Genomic and pangenomic analysis of Bacillus thuringiensis MH778713, formerly Bacillus cereus, a multi-heavy metal tolerant bacterium

Genomic and pangenomic analysis of Bacillus thuringiensis MH778713, formerly Bacillus cereus, a multi-heavy metal tolerant bacterium | 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 Genomic and pangenomic analysis of Bacillus thuringiensis MH778713, formerly Bacillus cereus, a multi-heavy metal tolerant bacterium Santiago-Valentín Galván-Gordillo, Brenda Roldán, Miguel Ángel Villalobos-López, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9227678/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract The Bacillus cereus group comprises several Gram-positive, spore-forming bacteria that are widespread in natural environments and exhibit varying degrees of pathogenic potential and industrial relevance. Species-level taxonomic classification of B. cereus group strains remains challenging. Phylogenetic analysis of housekeeping gene sequences is often employed to assign B. cereus group strains to the species level. Still, it is insufficient for accurate taxonomic classification on a genomic scale. Bacillus cereus MH778713 was described as a chromium hyper-tolerant strain, isolated from mesquite shrubs ( Prosopis laevigata ) in Mexico, and described as belonging to the B. cereus species by 16S rDNA and rpo B gene sequence analysis. Long-read genomic sequencing was conducted for Bacillus cereus MH778713, resulting in a high-quality, near-chromosome-level genome with an assembly length of 5.9 Mbp. An Average Nucleotide Identity (ANI) analysis showed that this strain belonged to the Bacillus thuringiensis species, reclassifying this microorganism as B. thuringiensis MH778713. Gene cluster families possibly related to heavy metal tolerance, such as antibiotic resistance, virulence factors, drug targets, and transporters, were found; no plasmids are carried by this strain. A pangenomic analysis of different bacterial species genomes allowed us to identify gene cluster families unique to this strain, possibly related to chromium hyper-tolerance. Bacillus chromium genomic heavy metals hyper tolerance pangenomic Figures Figure 1 Figure 2 Figure 3 Figure 4 INTRODUCTION Pollution by heavy metals represents an increasing global problem due to human and industrial activity (Mishra et al. 2019 ; Briffa et al. 2020 ; Adnan et al. 2024 ); even, it has become more common to find greater quantities of these toxic elements in soils and water resources. Chromium is a relatively abundant element in Earth’s crust, and it is found in nature in association with other elements in minerals. Due to its wide use in the automotive industry, electroplating, and other industrial applications, and its subsequent disposal in wastewater, chromium accumulation has caused fragility in ecosystems since the chromate (CrO 4 (−2) ) and hexavalent chromium (Cr 6+ ) ions are highly toxic forms due to its strongly oxidizing power and their capability to be erroneously transported by sulphate channels through cell membranes. Unlike organic contaminants, metals cannot be decomposed biologically, physically, or chemically, which limits remediation to confining these contaminants by altering the solubility, mobility, or toxicity, generating changes in their valences, and favouring their immobilization (chelation) (Bosecker 2001 ; Stephen and Macnaughton 1999 ; Dagdag et al. 2023 ). An alternative for the treatment of soils contaminated with heavy metals, for their elimination, is bioremediation, which involves the use of microorganisms due to their capacity to immobilize metals (Lovley and Coates 1997 ; Das et al. 2023 ). Bacillus is a bacterial genus belonging to the Firmicutes phylum, with about 142 species described. It’s a Gram-positive, spore-forming, rod-shaped, aerobic, or facultative bacterium (Shafi et al. 2017 ; Porwal et al. 2009 ). They are ubiquitous organisms that have been recovered from diverse environments, including soil, water, gastrointestinal tract, plant tissues, rhizosphere, and extreme environments (Alcaraz et al. 2010 ). This bacterial group is relevant because of the production of diverse antagonistic substances (Fira et al. 2018 ; Rabbee et al. 2019 ). B. velezensis , B. subtilis , B. amyloliquefaciens , and B. thuringiensis have been proven to be effective against a variety of pathogens because these species can produce a spectrum of bioactive secondary metabolites like antibacterial polyketides, cyclic lipopeptides, polyketide-type antimicrobial molecules, siderophores, and bacteriocins (Rabbee et al. 2023 ; Pesarini-Baptista et al. 2018 ; Abriouel et al. 2011 ). Bacillus strains have been reported as a safe and environmentally friendly bioremediation tool, highlighting the role of B. cereus , B. thuringiensis , and B. subtilis for their biosorption capacity, generating a biotechnological alternative for the recovery of contaminated soils with heavy metals (Srinath et al. 2002 ; Quintelas et al. 2008 ; Upadhyay et al. 2017 ; Sharman and Shukla 2021; Wróbel et al. 2023 ; Schommer et al. 2023 ). The Nexapa river, located in Chietla (Puebla), Mexico, represents an environmental and health problem, reporting high amounts of contaminants from various sources, such as domestic, industrial, and agrochemical discharges. Previous studies by Pérez-Castresana et al. ( 2018 ) reported a high concentration of heavy metals such as aluminium, titanium, and chromium (highlighting chromium due to its toxicity), in the river sediments. Ramirez et al. (2019) isolated a group of bacteria belonging to the genus Bacillus that tolerated the presence of large amounts of heavy metals in the environment; particularly Bacillus strain MH778713 showed a hyper-tolerance of up to 15,000 mg/kg of chromium. 16SrDNA and rpo B gene sequence analysis placed this strain in the Bacillus cereus group. Chromium tolerance in bacteria involves several mechanisms that allow them to survive and adapt to these stressful environments, particularly in its toxic hexavalent form Cr (VI) (Table 1 ) (Ackerley et al. 2004 ). Table 1 Mechanisms of bacterial tolerance to chromium compounds in bacteria. Chromium Reduction: Many bacteria have developed the ability to reduce toxic Cr (VI) to the less toxic trivalent form Cr (III). This reduction process can occur either enzymatically or non-enzymatically (Ramírez-Díaz et al. 2008 ). Enzymatic Reduction: Specific enzymes, such as chromate reductases, reduce Cr (VI) to Cr (III) under anaerobic or aerobic conditions. These enzymes may include flavoproteins, cytochromes, and reductases like ChrR, which are widely studied (Bopp et al. 1983; Ishibashi et al. 1990 ; Suzuki et al. 1992 ; Campos et al. 1995 ). Non-enzymatic Reduction : Reduced forms of cellular molecules, like glutathione and cysteine, can also facilitate the reduction of Cr (VI) to Cr (III). (Ackerley et al. 2004 ; Thatoi et al. 2014 ). Efflux Pumps : Certain bacteria express efflux pumps that actively expel Cr (VI) ions from the cell. These pumps, like the ChrA protein, are part of the resistance-nodulation-cell division (RND) superfamily and help maintain low intracellular concentrations of chromium, thereby reducing its toxicity (Nies 2003 ). Chromium Sequestration : Bacteria can sequester chromium by binding it to cell wall components, such as extracellular polysaccharides or proteins, thus preventing it from entering the cell or reducing its concentration inside the cell (Cervantes et al. 2001 ). Genetic Adaptation and Regulation : • Chromate Resistance Genes : Some bacteria possess chromate resistance genes (such as chr genes) located on plasmids or chromosomes. These genes code for proteins that help in chromium reduction, efflux, or protection from oxidative stress (Juhnke et al. 2002 ; Boop et al. 1983 ; Ramírez-Díaz et al. 2008 ; Viti et al. 2014 ). • Regulatory Proteins : Regulatory proteins like ChrB can modulate the expression of chromate resistance genes in response to chromium exposure (Ramírez-Díaz et al. 2008 ). Oxidative Stress Response: Chromium exposure leads to the generation of reactive oxygen species (ROS), which can damage cellular components. Bacteria use antioxidant enzymes (such as catalases, superoxide dismutases, and peroxidases) and other mechanisms (e.g., production of thiols) to mitigate oxidative damage and maintain cellular homeostasis. (López-Bucio et al. 2000 ). Metabolic Changes : Some bacteria alter their metabolic pathways to reduce chromium toxicity, such as switching from aerobic to anaerobic respiration, which can minimize the interaction of chromium with cellular components (Ramli et al. 2023 ). Biofilm Formation : Bacteria often form biofilms, which can provide a protective barrier against chromium toxicity. The extracellular polymeric substances (EPS) in biofilms can bind chromium and limit its access to bacterial cells (Teitzel and Parsek 2003 ). (Table 1) The genetic study of microorganisms has advanced considerably thanks to advances in sequencing techniques. Sanger sequencing, considered the first successful sequencing technology, has been the basis of genetic analysis for decades. However, the advent of Next-Generation Sequencing (NGS) technology has changed the research landscape, especially in microbial genetics. In this report, we compare both technologies, their applications in microbiology, and their advantages and limitations (Reuter et al. 2015 ). In the identification and classification of bacteria and other microorganisms, techniques based on DNA analysis have proven to be essential. Among these techniques, the Average Nucleotide Identity (ANI) technique stands out, which is used to determine the degree of genomic similarity between microorganisms, offering greater precision for identifying new species, especially when used to compare complete genomes (Richter and Rosselló-Móra 2009 ). On the other hand, the analysis of housekeeping genes (conserved genes essential for basic cellular functions) has been commonly used for phylogenetic classification and studies of microbial evolution (Janda and Abbott 2007 ). In this study, the genome of Bacillus cereus MH778713 has been analyzed, and a comparative genomic analysis was performed to clarify the taxonomic position of this strain at the species level and to identify those specialty genes of the strain that could be involved in chromium hypertolerance. MATERIALS AND METHODS Bacterial strain The chromium hyper-tolerant strain Bacillus cereus MH778713 was used for this study. This strain was isolated from soils surrounding the Nexapa river, in Mexico, where soil studies (Ramirez et al. 2019) reported a high prevalence of some metals, including some toxic heavy metals such as chromium. DNA Extraction, Library Preparation, and Whole Genome Sequencing Bacillus cereus MH778713 was grown in YM medium at 28°C, and genomic DNA was extracted using the Wizard® Genomic DNA Purification Kit (Promega), following the manufacturer's instructions. After extraction, the quality and quantity of the DNA were processed by spectral analysis using the Micro UV-Vis Spectrophotometer (NanoReady-RealLife). DNA