The biodiversity of microorganisms is maintained by intricate nets of interactions between competing species. Impaired functionality of human microbiomes correlates with their reduced biodiversity originating from aseptic environmental conditions and antibiotic use. Microbiomes of wild animals are free of these selective pressures. Microbiota provides a protecting shield from invasion by pathogens in the wild, outcompeting their growth in specific ecological niches. We applied ultrahigh-throughput microfluidic technologies for functional profiling of microbiomes of wild animals, including the skin beetle, Siberian lynx, common raccoon dog, and East Siberian brown bear. Single-cell screening of the most efficient killers of the common human pathogen Staphylococcus aureus resulted in repeated isolation of Bacillus pumilus strains. While isolated strains had different phenotypes, all of them displayed a similar set of biosynthetic gene clusters (BGCs) encoding antibiotic amicoumacin, siderophore bacillibactin, and putative analogs of antimicrobials including bacilysin, surfactin, desferrioxamine, and class IId cyclical bacteriocin. Amicoumacin A (Ami) was identified as a major antibacterial metabolite of these strains mediating their antagonistic activity. Genome mining indicates that Ami BGCs with this architecture subdivide into three distinct families, characteristic of the B. pumilus, B. subtilis, and Paenibacillus species. While Ami itself displays mediocre activity against the majority of Gram-negative bacteria, isolated B. pumilus strains efficiently inhibit the growth of both Gram-positive S. aureus and Gram-negative E. coli in coculture. We believe that the expanded antagonistic activity spectrum of Ami-producing B. pumilus can be attributed to the metabolomic profile predetermined by their biosynthetic fingerprint. Ultrahigh-throughput isolation of natural probiotic strains from wild animal microbiomes, as well as their metabolic reprogramming, opens up a new avenue for pathogen control and microbiome remodeling in the food industry, agriculture, and healthcare.

The cultivation of Panax ginseng C.A. Meyer, a valuable medicinal plant, presents a number of challenges due to its physiology and life cycle. The composition of the soil and the microbiome living in it are important for plant growth and root quality. Modern analytical methods were used to identify differences in the rhizosphere soils of plants in the forest and in the plots. Microbiological and molecular genetic methods were used to isolate and identify bacterial isolates from these soils, allowing for the establishment of a working collection of potentially useful bacterial strains. Increases in soil pH in the plots and changes in the amount of macronutrients partially explained the changes in the activity of the forest and plot isolates and the composition of the cultivated strains. The cultivated strains belonged to the rhizosphere-dominant phyla Pseudomonadota, Bacillota, and Actinomycetota of the main functional groups of soil potassium, phosphorus, and nitrogen transformations. The ratio of bacteria functional groups was comparable in the forest and in the plots. The most common phylum of cultured microorganisms was Bacillota, while the main differences were observed in the functional group of potassium-solubilizing bacteria belonging to the phyla Pseudomonadota.

A list of selected phenomena characterizing living systems on the main levels of the manifestation of life is considered. As a result of directed interpretation of these phenomena, life (in becomming of earthly reality) appears as an informational physico-mathematical process that solves the problem of optimal relations of its variability and heredity in a changing environment. Practical solutions found by living systems in the course of evolution can be used and are used in applied mathematics as approaches of working with information. Genetic and quantum algorithms, neural networks, annealing are important milestones of this use. On this way, mathematics discovers an unexpected efficiency in working with big data arrays, which represent the main content of modern biology. DOI 10.1134/S1061920822040082