Dissemination of pHK01-like incompatibility group IncFII plasmids encoding CTX-M-14 in Escherichia coli from human and animal sources

Few studies have compared CTX-M encoding plasmids identified in different ecological sources. This study aimed to analyze and compare the molecular epidemiology of plasmids encoding CTX-M-14 among strains from humans and animals. The CTX-M-14 encoding plasmids in 160 Escherichia coli isolates from animal faecal (14 pigs, 16 chickens, 12 cats, 8 cattle, 5 dogs and 3 rodents), human faecal (45 adults and 20 children) and human urine (37 adults) sources in 2002-2010 were characterized by molecular methods. The replicon types of the CTX-M-14 encoding plasmids were IncFII (n=61), I1-Iγ (n=24), other F types (n=23), B/O (n=10), K (n=6), N (n=3), A/C (n=1), HI1 (n=1), HI2 (n=1) and nontypeable (n=30). The genetic environment, ISEcp1 - bla CTX-M-14 - IS903 was found in 89.7% (52/58), 87.7% (57/65) and 86.5% (32/37) of the animal faecal, human faecal and human urine isolates, respectively. Subtyping of the 61 IncFII incompatibility group plasmids by replicon sequence typing, plasmid PCR-restriction fragment length polymorphism and marker genes (yac, malB, eitA/eitC and parB/A) profiles showed that 31% (18/58), 30.6% (20/65) and 37.8% (14/37) of the plasmids originating from animal faecal, human faecal and human urine isolates, respectively, were pHK01-like. These 52 pHK01-like plasmids originated from diverse human (20 faecal isolates from 2002, 2007 to 2008, 14 urinary isolates from 2004) and animal (all faecal, 1 cattle, 1 chicken, 5 pigs, 9 cats, 1 dog, 1 rodent from 2008 to 2010) sources. In conclusion, this study highlights the importance of the IncFII group, pHK01-like plasmids in the dissemination of CTX-M-14 among isolates from diverse sources. © 2012 Elsevier B.V.postprin

Generalized adjacency and the conservation of gene clusters in genetic networks defined by synthetic lethals

<p>Abstract</p> <p>Background</p> <p>Given genetic networks derived from two genomes, it may be difficult to decide if their local structures are similar enough in both genomes to infer some ancestral configuration or some conserved functional relationships. Current methods all depend on searching for identical substructures.</p> <p>Methods</p> <p>We explore a generalized vertex proximity criterion, and present analytic and probability results for the comparison of random lattice networks.</p> <p>Results</p> <p>We apply this criterion to the comparison of the genetic networks of two evolutionarily divergent yeasts, <it>Saccharomyces cerevisiae </it>and <it>Schizosaccharomyces pombe</it>, derived using the Synthetic Genetic Array screen. We show that the overlapping parts of the networks of the two yeasts share a common structure beyond the shared edges. This may be due to their conservation of redundant pathways containing many synthetic lethal pairs of genes.</p> <p>Conclusions</p> <p>Detecting the shared generalized adjacency clusters in the genetic networks of the two yeasts show that this analytical construct can be a useful tool in probing conserved network structure across divergent genomes.</p

Quantitative Epistasis Analysis and Pathway Inference from Genetic Interaction Data

Inferring regulatory and metabolic network models from quantitative genetic interaction data remains a major challenge in systems biology. Here, we present a novel quantitative model for interpreting epistasis within pathways responding to an external signal. The model provides the basis of an experimental method to determine the architecture of such pathways, and establishes a new set of rules to infer the order of genes within them. The method also allows the extraction of quantitative parameters enabling a new level of information to be added to genetic network models. It is applicable to any system where the impact of combinatorial loss-of-function mutations can be quantified with sufficient accuracy. We test the method by conducting a systematic analysis of a thoroughly characterized eukaryotic gene network, the galactose utilization pathway in Saccharomyces cerevisiae. For this purpose, we quantify the effects of single and double gene deletions on two phenotypic traits, fitness and reporter gene expression. We show that applying our method to fitness traits reveals the order of metabolic enzymes and the effects of accumulating metabolic intermediates. Conversely, the analysis of expression traits reveals the order of transcriptional regulatory genes, secondary regulatory signals and their relative strength. Strikingly, when the analyses of the two traits are combined, the method correctly infers ∼80% of the known relationships without any false positives

