Genetic diversity and sequence polymorphism of bacterial 16S rRNA isolates from beef tripe from Gaborone, Botswana

Genetic diversity and sequence polymorphism of bacterial 16S rRNA isolates from beef tripe from Gaborone, Botswana | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Genetic diversity and sequence polymorphism of bacterial 16S rRNA isolates from beef tripe from Gaborone, Botswana Koketso Motlhanka, Modiri D. Setlhoka, Nerve Zhou, Ernest Mochankana, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8590956/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Beef tripe is a commonly consumed organ meat in many parts of the world, including Botswana, but its porous texture and microbial richness pose potential food safety risks. Despite its widespread consumption, limited studies have characterized the genetic diversity of tripe-associated microbial populations at the molecular level. This study aimed to assess the genetic polymorphism and evolutionary dynamics of microbial isolates recovered from beef tripe sold in butcheries in Gaborone, Botswana, using nucleotide sequence data and population genetic tools implemented in R. Population genetic statistics were applied as descriptive measures of sequence heterogeneity across multiple bacterial taxa rather than as strict intraspecific evolutionary tests. A total of 99 aligned nucleotide sequences were analyzed for nucleotide composition, polymorphism, haplotype structure, and genetic diversity. Sliding window analyses of nucleotide diversity (π) and Tajima’s D were conducted. Additional analyses included haplotype network construction, PCA, minor allele frequency profiling, mismatch distribution, and genetic distance heatmaps. High nucleotide diversity (mean π = 0.7006) as well as 99 unique haplotypes were observed, indicating a highly diverse microbial population. Polymorphism was widely distributed across the genome, with no hyper-conserved or hypervariable regions. PCA and genetic distance heatmaps revealed diffuse clustering, consistent with a non-clonal structure. Tajima’s D values hovered around zero, suggesting neutral evolution. A unimodal mismatch distribution indicated recent population expansion. Notably, rare alleles with low minor allele frequencies were present across multiple isolates. The findings reveal a genetically diverse, neutrally evolving microbial community in beef tripe, shaped by environmental exposure and stochastic colonization. This underscores the need for genomic surveillance in food safety systems to detect emerging antimicrobial-resistant or pathogenic variants. However, pathogenic potential cannot be inferred from 16S rRNA sequence data alone. These findings describe genetic diversity patterns among bacterial isolates but do not directly infer pathogenicity or public health risk. Beef tripe nucleotide diversity Tajima’s D haplotypes microbial genetics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 1. Introduction Beef tripe, the edible lining of the stomach of ruminants, is a widely consumed protein source in many developing countries, including Botswana [ 1 ]. It is particularly popular in informal food systems due to its affordability and cultural acceptance. However, tripe is highly perishable and presents unique challenges in terms of food hygiene and safety [ 2 ]. Its complex surface texture and rich nutrient profile [ 3 , 4 ] provide an ideal environment for microbial colonization, making it a potential vehicle for foodborne pathogens and spoilage organisms if not properly handled. In food safety microbiology, assessing the microbial quality of animal-derived products is crucial for identifying potential risks to consumers [ 5 ]. Traditional microbiological methods often focus on the presence or absence of specific pathogens, but such approaches can overlook the broader genetic landscape of microbial populations that contribute to contamination, persistence, or resistance [ 6 ]. Modern molecular tools provide a more comprehensive understanding by targeting the genetic diversity within microbial communities. Nucleotide sequence-based analyses are particularly powerful in this regard. They allow researchers to assess not only the taxonomic composition of microbial communities but also their genetic variation, evolutionary trajectories, and functional potential. Nucleotide diversity (π), first described by Nei (1979), measures the average pairwise difference between sequences and reflects the extent of polymorphism in a population. High nucleotide diversity suggests the presence of a genetically varied population, which could include strains with distinct virulence factors, antimicrobial resistance (AMR) genes, or enhanced survival mechanisms. Another essential molecular tool in population genetics is Tajima’s D statistic [ 8 ], which compares the observed nucleotide diversity with the number of segregating sites to explore patterns consistent with neutrality or heterogeneity in sequence variation. Significant deviations from zero may indicate selection pressures acting on microbial populations. In foodborne microorganisms, such deviations may reflect adaptation to stressors such as antimicrobial treatments, host immune responses, or food processing environments. In contrast, non-significant values support the neutral theory of molecular evolution [ 9 ], suggesting that observed variation is largely shaped by mutation and genetic drift rather than selection. From a food safety perspective, understanding the underlying population dynamics of microorganisms in beef tripe is essential for several reasons [ 10 ]. First, genetically diverse microbial communities may harbor subpopulations with increased virulence or resistance potential, complicating control measures. Second, polymorphic markers can be used in molecular tracing to determine sources and routes of contamination within the food chain. Finally, such data contributes to risk assessment models that inform national food safety regulations and standards. Genetic heterogeneity within foodborne bacterial populations is increasingly recognized as a key determinant of persistence, transmission, and adaptation along the food chain. Genetic variation among food-associated bacteria highlights their ability to persist, adapt, and disseminate within food systems, making genomic diversity an important dimension of food safety surveillance. Despite its widespread consumption, beef tripe remains poorly characterized at the molecular level in southern Africa. Most studies focus on its microbial load rather than the genetic characteristics of its microbial residents. In Botswana, where foodborne illness remains a public health concern, there is a need for integrated molecular surveillance of high-risk animal products. This study aims to address that gap by characterizing the genetic polymorphism of microbial isolates from beef tripe using nucleotide sequence data by focusing on characterizing sequence-level diversity rather than assessing virulence or antimicrobial resistance. We assessed nucleotide diversity, haplotype frequencies, and Tajima’s D values to determine the extent of genetic variation and evolutionary pressures acting upon these microbial populations. The findings will enhance our understanding of microbial ecology in beef tripe and inform food safety monitoring and control strategies. 2. Materials and Methods 2.1 Sample Collection and DNA Sequencing The beef tripe samples stomach ( mogodu ) and reticulum ( ntshothwane ) were sampled from local butcheries in the South-east districts of Botswana. A total of 30 tripe samples were collected from 18 different butcheries around Gaborone. The butcheries were selected through a convenience sampling approach based on geographical spread within Gaborone, accessibility, and willingness to participate, which is a commonly accepted strategy for exploratory food microbiology studies in resource-limited settings. The samples were collected from different cattle and different butcheries. Each sample was placed in a separate sterile plastic bag and placed on ice in a cooler box which was immediately transported to the laboratory. Upon arrival at the microbiology laboratory, samples were held at 4°C and examined and analyzed within 2 hrs from the time of purchase. The mogodu and ntshothwane were analyzed collectively. Although the two originate from different bovine stomach compartments, in Botswana butcheries they are typically processed, displayed, and stored together, subjected to identical handling and environmental conditions. Because our study focused on overall microbial quality as encountered by consumers and because separating them would have resulted in insufficient sample sizes for meaningful statistical comparison, we combined them in the analysis. Although retail refrigeration was observed, storage temperatures were not recorded, representing a limitation of the sampling process. Genomic DNA was extracted from isolated bacteria (Table 1 ) using the GenElute ™ Bacterial Genomic DNA Kit (Sigma-Aldrich, USA) according to manufacturer’s instructions. PCR amplification was performed using ProFlex PCR Systems (Applied Biosystems, USA) in a 20 µL reaction volume containing 2.5 µL 10× PCR buffer, 2.0 µM of each primer, 0.2 mM dNTPs, and 1.25 U Taq DNA polymerase (Takara Bio Inc., Japan) using 16S rDNA and a pair of universal primers, 27F (5′-AGAGTTTGATCCTGGCTCAG-3′) and 1492R (5′-TACGGYTACCTTGTTACGACTT-3′) [ 11 ]. The following cycling conditions: initial denaturation at 98°C for 30 s, 35 cycles of denaturation (98°C for 30 s), annealing (48°C for 30 s), and extension (68°C for 30 s), a final extension step at 72°C for 7 min and held at 4°C ∞. Negative controls in which the template DNA in the PCR mixture was replaced with sterile distilled water were also included. All amplicons were purified using a QIAquick PCR product purification kit (Qiagen, GmBH, Germany) according to manufacturer´s instructions. The amplicons were sequenced by Inqaba Biotech (Pretoria, South Africa). SnapGene® Viewer software ver. 8.0.2 (GSL Biotech) sequence editing tool was used to generate contiguous sequences ( http://www.snapgene.com ). Species identification was done by using a BLAST nucleotide sequence analysis tool ( https://blast.ncbi.nlm.nih.gov/Blast.cgi?PROGRAM=blastn&PAGE_TYPE=BlastSearch&LINK_LOC=blasthome ). Table 1 shows all the identified microbes and the sampling locations from the beef tripe samples. Table 1 Bacterial isolates and their identities from tripe sampled different butcheries in the South-east district of Botswana [ 12 ]. Isolate Isolate code Sampling location Isolate name Accession number Percentage identity (%) TB1 TR1 Ledumang Aeromonas caviae MG737572.1 96.52 TB2 TR1 Ledumang Escherichia coli KY780341.1 97.54% TB3 TR1 Ledumang Enterobacteriaceae bacterium MT775448.1 87.06 TB4 TR1 Ledumang Aeromonas sobria GQ232759.1 97.21 TB5 TR1 Ledumang Escherichia coli MN208228.1 97.21 TB6 TR2 Phakalane Moellerella wisconsensis CP093242.1 97.00 TB7 TR2 Phakalane Escherichia coli MN094132.1 98.00 TB8 TR2 Phakalane Aeromonas veronii ON203104.1 97.86 TB9 TR2 Phakalane Aeromonas veronii AY928480.1 92.27 TB10 TR2 Phakalane Aeromonas allosaccharophila MK548513.1 97.10 TB11 TR3 Phase 2 Escherichia coli OR660422.1 96.82 TB12 TR3 Phase 2 Escherichia coli CP057829.1 97.12 TB13 TR3 Phase 2 Aeromonas veronii ON203104.1 97.12 TB14 TR3 Phase 2 Klebsiella oxytoca ON556594.1 97.28 TB15 TR3 Phase 2 Aeromonas sobria GQ232759.1 96.56 TB16 TR4 Sebele Escherichia spp. MT914503.1 98.02 TB17 TR4 