Exploring the Functional Potential of Ureibacillus thermophilus LM102 through comprehensive and comparative Genome Analysis

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The phylogeny using the TYGS web server revealed that Ureibacillus thermophilus LM102 shares both mesophilic and thermophilic strain traits. Digital DNA-DNA hybridization values ranged from 19.1% to 37.4%, confirming the strain unique taxonomic classification. BlastKOALA identified genes involved in complete biotin, riboflavin, and cobalamin biosynthesis pathways, highlighting its industrial relevance. The P2RP web tool identified stress-response mechanisms including WalRK (YycFG), DesKR, LiaRS, BceRS, and ResDE adaptations to temperature fluctuations, low oxygen levels, and antibiotic stresses through TCS mechanisms. The pangenome analysis using the power-law regression based on Heaps' law revealed the B P = 0.65, indicating the Ureibacillus genus has an open pangenome nature. Furthermore, the pan-genomic analysis using Roary revealed the stains possess 1006 genes encoding for hypothetical proteins. The gene ontology and physicochemical properties of the HPs were carried out using UPIMAPI and ExPASy. Of the total hypothetical protein, 49.36% is involved in molecular function, 41.71% in biological processes, and 6.36% in cellular components. Notably, 500 HPs exhibited known protein domains, and the average Aliphatic Index of these proteins is as high as 105.08, suggesting the high thermostability of the proteins. These findings suggest that Ureibacillus thermophilus LM102 has significant potential for applications in biotechnology and industrial processes. Ureibacillus thermophilus LM102 Digital DNA-DNA hybridization two-component systems open pangenome hypothetical proteins and aliphatic Index Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction In a 2016 study, it was estimated that Earth is home to one trillion microbial species. This estimation is based on combining scaling law and a lognormal model of biodiversity (Locey and Lennon 2016 ). Microbes are essential for life, playing crucial roles in fundamental functions and the survival of all organisms. They are the driving force behind evolution and evolve rapidly, exceeding 10,000 years' worth of laboratory experiments in just one day of natural evolution. Microbes can thrive in diverse environments like soil, air, water, and extreme habitats like hot springs, deep-sea vents, and caves. Moreover, they can biotransform radioactive materials into less toxic substances. We depend on them for various purposes such as the fermentation process, waste treatment, and the production of essential medicines like antibiotics (Rappuoli et al., 2023 ) (Patel et al., 2022 ). Despite their importance, only about one per cent of microbial species have been studied due to their unculturable nature, indicating significant gaps in our understanding of the microbial world (Barak et al., 2023 ). NGS, which is rapid, cost-effective, and culture-independent, has revolutionized microbial taxonomy and classification, dramatically expanding bacterial genome sequencing projects (Solieri et al., 2013 ). This method enables researchers to sequence microbial communities directly from environmental samples (Gupta et al., 2020 ). It facilitates the study of unculturable organisms without mimicking the pitfalls followed by traditional culturing practices (Motro and Moran-Gilad, 2017 ). NGS has increased our ability to identify microorganisms and paved the way to understanding the functional potential of microbes through genomics (Satam et al., 2023 ). By conducting a complete genome analysis, researchers gain valuable insights into genes responsible for important biological functions such as resistance to environmental stresses, metabolic versatility, and potential biotechnological applications. Subsequently, these genes can be cloned into a suitable vector and transformed into an appropriate host bacterium to express specific traits, such as antibiotics or enzyme production (Handelsman, 2004 ). Such advancements in microbial genomics uncover the potential of microorganisms to address real-world problems. For instance, comparative genome analysis of Acinetobacter pittii S-30, isolated from contaminated waste soil, revealed important genes related to metal resistance, motility, and stress resistance. The analysis of metabolic pathways showed its ability to metabolize aromatic compounds and utilize carbohydrate-active enzymes, indicating a strong potential for degrading biomass and promoting plant growth and for various biotechnological applications (Singh et al., 2024 ). Moreover, the in-depth genomic analysis of Bacillus pacificus RSA27 led to the discovery of a thermostable keratinase enzyme Analysis of the keratinase protein sequence confirmed the presence of a high-affinity calcium-binding site (Val168, Ile166, Asn164, Leu162, and Asp128) and a catalytic triad comprising Ser308, His151, and Asp119, indicating that the keratinase belongs to the serine protease family (Sharma et al., 2022 ). The rise of NGS technology has dramatically expanded the scope of microbial genomics, leading to the deposition of over 2.21 million assembled bacterial sequences in public repositories like NCBI. The abundance in the sequenced data represents challenges in analysis and data search (Arita et al., 2021 ). Notably, these databases predominantly feature a higher proportion of genomes from human pathogens, while the representation of environmental isolates is comparatively limited (Costessi et al., 2018 ). Therefore, a critical interest remains in exploring the genomic potential of environmental isolates such as Ureibacillus thermophilus LM102. The genus Ureibacillus , identified in 2001 (Fortina et al., 2001 ), represents a fascinating group of gram-positive, thermophilic bacteria within the family Caryophanaceae (formerly Planococcaceae). Originally classified as Bacillus thermosphaericus , the genus was redefined to reflect its distinctive ureolytic properties and its adaptation to high-temperature environments. Ureibacillus species are characterized by their motility, the formation of spherical endospores, and their robust thermotolerance, thriving within a temperature range of 45–70°C (Fortina et al., 2001 ; Yan et al., 2023 ). It is found in various environments such as saline soils, livestock compost, mineral-rich soils, marine waters, plant tissues, and animal guts (Yadav et al., 2024 ). Despite its discovery in 2001, the identification and study of Ureibacillus species are limited. Only a few species have been studied in detail, creating significant gaps in our knowledge of the genetic makeup and ecological roles. However, existing studies suggest that Ureibacillus plays a significant role in biotechnological applications due to its ability to withstand extreme conditions (Simoes Junior and MacLea, 2021 ). While generally considered non-pathogenic, there have been occasional reports of opportunistic infections in immunocompromised individuals (Glazunova et al., 2006 ). Given the potential biotechnological applications and the lack of functional studies, we have chosen to explore Ureibacillus thermophilus LM102 in this study. Our study aims to address the research gap by conducting a complete genomic analysis of Ureibacillus thermophilus LM102, an environment isolate. Upon examining the metabolic pathways, resistance mechanisms, and ecological adaptations of Ureibacillus thermophilus LM102, we aim to contribute a more balanced understanding of microbial diversity and reveal the biotechnological potential of the environment isolate. Materials and methods Genome annotation The complete genome sequence of the Ureibacillus thermophilus LM102 was downloaded in a fasta format from the NCBI genome database. The completeness of Ureibacillus thermophilus LM102 was assessed using BUSCO v1.0.0 (Simão et al., 2015), with the nucleotide fasta file as the input. BUSCO utilises universal single-copy orthologs to evaluate the assembly's completeness and repetitiveness. Furthermore, the genome (.fasta) was annotated in Prokka v1.14.5 (Seemann, 2014), to identify potential rRNA, protein coding sequences (CDS), tmRNA, repeat regions, and tRNA. Output files generated by Prokka (.fna, .fsa, .ffn, .gff, .tbl, .faa, .err, .gbk, and .sqn) which are used for further downstream analysis. Phylogenetic analysis of Ureibacillus thermophilus LM102 Ureibacillus thermophilus LM102 (. fasta) was submitted to the Type Strain Genome Server (TYGS) (Meier-Kolthoff and Göker, 2019) with the default parameters, enabling the identification of closely related genomes in the TYGS database. The Genomic BLAST Distance Phylogeny (GBDP) methodology involved comparative analysis of the query strain with type strains by accurately calculating intergenomic distances using the "trimming" algorithm and the d5 distance formula. Subsequently, digital DNA-DNA hybridization (dDDH) values and their confidence intervals were calculated following the predefined setteings of GGDC 3.0. A balanced minimum evolution tree was constructed using the Interactive Tree Of Life (iTOL v. 6) web-based software with 100 pseudo-bootstrap replicates for branch support based on the intergenomic distances (Letunic and Bork, 2021). The input file for the tree construction was downloaded in Newick format from the TYGS result page. Metabolic pathway analysis The protein sequence of the genome Ureibacillus thermophilus LM102 was annotated by the KEGG database using the BlastKOALA tool (Kanehisa et al., 2016) for KEGG ortholog (KO) annotations. This was carried out to conduct a comprehensive analysis of gene function and to reconstruct KEGG metabolic pathways. The output file is uploaded in KEGG Mapper Reconstruct to reconstruct the predicted metabolic pathways. Furthermore, the Predicted Prokaryotic Regulatory Proteins (P2RP) (Barakat et al., 2013) web tool was used to predict regulatory proteins and facilitate the annotation of Two Component System (TCS) within the genome of Ureibacillus thermophilus LM102. Comparative genome analysis The entire Ureibacillus genus sequences were retrieved from the NCBI for comparative genome analysis. Genomes tagged as 'Contaminated,' 'Unverified source of organism,' or 'Genome length too small' were not included in this study, ensuring the data accuracy and reliability. This carefully curated final subset of genomes is termed “ U_genome” (n=18). These genomes were analysed in Prokka to use the output files for subsequent analysis. The Proksee (Grant et al., 2023) web server tool was used to compare the U_genome . Initially, the Ureibacillus thermophilus LM102 (. fasta ) was employed as the input file for the analysis. Proksee BLAST was used to compare the genotypic differences between the U_genome while using Ureibacillus thermophilus LM102 as the template strand. Pan Genome analysis was conducted using Roary (Page et al., 2015) to compare multiple genomes aiming for identification of core and accessory genes. The GFF format file produced by prokka for the U_genome was used as the input file for the analysis. The process involved pre-clustering the filtered protein sequences with CD-HIT and comparing them with BLASTp using an identity threshold of 95%. Additionally, MCL was utilised to cluster the BLASTp results, and the pre-clustering outcomes from CD-HIT were subsequently merged with the clustering results from MCL. The Roary matrix was constructed using the output data generated from the pangenome analysis by the roary_plot.py script. Statistical analysis The pan-genome profiles, along with their projected size and trajectory, were determined using the approach outlined and described by (Tettelin et al., 2008, 2005), which employs models and regression algorithm. The process of curve fitting for the pan-genome followed a power-law regression based on Heaps' law, as detailed in earlier studies. In this model , y referred as pan-genome size, x referred as number of genomes, and the fitting parameters are , , and . Characterisation of hypothetical proteins The genes unique to the Ureibacillus thermophilus LM102 genome were initially sorted manually, and from this subset, genes encoding for hypothetical proteins were carefully selected. In the subsequent step, UPIMAPI (Sequeira et al., 2022) was employed for sequence homology-based annotation to ascertain Gene Ontology (GO), cross-references to external databases, protein names, and EC numbers. For the "--db" parameter UniProt (default) database was utilized and downloaded for this screening. The obtained GO terms underwent manual filtering before being compared to the 'GO set' using the NaviGO (Wei et al., 2017) web server tool. This allowed the segregation of GO IDs into three different GO categories. We utilized the ProtParam tool of ExPASy (Gasteiger et al., 2005) to determine various physicochemical properties of the hypothetical protein, including GRAVY (grand average of hydropathy), aliphatic index (AI), and instability index (II). Additionally, the subcellular location of the hypothetical protein was determined using the PSLpred tool (Bhasin et al., 2005). Results and discussion BUSCO Analysing in BUSCO, Ureibacillus thermophilus LM102 genome has completeness, with 99.2% of the 124 BUSCO groups. Specifically, 98.4% of the BUSCOs were determined to be single-copy complete, while 0.8% were duplicated. 