samples were stored at − 20°C until use. Library preparation was performed using Illumina DNA Prep (Illumina, USA) for the labelling and cleanup steps and the Nextera™ DNA CD Index Kit (Illumina, USA) for the indexing step, according to the manufacturer's instructions. Library quality control was performed using a Qubit 3.0 Fluorometer (Invitrogen, USA) and a Bioanalyzer 2100 (Agilent, USA). Sequencing of pooled and normalized libraries was performed using the MiSeq V2 reagent kit (300 cycles) on the Illumina MiSeq platform. Sequencing Quality Control, de Novo Assembly, and Annotation These steps were carried out using “The Bacterial and Viral Bioinformatics Resource Center” (BV-BRC) bacteria default pipeline ( https://www.bv-brc.org ) (Olson RD et al, 2022 ), i.e. Trim-Galore (v0.6.5dev) for reads trimming, BBNorm (October 19, 2017) for reads trimming normalization, Unicycler (v0.4.8) for genomic assembly, Pilon (v1.23) (2 rounds) for polishing, and QUAST (v5.2.0) for assembly quality control. Sufficient quality was defined as a base accuracy (Q30) of more than 95% for all reads. Genome annotation was done with the RAST toolkit (v.1.073) and Prokka (v1.12) using the default parameters. Assembled contigs were analyzed for the presence of resistance determinants as well as the presence of a myriad of resistance determinants such as: plasmids, virulence genes and whole anti-microbial resistance (AMR) genes using the ABRicate (v1.0.1), ResFinder, PlasmidFinder and Virulence Factor Database (VFDB) databases, on a command line on a Rocky Linux 8.7 (Green Obsidian) server (Fig. 1 ). Pairwise Average Nucleotide Identity (PyANI) Whole genome comparisons with PyANI (v.0.2.12) ( https://github.com/widdowquinn/pyani ) were used to calculate the Average Nucleotide Identity (ANI) based on MUMmer algorithm (default) against the 40 Bacillus genomic sequences (Fig. 2 ), using an in-house bash script (pyANI.sh), on a Rocky Linux 8.7 (Green Obsidian) server using a SLURM job scheduler. Sequences of strains selected for analysis were automatically retrieved from the NCBI RefSeq database, using an in-house bash script (NCBIdownload.sh), on a Rocky Linux 8.7 (Green Obsidian) server using a SLURM job scheduler. Pangenome Analysis Prokka (1.14.5) ( https://github.com/tseemann/prokka ) genome annotation followed by the Roary (3.13.0) pangenome pipeline ( https://sanger-pathogens.github.io/Roary ) were used for pangenome analysis of the whole genome sequence data from the Bacillus cereus MH778713 plus 39 other strains, 10 of each different group of B. cereus , B. thuringiensis , B. anthracis , and B. mycoides . To run Roary for the phylogenetic reconstruction, and visualize it together with the heatmap, based on the presence/absence genes approach, an in-house bash script was used (Roary.sh), which in turn runs Roary (3.13.0), FastTree (v2.1.11), and a locally modified version of roary_plots.py (v0.1.0) ( https://github.com/sanger-pathogens/Roary/tree/master/contrib/roary_plots ). RESULTS The genome of B. cereus MH778713 corresponds to a high-quality assembly, given by the quality values of the reads (Q 37.39 and 36.25 for R1 and R2, respectively), average short read coverage 21.95, and N's per-100 kbp 0.00. There were 30 contigs, an estimated genome length of 5,912,409 bp, and an average G + C content of 34.86%. The N50 is 378,292 bp. The L50 is 5 contigs (Table 2 ) (Wattam et al. 2017 ). (Table 2) Table 2 Genome content and quality data of B. cereus MH778713. Depth (Average) 21.95 # N's per 100 kbp 0 N50 378292 L50 5 # Contigs 39 Largest contig 1063386 Total length 5912409 GC (%) 34.86 Coarse consistency (%) 99.3 Fine consistency (%) 98.0 Completeness (%) 100 Contamination (%) 0 Protein-Encoding Genes with Functional Assignment 3680 Protein-Encoding Genes without Functional Assignment 2391 Total Distinct Roles 3431 All statistics are based on contigs of size > = 300 bp The most represented category was Amino Acids and Derivatives (n = 254 genes), followed by Carbohydrates (n = 197), Protein Metabolism (n = 112), and Dormancy and Sporulation (n = 97). Substantial representation was also observed in Nucleosides and Nucleotides (n = 87), DNA Metabolism (n = 62), Respiration (n = 57), and RNA Metabolism (n = 56). Additional functional groups included Iron acquisition and metabolism (n = 52), Fatty Acids, Lipids, and Isoprenoids (n = 49), Cell Wall and Capsule (n = 40), Stress Response (n = 34), Membrane Transport (n = 32), and Regulation and Cell Signaling (n = 29). Lower-frequency categories comprised Miscellaneous (n = 21), Nitrogen Metabolism (n = 18), Phosphorus Metabolism (n = 18), Phages, Prophages, Transposable Elements, and Plasmids (n = 13), Secondary Metabolism (n = 9), Metabolism of Aromatic Compounds (n = 9), Potassium Metabolism (n = 7), and Cell Division and Cell Cycle (n = 4). These assignments reflect the metabolic versatility and adaptive capacity of B. thuringiensis , with strong representation of core biosynthetic, carbohydrate utilization, and sporulation-related functions. An overview of the main categories for this genome is provided in Fig. 1 . (Fig. 1 ) Figure 2 shows the pyANI (pyANI-plus) identity heatmap, resulting in a group-specific matrix. Distribution of Bacillus genomes according to species and/or serotypes of Bacillus cereus , B. thuringiensis , B. anthracis and B. mycoides is shown; Bacillus strain MH778713 is located in the B. thuringiensis species group. Distinct clustering patterns are observed, separating members of the Bacillus anthracis , Bacillus cereus , Bacillus thuringiensis , and Bacillus mycoides lineages. Genomes within the same species cluster together with high similarity (deep red blocks along the diagonal), whereas interspecies comparisons show lower similarity (lighter shades of blue regions). The block-like structure highlights the genomic cohesion within species and the divergence among species within the Bacillus cereus group. (Fig. 2 ) Whole genome sequence data from the Bacillus strain MH778713 and 40 other genome sequences from B. cereus , B. thuringiensis , B. anthracis , and B. mycoides were used for pangenome construction. Annotated assemblies created by Prokka were used to score and visualize the core (95% > 99%), shell (15% > 95%), and cloud genomes (0% > 15%) using the default settings of the Roary software package (v.3.13.0) and a modified version of the roary_plots.py script ( https://github.com/sanger-pathogens/Roary/tree/master/contrib/roary_plots ). The complete genome sequences of 39 Bacillus strains were downloaded from the NCBI. Roary analysis of Bacillus pangenome identified a total of 1592 core, 151 softcore, 7667 shell, and 27808 cloud genes (Fig. 3 ). Although strain-specific genes are observed as accessory genes, all genomes clustered into a clade sharing a similar content of core genes, likely related to metabolic functions. (Fig. 3 ) The genes of the bacterial species studied in the pangenome analysis were also analysed by ABRcate (Fig. 4 ), to identify special genes such as AMR, virulence, and plasmids, looking for any element involved in hypertolerance to heavy metals (Saier et al. 2016 ; Mao et al. 2015 ; Chen et al. 2016 ; Zhu et al. 2009 ; Law et al. 2014 ; McArthur et al. 2013 ). Distinct patterns of gene distribution are observed among species within the Bacillus cereus group. Members of B. anthracis display a relatively conserved gene profile with limited variation, whereas B. cereus , B. thuringiensis , and B. mycoides exhibit greater heterogeneity in resistance and virulence-associated gene content. Genes related to β-lactam resistance, efflux systems, and virulence determinants (including enterotoxins and hemolysins) are variably distributed across strains. (Fig. 4 ) The pangenome analysis allowed us to identify a cluster of unique genes listed in Table 3 and to associate them with hypertolerance to heavy metals. The frequency of genes involved in bacterial oxidative stress and carbohydrate metabolism stands out. Table 3 Unique genes found in Bacillus thuringiensis MH778713. Gene Annotation Gene Annotation asn B_1 Asparagine synthetase [glutamine-hydrolyzing] 1 gld A Glycerol dehydrogenase dau A_1 C4-dicarboxylic acid transporter DauA hyf R DNA-binding transcriptional activator HyfR nrdG _1 Anaerobic ribonucleoside-triphosphate reductase-activating protein dha K_2 PTS-dependent dihydroxyacetone kinase, dihydroxyacetone-binding subunit DhaK nrd D_1 Anaerobic ribonucleoside-triphosphate reductase dha L PEP-dependent dihydroxyacetone kinase, ADP-binding subunit DhaL csg A C-factor pts H_2 Phosphocarrier protein HPr ybj J_2 Inner membrane protein YbjJ glp F_2 Glycerol uptake facilitator protein arn C_5 Undecaprenyl-phosphate 4-deoxy-4-formamido-L-arabinose transferase sor A PTS system sorbose-specific EIIC component arn C_6 Undecaprenyl-phosphate 4-deoxy-4-formamido-L-arabinose transferase lnr L_3 Linearmycin resistance ATP-binding protein LnrL ykn X_3 Putative efflux system component YknX lnr N_2 Linearmycin resistance permease protein LnrN lod B Putative FAD-dependent oxidoreductase LodB bae C Polyketide biosynthesis malonyl CoA-acyl carrier protein transacylase BaeC rpi A_2 Ribose-5-phosphate isomerase A acp P Acyl carrier protein psp A_4 Phosphoserine phosphatase 1 fab Z_3 3-hydroxyacyl-[acyl-carrier-protein] dehydratase FabZ prt S Protease PrtS gcv T_2 Aminomethyltransferase ssp C_1 Small, acid-soluble spore protein C moa A_6 GTP 3',8-cyclase ymf D_3 Bacillibactin exporter xkd G_2 Phage-like element PBSX protein XkdG cyp X Pulcherriminic acid synthase rsr IM Modification methylase RsrI yvm C Cyclo(L-leucyl-L-leucyl) synthase nor V Anaerobic nitric oxide reductase flavorubredoxin per A GDP-perosamine synthase nas D_2 Nitrite reductase [NAD(P)H] aro A_3 Protein AroA(G) rub 3 Rubredoxin 3 dad A_2 D-amino acid dehydrogenase glx R CRP-like cAMP-activated global transcriptional regulator trp G Anthranilate synthase component 2 nas D_3 Nitrite reductase [NAD(P)H] men F Isochorismate synthase MenF nir C_2 Nitrite transporter NirC lfr A Multidrug efflux pump LfrA spl G_2 Spore photoproduct lyase der _3 GTPase Der ytr A_4 HTH-type transcripcional repressor YtrA (Table 3 ) DISCUSSION Stress generated by an increase in the content of heavy metals in the environment has caused microorganisms to adapt to these environments. Chromium tolerance mechanisms in Bacillus thuringiensis and Bacillus cereus , two closely related bacterial species known for their diverse metabolic capabilities, including tolerance to other heavy metals, are examples of this adaptability. B. thuringiensis and B. cereus species are gram-positive, spore-forming bacteria found in a wide range of environments, including soil, water, and plants. They are known for their ability to withstand various environmental stresses. Mechanisms used to tolerate the presence of these toxic elements range from the modification of enzymes involved in controlling oxidative stress to the production of exopolysaccharides to generate an extracellular matrix that allows them to regulate the internal concentration of heavy metals. Bacillus cereus strain MH778713 was isolated