Dataset Information

Unifying themes in microbial associations with animal and plant hosts described using the gene ontology.

ABSTRACT: Microbes form intimate relationships with hosts (symbioses) that range from mutualism to parasitism. Common microbial mechanisms involved in a successful host association include adhesion, entry of the microbe or its effector proteins into the host cell, mitigation of host defenses, and nutrient acquisition. Genes associated with these microbial mechanisms are known for a broad range of symbioses, revealing both divergent and convergent strategies. Effective comparisons among these symbioses, however, are hampered by inconsistent descriptive terms in the literature for functionally similar genes. Bioinformatic approaches that use homology-based tools are limited to identifying functionally similar genes based on similarities in their sequences. An effective solution to these limitations is provided by the Gene Ontology (GO), which provides a standardized language to describe gene products from all organisms. The GO comprises three ontologies that enable one to describe the molecular function(s) of gene products, the biological processes to which they contribute, and their cellular locations. Beginning in 2004, the Plant-Associated Microbe Gene Ontology (PAMGO) interest group collaborated with the GO consortium to extend the GO to accommodate terms for describing gene products associated with microbe-host interactions. Currently, over 900 terms that describe biological processes common to diverse plant- and animal-associated microbes are incorporated into the GO database. Here we review some unifying themes common to diverse host-microbe associations and illustrate how the new GO terms facilitate a standardized description of the gene products involved. We also highlight areas where new terms need to be developed, an ongoing process that should involve the whole community.

SUBMITTER: Torto-Alalibo T

PROVIDER: S-EPMC3008171 | biostudies-literature | 2010 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Unifying themes in microbial associations with animal and plant hosts described using the gene ontology.

Torto-Alalibo Trudy T Collmer Candace W CW Gwinn-Giglio Michelle M Lindeberg Magdalen M Meng Shaowu S Chibucos Marcus C MC Tseng Tsai-Tien TT Lomax Jane J Biehl Bryan B Ireland Amelia A Bird David D Dean Ralph A RA Glasner Jeremy D JD Perna Nicole N Setubal Joao C JC Collmer Alan A Tyler Brett M BM

Microbiology and molecular biology reviews : MMBR 20101201 4

Microbes form intimate relationships with hosts (symbioses) that range from mutualism to parasitism. Common microbial mechanisms involved in a successful host association include adhesion, entry of the microbe or its effector proteins into the host cell, mitigation of host defenses, and nutrient acquisition. Genes associated with these microbial mechanisms are known for a broad range of symbioses, revealing both divergent and convergent strategies. Effective comparisons among these symbioses, ho ...[more]

PMID: 21119014

Similar Datasets

Project description:Drought stress negatively impacts microbial activity, but the magnitude of stress responses is likely dependent on a diversity of belowground interactions. Populus trichocarpa individuals and no-plant bulk soils were exposed to extended drought (∼0.03% gravimetric water content [GWC] after 12 days), rewet, and a 12-day "recovery" period to determine the effects of plant presence in mediating soil microbiome stability to water stress. Plant metabolomic analyses indicated that drought exposure increased host investment in C and N metabolic pathways (amino acids, fatty acids, phenolic glycosides) regardless of recovery. Several metabolites positively correlated with root-associated microbial alpha-diversity, but not those of soil communities. Soil bacterial community composition shifted with P. trichocarpa presence and with drought relative to irrigated controls, whereas soil fungal composition shifted only with plant presence. However, root fungal communities strongly shifted with drought, whereas root bacterial communities changed to a lesser degree. The proportion of bacterial water-stress opportunistic operational taxonomic units (OTUs) (enriched counts in drought) was high (∼11%) at the end of drying phases and maintained after rewet and recovery phases in bulk soils, but it declined over time in soils with plants present. For root fungi, opportunistic OTUs were high at the end of recovery in drought treatments (∼17% abundance), although relatively not responsive in soils, particularly planted soils (<0.5% abundance for sensitive or opportunistic). These data indicate that plants modulate soil and root-associated microbial drought responses via tight plant-microbe linkages during extreme drought scenarios, but trajectories after extreme drought vary with plant habitat and microbial functional groups.IMPORTANCE Climate change causes significant alterations in precipitation and temperature regimes that are predicted to become more extreme throughout the next century. Microorganisms are important members within ecosystems, and how they respond to these changing abiotic stressors has large implications for the functioning of ecosystems, the recycling of nutrients, and the health of the aboveground plant community. Drought stress negatively impacts microbial activity, but the magnitude of this stress response may be dependent on above- and belowground interactions. This study demonstrates that beneficial associations between plants and microbes can enhance tolerance to abiotic stress.

