Systematic association of genes to phenotypes by genome and literature mining

From genotype to phenotype: computational approaches for inferring microbial traits relevant to the food industry

When selecting microbial strains for the production of fermented foods, various microbial phenotypes need to be taken into account to achieve target product characteristics, such as biosafety, flavor, texture, and health-promoting effects. Through continuous advances in sequencing technologies, microbial whole-genome sequences of increasing quality can now be obtained both cheaper and faster, which increases the relevance of genome-based characterization of microbial phenotypes. Prediction of microbial phenotypes from genome sequences makes it possible to quickly screen large strain collections in silico to identify candidates with desirable traits. Several microbial phenotypes relevant to the production of fermented foods can be predicted using knowledge-based approaches, leveraging our existing understanding of the genetic and molecular mechanisms underlying those phenotypes. In the absence of this knowledge, data-driven approaches can be applied to estimate genotype-phenotype relationships based on large experimental datasets. Here, we review computational methods that implement knowledge- and data-driven approaches for phenotype prediction, as well as methods that combine elements from both approaches. Furthermore, we provide examples of how these methods have been applied in industrial biotechnology, with special focus on the fermented food industry.

Polyamine and Ethanolamine Metabolism in Bacteria as an Important Component of Nitrogen Assimilation for Survival and Pathogenicity

Nitrogen is an essential element required for bacterial growth. It serves as a building block for the biosynthesis of macromolecules and provides precursors for secondary metabolites. Bacteria have developed the ability to use various nitrogen sources and possess two enzyme systems for nitrogen assimilation involving glutamine synthetase/glutamate synthase and glutamate dehydrogenase. Microorganisms living in habitats with changeable availability of nutrients have developed strategies to survive under nitrogen limitation. One adaptation is the ability to acquire nitrogen from alternative sources including the polyamines putrescine, cadaverine, spermidine and spermine, as well as the monoamine ethanolamine. Bacterial polyamine and monoamine metabolism is not only important under low nitrogen availability, but it is also required to survive under high concentrations of these compounds. Such conditions can occur in diverse habitats such as soil, plant tissues and human cells. Strategies of pathogenic and non-pathogenic bacteria to survive in the presence of poly- and monoamines offer the possibility to combat pathogens by using their capability to metabolize polyamines as an antibiotic drug target. This work aims to summarize the knowledge on poly- and monoamine metabolism in bacteria and its role in nitrogen metabolism.

Mechanism of the Gut Microbiota Colonization Resistance and Enteric Pathogen Infection

The mammalian gut microbial community, known as the gut microbiota, comprises trillions of bacteria, which co-evolved with the host and has an important role in a variety of host functions that include nutrient acquisition, metabolism, and immunity development, and more importantly, it plays a critical role in the protection of the host from enteric infections associated with exogenous pathogens or indigenous pathobiont outgrowth that may result from healthy gut microbial community disruption. Microbiota evolves complex mechanisms to restrain pathogen growth, which included nutrient competition, competitive metabolic interactions, niche exclusion, and induction of host immune response, which are collectively termed colonization resistance. On the other hand, pathogens have also developed counterstrategies to expand their population and enhance their virulence to cope with the gut microbiota colonization resistance and cause infection. This review summarizes the available literature on the complex relationship occurring between the intestinal microbiota and enteric pathogens, describing how the gut microbiota can mediate colonization resistance against bacterial enteric infections and how bacterial enteropathogens can overcome this resistance as well as how the understanding of this complex interaction can inform future therapies against infectious diseases.

Deep semi-supervised learning ensemble framework for classifying co-mentions of human proteins and phenotypes

Background

Identifying human protein-phenotype relationships has attracted researchers in bioinformatics and biomedical natural language processing due to its importance in uncovering rare and complex diseases. Since experimental validation of protein-phenotype associations is prohibitive, automated tools capable of accurately extracting these associations from the biomedical text are in high demand. However, while the manual annotation of protein-phenotype co-mentions required for training such models is highly resource-consuming, extracting millions of unlabeled co-mentions is straightforward.

Results

In this study, we propose a novel deep semi-supervised ensemble framework that combines deep neural networks, semi-supervised, and ensemble learning for classifying human protein-phenotype co-mentions with the help of unlabeled data. This framework allows the ability to incorporate an extensive collection of unlabeled sentence-level co-mentions of human proteins and phenotypes with a small labeled dataset to enhance overall performance. We develop PPPredSS, a prototype of our proposed semi-supervised framework that combines sophisticated language models, convolutional networks, and recurrent networks. Our experimental results demonstrate that the proposed approach provides a new state-of-the-art performance in classifying human protein-phenotype co-mentions by outperforming other supervised and semi-supervised counterparts. Furthermore, we highlight the utility of PPPredSS in powering a curation assistant system through case studies involving a group of biologists.

Conclusions

This article presents a novel approach for human protein-phenotype co-mention classification based on deep, semi-supervised, and ensemble learning. The insights and findings from this work have implications for biomedical researchers, biocurators, and the text mining community working on biomedical relationship extraction.

Bacterial Microcompartment-Dependent 1,2-Propanediol Utilization of Propionibacterium freudenreichii

Bacterial microcompartments (BMCs) are proteinaceous prokaryotic organelles that enable the utilization of substrates such as 1,2-propanediol and ethanolamine. BMCs are mostly linked to the survival of particular pathogenic bacteria by providing a growth advantage through utilization of 1,2-propanediol and ethanolamine which are abundantly present in the human gut. Although a 1,2-propanediol utilization cluster was found in the probiotic bacterium Propionibacterium freudenreichii, BMC-mediated metabolism of 1,2-propanediol has not been demonstrated experimentally in P. freudenreichii. In this study we show that P. freudenreichii DSM 20271 metabolizes 1,2-propanediol in anaerobic conditions to propionate and 1-propanol. Furthermore, 1,2-propanediol induced the formation of BMCs, which were visualized by transmission electron microscopy and resembled BMCs found in other bacteria. Proteomic analysis of 1,2-propanediol grown cells compared to L-lactate grown cells showed significant upregulation of proteins involved in propanediol-utilization (pdu-cluster), DNA repair mechanisms and BMC shell proteins while proteins involved in oxidative phosphorylation were down-regulated. 1,2-Propanediol utilizing cells actively produced vitamin B₁₂ (cobalamin) in similar amounts as cells growing on L-lactate. The ability to metabolize 1,2-propanediol may have implications for human gut colonization and modulation, and can potentially aid in delivering propionate and vitamin B₁₂in situ.

The support of genetic evidence for cardiovascular risk induced by antineoplastic drugs

Cardiovascular dysfunction is one of the most common complications of long-term cancer treatment. Growing evidence has shown that antineoplastic drugs can increase cardiovascular risk during cancer therapy, seriously affecting patient survival. However, little is known about the genetic factors associated with the cardiovascular risk of antineoplastic drugs. We established a compendium of genetic evidence that supports cardiovascular risk induced by antineoplastic drugs. Most of this genetic evidence is attributed to causal alleles altering the expression of cardiovascular disease genes. We found that antineoplastic drugs predicted to induce cardiovascular risk are significantly enriched in drugs associated with cardiovascular adverse reactions, including many first-line cancer treatments. Functional experiments validated that retinoid X receptor agonists can reduce triglyceride lipolysis, thus modulating cardiovascular risk. Our results establish a link between the causal allele of cardiovascular disease genes and the direction of pharmacological modulation, which could facilitate cancer drug discovery and clinical trial design.