0.8 % were fragmented.The assembled genome consists of 1 scaffold and 24 contigs, totaling 3,017,325 base pairs with a minimal gap content of 0.076%. Phylogenetic analysis of Ureibacillus thermophilus LM102 : iTOL v. 6 software created the phylogenetic tree (Fig. 2) based on Genome Blast Distance Phylogeny (GBDP) distances calculated from 16S rRNA gene sequences, and adjacent to the phylogenetic tree the heatmap represents the 4 genomic characteristics featuring Genome size, G+C, Protein count, dDDH values. The phylogenetic tree showed that Ureibacillus thermophilus LM102 and Ureibacillus thermosphaericus DSM 10633 share a common internal node, signifying a close evolutionary relationship between the two species. The genome sizes are ranging from 2.72 Mb to 6.98 Mb base pairs. A bacterium with a large genome size, such as Anoxybacillus geothermalis ATCC BAA2555, needs more nutrients to survive, which limits its ecological distribution (Liu et al., 2023; Seppey et al., 2019). In contrast, bacteria with smaller genome sizes have fewer ATGC. They can thrive in environments with limited nutrients, as seen in Ureibacillus thermophilus LM102 with 3.02 Mb base pairs, suggesting the ability to survive in nutrient-limited environments. The dDDH values range from 19.1% to 37.4%. dDDH simulates the DNA-DNA hybridization method computationally without replicating its potential drawbacks and dDDH ≥70% between two strains indicating belonging to the same species (Li et al., 2021). The dDDH value shows that Ureibacillus thermophilus LM102 is distinct from the compared genomes, highlighting its unique species classification. In addition, a comparison was made between Ureibacillus thermophilus LM102 and the type strain genomes, focusing specifically on their growth temperatures ( Table 1 ). The phylogenetic tree analysis revealed that Ureibacillus thermophilus LM102 is related to both mesophilic and thermophilic strains, although the thermophilic strains have dDDH values relatively higher than mesophilic. This observation emphasizes that the strain Ureibacillus thermophilus LM102 despitebeing mesophilic shows genetic factors closely resembling thermophilic bacteria, indicating potential adaptation to both mesophilic and thermophilic conditions. As per ( Fig. 2) , the G+C content ranges from 34.8% in Caldibacillus pasinlerensis P1T to 53.83% in Bhargavaea massiliensis Marseille-Q1000T. Notably, U reibacillus thermophilus LM102 exhibits GC percentage of 38.51%. The GC content of a genome is a common factor used in taxonomic classification (Meier-Kolthoff et al., 2014) and typically falls between 13% and 75% (Bohlin et al., 2017). Moreover, G+C content significantly influences microbial ecology, impacting the amino acid composition of their proteomes (Barceló-Antemate et al., 2023; Teng et al., 2023) and contributing to the greater thermal stability in bacteria (Hu et al., 2022). In a previous study it was reported, on comparing the GC content of DNA from mesophilic bacteria to that of DNA from bacteria living at higher temperatures, it was found that the GC content of DNA from thermophilic bacteria was significantly greater (C. Wang et al., 2023). On the contrary, the G+C content of type strains falls within the range of 34.8% to 53.83%, encompassing thermophilic. This suggests that bacterial resistance to increasing temperatures relies not solely on G+C content but can also be influenced by physiological adaptations. Highlights of metabolic Pathway analysis The KEGG analysis identified genes responsible for various metabolic pathways and displayed the results in modules along with the counts of genes within each module. The findings show that 234 genes are associated with the synthesis of secondary metabolites, 123 genes with cellular Processes, 122 with cofactor biosynthesis, 116 with Microbial metabolism in diverse environments, 96 with Biosynthesis of amino acids, 84 with Membrane transport, 82 with Transcription & Translation, 81 with Signal transduction, and 31 with Xenobiotics biodegradation and metabolism. For starch and sucrose metabolism, the genes responsible for the enzymes involved in the conversion of starch and glycogen to maltose and dextrin have been identified. In cofactors and vitamin metabolism, genes responsible for Biotin biosynthesis, Riboflavin biosynthesis, and both aerobic and anaerobic biosynthesis of Cobalamin were predicted. The biosynthesis of biotin involves two different steps. The first step is the synthesis of the pimelate moiety, while the second step involves the assembly of the biotin molecule's bicyclic ring. The most commonly observed pathway for biotin biosynthesis is the BioC-BioH pathway (Lin and Cronan, 2011; Zhang et al., 2021)also identified in the studied genome. Important precursors in biotin production include pimeloyl-ACP/CoA and malonyl-ACP/CoA (Casals et al., 2016). The synthesis of the pimelate moiety follows several enzymatic steps (Mao et al., 2024), and the formation of a biotin ring is initiated by the activation of pimeloyl-ACP, and subsequent enzymatic reaction resulting in the formation of a biotin molecule (Lin et al., 2010; Sirithanakorn and Cronan, 2021). Biotin is a crucial coenzyme for carboxylation reactions and is vital for growth, development, and overall well-being in humans and animals. It is utilized in various areas such as food additives, animal feed, cosmetics, biomedicine, fermentation, and diagnostics. Therapeutically, biotin is used in treating chronic and acute eczema, diabetes, and contact dermatitis. It also plays a role in weight loss and normal fetal development (Hanna et al., 2022; Ma et al., 2024). Additionally, biotin has applications in immunological labelling, clinical diagnosis, drug targeting, and purification of biomolecular compounds (Fathi-Karkan et al., 2024). Economically, the global biotin market, valued at 1.6 billion dollars in 2023, is anticipated to approach 2 billion dollars by 2030, signifying a continuous upward trend (Zhao et al., 2024). Overall, the production and advancement of biotin methodologies have substantial ecological and biological impacts. The KEGG analysis identified the complete pathway for the biosynthesis of Riboflavin, with all the enzyme categories involved. Riboflavin, a commonly produced compound in the microbial industry, can be synthesized on an industrial scale by bacteria and fungi. It's important to note that riboflavin can be naturally synthesized by plants, fungi, and most bacteria. In industrial settings, B. subtilis and A. gossypii are the main producers of riboflavin (You et al., 2021). Moreover, mutants with overexpression of specific genes and resistance to purine analogs have been employed in the industry to achieve riboflavin overproduction (Averianova et al., 2020). Riboflavin, also referred to as vitamin B2, is an essential micronutrient that is water-soluble and plays a critical role in various physiological processes. It acts as a precursor for the coenzymes flavin mononucleotide (FMN) and flavin adenine dinucleotide (FAD), which are involved in oxidation-reduction reactions and the metabolism of carbohydrates, fats, ketone bodies, and proteins. Additionally, riboflavin is involved in the conversion of tryptophan to niacin and aids in iron mobilization. For humans, the recommended dietary intake of riboflavin is 0.4–0.6 mg/day, while for animals, it ranges from 0 to 17.5 mg/kg (EFSA Panel on Additives and Products or Substances used in Animal Feed (EFSA FEEDAP Panel) et al., 2018; Sepúlveda Cisternas et al., 2018; Vandamme and Revuelta, 2016). The KEGG analysis revealed that both aerobic and anaerobic pathways are involved in the biosynthesis of Cobalamin. De novo Cobalamin biosynthesis is only found in a small fraction of archaea and bacteria, but it is utilized by all life domains (Romine et al., 2017), indicating a high demand for its production in nature (Sultana et al., 2023). Prokaryotes have two alternative pathways for biotin biosynthesis, with the functionality of each pathway dependent on the availability of molecular oxygen and the timing of cobalt insertion. In the anaerobic pathway, oxygen is not required for ring-contraction and cobalt chelation through precorrin-2 with CbiK, while in the aerobic pathway, Cobalt chelation happens when hydrogenobyrinic a, c-diamide interacts with the CobNST complex and relies on oxygen to induce ring-contraction (Fang et al., 2017). Cobalamin has a wide range of applications, such as treating neurological and hematologic disorders, serving as a feed additive for domestic animals to enhance growth, and being used as a dietary supplement (Balabanova et al., 2021; Fang et al., 2017). The Ureibacillus genus is well-known for its motility and ability to form spherical endospores, which are dormant cells that contribute to high-stress tolerance in a diverse bacterial population (Beskrovnaya et al., 2021). In cell motility, it was found that the chemotaxis and motility pathways were incomplete. However, all the genes required for flagellar assembly, including the aerotaxis receptor, cheW, cheA, cheR, cheY, cheV, and cheB, were present. Bacteria utilize two-component regulatory system (TCS) to adapt to environmental changes and investigating the TCS mechanism can enhance our understanding of this adaptive process (Hirakawa et al., 2020). We screened the genome sequence against the Predicted Prokaryotic Regulatory Proteins Web server to identify the genomic TCS. The Ureibacillus thermophilus LM102 includes 44 ORFs that may play a role in TCSs or His-Asp phospho-relay. TCS signalling is an adaptive response within the host environment, typically described as the interaction between two proteins communicating via Histidine-Aspartic acid. In essence, a sensor protein (HK) that undergoes autophosphorylation on a histidine residue phosphorylates the receiver (REC) domain of a response regulator (RR) on a conserved Asp residue (Parvez et al., 2020). The genomic two-component system comprises 23 histidine kinases, 1 phosphotransfer protein, and 23 response regulators, accounting for approximately 1.47% of the open reading frame (ORF). Moreover, the detailed analysis predicted five TCS, including ResDE, DesKR, WalRK, LiaRS & BceRS. WalRK: The WalRK system, or YycFG, belongs to the EnvZ/OmpR TCS family and appears specific to bacteria with low G+C content (Dubrac et al., 2008). WalRK plays a key role in coordinating peptidoglycan synthesis with cell growth. Previous research studied the behaviour of WalRK during heat stress and its importance for cell proliferation. It has been established that disabling the walHI genes, which encode the negative modulator of WalK, results in abnormal growth and leads to cell death at high temperatures, indicating that Ureibacillus thermophilus LM102 can thrive in high temperatures (Takada et al., 2018). DesKR: Ureibacillus thermophilus LM102 maintains the fluidity of its membranes by modifying fatty acid composition in response to a decrease in temperature, a process known as homeoviscous adaptation. When the temperature drops, it triggers the bacteria to induce Des (FA desaturase) controlled by the TCS, which has phosphatase, phosphotransferase, and kinase activity (Fernández et al., 2019). DesK can sense the physical state of the membrane with the help of transmembrane segments. DesK facilitates the phosphorylation of the response regulator DesR, inducing Des to produce Delta5 fatty-acid desaturase. This increases the bilayer fluidity and restores DesK phosphatase activity in a negative feedback loop (Willdigg and Helmann, 2021). BceRS & LiaRS: BceS is a histidine kinase located within cells that lacks a domain for binding to extracellular ligands and does not directly bind to bacitracin. Instead, the BceS sensor kinase senses the structural changes of the BceAB transporter through the flux-sensing mechanism. Activation of BceS leads to phosphorylation of BceR, increasing the transcription of genes encoding BceAB transporters. This creates a positive feedback loop that regulates the level of BceAB for detoxifying bacitracin (George et al., 2022). The TCS transduction system LiaSR plays a crucial role in regulating cellular responses to various environmental stresses and factors damaging the cell envelope (Shankar et al., 2015). The downstream regulons controlled by the LiaSR system are associated with conferring tolerance to antibiotics, detergents, and acids (Shankar et al., 2015). Research has reported that this system regulates genes responsible for peptidoglycan synthesis, and deleting these genes leads to increased sensitivity to β-lactam and glycopeptide antibiotics (Butcher et al., 2007). ResDE: Additionally, it has been predicted that the genome can thrive in low-oxygen environments due to resD and resE. Both resD, a response regulator, and resE, a histidine sensor kinase, belong to the TCS transduction family of proteins (Nakano and Hulett, 2006). ResE undergoes autophosphorylation in response to a decrease in the NAD/NADH ratio due to restricted oxygen levels, in a manner reliant on the PAS domain. This initiates its kinase activity, subsequently facilitating the transfer of the phosphoryl group to ResD (Zhou et al., 2018). ResD was shown to regulate directly the expression of the ctaA, encoding a heme A synthase (Härtig and Jahn, 2012) as it serves as a cofactor for cellular respiration in many prokaryotic cytochrome c oxidases (CcO) (Zeng et al., 2020). This implies that understanding TCS provides insights into how bacteria develop resistance to antimicrobials and antibiotics and regulate metabolism (Thomas and Cook, 2020) in response to the environment. Comparative genome analysis Proksee facilitates the identification and visualisation of genotypic differences between the U_genome (n=18). The results show that the genome Ureibacillus thermosphaericus NRS-1739 has a high similarity with Ureibacillus thermophilus LM102while Ureibacillus chungkukjangi MER TA 176is a distantly related strainamong the U_genome ( Fig. 3 ) The BLAST ring analysis revealed that Ureibacillus thermosphaericus has a high degree of similarity with Ureibacillus thermophilus LM102potentially suggesting that the strains may have conserved core genes. Previous studies carried out on Ureibacillus thermosphaericus revealed that they have characteristics to survive in extreme conditions, able to thrive in high temperatures, and importantly they have been used in many industrial applications like biocatalysts for degradation of lignocellulosic biomass (Abbasalizadeh et al., 2012; Akita et al., 2017). Pan Genome analysis predominantly focuses on elucidating how genetic characteristics relate to physical traits in specific groups of organisms by capturing their complete genetic variation and minimising the predilection caused by utilising a single reference genome as it does not represent the genomic diversity of a species (J. Wang et al., 2023). As discussed in the material methods section of ‘Comparative studies’ scrutinizing the genus Ureibacillus resulted in 18 genomes, subsequently the Roary pipeline was employed for the pangenome analysis with the GFF file as input. Generally, Roary compartmentalises genes into four subsystems, core genes are found in 99% to 100% of strains, softcore genes are present in 95% to 99% of strains, shell genes appear in 15% to 95% of strains, and cloud cloud is found in less than 15% of strains. These genes are categorised according to their frequency of occurrence in each genome ( Fig. 4) . The analysis identified 14 core genes, a set of gene families found in nearly all (99% to 100%) strains within the interested clade. These genes generally play a predominant role in the basic survival and continuance of the clade (Kim et al., 2020), while the remaining 38,968 genes exhibited varying degrees of presence within the genomes. It is not unusual to get a small percentage of core genes which is also observed in previous studies (Podrzaj et al., 2022). However, a total of 8237 (21.13%) of gene clusters were tagged as shell genes or partially shared genes. Additionally, 73.64% of gene clusters accounted for strain-specific genes. Notably, the strain Ureibacillus thermophilus LM102accounted for 2384 strain-specific genes, nearly half of them about 1006 proteins accounting for hypothetical protein. Both the cloud and shell genes fall under accessory gene families, so understanding accessory genes benefits comprehension of knowledge about the evolutionary trajectories and variation among the clade (Kim et al., 2020), which elucidated an enormous number of strain-specific genes, suggesting that these genes have been acquired by gene duplication (Sanchez-Herrero