Comparative genomic analysis of ovine and other host associated isolates of Staphylococcus aureus exhibit the important role of mobile genetic elements and virulence factors in host adaptation.

Staphylococcus aureus is the main etiological agent of mastitis in small ruminants worldwide. This disease has a difficult cure and possible relapse, leading to significant economic losses in production, milk quality and livestock. This study performed comparative genomic analyses between 73 S. aureus genomes from different hosts (human, bovine, pig and others). This work isolated and sequenced 12 of these genomes from ovine. This study contributes to the knowledge of genomic specialization and the role of specific genes in establishing infection in ovine mastitis-associated S. aureus. The genomes of S. aureus isolated from sheep maintained a higher representation when grouped with clonal complexes 130 and 133. The genomes showed high genetic similarity, the species pan-genome consisting of 4200 genes (central = 2008, accessory = 1559 and unique = 634). Among these, 277 unique genes were related to the genomes isolated from sheep, with 39.6 % as hypothetical proteins, 6.4 % as phages, 6.4 % as toxins, 2.9 % as transporters, and 44.7 % as related to other proteins. Furthermore, at the pathogen level, they showed 80 genes associated with virulence factors and 19 with antibiotic resistance shared in almost all isolates. Although S. aureus isolated from ovine showed susceptibility to antimicrobials in vitro, ten genes were predicted to be associated with antibiotic inactivation and efflux pump, suggesting resistance to gentamicin and penicillin. This work may contribute to identifying genes acquired by horizontal transfer and their role in host adaptation, virulence, bacterial resistance, and characterization of strains affecting ovine.

The Space-Exposed Kombucha Microbial Community Member Komagataeibacter oboediens Showed Only Minor Changes in Its Genome After Reactivation on Earth.

Komagataeibacter is the dominant taxon and cellulose-producing bacteria in the Kombucha Microbial Community (KMC). This is the first study to isolate the K. oboediens genome from a reactivated space-exposed KMC sample and comprehensively characterize it. The space-exposed genome was compared with the Earth-based reference genome to understand the genome stability of K. oboediens under extraterrestrial conditions during a long time. Our results suggest that the genomes of K. oboediens IMBG180 (ground sample) and K. oboediens IMBG185 (space-exposed) are remarkably similar in topology, genomic islands, transposases, prion-like proteins, and number of plasmids and CRISPR-Cas cassettes. Nonetheless, there was a difference in the length of plasmids and the location of cas genes. A small difference was observed in the number of protein coding genes. Despite these differences, they do not affect any genetic metabolic profile of the cellulose synthesis, nitrogen-fixation, hopanoid lipids biosynthesis, and stress-related pathways. Minor changes are only observed in central carbohydrate and energy metabolism pathways gene numbers or sequence completeness. Altogether, these findings suggest that K. oboediens maintains its genome stability and functionality in KMC exposed to the space environment most probably due to the protective role of the KMC biofilm. Furthermore, due to its unaffected metabolic pathways, this bacterial species may also retain some promising potential for space applications.

Co-Expression Networks for Causal Gene Identification Based on RNA-Seq Data of Corynebacterium pseudotuberculosis.

Corynebacterium pseudotuberculosis is a Gram-positive bacterium that causes caseous lymphadenitis, a disease that predominantly affects sheep, goat, cattle, buffalo, and horses, but has also been recognized in other animals. This bacterium generates a severe economic impact on countries producing meat. Gene expression studies using RNA-Seq are one of the most commonly used techniques to perform transcriptional experiments. Computational analysis of such data through reverse-engineering algorithms leads to a better understanding of the genome-wide complexity of gene interactomes, enabling the identification of genes having the most significant functions inferred by the activated stress response pathways. In this study, we identified the influential or causal genes from four RNA-Seq datasets from different stress conditions (high iron, low iron, acid, osmosis, and PH) in C. pseudotuberculosis, using a consensus-based network inference algorithm called miRsigand next identified the causal genes in the network using the miRinfluence tool, which is based on the influence diffusion model. We found that over 50% of the genes identified as influential had some essential cellular functions in the genomes. In the strains analyzed, most of the causal genes had crucial roles or participated in processes associated with the response to extracellular stresses, pathogenicity, membrane components, and essential genes. This research brings new insight into the understanding of virulence and infection by C. pseudotuberculosis.