Comparative Genomic Analysis Reveals Novel Microcompartment-Associated Metabolic Pathways in the Human Gut Microbiome

Bacterial microcompartments are self-assembling subcellular structures surrounded by a semipermeable protein shell and found only in bacteria, but not archaea or eukaryotes. The general functions of the bacterial microcompartments are to concentrate enzymes, metabolites, and cofactors for multistep pathways; maintain the cofactor ratio; protect the cell from toxic metabolic intermediates; and protect the encapsulated pathway from unwanted side reactions. The bacterial microcompartments were suggested to play a significant role in organisms of the human gut microbiome, especially for various pathogens. Here, we used a comparative genomics approach to analyze the bacterial microcompartments in 646 individual genomes of organisms commonly found in the human gut microbiome. The bacterial microcompartments were found in 150 (23.2%) analyzed genomes. These microcompartments include previously known ones for the utilization of ethanolamine, 1,2-propanediol, choline, and fucose/rhamnose. Moreover, we reconstructed two novel pathways associated with the bacterial microcompartments. These pathways are catabolic pathways for the utilization of 1-amino-2-propanol/1-amino-2-propanone and xanthine. Remarkably, the xanthine utilization pathway does not demonstrate similarity to previously known microcompartment-associated pathways. Thus, we describe a novel type of bacterial microcompartment.

Navigating the disease landscape: knowledge representations for contextualizing molecular signatures

Large amounts of data emerging from experiments in molecular medicine are leading to the identification of molecular signatures associated with disease subtypes. The contextualization of these patterns is important for obtaining mechanistic insight into the aberrant processes associated with a disease, and this typically involves the integration of multiple heterogeneous types of data. In this review, we discuss knowledge representations that can be useful to explore the biological context of molecular signatures, in particular three main approaches, namely, pathway mapping approaches, molecular network centric approaches and approaches that represent biological statements as knowledge graphs. We discuss the utility of each of these paradigms, illustrate how they can be leveraged with selected practical examples and identify ongoing challenges for this field of research.

Metabolic adaptation of adherent-invasive Escherichia coli to exposure to bile salts

The adherent-invasive Escherichia coli (AIEC), which colonize the ileal mucosa of Crohn’s disease patients, adhere to intestinal epithelial cells, invade them and exacerbate intestinal inflammation. The high nutrient competition between the commensal microbiota and AIEC pathobiont requires the latter to occupy their own metabolic niches to survive and proliferate within the gut. In this study, a global RNA sequencing of AIEC strain LF82 has been used to observe the impact of bile salts on the expression of metabolic genes. The results showed a global up-regulation of genes involved in degradation and a down-regulation of those implicated in biosynthesis. The main up-regulated degradation pathways were ethanolamine, 1,2-propanediol and citrate utilization, as well as the methyl-citrate pathway. Our study reveals that ethanolamine utilization bestows a competitive advantage of AIEC strains that are metabolically capable of its degradation in the presence of bile salts. We observed that bile salts activated secondary metabolism pathways that communicate to provide an energy benefit to AIEC. Bile salts may be used by AIEC as an environmental signal to promote their colonization.

Ontology based text mining of gene-phenotype associations: application to candidate gene prediction

Gene-phenotype associations play an important role in understanding the disease mechanisms which is a requirement for treatment development. A portion of gene-phenotype associations are observed mainly experimentally and made publicly available through several standard resources such as MGI. However, there is still a vast amount of gene-phenotype associations buried in the biomedical literature. Given the large amount of literature data, we need automated text mining tools to alleviate the burden in manual curation of gene-phenotype associations and to develop comprehensive resources. In this study, we present an ontology-based approach in combination with statistical methods to text mine gene-phenotype associations from the literature. Our method achieved AUC values of 0.90 and 0.75 in recovering known gene-phenotype associations from HPO and MGI respectively. We posit that candidate genes and their relevant diseases should be expressed with similar phenotypes in publications. Thus, we demonstrate the utility of our approach by predicting disease candidate genes based on the semantic similarities of phenotypes associated with genes and diseases. To the best of our knowledge, this is the first study using an ontology based approach to extract gene-phenotype associations from the literature. We evaluated our disease candidate prediction model on the gene-disease associations from MGI. Our model achieved AUC values of 0.90 and 0.87 on OMIM (human) and MGI (mouse) datasets of gene-disease associations respectively. Our manual analysis on the text mined data revealed that our method can accurately extract gene-phenotype associations which are not currently covered by the existing public gene-phenotype resources. Overall, results indicate that our method can precisely extract known as well as new gene-phenotype associations from literature. All the data and methods are available at https://github.com/bio-ontology-research-group/genepheno.

Ethanolamine Influences Human Commensal Escherichia coli Growth, Gene Expression, and Competition with Enterohemorrhagic E. coli O157:H7

The microbiota protects the host from invading pathogens by limiting access to nutrients. In turn, bacterial pathogens selectively exploit metabolites not readily used by the microbiota to establish infection. Ethanolamine has been linked to pathogenesis of diverse pathogens by serving as a noncompetitive metabolite that enhances pathogen growth as well as a signal that modulates virulence. Although ethanolamine is abundant in the gastrointestinal tract, the prevailing idea is that commensal bacteria do not utilize EA, and thus, EA utilization has been particularly associated with pathogenesis. Here, we provide evidence that two human commensal Escherichia coli isolates readily utilize ethanolamine to enhance growth, modulate gene expression, and outgrow the pathogen enterohemorrhagic E. coli. These data indicate a more complex role for ethanolamine in host-microbiota-pathogen interactions.

A core principle of bacterial pathogenesis is that pathogens preferentially utilize metabolites that commensal bacteria do not in order to sidestep nutritional competition. The metabolite ethanolamine (EA) is well recognized to play a central role in host adaptation for diverse pathogens. EA promotes growth and influences virulence during host infection. Although genes encoding EA utilization have been identified in diverse bacteria (nonpathogenic and pathogenic), a prevailing idea is that commensal bacteria do not utilize EA to enhance growth, and thus, EA is a noncompetitive metabolite for pathogens. Here, we show that EA augments growth of two human commensal strains of Escherichia coli. Significantly, these commensal strains grow more rapidly than, and even outcompete, the pathogen enterohemorrhagic E. coli O157:H7 specifically when EA is provided as the sole nitrogen source. Moreover, EA-dependent signaling is similarly conserved in the human commensal E. coli strain HS and influences expression of adhesins. These findings suggest a more extensive role for EA utilization in bacterial physiology and host-microbiota-pathogen interactions than previously appreciated.

Automatic extraction of protein-protein interactions using grammatical relationship graph

Background

Relationships between bio-entities (genes, proteins, diseases, etc.) constitute a significant part of our knowledge. Most of this information is documented as unstructured text in different forms, such as books, articles and on-line pages. Automatic extraction of such information and storing it in structured form could help researchers more easily access such information and also make it possible to incorporate it in advanced integrative analysis. In this study, we developed a novel approach to extract bio-entity relationships information using Nature Language Processing (NLP) and a graph-theoretic algorithm.

Methods

Our method, called GRGT (Grammatical Relationship Graph for Triplets), not only extracts the pairs of terms that have certain relationships, but also extracts the type of relationship (the word describing the relationships). In addition, the directionality of the relationship can also be extracted. Our method is based on the assumption that a triplet exists for a pair of interactions. A triplet is defined as two terms (entities) and an interaction word describing the relationship of the two terms in a sentence. We first use a sentence parsing tool to obtain the sentence structure represented as a dependency graph where words are nodes and edges are typed dependencies. The shortest paths among the pairs of words in the triplet are then extracted, which form the basis for our information extraction method. Flexible pattern matching scheme was then used to match a triplet graph with unknown relationship to those triplet graphs with labels (True or False) in the database.

Results

We applied the method on three benchmark datasets to extract the protein-protein-interactions (PPIs), and obtained better precision than the top performing methods in literature.

Conclusions

We have developed a method to extract the protein-protein interactions from biomedical literature. PPIs extracted by our method have higher precision among other methods, suggesting that our method can be used to effectively extract PPIs and deposit them into databases. Beyond extracting PPIs, our method could be easily extended to extracting relationship information between other bio-entities.

