You are currently on the new version of our website. Access the old version .
MDPIMDPI
Search all articles
MicroorganismsMicroorganisms
Download PDF
  • Article
  • Open Access

26 January 2026

Genomic Insights into Antimicrobial Biosynthetic Potential of Bacillus velezensis Isolated from Traditional Peruvian Tocosh

,
,
,
,
,
,
,
,
and
1
Biomolecules Laboratory, Faculty of Health Sciences, Universidad Peruana de Ciencias Aplicadas, Lima 15067, Peru
2
Dairy and Lactic Acid Bacteria Technology Center, Institute of Food Technology (ITAL), Campinas 13083-015, Sao Paulo, Brazil
3
School of Biology, Faculty of Health Sciences, Universidad Peruana de Ciencias Aplicadas, Lima 15067, Peru
4
Grupo de Investigación en Dinámicas y Epidemiología de la Resistencia a Antimicrobianos-‘One Health’, Universidad Científica del Sur, Lima 15067, Peru
Microorganisms2026, 14(2), 287;https://doi.org/10.3390/microorganisms14020287
This article belongs to the Special Issue Genomics of Microorganisms from Traditional Fermented Products
Version Notes
Order Reprints

Abstract

Tocosh, a traditional Peruvian fermented potato product, is known for its health-promoting properties, including its antioxidant, anti-inflammatory, probiotic, and antibiotic effects, which have popularized its consumption, particularly in rural areas. To gain a better understanding of its antimicrobial properties, this study aimed to perform a comprehensive whole-genome analysis and functional assessment of the Bacillus velezensis TCSH0001 strain isolated from tocosh. The isolate was identified through whole-genome sequencing using the MinION nanopore platform. AntiSMASH analysis revealed nine biosynthetic gene clusters (BGCs) potentially responsible for producing secondary metabolites with antibiotic potential. Notably, seven BGCs showed a 100% similarity to known clusters involved in the biosynthesis of polyketide synthases (PKSs) and non-ribosomal peptides (NRPSs), including difficidin, bacillibactin, bacilysin, macrolactin H, bacillaene, fengycin, and bacillomycin D. In vitro analysis revealed antimicrobial activity against S. aureus strains. In addition, RT-qPCR indicated that the expression of the baeJ (bacillaene), bmyA (bacillomycin D), and pks2A (macrolactin H) occurs predominantly during the exponential growth phase. Our results suggest that this B. velezensis strain has the capacity to produce a diverse array of bioactive compounds, supporting the traditional use of tocosh as a natural antimicrobial agent, and revealing the potential of the strain as a high NRPS producer.

1. Introduction

The consumption of fermented foods and beverages, such as cheese, yogurt, and wine, is common in daily diets across various cultures due to their multiple associated nutritional benefits [1]. The practice of preserving food through microbial fermentation is ancient, with historical records documenting its application to a wide range of foods in different geographical areas [2]. In South America, for example, the Inca civilization fermented various foods, including tubers like potatoes (Solanum tuberosum) [3]. This fermented product is known as tocosh, popularly referred as “the natural penicillin of the Andes” [4]. The consumption of tocosh has been related to numerous health benefits, including antioxidant, anti-inflammatory, anti-diabetic, and probiotic activity [5]. In Peru, the departments of Ancash, Huánuco, and Junín are the primary producers of tocosh, largely due to the diversity of native potato varieties found in the Central Highlands [6]. The beneficial properties of tocosh are believed to come from microbial transformations that occur in potatoes during fermentation. Sequencing assays have identified genera such as Clostridium, Prevotella, and Lactobacillus as prominent members of the microbial community in tocosh [3,7].
Many health benefits associated with fermented foods are derived from the bioactive compounds produced by their microbiota or from the high abundance of specific beneficial microbial genera [8]. The traditional approach to identify and characterize microorganisms with biotechnological potential involves microbiology techniques [9,10]. However, culture-based methods often fail to capture the full diversity of species within natural microbiomes. It is estimated that only about 1% of bacterial species are culturable in laboratory settings due to the challenge of replicating their specific growth conditions [11,12]. The integration of molecular methods, such as DNA sequencing, has revolutionized microbiological research, providing a more comprehensive identification of microbial species [13].
Given the rise in drug-resistant pathogens, there is an urgent need to discover new sources of antibiotic-producing microorganisms [14]. Numerous candidate antimicrobial molecules have been reported from various natural sources [15,16]. Certain bacterial families, such as Actinomycetaceae, Streptomycetaceae, and Bacillaceae, are particularly well-known for their ability to produce antibiotics [17]. For example, the genus Bacillus includes several species noted for antibiotic production, such as bacitracin, bacilysin, and mersacidin [18,19]. In particular, Bacillus velezensis is recognized to produce a variety of bioactive compounds, including antimicrobial metabolites and lipopeptides such as fengycin, as well as digestive enzymes [20]. Due to these properties, B. velezensis strains have been widely and safely used as probiotics in animal feed and aquaculture [21,22]. In addition, this species is included in the European Food Safety Authority (EFSA) Qualified Presumption of Safety (QPS) list [23].
Although the antimicrobial cyclic lipopeptides (CLPs) produced by Bacillus species have been extensively studied, recent research has identified new variants of these compounds, likely arising from genetic mutations [24,25]. Therefore, the genomic analysis of novel Bacillus strains isolated from diverse sources is crucial for identifying known antimicrobial metabolites with promising biotechnological applications, as even minor changes in DNA sequences can significantly affect biological properties. Thus, the objective of this study was to investigate the B. velezensis TCSH0001 genome by searching for gene clusters associated with production of antimicrobial substances and evaluating their activity in vitro against Gram-positive and Gram-negative bacteria. In addition, the expression of the genes associated with bacillaene, bacillomycin D, and macrolactin H was examined by RT-qPCR to determine the production of antimicrobial peptides throughout the cultivation period.

2. Materials and Methods

2.1. Bacterial Isolation

The Bacillus velezensis isolate described in this study was obtained from tocosh samples made from potatoes (Solanum andigenum) collected in 2019, in the city of Heroínas Toledo, Junin, Peru (11°50′09″ S 75°17′36″ W). The sample was stored at −80 °C until processing. For the sample preparation, 3–5 g of tocosh was weighed and placed in a sterile mortar. The sample was then crushed and homogenized by adding 5 mL of sterile distilled water. The resulting mixture was transferred to a 50 mL tube and allowed to settle until the supernatant was clearly separated from the pellet. From the separated mixture, 1 mL of the supernatant was recovered and streaked onto Petri dishes with Luria–Bertani (LB) culture medium. The plate was incubated at 30 °C for 18 h. To obtain a pure culture, consecutive streaking was performed under the same culture conditions. Finally, the bacterial isolate was stored in LB media containing 20% of glycerol at −80 °C.

2.2. DNA Extraction and Whole-Genome Sequencing

The genomic DNA of the strain was extracted with the Quick-DNA fungal/bacterial Miniprep kit (#D6005, Zymo Research, Irvine, CA, USA) according to the manufacturer’s protocol. The quantity and quality of the extracted DNA were assessed with a NanoDrop Spectrophotometer (Thermo Fisher Scientific, Wilmington, DE, USA). DNA libraries were prepared using the Native Barcoding Kit 24 V14 SQK-NBD114.24 (Nanopore Technologies, Oxford, UK). The DNA Repair and End prep procedures were carried out with a total of 400 ng of input DNA using the FFPE DNA Repair Mix and Ultra II End repair/dA tailing Module kits (New England Biolabs, Ipswich, MA, USA). Then, barcode ligation was performed using the Blunt/TA Ligase Master Mix (NEB, cat #M0367), and native adaptors were ligated with the Quick T4 DNA Ligase (NEB, #E6065) and the Quick Ligation Reaction Buffer (NEB, #B6058), all according to the manufacturer’s protocol. The clean-up procedure was performed with AMPure XP (REF A63880, Beckman Coulter, Pasadena, CA, USA) magnetic microbeads and then the DNA library was quantified using a Qubit fluorometer (Thermo Fisher Scientific, Waltham, MA, USA). A total of 100 fmol of the purified library was loaded into R10.4.1 flow cells (FLO-MIN114) according to the manufacturer’s recommendations. Finally, sequencing was performed on the MinION Mk1B device (Oxford Nanopore Technologies, Oxford, UK) by configuring the demultiplexed data collection in the fast5 format with the MinKNOW software v 24.06.5 (Oxford Nanopore Technologies, Oxford, UK).

2.3. Assembly and Genome Annotation

The basecalling of the FAST5 files was performed with the Guppy software v6.5.7 (Oxford Nanopore Technologies, Oxford, UK) in the super accurate mode (SUP) to generate FASTQ files. The read quality metrics were assessed with NanoPlot v1.44.1 [26], and adapter and the barcode sequences were removed using Porechop v0.2.4 (https://github.com/rrwick/Porechop, accessed on 25 November 2025). Quality filtering was performed with NanoFilt v2.8.0 [27], retaining reads with an average quality score ≥ 14 and length ≥ 1000 bp. Consequently, de novo assembly was performed using Canu v2.2 [28], followed by polishing with Medaka v2.1.1 (https://github.com/nanoporetech/medaka, accessed on 25 November 2025). For the assembly evaluation (e.g., number of contigs, N50, and G + C content) QUAST v5.3.0 [29] was used. Additionally, assembly integrity and contamination were evaluated with BUSCO v6.0.0 [30] and CheckM v1.2.4 [31]. Genome annotation was performed with Prokka v1.14.6 [32] under default parameters. Circular genome representations and COG-based functional annotations were generated with GenoVi v0.2.16 (https://github.com/robotoD/GenoVi, accessed on 25 November 2025).
Antimicrobial resistance (AMR) and virulence genes were identified using CARD-RGI v6.0.5 [33] and abricate v1.2.1 (https://github.com/tseemann/abricate, accessed on 25 November 2025), querying the CARD [34] and VFDB [35] databases, respectively. Only hits with ≥80% identity and ≥80% coverage were considered. Additionally, the assembled genome sequence of B. velezensis TCSH0001 was analyzed for secondary metabolite biosynthetic potential using the AntiSMASH pipeline [36] in the strict detection mode. Biosynthetic gene clusters (BGC) associated with secondary metabolites production were identified through comparison with the MIBiG database of manually curated BGCs using the KnownClusterBLAST algorithm, both integrated within antiSMASH software (https://docs.antismash.secondarymetabolites.org/, accessed on 25 November 2025). The similarity value is determined by comparing the clusters to the well-characterized reference clusters, with homologous genes identified based on a sequence identity threshold (>30%) and short BLAST alignments (>25%). A ‘100% similarity’ indicates that the query cluster shares all homologous genes with the hit cluster, according to these criteria.