et al., 2020), and horizontal gene transfer (Redondo-Salvo et al., 2020; Sanchez-Herrero et al., 2020). HTG found in most bacterial genomes exemplifies the occurrence of unique genes (Arnold et al., 2022). The acquired genes promote rapid adaptation concerning environmental cues (Brito, 2021). Although, the significance of these transfers varies; sometimes they only reflect ongoing evolutionary processes periodically resulting in beneficial adaptations (Arnold et al., 2022). Statistical analysis Performing this Statistical analysis, we can estimate whether the closed or open nature of pangenome. In the model , the fitting parameters are , , and while is equivalent to the parameter used by Tettelin et al. ( 2008 ). When , the size of the pan-genome increases unboundedly with the subsequent addition of new genomes and can be considered open. Conversely, when the pan-genome trajectory approaches a plateau as further genomes are added and can be regarded as closed. From ( Fig. 5) , it is evident that the pan-genome size increases unboundedly with the subsequent addition of new genomes (n=18, and = 0.65). Additionally, the ( Supplementary Fig. 1) demonstrates a bar plot for new genes indicating that a number of new genes does not falls to zero when new genomes are added sequentially. Compiling the analysis of the new gene curve and pangenome curve using the regression model exhibit the open nature of the pangenome (Knight et al., 2017). The open pangenome of Ureibacillus means it can have various mechanisms for exchanging genetic material and adapt to multiple environments (Reis and Cunha, 2021). The genus Ureibacillus is isolated from a variety of environments, such as animal guts, marine waters, mineral-rich soils, livestock compost, plant tissues, and saline soils (Yadav et al., 2024). This demonstrates ability of genus Ureibacillus to be highly adapt and capable of thriving in diverse ecological niches. Characterisation of hypothetical proteins A recent study reported that as of October 2023, the RefSeq protein databases contained approximately 5,000,000 sequences, with approximately 10 per cent corresponding to hypothetical proteins (Vincent, 2024). This proportion highlights the large presence of hypothetical proteins within the database. This leads to the fundamental question: is studying hypothetical proteins beneficial? Certainly, the answer to the above problem is ‘Yes’, there are many findings on hypothetical proteins that benefit human health and understanding the mechanism of adaptation of bacteria to the environment. For example, studying the hypothetical protein of the SARS-CoV virus unveiled its ability to alter the antiviral inflammatory cytokine and interferon pathways in the host system (Rahman et al., 2023). The pan-genome analysis revealed that Ureibacillus thermophilus LM102 contains 33.88% (1006) of strain-specific gene codes for hypothetical proteins (HPs). Studying these hypothetical proteins is essential as they might contribute to the adaptations, pathogenicity, or other physiological traits of the strain and potentially identify the target for further biotechnological application. UPIMAPI was used for the initial prediction of the hypothetical proteins, resulting in 500 HPs that possess known protein domains or families, along with their corresponding GO IDs. NaviGO was then utilized to categorize the GO IDs into three different GO categories (Supplementary Fig. 2) , namely Cellular Component (CC), Molecular Function (MF), and Biological Process (BP). Among the three categories, largest cluster was molecular function (49.36%) followed by biological processes (41.71%) and cellular components (6.36%). Ureibacillus_thermophilus_LM102_01172_1 was predicted as Esterase. Esterase is a diverse class of enzymes that catalyzes the hydrolysis of alcohol and acid through the addition of water molecules (Ding et al., 2021). This versatile enzyme plays a significant role in the beverage and food industries. Its primary function is to modify fats and oil in fruit juices (Raveendran et al., 2018), ultimately enhancing the taste and aroma of the final yield (Sarnaik et al., 2023). Additionally, this enzyme finds application in a wide range of industrial sectors, including paper and pulp production, leather tanneries, detergents, textiles, cosmetics, biodiesel synthesis, pharmaceuticals, waste treatment and bioremediation (Akram et al., 2024). Furthermore, esterases are used for detoxification purposes, effectively neutralising inhaled, injected and ingested poisons. They are also utilized to inactivate certain drugs like aspirin, cocaine, and Ritalin, as well as to activate prodrugs to active drugs like irinotecan (Lockridge et al., 2018). The proteins YabQ (Ureibacillus_thermophilus_LM102_00385_1) and Spore coat protein (Ureibacillus_thermophilus_LM102_02528_1) were predicted as the potential role in spore formation. Many different organisms utilize sporulation as a method to adapt to changes in their environment and to survive in a dormant state until they encounter favourable conditions for active growth (Huang and Hull, 2017). The YabQ protein is located in the membrane of the forespore and plays a crucial role in cortex development (Asai et al., 2001). The spore coat, composed of multiple proteins, forms a layered shield that protects the bacterial genome in harsh conditions. It influences spore germination and determines the types of interactions spores can have with different environmental surfaces (McKenney et al., 2013). These spores exhibit high resistance to wet heat and protect against a wide range of threats, including exposure to various chemicals and attacks by predatory microbes (“The Spore Coat,” 2016; Yu et al., 2023). Furthermore, the protein encoded by Ureibacillus_thermophilus_LM102_01748_1 is responsible for the production of the beta-lactamase enzyme (EC 3.5.2.6). Beta-lactamases play a crucial role in the resistance mechanism of gram-negative bacteria. These particular enzymes work by breaking down the beta-lactam ring present in beta-lactam antibiotics, thus making them ineffective (Morrison and Zembower, 2020). The Ureibacillus_thermophilus_LM102_01444_1 protein is expected to contain the flagellar type III secretion protein SwrB, which enhances the number of flagellar hooks while moving in groups during swarming motility (Kearns et al., 2004; Phillips et al., 2021). Bacteria that swarm move together and display adaptive resistance to various antibiotics, and particularly advantageous in extreme temperatures(Butler et al., 2010; Rahman et al., 2022). It is anticipated that there is the presence of ComX (Ureibacillus_thermophilus_LM102_02851_1) within a group of hypothetical proteins. The ComX pheromone is a short chain of amino acids that goes through post-translational changes by ComQ and binds to its receptor ComP. This binding activates the corresponding response regulator, ComA, which then triggers different quorum-sensing genes (Dhiman, 2021). This activation results in the release of extracellular matrix components that induce natural competence in reaction to crowding (Okada et al., 2005). DNA possesses both informational and biochemical properties. When this DNA is taken up from the environment, it creates new combinations of alleles, which either increase or reduce fitness. However, the most important reason for uptake is nutrition. DNA is an ideal source of deoxyribonucleotides, which are essential for the replication of bacterial genome. Bacterial cells uptake extracellular DNA as a nutrient source instead of undergoing the expensive process of de novo nucleotide synthesis, which requires a lot of energy and molecular constituents (Mell and Redfield, 2014). The Protein Subcellular Localization (PSL) of proteins within the cell is a crucial factor in determining their functions. Understanding the subcellular location of proteins is essential for elucidating human disease mechanisms and protein interactions (Li et al., 2014). This knowledge is valuable in identifying potential drug targets in bacterial proteins (Imam et al., 2019). Proteins located in the cytoplasmic matrix are essential for maintaining cell metabolism (Ebner and Götz, 2019), while membrane proteins are involved in various cellular processes, including transport, communication, enzymatic reactions, and signal transduction (Jelokhani-Niaraki, 2022). The identification of protein subcellular localization also contributes to the enhancement of public databases, such as Swiss-Prot, and provides valuable data for the development of machine learning methods and computational protein location identification (Pan et al., 2020). The tool PSLpred was utilized to predict the PSL of 500 hypothetical proteins. Fig. 6 showed that most of these proteins were identified as Cytoplasmic Proteins (50%) and Inner-membrane Proteins (40.6%), with smaller percentages found in Extracellular Proteins (5.4%), Periplasmic Proteins (2.8%), and Outer Membrane Proteins (1.2%). The higher proportion of cytoplasmic proteins implies that nearly half of the hypothetical proteins are likely involved in cellular processes and maintain metabolic activities within living cells (Ebner and Götz, 2019). We also computed the physicochemical properties such as Grand Average of Hydropathy (GRAVY), aliphatic index (AI), and instability index (II) for all HPs. GRAVY values represent the average hydropathy value of a protein. Proteins with positive values are hydrophobic, while negative GRAVY values are hydrophilic (Wang et al., 2021). The analysis revealed that 291 HPs are hydrophilic and the remaining 209 HPs are hydrophobic. The instability index estimates the stability of a protein in a laboratory environment based on its amino acid composition. The stability index below 40 is considered stable, while the opposite is true for values above 40. Based on these metrics, approximately 70.6% (353) of HPs are stable in a test tube environment, while the remaining 29.4% are not stable (Prabhu et al., 2020; Wei et al., 2017). The AI of a protein is a measure of the relative volume occupied by the amino acid side chains of Valine, Leucine, Isoleucine, and Alanine. The AI of proteins derived from thermophilic bacteria was found to be significantly higher than that of typical proteins, which is used as a metric to evaluate protein thermostability. The proteins under study exhibited an aliphatic index with an average of 105.08. Notably, 98.4% of the proteins had an aliphatic index exceeding 70 and marking up to 172.9, indicating a relatively high value compared to proteins produced by other thermophilic bacteria (Akkaya et al., 2023). This suggests that these proteins have enhanced stability at higher temperatures compared to other thermophilic bacteria. Conclusion In the present study, genomic and metabolic characterization of Ureibacillus thermophilus LM102 has been conducted, predominantly focusing on its distinctive properties and potential biotechnological applications. The dDDH value indicates its unique species classification, expressing both mesophilic and thermophilic characteristics. The identification of TCS demonstrates Ureibacillus thermophilus LM102 ability to adapt to harsh environments. Metabolic pathway analysis shows biosynthesis of biotin, riboflavin, and cobalamin which has greater application in industries. Summarizing the results narrows down to the conclusion that Ureibacillus thermophilus LM102 is a perfect candidate for large-scale microbial production of the aforementioned vitamins due to its flexible growth temperature that can be exploited by industrial sectors. From 2020 to 2024, the data size in NCBI has grown exponentially due to new sequencing technologies. Researchers have begun focusing on identification of new microorganisms, leading to a significant gap in the functional exploration of potentially beneficial microbes. Hence, rather than allocating resources to sequencing, researchers must concentrate into the data already available in public repositories. Moreover, it is crucial to mention the constraints of this study. Mainly, the study utilized single genome data, whereas using metagenome data would enable us to explore more. Additionally, the research heavily relies on advanced computational tools, requiring high-end computational resources, time consuming and proficiency in programming. Declarations Acknowledgement I would like to acknowledge the SRM IST for funding to pursue my research. Author contributions PPVB - Conceptualization; Data curation; Formal analysis; Funding acquisition; Methodology; Validation; Visualization; Writing - original draft; Writing - review & editing. AA – Formal analysis; Funding acquisition; Methodology; Validation; Visualization; Writing - review & editing. LM – Conceptualization; Investigation; Project administration; Resources; Supervision. Declaration of competing interest The authors declare that there were no commercial or financial affiliations that could potentially create a conflict of interest during this study. Funding information This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. 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ResDE two-component regulatory system mediates oxygen limitation-induced biofilm formation by Bacillus amyloliquefaciens SQR9. Appl. Environ. Microbiol. 84, e02744–17. Table Table 1 Demonstrates the dDDH values and optimal growth temperature of U reibacillus thermophilus LM102 and its closely related 18 type strains. S. No. Type strain dDDH (%) Growth condition °C Optimum Growth °C Classification Reference 1 Parageobacillus galactosidasius DSM 18751 37.4 50–75 °C 70°C Thermophilic (Poli et al. 2011) 2 Parageobacillus yumthangensis AYN2 36.3 40–70 °C 60 °C Thermophilic (Najar et al. 2018) 3 Parageobacillus toebii DSM 14590 34.5 45-70 °C 60 °C Thermophilic (Sung 2002) 4 Anoxybacillus rupiensis DSM 17127 34.1 55–58°C 55–58°C Thermophilic (Derekova et al. 2007) 5 Bhargavaea massiliensis Marseille- Q1000 T 30.9 - - - - 6 Parageobacillus caldoxylosilyticus NBRC 107762 30.3 43–75 °C 65 °C Thermophilic (Ahmad et al. 2000) 7 Anoxybacillus geothermalis ATCC BAA2555 29.5 40–65 °C 60 °C Thermophilic (Filippidou et al. 2016) 8 Anoxybacillus karvacharensis K1 29.2 45–70 °C 60-65 °C Thermophilic (Panosyan et al. 2021) 9 Caldibacillus pasinlerensis P1T 28.5 40–60 °C 55 °C Thermophilic (Baltaci et al. 2020) 10 Planococcus koreensis DSM 15895 25.6 - - - - 11 Ureibacillus thermosphaericus DSM 10633 25.1 32–64 °C 60 °C Thermophilic (Fortina et al. 2001) 12 Ureibacillus terrenus ATCC BAA-384 24.3 40–64 °C 50-60 °C Thermophilic (Fortina et al. 2001) 13 Chryseomicrobium excrementi LMG 30119 23.1 20–40 °C 35-37 °C Mesophilic (Saha et al. 2018) 14 Planococcus mcmeekinii DSM 13963 23.1 15 Psychrobacillus lasiicapitis CGMCC 1.15308 21.7 25–35 °C 28-30 °C Mesophilic (Shen et al. 2017) 16 Bacillus ndiopicus FF3 21.4 30–45 °C 37°C Mesophilic (Lo et al. 2015) 17 Lysinibacillus antri SYSU K30002 20.2 28–40 °C 37 °C Mesophilic (Narsing Rao et al. 2020) 18 Planococcus salinus LCB217 19.1 10–45° C 30 °C Halophilic (Gan et al. 2018) 19 Ureibacillus thermophilus LM102 - 37-65 °C 37-65 °C Thermostable (Sunny et al. 2020) Supplementary Files SupplementaryFig.1.pdf SupplementaryFig.2.pdf Cite Share Download PDF Status: Posted Version 1 posted 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. 