Identifying genotype-phenotype relationships in biomedical text

BACKGROUND

One important type of information contained in biomedical research literature is the newly discovered relationships between phenotypes and genotypes. Because of the large quantity of literature, a reliable automatic system to identify this information for future curation is essential. Such a system provides important and up to date data for database construction and updating, and even text summarization. In this paper we present a machine learning method to identify these genotype-phenotype relationships. No large human-annotated corpus of genotype-phenotype relationships currently exists. So, a semi-automatic approach has been used to annotate a small labelled training set and a self-training method is proposed to annotate more sentences and enlarge the training set.

RESULTS

The resulting machine-learned model was evaluated using a separate test set annotated by an expert. The results show that using only the small training set in a supervised learning method achieves good results (precision: 76.47, recall: 77.61, F-measure: 77.03) which are improved by applying a self-training method (precision: 77.70, recall: 77.84, F-measure: 77.77).

CONCLUSIONS

Relationships between genotypes and phenotypes is biomedical information pivotal to the understanding of a patient's situation. Our proposed method is the first attempt to make a specialized system to identify genotype-phenotype relationships in biomedical literature. We achieve good results using a small training set. To improve the results other linguistic contexts need to be explored and an appropriately enlarged training set is required.

The Agr-Like Quorum Sensing System Is Required for Pathogenesis of Necrotic Enteritis Caused by Clostridium perfringens in Poultry

Clostridium perfringens encodes at least two different quorum sensing (QS) systems, the Agr-like and LuxS, and recent studies have highlighted their importance in the regulation of toxin production and virulence. The role of QS in the pathogenesis of necrotic enteritis (NE) in poultry and the regulation of NetB, the key toxin involved, has not yet been investigated. We have generated isogenic agrB-null and complemented strains from parent strain CP1 and demonstrated that the virulence of the agrB-null mutant was strongly attenuated in a chicken NE model system and restored by complementation. The production of NetB, a key NE-associated toxin, was dramatically reduced in the agrB mutant at both the transcriptional and protein levels, though not in a luxS mutant. Transwell assays confirmed that the Agr-like QS system controls NetB production through a diffusible signal. Global gene expression analysis of the agrB mutant identified additional genes modulated by Agr-like QS, including operons related to phospholipid metabolism and adherence, which may also play a role in NE pathogenesis. This study provides the first evidence that the Agr-like QS system is critical for NE pathogenesis and identifies a number of Agr-regulated genes, most notably netB, that are potentially involved in mediating its effects. The Agr-like QS system thus may serve as a target for developing novel interventions to prevent NE in chickens.

Respiration of Microbiota-Derived 1,2-propanediol Drives Salmonella Expansion during Colitis

Intestinal inflammation caused by Salmonella enterica serovar Typhimurium increases the availability of electron acceptors that fuel a respiratory growth of the pathogen in the intestinal lumen. Here we show that one of the carbon sources driving this respiratory expansion in the mouse model is 1,2-propanediol, a microbial fermentation product. 1,2-propanediol utilization required intestinal inflammation induced by virulence factors of the pathogen. S. Typhimurium used both aerobic and anaerobic respiration to consume 1,2-propanediol and expand in the murine large intestine. 1,2-propanediol-utilization did not confer a benefit in germ-free mice, but the pdu genes conferred a fitness advantage upon S. Typhimurium in mice mono-associated with Bacteroides fragilis or Bacteroides thetaiotaomicron. Collectively, our data suggest that intestinal inflammation enables S. Typhimurium to sidestep nutritional competition by respiring a microbiota-derived fermentation product.

Salmonella enterica serovar Typhimurium induces intestinal inflammation to induce the generation of host-derived respiratory electron acceptors, thereby driving a respiratory pathogen expansion, which aids infectious transmission by the fecal oral route. However, the identity of nutrients serving as electron donors to enable S. Typhimurium to edge out competing microbes in the competitive environment of the gut are just beginning to be worked out. Here we demonstrate that aerobic and anaerobic respiratory pathways cooperate to promote growth of Salmonella on the microbial fermentation product 1,2-propanediol. We propose that pathogen-induced intestinal inflammation enables Salmonella to sidestep nutritional competition with the largely anaerobic microbiota by respiring a microbe-derived metabolite that cannot be consumed by fermentation.

Metagenomics and CAZyme Discovery

Microorganisms play a primary role in regulating biogeochemical cycles and are a valuable source of enzymes that have biotechnological applications, such as carbohydrate-active enzymes (CAZymes). However, the inability to culture the majority of microorganisms that exist in natural ecosystems using common culture-dependent techniques restricts access to potentially novel cellulolytic bacteria and beneficial enzymes. The development of molecular-based culture-independent methods such as metagenomics enables researchers to study microbial communities directly from environmental samples, and presents a platform from which enzymes of interest can be sourced. We outline key methodological stages that are required as well as describe specific protocols that are currently used for metagenomic projects dedicated to CAZyme discovery.

When pathogenic bacteria meet the intestinal microbiota

The intestinal microbiota is a large and diverse microbial community that inhabits the intestinal tract, containing about 100 trillion bacteria from 500-1000 distinct species that, collectively, provide multiple benefits to the host. The gut microbiota contributes to nutrient absorption and maturation of the immune system, and also plays a central role in protection of the host from enteric bacterial infection. On the other hand, many enteric pathogens have developed strategies in order to be able to outcompete the intestinal community, leading to infection and/or chronic diseases. This review will summarize findings describing the complex relationship occurring between the intestinal microbiota and enteric pathogens, as well as how future therapies can ultimately benefit from such discoveries.This article is part of the themed issue 'The new bacteriology'.

The landscape of microbial phenotypic traits and associated genes

Bacteria and Archaea display a variety of phenotypic traits and can adapt to diverse ecological niches. However, systematic annotation of prokaryotic phenotypes is lacking. We have therefore developed ProTraits, a resource containing ∼545 000 novel phenotype inferences, spanning 424 traits assigned to 3046 bacterial and archaeal species. These annotations were assigned by a computational pipeline that associates microbes with phenotypes by text-mining the scientific literature and the broader World Wide Web, while also being able to define novel concepts from unstructured text. Moreover, the ProTraits pipeline assigns phenotypes by drawing extensively on comparative genomics, capturing patterns in gene repertoires, codon usage biases, proteome composition and co-occurrence in metagenomes. Notably, we find that gene synteny is highly predictive of many phenotypes, and highlight examples of gene neighborhoods associated with spore-forming ability. A global analysis of trait interrelatedness outlined clusters in the microbial phenotype network, suggesting common genetic underpinnings. Our extended set of phenotype annotations allows detection of 57 088 high confidence gene-trait links, which recover many known associations involving sporulation, flagella, catalase activity, aerobicity, photosynthesis and other traits. Over 99% of the commonly occurring gene families are involved in genetic interactions conditional on at least one phenotype, suggesting that epistasis has a major role in shaping microbial gene content.

Interactions between the microbiota and pathogenic bacteria in the gut

The microbiome has an important role in human health. Changes in the microbiota can confer resistance to or promote infection by pathogenic bacteria. Antibiotics have a profound impact on the microbiota that alters the nutritional landscape of the gut and can lead to the expansion of pathogenic populations. Pathogenic bacteria exploit microbiota-derived sources of carbon and nitrogen as nutrients and regulatory signals to promote their own growth and virulence. By eliciting inflammation, these bacteria alter the intestinal environment and use unique systems for respiration and metal acquisition to drive their expansion. Unravelling the interactions between the microbiota, the host and pathogenic bacteria will produce strategies for manipulating the microbiota against infectious diseases.

