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

Comparative assessment of performance and genome dependence among phylogenetic profiling methods

BACKGROUND: The rapidly increasing speed with which genome sequence data can be generated will be accompanied by an exponential increase in the number of sequenced eukaryotes. With the increasing number of sequenced eukaryotic genomes comes a need for bioinformatic techniques to aid in functional annotation. Ideally, genome context based techniques such as proximity, fusion, and phylogenetic profiling, which have been so successful in prokaryotes, could be utilized in eukaryotes. Here we explore the application of phylogenetic profiling, a method that exploits the evolutionary co-occurrence of genes in the assignment of functional linkages, to eukaryotic genomes. RESULTS: In order to evaluate the performance of phylogenetic profiling in eukaryotes, we assessed the relative performance of commonly used profile construction techniques and genome compositions in predicting functional linkages in both prokaryotic and eukaryotic organisms. When predicting linkages in E. coli with a prokaryotic profile, the use of continuous values constructed from transformed BLAST bit-scores performed better than profiles composed of discretized E-values; the use of discretized E-values resulted in more accurate linkages when using S. cerevisiae as the query organism. Extending this analysis by incorporating several eukaryotic genomes in profiles containing a majority of prokaryotes resulted in similar overall accuracy, but with a surprising reduction in pathway diversity among the most significant linkages. Furthermore, the application of phylogenetic profiling using profiles composed of only eukaryotes resulted in the loss of the strong correlation between common KEGG pathway membership and profile similarity score. Profile construction methods, orthology definitions, ontology and domain complexity were explored as possible sources of the poor performance of eukaryotic profiles, but with no improvement in results. CONCLUSION: Given the current set of completely sequenced eukaryotic organisms, phylogenetic profiling using profiles generated from any of the commonly used techniques was found to yield extremely poor results. These findings imply genome-specific requirements for constructing functionally relevant phylogenetic profiles, and suggest that differences in the evolutionary history between different kingdoms might generally limit the usefulness of phylogenetic profiling in eukaryotes

Genome-wide prioritization of disease genes and identification of disease-disease associations from an integrated human functional linkage network

An evidence-weighted functional-linkage network of human genes reveals associations among diseases that share no known disease genes and have dissimilar phenotype

Towards the identification of essential genes using targeted genome sequencing and comparative analysis

BACKGROUND: The identification of genes essential for survival is of theoretical importance in the understanding of the minimal requirements for cellular life, and of practical importance in the identification of potential drug targets in novel pathogens. With the great time and expense required for experimental studies aimed at constructing a catalog of essential genes in a given organism, a computational approach which could identify essential genes with high accuracy would be of great value. RESULTS: We gathered numerous features which could be generated automatically from genome sequence data and assessed their relationship to essentiality, and subsequently utilized machine learning to construct an integrated classifier of essential genes in both S. cerevisiae and E. coli. When looking at single features, phyletic retention, a measure of the number of organisms an ortholog is present in, was the most predictive of essentiality. Furthermore, during construction of our phyletic retention feature we for the first time explored the evolutionary relationship among the set of organisms in which the presence of a gene is most predictive of essentiality. We found that in both E. coli and S. cerevisiae the optimal sets always contain host-associated organisms with small genomes which are closely related to the reference. Using five optimally selected organisms, we were able to improve predictive accuracy as compared to using all available sequenced organisms. We hypothesize the predictive power of these genomes is a consequence of the process of reductive evolution, by which many parasites and symbionts evolved their gene content. In addition, essentiality is measured in rich media, a condition which resembles the environments of these organisms in their hosts where many nutrients are provided. Finally, we demonstrate that integration of our most highly predictive features using a probabilistic classifier resulted in accuracies surpassing any individual feature. CONCLUSION: Using features obtainable directly from sequence data, we were able to construct a classifier which can predict essential genes with high accuracy. Furthermore, our analysis of the set of genomes in which the presence of a gene is most predictive of essentiality may suggest ways in which targeted sequencing can be used in the identification of essential genes. In summary, the methods presented here can aid in the reduction of time and money invested in essential gene identification by targeting those genes for experimentation which are predicted as being essential with a high probability

