Genomic epidemiology of multidrug‐resistant Gram‐negative organisms

The emergence and spread of antibiotic‐resistant Gram‐negative bacteria (rGNB) across global healthcare networks presents a significant threat to public health. As the number of effective antibiotics available to treat these resistant organisms dwindles, it is essential that we devise more effective strategies for controlling their proliferation. Recently, whole‐genome sequencing has emerged as a disruptive technology that has transformed our understanding of the evolution and epidemiology of diverse rGNB species, and it has the potential to guide strategies for controlling the evolution and spread of resistance. Here, we review specific areas in which genomics has already made a significant impact, including outbreak investigations, regional epidemiology, clinical diagnostics, resistance evolution, and the study of epidemic lineages. While highlighting early successes, we also point to the next steps needed to translate this technology into strategies to improve public health and clinical medicine.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/147016/1/nyas13672.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/147016/2/nyas13672_am.pd

Partial inhibition and bilevel optimization in flux balance analysis

Motivation: Within Flux Balance Analysis, the investigation of complex subtasks, such as finding the optimal perturbation of the network or finding an optimal combination of drugs, often requires to set up a bilevel optimization problem. In order to keep the linearity and convexity of these nested optimization problems, an ON/OFF description of the effect of the perturbation (i.e. Boolean variable) is normally used. This restriction may not be realistic when one wants, for instance, to describe the partial inhibition of a reaction induced by a drug.Results: In this paper we present a formulation of the bilevel optimization which overcomes the oversimplified ON/OFF modeling while preserving the linear nature of the problem. A case study is considered: the search of the best multi-drug treatment which modulates an objective reaction and has the minimal perturbation on the whole network. The drug inhibition is described and modulated through a convex combination of a fixed number of Boolean variables. The results obtained from the application of the algorithm to the core metabolism of E.coli highlight the possibility of finding a broader spectrum of drug combinations compared to a simple ON/OFF modeling.Conclusions: The method we have presented is capable of treating partial inhibition inside a bilevel optimization, without loosing the linearity property, and with reasonable computational performances also on large metabolic networks. The more fine-graded representation of the perturbation allows to enlarge the repertoire of synergistic combination of drugs for tasks such as selective perturbation of cellular metabolism. This may encourage the use of the approach also for other cases in which a more realistic modeling is required. \ua9 2013 Facchetti and Altafini; licensee BioMed Central Ltd

Cohesive versus Flexible Evolution of Functional Modules in Eukaryotes

Although functionally related proteins can be reliably predicted from phylogenetic profiles, many functional modules do not seem to evolve cohesively according to case studies and systematic analyses in prokaryotes. In this study we quantify the extent of evolutionary cohesiveness of functional modules in eukaryotes and probe the biological and methodological factors influencing our estimates. We have collected various datasets of protein complexes and pathways in Saccheromyces cerevisiae. We define orthologous groups on 34 eukaryotic genomes and measure the extent of cohesive evolution of sets of orthologous groups of which members constitute a known complex or pathway. Within this framework it appears that most functional modules evolve flexibly rather than cohesively. Even after correcting for uncertain module definitions and potentially problematic orthologous groups, only 46% of pathways and complexes evolve more cohesively than random modules. This flexibility seems partly coupled to the nature of the functional module because biochemical pathways are generally more cohesively evolving than complexes

Rapid draft sequencing and real-time nanopore sequencing in a hospital outbreak of Salmonella

Background: Foodborne outbreaks of Salmonella remain a pressing public health concern. We recently detected a large outbreak of Salmonella enterica serovar Enteritidis phage type 14b affecting more than 30 patients in our hospital. This outbreak was linked to community, national and European-wide cases. Hospital patients with Salmonella are at high risk, and require a rapid response. We initially investigated this outbreak by whole-genome sequencing using a novel rapid protocol on the Illumina MiSeq; we then integrated these data with whole-genome data from surveillance sequencing, thereby placing the outbreak in a national context. Additionally, we investigated the potential of a newly released sequencing technology, the MinION from Oxford Nanopore Technologies, in the management of a hospital outbreak of Salmonella. Results: We demonstrate that rapid MiSeq sequencing can reduce the time to answer compared to the standard sequencing protocol with no impact on the results. We show, for the first time, that the MinION can acquire clinically relevant information in real time and within minutes of a DNA library being loaded. MinION sequencing permits confident assignment to species level within 20 min. Using a novel streaming phylogenetic placement method samples can be assigned to a serotype in 40 min and determined to be part of the outbreak in less than 2 h. Conclusions: Both approaches yielded reliable and actionable clinical information on the Salmonella outbreak in less than half a day. The rapid availability of such information may facilitate more informed epidemiological investigations and influence infection control practices

Capturing the cloud of diversity reveals complexity and heterogeneity of MRSA carriage, infection and transmission.

