Features for computational operon prediction in prokaryotes

Improved BioCyc Operon Prediction: Revisiting the Operon Prediction Problem

Introduction

Operon prediction is a valuable component of microbial-genome annotation because operon organization can yield inferences about gene function, and because knowledge of operon structure can aid the interpretation of gene expression data.

Methods

We present a number of improvements to the existing Pathway Tools operon predictor based mostly on 7 new features that we hypothesized would increase its performance. The new features include shared Gene Ontology biological process terms, similarity of codon usage and GC content, correlated gene expression, and shared protein complex.

Results

We evaluated the proposed 7 new features and found that the addition of 6 of them improved the performance of the operon predictor from 79.55% to 83.49%, a decrease in error rate of 19.3%. When gene expression data was not included, the accuracy decreased to 82.547, still an improvement of 14.7%. One of the proposed features as well as a previously used feature had no effect and were removed.

Discussion

Although some of the new features had strong predictive value individually, when combined with the other features they did not have a large impact on predictive accuracy, suggesting that they were not independent from the other features.

Prediction and analysis of metagenomic operons via MetaRon: a pipeline for prediction of Metagenome and whole-genome opeRons

Background

Efficient regulation of bacterial genes in response to the environmental stimulus results in unique gene clusters known as operons. Lack of complete operonic reference and functional information makes the prediction of metagenomic operons a challenging task; thus, opening new perspectives on the interpretation of the host-microbe interactions.

Results

In this work, we identified whole-genome and metagenomic operons via MetaRon (Metagenome and whole-genome opeRon prediction pipeline). MetaRon identifies operons without any experimental or functional information. MetaRon was implemented on datasets with different levels of complexity and information. Starting from its application on whole-genome to simulated mixture of three whole-genomes (E. coli MG1655, Mycobacterium tuberculosis H37Rv and Bacillus subtilis str. 16), E. coli c20 draft genome extracted from chicken gut and finally on 145 whole-metagenome data samples from human gut. MetaRon consistently achieved high operon prediction sensitivity, specificity and accuracy across E. coli whole-genome (97.8, 94.1 and 92.4%), simulated genome (93.7, 75.5 and 88.1%) and E. coli c20 (87, 91 and 88%,), respectively. Finally, we identified 1,232,407 unique operons from 145 paired-end human gut metagenome samples. We also report strong association of type 2 diabetes with Maltose phosphorylase (K00691), 3-deoxy-D-glycero-D-galacto-nononate 9-phosphate synthase (K21279) and an uncharacterized protein (K07101).

Conclusion

With MetaRon, we were able to remove two notable limitations of existing whole-genome operon prediction methods: (1) generalizability (ability to predict operons in unrelated bacterial genomes), and (2) whole-genome and metagenomic data management. We also demonstrate the use of operons as a subset to represent the trends of secondary metabolites in whole-metagenome data and the role of secondary metabolites in the occurrence of disease condition. Using operonic data from metagenome to study secondary metabolic trends will significantly reduce the data volume to more precise data. Furthermore, the identification of metabolic pathways associated with the occurrence of type 2 diabetes (T2D) also presents another dimension of analyzing the human gut metagenome. Presumably, this study is the first organized effort to predict metagenomic operons and perform a detailed analysis in association with a disease, in this case type 2 diabetes. The application of MetaRon to metagenomic data at diverse scale will be beneficial to understand the gene regulation and therapeutic metagenomics.

Flexible comparative genomics of prokaryotic transcriptional regulatory networks

Background

Comparative genomics methods enable the reconstruction of bacterial regulatory networks using available experimental data. In spite of their potential for accelerating research into the composition and evolution of bacterial regulons, few comparative genomics suites have been developed for the automated analysis of these regulatory systems. Available solutions typically rely on precomputed databases for operon and ortholog predictions, limiting the scope of analyses to processed complete genomes, and several key issues such as the transfer of experimental information or the integration of regulatory information in a probabilistic setting remain largely unaddressed.