Sebele Raoultella spp. DQ470482.1 95.20 TB18 TR4 Sebele Aeromonas caviae KP260655.1 97.07 TB19 TR4 Sebele Aeromonas spp. KC182747.1 97.22 TB20 TR4 Sebele Aeromonas veronii MF716720.1 97.20 TB21 TR5 Block 5 Escherichia coli PP593518.1 97.56 TB22 TR5 Block 5 Aeromonas allosaccharophila FJ940841.1 96.64 TB23 TR5 Block 5 Macrococcus caseolyticus OK138709.1 96.65 TB24 TR5 Block 5 Kurthia gibsonii ON385944.1 96.48 TB25A TR5 Block 5 Macrococcus caseolyticus MG996517.1 96.00 TB25B TR6 Maruapula Pseudomonas aeruginosa MH844577.1 96.52 TB26 TR6 Maruapula Macrococcus caseolyticus OK138709.1 95.65 TB27 TR6 Maruapula Lysinibacillus sphaericus OR439054.1 95.94 TB28 TR6 Maruapula Lysinibacillus sphaericus OR439054.1 96.94 TB29 TR6 Maruapula Macrococcus caseolyticus MG996517.1 96.13 TB30 TR7 Partial Metalysinibacillus jejuensis OR304284.1 96.31 TB31 TR7 Partial Klebsiella pneumoniae MN691735.1 97.76 TB31A TR7 Partial Klebsiella pneumoniae MN691735.1 96.69 TB32 TR7 Partial Kurthia gibsonii KT260521.1 96.64 TB33 TR7 Partial Macrococcus caseolyticus PQ780466.1 97.24 TB34 TR8 Phase 4 Acinetobacter spp. KX986339.1 96.22 TB35 TR8 Phase 4 Exiguobacterium profundum KP236216.1 80.51 TB36 TR8 Phase 4 Macrococcus caseolyticus FJ263452.1 94.66 TB37 TR8 Phase 4 Aeromonas veronii CP121810.1 97.25 TB37A TR8 Phase 4 Macrococcus caseolyticus OK138709.1 96.73 TB38 TR9 G-west Acinetobacter lwoffii MN704528.1 97.16 TB39 TR9 G-west Klebsiella pneumoniae MN691731.1 95.86 TB40 TR9 G-west Klebsiella pneumoniae MN691722.1 98.28 TB41 TR9 G-west Enterobacter hormaechei PP593567.1 95.99 TB42 TR9 G-west Escherichia coli MN704424.1 95.68 TB43 TR10 Extension 4 Kurthia gibsonii OP755821.1 95.95 TB44 TR10 Extension 4 Macrococcus caseolyticus OR030438.1 96.42 TB45 TR10 Extension 4 Enterobacter huaxiensis PQ782370.1 96.90 TB46 TR10 Extension 4 Escherichia coli PQ591631.1 96.00 TB47 TR10 Extension 4 Enterobacter cloacae PP716548.1 96.28 TB48 TR11 Station Enterobacter cloacae PP716548.1 96.28 TB49 TR11 Station Escherichia coli MN094132.1 97.08 TB50 TR11 Station Escherichia coli OR686182.1 97.40 TB51 TR11 Station Enterobacter kobei MN691834.1 97.01 TB52 TR11 Station Citrobacter freundii OP788129.1 96.12 TB53 TR12 BBS mall Kurthia spp. OR975503.1 94.48 TB54 TR12 BBS mall Kurthia gibsonii EU794728.1 96.68 TB55 TR12 BBS mall Shewanella spp. MK156203.1 96.30 TB56 TR12 BBS mall Moellerella wisconsensis CP093255.1 92.29 TB57 TR12 BBS mall Aeromonas rivipollensis ON479618.1 97.35 TB58 TR13 G-west Lysinibacillus sphaericus OR439054.1 95.68 TB59 TR13 G-west Macrococcus caseolyticus PQ780466.1 96.66 TB60 TR13 G-west Uncultured Kurthia spp. PQ865866.1 96.16 TB61 TR13 G-west Macrococcus caseolyticus AP009484.1 96.10 TB62 TR13 G-west Kurthia gibsonii ON858500.1 96.98 TB64 TR14 G-west industrial Hafnia alvei MH620744.1 97.70 TB64A TR14 G-west industrial Kurthia gibsonii OQ406180.1 96.67 TB65 TR14 G-west industrial Kurthia gibsonii OQ406180.1 96.98 TB66 TR14 G-west industrial Aeromonas allosaccharophila KC202277.1 97.17 TB67 TR14 G-west industrial Lelliottia nimipressuralis MK548531.1 98.10 TB68 TR15 Block 8 Exiguobacterium profundum MK934558.1 96.46 TB69 TR15 Block 8 Macrococcus caseolyticus OK138709.1 92.20 TB70 TR15 Block 8 Kurthia gibsonii MW405845.1 97.20 TB71 TR15 Block 8 Kurthia gibsonii OQ406180.1 96.76 TB72 TR15 Block 8 Aeromonas sobria GQ232759.1 97.10 TB73 TR16 Old Naledi Aeromonas hydrophila MT730008.1 97.73 TB74 TR16 Old Naledi Aeromonas veronii KC166864.1 97.32 TB75 TR16 Old Naledi Aeromonas veronii OR673529.1 97.79 TB76 TR16 Old Naledi Pseudomonas aeruginosa LC096954.1 98.06 TB77 TR16 Old Naledi Pseudomonas aeruginosa LC096954.1 97.98 TB78A TR16 Old Naledi Pseudomonas aeruginosa HF572851.1 97.96 TB78B TR16 Old Naledi Aeromonas veronii OP800196.1 96.47 TB80 TR17 Block 3 Escherichia fergusonii MT573771.1 96.91 TB81 TR17 Block 3 Klebsiella pneumoniae MN691735.1 97.01 TB82 TR17 Block 3 Aeromonas dhakensis OR879320.1 96.25 TB83 TR17 Block 3 Macrococcus caseolyticus PQ780466.1 97.24 TB84 TR17 Block 3 Enterobacteriaceae bacterium KM021070.1 86.02 TB85 TR17 Block 3 Klebsiella pneumoniae MN691735.1 97.74 TB86 TR17 Block 3 Klebsiella pneumoniae MN691735.1 98.11 TB87 TR18 Block 6 Lelliottia spp. PQ691757.1 97.78 TB88 TR18 Block 6 Klebsiella pneumoniae MN691732.1 97.39 TB89 TR18 Block 6 Klebsiella pneumoniae MN691735.1 96.65 TB90 TR18 Block 6 Acinetobacter spp. GU384266.1 97.19 TB91 TR18 Block 6 Pluralibacter gergoviae PQ187599.1 97.15 TB92 TR18 Block 6 Escherichia coli CP058070.1 96.31 TB93 TR18 Block 6 Aeromonas veronii MT929293.1 95.92 TB94 TR18 Block 6 Hafnia paralvei CP083737.1 97.69 TB95 TR18 Block 6 Buttiauxella agrestis AP023184.1 96.79 TB96 TR18 Block 6 Pantoea spp. PP809958.1 85.93 TB97 TR18 Block 6 Escherichia coli ON631208.1 97.54 2.2 Sequence Alignment and Preprocessing A total of 99 pre-aligned nucleotide sequences were analyzed. Sequences were curated for uniformity and aligned using the ClustalW algorithm via the msa package in R [ 13 ]. Ambiguous bases and misalignments were filtered prior to analysis. All downstream analyses were conducted in R (version 4.3.1; R Core Team, 2023). Although sequences represented multiple bacterial genera, alignment was conducted to enable comparative assessment of overall sequence variability rather than species-specific evolutionary inference. 2.3 Nucleotide Composition and Polymorphism Analysis Nucleotide composition analysis was conducted using the seqinr [ 14 ] and Biostrings packages [ 15 ]. A sequence logo plot was generated using ggseqlogo to visualize base conservation and variability [ 16 ]. Polymorphic sites were identified with PopGenome [ 17 ], and a heatmap of polymorphism frequencies across nucleotide positions was visualized using pheatmap [ 18 ]. 2.4 Nucleotide Diversity and Sliding Window Analysis Nucleotide diversity (π) was estimated to quantify average sequence variation [ 7 ]. A sliding window analysis (100 bp window, 25 bp step) was implemented in PopGenome to detect localized regions of high and low diversity. Conserved regions with minimal diversity were highlighted on the diversity curve to identify stable genomic segments. 2.5 Haplotype Analysis Unique haplotypes were inferred and their frequencies calculated using the pegas package [ 19 ]. A bar plot visualizing the abundance of each haplotype provided insights into the richness and distribution of genetic types. In this study, haplotypes refer to unique 16S rRNA sequence variants across isolates rather than intraspecific haplotypes in the classical population genetic sense. 2.6 Haplotype Network A haplotype network was constructed using a minimum spanning network method in pegas to depict evolutionary relationships and mutational steps among haplotypes [ 19 ]. A minimum-spanning haplotype network was constructed using the haploNet() function from the pegas package (Paradis et al . 2023). This method calculates pairwise distances between haplotypes and connects them using the smallest number of mutational steps. The resulting network was visualized with node sizes representing haplotype frequency and edge lengths reflecting mutational distances. The Unweighted Pair Group Method with Arithmetic Mean (UPGMA) dendrogram was generated to explore hierarchical clustering among haplotypes. Pairwise genetic distances were computed using dist.dna() from the ape package, and the dendrogram was built using the upgma() function from the phangorn package [ 20 ]. Visualization was done using the ggtree package for clarity and annotation. To examine spatial patterns, a presence/absence matrix of haplotypes across sampling sites was constructed. This matrix was used to create a heatmap using the ggplot2 and reshape2 packages [ 21 ]. Each cell in the heatmap indicates whether a particular haplotype was found at a given location, providing a clear view of geographic distribution. 2.7 Principal Component Analysis (PCA) Principal Component Analysis was performed using adegenet [ 22 ] and factoextra [ 23 ]. PCA facilitated visualization of genetic clustering among isolates based on binary SNP matrices derived from the aligned sequences. 2.8 Minor Allele Frequency (MAF) Distribution Minor allele frequencies were calculated for all polymorphic sites to assess allele distribution and detect dominant variants using vcfR [ 24 ] and base R functions. 2.9 Genetic Distance and Heatmap Pairwise genetic distances were calculated using the Kimura 2-parameter model in ape [ 25 ]. The resulting distance matrix was visualized using pheatmap to detect clustering and isolate similarity. 2.10 Mismatch Distribution Mismatch distribution analysis was conducted using pegas to evaluate the frequency of pairwise nucleotide differences and infer possible demographic events such as population expansion [ 19 ]. 2.11 Tajima’s D and Sliding Window Neutrality Test Tajima’s D statistic was calculated across the alignment in sliding windows using PopGenome [ 17 ] to test for neutrality [ 8 ]. Regions exceeding ± 2 were flagged as potentially under selection or demographic influence. 2.12 Software and Supplementary Materials All analyses were performed using R version 4.3.1 (R Core Team, 2023). Full scripts used for data analysis are included in the Supplementary Materials. The following R packages were used: Biostrings [ 15 ], msa [ 13 ], DECIPHER [ 26 , 27 ], seqinr [ 14 ], ggseqlogo [ 16 ], PopGenome [ 17 ], pegas [ 19 ], adegenet [ 22 ], factoextra [ 23 ], vcfR [ 24 ], ape [ 25 ], and pheatmap [ 18 ]. All downstream genetic diversity and population structure analyses were conducted in R using validated population genetics and visualization packages to ensure reproducibility.lina 3. Results and Discussion 3.1 Nucleotide Diversity Nucleotide diversity (π) shows the average number of nucleotide differences per site between any two sequences in your alignment [ 28 ]. The mean nucleotide diversity (π) across the aligned microbial sequences was 0.7006, with values ranging from 0.0582 to 0.7498 (Fig. 1 ). The high π value largely reflects sequence divergence among multiple bacterial taxa rather than within-species polymorphism. These findings demonstrate substantial genetic diversity among bacterial 16S rRNA isolates recovered from beef tripe, reflecting heterogeneous microbial populations rather than direct evidence of pathogenicity. No positions along the aligned 16S rRNA gene region exceeded a diversity threshold of 0.8, suggesting that while diversity is elevated across the alignment, extreme hypervariable sites are absent. High nucleotide diversity (π) is often indicative of a large, genetically heterogeneous population, potentially arising from varied environmental sources or long-standing microbial colonization within the beef tripe ecosystem [ 7 , 9 ]. The heterogeneous nature of the tripe microbiota reflects possible exposure to multiple contamination points from the animal gut flora, processing environments, or water sources used during preparation. From a food safety perspective, high genetic diversity may complicate pathogen tracking and outbreak source attribution [ 29 ]. Moreover, it increases the likelihood of encountering strains with unique virulence factors or antimicrobial resistance genes [ 30 ], which emphasizes the need for comprehensive molecular surveillance. While the observed genetic diversity highlights potential food safety concerns, pathogenicity and antimicrobial resistance cannot be inferred from 16S rRNA data alone. Sliding window analysis is a commonly used method for studying the properties of molecular sequences: data are plotted as moving averages of a particular criterion, such as the number of nucleotide changes, for a window of a certain length slid along a sequence or sequence alignment [ 8 , 31 ]. The sliding window analysis revealed moderate fluctuations in nucleotide diversity along the alignment (Fig. 2 ). Some windows showed dips in diversity, interpreted as conserved regions possibly associated with housekeeping genes or essential structural loci. These conserved zones were marked in red on the diversity curve, helping to distinguish them from more variable intergenic or mobile element-associated regions. Such insights are essential for identifying candidate genes for typing or functional assays, as conserved regions may serve as PCR targets in diagnostic applications, while highly variable regions could be used in strain differentiation protocols [ 17 ]. 