from soils surrounding the Nexapa River, Chietla, Puebla, which has reported high contamination rates of metals such as Aluminum (Al), Titanium (Ti), Silicon (Si), Iron (Fe), and Chromium (Cr), the latter being relevant for its impact on health since it is associated with cancer and skin and respiratory diseases. Previous experiments carried out by Ramírez et al. ( 2019 ) reported that B. cereus MH778713 was capable of growing in a medium with up to 15,000 mg of chromium, being stated as a hypertolerant bacterium. In this study, the genome of Bacillus cereus MH778713 has been analyzed, and comparative genomics analyses were performed to identify those specialty genes of the strain that could be involved in chromium hypertolerance. We utilize next-generation sequencing (NGS) technology due to its significant advantages over traditional methods. In this way, we were able to encompass a significant portion of the genomic coverage, enabling us to conduct a detailed analysis of genes, regulatory sequences, mutations, and mobile elements, such as plasmids. These advantages also provide high resolution for species identification and strain classification, which is especially useful in microorganisms closely related, such as members of the Bacillus cereus group species, where differences in housekeeping gene sequences could be insufficient for accurate identification. Besides, NGS allows us to detect differences at the level of accessory or resistance genes. Illumina miSeq technology, used for this analysis, allowed us to obtain an ensembled genome of 5.9 Mpb, with an average short read coverage of 22,132, with 38 contigs, and a L50 value of 5, and 6075 CDS reported (Fig. 1 ). The average nucleotide identity analysis (ANI), evaluating the degree of sequence identity between the nucleotides of the homologous fragments in the complete genomes of different Bacillus species, comparing a group of 40 strains including B. cereus , B. thuringiensis , B. mycoides and B. anthracis , was a useful tool to verify taxonomic identities in prokaryotic genomes, clarifying the taxonomic position of Bacillus strain MH778713, indicating its inclusion into Bacillus thuringiensis species. Figure 2 shows the heatmap of the average nucleotide identity (ANI) performed with the pyANI program, which provided a way to automate genome comparison. This tool performs ANI calculations using alignment algorithms. pyANI can use different alignment methods, using the MUMmer method because of its specificity for nucleotide sequences, calculating the average percentage of identical nucleotides in all orthologous gene pairs shared between these genomes; we observed that MH778713 strain is grouped in Bacillus thuringiensis species. This finding was interesting, since the 16SrDNA and rpo B gene sequences analyses classified these strains as belonging to the Bacillus cereus species. Furthermore, in rapid analysis programs such as Dragen Metagenomics, we were able to classify it as a group 2 Bacillus cereus . Still, when performing gene alignments and genome comparisons, most individual genes coincided (> 90%) with Bacillus thuringiensis . To corroborate this finding, a pangenome analysis was performed (Fig. 3 ), where the genome of the MH778713 strain was compared with genomes from other Bacillus species, using tools like Roary; this analysis allows us to understand the genetic diversity within the entire collection of genomes we used in this study, identifying key genes for specific traits. Roary is a tool capable of taking sets of genomic sequences and dividing the genes into core, accessory, and unique gene clusters. It generates a clustering of orthologous genes that allows us to identify those that are common and those that vary between strains, tools that could help us explain the tolerance to heavy metals observed in the strain MH778713. The analysis of the core genome organization identified conserved and unique gene clusters that could be related to heavy metal tolerance, analyzing their variability, and understanding the evolution of tolerance within the Bacillus group. We know that both B. thuringiensis and B. cereus possess chromate reductase enzymes that can reduce hexavalent chromium (Cr [VI]) to trivalent chromium (Cr [III]), a less toxic and less soluble form. Bacillus cereus strain DCNW, isolated from chromium-contaminated soils, has been shown to produce chromate reductases that effectively reduce Cr (VI) under aerobic conditions. The reduction typically involves transferring electrons from electron donors such as NADH or NADPH to Cr (VI), converting it to Cr (III). This activity helps to mitigate the toxic effects of Cr (VI) by lowering its oxidative potential and reducing its mobility in the environment (Li et al., 2020 ). Data obtained from the pangenomic analysis showed, when comparing unique genes of the MH778713 strain with other Bacillus genomes (Table 3 ), the presence of men F genes that catalyze the conversion of chorismate into isochorismate. This metabolite is involved in the synthesis of pyocyanin, a blue-colored secondary metabolite with the ability to oxidize and reduce other molecules. This ability could be occupied by Bacillus thuringiensis MH778713 to tolerate high concentrations of metals. Within the genome sequences analyzed in this study, genes related to the tolerance to different heavy metals were also identified, such as a cluster of genes related to arsenic and antimony tolerance, where transporters proteins facilitate the efflux of trivalent arsenic and antimony; ArsR is a member of the family of proteins categorized as DNA-binding transcriptional repressors; ArsR/SmtB family can sense a variety of metals and undergo allosteric conformational changes upon metal binding, resulting in derepressing of genes involved in detoxification (Busenlehner et al. 2003 ). We found cation diffusion facilitators proteins for cobalt, zinc, and cadmium ions (Bruins et al. 2000 ); we also detected a heavy metal translocating P-type ATPase for metals like cadmium, zinc, and mercury, whose function could be playing a role in incrementing the tolerance to metals in microorganisms (Chien et al. 2013 ). Furthermore, the presence of efflux pumps (involved in the movement of metals across cell membranes), the protection system against oxidative stress due to OxyR (a master regulator that detects the presence of H 2 O 2 and activates the massive formation of catalase and peroxidase enzymes for microorganism protection) (Storz et al. 1992), and the production of biofilm (where the polysaccharides of the glycocalyx could play an important role in forming a network where the metal found in the environment can be sequestered without it ever entering the interior of the cell), are mechanisms that could be involved in the prevention of damage, and its potentially toxic effect in microbial cells (Jasu and Ray 2021 ). CONCLUSIONS In conclusion, Bacillus spp. genome sequences within the Bacillus cereus group exhibited a high degree of similarity when compared to those from separate clades. This similarity became more apparent upon analysing the organization of their genomic DNA. Nevertheless, a more noticeable variation is observed when examining the arrangement of genes across the entire genome throughout the bacterium’s evolution, highlighting its genomic diversity. When compared with the pangenome, it is evident that Bacillus strain MH778713, previously classified as Bacillus cereus , must be reclassified as Bacillus thuringiensis MH778713. The scope of the study was to identify the heavy metal tolerance-related genes. The pangenomic analysis showed the presence of a cluster of unique genes in Bacillus thuringiensis MH778713, despite the closed pangenome similarity with Bacillus cereus . These genes may contribute to the functional diversity within the species, even though their specific roles remain unknown. Information obtained from this analysis is crucial for improving our understanding of the heavy metal tolerance evolution. Declarations ACKNOWLEDMENTS This work was supported by Vicerrectoría de Investigación y Estudios de Posgrado (VIEP-BUAP), Instituto de Ciencias (ICUAP), Posgrado en Ciencias (Microbiología) and SECIHTI research grant number 524111. We thank the Laboratorio Nacional de Supercómputo del Sureste de México for granting us the Access to its facilities (Project Number 202401001C: Genómica comparativa y evolutiva de poblaciones microbianas y eucariotes en condiciones extremas, con un enfoque en “Una Salud” (One Health). Data Availability: The full genome sequence of Bacillus thuringiensis MH778713 was made available in the GenBank database (Accession No: JBLWMS000000000). Conflict of interest : No conflict of interest declared. Ethical Statement : No animal uses. Author contributions : MAV, AB, VR and JAM conceived, designed, and directed the project, contributed to the interpretation of the results, and designed the figures. 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FEMS Microbiol Rev 38(4):633–659. https://doi.org/10.1111/1574-6976.12051 Wattam AR, Davis JJ, Assaf R, Boisvert S, Brettin T, Bun C, Conrad N, Dietrich EM, Disz T, Gabbard JL et al (2017) Improvements to PATRIC, the all-bacterial bioinformatics database and analysis resource center. Nucleic Acids Res 45(D1):D535–D542. https://doi.org/10.1093/nar/gkw1017 Wróbel M, Śliwakowski W, Kowalczyk P, Kramkowski K, Dobrzyński J (2023) Bioremediation of heavy metals by the genus Bacillus . Int J Environ Res Public Health 20(6):4964. https://doi.org/10.3390/ijerph20064964 Zhu F, Han B, Kumar P, Liu X, Ma X, Wei X, Huang L, Guo Y, Han L, Zheng C (2009) Update of TTD: therapeutic target database. Nucleic Acids Res 38:D787–D791. https://doi.org/10.1093/nar/gkp1014 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 22 Apr, 2026 Reviewers agreed at journal 05 Apr, 2026 Reviewers agreed at journal 02 Apr, 2026 Reviewers invited by journal 31 Mar, 2026 Editor assigned by journal 26 Mar, 2026 Submission checks completed at journal 26 Mar, 2026 First submitted to journal 25 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9227678","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":615937766,"identity":"d8ee1913-f303-4036-826d-b40a39365043","order_by":0,"name":"Santiago-Valentín Galván-Gordillo","email":"","orcid":"","institution":"Instituto Politécnico Nacional","correspondingAuthor":false,"prefix":"","firstName":"Santiago-Valentín","middleName":"","lastName":"Galván-Gordillo","suffix":""},{"id":615937774,"identity":"732e9ae0-1f16-4548-b348-bb255bcfefbd","order_by":1,"name":"Brenda Roldán","email":"","orcid":"","institution":"Benemérita Universidad Autónoma de Puebla","correspondingAuthor":false,"prefix":"","firstName":"Brenda","middleName":"","lastName":"Roldán","suffix":""},{"id":615937776,"identity":"8f588c39-e01a-4b77-a82c-50e320042b00","order_by":2,"name":"Miguel Ángel Villalobos-López","email":"","orcid":"","institution":"Instituto Politécnico Nacional","correspondingAuthor":false,"prefix":"","firstName":"Miguel","middleName":"Ángel","lastName":"Villalobos-López","suffix":""},{"id":615937780,"identity":"e8db3806-4402-44b1-a08d-b70c3e59c165","order_by":3,"name":"Antonino