Detection of operons

Genomic and Proteomic Analysis of Six Vi01-like Phages Reveals Wide Host Range and Multiple Tail Spike Proteins

Enterobacteriaceae is a large family of Gram-negative bacteria composed of many pathogens, including Salmonella and Shigella. Here, we characterize six bacteriophages that infect Enterobacteriaceae, which were isolated from wastewater plants in the Wasatch front (Utah, United States). These phages are highly similar to the Kuttervirus vB_SenM_Vi01 (Vi01), which was isolated using wastewater from Kiel, Germany. The phages vary little in genome size and are between 157 kb and 164 kb, which is consistent with the sizes of other phages in the Vi01-like phage family. These six phages were characterized through genomic and proteomic comparison, mass spectrometry, and both laboratory and clinical host range studies. While their proteomes are largely unstudied, mass spectrometry analysis confirmed the production of five hypothetical proteins, several of which unveiled a potential operon that suggests a ferritin-mediated entry system on the Vi01-like phage family tail. However, no dependence on this pathway was observed for the single host tested herein. While unable to infect every genus of Enterobacteriaceae tested, these phages are extraordinarily broad ranged, with several demonstrating the ability to infect Salmonella enterica and Citrobacter freundii strains with generally high efficiency, as well as several clinical Salmonella enterica isolates, most likely due to their multiple tail fibers.

Detecting operons in bacterial genomes via visual representation learning

Contiguous genes in prokaryotes are often arranged into operons. Detecting operons plays a critical role in inferring gene functionality and regulatory networks. Human experts annotate operons by visually inspecting gene neighborhoods across pileups of related genomes. These visual representations capture the inter-genic distance, strand direction, gene size, functional relatedness, and gene neighborhood conservation, which are the most prominent operon features mentioned in the literature. By studying these features, an expert can then decide whether a genomic region is part of an operon. We propose a deep learning based method named Operon Hunter that uses visual representations of genomic fragments to make operon predictions. Using transfer learning and data augmentation techniques facilitates leveraging the powerful neural networks trained on image datasets by re-training them on a more limited dataset of extensively validated operons. Our method outperforms the previously reported state-of-the-art tools, especially when it comes to predicting full operons and their boundaries accurately. Furthermore, our approach makes it possible to visually identify the features influencing the network’s decisions to be subsequently cross-checked by human experts.

Systematic prediction of functionally linked genes in bacterial and archaeal genomes

Functionally linked genes in bacterial and archaeal genomes are often organized into operons. However, the composition and architecture of operons are highly variable and frequently differ even among closely related genomes. Therefore, to efficiently extract reliable functional predictions for uncharacterized genes from comparative analyses of the rapidly growing genomic databases, dedicated computational approaches are required. We developed a protocol to systematically and automatically identify genes that are likely to be functionally associated with a 'bait' gene or locus by using relevance metrics. Given a set of bait loci and a genomic database defined by the user, this protocol compares the genomic neighborhoods of the baits to identify genes that are likely to be functionally linked to the baits by calculating the abundance of a given gene within and outside the bait neighborhoods and the distance to the bait. We exemplify the performance of the protocol with three test cases, namely, genes linked to CRISPR-Cas systems using the 'CRISPRicity' metric, genes associated with archaeal proviruses and genes linked to Argonaute genes in halobacteria. The protocol can be run by users with basic computational skills. The computational cost depends on the sizes of the genomic dataset and the list of reference loci and can vary from one CPU-hour to hundreds of hours on a supercomputer.

Operon prediction using chaos embedded particle swarm optimization

Operons contain valuable information for drug design and determining protein functions. Genes within an operon are co-transcribed to a single-strand mRNA and must be coregulated. The identification of operons is, thus, critical for a detailed understanding of the gene regulations. However, currently used experimental methods for operon detection are generally difficult to implement and time consuming. In this paper, we propose a chaotic binary particle swarm optimization (CBPSO) to predict operons in bacterial genomes. The intergenic distance, participation in the same metabolic pathway and the cluster of orthologous groups (COG) properties of the Escherichia coli genome are used to design a fitness function. Furthermore, the Bacillus subtilis, Pseudomonas aeruginosa PA01, Staphylococcus aureus and Mycobacterium tuberculosis genomes are tested and evaluated for accuracy, sensitivity, and specificity. The computational results indicate that the proposed method works effectively in terms of enhancing the performance of the operon prediction. The proposed method also achieved a good balance between sensitivity and specificity when compared to methods from the literature.