2.4. Taxonomic Identification and Comparative Genomics Analysis

The phylogenetic relationships of Bacillus velezensis TCSH0001 within the Bacillus genus were inferred using a phylogenomic approach based on core genome single-nucleotide polymorphisms (SNPs). The pangenome was reconstructed using Panaroo v1.5.2 [37] using GFF files obtained with Prokka from different genomes of representative Bacillus retrieved from the NCBI Genomes database (https://www.ncbi.nlm.nih.gov/datasets/genome/, accessed on 25 November 2025) to obtain a high-quality core–gene alignment. This alignment was processed with Gubbins v3.4.3 [38] to identify and mask regions of putative recombination. Subsequently, SNP-sites v2.5.1 [39] was utilized to extract polymorphic sites from the recombination-filtered alignment. The maximum likelihood (ML) phylogenetic tree was reconstructed using IQ-TREE v3.0.1 [40] with the best-fit model of nucleotide substitution automatically selected by ModelFinder and branch support assessed using the UltraFast Bootstrap approximation with 1000 replicates. Average nucleotide identity (ANI) analysis between B. velezensis TCSH0001 and other representative B. velezensis strains obtained from NCBI Genomes database was performed using ANIclustermap v2.0.1 (https://github.com/moshi4/ANIclustermap, accessed on 25 November 2025). The orthologous relationships between the B. velezensis strains were determined with Orthovenn3 [41], based on protein sequence comparisons from each strain’s genome annotation, enabling the identification of specific ortholog clusters. The functional annotation of Bacillus velezensis TCSH0001-specific orthogroups was performed with eggNOG-mapper [42]. The accession numbers for the representative Bacillus genomes used in these analyses are listed in Table S1.

2.5. Antagonism Assay

The ability of the B. velezensis TCSH0001 strain to inhibit the growth of bacterial pathogens through the production of secondary metabolites was evaluated by the formation of an inhibition zone in LB agar plates. Challenge cultures were prepared by streaking Escherichia coli ATCC 25922, used as a reference for antibiotic susceptibility testing onto LB agar plates, and three Staphylococcus aureus isolates, including the FRI913 strain and the MNHOCH, which are both methicillin-sensitive and enterotoxins producers, and the HT20020455, which is methicillin-sensitive without documented enterotoxin production. All the cultures were incubated at 37 °C for 12 h prior to assay. The next day a colony was transferred to LB broth and incubated at 37 °C to reach a concentration of 1.5 × 108 CFU/mL. Finally, each culture was streaked onto LB plates and incubated at 37 °C for 6 h. B. velezensis TCSH0001 isolate, stored in cryovials at −80 °C, was directly inoculated onto a central round area in LB agar plates. The plates were incubated at 30 °C for 12 h. Then, challenge cultures were transferred to the plates with the B. velezensis culture according to the replica plate protocol [43,44,45]. Finally, the plates were incubated at 37 °C for 12 h. The formation of an inhibition zone, indicating the potential production of secondary metabolites by the TCSH0001 isolate that inhibits the growth of the E. coli and S. aureus strains, was photographed, and the diameter of the inhibition zone was measured.

2.6. RNA Isolation and RT-qPCR

The expression of the selected secondary metabolites, bacillaene (baeJ gene), bacillomycin D (bmyA gene), and macrolactin H (pks2A), was examined by RT-qPCR. B. velezensis TCSH0001 was cultured at 30 °C and 180 rpm in LB medium at a pH of 7.0 for RNA isolation [46]. The optical density at 600 nm (OD) was measured with the GENESYS™ 180 UV-Vis spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA) every hour and half an hour for the last measurement. The OD in which RNA was extracted was 0.55 and 1.45 for the logarithmic and stationary phases, respectively. A standard curve was developed with the data. After the incubation period (5 h for the exponential phase and 19 h for the stationary phase), B. velezensis TCSH0001 cells were harvested by centrifugation at 4700 rpm for 10 min, and the total RNA was purified using the Quick-DNA/RNA Miniprep Plus Kit (D7003, Zymo Research, CA, USA) following the manufacturer’s protocol with a modification in the DNase step to ensure complete degradation of DNA (less than 1 ng or undetectable). The quantity and quality of the extracted RNA was then evaluated using a Qubit fluorometer with Qubit RNA HS Assay Kit (Q32852, Thermo Fisher Scientific, Waltham, MA, USA) and an NP80 spectrophotometer (Implen GmbH, Munich, Germany). Genomic DNA contamination was evaluated with the Qubit dsDNA HS Assay Kit Q32851. For retrotranscription, we used the RevertAid RT Kit (K1691, ThermoFisher Scientific, Waltham, MA, USA) using random primers according to the manufacturer’s protocol. The primers used in this study were designed using the NCBI Primer Designing Tool, targeting the baeJ, bmyA, and pks2A genes from the bacillaene, bacillomycin D, and macrolactin H BGC, respectively (Table S2). The gene sequences were obtained from the available information in the MIBiG Repository of Known biosynthetic gene clusters within antiSMASH. The primers were synthesized by Macrogen Inc. (Seoul, Republic of Korea). The amplification conditions consisted of the following: initial denaturation and enzyme activation at 95 °C for 3 min, followed by 40 cycles of denaturation at 95 °C for 10 s, and annealing and extension at 59 °C for 30 s, and, finally, the melt curve was performed at 60 °C to 95 °C for 10 min, with a heating rate of 1 °C per minute.

2.7. Statistical Analyses

For each bacterial strain, eight independent experimental replicates were performed on separate agar plates, and the diameter of the inhibition zone (mm) was recorded for each replicate. From these measurements, the mean, standard deviation, and standard error were calculated for each strain. Differences in the inhibition zones among the bacterial strains were assessed using the non-parametric Kruskal–Wallis test, which is appropriate for comparing multiple independent groups without assuming normality and given that the control strain (E. coli ATCC 25922) exhibited relatively constant inhibition values. Following the detection of overall differences, post hoc comparisons were conducted using Dunn’s test, restricted to pairwise contrasts between each strain and the negative control. A Bonferroni correction was applied specifically to these three contrasts to control for type I error. The adjusted p-values were interpreted according to standard significance thresholds (* < 0.05; ** < 0.01; and *** < 0.001). The gene expression results were evaluated using t-tests to compare the differences between growth phases. Statistical significance was marked as (*** < 0.001, ** < 0.01, and * < 0.05). The analyses were performed using the qPCRtools package v1.0.1 (https://cran.r-project.org/web/packages/qPCRtools/, accessed on 25 November 2025) to ensure the reliability and accuracy of the obtained results.

3. Results

3.1. Microbial Isolation and Antagonism Assay

After isolation, the B. velezensis TCSH0001 colony on LB medium was circular in shape, with filamentous edges, a smooth surface, and a dry consistency. No pigmentation was observed under the tested growth conditions, either during isolation or in confrontation with the E. coli or S. aureus strains (Figure 1A). In the antagonism assay, the TCSH0001 isolate demonstrated inhibitory activity against all testers, including the three Gram-positive S. aureus strains, and the E. coli ATCC 25922. The largest inhibition zone was observed against S. aureus MNHOCH, with a mean diameter of approximately 14 mm (Figure 1). Although this value is considered modest compared to inhibition zones reported for other antimicrobial agents, it indicates that the B. velezensis TCSH0001 isolate produces secondary metabolites with specific antimicrobial activity against the S. aureus strains.
Figure 1. (A) Representative images of inhibition zones on LB agar plates for each tested strain, showing the antagonistic effect of B. velezensis TCSH0001. (B) Quantification of the inhibition zones (mm) presented as mean ± standard error (n = 8), with individual replicates indicated as gray points. Significant differences relative to E. coli ATCC 25922 were assessed using Dunn’s test with Bonferroni correction (* p < 0.05 and *** p < 0.001).

3.2. Whole Genome Assembly and Annotation

The 286,813 cleaned reads with a quality value above 13 were assembled into a single circular chromosome. BUSCO completeness assessments of the assembled genome using the bacillus_odb12 database as a reference revealed that 769 genes (98.8%) were complete, while five genes (0.6%) were partial, which is very similar to that which was observed in B. velezensis FZB42 and B. velezensis IBUN2755 (Figure 2). These results were confirmed by genome integrity and contamination analysis with CheckM, where 99.81% and 0.61% values were obtained in B. velezensis TCSH0001, respectively.
Figure 2. BUSCO completeness comparison between B. velezensis TCSH0001 genome and other representative B. velezensis genomes.
The complete genome of B. velezensis TCSH0001 presented a length of 4,282,411 bp with 45.7% GC content and 4246 protein-coding sequences (CDS), 28 rRNAs, 86 tRNAs, and 1 tmRNA (Figure 3A). The comparison of genome size and GC% between B. velezensis TCSH0001 and the other strains are shown in Table 1. The functional annotation of the B.velezensis TCSH0001 genome using the COG database resulted in the assignment of 4802 genes into 26 COG categories (Figure 3B). The distribution revealed a robust metabolic and regulatory framework. The most abundant category was transcription (394 genes; 8.2% of all CDS) followed by general function prediction only (370 genes; 7.7%), and amino acid transport and metabolism (332 genes; 6.9%). Other major categories contributing to the strain’s physiological versatility include carbohydrate transport and metabolism (312 genes; 6.5%), signal transduction mechanisms (289 genes; 6.0%), and inorganic ion transport and metabolism (233 genes; 4.9%). Notably, defense mechanisms (173 genes; 3.6%) and secondary metabolites biosynthesis, transport, and catabolism (113 genes; 2.4%) were well-represented, providing a genomic basis for the strain’s survival and antimicrobial potential. No plasmids or significant resistance genes were detected in the genome, although the virulence gene clbA was identified. In B. velezensis, this gene is typically associated with the activation of non-ribosomal polyketide synthetases (NRPS) and polyketide synthases (PKS), which are essential to produce antimicrobial compounds rather than clinical toxicity.
Figure 3. Genome circular map (A) and functional classification of protein-coding genes based on Cluster of Orthologous Groups (COG) categories (B) for Bacillus velezensis TCSH0001. The bar chart displays the distribution of genes across four major functional classes: blue and teal tones represent cellular processes and signaling (categories D, M, N, O, T, U, V, W, Y, Z); purple tones indicate information storage and processing (J, K, L, X); green and orange tones denote metabolism (C, E, F, G, H, I, P, Q); and grey tones correspond to poorly characterized proteins (R, S).
Table 1. Genomic features of B. velezensis TCSH0001 compared to reference strains. GC%: GC content, CDS: Coding DNA Sequences, and BGC: biosynthetic gene clusters.