Genome sequencing is revolutionizing clinical microbiology and our understanding of infectious diseases. Previous studies have largely relied on the sequencing of a single isolate from each individual. However, it is not clear what degree of bacterial diversity exists within, and is transmitted between individuals. Understanding this 'cloud of diversity' is key to accurate identification of transmission pathways. Here, we report the deep sequencing of methicillin-resistant Staphylococcus aureus among staff and animal patients involved in a transmission network at a veterinary hospital. We demonstrate considerable within-host diversity and that within-host diversity may rise and fall over time. Isolates from invasive disease contained multiple mutations in the same genes, including inactivation of a global regulator of virulence and changes in phage copy number. This study highlights the need for sequencing of multiple isolates from individuals to gain an accurate picture of transmission networks and to further understand the basis of pathogenesis.Thanks to Dr Alex O’Neill, University of Leeds and Dr Matthew Ellington, Public Health England for provision of RN4220 and RN4200mutS. We thank the core sequencing and informatics team at the Wellcome Trust Sanger Institute for sequencing of the isolates described in this study. This work was supported by a Medical Research Council Partnership grant (G1001787/1) held between the Department of Veterinary Medicine, University of Cambridge (M.A.H.), the School of Clinical Medicine, University of Cambridge (S.J.P.), the Moredun Research Institute, and the Wellcome Trust Sanger Institute (J.P. and S.J.P). S.J.P. receives support from the NIHR Cambridge Biomedical Research Centre. M.T.G.H., S.R.H. and J.P. were funded by Wellcome Trust grant no. 098051. G.G.R.M. was funded by an MRC studentship.This is the final version of the article. It first appeared from Nature Publishing Group via http://dx.doi.org/10.1038/ncomms756

Identification of a General O-linked Protein Glycosylation System in Acinetobacter baumannii and Its Role in Virulence and Biofilm Formation

Acinetobacter baumannii is an emerging cause of nosocomial infections. The isolation of strains resistant to multiple antibiotics is increasing at alarming rates. Although A. baumannii is considered as one of the more threatening “superbugs” for our healthcare system, little is known about the factors contributing to its pathogenesis. In this work we show that A. baumannii ATCC 17978 possesses an O-glycosylation system responsible for the glycosylation of multiple proteins. 2D-DIGE and mass spectrometry methods identified seven A. baumannii glycoproteins, of yet unknown function. The glycan structure was determined using a combination of MS and NMR techniques and consists of a branched pentasaccharide containing N-acetylgalactosamine, glucose, galactose, N-acetylglucosamine, and a derivative of glucuronic acid. A glycosylation deficient strain was generated by homologous recombination. This strain did not show any growth defects, but exhibited a severely diminished capacity to generate biofilms. Disruption of the glycosylation machinery also resulted in reduced virulence in two infection models, the amoebae Dictyostelium discoideum and the larvae of the insect Galleria mellonella, and reduced in vivo fitness in a mouse model of peritoneal sepsis. Despite A. baumannii genome plasticity, the O-glycosylation machinery appears to be present in all clinical isolates tested as well as in all of the genomes sequenced. This suggests the existence of a strong evolutionary pressure to retain this system. These results together indicate that O-glycosylation in A. baumannii is required for full virulence and therefore represents a novel target for the development of new antibiotics

Discovering functional linkages and uncharacterized cellular pathways using phylogenetic profile comparisons: a comprehensive assessment