Binary particle swarm optimization for operon prediction

An operon is a fundamental unit of transcription and contains specific functional genes for the construction and regulation of networks at the entire genome level. The correct prediction of operons is vital for understanding gene regulations and functions in newly sequenced genomes. As experimental methods for operon detection tend to be nontrivial and time consuming, various methods for operon prediction have been proposed in the literature. In this study, a binary particle swarm optimization is used for operon prediction in bacterial genomes. The intergenic distance, participation in the same metabolic pathway, the cluster of orthologous groups, the gene length ratio and the operon length are used to design a fitness function. We trained the proper values on the Escherichia coli genome, and used the above five properties to implement feature selection. Finally, our study used the intergenic distance, metabolic pathway and the gene length ratio property to predict operons. Experimental results show that the prediction accuracy of this method reached 92.1%, 93.3% and 95.9% on the Bacillus subtilis genome, the Pseudomonas aeruginosa PA01 genome and the Staphylococcus aureus genome, respectively. This method has enabled us to predict operons with high accuracy for these three genomes, for which only limited data on the properties of the operon structure exists.

High accuracy operon prediction method based on STRING database scores

We present a simple and highly accurate computational method for operon prediction, based on intergenic distances and functional relationships between the protein products of contiguous genes, as defined by STRING database (Jensen,L.J., Kuhn,M., Stark,M., Chaffron,S., Creevey,C., Muller,J., Doerks,T., Julien,P., Roth,A., Simonovic,M. et al. (2009) STRING 8–a global view on proteins and their functional interactions in 630 organisms. Nucleic Acids Res., 37, D412–D416). These two parameters were used to train a neural network on a subset of experimentally characterized Escherichia coli and Bacillus subtilis operons. Our predictive model was successfully tested on the set of experimentally defined operons in E. coli and B. subtilis, with accuracies of 94.6 and 93.3%, respectively. As far as we know, these are the highest accuracies ever obtained for predicting bacterial operons. Furthermore, in order to evaluate the predictable accuracy of our model when using an organism's data set for the training procedure, and a different organism's data set for testing, we repeated the E. coli operon prediction analysis using a neural network trained with B. subtilis data, and a B. subtilis analysis using a neural network trained with E. coli data. Even for these cases, the accuracies reached with our method were outstandingly high, 91.5 and 93%, respectively. These results show the potential use of our method for accurately predicting the operons of any other organism. Our operon predictions for fully-sequenced genomes are available at http://operons.ibt.unam.mx/OperonPredictor/.

Deciphering chemotaxis pathways using cross species comparisons

Background

Chemotaxis is the process by which motile bacteria sense their chemical environment and move towards more favourable conditions. Escherichia coli utilises a single sensory pathway, but little is known about signalling pathways in species with more complex systems.

Results

To investigate whether chemotaxis pathways in other bacteria follow the E. coli paradigm, we analysed 206 species encoding at least 1 homologue of each of the 5 core chemotaxis proteins (CheA, CheB, CheR, CheW and CheY). 61 species encode more than one of all of these 5 proteins, suggesting they have multiple chemotaxis pathways. Operon information is not available for most bacteria, so we developed a novel statistical approach to cluster che genes into putative operons. Using operon-based models, we reconstructed putative chemotaxis pathways for all 206 species. We show that cheA-cheW and cheR-cheB have strong preferences to occur in the same operon as two-gene blocks, which may reflect a functional requirement for co-transcription. However, other che genes, most notably cheY, are more dispersed on the genome. Comparison of our operons with shuffled equivalents demonstrates that specific patterns of genomic location may be a determining factor for the observed in vivo chemotaxis pathways.

We then examined the chemotaxis pathways of Rhodobacter sphaeroides. Here, the PpfA protein is known to be critical for correct partitioning of proteins in the cytoplasmically-localised pathway. We found ppfA in che operons of many species, suggesting that partitioning of cytoplasmic Che protein clusters is common. We also examined the apparently non-typical chemotaxis components, CheA3, CheA4 and CheY6. We found that though variants of CheA proteins are rare, the CheY6 variant may be a common type of CheY, with a significantly disordered C-terminal region which may be functionally significant.