Báez","email":"","orcid":"","institution":"Benemérita Universidad Autónoma de Puebla","correspondingAuthor":false,"prefix":"","firstName":"Antonino","middleName":"","lastName":"Báez","suffix":""},{"id":615937783,"identity":"f75f6fc2-fcd4-4771-a67b-cffe5d8d3721","order_by":4,"name":"Jorge Raúl Cerna","email":"","orcid":"","institution":"Benemérita Universidad Autónoma de Puebla","correspondingAuthor":false,"prefix":"","firstName":"Jorge","middleName":"Raúl","lastName":"Cerna","suffix":""},{"id":615937785,"identity":"d70508d4-1bc5-4b9b-9e43-f891684c8f73","order_by":5,"name":"Verónica Ramírez","email":"","orcid":"","institution":"Benemérita Universidad Autónoma de Puebla","correspondingAuthor":false,"prefix":"","firstName":"Verónica","middleName":"","lastName":"Ramírez","suffix":""},{"id":615937787,"identity":"6bfacc71-644c-4936-83a3-124e754449f5","order_by":6,"name":"José-Antonio Munive","email":"data:image/png;base64,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","orcid":"","institution":"Benemérita Universidad Autónoma de Puebla","correspondingAuthor":true,"prefix":"","firstName":"José-Antonio","middleName":"","lastName":"Munive","suffix":""}],"badges":[],"createdAt":"2026-03-26 00:38:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9227678/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9227678/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106110409,"identity":"89525a3b-7d6f-4608-8965-96055261adc3","added_by":"auto","created_at":"2026-04-03 14:55:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1766338,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSubsystem-based functional annotation of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eBacillus thuringiensis\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e strain MH778713 using the RAST-tk pipeline.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDistribution of protein-coding genes assigned to functional subsystems following annotation with the RAST-tk pipeline. A total of 6,071 coding sequences were predicted, of which 339 subsystems were identified. Genes were categorized into 25 major metabolic and functional classes.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9227678/v1/7094f4342cead70c0a2defa9.png"},{"id":106110413,"identity":"615f37e6-d6c6-4efe-970b-fe878046ce84","added_by":"auto","created_at":"2026-04-03 14:55:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":22548227,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePairwise genomic similarity heatmap of 41 \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eBacillus\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e genomes with hierarchical clustering.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHeatmap representing pairwise genomic similarity, obtained with pyANI-plus, among 41 \u003cem\u003eBacillus\u003c/em\u003egenomes. Color intensity reflects the degree of similarity between genome pairs, with red indicating higher similarity values and blue indicating lower similarity values. The diagonal corresponds to self-comparisons (maximum similarity). Hierarchical clustering was applied to both rows and columns, grouping genomes according to their genomic relatedness.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9227678/v1/f013544cdfd51a45789c9c5c.png"},{"id":106110415,"identity":"f2ce0b26-9172-4d15-b635-d3cc81bb65b4","added_by":"auto","created_at":"2026-04-03 14:55:30","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":3332031,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePan-genome presence–absence matrix of 41 \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eBacillus\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e genomes generated using Roary.\u003c/strong\u003e\u003cbr\u003e\nLeft panel: phylogenetic tree constructed from core genome alignment, representing the evolutionary relationships among 41 taxa. Right panel: binary presence–absence matrix comprising 37,250 gene clusters (core + accessory genome). Each row corresponds to a genome and each column to an orthologous gene cluster. Blue cells indicate gene presence, whereas white cells indicate absence. A conserved block of genes shared across all genomes is visible on the left side of the matrix, representing the core genome. The progressively fragmented pattern toward the right reflects accessory and strain-specific (cloud) genes, illustrating the genomic diversity within the analyzed \u003cem\u003eBacillus\u003c/em\u003especies complex.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9227678/v1/30ff46b90df485d377b78a2d.png"},{"id":106110441,"identity":"f89ef1be-16b5-46c1-9cd7-633c4d7f9598","added_by":"auto","created_at":"2026-04-03 14:55:34","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3240082,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of resistance and virulence genes across 41 \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eBacillus\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e genomes identified using ABRicate.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHeatmap representing the detection of antimicrobial resistance and virulence-associated genes across 41 \u003cem\u003eBacillus\u003c/em\u003e genomes using ABRicate. Rows correspond to genomes and columns represent detected genes, grouped by reference database (CARD, MEGARes, NCBI AMR, PlasmidFinder, ResFinder, and VFDB). Color intensity reflects gene detection counts, ranging from absence (white) to higher detection frequency (red scale).\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9227678/v1/06c2adb1dd5569bf4bb363fd.png"},{"id":106959430,"identity":"273405e4-3a1c-4e77-a0eb-51fd12d85af2","added_by":"auto","created_at":"2026-04-15 09:09:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":30652834,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9227678/v1/637dc5f3-6df8-4792-bf07-c0d9b3b06a36.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genomic and pangenomic analysis of Bacillus thuringiensis MH778713, formerly Bacillus cereus, a multi-heavy metal tolerant bacterium","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003ePollution by heavy metals represents an increasing global problem due to human and industrial activity (Mishra et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Briffa et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Adnan et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); even, it has become more common to find greater quantities of these toxic elements in soils and water resources. Chromium is a relatively abundant element in Earth\u0026rsquo;s crust, and it is found in nature in association with other elements in minerals. Due to its wide use in the automotive industry, electroplating, and other industrial applications, and its subsequent disposal in wastewater, chromium accumulation has caused fragility in ecosystems since the chromate (CrO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e(\u0026minus;2)\u003c/sup\u003e) and hexavalent chromium (Cr\u003csup\u003e6+\u003c/sup\u003e) ions are highly toxic forms due to its strongly oxidizing power and their capability to be erroneously transported by sulphate channels through cell membranes. Unlike organic contaminants, metals cannot be decomposed biologically, physically, or chemically, which limits remediation to confining these contaminants by altering the solubility, mobility, or toxicity, generating changes in their valences, and favouring their immobilization (chelation) (Bosecker \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Stephen and Macnaughton \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Dagdag et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). An alternative for the treatment of soils contaminated with heavy metals, for their elimination, is bioremediation, which involves the use of microorganisms due to their capacity to immobilize metals (Lovley and Coates \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Das et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eBacillus\u003c/em\u003e is a bacterial genus belonging to the Firmicutes phylum, with about 142 species described. It\u0026rsquo;s a Gram-positive, spore-forming, rod-shaped, aerobic, or facultative bacterium (Shafi et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Porwal et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). They are ubiquitous organisms that have been recovered from diverse environments, including soil, water, gastrointestinal tract, plant tissues, rhizosphere, and extreme environments (Alcaraz et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). This bacterial group is relevant because of the production of diverse antagonistic substances (Fira et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Rabbee et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). \u003cem\u003eB. velezensis\u003c/em\u003e, \u003cem\u003eB. subtilis\u003c/em\u003e, \u003cem\u003eB. amyloliquefaciens\u003c/em\u003e, and \u003cem\u003eB. thuringiensis\u003c/em\u003e have been proven to be effective against a variety of pathogens because these species can produce a spectrum of bioactive secondary metabolites like antibacterial polyketides, cyclic lipopeptides, polyketide-type antimicrobial molecules, siderophores, and bacteriocins (Rabbee et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Pesarini-Baptista et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Abriouel et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eBacillus\u003c/em\u003e strains have been reported as a safe and environmentally friendly bioremediation tool, highlighting the role of \u003cem\u003eB. cereus\u003c/em\u003e, \u003cem\u003eB. thuringiensis\u003c/em\u003e, and \u003cem\u003eB. subtilis\u003c/em\u003e for their biosorption capacity, generating a biotechnological alternative for the recovery of contaminated soils with heavy metals (Srinath et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Quintelas et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Upadhyay et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Sharman and Shukla 2021; Wr\u0026oacute;bel et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Schommer et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The Nexapa river, located in Chietla (Puebla), Mexico, represents an environmental and health problem, reporting high amounts of contaminants from various sources, such as domestic, industrial, and agrochemical discharges. Previous studies by P\u0026eacute;rez-Castresana et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) reported a high concentration of heavy metals such as aluminium, titanium, and chromium (highlighting chromium due to its toxicity), in the river sediments. Ramirez et al. (2019) isolated a group of bacteria belonging to the genus \u003cem\u003eBacillus\u003c/em\u003e that tolerated the presence of large amounts of heavy metals in the environment; particularly \u003cem\u003eBacillus\u003c/em\u003e strain MH778713 showed a hyper-tolerance of up to 15,000 mg/kg of chromium. 