Comparative, Pangenomic and Functional Analyses of two Bacillus paralicheniformis Soil- Isolated Strains from Bahia Sequenced by WGS Reveal Species Homogeneity and Bioactive Metabolites with Biotechnological Potential

Comparative, Pangenomic and Functional Analyses of two Bacillus paralicheniformis Soil- Isolated Strains from Bahia Sequenced by WGS Reveal Species Homogeneity and Bioactive Metabolites with Biotechnological Potential | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Comparative, Pangenomic and Functional Analyses of two Bacillus paralicheniformis Soil- Isolated Strains from Bahia Sequenced by WGS Reveal Species Homogeneity and Bioactive Metabolites with Biotechnological Potential Gabriel Camargos Gomes, Eduarda Guimarães Sousa, Ludmila Silva Quaresma, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7143786/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 31 Dec, 2025 Read the published version in World Journal of Microbiology and Biotechnology → Version 1 posted 12 You are reading this latest preprint version Abstract The Bacillus genus includes plant growth-promoting rhizobacteria (PGPR), and the discovery of new strains within this group is of great biotechnological interest due to their ability to produce antimicrobial compounds (AMCs), vitamins, enzymes, and heterologous proteins. Among these, Bacillus paralicheniformis is a recently described species whose phylogeny remains poorly resolved, highlighting the need for further investigation. This study aimed to identify and characterize the isolates BAC30 and BAC220 using whole-genome sequencing (WGS). Both were confirmed as B. paralicheniformis and included in phylogenomic and comparative analyses with 28 other strains to assess the species’ genetic structure and inter-strain similarity. Functional annotation of BAC30 and BAC220 was also performed, focusing on biotechnological potential. Comparative analysis revealed high genomic similarity among strains, including the two isolates. Pangenome analysis showed a low proportion of core genes relative to accessory genes (shell and cloud), and the rarefaction curve suggested an open pangenome, indicating the species’ ubiquity and co-evolution with other organisms. Functional analysis identified genes of defense mechanisms related to beta-lactam resistance. Regarding secondary metabolite production, genes involved in the biosynthesis of vitamins (e.g., riboflavin) and AMCs (e.g., bacitracin) were detected. Although further in vitro and in vivo assays are needed to confirm gene expression, the findings support the biotechnological relevance of these isolates as potential biocontrol agents and/or producers of industrially valuable compounds. Bacillus Biocontrol Agriculture Industry Metabolites Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction The Bacillus genus is highly diverse and extensively studied. Its main characteristics include rod-shaped bacteria capable of sporulating when exposed to environmental stress factors, primarily nutrient deprivation (Paredes-Sabja et al. 2011 ), but also sudden changes in pH, salinity, and temperature (Gauvry et al. 2021 ). Originally, the genus included species such as B. subtilis (the most extensively studied and a global reference among Gram-positive bacteria (Errington and Aart 2020 )), B. licheniformis , B. pumilus , and B. amyloliquefaciens . Later, additional species were incorporated, such as pathogenic ones, like B. cereus and B. anthracis , as well as others of importance to agriculture and industry, such as B. thuringiensis (Blanco Crivelli et al. 2024 ). With the advancement of sequencing and genome annotation technologies, new species and subspecies have been identified, such as B. paralicheniformis , expanding the genetic landscape of the genus. However, as B. paralicheniformis was only recently established (many of its strains were previously classified as B. licheniformis ), the number of available studies and deposited genomes remains limited compared to other species in the group. This highlights the need for further investigations to characterize its genomic profile, particularly regarding evolutionary and phylogenomic patterns. This species, recently described and separated from B. licheniformis (Dunlap et al. 2015), already shows industrial application potential, with notable prominence in agriculture, where it is recognized as a plant growth-promoting rhizobacterium (PGPR) (Du et al. 2019). It is known to produce bacteriocins and other metabolites (Choyam et al. 2021 ) that help maintain the local microbiota by inhibiting pathogens such as fungi (Ramírez-Cariño et al. 2020 ), bacteria (Zhao et al. 2023 ), viruses (Yu et al. 2024), and nematodes (Chavarria-Quicaño et al. 2023 ). In addition, these bacteria promote the growth of plants like wheat and cotton (Xu et al. 2023 ), and may contribute to nitrogen fixation, phosphate solubilization, potassium release, and the production and release of phytohormones (Olanrewaju et al. 2017 ). Although the induction of biosynthesis of molecules of interest and the establishment of optimal conditions for efficient production require the evaluation of multiple variables, studied under the scope of metabolic engineering (Kim et al. 2016 ), the use of individual bacterial strains that produce secondary metabolites, such as bacitracin, bacillibactin, fengycin, and surfactin, has proven valuable. These metabolites have potential applications as natural preservatives and surfactants, offering a promising alternative to chemical additives currently used in the food industry, which are associated with potential toxicological and teratogenic effects harmful to human health. In this context, consumer demand for healthier and more natural biopreservatives has increased (Kumariya et al. 2019 ). Moreover, previous studies have assessed the application of B. paralicheniformis strains in animal production, including strain BAC220, which is the focus of the present study (initially identified as B. subtilis via MALDI-TOF analysis) (Dos Reis et al. 2022 ). This strain was shown to reduce necrosis rates and inflammatory markers in chicken embryos challenged with Salmonella Pullorum ( Salmonella enterica subsp. enterica serovar Gallinarum biovar Pullorum ). Given the biotechnological relevance of the Bacillus genus, this study aimed to explore the evolutionary profile of the B. paralicheniformis species, conducting phylogenomic and comparative analyses of strains obtained from the National Center for Biotechnology Information (NCBI) database, along with functional analyses of two isolated strains, BAC30 and BAC220. These analyses focused on functional genes related to defense mechanisms and the biosynthesis of secondary metabolites with potential for biotechnological application. Materials and methods Genome assembly, contamination check, and completeness evaluation The strains identified as BAC30 and BAC220 were isolated from soil samples collected in Bahia, Brazil and previously identified as unknown and Bacillus cereus , respectively, in MALDI-TOF analysis. DNA extraction was performed using the Wizard® Genomic DNA Purification Kit (Promega), following the manufacturer's instructions. Next-generation sequencing was conducted on the HiSeq 2500 platform (2×150 bp) (Illumina®, United States), and paired-end libraries were constructed using the ThruPLEX DNA-Seq Kit (Takara). For genome assembly, annotation, and taxonomic identification, a customized pipeline was employed. Initial trimming was performed using FastP (v0.24.0), a tool capable of detecting and removing adapters and low-quality reads, thereby improving the overall quality of the sequenced genome (Chen et al. 2018 ). This was followed by a quality control assessment using FastQC (v0.12.1), which generates a comprehensive report on several sequencing metrics (Babraham Bioinformatics, 2010). Genome assembly was carried out with Unicycler (v0.5.1), a tool optimized for short-read data generated by platforms such as Illumina. The software uses a De Bruijn graph-based approach to construct initial contigs and then applies refinement strategies to resolve ambiguities and repetitive regions commonly associated with short-read data. Unicycler further performs assembly graph polishing, leveraging the high accuracy of short reads to improve the continuity and quality of the assembled genome (Wick et al. 2017 ). Contamination and completeness assessments were performed using CheckM2 (v1.0.2), which applies a machine learning model independent of phylogeny. It estimates genome completeness based on the proportion of conserved marker genes present and predicts potential contamination or misassemblies based on redundancy among these markers (Chklovski et al. 2023 ). Additional quality control analyses were conducted using GUNC (v1.0.6), which detects possible chimeric contigs (fragments erroneously assembled from different genomic sources) (Orakov et al. 2021 ). Moreover, Barrnap (v0.9) was employed to predict conserved ribosomal genes such as 5S, 16S, and 23S rRNA, providing an additional metric for evaluating the completeness of the assembled genomes (Seeman T., 2013 ) Taxonomic Identification Taxonomic identification was performed using the Genome Taxonomy Database Toolkit (GTDB-Tk) (v2.4.1) and the Type (Strain) Genome Server (TYGS), to ensure high confidence in the classification results. GTDB-Tk infers taxonomic assignments based on the standardized taxonomy of the Genome Taxonomy Database (GTDB), applying the Relative Evolutionary Divergence (RED) criteria, followed by Average Nucleotide Identity (ANI) validation for species-level resolution (Chaumeil et al. 2020 ). The commonly accepted ANI threshold for confident species-level identification is > 95–96% (Elbir 2024 ). TYGS performs digital DNA-DNA hybridization (dDDH) analysis, which assesses phylogenomic relatedness between submitted genomes and database entries based on the degree of hybridization between complete genomes or genomic fragments, considering overlap, segment size, and similarity (Meier-Kolthoff and Göker 2019 ). For dDDH, the standard threshold for confident species-level identification is > 70% (Meier-Kolthoff et al. 2013 ). In addition, the pyANI tool was used to perform all-vs-all ANI comparisons, generating a pairwise similarity distance matrix (Pritchard et al. 2016 ). This analysis included 28 complete B. paralicheniformis genomes retrieved from the NCBI database (Table 1 ), along with the two isolates from this study (BAC30 and BAC220). A heatmap was generated using a custom script in RStudio (v2024.12.1) (Posit Team, 2025 ). Subsequently, using the pyANI software, the two isolates were compared against 14 genomes representing other Bacillus species. All genomes, except for the isolates, are designated as reference genomes for their respective species according to NCBI and were retrieved directly from the NCBI database. Compilation of the Bacillus paralicheniformis dataset Table 1 presents 28 complete Bacillus paralicheniformis genomes (RefSeq), all obtained from the NCBI using the NCBI Datasets tool (v16.30.0), which enables the retrieval and download of sequences, annotations, and metadata for genes and genomes via command line interface (O’Leary et al. 2024 ). This same dataset was used in all subsequent analyses, with the addition of the two isolates from this study, BAC30 and BAC220. The table includes information such as strain names, NCBI assembly accession numbers, approximate total genome size (in Megabases, Mb), as well as the source and country of isolation, in order to assess genomic similarities and differences among the strains. Table 1 Information on the 28 complete Bacillus paralicheniformis genomes retrieved via NCBI Datasets. Strain Name NCBI RefSeq Assembly Genome size (Mb) Isolation source Country of origin Bac84 GCF_002993925.1 4.38 Red Sea Lagoon Saudi Arabia PRO109 GCF_029536955.1 4.62 Soy-based food Ghana A4-3 GCF_008365255.1 4.58 Tomato South Korea CP47 GCF_031326025.1 4.54 Fermented soy-based food South Korea 14DA11 GCF_002393225.1 4.54 Fermented soy-based food South Korea J41TS8 GCF_021654755.1 4.50 Japanese honey Japan DY4 GCF_030166695.1 4.55 Pig feces China CBMAI 1303 GCF_003711025.1 4.48 - Brazil Bac48 GCF_002993945.1 4.46 Red Sea mangrove mud Saudi Arabia A5 GCF_035985545.1 4.46 Marine sediment China CamBx3 GCF_026210435.1 4.45 Hot spring water - FA6 GCF_009497935.1 4.45 Grass carp China NCTC8721 GCF_900635765.1 4.43 - - J36TS2 GCF_021654735.1 4.60 Japanese honey Japan DSM 28591 GCF_029537035.1 4.39 C57BL/6 mouse feces Germany J25TS1 GCF_021654715.1 4.39 Japanese honey Japan BL-09 GCF_000876525.1 4.39 Fermented Congee Soup China MDJK30 GCF_002068155.1 4.35 Peony rhizosphere China RP01 GCF_029000405.1 4.34 Soil China CEW 1W GCF_030123025.1 4.34 Marine sediment India 285-3 GCF_026723705.1 4.32 Soil China RSC-1 GCF_018324505.1 4.32 Red sea water Saudi Arabia RSC-2 GCF_018324565.1 4.32 Red sea water Saudi Arabia SUBG0010 GCF_003171815.2 4.32 Rhizosphere India SYN-191 GCF_029590455.1 4.29 Soil China ATCC 9945a GCF_000408885.1 4.38 - - Baplich1 GCF_038049955.1 4.38 Tomato rhizosphere China FUA2150 GCF_039619465.1 4.45 Fermentation starter Daqu China Phylogenomic analyses Starting from the 28 complete genomes and the two isolated genomes (BAC30 and BAC220), one outgroup was included: Bacillus sonorensis ASM3405511v1 (GCF_035804855.1). This strain was chosen as an