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13:10:59","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":119489,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBLAST comparison of the genome \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eUreibacillus thermophilus \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eLM102\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003ewith 17 other \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eUreibacillus \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003especies.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-6643773/v1/8d30d0f3b9d607359c1be91a.png"},{"id":85948036,"identity":"49144c63-d54c-49d4-9024-27d73b0254ce","added_by":"auto","created_at":"2025-07-03 13:10:59","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":159145,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePangenome ROARY matrix diagram representing the genes shared by the \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eUreibacillus \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003especies.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-6643773/v1/a60cacb2f5d23b28f2b9e31b.png"},{"id":85948031,"identity":"3f24a722-104d-4da6-8861-0885c6c47496","added_by":"auto","created_at":"2025-07-03 13:10:59","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":25150,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe plotted gene addition curves represent the sequential addition of genomes (n=18) using a power-law regression model. The trajectory of the pangenome increases upon the addition of gene families that exhibit features of an open pangenome.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-6643773/v1/76b0280e1d2f2e07ac0a997c.png"},{"id":85948037,"identity":"62a78bb8-5414-483f-9783-47ce455807bf","added_by":"auto","created_at":"2025-07-03 13:10:59","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1830511,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe graph represents the physicochemical properties and subcellular localization of Hypothetical proteins.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-6643773/v1/973f08397dc65540074fe2ed.png"},{"id":102398336,"identity":"602c9f81-ac5a-4ab9-a835-f81999c2c368","added_by":"auto","created_at":"2026-02-11 10:22:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3860804,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6643773/v1/3808aca8-17f0-4849-a88b-733362c531d8.pdf"},{"id":85948370,"identity":"ed2cf598-ccef-4561-8480-6d9f83c1b7bb","added_by":"auto","created_at":"2025-07-03 13:18:59","extension":"pdf","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":12241,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFig.1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6643773/v1/83aa8cc911e7e71a1d1615bd.pdf"},{"id":85948379,"identity":"e712eab9-d8e9-40ca-afd8-50a800c6cfb5","added_by":"auto","created_at":"2025-07-03 13:18:59","extension":"pdf","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":8994781,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFig.2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6643773/v1/f67b5d866e8d88e4f8494442.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003e\u003cstrong\u003eExploring the Functional Potential of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eUreibacillus thermophilus\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e LM102 through comprehensive and comparative Genome Analysis\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn a 2016 study, it was estimated that Earth is home to one trillion microbial species. This estimation is based on combining scaling law and a lognormal model of biodiversity (Locey and Lennon \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e2016\u003c/span\u003e). Microbes are essential for life, playing crucial roles in fundamental functions and the survival of all organisms. They are the driving force behind evolution and evolve rapidly, exceeding 10,000 years' worth of laboratory experiments in just one day of natural evolution. Microbes can thrive in diverse environments like soil, air, water, and extreme habitats like hot springs, deep-sea vents, and caves. Moreover, they can biotransform radioactive materials into less toxic substances. We depend on them for various purposes such as the fermentation process, waste treatment, and the production of essential medicines like antibiotics (Rappuoli et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) (Patel et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Despite their importance, only about one per cent of microbial species have been studied due to their unculturable nature, indicating significant gaps in our understanding of the microbial world (Barak et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNGS, which is rapid, cost-effective, and culture-independent, has revolutionized microbial taxonomy and classification, dramatically expanding bacterial genome sequencing projects (Solieri et al., \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). This method enables researchers to sequence microbial communities directly from environmental samples (Gupta et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). It facilitates the study of unculturable organisms without mimicking the pitfalls followed by traditional culturing practices (Motro and Moran-Gilad, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). NGS has increased our ability to identify microorganisms and paved the way to understanding the functional potential of microbes through genomics (Satam et al., \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBy conducting a complete genome analysis, researchers gain valuable insights into genes responsible for important biological functions such as resistance to environmental stresses, metabolic versatility, and potential biotechnological applications. Subsequently, these genes can be cloned into a suitable vector and transformed into an appropriate host bacterium to express specific traits, such as antibiotics or enzyme production (Handelsman, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Such advancements in microbial genomics uncover the potential of microorganisms to address real-world problems. For instance, comparative genome analysis of \u003cem\u003eAcinetobacter pittii\u003c/em\u003e S-30, isolated from contaminated waste soil, revealed important genes related to metal resistance, motility, and stress resistance. The analysis of metabolic pathways showed its ability to metabolize aromatic compounds and utilize carbohydrate-active enzymes, indicating a strong potential for degrading biomass and promoting plant growth and for various biotechnological applications (Singh et al., \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Moreover, the in-depth genomic analysis of \u003cem\u003eBacillus pacificus\u003c/em\u003e RSA27 led to the discovery of a thermostable keratinase enzyme Analysis of the keratinase protein sequence confirmed the presence of a high-affinity calcium-binding site (Val168, Ile166, Asn164, Leu162, and Asp128) and a catalytic triad comprising Ser308, His151, and Asp119, indicating that the keratinase belongs to the serine protease family (Sharma et al., \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe rise of NGS technology has dramatically expanded the scope of microbial genomics, leading to the deposition of over 2.21\u0026nbsp;million assembled bacterial sequences in public repositories like NCBI. The abundance in the sequenced data represents challenges in analysis and data search (Arita et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Notably, these databases predominantly feature a higher proportion of genomes from human pathogens, while the representation of environmental isolates is comparatively limited (Costessi et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Therefore, a critical interest remains in exploring the genomic potential of environmental isolates such as \u003cem\u003eUreibacillus thermophilus\u003c/em\u003e LM102.\u003c/p\u003e \u003cp\u003eThe genus \u003cem\u003eUreibacillus\u003c/em\u003e, identified in 2001 (Fortina et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), represents a fascinating group of gram-positive, thermophilic bacteria within the family Caryophanaceae (formerly Planococcaceae). Originally classified as \u003cem\u003eBacillus thermosphaericus\u003c/em\u003e, the genus was redefined to reflect its distinctive ureolytic properties and its adaptation to high-temperature environments. \u003cem\u003eUreibacillus\u003c/em\u003e species are characterized by their motility, the formation of spherical endospores, and their robust thermotolerance, thriving within a temperature range of 45\u0026ndash;70\u0026deg;C (Fortina et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Yan et al., \u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). It is found in various environments such as saline soils, livestock compost, mineral-rich soils, marine waters, plant tissues, and animal guts (Yadav et al., \u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite its discovery in 2001, the identification and study of \u003cem\u003eUreibacillus\u003c/em\u003e species are limited. Only a few species have been studied in detail, creating significant gaps in our knowledge of the genetic makeup and ecological roles. However, existing studies suggest that \u003cem\u003eUreibacillus\u003c/em\u003e plays a significant role in biotechnological applications due to its ability to withstand extreme conditions (Simoes Junior and MacLea, \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). While generally considered non-pathogenic, there have been occasional reports of opportunistic infections in immunocompromised individuals (Glazunova et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Given the potential biotechnological applications and the lack of functional studies, we have chosen to explore \u003cem\u003eUreibacillus thermophilus\u003c/em\u003e LM102 in this study. Our study aims to address the research gap by conducting a complete genomic analysis of \u003cem\u003eUreibacillus thermophilus\u003c/em\u003e LM102, an environment isolate. Upon examining the metabolic pathways, resistance mechanisms, and ecological adaptations of \u003cem\u003eUreibacillus thermophilus\u003c/em\u003e LM102, we aim to contribute a more balanced understanding of microbial diversity and reveal the biotechnological potential of the environment isolate.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cstrong\u003eGenome annotation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe complete genome sequence of the \u003cem\u003eUreibacillus thermophilus\u0026nbsp;\u003c/em\u003eLM102 was downloaded in a fasta format from the NCBI genome database. The completeness of \u003cem\u003eUreibacillus thermophilus\u0026nbsp;\u003c/em\u003eLM102 was assessed using BUSCO v1.0.0 (Sim\u0026atilde;o et al., 2015), with the nucleotide fasta file as the input. BUSCO utilises universal single-copy orthologs to evaluate the assembly\u0026apos;s completeness and repetitiveness. Furthermore, the genome\u003cem\u003e\u0026nbsp;(.fasta)\u003c/em\u003e was annotated in Prokka v1.14.5 (Seemann, 2014), to identify potential rRNA, protein coding sequences (CDS), tmRNA, repeat regions, and tRNA. Output files generated by Prokka (.fna, .fsa, .ffn, .gff, .tbl, .faa, .err, .gbk, and .sqn) which are used for further downstream analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePhylogenetic analysis of \u003cem\u003eUreibacillus thermophilus\u0026nbsp;\u003c/em\u003eLM102\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eUreibacillus thermophilus\u0026nbsp;\u003c/em\u003eLM102 (.\u003cem\u003efasta)\u0026nbsp;\u003c/em\u003ewas submitted to the Type Strain Genome Server (TYGS) (Meier-Kolthoff and G\u0026ouml;ker, 2019) with the default parameters, enabling the identification of closely related genomes in the TYGS database. The Genomic BLAST Distance Phylogeny (GBDP) methodology involved comparative analysis of the query strain with type strains by accurately calculating intergenomic distances using the \u0026quot;trimming\u0026quot; algorithm and the d5 distance formula. Subsequently, digital DNA-DNA hybridization (dDDH) values and their confidence intervals were calculated following the predefined setteings of GGDC 3.0. A balanced minimum evolution tree was constructed using the Interactive Tree Of Life (iTOL v. 6) web-based software with 100 pseudo-bootstrap replicates for branch support based on the intergenomic distances (Letunic and Bork, 2021). The input file for the tree construction was downloaded in Newick format from the TYGS result page.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMetabolic pathway analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe protein sequence of the genome \u003cem\u003eUreibacillus thermophilus\u0026nbsp;\u003c/em\u003eLM102 was annotated by the KEGG database using the BlastKOALA tool (Kanehisa et al., 2016) for KEGG ortholog (KO) annotations. This was carried out to conduct a comprehensive analysis of gene function and to reconstruct KEGG metabolic pathways. The output file is uploaded in KEGG Mapper Reconstruct to reconstruct the predicted metabolic pathways. Furthermore, the Predicted Prokaryotic Regulatory Proteins (P2RP) (Barakat et al., 2013) web tool was used to predict regulatory proteins and facilitate the annotation of Two Component System (TCS) within the genome of \u003cem\u003e\u0026nbsp;Ureibacillus thermophilus\u0026nbsp;\u003c/em\u003eLM102.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComparative genome analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe entire \u003cem\u003eUreibacillus\u0026nbsp;\u003c/em\u003egenus sequences were retrieved from the NCBI for comparative genome analysis. Genomes tagged as \u0026apos;Contaminated,\u0026apos; \u0026apos;Unverified source of organism,\u0026apos; or \u0026apos;Genome length too small\u0026apos; were not included in this study, ensuring the data accuracy and reliability. This carefully curated final subset of genomes is termed \u0026ldquo;\u003cem\u003eU_genome\u0026rdquo;\u0026nbsp;\u003c/em\u003e(n=18). These genomes were analysed in Prokka to use the output files for subsequent analysis.\u003c/p\u003e\n\u003cp\u003eThe Proksee (Grant et al., 2023) web server tool was used to compare the \u003cem\u003eU_genome\u003c/em\u003e. Initially, the \u003cem\u003eUreibacillus thermophilus\u0026nbsp;\u003c/em\u003eLM102 (.