16SrDNA and \u003cem\u003erpo\u003c/em\u003eB gene sequence analysis placed this strain in the \u003cem\u003eBacillus cereus\u003c/em\u003e group. Chromium tolerance in bacteria involves several mechanisms that allow them to survive and adapt to these stressful environments, particularly in its toxic hexavalent form Cr (VI) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) (Ackerley et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\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\u003eMechanisms of bacterial tolerance to chromium compounds in bacteria.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eChromium Reduction: Many bacteria have developed the ability to reduce toxic Cr (VI) to the less toxic trivalent form Cr (III). This reduction process can occur either enzymatically or non-enzymatically (Ram\u0026iacute;rez-D\u0026iacute;az et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnzymatic Reduction: Specific enzymes, such as chromate reductases, reduce Cr (VI) to Cr (III) under anaerobic or aerobic conditions. These enzymes may include flavoproteins, cytochromes, and reductases like ChrR, which are widely studied (Bopp et al. 1983; Ishibashi et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Suzuki et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Campos et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1995\u003c/span\u003e).\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNon-enzymatic Reduction\u003c/b\u003e: Reduced forms of cellular molecules, like glutathione and cysteine, can also facilitate the reduction of Cr (VI) to Cr (III). (Ackerley et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Thatoi et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEfflux Pumps\u003c/b\u003e: Certain bacteria express efflux pumps that actively expel Cr (VI) ions from the cell. These pumps, like the ChrA protein, are part of the resistance-nodulation-cell division (RND) superfamily and help maintain low intracellular concentrations of chromium, thereby reducing its toxicity (Nies \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChromium Sequestration\u003c/b\u003e: Bacteria can sequester chromium by binding it to cell wall components, such as extracellular polysaccharides or proteins, thus preventing it from entering the cell or reducing its concentration inside the cell (Cervantes et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eGenetic Adaptation and Regulation\u003c/b\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026bull; \u003cb\u003eChromate Resistance Genes\u003c/b\u003e: Some bacteria possess chromate resistance genes (such as \u003cem\u003echr\u003c/em\u003e genes) located on plasmids or chromosomes. These genes code for proteins that help in chromium reduction, efflux, or protection from oxidative stress (Juhnke et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Boop et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1983\u003c/span\u003e; Ram\u0026iacute;rez-D\u0026iacute;az et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Viti et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e\u0026bull; Regulatory Proteins\u003c/b\u003e: Regulatory proteins like ChrB can modulate the expression of chromate resistance genes in response to chromium exposure (Ram\u0026iacute;rez-D\u0026iacute;az et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOxidative Stress\u003c/b\u003e Response: Chromium exposure leads to the generation of reactive oxygen species (ROS), which can damage cellular components. Bacteria use antioxidant enzymes (such as catalases, superoxide dismutases, and peroxidases) and other mechanisms (e.g., production of thiols) to mitigate oxidative damage and maintain cellular homeostasis. (L\u0026oacute;pez-Bucio et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2000\u003c/span\u003e).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMetabolic Changes\u003c/b\u003e: Some bacteria alter their metabolic pathways to reduce chromium toxicity, such as switching from aerobic to anaerobic respiration, which can minimize the interaction of chromium with cellular components (Ramli et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cb\u003eBiofilm Formation\u003c/b\u003e: Bacteria often form biofilms, which can provide a protective barrier against chromium toxicity. The extracellular polymeric substances (EPS) in biofilms can bind chromium and limit its access to bacterial cells (Teitzel and Parsek \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\u003cp\u003e\u003cstrong\u003e(Table 1)\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eThe genetic study of microorganisms has advanced considerably thanks to advances in sequencing techniques. Sanger sequencing, considered the first successful sequencing technology, has been the basis of genetic analysis for decades. However, the advent of Next-Generation Sequencing (NGS) technology has changed the research landscape, especially in microbial genetics. In this report, we compare both technologies, their applications in microbiology, and their advantages and limitations (Reuter et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In the identification and classification of bacteria and other microorganisms, techniques based on DNA analysis have proven to be essential. Among these techniques, the Average Nucleotide Identity (ANI) technique stands out, which is used to determine the degree of genomic similarity between microorganisms, offering greater precision for identifying new species, especially when used to compare complete genomes (Richter and Rossell\u0026oacute;-M\u0026oacute;ra \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). On the other hand, the analysis of housekeeping genes (conserved genes essential for basic cellular functions) has been commonly used for phylogenetic classification and studies of microbial evolution (Janda and Abbott \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, the genome of \u003cem\u003eBacillus cereus\u003c/em\u003e MH778713 has been analyzed, and a comparative genomic analysis was performed to clarify the taxonomic position of this strain at the species level and to identify those specialty genes of the strain that could be involved in chromium hypertolerance.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eBacterial strain\u003c/h2\u003e \u003cp\u003eThe chromium hyper-tolerant strain \u003cem\u003eBacillus cereus\u003c/em\u003e MH778713 was used for this study. This strain was isolated from soils surrounding the Nexapa river, in Mexico, where soil studies (Ramirez et al. 2019) reported a high prevalence of some metals, including some toxic heavy metals such as chromium.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDNA Extraction, Library Preparation, and Whole Genome Sequencing\u003c/h3\u003e\n\u003cp\u003e \u003cem\u003eBacillus cereus\u003c/em\u003e MH778713 was grown in YM medium at 28\u0026deg;C, and genomic DNA was extracted using the Wizard\u0026reg; Genomic DNA Purification Kit (Promega), following the manufacturer's instructions. After extraction, the quality and quantity of the DNA were processed by spectral analysis using the Micro UV-Vis Spectrophotometer (NanoReady-RealLife). DNA samples were stored at \u0026minus;\u0026thinsp;20\u0026deg;C until use. Library preparation was performed using Illumina DNA Prep (Illumina, USA) for the labelling and cleanup steps and the Nextera\u0026trade; DNA CD Index Kit (Illumina, USA) for the indexing step, according to the manufacturer's instructions. Library quality control was performed using a Qubit 3.0 Fluorometer (Invitrogen, USA) and a Bioanalyzer 2100 (Agilent, USA). Sequencing of pooled and normalized libraries was performed using the MiSeq V2 reagent kit (300 cycles) on the Illumina MiSeq platform.\u003c/p\u003e\n\u003ch3\u003eSequencing Quality Control, de Novo Assembly, and Annotation\u003c/h3\u003e\n\u003cp\u003eThese steps were carried out using \u0026ldquo;The Bacterial and Viral Bioinformatics Resource Center\u0026rdquo; (BV-BRC) bacteria default pipeline (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.bv-brc.org\u003c/span\u003e\u003cspan address=\"https://www.bv-brc.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (Olson RD et al, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), i.e. Trim-Galore (v0.6.5dev) for reads trimming, BBNorm (October 19, 2017) for reads trimming normalization, Unicycler (v0.4.8) for genomic assembly, Pilon (v1.23) (2 rounds) for polishing, and QUAST (v5.2.0) for assembly quality control. Sufficient quality was defined as a base accuracy (Q30) of more than 95% for all reads. Genome annotation was done with the RAST toolkit (v.1.073) and Prokka (v1.12) using the default parameters. Assembled contigs were analyzed for the presence of resistance determinants as well as the presence of a myriad of resistance determinants such as: plasmids, virulence genes and whole anti-microbial resistance (AMR) genes using the ABRicate (v1.0.1), ResFinder, PlasmidFinder and Virulence Factor Database (VFDB) databases, on a command line on a Rocky Linux 8.7 (Green Obsidian) server (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003ePairwise Average Nucleotide Identity (PyANI)\u003c/h3\u003e\n\u003cp\u003eWhole genome comparisons with PyANI (v.0.2.12) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/widdowquinn/pyani\u003c/span\u003e\u003cspan address=\"https://github.com/widdowquinn/pyani\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) were used to calculate the Average Nucleotide Identity (ANI) based on MUMmer algorithm (default) against the 40 \u003cem\u003eBacillus\u003c/em\u003e genomic sequences (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), using an in-house bash script (pyANI.sh), on a Rocky Linux 8.7 (Green Obsidian) server using a SLURM job scheduler. Sequences of strains selected for analysis were automatically retrieved from the NCBI RefSeq database, using an in-house bash script (NCBIdownload.sh), on a Rocky Linux 8.7 (Green Obsidian) server using a SLURM job scheduler.