Analysis of the Phlebiopsis gigantea genome, transcriptome and secretome provides insight into its pioneer colonization strategies of wood.

Collectively classified as white-rot fungi, certain basidiomycetes efficiently degrade the major structural polymers of wood cell walls. A small subset of these Agaricomycetes, exemplified by Phlebiopsis gigantea, is capable of colonizing freshly exposed conifer sapwood despite its high content of extractives, which retards the establishment of other fungal species. The mechanism(s) by which P. gigantea tolerates and metabolizes resinous compounds have not been explored. Here, we report the annotated P. gigantea genome and compare profiles of its transcriptome and secretome when cultured on fresh-cut versus solvent-extracted loblolly pine wood. The P. gigantea genome contains a conventional repertoire of hydrolase genes involved in cellulose/hemicellulose degradation, whose patterns of expression were relatively unperturbed by the absence of extractives. The expression of genes typically ascribed to lignin degradation was also largely unaffected. In contrast, genes likely involved in the transformation and detoxification of wood extractives were highly induced in its presence. Their products included an ABC transporter, lipases, cytochrome P450s, glutathione S-transferase and aldehyde dehydrogenase. Other regulated genes of unknown function and several constitutively expressed genes are also likely involved in P. gigantea's extractives metabolism. These results contribute to our fundamental understanding of pioneer colonization of conifer wood and provide insight into the diverse chemistries employed by fungi in carbon cycling processes.

Identification of host-microbe interaction factors in the genomes of soft rot-associated pathogens Dickeya dadantii 3937 and Pectobacterium carotovorum WPP14 with supervised machine learning.

BACKGROUND: A wealth of genome sequences has provided thousands of genes of unknown function, but identification of functions for the large numbers of hypothetical genes in phytopathogens remains a challenge that impacts all research on plant-microbe interactions. Decades of research on the molecular basis of pathogenesis focused on a limited number of factors associated with long-known host-microbe interaction systems, providing limited direction into this challenge. Computational approaches to identify virulence genes often rely on two strategies: searching for sequence similarity to known host-microbe interaction factors from other organisms, and identifying islands of genes that discriminate between pathogens of one type and closely related non-pathogens or pathogens of a different type. The former is limited to known genes, excluding vast collections of genes of unknown function found in every genome. The latter lacks specificity, since many genes in genomic islands have little to do with host-interaction. RESULT: In this study, we developed a supervised machine learning approach that was designed to recognize patterns from large and disparate data types, in order to identify candidate host-microbe interaction factors. The soft rot Enterobacteriaceae strains Dickeya dadantii 3937 and Pectobacterium carotovorum WPP14 were used for development of this tool, because these pathogens are important on multiple high value crops in agriculture worldwide and more genomic and functional data is available for the Enterobacteriaceae than any other microbial family. Our approach achieved greater than 90% precision and a recall rate over 80% in 10-fold cross validation tests. CONCLUSION: Application of the learning scheme to the complete genome of these two organisms generated a list of roughly 200 candidates, many of which were previously not implicated in plant-microbe interaction and many of which are of completely unknown function. These lists provide new targets for experimental validation and further characterization, and our approach presents a promising pattern-learning scheme that can be generalized to create a resource to study host-microbe interactions in other bacterial phytopathogens.

Evolution of the metabolic and regulatory networks associated with oxygen availability in two phytopathogenic enterobacteria.