3.2 Haplotype Structure and Distribution A haplotype (short for haploid genotype) is a group of alleles (or DNA sequence variants) that are inherited together from a single parent and are located close to each other on the same chromosome or region of DNA [ 32 , 33 ]. Haplotypes help identify genetic variation and track evolutionary relationships while a haplotype network shows how these different sequence types are connected, may represent more centrally connected sequence variants within the network. A total of 99 unique haplotypes were identified among the sequences analyzed, with the most frequent haplotype found only twice (Fig. 3 ). This implies that nearly all isolates carry unique nucleotide signatures, further reinforcing the idea of a non-clonal, highly diverse population structure in the beef tripe microbial diversity. The haplotype frequency distribution was right-skewed, suggesting a population in which most genotypes are rare, a pattern typical of microbial communities in complex environments, where niche specialization and local adaptations drive diversification [ 22 ]. The haplotype network revealed minimal reticulation, consistent with neutral evolutionary dynamics and limited recombination. Isolates formed loosely connected nodes, reflecting micro-evolutionary divergence rather than tight clonal expansion, a hallmark of environments not under strong selective pressure [ 8 ]. The minimum-spanning haplotype network graphically illustrates the genetic relationships and mutational steps among haplotypes. Central haplotypes such as H5 ( Lysinibacillus sphaericus ), H43 ( Aeromonas caviae ), H54 ( Aeromonas allosaccharophila ), and H61 ( Moellerella wisconsensis ) appear as hubs (Fig. 3 ), each connected to several others by short mutational paths. This suggests they may represent ancestral or more common haplotypes within the population [ 34 ]. Peripheral haplotypes like H8 ( Kurthia gibsonii ), H28 ( Macrococcus caseolyticus ) and H84 ( Klebsiella pneumoniae ) show fewer connections, indicating more recent divergence or lower frequency. Notably, H72 ( Escherichia fergusonii ), H74 ( Escherichia spp.), and H93 ( Enterobacter hormaechei ) create branching structures, suggesting sub-lineages or potential site-specific evolutionary events (Fig. 3 ). Such star-like patterns around central haplotypes often imply recent population expansions which indicates heterogeneous sequence variation among isolates [ 35 ]. The UPGMA (Unweighted Pair Group Method with Arithmetic Mean) dendrogram further supports the relationships seen in the network but emphasizes clustering based on genetic distance (Fig. 4 ). Figure 4 shows localized variation in nucleotide diversity along the aligned sequences. Haplotypes such as H5 ( Lysinibacillus sphaericus ), H10 ( Kurthia gibsonii ) and H18 ( Macrococcus caseolyticus ) cluster together, suggesting close genetic affinity, potentially due to geographic proximity or shared evolutionary pressures (Fig. 4 ). Interestingly, H3 ( Lysinibacillus sphaericus ) and H27 ( Macrococcus caseolyticus ) diverge early, indicating significant genetic separation from the rest. This could imply ancient divergence or isolated mutation events. These dendrogram results can inform hypotheses about microevolution and even help predict phenotypic traits or pathogenic potential in microbial studies [ 36 ]. The UPGMA dendrogram based on genetic distance (0.0–0.7) revealed four major clades (I–IV), reflecting phylogenetic relationships among microbial haplotypes isolated from beef tripe. Clade I (0.0–0.2) was dominated by Escherichia coli (H1, H3, H5) and Klebsiella pneumoniae (H7), suggesting higher sequence similarity that may reflect shared ecological sources or taxonomic relatedness, possibly linked to shared environmental sources such as slaughterhouse contamination [ 37 ]. Clade II (0.2–0.4) consisted primarily of Aeromonas veronii (H12, H15) and A. caviae (H18), with sub-clustering indicative of strain-level diversity potentially related to adaptive traits such as antimicrobial resistance [ 38 , 39 ]. Clade III (0.4–0.6), featuring Pseudomonas aeruginosa (H22) and Macrococcus caseolyticus (H25), showed higher divergence, which may reflect ecological specialization. For instance, P. aeruginosa 's known ability to form biofilms on meat surfaces [ 40 ]. Finally, Clade IV (distance > 0.6) included the psychrotrophic Exiguobacterium profundum (H30), an outlier whose substantial divergence may be attributed to its adaptation to low-temperature environments such as cold storage [ 41 ]. The dominance of Clades I and II highlights potential hygiene lapses during tripe processing, whereas Clades III and IV underscore the survival of niche-adapted taxa even after post-processing interventions. This heatmap reveals the geographic occurrence of haplotypes across the sampled locations in Gaborone, Botswana. Haplotypes like H88 ( Klebsiella pneumoniae ) and H60 ( Aeromonas veronii ) are found in multiple locations (e.g., Block 5, G-west Industrial), suggesting possible environmental stability or widespread dissemination mechanisms, potentially via shared food handling practices or market routes. These patterns should be interpreted cautiously, as 16S rRNA data alone cannot resolve fine-scale spatial adaptation. In contrast, unique haplotypes like H3 ( Lysinibacillus sphaericus from Old Naledi) and H12 ( Kurthia gibsonii from Block 3) suggest local adaptations or micro-environmental influences that limit gene flow (Fig. 5 ). The distribution of H27 ( Macrococcus caseolyticus ) in multiple unrelated locations could indicate a more ubiquitous presence or historical spread. Spatial structuring of haplotypes highlights the importance of geography in shaping genetic diversity and can provide insights into contamination sources, especially in food safety research [ 42 , 43 ]. 3.3 Principal Component Analysis (PCA) Principal Component Analysis is about the creation of new set of uncorrelated variables from a set of possibly correlated variables [ 44 ]. PCA revealed a diffuse clustering pattern, with no clearly defined groups among isolates. This indicates that genetic variation is broadly distributed rather than structured into distinct subpopulations (Fig. 6 ). The lack of major clustering implies no dominant source of contamination, supporting the hypothesis of diverse microbial inputs during tripe handling and processing. In similar food matrices, PCA has successfully discriminated between strains based on geographic origin or processing method [ 45 ]. However, in this case, the absence of structure may reflect random colonization events, frequent microbial turnover, and a lack of ecological filtering in the tripe environment. 3.4 Minor Allele Frequencies (MAF) The Minor Allele Frequency (MAF) refers to the frequency at which the less common allele occurs at a specific genetic locus in a population. At any given single nucleotide polymorphism (SNP) site, there are typically two possible nucleotide variants, known as alleles [ 46 ]. For example, if the alleles at a particular site are adenine (A) and guanine (G), and within a population, allele A is observed in 80% of the sequences while allele G is present in 20%, then allele G is considered the minor allele because it is less frequent. The MAF, which quantifies the proportion of the less common allele at that site, would be 0.20 or 20% in this case. MAF is a key measure used in population genetics to assess the level of genetic variation at specific loci. Analysis of MAF showed that most alleles were rare, consistent with a recent population expansion or mutation accumulation without purging (Fig. 7 ). Rare alleles are especially important in microbial ecology as they may represent low-frequency sequence variants, some of which could warrant further functional investigation [ 47 ]. For food safety, this has direct implications. Rare but functional variants can go undetected in routine surveillance but may pose significant risks if positively selected under stress conditions, such as exposure to antibiotics or sanitizers. 3.5 Pairwise Genetic Distance Genetic distance refers to a measure of evolutionary divergence between DNA sequences from different individuals or isolates. It quantifies how different two sequences are based on the number of nucleotide substitutions between them [ 28 ]. In this study, a pairwise genetic distance matrix was generated from the aligned sequences of microbial isolates obtained from beef tripe. The results revealed generally high levels of divergence among the sequences, consistent with the high nucleotide and haplotype diversity observed. A heatmap visualization of the distance matrix showed no clear clustering, indicating the absence of dominant or closely related groups among the isolates (Fig. 8 ). As seen in Fig. 8 , E. coli isolate TB87 clusters distantly from other enteric bacteria, consistent with its unique haplotype. This suggests that the microbial population is highly heterogeneous, likely due to multiple contamination sources, such as animal gut flora, water, handling surfaces, or post-slaughter environments [ 29 ]. The lack of genetic clustering supports the hypothesis that the microbial population is shaped more by random colonization and microevolution than by clonal expansion or selective sweeps. In food microbiology, high genetic distance among isolates is a red flag, as it may reflect the presence of multiple strains, some of which could harbor novel antimicrobial resistance or virulence factors [ 29 , 30 ]. 3.6 Mismatch Distribution Mismatch distribution analysis examines the number of nucleotide differences (mismatches) between all pairs of sequences in a population. This distribution provides insight into the demographic history of the population, such as expansion, contraction, or bottlenecks [ 48 ]. In this study, the mismatch distribution produced a unimodal (single peak) curve, which is typically consistent with a unimodal pattern of sequence divergence across isolates (Fig. 9 ). This pattern suggests that the microbial community has undergone a recent increase in size, possibly due to favorable environmental conditions, such as nutrient availability in beef tripe or lack of effective cleaning and preservation during post-slaughter processing. A unimodal mismatch distribution is also consistent with neutral evolution, where mutations accumulate randomly rather than being driven by strong selective pressures. In contrast, multimodal or irregular distributions are often associated with population structure, bottlenecks, or balancing selection [ 49 ]. The genetic heterogeneity observed may influence bacterial persistence and adaptability within the food chain, but direct health risk requires confirmation through virulence and resistance profiling. Figure 9 : The mismatch distribution, showing the frequency of pairwise differences among all sequence pairs. This distribution is often used to infer demographic events such as population expansion or bottlenecks. A unimodal distribution typically suggests a recent population expansion, while a multimodal or ragged distribution may indicate demographic stability or structure. Given the multi-species nature of the dataset, population genetic statistics are interpreted here as indicators of overall sequence variability rather than evidence of demographic history. 