Seamless Gene Tagging by Endonuclease-Driven Homologous Recombination

Gene tagging facilitates systematic genomic and proteomic analyses but chromosomal tagging typically disrupts gene regulatory sequences. Here we describe a seamless gene tagging approach that preserves endogenous gene regulation and is potentially applicable in any species with efficient DNA double-strand break repair by homologous recombination. We implement seamless tagging in Saccharomyces cerevisiae and demonstrate its application for protein tagging while preserving simultaneously upstream and downstream gene regulatory elements. Seamless tagging is compatible with high-throughput strain construction using synthetic genetic arrays (SGA), enables functional analysis of transcription antisense to open reading frames and should facilitate systematic and minimally-invasive analysis of gene functions

Structure of Protein Interaction Networks and Their Implications on Drug Design

Protein-protein interaction networks (PINs) are rich sources of information that enable the network properties of biological systems to be understood. A study of the topological and statistical properties of budding yeast and human PINs revealed that they are scale-rich and configured as highly optimized tolerance (HOT) networks that are similar to the router-level topology of the Internet. This is different from claims that such networks are scale-free and configured through simple preferential-attachment processes. Further analysis revealed that there are extensive interconnections among middle-degree nodes that form the backbone of the networks. Degree distributions of essential genes, synthetic lethal genes, synthetic sick genes, and human drug-target genes indicate that there are advantageous drug targets among nodes with middle- to low-degree nodes. Such network properties provide the rationale for combinatorial drugs that target less prominent nodes to increase synergetic efficacy and create fewer side effects

MTSS1 and SCAMP1 cooperate to prevent invasion in breast cancer

Cell–cell adhesions constitute the structural “glue” that retains cells together and contributes to tissue organisation and physiological function. The integrity of these structures is regulated by extracellular and intracellular signals and pathways that act on the functional units of cell adhesion such as the cell adhesion molecules/adhesion receptors, the extracellular matrix (ECM) proteins and the cytoplasmic plaque/peripheral membrane proteins. In advanced cancer, these regulatory pathways are dysregulated and lead to cell–cell adhesion disassembly, increased invasion and metastasis. The Metastasis suppressor protein 1 (MTSS1) plays a key role in the maintenance of cell–cell adhesions and its loss correlates with tumour progression in a variety of cancers. However, the mechanisms that regulate its function are not well-known. Using a system biology approach, we unravelled potential interacting partners of MTSS1. We found that the secretory carrier-associated membrane protein 1 (SCAMP1), a molecule involved in post-Golgi recycling pathways and in endosome cell membrane recycling, enhances Mtss1 anti-invasive function in HER2+/ER−/PR− breast cancer, by promoting its protein trafficking leading to elevated levels of RAC1-GTP and increased cell–cell adhesions. This was clinically tested in HER2 breast cancer tissue and shown that loss of MTSS1 and SCAMP1 correlates with reduced disease-specific survival. In summary, we provide evidence of the cooperative roles of MTSS1 and SCAMP1 in preventing HER2+/ER−/PR− breast cancer invasion and we show that the loss of Mtss1 and Scamp1 results in a more aggressive cancer cell phenotype