BACKGROUND: Dickeya dadantii and Pectobacterium atrosepticum are phytopathogenic enterobacteria capable of facultative anaerobic growth in a wide range of O2 concentrations found in plant and natural environments. The transcriptional response to O2 remains under-explored for these and other phytopathogenic enterobacteria although it has been well characterized for animal-associated genera including Escherichia coli and Salmonella enterica. Knowledge of the extent of conservation of the transcriptional response across orthologous genes in more distantly related species is useful to identify rates and patterns of regulon evolution. Evolutionary events such as loss and acquisition of genes by lateral transfer events along each evolutionary branch results in lineage-specific genes, some of which may have been subsequently incorporated into the O2-responsive stimulon. Here we present a comparison of transcriptional profiles measured using densely tiled oligonucleotide arrays for two phytopathogens, Dickeya dadantii 3937 and Pectobacterium atrosepticum SCRI1043, grown to mid-log phase in MOPS minimal medium (0.1% glucose) with and without O2. RESULTS: More than 7% of the genes of each phytopathogen are differentially expressed with greater than 3-fold changes under anaerobic conditions. In addition to anaerobic metabolism genes, the O2 responsive stimulon includes a variety of virulence and pathogenicity-genes. Few of these genes overlap with orthologous genes in the anaerobic stimulon of E. coli. We define these as the conserved core, in which the transcriptional pattern as well as genetic architecture are well preserved. This conserved core includes previously described anaerobic metabolic pathways such as fermentation. Other components of the anaerobic stimulon show variation in genetic content, genome architecture and regulation. Notably formate metabolism, nitrate/nitrite metabolism, and fermentative butanediol production, differ between E. coli and the phytopathogens. Surprisingly, the overlap of the anaerobic stimulon between the phytopathogens is also relatively small considering that they are closely related, occupy similar niches and employ similar strategies to cause disease. There are cases of interesting divergences in the pattern of transcription of genes between Dickeya and Pectobacterium for virulence-associated subsystems including the type VI secretion system (T6SS), suggesting that fine-tuning of the stimulon impacts interaction with plants or competing microbes. CONCLUSIONS: The small number of genes (an even smaller number if we consider operons) comprising the conserved core transcriptional response to O2 limitation demonstrates the extent of regulatory divergence prevalent in the Enterobacteriaceae. Our orthology-driven comparative transcriptomics approach indicates that the adaptive response in the eneterobacteria is a result of interaction of core (regulators) and lineage-specific (structural and regulatory) genes. Our subsystems based approach reveals that similar phenotypic outcomes are sometimes achieved by each organism using different genes and regulatory strategies.

chipD: a web tool to design oligonucleotide probes for high-density tiling arrays.

chipD is a web server that facilitates design of DNA oligonucleotide probes for high-density tiling arrays, which can be used in a number of genomic applications such as ChIP-chip or gene-expression profiling. The server implements a probe selection algorithm that takes as an input, in addition to the target sequences, a set of parameters that allow probe design to be tailored to specific applications, protocols or the array manufacturer's requirements. The algorithm optimizes probes to meet three objectives: (i) probes should be specific; (ii) probes should have similar thermodynamic properties; and (iii) the target sequence coverage should be homogeneous and avoid significant gaps. The output provides in a text format, the list of probe sequences with their genomic locations, targeted strands and hybridization characteristics. chipD has been used successfully to design tiling arrays for bacteria and yeast. chipD is available at http://chipd.uwbacter.org/.

Genome sequence of the plant-pathogenic bacterium Dickeya dadantii 3937.

Dickeya dadantii is a plant-pathogenic enterobacterium responsible for the soft rot disease of many plants of economic importance. We present here the sequence of strain 3937, a strain widely used as a model system for research on the molecular biology and pathogenicity of this group of bacteria.

Previsit Multidomain Psychosocial Screening Tools for Adolescents and Young Adults: A Systematic Review.

Adolescence and young adulthood constitute a period when exploratory behaviors can evolve into risky behaviors. Most causes of adolescent ill health are preventable; therefore, it is a priority to detect them early before they turn into health problems. Previsit multidomain psychosocial screening tools are used by professionals to detect and prioritize potentially problematic issues. In conjunction with appropriate clinician training, these tools have improved clinician screening rates in several areas of adolescent health. This article reviews existing multidomain previsit psychosocial screening tools developed in the 21st century and describes their characteristics using a systematic methodology. We reviewed 10,623 records to identify 15 different tools in use since 2000 and described their characteristics. Results show that all tools were developed in high-income countries. The tools provide sufficient coverage of many psychosocial domains relevant to young people's health. However, some psychosocial domains such as screen use and strengths are seldomly addressed. Furthermore, the tools rarely focus on young adults as a target population. Future research should assess the effectiveness, acceptability, and psychometric properties of validated psychosocial screening tools and examine how to expand their use in low- and middle-income countries.

Annotation and overview of the Pseudomonas savastanoi pv. savastanoi NCPPB 3335 draft genome reveals the virulence gene complement of a tumour-inducing pathogen of woody hosts.