Conclusions

We find that many bacterial species potentially have multiple chemotaxis pathways, with grouping of che genes into operons likely to be a major factor in keeping signalling pathways distinct. Gene order is highly conserved with cheA-cheW and cheR-cheB blocks, perhaps reflecting functional linkage. CheY behaves differently to other Che proteins, both in its genomic location and its putative protein interactions, which should be considered when modelling chemotaxis pathways.

Biochemical and structural characterization of a novel family of cystathionine beta-synthase domain proteins fused to a Zn ribbon-like domain

We have identified a novel family of proteins, in which the N-terminal cystathionine beta-synthase (CBS) domain is fused to the C-terminal Zn ribbon domain. Four proteins were overexpressed in Escherichia coli and purified: TA0289 from Thermoplasma acidophilum, TV1335 from Thermoplasma volcanium, PF1953 from Pyrococcus furiosus, and PH0267 from Pyrococcus horikoshii. The purified proteins had a red/purple color in solution and an absorption spectrum typical of rubredoxins (Rds). Metal analysis of purified proteins revealed the presence of several metals, with iron and zinc being the most abundant metals (2-67% of iron and 12-74% of zinc). Crystal structures of both mercury- and iron-bound TA0289 (1.5-2.0 A resolution) revealed a dimeric protein whose intersubunit contacts are formed exclusively by the alpha-helices of two cystathionine beta-synthase subdomains, whereas the C-terminal domain has a classical Zn ribbon planar architecture. All proteins were reversibly reduced by chemical reductants (ascorbate or dithionite) or by the general Rd reductase NorW from E. coli in the presence of NADH. Reduced TA0289 was found to be capable of transferring electrons to cytochrome C from horse heart. Likewise, the purified Zn ribbon protein KTI11 from Saccharomyces cerevisiae had a purple color in solution and an Rd-like absorption spectrum, contained both iron and zinc, and was reduced by the Rd reductase NorW from E. coli. Thus, recombinant Zn ribbon domains from archaea and yeast demonstrate an Rd-like electron carrier activity in vitro. We suggest that, in vivo, some Zn ribbon domains might also bind iron and therefore possess an electron carrier activity, adding another physiological role to this large family of important proteins.

Quantitative assessment of protein function prediction from metagenomics shotgun sequences

To assess the potential of protein function prediction in environmental genomics data, we analyzed shotgun sequences from four diverse and complex habitats. Using homology searches as well as customized gene neighborhood methods that incorporate intergenic and evolutionary distances, we inferred specific functions for 76% of the 1.4 million predicted ORFs in these samples (83% when nonspecific functions are considered). Surprisingly, these fractions are only slightly smaller than the corresponding ones in completely sequenced genomes (83% and 86%, respectively, by using the same methodology) and considerably higher than previously thought. For as many as 75,448 ORFs (5% of the total), only neighborhood methods can assign functions, illustrated here by a previously undescribed gene associated with the well characterized heme biosynthesis operon and a potential transcription factor that might regulate a coupling between fatty acid biosynthesis and degradation. Our results further suggest that, although functions can be inferred for most proteins on earth, many functions remain to be discovered in numerous small, rare protein families.

The major chemotaxis gene cluster of Rhizobium leguminosarum bv. viciae is essential for competitive nodulation

Rhizobium leguminosarum biovar viciae strain 3841 is a motile alpha-proteobacterium that can establish a nitrogen-fixing symbiosis within the roots of pea plants. In order to determine the contribution of chemotaxis to the lifestyle of R. leguminosarum, we have characterized the function of two chemotaxis gene clusters (che1 and che2) in controlling motility behaviour. We have found that both chemotaxis gene clusters modulate the motility swimming bias of R. leguminosarum cells and that the che1 cluster is the major pathway controlling swimming bias and chemotaxis. The che2 cluster also contributes to swimming bias, but has a minor effect on chemotaxis. Using competitive nodulation assays, we have demonstrated that a functional che1 cluster, but not the che2 cluster, promotes competitive nodulation of the peas. This finding implies that the environmental cue(s) triggering chemotaxis of R. leguminosarum bv. viciae cells towards the roots of pea and facilitating colonization are likely to be processed through the che1 cluster despite the contribution of both che clusters to swimming behaviour. A phylogenetic analysis of the distribution of che1 and che2 orthologues in the alpha-proteobacteria together with our results allow us to propose that che1 homologues are major controllers of chemotaxis and host association in the Rhizobiaceae.