3.3. Taxonomic Identification

The phylogenetic tree constructed with a phylogenomic approach based on core-genome SNPs (Figure 4A) showed that strain TCSH0001 clustered monophyletically within the Bacillus velezensis clade, showing a minimal genetic distance from the reference genomes of this species. After the initial phylogenetic location, the ANI analysis was performed to determine the relationship of species with the other 39 genomes of B. velezensis available on NCBI. According to the ANI values (Figure 4B), the B. velezensis TCSH0001 genome was most similar (98.8%) to the B. velezensis IBUN 2755, which was isolated from soil and studied as a biocontrol agent for rice plants [47]. As observed in heatmap, there are four well-defined groups with a high species threshold between the analyzed B. velezensis strains, a finding consistent with results observed by Mullins [48], where it was suggested that this species could be divided into at least four distinct clades.
Figure 4. (A) Phylogenetic analysis of Bacillus genomes based on core genome SNPs using the maximum likelihood method; light blue branches and labels represent Bacillus velezensis reference genomes, while the green branch and label highlight the B. velezensis TCSH0001 strain. (B) Heatmap showing the relative average nucleotide identity (ANI) between different B. velezensis genomes. The color scale indicates the degree of genomic similarity: red represents 99–100%, orange to yellow indicates 98.5–99%, and green tones represent 97–98.5%.

3.4. Comparative Genomics

A key question in this comparative study is how B. velezensis TCSH0001 differentiates itself from both the global model strain FZB42 and its closest phylogenetic relative, IBUN 2755 (as identified by ANI analysis). Although TCSH0001 shares a core functional backbone with these reference strains, its distinct biological advantages are driven by its specialized accessory genome and targeted paralogous expansions. OrthoVenn3 analysis revealed that TCSH0001 possesses unique protein clusters absent in both reference strains (Figure 5). Specifically, the identification of exclusive clusters such as cluster 3569 (NodL-like acetyltransferase) and cluster 3661 (cell shape regulation) suggests a specialized evolutionary trajectory for root colonization and higher-density biofilms, supporting the structural resilience model described by Blaznik et al. [49]. The robustness of this strain is rooted in a highly coordinated system for persistence and DNA protection; clusters 51, 53, and 3646 optimize sporulation efficiency under stress, while cluster 3597 (chromosome condensation) and 3579 (DNA binding) ensure precise genomic packaging to prevent damage during latency. This stability is further reinforced by a high-fidelity machinery where cluster 3580 (dTTP biosynthesis) accelerates the repair of environmental damage and cluster 3675 (dUTP catabolic process) acts as a critical quality filter to prevent lethal mutations. Finally, the strategic control over mobile genetic elements, indicated by cluster 3583 (latency–replication decision) coupled with cluster 3669 (restriction–modification system), may provide TCSH0001 with specialized defense mechanisms and the ability to domesticate phages as competitive weapons. These unique features suggest TCSH0001 strain to be a superior biotechnological platform compared to standard commercial reference strains.
Figure 5. Comparative genome analysis of Bacillus velezensis strains. The Venn diagram illustrates the shared and unique orthologous gene clusters among B. velezensis TCSH0001 (green), B. velezensis FZB42 (light blue), and B. velezensis IBUN_2755 (pink), identified using OrthoVenn3. The central intersection shows the core genome consisting of 3509 gene clusters common to all three strains. The bar charts at the bottom provide a quantitative summary: the upper bar indicates the total number of genes per strain, while the lower horizontal bar highlights the distribution of elements, emphasizing that the vast majority of clusters (3509) are shared among all three compared genomes.

3.5. Secondary Metabolite Biosynthetic Gene Cluster Identification

The AntiSMASH software was used for the prediction and analysis of potential secondary metabolites BGC present in the B. velezensis TCSH0001 genome. The results indicated the presence of nine secondary metabolites producing gene clusters, of which seven showed over 90% similarity with already known clusters (Table 2). The relevant types of BGCs found in the TCSH0001 genome were transAT-PKS, NRPS Type I, transAT-PKS, and RiPP:LAPP. The seven fully conserved clusters, determined using the MIBiG comparison tool within antiSMASH, were responsible for the production of difficidin, bacillibactin, bacilysin, macrolactin H, bacillaene, fengycin, and bacillomycin D, when compared to B. velezensis FZB42 (Figure 6). The comprehensive details of contigs, gene products, and identifiers for the selected secondary metabolites identified are provided in Tables S3–S9, supporting a thorough characterization of the strain’s biosynthetic potential.
Table 2. BGCs found in the B. velezensis TCSH0001 genome with the antiSMASH software. Similarity percentages refer to the proportion of homologous genes shared between the query cluster and the reference clusters.
Figure 6. Results of the analysis with MIBiG comparison with the respective secondary metabolites based in the B. velezensis FZB42 genome, as reference. The nine clusters with greater than 90% similarity are shown. The core biosynthetic genes are highlighted with cherry, the additional biosynthetic genes with pink color, the transport-related genes with blue color, the regulatory genes with green color, and the other genes with gray color.
The identified fengycin BGC consists of seven distinct genes encoding YngEFGHIJK, five fengycin synthetase proteins (ABCDE), a hypothetical protein, and a gene designated as dacC (Table S3). Bacillomycin D BGC is composed of several genes involved in the biosynthesis of this secondary metabolite. The cluster includes genes encoding bacillomycin D synthetase A (bmyA), bacillomycin D synthetase B (bmyB), and bacillomycin D synthetase C (bmyC), which are responsible for scaffold biosynthesis. Additionally, the cluster contains genes encoding a malonyl-CoA transacylase (bmyD), which plays a key role in the extension of the polyketide backbone, and xynD, which is involved in the final modifications of the metabolite structure. The BGC also includes yxjF and yxjC, which may be involved in the transport or modification of intermediates, as well as scoA and scoB, genes potentially related to the regulation and final assembly of bacillomycin D (Table S4). The bacillaene BGC begins with a gene encoding a hydroxyacylglutathione hydrolase, followed by three genes encoding malonyl-CoA transacylase, the final one of which may function as an oxidoreductase. Subsequently, the cluster includes a hydroxymethylglutaryl-CoA synthase, two distinct enoyl-CoA hydratases involved in polyketide biosynthesis, two hybrid NRPS/PKS proteins, and three type I polyketide synthases, and a cytochrome P450 (Table S5). The macrolactin H cluster starts with a gene encoding a malonyl-CoA transacylase, followed by seven distinct genes encoding polyketide synthases (PKS) and a penicillin-binding protein-related beta-lactamase precursor. The cluster concludes with a gene encoding the pyruvate dehydrogenase protein alpha subunit (Table S6). The BGC responsible for the production of the secondary metabolite bacilysin consists of seven genes: ywfG, bacA, bacB, bacC, bacD, bacE, and ywfA (Table S7). Bacillibactin production was identified, comprising five genes: dhbF, dhbB, dhbE, dhbC, and dhbA (Table S8). Additionally, a biosynthetic gene cluster (BGC) responsible for difficidin initiates with three genes encoding an enoyl-CoA hydratase, a hydroxymethylglutaryl-CoA synthetase, and a putative cytochrome P450 monooxygenase. These are followed by seven genes encoding type I polyketide synthases. The cluster terminates with genes encoding a 3-oxoacyl reductase, an acyl-CoA synthetase/AMP-acid ligase II, a putative long-chain acid CoA ligase, and a malonyl-CoA transacylase (Table S9).

3.6. Gene Expression Analysis

The expression of the secondary metabolite genes, bacillaene (baeJ), bacillomycinD (bmyA), and macrolactin (pks2A), were evaluated during the logarithmic and exponential phases of growth using RT-qPCR. These genes are core components of the BGC. Previously, antiSMASH identified nine clusters, and three of them were evaluated, namely, region 2 (NRPS Type I for bacillomycin D), region 3 (NRPS Type Ifor bacillaene), and region 4 (Trans-AT PKS for macrolactin H). These clusters were selected over others because bacillaene and macrolactin represent the primary polyketide arsenal for rapid niche establishment and broad-spectrum antibiosis during active growth, while bacillomycin is a lipopeptide that promotes iron acquisition, providing a competitive advantage in nutrient-limited environments. The median RNA/DNA ratio was 146.8, indicating efficient removal of genomic DNA and ensuring that subsequent gene expression analyses were not confounded by DNA contamination (Figure S1; Table S10). The RT-qPCR assays showed high reliability, as indicated by standard curve coefficients of determination (R2) ranging from 0.99688 to 0.99966, enabling robust normalization to the reference gene and accurate comparison of transcript levels (Figure S2, Table S11).
As shown in Figure 7, baeJ, bmyA, and pks2A expression displayed significant up-regulation during the logarithmic phase of growth evaluated at 5 h, as compared with the stationary phase evaluated at 19 h. These results indicate that these genes are mainly up-regulated during the phase with higher metabolic activity. Expression patterns were consistent across three independent biological replicates, confirming the reproducibility of the observed trends.
Figure 7. Relative expression levels of secondary metabolite biosynthetic genes encoding synthases for bacillaene (baeJ, located in antiSMASH Region 7; Type I PKS), bacillibactin D (bacD, located in antiSMASH Region2; NRPS), and macrolactin H (macH, located in antiSMASH Region 6; TransAT-PKS) during the logarithmic and stationary growth phases of B. velezensis TCSH0001. Gene expression values were normalized to the reference gene rpoB. Data represent the mean ± standard error of three independent biological replicates (n = 3), each analyzed in two technical replicates (n = 2). (***) indicates statistically significant differences (p < 0.001) between growth phases.