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePangenome Analysis\u003c/h2\u003e \u003cp\u003eProkka (1.14.5) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/tseemann/prokka\u003c/span\u003e\u003cspan address=\"https://github.com/tseemann/prokka\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) genome annotation followed by the Roary (3.13.0) pangenome pipeline (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://sanger-pathogens.github.io/Roary\u003c/span\u003e\u003cspan address=\"https://sanger-pathogens.github.io/Roary\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) were used for pangenome analysis of the whole genome sequence data from the \u003cem\u003eBacillus cereus\u003c/em\u003e MH778713 plus 39 other strains, 10 of each different group of \u003cem\u003eB. cereus\u003c/em\u003e, \u003cem\u003eB. thuringiensis\u003c/em\u003e, \u003cem\u003eB. anthracis\u003c/em\u003e, and \u003cem\u003eB. mycoides\u003c/em\u003e. To run Roary for the phylogenetic reconstruction, and visualize it together with the heatmap, based on the presence/absence genes approach, an in-house bash script was used (Roary.sh), which in turn runs Roary (3.13.0), FastTree (v2.1.11), and a locally modified version of roary_plots.py (v0.1.0) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/sanger-pathogens/Roary/tree/master/contrib/roary_plots\u003c/span\u003e\u003cspan address=\"https://github.com/sanger-pathogens/Roary/tree/master/contrib/roary_plots\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eThe genome of \u003cem\u003eB. cereus\u003c/em\u003e MH778713 corresponds to a high-quality assembly, given by the quality values of the reads (Q 37.39 and 36.25 for R1 and R2, respectively), average short read coverage 21.95, and N's per-100 kbp 0.00. There were 30 contigs, an estimated genome length of 5,912,409 bp, and an average G\u0026thinsp;+\u0026thinsp;C content of 34.86%. The N50 is 378,292 bp. The L50 is 5 contigs (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) (Wattam et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e\u003cstrong\u003e(Table 2)\u003c/strong\u003e\u003c/p\u003e\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGenome content and quality data of \u003cem\u003eB. cereus\u003c/em\u003e MH778713.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepth (Average)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.95\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e# N's per 100 kbp\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eN50\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e378292\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eL50\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e# Contigs\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLargest contig\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1063386\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal length\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5912409\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGC (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCoarse consistency (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e99.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFine consistency (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCompleteness (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eContamination (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProtein-Encoding Genes with Functional Assignment\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3680\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProtein-Encoding Genes without Functional Assignment\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2391\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal Distinct Roles\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3431\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eAll statistics are based on contigs of size\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;300 bp\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\u003cp\u003eThe most represented category was Amino Acids and Derivatives (n\u0026thinsp;=\u0026thinsp;254 genes), followed by Carbohydrates (n\u0026thinsp;=\u0026thinsp;197), Protein Metabolism (n\u0026thinsp;=\u0026thinsp;112), and Dormancy and Sporulation (n\u0026thinsp;=\u0026thinsp;97). Substantial representation was also observed in Nucleosides and Nucleotides (n\u0026thinsp;=\u0026thinsp;87), DNA Metabolism (n\u0026thinsp;=\u0026thinsp;62), Respiration (n\u0026thinsp;=\u0026thinsp;57), and RNA Metabolism (n\u0026thinsp;=\u0026thinsp;56).\u003c/p\u003e \u003cp\u003eAdditional functional groups included Iron acquisition and metabolism (n\u0026thinsp;=\u0026thinsp;52), Fatty Acids, Lipids, and Isoprenoids (n\u0026thinsp;=\u0026thinsp;49), Cell Wall and Capsule (n\u0026thinsp;=\u0026thinsp;40), Stress Response (n\u0026thinsp;=\u0026thinsp;34), Membrane Transport (n\u0026thinsp;=\u0026thinsp;32), and Regulation and Cell Signaling (n\u0026thinsp;=\u0026thinsp;29). Lower-frequency categories comprised Miscellaneous (n\u0026thinsp;=\u0026thinsp;21), Nitrogen Metabolism (n\u0026thinsp;=\u0026thinsp;18), Phosphorus Metabolism (n\u0026thinsp;=\u0026thinsp;18), Phages, Prophages, Transposable Elements, and Plasmids (n\u0026thinsp;=\u0026thinsp;13), Secondary Metabolism (n\u0026thinsp;=\u0026thinsp;9), Metabolism of Aromatic Compounds (n\u0026thinsp;=\u0026thinsp;9), Potassium Metabolism (n\u0026thinsp;=\u0026thinsp;7), and Cell Division and Cell Cycle (n\u0026thinsp;=\u0026thinsp;4).\u003c/p\u003e \u003cp\u003eThese assignments reflect the metabolic versatility and adaptive capacity of \u003cem\u003eB. thuringiensis\u003c/em\u003e, with strong representation of core biosynthetic, carbohydrate utilization, and sporulation-related functions. An overview of the main categories for this genome is provided in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the pyANI (pyANI-plus) identity heatmap, resulting in a group-specific matrix. Distribution of \u003cem\u003eBacillus\u003c/em\u003e genomes according to species and/or serotypes of \u003cem\u003eBacillus cereus\u003c/em\u003e, \u003cem\u003eB. thuringiensis\u003c/em\u003e, \u003cem\u003eB. anthracis\u003c/em\u003e and \u003cem\u003eB. mycoides\u003c/em\u003e is shown; \u003cem\u003eBacillus\u003c/em\u003e strain MH778713 is located in the \u003cem\u003eB. thuringiensis\u003c/em\u003e species group.\u003c/p\u003e \u003cp\u003eDistinct clustering patterns are observed, separating members of the \u003cem\u003eBacillus anthracis\u003c/em\u003e, \u003cem\u003eBacillus cereus\u003c/em\u003e, \u003cem\u003eBacillus thuringiensis\u003c/em\u003e, and \u003cem\u003eBacillus mycoides\u003c/em\u003e lineages. Genomes within the same species cluster together with high similarity (deep red blocks along the diagonal), whereas interspecies comparisons show lower similarity (lighter shades of blue regions). The block-like structure highlights the genomic cohesion within species and the divergence among species within the \u003cem\u003eBacillus cereus\u003c/em\u003e group.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/h2\u003e \u003cp\u003eWhole genome sequence data from the \u003cem\u003eBacillus\u003c/em\u003e strain MH778713 and 40 other genome sequences from \u003cem\u003eB. cereus\u003c/em\u003e, \u003cem\u003eB. thuringiensis\u003c/em\u003e, \u003cem\u003eB. anthracis\u003c/em\u003e, and \u003cem\u003eB. mycoides\u003c/em\u003e were used for pangenome construction. Annotated assemblies created by Prokka were used to score and visualize the core (95% \u0026gt; 99%), shell (15% \u0026gt; 95%), and cloud genomes (0% \u0026gt; 15%) using the default settings of the Roary software package (v.3.13.0) and a modified version of the roary_plots.py script (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/sanger-pathogens/Roary/tree/master/contrib/roary_plots\u003c/span\u003e\u003cspan address=\"https://github.com/sanger-pathogens/Roary/tree/master/contrib/roary_plots\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe complete genome sequences of 39 \u003cem\u003eBacillus\u003c/em\u003e strains were downloaded from the NCBI. Roary analysis of \u003cem\u003eBacillus\u003c/em\u003e pangenome identified a total of 1592 core, 151 softcore, 7667 shell, and 27808 cloud genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Although strain-specific genes are observed as accessory genes, all genomes clustered into a clade sharing a similar content of core genes, likely related to metabolic functions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003c/h2\u003e \u003cp\u003eThe genes of the bacterial species studied in the pangenome analysis were also analysed by ABRcate (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), to identify special genes such as AMR, virulence, and plasmids, looking for any element involved in hypertolerance to heavy metals (Saier et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Mao et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Chen et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Zhu et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Law et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; McArthur et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDistinct patterns of gene distribution are observed among species within the \u003cem\u003eBacillus cereus\u003c/em\u003e group. Members of \u003cem\u003eB. anthracis\u003c/em\u003e display a relatively conserved gene profile with limited variation, whereas \u003cem\u003eB. cereus\u003c/em\u003e, \u003cem\u003eB. thuringiensis\u003c/em\u003e, and \u003cem\u003eB. mycoides\u003c/em\u003e exhibit greater heterogeneity in resistance and virulence-associated gene content. Genes related to β-lactam resistance, efflux systems, and virulence determinants (including enterotoxins and hemolysins) are variably distributed across strains.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e)\u003c/h2\u003e \u003cp\u003eThe pangenome analysis allowed us to identify a cluster of unique genes listed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and to associate them with hypertolerance to heavy metals. The frequency of genes involved in bacterial oxidative stress and carbohydrate metabolism stands out.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnique genes found in \u003cem\u003eBacillus thuringiensis\u003c/em\u003e MH778713.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnnotation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnnotation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003easn\u003c/em\u003eB_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAsparagine synthetase [glutamine-hydrolyzing] 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003egld\u003c/em\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGlycerol dehydrogenase\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003edau\u003c/em\u003eA_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC4-dicarboxylic acid transporter DauA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ehyf\u003c/em\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDNA-binding transcriptional activator HyfR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003enrdG\u003c/em\u003e_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnaerobic ribonucleoside-triphosphate reductase-activating protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003edha\u003c/em\u003eK_2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePTS-dependent dihydroxyacetone kinase, dihydroxyacetone-binding subunit DhaK\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003enrd\u003c/em\u003eD_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnaerobic ribonucleoside-triphosphate reductase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003edha\u003c/em\u003eL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePEP-dependent dihydroxyacetone kinase, ADP-binding subunit DhaL\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ecsg\u003c/em\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC-factor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003epts\u003c/em\u003eH_2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePhosphocarrier protein