Analysis of multipartite bacterial genomes using alignment free and alignment-based pipelines.

Since the discovery of second chromosome in Rhodobacter sphaeroides 2.4.1 in 1989, multipartite genomes have been reported in over three hundred bacterial species under nine different phyla. This has shattered the unipartite (single chromosome) genome dogma in bacteria. Since then, many questions on various aspects of multipartite genomes in bacteria have been addressed. However, our understanding of how multipartite genomes emerge and evolve is still lacking. Importantly, the knowledge of genetic factors underlying the differences in multipartite and single-chromosome genomes is lacking. In this work, we have performed comparative evolutionary and functional genomics analyses to identify molecular factors that discriminate multipartite from unipartite bacteria, with the goal to decipher taxon-specific factors, and those that are prevalent across the taxa, underlying these traits. We assessed the roles of evolutionary mechanisms, specifically gene gain, in driving the divergence of bacteria with single and multiple chromosomes. In addition, we performed functional genomic analysis to garner support for our findings from comparative evolutionary analysis. We found genes such as those encoding conserved hypothetical proteins in Deinococcus radiodurans R1, and putative phage phi-C31 gp36 major capsid like and hypothetical proteins in Rhodobacter sphaeroides 2.4.1, which are located on accessory chromosomes in these bacteria but were not found in the inferred ancestral sequences, and on the primary chromosomes, as well as were not found in their closest relatives with single chromosome within the same clade. Our study shines a new light on the potential roles of the secondary chromosomes in helping bacteria with multipartite genomes to adapt to specialized environments or growth conditions.

Computational evaluation of efflux pump homologues and lignans as potent inhibitors against multidrug-resistant Salmonella typhi.

Typhoid fever, caused by Salmonella enterica serovar typhi, presents a substantial global health threat, particularly in regions with limited healthcare infrastructure. The rise of multidrug-resistant strains of S. typhi exacerbates this challenge, severely compromising conventional treatment efficacy due to over activity of efflux pumps. In our study, a comprehensive exploration of two fundamental aspects to combat MDR in S. typhi is carried out; i.e. employing advanced bioinformatics analyses and AlphaFold AI, We successfully identified and characterised a putative homologue, ABC-TPA, reminiscent of the P-glycoprotein (P-gp) known for its role in multidrug resistance in diverse pathogens. This discovery provides a critical foundation for understanding the potential mechanisms driving antibiotic resistance in S. typhi. Furthermore, employing computational methodologies, We meticulously assessed the potential of lignans, specifically Schisandrin A, B, and C, as promising Efflux Pump Inhibitors (EPIs) against the identified P-gp homologue in S. typhi. Noteworthy findings revealed robust binding interactions of Schisandrin A and B with the target protein, indicating substantial inhibitory capabilities. In contrast, Schisandrin C exhibited instability, showing varied effectiveness among the evaluated lignans. Pharmacokinetics and toxicity predictions underscored the favourable attributes of Schisandrin A, including prolonged action duration. Furthermore, high systemic stability and demanished toxicity profile of SA and SB present their therapeutic efficacy against MDR. This comprehensive investigation not only elucidates potential therapeutic strategies against MDR strains of S. typhi but also highlights the relevance of computational approaches in identifying and evaluating promising candidates. These findings lay a robust foundation for future empirical studies to address the formidable challenges antibiotic resistance poses in this clinically significant infectious diseases.

Leverage of Salvadora persica and Pulicaria undulata extracts in Escherichia coli-challenged broiler chickens.

Escherichia coli (E. coli) is a significant challenge in the poultry industry due to their related use of antimicrobial compounds and the drastic losses in production and livability. This study investigated the preventive impacts of dietary supplementation of Salvadora persica (SP) and/or Pulicaria undulata (PU) extracts on growth traits, biochemical and immune parameters, and related gene expression of E. coli-infected broilers. A total of 120 one-day-old Cobb broilers were used. The chicks were allocated into eight equal groups (3 replicates/ group; 5 chicks per each replicate) as follows: G1; control negative, G2; SP-treated, G3; PU-treated, G4; SP/PU-treated, G5; E. coli infected, G6; E. coli infected and SP-treated, G7; E. coli infected and PU-treated, G8; E. coli infected and SP/PU-treated groups. Results revealed significant improvement in average body weight (ABW), average weight gain (AWG) and feed conversion ratio (FCR) in broilers fed diets supplemented with SP and/or PU compared to control and E. coli infected groups. Moreover, significant (P < 0.05) reduction in ALT, AST, creatinine, and uric acid was reported in other treated groups compared to the single E. coli-infected broilers. On the contrary, a significant increase (P < 0.05) in serum immunoglobulin and protein concentration was also reported in treated groups when compared to E. coli-infected untreated group. In addition, feeding broilers with SP and/or PU significantly improved (P < 0.05) the relative weight of immune-related organs and gene expression of TLR-15, with subsequent down-regulation of IL-1ß and TNF-α mRNA transcripts. Supplementing broilers with dietary SP and/or PU could be promising in the prevention of E. coli infection via stimulating significant improvement of immune-related gene expression, immune-related organ weight, and down-regulation of inflammatory-related genes, with subsequent enhancement of the growth performance of broiler chickens.

