The increase in microbial sequenced genomes from pure cultures and metagenomic samples reflects the current attainability of whole-genome and shotgun sequencing methods. However, software for genome visualization still lacks automation, integration of different analyses, and customizable options for non-experienced users. In this study, we introduce GenoVi, a Python command-line tool able to create custom circular genome representations for the analysis and visualization of microbial genomes and sequence elements. It is designed to work with complete or draft genomes, featuring customizable options including 25 different built-in color palettes (including 5 color-blind safe palettes), text formatting options, and automatic scaling for complete genomes or sequence elements with more than one replicon/sequence. Using a Genbank format file as the input file or multiple files within a directory, GenoVi (i) visualizes genomic features from the GenBank annotation file, (ii) integrates a Cluster of Orthologs Group (COG) categories analysis using DeepNOG, (iii) automatically scales the visualization of each replicon of complete genomes or multiple sequence elements, (iv) and generates COG histograms, COG frequency heatmaps and output tables including general stats of each replicon or contig processed. GenoVi’s potential was assessed by analyzing single and multiple genomes of Bacteria and Archaea. Paraburkholderia genomes were analyzed to obtain a fast classification of replicons in large multipartite genomes. GenoVi works as an easy-to-use command-line tool and provides customizable options to automatically generate genomic maps for scientific publications, educational resources, and outreach activities. GenoVi is freely available and can be downloaded from https://github.com/robotoD/GenoVi.

Burkholderia sensu lato (s.l.) species have a versatile metabolism. The aims of this review are the genomic reconstruction of the metabolic pathways involved in the synthesis of polyhydroxyalkanoates (PHAs) by Burkholderia s.l. genera, and the characterization of the PHA synthases and the pha genes organization. The reports of the PHA synthesis from different substrates by Burkholderia s.l. strains were reviewed. Genome-guided metabolic reconstruction involving the conversion of sugars and fatty acids into PHAs by 37 Burkholderia s.l. species was performed. Sugars are metabolized via the Entner–Doudoroff (ED), pentose-phosphate (PP), and lower Embden–Meyerhoff–Parnas (EMP) pathways, which produce reducing power through NAD(P)H synthesis and PHA precursors. Fatty acid substrates are metabolized via β-oxidation and de novo synthesis of fatty acids into PHAs. The analysis of 194 Burkholderia s.l. genomes revealed that all strains have the phaC, phaA, and phaB genes for PHA synthesis, wherein the phaC gene is generally present in ≥2 copies. PHA synthases were classified into four phylogenetic groups belonging to class I II and III PHA synthases and one outlier group. The reconstruction of PHAs synthesis revealed a high level of gene redundancy probably reflecting complex regulatory layers that provide fine tuning according to diverse substrates and physiological conditions.

Background: Aerobic metabolism generates reactive oxygen species that may cause critical harm to the cell. The aim of this study is the characterization of the stress responses in the model aromatic-degrading bacterium Paraburkholderia xenovorans LB400 to the oxidizing agents paraquat and H₂O₂. Methods: Antioxidant genes were identified by bioinformatic methods in the genome of P. xenovorans LB400, and the phylogeny of its OxyR and SoxR transcriptional regulators were studied. Functionality of the transcriptional regulators from strain LB400 was assessed by complementation with LB400 SoxR of null mutant P. aeruginosa ΔsoxR, and the construction of P. xenovorans pIZoxyR that overexpresses OxyR. The effects of oxidizing agents on P. xenovorans were studied measuring bacterial susceptibility, survival and ROS formation after exposure to paraquat and H₂O₂. The effects of these oxidants on gene expression (qRT-PCR) and the proteome (LC–MS/MS) were quantified. Results: P. xenovorans LB400 possesses a wide repertoire of genes for the antioxidant defense including the oxyR, ahpC, ahpF, kat, trxB, dpsA and gorA genes, whose orthologous genes are regulated by the transcriptional regulator OxyR in E. coli. The LB400 genome also harbors the soxR, fumC, acnA, sodB, fpr and fldX genes, whose orthologous genes are regulated by the transcriptional regulator SoxR in E. coli. The functionality of the LB400 soxR gene was confirmed by complementation of null mutant P. aeruginosa ΔsoxR. Growth, susceptibility, and ROS formation assays revealed that LB400 cells were more susceptible to paraquat than H₂O₂. Transcriptional analyses indicated the upregulation of the oxyR, ahpC1, katE and ohrB genes in LB400 cells after exposure to H₂O₂, whereas the oxyR, fumC, ahpC1, sodB1 and ohrB genes were induced in presence of paraquat. Proteome analysis revealed that paraquat induced the oxidative stress response proteins AhpCF and DpsA, the universal stress protein UspA and the RNA chaperone CspA. Both oxidizing agents induced the Ohr protein, which is involved in organic peroxide resistance. Notably, the overexpression of the LB400 oxyR gene in P. xenovorans significantly decreased the ROS formation and the susceptibility to paraquat, suggesting a broad OxyR-regulated antioxidant response. Conclusions: This study showed that P. xenovorans LB400 possess a broad range oxidative stress response, which explain the high resistance of this strain to the oxidizing compounds paraquat and H₂O₂.

