Unravelling the genomic secrets of bacterial fish pathogens: a roadmap to aquaculture sustainability.

In the field of aquaculture, bacterial pathogens pose significant challenges to fish health and production. Advancements in genomic technologies have revolutionized our understanding of bacterial fish pathogens and their interactions with their host species. This review explores the application of genomic approaches in the identification, classification, and characterization of bacterial fish pathogens. Through an extensive analysis of the literature, we have compiled valuable data on 79 bacterial fish pathogens spanning 13 different phyla, encompassing their whole genome sequences. By leveraging high-throughput sequencing techniques, researchers have gained valuable insights into the genomic makeup of these pathogens, enabling a deeper understanding of their virulence factors and mechanisms of host interaction. Furthermore, genomic approaches have facilitated the discovery of potential vaccine and drug targets, opening up new avenues for the development of effective interventions against fish pathogens. Additionally, the utilization of genomics in fish disease resistance and control in aquaculture has shown promising results, enabling the identification of genetic markers associated with disease resistance traits. This review highlights the significant contributions of genomics to the field of fish pathogen research and underscores its potential for improving disease management strategies and enhancing the sustainability of aquaculture practices.

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.

Combined In Silico and In Vitro Study to Reveal the Structural Insights and Nucleotide-Binding Ability of the Transcriptional Regulator PehR from the Phytopathogen Ralstonia solanacearum.

The transcriptional regulator PehR regulates the synthesis of the extracellular plant cell wall-degrading enzyme polygalacturonase, which is essential in the bacterial wilt of plants caused by one of the most devastating plant phytopathogens, Ralstonia solanacearum. The bacterium has a wide global distribution infecting many different plant species, resulting in massive agricultural and economic losses. Because the PehR molecular structure has not yet been determined and the structural consequences of PehR on ligand binding have not been thoroughly investigated, we have used an in silico approach combined with in vitro experiments for the first time to characterize the PehR regulator from a local isolate (Tezpur, Assam, India) of the phytopathogenic bacterium R. solanacearum F1C1. In this study, an in silico approach was employed to model the 3D structure of the PehR regulator, followed by the binding analysis of different ligands against this regulatory protein. Molecular docking studies suggest that ATP has the highest binding affinity for the PehR regulator. By using molecular dynamics (MD) simulation analysis, involving root-mean-square deviation, root-mean-square fluctuations, hydrogen bonding, radius of gyration, solvent-accessible surface area, and principal component analysis, it was possible to confirm the sudden conformational changes of the PehR regulator caused by the presence of ATP. We used an in vitro approach to further validate the formation of the PehR-ATP complex. In this approach, recombinant DNA technology was used to clone, express, and purify the gene encoding the PehR regulator from R. solanacearum F1C1. Purified PehR was used in ATP-binding experiments using fluorescence spectroscopy and Fourier transform infrared spectroscopy, the outcomes of which showed a potent binding to ATP. The putative PehR-ATP-binding analysis revealed the importance of the amino acids Lys190, Glu191, Arg192, Arg375, and Asp378 for the ATP-binding process, but further study is required to confirm this. It will be simpler to comprehend the catalytic mechanisms of a crucial PehR regulator process in R. solanacearum with the aid of the ATP-binding process hints provided by these structural biology applications.

DNA structural properties of DNA binding sites for 21 transcription factors in the mycobacterial genome.

Mycobacterium tuberculosis, the causative agent of tuberculosis, has evolved over time into a multidrug resistance strain that poses a serious global pandemic health threat. The ability to survive and remain dormant within the host macrophage relies on multiple transcription factors contributing to virulence. To date, very limited structural insights from crystallographic and NMR studies are available for TFs and TF-DNA binding events. Understanding the role of DNA structure in TF binding is critical to deciphering MTB pathogenicity and has yet to be resolved at the genome scale. In this work, we analyzed the compositional and conformational preference of 21 mycobacterial TFs, evident at their DNA binding sites, in local and global scales. Results suggest that most TFs prefer binding to genomic regions characterized by unique DNA structural signatures, namely, high electrostatic potential, narrow minor grooves, high propeller twist, helical twist, intrinsic curvature, and DNA rigidity compared to the flanking sequences. Additionally, preference for specific trinucleotide motifs, with clear periodic signals of tetranucleotide motifs, are observed in the vicinity of the TF-DNA interactions. Altogether, our study reports nuanced DNA shape and structural preferences of 21 TFs.