High-precision high-coverage functional inference from integrated data sources

<p>Abstract</p> <p>Background</p> <p>Information obtained from diverse data sources can be combined in a principled manner using various machine learning methods to increase the reliability and range of knowledge about protein function. The result is a weighted functional linkage network (FLN) in which linked neighbors share at least one function with high probability. Precision is, however, low. Aiming to provide precise functional annotation for as many proteins as possible, we explore and propose a two-step framework for functional annotation (1) construction of a high-coverage and reliable FLN via machine learning techniques (2) development of a decision rule for the constructed FLN to optimize functional annotation.</p> <p>Results</p> <p>We first apply this framework to <it>Saccharomyces cerevisiae</it>. In the first step, we demonstrate that four commonly used machine learning methods, Linear SVM, Linear Discriminant Analysis, Naïve Bayes, and Neural Network, all combine heterogeneous data to produce reliable and high-coverage FLNs, in which the linkage weight more accurately estimates functional coupling of linked proteins than use individual data sources alone. In the second step, empirical tuning of an adjustable decision rule on the constructed FLN reveals that basing annotation on maximum edge weight results in the most precise annotation at high coverages. In particular at low coverage all rules evaluated perform comparably. At coverage above approximately 50%, however, they diverge rapidly. At full coverage, the maximum weight decision rule still has a precision of approximately 70%, whereas for other methods, precision ranges from a high of slightly more than 30%, down to 3%. In addition, a scoring scheme to estimate the precisions of individual predictions is also provided. Finally, tests of the robustness of the framework indicate that our framework can be successfully applied to less studied organisms.</p> <p>Conclusion</p> <p>We provide a general two-step function-annotation framework, and show that high coverage, high precision annotations can be achieved by constructing a high-coverage and reliable FLN via data integration followed by applying a maximum weight decision rule.</p

Due Diligence im Private Equity – Einsatz und Rolle quantitativer Methoden bei Private Equity Transaktionen

Die vorliegende Arbeit untersucht auf Basis einer umfassenden weltweiten empirischen Befragung die Arbeitsweise und das Entscheidungsverhalten von Private Equity Professionals in der Transaktionspraxis. Ausgehend von einer empirischen Beurteilung von Due Diligence mit Hinblick auf die Notwendigkeit ihrer Durchführung im Kontext von Informations- und Prognoseproblem zeigt die Untersuchung auf, wie sich die quantitative Arbeit in der Private Equity Praxis ausgestaltet und ordnet diese in ihrem Zusammenspiel mit weichen Faktoren und Expertenwissen bzw. persönlicher Erfahrung ein. Zuletzt wird auf Basis der verfügbaren Daten eine Brücke zwischen Einsatz & Rolle quantitativer Methoden und weicher Faktoren bei der Investitionsentscheidung und der Performance von Private Equity Gesellschaften geschlagen. Die Arbeit gliedert sich in folgende sechs Hauptabschnitte: - Einleitung - Aktuelle Forschung zu Private Equity und Motivation der Arbeit - Private Equity – eine Einführung - Grundlegende Informationsprobleme und Unsicherheiten bei einem Unternehmenskauf: Informationsasymmetrien und Prognoseproblem - Empirische Untersuchung: Due Diligence im Private Equity – Einsatz und Rolle quantitativer Methoden bei Private Equity Transaktionen - Schlussbemerkung und Ausblic

Model-driven analysis of experimentally determined growth phenotypes for 465 yeast gene deletion mutants under 16 different conditions

An iterative approach that integrates high-throughput measurements of yeast deletion mutants and flux balance model predictions improves understanding of both experimental and computational results

Algorithm for management of patients with X-ray endovascular artery embolization of the prostate for its benign hyperplasia

X-ray endovascular prostatic artery embolization (XEPAE) is a comparatively new alternative treatment for benign prostatic hyperplasia (BPH), which has shown good results in Russia and foreign countries. The dissimilarity from other treatments for this nosology is the use of superselective artery ischemia of the prostate, which leads to a decrease in its sizes and a progressive reduction in lower urinary tract symptoms. Twelve patients aged 59–71 years (mean age, 66 years) with BPH have been operated on since 2014. The mean follow-up period was 8.4 months (3–17 months). The patients were divided into 3 groups in accordance with prostate volume: 1) 60–100 cm3 (n = 3); 2) 100–200 сm3 (n = 5); 3) ≥ 200 см3 (n = 4). The mean prostatic specific antigen level was 5.1 ± 2.7 ng/ml (2.7–6.3 ng/ml). Due to the increased pro static specific antigen level up to 4 ng/ml, 6 patients underwent transrectal and/or targeted biopsy of the prostate, which failed to reveal the morphological signs of its malignant process. The examination algorithm included all necessary laboratory and clinical examinations, as those during surgery for BPH, as well as multislice spiral computed tomography and/or magnetic resonance imaging, angiography of small pelvic vessels and organs.  Postoperative patient monitoring revealed that the total International Prognostic Scoring System scores decreased by 41.3 and 61.4 % at 3- and 6-month follow-ups, respectively; then these values were insignificant. The prostate volume substantially decreased at 3 months; but at 6 months it was 47 % of the baseline one. The urine flow rate rose from 6.7 to 15.9 ml/sec. The quality of life index became summarily quite satisfactory at 6-month follow-up and amounted to 3.3 ± 1.3. Thus, it may be stated that XEPAE is a safe and highly effective treatment for BPH if its medical treatment is ineffective. This surgical treatment performed for 3–6 months substantially decreases the prostate, normalizes urination, and restores quality of life. Patients with severe comorbidity and a high operative-anesthetic risk should be a promising area in the use of this technique. Patients with cystostome and preserved bladder capacity and those with acute urinary retention in the presence of BPH will serve as important additional areas in the application of XEPAE