3.7 Tajima’s D Analysis Tajima’s D is a widely used statistical test in population genetics that compares two measures of genetic variation: the number of segregating sites (S) and the average number of nucleotide differences (π) between sequences. It is used to detect departures from the neutral theory of evolution, where genetic variation is assumed to arise purely from random mutation and genetic drift, without the influence of selection [ 8 ]. In this study, Tajima’s D was calculated across the aligned sequences of microbial isolates from beef tripe using a sliding window approach (Fig. 9 ). This method enables the identification of localized regions of the genome that may be under different evolutionary pressures. The window size and step parameters (e.g., 100 bp window with 25 bp step) allowed for fine-scale resolution across the alignment. The results showed that Tajima’s D values did not exceed + 2 or drop below − 2, which indicates that no strong evidence of positive or purifying selection was observed in any region of the genome. Most Tajima’s D values hovered around zero, suggesting no strong deviation from neutral-like patterns of sequence variation across the aligned region. 3.8 Intensity of genetic variability and distribution Polymorphism refers to the occurrence of multiple genetic variants at particular loci within a population and is a key indicator of evolutionary potential and adaptation [ 7 ]. The heatmap of polymorphic sites across bacterial isolates from beef tripe reveals a broad distribution of sequence variation, with diverse bacterial taxa exhibiting extensive nucleotide polymorphism across the alignment. Each vertical band represents a nucleotide position, and the variability in color intensity reflects base substitutions among isolates. Notably, isolates such as Aeromonas veronii (e.g., TB1, TB9, TB35), Escherichia coli (e.g., TB2, TB42, TB87), and Klebsiella pneumoniae (e.g., TB34, TB44, TB88) show continuous and dense polymorphic patterns, suggesting that these species harbor high intra-species genetic diversity (Fig. 10 ). In contrast, isolates such as Kurthia gibsonii (TB27, TB84) and Macrococcus caseolyticus (TB33, TB40, TB85) display patchier and more conserved profiles, implying either a more recent evolutionary origin, lower mutation rates, or more limited genetic input. Interestingly, some species like Pseudomonas aeruginosa (TB46, TB61) and Enterobacter cloacae (TB63) demonstrate mixed profiles, with regions of both high and low variability, which may point to recombination events or selective pressures acting differentially across the genome [ 47 ]. The overall heatmap does not show dominant conserved blocks, indicating no universal loci under purifying selection, further supporting the results of Tajima’s D analysis that showed near-neutral evolution. The wide variation across both Gram-negative (e.g., Klebsiella , Pseudomonas , Aeromonas ) and Gram-positive genera (e.g., Macrococcus , Kurthia ) suggests that the microbial community inhabiting beef tripe is shaped more by ecological diversity and environmental exposure than by vertical transmission or clonal expansion [ 30 ]. From a food safety perspective, such diversity is a double-edged sword. On one hand, it reduces the dominance of any single pathogenic strain, but on the other hand, it increases the likelihood that rare but high-risk variants, such as those carrying resistance or virulence determinants, may be overlooked by culture-dependent or marker-based surveillance systems. The heatmap reinforces the need for whole-genome or metagenomic surveillance to capture the full spectrum of microbial diversity in food products, particularly in under-regulated environments like informal butcheries. This study provides a comprehensive molecular characterization of microbial populations isolated from beef tripe sold in butcheries in Gaborone, Botswana, using a suite of population genetic tools. The analyses revealed high levels of nucleotide and haplotype diversity, with 99 unique haplotypes and a mean nucleotide diversity (π) of 0.7006, indicating a genetically diverse and heterogeneous microbial community. The polymorphism heatmap and genetic distance matrices further illustrated this diversity, with no clear clustering or dominant lineages among isolates such as Aeromonas veronii , Klebsiella pneumoniae , and Escherichia coli . These findings reflect microbiota shaped by multiple sources of contamination and neutral evolutionary dynamics, as supported by near-zero Tajima’s D values and a unimodal mismatch distribution, both of which suggest recent population expansion without strong selective pressures. Haplotypes like H5, H43, and H54 emerge as genetically central and geographically widespread, potentially serving as molecular markers for monitoring foodborne pathogens in beef tripe. The presence of unique, location-specific haplotypes such as H3, H12, and H84 underscores the importance of localized surveillance to detect emergent strains. While the lack of evidence for positive or purifying selection may appear reassuring from a food safety standpoint, the presence of low-frequency variants (as shown by minor allele frequency analysis) highlights the potential risk posed by rare but potentially harmful strains, including those with antimicrobial resistance or virulent genes. These variants may persist and expand under changing environmental or handling conditions. The diffuse clustering pattern observed in PCA, coupled with widespread polymorphism across the alignment, further supports the hypothesis of non-clonal, environment-driven population dynamics. 3.9 Study Limitations This study has several limitations that should be considered when interpreting the findings. First, the analyses were conducted on a multi-species dataset comprising bacterial isolates from diverse genera and families. While this approach provides a broad overview of sequence heterogeneity associated with beef tripe, it limits the application of classical population genetic frameworks, which typically assume a single, panmictic population with a shared evolutionary history. As a result, metrics such as nucleotide diversity, haplotype structure, Tajima’s D, and mismatch distributions are best interpreted as descriptive indicators of overall genetic variability rather than definitive evidence of demographic processes or selective pressures operating within individual species. Second, the study relied on partial 16S rRNA gene sequences. Although this marker is widely used for bacterial identification and comparative diversity analyses, it represents a relatively conserved genomic region subject to functional constraint. This limits resolution for fine-scale evolutionary inference, strain-level discrimination, and the detection of recombination or adaptive selection. Consequently, signals of neutrality or population expansion inferred from 16S rRNA data should be interpreted cautiously, as they may reflect taxonomic breadth and interspecific divergence rather than true within-species evolutionary dynamics. Third, the absence of functional gene analyses, including antimicrobial resistance and virulence determinants, restricts the ability to directly link observed genetic diversity to phenotypic risk. While the presence of rare variants and high overall heterogeneity raises important food safety considerations, functional validation through whole-genome sequencing or targeted gene screening is required to confirm their public health relevance. Despite these limitations, the study provides valuable baseline molecular data on beef tripe–associated microbiota in Botswana and highlights the importance of integrative genetic surveillance as a foundation for future high-resolution investigations. 4. Conclusion These findings underscore the importance of integrating molecular surveillance into food safety protocols, particularly in informal meat supply chains where regulatory oversight may be limited. Monitoring microbial diversity at the genomic level allows for early detection of emerging threats, enhances epidemiological tracing, and informs risk management strategies. Because analyses were based on partial 16S rRNA gene sequences from multiple bacterial taxa, evolutionary interpretations should be viewed as descriptive indicators of diversity rather than definitive population genetic inferences. In conclusion, while beef tripe remains a culturally and nutritionally important food source, it also represents a complex and dynamic reservoir of microbial diversity that warrants continued attention, regulation, and molecular risk assessment. Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable Competing interests The authors declare no competing interests. Funding Funds for sequencing were provided by the Botswana University of Agriculture and Natural Resources Graduate Office. Author Contribution K.M. - Conceptualization, Methodology, Investigation, Formal analysis, Writing – Original DraftM.D.S - Investigation, Data curation, Writing – Review & EditingN.Z. - Writing – Review & EditingE.M. - Writing – Review & EditingJ.M.- Writing – Review & EditingM.H.D.M - Writing – Review & EditingG.M. - Writing – Review & Editing & Supervision Acknowledgement A special thank you to Botswana University of Agriculture and Natural Resources (BUAN) for the sequence’s funds and Inqaba Biotech (Pretoria, South Africa) for sequencing the data. Data Availability All 16S rRNA gene sequences generated during this study have been deposited and published in the NCBI GenBank database under accession numbers PV686902–PV686997. References Berrada H, Font G. HANDBOOK OF Analysis of Edible Animal By-Products ; NOLLET, L.M.L., TOLDRÁ, F., Eds.; CRC Press, 2011; ISBN 9781439803608. Bensink JC, Dobrenov B, Mulenga MP, Bensink ZS, McKee JJ. 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In Global Water Pathogen Project ; 2019; Vol. 880. Stellato G, De Filippis F, La Storia A, Ercolini D. Coexistence of Lactic Acid Bacteria and Potential Spoilage Microbiota in a Dairy Processing Environment. Appl Environ Microbiol. 2015;81:7893–904. 10.1128/AEM.02294-15 . Vishnivetskaya TA, Kathariou S, Tiedje JM. The Exiguobacterium Genus: Biodiversity and Biogeography The Exiguobacterium Genus : Biodiversity and Biogeography. 2009, 10.1007/s00792-009-0243-5 Excoffier L, Lischer HEL, Arlequin Suite. Ver 3.5: A New Series of Programs to Perform Population Genetics Analyses under Linux and Windows. Mol Ecol Resour. 2010;10:564–7. 10.1111/j.1755-0998.2010.02847.x . Ravel A, Smolina E, Sargeant JM, Cook A, Marshall B, Fleury MD, Pollari F. Seasonality in Human Salmonellosis: Assessment of Human Activities and Chicken Contamination as Driving Factors. Foodborne Pathog Dis. 2010;7:785–94. 10.1089/fpd.2009.0460 . Saha D, Manickavasagan A. 