Phydbac "Gene Function Predictor" : a gene annotation tool based on genomic context analysis

Background  

The large amount of completely sequenced genomes allows genomic context analysis to predict reliable functional associations between prokaryotic proteins. Major methods rely on the fact that genes encoding physically interacting partners or members of shared metabolic pathways tend to be proximate on the genome, to evolve in a correlated manner and to be fused as a single sequence in another organism.     

Genes2Networks: connecting lists of gene symbols using mammalian protein interactions databases

Background  

In recent years, mammalian protein-protein interaction network databases have been developed. The interactions in these databases are either extracted manually from low-throughput experimental biomedical research literature, extracted automatically from literature using techniques such as natural language processing (NLP), generated experimentally using high-throughput methods such as yeast-2-hybrid screens, or interactions are predicted using an assortment of computational approaches. Genes or proteins identified as significantly changing in proteomic experiments, or identified as susceptibility disease genes in genomic studies, can be placed in the context of protein interaction networks in order to assign these genes and proteins to pathways and protein complexes.     

Detection of gene pathways with predictive power for breast cancer prognosis

Background  

Prognosis is of critical interest in breast cancer research. Biomedical studies suggest that genomic measurements may have independent predictive power for prognosis. Gene profiling studies have been conducted to search for predictive genomic measurements. Genes have the inherent pathway structure, where pathways are composed of multiple genes with coordinated functions. The goal of this study is to identify gene pathways with predictive power for breast cancer prognosis. Since our goal is fundamentally different from that of existing studies, a new pathway analysis method is proposed.     

Signalogs: orthology-based identification of novel signaling pathway components in three metazoans

Background

Uncovering novel components of signal transduction pathways and their interactions within species is a central task in current biological research. Orthology alignment and functional genomics approaches allow the effective identification of signaling proteins by cross-species data integration. Recently, functional annotation of orthologs was transferred across organisms to predict novel roles for proteins. Despite the wide use of these methods, annotation of complete signaling pathways has not yet been transferred systematically between species.

Principal Findings

Here we introduce the concept of ‘signalog’ to describe potential novel signaling function of a protein on the basis of the known signaling role(s) of its ortholog(s). To identify signalogs on genomic scale, we systematically transferred signaling pathway annotations among three animal species, the nematode Caenorhabditis elegans, the fruit fly Drosophila melanogaster, and humans. Using orthology data from InParanoid and signaling pathway information from the SignaLink database, we predict 88 worm, 92 fly, and 73 human novel signaling components. Furthermore, we developed an on-line tool and an interactive orthology network viewer to allow users to predict and visualize components of orthologous pathways. We verified the novelty of the predicted signalogs by literature search and comparison to known pathway annotations. In C. elegans, 6 out of the predicted novel Notch pathway members were validated experimentally. Our approach predicts signaling roles for 19 human orthodisease proteins and 5 known drug targets, and suggests 14 novel drug target candidates.

Conclusions

Orthology-based pathway membership prediction between species enables the identification of novel signaling pathway components that we referred to as signalogs. Signalogs can be used to build a comprehensive signaling network in a given species. Such networks may increase the biomedical utilization of C. elegans and D. melanogaster. In humans, signalogs may identify novel drug targets and new signaling mechanisms for approved drugs.     

Extensive ssDNA end formation at DNA double-strand breaks in non-homologous end-joining deficient cells during the S phase

Background  

Efficient and correct repair of DNA damage, especially DNA double-strand breaks, is critical for cellular survival. Defects in the DNA repair may lead to cell death or genomic instability and development of cancer. Non-homologous end-joining (NHEJ) is the major repair pathway for DNA double-strand breaks in mammalian cells. The ability of other repair pathways, such as homologous recombination, to compensate for loss of NHEJ and the ways in which contributions of different pathways are regulated are far from fully understood.     

The fission yeast DNA structure checkpoint protein Rad26ATRIP/LCD1/UVSD accumulates in the cytoplasm following microtubule destabilization

Background  

DNA structure checkpoints are conserved eukaryotic signal transduction pathways that help preserve genomic integrity. Upon detecting checkpoint signals such as stalled replication forks or double-stranded DNA breaks, these pathways coordinate appropriate stress responses. Members of the PI-3 kinase related kinase (PIKK) family are essential elements of DNA structure checkpoints. In fission yeast, the Rad3 PIKK and its regulatory subunit Rad26 coordinate the detection of checkpoint signals with pathway outputs.     