outgroup due to its close taxonomic relationship, although it does not belong to the same species (Olajide et al. 2021 ). For phylogenomic tree construction, the programs Gegenees and SplitsTree4 (v4.14.4) were used. Gegenees generates a pairwise distance matrix (all vs all) between genomes by fragmenting them and assigning scores to gene regions, thus standardizing the comparison, which is performed using BLAST (Ågren et al. 2012 ). SplitsTree4 then uses this distance matrix to infer phylogenetic relationships through the Neighbor Joining (NJ) method (Huson and Bryant 2006 ). Additionally, Orthofinder (v3.01b1) was used to construct a phylogenetic tree based on all-vs-all comparisons of protein sequences, leveraging orthologous and paralogous relationships. The tree was generated using the Maximum Likelihood (ML) method with 1000 bootstrap replicates (Emms and Kelly 2019 ). The resulting tree was visualized and edited using the Interactive Tree of Life (iTOL) platform (Letunic and Bork 2024 ). Comparative analyses The 28 complete Bacillus paralicheniformis genomes obtained from NCBI were annotated using Prokka (v1.14.6), in order to standardize them with the two isolated genomes from this study. Prokka annotates sequences in FASTA format, identifying the coordinates of genomic features present in contigs using tools like Prodigal (Hyatt et al. 2010 ), RNAmmer (Lagesen et al. 2007 ), and others (Seemann 2014 ). A comparative circular genome map of the 30 genomes was then generated using the Proksee platform (Grant et al. 2023 ), with the NCBI reference genome Bacillus paralicheniformis Bac84 (GCF_002993925.1) as the base. From this reference genome, a BLAST comparison was performed against each of the other genomes using BLAST+ (v2.16.0), integrated within the Proksee software. Pangenomic Analyses The PPanGGolin software (v2.0.5) was used to perform the pangenome analysis. This tool integrates information from protein-coding genes and their genomic neighborhoods to construct a gene family graph, where each node represents a gene family, and edges between nodes reflect similarity relationships among families (Gautreau et al. 2020 ). The analysis included the 28 previously deposited genomes along with the two newly sequenced isolates. The program outputs several key parameters, including the number and percentage of gene families, categorized into accessory (cloud), shared (shell), and persistent (core) genes. A rarefaction curve was also constructed based on Heap’s Law, a mathematical model describing the increase in the number of gene families (or unique genes) as additional genomes are included in the analysis. This curve allows inference of whether the pangenome is open or closed, based on the α (alpha) parameter. Alpha values below 1 indicate an open pangenome, suggesting that the inclusion of new genomes continues to yield novel genes (cloud). In contrast, alpha values equal to or greater than 1 indicate a closed pangenome, in which most genes have already been captured and the addition of new genomes has minimal impact on the total gene count (Felice et al. 2023 ). Functional analysis For the functional analyses, the COG classifier tool was first used to perform functional annotation, classification, and analysis of all genes based on comparisons with the COG (Clusters of Orthologous Groups) database (Shimoyama 2022 ). This tool was used to compare the core genes of the isolated strains with those of the other genomes in the dataset, in order to evaluate the impact of the isolates on the overall core genome of the species and to assess differences in the gene proportions across COG functional categories. Next, the BlastKOALA tool, integrated with the KEGG platform, was used to functionally annotate the sequences based on the KEGG database, assigning KEGG Orthology (KO) identifiers. This enabled the characterization of individual gene functions and the reconstruction of specific KEGG pathways and modules (Kanehisa et al. 2016 ), considered relevant to the biotechnological potential of the isolates, with emphasis on genes associated with defense mechanisms, secondary metabolites prodduction, and genes related to nitrogen metabolism. Finally, the antiSMASH software (v8.0) was used, a tool focused on the analysis of microbial genomes with an emphasis on the annotation of genes involved in biosynthetic gene clusters (BGCs), particularly those encoding nonribosomal peptide synthetases (NRPS), which are of considerable biotechnological interest (Blin et al. 2025 ). Results Assembly, quality assessment, and taxonomic identification of the isolated strains The genomes of the isolates were designated BAC30 and BAC220 and were subsequently identified as Bacillus paralicheniformis by both applied methods (GTDB-Tk and TYGS), showing an acceptable degree of similarity (with thresholds above 95% for ANI and 70% for dDDH) (Table S1 ). The GC content, ranging between 45% and 46%, is consistent with previously reported B. paralicheniformis genomes (Du et al. 2019; Asif et al. 2023 ). Quality assessments indicated 100% completeness with no detectable contamination (below 5%), and both samples were confirmed to be free of chimeric sequences. Furthermore, both isolates harbored the three conserved ribosomal RNA genes (5S, 16S, and 23S), further supporting genome completeness. These findings confirm that the isolation, genomic DNA extraction, and sequencing procedures were properly conducted, yielding high-quality and contamination-free samples (Table S2). The genomes have been deposited in the public GenBank database at NCBI: BAC220 as Bacillus paralicheniformis Bp Uniclon 01 (SAMN37735107) and BAC30 as B. paralicheniformis Bp Uniclon 02 (SAMN43408015). Taxonomic and phylogenomic analysis Using the pyANI software, all genomes of B. paralicheniformis showed high ANI values (0.97–1.00), including the publicly available genomes and the newly isolated ones. In contrast, the outgroup B. sonorensis , highlighted in blue, displayed considerably lower similarity scores (0.81–0.82) (Fig. S1 ). Subsequently, the isolates were compared to other members of the Bacillus genus (Fig. 1a), and showed a high level of similarity with strain Bac84 (0.99–1.00), followed by the reference genome of B. licheniformis (0.82), thereby supporting the previous taxonomic identifications. The analysis of the full B. paralicheniformis dataset (30 genomes) using Gegenees and SplitsTree4, in a Neighbor Joining (NJ) phylogenetic tree, revealed high similarity scores among strains (81–100), while the outgroup exhibited significantly lower values (20–22). Notably, the strains CamBx3 and J41TS8 appeared more phylogenetically distant from the rest of the species (Fig. S2). This pattern was consistent with the maximum likelihood (ML) phylogenetic tree generated using OrthoFinder (Fig. 1b), which also positioned the outgroup distinctly apart, followed by the same two divergent strains. Additionally, the ML tree suggested the formation of four subgroups, supported by satisfactory bootstrap values, all above 0.7 (minimum of 0.729). Figure 1 Taxonomic and phylogenomic analysis of the B. paralicheniformis species (a) ANI analysis of 16 Bacillus genomes using the pyANI software, including reference genomes for each species and the addition of the isolated strains BAC30 and BAC220 (b) Phylogenomic analysis of B. paralicheniformis strains performed using OrthoFinder based on the Maximum Likelihood (ML) method. The isolated strains BAC30 and BAC220 are highlighted in red. Comparative analysis Circular map of Bacillus paralicheniformis strains and pangenome analysis The comparative circular map (Fig. 2 a) displays the reference genome Bac84, retrieved from the NCBI database, aligned against the remaining 29 B. paralicheniformis genomes, including the isolated strains BAC30 and BAC220. As observed in the phylogenomic analyses, there is a high degree of similarity among the strains and the reference genome (Bac84), evidenced by the minimal presence of white gaps. The pangenome analysis revealed a total of 129,406 genes grouped into 6,973 gene families. Of these, 3,935 (56.44%) were classified as core genes, 2,581 (37.01%) as cloud genes, and only 457 (6.55%) as shell genes (Fig. S3). In the rarefaction curve of B. paralicheniformis genomes (Fig. 2 b), the calculated gamma (Ɣ) value was 0.20309. From this, the corresponding alpha (α) value, calculated as 1 – Ɣ, was 0.79691. Since α < 1, the pangenome is considered open, meaning that as more genomes are added, the number of genes in the pangenome continues to increase. To assess the impact of the isolates on the overall species core, α values were analyzed using only the 28 public genomes, with the isolates excluded. This analysis yielded a γ value of 0.18142, corresponding to an α value of 0.81858 (Fig. S5). Gene categories comparison in COG Classifier Using the COG Classifier software, the core genes of the two isolates (BAC30 and BAC220) were first analyzed (Fig. S4a), followed by an assessment of their impact on the overall gene repertoire of the species (Fig. S4b & S4c). No significant difference in gene quantity was observed, highlighting the similarity between the isolates and the other 28 genomes available in the NCBI database. Additionally, in the core genome of the isolates (Fig. S4a), a proportionally higher number of genes classified under category C (Energy production and conversion) was noted, in contrast to category G (Carbohydrate transport and metabolism). This pattern is reversed when analyzing the core genome of the entire B. paralicheniformis dataset (Fig. S4a). Functional analysis The genomes of the two isolated strains, BAC30 and BAC220, were analyzed using the KEGG database, focusing on genes of interest within categories considered relevant for the biotechnological application of these organisms (defense mechanisms, secondary metabolite production, and nitrogen metabolism). Defense mechanisms A gene module related to beta-lactam resistance was identified, involving the Bla system ( BlaR1, BlaI, penP ), which operates through a negative feedback mechanism (Fig. 3 ). In this system, the presence of a beta-lactam compound in the intracellular environment binds to the BlaR1 repressor, forming the B-lactam + BlaR1 complex and inhibiting the repressor’s activity. This inhibition activates the penP gene, which induces the production of beta-lactamase. The enzyme then degrades both the complex and the beta-lactam molecule, thereby releasing the BlaR1 repressor. Moreover, a multidrug efflux system AbcA ( abcA, norG ) was also identified. Secondary metabolites production Using KEGG, modules related to the biosynthesis of vitamins (B1, B2, B5, B6, B7, and B9) and the cofactors coenzyme A (CoA) and flavin adenine dinucleotide (FAD) were identified. In this context, the metabolic pathway responsible for the biosynthesis of the most relevant vitamin, riboflavin (vitamin B2), is illustrated (Fig. 4 ). Riboflavin biosynthesis proceeds through the conversion of guanosine triphosphate (GTP) and ribulose 5-phosphate (Ru5P) which are converted into 5-Amino-6(ribityl-amino)uracil (ARU) and 3,4-Dihydroxy-2-butanone 4-phosphate (DHBP), respectively. Subsequently, both form the intermediate 6,7-dimethyl-8-ribityllumazine (DMRL) via the genes ribBA , ribD , ribH , ybjI , and ycsE , followed by conversion to riboflavin mediated by the ribE gene. Riboflavin can then be converted into the cofactor flavin adenine dinucleotide (FAD) through the action of the ribF gene. Nitrogen metabolism Genes related to nitrogen metabolism were analyzed using KEGG (Fig. 5 ), revealing a complete module associated with assimilatory nitrate reduction, in which nitrate is converted to nitrite and subsequently to ammonia through a set of nas genes ( nasB, nasC, nasD, nasE ). Additionally, the nar operon ( narG, narH, narI, narJ ), responsible for the interconversion of nitrate and nitrite, was identified, along with the narK gene involved in the transport of nitrate and nitrite from the extracellular environment to the intracellular space. Furthermore, a complete biosynthesis module for siroheme was detected, which is involved in the formation of the nitrite reductase enzyme (responsible for reducing nitrite to ammonium). This pathway includes hem genes ( hemA, hemB, hemC, hemD, hemL ), which convert L-glutamine (Glu) into uroporphyrinogen III, subsequently transformed into siroheme ( cysG ) and heme ( hemY, hemH, hemQ ). Clusters of Non-Ribosomal Peptide Synthesis (NRPS) Using the AntiSMASH software, the genomes of the BAC30 and BAC220 strains were analyzed, identifying nonribosomal peptide synthesis (NRPS) regions associated with secondary metabolite production, exhibiting high identity (above 70%). In the BAC30 strain (Fig. 6 a), regions linked to the biosynthesis of bacitracin, bacillibactin, lichenysin, and fengycin were detected, along with the biosynthetic genes involved in these processes. In contrast, the BAC220 strain exhibited only three regions associated with the production of bacillibactin, bacitracin, and lichenysin (Fig. 6 b). Discussion Due to the relatively recent description of the species Bacillus paralicheniformis and the limited number of comprehensive studies on its genomic and functional landscape, this study aimed to deepen the understanding of its phylogeny and behavior in a comparative context. Despite current knowledge limitations, B. paralicheniformis