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Theory - Proc. 2021, 2021 - July , 2912–2917. 10.1109/ISIT45174.2021.9517732 Rogers AR, Harpending H. Population Growth Makes Waves in the Distribution of Pairwise Genetic Differences ’. 1992, 9 , 552–69. Additional Declarations No competing interests reported. Supplementary Files supplementarymaterial.xlsx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 06 Apr, 2026 Reviewers agreed at journal 23 Feb, 2026 Reviewers invited by journal 17 Feb, 2026 Editor assigned by journal 27 Jan, 2026 Submission checks completed at journal 27 Jan, 2026 First submitted to journal 27 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8590956","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":593761300,"identity":"0bdc07f6-14a2-4b90-b962-c48686543b7a","order_by":0,"name":"Koketso Motlhanka","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIiWNgGAWjYBACfmbGhgMJBhJyjO09MDEe/Fok25sbD3yosDBm7jlDpBaDM8ebD844U5HYPiOHSC0MNxIbDvO2SST2znx7TOLnDgZ5/gbeYxL4dDDOgGgxnjk7L02y9wyD4YwDfGl4tTBLQLTIbpydYybB28bAuIGBx+wGPi1sUC2M+2+eMZP828ZgT1ALD8/BBqD3JRQbZ/CYSQNtSSSoRYK9sQEYyBLGjD15ydaybRLJMw7zpf/Ap8X+MPvjDwkGdcCoPHvw5ts2G9v+9t7DBvi0YNgKDBFS1I+CUTAKRsEowAoAY0VOSVkwKnkAAAAASUVORK5CYII=","orcid":"","institution":"Botswana University of Agriculture and Natural Resources","correspondingAuthor":true,"prefix":"","firstName":"Koketso","middleName":"","lastName":"Motlhanka","suffix":""},{"id":593761301,"identity":"ddb26ed2-50de-498e-b4a6-f5a95ac264e9","order_by":1,"name":"Modiri D. Setlhoka","email":"","orcid":"","institution":"Botswana University of Agriculture and Natural Resources","correspondingAuthor":false,"prefix":"","firstName":"Modiri","middleName":"D.","lastName":"Setlhoka","suffix":""},{"id":593761302,"identity":"25edcd2e-4547-48dd-8aad-8d59f73d83d9","order_by":2,"name":"Nerve Zhou","email":"","orcid":"","institution":"Botswana International University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Nerve","middleName":"","lastName":"Zhou","suffix":""},{"id":593761303,"identity":"8e7e14b3-dd5b-4d65-8864-c9ac49153657","order_by":3,"name":"Ernest Mochankana","email":"","orcid":"","institution":"Botswana University of Agriculture and Natural Resources","correspondingAuthor":false,"prefix":"","firstName":"Ernest","middleName":"","lastName":"Mochankana","suffix":""},{"id":593761304,"identity":"9e783f63-dc19-4ae1-bd79-9140795c0a41","order_by":4,"name":"James Machete","email":"","orcid":"","institution":"Botswana University of Agriculture and Natural Resources","correspondingAuthor":false,"prefix":"","firstName":"James","middleName":"","lastName":"Machete","suffix":""},{"id":593761305,"identity":"f07b587f-ecb9-49bc-9368-ea99d685200f","order_by":5,"name":"Molebeledi H. D. Mareko","email":"","orcid":"","institution":"Botswana University of Agriculture and Natural Resources","correspondingAuthor":false,"prefix":"","firstName":"Molebeledi","middleName":"H. D.","lastName":"Mareko","suffix":""},{"id":593761306,"identity":"bf9cb848-8f36-4d08-bec5-cfbac81cd8ce","order_by":6,"name":"Goitseone Malambane","email":"","orcid":"","institution":"Botswana University of Agriculture and Natural Resources","correspondingAuthor":false,"prefix":"","firstName":"Goitseone","middleName":"","lastName":"Malambane","suffix":""}],"badges":[],"createdAt":"2026-01-13 10:53:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8590956/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8590956/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103052236,"identity":"39c96d31-eaff-4622-96fc-6da697f22192","added_by":"auto","created_at":"2026-02-20 08:05:50","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":65657,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNucleotide diversity (π) at each position across your alignment. \u003c/strong\u003eMost positions show high diversity, with a mean value of 0.70, indicating substantial variation among your isolates. No positions exceed a diversity of 0.8, and the minimum observed is about 0.06. This pattern suggests a generally diverse population with few highly conserved sites.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8590956/v1/57d44d7782a0bcb189abe0d9.jpg"},{"id":103052174,"identity":"0de915e4-c993-439d-8a7d-a1a45784c04c","added_by":"auto","created_at":"2026-02-20 08:05:39","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":82433,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSliding window analysis of nucleotide diversity. \u0026nbsp;\u003c/strong\u003eThe plot highlights these conserved regions in red on the nucleotide diversity curve, making it easy to see where the most conserved stretches are located along your sequence.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8590956/v1/b4de65a28b997e019362c394.jpg"},{"id":103052099,"identity":"16ff8910-b73f-465e-b355-9becdbcb8bf3","added_by":"auto","created_at":"2026-02-20 08:05:16","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":86705,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe network diagram shows each haplotype as a blue node; node size reflects the number of isolates carrying that haplotype (all singletons here, so sizes are similar). \u003c/strong\u003eEdges connect haplotypes by the minimum-spanning tree, labelled with the Hamming distance (number of nucleotide differences). Closer nodes are genetically more similar. Because every isolate formed its own haplotype, the graph is a sparse star-like web where most edges are long (large distances). This confirms the high sequence diversity suggested earlier.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8590956/v1/0f2a515d0d0539a48d82b916.jpg"},{"id":103052476,"identity":"cfe53b52-3ad6-4482-b604-3278b2e2e7a0","added_by":"auto","created_at":"2026-02-20 08:06:25","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":197284,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eUPGMA dendrogram clustering haplotypes by genetic distance, illustrating how they group into related clades.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8590956/v1/1571d7f71c3a655c963cbfa7.jpg"},{"id":103052240,"identity":"3d8ab3a5-16b7-4062-b380-333fc69f4aee","added_by":"auto","created_at":"2026-02-20 08:05:52","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":197737,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHeatmap of haplotype distribution across sampling areas.\u003c/strong\u003e Haplotypes like H88 (\u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e) and H60 (\u003cem\u003eAeromonas veronii\u003c/em\u003e) are found in multiple locations while unique haplotypes include H3 (\u003cem\u003eLysinibacillus sphaericus\u003c/em\u003e) and H12 (\u003cem\u003eKurthia gibsonii\u003c/em\u003e).\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8590956/v1/698ff00c6841d01a17c49a9c.jpg"},{"id":103052101,"identity":"f60befc2-88d2-471b-bbab-ebb6576795ad","added_by":"auto","created_at":"2026-02-20 08:05:16","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":38492,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePCA plot of the aligned nucleotide sequences.\u003c/strong\u003e Each point represents an isolate, projected onto the first two principal components (PC1 and PC2), which capture the major axes of genetic variation in the dataset\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8590956/v1/b62d3a09c4870057fabea09e.jpg"},{"id":103052100,"identity":"39057f61-f848-4277-9bbd-696cff71f61e","added_by":"auto","created_at":"2026-02-20 08:05:16","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":54019,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe histogram of minor allele frequencies (MAF) across all sites in your alignment.\u003c/strong\u003e This plot shows the distribution of the frequency of the less common allele at each polymorphic site. Most sites have low MAF, which is typical for population genetic data, indicating that most variants are rare.\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8590956/v1/f637aa50414762fd98ef2a83.jpg"},{"id":103504217,"identity":"0cf3b530-e433-41e9-8ab7-d13ba8a9eb74","added_by":"auto","created_at":"2026-02-26 13:18:29","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":237985,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHeatmap of pairwise genetic distances showing high sequence divergence among microbial isolates. \u003c/strong\u003eThis heatmap illustrates extensive genetic divergence among the microbial isolates, with no clear clustering or dominant lineages. The absence of structured patterns indicates a heterogeneous microbial community shaped by multiple contamination sources rather than clonal expansion.\u003c/p\u003e","description":"","filename":"8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8590956/v1/27b97d8e2d0ef88c34c3f30d.jpg"},{"id":103052483,"identity":"cda3017b-d39e-401a-818e-6c5915f78083","added_by":"auto","created_at":"2026-02-20 08:06:29","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":31442,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe mismatch distribution, showing the frequency of pairwise differences among all sequence pairs. \u003c/strong\u003eThis distribution is often used to infer demographic events such as population\u003c/p\u003e","description":"","filename":"9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8590956/v1/1ea52b3eb1d5f17c5cc5ed17.jpg"},{"id":103052473,"identity":"78e73617-bc43-4764-aa08-e0ce77d93833","added_by":"auto","created_at":"2026-02-20 08:06:25","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":52768,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 9: The plot above shows Tajima's D values across the alignment in sliding windows.\u003c/strong\u003e No regions exceeded the threshold (D\u0026gt;2), so there is no strong evidence of positive selection in these regions based on this analysis.\u003c/p\u003e","description":"","filename":"10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8590956/v1/d61192d0267fd350a04c8d95.jpg"},{"id":103052388,"identity":"731aee98-78f0-464f-af36-2a9e40032401","added_by":"auto","created_at":"2026-02-20 08:06:10","extension":"jpg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":270105,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 10: Polymorphism heatmap showing genome-wide nucleotide variability across microbial isolates. \u003c/strong\u003eThe heatmap reveals widespread polymorphism across isolates, with both Gram-negative and Gram-positive taxa exhibiting high levels of nucleotide variability. The lack of universally conserved regions supports neutral evolutionary dynamics and underscores the complexity of microbial diversity in beef tripe.\u003c/p\u003e","description":"","filename":"11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8590956/v1/ab72acb1159f1842893fd6c8.jpg"},{"id":104397744,"identity":"96993975-6a52-4318-8a0a-86b587873390","added_by":"auto","created_at":"2026-03-11 11:55:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3324643,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8590956/v1/da309dd1-39d6-4179-9833-910a3c6c14b1.pdf"},{"id":103052480,"identity":"1b47f6ac-5ecb-48d8-a1ed-5c8c47ecd8c5","added_by":"auto","created_at":"2026-02-20 08:06:27","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":53998,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarymaterial.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8590956/v1/44967853cf42fb39270a501f.