\u003cem\u003efasta\u003c/em\u003e) was employed as the input file for the analysis. Proksee BLAST was used to compare the genotypic differences between the \u003cem\u003eU_genome\u003c/em\u003e while using \u003cem\u003eUreibacillus thermophilus\u0026nbsp;\u003c/em\u003eLM102 as the template strand. Pan Genome analysis was conducted using Roary (Page et al., 2015) to compare multiple genomes aiming for identification of core and accessory genes. The GFF format file produced by prokka for the \u003cem\u003eU_genome\u003c/em\u003e was used as the input file for the analysis. The process involved pre-clustering the filtered protein sequences with CD-HIT and comparing them with BLASTp using an identity threshold of 95%. Additionally, MCL was utilised to cluster the BLASTp results, and the pre-clustering outcomes from CD-HIT were subsequently merged with the clustering results from MCL. The Roary matrix was constructed using the output data generated from the pangenome analysis by the roary_plot.py script.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe pan-genome profiles, along with their projected size and trajectory, were determined using the approach outlined and described by (Tettelin et al., 2008, 2005), which employs models and regression algorithm. The process of curve fitting for the pan-genome followed a power-law regression based on Heaps\u0026apos; law, as detailed in earlier studies. In this model \u003cimg width=\"108\" height=\"25\" src=\"data:image/png;base64,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\" alt=\"{\u0026quot;mathml\u0026quot;:\u0026quot;\u0026lt;math xmlns=\\\u0026quot;http://www.w3.org/1998/Math/MathML\\\u0026quot;\u0026gt;\u0026lt;mstyle mathsize=\\\u0026quot;16px\\\u0026quot;\u0026gt;\u0026lt;mi\u0026gt;y\u0026lt;/mi\u0026gt;\u0026lt;mo\u0026gt; \u0026lt;/mo\u0026gt;\u0026lt;mo\u0026gt;=\u0026lt;/mo\u0026gt;\u0026lt;mo\u0026gt; \u0026lt;/mo\u0026gt;\u0026lt;msub\u0026gt;\u0026lt;mi\u0026gt;A\u0026lt;/mi\u0026gt;\u0026lt;mi\u0026gt;p\u0026lt;/mi\u0026gt;\u0026lt;/msub\u0026gt;\u0026lt;msup\u0026gt;\u0026lt;mi\u0026gt;x\u0026lt;/mi\u0026gt;\u0026lt;msub\u0026gt;\u0026lt;mi\u0026gt;B\u0026lt;/mi\u0026gt;\u0026lt;mi\u0026gt;p\u0026lt;/mi\u0026gt;\u0026lt;/msub\u0026gt;\u0026lt;/msup\u0026gt;\u0026lt;mo\u0026gt;+\u0026lt;/mo\u0026gt;\u0026lt;msub\u0026gt;\u0026lt;mi\u0026gt;C\u0026lt;/mi\u0026gt;\u0026lt;mi\u0026gt;p\u0026lt;/mi\u0026gt;\u0026lt;/msub\u0026gt;\u0026lt;/mstyle\u0026gt;\u0026lt;/math\u0026gt;\u0026quot;,\u0026quot;truncated\u0026quot;:false}\"\u003e, y referred as pan-genome size, x referred as number of genomes, and the fitting parameters are \u003cimg width=\"19\" height=\"19\" src=\"data:image/png;base64,R0lGODlhHAAcAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAAAAAbABwAgAAAAAAAAAJHhI+By33qYgtUWqPqlRlu13lf8ogj4FHmKGonmsHrZrrs9M4qO8Ofutq9hsTiD4UsumynpbHVs9ii0tTzYfRlMVsM9cc0FAAAOw==\" alt=\"{\u0026quot;mathml\u0026quot;:\u0026quot;\u0026lt;math xmlns=\\\u0026quot;http://www.w3.org/1998/Math/MathML\\\u0026quot;\u0026gt;\u0026lt;mstyle mathsize=\\\u0026quot;16px\\\u0026quot;\u0026gt;\u0026lt;msub\u0026gt;\u0026lt;mi\u0026gt;A\u0026lt;/mi\u0026gt;\u0026lt;mi\u0026gt;p\u0026lt;/mi\u0026gt;\u0026lt;/msub\u0026gt;\u0026lt;/mstyle\u0026gt;\u0026lt;/math\u0026gt;\u0026quot;,\u0026quot;truncated\u0026quot;:false}\"\u003e, \u003cimg width=\"17\" height=\"19\" src=\"data:image/png;base64,R0lGODlhGgAcAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAAAAAZABwAgAAAAAAAAAJIhBGpce2P2JGwqpaqpuZql2HcF3EhKXYnqjIuC5orunqsrcJdPH7z7DvhSAsXUIdMKm+I5jJ0pE2eqaSQulNClxHu1Nv1vh4FADs=\" alt=\"{\u0026quot;mathml\u0026quot;:\u0026quot;\u0026lt;math xmlns=\\\u0026quot;http://www.w3.org/1998/Math/MathML\\\u0026quot;\u0026gt;\u0026lt;mstyle mathsize=\\\u0026quot;16px\\\u0026quot;\u0026gt;\u0026lt;msub\u0026gt;\u0026lt;mi\u0026gt;B\u0026lt;/mi\u0026gt;\u0026lt;mi\u0026gt;p\u0026lt;/mi\u0026gt;\u0026lt;/msub\u0026gt;\u0026lt;/mstyle\u0026gt;\u0026lt;/math\u0026gt;\u0026quot;,\u0026quot;truncated\u0026quot;:false}\"\u003e, and \u003cimg width=\"19\" height=\"19\" src=\"data:image/png;base64,R0lGODlhHAAcAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAEAAAAaABwAgAAAAAAAAAJFhG+hCoEPG0szxoqchevu131iKH5kaZ0oiK2c5r5wzM40Zd8S2NCL6uvphkScJIeqIJOZ5SgzhLVWSqdJGi1StDutI1cAADs=\" alt=\"{\u0026quot;mathml\u0026quot;:\u0026quot;\u0026lt;math xmlns=\\\u0026quot;http://www.w3.org/1998/Math/MathML\\\u0026quot;\u0026gt;\u0026lt;mstyle mathsize=\\\u0026quot;16px\\\u0026quot;\u0026gt;\u0026lt;msub\u0026gt;\u0026lt;mi\u0026gt;C\u0026lt;/mi\u0026gt;\u0026lt;mi\u0026gt;p\u0026lt;/mi\u0026gt;\u0026lt;/msub\u0026gt;\u0026lt;/mstyle\u0026gt;\u0026lt;/math\u0026gt;\u0026quot;,\u0026quot;truncated\u0026quot;:false}\"\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCharacterisation of hypothetical proteins\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe genes unique to the \u003cem\u003eUreibacillus thermophilus\u003c/em\u003e LM102 genome were initially sorted manually, and from this subset, genes encoding for hypothetical proteins were carefully selected. In the subsequent step, UPIMAPI (Sequeira et al., 2022) was employed for sequence homology-based annotation to ascertain Gene Ontology (GO), cross-references to external databases, protein names, and EC numbers. For the \u0026quot;--db\u0026quot; parameter UniProt (default) database was utilized and downloaded for this screening. The obtained GO terms underwent manual filtering before being compared to the \u0026apos;GO set\u0026apos; using the NaviGO (Wei et al., 2017) web server tool. This allowed the segregation of GO IDs into three different GO categories. We utilized the ProtParam tool of ExPASy (Gasteiger et al., 2005) to determine various physicochemical properties of the hypothetical protein, including GRAVY (grand average of hydropathy), aliphatic index (AI), and instability index (II). Additionally, the subcellular location of the hypothetical protein was determined using the PSLpred tool (Bhasin et al., 2005).\u003c/p\u003e"},{"header":"Results and discussion","content":"\u003cp\u003e\u003cstrong\u003eBUSCO\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnalysing in BUSCO, \u003cem\u003eUreibacillus thermophilus\u0026nbsp;\u003c/em\u003eLM102 genome has completeness, with 99.2% of the 124 BUSCO groups. Specifically, 98.4% of the BUSCOs were determined to be single-copy complete, while 0.8% were duplicated. 0.8 % were fragmented.The assembled genome consists of 1 scaffold and 24 contigs, totaling 3,017,325 base pairs with a minimal gap content of 0.076%.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePhylogenetic analysis of \u003cem\u003eUreibacillus thermophilus\u0026nbsp;\u003c/em\u003eLM102\u003cem\u003e:\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eiTOL v. 6 software created the phylogenetic tree \u003cstrong\u003e(Fig. 2)\u003c/strong\u003e based on Genome Blast Distance Phylogeny (GBDP) distances calculated from 16S rRNA gene sequences, and adjacent to the phylogenetic tree the heatmap represents the 4 genomic characteristics featuring Genome size, G+C, Protein count, dDDH values. The phylogenetic tree showed that \u003cem\u003eUreibacillus thermophilus\u0026nbsp;\u003c/em\u003eLM102 and \u003cem\u003eUreibacillus thermosphaericus\u0026nbsp;\u003c/em\u003eDSM 10633 share a common internal node, signifying a close evolutionary relationship between the two species. The genome sizes are ranging from 2.72 Mb to 6.98 Mb base pairs. A bacterium with a large genome size, such as\u003cem\u003e\u0026nbsp;Anoxybacillus geothermalis\u0026nbsp;\u003c/em\u003eATCC BAA2555, needs more nutrients to survive, which limits its ecological distribution (Liu et al., 2023; Seppey et al., 2019). In contrast, bacteria with smaller genome sizes have fewer ATGC. They can thrive in environments with limited nutrients, as seen in \u003cem\u003eUreibacillus thermophilus\u003c/em\u003e LM102 with 3.02 Mb base pairs, suggesting the ability to survive in nutrient-limited environments.\u003c/p\u003e\n\u003cp\u003eThe dDDH values range from 19.1% to 37.4%. dDDH simulates the DNA-DNA hybridization method computationally without replicating its potential drawbacks and dDDH \u0026ge;70% between two strains indicating belonging to the same species (Li et al., 2021). The dDDH value shows that \u003cem\u003eUreibacillus thermophilus\u0026nbsp;\u003c/em\u003eLM102 is distinct from the compared genomes, highlighting its unique species classification.\u003c/p\u003e\n\u003cp\u003eIn addition, a comparison was made between \u003cem\u003eUreibacillus thermophilus\u0026nbsp;\u003c/em\u003eLM102 and the type strain genomes, focusing specifically on their growth temperatures (\u003cstrong\u003eTable 1\u003c/strong\u003e). The phylogenetic tree analysis revealed that \u003cem\u003eUreibacillus thermophilus\u0026nbsp;\u003c/em\u003eLM102 is related to both mesophilic and thermophilic strains, although the thermophilic strains have dDDH values relatively higher than mesophilic. This observation emphasizes that the strain \u003cem\u003eUreibacillus thermophilus\u0026nbsp;\u003c/em\u003eLM102 despitebeing mesophilic shows genetic factors closely resembling thermophilic bacteria, indicating potential adaptation to both mesophilic and thermophilic conditions.\u003c/p\u003e\n\u003cp\u003eAs per (\u003cstrong\u003eFig. 2)\u003c/strong\u003e, the G+C content ranges from 34.8% in \u003cem\u003eCaldibacillus pasinlerensis\u0026nbsp;\u003c/em\u003eP1T to 53.83% in \u003cem\u003eBhargavaea massiliensis\u0026nbsp;\u003c/em\u003eMarseille-Q1000T. Notably, U\u003cem\u003ereibacillus thermophilus\u0026nbsp;\u003c/em\u003eLM102 exhibits GC percentage of 38.51%. The GC content of a genome is a common factor used in taxonomic classification (Meier-Kolthoff et al., 2014) and typically falls between 13% and 75% (Bohlin et al., 2017). Moreover, G+C content significantly influences microbial ecology, impacting the amino acid composition of their proteomes (Barcel\u0026oacute;-Antemate et al., 2023; Teng et al., 2023) and contributing to the greater thermal stability in bacteria (Hu et al., 2022). In a previous study it was reported, on comparing the GC content of DNA from mesophilic bacteria to that of DNA from bacteria living at higher temperatures, it was found that the GC content of DNA from thermophilic bacteria was significantly greater (C. Wang et al., 2023). On the contrary, the G+C content of type strains falls within the range of 34.8% to 53.83%, encompassing thermophilic. This suggests that bacterial resistance to increasing temperatures relies not solely on G+C content but can also be influenced by physiological adaptations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHighlights of metabolic Pathway analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe KEGG analysis identified genes responsible for various metabolic pathways and displayed the results in modules along with the counts of genes within each module. The findings show that 234 genes are associated with the synthesis of secondary metabolites, 123 genes with cellular Processes, 122 with cofactor biosynthesis, 116 with Microbial metabolism in diverse environments, 96 with Biosynthesis of amino acids, 84 with Membrane transport, 82 with Transcription \u0026amp; Translation, 81 with Signal transduction, and 31 with Xenobiotics biodegradation and metabolism.\u003c/p\u003e\n\u003cp\u003eFor starch and sucrose metabolism, the genes responsible for the enzymes involved in the conversion of starch and glycogen to maltose and dextrin have been identified. In cofactors and vitamin metabolism, genes responsible for Biotin biosynthesis, Riboflavin biosynthesis, and both aerobic and anaerobic biosynthesis of Cobalamin were predicted.\u003c/p\u003e\n\u003cp\u003eThe biosynthesis of biotin involves two different steps. The first step is the synthesis of the pimelate moiety, while the second step involves the assembly of the biotin molecule\u0026apos;s bicyclic ring. The most commonly observed pathway for biotin biosynthesis is the BioC-BioH pathway (Lin and Cronan, 2011; Zhang et al., 2021)also identified in the studied genome. Important precursors in biotin production include pimeloyl-ACP/CoA and malonyl-ACP/CoA (Casals et al., 2016). The synthesis of the pimelate moiety follows several enzymatic steps (Mao et al., 2024), and the formation of a biotin ring is initiated by the activation of pimeloyl-ACP, and subsequent enzymatic reaction resulting in the formation of a biotin molecule (Lin et al., 2010; Sirithanakorn and Cronan, 2021). Biotin is a crucial coenzyme for carboxylation reactions and is vital for growth, development, and overall well-being in humans and animals. It is utilized in various areas such as food additives, animal feed, cosmetics, biomedicine, fermentation, and diagnostics. Therapeutically, biotin is used in treating chronic and acute eczema, diabetes, and contact dermatitis. It also plays a role in weight loss and normal fetal development (Hanna et al., 2022; Ma et al., 2024). Additionally, biotin has applications in immunological labelling, clinical diagnosis, drug targeting, and purification of biomolecular compounds (Fathi-Karkan et al., 2024). Economically, the global biotin market, valued at 1.6 billion dollars in 2023, is anticipated to approach 2 billion dollars by 2030, signifying a continuous upward trend (Zhao et al., 2024). Overall, the production and advancement of biotin methodologies have substantial ecological and biological impacts.\u003c/p\u003e\n\u003cp\u003eThe KEGG analysis identified the complete pathway for the biosynthesis of Riboflavin, with all the enzyme categories involved. Riboflavin, a commonly produced compound in the microbial industry, can be synthesized on an industrial scale by bacteria and fungi. It\u0026apos;s important to note that riboflavin can be naturally synthesized by plants, fungi, and most bacteria. In industrial settings, \u003cem\u003eB. subtilis\u003c/em\u003e and \u003cem\u003eA. gossypii\u003c/em\u003e are the main producers of riboflavin (You et al., 2021). Moreover, mutants with overexpression of specific genes and resistance to purine analogs have been employed in the industry to achieve riboflavin overproduction (Averianova et al., 2020). Riboflavin, also referred to as vitamin B2, is an essential micronutrient that is water-soluble and plays a critical role in various physiological processes. It acts as a precursor for the coenzymes flavin mononucleotide (FMN) and flavin adenine dinucleotide (FAD), which are involved in oxidation-reduction reactions and the metabolism of carbohydrates, fats, ketone bodies, and proteins. Additionally, riboflavin is involved in the conversion of tryptophan to niacin and aids in iron mobilization. For humans, the recommended dietary intake of riboflavin is 0.4\u0026ndash;0.6 mg/day, while for animals, it ranges from 0 to 17.5 mg/kg (EFSA Panel on Additives and Products or Substances used in Animal Feed (EFSA FEEDAP Panel) et al., 2018; Sep\u0026uacute;lveda Cisternas et al., 2018; Vandamme and Revuelta, 2016).