has been identified as a species capable of producing bacitracin, a metabolite of industrial interest, which has demonstrated pathogen-inhibitory activity both in vitro and in vivo . In this context, the characterization of new strains is essential to broaden the biotechnological application potential of the species, as exemplified by the BAC30 and BAC220 isolates analyzed in this study. In the context of isolates identification, both strains were successfully sequenced and subsequently processed through genome assembly and annotation. Additionally, they showed high similarity to each other in terms of genome size, GC content (%), and taxonomic classification, as determined by the TYGS and GTDB-Tk tools (Tables S1 & S2). When comparing B. paralicheniformis strains among themselves, a high degree of genomic similarity was observed across all genomes (Fig. S1 ). Furthermore, comparison of the BAC30 and BAC220 isolates with other Bacillus species (Fig. 1) revealed strong similarity to the reference genome of B. paralicheniformis (Bac84). These three B. paralicheniformis genomes also showed greater similarity to the B. licheniformis reference genome than to other species within the Bacillus genus. Additionally, the clustering of other Bacillus species aligned with previous studies that reported similar associations, supporting the reliability of our analysis (Nannan et al. 2021 ). These results further confirm the relevance and reliability of WGS analyses, considering that the initial identification of the isolates was incorrect, having been previously classified as unknown and Bacillus cereus , respectively. In the phylogenomic analyses performed using Gegenees and SplitsTree4 (Fig. S2), the outgroup Bacillus sonorensis (ASM3405511v1) exhibited a relatively high similarity score, reflecting its close phylogenetic relationship due to its inclusion within the same genus as the other analyzed genomes. This similarity contributes to the robustness of the phylogenomic inferences, minimizing potential biases and branching errors that could arise from the use of an excessively divergent outgroup (Wilberg 2015 ). In the analysis conducted with Orthofinder (Fig. 1b), the formation of four distinct subgroups was observed, consistent with the results obtained through Gegenees. The structure of these groups remained stable, with only minor changes in the relative positioning of genomes across different approaches. However, to date, no studies have conducted specific and in-depth analyses focused solely on Bacillus paralicheniformis genomes, with most comparative studies involving closely related species within the same genus, such as Bacillus licheniformis , Bacillus subtilis , and others (Asif et al. 2023 ). In both analyses, the strains CamBx3 (Narsing Rao et al. 2024 ) and J41TS8 (Okumura et al. 2022 ) stood out by clustering farther from the remaining genomes, potentially indicating a relevant phylogenetic divergence. Although these strains share over 95% ANI identity, they may represent a sub-lineage or even a distinct lineage from the currently recognized B. paralicheniformis , considering the limited number of available phylogenetic markers for this species. Furthermore, the circular map (Fig. 2 a) generated for all B. paralicheniformis strains using Proksee once again revealed a high degree of genomic similarity. This finding corroborates previous results from the taxonomic analysis (Fig. S1 ), the minimal impact observed when comparing the gene repertoire of the isolates with that of the overall species (Fig. S4), and the negligible difference in the alpha value when comparing the pangenome with and without the isolates (Fig. S5). The analysis performed using the PPanGGolin tool revealed that, despite the high genomic similarity among the Bacillus paralicheniformis strains in the dataset, the proportion of persistent genes (Fig. S3) is relatively low when compared to other Bacillus species, such as B. amyloliquefaciens (75.20%) and B. anthracis (74.01%) (Kim et al. 2017 ). The high number of unique genes and the limited number of shared genes among the analyzed genomes, suggests a pattern of ubiquity for the species. Such dispersion reflects the diversity of isolation sources and the broad geographic distribution of the strains, as shown in Table 1 , and highlights the adaptive capacity of the species to acquire new genes. The rarefaction curve (Fig. 2 b) also indicates a continuing trend of gene acquisition among the strains analyzed. Although the strains are highly similar and the inclusion of the isolates did not significantly impact the species' overall gene repertoire (Fig. S4b & S4c) (Fig. S5), this pattern may indicate the species' adaptive capacity to different ecological niches, as well as its widespread environmental distribution, consistent with the distribution of gene families (Fig. S3). However, to confirm this genomic plasticity and adaptive potential, additional analyses are still required, particularly regarding gene synteny and the presence of mobile genetic elements. Regarding the functional analyses, the results related to defense mechanisms obtained through KEGG revealed the presence of two distinct β-lactam resistance modules, which may act synergistically (Fig. 3 ). This resistance may be considered intrinsic to organisms of the Bacillus genus as, for example, a study analyzing 114 Bacillus genomes, including B. paralicheniformis , found that approximately 86% of the strains exhibited resistance to penicillin (Zhai et al. 2023 ). The low abundance of antibiotic resistance genes (ARGs) is crucial for the efficient disposal of these microorganisms during equipment cleaning and for mitigating the spread of ARGs from industrial waste (Li et al. 2022 ). It also allows for the introduction of classical vectors containing expression inducers or heterologous protein genes, which frequently include resistance genes used for clonal culture selection (Dong and Zhang 2014 ). Regarding B-complex vitamin biosynthesis (Fig. 4 ), recent studies have primarily focused on the development of food formulations using Bacillus strains capable of producing riboflavin (vitamin B2), an essential compound for monogastric organisms such as humans, poultry, and swine (Lambertz et al. 2020 ). In this context, several studies have characterized and assessed the safety of riboflavin produced by B. subtilis strains, considering it safe for consumption (EFSA Panel on Additives and Products or Substances used in Animal Feed (FEEDAP) et al. 2022 ). However, there is a potential risk of residual genes in the final product, which may include resistance or virulence genes with the potential for horizontal transfer to pathogenic bacteria (EFSA Panel on Additives and Products or Substances used in Animal Feed (FEEDAP) et al. 2018 ). Another limiting factor for the commercialization of isolated vitamin compounds is the high production cost, associated with low yield and the availability of more efficient alternatives, which currently makes the process economically unfeasible (Revuelta et al. 2016 ). Regarding nitrogen metabolism (Fig. 5 ), the nas and nar genes, associated with nitrate reduction and conversion, were identified. These genes indicate the adaptability of the strains to different forms of nitrogen compounds (nitrate, nitrite, and ammonia), which vary in concentration depending on soil type, fertilizers, and animal-derived inputs (Ferraz-Almeida 2024 ), enabling the balancing of these compounds. Additionally, the conversion of nitrogen compounds may reduce NO₃⁻ (nitrate) levels, whose elevated concentrations contribute to soil acidification and nutrient depletion, ultimately hindering plant growth (Florio et al. 2025 ). It is also considered that these bacteria may increase nitrogen retention time in the soil, preventing its volatilization as nitrous oxide (N₂O), a greenhouse gas that lowers fertilizer efficiency (Wu et al. 2018 ), as well as enabling a reduction in excessive fertilizer use, which is recognized as a major source of environmental pollution (Ahmed et al. 2017 ). Although nitrogenous compounds generated by these bacteria can be taken up by plants, no genes related to their extracellular transport were found. However, due to the rapid cell cycle of Bacillus strains, intracellular content, including metabolites and nitrogen compounds, can be released into the rhizosphere after bacterial lysis, making these compounds available to plants as necromass (Pausch et al. 2024 ). The identified heme biosynthesis pathway is also noteworthy, as microbial heme production has been explored in pharmaceutical and food industries as an economically viable and animal-free alternative to traditional production methods involving animal blood (Yang et al. 2023 ). In the analysis of nonribosomal peptides (NRPs) associated with the production of AMCs (Fig. 6 ), bacitracin stands out as the most relevant, being primarily produced by strains of Bacillus licheniformis and B. subtilis (Cai et al. 2020 ). This antibiotic is widely used in topical formulations, such as ointments, often in combination with other antimicrobials like neomycin (Jones et al. 2006 ), contributing to the prevention of infections caused by pathogens and/or opportunistic microorganisms. Its efficacy has been demonstrated, for example, in a study indicating bacitracin as the main metabolite responsible for inhibitory activity against the pathogen Staphylococcus aureus , compared to other metabolites such as bacilysin, fengycin, and bacillibactin (Luo et al. 2023 ). Furthermore, the production of natural antibiotics and preservatives like bacitracin is highly relevant, considering that the consumption of artificial preservatives have been associated with gut diseases, obesity (Reardon 2015 ), lung and liver damage, and cancer (Aldabayan 2025 ). However, efficient bacitracin production requires, for example the genes bcrABC , which confers resistance to bacitracin itself (Podlesek et al. 2006 ), and were not found in the BAC30 and BAC220 strains. Additionally, limiting factors such as optical density, nitrate concentration, and NADH oxidation must be carefully considered in in vitro assays (Zhu et al. 2023 ). Nevertheless, the NRPs, combined with vitamin biosynthesis genes such as those for riboflavin, suggest a diversified potential for the biotechnological application of the isolated BAC30 and BAC220 strains. Conclusion The isolated Bacillus paralicheniformis strains, BAC30 and BAC220, demonstrated high reliability in sequencing, genome assembly, and annotation. Phylogenomic analyses revealed high similarity between the genomes, with particular attention to the CamBx3 and J41TS8 strains, which may warrant further investigation due to their lower similarity relative to the others. Additionally, the formation of subgroups was proposed, although more specific phylogenomic analyses are still needed to support these subgroup distinctions. Regarding comparative analyses, the pangenomic approach provided insight into the species’ tendency to adapt to different ecological niches and acquire new genes, helping to explain the speciation and divergence between B. licheniformis and B. paralicheniformis . In silico analyses suggest that the isolated strains BAC30 and BAC220 hold biotechnological potential, as they only exhibited KEGG modules related to beta-lactam resistance. Moreover, modules involved in the biosynthesis of metabolites relevant to various industrial sectors were identified, including those responsible for the production of vitamins, cofactors, and natural antibiotics, which can be incorporated into commercial products or produced independently. Nevertheless, despite the observed potential, further in vitro and in vivo assays are required to confirm the expression of genes involved in metabolite biosynthesis and antibiotic resistance, in order to properly assess the actual effectiveness of these strains as producers of biotechnologically valuable compounds. Declarations Acknowledgements The authors would like to acknowledge the Pró-Reitoria de Pesquisa—Universidade Federal de Minas Gerais, Rede de Ciências Ômicas (RECOM), the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) and Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG) for their financial support and fellowships. Funding This research was funded by Fundação de Amparo à Pesquisa do Estado de Minas Gerais -FAPEMIG (5.18/2022). Competing Interests The authors declare no conflict of interest. Consent for Publication All authors consent to the publication of this article. Author Contributions Conceptualization: Gabriel Camargos Gomes, Eduarda Guimarães Sousa; Methodology: Gabriel Camargos Gomes, Eduarda Guimarães Sousa, Marcus Vinícius Canário Viana, Bertram Brenig; Formal analysis and investigation: Gabriel Camargos Gomes, Giovanna Karine Viana Silva, Rafael Junio de oliveira; Writing-original draft preparation: Gabriel Camargos Gomes, Eduarda Guimarães Sousa, Janaíne Aparecida de Paula; Writing-review and editing: Gabriel Camargos Gomes, Eduarda Guimarães Sousa, Ludmila Silva Quaresma, Rhayane Cristina Viegas Santos, Gabriela Munis Campos, Vasco Azevedo; Supervision: Vasco Azevedo; Funding acquisition: Vasco Azevedo. All authors read and approved the final manuscript. Data Availability The current study’s data are available from the corresponding author upon reasonable request. 