Cellular Notch responsiveness is defined by phosphoinositide 3-kinase-dependent signals

Background  

Notch plays a wide-ranging role in controlling cell fate, differentiation and development. The PI3K-Akt pathway is a similarly conserved signalling pathway which regulates processes such as differentiation, proliferation and survival. Mice with disrupted Notch and PI3K signalling show phenotypic similarities during haematopoietic cell development, suggesting functional interaction between these pathways.     

Explore the Features of Brain-Derived Neurotrophic Factor in Mood Disorders

Objectives

Brain-derived neurotrophic factor (BDNF) plays important roles in neuronal survival and differentiation; however, the effects of BDNF on mood disorders remain unclear. We investigated BDNF from the perspective of various aspects of systems biology, including its molecular evolution, genomic studies, protein functions, and pathway analysis.

Methods

We conducted analyses examining sequences, multiple alignments, phylogenetic trees and positive selection across 12 species and several human populations. We summarized the results of previous genomic and functional studies of pro-BDNF and mature-BDNF (m-BDNF) found in a literature review. We identified proteins that interact with BDNF and performed pathway-based analysis using large genome-wide association (GWA) datasets obtained for mood disorders.

Results

BDNF is encoded by a highly conserved gene. The chordate BDNF genes exhibit an average of 75% identity with the human gene, while vertebrate orthologues are 85.9%-100% identical to human BDNF. No signs of recent positive selection were found. Associations between BDNF and mood disorders were not significant in most of the genomic studies (e.g., linkage, association, gene expression, GWA), while relationships between serum/plasma BDNF level and mood disorders were consistently reported. Pro-BDNF is important in the response to stress; the literature review suggests the necessity of studying both pro- and m-BDNF with regard to mood disorders. In addition to conventional pathway analysis, we further considered proteins that interact with BDNF (I-Genes) and identified several biological pathways involved with BDNF or I-Genes to be significantly associated with mood disorders.

Conclusions

Systematically examining the features and biological pathways of BDNF may provide opportunities to deepen our understanding of the mechanisms underlying mood disorders.     

Implications for domain fusion protein-protein interactions based on structural information

Background  

Several in silico methods exist that were developed to predict protein interactions from the copious amount of genomic and proteomic data. One of these methods is Domain Fusion, which has proven to be effective in predicting functional links between proteins.     

Revealing and avoiding bias in semantic similarity scores for protein pairs

Background  

Semantic similarity scores for protein pairs are widely applied in functional genomic researches for finding functional clusters of proteins, predicting protein functions and protein-protein interactions, and for identifying putative disease genes. However, because some proteins, such as those related to diseases, tend to be studied more intensively, annotations are likely to be biased, which may affect applications based on semantic similarity measures. Thus, it is necessary to evaluate the effects of the bias on semantic similarity scores between proteins and then find a method to avoid them.     

Evaluation of Physical and Functional Protein-Protein Interaction Prediction Methods for Detecting Biological Pathways

Background

Cellular activities are governed by the physical and the functional interactions among several proteins involved in various biological pathways. With the availability of sequenced genomes and high-throughput experimental data one can identify genome-wide protein-protein interactions using various computational techniques. Comparative assessments of these techniques in predicting protein interactions have been frequently reported in the literature but not their ability to elucidate a particular biological pathway.

Methods

Towards the goal of understanding the prediction capabilities of interactions among the specific biological pathway proteins, we report the analyses of 14 biological pathways of Escherichia coli catalogued in KEGG database using five protein-protein functional linkage prediction methods. These methods are phylogenetic profiling, gene neighborhood, co-presence of orthologous genes in the same gene clusters, a mirrortree variant, and expression similarity.

Conclusions

Our results reveal that the prediction of metabolic pathway protein interactions continues to be a challenging task for all methods which possibly reflect flexible/independent evolutionary histories of these proteins. These methods have predicted functional associations of proteins involved in amino acids, nucleotide, glycans and vitamins & co-factors pathways slightly better than the random performance on carbohydrate, lipid and energy metabolism. We also make similar observations for interactions involved among the environmental information processing proteins. On the contrary, genetic information processing or specialized processes such as motility related protein-protein linkages that occur in the subset of organisms are predicted with comparable accuracy. Metabolic pathways are best predicted by using neighborhood of orthologous genes whereas phyletic pattern is good enough to reconstruct central dogma pathway protein interactions. We have also shown that the effective use of a particular prediction method depends on the pathway under investigation. In case one is not focused on specific pathway, gene expression similarity method is the best option.     