Results

Here we introduce CGB, a flexible platform for comparative genomics of prokaryotic regulons. CGB has few external dependencies and enables fully customized analyses of newly available genome data. The platform automates the merging of experimental information and uses a gene-centered, Bayesian framework to generate and integrate easily interpretable results. We demonstrate its flexibility and power by analyzing the evolution of type III secretion system regulation in pathogenic Proteobacteria and by characterizing the SOS regulon of a new bacterial phylum, the Balneolaeota.

Conclusions

Our results demonstrate the applicability of the CGB pipeline in multiple settings. CGB’s ability to automatically integrate experimental information from multiple sources and use complete and draft genomic data, coupled with its non-reliance on precomputed databases and its easily interpretable display of gene-centered posterior probabilities of regulation provide users with an unprecedented level of flexibility in launching comparative genomics analyses of prokaryotic transcriptional regulatory networks. The analyses of type III secretion and SOS response regulatory networks illustrate instances of convergent and divergent evolution of these regulatory systems, showcasing the power of formal ancestral state reconstruction at inferring the evolutionary history of regulatory networks.

Photosynthetic protein classification using genome neighborhood-based machine learning feature

Identification of novel photosynthetic proteins is important for understanding and improving photosynthetic efficiency. Synergistically, genome neighborhood can provide additional useful information to identify photosynthetic proteins. We, therefore, expected that applying a computational approach, particularly machine learning (ML) with the genome neighborhood-based feature should facilitate the photosynthetic function assignment. Our results revealed a functional relationship between photosynthetic genes and their conserved neighboring genes observed by ‘Phylo score’, indicating their functions could be inferred from the genome neighborhood profile. Therefore, we created a new method for extracting patterns based on the genome neighborhood network (GNN) and applied them for the photosynthetic protein classification using ML algorithms. Random forest (RF) classifier using genome neighborhood-based features achieved the highest accuracy up to 87% in the classification of photosynthetic proteins and also showed better performance (Mathew’s correlation coefficient = 0.718) than other available tools including the sequence similarity search (0.447) and ML-based method (0.361). Furthermore, we demonstrated the ability of our model to identify novel photosynthetic proteins compared to the other methods. Our classifier is available at http://bicep2.kmutt.ac.th/photomod_standalone, https://bit.ly/2S0I2Ox and DockerHub: https://hub.docker.com/r/asangphukieo/photomod.

A computational system for identifying operons based on RNA-seq data

An operon is a set of neighboring genes in a genome that is transcribed as a single polycistronic message. Genes that are part of the same operon often have related functional roles or participate in the same metabolic pathways. The majority of all bacterial genes are co-transcribed with one or more other genes as part of a multi-gene operon. Thus, accurate identification of operons is important in understanding co-regulation of genes and their functional relationships. Here, we present a computational system that uses RNA-seq data to determine operons throughout a genome. The system takes the name of a genome and one or more files of RNA-seq data as input. Our method combines primary genomic sequence information with expression data from the RNA-seq files in a unified probabilistic model in order to identify operons. We assess our method's ability to accurately identify operons in a range of species through comparison to external databases of operons, both experimentally confirmed and computationally predicted, and through focused experiments that confirm new operons identified by our method. Our system is freely available at https://cs.wellesley.edu/~btjaden/Rockhopper/.

