Operon prediction by comparative genomics: an application to the Synechococcus sp. WH8102 genome

We present a computational method for operon prediction based on a comparative genomics approach. A group of consecutive genes is considered as a candidate operon if both their gene sequences and functions are conserved across several phylogenetically related genomes. In addition, various supporting data for operons are also collected through the application of public domain computer programs, and used in our prediction method. These include the prediction of conserved gene functions, promoter motifs and terminators. An apparent advantage of our approach over other operon prediction methods is that it does not require many experimental data (such as gene expression data and pathway data) as input. This feature makes it applicable to many newly sequenced genomes that do not have extensive experimental information. In order to validate our prediction, we have tested the method on Escherichia coli K12, in which operon structures have been extensively studied, through a comparative analysis against Haemophilus influenzae Rd and Salmonella typhimurium LT2. Our method successfully predicted most of the 237 known operons. After this initial validation, we then applied the method to a newly sequenced and annotated microbial genome, Synechococcus sp. WH8102, through a comparative genome analysis with two other cyanobacterial genomes, Prochlorococcus marinus sp. MED4 and P.marinus sp. MIT9313. Our results are consistent with previously reported results and statistics on operons in the literature.     

Quantitative Genome-Wide Genetic Interaction Screens Reveal Global Epistatic Relationships of Protein Complexes in Escherichia coli

Large-scale proteomic analyses in Escherichia coli have documented the composition and physical relationships of multiprotein complexes, but not their functional organization into biological pathways and processes. Conversely, genetic interaction (GI) screens can provide insights into the biological role(s) of individual gene and higher order associations. Combining the information from both approaches should elucidate how complexes and pathways intersect functionally at a systems level. However, such integrative analysis has been hindered due to the lack of relevant GI data. Here we present a systematic, unbiased, and quantitative synthetic genetic array screen in E. coli describing the genetic dependencies and functional cross-talk among over 600,000 digenic mutant combinations. Combining this epistasis information with putative functional modules derived from previous proteomic data and genomic context-based methods revealed unexpected associations, including new components required for the biogenesis of iron-sulphur and ribosome integrity, and the interplay between molecular chaperones and proteases. We find that functionally-linked genes co-conserved among γ-proteobacteria are far more likely to have correlated GI profiles than genes with divergent patterns of evolution. Overall, examining bacterial GIs in the context of protein complexes provides avenues for a deeper mechanistic understanding of core microbial systems.     

Operon prediction in Pyrococcus furiosus

Identification of operons in the hyperthermophilic archaeon Pyrococcus furiosus represents an important step to understanding the regulatory mechanisms that enable the organism to adapt and thrive in extreme environments. We have predicted operons in P.furiosus by combining the results from three existing algorithms using a neural network (NN). These algorithms use intergenic distances, phylogenetic profiles, functional categories and gene-order conservation in their operon prediction. Our method takes as inputs the confidence scores of the three programs, and outputs a prediction of whether adjacent genes on the same strand belong to the same operon. In addition, we have applied Gene Ontology (GO) and KEGG pathway information to improve the accuracy of our algorithm. The parameters of this NN predictor are trained on a subset of all experimentally verified operon gene pairs of Bacillus subtilis. It subsequently achieved 86.5% prediction accuracy when applied to a subset of gene pairs for Escherichia coli, which is substantially better than any of the three prediction programs. Using this new algorithm, we predicted 470 operons in the P.furiosus genome. Of these, 349 were validated using DNA microarray data.     

Gene co-regulation is highly conserved in the evolution of eukaryotes and prokaryotes

Differences between species have been suggested to largely reside in the network of connections among the genes. Nevertheless, the rate at which these connections evolve has not been properly quantified. Here, we measure the extent to which co-regulation between pairs of genes is conserved over large phylogenetic distances; between two eukaryotes Caenorhabditis elegans and Saccharomyces cerevisiae, and between two prokaryotes Escherichia coli and Bacillus subtilis. We first construct a reliable set of co-regulated genes by combining various functional genomics data from yeast, and subsequently determine conservation of co-regulation in worm from the distribution of co-expression values. For B.subtilis and E.coli, we use known operons and regulons. We find that between 76 and 80% of the co-regulatory connections are conserved between orthologous pairs of genes, which is very high compared with previous estimates and expectations regarding network evolution. We show that in the case of gene duplication after speciation, one of the two inparalogous genes tends to retain its original co-regulatory relationship, while the other loses this link and is presumably free for differentiation or sub-functionalization. The high level of co-regulation conservation implies that reliably predicted functional relationships from functional genomics data in one species can be transferred with high accuracy to another species when that species also harbours the associated genes.     