Urinary Colorimetric Sensor Array and Algorithm to Distinguish Kawasaki Disease from Other Febrile Illnesses

Objectives

Kawasaki disease (KD) is an acute pediatric vasculitis of infants and young children with unknown etiology and no specific laboratory-based test to identify. A specific molecular diagnostic test is urgently needed to support the clinical decision of proper medical intervention, preventing subsequent complications of coronary artery aneurysms. We used a simple and low-cost colorimetric sensor array to address the lack of a specific diagnostic test to differentiate KD from febrile control (FC) patients with similar rash/fever illnesses.

Study Design

Demographic and clinical data were prospectively collected for subjects with KD and FCs under standard protocol. After screening using a genetic algorithm, eleven compounds including metalloporphyrins, pH indicators, redox indicators and solvatochromic dye categories, were selected from our chromatic compound library (n = 190) to construct a colorimetric sensor array for diagnosing KD. Quantitative color difference analysis led to a decision-tree-based KD diagnostic algorithm.

Results

This KD sensing array allowed the identification of 94% of KD subjects (receiver operating characteristic [ROC] area under the curve [AUC] 0.981) in the training set (33 KD, 33 FC) and 94% of KD subjects (ROC AUC: 0.873) in the testing set (16 KD, 17 FC). Color difference maps reconstructed from the digital images of the sensing compounds demonstrated distinctive patterns differentiating KD from FC patients.

Conclusions

The colorimetric sensor array, composed of common used chemical compounds, is an easily accessible, low-cost method to realize the discrimination of subjects with KD from other febrile illness.

Vitamin B12 Uptake by the Gut Commensal Bacteria Bacteroides thetaiotaomicron Limits the Production of Shiga Toxin by Enterohemorrhagic Escherichia coli

Enterohemorrhagic Escherichia coli (EHEC) are foodborne pathogens responsible for the development of bloody diarrhea and renal failure in humans. Many environmental factors have been shown to regulate the production of Shiga toxin 2 (Stx2), the main virulence factor of EHEC. Among them, soluble factors produced by human gut microbiota and in particular, by the predominant species Bacteroides thetaiotaomicron (B. thetaiotaomicron), inhibit Stx2 gene expression. In this study, we investigated the molecular mechanisms underlying the B. thetaiotaomicron-dependent inhibition of Stx2 production by EHEC. We determined that Stx2-regulating molecules are resistant to heat treatment but do not correspond to propionate and acetate, two short-chain fatty acids produced by B. thetaiotaomicron. Moreover, screening of a B. thetaiotaomicron mutant library identified seven mutants that do not inhibit Stx2 synthesis by EHEC. One mutant has impaired production of BtuB, an outer membrane receptor for vitamin B₁₂. Together with restoration of Stx2 level after vitamin B₁₂ supplementation, these data highlight vitamin B₁₂ as a molecule produced by gut microbiota that modulates production of a key virulence factor of EHEC and consequently may affect the outcome of an infection.

Phenome-driven disease genetics prediction toward drug discovery

MOTIVATION

Discerning genetic contributions to diseases not only enhances our understanding of disease mechanisms, but also leads to translational opportunities for drug discovery. Recent computational approaches incorporate disease phenotypic similarities to improve the prediction power of disease gene discovery. However, most current studies used only one data source of human disease phenotype. We present an innovative and generic strategy for combining multiple different data sources of human disease phenotype and predicting disease-associated genes from integrated phenotypic and genomic data.

RESULTS

To demonstrate our approach, we explored a new phenotype database from biomedical ontologies and constructed Disease Manifestation Network (DMN). We combined DMN with mimMiner, which was a widely used phenotype database in disease gene prediction studies. Our approach achieved significantly improved performance over a baseline method, which used only one phenotype data source. In the leave-one-out cross-validation and de novo gene prediction analysis, our approach achieved the area under the curves of 90.7% and 90.3%, which are significantly higher than 84.2% (P < e(-4)) and 81.3% (P < e(-12)) for the baseline approach. We further demonstrated that our predicted genes have the translational potential in drug discovery. We used Crohn's disease as an example and ranked the candidate drugs based on the rank of drug targets. Our gene prediction approach prioritized druggable genes that are likely to be associated with Crohn's disease pathogenesis, and our rank of candidate drugs successfully prioritized the Food and Drug Administration-approved drugs for Crohn's disease. We also found literature evidence to support a number of drugs among the top 200 candidates. In summary, we demonstrated that a novel strategy combining unique disease phenotype data with system approaches can lead to rapid drug discovery.

AVAILABILITY AND IMPLEMENTATION

nlp.

CASE

edu/public/data/DMN

Transcriptomic Analysis of the Adaptation of Listeria monocytogenes to Growth on Vacuum-Packed Cold Smoked Salmon

The foodborne pathogen Listeria monocytogenes is able to survive and grow in ready-to-eat foods, in which it is likely to experience a number of environmental stresses due to refrigerated storage and the physicochemical properties of the food. Little is known about the specific molecular mechanisms underlying survival and growth of L. monocytogenes under different complex conditions on/in specific food matrices. Transcriptome sequencing (RNA-seq) was used to understand the transcriptional landscape of L. monocytogenes strain H7858 grown on cold smoked salmon (CSS; water phase salt, 4.65%; pH 6.1) relative to that in modified brain heart infusion broth (MBHIB; water phase salt, 4.65%; pH 6.1) at 7°C. Significant differential transcription of 149 genes was observed (false-discovery rate [FDR], <0.05; fold change, ≥2.5), and 88 and 61 genes were up- and downregulated, respectively, in H7858 grown on CSS relative to the genes in H7858 grown in MBHIB. In spite of these differences in transcriptomes under these two conditions, growth parameters for L. monocytogenes were not significantly different between CSS and MBHIB, indicating that the transcriptomic differences reflect how L. monocytogenes is able to facilitate growth under these different conditions. Differential expression analysis and Gene Ontology enrichment analysis indicated that genes encoding proteins involved in cobalamin biosynthesis as well as ethanolamine and 1,2-propanediol utilization have significantly higher transcript levels in H7858 grown on CSS than in that grown in MBHIB. Our data identify specific transcriptional profiles of L. monocytogenes growing on vacuum-packaged CSS, which may provide targets for the development of novel and improved strategies to control L. monocytogenes growth on this ready-to-eat food.

The digital revolution in phenotyping

Phenotypes have gained increased notoriety in the clinical and biological domain owing to their application in numerous areas such as the discovery of disease genes and drug targets, phylogenetics and pharmacogenomics. Phenotypes, defined as observable characteristics of organisms, can be seen as one of the bridges that lead to a translation of experimental findings into clinical applications and thereby support 'bench to bedside' efforts. However, to build this translational bridge, a common and universal understanding of phenotypes is required that goes beyond domain-specific definitions. To achieve this ambitious goal, a digital revolution is ongoing that enables the encoding of data in computer-readable formats and the data storage in specialized repositories, ready for integration, enabling translational research. While phenome research is an ongoing endeavor, the true potential hidden in the currently available data still needs to be unlocked, offering exciting opportunities for the forthcoming years. Here, we provide insights into the state-of-the-art in digital phenotyping, by means of representing, acquiring and analyzing phenotype data. In addition, we provide visions of this field for future research work that could enable better applications of phenotype data.

Frenemies: Signaling and Nutritional Integration in Pathogen-Microbiota-Host Interactions

The mammalian gastrointestinal (GI) microbiota is highly adapted to thrive in the GI environment and performs key functions related to host nutrition, physiology, development, immunity, and behavior. Successful host-bacterial associations require chemical signaling and optimal nutrient utilization and exchange. However, this important balance can be severely disrupted by environmental stimuli, with one of the most common insults upon the microbiota being infectious diseases. Although the microbiota acts as a barrier toward enteric pathogens, many enteric pathogens exploit signals and nutrients derived from both the microbiota and host to regulate their virulence programs. Here we review several signaling and nutrient recognition systems employed by GI pathogens to regulate growth and virulence. We discuss how shifts in the microbiota composition change host susceptibility to infection and how dietary changes or manipulation of the microbiota could potentially prevent and/or ameliorate GI infections.