4. Discussion

Genome sequence analysis provides detailed information on the functional potential and taxonomic identification of microorganisms. In this context, the use of whole-genome sequencing is an important approach to determine the exact taxonomical location of a strain and to make an in-depth inspection of its industrial potential. The COG functional classification of the B. velezensis TCSH0001 genome reveals a genomic architecture specialized for metabolic versatility and complex genetic regulation. The predominant category, transcription, serves as a key indicator of this strain’s capacity to respond to environmental stimuli. Similarly to the recently characterized strain BN, where the category of transcription is also a major functional group, this high representation of transcriptional regulators suggests a sophisticated control system that coordinates the expression of biosynthetic gene clusters in response to external signals [50]. This regulatory infrastructure allows the bacterium to rapidly adapt its physiology to stress or competitive conditions, laying the genetic groundwork for the potential synthesis of bioactive compounds in the later growth stages [51]. The observed enrichment in the categories of amino acid transport and metabolism, and carbohydrate transport and metabolism, which together represent 13.4% of the analyzed CDS, mirrors the metabolic profile of strain LOH112 [52]. This robust metabolic profile is characteristic of strains with high colonization capacity, suggesting that B. velezensis TCSH0001 is well adapted to occupy ecological niches where microbial competition and resource limitation are prevalent. While in other B. velezensis strains these categories are linked to rhizosphere colonization, in TCSH0001 they likely facilitate the degradation of potato starches and the uptake of nitrogenous precursors. This ensures not only basal survival but also provides the necessary substrates required for the assembly of non-ribosomal peptides and polyketides, effectively transforming environmental nutrients into functional metabolites.
Regarding the safety profile, the significant presence of genes in defense mechanisms and signal transduction mechanisms offers insights into the competitive behavior of this strain. Crucially, no resistance genes, including functional homologs of the cfr gene reported in other Bacillales [53] were detected. The absence of cfr methyltransferase is a key finding, as this gene confers resistance to the PhLOPSA (phenicol, lincosamide, oxazolidinone, pleuromutilin, and streptogramin A) suite of antibiotics and its absence reduces the risk of horizontal gene transfer of high concern resistance markers to clinical pathogens [53]. A notable finding in the genome was the identification of the clbA gene, a phosphopantetheinyl transferase. While clbA is traditionally cataloged in databases like VFDB due to its involvement in the synthesis of colibactin (genotoxin found in E. coli) its presence in Bacillus must be interpreted within its specific genomic context. As demonstrated in recent comparative genomic surveys, the presence of isolated clbA homologs in the B. amyloliquefaciens group does not correlate with pathogenicity. In these beneficial strains, clbA serves as a functional pleiotropic activator for the 4′-phosphopantetheinylation of carrier proteins in both non-ribosomal peptide synthetases (NRPS) and polyketide synthases (PKS). In TCSH0001, clbA is likely the enzymatic trigger for the synthesis of bioactive lipopeptides and siderophores rather than a virulence factor. This interpretation is reinforced by the absence of the remaining 17 genes that constitute the pks pathogenicity island (colibactin gene cluster) required for DNA-damaging activity. Therefore, the presence of clbA in TCSH0001 represents a metabolic asset for biocontrol, enabling the production of antimicrobial compounds. Furthermore, the lack of hemolysin, enterotoxin, and emetic toxin genes common in the B. cereus group, as highlighted by Liu et al. [54], as a differentiator for safe Bacillus species further confirms that the genomic repertoire of this strain is evolutionarily tuned for “sensing and reacting” to environmental competition rather than for host infection. This comprehensive safety profile reinforces the strain’s status as a robust and safe candidate for biotechnological applications in agriculture and food industries. As observed in strain D103, the coordination between the external signal sensing and the activation of defense systems is what enables B. velezensis to dominate in polymicrobial environments [55]. This genetic arsenal suggests that TCSH0001 is not a passive component of the tocosh microbiota; rather, it is equipped to interact with and potentially suppress other microorganisms through competitive exclusion systems.
The identification of 171 genes in the secondary metabolites’ biosynthesis, transport, and catabolism category reinforces the idea that this strain harbors a chemical potential yet to be fully explored. When compared to strain BN, which shows a similar distribution in the category of secondary metabolites, it becomes evident that TCSH0001 maintains a core set of genes dedicated to the production and transport of antimicrobials [56]. The robustness of these functional categories across the complete genome suggests that regulatory and metabolic mechanisms are aligned to sustain complex biological activity, justifying the detailed investigation of its specific antimicrobial capabilities in the subsequent sections. Additionally, the functional mapping of TCSH0001 reveals a significant investment in genes related to environmental stress adaptation, a trait essential for surviving the dynamic chemical conditions and pH fluctuations inherent in traditional potato fermentation [50]. This adaptive resilience, coupled with the robust signaling pathways identified, explains the strain’s dominance within the tocosh antimicrobial compounds, even under challenging conditions. The absence of virulence and resistance genes highlights the high genetic stability and the low propensity to involve horizontal gene transfer events, confirming the safety of this strain in the food matrix. Although some Bacillus strains have shown probiotic properties, including the absence of ARG, further characterization is needed of B. velezensis [57]. The phylogenetic analysis allowed us to understand more about the position of B. velezensis TCSH0001 inside the B. velezensis, even though it originates from different sources and ANI analysis showed the diversity between the B. velezensis group. The core genome phylogenetic reconstruction showed that our tocosh strain belongs to the same clade of the biocontrol strain IBUN 2755 [58], revealing that the B. velezensis TCSH0001 may exhibit the same biotechnological properties and plant biocontrol abilities.
The B. velezensis TCSH0001 strain isolated from a tocosh sample showed an ability to inhibit the growth of three different S. aureus strains through the production of antimicrobial compounds. The greater inhibition capacity of B. velezensis strains against the tested Gram-positive bacteria like S. aureus has been reported by several authors, and it is related to the lipopeptide fengycin [24], demonstrating the great potential of this species as an antimicrobial compound producer. The production of these compounds is related to competition and/or cooperation mechanisms among bacterial communities, aimed at competing for space and resources [59]. However, its production is highly dependent on media composition, temperature, and the growth phase, resulting in a specific synthesis of antimicrobial compounds that can inhibit only Gram-positive bacteria, Gram-negative bacteria, or both.
The genomic architecture of B. velezensis TCSH0001 reveals an evolutionary specialization; the presence of unique protein clusters in this strain demonstrates that its accessory genome has been shaped to maximize fitness in competitive ecological niches. This supports the framework proposed by Belbahri et al. [60], which defines the variable genome of Bacillales as a reservoir for habitat-specific adaptation and specialized metabolic traits. The exclusive presence of clusters 51, 53, and 3646 suggests a specialized evolutionary investment in the sporulation commitment phase. Sporulation is a metabolically expensive process, and its efficiency is often the deciding factor in the persistence of biocontrol agents in the rhizosphere. However, the identification of cluster 3597 (chromosome condensation) and cluster 3579 (DNA binding) indicates that machinery is ensured by precise nucleoid packaging, shielding the genetic material from physical and chemical degradation during prolonged latency. The transition into spore requires the precise segregation of the chromosome into the forespore. The role of cluster 3597 in chromosome condensation is vital during the asymmetric division. If DNA is not properly condensed and tethered, the resulting spore may be anucleated or contain damaged genetic material. By linking these condensation clusters to the gene dosage effect described by Zhang et al. [61], we can argue that TCSH0001 possesses a redundant and highly expressive toolkit for DNA packaging. Furthermore, this structural robustness extends to cellular morphology through cluster 3661 (cell shape regulation). The ability to optimize cells’ surface-to-volume ratios allows TCSH0001 to form more dense biofilms with superior mechanical properties. According to Blaznik et al. [49], such structural modifications in the biofilm matrix are critical determinants for resisting osmotic pressure and environmental microbials, providing an advantage over other strains. An exceptional feature is its multi-layered genomic shield against mutations. While cluster 3580 (dTTP biosynthesis) ensures a rapid supply of precursors for DNA repair, cluster 3675 (dUTP catabolic process) serves as a metabolic sentinel by actively eliminating dUTP, which DNA polymerase often confuses with dTTP. This cluster prevents lethal mutagenic events. The efficacy of these repair systems is significantly amplified by the tandem duplication observed in clusters 3586 and 3587 (DNA recombination and repair); by harboring multiple paralogs, TCSH0001 exploits a gene dosage effect, allowing for accelerated chromosomal reconstruction following severe stress. This mechanism aligns with the findings of Zhang et al. [61], who demonstrated that tandem gene duplications are a rapid evolutionary strategy to optimize expression levels and increase fitness in high-stress agricultural environments. The cluster 3669 (restriction–modification) acts like a biological barrier that modulates the rate of horizontal gene transfer, suggesting TCSH0001 possesses a unique methylation signature, allowing it to reject harmful viral DNA while potentially accepting beneficial plasmids that carry biocontrol or antibiotic resistance genes [62]. This selective advantage is crucial for maintaining the stability of the strain in complex microbial communities. Cluster 3583 (latency–replication decision) is perhaps one of the most striking features; it indicates that the expression of molecular syringes under specific stress cues can be controlled, effectively eliminating competitors in the rhizosphere.
The AntiSMASH software was used to analyze the B. velezensis TCSH0001 genome with the aim of predicting the secondary metabolites responsible for the antibiotic activity. The analysis revealed nine gene clusters potentially responsible for secondary metabolite production including NRPS, mixed modular NRPS/PKS, RiPP:LAPP, and trans-AT PKS, with seven of them displaying complete (100%) similarity to previously identified clusters: difficidin, bacillibactin, bacilysin, macrolactin H, bacillaene, fengycin, and bacillomycin D, respectively. The B. velezensis TCSH0001 genome included other antimicrobial gene clusters with similarity higher than 90% (surfactin and plantazolicin). The results of the MIBiG comparison with the B. velezensis FZB42 genome indicated a high level of genetic similarity in the BGCs evaluated. This information is valuable since B. velezensis FZB42 is recognized by for its properties as a plant growth promoter (PGP), which could be an interesting feature that could be investigated in B. velezensis TCSH0001.
Macrolactins are a large group of macrolide-type antibiotics with diverse biological activities, including the inhibition of cancer cell proliferation, antiviral activity against HIV, anti-inflammatory effects, and antibacterial activity against Staphylococcus aureus [63,64]. In this study, the BGC responsible for macrolactin H production begins with a malonyl-CoA transacylase involved in polyketide biosynthesis, followed by seven distinct Type I polyketide synthase (PKS) genes, and concludes with pks21 and pdhA genes. Macrolactins are known for their antimicrobial properties; their antibacterial activity was initially reported by Nagao and Adachi [65]. They evaluated the mentioned activity of nine macrolactin variants—A, F, and G–M (including H)—produced by a marine Bacillus sp. against S. aureus and Bacillus subtilis. Their results showed that these macrolactin metabolites exhibited an agonistic activity against both bacteria, although it was relatively weak [65].
Bacillaene is a linear and non-ribosomal polyketide/peptide produced by many species of Bacillus, which inhibits bacterial protein synthesis [66]. It is synthesized by the trans-acyltransferase polyketide synthetase [67]. The BGC of this secondary metabolite starts with a gene coding for hydroxyacylglutathione hydrolase that is present in the glyoxalase system, malonyl-CoA transacylase genes required for polyketide biosynthesis in bacteria, a HMG-CoA gene for the condensation of acetyl-CoA, hydratases for polyketide biosynthesis, polyketide synthases, and a cytochrome protein. [68,69]. It has been proven to be an efficient antagonist against competitors. In studies with the Bacillus subtilis PS-216 strain, this polyketide has been observed to prevent the adhesion and biofilms formation of Campylobacter jejuni on abiotic surfaces [70]. Similarly, B. subtilis PS-216 inhibits the growth and biofilm formation of Salmonella enterica serovar Typhimurium SL1344, a response also mediated by bacillaene [71]. In addition to these antimicrobial properties, bacillaene has also been shown to provide a predation-resistant effect to B. subtilis against Myxococcus xanthus [72].
The categorization of bacillomycin D as a non-ribosomal peptide synthetase (NRPS) product is fundamental to the antifungal identity of B. velezensis TCSH0001. Unlike the broad-spectrum antibacterial polyketides also identified in this strain, bacillomycin D belongs to the iturin family, characterized by its unique ability to disrupt the structural integrity of eukaryotic microorganisms. The molecular mechanism involves a stoichiometric interaction with fungal membrane sterols, primarily ergosterol. This interaction initiates the self-aggregation of lipopeptide molecules into transmembrane pores, and these pores cause an immediate increase in membrane permeability, resulting in the leakage of vital ions and larger cytoplasmic components, ultimately leading to the osmotic collapse and death of the pathogen [73]. This specific mode of action suggests that TCSH0001 could bypass the cell wall defenses of oomycetes and filamentous fungi that would otherwise be resistant to the strain’s antibacterial PKS arsenal. The critical nature of this cluster is emphasized by Han et al. [74], whose research with the closely related strain FZB42 demonstrated that the genetic deletion of the bacillomycin D operon leads to a near-total loss of antagonistic activity against Phytophthora sojae. This suggests that for TCSH0001, this region is not merely an auxiliary defense but the primary driver of fungal suppression.
Beyond the three primary clusters, the genome of B. velezensis TCSH001 maintains a “latent arsenal” of six additional BGCs that provide functional redundancy and specialized defense mechanisms. Difficidin is a highly unsaturated macrocyclic polyketide recognized as one of the most potent inhibitors of bacterial protein synthesis in the Bacillus species. Its clinical and agricultural relevance is significant, as it has demonstrated high efficacy against multidrug-resistant pathogens and the tomato wilt Ralstonia solanacearum [75]. Bacilysin, while structurally simpler as a non-ribosomal dipeptide, acts as an antibiotic. Once transported into a competitor’s cell, it is hydrolyzed to release anticaspin, which irreversibly inhibits glucosamine-6-phosphate synthetase, thereby halting cell wall peptidoglycan synthesis [76]. The coexistence of these metabolites suggests that TCSH0001 can suppress a broad range of bacterial competitors, including those that might have developed resistance to primary polyketides. The lipopeptide clusters for fengycin and surfactin provide critical advantages in late-stage colonization. Fengycon is a cyclic lipopeptide that complements bacillomycin D by specifically targeting filamentous fungi. Its mechanism involves inducing reactive oxygen species bursts and mitochondrial membrane potential loss in fungal pathogens such as Magnaporthe grisea [77]. Surfactin, although a less potent antibiotic, is essential for its surfactant properties. It facilitates swarming motility and is a primary driver of biofilm architecture. Finally, the plantazolicin cluster encodes a ribosomally synthesized and post-translationally modified peptide with narrow-spectrum activity. This metabolite is particularly noted for its high selectivity against closely related Bacillus species, potentially serving as a tool for intragenus competition to ensure TCSH0001 remains the dominant strain within its ecological niche [78].
The expression levels of genes associated with bacillaene, bacillomycin D, and macrolactin H were analyzed. While the genome mining of B. velezensis TCSH0001 revealed nine high-confidence biosynthetic clusters, three were prioritized due to their critical role in early stage competition. baeJ, bmyA, and pks2A were significantly upregulated during the logarithmic phase, indicating that the synthesis of their respective secondary metabolites correlates with bacterial growth. On the other hand, the reduction in pH and glucose concentration, common in the stationary phase, could affect the synthesis of antimicrobial peptides. Similar results were reported in the production of CLP by Bacillus P45 growing in feather meals and BHI media [58]. According to Chen et al. [79], macrolactin and bacillaene are essential polyketides for suppressing soil-borne pathogens during active growth, and macrolactin is particularly effective inhibiting the growth of competing bacteria by targeting the H+-transporting ATP synthase, while bacillaene serves as a broad-spectrum selective agent. Their early expression allows TCSH0001 to sanitize the surrounding niche before the competitors can reach a high population density. Furthermore, the expression of bmyA, a gene encoding bacillomycin D synthetase A, which has been reported to play an important role in antibacterial activity by increasing intracellular iron concentrations as a cue for biofilm development [80], was significantly upregulated at 5 h. However, its expression decreased at 19 h. This expression pattern is consistent with a study evaluating the three bacillomycin D synthetases (bmyA, bmyB, and bmyC) at approximately 20 h (early stationary phase). Notably, in that study, the expression of the synthetases increased again at 48 h, with peak levels observed at 72 h (late stationary phase) [81]. Another recent study reported that bacillomycin D was responsible for inhibiting Staphylococcus human pathogens, further reinforcing our results [82]. These findings highlight the dynamic regulation of bacillomycin D synthetases and their potential role in both iron acquisition and biofilm formation.