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Gerais","correspondingAuthor":false,"prefix":"","firstName":"Eduarda","middleName":"Guimarães","lastName":"Sousa","suffix":""},{"id":490500014,"identity":"430fde51-b8ed-424e-9bb3-b4cf2a3d1b9a","order_by":2,"name":"Ludmila Silva Quaresma","email":"","orcid":"","institution":"Federal University of Minas Gerais","correspondingAuthor":false,"prefix":"","firstName":"Ludmila","middleName":"Silva","lastName":"Quaresma","suffix":""},{"id":490500015,"identity":"2852fd98-baee-432c-8e90-ae7db9adc16a","order_by":3,"name":"Rhayane Cristina Viegas Santos","email":"","orcid":"","institution":"Federal University of Minas Gerais","correspondingAuthor":false,"prefix":"","firstName":"Rhayane","middleName":"Cristina Viegas","lastName":"Santos","suffix":""},{"id":490500017,"identity":"8851dc3c-8bc0-4e3c-a10b-0e88848b0c69","order_by":4,"name":"Gabriela Munis Campos","email":"","orcid":"","institution":"Federal University of Minas Gerais","correspondingAuthor":false,"prefix":"","firstName":"Gabriela","middleName":"Munis","lastName":"Campos","suffix":""},{"id":490500023,"identity":"d7a5f639-7b47-451a-9de9-f54e542063c7","order_by":5,"name":"Janaíne Aparecida de Paula","email":"","orcid":"","institution":"Federal University of Minas Gerais","correspondingAuthor":false,"prefix":"","firstName":"Janaíne","middleName":"Aparecida","lastName":"de Paula","suffix":""},{"id":490500027,"identity":"660ee42d-8eb0-4ec4-9fca-e5d249de1770","order_by":6,"name":"Marcus Vinícius Canário Viana","email":"","orcid":"","institution":"Federal University of Minas Gerais","correspondingAuthor":false,"prefix":"","firstName":"Marcus","middleName":"Vinícius Canário","lastName":"Viana","suffix":""},{"id":490500030,"identity":"1bccde88-eb63-4cb7-aa12-37a4730b7fe9","order_by":7,"name":"Rafael Junio de Oliveira","email":"","orcid":"","institution":"Federal University of Minas Gerais","correspondingAuthor":false,"prefix":"","firstName":"Rafael","middleName":"Junio","lastName":"de Oliveira","suffix":""},{"id":490500036,"identity":"e409d7c6-b997-40f8-a0cc-910b593e29a4","order_by":8,"name":"Giovanna Karine Viana Silva","email":"","orcid":"","institution":"Federal University of Minas Gerais","correspondingAuthor":false,"prefix":"","firstName":"Giovanna","middleName":"Karine Viana","lastName":"Silva","suffix":""},{"id":490500038,"identity":"acd563e8-ba74-41ec-a2e0-4fc346e8c83e","order_by":9,"name":"Bertram Brenig","email":"","orcid":"","institution":"University of Göttingen","correspondingAuthor":false,"prefix":"","firstName":"Bertram","middleName":"","lastName":"Brenig","suffix":""},{"id":490500039,"identity":"91688f8c-a7be-4470-8b57-740c2e65776e","order_by":10,"name":"Vasco Azevedo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIiWNgGAWjYBADHjD5AYjZ2InWwsbAwDgDpIWZaHuAWpjBdhHSYnD8+MMPH2ruycjPbz722ObXNnk+ZgbGDx9z8Gg5k2MsOeNYMY/BMbZ049y+24ZtzAzMkjO34dYi2ZDDxszDlsBjwMZjJp3bc5sRqIWNmReflv7nz5h5/iXwyLcBtVj23LYnqIVfIsGMmbctgYfhGFALw4/biURoeWMsObMP6LBjaWmSvQ23k9uYGZvx+oWNPx0YYt8S7OWbDx+T+PHntu389uaDHz7i0YIKGNvAZAOx6kHgDymKR8EoGAWjYKQAACOJRsxl4YSYAAAAAElFTkSuQmCC","orcid":"","institution":"Federal University of Minas Gerais","correspondingAuthor":true,"prefix":"","firstName":"Vasco","middleName":"","lastName":"Azevedo","suffix":""}],"badges":[],"createdAt":"2025-07-17 00:53:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7143786/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7143786/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11274-025-04759-z","type":"published","date":"2025-12-31T15:57:17+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":87583994,"identity":"dd78556c-39fe-41d0-b0bd-c7255214fd4f","added_by":"auto","created_at":"2025-07-25 13:25:49","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":415736,"visible":true,"origin":"","legend":"\u003cp\u003eTaxonomic and phylogenomic analysis of the \u003cem\u003eB. paralicheniformis\u003c/em\u003e species\u003cstrong\u003e (a) \u003c/strong\u003eANI analysis of 16 \u003cem\u003eBacillus\u003c/em\u003e genomes using the pyANI software, including reference genomes for each species and the addition of the isolated strains BAC30 and BAC220 \u003cstrong\u003e(b)\u003c/strong\u003ePhylogenomic analysis of \u003cem\u003eB. paralicheniformis\u003c/em\u003e strains performed using OrthoFinder based on the Maximum Likelihood (ML) method. The isolated strains BAC30 and BAC220 are highlighted in red.\u003c/p\u003e","description":"","filename":"Fig.1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7143786/v1/0ca333752e43b21bedb0d081.jpg"},{"id":87583351,"identity":"7b000822-08f0-44f6-ac37-06775cfad2da","added_by":"auto","created_at":"2025-07-25 13:17:49","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":610125,"visible":true,"origin":"","legend":"\u003cp\u003eComparative analyses of the \u003cem\u003eB. paralicheniformis\u003c/em\u003edataset \u003cstrong\u003e(a)\u003c/strong\u003e Circular map generated in Proksee for the 30 \u003cem\u003eB. paralicheniformis\u003c/em\u003e strains, with the reference genome Bac84 at the center. Comparisons with the remaining strains were performed using NCBI’s BLAST+ \u003cstrong\u003e(b)\u003c/strong\u003e Rarefaction curve of the 30 \u003cem\u003eB. paralicheniformis\u003c/em\u003e genomes, generated using PPanGGolin. The curve for persistent (core) genes is shown in orange, and the pangenome curve is shown in black. The gamma (Ɣ) value, derived from parameter F, is displayed on both curves.\u003c/p\u003e","description":"","filename":"Fig.2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7143786/v1/a64f1f28d540f9c6b6d599be.jpg"},{"id":87583353,"identity":"d17a86c6-10d9-4bf2-be30-f388402ee30a","added_by":"auto","created_at":"2025-07-25 13:17:49","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":46837,"visible":true,"origin":"","legend":"\u003cp\u003eMetabolic map of beta-lactam resistance mechanisms based on genes from the BAC30 and BAC220 strains identified through KEGG analysis.\u003c/p\u003e","description":"","filename":"Fig.3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7143786/v1/866f9f49dcf7ea00c1b62efd.jpg"},{"id":87583343,"identity":"a86a8a94-310e-44d6-8931-40b785418337","added_by":"auto","created_at":"2025-07-25 13:17:49","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":25112,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic representation of the metabolic pathway responsible for riboflavin (vitamin B2) biosynthesis, based on gene analysis of the BAC30 and BAC220 strains obtained from KEGG.\u003c/p\u003e","description":"","filename":"Fig.4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7143786/v1/fe7771f656658da9d6a2395c.jpg"},{"id":87583348,"identity":"e539abf2-593e-46ab-9cef-a2e020e9cfa1","added_by":"auto","created_at":"2025-07-25 13:17:49","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":72611,"visible":true,"origin":"","legend":"\u003cp\u003eMetabolic map related to nitrogen metabolism based on genes from the BAC30 and BAC220 strains identified through KEGG analysis.\u003c/p\u003e","description":"","filename":"Fig.5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7143786/v1/97d58ef3378f9d7e7197c37e.jpg"},{"id":87584003,"identity":"f119dcf9-05a9-469c-9226-e1454d904b61","added_by":"auto","created_at":"2025-07-25 13:25:50","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":396513,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of regions in the genomes of strains BAC30 and BAC220 associated with nonribosomal peptide synthesis (NRPS), specifically bacillibactin, fengycin, bacitracin, and lichenysin, using the AntiSMASH software. The figure also shows the NRPS region sizes in kilobases (kb), identity scores based on BLAST analysis (NCBI), and the core biosynthetic genes for each NRPS cluster.\u003cstrong\u003e (a)\u003c/strong\u003e BAC30 \u003cstrong\u003e(b)\u003c/strong\u003e BAC220\u003c/p\u003e","description":"","filename":"Fig.6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7143786/v1/76a56e84e550f869b21abeb6.jpg"},{"id":99545285,"identity":"11a0543b-3576-468a-9c30-2e058d420cd0","added_by":"auto","created_at":"2026-01-05 16:05:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2723496,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7143786/v1/a582a23f-a91c-4c57-ae22-8168e0285a94.pdf"},{"id":87583993,"identity":"d39070d1-32cc-4d9e-81b3-5d12b3b48dcd","added_by":"auto","created_at":"2025-07-25 13:25:49","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1481896,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7143786/v1/90a396e5f0f86940100bfab7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comparative, Pangenomic and Functional Analyses of two Bacillus paralicheniformis Soil- Isolated Strains from Bahia Sequenced by WGS Reveal Species Homogeneity and Bioactive Metabolites with Biotechnological Potential","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe \u003cem\u003eBacillus\u003c/em\u003e genus is highly diverse and extensively studied. Its main characteristics include rod-shaped bacteria capable of sporulating when exposed to environmental stress factors, primarily nutrient deprivation (Paredes-Sabja et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), but also sudden changes in pH, salinity, and temperature (Gauvry et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Originally, the genus included species such as \u003cem\u003eB. subtilis\u003c/em\u003e (the most extensively studied and a global reference among Gram-positive bacteria (Errington and Aart \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)), \u003cem\u003eB. licheniformis\u003c/em\u003e, \u003cem\u003eB. pumilus\u003c/em\u003e, and \u003cem\u003eB. amyloliquefaciens\u003c/em\u003e. Later, additional species were incorporated, such as pathogenic ones, like \u003cem\u003eB. cereus\u003c/em\u003e and \u003cem\u003eB. anthracis\u003c/em\u003e, as well as others of importance to agriculture and industry, such as \u003cem\u003eB. thuringiensis\u003c/em\u003e (Blanco Crivelli et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). With the advancement of sequencing and genome annotation technologies, new species and subspecies have been identified, such as \u003cem\u003eB. paralicheniformis\u003c/em\u003e, expanding the genetic landscape of the genus. However, as \u003cem\u003eB. paralicheniformis\u003c/em\u003e was only recently established (many of its strains were previously classified as \u003cem\u003eB. licheniformis\u003c/em\u003e), the number of available studies and deposited genomes remains limited compared to other species in the group. This highlights the need for further investigations to characterize its genomic profile, particularly regarding evolutionary and phylogenomic patterns.\u003c/p\u003e\u003cp\u003eThis species, recently described and separated from \u003cem\u003eB. licheniformis\u003c/em\u003e (Dunlap et al. 2015), already shows industrial application potential, with notable prominence in agriculture, where it is recognized as a plant growth-promoting rhizobacterium (PGPR) (Du et al. 2019). It is known to produce bacteriocins and other metabolites (Choyam et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) that help maintain the local microbiota by inhibiting pathogens such as fungi (Ram\u0026iacute;rez-Cari\u0026ntilde;o et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), bacteria (Zhao et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), viruses (Yu et al. 2024), and nematodes (Chavarria-Quica\u0026ntilde;o et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In addition, these bacteria promote the growth of plants like wheat and cotton (Xu et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and may contribute to nitrogen fixation, phosphate solubilization, potassium release, and the production and release of phytohormones (Olanrewaju et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAlthough the induction of biosynthesis of molecules of interest and the establishment of optimal conditions for efficient production require the evaluation of multiple variables, studied under the scope of metabolic engineering (Kim et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), the use of individual bacterial strains that produce secondary metabolites, such as bacitracin, bacillibactin, fengycin, and surfactin, has proven valuable. These metabolites have potential applications as natural preservatives and surfactants, offering a promising alternative to chemical additives currently used in the food industry, which are associated with potential toxicological and teratogenic effects harmful to human health. In this context, consumer demand for healthier and more natural biopreservatives has increased (Kumariya et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMoreover, previous studies have assessed the application of \u003cem\u003eB. paralicheniformis\u003c/em\u003e strains in animal production, including strain BAC220, which is the focus of the present study (initially identified as \u003cem\u003eB. subtilis\u003c/em\u003e via MALDI-TOF analysis) (Dos Reis et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This strain was shown to reduce necrosis rates and inflammatory markers in chicken embryos challenged with \u003cem\u003eSalmonella Pullorum\u003c/em\u003e (\u003cem\u003eSalmonella enterica subsp. enterica serovar Gallinarum biovar Pullorum\u003c/em\u003e).