Exploring the cellular basis of human disease through a large-scale mapping of deleterious genes to cell types

BACKGROUND

Each cell type found within the human body performs a diverse and unique set of functions, the disruption of which can lead to disease. However, there currently exists no systematic mapping between cell types and the diseases they can cause.

METHODS

In this study, we integrate protein-protein interaction data with high-quality cell-type-specific gene expression data from the FANTOM5 project to build the largest collection of cell-type-specific interactomes created to date. We develop a novel method, called gene set compactness (GSC), that contrasts the relative positions of disease-associated genes across 73 cell-type-specific interactomes to map genes associated with 196 diseases to the cell types they affect. We conduct text-mining of the PubMed database to produce an independent resource of disease-associated cell types, which we use to validate our method.

RESULTS

The GSC method successfully identifies known disease-cell-type associations, as well as highlighting associations that warrant further study. This includes mast cells and multiple sclerosis, a cell population currently being targeted in a multiple sclerosis phase 2 clinical trial. Furthermore, we build a cell-type-based diseasome using the cell types identified as manifesting each disease, offering insight into diseases linked through etiology.

CONCLUSIONS

The data set produced in this study represents the first large-scale mapping of diseases to the cell types in which they are manifested and will therefore be useful in the study of disease systems. Overall, we demonstrate that our approach links disease-associated genes to the phenotypes they produce, a key goal within systems medicine.

Event-based text mining for biology and functional genomics

The assessment of genome function requires a mapping between genome-derived entities and biochemical reactions, and the biomedical literature represents a rich source of information about reactions between biological components. However, the increasingly rapid growth in the volume of literature provides both a challenge and an opportunity for researchers to isolate information about reactions of interest in a timely and efficient manner. In response, recent text mining research in the biology domain has been largely focused on the identification and extraction of 'events', i.e. categorised, structured representations of relationships between biochemical entities, from the literature. Functional genomics analyses necessarily encompass events as so defined. Automatic event extraction systems facilitate the development of sophisticated semantic search applications, allowing researchers to formulate structured queries over extracted events, so as to specify the exact types of reactions to be retrieved. This article provides an overview of recent research into event extraction. We cover annotated corpora on which systems are trained, systems that achieve state-of-the-art performance and details of the community shared tasks that have been instrumental in increasing the quality, coverage and scalability of recent systems. Finally, several concrete applications of event extraction are covered, together with emerging directions of research.

An allosteric model for control of pore opening by substrate binding in the EutL microcompartment shell protein

The ethanolamine utilization (Eut) microcompartment is a protein-based metabolic organelle that is strongly associated with pathogenesis in bacteria that inhabit the human gut. The exterior shell of this elaborate protein complex is composed from a few thousand copies of BMC-domain shell proteins, which form a semi-permeable diffusion barrier that provides the interior enzymes with substrates and cofactors while simultaneously retaining metabolic intermediates. The ability of this protein shell to regulate passage of substrate and cofactor molecules is critical for microcompartment function, but the details of how this diffusion barrier can allow the passage of large cofactors while still retaining small intermediates remain unclear. Previous work has revealed two conformations of the EutL shell protein, providing substantial evidence for a gated pore that might allow the passage of large cofactors. Here we report structural and biophysical evidence to show that ethanolamine, the substrate of the Eut microcompartment, acts as a negative allosteric regulator of EutL pore opening. Specifically, a series of X-ray crystal structures of EutL from Clostridium perfringens, along with equilibrium binding studies, reveal that ethanolamine binds to EutL at a site that exists in the closed-pore conformation and which is incompatible with opening of the large pore for cofactor transport. The allosteric mechanism we propose is consistent with the cofactor requirements of the Eut microcompartment, leading to a new model for EutL function. Furthermore, our results suggest the possibility of redox modulation of the allosteric mechanism, opening potentially new lines of investigation.

Inflammation-associated adherent-invasive Escherichia coli are enriched in pathways for use of propanediol and iron and M-cell translocation

BACKGROUND

Perturbations of the intestinal microbiome, termed dysbiosis, are linked to intestinal inflammation. Isolation of adherent-invasive Escherichia coli (AIEC) from intestines of patients with Crohn's disease (CD), dogs with granulomatous colitis, and mice with acute ileitis suggests these bacteria share pathoadaptive virulence factors that promote inflammation.

METHODS

To identify genes associated with AIEC, we sequenced the genomes of phylogenetically diverse AIEC strains isolated from people with CD (4), dogs with granulomatous colitis (2), and mice with ileitis (2) and 1 non-AIEC strain from CD ileum and compared them with 38 genome sequences of E. coli and Shigella. We then determined the prevalence of AIEC-associated genes in 49 E. coli strains from patients with CD and controls and correlated genotype with invasion of intestinal epithelial cells, persistence within macrophages, AIEC pathotype, and growth in standardized conditions.

RESULTS

Genes encoding propanediol utilization (pdu operon) and iron acquisition (yersiniabactin, chu operon) were overrepresented in AIEC relative to nonpathogenic E. coli. PduC (propanediol dehydratase) was enriched in CD-derived AIEC, correlated with increased cellular invasion, and persistence in vitro and was increasingly expressed in fucose-containing media. Growth of AIEC required iron, and the presence of chuA (heme acquisition) correlated with persistence in macrophages. CD-associated AIEC with lpfA 154 (long polar fimbriae) demonstrated increased invasion of epithelial cells and translocation across M cells.

CONCLUSIONS

Our findings provide novel insights into the genetic basis of the AIEC pathotype, supporting the concept that AIEC are equipped to exploit and promote intestinal inflammation and reveal potential targets for intervention against AIEC and inflammation-associated dysbiosis.

Comparative analysis of a novel disease phenotype network based on clinical manifestations

Systems approaches to analyzing disease phenotype networks in combination with protein functional interaction networks have great potential in illuminating disease pathophysiological mechanisms. While many genetic networks are readily available, disease phenotype networks remain largely incomplete. In this study, we built a large-scale Disease Manifestation Network (DMN) from 50,543 highly accurate disease-manifestation semantic relationships in the United Medical Language System (UMLS). Our new phenotype network contains 2305 nodes and 373,527 weighted edges to represent the disease phenotypic similarities. We first compared DMN with the networks representing genetic relationships among diseases, and demonstrated that the phenotype clustering in DMN reflects common disease genetics. Then we compared DMN with a widely-used disease phenotype network in previous gene discovery studies, called mimMiner, which was extracted from the textual descriptions in Online Mendelian Inheritance in Man (OMIM). We demonstrated that DMN contains different knowledge from the existing phenotype data source. Finally, a case study on Marfan syndrome further proved that DMN contains useful information and can provide leads to discover unknown disease causes. Integrating DMN in systems approaches with mimMiner and other data offers the opportunities to predict novel disease genetics. We made DMN publicly available at nlp/case.edu/public/data/DMN.

The humankind genome: from genetic diversity to the origin of human diseases

Genome-wide association studies have failed to establish common variant risk for the majority of common human diseases. The underlying reasons for this failure are explained by recent studies of resequencing and comparison of over 1200 human genomes and 10 000 exomes, together with the delineation of DNA methylation patterns (epigenome) and full characterization of coding and noncoding RNAs (transcriptome) being transcribed. These studies have provided the most comprehensive catalogues of functional elements and genetic variants that are now available for global integrative analysis and experimental validation in prospective cohort studies. With these datasets, researchers will have unparalleled opportunities for the alignment, mining, and testing of hypotheses for the roles of specific genetic variants, including copy number variations, single nucleotide polymorphisms, and indels as the cause of specific phenotypes and diseases. Through the use of next-generation sequencing technologies for genotyping and standardized ontological annotation to systematically analyze the effects of genomic variation on humans and model organism phenotypes, we will be able to find candidate genes and new clues for disease's etiology and treatment. This article describes essential concepts in genetics and genomic technologies as well as the emerging computational framework to comprehensively search websites and platforms available for the analysis and interpretation of genomic data.