5. Conclusions

The genomic and functional analysis of the B. velezensis TCSH0001 strain isolated from traditional Peruvian tocosh reveals its remarkable potential as a source of antimicrobial compounds. The identification of multiple biosynthetic gene clusters, including those responsible for difficidin, bacillibactin, bacilysin, macrolactin H, bacillaene, bacillomycin D, and fengycin, underscore the strain’s capacity to produce a diverse array of bioactive metabolites with antibacterial and antifungal activity. Experimental assays confirmed its inhibitory effect against pathogenic bacteria, such as S. aureus, and gene expression studies reveal that the production of key antimicrobial biosynthetic genes is closely linked to the bacterial growth phase. However, further research is needed to determine which antimicrobial compounds are associated with the observed phenotypical inhibition. In addition, we report an increase in the expression of genes related to the logarithmic phase with a decrease observed at 19 h. Although this pattern is relatively novel, several authors support our findings. These findings support the traditional use of tocosh as a natural source of antimicrobial activity and suggest that B. velezensis TCSH0001 could be further explored for industrial, agricultural, and medical applications, emphasizing the value of integrating genomic sequencing with functional assays for the discovery of novel microorganisms with biotechnological relevance.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/microorganisms14020287/s1, Figure S1: RNA and DNA yields quantified using a Qubit fluorometer in n = 3 biological replicates per growth phase; Figure S2: qPCR standard curves for the biosynthetic synthase genes baeJ (bacillaene), bacD (bacilysin), and pks2A (macrolactin H) in B. velezensis TCSH0001; Table S1: List of representative Bacillus genomes used in the genomic evolutionary analysis of genomic SNP and ANI analysis; Table S2: Primers used in the RT-qPCR analysis of secondary metabolite genes (bacD, bae, and pks2A) and the reference gene (rpoB); Table S3: Contigs, gene products, and identifiers for each gene found in the secondary metabolite fengycin BGC; Table S4: Contigs, gene products, and identifiers for each gene found in the secondary metabolite bacillomycin DBGC; Table S5: Contigs, gene products, and identifiers for each gene found in the secondary metabolite bacillaene BGC; Table S6: Contigs, gene products, and identifiers for each gene found in the secondary metabolite macrolactin HBGC; Table S7: Contigs, gene products, and identifiers for each gene found in the secondary metabolite bacilysin BGC; Table S8: Contigs, gene products, and identifiers for each gene found in the secondary metabolite bacillibatin BGC; Table S9: Contigs, gene products, and identifiers for each gene found in the secondary metabolite difficidin BGC; Table S10: RNA and DNA yields (ng) and the corresponding RNA/DNA ratios measured in exponential and stationary growth phases (n = 3 biological replicates per phase); and Table S11: RT-qPCR efficiency for secondary metabolite genes (baeJ, bmyA, and pks2A) and reference gene (rpoB) in B. velezensis TCSH0001, showing the slope, R2, p-value, and amplification efficiency (E).

