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The Bacillus cereus sensu stricto (s.s.) species comprises strains of biovar Thuringiensis (Bt) known for their bioinsecticidal activity, as well as strains with foodborne pathogenic potential. Bt strains are identified (i) based on the production of insecticidal crystal proteins, also known as Bt toxins, or (ii) based on the presence of cry, cyt, and vip genes, which encode Bt toxins. Multiple bioinformatics tools have been developed for the detection of crystal protein-encoding genes based on whole-genome sequencing (WGS) data. However, the performance of these tools is yet to be evaluated using phenotypic data. Thus, the goal of this study was to assess the performance of four bioinformatics tools for the detection of crystal protein-encoding genes. The accuracy of sequence-based identification of Bt was determined in reference to phenotypic microscope-based screening for the production of crystal proteins. A total of 58 diverse B. cereus sensu lato strains isolated from clinical, food, environmental, and commercial biopesticide products underwent WGS. Isolates were examined for crystal protein production using phase contrast microscopy. Crystal protein-encoding genes were detected using BtToxin_Digger, BTyper3, IDOPS (identification of pesticidal sequences), and Cry_processor. Out of 58 isolates, the phenotypic production of crystal proteins was confirmed for 18 isolates. Specificity and sensitivity of Bt identification based on sequences were 0.85 and 0.94 for BtToxin_Digger, 0.97 and 0.89 for BTyper3, 0.95 and 0.94 for IDOPS, and 0.88 and 1.00 for Cry_processor, respectively. Cry_processor predicted crystal protein production with the highest specificity, and BtToxin_Digger and IDOPS predicted crystal protein production with the highest sensitivity. Three out of four tested bioinformatics tools performed well overall, with IDOPS achieving high sensitivity and specificity (>0.90).IMPORTANCEStrains of Bacillus cereus sensu stricto (s.s.) biovar Thuringiensis

Developments in computational omics technologies have provided new means to access the hidden diversity of natural products, unearthing new potential for drug discovery. In parallel, artificial intelligence approaches such as machine learning have led to exciting developments in the computational drug design field, facilitating biological activity prediction and de novo drug design for molecular targets of interest. Here, we describe current and future synergies between these developments to effectively identify drug candidates from the plethora of molecules produced by nature. We also discuss how to address key challenges in realizing the potential of these synergies, such as the need for high-quality datasets to train deep learning algorithms and appropriate strategies for algorithm validation.|Advances in computational omics technologies are enabling access to the hidden diversity of natural products, and artificial intelligence approaches are facilitating key steps in harnessing the therapeutic potential of such compounds, including biological activity prediction. This article discusses synergies between these fields to effectively identify drug candidates from the plethora of molecules produced by nature, and how to address the challenges in realizing the potential of these synergies.

Introduction: Macrococcus species have been isolated from a range of mammals and mammal-derived food products. While they are largely considered to be animal commensals, Macrococcus spp. can be opportunistic pathogens in both veterinary and human clinical settings. This study aimed to provide insight into the evolution, population structure, and functional potential of the Macrococcus genus, with an emphasis on antimicrobial resistance (AMR) and virulence potential. Methods: All high-quality, publicly available Macrococcus genomes (n = 104, accessed 27 August 2022), plus six South African genomes sequenced here (two strains from bovine clinical mastitis cases and four strains from beef products), underwent taxonomic assignment (using four different approaches), AMR determinant detection (via AMRFinderPlus), and virulence factor detection (using DIAMOND and the core Virulence Factor Database). Results: Overall, the 110 Macrococcus genomes were of animal commensal, veterinary clinical, food-associated (including food spoilage), and environmental origins; five genomes (4.5%) originated from human clinical cases. Notably, none of the taxonomic assignment methods produced identical results, highlighting the potential for Macrococcus species misidentifications. The most common predicted antimicrobial classes associated with AMR determinants identified across Macrococcus included macrolides, beta-lactams, and aminoglycosides (n = 81, 61, and 44 of 110 genomes; 73.6, 55.5, and 40.0%, respectively). Genes showing homology to Staphylococcus aureus exoenzyme aureolysin were detected across multiple species (using 90% coverage, n = 40 and 77 genomes harboring aureolysin-like genes at 60 and 40% amino acid [AA] identity, respectively). S. aureus Panton-Valentine leucocidin toxin-associated lukF-PV and lukS-PV homologs were identified in eight M. canis genomes (≥40% AA identity, >85% coverage). Using a method that delineates populations using recent gene flow (PopCOGenT), two

Salmonella enterica subsp. enterica serotype Typhimurium definitive type 104 (DT104) can infect both humans and animals and is often multidrug-resistant (MDR). Previous studies have indicated that, unlike most S. Typhimurium, the overwhelming majority of DT104 strains produce pertussis-like toxin ArtAB via prophage-encoded genes artAB. However, DT104 that lack artAB have been described on occasion. Here, we identify an MDR DT104 complex lineage circulating among humans and cattle in the USA, which lacks artAB (i.e. the ‘U.S. artAB-negative major clade’; n=42 genomes). Unlike most other bovine- and human-associated DT104 complex strains from the USA (n=230 total genomes), which harbour artAB on prophage Gifsy-1 (n=177), members of the U.S. artAB-negative major clade lack Gifsy-1, as well as anti-inflammatory effector gogB. The U.S. artAB-negative major clade encompasses human- and cattle-associated strains isolated from ≥11 USA states over a 20-year period. The clade was predicted to have lost artAB, Gifsy-1 and gogB circa 1985–1987 (95 % highest posterior density interval 1979.0–1992.1). When compared to DT104 genomes from other regions of the world (n=752 total genomes), several additional, sporadic artAB, Gifsy-1 and/or gogB loss events among clades encompassing five or fewer genomes were observed. Using phenotypic assays that simulate conditions encountered during human and/or bovine digestion, members of the U.S. artAB-negative major clade did not differ from closely related Gifsy-1/artAB/gogB-harbouring U.S. DT104 complex strains (ANOVA raw P>0.05); thus, future research is needed to elucidate the roles that artAB, gogB and Gifsy-1 play in DT104 virulence in humans and animals.