Prevalent structural disorder carries signature of prokaryotic adaptation to oxic atmosphere

Microbes have adopted efficient mechanisms to contend with environmental changes. The emergence of oxygen was a major event that led to an abrupt change in Earth's atmosphere. To adjust with this shift in environmental condition ancient microbes must have undergone several modifications. Although some proteomic and genomic attributes were proposed to facilitate survival of microorganisms in the presence of oxygen, the process of adaptation still remains elusive. Recent studies have focused that intrinsically disordered proteins play crucial roles in adaptation to a wide range of ecological conditions. Therefore, it is likely that disordered proteins could also play indispensable roles in microbial adaptation to the aerobic environment. To test this hypothesis we measured the disorder content of 679 prokaryotes from four oxygen requirement groups. Our result revealed that aerobic proteomes are endowed with the highest protein disorder followed by facultative microbes. Minimal disorder was observed in anaerobic and microaerophilic microbes with no significant difference in their disorder content. Considering all the potential confounding factors that can modulate protein disorder, here we established that the high protein disorder in aerobic microbe is not a by-product of adaptation to any other selective pressure. On the functional level, we found that the high disorder in aerobic proteomes has been utilized for processes that are important for their aerobic lifestyle. Moreover, aerobic proteomes were found to be enriched with disordered binding sites and to contain transcription factors with high disorder propensity. Based on our results, here we proposed that the high protein disorder is an adaptive opportunity for aerobic microbes to fit with the genomic and functional complexities of the aerobic lifestyle.

From food to cell: nutrient exploitation strategies of enteropathogens

Upon entering the human gastrointestinal tract, foodborne bacterial enteropathogens encounter, among numerous other stress conditions, nutrient competition with the host organism and the commensal microbiota. The main carbon, nitrogen and energy sources exploited by pathogens during proliferation in, and colonization of, the gut have, however, not been identified completely. In recent years, a huge body of literature has provided evidence that most enteropathogens are equipped with a large set of specific metabolic pathways to overcome nutritional limitations in vivo, thus increasing bacterial fitness during infection. These adaptations include the degradation of myo-inositol, ethanolamine cleaved from phospholipids, fucose derived from mucosal glycoconjugates, 1,2-propanediol as the fermentation product of fucose or rhamnose and several other metabolites not accessible for commensal bacteria or present in competition-free microenvironments. Interestingly, the data reviewed here point to common metabolic strategies of enteric pathogens allowing the exploitation of nutrient sources that not only are present in the gut lumen, the mucosa or epithelial cells, but also are abundant in food. An increased knowledge of the metabolic strategies developed by enteropathogens is therefore a key factor to better control foodborne diseases.

Inferring gene function from evolutionary change in signatures of translation efficiency

Background

The genetic code is redundant, meaning that most amino acids can be encoded by more than one codon. Highly expressed genes tend to use optimal codons to increase the accuracy and speed of translation. Thus, codon usage biases provide a signature of the relative expression levels of genes, which can, uniquely, be quantified across the domains of life.

Results

Here we describe a general statistical framework to exploit this phenomenon and to systematically associate genes with environments and phenotypic traits through changes in codon adaptation. By inferring evolutionary signatures of translation efficiency in 911 bacterial and archaeal genomes while controlling for confounding effects of phylogeny and inter-correlated phenotypes, we linked 187 gene families to 24 diverse phenotypic traits. A series of experiments in Escherichia coli revealed that 13 of 15, 19 of 23, and 3 of 6 gene families with changes in codon adaptation in aerotolerant, thermophilic, or halophilic microbes. Respectively, confer specific resistance to, respectively, hydrogen peroxide, heat, and high salinity. Further, we demonstrate experimentally that changes in codon optimality alone are sufficient to enhance stress resistance. Finally, we present evidence that multiple genes with altered codon optimality in aerobes confer oxidative stress resistance by controlling the levels of iron and NAD(P)H.

Conclusions

Taken together, these results provide experimental evidence for a widespread connection between changes in translation efficiency and phenotypic adaptation. As the number of sequenced genomes increases, this novel genomic context method for linking genes to phenotypes based on sequence alone will become increasingly useful.

Text mining and medicine: usefulness in respiratory diseases

It is increasingly common to have medical information in electronic format. This includes scientific articles as well as clinical management reviews, and even records from health institutions with patient data. However, traditional instruments, both individual and institutional, are of little use for selecting the most appropriate information in each case, either in the clinical or research field. So-called text or data «mining» enables this huge amount of information to be managed, extracting it from various sources using processing systems (filtration and curation), integrating it and permitting the generation of new knowledge. This review aims to provide an overview of text and data mining, and of the potential usefulness of this bioinformatic technique in the exercise of care in respiratory medicine and in research in the same field.

Coupling Graphs, Efficient Algorithms and B-Cell Epitope Prediction

Coupling graphs are newly introduced in this paper to meet many application needs particularly in the field of bioinformatics. A coupling graph is a two-layer graph complex, in which each node from one layer of the graph complex has at least one connection with the nodes in the other layer, and vice versa. The coupling graph model is sufficiently powerful to capture strong and inherent associations between subgraph pairs in complicated applications. The focus of this paper is on mining algorithms of frequent coupling subgraphs and bioinformatics application. Although existing frequent subgraph mining algorithms are competent to identify frequent subgraphs from a graph database, they perform poorly on frequent coupling subgraph mining because they generate many irrelevant subgraphs. We propose a novel graph transformation technique to transform a coupling graph into a generic graph. Based on the transformed coupling graphs, existing graph mining methods are then utilized to discover frequent coupling subgraphs. We prove that the transformation is precise and complete and that the restoration is reversible. Experiments carried out on a database containing 10,511 coupling graphs show that our proposed algorithm reduces the mining time very much in comparison with the existing subgraph mining algorithms. Moreover, we demonstrate the usefulness of frequent coupling subgraphs by applying our algorithm to make accurate predictions of epitopes in antibody-antigen binding.

A mariner transposon-based signature-tagged mutagenesis system for the analysis of oral infection by Listeria monocytogenes

Listeria monocytogenes is a Gram-positive foodborne pathogen and the causative agent of listerosis a disease that manifests predominately as meningitis in the non-pregnant individual or infection of the fetus and spontaneous abortion in pregnant women. Common-source outbreaks of foodborne listeriosis are associated with significant morbidity and mortality. However, relatively little is known concerning the mechanisms that govern infection via the oral route. In order to aid functional genetic analysis of the gastrointestinal phase of infection we designed a novel signature-tagged mutagenesis (STM) system based upon the invasive L. monocytogenes 4b serotype H7858 strain. To overcome the limitations of gastrointestinal infection by L. monocytogenes in the mouse model we created a H7858 strain that is genetically optimised for oral infection in mice. Furthermore our STM system was based upon a mariner transposon to favour numerous and random transposition events throughout the L. monocytogenes genome. Use of the STM bank to investigate oral infection by L. monocytogenes identified 21 insertion mutants that demonstrated significantly reduced potential for infection in our model. The sites of transposon insertion included lmOh7858_0671 (encoding an internalin homologous to Lmo0610), lmOh7858_0898 (encoding a putative surface-expressed LPXTG protein homologous to Lmo0842), lmOh7858_2579 (encoding the HupDGC hemin transport system) and lmOh7858_0399 (encoding a putative fructose specific phosphotransferase system). We propose that this represents an optimised STM system for functional genetic analysis of foodborne/oral infection by L. monocytogenes.