Pseudomonas savastanoi pv. savastanoi is a tumour-inducing pathogen of Olea europaea L. causing olive knot disease. Bioinformatic analysis of the draft genome sequence of strain NCPPB 3335, which encodes 5232 predicted coding genes on a total length of 5856 998 bp and a 57.12% G + C, revealed a large degree of conservation with Pseudomonas syringae pv. phaseolicola 1448A and P. syringae pv. tabaci 11528. However, NCPPB 3335 contains twelve variable genomic regions, which are absent in all previously sequenced P. syringae strains. Various features that could contribute to the ability of this strain to survive in a woody host were identified, including broad catabolic and transport capabilities for degrading plant-derived aromatic compounds, the duplication of sequences related to the biosynthesis of the phytohormone indoleacetic acid (iaaM, iaaH) and its amino acid conjugate indoleacetic acid-lysine (iaaL gene), and the repertoire of strain-specific putative type III secretion system effectors. Access to this seventh genome sequence belonging to the 'P. syringae complex' allowed us to identify 73 predicted coding genes that are NCPPB 3335-specific. Results shown here provide the basis for detailed functional analysis of a tumour-inducing pathogen of woody hosts and for the study of specific adaptations of a P. savastanoi pathovar.

Reordering contigs of draft genomes using the Mauve aligner.

SUMMARY: Mauve Contig Mover provides a new method for proposing the relative order of contigs that make up a draft genome based on comparison to a complete or draft reference genome. A novel application of the Mauve aligner and viewer provides an automated reordering algorithm coupled with a powerful drill-down display allowing detailed exploration of results. AVAILABILITY: The software is available for download at http://gel.ahabs.wisc.edu/mauve.

Enteropathogen Resource Integration Center (ERIC): bioinformatics support for research on biodefense-relevant enterobacteria.

ERIC, the Enteropathogen Resource Integration Center (www.ericbrc.org), is a new web portal serving as a rich source of information about enterobacteria on the NIAID established list of Select Agents related to biodefense-diarrheagenic Escherichia coli, Shigella spp., Salmonella spp., Yersinia enterocolitica and Yersinia pestis. More than 30 genomes have been completely sequenced, many more exist in draft form and additional projects are underway. These organisms are increasingly the focus of studies using high-throughput experimental technologies and computational approaches. This wealth of data provides unprecedented opportunities for understanding the workings of basic biological systems and discovery of novel targets for development of vaccines, diagnostics and therapeutics. ERIC brings information together from disparate sources and supports data comparison across different organisms, analysis of varying data types and visualization of analyses in human and computer-readable formats.

Tailoring a Global Iron Regulon to a Uropathogen.

Pathogenicity islands and plasmids bear genes for pathogenesis of various Escherichia coli pathotypes. Although there is a basic understanding of the contribution of these virulence factors to disease, less is known about variation in regulatory networks in determining disease phenotypes. Here, we dissected a regulatory network directed by the conserved iron homeostasis regulator, ferric uptake regulator (Fur), in uropathogenic E. coli (UPEC) strain CFT073. Comparing anaerobic genome-scale Fur DNA binding with Fur-dependent transcript expression and protein levels of the uropathogen to that of commensal E. coli K-12 strain MG1655 showed that the Fur regulon of the core genome is conserved but also includes genes within the pathogenicity/genetic islands. Unexpectedly, regulons indicative of amino acid limitation and the general stress response were also indirectly activated in the uropathogen fur mutant, suggesting that induction of the Fur regulon increases amino acid demand. Using RpoS levels as a proxy, addition of amino acids mitigated the stress. In addition, iron chelation increased RpoS to the same levels as in the fur mutant. The increased amino acid demand of the fur mutant or iron chelated cells was exacerbated by aerobic conditions, which could be partly explained by the O2-dependent synthesis of the siderophore aerobactin, encoded by an operon within a pathogenicity island. Taken together, these data suggest that in the iron-poor environment of the urinary tract, amino acid availability could play a role in the proliferation of this uropathogen, particularly if there is sufficient O2 to produce aerobactin.IMPORTANCE Host iron restriction is a common mechanism for limiting the growth of pathogens. We compared the regulatory network controlled by Fur in uropathogenic E. coli (UPEC) to that of nonpathogenic E. coli K-12 to uncover strategies that pathogenic bacteria use to overcome iron limitation. Although iron homeostasis functions were regulated by Fur in the uropathogen as expected, a surprising finding was the activation of the stringent and general stress responses in the uropathogen fur mutant, which was rescued by amino acid addition. This coordinated global response could be important in controlling growth and survival under nutrient-limiting conditions and during transitions from the nutrient-rich environment of the lower gastrointestinal (GI) tract to the more restrictive environment of the urinary tract. The coupling of the response of iron limitation to increased demand for amino acids could be a critical attribute that sets UPEC apart from other E. coli pathotypes.