A computational approach to identify genes for functional RNAs in genomic sequences

Currently there is no successful computational approach for identification of genes encoding novel functional RNAs (fRNAs) in genomic sequences. We have developed a machine learning approach using neural networks and support vector machines to extract common features among known RNAs for prediction of new RNA genes in the unannotated regions of prokaryotic and archaeal genomes. The Escherichia coli genome was used for development, but we have applied this method to several other bacterial and archaeal genomes. Networks based on nucleotide composition were 80–90% accurate in jackknife testing experiments for bacteria and 90–99% for hyperthermophilic archaea. We also achieved a significant improvement in accuracy by combining these predictions with those obtained using a second set of parameters consisting of known RNA sequence motifs and the calculated free energy of folding. Several known fRNAs not included in the training datasets were identified as well as several hundred predicted novel RNAs. These studies indicate that there are many unidentified RNAs in simple genomes that can be predicted computationally as a precursor to experimental study. Public access to our RNA gene predictions and an interface for user predictions is available via the web.     

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.     

Interplay between Siderophores and Colibactin Genotoxin Biosynthetic Pathways in Escherichia coli

In Escherichia coli, the biosynthetic pathways of several small iron-scavenging molecules known as siderophores (enterobactin, salmochelins and yersiniabactin) and of a genotoxin (colibactin) are known to require a 4′-phosphopantetheinyl transferase (PPTase). Only two PPTases have been clearly identified: EntD and ClbA. The gene coding for EntD is part of the core genome of E. coli, whereas ClbA is encoded on the pks pathogenicity island which codes for colibactin. Interestingly, the pks island is physically associated with the high pathogenicity island (HPI) in a subset of highly virulent E. coli strains. The HPI carries the gene cluster required for yersiniabactin synthesis except for a gene coding its cognate PPTase. Here we investigated a potential interplay between the synthesis pathways leading to the production of siderophores and colibactin, through a functional interchangeability between EntD and ClbA. We demonstrated that ClbA could contribute to siderophores synthesis. Inactivation of both entD and clbA abolished the virulence of extra-intestinal pathogenic E. coli (ExPEC) in a mouse sepsis model, and the presence of either functional EntD or ClbA was required for the survival of ExPEC in vivo. This is the first report demonstrating a connection between multiple phosphopantetheinyl-requiring pathways leading to the biosynthesis of functionally distinct secondary metabolites in a given microorganism. Therefore, we hypothesize that the strict association of the pks island with HPI has been selected in highly virulent E. coli because ClbA is a promiscuous PPTase that can contribute to the synthesis of both the genotoxin and siderophores. The data highlight the complex regulatory interaction of various virulence features with different functions. The identification of key points of these networks is not only essential to the understanding of ExPEC virulence but also an attractive and promising target for the development of anti-virulence therapy strategies.     

Genome-scale gene function prediction using multiple sources of high-throughput data in yeast Saccharomyces cerevisiae

Characterizing gene function is one of the major challenging tasks in the post-genomic era. To address this challenge, we have developed GeneFAS (Gene Function Annotation System), a new integrated probabilistic method for cellular function prediction by combining information from protein-protein interactions, protein complexes, microarray gene expression profiles, and annotations of known proteins through an integrative statistical model. Our approach is based on a novel assessment for the relationship between (1) the interaction/correlation of two proteins' high-throughput data and (2) their functional relationship in terms of their Gene Ontology (GO) hierarchy. We have developed a Web server for the predictions. We have applied our method to yeast Saccharomyces cerevisiae and predicted functions for 1548 out of 2472 unannotated proteins.     

A Hybrid Approach of Gene Sets and Single Genes for the Prediction of Survival Risks with Gene Expression Data

Accumulated biological knowledge is often encoded as gene sets, collections of genes associated with similar biological functions or pathways. The use of gene sets in the analyses of high-throughput gene expression data has been intensively studied and applied in clinical research. However, the main interest remains in finding modules of biological knowledge, or corresponding gene sets, significantly associated with disease conditions. Risk prediction from censored survival times using gene sets hasn’t been well studied. In this work, we propose a hybrid method that uses both single gene and gene set information together to predict patient survival risks from gene expression profiles. In the proposed method, gene sets provide context-level information that is poorly reflected by single genes. Complementarily, single genes help to supplement incomplete information of gene sets due to our imperfect biomedical knowledge. Through the tests over multiple data sets of cancer and trauma injury, the proposed method showed robust and improved performance compared with the conventional approaches with only single genes or gene sets solely. Additionally, we examined the prediction result in the trauma injury data, and showed that the modules of biological knowledge used in the prediction by the proposed method were highly interpretable in biology. A wide range of survival prediction problems in clinical genomics is expected to benefit from the use of biological knowledge.     

