Regions In Bacterial Genomes Research Articles

The Rise of Bacterial G-Quadruplexes in Current Antimicrobial Discovery.

Antimicrobial resistance (AMR) is a silent critical issue that poses several challenges to health systems. While the discovery of novel antibiotics is currently stalled and prevalently focused on chemical variations of the scaffolds of available drugs, novel targets and innovative strategies are urgently needed to face this global threat. In this context, bacterial G-quadruplexes (G4s) are emerging as timely and profitable targets for the design and development of antimicrobial agents. Indeed, they are expressed in regulatory regions of bacterial genomes, and their modulation has been observed to provide antimicrobial effects with translational perspectives in the context of AMR. In this work, we review the current knowledge of bacterial G4s as well as their modulation by small molecules, including tools and techniques suitable for these investigations. Finally, we critically analyze the needs and future directions in the field, with a focus on the development of small molecules as bacterial G4s modulators endowed with remarkable drug-likeness.

MLDSPP: Bacterial Promoter Prediction Tool Using DNA Structural Properties with Machine Learning and Explainable AI.

Bacterial promoters play a crucial role in gene expression by serving as docking sites for the transcription initiation machinery. However, accurately identifying promoter regions in bacterial genomes remains a challenge due to their diverse architecture and variations. In this study, we propose MLDSPP (Machine Learning and Duplex Stability based Promoter prediction in Prokaryotes), a machine learning-based promoter prediction tool, to comprehensively screen bacterial promoter regions in 12 diverse genomes. We leveraged biologically relevant and informative DNA structural properties, such as DNA duplex stability and base stacking, and state-of-the-art machine learning (ML) strategies to gain insights into promoter characteristics. We evaluated several machine learning models, including Support Vector Machines, Random Forests, and XGBoost, and assessed their performance using accuracy, precision, recall, specificity, F1 score, and MCC metrics. Our findings reveal that XGBoost outperformed other models and current state-of-the-art promoter prediction tools, namely Sigma70pred and iPromoter2L, achieving F1-scores >95% in most systems. Significantly, the use of one-hot encoding for representing nucleotide sequences complements these structural features, enhancing our XGBoost model's predictive capabilities. To address the challenge of model interpretability, we incorporated explainable AI techniques using Shapley values. This enhancement allows for a better understanding and interpretation of the predictions of our model. In conclusion, our study presents MLDSPP as a novel, generic tool for predicting promoter regions in bacteria, utilizing original downstream sequences as nonpromoter controls. This tool has the potential to significantly advance the field of bacterial genomics and contribute to our understanding of gene regulation in diverse bacterial systems.

DBSCAN-SWA: An Integrated Tool for Rapid Prophage Detection and Annotation

As an intracellular form of a bacteriophage in the bacterial host genome, a prophage usually integrates into bacterial DNA with high specificity and contributes to horizontal gene transfer (HGT). With the exponentially increasing number of microbial sequences uncovered in genomic or metagenomics studies, there is a massive demand for a tool that is capable of fast and accurate identification of prophages. Here, we introduce DBSCAN-SWA, a command line software tool developed to predict prophage regions in bacterial genomes. DBSCAN-SWA runs faster than any previous tools. Importantly, it has great detection power based on analysis using 184 manually curated prophages, with a recall of 85% compared with Phage_Finder (63%), VirSorter (74%), and PHASTER (82%) for (Multi-) FASTA sequences. Moreover, DBSCAN-SWA outperforms the existing standalone prophage prediction tools for high-throughput sequencing data based on the analysis of 19,989 contigs of 400 bacterial genomes collected from Human Microbiome Project (HMP) project. DBSCAN-SWA also provides user-friendly result visualizations including a circular prophage viewer and interactive DataTables. DBSCAN-SWA is implemented in Python3 and is available under an open source GPLv2 license from https://github.com/HIT-ImmunologyLab/DBSCAN-SWA/.