\u003c/p\u003e\u003cp\u003eGiven the biotechnological relevance of the \u003cem\u003eBacillus\u003c/em\u003e genus, this study aimed to explore the evolutionary profile of the \u003cem\u003eB. paralicheniformis\u003c/em\u003e species, conducting phylogenomic and comparative analyses of strains obtained from the National Center for Biotechnology Information (NCBI) database, along with functional analyses of two isolated strains, BAC30 and BAC220. These analyses focused on functional genes related to defense mechanisms and the biosynthesis of secondary metabolites with potential for biotechnological application.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cb\u003eGenome assembly, contamination check, and completeness evaluation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe strains identified as BAC30 and BAC220 were isolated from soil samples collected in Bahia, Brazil and previously identified as unknown and \u003cem\u003eBacillus cereus\u003c/em\u003e, respectively, in MALDI-TOF analysis. DNA extraction was performed using the Wizard\u0026reg; Genomic DNA Purification Kit (Promega), following the manufacturer's instructions. Next-generation sequencing was conducted on the HiSeq 2500 platform (2\u0026times;150 bp) (Illumina\u0026reg;, United States), and paired-end libraries were constructed using the ThruPLEX DNA-Seq Kit (Takara).\u003c/p\u003e\u003cp\u003eFor genome assembly, annotation, and taxonomic identification, a customized pipeline was employed. Initial trimming was performed using FastP (v0.24.0), a tool capable of detecting and removing adapters and low-quality reads, thereby improving the overall quality of the sequenced genome (Chen et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This was followed by a quality control assessment using FastQC (v0.12.1), which generates a comprehensive report on several sequencing metrics (Babraham Bioinformatics, 2010).\u003c/p\u003e\u003cp\u003eGenome assembly was carried out with Unicycler (v0.5.1), a tool optimized for short-read data generated by platforms such as Illumina. The software uses a De Bruijn graph-based approach to construct initial contigs and then applies refinement strategies to resolve ambiguities and repetitive regions commonly associated with short-read data. Unicycler further performs assembly graph polishing, leveraging the high accuracy of short reads to improve the continuity and quality of the assembled genome (Wick et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eContamination and completeness assessments were performed using CheckM2 (v1.0.2), which applies a machine learning model independent of phylogeny. It estimates genome completeness based on the proportion of conserved marker genes present and predicts potential contamination or misassemblies based on redundancy among these markers (Chklovski et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Additional quality control analyses were conducted using GUNC (v1.0.6), which detects possible chimeric contigs (fragments erroneously assembled from different genomic sources) (Orakov et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Moreover, Barrnap (v0.9) was employed to predict conserved ribosomal genes such as 5S, 16S, and 23S rRNA, providing an additional metric for evaluating the completeness of the assembled genomes (Seeman T., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2013\u003c/span\u003e)\u003c/p\u003e\u003cp\u003e\u003cb\u003eTaxonomic Identification\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTaxonomic identification was performed using the Genome Taxonomy Database Toolkit (GTDB-Tk) (v2.4.1) and the Type (Strain) Genome Server (TYGS), to ensure high confidence in the classification results. GTDB-Tk infers taxonomic assignments based on the standardized taxonomy of the Genome Taxonomy Database (GTDB), applying the Relative Evolutionary Divergence (RED) criteria, followed by Average Nucleotide Identity (ANI) validation for species-level resolution (Chaumeil et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The commonly accepted ANI threshold for confident species-level identification is \u0026gt;\u0026thinsp;95\u0026ndash;96% (Elbir \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). TYGS performs digital DNA-DNA hybridization (dDDH) analysis, which assesses phylogenomic relatedness between submitted genomes and database entries based on the degree of hybridization between complete genomes or genomic fragments, considering overlap, segment size, and similarity (Meier-Kolthoff and G\u0026ouml;ker \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). For dDDH, the standard threshold for confident species-level identification is \u0026gt;\u0026thinsp;70% (Meier-Kolthoff et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn addition, the pyANI tool was used to perform all-vs-all ANI comparisons, generating a pairwise similarity distance matrix (Pritchard et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This analysis included 28 complete \u003cem\u003eB. paralicheniformis\u003c/em\u003e genomes retrieved from the NCBI database (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), along with the two isolates from this study (BAC30 and BAC220). A heatmap was generated using a custom script in RStudio (v2024.12.1) (Posit Team, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Subsequently, using the pyANI software, the two isolates were compared against 14 genomes representing other \u003cem\u003eBacillus\u003c/em\u003e species. All genomes, except for the isolates, are designated as reference genomes for their respective species according to NCBI and were retrieved directly from the NCBI database.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCompilation of the\u003c/b\u003e \u003cb\u003eBacillus paralicheniformis\u003c/b\u003e \u003cb\u003edataset\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents 28 complete \u003cem\u003eBacillus paralicheniformis\u003c/em\u003e genomes (RefSeq), all obtained from the NCBI using the NCBI Datasets tool (v16.30.0), which enables the retrieval and download of sequences, annotations, and metadata for genes and genomes via command line interface (O\u0026rsquo;Leary et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This same dataset was used in all subsequent analyses, with the addition of the two isolates from this study, BAC30 and BAC220. The table includes information such as strain names, NCBI assembly accession numbers, approximate total genome size (in Megabases, Mb), as well as the source and country of isolation, in order to assess genomic similarities and differences among the strains.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eInformation on the 28 complete \u003cem\u003eBacillus paralicheniformis\u003c/em\u003e genomes retrieved via NCBI Datasets.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStrain Name\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNCBI RefSeq\u003c/p\u003e\u003cp\u003eAssembly\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGenome size (Mb)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIsolation source\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCountry of origin\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBac84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGCF_002993925.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRed Sea Lagoon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSaudi Arabia\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePRO109\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGCF_029536955.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSoy-based food\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGhana\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA4-3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGCF_008365255.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTomato\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSouth Korea\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCP47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGCF_031326025.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFermented soy-based food\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSouth Korea\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e14DA11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGCF_002393225.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFermented soy-based food\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSouth Korea\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJ41TS8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGCF_021654755.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eJapanese honey\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eJapan\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDY4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGCF_030166695.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePig feces\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eChina\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCBMAI 1303\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGCF_003711025.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBrazil\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBac48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGCF_002993945.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRed Sea mangrove mud\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSaudi Arabia\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGCF_035985545.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMarine sediment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eChina\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCamBx3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGCF_026210435.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHot spring water\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFA6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGCF_009497935.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGrass carp\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eChina\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNCTC8721\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGCF_900635765.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJ36TS2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGCF_021654735.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eJapanese honey\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eJapan\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDSM 28591\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGCF_029537035.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC57BL/6 mouse feces\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGermany\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJ25TS1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGCF_021654715.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eJapanese honey\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eJapan\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBL-09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGCF_000876525.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFermented Congee Soup\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eChina\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMDJK30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGCF_002068155.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePeony rhizosphere\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eChina\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRP01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGCF_029000405.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSoil\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eChina\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCEW 1W\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGCF_030123025.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMarine sediment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIndia\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e285-3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGCF_026723705.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSoil\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eChina\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRSC-1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGCF_018324505.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRed sea water\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSaudi Arabia\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRSC-2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGCF_018324565.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRed sea water\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSaudi Arabia\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSUBG0010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGCF_003171815.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRhizosphere\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIndia\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSYN-191\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGCF_029590455.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSoil\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eChina\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eATCC 9945a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGCF_000408885.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBaplich1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGCF_038049955.