<p>Abstract</p> <p>Background</p> <p>A widely-used approach for discovering functional and physical interactions among proteins involves phylogenetic profile comparisons (PPCs). Here, proteins with similar profiles are inferred to be functionally related under the assumption that proteins involved in the same metabolic pathway or cellular system are likely to have been co-inherited during evolution.</p> <p>Results</p> <p>Our experimentation with <it>E. coli </it>and yeast proteins with 16 different carefully composed reference sets of genomes revealed that the phyletic patterns of proteins in prokaryotes alone could be adequate enough to make reasonably accurate functional linkage predictions. A slight improvement in performance is observed on adding few eukaryotes into the reference set, but a noticeable drop-off in performance is observed with increased number of eukaryotes. Inclusion of most parasitic, pathogenic or vertebrate genomes and multiple strains of the same species into the reference set do not necessarily contribute to an improved sensitivity or accuracy. Interestingly, we also found that evolutionary histories of individual pathways have a significant affect on the performance of the PPC approach with respect to a particular reference set. For example, to accurately predict functional links in carbohydrate or lipid metabolism, a reference set solely composed of prokaryotic (or bacterial) genomes performed among the best compared to one composed of genomes from all three super-kingdoms; this is in contrast to predicting functional links in translation for which a reference set composed of prokaryotic (or bacterial) genomes performed the worst. We also demonstrate that the widely used random null model to quantify the statistical significance of profile similarity is incomplete, which could result in an increased number of false-positives.</p> <p>Conclusion</p> <p>Contrary to previous proposals, it is not merely the number of genomes but a careful selection of informative genomes in the reference set that influences the prediction accuracy of the PPC approach. We note that the predictive power of the PPC approach, especially in eukaryotes, is heavily influenced by the primary endosymbiosis and subsequent bacterial contributions. The over-representation of parasitic unicellular eukaryotes and vertebrates additionally make eukaryotes less useful in the reference sets. Reference sets composed of highly non-redundant set of genomes from all three super-kingdoms fare better with pathways showing considerable vertical inheritance and strong conservation (e.g. translation apparatus), while reference sets solely composed of prokaryotic genomes fare better for more variable pathways like carbohydrate metabolism. Differential performance of the PPC approach on various pathways, and a weak positive correlation between functional and profile similarities suggest that caution should be exercised while interpreting functional linkages inferred from genome-wide large-scale profile comparisons using a single reference set.</p

A framework for evolutionary systems biology

<p>Abstract</p> <p>Background</p> <p>Many difficult problems in evolutionary genomics are related to mutations that have weak effects on fitness, as the consequences of mutations with large effects are often simple to predict. Current systems biology has accumulated much data on mutations with large effects and can predict the properties of knockout mutants in some systems. However experimental methods are too insensitive to observe small effects.</p> <p>Results</p> <p>Here I propose a novel framework that brings together evolutionary theory and current systems biology approaches in order to quantify small effects of mutations and their epistatic interactions <it>in silico</it>. Central to this approach is the definition of fitness correlates that can be computed in some current systems biology models employing the rigorous algorithms that are at the core of much work in computational systems biology. The framework exploits synergies between the realism of such models and the need to understand real systems in evolutionary theory. This framework can address many longstanding topics in evolutionary biology by defining various 'levels' of the adaptive landscape. Addressed topics include the distribution of mutational effects on fitness, as well as the nature of advantageous mutations, epistasis and robustness. Combining corresponding parameter estimates with population genetics models raises the possibility of testing evolutionary hypotheses at a new level of realism.</p> <p>Conclusion</p> <p>EvoSysBio is expected to lead to a more detailed understanding of the fundamental principles of life by combining knowledge about well-known biological systems from several disciplines. This will benefit both evolutionary theory and current systems biology. Understanding robustness by analysing distributions of mutational effects and epistasis is pivotal for drug design, cancer research, responsible genetic engineering in synthetic biology and many other practical applications.</p

Bioinspired bactericidal surfaces with polymer nanocone arrays

Infections resulting from bacterial biofilm formation on the surface of medical devices are challenging to treat and can cause significant patient morbidity. Recently, it has become apparent that regulation of surface nanotopography can render surfaces bactericidal. In this study, poly(ethylene terephthalate) nanocone arrays are generated through a polystyrene nanosphere-mask colloidal lithographic process. It is shown that modification of the mask diameter leads to a direct modification of centre-to-centre spacing between nanocones. By altering the oxygen plasma etching time it is possible to modify the height, tip width and base diameter of the individual nanocone features. The bactericidal activity of the nanocone arrays was investigated against Escherichia coli and Klebsiella pneumoniae. It is shown that surfaces with the most densely populated nanocone arrays (center-to-center spacing of 200 nm), higher aspect ratios (>3) and tip widths <20 nm kill the highest percentage of bacteria (∼30%)