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genetic diversity and sequence polymorphism of bacterial 16S rRNA isolates from beef tripe from Gaborone, Botswana","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eBeef tripe, the edible lining of the stomach of ruminants, is a widely consumed protein source in many developing countries, including Botswana [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. It is particularly popular in informal food systems due to its affordability and cultural acceptance. However, tripe is highly perishable and presents unique challenges in terms of food hygiene and safety [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Its complex surface texture and rich nutrient profile [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] provide an ideal environment for microbial colonization, making it a potential vehicle for foodborne pathogens and spoilage organisms if not properly handled. In food safety microbiology, assessing the microbial quality of animal-derived products is crucial for identifying potential risks to consumers [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Traditional microbiological methods often focus on the presence or absence of specific pathogens, but such approaches can overlook the broader genetic landscape of microbial populations that contribute to contamination, persistence, or resistance [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Modern molecular tools provide a more comprehensive understanding by targeting the genetic diversity within microbial communities.\u003c/p\u003e \u003cp\u003eNucleotide sequence-based analyses are particularly powerful in this regard. They allow researchers to assess not only the taxonomic composition of microbial communities but also their genetic variation, evolutionary trajectories, and functional potential. Nucleotide diversity (π), first described by Nei (1979), measures the average pairwise difference between sequences and reflects the extent of polymorphism in a population. High nucleotide diversity suggests the presence of a genetically varied population, which could include strains with distinct virulence factors, antimicrobial resistance (AMR) genes, or enhanced survival mechanisms. Another essential molecular tool in population genetics is Tajima\u0026rsquo;s D statistic [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], which compares the observed nucleotide diversity with the number of segregating sites to explore patterns consistent with neutrality or heterogeneity in sequence variation. Significant deviations from zero may indicate selection pressures acting on microbial populations. In foodborne microorganisms, such deviations may reflect adaptation to stressors such as antimicrobial treatments, host immune responses, or food processing environments. In contrast, non-significant values support the neutral theory of molecular evolution [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], suggesting that observed variation is largely shaped by mutation and genetic drift rather than selection.\u003c/p\u003e \u003cp\u003eFrom a food safety perspective, understanding the underlying population dynamics of microorganisms in beef tripe is essential for several reasons [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. First, genetically diverse microbial communities may harbor subpopulations with increased virulence or resistance potential, complicating control measures. Second, polymorphic markers can be used in molecular tracing to determine sources and routes of contamination within the food chain. Finally, such data contributes to risk assessment models that inform national food safety regulations and standards. Genetic heterogeneity within foodborne bacterial populations is increasingly recognized as a key determinant of persistence, transmission, and adaptation along the food chain. Genetic variation among food-associated bacteria highlights their ability to persist, adapt, and disseminate within food systems, making genomic diversity an important dimension of food safety surveillance. Despite its widespread consumption, beef tripe remains poorly characterized at the molecular level in southern Africa. Most studies focus on its microbial load rather than the genetic characteristics of its microbial residents. In Botswana, where foodborne illness remains a public health concern, there is a need for integrated molecular surveillance of high-risk animal products. This study aims to address that gap by characterizing the genetic polymorphism of microbial isolates from beef tripe using nucleotide sequence data by focusing on characterizing sequence-level diversity rather than assessing virulence or antimicrobial resistance. We assessed nucleotide diversity, haplotype frequencies, and Tajima\u0026rsquo;s D values to determine the extent of genetic variation and evolutionary pressures acting upon these microbial populations. The findings will enhance our understanding of microbial ecology in beef tripe and inform food safety monitoring and control strategies.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Sample Collection and DNA Sequencing\u003c/h2\u003e \u003cp\u003eThe beef tripe samples stomach (\u003cem\u003emogodu\u003c/em\u003e) and reticulum (\u003cem\u003entshothwane\u003c/em\u003e) were sampled from local butcheries in the South-east districts of Botswana. A total of 30 tripe samples were collected from 18 different butcheries around Gaborone. The butcheries were selected through a convenience sampling approach based on geographical spread within Gaborone, accessibility, and willingness to participate, which is a commonly accepted strategy for exploratory food microbiology studies in resource-limited settings. The samples were collected from different cattle and different butcheries. Each sample was placed in a separate sterile plastic bag and placed on ice in a cooler box which was immediately transported to the laboratory. Upon arrival at the microbiology laboratory, samples were held at 4\u0026deg;C and examined and analyzed within 2 hrs from the time of purchase. The \u003cem\u003emogodu\u003c/em\u003e and \u003cem\u003entshothwane\u003c/em\u003e were analyzed collectively. Although the two originate from different bovine stomach compartments, in Botswana butcheries they are typically processed, displayed, and stored together, subjected to identical handling and environmental conditions. Because our study focused on overall microbial quality as encountered by consumers and because separating them would have resulted in insufficient sample sizes for meaningful statistical comparison, we combined them in the analysis. Although retail refrigeration was observed, storage temperatures were not recorded, representing a limitation of the sampling process.\u003c/p\u003e \u003cp\u003eGenomic DNA was extracted from isolated bacteria (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) using the GenElute \u0026trade; Bacterial Genomic DNA Kit (Sigma-Aldrich, USA) according to manufacturer\u0026rsquo;s instructions. PCR amplification was performed using ProFlex PCR Systems (Applied Biosystems, USA) in a 20 \u0026micro;L reaction volume containing 2.5 \u0026micro;L 10\u0026times; PCR buffer, 2.0 \u0026micro;M of each primer, 0.2 mM dNTPs, and 1.25 U Taq DNA polymerase (Takara Bio Inc., Japan) using 16S rDNA and a pair of universal primers, 27F (5\u0026prime;-AGAGTTTGATCCTGGCTCAG-3\u0026prime;) and 1492R (5\u0026prime;-TACGGYTACCTTGTTACGACTT-3\u0026prime;) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The following cycling conditions: initial denaturation at 98\u0026deg;C for 30 s, 35 cycles of denaturation (98\u0026deg;C for 30 s), annealing (48\u0026deg;C for 30 s), and extension (68\u0026deg;C for 30 s), a final extension step at 72\u0026deg;C for 7 min and held at 4\u0026deg;C \u0026infin;. Negative controls in which the template DNA in the PCR mixture was replaced with sterile distilled water were also included. All amplicons were purified using a QIAquick PCR product purification kit (Qiagen, GmBH, Germany) according to manufacturer\u0026acute;s instructions. The amplicons were sequenced by Inqaba Biotech (Pretoria, South Africa). SnapGene\u0026reg; Viewer software ver. 8.0.2 (GSL Biotech) sequence editing tool was used to generate contiguous sequences (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.snapgene.com\u003c/span\u003e\u003cspan address=\"http://www.snapgene.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Species identification was done by using a BLAST nucleotide sequence analysis tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://blast.ncbi.nlm.nih.gov/Blast.cgi?PROGRAM=blastn\u0026amp;PAGE_TYPE=BlastSearch\u0026amp;LINK_LOC=blasthome\u003c/span\u003e\u003cspan address=\"https://blast.ncbi.nlm.nih.gov/Blast.cgi?PROGRAM=blastn\u0026amp;PAGE_TYPE=BlastSearch\u0026amp;LINK_LOC=blasthome\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows all the identified microbes and the sampling locations from the beef tripe samples.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eBacterial isolates and their identities from tripe sampled different butcheries in the South-east district of Botswana\u003c/b\u003e [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIsolate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIsolate code\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSampling location\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIsolate name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAccession number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePercentage identity (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLedumang\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eAeromonas caviae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMG737572.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e96.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLedumang\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKY780341.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e97.54%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLedumang\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eEnterobacteriaceae bacterium\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMT775448.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e87.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLedumang\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eAeromonas sobria\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGQ232759.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e97.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLedumang\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMN208228.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e97.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePhakalane\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eMoellerella wisconsensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCP093242.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e97.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePhakalane\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMN094132.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e98.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePhakalane\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eAeromonas veronii\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eON203104.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e97.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePhakalane\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eAeromonas veronii\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAY928480.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e92.