\u003c/p\u003e\n\u003cp\u003eThe KEGG analysis revealed that both aerobic and anaerobic pathways are involved in the biosynthesis of Cobalamin. De novo Cobalamin biosynthesis is only found in a small fraction of archaea and bacteria, but it is utilized by all life domains (Romine et al., 2017), indicating a high demand for its production in nature (Sultana et al., 2023). Prokaryotes have two alternative pathways for biotin biosynthesis, with the functionality of each pathway dependent on the availability of molecular oxygen and the timing of cobalt insertion. In the anaerobic pathway, oxygen is not required for ring-contraction and cobalt chelation through precorrin-2 with CbiK, while in the aerobic pathway, Cobalt chelation happens when hydrogenobyrinic a, c-diamide interacts with the CobNST complex and relies on oxygen to induce ring-contraction (Fang et al., 2017). Cobalamin has a wide range of applications, such as treating neurological and hematologic disorders, serving as a feed additive for domestic animals to enhance growth, and being used as a dietary supplement (Balabanova et al., 2021; Fang et al., 2017).\u003c/p\u003e\n\u003cp\u003eThe \u003cem\u003eUreibacillus\u003c/em\u003e genus is well-known for its motility and ability to form spherical endospores, which are dormant cells that contribute to high-stress tolerance in a diverse bacterial population (Beskrovnaya et al., 2021). In cell motility, it was found that the chemotaxis and motility pathways were incomplete. However, all the genes required for flagellar assembly, including the aerotaxis receptor, cheW, cheA, cheR, cheY, cheV, and cheB, were present.\u003c/p\u003e\n\u003cp\u003eBacteria utilize two-component regulatory system (TCS) to adapt to environmental changes and investigating the TCS mechanism can enhance our understanding of this adaptive process (Hirakawa et al., 2020). We screened the genome sequence against the Predicted Prokaryotic Regulatory Proteins Web server to identify the genomic TCS.\u003c/p\u003e\n\u003cp\u003eThe \u003cem\u003eUreibacillus thermophilus\u0026nbsp;\u003c/em\u003eLM102 includes 44 ORFs that may play a role in TCSs or His-Asp phospho-relay. TCS signalling is an adaptive response within the host environment, typically described as the interaction between two proteins communicating via Histidine-Aspartic acid. In essence, a sensor protein (HK) that undergoes autophosphorylation on a histidine residue phosphorylates the receiver (REC) domain of a response regulator (RR) on a conserved Asp residue (Parvez et al., 2020). The genomic two-component system comprises 23 histidine kinases, 1 phosphotransfer protein, and 23 response regulators, accounting for approximately 1.47% of the open reading frame (ORF).\u003c/p\u003e\n\u003cp\u003eMoreover, the detailed analysis predicted five TCS, including ResDE, DesKR, WalRK, LiaRS \u0026amp; BceRS.\u003c/p\u003e\n\u003cp\u003eWalRK:\u003c/p\u003e\n\u003cp\u003eThe WalRK system, or YycFG, belongs to the EnvZ/OmpR TCS family and appears specific to bacteria with low G+C content (Dubrac et al., 2008). WalRK plays a key role in coordinating peptidoglycan synthesis with cell growth. Previous research studied the behaviour of WalRK during heat stress and its importance for cell proliferation. It has been established that disabling the walHI genes, which encode the negative modulator of WalK, results in abnormal growth and leads to cell death at high temperatures, indicating that \u003cem\u003eUreibacillus thermophilus\u0026nbsp;\u003c/em\u003eLM102 can thrive in high temperatures (Takada et al., 2018).\u003c/p\u003e\n\u003cp\u003eDesKR:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eUreibacillus thermophilus\u0026nbsp;\u003c/em\u003eLM102 maintains the fluidity of its membranes by modifying fatty acid composition in response to a decrease in temperature, a process known as homeoviscous adaptation. When the temperature drops, it triggers the bacteria to induce Des (FA desaturase) controlled by the TCS, which has phosphatase, phosphotransferase, and kinase activity (Fern\u0026aacute;ndez et al., 2019). DesK can sense the physical state of the membrane with the help of transmembrane segments. DesK facilitates the phosphorylation of the response regulator DesR, inducing Des to produce Delta5 fatty-acid desaturase. This increases the bilayer fluidity and restores DesK phosphatase activity in a negative feedback loop (Willdigg and Helmann, 2021).\u003c/p\u003e\n\u003cp\u003eBceRS \u0026amp; LiaRS:\u003c/p\u003e\n\u003cp\u003eBceS is a histidine kinase located within cells that lacks a domain for binding to extracellular ligands and does not directly bind to bacitracin. Instead, the BceS sensor kinase senses the structural changes of the BceAB transporter through the flux-sensing mechanism. Activation of BceS leads to phosphorylation of BceR, increasing the transcription of genes encoding BceAB transporters. This creates a positive feedback loop that regulates the level of BceAB for detoxifying bacitracin (George et al., 2022).\u003c/p\u003e\n\u003cp\u003eThe TCS transduction system LiaSR plays a crucial role in regulating cellular responses to various environmental stresses and factors damaging the cell envelope (Shankar et al., 2015). The downstream regulons controlled by the LiaSR system are associated with conferring tolerance to antibiotics, detergents, and acids (Shankar et al., 2015). Research has reported that this system regulates genes responsible for peptidoglycan synthesis, and deleting these genes leads to increased sensitivity to \u0026beta;-lactam and glycopeptide antibiotics (Butcher et al., 2007).\u003c/p\u003e\n\u003cp\u003eResDE:\u003c/p\u003e\n\u003cp\u003eAdditionally, it has been predicted that the genome can thrive in low-oxygen environments due to resD and resE. Both resD, a response regulator, and resE, a histidine sensor kinase, belong to the TCS transduction family of proteins (Nakano and Hulett, 2006). ResE undergoes autophosphorylation in response to a decrease in the NAD/NADH ratio due to restricted oxygen levels, in a manner reliant on the PAS domain. This initiates its kinase activity, subsequently facilitating the transfer of the phosphoryl group to ResD (Zhou et al., 2018). ResD was shown to regulate directly the expression of the ctaA, encoding a heme A synthase (H\u0026auml;rtig and Jahn, 2012) as it serves as a cofactor for cellular respiration in many prokaryotic cytochrome c oxidases (CcO) (Zeng et al., 2020).\u003c/p\u003e\n\u003cp\u003eThis implies that understanding TCS provides insights into how bacteria develop resistance to antimicrobials and antibiotics and regulate metabolism (Thomas and Cook, 2020) in response to the environment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComparative genome analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eProksee facilitates the identification and visualisation of genotypic differences between the \u003cem\u003eU_genome\u0026nbsp;\u003c/em\u003e(n=18). The results show that the genome \u003cem\u003eUreibacillus thermosphaericus\u0026nbsp;\u003c/em\u003eNRS-1739 has a high similarity with \u003cem\u003eUreibacillus thermophilus\u0026nbsp;\u003c/em\u003eLM102while \u003cem\u003eUreibacillus chungkukjangi\u0026nbsp;\u003c/em\u003eMER TA 176is a distantly related strainamong the \u003cem\u003eU_genome\u003c/em\u003e (\u003cstrong\u003eFig. 3\u003c/strong\u003e)\u003c/p\u003e\n\u003cp\u003eThe BLAST ring analysis revealed that \u003cem\u003eUreibacillus thermosphaericus\u0026nbsp;\u003c/em\u003ehas a high degree of similarity with \u003cem\u003eUreibacillus thermophilus\u0026nbsp;\u003c/em\u003eLM102potentially suggesting that the strains may have conserved core genes. Previous studies carried out on \u003cem\u003eUreibacillus thermosphaericus\u0026nbsp;\u003c/em\u003erevealed that they have characteristics to survive in extreme conditions, able to thrive in high temperatures, and importantly they have been used in many industrial applications like biocatalysts for degradation of lignocellulosic biomass (Abbasalizadeh et al., 2012; Akita et al., 2017).\u003c/p\u003e\n\u003cp\u003ePan Genome analysis predominantly focuses on elucidating how genetic characteristics relate to physical traits in specific groups of organisms by capturing their complete genetic variation and minimising the predilection caused by utilising a single reference genome as it does not represent the genomic diversity of a species (J. Wang et al., 2023).\u003c/p\u003e\n\u003cp\u003eAs discussed in the material methods section of \u0026lsquo;Comparative studies\u0026rsquo; scrutinizing the genus \u003cem\u003eUreibacillus\u0026nbsp;\u003c/em\u003eresulted in 18 genomes, subsequently the Roary pipeline was employed for the pangenome analysis with the GFF file as input. Generally, Roary compartmentalises genes into four subsystems, core genes are found in 99% to 100% of strains, softcore genes are present in 95% to 99% of strains, shell genes appear in 15% to 95% of strains, and cloud cloud is found in less than 15% of strains. These genes are categorised according to their frequency of occurrence in each genome (\u003cstrong\u003eFig. 4)\u003c/strong\u003e. The analysis identified 14 core genes, a set of gene families found in nearly all (99% to 100%) strains within the interested clade. These genes generally play a predominant role in the basic survival and continuance of the clade (Kim et al., 2020), while the remaining 38,968 genes exhibited varying degrees of presence within the genomes. It is not unusual to get a small percentage of core genes which is also observed in previous studies (Podrzaj et al., 2022).\u003c/p\u003e\n\u003cp\u003eHowever, a total of 8237 (21.13%) of gene clusters were tagged as shell genes or partially shared genes. Additionally, 73.64% of gene clusters accounted for strain-specific genes. Notably, the strain \u003cem\u003eUreibacillus thermophilus\u0026nbsp;\u003c/em\u003eLM102accounted for 2384 strain-specific genes, nearly half of them about 1006 proteins accounting for hypothetical protein.\u003c/p\u003e\n\u003cp\u003eBoth the cloud and shell genes fall under accessory gene families, so understanding accessory genes benefits comprehension of knowledge about the evolutionary trajectories and variation among the clade (Kim et al., 2020), which elucidated an enormous number of strain-specific genes, suggesting that these genes have been acquired by gene duplication (Sanchez-Herrero et al., 2020), and horizontal gene transfer (Redondo-Salvo et al., 2020; Sanchez-Herrero et al., 2020). HTG found in most bacterial genomes exemplifies the occurrence of unique genes (Arnold et al., 2022). The acquired genes promote rapid adaptation concerning environmental cues (Brito, 2021). Although, the significance of these transfers varies; sometimes they only reflect ongoing evolutionary processes periodically resulting in beneficial adaptations (Arnold et al., 2022).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePerforming this Statistical analysis, we can estimate whether the closed or open nature of pangenome. In the model \u003cimg width=\"108\" height=\"25\" src=\"data:image/png;base64,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\" alt=\"{\u0026quot;mathml\u0026quot;:\u0026quot;\u0026lt;math xmlns=\\\u0026quot;http://www.w3.org/1998/Math/MathML\\\u0026quot;\u0026gt;\u0026lt;mstyle mathsize=\\\u0026quot;16px\\\u0026quot;\u0026gt;\u0026lt;mi\u0026gt;y\u0026lt;/mi\u0026gt;\u0026lt;mo\u0026gt; \u0026lt;/mo\u0026gt;\u0026lt;mo\u0026gt;=\u0026lt;/mo\u0026gt;\u0026lt;mo\u0026gt; \u0026lt;/mo\u0026gt;\u0026lt;msub\u0026gt;\u0026lt;mi\u0026gt;A\u0026lt;/mi\u0026gt;\u0026lt;mi\u0026gt;p\u0026lt;/mi\u0026gt;\u0026lt;/msub\u0026gt;\u0026lt;msup\u0026gt;\u0026lt;mi\u0026gt;x\u0026lt;/mi\u0026gt;\u0026lt;msub\u0026gt;\u0026lt;mi\u0026gt;B\u0026lt;/mi\u0026gt;\u0026lt;mi\u0026gt;p\u0026lt;/mi\u0026gt;\u0026lt;/msub\u0026gt;\u0026lt;/msup\u0026gt;\u0026lt;mo\u0026gt;+\u0026lt;/mo\u0026gt;\u0026lt;msub\u0026gt;\u0026lt;mi\u0026gt;C\u0026lt;/mi\u0026gt;\u0026lt;mi\u0026gt;p\u0026lt;/mi\u0026gt;\u0026lt;/msub\u0026gt;\u0026lt;/mstyle\u0026gt;\u0026lt;/math\u0026gt;\u0026quot;,\u0026quot;truncated\u0026quot;:false}\"\u003e, the fitting parameters are \u003cimg width=\"19\" height=\"19\" src=\"data:image/png;base64,R0lGODlhHAAcAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAAAAAbABwAgAAAAAAAAAJGhI+By33qYgtUWqPqlRlu13lf8ogj4FHmKGonGrqnKX8r+t4qe8P2vgC+hsTiJoMrumq8h7HVs8ii0tTT+aSyjAntkQkoAAA7\" alt=\"{\u0026quot;mathml\u0026quot;:\u0026quot;\u0026lt;math xmlns=\\\u0026quot;http://www.w3.org/1998/Math/MathML\\\u0026quot;\u0026gt;\u0026lt;mstyle mathsize=\\\u0026quot;16px\\\u0026quot;\u0026gt;\u0026lt;msub\u0026gt;\u0026lt;mi\u0026gt;A\u0026lt;/mi\u0026gt;\u0026lt;mi\u0026gt;p\u0026lt;/mi\u0026gt;\u0026lt;/msub\u0026gt;\u0026lt;/mstyle\u0026gt;\u0026lt;/math\u0026gt;\u0026quot;,\u0026quot;truncated\u0026quot;:false}\"\u003e, \u003cimg width=\"17\" height=\"19\" src=\"data:image/png;base64,R0lGODlhGgAcAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAAAAAZABwAgAAAAAAAAAJKhBGpce2P2JGwqpaqpuZql2HcF3EhKZYjejEra55oJL6WTM8diH94vzl5ZgsXUIdMKpMuxLIjOZIotmlKKXxStdFnzusEf7VhRwEAOw==\" alt=\"{\u0026quot;mathml\u0026quot;:\u0026quot;\u0026lt;math xmlns=\\\u0026quot;http://www.w3.org/1998/Math/MathML\\\u0026quot;\u0026gt;\u0026lt;mstyle mathsize=\\\u0026quot;16px\\\u0026quot;\u0026gt;\u0026lt;msub\u0026gt;\u0026lt;mi\u0026gt;B\u0026lt;/mi\u0026gt;\u0026lt;mi\u0026gt;p\u0026lt;/mi\u0026gt;\u0026lt;/msub\u0026gt;\u0026lt;/mstyle\u0026gt;\u0026lt;/math\u0026gt;\u0026quot;,\u0026quot;truncated\u0026quot;:false}\"\u003e, and \u003cimg width=\"19\" height=\"19\" src=\"data:image/png;base64,R0lGODlhHAAcAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAEAAAAaABwAgAAAAAAAAAJFhG+hCoEPG0szxoqchevu131iKH5kaZ0oiK2c5r5wzM40Zd8S2NCL6uvphkScJIeqIJOZ5SgzhLVWSqdJGi1StDutI1cAADs=\" alt=\"{\u0026quot;mathml\u0026quot;:\u0026quot;\u0026lt;math xmlns=\\\u0026quot;http://www.w3.org/1998/Math/MathML\\\u0026quot;\u0026gt;\u0026lt;mstyle mathsize=\\\u0026quot;16px\\\u0026quot;\u0026gt;\u0026lt;msub\u0026gt;\u0026lt;mi\u0026gt;C\u0026lt;/mi\u0026gt;\u0026lt;mi\u0026gt;p\u0026lt;/mi\u0026gt;\u0026lt;/msub\u0026gt;\u0026lt;/mstyle\u0026gt;\u0026lt;/math\u0026gt;\u0026quot;,\u0026quot;truncated\u0026quot;:false}\"\u003e\u0026nbsp;while \u003cimg width=\"17\" height=\"19\" src=\"data:image/png;base64,R0lGODlhGgAcAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAAAAAZABwAgAAAAAAAAAJKhBGpce2P2JGwqpaqpuZql2HcF3EhKZYjejEra55oJL6WTM8diH94vzl5ZgsXUIdMKpMuxLIjOZIotmlKKXxStdFnzusEf7VhRwEAOw==\" alt=\"{\u0026quot;mathml\u0026quot;:\u0026quot;\u0026lt;math xmlns=\\\u0026quot;http://www.w3.org/1998/Math/MathML\\\u0026quot;\u0026gt;\u0026lt;mstyle mathsize=\\\u0026quot;16px\\\u0026quot;\u0026gt;\u0026lt;msub\u0026gt;\u0026lt;mi\u0026gt;B\u0026lt;/mi\u0026gt;\u0026lt;mi\u0026gt;p\u0026lt;/mi\u0026gt;\u0026lt;/msub\u0026gt;\u0026lt;/mstyle\u0026gt;\u0026lt;/math\u0026gt;\u0026quot;,\u0026quot;truncated\u0026quot;:false}\"\u003e\u0026nbsp;is equivalent to the parameter \u003cimg width=\"7\" height=\"9\" src=\"data:image/png;base64,R0lGODlhCgAOAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAAAAAKAA4AgAAAAAAAAAIaBGIWeK35DktowTfhypTTq4GKF4GbaKEiUAAAOw==\" alt=\"{\u0026quot;mathml\u0026quot;:\u0026quot;\u0026lt;math