Genome-Wide Analysis of Effectors of Peroxisome Biogenesis

Peroxisomes are intracellular organelles that house a number of diverse metabolic processes, notably those required for β-oxidation of fatty acids. Peroxisomes biogenesis can be induced by the presence of peroxisome proliferators, including fatty acids, which activate complex cellular programs that underlie the induction process. Here, we used multi-parameter quantitative phenotype analyses of an arrayed mutant collection of yeast cells induced to proliferate peroxisomes, to establish a comprehensive inventory of genes required for peroxisome induction and function. The assays employed include growth in the presence of fatty acids, and confocal imaging and flow cytometry through the induction process. In addition to the classical phenotypes associated with loss of peroxisomal functions, these studies identified 169 genes required for robust signaling, transcription, normal peroxisomal development and morphologies, and transmission of peroxisomes to daughter cells. These gene products are localized throughout the cell, and many have indirect connections to peroxisome function. By integration with extant data sets, we present a total of 211 genes linked to peroxisome biogenesis and highlight the complex networks through which information flows during peroxisome biogenesis and function

Simultaneous Genome-Wide Inference of Physical, Genetic, Regulatory, and Functional Pathway Components

Biomolecular pathways are built from diverse types of pairwise interactions, ranging from physical protein-protein interactions and modifications to indirect regulatory relationships. One goal of systems biology is to bridge three aspects of this complexity: the growing body of high-throughput data assaying these interactions; the specific interactions in which individual genes participate; and the genome-wide patterns of interactions in a system of interest. Here, we describe methodology for simultaneously predicting specific types of biomolecular interactions using high-throughput genomic data. This results in a comprehensive compendium of whole-genome networks for yeast, derived from ∼3,500 experimental conditions and describing 30 interaction types, which range from general (e.g. physical or regulatory) to specific (e.g. phosphorylation or transcriptional regulation). We used these networks to investigate molecular pathways in carbon metabolism and cellular transport, proposing a novel connection between glycogen breakdown and glucose utilization supported by recent publications. Additionally, 14 specific predicted interactions in DNA topological change and protein biosynthesis were experimentally validated. We analyzed the systems-level network features within all interactomes, verifying the presence of small-world properties and enrichment for recurring network motifs. This compendium of physical, synthetic, regulatory, and functional interaction networks has been made publicly available through an interactive web interface for investigators to utilize in future research at http://function.princeton.edu/bioweaver/

A network perspective on the topological importance of enzymes and their phylogenetic conservation

<p>Abstract</p> <p>Background</p> <p>A metabolic network is the sum of all chemical transformations or reactions in the cell, with the metabolites being interconnected by enzyme-catalyzed reactions. Many enzymes exist in numerous species while others occur only in a few. We ask if there are relationships between the phylogenetic profile of an enzyme, or the number of different bacterial species that contain it, and its topological importance in the metabolic network. Our null hypothesis is that phylogenetic profile is independent of topological importance. To test our null hypothesis we constructed an enzyme network from the KEGG (Kyoto Encyclopedia of Genes and Genomes) database. We calculated three network indices of topological importance: the degree or the number of connections of a network node; closeness centrality, which measures how close a node is to others; and betweenness centrality measuring how frequently a node appears on all shortest paths between two other nodes.</p> <p>Results</p> <p>Enzyme phylogenetic profile correlates best with betweenness centrality and also quite closely with degree, but poorly with closeness centrality. Both betweenness and closeness centralities are non-local measures of topological importance and it is intriguing that they have contrasting power of predicting phylogenetic profile in bacterial species. We speculate that redundancy in an enzyme network may be reflected by betweenness centrality but not by closeness centrality. We also discuss factors influencing the correlation between phylogenetic profile and topological importance.</p> <p>Conclusion</p> <p>Our analysis falsifies the hypothesis that phylogenetic profile of enzymes is independent of enzyme network importance. Our results show that phylogenetic profile correlates better with degree and betweenness centrality, but less so with closeness centrality. Enzymes that occur in many bacterial species tend to be those that have high network importance. We speculate that this phenomenon originates in mechanisms driving network evolution. Closeness centrality reflects phylogenetic profile poorly. This is because metabolic networks often consist of distinct functional modules and some are not in the centre of the network. Enzymes in these peripheral parts of a network might be important for cell survival and should therefore occur in many bacterial species. They are, however, distant from other enzymes in the same network.</p