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePhakalane\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eAeromonas allosaccharophila\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMK548513.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e97.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePhase 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR660422.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e96.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePhase 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCP057829.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e97.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePhase 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eAeromonas veronii\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eON203104.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e97.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePhase 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eKlebsiella oxytoca\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eON556594.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e97.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePhase 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eAeromonas sobria\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGQ232759.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e96.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSebele\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eEscherichia\u003c/em\u003e spp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMT914503.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e98.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSebele\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eRaoultella\u003c/em\u003e spp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDQ470482.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e95.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSebele\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eAeromonas caviae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKP260655.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e97.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSebele\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eAeromonas\u003c/em\u003e spp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKC182747.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e97.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSebele\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eAeromonas veronii\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMF716720.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e97.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBlock 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePP593518.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e97.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBlock 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eAeromonas allosaccharophila\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFJ940841.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e96.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBlock 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eMacrococcus caseolyticus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOK138709.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e96.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBlock 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eKurthia gibsonii\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eON385944.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e96.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB25A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBlock 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eMacrococcus caseolyticus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMG996517.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e96.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB25B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMaruapula\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMH844577.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e96.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMaruapula\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eMacrococcus caseolyticus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOK138709.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e95.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMaruapula\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eLysinibacillus sphaericus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR439054.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e95.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMaruapula\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eLysinibacillus sphaericus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR439054.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e96.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMaruapula\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eMacrococcus caseolyticus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMG996517.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e96.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePartial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eMetalysinibacillus jejuensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR304284.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e96.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePartial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMN691735.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e97.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB31A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePartial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMN691735.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e96.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePartial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eKurthia gibsonii\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKT260521.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e96.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePartial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eMacrococcus caseolyticus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePQ780466.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e97.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePhase 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eAcinetobacter\u003c/em\u003e spp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKX986339.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e96.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePhase 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eExiguobacterium profundum\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKP236216.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e80.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePhase 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eMacrococcus caseolyticus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFJ263452.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e94.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePhase 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eAeromonas veronii\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCP121810.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e97.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB37A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePhase 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eMacrococcus caseolyticus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOK138709.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e96.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG-west\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eAcinetobacter lwoffii\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMN704528.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e97.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG-west\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMN691731.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e95.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG-west\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMN691722.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e98.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG-west\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eEnterobacter hormaechei\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePP593567.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e95.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG-west\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMN704424.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e95.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExtension 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eKurthia gibsonii\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOP755821.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e95.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExtension 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eMacrococcus caseolyticus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR030438.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e96.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExtension 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eEnterobacter huaxiensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePQ782370.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e96.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExtension 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePQ591631.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e96.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExtension 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eEnterobacter cloacae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePP716548.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e96.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eEnterobacter cloacae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePP716548.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e96.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMN094132.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e97.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR686182.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e97.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eEnterobacter kobei\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMN691834.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e97.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eCitrobacter freundii\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOP788129.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e96.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBBS mall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eKurthia\u003c/em\u003e spp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR975503.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e94.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBBS mall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eKurthia gibsonii\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEU794728.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e96.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBBS mall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eShewanella spp.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMK156203.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e96.