Text-mining of PubMed abstracts by natural language processing to create a public knowledge base on molecular mechanisms of bacterial enteropathogens.

BACKGROUND: The Enteropathogen Resource Integration Center (ERIC; http://www.ericbrc.org) has a goal of providing bioinformatics support for the scientific community researching enteropathogenic bacteria such as Escherichia coli and Salmonella spp. Rapid and accurate identification of experimental conclusions from the scientific literature is critical to support research in this field. Natural Language Processing (NLP), and in particular Information Extraction (IE) technology, can be a significant aid to this process. DESCRIPTION: We have trained a powerful, state-of-the-art IE technology on a corpus of abstracts from the microbial literature in PubMed to automatically identify and categorize biologically relevant entities and predicative relations. These relations include: Genes/Gene Products and their Roles; Gene Mutations and the resulting Phenotypes; and Organisms and their associated Pathogenicity. Evaluations on blind datasets show an F-measure average of greater than 90% for entities (genes, operons, etc.) and over 70% for relations (gene/gene product to role, etc). This IE capability, combined with text indexing and relational database technologies, constitute the core of our recently deployed text mining application. CONCLUSION: Our Text Mining application is available online on the ERIC website (http://www.ericbrc.org/portal/eric/articles). The information retrieval interface displays a list of recently published enteropathogen literature abstracts, and also provides a search interface to execute custom queries by keyword, date range, etc. Upon selection, processed abstracts and the entities and relations extracted from them are retrieved from a relational database and marked up to highlight the entities and relations. The abstract also provides links from extracted genes and gene products to the ERIC Annotations database, thus providing access to comprehensive genomic annotations and adding value to both the text-mining and annotations systems.

Gene Ontology annotation highlights shared and divergent pathogenic strategies of type III effector proteins deployed by the plant pathogen Pseudomonas syringae pv tomato DC3000 and animal pathogenic Escherichia coli strains.

Genome-informed identification and characterization of Type III effector repertoires in various bacterial strains and species is revealing important insights into the critical roles that these proteins play in the pathogenic strategies of diverse bacteria. However, non-systematic discipline-specific approaches to their annotation impede analysis of the accumulating wealth of data and inhibit easy communication of findings among researchers working on different experimental systems. The development of Gene Ontology (GO) terms to capture biological processes occurring during the interaction between organisms creates a common language that facilitates cross-genome analyses. The application of these terms to annotate type III effector genes in different bacterial species - the plant pathogen Pseudomonas syringae pv tomato DC3000 and animal pathogenic strains of Escherichia coli - illustrates how GO can effectively describe fundamental similarities and differences among different gene products deployed as part of diverse pathogenic strategies. In depth descriptions of the GO annotations for P. syringae pv tomato DC3000 effector AvrPtoB and the E. coli effector Tir are described, with special emphasis given to GO capability for capturing information about interacting proteins and taxa. GO-highlighted similarities in biological process and molecular function for effectors from additional pathosystems are also discussed.

Complete Genome Sequence of Dickeya dianthicola ME23, a Pathogen Causing Blackleg and Soft Rot Diseases of Potato.

In 2014, an outbreak of potato blackleg and soft rot disease emerged in North America and continues to impact potato production. Here, we report the annotated genome sequence of Dickeya dianthicola ME23, a strain hypothesized to be representative of the bacterial population responsible for this disease outbreak.

ASAP: a resource for annotating, curating, comparing, and disseminating genomic data.

ASAP is a comprehensive web-based system for community genome annotation and analysis. ASAP is being used for a large-scale effort to augment and curate annotations for genomes of enterobacterial pathogens and for additional genome sequences. New tools, such as the genome alignment program Mauve, have been incorporated into ASAP in order to improve display and analysis of related genomes. Recent improvements to the database and challenges for future development of the system are discussed. ASAP is available on the web at https://asap.ahabs.wisc.edu/asap/logon.php.