Cohesive versus Flexible Evolution of Functional Modules in Eukaryotes

Although functionally related proteins can be reliably predicted from phylogenetic profiles, many functional modules do not seem to evolve cohesively according to case studies and systematic analyses in prokaryotes. In this study we quantify the extent of evolutionary cohesiveness of functional modules in eukaryotes and probe the biological and methodological factors influencing our estimates. We have collected various datasets of protein complexes and pathways in Saccheromyces cerevisiae. We define orthologous groups on 34 eukaryotic genomes and measure the extent of cohesive evolution of sets of orthologous groups of which members constitute a known complex or pathway. Within this framework it appears that most functional modules evolve flexibly rather than cohesively. Even after correcting for uncertain module definitions and potentially problematic orthologous groups, only 46% of pathways and complexes evolve more cohesively than random modules. This flexibility seems partly coupled to the nature of the functional module because biochemical pathways are generally more cohesively evolving than complexes.     

Human Intellectual Disability Genes Form Conserved Functional Modules in Drosophila

Intellectual Disability (ID) disorders, defined by an IQ below 70, are genetically and phenotypically highly heterogeneous. Identification of common molecular pathways underlying these disorders is crucial for understanding the molecular basis of cognition and for the development of therapeutic intervention strategies. To systematically establish their functional connectivity, we used transgenic RNAi to target 270 ID gene orthologs in the Drosophila eye. Assessment of neuronal function in behavioral and electrophysiological assays and multiparametric morphological analysis identified phenotypes associated with knockdown of 180 ID gene orthologs. Most of these genotype-phenotype associations were novel. For example, we uncovered 16 genes that are required for basal neurotransmission and have not previously been implicated in this process in any system or organism. ID gene orthologs with morphological eye phenotypes, in contrast to genes without phenotypes, are relatively highly expressed in the human nervous system and are enriched for neuronal functions, suggesting that eye phenotyping can distinguish different classes of ID genes. Indeed, grouping genes by Drosophila phenotype uncovered 26 connected functional modules. Novel links between ID genes successfully predicted that MYCN, PIGV and UPF3B regulate synapse development. Drosophila phenotype groups show, in addition to ID, significant phenotypic similarity also in humans, indicating that functional modules are conserved. The combined data indicate that ID disorders, despite their extreme genetic diversity, are caused by disruption of a limited number of highly connected functional modules.     

Probing Oral Microbial Functionality – Expression of spxB in Plaque Samples

The Human Oral Microbiome Database (HOMD) provides an extensive collection of genome sequences from oral bacteria. The sequence information is a static snapshot of the microbial potential of the so far sequenced species. A major challenge is to connect the microbial potential encoded in the metagenome to an actual function in the in vivo oral biofilm. In the present study we took a reductionist approach and identified a considerably conserved metabolic gene, spxB to be encoded by a majority of oral streptococci using the HOMD metagenome information. spxB encodes the pyruvate oxidase responsible for the production of growth inhibiting amounts of hydrogen peroxide (H₂O₂) and has previously been shown as important in the interspecies competition in the oral biofilm. Here we demonstrate a strong correlation of H₂O₂ production and the presence of the spxB gene in dental plaque. Using Real-Time RT PCR we show that spxB is expressed in freshly isolated human plaque samples from several donors and that the expression is relative constant when followed over time in one individual. This is the first demonstration of an oral community encoded gene expressed in vivo suggesting a functional role of spxB in oral biofilm physiology. This also demonstrates a possible strategy to connect the microbial potential of the metagenome to its functionality in future studies by identifying similar highly conserved genes in the oral microbial community.     

Combinatorial network of transcriptional regulation and microRNA regulation in human cancer

Background

In order to reduce time and efforts to develop microbial strains with better capability of producing desired bioproducts, genome-scale metabolic simulations have proven useful in identifying gene knockout and amplification targets. Constraints-based flux analysis has successfully been employed for such simulation, but is limited in its ability to properly describe the complex nature of biological systems. Gene knockout simulations are relatively straightforward to implement, simply by constraining the flux values of the target reaction to zero, but the identification of reliable gene amplification targets is rather difficult. Here, we report a new algorithm which incorporates physiological data into a model to improve the model??s prediction capabilities and to capitalize on the relationships between genes and metabolic fluxes.

Results

We developed an algorithm, flux variability scanning based on enforced objective flux (FVSEOF) with grouping reaction (GR) constraints, in an effort to identify gene amplification targets by considering reactions that co-carry flux values based on physiological omics data via ??GR constraints??. This method scans changes in the variabilities of metabolic fluxes in response to an artificially enforced objective flux of product formation. The gene amplification targets predicted using this method were validated by comparing the predicted effects with the previous experimental results obtained for the production of shikimic acid and putrescine in Escherichia coli. Moreover, new gene amplification targets for further enhancing putrescine production were validated through experiments involving the overexpression of each identified targeted gene under condition-controlled batch cultivation.

Conclusions

FVSEOF with GR constraints allows identification of gene amplification targets for metabolic engineering of microbial strains in order to enhance the production of desired bioproducts. The algorithm was validated through the experiments on the enhanced production of putrescine in E. coli, in addition to the comparison with the previously reported experimental data. The FVSEOF strategy with GR constraints will be generally useful for developing industrially important microbial strains having enhanced capabilities of producing chemicals of interest.