HPr\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eybj\u003c/em\u003eJ_2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInner membrane protein YbjJ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eglp\u003c/em\u003eF_2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGlycerol uptake facilitator protein\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003earn\u003c/em\u003eC_5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUndecaprenyl-phosphate 4-deoxy-4-formamido-L-arabinose transferase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003esor\u003c/em\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePTS system sorbose-specific EIIC component\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003earn\u003c/em\u003eC_6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUndecaprenyl-phosphate 4-deoxy-4-formamido-L-arabinose transferase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003elnr\u003c/em\u003eL_3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLinearmycin resistance ATP-binding protein LnrL\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eykn\u003c/em\u003eX_3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePutative efflux system component YknX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003elnr\u003c/em\u003eN_2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLinearmycin resistance permease protein LnrN\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003elod\u003c/em\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePutative FAD-dependent oxidoreductase LodB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ebae\u003c/em\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePolyketide biosynthesis malonyl CoA-acyl carrier protein transacylase BaeC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003erpi\u003c/em\u003eA_2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRibose-5-phosphate isomerase A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eacp\u003c/em\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAcyl carrier protein\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003epsp\u003c/em\u003eA_4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhosphoserine phosphatase 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003efab\u003c/em\u003eZ_3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3-hydroxyacyl-[acyl-carrier-protein] dehydratase FabZ\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eprt\u003c/em\u003eS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProtease PrtS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003egcv\u003c/em\u003eT_2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAminomethyltransferase\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003essp\u003c/em\u003eC_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSmall, acid-soluble spore protein C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003emoa\u003c/em\u003eA_6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGTP 3',8-cyclase\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eymf\u003c/em\u003eD_3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBacillibactin exporter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003exkd\u003c/em\u003eG_2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePhage-like element PBSX protein XkdG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ecyp\u003c/em\u003eX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePulcherriminic acid synthase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ersr\u003c/em\u003eIM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModification methylase RsrI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eyvm\u003c/em\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCyclo(L-leucyl-L-leucyl) synthase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003enor\u003c/em\u003eV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnaerobic nitric oxide reductase flavorubredoxin\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eper\u003c/em\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGDP-perosamine synthase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003enas\u003c/em\u003eD_2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNitrite reductase [NAD(P)H]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003earo\u003c/em\u003eA_3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProtein AroA(G)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003erub\u003c/em\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRubredoxin 3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003edad\u003c/em\u003eA_2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eD-amino acid dehydrogenase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eglx\u003c/em\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCRP-like cAMP-activated global transcriptional regulator\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003etrp\u003c/em\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnthranilate synthase component 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003enas\u003c/em\u003eD_3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNitrite reductase [NAD(P)H]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003emen\u003c/em\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIsochorismate synthase MenF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003enir\u003c/em\u003eC_2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNitrite transporter NirC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003elfr\u003c/em\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMultidrug efflux pump LfrA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003espl\u003c/em\u003eG_2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSpore photoproduct lyase\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eder\u003c/em\u003e_3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGTPase Der\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eytr\u003c/em\u003eA_4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHTH-type transcripcional repressor YtrA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e(Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003c/h2\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eStress generated by an increase in the content of heavy metals in the environment has caused microorganisms to adapt to these environments. Chromium tolerance mechanisms in \u003cem\u003eBacillus thuringiensis\u003c/em\u003e and \u003cem\u003eBacillus cereus\u003c/em\u003e, two closely related bacterial species known for their diverse metabolic capabilities, including tolerance to other heavy metals, are examples of this adaptability. \u003cem\u003eB. thuringiensis\u003c/em\u003e and \u003cem\u003eB. cereus\u003c/em\u003e species are gram-positive, spore-forming bacteria found in a wide range of environments, including soil, water, and plants. They are known for their ability to withstand various environmental stresses. Mechanisms used to tolerate the presence of these toxic elements range from the modification of enzymes involved in controlling oxidative stress to the production of exopolysaccharides to generate an extracellular matrix that allows them to regulate the internal concentration of heavy metals. \u003cem\u003eBacillus cereus\u003c/em\u003e strain MH778713 was isolated from soils surrounding the Nexapa River, Chietla, Puebla, which has reported high contamination rates of metals such as Aluminum (Al), Titanium (Ti), Silicon (Si), Iron (Fe), and Chromium (Cr), the latter being relevant for its impact on health since it is associated with cancer and skin and respiratory diseases. Previous experiments carried out by Ram\u0026iacute;rez et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) reported that \u003cem\u003eB. cereus\u003c/em\u003e MH778713 was capable of growing in a medium with up to 15,000 mg of chromium, being stated as a hypertolerant bacterium. In this study, the genome of \u003cem\u003eBacillus cereus\u003c/em\u003e MH778713 has been analyzed, and comparative genomics analyses were performed to identify those specialty genes of the strain that could be involved in chromium hypertolerance. We utilize next-generation sequencing (NGS) technology due to its significant advantages over traditional methods. In this way, we were able to encompass a significant portion of the genomic coverage, enabling us to conduct a detailed analysis of genes, regulatory sequences, mutations, and mobile elements, such as plasmids. These advantages also provide high resolution for species identification and strain classification, which is especially useful in microorganisms closely related, such as members of the \u003cem\u003eBacillus cereus\u003c/em\u003e group species, where differences in housekeeping gene sequences could be insufficient for accurate identification. Besides, NGS allows us to detect differences at the level of accessory or resistance genes.\u003c/p\u003e \u003cp\u003eIllumina miSeq technology, used for this analysis, allowed us to obtain an ensembled genome of 5.9 Mpb, with an average short read coverage of 22,132, with 38 contigs, and a L50 value of 5, and 6075 CDS reported (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The average nucleotide identity analysis (ANI), evaluating the degree of sequence identity between the nucleotides of the homologous fragments in the complete genomes of different \u003cem\u003eBacillus\u003c/em\u003e species, comparing a group of 40 strains including \u003cem\u003eB. cereus\u003c/em\u003e, \u003cem\u003eB. thuringiensis\u003c/em\u003e, \u003cem\u003eB. mycoides\u003c/em\u003e and \u003cem\u003eB. anthracis\u003c/em\u003e, was a useful tool to verify taxonomic identities in prokaryotic genomes, clarifying the taxonomic position of \u003cem\u003eBacillus\u003c/em\u003e strain MH778713, indicating its inclusion into \u003cem\u003eBacillus thuringiensis\u003c/em\u003e species.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the heatmap of the average nucleotide identity (ANI) performed with the pyANI program, which provided a way to automate genome comparison. This tool performs ANI calculations using alignment algorithms. pyANI can use different alignment methods, using the MUMmer method because of its specificity for nucleotide sequences, calculating the average percentage of identical nucleotides in all orthologous gene pairs shared between these genomes; we observed that MH778713 strain is grouped in \u003cem\u003eBacillus thuringiensis\u003c/em\u003e species. This finding was interesting, since the 16SrDNA and \u003cem\u003erpo\u003c/em\u003eB gene sequences analyses classified these strains as belonging to the \u003cem\u003eBacillus cereus\u003c/em\u003e species. Furthermore, in rapid analysis programs such as Dragen Metagenomics, we were able to classify it as a group 2 \u003cem\u003eBacillus cereus\u003c/em\u003e. Still, when performing gene alignments and genome comparisons, most individual genes coincided (\u0026gt;\u0026thinsp;90%) with \u003cem\u003eBacillus thuringiensis\u003c/em\u003e. To corroborate this finding, a pangenome analysis was performed (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), where the genome of the MH778713 strain was compared with genomes from other \u003cem\u003eBacillus\u003c/em\u003e species, using tools like Roary; this analysis allows us to understand the genetic diversity within the entire collection of genomes we used in this study, identifying key genes for specific traits. Roary is a tool capable of taking sets of genomic sequences and dividing the genes into core, accessory, and unique gene clusters. It generates a clustering of orthologous genes that allows us to identify those that are common and those that vary between strains, tools that could help us explain the tolerance to heavy metals observed in the strain MH778713. The analysis of the core genome organization identified conserved and unique gene clusters that could be related to heavy metal tolerance, analyzing their variability, and understanding the evolution of tolerance within the \u003cem\u003eBacillus\u003c/em\u003e group.