EutR is a direct regulator of genes that contribute to metabolism and virulence in enterohemorrhagic Escherichia coli O157:H7

Ethanolamine (EA) metabolism is a trait associated with enteric pathogens, including enterohemorrhagic Escherichia coli O157:H7 (EHEC). EHEC causes severe bloody diarrhea and hemolytic uremic syndrome. EHEC encodes the ethanolamine utilization (eut) operon that allows EHEC to metabolize EA and gain a competitive advantage when colonizing the gastrointestinal tract. The eut operon encodes the transcriptional regulator EutR. Genetic studies indicated that EutR expression is induced by EA and vitamin B12 and that EutR promotes expression of the eut operon; however, biochemical evidence for these interactions has been lacking. We performed EA-binding assays and electrophoretic mobility shift assays (EMSAs) to elucidate a mechanism for EutR gene regulation. These studies confirmed EutR interaction with EA, as well as direct binding to the eutS promoter. EutR also contributes to expression of the locus of enterocyte effacement (LEE) in an EA-dependent manner. We performed EMSAs to examine EutR activation of the LEE. The results demonstrated that EutR directly binds the regulatory region of the ler promoter. These results present the first mechanistic description of EutR gene regulation and reveal a novel role for EutR in EHEC pathogenesis.

Production of bulk chemicals via novel metabolic pathways in microorganisms

Metabolic engineering has been playing important roles in developing high performance microorganisms capable of producing various chemicals and materials from renewable biomass in a sustainable manner. Synthetic and systems biology are also contributing significantly to the creation of novel pathways and the whole cell-wide optimization of metabolic performance, respectively. In order to expand the spectrum of chemicals that can be produced biotechnologically, it is necessary to broaden the metabolic capacities of microorganisms. Expanding the metabolic pathways for biosynthesizing the target chemicals requires not only the enumeration of a series of known enzymes, but also the identification of biochemical gaps whose corresponding enzymes might not actually exist in nature; this issue is the focus of this paper. First, pathway prediction tools, effectively combining reactions that lead to the production of a target chemical, are analyzed in terms of logics representing chemical information, and designing and ranking the proposed metabolic pathways. Then, several approaches for potentially filling in the gaps of the novel metabolic pathway are suggested along with relevant examples, including the use of promiscuous enzymes that flexibly utilize different substrates, design of novel enzymes for non-natural reactions, and exploration of hypothetical proteins. Finally, strain optimization by systems metabolic engineering in the context of novel metabolic pathways constructed is briefly described. It is hoped that this review paper will provide logical ways of efficiently utilizing 'big' biological data to design and develop novel metabolic pathways for the production of various bulk chemicals that are currently produced from fossil resources.

Ethanolamine: a signal to commence a host-associated lifestyle?

Ethanolamine (EA) is a compound prevalent in the gastrointestinal (GI) environment. The ability to catabolize this compound has been associated with important GI pathogens, including enterohemorrhagic Escherichia coli O157:H7 (EHEC). It has been hypothesized that the ability of EHEC to utilize EA as a source of nitrogen provides EHEC with an important competitive advantage in the gut. However, new work by Kendall et al. (mBio 3:e00050-12, 2012) suggests that the role of EA in EHEC pathogenesis may be more fundamental; EA appears to be a signal for EHEC to commence its virulence program. In this commentary, I review the previously described connections of EA to bacterial pathogenesis in the GI tract, highlight the important findings of this new study, and note areas where further research is needed to fully comprehend the connection of EA utilization to bacterial pathogenesis.

Event extraction of bacteria biotopes: a knowledge-intensive NLP-based approach

Background

Bacteria biotopes cover a wide range of diverse habitats including animal and plant hosts, natural, medical and industrial environments. The high volume of publications in the microbiology domain provides a rich source of up-to-date information on bacteria biotopes. This information, as found in scientific articles, is expressed in natural language and is rarely available in a structured format, such as a database. This information is of great importance for fundamental research and microbiology applications (e.g., medicine, agronomy, food, bioenergy). The automatic extraction of this information from texts will provide a great benefit to the field.

Methods

We present a new method for extracting relationships between bacteria and their locations using the Alvis framework. Recognition of bacteria and their locations was achieved using a pattern-based approach and domain lexical resources. For the detection of environment locations, we propose a new approach that combines lexical information and the syntactic-semantic analysis of corpus terms to overcome the incompleteness of lexical resources. Bacteria location relations extend over sentence borders, and we developed domain-specific rules for dealing with bacteria anaphors.

Results

We participated in the BioNLP 2011 Bacteria Biotope (BB) task with the Alvis system. Official evaluation results show that it achieves the best performance of participating systems. New developments since then have increased the F-score by 4.1 points.

Conclusions

We have shown that the combination of semantic analysis and domain-adapted resources is both effective and efficient for event information extraction in the bacteria biotope domain. We plan to adapt the method to deal with a larger set of location types and a large-scale scientific article corpus to enable microbiologists to integrate and use the extracted knowledge in combination with experimental data.

A novel extracellular metallopeptidase domain shared by animal host-associated mutualistic and pathogenic microbes

The mucosal microbiota is recognised as an important factor for our health, with many disease states linked to imbalances in the normal community structure. Hence, there is considerable interest in identifying the molecular basis of human-microbe interactions. In this work we investigated the capacity of microbes to thrive on mucosal surfaces, either as mutualists, commensals or pathogens, using comparative genomics to identify co-occurring molecular traits. We identified a novel domain we named M60-like/PF13402 (new Pfam entry PF13402), which was detected mainly among proteins from animal host mucosa-associated prokaryotic and eukaryotic microbes ranging from mutualists to pathogens. Lateral gene transfers between distantly related microbes explained their shared M60-like/PF13402 domain. The novel domain is characterised by a zinc-metallopeptidase-like motif and is distantly related to known viral enhancin zinc-metallopeptidases. Signal peptides and/or cell surface anchoring features were detected in most microbial M60-like/PF13402 domain-containing proteins, indicating that these proteins target an extracellular substrate. A significant subset of these putative peptidases was further characterised by the presence of associated domains belonging to carbohydrate-binding module family 5/12, 32 and 51 and other glycan-binding domains, suggesting that these novel proteases are targeted to complex glycoproteins such as mucins. An in vitro mucinase assay demonstrated degradation of mammalian mucins by a recombinant form of an M60-like/PF13402-containing protein from the gut mutualist Bacteroides thetaiotaomicron. This study reveals that M60-like domains are peptidases targeting host glycoproteins. These peptidases likely play an important role in successful colonisation of both vertebrate mucosal surfaces and the invertebrate digestive tract by both mutualistic and pathogenic microbes. Moreover, 141 entries across various peptidase families described in the MEROPS database were also identified with carbohydrate-binding modules defining a new functional context for these glycan-binding domains and providing opportunities to engineer proteases targeting specific glycoproteins for both biomedical and industrial applications.

Literature mining, ontologies and information visualization for drug repurposing

The immense growth of MEDLINE coupled with the realization that a vast amount of biomedical knowledge is recorded in free-text format, has led to the appearance of a large number of literature mining techniques aiming to extract biomedical terms and their inter-relations from the scientific literature. Ontologies have been extensively utilized in the biomedical domain either as controlled vocabularies or to provide the framework for mapping relations between concepts in biology and medicine. Literature-based approaches and ontologies have been used in the past for the purpose of hypothesis generation in connection with drug discovery. Here, we review the application of literature mining and ontology modeling and traversal to the area of drug repurposing (DR). In recent years, DR has emerged as a noteworthy alternative to the traditional drug development process, in response to the decreased productivity of the biopharmaceutical industry. Thus, systematic approaches to DR have been developed, involving a variety of in silico, genomic and high-throughput screening technologies. Attempts to integrate literature mining with other types of data arising from the use of these technologies as well as visualization tools assisting in the discovery of novel associations between existing drugs and new indications will also be presented.