Phenotypic and Genomic Diversification in Complex Carbohydrate-Degrading Human Gut Bacteria

Symbiotic bacteria are responsible for the majority of complex carbohydrate digestion in the human colon. Since the identities and amounts of dietary polysaccharides directly impact the gut microbiota, determining which microorganisms consume specific nutrients is central for defining the relationship between diet and gut microbial ecology. Using a custom phenotyping array, we determined carbohydrate utilization profiles for 354 members of the Bacteroidetes, a dominant saccharolytic phylum. There was wide variation in the numbers and types of substrates degraded by individual bacteria, but phenotype-based clustering grouped members of the same species indicating that each species performs characteristic roles. The ability to utilize dietary polysaccharides and endogenous mucin glycans was negatively correlated, suggesting exclusion between these niches. By analyzing related Bacteroides ovatus/Bacteroides xylanisolvens strains that vary in their ability to utilize mucin glycans, we addressed whether gene clusters that confer this complex, multilocus trait are being gained or lost in individual strains. Pangenome reconstruction of these strains revealed a remarkably mosaic architecture in which genes involved in polysaccharide metabolism are highly variable and bioinformatics data provide evidence of interspecies gene transfer that might explain this genomic heterogeneity. Global transcriptomic analyses suggest that the ability to utilize mucin has been lost in some lineages of B. ovatus and B. xylanisolvens, which harbor residual gene clusters that are involved in mucin utilization by strains that still actively express this phenotype. Our data provide insight into the breadth and complexity of carbohydrate metabolism in the microbiome and the underlying genomic events that shape these behaviors

Genomic epidemiology of a protracted hospital outbreak caused by multidrug-resistant Acinetobacter baumannii in Birmingham, England

BACKGROUND: Multidrug-resistant Acinetobacter baumannii commonly causes hospital outbreaks. However, within an outbreak, it can be difficult to identify the routes of cross-infection rapidly and accurately enough to inform infection control. Here, we describe a protracted hospital outbreak of multidrug-resistant A. baumannii, in which whole-genome sequencing (WGS) was used to obtain a high-resolution view of the relationships between isolates. METHODS: To delineate and investigate the outbreak, we attempted to genome-sequence 114 isolates that had been assigned to the A. baumannii complex by the Vitek2 system and obtained informative draft genome sequences from 102 of them. Genomes were mapped against an outbreak reference sequence to identify single nucleotide variants (SNVs). RESULTS: We found that the pulsotype 27 outbreak strain was distinct from all other genome-sequenced strains. Seventy-four isolates from 49 patients could be assigned to the pulsotype 27 outbreak on the basis of genomic similarity, while WGS allowed 18 isolates to be ruled out of the outbreak. Among the pulsotype 27 outbreak isolates, we identified 31 SNVs and seven major genotypic clusters. In two patients, we documented within-host diversity, including mixtures of unrelated strains and within-strain clouds of SNV diversity. By combining WGS and epidemiological data, we reconstructed potential transmission events that linked all but 10 of the patients and confirmed links between clinical and environmental isolates. Identification of a contaminated bed and a burns theatre as sources of transmission led to enhanced environmental decontamination procedures. CONCLUSIONS: WGS is now poised to make an impact on hospital infection prevention and control, delivering cost-effective identification of routes of infection within a clinically relevant timeframe and allowing infection control teams to track, and even prevent, the spread of drug-resistant hospital pathogens