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTomato rhizosphere\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eChina\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFUA2150\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGCF_039619465.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFermentation starter Daqu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eChina\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003ePhylogenomic analyses\u003c/b\u003e\u003c/p\u003e\u003cp\u003eStarting from the 28 complete genomes and the two isolated genomes (BAC30 and BAC220), one outgroup was included: \u003cem\u003eBacillus sonorensis\u003c/em\u003e ASM3405511v1 (GCF_035804855.1). This strain was chosen as an outgroup due to its close taxonomic relationship, although it does not belong to the same species (Olajide et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). For phylogenomic tree construction, the programs Gegenees and SplitsTree4 (v4.14.4) were used. Gegenees generates a pairwise distance matrix (all vs all) between genomes by fragmenting them and assigning scores to gene regions, thus standardizing the comparison, which is performed using BLAST (\u0026Aring;gren et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). SplitsTree4 then uses this distance matrix to infer phylogenetic relationships through the Neighbor Joining (NJ) method (Huson and Bryant \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAdditionally, Orthofinder (v3.01b1) was used to construct a phylogenetic tree based on all-vs-all comparisons of protein sequences, leveraging orthologous and paralogous relationships. The tree was generated using the Maximum Likelihood (ML) method with 1000 bootstrap replicates (Emms and Kelly \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The resulting tree was visualized and edited using the Interactive Tree of Life (iTOL) platform (Letunic and Bork \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eComparative analyses\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe 28 complete \u003cem\u003eBacillus paralicheniformis\u003c/em\u003e genomes obtained from NCBI were annotated using Prokka (v1.14.6), in order to standardize them with the two isolated genomes from this study. Prokka annotates sequences in FASTA format, identifying the coordinates of genomic features present in contigs using tools like Prodigal (Hyatt et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), RNAmmer (Lagesen et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), and others (Seemann \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). A comparative circular genome map of the 30 genomes was then generated using the Proksee platform (Grant et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), with the NCBI reference genome \u003cem\u003eBacillus paralicheniformis\u003c/em\u003e Bac84 (GCF_002993925.1) as the base. From this reference genome, a BLAST comparison was performed against each of the other genomes using BLAST+ (v2.16.0), integrated within the Proksee software.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePangenomic Analyses\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe PPanGGolin software (v2.0.5) was used to perform the pangenome analysis. This tool integrates information from protein-coding genes and their genomic neighborhoods to construct a gene family graph, where each node represents a gene family, and edges between nodes reflect similarity relationships among families (Gautreau et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The analysis included the 28 previously deposited genomes along with the two newly sequenced isolates. The program outputs several key parameters, including the number and percentage of gene families, categorized into accessory (cloud), shared (shell), and persistent (core) genes.\u003c/p\u003e\u003cp\u003eA rarefaction curve was also constructed based on Heap\u0026rsquo;s Law, a mathematical model describing the increase in the number of gene families (or unique genes) as additional genomes are included in the analysis. This curve allows inference of whether the pangenome is open or closed, based on the α (alpha) parameter. Alpha values below 1 indicate an open pangenome, suggesting that the inclusion of new genomes continues to yield novel genes (cloud). In contrast, alpha values equal to or greater than 1 indicate a closed pangenome, in which most genes have already been captured and the addition of new genomes has minimal impact on the total gene count (Felice et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eFunctional analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFor the functional analyses, the COG classifier tool was first used to perform functional annotation, classification, and analysis of all genes based on comparisons with the COG (Clusters of Orthologous Groups) database (Shimoyama \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This tool was used to compare the core genes of the isolated strains with those of the other genomes in the dataset, in order to evaluate the impact of the isolates on the overall core genome of the species and to assess differences in the gene proportions across COG functional categories.\u003c/p\u003e\u003cp\u003eNext, the BlastKOALA tool, integrated with the KEGG platform, was used to functionally annotate the sequences based on the KEGG database, assigning KEGG Orthology (KO) identifiers. This enabled the characterization of individual gene functions and the reconstruction of specific KEGG pathways and modules (Kanehisa et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), considered relevant to the biotechnological potential of the isolates, with emphasis on genes associated with defense mechanisms, secondary metabolites prodduction, and genes related to nitrogen metabolism.\u003c/p\u003e\u003cp\u003eFinally, the antiSMASH software (v8.0) was used, a tool focused on the analysis of microbial genomes with an emphasis on the annotation of genes involved in biosynthetic gene clusters (BGCs), particularly those encoding nonribosomal peptide synthetases (NRPS), which are of considerable biotechnological interest (Blin et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eAssembly, quality assessment, and taxonomic identification of the isolated strains\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe genomes of the isolates were designated BAC30 and BAC220 and were subsequently identified as \u003cem\u003eBacillus paralicheniformis\u003c/em\u003e by both applied methods (GTDB-Tk and TYGS), showing an acceptable degree of similarity (with thresholds above 95% for ANI and 70% for dDDH) (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The GC content, ranging between 45% and 46%, is consistent with previously reported \u003cem\u003eB. paralicheniformis\u003c/em\u003e genomes (Du et al. 2019; Asif et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Quality assessments indicated 100% completeness with no detectable contamination (below 5%), and both samples were confirmed to be free of chimeric sequences. Furthermore, both isolates harbored the three conserved ribosomal RNA genes (5S, 16S, and 23S), further supporting genome completeness. These findings confirm that the isolation, genomic DNA extraction, and sequencing procedures were properly conducted, yielding high-quality and contamination-free samples (Table S2). The genomes have been deposited in the public GenBank database at NCBI: BAC220 as \u003cem\u003eBacillus paralicheniformis\u003c/em\u003e Bp Uniclon 01 (SAMN37735107) and BAC30 as \u003cem\u003eB. paralicheniformis\u003c/em\u003e Bp Uniclon 02 (SAMN43408015).\u003c/p\u003e\u003cp\u003e\u003cb\u003eTaxonomic and phylogenomic analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eUsing the pyANI software, all genomes of \u003cem\u003eB. paralicheniformis\u003c/em\u003e showed high ANI values (0.97\u0026ndash;1.00), including the publicly available genomes and the newly isolated ones. In contrast, the outgroup \u003cem\u003eB. sonorensis\u003c/em\u003e, highlighted in blue, displayed considerably lower similarity scores (0.81\u0026ndash;0.82) (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Subsequently, the isolates were compared to other members of the \u003cem\u003eBacillus\u003c/em\u003e genus (Fig.\u0026nbsp;1a), and showed a high level of similarity with strain Bac84 (0.99\u0026ndash;1.00), followed by the reference genome of \u003cem\u003eB. licheniformis\u003c/em\u003e (0.82), thereby supporting the previous taxonomic identifications.\u003c/p\u003e\u003cp\u003eThe analysis of the full \u003cem\u003eB. paralicheniformis\u003c/em\u003e dataset (30 genomes) using Gegenees and SplitsTree4, in a Neighbor Joining (NJ) phylogenetic tree, revealed high similarity scores among strains (81\u0026ndash;100), while the outgroup exhibited significantly lower values (20\u0026ndash;22). Notably, the strains CamBx3 and J41TS8 appeared more phylogenetically distant from the rest of the species (Fig. S2). This pattern was consistent with the maximum likelihood (ML) phylogenetic tree generated using OrthoFinder (Fig.\u0026nbsp;1b), which also positioned the outgroup distinctly apart, followed by the same two divergent strains. Additionally, the ML tree suggested the formation of four subgroups, supported by satisfactory bootstrap values, all above 0.7 (minimum of 0.729).\u003c/p\u003e\u003cp\u003e\u003cb\u003eFigure\u0026nbsp;1\u003c/b\u003e Taxonomic and phylogenomic analysis of the \u003cem\u003eB. paralicheniformis\u003c/em\u003e species \u003cb\u003e(a)\u003c/b\u003e ANI analysis of 16 \u003cem\u003eBacillus\u003c/em\u003e genomes using the pyANI software, including reference genomes for each species and the addition of the isolated strains BAC30 and BAC220 \u003cb\u003e(b)\u003c/b\u003e Phylogenomic analysis of \u003cem\u003eB. paralicheniformis\u003c/em\u003e strains performed using OrthoFinder based on the Maximum Likelihood (ML) method. The isolated strains BAC30 and BAC220 are highlighted in red.\u003c/p\u003e\u003cp\u003e\u003cb\u003eComparative analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eCircular map of Bacillus paralicheniformis strains and pangenome analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe comparative circular map (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003ea) displays the reference genome Bac84, retrieved from the NCBI database, aligned against the remaining 29 \u003cem\u003eB. paralicheniformis\u003c/em\u003e genomes, including the isolated strains BAC30 and BAC220. As observed in the phylogenomic analyses, there is a high degree of similarity among the strains and the reference genome (Bac84), evidenced by the minimal presence of white gaps.\u003c/p\u003e\u003cp\u003eThe pangenome analysis revealed a total of 129,406 genes grouped into 6,973 gene families. Of these, 3,935 (56.44%) were classified as core genes, 2,581 (37.01%) as cloud genes, and only 457 (6.55%) as shell genes (Fig. S3). In the rarefaction curve of \u003cem\u003eB. paralicheniformis\u003c/em\u003e genomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eb), the calculated gamma (Ɣ) value was 0.20309. From this, the corresponding alpha (α) value, calculated as 1 \u0026ndash; Ɣ, was 0.79691. Since α\u0026thinsp;\u0026lt;\u0026thinsp;1, the pangenome is considered open, meaning that as more genomes are added, the number of genes in the pangenome continues to increase. To assess the impact of the isolates on the overall species core, α values were analyzed using only the 28 public genomes, with the isolates excluded. This analysis yielded a γ value of 0.18142, corresponding to an α value of 0.81858 (Fig. S5).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eGene categories comparison in COG Classifier\u003c/b\u003e\u003c/p\u003e\u003cp\u003eUsing the COG Classifier software, the core genes of the two isolates (BAC30 and BAC220) were first analyzed (Fig. S4a), followed by an assessment of their impact on the overall gene repertoire of the species (Fig. S4b \u0026amp; S4c). No significant difference in gene quantity was observed, highlighting the similarity between the isolates and the other 28 genomes available in the NCBI database. Additionally, in the core genome of the isolates (Fig. S4a), a proportionally higher number of genes classified under category C (Energy production and conversion) was noted, in contrast to category G (Carbohydrate transport and metabolism). This pattern is reversed when analyzing the core genome of the entire \u003cem\u003eB. paralicheniformis\u003c/em\u003e dataset (Fig. S4a).\u003c/p\u003e\u003cp\u003e\u003cb\u003eFunctional analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe genomes of the two isolated strains, BAC30 and BAC220, were analyzed using the KEGG database, focusing on genes of interest within categories considered relevant for the biotechnological application of these organisms (defense mechanisms, secondary metabolite production, and nitrogen metabolism).