Mobilized colistin resistance genes (mcr) may confer resistance to the last-resort antimicrobial colistin and can often be transmitted horizontally. mcr encode phosphoethanolamine transferases (PET), which are closely related to chromosomally encoded, intrinsic lipid modification PET (i-PET; e.g., EptA, EptB, CptA). To gain insight into the evolution of mcr within the context of i-PET, we identified 69,814 MCR-like proteins present across 256 bacterial genera (obtained by querying known MCR family representatives against the National Center for Biotechnology Information [NCBI] non-redundant protein database via protein BLAST). We subsequently identified 125 putative novel mcr-like genes, which were located on the same contig as (i) ≥1 plasmid replicon and (ii) ≥1 additional antimicrobial resistance gene (obtained by querying the PlasmidFinder database and NCBI’s National Database of Antibiotic Resistant Organisms, respectively, via nucleotide BLAST). At 80% amino acid identity, these putative novel MCR-like proteins formed 13 clusters, five of which represented putative novel MCR families. Sequence similarity and a maximum likelihood phylogeny of mcr, putative novel mcr-like, and ipet genes indicated that sequence similarity was insufficient to discriminate mcr from ipet genes. A mixed-effect model of evolution (MEME) indicated that site- and branch-specific positive selection played a role in the evolution of alleles within the mcr-2 and mcr-9 families. MEME suggested that positive selection played a role in the diversification of several residues in structurally important regions, including (i) a bridging region that connects the membrane-bound and catalytic periplasmic domains, and (ii) a periplasmic loop juxtaposing the substrate entry tunnel. Moreover, eptA and mcr were localized within different genomic contexts. Canonical eptA genes were typically chromosomally encoded in an operon with a two-component regulatory system or adjacent to a TetR-type regulator. C

Salmonella enterica subsp. enterica (S.) serovar Cerro is rarely isolated from human clinical cases of salmonellosis but represents the most common serovar isolated from cattle without clinical signs of illness in the United States. In this study, using a large, diverse set of 316 isolates, we utilized genomic methods to further elucidate the evolutionary history of S. Cerro and to identify genomic features associated with its apparent virulence attenuation in humans. Phylogenetic analyses showed that within this polyphyletic serovar, 98.4% of isolates (311/316) represent a monophyletic clade within section Typhi and the remaining 1.6% of isolates (5/316) form a monophyletic clade within subspecies enterica Clade A1. Of the section Typhi S. Cerro isolates, 93.2% of isolates (290/311) clustered into a large clonal clade comprised of predominantly sequence type (ST) 367 cattle and environmental isolates, while the remaining 6.8% of isolates (21/311), primarily from human clinical sources, clustered outside of this clonal clade. A tip-dated phylogeny of S. Cerro ST367 identified two major clades (I and II), one of which overwhelmingly consisted of cattle isolates that share a most recent common ancestor that existed circa 1975. Gene presence/absence and rarefaction curve analyses suggested that the pangenome of section Typhi S. Cerro is open, potentially reflecting the gain/loss of prophage; human isolates contained the most open pangenome, while cattle isolates had the least open pangenome. Hypothetically disrupted coding sequences (HDCs) displayed clade-specific losses of intact speC and sopA virulence genes within the large clonal S. Cerro clade, while loss of intact vgrG, araH, and vapC occurred in all section Typhi S. Cerro isolates. Further phenotypic analysis suggested that the presence of a premature stop codon in speC does not abolish ornithine decarboxylase activity in S. Cerro, likely due to the activity of the second ornithine decarboxylase encoded by speF

Natural microbial communities are phylogenetically and metabolically diverse. In addition to underexplored organismal groups1, this diversity encompasses a rich discovery potential for ecologically and biotechnologically relevant enzymes and biochemical compounds2,3. However, studying this diversity to identify genomic pathways for the synthesis of such compounds4 and assigning them to their respective hosts remains challenging. The biosynthetic potential of microorganisms in the open ocean remains largely uncharted owing to limitations in the analysis of genome-resolved data at the global scale. Here we investigated the diversity and novelty of biosynthetic gene clusters in the ocean by integrating around 10,000 microbial genomes from cultivated and single cells with more than 25,000 newly reconstructed draft genomes from more than 1,000 seawater samples. These efforts revealed approximately 40,000 putative mostly new biosynthetic gene clusters, several of which were found in previously unsuspected phylogenetic groups. Among these groups, we identified a lineage rich in biosynthetic gene clusters (‘Candidatus Eudoremicrobiaceae’) that belongs to an uncultivated bacterial phylum and includes some of the most biosynthetically diverse microorganisms in this environment. From these, we characterized the phospeptin and pythonamide pathways, revealing cases of unusual bioactive compound structure and enzymology, respectively. Together, this research demonstrates how microbiomics-driven strategies can enable the investigation of previously undescribed enzymes and natural products in underexplored microbial groups and environments