A Comparative Analysis of the Core Proteomes within and among the Bacillus subtilis and Bacillus cereus Evolutionary Groups Reveals the Patterns of Lineage- and Species-Specific Adaptations

Bacillus-based biocontrol beyond chemical control in central Africa: the challenge of turning myth into reality

Agricultural productivity in the Great Lakes Countries of Central Africa, including Burundi, Rwanda, and the Democratic Republic of Congo, is affected by a wide range of diseases and pests which are mainly controlled by chemical pesticides. However, more than 30% of the pesticides used in the region are banned in European Union due to their high toxicity. Globally available safe and eco-friendly biological alternatives to chemicals are virtually non-existent in the region. Bacillus PGPR-based biocontrol products are the most dominant in the market and have proven their efficacy in controlling major plant diseases reported in the region. With this review, we present the current situation of disease and pest management and urge the need to utilize Bacillus-based control as a possible sustainable alternative to chemical pesticides. A repertoire of strains from the Bacillus subtilis group that have shown great potential to antagonize local pathogens is provided, and efforts to promote their use, as well as the search for indigenous and more adapted Bacillus strains to local agro-ecological conditions, should be undertaken to make sustainable agriculture a reality in the region.

pyPGCF: A Python Software for Phylogenomic Analysis, Species Demarcation, Identification of Core, and Fingerprint Proteins of Bacterial Genomes That Are Important for Plants

This computational protocol describes how to use pyPGCF, a python software package that runs in the linux environment, in order to analyze bacterial genomes and perform: (i) phylogenomic analysis, (ii) species demarcation, (iii) identification of the core proteins of a bacterial genus and its individual species, (iv) identification of species-specific fingerprint proteins that are found in all strains of a species and, at the same time, are absent from all other species of the genus, (v) functional annotation of the core and fingerprint proteins with eggNOG, and (vi) identification of secondary metabolite biosynthetic gene clusters (smBGCs) with antiSMASH. This software has already been implemented to analyze bacterial genera and species that are important for plants (e.g., Pseudomonas, Bacillus, Streptomyces). In addition, we provide a test dataset and example commands showing how to analyze 165 genomes from 55 species of the genus Bacillus. The main advantages of pyPGCF are that: (i) it uses adjustable orthology cut-offs, (ii) it identifies species-specific fingerprints, and (iii) its computational cost scales linearly with the number of genomes being analyzed. Therefore, pyPGCF is able to deal with a very large number of bacterial genomes, in reasonable timescales, using widely available levels of computing power.

Comprehensive genomic analysis of Bacillus paralicheniformis strain BP9, pan-genomic and genetic basis of biocontrol mechanism

Many Bacillus species are essential antibacterial agents, but their antibiosis potential still needs to be elucidated to its full extent. Here, we isolated a soil bacterium, BP9, which has significant antibiosis activity against fungal and bacterial pathogens. BP9 improved the growth of wheat seedlings via active colonization and demonstrated effective biofilm and swarming activity. BP9 sequenced genome contains 4282 genes with a mean G-C content of 45.94% of the whole genome. A single copy concatenated 802 core genes of 28 genomes, and their calculated average nucleotide identity (ANI) discriminated the strain BP9 from Bacillus licheniformis and classified it as Bacillus paralicheniformis. Furthermore, a comparative pan-genome analysis of 40 B. paralicheniformis strains suggested that the genetic repertoire of BP9 belongs to open-type genome species. A comparative analysis of a pan-genome dataset using the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Cluster of Orthologous Gene groups (COG) revealed the diversity of secondary metabolic pathways, where BP9 distinguishes itself by exhibiting a greater prevalence of loci associated with the metabolism and transportation of organic and inorganic substances, carbohydrate and amino acid for effective inhabitation in diverse environments. The primary secondary metabolites and their genes involved in synthesizing bacillibactin, fencing, bacitracin, and lantibiotics were identified as acquired through a recent Horizontal gene transfer (HGT) event, which contributes to a significant part of the strain`s antimicrobial potential. Finally, we report some genes essential for plant-host interaction identified in BP9, which reduce spore germination and virulence of multiple fungal and bacterial species. The effective colonization, diverse predicted metabolic pathways and secondary metabolites (antibiotics) suggest testing the suitability of strain BP9 as a potential bio-preparation in agricultural fields.

Leveraging Scheme for Cross-Study Microbiome Machine Learning Prediction and Feature Evaluations

The microbiota has proved to be one of the critical factors for many diseases, and researchers have been using microbiome data for disease prediction. However, models trained on one independent microbiome study may not be easily applicable to other independent studies due to the high level of variability in microbiome data. In this study, we developed a method for improving the generalizability and interpretability of machine learning models for predicting three different diseases (colorectal cancer, Crohn's disease, and immunotherapy response) using nine independent microbiome datasets. Our method involves combining a smaller dataset with a larger dataset, and we found that using at least 25% of the target samples in the source data resulted in improved model performance. We determined random forest as our top model and employed feature selection to identify common and important taxa for disease prediction across the different studies. Our results suggest that this leveraging scheme is a promising approach for improving the accuracy and interpretability of machine learning models for predicting diseases based on microbiome data.

Enzyme Profiling and Identification of Endophytic and Rhizospheric Bacteria Isolated from Arthrocnemum macrostachyum

Endophytic and rhizospheric bacteria isolated from halophytic plants support their host to survive in hyper-saline soil. These bacteria are also known to produce various enzymes with potential industrial applications. In this study, the endophytic and rhizospheric bacteria were isolated from Arthrocnemum macrostachyum collected from Karachi, Pakistan, and their ability to produce various extracellular enzymes was assessed using commercial and natural substrates. In total, 11 bacterial strains were isolated (four endophytic; seven rhizospheric). Bacillus was found to be the most abundant genus (73%), followed by Glutamicibacter (27%). The isolates including Glutamicibacter endophyticus and Bacillus licheniformis are reported for the first time from A. macrostachyum. All of the isolates were capable of producing at least two of the five industrially important hydrolytic enzymes tested, i.e., xylanase, cellulase, amylase, pectinase, and lipase. Lipase production was found to be highest among the isolates, i.e., up to 18 IU mL⁻¹. Although most of the isolates could grow at a wide range of temperatures (4–55 °C), pH (1–11), and salt concentrations (2–12%), under extreme conditions, very little growth was observed and the optimal growth was recorded between 2% and 6% NaCl, 25 and 45 °C, and 7 and 9 pH. Our results suggest that these isolates could be potential producers of enzymes with several biotechnological applications.