Integrated bio-entity network: a system for biological knowledge discovery

A significant part of our biological knowledge is centered on relationships between biological entities (bio-entities) such as proteins, genes, small molecules, pathways, gene ontology (GO) terms and diseases. Accumulated at an increasing speed, the information on bio-entity relationships is archived in different forms at scattered places. Most of such information is buried in scientific literature as unstructured text. Organizing heterogeneous information in a structured form not only facilitates study of biological systems using integrative approaches, but also allows discovery of new knowledge in an automatic and systematic way. In this study, we performed a large scale integration of bio-entity relationship information from both databases containing manually annotated, structured information and automatic information extraction of unstructured text in scientific literature. The relationship information we integrated in this study includes protein–protein interactions, protein/gene regulations, protein–small molecule interactions, protein–GO relationships, protein–pathway relationships, and pathway–disease relationships. The relationship information is organized in a graph data structure, named integrated bio-entity network (IBN), where the vertices are the bio-entities and edges represent their relationships. Under this framework, graph theoretic algorithms can be designed to perform various knowledge discovery tasks. We designed breadth-first search with pruning (BFSP) and most probable path (MPP) algorithms to automatically generate hypotheses—the indirect relationships with high probabilities in the network. We show that IBN can be used to generate plausible hypotheses, which not only help to better understand the complex interactions in biological systems, but also provide guidance for experimental designs.

PREDICT: a method for inferring novel drug indications with application to personalized medicine

The authors present a new method, PREDICT, for the large-scale prediction of drug indications, and demonstrate its use on both approved drugs and novel molecules. They also provide a proof-of-concept for its potential utility in predicting patient-specific medications.

We present a novel method for the large-scale prediction of drug indications that can handle both approved drugs and novel molecules.

Our method utilizes multiple drug–drug and disease–disease similarity measures for the prediction task, obtaining high specificity and sensitivity rates (AUC=0.9).

Our drug repositioning predictions cover 27% of the indications currently tested on clinical trials (P<2 × 10⁻²²⁰).

We show comparable performance using a gene expression signature-based disease–disease similarity, laying the computational foundation for predicting patient-specific indications.

Predicting indications for new molecules or finding alternative indications for approved drugs is a laborious and costly process (DiMasi et al, 2003), calling for computational solutions that would minimize production time and development costs (Terstappen and Reggiani, 2001). Here, we present a novel method for predicting drug indications, PREDICT, capable of handling both approved drugs and novel molecules. Our method is based on the assumption that similar drugs are indicated for similar diseases. To score a possible drug–disease association, we compute its similarity to known associations by combining drug–drug and disease–disease similarity computations. This strategy achieves high specificity and sensitivity rates in a cross-validation setting, where part of the known associations are hidden and the method is assessed based on how well it can retrieve them based on the rest of the associations. Assessing its predictions of novel indications for existing drugs, we find that it covers a significant portion (27%, P<2 × 10⁻²²⁰) of drug indications currently tested on clinical trials. Examples of such predictions include: (i) Cabergoline, indicated for Hyperprolactinemia, which is predicted to treat Migrane, a prediction supported by two separate studies (Verhelst et al, 1999; Cavestro et al, 2006) and (ii) Progesterone, which is predicted to treat renal cell cancer, non-papillary (npRCC), supported by the study of Izumi et al (2007). In addition, we provide indication predictions for novel molecules. For example, Cycloleucine is predicted for the treatment of Alzheimer's disease (AD); indeed, Cycloleucine was found to be a potent and selective antagonist of NMDA receptor-mediated responses (Hershkowitz and Rogawski, 1989), a new promising class of chemicals for the treatment of AD (Farlow, 2004). As another example, Hyperforin, St John's wort extract, is predicted to treat hyperthermia. Interestingly, St John's wort extract was found to have anxiolytic effects on stress-induced hyperthermia in mice (Grundmann et al, 2006). We further introduce a disease–disease similarity measure based on disease-specific gene signatures and show that such a measure can be used by our method to accurately predict drug indications. Importantly, this suggests the potential utility of our approach also in a personalized medicine setting, whereby future gene expression signatures from individual patients would replace these disease-specific signatures.

Inferring potential drug indications, for either novel or approved drugs, is a key step in drug development. Previous computational methods in this domain have focused on either drug repositioning or matching drug and disease gene expression profiles. Here, we present a novel method for the large-scale prediction of drug indications (PREDICT) that can handle both approved drugs and novel molecules. Our method is based on the observation that similar drugs are indicated for similar diseases, and utilizes multiple drug–drug and disease–disease similarity measures for the prediction task. On cross-validation, it obtains high specificity and sensitivity (AUC=0.9) in predicting drug indications, surpassing existing methods. We validate our predictions by their overlap with drug indications that are currently under clinical trials, and by their agreement with tissue-specific expression information on the drug targets. We further show that disease-specific genetic signatures can be used to accurately predict drug indications for new diseases (AUC=0.92). This lays the computational foundation for future personalized drug treatments, where gene expression signatures from individual patients would replace the disease-specific signatures.

Metabolomes of the psychrotolerant bacterium Listeria monocytogenes 10403S grown at 37 °C and 8 °C

Listeria monocytogenes is a food-borne pathogen with the ability to grow at refrigeration temperatures. Knowledge of the mechanisms involved in low temperature growth is incomplete and here we report the results of a metabolomics investigation of this. The small molecule contents of L. monocytogenes 10403S grown at 37 °C and 8 °C were compared by gas chromatography/mass spectrometry (GC/MS). Over 500 peaks were detected in both 37 °C and 8 °C-grown cells, and 103 were identified. Of the identified metabolites, the concentrations of 56 metabolites were increased (P<0.05), while the concentrations of 8 metabolites were decreased at low temperature. Metabolites increasing in concentration at 8 °C included amino acids, sugars, organic acids, urea cycle intermediates, polyamines, and different compatible solutes. A principal component analysis (PCA) was used to visualize and compare the matrix containing the data in 6 samples, and this clearly identified the 37 °C and 8 °C metabolomes as different. The results indicated that an increase in solute concentrations in the cytoplasm was associated with low temperature adaptation, which may be a response to chill stress with the effect of lowering the freezing point of intracellular water and decreasing ice crystal formation.

Transcription antitermination by a phosphorylated response regulator and cobalamin-dependent termination at a B₁₂ riboswitch contribute to ethanolamine utilization in Enterococcus faecalis

The ability of bacteria to utilize ethanolamine (EA) as a carbon and nitrogen source may confer an advantage for survival, colonization, and pathogenicity in the human intestinal tract. Enterococcus faecalis, a Gram-positive human commensal organism, depends on a two-component signaling system (TCS-17) for sensing EA and regulating the expression of the ethanolamine utilization genes. Multiple promoters participate in eut gene expression in the presence of EA as the sole carbon source and cobalamin (CoB12), an essential cofactor in the enzymatic degradation process. By means of in vivo and in vitro approaches, this study characterized the transcriptional activity identified in the eutT-eutG intergenic region of the E. faecalis eut cluster. Two novel promoters in this region were shown to be active in vivo. The distal P2-1 promoter was associated with a B12 riboswitch that terminated transcription in the presence of CoB12. Transcription elongation from the proximal P2-2 promoter was regulated by antitermination mediated by the phosphorylated form of the response regulator of TCS-17 (RR17). 3'-Rapid amplification of cDNA ends (RACE) analyses of the terminated RNA products allowed precise identification of the hairpin loop structures involved in termination/antitermination. The results uncovered the role of the B12 riboswitch and RR17 in eut gene expression, adding to the complexity of this regulatory pathway and extending the knowledge of possible means of transcription regulation in Gram-positive organisms.