Author Contributions

Conceptualization, F.G.E.; funding acquisition, F.G.E.; resources, D.E.B., R.A., J.R. and F.G.E.; supervision, D.E.B. and F.G.E.; methodology, D.E.B., C.M.B.P., R.A., J.R. and F.G.E.; investigation, D.E.B., C.M.B.P., J.G.F., B.M.S., J.V.N., J.R.T. and K.F.J.; formal analysis, D.E.B., C.M.B.P., J.G.F., B.M.S., J.V.N., J.R.T., K.F.J. and F.G.E.; visualization, J.G.F. and B.M.S.; writing—original draft preparation, D.E.B., C.M.B.P., R.A. and F.G.E.; and writing—review and editing, D.E.B., C.M.B.P. and F.G.E. All authors have read and agreed to the published version of the manuscript.

Funding

The study was supported by the Universidad Peruana de Ciencias Aplicadas (A-015-2025).

Institutional Review Board Statement

Not applicable.

Data Availability Statement

The data presented in this study are openly available in [NCBI] at [https://www.ncbi.nlm.nih.gov/, accessed on 25 November 2025], reference number [PRJNA1394479].

Acknowledgments

The following reagents were provided by the Network on Antimicrobial Resistance in Staphylococcus aureus (NARSA) for distribution by BEI Resources, NIAID, NIH: Staphylococcus aureus, Strain FRI913, NR-45917; Staphylococcus aureus, Strain MNHOCH, NR-45920; and Staphylococcus aureus, Strain HT20020455, NR-46059.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Marco, M.L.; Heeney, D.; Binda, S.; Cifelli, C.J.; Cotter, P.D.; Foligné, B.; Gänzle, M.; Kort, R.; Pasin, G.; Pihlanto, A.; et al. Health Benefits of Fermented Foods: Microbiota and Beyond. Curr. Opin. Biotechnol. 2017, 44, 94–102. [Google Scholar] [CrossRef]
  2. Şanlier, N.; Gökcen, B.B.; Sezgin, A.C. Health Benefits of Fermented Foods. Crit. Rev. Food Sci. Nutr. 2019, 59, 506–527. [Google Scholar] [CrossRef] [PubMed]
  3. Jimenez, M.E.; O’Donovan, C.M.; de Ullivarri, M.F.; Cotter, P.D. Microorganisms Present in Artisanal Fermented Food from South America. Front. Microbiol. 2022, 13, 941866. [Google Scholar] [CrossRef] [PubMed]
  4. Velasco-Chong, J.R.; Herrera-Calderón, O.; Rojas-Armas, J.P.; Hañari-Quispe, R.D.; Figueroa-Salvador, L.; Peña-Rojas, G.; Andía-Ayme, V.; Yuli-Posadas, R.Á.; Yepes-Perez, A.F.; Aguilar, C. TOCOSH FLOUR (Solanum tuberosum L.): A Toxicological Assessment of Traditional Peruvian Fermented Potatoes. Foods 2020, 9, 719. [Google Scholar] [CrossRef] [PubMed]
  5. Escandón, K.; Gonzalez-Rojas, J.C.; Carrera Flores, M.J.; Felix, D.G.; Lazo-Vélez, M.A. In Vitro Digestibility and Physico-Chemical Properties of Potato (Solanum tuberosum) Fermented by Traditional and Alternative Processes. ACS Food Sci. Technol. 2023, 3, 465–469. [Google Scholar] [CrossRef]
  6. Bellumori, M.; Silva, N.A.C.; Vilca, L.; Andrenelli, L.; Cecchi, L.; Innocenti, M.; Balli, D.; Mulinacci, N. A Study on the Biodiversity of Pigmented Andean Potatoes: Nutritional Profile and Phenolic Composition. Molecules 2020, 25, 3169. [Google Scholar] [CrossRef]
  7. Jiménez, E.; Yépez, A.; Pérez-Cataluña, A.; Ramos Vásquez, E.; Zúñiga Dávila, D.; Vignolo, G.; Aznar, R. Exploring Diversity and Biotechnological Potential of Lactic Acid Bacteria from Tocosh-traditional Peruvian fermented potatoes-by High Throughput Sequencing (HTS) and Culturing. LWT 2018, 87, 567–574. [Google Scholar] [CrossRef]
  8. Valentino, V.; Magliulo, R.; Farsi, D.; Cotter, P.D.; O’Sullivan, O.; Ercolini, D.; De Filippis, F. Fermented Foods, Their Microbiome and Its Potential in Boosting Human Health. Microb. Biotechnol. 2024, 17, e14428. [Google Scholar] [CrossRef]
  9. Amenu, D.; Bacha, K. Probiotic Potential and Safety Analysis of Lactic Acid Bacteria Isolated from Ethiopian Traditional Fermented Foods and beverages. Ann. Microbiol. 2023, 73, 37. [Google Scholar] [CrossRef]
  10. Ricci, A.; Bertani, G.; Maoloni, A.; Bernini, V.; Levante, A.; Neviani, E.; Lazzi, C. Antimicrobial Activity of Fermented Vegetable Byproduct Extracts for Food Applications. Foods 2021, 10, 1092. [Google Scholar] [CrossRef]
  11. Bodor, A.; Bounedjoum, N.; Vincze, G.E.; Erdeiné Kis, Á.; Laczi, K.; Bende, G.; Szilágyi, Á.; Kovács, T.; Perei, K.; Rákhely, G. Challenges of Unculturable Bacteria: Environmental Perspectives. Rev. Environ. Sci. Biotechnol. 2020, 19, 1–22. [Google Scholar] [CrossRef]
  12. Austin, B. The Value of Cultures to Modern Microbiology. Antonie Van Leeuwenhoek 2017, 110, 1247–1256. [Google Scholar] [CrossRef]
  13. Zimmerman, N.; Izard, J.; Klatt, C.; Zhou, J.; Aronson, E. The Unseen World: Environmental Microbial Sequencing and Identification Methods for Ecologists. Front. Ecol. Environ. 2014, 12, 224–231. [Google Scholar] [CrossRef]
  14. Rani, A.; Saini, K.C.; Bast, F.; Varjani, S.; Mehariya, S.; Bhatia, S.K.; Sharma, N.; Funk, C. A Review on Microbial Products and Their Perspective Application as Antimicrobial Agents. Biomolecules 2021, 11, 1860. [Google Scholar] [CrossRef]
  15. Dini, I.; De Biasi, M.G.; Mancusi, A. An Overview of the Potentialities of Antimicrobial Peptides Derived from Natural Sources. Antibiotics 2022, 11, 1483. [Google Scholar] [CrossRef] [PubMed]
  16. López-García, G.; Dublan-García, O.; Arizmendi-Cotero, D.; Oliván, L.M.G. Antioxidant and Antimicrobial Peptides Derived from Food Proteins. Molecules 2022, 27, 1343. [Google Scholar] [CrossRef] [PubMed]
  17. Ueda, K.; Beppu, T. Antibiotics in Microbial Coculture. J. Antibiot. 2017, 70, 361–365. [Google Scholar] [CrossRef]
  18. Tran, C.; Cock, I.E.; Chen, X.; Feng, Y. Antimicrobial Bacillus: Metabolites and Their Mode of Action. Antibiotics 2022, 11, 88. [Google Scholar] [CrossRef]
  19. Zhu, J.; Wang, S.; Wang, C.; Wang, Z.; Luo, G.; Li, J.; Zhan, Y.; Cai, D.; Chen, S. Microbial Synthesis of Bacitracin: Recent Progress, Challenges, and Prospects. Synth. Syst. Biotechnol. 2023, 8, 314–324. [Google Scholar] [CrossRef] [PubMed]
  20. Adeniji, A.A.; Loots, D.T.; Babalola, O.O. Bacillus velezensis: Phylogeny, Useful Applications, and Avenues for Exploitation. Appl. Microbiol. Biotechnol. 2019, 103, 3669–3682. [Google Scholar] [CrossRef]
  21. Monzón-Atienza, L.; Bravo, J.; Torrecillas, S.; Montero, D.; Canales, A.F.G.d.; de la Banda, I.G.; Galindo-Villegas, J.; Ramos-Vivas, J.; Acosta, F. Isolation and Characterization of a Bacillus velezensis D-18 Strain, as a Potential Probiotic in European Seabass Aquaculture. Probiotics Antimicrob. Proteins 2021, 13, 1404–1412. [Google Scholar] [CrossRef]
  22. Tsai, C.F.; Lin, L.J.; Wang, C.H.; Tsai, C.S.; Chang, S.C.; Lee, T.T. Effects of Fermented Soybean Meal with Bacillus velezensis, Lactobacillus spp. or Their Combination on Broiler Performance, Gut Antioxidant Activity and Microflora. Anim. Biosci. 2022, 35, 1892–1903. [Google Scholar] [CrossRef]
  23. EFSA Panel (BIOHAZ); Koutsoumanis, K.; Allende, A.; Álvarez-Ordóñez, A.; Bolton, D.; Bover-Cid, S.; Chemaly, M.; de Cesare, A.; Hilbert, F.; Lindqvist, R.; et al. Update of the List of Qualified Presumption of Safety (QPS) Recommended Microorganisms Intentionally Added to Food or Feed as Notified to EFSA. EFSA J. 2023, 21, 7747. [Google Scholar] [CrossRef]
  24. Stincone, P.; Veras, F.F.; Pereira, J.Q.; Mayer, F.Q.; Varela, A.P.M.; Brandelli, A. Diversity of Cyclic Antimicrobial Lipopeptides from Bacillus P34 Revealed by Functional Annotation and Comparative Genome Analysis. Microbiol. Res. 2020, 238, 126515. [Google Scholar] [CrossRef] [PubMed]
  25. Bach, E.; Ritter, A.C.; Silveira, R.D.; de Souza, M.Á.; Passaglia, L.M.P.; Welke, J.E.; Brandelli, A. Pangenome Analysis of Bacillus velezensis Exploring the Probiotic Potential and Plant Growth Promotion Traits of Strains Isolated from Fish Intestines. Mol. Genet. Genom. 2025, 300, 20. [Google Scholar] [CrossRef]