Impact of packaging atmosphere, oregano essential oil, and storage temperature on cold-adapted Salmonella Enteritidis and Salmonella Typhimurium on ready-to-eat smoked turkey.

The hazard of diseases created by S. Enteritidis and S. Typhimurium is relatively high in turkey meat products. Combinations of preservation methods are utilized in many strategies, such as mild heat with decreased water activity, a changed atmosphere, refrigerated storage, and decreased heat treatment with some acidification. Within the domain of ready-to-eat food technology, a range of preservation methods are typically utilized to enhance shelf life, such as applying mild heat in tandem with reduced water activity, employing modified atmosphere packaging, utilizing refrigerated storage, and utilizing reduced heat treatment combined with acidification. This investigation aimed to determine how S. Enteritidis and S. Typhimurium grew when sliced ready-to-eat smoked turkey (RTE-SM) was stored at 0, 5, 10, and 15°C for various periods. The study also examined the effects of modified atmosphere packaging (MAP) (40% CO2 and 60% N2) and VP on these growth patterns. Total viable count (TVC), lactic acid bacteria (LAB), pH, and redox potential levels were determined. The control experiment on RTE-SM showed no Salmonella growth within 30 d of storage at any temperature. This indicated that the RTE-SM in use did not initially contain S. Typhimurium and S. Enteritidis. Results indicated that the storage of RTE-SM using a combination of VP, MAP, and MAPEO with storage at 0 and 5°C did not allow for the pathogen to grow throughout storage. In comparison, at 10 and 15°C after one day, which allowed for minor growth (0.17-0.5 log CFU/g)? In contrast, at 0 and 5°C, Salmonella survives until the end of storage (173 d). However, the combination of MAPEO with the same storage temperatures achieved the elimination of the pathogen in the meat after 80 d. The combination of both packaging systems with high temperatures (10 or 15°C) allowed for the multiplication and growth of the bacterium through the product's shelf life of more than 1 log CFU/g. Thus, a combination of MAP or MAPEO with low storage temperatures (0 or 5°C) inhibited the growth of the pathogen.

Using Machine Learning to Predict Genes Underlying Differentiation of Multipartite and Unipartite Traits in Bacteria.

Since the discovery of the second chromosome in the Rhodobacter sphaeroides 2.4.1 by Suwanto and Kaplan in 1989 and the revelation of gene sequences, multipartite genomes have been reported in over three hundred bacterial species under nine different phyla. This phenomenon shattered the dogma of a unipartite genome (a single circular chromosome) in bacteria. Recently, Artificial Intelligence (AI), machine learning (ML), and Deep Learning (DL) have emerged as powerful tools in the investigation of big data in a plethora of disciplines to decipher complex patterns in these data, including the large-scale analysis and interpretation of genomic data. An important inquiry in bacteriology pertains to the genetic factors that underlie the structural evolution of multipartite and unipartite bacterial species. Towards this goal, here we have attempted to leverage machine learning as a means to identify the genetic factors that underlie the differentiation of, in general, bacteria with multipartite genomes and bacteria with unipartite genomes. In this study, deploying ML algorithms yielded two gene lists of interest: one that contains 46 discriminatory genes obtained following an assessment on all gene sets, and another that contains 35 discriminatory genes obtained based on an investigation of genes that are differentially present (or absent) in the genomes of the multipartite bacteria and their respective close relatives. Our study revealed a small pool of genes that discriminate bacteria with multipartite genomes and their close relatives with single-chromosome genomes. Machine learning thus aided in uncovering the genetic factors that underlie the differentiation of bacterial multipartite and unipartite traits.