Extending pathways based on gene lists using InterPro domain signatures

Background

High-throughput technologies like functional screens and gene expression analysis produce extended lists of candidate genes. Gene-Set Enrichment Analysis is a commonly used and well established technique to test for the statistically significant over-representation of particular pathways. A shortcoming of this method is however, that most genes that are investigated in the experiments have very sparse functional or pathway annotation and therefore cannot be the target of such an analysis. The approach presented here aims to assign lists of genes with limited annotation to previously described functional gene collections or pathways. This works by comparing InterPro domain signatures of the candidate gene lists with domain signatures of gene sets derived from known classifications, e.g. KEGG pathways.

Results

In order to validate our approach, we designed a simulation study. Based on all pathways available in the KEGG database, we create test gene lists by randomly selecting pathway genes, removing these genes from the known pathways and adding variable amounts of noise in the form of genes not annotated to the pathway. We show that we can recover pathway memberships based on the simulated gene lists with high accuracy. We further demonstrate the applicability of our approach on a biological example.

Conclusion

Results based on simulation and data analysis show that domain based pathway enrichment analysis is a very sensitive method to test for enrichment of pathways in sparsely annotated lists of genes. An R based software package domainsignatures, to routinely perform this analysis on the results of high-throughput screening, is available via Bioconductor.     

PathFinder: mining signal transduction pathway segments from protein-protein interaction networks

Background  

A Signal transduction pathway is the chain of processes by which a cell converts an extracellular signal into a response. In most unicellular organisms, the number of signal transduction pathways influences the number of ways the cell can react and respond to the environment. Discovering signal transduction pathways is an arduous problem, even with the use of systematic genomic, proteomic and metabolomic technologies. These techniques lead to an enormous amount of data and how to interpret and process this data becomes a challenging computational problem.     

Evolutionary rate patterns of the Gibberellin pathway genes

Background  

Analysis of molecular evolutionary patterns of different genes within metabolic pathways allows us to determine whether these genes are subject to equivalent evolutionary forces and how natural selection shapes the evolution of proteins in an interacting system. Although previous studies found that upstream genes in the pathway evolved more slowly than downstream genes, the correlation between evolutionary rate and position of the genes in metabolic pathways as well as its implications in molecular evolution are still less understood.     

MetaMine – A tool to detect and analyse gene patterns in their environmental context

Background  

Modern sequencing technologies allow rapid sequencing and bioinformatic analysis of genomes and metagenomes. With every new sequencing project a vast number of new proteins become available with many genes remaining functionally unclassified based on evidences from sequence similarities alone. Extending similarity searches with gene pattern approaches, defined as genes sharing a distinct genomic neighbourhood, have shown to significantly improve the number of functional assignments. Further functional evidences can be gained by correlating these gene patterns with prevailing environmental parameters. MetaMine was developed to approach the large pool of unclassified proteins by searching for recurrent gene patterns across habitats based on key genes.     

PathMAPA: a tool for displaying gene expression and performing statistical tests on metabolic pathways at multiple levels for <Emphasis Type="Italic">Arabidopsis</Emphasis>

Background  

To date, many genomic and pathway-related tools and databases have been developed to analyze microarray data. In published web-based applications to date, however, complex pathways have been displayed with static image files that may not be up-to-date or are time-consuming to rebuild. In addition, gene expression analyses focus on individual probes and genes with little or no consideration of pathways. These approaches reveal little information about pathways that are key to a full understanding of the building blocks of biological systems. Therefore, there is a need to provide useful tools that can generate pathways without manually building images and allow gene expression data to be integrated and analyzed at pathway levels for such experimental organisms as Arabidopsis.     

An <Emphasis Type="Italic">in vivo</Emphasis> reporter of BMP signaling in organogenesis reveals targets in the developing kidney

Background  

Bone morphogenetic proteins (BMPs) regulate essential processes during organogenesis, and a functional understanding of these secreted proteins depends on identification of their target cells. In this study, we generate a transgenic reporter for organogenesis studies that we use to define BMP pathway activation in the developing kidney.     

Discovering functional linkages and uncharacterized cellular pathways using phylogenetic profile comparisons: a comprehensive assessment

Background  

A widely-used approach for discovering functional and physical interactions among proteins involves phylogenetic profile comparisons (PPCs). Here, proteins with similar profiles are inferred to be functionally related under the assumption that proteins involved in the same metabolic pathway or cellular system are likely to have been co-inherited during evolution.     

Mating changes the subcellular distribution and the functionality of estrogen receptors in the rat oviduct

Background  

Mating changes the mode of action of 17beta-estradiol (E2) to accelerate oviductal egg transport from a nongenomic to a genomic mode, although in both pathways estrogen receptors (ER) are required. This change was designated as intracellular path shifting (IPS).