Genomic landscape of single-stranded DNA gapped intermediates in Escherichia coli.

Single-stranded (ss) gapped regions in bacterial genomes (gDNA) are formed on W- and C-strands during replication, repair, and recombination. Using non-denaturing bisulfite treatment to convert C to U on ssDNA, combined with deep sequencing, we have mapped gDNA gap locations, sizes, and distributions in Escherichia coli for cells grown in mid-log phase in the presence and absence of UV irradiation, and in stationary phase cells. The fraction of ssDNA on gDNA is similar for W- and C-strands, ∼1.3% for log phase cells, ∼4.8% for irradiated log phase cells, and ∼8.5% for stationary phase cells. After UV irradiation, gaps increased in numbers and average lengths. A monotonic reduction in ssDNA occurred symmetrically between the DNA replication origin of (OriC) and terminus (Ter) for log phase cells with and without UV, a hallmark feature of DNA replication. Stationary phase cells showed no OriC → Ter ssDNA gradient. We have identified a spatially diverse gapped DNA landscape containing thousands of highly enriched ‘hot’ ssDNA regions along with smaller numbers of ‘cold’ regions. This analysis can be used for a wide variety of conditions to map ssDNA gaps generated when DNA metabolic pathways have been altered, and to identify proteins bound in the gaps.

DACCOR-Detection, characterization, and reconstruction of repetitive regions in bacterial genomes.

The reconstruction of genomes using mapping-based approaches with short reads experiences difficulties when resolving repetitive regions. These repetitive regions in genomes result in low mapping qualities of the respective reads, which in turn lead to many unresolved bases. Currently, the reconstruction of these regions is often based on modified references in which the repetitive regions are masked. However, for many references, such masked genomes are not available or are based on repetitive regions of other genomes. Our idea is to identify repetitive regions in the reference genome de novo. These regions can then be used to reconstruct them separately using short read sequencing data. Afterward, the reconstructed repetitive sequence can be inserted into the reconstructed genome. We present the program detection, characterization, and reconstruction of repetitive regions, which performs these steps automatically. Our results show an increased base pair resolution of the repetitive regions in the reconstruction of Treponema pallidum samples, resulting in fewer unresolved bases.

An Efficient Approach to Explore and Discriminate Anomalous Regions in Bacterial Genomes Based on Maximum Entropy.

Recently, there has been an increase in the number of whole bacterial genomes sequenced, mainly due to the advancing of next-generation sequencing technologies. In face of this, there is a need to provide new analytical alternatives that can follow this advance. Given our current knowledge about the genomic plasticity of bacteria and that those genomic regions can uncover important features about this microorganism, our goal was to develop a fast methodology based on maximum entropy (ME) to guide the researcher to regions that could be prioritized during the analysis. This methodology was compared with other available methods. In addition, ME was applied to eight different bacterial genera. The methodology consists of two main steps: processing the nucleotide sequence and ME calculation. We applied ME to Xanthomonas axonopodis pv. citri 306 (XAC) and Xanthomonas campestris pv. campestris ATCC 33913 (XCC), both of which have their anomalous regions well documented. We then compared our results against those from Alien Hunter, HGT-DB, Islander, IslandPath, and SIGI-HMM. ME was shown to be superior in terms of efficiency and analysis duration. Besides, ME only needs the genome sequence in FASTA format as input. The proposed strategy based on ME is able to help in bacterial genome exploration. This is a simple and fast strategy for individual genomes in comparison with other available methods, without relying on previous annotation and alignments. This methodology can also be a new option in the early stages of analysis of newly sequenced bacterial genomes.

Detection of drug resistance in Mycobacterium tuberculosis.

Detection of drug resistance in Mycobacterium tuberculosis.

A Novel Method to Predict Genomic Islands Based on Mean Shift Clustering Algorithm.

