypothesis of a mitochondrial ribosome rescue mechanism.

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    Contains fulltext : 109616.pdf (publisher's version ) (Open Access)ABSTRACT: BACKGROUND: mtRF1 is a vertebrate mitochondrial protein with an unknown function that arose from a duplication of the mitochondrial release factor mtRF1a. To elucidate the function of mtRF1, we determined the positions that are conserved among mtRF1 sequences but that are different in their mtRF1a paralogs. We subsequently modeled the 3D structure of mtRF1a and mtRF1 bound to the ribosome, highlighting the structural implications of these differences to derive a hypothesis for the function of mtRF1. RESULTS: Our model predicts, in agreement with the experimental data, that the 3D structure of mtRF1a allows it to recognize the stop codons UAA and UAG in the A-site of the ribosome. In contrast, we show that mtRF1 likely can only bind the ribosome when the A-site is devoid of mRNA. Furthermore, while mtRF1a will adopt its catalytic conformation, in which it functions as a peptidyl-tRNA hydrolase in the ribosome, only upon binding of a stop codon in the A-site, mtRF1 appears specifically adapted to assume this extended, peptidyl-tRNA hydrolyzing conformation in the absence of mRNA in the A-site. CONCLUSIONS: We predict that mtRF1 specifically recognizes ribosomes with an empty A-site and is able to function as a peptidyl-tRNA hydrolase in those situations. Stalled ribosomes with empty A-sites that still contain a tRNA bound to a peptide chain can result from the translation of truncated, stop-codon less mRNAs. We hypothesize that mtRF1 recycles such stalled ribosomes, performing a function that is analogous to that of tmRNA in bacteria. REVIEWERS: This article was reviewed by Dr. Eugene Koonin, Prof. Knud H. Nierhaus (nominated by Dr. Sarah Teichmann) and Dr. Shamil Sunyaev
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