Computational operon prediction in whole-genomes and metagenomes

Microbial diversity in unique environmental settings enables abrupt responses catalysed by altering the gene regulation and formation of gene clusters called operons. Operons increases bacterial adaptability, which in turn increases their survival. This review article presents the emergence of computational operon prediction methods for whole microbial genomes and metagenomes, and discusses their strengths and limitations. Most of the whole-genome operon prediction methods struggle to generalize on unrelated genomes. The applicability of universal whole-genome operon prediction methods to metagenomic data is an interesting yet less investigated question. We have evaluated the potential of various operon prediction features for genomic and metagenomic data. Most of operon prediction methods with high accuracy have been compiled into databases. Despite of the high predictive performance, the data among many databases are not completely consistent for similar species. We performed a correlation analysis between the computationally predicted operon databases and experimentally validated data for Escherichia coli, Bacillus subtilis and Mycobacterium tuberculosis. Operon prediction for most of the less characterized microbes cannot be verified due to absence of experimentally validated operons. The generation of validated information for other microbes would test the authenticity of operon databases for other less annotated microbes as well. Advances in sequencing technologies and development of better analysis methods will help researchers to overcome the technological hurdles (such as long sequencing reads and improved contig size) and further improve operon predictions and better utilize operonic information.

The power of operon rearrangements for predicting functional associations

In this mini-review I aim to make the case that operons might be the most powerful source for predicted associations among gene products. Such associations can help identify potential processes where the products of unannotated genes might play a role. The power of the operon for providing insight into functional associations stems from four features: (1) on average, around 60% of the genes in prokaryotes are associated into operons; (2) the functional associations between genes in operons tend to be highly conserved; (3) operons can be predicted with high accuracy by conservation of gene order and by the distances between adjacent genes in the same DNA strand; and (4) operons frequently reorganize, providing further insight into functional associations that would not be evident without these reorganization events.

An empirical strategy to detect bacterial transcript structure from directional RNA-seq transcriptome data

Background

As sequencing costs are being lowered continuously, RNA-seq has gradually been adopted as the first choice for comparative transcriptome studies with bacteria. Unlike microarrays, RNA-seq can directly detect cDNA derived from mRNA transcripts at a single nucleotide resolution. Not only does this allow researchers to determine the absolute expression level of genes, but it also conveys information about transcript structure. Few automatic software tools have yet been established to investigate large-scale RNA-seq data for bacterial transcript structure analysis.

Results

In this study, 54 directional RNA-seq libraries from Salmonella serovar Typhimurium (S. Typhimurium) 14028s were examined for potential relationships between read mapping patterns and transcript structure. We developed an empirical method, combined with statistical tests, to automatically detect key transcript features, including transcriptional start sites (TSSs), transcriptional termination sites (TTSs) and operon organization. Using our method, we obtained 2,764 TSSs and 1,467 TTSs for 1331 and 844 different genes, respectively. Identification of TSSs facilitated further discrimination of 215 putative sigma 38 regulons and 863 potential sigma 70 regulons. Combining the TSSs and TTSs with intergenic distance and co-expression information, we comprehensively annotated the operon organization in S. Typhimurium 14028s.

Conclusions

Our results show that directional RNA-seq can be used to detect transcriptional borders at an acceptable resolution of ±10-20 nucleotides. Technical limitations of the RNA-seq procedure may prevent single nucleotide resolution. The automatic transcript border detection methods, statistical models and operon organization pipeline that we have described could be widely applied to RNA-seq studies in other bacteria. Furthermore, the TSSs, TTSs, operons, promoters and unstranslated regions that we have defined for S. Typhimurium 14028s may constitute valuable resources that can be used for comparative analyses with other Salmonella serotypes.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1555-8) contains supplementary material, which is available to authorized users.

The FlxABCD-HdrABC proteins correspond to a novel NADH dehydrogenase/heterodisulfide reductase widespread in anaerobic bacteria and involved in ethanol metabolism in Desulfovibrio vulgaris Hildenborough

Flavin-based electron bifurcation (FBEB) is an important mechanism for the energy metabolism of anaerobes. A new family of NADH dehydrogenases, the flavin oxidoreductase (FlxABCD, previously called FloxABCD), was proposed to perform FBEB in sulphate-reducing organisms coupled with heterodisulfide reductase (HdrABC). We found that the hdrABC-flxABCD gene cluster is widespread among anaerobic bacteria, pointing to a general and important role in their bioenergetics. In this work, we studied FlxABCD of Desulfovibrio vulgaris Hildenborough. The hdr-flx genes are part of the same transcriptional unit and are increased in transcription during growth in ethanol-sulfate, and to a less extent during pyruvate fermentation. Two mutant strains were generated: one where expression of the hdr-flx genes was interrupted and another lacking the flxA gene. Both strains were unable to grow with ethanol-sulfate, whereas growth was restored in a flxA-complemented strain. The mutant strains also produced very reduced amounts of ethanol compared with the wild type during pyruvate fermentation. Our results show that in D. vulgaris, the FlxABCD-HdrABC proteins are essential for NADH oxidation during growth on ethanol, probably involving a FBEB mechanism that leads to reduction of ferredoxin and the small protein DsrC, while in fermentation they operate in reverse, reducing NAD(+) for ethanol production.

Predicting Functional Interactions Among Genes in Prokaryotes by Genomic Context

Genomic context methods for finding functions of unannotated genes were implemented very early after the publication of the first few prokaryotic genomes. The ideas behind these methods include gene fusions, conservation of gene adjacency, and the patters of co-occurrence of genes across available genomes. A later addition was the prediction of features related to functional organization, such as operons, stretches of genes co-transcribed into a single messenger RNA. The ideas behind these methods tend to be easy to understand, while the strategies for transforming those basic ideas into predictions can vary in complexity, mostly because genes whose products are known to functionally interact vary in the way they relate to those basic ideas. We present here a view of genomic context methods for predicting functional interactions, with simple examples of their implementation as compared and evaluated using genes whose products are known to functionally interact.

Natural selection for operons depends on genome size

In prokaryotes, genome size is associated with metabolic versatility, regulatory complexity, effective population size, and horizontal transfer rates. We therefore analyzed the covariation of genome size and operon conservation to assess the evolutionary models of operon formation and maintenance. In agreement with previous results, intraoperonic pairs of essential and of highly expressed genes are more conserved. Interestingly, intraoperonic pairs of genes are also more conserved when they encode proteins at similar cell concentrations, suggesting a role of cotranscription in diminishing the cost of waste and shortfall in gene expression. Larger genomes have fewer and smaller operons that are also less conserved. Importantly, lower conservation in larger genomes was observed for all classes of operons in terms of gene expression, essentiality, and balanced protein concentration. We reached very similar conclusions in independent analyses of three major bacterial clades (α- and β-Proteobacteria and Firmicutes). Operon conservation is inversely correlated to the abundance of transcription factors in the genome when controlled for genome size. This suggests a negative association between the complexity of genetic networks and operon conservation. These results show that genome size and/or its proxies are key determinants of the intensity of natural selection for operon organization. Our data fit better the evolutionary models based on the advantage of coregulation than those based on genetic linkage or stochastic gene expression. We suggest that larger genomes with highly complex genetic networks and many transcription factors endure weaker selection for operons than smaller genomes with fewer alternative tools for genetic regulation.

A horizontally acquired transcription factor coordinates Salmonella adaptations to host microenvironments

The transcription factors HilA and SsrB activate expression of two type III secretion systems (T3SSs) and cognate effectors that reprogram host cell functions to benefit infecting Salmonella in the host. These transcription factors, the secretion systems, and the effectors are all encoded by horizontally acquired genes. Using quantitative proteomics, we quantified the abundance of 2,149 proteins from hilA or ssrB Salmonella in vitro. Our results suggest that the HilA regulon does not extend significantly beyond proteins known to be involved in direct interactions with intestinal epithelium. On the other hand, SsrB influences the expression of a diverse range of proteins, many of which are ancestral to the acquisition of ssrB. In addition to the known regulon of T3SS-related proteins, we show that, through SodCI and bacterioferritin, SsrB controls resistance to reactive oxygen species and that SsrB down-regulates flagella and motility. This indicates that SsrB-controlled proteins not only redirect host cell membrane traffic to establish a supportive niche within host cells but also have adapted to the chemistry and physical constraints of that niche.

Expression of T3SSs typically requires a transcription factor that is linked in a genomic island. Studies of the targets of HilA and SsrB have focused on almost exclusively on T3SS substrates that are either linked or encoded in distinct genomic islands. By broadening our focus, we found that the regulon of SsrB extended considerably beyond T3SS-2 and its substrates, while that of HilA did not. That at least two SsrB-regulated processes streamline existence in the intracellular niche afforded by T3SS-2 seems to be a predictable outcome of evolution and natural selection. However, and importantly, these are the first such functions to be implicated as being SsrB dependent. The concept of T3SS-associated transcription factors coordinating manipulations of host cells together with distinct bacterial processes for increased efficiency has unrealized implications for numerous host-pathogen systems.

The multifunctional role of the pallilysin-associated Treponema pallidum protein, Tp0750, in promoting fibrinolysis and extracellular matrix component degradation

The mechanisms that facilitate dissemination of the highly invasive spirochaete, Treponema pallidum, are incompletely understood. Previous studies showed the treponemal metalloprotease pallilysin (Tp0751) possesses fibrin clot degradation capability, suggesting a role in treponemal dissemination. In the current study we report characterization of the functionally linked protein Tp0750. Structural modelling predicts Tp0750 contains a von Willebrand factor type A (vWFA) domain, a protein-protein interaction domain commonly observed in extracellular matrix (ECM)-binding proteins. We report Tp0750 is a serine protease that degrades the major clot components fibrinogen and fibronectin. We also demonstrate Tp0750 cleaves a matrix metalloprotease (MMP) peptide substrate that is targeted by several MMPs, enzymes central to ECM remodelling. Through proteomic analyses we show Tp0750 binds the endothelial fibrinolytic receptor, annexin A2, in a specific and dose-dependent manner. These results suggest Tp0750 constitutes a multifunctional protein that is able to (1) degrade infection-limiting clots by both inhibiting clot formation through degradation of host coagulation cascade proteins and promoting clot dissolution by complexing with host proteins involved in the fibrinolytic cascade and (2) facilitate ECM degradation via MMP-like proteolysis of host components. We propose that through these activities Tp0750 functions in concert with pallilysin to enable T. pallidum dissemination.

A combination of improved differential and global RNA-seq reveals pervasive transcription initiation and events in all stages of the life-cycle of functional RNAs in Propionibacterium acnes, a major contributor to wide-spread human disease

BACKGROUND

Sequencing of the genome of Propionibacterium acnes produced a catalogue of genes many of which enable this organism to colonise skin and survive exposure to the elements. Despite this platform, there was little understanding of the gene regulation that gives rise to an organism that has a major impact on human health and wellbeing and causes infections beyond the skin. To address this situation, we have undertaken a genome-wide study of gene regulation using a combination of improved differential and global RNA-sequencing and an analytical approach that takes into account the inherent noise within the data.

RESULTS

We have produced nucleotide-resolution transcriptome maps that identify and differentiate sites of transcription initiation from sites of stable RNA processing and mRNA cleavage. Moreover, analysis of these maps provides strong evidence for 'pervasive' transcription and shows that contrary to initial indications it is not biased towards the production of antisense RNAs. In addition, the maps reveal an extensive array of riboswitches, leaderless mRNAs and small non-protein-coding RNAs alongside vegetative promoters and post-transcriptional events, which includes unusual tRNA processing. The identification of such features will inform models of complex gene regulation, as illustrated here for ribonucleotide reductases and a potential quorum-sensing, two-component system.

CONCLUSIONS

The approach described here, which is transferable to any bacterial species, has produced a step increase in whole-cell knowledge of gene regulation in P. acnes. Continued expansion of our maps to include transcription associated with different growth conditions and genetic backgrounds will provide a new platform from which to computationally model the gene expression that determines the physiology of P. acnes and its role in human disease.