Genomic Islands (GIs) are regions of bacterial genomes that are acquired from other organisms by the phenomenon of horizontal transfer. These regions are often responsible for many important acquired adaptations of the bacteria, with great impact on their evolution and behavior. Nevertheless, these adaptations are usually associated with pathogenicity, antibiotic resistance, degradation and metabolism. Identification of such regions is of medical and industrial interest. For this reason, different approaches for genomic islands prediction have been proposed. However, none of them are capable of predicting precisely the complete repertory of GIs in a genome. The difficulties arise due to the changes in performance of different algorithms in the face of the variety of nucleotide distribution in different species. In this paper, we present a novel method to predict GIs that is built upon mean shift clustering algorithm. It does not require any information regarding the number of clusters, and the bandwidth parameter is automatically calculated based on a heuristic approach. The method was implemented in a new user-friendly tool named MSGIP—Mean Shift Genomic Island Predictor. Genomes of bacteria with GIs discussed in other papers were used to evaluate the proposed method. The application of this tool revealed the same GIs predicted by other methods and also different novel unpredicted islands. A detailed investigation of the different features related to typical GI elements inserted in these new regions confirmed its effectiveness. Stand-alone and user-friendly versions for this new methodology are available at http://msgip.integrativebioinformatics.me.

Application of Graph Entropy in CRISPR and Repeats Detection in DNA Sequences

We analyzed DNA sequences using a new measure of entropy. The general aim was to analyze DNA sequences and find interesting sections of a genome using a new formulation of Shannon like entropy. We developed this new measure of entropy for any non-trivial graph or, more broadly, for any square matrix whose non-zero elements represent probabilistic weights assigned to connections or transitions between pairs of vertices. The new measure is called the graph entropy and it quantifies the aggregate indeterminacy effected by the variety of unique walks that exist between each pair of vertices. The new tool is shown to be uniquely capable of revealing CRISPR regions in bacterial genomes and to identify Tandem repeats and Direct repeats of genome. We have done experiment on 26 species and found many tandem repeats and direct repeats (CRISPR for bacteria or archaea). There are several existing separate CRISPR or Tandem finder tools but our entropy can find both of these features if present in genome.

Recent Advances in the Identification of Replication Origins Based on the Z-curve Method

Precise DNA replication is critical for the maintenance of genetic integrity in all organisms. In all three domains of life, DNA replication starts at a specialized locus, termed as the replication origin, oriC or ORI, and its identification is vital to understanding the complex replication process. In bacteria and eukaryotes, replication initiates from single and multiple origins, respectively, while archaea can adopt either of the two modes. The Z-curve method has been successfully used to identify replication origins in genomes of various species, including multiple oriCs in some archaea. Based on the Z-curve method and comparative genomics analysis, we have developed a web-based system, Ori-Finder, for finding oriCs in bacterial genomes with high accuracy. Predicted oriC regions in bacterial genomes are organized into an online database, DoriC. Recently, archaeal oriC regions identified by both in vivo and in silico methods have also been included in the database. Here, we summarize the recent advances of in silico prediction of oriCs in bacterial and archaeal genomes using the Z-curve based method.

A Hidden Markov Model for identifying essential and growth-defect regions in bacterial genomes from transposon insertion sequencing data