xmlns=\\\u0026quot;http://www.w3.org/1998/Math/MathML\\\u0026quot;\u0026gt;\u0026lt;mstyle mathsize=\\\u0026quot;16px\\\u0026quot;\u0026gt;\u0026lt;mi\u0026gt;γ\u0026lt;/mi\u0026gt;\u0026lt;/mstyle\u0026gt;\u0026lt;/math\u0026gt;\u0026quot;,\u0026quot;truncated\u0026quot;:false}\"\u003e used by Tettelin et al. (\u003cu\u003e2008\u003c/u\u003e). When \u003cimg width=\"25\" height=\"11\" src=\"data:image/png;base64,R0lGODlhJgAQAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAAAAAjABAAgAAAAAAAAAI/hBF5y+0djnq0ThOrZinLrWUXNoLS5C2piVwdV4bxSpqvM9NwrOKyDtttbo8ZiwgJslo6F2+Yqy2FHMS0CCgAADs=\" alt=\"{\u0026quot;mathml\u0026quot;:\u0026quot;\u0026lt;math xmlns=\\\u0026quot;http://www.w3.org/1998/Math/MathML\\\u0026quot;\u0026gt;\u0026lt;mstyle mathsize=\\\u0026quot;16px\\\u0026quot;\u0026gt;\u0026lt;mn\u0026gt;0\u0026lt;/mn\u0026gt;\u0026lt;mo\u0026gt; \u0026lt;/mo\u0026gt;\u0026lt;mo\u0026gt;\u0026lt;\u0026lt;/mo\u0026gt;\u0026lt;/mstyle\u0026gt;\u0026lt;/math\u0026gt;\u0026quot;,\u0026quot;truncated\u0026quot;:false}\"\u003e\u0026nbsp;\u003cimg width=\"17\" height=\"19\" src=\"data:image/png;base64,R0lGODlhGgAcAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAAAAAZABwAgAAAAAAAAAJKhBGpce2P2JGwqpaqpuZql2HcF3EhKZYjejEra55oJL6WTM8diH94vzl5ZgsXUIdMKpMuxLIjOZIotmlKKXxStdFnzusEf7VhRwEAOw==\" alt=\"{\u0026quot;mathml\u0026quot;:\u0026quot;\u0026lt;math xmlns=\\\u0026quot;http://www.w3.org/1998/Math/MathML\\\u0026quot;\u0026gt;\u0026lt;mstyle mathsize=\\\u0026quot;16px\\\u0026quot;\u0026gt;\u0026lt;msub\u0026gt;\u0026lt;mi\u0026gt;B\u0026lt;/mi\u0026gt;\u0026lt;mi\u0026gt;p\u0026lt;/mi\u0026gt;\u0026lt;/msub\u0026gt;\u0026lt;/mstyle\u0026gt;\u0026lt;/math\u0026gt;\u0026quot;,\u0026quot;truncated\u0026quot;:false}\"\u003e\u0026nbsp;\u003cimg width=\"25\" height=\"11\" src=\"data:image/png;base64,R0lGODlhJgAQAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAMAAAAhABAAgAAAAAAAAAI2hI+pCxEMo3FP2uXuqZdLqoGRqDVjVk4eQqaT0roNOq/yy9L3zMSpvfFZhDngKGTE6D4d06MAADs=\" alt=\"{\u0026quot;mathml\u0026quot;:\u0026quot;\u0026lt;math xmlns=\\\u0026quot;http://www.w3.org/1998/Math/MathML\\\u0026quot;\u0026gt;\u0026lt;mstyle mathsize=\\\u0026quot;16px\\\u0026quot;\u0026gt;\u0026lt;mo\u0026gt;\u0026lt;\u0026lt;/mo\u0026gt;\u0026lt;mo\u0026gt; \u0026lt;/mo\u0026gt;\u0026lt;mn\u0026gt;1\u0026lt;/mn\u0026gt;\u0026lt;/mstyle\u0026gt;\u0026lt;/math\u0026gt;\u0026quot;,\u0026quot;truncated\u0026quot;:false}\"\u003e, the size of the pan-genome increases unboundedly with the subsequent addition of new genomes and can be considered open. Conversely, when \u003cimg width=\"99\" height=\"20\" src=\"data:image/png;base64,R0lGODlhlAAeAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAAAACSAB4AgAAAAAAAAAL/hBGXx+0Po2ShzouzhsvVBG7iaHQdiaYeYnknq8bN+8r21rpnfnMTX+oJMx/drDZEXIDFJAeJAiqOUFvz96iuZFdVt8ST3j5ahVRMWYQtX0kbVyuGylF0BGmHTSnK95N+d6aWV0dodBSIaObl5xa31tO4lAX1FWZlSKVoBrgk6fj45MKJ2Qk2ullKgneXlRYjJ9JlmSlbu5iGF4pbWLf16sRiylRFs3Obq9TLC2a6+smsdZyqAe2Zy4YMmzn3w6a8JwseJRxsPlMdeb7O3u6+Gv4uPz/y7UyP/z6dz9/fHO8v4LwdAgvK22cw4bkc9xQ6xAHwoUQuDSda9HYxoxWNBxxTWOtYsAAAOw==\" alt=\"{\u0026quot;mathml\u0026quot;:\u0026quot;\u0026lt;math xmlns=\\\u0026quot;http://www.w3.org/1998/Math/MathML\\\u0026quot;\u0026gt;\u0026lt;mstyle mathsize=\\\u0026quot;16px\\\u0026quot;\u0026gt;\u0026lt;msub\u0026gt;\u0026lt;mi\u0026gt;B\u0026lt;/mi\u0026gt;\u0026lt;mi\u0026gt;p\u0026lt;/mi\u0026gt;\u0026lt;/msub\u0026gt;\u0026lt;mo\u0026gt; \u0026lt;/mo\u0026gt;\u0026lt;mo\u0026gt;\u0026lt;\u0026lt;/mo\u0026gt;\u0026lt;mo\u0026gt; \u0026lt;/mo\u0026gt;\u0026lt;mn\u0026gt;0\u0026lt;/mn\u0026gt;\u0026lt;mo\u0026gt; \u0026lt;/mo\u0026gt;\u0026lt;mi\u0026gt;o\u0026lt;/mi\u0026gt;\u0026lt;mi\u0026gt;r\u0026lt;/mi\u0026gt;\u0026lt;mo\u0026gt; \u0026lt;/mo\u0026gt;\u0026lt;mo\u0026gt;\u0026gt;\u0026lt;/mo\u0026gt;\u0026lt;mo\u0026gt; \u0026lt;/mo\u0026gt;\u0026lt;mn\u0026gt;1\u0026lt;/mn\u0026gt;\u0026lt;/mstyle\u0026gt;\u0026lt;/math\u0026gt;\u0026quot;,\u0026quot;truncated\u0026quot;:false}\"\u003e the pan-genome trajectory approaches a plateau as further genomes are added and can be regarded as closed. From (\u003cstrong\u003eFig. 5)\u003c/strong\u003e, it is evident that the pan-genome size increases unboundedly with the subsequent addition of new genomes (n=18, and \u003cimg width=\"17\" height=\"19\" src=\"data:image/png;base64,R0lGODlhGgAcAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAAAAAZABwAgAAAAAAAAAJKhBGpce2P2JGwqpaqpuZql2HcF3EhKZYjejEra55oJL6WTM8diH94vzl5ZgsXUIdMKpMuxLIjOZIotmlKKXxStdFnzusEf7VhRwEAOw==\" alt=\"{\u0026quot;mathml\u0026quot;:\u0026quot;\u0026lt;math xmlns=\\\u0026quot;http://www.w3.org/1998/Math/MathML\\\u0026quot;\u0026gt;\u0026lt;mstyle mathsize=\\\u0026quot;16px\\\u0026quot;\u0026gt;\u0026lt;msub\u0026gt;\u0026lt;mi\u0026gt;B\u0026lt;/mi\u0026gt;\u0026lt;mi\u0026gt;p\u0026lt;/mi\u0026gt;\u0026lt;/msub\u0026gt;\u0026lt;/mstyle\u0026gt;\u0026lt;/math\u0026gt;\u0026quot;,\u0026quot;truncated\u0026quot;:false}\"\u003e = 0.65). Additionally, the (\u003cstrong\u003eSupplementary\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eFig. 1)\u0026nbsp;\u003c/strong\u003edemonstrates a bar plot for new genes indicating that a number of new genes does not falls to zero when new genomes are added sequentially. Compiling the analysis of the new gene curve and pangenome curve using the regression model exhibit the open nature of the pangenome (Knight et al., 2017). The open pangenome of \u003cem\u003eUreibacillus\u0026nbsp;\u003c/em\u003emeans it can have various mechanisms for exchanging genetic material and adapt to multiple environments (Reis and Cunha, 2021). The genus \u003cem\u003eUreibacillus\u0026nbsp;\u003c/em\u003eis isolated from a variety of environments, such as animal guts, marine waters, mineral-rich soils, livestock compost, plant tissues, and saline soils (Yadav et al., 2024). This demonstrates ability of genus \u003cem\u003eUreibacillus\u0026nbsp;\u003c/em\u003eto be highly adapt and capable of thriving in diverse ecological niches.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCharacterisation of hypothetical proteins\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA recent study reported that as of October 2023, the RefSeq protein databases contained approximately 5,000,000 sequences, with approximately 10 per cent corresponding to hypothetical proteins (Vincent, 2024). This proportion highlights the large presence of hypothetical proteins within the database. This leads to the fundamental question: is studying hypothetical proteins beneficial? Certainly, the answer to the above problem is \u0026lsquo;Yes\u0026rsquo;, there are many findings on hypothetical proteins that benefit human health and understanding the mechanism of adaptation of bacteria to the environment. For example, studying the hypothetical protein of the SARS-CoV virus unveiled its ability to alter the antiviral inflammatory cytokine and interferon pathways in the host system (Rahman et al., 2023).\u003c/p\u003e\n\u003cp\u003eThe pan-genome analysis revealed that \u003cem\u003eUreibacillus thermophilus\u0026nbsp;\u003c/em\u003eLM102 contains 33.88% (1006) of strain-specific gene codes for hypothetical proteins (HPs). Studying these hypothetical proteins is essential as they might contribute to the adaptations, pathogenicity, or other physiological traits of the strain and potentially identify the target for further biotechnological application.\u003c/p\u003e\n\u003cp\u003eUPIMAPI was used for the initial prediction of the hypothetical proteins, resulting in 500 HPs that possess known protein domains or families, along with their corresponding GO IDs. NaviGO was then utilized to categorize the GO IDs into three different GO categories \u003cstrong\u003e(Supplementary Fig. 2)\u003c/strong\u003e, namely Cellular Component (CC), Molecular Function (MF), and Biological Process (BP). Among the three categories, largest cluster was molecular function (49.36%) followed by biological processes (41.71%) and cellular components (6.36%).\u003c/p\u003e\n\u003cp\u003eUreibacillus_thermophilus_LM102_01172_1 was predicted as Esterase. Esterase is a diverse class of enzymes that catalyzes the hydrolysis of alcohol and acid through the addition of water molecules (Ding et al., 2021). This versatile enzyme plays a significant role in the beverage and food industries. Its primary function is to modify fats and oil in fruit juices (Raveendran et al., 2018), ultimately enhancing the taste and aroma of the final yield (Sarnaik et al., 2023). Additionally, this enzyme finds application in a wide range of industrial sectors, including paper and pulp production, leather tanneries, detergents, textiles, cosmetics, biodiesel synthesis, pharmaceuticals, waste treatment and bioremediation (Akram et al., 2024). Furthermore, esterases are used for detoxification purposes, effectively neutralising inhaled, injected and ingested poisons. They are also utilized to inactivate certain drugs like aspirin, cocaine, and Ritalin, as well as to activate prodrugs to active drugs like irinotecan (Lockridge et al., 2018).\u003c/p\u003e\n\u003cp\u003eThe proteins YabQ (Ureibacillus_thermophilus_LM102_00385_1) and Spore coat protein (Ureibacillus_thermophilus_LM102_02528_1) were predicted as the potential role in spore formation. Many different organisms utilize sporulation as a method to adapt to changes in their environment and to survive in a dormant state until they encounter favourable conditions for active growth (Huang and Hull, 2017). The YabQ protein is located in the membrane of the forespore and plays a crucial role in cortex development (Asai et al., 2001). The spore coat, composed of multiple proteins, forms a layered shield that protects the bacterial genome in harsh conditions. It influences spore germination and determines the types of interactions spores can have with different environmental surfaces (McKenney et al., 2013). These spores exhibit high resistance to wet heat and protect against a wide range of threats, including exposure to various chemicals and attacks by predatory microbes (\u0026ldquo;The Spore Coat,\u0026rdquo; 2016; Yu et al., 2023). Furthermore, the protein encoded by Ureibacillus_thermophilus_LM102_01748_1 is responsible for the production of the beta-lactamase enzyme (EC 3.5.2.6). Beta-lactamases play a crucial role in the resistance mechanism of gram-negative bacteria. These particular enzymes work by breaking down the beta-lactam ring present in beta-lactam antibiotics, thus making them ineffective (Morrison and Zembower, 2020).\u003c/p\u003e\n\u003cp\u003eThe Ureibacillus_thermophilus_LM102_01444_1 protein is expected to contain the flagellar type III secretion protein SwrB, which enhances the number of flagellar hooks while moving in groups during swarming motility (Kearns et al., 2004; Phillips et al., 2021). Bacteria that swarm move together and display adaptive resistance to various antibiotics, and particularly advantageous in extreme temperatures(Butler et al., 2010; Rahman et al., 2022).\u003c/p\u003e\n\u003cp\u003eIt is anticipated that there is the presence of ComX (Ureibacillus_thermophilus_LM102_02851_1) within a group of hypothetical proteins. The ComX pheromone is a short chain of amino acids that goes through post-translational changes by ComQ and binds to its receptor ComP. This binding activates the corresponding response regulator, ComA, which then triggers different quorum-sensing genes (Dhiman, 2021). This activation results in the release of extracellular matrix components that induce natural competence in reaction to crowding (Okada et al., 2005). DNA possesses both informational and biochemical properties. When this DNA is taken up from the environment, it creates new combinations of alleles, which either increase or reduce fitness. However, the most important reason for uptake is nutrition. DNA is an ideal source of deoxyribonucleotides, which are essential for the replication of bacterial genome. Bacterial cells uptake extracellular DNA as a nutrient source instead of undergoing the expensive process of de novo nucleotide synthesis, which requires a lot of energy and molecular constituents (Mell and Redfield, 2014).\u003c/p\u003e\n\u003cp\u003eThe Protein Subcellular Localization (PSL) of proteins within the cell is a crucial factor in determining their functions. Understanding the subcellular location of proteins is essential for elucidating human disease mechanisms and protein interactions (Li et al., 2014). This knowledge is valuable in identifying potential drug targets in bacterial proteins (Imam et al., 2019). Proteins located in the cytoplasmic matrix are essential for maintaining cell metabolism (Ebner and G\u0026ouml;tz, 2019), while membrane proteins are involved in various cellular processes, including transport, communication, enzymatic reactions, and signal transduction (Jelokhani-Niaraki, 2022). The identification of protein subcellular localization also contributes to the enhancement of public databases, such as Swiss-Prot, and provides valuable data for the development of machine learning methods and computational protein location identification (Pan et al., 2020). The tool PSLpred was utilized to predict the PSL of 500 hypothetical proteins. Fig. 6 showed that most of these proteins were identified as Cytoplasmic Proteins (50%) and Inner-membrane Proteins (40.6%), with smaller percentages found in Extracellular Proteins (5.4%), Periplasmic Proteins (2.8%), and Outer Membrane Proteins (1.2%). The higher proportion of cytoplasmic proteins implies that nearly half of the hypothetical proteins are likely involved in cellular processes and maintain metabolic activities within living cells (Ebner and G\u0026ouml;tz, 2019).\u003c/p\u003e\n\u003cp\u003eWe also computed the physicochemical properties such as Grand Average of Hydropathy (GRAVY), aliphatic index (AI), and instability index (II) for all HPs. GRAVY values represent the average hydropathy value of a protein. Proteins with positive values are hydrophobic, while negative GRAVY values are hydrophilic (Wang et al., 2021). The analysis revealed that 291 HPs are hydrophilic and the remaining 209 HPs are hydrophobic. The instability index estimates the stability of a protein in a laboratory environment based on its amino acid composition. The stability index below 40 is considered stable, while the opposite is true for values above 40. Based on these metrics, approximately 70.6% (353) of HPs are stable in a test tube environment, while the remaining 29.4% are not stable (Prabhu et al., 2020; Wei et al., 2017). The AI of a protein is a measure of the relative volume occupied by the amino acid side chains of Valine, Leucine, Isoleucine, and Alanine. The AI of proteins derived from thermophilic bacteria was found to be significantly higher than that of typical proteins, which is used as a metric to evaluate protein thermostability. The proteins under study exhibited an aliphatic index with an average of 105.08. Notably, 98.4% of the proteins had an aliphatic index exceeding 70 and marking up to 172.9, indicating a relatively high value compared to proteins produced by other thermophilic bacteria (Akkaya et al., 2023). This suggests that these proteins have enhanced stability at higher temperatures compared to other thermophilic bacteria.