Alcaligenes aquatilis QD168 is a marine, aromatic hydrocarbon-degrading bacterium, isolated from an oil-polluted sediment of Quintero Bay, an industrial-coastal zone that has been chronically impacted by diverse pollutants. The aims of this study were to characterize the phylogenomic positions of Alcaligenes spp. and to characterize the genetic determinants and the physiological response of A. aquatilis QD168 to model environmental stressors (benzene, oxidizing agents, and salt). Phylogenomic analyses, using 35 housekeeping genes, clustered A. aquatilis QD168 with four other strains of Alcaligenes spp. (A. aquatilis BU33N, A. faecalis JQ135, A. faecalis UBA3227, and A. faecalis UBA7629). Genomic sequence analyses of A. aquatilis QD168 with 25 Alcaligenes spp., using ANIb, indicated that A. aquatilis BU33N is the closest related strain, with 96.8% ANIb similarity. Strain QD168 harbors 95 genes encoding proteins of seven central catabolic pathways, as well as sixteen peripheral catabolic pathways/reactions for aromatic compounds. A. aquatilis QD168 was able to grow on 3-hydroxybenzoate, 4-hydroxybenzoate, benzoate, benzene, 3-hydroxycinnamate, cinnamate, anthranilate, benzamide, 4-aminobenzoate, nicotinate, toluene, biphenyl and tryptophan, as sole carbon or nitrogen source. Benzene degradation was further analyzed by growth, metabolite identification and gene expression analyses. Benzene strongly induced the expression of the genes encoding phenol hydroxylase (dmpP) and catechol 1,2-dioxygenase (catA). Additionally, 30 genes encoding transcriptional regulators, scavenging enzymes, oxidative damage repair systems and isozymes involved in oxidative stress response were identified. Oxidative stress response of strain QD168 to hydrogen peroxide and paraquat was characterized, demonstrating that A. aquatilis QD168 is notably more resistant to paraquat than to H₂O₂. Genetic determinants (47 genes) for osmoprotective responses were identified, correlating with observed high halotolerance by strain QD168. The physiological adaptation of A. aquatilis QD168 to environmental stressors such as pollutants, oxidative stress and salinity may be exploited for bioremediation of oil-polluted saline sites.

Heavy metal co-contamination in crude oil-polluted environments may inhibit microbial bioremediation of hydrocarbons. The model heavy metal-resistant bacterium Cupriavidus metallidurans CH34 possesses cadmium and mercury resistance, as well as genes related to the catabolism of hazardous BTEX aromatic hydrocarbons. The aims of this study were to analyze the aromatic catabolic potential of C. metallidurans CH34 and to determine the functionality of the predicted benzene catabolic pathway and the influence of cadmium and mercury on benzene degradation. Three chromosome-encoded bacterial multicomponent monooxygenases (BMMs) are involved in benzene catabolic pathways. Growth assessment, intermediates identification, and gene expression analysis indicate the functionality of the benzene catabolic pathway. Strain CH34 degraded benzene via phenol and 2-hydroxymuconic semialdehyde. Transcriptional analyses revealed a transition from the expression of catechol 2,3-dioxygenase (tomB) in the early exponential phase to catechol 1,2-dioxygenase (catA1 and catA2) in the late exponential phase. The minimum inhibitory concentration to Hg (II) and Cd (II) was significantly lower in the presence of benzene, demonstrating the effect of co-contamination on bacterial growth. Notably, this study showed that C. metallidurans CH34 degraded benzene in the presence of Hg (II) or Cd (II).

Promoter identification is a fundamental step in understanding bacterial gene regulation mechanisms. However, accurate and fast classification of bacterial promoters continues to be challenging. New methods based on deep convolutional networks have been applied to identify and classify bacterial promoters recognized by sigma ($\sigma$) factors and RNA polymerase subunits which increase affinity to specific DNA sequences to modulate transcription and respond to nutritional or environmental changes. This work presents a new multiclass promoter prediction model by using convolutional neural networks (CNNs), denoted as PromoterLCNN, which classifies Escherichia coli promoters into subclasses ${\sigma }^{70}$, ${\sigma }^{24}$, ${\sigma }^{32}$, ${\sigma }^{38}$, ${\sigma }^{28}$, and ${\sigma }^{54}$. We present a light, fast, and simple two-stage multiclass CNN architecture for promoter identification and classification. Training and testing were performed on a benchmark dataset, part of RegulonDB. Comparative performance of PromoterLCNN against other CNN-based classifiers using four parameters (Acc, Sn, Sp, MCC) resulted in similar or better performance than those that commonly use cascade architecture, reducing time by approximately 30–90% for training, prediction, and hyperparameter optimization without compromising classification quality.