BackgroundKnowledge of which genes are essential to the survival of an organism is critical to understanding the function of genes, and for the identification of potential drug targets for antimicrobial treatment. Previous statistical methods for assessing essentiality based on sequencing of tranposon libraries have usually limited their assessment to strict 'essential’ or 'non-essential’ categories. However, this binary view of essentiality does not accurately represent the more nuanced ways the growth of an organism might be affected by the disruption of its genes. In addition, these methods often limit their analysis to open-reading frames. We propose a novel method for analyzing sequence data from transposon mutant libraries using a Hidden Markov Model (HMM), along with formulas to adapt the parameters of the model to different datasets for robustness. This approach allows for the clustering of insertion sites into distinct regions of essentiality across the entire genome in a statistically rigorous manner, while also allowing for the detection of growth-defect and growth-advantage regions.ResultsWe evaluate the performance of a 4-state HMM on a sequence dataset of M. tuberculosis transposon mutants. We also test the HMM on several synthetic datasets representing different levels of transposon insertion density and sequence coverage. We show that the HMM produces results that are highly correlated with previous assignments of essentiality for this organism. We also show that it detects growth-defect and growth-advantage genes previously shown to impair or enhance growth when disrupted.ConclusionsA 4-state HMM provides an improved way of analyzing Tn-seq data and assessing different levels of essentiality that enables not only the characterization of essential and non-essential genes, but also genes whose disruption leads to impairment (or enhancement) of growth.

Evolution of transcriptional regulation in closely related bacteria

BackgroundThe exponential growth of the number of fully sequenced genomes at varying taxonomic closeness allows one to characterize transcriptional regulation using comparative-genomics analysis instead of time-consuming experimental methods. A transcriptional regulatory unit consists of a transcription factor, its binding site and a regulated gene. These units constitute a graph which contains so-called “network motifs”, subgraphs of a given structure. Here we consider genomes of closely related Enterobacteriales and estimate the fraction of conserved network motifs and sites as well as positions under selection in various types of non-coding regions.ResultsUsing a newly developed technique, we found that the highest fraction of positions under selection, approximately 50%, was observed in synvergon spacers (between consecutive genes from the same strand), followed by ~45% in divergon spacers (common 5’-regions), and ~10% in convergon spacers (common 3’-regions). The fraction of selected positions in functional regions was higher, 60% in transcription factor-binding sites and ~45% in terminators and promoters. Small, but significant differences were observed between Escherichia coli and Salmonella enterica. This fraction is similar to the one observed in eukaryotes.The conservation of binding sites demonstrated some differences between types of regulatory units. In E. coli, strains the interactions of the type “local transcriptional factor ➝ gene” turned out to be more conserved in feed-forward loops (FFLs) compared to non-motif interactions. The coherent FFLs tend to be less conserved than the incoherent FFLs. A natural explanation is that the former imply functional redundancy.ConclusionsA naïve hypothesis that FFL would be highly conserved turned out to be not entirely true: its conservation depends on its status in the transcriptional network and also from its usage. The fraction of positions under selection in intergenic regions of bacterial genomes is roughly similar to that of eukaryotes. Known regulatory sites explain 20±5% of selected positions.

Evolution of simple sequence repeat–mediated phase variation in bacterial genomes

Mutability as mechanism for rapid adaptation to environmental challenge is an alluringly simple concept whose apotheosis is realized in simple sequence repeats (SSR). Bacterial genomes of several species contain SSRs with a proven role in adaptation to environmental fluctuations. SSRs are hypermutable and generate reversible mutations in localized regions of bacterial genomes, leading to phase variable ON/OFF switches in gene expression. The application of genetic, bioinformatic, and mathematical/computational modeling approaches are revolutionizing our current understanding of how genomic molecular forces and environmental factors influence SSR-mediated adaptation and led to evolution of this mechanism of localized hypermutation in bacterial genomes.