\u003c/p\u003e\u003cp\u003e\u003cb\u003eDefense mechanisms\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA gene module related to beta-lactam resistance was identified, involving the Bla system (\u003cem\u003eBlaR1, BlaI, penP\u003c/em\u003e), which operates through a negative feedback mechanism (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In this system, the presence of a beta-lactam compound in the intracellular environment binds to the \u003cem\u003eBlaR1\u003c/em\u003e repressor, forming the B-lactam\u0026thinsp;+\u0026thinsp;BlaR1 complex and inhibiting the repressor\u0026rsquo;s activity. This inhibition activates the \u003cem\u003epenP\u003c/em\u003e gene, which induces the production of beta-lactamase. The enzyme then degrades both the complex and the beta-lactam molecule, thereby releasing the \u003cem\u003eBlaR1\u003c/em\u003e repressor. Moreover, a multidrug efflux system AbcA (\u003cem\u003eabcA, norG\u003c/em\u003e) was also identified.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eSecondary metabolites production\u003c/b\u003e\u003c/p\u003e\u003cp\u003eUsing KEGG, modules related to the biosynthesis of vitamins (B1, B2, B5, B6, B7, and B9) and the cofactors coenzyme A (CoA) and flavin adenine dinucleotide (FAD) were identified. In this context, the metabolic pathway responsible for the biosynthesis of the most relevant vitamin, riboflavin (vitamin B2), is illustrated (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Riboflavin biosynthesis proceeds through the conversion of guanosine triphosphate (GTP) and ribulose 5-phosphate (Ru5P) which are converted into 5-Amino-6(ribityl-amino)uracil (ARU) and 3,4-Dihydroxy-2-butanone 4-phosphate (DHBP), respectively. Subsequently, both form the intermediate 6,7-dimethyl-8-ribityllumazine (DMRL) via the genes \u003cem\u003eribBA\u003c/em\u003e, \u003cem\u003eribD\u003c/em\u003e, \u003cem\u003eribH\u003c/em\u003e, \u003cem\u003eybjI\u003c/em\u003e, and \u003cem\u003eycsE\u003c/em\u003e, followed by conversion to riboflavin mediated by the \u003cem\u003eribE\u003c/em\u003e gene. Riboflavin can then be converted into the cofactor flavin adenine dinucleotide (FAD) through the action of the \u003cem\u003eribF\u003c/em\u003e gene.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eNitrogen metabolism\u003c/b\u003e\u003c/p\u003e\u003cp\u003eGenes related to nitrogen metabolism were analyzed using KEGG (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e), revealing a complete module associated with assimilatory nitrate reduction, in which nitrate is converted to nitrite and subsequently to ammonia through a set of \u003cem\u003enas\u003c/em\u003e genes (\u003cem\u003enasB, nasC, nasD, nasE\u003c/em\u003e). Additionally, the \u003cem\u003enar\u003c/em\u003e operon (\u003cem\u003enarG, narH, narI, narJ\u003c/em\u003e), responsible for the interconversion of nitrate and nitrite, was identified, along with the narK gene involved in the transport of nitrate and nitrite from the extracellular environment to the intracellular space. Furthermore, a complete biosynthesis module for siroheme was detected, which is involved in the formation of the nitrite reductase enzyme (responsible for reducing nitrite to ammonium). This pathway includes \u003cem\u003ehem\u003c/em\u003e genes (\u003cem\u003ehemA, hemB, hemC, hemD, hemL\u003c/em\u003e), which convert L-glutamine (Glu) into uroporphyrinogen III, subsequently transformed into siroheme (\u003cem\u003ecysG\u003c/em\u003e) and heme (\u003cem\u003ehemY, hemH, hemQ\u003c/em\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eClusters of Non-Ribosomal Peptide Synthesis (NRPS)\u003c/b\u003e\u003c/p\u003e\u003cp\u003eUsing the AntiSMASH software, the genomes of the BAC30 and BAC220 strains were analyzed, identifying nonribosomal peptide synthesis (NRPS) regions associated with secondary metabolite production, exhibiting high identity (above 70%). In the BAC30 strain (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003ea), regions linked to the biosynthesis of bacitracin, bacillibactin, lichenysin, and fengycin were detected, along with the biosynthetic genes involved in these processes. In contrast, the BAC220 strain exhibited only three regions associated with the production of bacillibactin, bacitracin, and lichenysin (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eb).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eDue to the relatively recent description of the species \u003cem\u003eBacillus paralicheniformis\u003c/em\u003e and the limited number of comprehensive studies on its genomic and functional landscape, this study aimed to deepen the understanding of its phylogeny and behavior in a comparative context. Despite current knowledge limitations, \u003cem\u003eB. paralicheniformis\u003c/em\u003e has been identified as a species capable of producing bacitracin, a metabolite of industrial interest, which has demonstrated pathogen-inhibitory activity both \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e. In this context, the characterization of new strains is essential to broaden the biotechnological application potential of the species, as exemplified by the BAC30 and BAC220 isolates analyzed in this study.\u003c/p\u003e\u003cp\u003eIn the context of isolates identification, both strains were successfully sequenced and subsequently processed through genome assembly and annotation. Additionally, they showed high similarity to each other in terms of genome size, GC content (%), and taxonomic classification, as determined by the TYGS and GTDB-Tk tools (Tables S1 \u0026amp; S2). When comparing \u003cem\u003eB. paralicheniformis\u003c/em\u003e strains among themselves, a high degree of genomic similarity was observed across all genomes (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Furthermore, comparison of the BAC30 and BAC220 isolates with other \u003cem\u003eBacillus\u003c/em\u003e species (Fig.\u0026nbsp;1) revealed strong similarity to the reference genome of \u003cem\u003eB. paralicheniformis\u003c/em\u003e (Bac84). These three \u003cem\u003eB. paralicheniformis\u003c/em\u003e genomes also showed greater similarity to the \u003cem\u003eB. licheniformis\u003c/em\u003e reference genome than to other species within the \u003cem\u003eBacillus\u003c/em\u003e genus. Additionally, the clustering of other \u003cem\u003eBacillus\u003c/em\u003e species aligned with previous studies that reported similar associations, supporting the reliability of our analysis (Nannan et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These results further confirm the relevance and reliability of WGS analyses, considering that the initial identification of the isolates was incorrect, having been previously classified as \u003cem\u003eunknown\u003c/em\u003e and \u003cem\u003eBacillus cereus\u003c/em\u003e, respectively.\u003c/p\u003e\u003cp\u003eIn the phylogenomic analyses performed using Gegenees and SplitsTree4 (Fig. S2), the outgroup \u003cem\u003eBacillus sonorensis\u003c/em\u003e (ASM3405511v1) exhibited a relatively high similarity score, reflecting its close phylogenetic relationship due to its inclusion within the same genus as the other analyzed genomes. This similarity contributes to the robustness of the phylogenomic inferences, minimizing potential biases and branching errors that could arise from the use of an excessively divergent outgroup (Wilberg \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In the analysis conducted with Orthofinder (Fig.\u0026nbsp;1b), the formation of four distinct subgroups was observed, consistent with the results obtained through Gegenees. The structure of these groups remained stable, with only minor changes in the relative positioning of genomes across different approaches. However, to date, no studies have conducted specific and in-depth analyses focused solely on \u003cem\u003eBacillus paralicheniformis\u003c/em\u003e genomes, with most comparative studies involving closely related species within the same genus, such as \u003cem\u003eBacillus licheniformis\u003c/em\u003e, \u003cem\u003eBacillus subtilis\u003c/em\u003e, and others (Asif et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In both analyses, the strains CamBx3 (Narsing Rao et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and J41TS8 (Okumura et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) stood out by clustering farther from the remaining genomes, potentially indicating a relevant phylogenetic divergence. Although these strains share over 95% ANI identity, they may represent a sub-lineage or even a distinct lineage from the currently recognized \u003cem\u003eB. paralicheniformis\u003c/em\u003e, considering the limited number of available phylogenetic markers for this species. Furthermore, the circular map (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003ea) generated for all \u003cem\u003eB. paralicheniformis\u003c/em\u003e strains using Proksee once again revealed a high degree of genomic similarity. This finding corroborates previous results from the taxonomic analysis (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), the minimal impact observed when comparing the gene repertoire of the isolates with that of the overall species (Fig. S4), and the negligible difference in the alpha value when comparing the pangenome with and without the isolates (Fig. S5).\u003c/p\u003e\u003cp\u003eThe analysis performed using the PPanGGolin tool revealed that, despite the high genomic similarity among the \u003cem\u003eBacillus paralicheniformis\u003c/em\u003e strains in the dataset, the proportion of persistent genes (Fig. S3) is relatively low when compared to other \u003cem\u003eBacillus\u003c/em\u003e species, such as \u003cem\u003eB. amyloliquefaciens\u003c/em\u003e (75.20%) and \u003cem\u003eB. anthracis\u003c/em\u003e (74.01%) (Kim et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The high number of unique genes and the limited number of shared genes among the analyzed genomes, suggests a pattern of ubiquity for the species. Such dispersion reflects the diversity of isolation sources and the broad geographic distribution of the strains, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, and highlights the adaptive capacity of the species to acquire new genes. The rarefaction curve (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eb) also indicates a continuing trend of gene acquisition among the strains analyzed. Although the strains are highly similar and the inclusion of the isolates did not significantly impact the species' overall gene repertoire (Fig. S4b \u0026amp; S4c) (Fig. S5), this pattern may indicate the species' adaptive capacity to different ecological niches, as well as its widespread environmental distribution, consistent with the distribution of gene families (Fig. S3). However, to confirm this genomic plasticity and adaptive potential, additional analyses are still required, particularly regarding gene synteny and the presence of mobile genetic elements.\u003c/p\u003e\u003cp\u003eRegarding the functional analyses, the results related to defense mechanisms obtained through KEGG revealed the presence of two distinct β-lactam resistance modules, which may act synergistically (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This resistance may be considered intrinsic to organisms of the \u003cem\u003eBacillus\u003c/em\u003e genus as, for example, a study analyzing 114 \u003cem\u003eBacillus\u003c/em\u003e genomes, including \u003cem\u003eB. paralicheniformis\u003c/em\u003e, found that approximately 86% of the strains exhibited resistance to penicillin (Zhai et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The low abundance of antibiotic resistance genes (ARGs) is crucial for the efficient disposal of these microorganisms during equipment cleaning and for mitigating the spread of ARGs from industrial waste (Li et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). It also allows for the introduction of classical vectors containing expression inducers or heterologous protein genes, which frequently include resistance genes used for clonal culture selection (Dong and Zhang \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Regarding B-complex vitamin biosynthesis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e), recent studies have primarily focused on the development of food formulations using \u003cem\u003eBacillus\u003c/em\u003e strains capable of producing riboflavin (vitamin B2), an essential compound for monogastric organisms such as humans, poultry, and swine (Lambertz et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In this context, several studies have characterized and assessed the safety of riboflavin produced by \u003cem\u003eB. subtilis\u003c/em\u003e strains, considering it safe for consumption (EFSA Panel on Additives and Products or Substances used in Animal Feed (FEEDAP) et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, there is a potential risk of residual genes in the final product, which may include resistance or virulence genes with the potential for horizontal transfer to pathogenic bacteria (EFSA Panel on Additives and Products or Substances used in Animal Feed (FEEDAP) et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Another limiting factor for the commercialization of isolated vitamin compounds is the high production cost, associated with low yield and the availability of more efficient alternatives, which currently makes the process economically unfeasible (Revuelta et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eRegarding nitrogen metabolism (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e), the \u003cem\u003enas\u003c/em\u003e and \u003cem\u003enar\u003c/em\u003e genes, associated with nitrate reduction and conversion, were identified. These