  26. De Coster, W.; Rademakers, R. NanoPack2: Population-Scale Evaluation of Long-Read Sequencing Data. Bioinformatics 2023, 39, btad311. [Google Scholar] [CrossRef] [PubMed]
  27. De Coster, W.; D’hert, S.; Schultz, D.T.; Cruts, M.; van Broeckhoven, C. NanoPack: Visualizing and Processing Long-Read Sequencing Data. Bioinformatics 2018, 34, 2666–2669. [Google Scholar] [CrossRef]
  28. Koren, S.; Walenz, B.P.; Berlin, K.; Miller, J.R.; Bergman, N.H.; Phillippy, A.M. Canu: Scalable and Accurate Long-Read Assembly via Adaptive k-mer Weighting and repeat separation. Genome Res. 2017, 27, 722–736. [Google Scholar] [CrossRef]
  29. Gurevich, A.; Saveliev, V.; Vyahhi, N.; Tesler, G. QUAST: Quality Assessment Tool for Genome Assemblies. Bioinformatics 2013, 29, 1072–1075. [Google Scholar] [CrossRef]
  30. Tegenfeldt, F.; Kuznetsov, D.; Manni, M.; Berkeley, M.; Zdobnov, E.M.; Kriventseva, E.V. OrthoDB and BUSCO update: Annotation of orthologs with wider sampling of genomes. Nucleic Acids Res. 2025, 53, D516–D522. [Google Scholar] [CrossRef] [PubMed]
  31. Parks, D.H.; Imelfort, M.; Skennerton, C.T.; Hugenholtz, P.; Tyson, G.W. CheckM: Assessing the Quality of Microbial Genomes Recovered from Isolates, Single Cells, and Metagenomes. Genome Res. 2015, 25, 1043–1055. [Google Scholar] [CrossRef]
  32. Seemann, T. Prokka: Rapid Prokaryotic Genome Annotation. Bioinformatics 2014, 30, 2068–2069. [Google Scholar] [CrossRef]
  33. Alcock, B.P.; Huynh, W.; Chalil, R.; Smith, K.W.; Raphenya, A.R.; Wlodarski, M.A.; Edalatmand, A.; Petkau, A.; Syed, S.A.; Tsang, K.K.; et al. CARD 2023: Expanded Curation, Support for Machine Learning, and Resistome Prediction at the Comprehensive Antibiotic Resistance Database. Nucleic Acids Res. 2023, 51, D690–D699. [Google Scholar] [CrossRef]
  34. Jia, B.; Raphenya, A.R.; Alcock, B.; Waglechner, N.; Guo, P.; Tsang, K.K.; Lago, B.A.; Dave, B.M.; Pereira, S.; Sharma, A.N.; et al. CARD 2017: Expansion and Model-Centric Curation of the Comprehensive Antibiotic Resistance Database. Nucleic Acids Res. 2017, 45, D566–D573. [Google Scholar] [CrossRef] [PubMed]
  35. Chen, L.; Zheng, D.; Liu, B.; Yang, J.; Jin, Q. VFDB 2016: Hierarchical and Refined Dataset for Big Data Analysis—10 Years on. Nucleic Acids Res. 2016, 44, D694–D697. [Google Scholar] [CrossRef] [PubMed]
  36. Medema, M.H.; Blin, K.; Cimermancic, P.; de Jager, V.; Zakrzewski, P.; Fischbach, M.A.; Weber, T.; Takano, E.; Breitling, R. antiSMASH: Rapid identification, annotation and analysis of secondary metabolite biosynthesis gene clusters in bacterial and fungal genome sequences. Nucleic Acids Res. 2011, 39, W339–W346. [Google Scholar] [CrossRef]
  37. Tonkin-Hill, G.; MacAlasdair, N.; Ruis, C.; Weimann, A.; Horesh, G.; Lees, J.A.; Gladstone, R.A.; Lo, S.; Beaudoin, C.; Floto, R.A.; et al. Producing Polished Prokaryotic Pangenomes with the Panaroo Pipeline. Genome Biol. 2020, 21, 180. [Google Scholar] [CrossRef]
  38. Croucher, N.J.; Page, A.J.; Connor, T.R.; Delaney, A.J.; Keane, J.A.; Bentley, S.D.; Parkhill, J.; Harris, S.R. Rapid Phylogenetic Analysis of Large Samples of Recombinant Bacterial Whole Genome Sequences Using Gubbins. Nucleic Acids Res. 2015, 43, e15. [Google Scholar] [CrossRef] [PubMed]
  39. Page, A.J.; Taylor, B.; Delaney, A.J.; Soares, J.; Seemann, T.; Keane, J.A.; Harris, S.R. SNP-sites: Rapid Efficient Extraction of SNPs from Multi-FASTA Alignments. Microb. Genom. 2016, 2, e000056. [Google Scholar] [CrossRef]
  40. Wong, T.K.; Ly-Trong, N.; Ren, H.; Baños, H.; Roger, A.J.; Susko, E.; Bielow, C.; de Maio, N.; Goldman, N.; Hahn, M.W.; et al. IQ-TREE 3: Phylogenomic Inference Software using Complex Evolutionary Models. Mol. Biol. Evol. 2025, 42, msad215. [Google Scholar] [CrossRef]
  41. Sun, J.; Lu, F.; Luo, Y.; Bie, L.; Xu, L.; Wang, Y. OrthoVenn3: An integrated platform for exploring and visualizing orthologous data across genomes. Nucleic Acids Res. 2023, 51, W397–W403. [Google Scholar] [CrossRef]
  42. Cantalapiedra, C.P.; Hern Andez-Plaza, A.; Letunic, I.; Bork, P.; Huerta-Cepas, J. eggNOG-mapper v2: Functional Annotation, Orthology Assignments, and Domain Prediction at the Metagenomic Scale. Mol. Biol. Evol. 2021, 38, 5825–5829. [Google Scholar] [CrossRef]
  43. Lederberg, J.; Lederberg, E.M. Replica plating and indirect selection of bacterial mutants. J. Bacteriol. 1952, 63, 399–406. [Google Scholar] [CrossRef]
  44. Osterblad, M.; Leistevuo, T.; Huovinen, P. Screening for antimicrobial resistance in fecal samples by the replica plating method. J. Clin. Microbiol. 1995, 33, 3146–3149. [Google Scholar] [CrossRef]
  45. Kirk, A.; Stavrinides, J. A replica plating method for efficient, high-throughput screening of antibiotic gene clusters in bacteria uncovers a holomycin-like cluster in the clinical isolate, Pantoea agglomerans 20KB447973. J. Microbiol. Methods 2023, 214, 106822. [Google Scholar] [CrossRef]
  46. Ezraty, B.; Henry, C.; Hérisse, M.; Denamur, E.; Barras, F. Commercial Lysogeny Broth Culture Media and Oxidative Stress: A Cautious Tale. Free Radic. Biol. Med. 2014, 74, 245–251. [Google Scholar] [CrossRef] [PubMed]
  47. Perea-Molina, P.A.; Pedraza-Herrera, L.A.; Beauregard, P.B.; Uribe-Vélez, D. A Biocontrol Bacillus velezensis Strain Decreases Pathogen Burkholderia glumae Population and occupies a similar niche in rice plants. Biol. Control 2022, 76, 105067. [Google Scholar] [CrossRef]
  48. Mullins, A.J.; Li, Y.; Qin, L.; Hu, X.; Xie, L.; Gu, C.; Mahenthiralingam, E.; Liao, X.; Webster, G. Reclassification of the Biocontrol Agents Bacillus subtilis BY-2 and Tu-100 as Bacillus velezensis and insights into the genomic and specialized metabolite diversity of the species. Microbiology 2020, 166, 1121–1133. [Google Scholar] [CrossRef]
  49. Blaznik, M.; Volk, M.; Kraigher, B.; Calonge-Sanz, A.; Barco-García, G.; Stopar, D.; Dogsa, I. Biofilm structure as a key factor in antibiotic tolerance: Insights from Bacillus subtilis model systems. npj Biofilms Microbiomes 2025, 11, 34. [Google Scholar] [CrossRef]
  50. Zheng, Y.; Liu, T.; Wang, Z.; Wang, X.; Wang, H.; Li, Y.; Zheng, W.; Wei, S.; Leng, Y.; Li, J.; et al. Whole-Genome Sequencing and Secondary Metabolite Exploration of the Novel Bacillus velezensis BN with broad-spectrum antagonistic activity against fungal plant pathogens. Front. Microbiol. 2025, 15, 1498653. [Google Scholar] [CrossRef] [PubMed]
  51. Borriss, R.; Chen, X.H.; Rueckert, C.; Blom, J.; Becker, A.; Baumgarth, B.; Fan, B.; Henne, R.; Kernine, M.; Leifert, C. Comparative Analysis of the Complete Genome Sequence of the Plant Growth-Promoting Bacterium Bacillus amyloliquefaciens FZB42. J. Biotechnol. 2011, 151, 73–90. [Google Scholar] [CrossRef]
  52. Ge, Z.; Kuang, Z.; Chen, J.; Chen, J.; Liu, T.; She, Z.; Lu, Y. Comparative Genomics Analysis of Bacillus velezensis LOH112 Isolated from a Nonagenarian. Genomics 2023, 115, 110626. [Google Scholar] [CrossRef]
  53. Hansen, L.H.; Planellas, M.H.; Long, K.S.; Vester, B. The order Bacillales hosts functional homologs of the worrisome cfr antibiotic resistance gene. Antimicrob. Agents Chemother. 2012, 56, 3563–3567. [Google Scholar] [CrossRef] [PubMed]
  54. Liu, Y.; Lai, Q.; Göker, M.; Meier-Kolthoff, J.P.; Wang, M.; Sun, Y.; Wang, L.; Shao, Z. Genomic insights into the taxonomic status of the Bacillus cereus group. Sci. Rep. 2015, 5, 14082. [Google Scholar] [CrossRef]
  55. Zhang, Y.; Zhang, N.; Bi, X.; Bi, T.; Baloch, F.B.; Miao, J.; Zeng, N.; Li, B.; An, Y. Growth Promotion on Maize and Whole-Genome Sequence Analysis of Bacillus velezensis D103. Microbiol. Spectr. 2024, 12, e01147-24. [Google Scholar] [CrossRef]