Project description:Scientists and clinicians who study genetic alterations and disease have traditionally described phenotypes in natural language. The considerable variation in these free-text descriptions has posed a hindrance to the important task of identifying candidate genes and models for human diseases and indicates the need for a computationally tractable method to mine data resources for mutant phenotypes. In this study, we tested the hypothesis that ontological annotation of disease phenotypes will facilitate the discovery of new genotype-phenotype relationships within and across species. To describe phenotypes using ontologies, we used an Entity-Quality (EQ) methodology, wherein the affected entity (E) and how it is affected (Q) are recorded using terms from a variety of ontologies. Using this EQ method, we annotated the phenotypes of 11 gene-linked human diseases described in Online Mendelian Inheritance in Man (OMIM). These human annotations were loaded into our Ontology-Based Database (OBD) along with other ontology-based phenotype descriptions of mutants from various model organism databases. Phenotypes recorded with this EQ method can be computationally compared based on the hierarchy of terms in the ontologies and the frequency of annotation. We utilized four similarity metrics to compare phenotypes and developed an ontology of homologous and analogous anatomical structures to compare phenotypes between species. Using these tools, we demonstrate that we can identify, through the similarity of the recorded phenotypes, other alleles of the same gene, other members of a signaling pathway, and orthologous genes and pathway members across species. We conclude that EQ-based annotation of phenotypes, in conjunction with a cross-species ontology, and a variety of similarity metrics can identify biologically meaningful similarities between genes by comparing phenotypes alone. This annotation and search method provides a novel and efficient means to identify gene candidates and animal models of human disease, which may shorten the lengthy path to identification and understanding of the genetic basis of human disease.

Project description:Analysing the relationships between biomolecules and the genetic diseases is a highly active area of research, where the aim is to identify the genes and their products that cause a particular disease due to functional changes originated from mutations. Biological ontologies are frequently employed in these studies, which provides researchers with extensive opportunities for knowledge discovery through computational data analysis. In this study, a novel approach is proposed for the identification of relationships between biomedical entities by automatically mapping phenotypic abnormality defining HPO terms with biomolecular function defining GO terms, where each association indicates the occurrence of the abnormality due to the loss of the biomolecular function expressed by the corresponding GO term. The proposed HPO2GO mappings were extracted by calculating the frequency of the co-annotations of the terms on the same genes/proteins, using already existing curated HPO and GO annotation sets. This was followed by the filtering of the unreliable mappings that could be observed due to chance, by statistical resampling of the co-occurrence similarity distributions. Furthermore, the biological relevance of the finalized mappings were discussed over selected cases, using the literature. The resulting HPO2GO mappings can be employed in different settings to predict and to analyse novel gene/protein-ontology term-disease relations. As an application of the proposed approach, HPO term-protein associations (i.e., HPO2protein) were predicted. In order to test the predictive performance of the method on a quantitative basis, and to compare it with the state-of-the-art, CAFA2 challenge HPO prediction target protein set was employed. The results of the benchmark indicated the potential of the proposed approach, as HPO2GO performance was among the best (Fmax = 0.35). The automated cross ontology mapping approach developed in this work may be extended to other ontologies as well, to identify unexplored relation patterns at the systemic level. The datasets, results and the source code of HPO2GO are available for download at: https://github.com/cansyl/HPO2GO.

Project description:Methane (CH4) is a valuable fuel, constituting 70-95% of natural gas, and a potent greenhouse gas. Release of CH4 into the atmosphere contributes to climate change. Biological CH4 production or methanogenesis is mostly performed by methanogens, a group of strictly anaerobic archaea. The direct substrates for methanogenesis are H2 plus CO2, acetate, formate, methylamines, methanol, methyl sulfides, and ethanol or a secondary alcohol plus CO2. In numerous anaerobic niches in nature, methanogenesis facilitates mineralization of complex biopolymers such as carbohydrates, lipids and proteins generated by primary producers. Thus, methanogens are critical players in the global carbon cycle. The same process is used in anaerobic treatment of municipal, industrial and agricultural wastes, reducing the biological pollutants in the wastes and generating methane. It also holds potential for commercial production of natural gas from renewable resources. This process operates in digestive systems of many animals, including cattle, and humans. In contrast, in deep-sea hydrothermal vents methanogenesis is a primary production process, allowing chemosynthesis of biomaterials from H2 plus CO2. In this report we present Gene Ontology (GO) terms that can be used to describe processes, functions and cellular components involved in methanogenic biodegradation and biosynthesis of specialized coenzymes that methanogens use. Some of these GO terms were previously available and the rest were generated in our Microbial Energy Gene Ontology (MENGO) project. A recently discovered non-canonical CH4 production process is also described. We have performed manual GO annotation of selected methanogenesis genes, based on experimental evidence, providing "gold standards" for machine annotation and automated discovery of methanogenesis genes or systems in diverse genomes. Most of the GO-related information presented in this report is available at the MENGO website (http://www.mengo.biochem.vt.edu/).

Dataset Information

Unifying themes in microbial associations with animal and plant hosts described using the gene ontology.

Publications

Unifying themes in microbial associations with animal and plant hosts described using the gene ontology.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets