Analysis of genomic context: prediction of functional associations from conserved bidirectionally transcribed gene pairs

Transcription Attenuation in Synthetic Promoters in Nonoverlapping Tandem Formation

Closely spaced promoters are ubiquitous in prokaryotic and eukaryotic genomes. How their structure and dynamics relate remains unclear, particularly for tandem formations. To study their transcriptional interference, we engineered two pairs and one trio of synthetic promoters in nonoverlapping, tandem formation, in single-copy plasmids transformed into Escherichia coli cells. From in vivo measurements, we found that these promoters in tandem formation can have attenuated transcription rates. The attenuation strength can be widely fine-tuned by the promoters' positioning, natural regulatory mechanisms, and other factors, including the antibiotic rifampicin, which is known to hamper RNAP promoter escape. From this, and supported by in silico models, we concluded that the attenuation in these constructs emerges from premature terminations generated by collisions between RNAPs elongating from upstream promoters and RNAPs occupying downstream promoters. Moreover, we found that these collisions can cause one or both RNAPs to falloff. Finally, the broad spectrum of possible, externally regulated, attenuation strengths observed in our synthetic tandem promoters suggests that they could become useful as externally controllable regulators of future synthetic circuits.

Challenges in computational discovery of bioactive peptides in 'omics data

Peptides have a plethora of activities in biological systems that can potentially be exploited biotechnologically. Several peptides are used clinically, as well as in industry and agriculture. The increase in available 'omics data has recently provided a large opportunity for mining novel enzymes, biosynthetic gene clusters, and molecules. While these data primarily consist of DNA sequences, other types of data provide important complementary information. Due to their size, the approaches proven successful at discovering novel proteins of canonical size cannot be naïvely applied to the discovery of peptides. Peptides can be encoded directly in the genome as short open reading frames (smORFs), or they can be derived from larger proteins by proteolysis. Both of these peptide classes pose challenges as simple methods for their prediction result in large numbers of false positives. Similarly, functional annotation of larger proteins, traditionally based on sequence similarity to infer orthology and then transferring functions between characterized proteins and uncharacterized ones, cannot be applied for short sequences. The use of these techniques is much more limited and alternative approaches based on machine learning are used instead. Here, we review the limitations of traditional methods as well as the alternative methods that have recently been developed for discovering novel bioactive peptides with a focus on prokaryotic genomes and metagenomes.

AnnoView enables large-scale analysis, comparison, and visualization of microbial gene neighborhoods

The analysis and comparison of gene neighborhoods is a powerful approach for exploring microbial genome structure, function, and evolution. Although numerous tools exist for genome visualization and comparison, genome exploration across large genomic databases or user-generated datasets remains a challenge. Here, we introduce AnnoView, a web server designed for interactive exploration of gene neighborhoods across the bacterial and archaeal tree of life. Our server offers users the ability to identify, compare, and visualize gene neighborhoods of interest from 30 238 bacterial genomes and 1672 archaeal genomes, through integration with the comprehensive Genome Taxonomy Database and AnnoTree databases. Identified gene neighborhoods can be visualized using pre-computed functional annotations from different sources such as KEGG, Pfam and TIGRFAM, or clustered based on similarity. Alternatively, users can upload and explore their own custom genomic datasets in GBK, GFF or CSV format, or use AnnoView as a genome browser for relatively small genomes (e.g. viruses and plasmids). Ultimately, we anticipate that AnnoView will catalyze biological discovery by enabling user-friendly search, comparison, and visualization of genomic data. AnnoView is available at http://annoview.uwaterloo.ca.

The L-lactate dehydrogenases of Pseudomonas aeruginosa are conditionally regulated but both contribute to survival during macrophage infection

Pseudomonas aeruginosa is an opportunistic pathogen that thrives in environments associated with human activity, including soil and water altered by agriculture or pollution. Because L-lactate is a significant product of plant and animal metabolism, it is available to serve as a carbon source for P. aeruginosa in the diverse settings it inhabits. Here, we evaluate P. aeruginosa's production and use of its redundant L-lactate dehydrogenases, termed LldD and LldA. We confirm that the protein LldR represses lldD and identify a new transcription factor, called LldS, that activates lldA; these distinct regulators and the genomic contexts of lldD and lldA contribute to their differential expression. We demonstrate that the lldD and lldA genes are conditionally controlled in response to lactate isomers as well as to glycolate and - hydroxybutyrate, which, like lactate, are -hydroxycarboxylates. We also show that lldA is induced when iron availability is low. Our examination of lldD and lldA expression across depth in biofilms indicates a complex pattern that is consistent with the effects of glycolate production, iron availability, and cross-regulation on enzyme preference. Finally, macrophage infection assays revealed that both lldD and lldA contribute to persistence within host cells, underscoring the potential role of L-lactate as a carbon source during P. aeruginosa-eukaryote interactions. Together, these findings help us understand the metabolism of a key resource that may promote P. aeruginosa's success as a resident of contaminated environments and animal hosts.

Effects of Chronic Sleep Restriction on Transcriptional Sirtuin 1 Signaling Regulation in Male Mice White Adipose Tissue

Chronic sleep restriction (CSR) is a prevalent issue in modern society that is associated with several pathological states, ranging from neuropsychiatric to metabolic diseases. Despite its known impact on metabolism, the specific effects of CSR on the molecular mechanisms involved in maintaining metabolic homeostasis at the level of white adipose tissue (WAT) remain poorly understood. Therefore, this study aimed to investigate the influence of CSR on sirtuin 1 (SIRT1) and the peroxisome proliferator-activated receptor γ (PPARγ) signaling pathway in the WAT of young male mice. Both genes interact with specific targets involved in multiple metabolic processes, including adipocyte differentiation, browning, and lipid metabolism. The quantitative PCR (qPCR) results demonstrated a significant upregulation of SIRT-1 and some of its target genes associated with the transcriptional regulation of lipid homeostasis (i.e., PPARα, PPARγ, PGC-1α, and SREBF) and adipose tissue development (i.e., leptin, adiponectin) in CSR mice. On the contrary, DNA-binding transcription factors (i.e., CEBP-β and C-myc), which play a pivotal function during the adipogenesis process, were found to be down-regulated. Our results also suggest that the induction of SIRT1-dependent molecular pathways prevents weight gain. Overall, these findings offer new, valuable insights into the molecular adaptations of WAT to CSR, in order to support increased energy demand due to sleep loss.

RNA polymerase sliding on DNA can couple the transcription of nearby bacterial operons

DNA transcription initiates after an RNA polymerase (RNAP) molecule binds to the promoter of a gene. In bacteria, the canonical picture is that RNAP comes from the cytoplasmic pool of freely diffusing RNAP molecules. Recent experiments suggest the possible existence of a separate pool of polymerases, competent for initiation, which freely slide on the DNA after having terminated one round of transcription. Promoter-dependent transcription reinitiation from this pool of posttermination RNAP may lead to coupled initiation at nearby operons, but it is unclear whether this can occur over the distance and timescales needed for it to function widely on a bacterial genome in vivo. Here, we mathematically model the hypothesized reinitiation mechanism as a diffusion-to-capture process and compute the distances over which significant interoperon coupling can occur and the time required. These quantities depend on molecular association and dissociation rate constants between DNA, RNAP, and the transcription initiation factor σ70; we measure these rate constants using single-molecule experiments in vitro. Our combined theory/experimental results demonstrate that efficient coupling can occur at physiologically relevant σ70 concentrations and on timescales appropriate for transcript synthesis. Coupling is efficient over terminator-promoter distances up to ∼1,000 bp, which includes the majority of terminator-promoter nearest neighbor pairs in the Escherichia coli genome. The results suggest a generalized mechanism that couples the transcription of nearby operons and breaks the paradigm that each binding of RNAP to DNA can produce at most one messenger RNA.

RNA polymerase sliding on DNA can couple the transcription of nearby bacterial operons

DNA transcription initiates after an RNA polymerase (RNAP) molecule binds to the promoter of a gene. In bacteria, the canonical picture is that RNAP comes from the cytoplasmic pool of freely diffusing RNAP molecules. Recent experiments suggest the possible existence of a separate pool of polymerases, competent for initiation, which freely slide on the DNA after having terminated one round of transcription. Promoter-dependent transcription reinitiation from this pool of post-termination RNAP may lead to coupled initiation at nearby operons, but it is unclear whether this can occur over the distance- and time-scales needed for it to function widely on a bacterial genome in vivo. Here, we mathematically model the hypothesized reinitiation mechanism as a diffusion-to-capture process and compute the distances over which significant inter-operon coupling can occur and the time required. These quantities depend on previously uncharacterized molecular association and dissociation rate constants between DNA, RNAP and the transcription initiation factor σ 70 ; we measure these rate constants using single-molecule experiments in vitro. Our combined theory/experimental results demonstrate that efficient coupling can occur at physiologically relevant σ 70 concentrations and on timescales appropriate for transcript synthesis. Coupling is efficient over terminator-promoter distances up to ∼ 1, 000 bp, which includes the majority of terminator-promoter nearest neighbor pairs in the E. coli genome. The results suggest a generalized mechanism that couples the transcription of nearby operons and breaks the paradigm that each binding of RNAP to DNA can produce at most one messenger RNA.

SIGNIFICANCE STATEMENT

After transcribing an operon, a bacterial RNA polymerase can stay bound to DNA, slide along it, and reini-tiate transcription of the same or a different operon. Quantitative single-molecule biophysics experiments combined with mathematical theory demonstrate that this reinitiation process can be quick and efficient over gene spacings typical of a bacterial genome. Reinitiation may provide a mechanism to orchestrate the transcriptional activities of groups of nearby operons.

Deciphering microbial gene function using natural language processing

Revealing the function of uncharacterized genes is a fundamental challenge in an era of ever-increasing volumes of sequencing data. Here, we present a concept for tackling this challenge using deep learning methodologies adopted from natural language processing (NLP). We repurpose NLP algorithms to model "gene semantics" based on a biological corpus of more than 360 million microbial genes within their genomic context. We use the language models to predict functional categories for 56,617 genes and find that out of 1369 genes associated with recently discovered defense systems, 98% are inferred correctly. We then systematically evaluate the "discovery potential" of different functional categories, pinpointing those with the most genes yet to be characterized. Finally, we demonstrate our method's ability to discover systems associated with microbial interaction and defense. Our results highlight that combining microbial genomics and language models is a promising avenue for revealing gene functions in microbes.

GeCoViz: genomic context visualisation of prokaryotic genes from a functional and evolutionary perspective

Synteny conservation analysis is a well-established methodology to investigate the potential functional role of unknown prokaryotic genes. However, bioinformatic tools to reconstruct and visualise genomic contexts usually depend on slow computations, are restricted to narrow taxonomic ranges, and/or do not allow for the functional and interactive exploration of neighbouring genes across different species. Here, we present GeCoViz, an online resource built upon 12 221 reference prokaryotic genomes that provides fast and interactive visualisation of custom genomic regions anchored by any target gene, which can be sought by either name, orthologous group (KEGGs, eggNOGs), protein domain (PFAM) or sequence. To facilitate functional and evolutionary interpretation, GeCoViz allows to customise the taxonomic scope of each analysis and provides comprehensive annotations of the neighbouring genes. Interactive visualisation options include, among others, the scaled representations of gene lengths and genomic distances, and on the fly calculation of synteny conservation of neighbouring genes, which can be highlighted based on custom thresholds. The resulting plots can be downloaded as high-quality images for publishing purposes. Overall, GeCoViz offers an easy-to-use, comprehensive, fast and interactive web-based tool for investigating the genomic context of prokaryotic genes, and is freely available at https://gecoviz.cgmlab.org.

RecPD: A Recombination-aware measure of phylogenetic diversity

A critical step in studying biological features (e.g., genetic variants, gene families, metabolic capabilities, or taxa) is assessing their diversity and distribution among a sample of individuals. Accurate assessments of these patterns are essential for linking features to traits or outcomes of interest and understanding their functional impact. Consequently, it is of crucial importance that the measures employed for quantifying feature diversity can perform robustly under any evolutionary scenario. However, the standard measures used for quantifying and comparing the distribution of features, such as prevalence, phylogenetic diversity, and related approaches, either do not take into consideration evolutionary history, or assume strictly vertical patterns of inheritance. Consequently, these approaches cannot accurately assess diversity for features that have undergone recombination or horizontal transfer. To address this issue, we have devised RecPD, a novel recombination-aware phylogenetic-diversity statistic for measuring the distribution and diversity of features under all evolutionary scenarios. RecPD utilizes ancestral-state reconstruction to map the presence / absence of features onto ancestral nodes in a species tree, and then identifies potential recombination events in the evolutionary history of the feature. We also derive several related measures from RecPD that can be used to assess and quantify evolutionary dynamics and correlation of feature evolutionary histories. We used simulation studies to show that RecPD reliably reconstructs feature evolutionary histories under diverse recombination and loss scenarios. We then applied RecPD in two diverse real-world scenarios including a preliminary study type III effector protein families secreted by the plant pathogenic bacterium Pseudomonas syringae and growth phenotypes of the Pseudomonas genus and demonstrate that prevalence is an inadequate measure that obscures the potential impact of recombination. We believe RecPD will have broad utility for revealing and quantifying complex evolutionary processes for features at any biological level.

Phylogenetic diversity is an important concept utilized in evolutionary ecology which has extensive applications in population genetics to help us understand how evolutionary processes have distributed genetic variation among individuals of a species, and how this impacts phenotypic diversification over time. However, existing approaches for studying phylogenetic diversity largely assume that the genetic features follow vertical inheritance, which is frequently violated in the case of microbial genomes due to horizontal transfer. To address this shortcoming, we present RecPD, a recombination-aware phylogenetic diversity measure, which incorporates ancestral state reconstruction to quantify the phylogenetic diversity of genetic features mapped onto a species phylogeny. Through simulation experiments we show that RecPD robustly reconstructs the evolutionary histories of features evolving under various scenarios of recombination and loss. When applied to a real-world example of type III secreted effector protein families from the plant pathogenic bacterium Pseudomonas syringae, RecPD reveals that horizontal transfer has played an important role in shaping the phylogenetic distributions of a substantial proportion of families across the P. syringae species complex. Furthermore, we demonstrate that the traditional measures of feature prevalence are unsuitable as a measure for comparing feature diversity. We also provide a R package implementation of RecPD for public use: https://github.com/cedatorma/recpd.

Analytical kinetic model of native tandem promoters in E. coli

Closely spaced promoters in tandem formation are abundant in bacteria. We investigated the evolutionary conservation, biological functions, and the RNA and single-cell protein expression of genes regulated by tandem promoters in E. coli. We also studied the sequence (distance between transcription start sites ‘d_TSS’, pause sequences, and distances from oriC) and potential influence of the input transcription factors of these promoters. From this, we propose an analytical model of gene expression based on measured expression dynamics, where RNAP-promoter occupancy times and d_TSS are the key regulators of transcription interference due to TSS occlusion by RNAP at one of the promoters (when d_TSS ≤ 35 bp) and RNAP occupancy of the downstream promoter (when d_TSS > 35 bp). Occlusion and downstream promoter occupancy are modeled as linear functions of occupancy time, while the influence of d_TSS is implemented by a continuous step function, fit to in vivo data on mean single-cell protein numbers of 30 natural genes controlled by tandem promoters. The best-fitting step is at 35 bp, matching the length of DNA occupied by RNAP in the open complex formation. This model accurately predicts the squared coefficient of variation and skewness of the natural single-cell protein numbers as a function of d_TSS. Additional predictions suggest that promoters in tandem formation can cover a wide range of transcription dynamics within realistic intervals of parameter values. By accurately capturing the dynamics of these promoters, this model can be helpful to predict the dynamics of new promoters and contribute to the expansion of the repertoire of expression dynamics available to synthetic genetic constructs.

Tandem promoters are common in nature, but investigations on their dynamics have so far largely relied on synthetic constructs. Thus, their regulation and potentially unique dynamics remain unexplored. We first performed a comprehensive exploration of the conservation of genes regulated by these promoters in E. coli and the properties of their input transcription factors. We then measured protein and RNA levels expressed by 30 Escherichia coli tandem promoters, to establish an analytical model of the expression dynamics of genes controlled by such promoters. We show that start site occlusion and downstream RNAP occupancy can be realistically captured by a model with RNAP binding affinity, the time length of open complex formation, and the nucleotide distance between transcription start sites. This study contributes to a better understanding of the unique dynamics tandem promoters can bring to the dynamics of gene networks and will assist in their use in synthetic genetic circuits.

DNA supercoiling-mediated collective behavior of co-transcribing RNA polymerases

Multiple RNA polymerases (RNAPs) transcribing a gene have been known to exhibit collective group behavior, causing the transcription elongation rate to increase with the rate of transcription initiation. Such behavior has long been believed to be driven by a physical interaction or ‘push’ between closely spaced RNAPs. However, recent studies have posited that RNAPs separated by longer distances may cooperate by modifying the DNA segment under transcription. Here, we present a theoretical model incorporating the mechanical coupling between RNAP translocation and the DNA torsional response. Using stochastic simulations, we demonstrate DNA supercoiling-mediated long-range cooperation between co-transcribing RNAPs. We find that inhibiting transcription initiation can slow down the already recruited RNAPs, in agreement with recent experimental observations, and predict that the average transcription elongation rate varies non-monotonically with the rate of transcription initiation. We further show that while RNAPs transcribing neighboring genes oriented in tandem can cooperate, those transcribing genes in divergent or convergent orientations can act antagonistically, and that such behavior holds over a large range of intergenic separations. Our model makes testable predictions, revealing how the mechanical interplay between RNAPs and the DNA they transcribe can govern transcriptional dynamics.

GABA Production by Human Intestinal Bacteroides spp.: Prevalence, Regulation, and Role in Acid Stress Tolerance

The high neuroactive potential of metabolites produced by gut microbes has gained traction over the last few years, with metagenomic-based studies suggesting an important role of microbiota-derived γ-aminobutyric acid (GABA) in modulating mental health. Emerging evidence has revealed the presence of the glutamate decarboxylase (GAD)-encoding gene, a key enzyme to produce GABA, in the prominent human intestinal genus Bacteroides. Here, we investigated GABA production by Bacteroides in culture and metabolic assays combined with comparative genomics and phylogenetics. A total of 961 Bacteroides genomes were analyzed in silico and 17 metabolically and genetically diverse human intestinal isolates representing 11 species were screened in vitro. Using the model organism Bacteroides thetaiotaomicron DSM 2079, we determined GABA production kinetics, its impact on milieu pH, and we assessed its role in mitigating acid-induced cellular damage. We showed that the GAD-system consists of at least four highly conserved genes encoding a GAD, a glutaminase, a glutamate/GABA antiporter, and a potassium channel. We demonstrated a high prevalence of the GAD-system among Bacteroides with 90% of all Bacteroides genomes (96% in human gut isolates only) harboring all genes of the GAD-system and 16 intestinal Bacteroides strains producing GABA in vitro (ranging from 0.09 to 60.84 mM). We identified glutamate and glutamine as precursors of GABA production, showed that the production is regulated by pH, and that the GAD-system acts as a protective mechanism against acid stress in Bacteroides, mitigating cell death and preserving metabolic activity. Our data also indicate that the GAD-system might represent the only amino acid-dependent acid tolerance system in Bacteroides. Altogether, our results suggest an important contribution of Bacteroides in the regulation of the GABAergic system in the human gut.

Implementation of homology based and non-homology based computational methods for the identification and annotation of orphan enzymes: using Mycobacterium tuberculosis H37Rv as a case study

Background

Homology based methods are one of the most important and widely used approaches for functional annotation of high-throughput microbial genome data. A major limitation of these methods is the absence of well-characterized sequences for certain functions. The non-homology methods based on the context and the interactions of a protein are very useful for identifying missing metabolic activities and functional annotation in the absence of significant sequence similarity. In the current work, we employ both homology and context-based methods, incrementally, to identify local holes and chokepoints, whose presence in the Mycobacterium tuberculosis genome is indicated based on its interaction with known proteins in a metabolic network context, but have not been annotated. We have developed two computational procedures using network theory to identify orphan enzymes (‘Hole finding protocol’) coupled with the identification of candidate proteins for the predicted orphan enzyme (‘Hole filling protocol’). We propose an integrated interaction score based on scores from the STRING database to identify candidate protein sequences for the orphan enzymes from M. tuberculosis, as a case study, which are most likely to perform the missing function.

Results

The application of an automated homology-based enzyme identification protocol, ModEnzA, on M. tuberculosis genome yielded 56 novel enzyme predictions. We further predicted 74 putative local holes, 6 choke points, and 3 high confidence local holes in the genome using ‘Hole finding protocol’. The ‘Hole-filling protocol’ was validated on the E. coli genome using artificial in-silico enzyme knockouts where our method showed 25% increased accuracy, compared to other methods, in assigning the correct sequence for the knocked-out enzyme amongst the top 10 ranks. The method was further validated on 8 additional genomes.

Conclusions

We have developed methods that can be generalized to augment homology-based annotation to identify missing enzyme coding genes and to predict a candidate protein for them. For pathogens such as M. tuberculosis, this work holds significance in terms of increasing the protein repertoire and thereby, the potential for identifying novel drug targets.

Discovery of multi-operon colinear syntenic blocks in microbial genomes

MOTIVATION

An important task in comparative genomics is to detect functional units by analyzing gene-context patterns. Colinear syntenic blocks (CSBs) are groups of genes that are consistently encoded in the same neighborhood and in the same order across a wide range of taxa. Such CSBs are likely essential for the regulation of gene expression in prokaryotes. Recent results indicate that colinearity can be conserved across multiple operons, thus motivating the discovery of multi-operon CSBs. This computational task raises scalability challenges in large datasets.

RESULTS

We propose an efficient algorithm for the discovery of cross-strand multi-operon CSBs in large genomic datasets. The proposed algorithm uses match-point arithmetic, which is scalable for large datasets of microbial genomes in terms of running time and space requirements. The algorithm is implemented and incorporated into a tool with a graphical user interface, called CSBFinder-S. We applied CSBFinder-S to data mine 1485 prokaryotic genomes and analyzed the identified cross-strand CSBs. Our results indicate that most of the syntenic blocks are exclusively colinear. Additional results indicate that transcriptional regulation by overlapping transcriptional genes is abundant in bacteria. We demonstrate the utility of CSBFinder-S to identify common function of the gene-pair PulEF in multiple contexts, including Type 2 Secretion System, Type 4 Pilus System and DNA uptake machinery.

AVAILABILITY AND IMPLEMENTATION

CSBFinder-S software and code are publicly available at https://github.com/dinasv/CSBFinder.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

HumanNet v2: human gene networks for disease research

Human gene networks have proven useful in many aspects of disease research, with numerous network-based strategies developed for generating hypotheses about gene-disease-drug associations. The ability to predict and organize genes most relevant to a specific disease has proven especially important. We previously developed a human functional gene network, HumanNet, by integrating diverse types of omics data using Bayesian statistics framework and demonstrated its ability to retrieve disease genes. Here, we present HumanNet v2 (http://www.inetbio.org/humannet), a database of human gene networks, which was updated by incorporating new data types, extending data sources and improving network inference algorithms. HumanNet now comprises a hierarchy of human gene networks, allowing for more flexible incorporation of network information into studies. HumanNet performs well in ranking disease-linked gene sets with minimal literature-dependent biases. We observe that incorporating model organisms’ protein–protein interactions does not markedly improve disease gene predictions, suggesting that many of the disease gene associations are now captured directly in human-derived datasets. With an improved interactive user interface for disease network analysis, we expect HumanNet will be a useful resource for network medicine.

A systematic pipeline for classifying bacterial operons reveals the evolutionary landscape of biofilm machineries

In bacteria functionally related genes comprising metabolic pathways and protein complexes are frequently encoded in operons and are widely conserved across phylogenetically diverse species. The evolution of these operon-encoded processes is affected by diverse mechanisms such as gene duplication, loss, rearrangement, and horizontal transfer. These mechanisms can result in functional diversification, increasing the potential evolution of novel biological pathways, and enabling pre-existing pathways to adapt to the requirements of particular environments. Despite the fundamental importance that these mechanisms play in bacterial environmental adaptation, a systematic approach for studying the evolution of operon organization is lacking. Herein, we present a novel method to study the evolution of operons based on phylogenetic clustering of operon-encoded protein families and genomic-proximity network visualizations of operon architectures. We applied this approach to study the evolution of the synthase dependent exopolysaccharide (EPS) biosynthetic systems: cellulose, acetylated cellulose, poly-β-1,6-N-acetyl-D-glucosamine (PNAG), Pel, and alginate. These polymers have important roles in biofilm formation, antibiotic tolerance, and as virulence factors in opportunistic pathogens. Our approach revealed the complex evolutionary landscape of EPS machineries, and enabled operons to be classified into evolutionarily distinct lineages. Cellulose operons show phyla-specific operon lineages resulting from gene loss, rearrangement, and the acquisition of accessory loci, and the occurrence of whole-operon duplications arising through horizonal gene transfer. Our evolution-based classification also distinguishes between PNAG production from Gram-negative and Gram-positive bacteria on the basis of structural and functional evolution of the acetylation modification domains shared by PgaB and IcaB loci, respectively. We also predict several pel-like operon lineages in Gram-positive bacteria and demonstrate in our companion paper (Whitfield et al PLoS Pathogens, in press) that Bacillus cereus produces a Pel-dependent biofilm that is regulated by cyclic-3',5'-dimeric guanosine monophosphate (c-di-GMP).

Transcription closed and open complex formation coordinate expression of genes with a shared promoter region

Many genes are spaced closely, allowing coordination without explicit control through shared regulatory elements and molecular interactions. We study the dynamics of a stochastic model of a gene-pair in a head-to-head configuration, sharing promoter elements, which accounts for the rate-limiting steps in transcription initiation. We find that only in specific regions of the parameter space of the rate-limiting steps is orderly coexpression exhibited, suggesting that successful cooperation between closely spaced genes requires the coevolution of compatible rate-limiting step configuration. The model predictions are validated using in vivo single-cell, single-RNA measurements of the dynamics of pairs of genes sharing promoter elements. Our results suggest that, in E. coli, the kinetics of the rate-limiting steps in active transcription can play a central role in shaping the dynamics of gene-pairs sharing promoter elements.

Towards the competent conformation for catalysis in the ferredoxin-NADP+ reductase from the Brucella ovis pathogen

Brucella ovis encodes a bacterial subclass 1 ferredoxin-NADP(H) reductase (BoFPR) that, by similarity with other FPRs, is expected either to deliver electrons from NADPH to the redox-based metabolism and/or to oxidize NADPH to regulate the soxRS regulon that protects bacteria against oxidative damage. Such potential roles for the pathogen survival under infection conditions make of interest to understand and to act on the BoFPR mechanism. Here, we investigate the NADP⁺/H interaction and NADPH oxidation by hydride transfer (HT) to BoFPR. Crystal structures of BoFPR in free and in complex with NADP⁺ hardly differ. The latter shows binding of the NADP⁺ adenosine moiety, while its redox-reactive nicotinamide protrudes towards the solvent. Nonetheless, pre-steady-state kinetics show formation of a charge-transfer complex (CTC-1) prior to the hydride transfer, as well as conversion of CTC-1 into a second charge-transfer complex (CTC-2) concomitantly with the HT event. Thus, during catalysis nicotinamide and flavin reacting rings stack. Kinetic data also identify the HT itself as the rate limiting step in the reduction of BoFPR by NADPH, as well as product release limiting the overall reaction. Using all-atom molecular dynamics simulations with a thermal effect approach we are able to visualise a potential transient catalytically competent interaction of the reacting rings. Simulations indicate that the architecture of the FAD folded conformation in BoFPR might be key in catalysis, pointing to its adenine as an element to orient the reactive atoms in conformations competent for HT.

Different Ways of Doing the Same: Variations in the Two Last Steps of the Purine Biosynthetic Pathway in Prokaryotes

The last two steps of the purine biosynthetic pathway may be catalyzed by different enzymes in prokaryotes. The genes that encode these enzymes include homologs of purH, purP, purO and those encoding the AICARFT and IMPCH domains of PurH, here named purV and purJ, respectively. In Bacteria, these reactions are mainly catalyzed by the domains AICARFT and IMPCH of PurH. In Archaea, these reactions may be carried out by PurH and also by PurP and PurO, both considered signatures of this domain and analogous to the AICARFT and IMPCH domains of PurH, respectively. These genes were searched for in 1,403 completely sequenced prokaryotic genomes publicly available. Our analyses revealed taxonomic patterns for the distribution of these genes and anticorrelations in their occurrence. The analyses of bacterial genomes revealed the existence of genes coding for PurV, PurJ, and PurO, which may no longer be considered signatures of the domain Archaea. Although highly divergent, the PurOs of Archaea and Bacteria show a high level of conservation in the amino acids of the active sites of the protein, allowing us to infer that these enzymes are analogs. Based on the results, we propose that the gene purO was present in the common ancestor of all living beings, whereas the gene encoding PurP emerged after the divergence of Archaea and Bacteria and their isoforms originated in duplication events in the common ancestor of phyla Crenarchaeota and Euryarchaeota. The results reported here expand our understanding of the diversity and evolution of the last two steps of the purine biosynthetic pathway in prokaryotes.

A New Microbial Pathway for Organophosphonate Degradation Catalyzed by Two Previously Misannotated Non-Heme-Iron Oxygenases

The assignment of biochemical functions to hypothetical proteins is challenged by functional diversification within many protein structural superfamilies. This diversification, which is particularly common for metalloenzymes, renders functional annotations that are founded solely on sequence and domain similarities unreliable and often erroneous. Definitive biochemical characterization to delineate functional subgroups within these superfamilies will aid in improving bioinformatic approaches for functional annotation. We describe here the structural and functional characterization of two non-heme-iron oxygenases, TmpA and TmpB, which are encoded by a genomically clustered pair of genes found in more than 350 species of bacteria. TmpA and TmpB are functional homologues of a pair of enzymes (PhnY and PhnZ) that degrade 2-aminoethylphosphonate but instead act on its naturally occurring, quaternary ammonium analogue, 2-(trimethylammonio)ethylphosphonate (TMAEP). TmpA, an iron(II)- and 2-(oxo)glutarate-dependent oxygenase misannotated as a γ-butyrobetaine (γbb) hydroxylase, shows no activity toward γbb but efficiently hydroxylates TMAEP. The product, ( R)-1-hydroxy-2-(trimethylammonio)ethylphosphonate [( R)-OH-TMAEP], then serves as the substrate for the second enzyme, TmpB. By contrast to its purported phosphohydrolytic activity, TmpB is an HD-domain oxygenase that uses a mixed-valent diiron cofactor to enact oxidative cleavage of the C-P bond of its substrate, yielding glycine betaine and phosphate. The high specificities of TmpA and TmpB for their N-trimethylated substrates suggest that they have evolved specifically to degrade TMAEP, which was not previously known to be subject to microbial catabolism. This study thus adds to the growing list of known pathways through which microbes break down organophosphonates to harvest phosphorus, carbon, and nitrogen in nutrient-limited niches.

Whole-genome-based phylogeny of Bacillus cytotoxicus reveals different clades within the species and provides clues on ecology and evolution

Bacillus cytotoxicus is a member of the Bacillus cereus group linked to fatal cases of diarrheal disease. Information on B. cytotoxicus is very limited; in particular comprehensive genomic data is lacking. Thus, we applied a genomic approach to characterize B. cytotoxicus and decipher its population structure. To this end, complete genomes of ten B. cytotoxicus were sequenced and compared to the four publicly available full B. cytotoxicus genomes and genomes of other B. cereus group members. Average nucleotide identity, core genome, and pan genome clustering resulted in clear distinction of B. cytotoxicus strains from other strains of the B. cereus group. Genomic content analyses showed that a hydroxyphenylalanine operon is present in B. cytotoxicus, but absent in all other members of the B. cereus group. It enables degradation of aromatic compounds to succinate and pyruvate and was likely acquired from another Bacillus species. It allows for utilization of tyrosine and might have given a B. cytotoxicus ancestor an evolutionary advantage resulting in species differentiation. Plasmid content showed that B. cytotoxicus is flexible in exchanging genes, allowing for quick adaptation to the environment. Genome-based phylogenetic analyses divided the B. cytotoxicus strains into four clades that also differed in virulence gene content.

Solution NMR structure of CHU_1110 from Cytophaga hutchinsonii, an AHSA1 protein potentially involved in metal ion stress response

We report the solution nuclear magnetic resonance (NMR) structure of CHU_1110 from Cytophaga hutchinsonii. CHU_1110 contains three α-helices and one antiparallel β-sheet, forming a large cavity in the center of the protein, which are consistent with the structural characteristics of AHSA1 protein family. This protein shows high structural similarities to the prokaryotic proteins RHE_CH02687 from Rhizobium etli and YndB from Bacillus subtilis, which can bind with flavinoids. Unlike these two homologs, CHU_1110 shows no obvious interaction with flavonoids in NMR titration experiments. In addition, no direct interaction has been observed between CHU_1110 and ATP, although many homologous sequences of CHU_1110 have been annotated as ATPase. Combining the analysis of structural similarity of CHU_1110 and genomic context of its encoding gene, we speculate that CHU_1110 may be involved in the stress response of bacteria to heavy metal ions, even though its specific biological functions that need to be further investigated.

Genome-wide analysis of fitness data and its application to improve metabolic models

Background

Synthetic biology and related techniques enable genome scale high-throughput investigation of the effect on organism fitness of different gene knock-downs/outs and of other modifications of genomic sequence.

Results

We develop statistical and computational pipelines and frameworks for analyzing high throughput fitness data over a genome scale set of sequence variants. Analyzing data from a high-throughput knock-down/knock-out bacterial study, we investigate differences and determinants of the effect on fitness in different conditions. Comparing fitness vectors of genes, across tens of conditions, we observe that fitness consequences strongly depend on genomic location and more weakly depend on gene sequence similarity and on functional relationships. In analyzing promoter sequences, we identified motifs associated with conditions studied in bacterial media such as Casaminos, D-glucose, Sucrose, and other sugars and amino-acid sources.

We also use fitness data to infer genes associated with orphan metabolic reactions in the iJO1366 E. coli metabolic model. To do this, we developed a new computational method that integrates gene fitness and gene expression profiles within a given reaction network neighborhood to associate this reaction with a set of genes that potentially encode the catalyzing proteins. We then apply this approach to predict candidate genes for 107 orphan reactions in iJO1366. Furthermore - we validate our methodology with known reactions using a leave-one-out approach. Specifically, using top-20 candidates selected based on combined fitness and expression datasets, we correctly reconstruct 39.7% of the reactions, as compared to 33% based on fitness and to 26% based on expression separately, and to 4.02% as a random baseline. Our model improvement results include a novel association of a gene to an orphan cytosine nucleosidation reaction.

Conclusion

Our pipeline for metabolic modeling shows a clear benefit of using fitness data for predicting genes of orphan reactions. Along with the analysis pipelines we developed, it can be used to analyze similar high-throughput data.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2341-9) contains supplementary material, which is available to authorized users.

New Insight Into the Diversity of SemiSWEET Sugar Transporters and the Homologs in Prokaryotes

Sugars will eventually be exported transporters (SWEETs) and SemiSWEETs represent a family of sugar transporters in eukaryotes and prokaryotes, respectively. SWEETs contain seven transmembrane helices (TMHs), while SemiSWEETs contain three. The functions of SemiSWEETs are less studied. In this perspective article, we analyzed the diversity and conservation of SemiSWEETs and further proposed the possible functions. 1,922 SemiSWEET homologs were retrieved from the UniProt database, which is not proportional to the sequenced prokaryotic genomes. However, these proteins are very diverse in sequences and can be classified into 19 clusters when >50% sequence identity is required. Moreover, a gene context analysis indicated that several SemiSWEETs are located in the operons that are related to diverse carbohydrate metabolism. Several proteins with seven TMHs can be found in bacteria, and sequence alignment suggested that these proteins in bacteria may be formed by the duplication and fusion. Multiple sequence alignments showed that the amino acids for sugar translocation are still conserved and coevolved, although the sequences show diversity. Among them, the functions of a few amino acids are still not clear. These findings highlight the challenges that exist in SemiSWEETs and provide future researchers the foundation to explore these uncharted areas.

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.

Variations on a theme: evolution of the phage-shock-protein system in Actinobacteria

The phage shock protein (Psp) stress-response system protects bacteria from envelope stress through a cascade of interactions with other proteins and membrane lipids to stabilize the cell membrane. A key component of this multi-gene system is PspA, an effector protein that is found in diverse bacterial phyla, archaea, cyanobacteria, and chloroplasts. Other members of the Psp system include the cognate partners of PspA that are part of known operons: pspF||pspABC in Proteobacteria, liaIHGFSR in Firmicutes, and clgRpspAMN in Actinobacteria. Despite the functional significance of the Psp system, the conservation of PspA and other Psp functions, as well as the various genomic contexts of PspA, remain poorly characterized in Actinobacteria. Here we utilize a computational evolutionary approach to systematically identify the variations of the Psp system in ~450 completed actinobacterial genomes. We first determined the homologs of PspA and its cognate partners (as reported in Escherichia coli, Bacillus subtilis, and Mycobacterium tuberculosis) across Actinobacteria. This survey revealed that PspA and most of its functional partners are prevalent in Actinobacteria. We then found that PspA occurs in four predominant genomic contexts within Actinobacteria, the primary context being the clgRpspAM system previously identified in Mycobacteria. We also constructed a phylogenetic tree of PspA homologs (including paralogs) to trace the conservation and evolution of PspA across Actinobacteria. The genomic context revealed that PspA shows changes in its gene-neighborhood. The presence of multiple PspA contexts or of other known Psp members in genomic neighborhoods that do not carry pspA suggests yet undiscovered functional implications in envelope stress response mechanisms.

Functional association prediction by community profiling

Recent years have witnessed unprecedented accumulation of DNA sequences and therefore protein sequences (predicted from DNA sequences), due to the advances of sequencing technology. One of the major sources of the hypothetical proteins is the metagenomics research. Current annotation of metagenomes (collections of short metagenomic sequences or assemblies) relies on similarity searches against known gene/protein families, based on which functional profiles of microbial communities can be built. This practice, however, leaves out the hypothetical proteins, which may outnumber the known proteins for many microbial communities. On the other hand, we may ask: what can we gain from the large number of metagenomes made available by the metagenomic studies, for the annotation of metagenomic sequences as well as functional annotation of hypothetical proteins in general? Here we propose a community profiling approach for predicting functional associations between proteins: two proteins are predicted to be associated if they share similar presence and absence profiles (called community profiles) across microbial communities. Community profiling is conceptually similar to the phylogenetic profiling approach to functional prediction, however with fundamental differences. We tested different profile construction methods, the selection of reference metagenomes, and correlation metrics, among others, to optimize the performance of this new approach. We demonstrated that the community profiling approach alone slightly outperforms the phylogenetic profiling approach for associating proteins in species that are well represented by sequenced genomes, and combining phylogenetic and community profiling further improves (though only marginally) the prediction of functional association. Further we showed that community profiling method significantly outperforms phylogenetic profiling, revealing more functional associations, when applied to a more recently sequenced bacterial genome.

Effect of genomic distance on coexpression of coregulated genes in E. coli

In prokaryotes, genomic distance is a feature that in addition to coregulation affects coexpression. Several observations, such as genomic clustering of highly coexpressed small regulons, support the idea that coexpression behavior of coregulated genes is affected by the distance between the coregulated genes. However, the specific contribution of distance in addition to coregulation in determining the degree of coexpression has not yet been studied systematically. In this work, we exploit the rich information in RegulonDB to study how the genomic distance between coregulated genes affects their degree of coexpression, measured by pairwise similarity of expression profiles obtained under a large number of conditions. We observed that, in general, coregulated genes display higher degrees of coexpression as they are more closely located on the genome. This contribution of genomic distance in determining the degree of coexpression was relatively small compared to the degree of coexpression that was determined by the tightness of the coregulation (degree of overlap of regulatory programs) but was shown to be evolutionary constrained. In addition, the distance effect was sufficient to guarantee coexpression of coregulated genes that are located at very short distances, irrespective of their tightness of coregulation. This is partly but definitely not always because the close distance is also the cause of the coregulation. In cases where it is not, we hypothesize that the effect of the distance on coexpression could be caused by the fact that coregulated genes closely located to each other are also relatively more equidistantly located from their common TF and therefore subject to more similar levels of TF molecules. The absolute genomic distance of the coregulated genes to their common TF-coding gene tends to be less important in determining the degree of coexpression. Our results pinpoint the importance of taking into account the combined effect of distance and coregulation when studying prokaryotic coexpression and transcriptional regulation.

HAPPI-2: a Comprehensive and High-quality Map of Human Annotated and Predicted Protein Interactions

BACKGROUND

Human protein-protein interaction (PPI) data is essential to network and systems biology studies. PPI data can help biochemists hypothesize how proteins form complexes by binding to each other, how extracellular signals propagate through post-translational modification of de-activated signaling molecules, and how chemical reactions are coupled by enzymes involved in a complex biological process. Our capability to develop good public database resources for human PPI data has a direct impact on the quality of future research on genome biology and medicine.

RESULTS

The database of Human Annotated and Predicted Protein Interactions (HAPPI) version 2.0 is a major update to the original HAPPI 1.0 database. It contains 2,922,202 unique protein-protein interactions (PPI) linked by 23,060 human proteins, making it the most comprehensive database covering human PPI data today. These PPIs contain both physical/direct interactions and high-quality functional/indirect interactions. Compared with the HAPPI 1.0 database release, HAPPI database version 2.0 (HAPPI-2) represents a 485% of human PPI data coverage increase and a 73% protein coverage increase. The revamped HAPPI web portal provides users with a friendly search, curation, and data retrieval interface, allowing them to retrieve human PPIs and available annotation information on the interaction type, interaction quality, interacting partner drug targeting data, and disease information. The updated HAPPI-2 can be freely accessed by Academic users at http://discovery.informatics.uab.edu/HAPPI .

CONCLUSIONS

While the underlying data for HAPPI-2 are integrated from a diverse data sources, the new HAPPI-2 release represents a good balance between data coverage and data quality of human PPIs, making it ideally suited for network biology.

Inferring Functional Relationships from Conservation of Gene Order

Predicting functional associations using the Gene Neighbor Method depends on the simple idea that if genes are conserved next to each other in evolutionarily distant prokaryotes they might belong to a polycistronic transcription unit. The procedure presented in this chapter starts with the organization of the genes within genomes into pairs of adjacent genes. Then, the pairs of adjacent genes in a genome of interest are mapped to their corresponding orthologs in other, informative, genomes. The final step is to verify if the mapped orthologs are also pairs of adjacent genes in the informative genomes.

Finding approximate gene clusters with Gecko 3

Gene-order-based comparison of multiple genomes provides signals for functional analysis of genes and the evolutionary process of genome organization. Gene clusters are regions of co-localized genes on genomes of different species. The rapid increase in sequenced genomes necessitates bioinformatics tools for finding gene clusters in hundreds of genomes. Existing tools are often restricted to few (in many cases, only two) genomes, and often make restrictive assumptions such as short perfect conservation, conserved gene order or monophyletic gene clusters. We present Gecko 3, an open-source software for finding gene clusters in hundreds of bacterial genomes, that comes with an easy-to-use graphical user interface. The underlying gene cluster model is intuitive, can cope with low degrees of conservation as well as misannotations and is complemented by a sound statistical evaluation. To evaluate the biological benefit of Gecko 3 and to exemplify our method, we search for gene clusters in a dataset of 678 bacterial genomes using Synechocystis sp. PCC 6803 as a reference. We confirm detected gene clusters reviewing the literature and comparing them to a database of operons; we detect two novel clusters, which were confirmed by publicly available experimental RNA-Seq data. The computational analysis is carried out on a laptop computer in <40 min.

A Bioinformatics Analysis Reveals a Group of MocR Bacterial Transcriptional Regulators Linked to a Family of Genes Coding for Membrane Proteins

The MocR bacterial transcriptional regulators are characterized by an N-terminal domain, 60 residues long on average, possessing the winged-helix-turn-helix (wHTH) architecture responsible for DNA recognition and binding, linked to a large C-terminal domain (350 residues on average) that is homologous to fold type-I pyridoxal 5′-phosphate (PLP) dependent enzymes like aspartate aminotransferase (AAT). These regulators are involved in the expression of genes taking part in several metabolic pathways directly or indirectly connected to PLP chemistry, many of which are still uncharacterized. A bioinformatics analysis is here reported that studied the features of a distinct group of MocR regulators predicted to be functionally linked to a family of homologous genes coding for integral membrane proteins of unknown function. This group occurs mainly in the Actinobacteria and Gammaproteobacteria phyla. An analysis of the multiple sequence alignments of their wHTH and AAT domains suggested the presence of specificity-determining positions (SDPs). Mapping of SDPs onto a homology model of the AAT domain hinted at possible structural/functional roles in effector recognition. Likewise, SDPs in wHTH domain suggested the basis of specificity of Transcription Factor Binding Site recognition. The results reported represent a framework for rational design of experiments and for bioinformatics analysis of other MocR subgroups.

Investigating Evolutionary Dynamics of RHA1 Operons

Grouping genes as operons is an important genomic feature of prokaryotic organisms. The comprehensive understanding of the operon organizations would be helpful to decipher transcriptional mechanisms, cellular pathways, and the evolutionary landscape of prokaryotic genomes. Although thousands of prokaryotes have been sequenced, genome-wide investigation of the evolutionary dynamics (division and recombination) of operons among these genomes remains unexplored. Here, we systematically analyzed the operon dynamics of Rhodococcus jostii RHA1 (RHA1), an oleaginous bacterium with high potential applications in biofuel, by comparing 340 prokaryotic genomes that were carefully selected from different genera. Interestingly, 99% of RHA1 operons were observed to exhibit evolutionary events of division and recombination among the 340 compared genomes. An operon that encodes all enzymes related to histidine biosynthesis in RHA1 (His-operon) was found to be segmented into smaller gene groups (sub-operons) in diverse genomes. These sub-operons were further reorganized with different functional genes as novel operons that are related to different biochemical processes. Comparatively, the operons involved in the functional categories of lipid transport and metabolism are relatively conserved among the 340 compared genomes. At the pathway level, RHA1 operons found to be significantly conserved were involved in ribosome synthesis, oxidative phosphorylation, and fatty acid synthesis. These analyses provide evolutionary insights of operon organization and the dynamic associations of various biochemical pathways in different prokaryotes.

Conserved Units of Co-Expression in Bacterial Genomes: An Evolutionary Insight into Transcriptional Regulation

Genome-wide measurements of transcriptional activity in bacteria indicate that the transcription of successive genes is strongly correlated beyond the scale of operons. Here, we analyze hundreds of bacterial genomes to identify supra-operonic segments of genes that are proximal in a large number of genomes. We show that these synteny segments correspond to genomic units of strong transcriptional co-expression. Structurally, the segments contain operons with specific relative orientations (co-directional or divergent) and nucleoid-associated proteins are found to bind at their boundaries. Functionally, operons inside a same segment are highly co-expressed even in the apparent absence of regulatory factors at their promoter regions. Remote operons along DNA can also be co-expressed if their corresponding segments share a transcriptional or sigma factor, without requiring these factors to bind directly to the promoters of the operons. As evidence that these results apply across the bacterial kingdom, we demonstrate them both in the Gram-negative bacterium Escherichia coli and in the Gram-positive bacterium Bacillus subtilis. The underlying process that we propose involves only RNA-polymerases and DNA: it implies that the transcription of an operon mechanically enhances the transcription of adjacent operons. In support of a primary role of this regulation by facilitated co-transcription, we show that the transcription en bloc of successive operons as a result of transcriptional read-through is strongly and specifically enhanced in synteny segments. Finally, our analysis indicates that facilitated co-transcription may be evolutionary primitive and may apply beyond bacteria.

Transcription-coupled DNA supercoiling dictates the chromosomal arrangement of bacterial genes

Over the recent decade, the central importance of DNA supercoiling in chromosome organization and global gene regulation of bacteria became more and more visible. With a regulon comprising more than 2000 genes in Escherichia coli, DNA supercoiling is among the most influential regulators of gene expression found in bacteria so far. However, the mechanism creating thousands of diverse temporal gene expression patterns coordinated by DNA supercoiling remains unclear. In this study we show that a specific chromosomal arrangement of genes modulates the local levels of DNA supercoiling at gene promoters via transcription-coupled DNA supercoiling (TCDS) in the model organism E. coli. Our findings provide a consistent explanation for the strong positive coupling of temporal gene expression patterns of neighboring genes. Using comparative genomics we are furthermore able to provide evidence that TCDS is a driving force for the evolution of chromosomal gene arrangement patterns in other Enterobacteriaceae. With the currently available data of promoter supercoiling sensitivity we prove that the same principle is applicable also for the evolutionary distant gram-positive pathogenic bacterium Streptococcus pneumoniae. Moreover, our findings are fully consistent with recent investigations concerning the regulatory impact of TCDS on gene pairs in eukaryots underpinning the broad applicability of our analysis.

Coevolution of the Organization and Structure of Prokaryotic Genomes

The cytoplasm of prokaryotes contains many molecular machines interacting directly with the chromosome. These vital interactions depend on the chromosome structure, as a molecule, and on the genome organization, as a unit of genetic information. Strong selection for the organization of the genetic elements implicated in these interactions drives replicon ploidy, gene distribution, operon conservation, and the formation of replication-associated traits. The genomes of prokaryotes are also very plastic with high rates of horizontal gene transfer and gene loss. The evolutionary conflicts between plasticity and organization lead to the formation of regions with high genetic diversity whose impact on chromosome structure is poorly understood. Prokaryotic genomes are remarkable documents of natural history because they carry the imprint of all of these selective and mutational forces. Their study allows a better understanding of molecular mechanisms, their impact on microbial evolution, and how they can be tinkered in synthetic biology.

Remote homology and the functions of metagenomic dark matter

Predicted open reading frames (ORFs) that lack detectable homology to known proteins are termed ORFans. Despite their prevalence in metagenomes, the extent to which ORFans encode real proteins, the degree to which they can be annotated, and their functional contributions, remain unclear. To gain insights into these questions, we applied sensitive remote-homology detection methods to functionally analyze ORFans from soil, marine, and human gut metagenome collections. ORFans were identified, clustered into sequence families, and annotated through profile-profile comparison to proteins of known structure. We found that a considerable number of metagenomic ORFans (73,896 of 484,121, 15.3%) exhibit significant remote homology to structurally characterized proteins, providing a means for ORFan functional profiling. The extent of detected remote homology far exceeds that obtained for artificial protein families (1.4%). As expected for real genes, the predicted functions of ORFans are significantly similar to the functions of their gene neighbors (p < 0.001). Compared to the functional profiles predicted through standard homology searches, ORFans show biologically intriguing differences. Many ORFan-enriched functions are virus-related and tend to reflect biological processes associated with extreme sequence diversity. Each environment also possesses a large number of unique ORFan families and functions, including some known to play important community roles such as gut microbial polysaccharide digestion. Lastly, ORFans are a valuable resource for finding novel enzymes of interest, as we demonstrate through the identification of hundreds of novel ORFan metalloproteases that all possess a signature catalytic motif despite a general lack of similarity to known proteins. Our ORFan functional predictions are a valuable resource for discovering novel protein families and exploring the boundaries of protein sequence space. All remote homology predictions are available at http://doxey.uwaterloo.ca/ORFans.

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.

Molecular mechanism of PdxR – a transcriptional activator involved in the regulation of vitamin B6 biosynthesis in the probiotic bacterium Bacillus clausii

Pyridoxal 5'-phosphate (PLP), the well-known active form of vitamin B6 , is an essential enzyme cofactor involved in a large number of metabolic processes. PLP levels need to be finely tuned in response to cell requirements; however, little is known about the regulation of PLP biosynthesis and recycling pathways. The transcriptional regulator PdxR activates transcription of the pdxST genes encoding PLP synthase. It is characterized by an N-terminal helix-turn-helix motif that binds DNA and an effector-binding C-terminal domain homologous to PLP-dependent enzymes. Although it is known that PLP acts as an anti-activator, the mechanism of action of PdxR is unknown. In the present study, we analyzed the biochemical and DNA-binding properties of PdxR from the probiotic Bacillus clausii. Spectroscopic measurements showed that PLP is the only B6 vitamer that acts as an effector molecule of PdxR. Binding of PLP to PdxR determines a protein conformational change, as detected by gel filtration chromatography and limited proteolysis experiments. We showed that two direct repeats and one inverted repeat are present in the DNA promoter region and PdxR is able to bind DNA fragments containing any combination of two of them. However, when PLP binds to PdxR, it modifies the DNA-binding properties of the protein, making it selective for inverted repeats. A molecular mechanism is proposed in which the two different DNA binding modalities of PdxR determined by the presence or absence of PLP are responsible for the control of pdxST transcription.

Extensive Identification of Bacterial Riboflavin Transporters and Their Distribution across Bacterial Species

Riboflavin, the precursor for the cofactors flavin mononucleotide (FMN) and flavin adenine dinucleotide, is an essential metabolite in all organisms. While the functions for de novo riboflavin biosynthesis and riboflavin import may coexist in bacteria, the extent of this co-occurrence is undetermined. The RibM, RibN, RfuABCD and the energy-coupling factor-RibU bacterial riboflavin transporters have been experimentally characterized. In addition, ImpX, RfnT and RibXY are proposed as riboflavin transporters based on positional clustering with riboflavin biosynthetic pathway (RBP) genes or conservation of the FMN riboswitch regulatory element. Here, we searched for the FMN riboswitch in bacterial genomes to identify genes encoding riboflavin transporters and assessed their distribution among bacteria. Two new putative riboflavin transporters were identified: RibZ in Clostridium and RibV in Mesoplasma florum. Trans-complementation of an Escherichia coli riboflavin auxotroph strain confirmed the riboflavin transport activity of RibZ from Clostridium difficile, RibXY from Chloroflexus aurantiacus, ImpX from Fusobacterium nucleatum and RfnT from Ochrobactrum anthropi. The analysis of the genomic distribution of all known bacterial riboflavin transporters revealed that most occur in species possessing the RBP and that some bacteria may even encode functional riboflavin transporters from two different families. Our results indicate that some species possess ancestral riboflavin transporters, while others possess transporters that appear to have evolved recently. Moreover, our data suggest that unidentified riboflavin transporters also exist. The present study doubles the number of experimentally characterized riboflavin transporters and suggests a specific, non-accessory role for these proteins in riboflavin-prototrophic bacteria.

Network-assisted crop systems genetics: network inference and integrative analysis

Although next-generation sequencing (NGS) technology has enabled the decoding of many crop species genomes, most of the underlying genetic components for economically important crop traits remain to be determined. Network approaches have proven useful for the study of the reference plant, Arabidopsis thaliana, and the success of network-based crop genetics will also require the availability of a genome-scale functional networks for crop species. In this review, we discuss how to construct functional networks and elucidate the holistic view of a crop system. The crop gene network then can be used for gene prioritization and the analysis of resequencing-based genome-wide association study (GWAS) data, the amount of which will rapidly grow in the field of crop science in the coming years.

Spatial features for Escherichia coli genome organization

Background

In bacterial genomes, the compactly encoded genes and operons are well organized, with genes in the same biological pathway or operons in the same regulon close to each other on the genome sequence. In addition, the linearly close genes have a higher probability of co-expression and their protein products tend to form protein–protein interactions. However, the organization features of bacterial genomes in a three-dimensional space remain elusive. The DNA interaction data of Escherichia coli, measured by the genome conformation capture (GCC) technique, have recently become available, which allowed us to investigate the spatial features of bacterial genome organization.

Results

By renormalizing the GCC data, we compared the interaction frequency of operon pairs in the same regulon with that of random operon pairs. The results showed that arrangements of operons in the E. coli genome tend to minimize the spatial distance between operons in the same regulon. A similar global organization feature exists for genes in biological pathways of E. coli. In addition, the genes close to each other spatially (even if they are far from each other on the genome sequence) tend to be co-expressed and form protein–protein interactions. These results provided new insights into the organization principles of bacterial genomes and support the notion of transcription factory.

Conclusions

This study revealed the organization features of Escherichia coli genomic functional units in the 3D space and furthered our understanding of the link between the three-dimensional structure of chromosomes and biological function.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1258-1) contains supplementary material, which is available to authorized users.

Comparative Genomics and Evolutionary Modularity of Prokaryotes

The soaring number of high-quality genomic sequences has ushered in the era of post-genomic research where our understanding of organisms has dramatically shifted towards defining the function of genes within their larger biological contexts. As a result, novel high-throughput experimental technologies are being increasingly employed to uncover physical and functional associations of genes and proteins in complex biological processes. Through the construction and analysis of physical, genetic and metabolic networks generated for the model organisms, such as Escherichia coli, organizational principles of the genome have been deduced, such as modularity, which has important implications toward understanding prokaryotic evolution and adaptation to novel lifestyles.

LocFuse: human protein-protein interaction prediction via classifier fusion using protein localization information

Protein-protein interaction (PPI) detection is one of the central goals of functional genomics and systems biology. Knowledge about the nature of PPIs can help fill the widening gap between sequence information and functional annotations. Although experimental methods have produced valuable PPI data, they also suffer from significant limitations. Computational PPI prediction methods have attracted tremendous attentions. Despite considerable efforts, PPI prediction is still in its infancy in complex multicellular organisms such as humans. Here, we propose a novel ensemble learning method, LocFuse, which is useful in human PPI prediction. This method uses eight different genomic and proteomic features along with four types of different classifiers. The prediction performance of this classifier selection method was found to be considerably better than methods employed hitherto. This confirms the complex nature of the PPI prediction problem and also the necessity of using biological information for classifier fusion. The LocFuse is available at: http://lbb.ut.ac.ir/Download/LBBsoft/LocFuse.

BIOLOGICAL SIGNIFICANCE

The results revealed that if we divide proteome space according to the cellular localization of proteins, then the utility of some classifiers in PPI prediction can be improved. Therefore, to predict the interaction for any given protein pair, we can select the most accurate classifier with regard to the cellular localization information. Based on the results, we can say that the importance of different features for PPI prediction varies between differently localized proteins; however in general, our novel features, which were extracted from position-specific scoring matrices (PSSMs), are the most important ones and the Random Forest (RF) classifier performs best in most cases. LocFuse was developed with a user-friendly graphic interface and it is freely available for Linux, Mac OSX and MS Windows operating systems.