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn the present study, genomic and metabolic characterization of \u003cem\u003eUreibacillus thermophilus\u003c/em\u003e LM102 has been conducted, predominantly focusing on its distinctive properties and potential biotechnological applications. The dDDH value indicates its unique species classification, expressing both mesophilic and thermophilic characteristics. The identification of TCS demonstrates \u003cem\u003eUreibacillus thermophilus\u003c/em\u003e LM102 ability to adapt to harsh environments. Metabolic pathway analysis shows biosynthesis of biotin, riboflavin, and cobalamin which has greater application in industries. Summarizing the results narrows down to the conclusion that \u003cem\u003eUreibacillus thermophilus\u003c/em\u003e LM102 is a perfect candidate for large-scale microbial production of the aforementioned vitamins due to its flexible growth temperature that can be exploited by industrial sectors. From 2020 to 2024, the data size in NCBI has grown exponentially due to new sequencing technologies. Researchers have begun focusing on identification of new microorganisms, leading to a significant gap in the functional exploration of potentially beneficial microbes. Hence, rather than allocating resources to sequencing, researchers must concentrate into the data already available in public repositories. Moreover, it is crucial to mention the constraints of this study. Mainly, the study utilized single genome data, whereas using metagenome data would enable us to explore more. Additionally, the research heavily relies on advanced computational tools, requiring high-end computational resources, time consuming and proficiency in programming.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI would like to acknowledge the SRM IST for funding to pursue my research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePPVB - Conceptualization; Data curation; Formal analysis; Funding acquisition; Methodology; Validation; Visualization; Writing - original draft; Writing - review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eAA – Formal analysis; Funding acquisition; Methodology; Validation; Visualization; Writing - review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eLM – Conceptualization; Investigation; Project administration; Resources; Supervision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there were no commercial or financial affiliations that could potentially create a conflict of interest during this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSubmission declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the article has not been submitted elsewhere or not under any other considerations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data presented in the study are deposited in the NCBI repository under the BioProject PRJNA472232\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbbasalizadeh, S., Salehi Jouzani, G., Motamedi Juibari, M., Azarbaijani, R., Parsa Yeganeh, L., Ahmad Raji, M., Mardi, M., Salekdeh, G.H., 2012. 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Microbiol. 84, e02744\u0026ndash;17.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eDemonstrates the dDDH values and optimal growth temperature of U\u003cem\u003ereibacillus thermophilus\u0026nbsp;\u003c/em\u003eLM102 and its closely related 18 type strains.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eS. No.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eType strain\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003edDDH (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGrowth condition\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026deg;C\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOptimum Growth\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u0026deg;C\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eClassification\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReference\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cem\u003eParageobacillus galactosidasius\u0026nbsp;\u003c/em\u003eDSM 18751\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e37.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;50\u0026ndash;75 \u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e70\u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eThermophilic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(Poli et al. 2011)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cem\u003eParageobacillus yumthangensis\u003c/em\u003e AYN2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e36.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e40\u0026ndash;70\u0026thinsp;\u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e60\u0026thinsp;\u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eThermophilic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(Najar et al. 2018)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cem\u003eParageobacillus toebii\u0026nbsp;\u003c/em\u003eDSM 14590\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e34.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e45-70 \u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e60\u0026thinsp;\u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eThermophilic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(Sung 2002)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cem\u003eAnoxybacillus rupiensis\u0026nbsp;\u003c/em\u003eDSM 17127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e34.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e55\u0026ndash;58\u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e55\u0026ndash;58\u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eThermophilic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(Derekova et al. 2007)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cem\u003eBhargavaea massiliensis\u0026nbsp;\u003c/em\u003eMarseille- Q1000 T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e30.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cem\u003eParageobacillus caldoxylosilyticus\u0026nbsp;\u003c/em\u003eNBRC 107762\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e30.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;43\u0026ndash;75 \u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e65 \u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eThermophilic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(Ahmad et al. 2000)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cem\u003eAnoxybacillus geothermalis\u0026nbsp;\u003c/em\u003eATCC BAA2555\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e29.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e40\u0026ndash;65\u0026thinsp;\u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e60\u0026thinsp;\u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eThermophilic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(Filippidou et al. 2016)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cem\u003eAnoxybacillus karvacharensis\u0026nbsp;\u003c/em\u003eK1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e29.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e45\u0026ndash;70\u0026thinsp;\u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e60-65 \u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eThermophilic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(Panosyan et al. 2021)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cem\u003eCaldibacillus pasinlerensis\u0026nbsp;\u003c/em\u003eP1T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e28.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e40\u0026ndash;60\u0026thinsp;\u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e55 \u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eThermophilic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(Baltaci et al. 2020)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cem\u003ePlanococcus koreensis\u0026nbsp;\u003c/em\u003eDSM 15895\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e25.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cem\u003eUreibacillus thermosphaericus\u0026nbsp;\u003c/em\u003eDSM 10633\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e25.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e32\u0026ndash;64\u0026thinsp;\u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;60 \u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eThermophilic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(Fortina et al. 2001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cem\u003eUreibacillus terrenus\u0026nbsp;\u003c/em\u003eATCC BAA-384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e24.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e40\u0026ndash;64\u0026thinsp;\u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e50-60 \u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eThermophilic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(Fortina et al. 2001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cem\u003eChryseomicrobium excrementi\u0026nbsp;\u003c/em\u003eLMG 30119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e23.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e20\u0026ndash;40\u0026thinsp;\u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e35-37 \u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eMesophilic\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(Saha et al. 2018)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cem\u003ePlanococcus mcmeekinii\u0026nbsp;\u003c/em\u003eDSM 13963\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e23.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cem\u003ePsychrobacillus lasiicapitis\u0026nbsp;\u003c/em\u003eCGMCC 1.15308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e21.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e25\u0026ndash;35\u0026thinsp;\u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e28-30 \u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eMesophilic\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(Shen et al. 2017)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cem\u003eBacillus ndiopicus\u0026nbsp;\u003c/em\u003eFF3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e21.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e30\u0026ndash;45\u0026thinsp;\u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e37\u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eMesophilic\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(Lo et al. 2015)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cem\u003eLysinibacillus antri\u0026nbsp;\u003c/em\u003eSYSU K30002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e20.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e28\u0026ndash;40\u0026thinsp;\u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e37 \u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eMesophilic\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(Narsing Rao et al. 2020)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cem\u003ePlanococcus salinus\u0026nbsp;\u003c/em\u003eLCB217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e19.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e10\u0026ndash;45\u0026deg; C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e30\u0026thinsp;\u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eHalophilic\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(Gan et al. 2018)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cem\u003eUreibacillus thermophilus\u0026nbsp;\u003c/em\u003eLM102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e37-65 \u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e37-65 \u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eThermostable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e(Sunny et al. 2020)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Ureibacillus thermophilus LM102, Digital DNA-DNA hybridization, two-component systems, open pangenome, hypothetical proteins, and aliphatic Index","lastPublishedDoi":"10.21203/rs.3.rs-6643773/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6643773/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study aimed to conduct a phylogenetic and functional analysis of \u003cem\u003eUreibacillus\u003c/em\u003e \u003cem\u003ethermophilus \u003c/em\u003eLM102. The phylogeny using the TYGS web server revealed that \u003cem\u003eUreibacillus\u003c/em\u003e \u003cem\u003ethermophilus\u003c/em\u003e LM102 shares both mesophilic and thermophilic strain traits. Digital DNA-DNA hybridization values ranged from 19.1% to 37.4%, confirming the strain unique taxonomic classification. BlastKOALA identified genes involved in complete biotin, riboflavin, and cobalamin biosynthesis pathways, highlighting its industrial relevance. The P2RP web tool identified stress-response mechanisms including WalRK (YycFG), DesKR, LiaRS, BceRS, and ResDE adaptations to temperature fluctuations, low oxygen levels, and antibiotic stresses through TCS mechanisms. The pangenome analysis using the power-law regression based on Heaps' law revealed the \u003csup\u003e\u003cem\u003e\u003cstrong\u003eB\u003c/strong\u003e\u003c/em\u003e\u003c/sup\u003e\u003csub\u003e\u003cem\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/em\u003e\u003c/sub\u003e = 0.65, indicating the \u003cem\u003eUreibacillus\u003c/em\u003e genus has an open pangenome nature. Furthermore, the pan-genomic analysis using Roary revealed the stains possess 1006 genes encoding for hypothetical proteins. The gene ontology and physicochemical properties of the HPs were carried out using UPIMAPI and ExPASy. Of the total hypothetical protein, 49.36% is involved in molecular function, 41.71% in biological processes, and 6.36% in cellular components. Notably, 500 HPs exhibited known protein domains, and the average Aliphatic Index of these proteins is as high as 105.08, suggesting the high thermostability of the proteins. These findings suggest that \u003cem\u003eUreibacillus thermophilus\u003c/em\u003e LM102 has significant potential for applications in biotechnology and industrial processes.\u003c/p\u003e","manuscriptTitle":"Exploring the Functional Potential of Ureibacillus thermophilus LM102 through comprehensive and comparative Genome Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-03 13:10:55","doi":"10.21203/rs.3.rs-6643773/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0bc04ef0-2cb9-4fd4-8de7-577cae588edb","owner":[],"postedDate":"July 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-10T21:13:25+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-03 13:10:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6643773","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6643773","identity":"rs-6643773","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}