  56. Shen, Y.; Shi, Z.; Zhao, J.; Li, M.; Tang, J.; Wang, N.; Mo, Y.; Yang, T.; Zhou, X.; Chen, Q.; et al. Whole Genome Sequencing Provides Evidence for Bacillus velezensis SH-1471 as a Beneficial Rhizosphere Bacterium in plants. Sci. Rep. 2023, 13, 16259. [Google Scholar] [CrossRef] [PubMed]
  57. Bini, F.; Soffritti, I.; D’Accolti, M.; Mazziga, E.; Caballero, J.D.; David, S.; Argimon, S.; Aanensen, D.M.; Volta, A.; Bisi, M.; et al. Profiling the Resistome and Virulome of Bacillus Strains Used for Probiotic-Based Sanitation: A Multicenter WGS Analysis. BMC Genom. 2025, 26, 382. [Google Scholar] [CrossRef]
  58. Piewngam, P.; Zheng, Y.; Nguyen #1, T.H.; Dickey, S.W.; Joo, H.-S.; Villaruz, A.E.; Glose, K.A.; Fisher, E.L.; Hunt, R.L.; Li, B.; et al. Pathogen elimination by probiotic Bacillus via signalling interference. Nature 2018, 562, 532–537. [Google Scholar] [CrossRef]
  59. Xu, C.; Xia, B.; Zhang, Z.; Lin, Y.; Li, C.; Lin, L. Research Progress in Steroidal Saponins from the Genus Polygonatum: Chemical Components, Biosynthetic Pathways and Pharmacological Effects. Phytochemistry 2023, 213, 113731. [Google Scholar] [CrossRef] [PubMed]
  60. Belbahri, L.; Bouket, A.C.; Rekik, I.; Alenezi, F.N.; Vallat, A.; Luptakova, L.; Petrovova, E.; Oszako, T.; Cherrad, S.; Vacher, S.; et al. Comparative Genomics of Bacillus amyloliquefaciens Strains Reveals a Core Genome with Traits for Habitat Adaptation and a Secondary Metabolites Rich Accessory Genome. Front. Microbiol. 2017, 8, 1438. [Google Scholar] [CrossRef]
  61. Zhang, F.; Shi, X.; Xu, J.; Yuan, W.; Li, Z. Tandem gene duplication selected by activation of horizontally transferred gene in bacteria. Appl. Microbiol. Biotechnol. 2024, 108, 340. [Google Scholar] [CrossRef]
  62. Vasu, K.; Nagaraja, V. Diverse Functions of Restriction-Modification Systems in Addition to Cellular Defense. Microbiol. Mol. Biol. Rev. 2013, 77, 53–72. [Google Scholar] [CrossRef] [PubMed]
  63. Gustafson, K.; Roman, M.; Fenical, W. The Macrolactins, a Novel Class of Antiviral and Cytotoxic Macrolides from a Deep-Sea Marine Bacterium. J. Am. Chem. Soc. 1989, 111, 7519–7524. [Google Scholar] [CrossRef]
  64. Zhang, L.; Jin, M.; Shi, X.; Jin, L.; Hou, X.; Yu, Y.; Liu, B.; Cao, J.; Quan, C. Macrolactin Metabolite Production by Bacillus sp. ZJ318 Isolated from Marine Sediment. Appl. Biochem. Biotechnol. 2022, 194, 2581–2593. [Google Scholar] [CrossRef]
  65. Nagao, T.; Adachi, K.; Sakai, M.; Nishijima, M.; Sanoll, H. Novel macrolactins as antibiotic lactones from a marine bacterium. J. Antibiot. 2001, 54, 333–339. [Google Scholar] [CrossRef]
  66. Mayerl, F.; Fisher, S.; Pirnik, D.; Aklonis, C.; Dean, L.; Meyers, E.; Fernandes, P. Bacillaene, a Novel Inhibitor of Procaryotic Protein Synthesis Produced by Bacillus subtilis: Production, Taxonomy, Isolation, Physico-Chemical Characterization and Biological Activity. J. Antibiot. 1995, 48, 997–1003. [Google Scholar] [CrossRef]
  67. Miao, S.; Liang, J.; Xu, Y.; Yu, G.; Shao, M. Bacillaene: Sharp Objects Consist in the Arsenal of Antibiotics Produced by Bacillus. J. Cell. Physiol. 2024, 239, e30974, Erratum in J. Cell. Physiol. 2024, 239, e31228. [Google Scholar] [CrossRef] [PubMed]
  68. Li, T.; Cheng, X.; Wang, Y.; Yin, X.; Li, Z.; Liu, R.; Liu, G.; Wang, Y.; Xu, Y. Genome-Wide Analysis of Glyoxalase-Like Gene Families in Grape (Vitis vinifera L.) and Their Expression Profiling in Response to Downy Mildew Infection. BMC Genom. 2019, 20, 5733. [Google Scholar] [CrossRef]
  69. Fan, B.; Li, Y.; Li, L.; Peng, X.; Bu, C.; Wu, X. Malonylome Analysis of Rhizobacterium Bacillus amyloliquefaciens FZB42 Reveals Involvement of Lysine Malonylation in Polyketide Synthesis and Plant-Bacteria Interactions. J. Proteom. 2017, 154, 1–12. [Google Scholar] [CrossRef]
  70. Erega, A.; Stefanie, P.; Dogsa, I.; Danevčič, T.; Simunovic, K.; Klančnik, A.; Možina, S.S.; Mulec, I.M. Bacillaene Mediates the Inhibitory Effect of Bacillus subtilis on Campylobacter jejuni Biofilms. Appl. Environ. Microbiol. 2021, 87, e02955-20. [Google Scholar] [CrossRef]
  71. Podnar, E.; Erega, A.; Danevčič, T.; Kovačec, E.; Lories, B.; Steenackers, H.; Mandic-Mulec, I. Nutrient Availability and Biofilm Polysaccharide Shape the Bacillaene-Dependent Antagonism of Bacillus subtilis Against Salmonella Typhimurium. Microbiol. Spectr. 2022, 10, e01836-22. [Google Scholar] [CrossRef]
  72. Müller, S.; Strack, S.N.; Hoefler, B.C.; Straight, P.D.; Kearns, D.B.; Kirby, J.R. Bacillaene and Sporulation Protect Bacillus subtilis from Predation by Myxococcus xanthus. Appl. Environ. Microbiol. 2014, 80, 5603–5610. [Google Scholar] [CrossRef]
  73. Arguelles-Arias, A.; Ongena, M.; Halimi, B.; Lara, Y.; Brans, A.; Joris, B.; Fickers, P. Bacillus amyloliquefaciens GA1 as a source of potent antibiotics and other secondary metabolites for biocontrol of plant pathogens. Microb. Cell Factories 2009, 8, 63. [Google Scholar] [CrossRef]
  74. Han, X.; Shen, D.; Xiong, Q.; Bao, B.; Zhang, W.; Dai, T.; Zhao, Y.; Borriss, R.; Fan, B. The Plant-Beneficial Rhizobacterium Bacillus velezensis FZB42 Controls the Soybean Pathogen Phytophthora sojae. Appl. Environ. Microbiol. 2021, 87, e01601-21. [Google Scholar] [CrossRef]
  75. Im, S.M.; Yu, N.H.; Joen, H.W.; Kim, S.O.; Park, H.W.; Park, A.R.; Kim, J.C. Biological control of tomato bacterial wilt by oxydifficidin and difficidin-producing Bacillus methylotrophicus DR-08. Pestic. Biochem. Physiol. 2020, 163, 170–178. [Google Scholar] [CrossRef]
  76. Rajavel, M.; Mitra, A.; Gopal, B. Role of Bacillus subtilis BacB in the synthesis of bacilysin. J. Biol. Chem. 2009, 284, 31882–31892. [Google Scholar] [CrossRef] [PubMed]
  77. Zhang, L.; Sun, C. Fengycins from Marine Bacillus subtilis Strains Kill the Plant-Pathogenic Fungus Magnaporthe grisea by Inducing Reactive Oxygen Species Production and Chromatin Condensation. Appl. Environ. Microbiol. 2018, 84, e00445-18. [Google Scholar] [CrossRef]
  78. Molohon, K.J.; Blair, P.M.; Park, S.; Doroghazi, J.R.; Maxson, T.; Hershfield, J.R.; Flatt, K.M.; Schroeder, N.E.; Ha, T.; Mitchell, D.A. Plantazolicin is an ultranarrow-spectrum antibiotic that targets the Bacillus anthracis membrane. ACS Infect. Dis. 2015, 2, 207–220. [Google Scholar] [CrossRef] [PubMed]
  79. Chen, X.H.; Koumoutsi, A.; Scholz, R.; Schneider, K.; Vater, J.; Süssmuth, R.; Piel, J.; Borriss, R. Genome analysis of Bacillus amyloliquefaciens FZB42 reveals its potential for biocontrol of plant pathogens. J. Biotechnol. 2009, 140, 27–37. [Google Scholar] [CrossRef] [PubMed]
  80. Xu, Z.; Mandic-Mulec, I.; Zhang, H.; Liu, Y.; Sun, X. Antibiotic Bacillomycin D Affects Iron Acquisition and Biofilm Formation in Bacillus velezensis Through a Btr-Mediated FeuABC-Dependent Pathway. Cell Rep. 2019, 29, 1192–1202. [Google Scholar] [CrossRef]
  81. Zhang, A.; Yang, B.; Ma, Y.; Li, R.; Zhou, Z.; Luo, C.; Zhang, Y.; Zhang, Y.; Dan, Y.; Sun, Q. Improving the bacillomycin L production in Bacillus amyloliquefaciens by atmospheric and room-temperature plasma combined with Box-Behnken design. Microb. Cell Factories 2025, 24, 144. [Google Scholar] [CrossRef] [PubMed]
  82. Nam, J.; Alam, S.T.; Kang, K.; Choi, J.; Seo, M.H. Anti-staphylococcal activity of a cyclic lipopeptide, C15-bacillomycin D, produced by Bacillus velezensis NST6. J. Appl. Microbiol. 2021, 131, 93–104. [Google Scholar] [CrossRef] [PubMed]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Article Metrics

Citations

Article Access Statistics

Journal Statistics
Article metric data becomes available approximately 24 hours after publication online.
Download PDF
The Impact of Disinfecting Non-Sterile Disposable Gloves on the Level of Microbiological Contamination in Clinical Practice
Previous