\u003c/p\u003e \u003cp\u003eWe know that both \u003cem\u003eB. thuringiensis\u003c/em\u003e and \u003cem\u003eB. cereus\u003c/em\u003e possess chromate reductase enzymes that can reduce hexavalent chromium (Cr [VI]) to trivalent chromium (Cr [III]), a less toxic and less soluble form. \u003cem\u003eBacillus cereus\u003c/em\u003e strain DCNW, isolated from chromium-contaminated soils, has been shown to produce chromate reductases that effectively reduce Cr (VI) under aerobic conditions. The reduction typically involves transferring electrons from electron donors such as NADH or NADPH to Cr (VI), converting it to Cr (III). This activity helps to mitigate the toxic effects of Cr (VI) by lowering its oxidative potential and reducing its mobility in the environment (Li et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eData obtained from the pangenomic analysis showed, when comparing unique genes of the MH778713 strain with other \u003cem\u003eBacillus\u003c/em\u003e genomes (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), the presence of \u003cem\u003emen\u003c/em\u003eF genes that catalyze the conversion of chorismate into isochorismate. This metabolite is involved in the synthesis of pyocyanin, a blue-colored secondary metabolite with the ability to oxidize and reduce other molecules. This ability could be occupied by \u003cem\u003eBacillus thuringiensis\u003c/em\u003e MH778713 to tolerate high concentrations of metals.\u003c/p\u003e \u003cp\u003eWithin the genome sequences analyzed in this study, genes related to the tolerance to different heavy metals were also identified, such as a cluster of genes related to arsenic and antimony tolerance, where transporters proteins facilitate the efflux of trivalent arsenic and antimony; ArsR is a member of the family of proteins categorized as DNA-binding transcriptional repressors; ArsR/SmtB family can sense a variety of metals and undergo allosteric conformational changes upon metal binding, resulting in derepressing of genes involved in detoxification (Busenlehner et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). We found cation diffusion facilitators proteins for cobalt, zinc, and cadmium ions (Bruins et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2000\u003c/span\u003e); we also detected a heavy metal translocating P-type ATPase for metals like cadmium, zinc, and mercury, whose function could be playing a role in incrementing the tolerance to metals in microorganisms (Chien et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Furthermore, the presence of efflux pumps (involved in the movement of metals across cell membranes), the protection system against oxidative stress due to OxyR (a master regulator that detects the presence of H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e and activates the massive formation of catalase and peroxidase enzymes for microorganism protection) (Storz et al. 1992), and the production of biofilm (where the polysaccharides of the glycocalyx could play an important role in forming a network where the metal found in the environment can be sequestered without it ever entering the interior of the cell), are mechanisms that could be involved in the prevention of damage, and its potentially toxic effect in microbial cells (Jasu and Ray \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eIn conclusion, \u003cem\u003eBacillus\u003c/em\u003e spp. genome sequences within the \u003cem\u003eBacillus cereus\u003c/em\u003e group exhibited a high degree of similarity when compared to those from separate clades. This similarity became more apparent upon analysing the organization of their genomic DNA. Nevertheless, a more noticeable variation is observed when examining the arrangement of genes across the entire genome throughout the bacterium\u0026rsquo;s evolution, highlighting its genomic diversity. When compared with the pangenome, it is evident that \u003cem\u003eBacillus\u003c/em\u003e strain MH778713, previously classified as \u003cem\u003eBacillus cereus\u003c/em\u003e, must be reclassified as \u003cem\u003eBacillus thuringiensis\u003c/em\u003e MH778713. The scope of the study was to identify the heavy metal tolerance-related genes. The pangenomic analysis showed the presence of a cluster of unique genes in \u003cem\u003eBacillus thuringiensis\u003c/em\u003e MH778713, despite the closed pangenome similarity with \u003cem\u003eBacillus cereus\u003c/em\u003e. These genes may contribute to the functional diversity within the species, even though their specific roles remain unknown. Information obtained from this analysis is crucial for improving our understanding of the heavy metal tolerance evolution.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eACKNOWLEDMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Vicerrector\u0026iacute;a de Investigaci\u0026oacute;n y Estudios de Posgrado (VIEP-BUAP), Instituto de Ciencias (ICUAP), Posgrado en Ciencias (Microbiolog\u0026iacute;a) and SECIHTI research grant number 524111. We thank the Laboratorio Nacional de Superc\u0026oacute;mputo del Sureste de M\u0026eacute;xico for granting us the Access to its facilities (Project Number 202401001C: Gen\u0026oacute;mica comparativa y evolutiva de poblaciones microbianas y eucariotes en condiciones extremas, con un enfoque en \u0026ldquo;Una Salud\u0026rdquo; (One Health). \u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eData Availability:\u003c/strong\u003e \u003c/p\u003e\n\u003cp\u003eThe full genome sequence of \u003cem\u003eBacillus thuringiensis\u003c/em\u003e MH778713 was made available in the GenBank database (Accession No: JBLWMS000000000).\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e: No conflict of interest declared.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eEthical Statement\u003c/strong\u003e: No animal uses.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eMAV, AB, VR and JAM conceived, designed, and directed the project, contributed to the interpretation of the results, and designed the figures. SVGG, BR and JRC participated in the acquisition of the data and developed the methodologies. JAM took the lead in the writing of the manuscript. All authors provided critical feedback, helped to shape the research, discussed the results, and contributed to the final version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbriouel H, Franz CM, Ben-Omar N, G\u0026aacute;lvez A (2011) Diversity and applications of \u003cem\u003eBacillus\u003c/em\u003e bacteriocins. 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Nucleic Acids Res 38:D787\u0026ndash;D791. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/nar/gkp1014\u003c/span\u003e\u003cspan address=\"10.1093/nar/gkp1014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"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, chromium, genomic, heavy metals, hyper tolerance, pangenomic","lastPublishedDoi":"10.21203/rs.3.rs-9227678/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9227678/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe \u003cem\u003eBacillus cereus\u003c/em\u003e group comprises several Gram-positive, spore-forming bacteria that are widespread in natural environments and exhibit varying degrees of pathogenic potential and industrial relevance. Species-level taxonomic classification of \u003cem\u003eB. cereus\u003c/em\u003e group strains remains challenging. Phylogenetic analysis of housekeeping gene sequences is often employed to assign \u003cem\u003eB. cereus\u003c/em\u003e group strains to the species level. Still, it is insufficient for accurate taxonomic classification on a genomic scale. \u003cem\u003eBacillus cereus\u003c/em\u003e MH778713 was described as a chromium hyper-tolerant strain, isolated from mesquite shrubs (\u003cem\u003eProsopis laevigata\u003c/em\u003e) in Mexico, and described as belonging to the \u003cem\u003eB. cereus\u003c/em\u003e species by \u003cem\u003e16S rDNA\u003c/em\u003e and \u003cem\u003erpo\u003c/em\u003eB gene sequence analysis. Long-read genomic sequencing was conducted for \u003cem\u003eBacillus cereus\u003c/em\u003e MH778713, resulting in a high-quality, near-chromosome-level genome with an assembly length of 5.9 Mbp. An Average Nucleotide Identity (ANI) analysis showed that this strain belonged to the \u003cem\u003eBacillus thuringiensis\u003c/em\u003e species, reclassifying this microorganism as \u003cem\u003eB. thuringiensis\u003c/em\u003e MH778713. Gene cluster families possibly related to heavy metal tolerance, such as antibiotic resistance, virulence factors, drug targets, and transporters, were found; no plasmids are carried by this strain. A pangenomic analysis of different bacterial species genomes allowed us to identify gene cluster families unique to this strain, possibly related to chromium hyper-tolerance.\u003c/p\u003e","manuscriptTitle":"Genomic and pangenomic analysis of Bacillus thuringiensis MH778713, formerly Bacillus cereus, a multi-heavy metal tolerant bacterium","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-03 14:53:50","doi":"10.21203/rs.3.rs-9227678/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-22T08:59:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"21064711141244467563299921271779374511","date":"2026-04-05T10:41:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"260464106871361812197276861314802820830","date":"2026-04-03T02:44:13+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-31T09:47:49+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-26T08:25:59+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-26T07:39:17+00:00","index":"","fulltext":""},{"type":"submitted","content":"World Journal of Microbiology and Biotechnology","date":"2026-03-26T00:22:10+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":"62ebdb80-c34e-4da2-ae2b-c7399fc8770f","owner":[],"postedDate":"April 3rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-03T14:53:56+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-03 14:53:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9227678","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9227678","identity":"rs-9227678","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}