Escherichia coli K-12: a cooperatively developed annotation snapshot--2005.

The goal of this group project has been to coordinate and bring up-to-date information on all genes of Escherichia coli K-12. Annotation of the genome of an organism entails identification of genes, the boundaries of genes in terms of precise start and end sites, and description of the gene products. Known and predicted functions were assigned to each gene product on the basis of experimental evidence or sequence analysis. Since both kinds of evidence are constantly expanding, no annotation is complete at any moment in time. This is a snapshot analysis based on the most recent genome sequences of two E.coli K-12 bacteria. An accurate and up-to-date description of E.coli K-12 genes is of particular importance to the scientific community because experimentally determined properties of its gene products provide fundamental information for annotation of innumerable genes of other organisms. Availability of the complete genome sequence of two K-12 strains allows comparison of their genotypes and mutant status of alleles.

Genomic mining for Aspergillus natural products.

The genus Aspergillus is renowned for its ability to produce a myriad of bioactive secondary metabolites. Although the propensity of biosynthetic genes to form contiguous clusters greatly facilitates assignment of putative secondary metabolite genes in the completed Aspergillus genomes, such analysis cannot predict gene expression and, ultimately, product formation. To circumvent this deficiency, we have examined Aspergillus nidulans microarrays for expressed secondary metabolite gene clusters by using the transcriptional regulator LaeA. Deletion or overexpression of laeA clearly identified numerous secondary metabolite clusters. A gene deletion in one of the clusters eliminated the production of the antitumor compound terrequinone A, a metabolite not described, from A. nidulans. In this paper, we highlight that LaeA-based genome mining helps decipher the secondary metabolome of Aspergilli and provides an unparalleled view to assess secondary metabolism gene regulation.

Host range and molecular phylogenies of the soft rot enterobacterial genera pectobacterium and dickeya.

ABSTRACT Pectobacterium and Dickeya spp. are related broad-host-range entero-bacterial pathogens of angiosperms. A review of the literature shows that these genera each cause disease in species from at least 35% of angiosperm plant orders. The known host ranges of these pathogens partially overlap and, together, these two genera are pathogens of species from 50% of angiosperm plant orders. Notably, there are no reported hosts for either genus in the eudicots clade and no reported Dickeya hosts in the magnoliids or eurosids II clades, although Pectobacterium spp. are pathogens of at least one plant species in the magnoliids and at least one in each of the three eurosids II plant orders. In addition, Dickeya but not Pectobacterium spp. have been reported on a host in the rosids clade and, unlike Pectobacterium spp., have been reported on many Poales species. Natural disease among nonangiosperms has not been reported for either genus. Phylogenetic analyses of sequences concatenated from regions of seven housekeeping genes (acnA, gapA, icdA, mdh, mtlD, pgi, and proA) from representatives of these genera demonstrated that Dickeya spp. and the related tree pathogens, the genus Brenneria, are more diverse than Pectobacterium spp. and that the Pectobacterium strains can be divided into at least five distinct clades, three of which contain strains from multiple host plants.

A new asset for pathogen informatics--the Enteropathogen Resource Integration Center (ERIC), an NIAID Bioinformatics Resource Center for Biodefense and Emerging/Re-emerging Infectious Disease.

ERIC (Enteropathogen Resource Information Center) is one of the National Institute of Allergy and Infectious Diseases (NIAID) Bioinformatics Resource Centers for Biodefense and Emerging/Re-emerging Infectious Disease. ERIC serves as a comprehensive information resource for five related pathogens: Yersinia enterocolitica, Yersinia pestis, diarrheagenic E. coli, Shigella spp., and Salmonella spp. ERIC integrates genomics, proteomics, biochemical and microbiological information to facilitate the interpretation and understanding of ERIC pathogens and select related non-pathogens for the advancement of diagnostics, therapeutics, and vaccines. ERIC (www.ericbrc.org) is evolving to provide state-of-the-art analysis tools and data types, such as genome sequencing, comparative genomics, genome polymorphisms, gene expression, proteomics, and pathways as well as expertly curated community genome annotation. Genome sequence and genome annotation data and a variety of analysis and tools for eight strains of Yersinia enterocolitica and Yersinia pestis pathogens (Yersinia pestis biovars Mediaevalis KIM, Mediaevalis 91001, Orientalis CO92, Orientalis IP275, Antiqua Angola, Antiqua Antiqua, Antiqua Nepal516, and Yersinia enterocolitica 8081) and two strains of Yersinia pseudotuberculosis (Yersinia pseudotuberculosis IP32953 and IP31758) are currently available through the ERIC portal. ERIC seeks to maintain a strong collaboration with the scientific community so that we can continue to identify and incorporate the latest research data, tools, and training to best meet the current and future needs of the enteropathogen research community. All tools and data developed under this NIAID contract will be freely available. Please contact info@ericbrc.org for more information.