genes indicate the adaptability of the strains to different forms of nitrogen compounds (nitrate, nitrite, and ammonia), which vary in concentration depending on soil type, fertilizers, and animal-derived inputs (Ferraz-Almeida \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), enabling the balancing of these compounds. Additionally, the conversion of nitrogen compounds may reduce NO₃⁻ (nitrate) levels, whose elevated concentrations contribute to soil acidification and nutrient depletion, ultimately hindering plant growth (Florio et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). It is also considered that these bacteria may increase nitrogen retention time in the soil, preventing its volatilization as nitrous oxide (N₂O), a greenhouse gas that lowers fertilizer efficiency (Wu et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), as well as enabling a reduction in excessive fertilizer use, which is recognized as a major source of environmental pollution (Ahmed et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Although nitrogenous compounds generated by these bacteria can be taken up by plants, no genes related to their extracellular transport were found. However, due to the rapid cell cycle of \u003cem\u003eBacillus\u003c/em\u003e strains, intracellular content, including metabolites and nitrogen compounds, can be released into the rhizosphere after bacterial lysis, making these compounds available to plants as necromass (Pausch et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The identified heme biosynthesis pathway is also noteworthy, as microbial heme production has been explored in pharmaceutical and food industries as an economically viable and animal-free alternative to traditional production methods involving animal blood (Yang et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn the analysis of nonribosomal peptides (NRPs) associated with the production of AMCs (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e), bacitracin stands out as the most relevant, being primarily produced by strains of \u003cem\u003eBacillus licheniformis\u003c/em\u003e and \u003cem\u003eB. subtilis\u003c/em\u003e (Cai et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This antibiotic is widely used in topical formulations, such as ointments, often in combination with other antimicrobials like neomycin (Jones et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), contributing to the prevention of infections caused by pathogens and/or opportunistic microorganisms. Its efficacy has been demonstrated, for example, in a study indicating bacitracin as the main metabolite responsible for inhibitory activity against the pathogen \u003cem\u003eStaphylococcus aureus\u003c/em\u003e, compared to other metabolites such as bacilysin, fengycin, and bacillibactin (Luo et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Furthermore, the production of natural antibiotics and preservatives like bacitracin is highly relevant, considering that the consumption of artificial preservatives have been associated with gut diseases, obesity (Reardon \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), lung and liver damage, and cancer (Aldabayan \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). However, efficient bacitracin production requires, for example the genes \u003cem\u003ebcrABC\u003c/em\u003e, which confers resistance to bacitracin itself (Podlesek et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), and were not found in the BAC30 and BAC220 strains. Additionally, limiting factors such as optical density, nitrate concentration, and NADH oxidation must be carefully considered in in vitro assays (Zhu et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Nevertheless, the NRPs, combined with vitamin biosynthesis genes such as those for riboflavin, suggest a diversified potential for the biotechnological application of the isolated BAC30 and BAC220 strains.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe isolated \u003cem\u003eBacillus paralicheniformis\u003c/em\u003e strains, BAC30 and BAC220, demonstrated high reliability in sequencing, genome assembly, and annotation. Phylogenomic analyses revealed high similarity between the genomes, with particular attention to the CamBx3 and J41TS8 strains, which may warrant further investigation due to their lower similarity relative to the others. Additionally, the formation of subgroups was proposed, although more specific phylogenomic analyses are still needed to support these subgroup distinctions. Regarding comparative analyses, the pangenomic approach provided insight into the species\u0026rsquo; tendency to adapt to different ecological niches and acquire new genes, helping to explain the speciation and divergence between \u003cem\u003eB. licheniformis\u003c/em\u003e and \u003cem\u003eB. paralicheniformis\u003c/em\u003e.\u003c/p\u003e\u003cp\u003e\u003cem\u003eIn silico\u003c/em\u003e analyses suggest that the isolated strains BAC30 and BAC220 hold biotechnological potential, as they only exhibited KEGG modules related to beta-lactam resistance. Moreover, modules involved in the biosynthesis of metabolites relevant to various industrial sectors were identified, including those responsible for the production of vitamins, cofactors, and natural antibiotics, which can be incorporated into commercial products or produced independently.\u003c/p\u003e\u003cp\u003eNevertheless, despite the observed potential, further \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e assays are required to confirm the expression of genes involved in metabolite biosynthesis and antibiotic resistance, in order to properly assess the actual effectiveness of these strains as producers of biotechnologically valuable compounds.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to acknowledge the Pr\u0026oacute;-Reitoria de Pesquisa\u0026mdash;Universidade Federal de Minas Gerais, Rede de Ci\u0026ecirc;ncias \u0026Ocirc;micas (RECOM), the Conselho Nacional de Desenvolvimento Cient\u0026iacute;fico e Tecnol\u0026oacute;gico (CNPq), Coordena\u0026ccedil;\u0026atilde;o de Aperfei\u0026ccedil;oamento de Pessoal de N\u0026iacute;vel Superior (CAPES) and Funda\u0026ccedil;\u0026atilde;o de Amparo \u0026agrave; Pesquisa do Estado de Minas Gerais (FAPEMIG) for their financial support and fellowships.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis research was funded by Funda\u0026ccedil;\u0026atilde;o de Amparo \u0026agrave; Pesquisa do Estado de Minas Gerais -FAPEMIG (5.18/2022).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll authors consent to the publication of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConceptualization: Gabriel Camargos Gomes, Eduarda Guimar\u0026atilde;es Sousa; Methodology: Gabriel Camargos Gomes, Eduarda Guimar\u0026atilde;es Sousa, Marcus Vin\u0026iacute;cius Can\u0026aacute;rio Viana, Bertram Brenig; Formal analysis and investigation: Gabriel Camargos Gomes, Giovanna Karine Viana Silva, Rafael Junio de oliveira; Writing-original draft preparation: Gabriel Camargos Gomes, Eduarda Guimar\u0026atilde;es Sousa, Jana\u0026iacute;ne Aparecida de Paula; \u0026nbsp;Writing-review and editing: Gabriel Camargos Gomes, Eduarda Guimar\u0026atilde;es Sousa, Ludmila Silva Quaresma, Rhayane Cristina Viegas Santos, Gabriela Munis Campos, Vasco Azevedo; Supervision: Vasco Azevedo; Funding acquisition: Vasco Azevedo. All authors read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe current study\u0026rsquo;s data are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003e\u0026Aring;gren J, Sundstr\u0026ouml;m A, H\u0026aring;fstr\u0026ouml;m T, Segerman B (2012) Gegenees: Fragmented Alignment of Multiple Genomes for Determining Phylogenomic Distances and Genetic Signatures Unique for Specified Target Groups. PLoS ONE 7(6):e39107. https://doi.org/10.1371/journal.pone.0039107\u003c/li\u003e\n\u003cli\u003eAhmed M, Rauf M, Mukhtar Z, Saeed NA (2017) Excessive use of nitrogenous fertilizers: an unawareness causing serious threats to environment and human health. 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Synthetic and Systems Biotechnology 8(2):314\u0026ndash;322. https://doi.org/10.1016/j.synbio.2023.03.009\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"world-journal-of-microbiology-and-biotechnology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wibi","sideBox":"Learn more about [World Journal of Microbiology and Biotechnology](https://www.springer.com/journal/11274)","snPcode":"11274","submissionUrl":"https://submission.nature.com/new-submission/11274/3","title":"World Journal of Microbiology and Biotechnology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Bacillus, Biocontrol, Agriculture, Industry, Metabolites","lastPublishedDoi":"10.21203/rs.3.rs-7143786/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7143786/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe \u003cem\u003eBacillus\u003c/em\u003e genus includes plant growth-promoting rhizobacteria (PGPR), and the discovery of new strains within this group is of great biotechnological interest due to their ability to produce antimicrobial compounds (AMCs), vitamins, enzymes, and heterologous proteins. Among these, \u003cem\u003eBacillus paralicheniformis\u003c/em\u003e is a recently described species whose phylogeny remains poorly resolved, highlighting the need for further investigation. This study aimed to identify and characterize the isolates BAC30 and BAC220 using whole-genome sequencing (WGS). Both were confirmed as \u003cem\u003eB. paralicheniformis\u003c/em\u003e and included in phylogenomic and comparative analyses with 28 other strains to assess the species\u0026rsquo; genetic structure and inter-strain similarity. Functional annotation of BAC30 and BAC220 was also performed, focusing on biotechnological potential. Comparative analysis revealed high genomic similarity among strains, including the two isolates. Pangenome analysis showed a low proportion of core genes relative to accessory genes (shell and cloud), and the rarefaction curve suggested an open pangenome, indicating the species\u0026rsquo; ubiquity and co-evolution with other organisms. Functional analysis identified genes of defense mechanisms related to beta-lactam resistance. Regarding secondary metabolite production, genes involved in the biosynthesis of vitamins (e.g., riboflavin) and AMCs (e.g., bacitracin) were detected. Although further \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e assays are needed to confirm gene expression, the findings support the biotechnological relevance of these isolates as potential biocontrol agents and/or producers of industrially valuable compounds.\u003c/p\u003e","manuscriptTitle":"Comparative, Pangenomic and Functional Analyses of two Bacillus paralicheniformis Soil- Isolated Strains from Bahia Sequenced by WGS Reveal Species Homogeneity and Bioactive Metabolites with Biotechnological Potential","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-25 13:17:44","doi":"10.21203/rs.3.rs-7143786/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-21T18:56:42+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-19T14:56:05+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-07T23:11:29+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-25T20:03:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"5400421146465676998374721211000044273","date":"2025-07-25T10:55:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"70296713092041314294374281133892786842","date":"2025-07-24T19:10:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"261795702720834974356835054738198631257","date":"2025-07-23T17:22:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"162992045594296682389957955568006360423","date":"2025-07-23T12:53:21+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-23T10:40:47+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-18T02:20:02+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-17T05:40:46+00:00","index":"","fulltext":""},{"type":"submitted","content":"World Journal of Microbiology and Biotechnology","date":"2025-07-17T00:41:02+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"world-journal-of-microbiology-and-biotechnology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wibi","sideBox":"Learn more about [World Journal of Microbiology and Biotechnology](https://www.springer.com/journal/11274)","snPcode":"11274","submissionUrl":"https://submission.nature.com/new-submission/11274/3","title":"World Journal of Microbiology and Biotechnology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"7a63fbb2-1261-42f0-862f-a3734123cfd6","owner":[],"postedDate":"July 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-05T16:00:41+00:00","versionOfRecord":{"articleIdentity":"rs-7143786","link":"https://doi.org/10.1007/s11274-025-04759-z","journal":{"identity":"world-journal-of-microbiology-and-biotechnology","isVorOnly":false,"title":"World Journal of Microbiology and Biotechnology"},"publishedOn":"2025-12-31 15:57:17","publishedOnDateReadable":"December 31st, 2025"},"versionCreatedAt":"2025-07-25 13:17:44","video":"","vorDoi":"10.1007/s11274-025-04759-z","vorDoiUrl":"https://doi.org/10.1007/s11274-025-04759-z","workflowStages":[]},"version":"v1","identity":"rs-7143786","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7143786","identity":"rs-7143786","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}