Genome-wide detection of novel regulatory RNAs in E. coli

The intergenic regions in bacterial genomes can contain regulatory leader sequences and small RNAs (sRNAs), which both serve to modulate gene expression. Computational analyses have predicted the presence of hundreds of these noncoding regulatory RNAs in Escherichia coli; however, only about 80 have been experimentally validated. By applying a deep-sequencing approach, we detected and quantified the vast majority of the previously validated regulatory elements and identified 10 new sRNAs and nine new regulatory leader sequences in the intergenic regions of E. coli. Half of the newly discovered sRNAs displayed enhanced stability in the presence of the RNA-binding protein Hfq, which is vital to the function of many of the known E. coli sRNAs. Whereas previous methods have often relied on phylogenetic conservation to identify regulatory leader sequences, only five of the newly discovered E. coli leader sequences were present in the genomes of other enteric species. For those newly identified regulatory elements having orthologs in Salmonella, evolutionary analyses showed that these regions encoded new noncoding elements rather than small, unannotated protein-coding transcripts. In addition to discovering new noncoding regulatory elements, we validated 53 sRNAs that were previously predicted but never detected and showed that the presence, within intergenic regions, of σ(70) promoters and sequences with compensatory mutations that maintain stable RNA secondary structures across related species is a good predictor of novel sRNAs.

Alternative Sigma Factor σH Modulates Prophage Integration and Excision in Staphylococcus aureus

The prophage is one of the most important components of variable regions in bacterial genomes. Some prophages carry additional genes that may enhance the toxicity and survival ability of their host bacteria. This phenomenon is predominant in Staphylococcus aureus, a very common human pathogen. Bioinformatics analysis of several staphylococcal prophages revealed a highly conserved 40-bp untranslated region upstream of the int gene. A small transcript encoding phage integrase was identified to be initiated from the region, demonstrating that the untranslated region contained a promoter for int. No typical recognition sequence for either σA or σB was identified in the 40-bp region. Experiments both in vitro and in vivo demonstrated that σH recognized the promoter and directed transcription. Genetic deletion of sigH altered the int expression, and subsequently, the excision proportion of prophage DNAs. Phage assays further showed that sigH affected the ability of spontaneous lysis and lysogenization in S. aureus, suggesting that sigH plays a role in stabilizing the lysogenic state. These findings revealed a novel mechanism of prophage integration specifically regulated by a host-source alternative sigma factor. This mechanism suggests a co-evolution strategy of staphylococcal prophages and their host bacteria.

Origins of replication in Cyanothece 51142

Welsh et al. (1) recently reported the complete genome of Cyanothece 51142 in PNAS. The authors concluded that “The origin of replication could not be determined for either the circular or linear chromosome by using standard GC skew and DnaA box analysis” (1). However, by utilizing a web-based system Ori-Finder (2), we have identified the locations of oriC for both the circular and linear chromosomes in Cyanothece 51142. For the circular chromosome, the oriC is predicted within the intergenic region between the ORF cce_1862 and the dnaN gene (cce_1864), from 1,886,587 nt to 1,887,114 nt of the chromosome. The feature that oriC is adjacent to the dnaN gene is universal among the bacteria within the class Cyanobacteria (for more detail, please visit DoriC, a database of oriC regions in bacterial genomes at http://tubic.tju.edu.cn/doric/). This oriC region also contains 3 putative DnaA boxes (Fig. 1A), each with perfect match to TTTTCCACA, the “species-specific” DnaA box motif for the class Cyanobacteria. For the linear chromosome, the oriC is predicted within the intergenic region between 2 ORFs, cce_5168 and cce_5169, from 373,518 nt to 373,849 nt of the chromosome. This identified oriC region is around the minimum of the GC disparity curve (348,971 nt) and contains 3 putative DnaA boxes (Fig. 1B), each with no more than 1 mismatch from TTATCCACA, the DnaA box motif of Escherichia coli. It is also observed that the oriC for the linear chromosome contains a reverse repeat, TGAAGATATAGAAGT (Fig. 1B).

Visualizing the Repeat Structure of Genomic Sequences

Repeats are a common feature of genomic sequences and much remains to be understood of their origin and structure. The identification of repeated strings in genomic sequences is therefore of importance for a variety of applications in biology. In this paper a new method for finding all repeats and visualizing them in a two-dimensional plot is presented. The method is first applied to a set of constructed sequences in order to develop a comparative framework. Several complete genomes are then analyzed, including the whole human genome. The technique reveals the complex repeat structure of genomic sequences. In particular, interesting differences in the repeat character of the coding and noncoding regions of bacterial genomes are noted. The method allows fast identification of all repeats and easy inter-genome comparison. In doing this the plot effectively creates a signature of a sequence which allows some classes of repeats present in a sequence to be identified by simple visual inspection. To our knowledge this is the first time all exact repeats have been visualized in a single plot that highlights the degree to which repeats occur within a genomic sequence, giving an indication of the important role repeats play. From this it is clear that large scale repeat analysis remains an important and unsolved problem in bioinformatics.

MOSAIC: an online database dedicated to the comparative genomics of bacterial strains at the intra-species level

BackgroundThe recent availability of complete sequences for numerous closely related bacterial genomes opens up new challenges in comparative genomics. Several methods have been developed to align complete genomes at the nucleotide level but their use and the biological interpretation of results are not straightforward. It is therefore necessary to develop new resources to access, analyze, and visualize genome comparisons.DescriptionHere we present recent developments on MOSAIC, a generalist comparative bacterial genome database. This database provides the bacteriologist community with easy access to comparisons of complete bacterial genomes at the intra-species level. The strategy we developed for comparison allows us to define two types of regions in bacterial genomes: backbone segments (i.e., regions conserved in all compared strains) and variable segments (i.e., regions that are either specific to or variable in one of the aligned genomes). Definition of these segments at the nucleotide level allows precise comparative and evolutionary analyses of both coding and non-coding regions of bacterial genomes. Such work is easily performed using the MOSAIC Web interface, which allows browsing and graphical visualization of genome comparisons.ConclusionThe MOSAIC database now includes 493 pairwise comparisons and 35 multiple maximal comparisons representing 78 bacterial species. Genome conserved regions (backbones) and variable segments are presented in various formats for further analysis. A graphical interface allows visualization of aligned genomes and functional annotations. The MOSAIC database is available online at .

The SeqWord Genome Browser: an online tool for the identification and visualization of atypical regions of bacterial genomes through oligonucleotide usage

BackgroundData mining in large DNA sequences is a major challenge in microbial genomics and bioinformatics. Oligonucleotide usage (OU) patterns provide a wealth of information for large scale sequence analysis and visualization. The purpose of this research was to make OU statistical analysis available as a novel web-based tool for functional genomics and annotation. The tool is also available as a downloadable package.ResultsThe SeqWord Genome Browser (SWGB) was developed to visualize the natural compositional variation of DNA sequences. The applet is also used for identification of divergent genomic regions both in annotated sequences of bacterial chromosomes, plasmids, phages and viruses, and in raw DNA sequences prior to annotation by comparing local and global OU patterns. The applet allows fast and reliable identification of clusters of horizontally transferred genomic islands, large multi-domain genes and genes for ribosomal RNA. Within the majority of genomic fragments (also termed genomic core sequence), regions enriched with housekeeping genes, ribosomal proteins and the regions rich in pseudogenes or genetic vestiges may be contrasted.ConclusionThe SWGB applet presents a range of comprehensive OU statistical parameters calculated for a range of bacterial species, plasmids and phages. It is available on the Internet at .

DoriC: a database oforiCregions in bacterial genomes

Replication origins (oriCs) of bacterial genomes currently available in GenBank have been predicted by using a systematic method comprising the Z-curve analysis for nucleotide distribution asymmetry, DnaA box distribution, genes adjacent to candidate oriCs and phylogenetic relationships. These oriCs are organized into a MySQL database, DoriC, which provides extensive information and graphical views of the oriC regions. In addition, users can Blast a query sequence or even a whole genome against DoriC to find a homologous one. DoriC will be updated timely and the latest version is DoriC 1.8, in which oriCs of 425 genomes (468 chromosomes) are identified. DoriC can be accessed from http://tubic.tju.edu.cn/doric/. Supplementary data are available at http://tubic.tju.edu.cn/doric/supplementary.htm.