Analyzing the dose-dependence of the Saccharomyces cerevisiae global transcriptional response to methyl methanesulfonate and ionizing radiation.

BACKGROUND: One of the most crucial tasks for a cell to ensure its long term survival is preserving the integrity of its genetic heritage via maintenance of DNA structure and sequence. While the DNA damage response in the yeast Saccharomyces cerevisiae, a model eukaryotic organism, has been extensively studied, much remains to be elucidated about how the organism senses and responds to different types and doses of DNA damage. We have measured the global transcriptional response of S. cerevisiae to multiple doses of two representative DNA damaging agents, methyl methanesulfonate (MMS) and gamma radiation. RESULTS: Hierarchical clustering of genes with a statistically significant change in transcription illustrated the differences in the cellular responses to MMS and gamma radiation. Overall, MMS produced a larger transcriptional response than gamma radiation, and many of the genes modulated in response to MMS are involved in protein and translational regulation. Several clusters of coregulated genes whose responses varied with DNA damaging agent dose were identified. Perhaps the most interesting cluster contained four genes exhibiting biphasic induction in response to MMS dose. All of the genes (DUN1, RNR2, RNR4, and HUG1) are involved in the Mec1p kinase pathway known to respond to MMS, presumably due to stalled DNA replication forks. The biphasic responses of these genes suggest that the pathway is induced at lower levels as MMS dose increases. The genes in this cluster with a threefold or greater transcriptional response to gamma radiation all showed an increased induction with increasing gamma radiation dosage. CONCLUSION: Analyzing genome-wide transcriptional changes to multiple doses of external stresses enabled the identification of cellular responses that are modulated by magnitude of the stress, providing insights into how a cell deals with genotoxicity.

CGHScan: finding variable regions using high-density microarray comparative genomic hybridization data.

BACKGROUND: Comparative genomic hybridization can rapidly identify chromosomal regions that vary between organisms and tissues. This technique has been applied to detecting differences between normal and cancerous tissues in eukaryotes as well as genomic variability in microbial strains and species. The density of oligonucleotide probes available on current microarray platforms is particularly well-suited for comparisons of organisms with smaller genomes like bacteria and yeast where an entire genome can be assayed on a single microarray with high resolution. Available methods for analyzing these experiments typically confine analyses to data from pre-defined annotated genome features, such as entire genes. Many of these methods are ill suited for datasets with the number of measurements typical of high-density microarrays. RESULTS: We present an algorithm for analyzing microarray hybridization data to aid identification of regions that vary between an unsequenced genome and a sequenced reference genome. The program, CGHScan, uses an iterative random walk approach integrating multi-layered significance testing to detect these regions from comparative genomic hybridization data. The algorithm tolerates a high level of noise in measurements of individual probe intensities and is relatively insensitive to the choice of method for normalizing probe intensity values and identifying probes that differ between samples. When applied to comparative genomic hybridization data from a published experiment, CGHScan identified eight of nine known deletions in a Brucella ovis strain as compared to Brucella melitensis. The same result was obtained using two different normalization methods and two different scores to classify data for individual probes as representing conserved or variable genomic regions. The undetected region is a small (58 base pair) deletion that is below the resolution of CGHScan given the array design employed in the study. CONCLUSION: CGHScan is an effective tool for analyzing comparative genomic hybridization data from high-density microarrays. The algorithm is capable of accurately identifying known variable regions and is tolerant of high noise and varying methods of data preprocessing. Statistical analysis is used to define each variable region providing a robust and reliable method for rapid identification of genomic differences independent of annotated gene boundaries.