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBBS mall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eMoellerella wisconsensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCP093255.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e92.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBBS mall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eAeromonas rivipollensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eON479618.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e97.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG-west\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eLysinibacillus sphaericus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR439054.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e95.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG-west\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eMacrococcus caseolyticus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePQ780466.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e96.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG-west\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eUncultured Kurthia spp.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePQ865866.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e96.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG-west\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eMacrococcus caseolyticus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAP009484.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e96.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG-west\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eKurthia gibsonii\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eON858500.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e96.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG-west industrial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eHafnia alvei\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMH620744.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e97.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB64A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG-west industrial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eKurthia gibsonii\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOQ406180.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e96.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG-west industrial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eKurthia gibsonii\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOQ406180.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e96.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG-west industrial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eAeromonas allosaccharophila\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKC202277.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e97.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG-west industrial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eLelliottia nimipressuralis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMK548531.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e98.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBlock 8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eExiguobacterium profundum\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMK934558.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e96.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBlock 8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eMacrococcus caseolyticus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOK138709.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e92.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBlock 8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eKurthia gibsonii\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMW405845.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e97.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBlock 8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eKurthia gibsonii\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOQ406180.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e96.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBlock 8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eAeromonas sobria\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGQ232759.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e97.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOld Naledi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eAeromonas hydrophila\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMT730008.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e97.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOld Naledi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eAeromonas veronii\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKC166864.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e97.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOld Naledi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eAeromonas veronii\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR673529.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e97.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOld Naledi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLC096954.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e98.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOld Naledi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLC096954.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e97.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB78A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOld Naledi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHF572851.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e97.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB78B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOld Naledi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eAeromonas veronii\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOP800196.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e96.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBlock 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eEscherichia fergusonii\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMT573771.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e96.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBlock 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMN691735.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e97.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBlock 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eAeromonas dhakensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR879320.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e96.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBlock 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eMacrococcus caseolyticus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePQ780466.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e97.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBlock 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eEnterobacteriaceae bacterium\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKM021070.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e86.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBlock 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMN691735.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e97.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBlock 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMN691735.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e98.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBlock 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eLelliottia\u003c/em\u003e spp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePQ691757.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e97.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBlock 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMN691732.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e97.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBlock 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMN691735.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e96.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBlock 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eAcinetobacter\u003c/em\u003e spp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGU384266.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e97.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBlock 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ePluralibacter gergoviae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePQ187599.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e97.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBlock 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCP058070.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e96.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBlock 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eAeromonas veronii\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMT929293.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e95.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBlock 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eHafnia paralvei\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCP083737.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e97.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBlock 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eButtiauxella agrestis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAP023184.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e96.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBlock 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ePantoea\u003c/em\u003e spp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePP809958.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e85.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBlock 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eON631208.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e97.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Sequence Alignment and Preprocessing\u003c/h2\u003e \u003cp\u003eA total of 99 pre-aligned nucleotide sequences were analyzed. Sequences were curated for uniformity and aligned using the ClustalW algorithm via the msa package in R [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Ambiguous bases and misalignments were filtered prior to analysis. All downstream analyses were conducted in R (version 4.3.1; R Core Team, 2023). Although sequences represented multiple bacterial genera, alignment was conducted to enable comparative assessment of overall sequence variability rather than species-specific evolutionary inference.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Nucleotide Composition and Polymorphism Analysis\u003c/h2\u003e \u003cp\u003eNucleotide composition analysis was conducted using the seqinr [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] and Biostrings packages [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. A sequence logo plot was generated using ggseqlogo to visualize base conservation and variability [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Polymorphic sites were identified with PopGenome [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], and a heatmap of polymorphism frequencies across nucleotide positions was visualized using pheatmap [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Nucleotide Diversity and Sliding Window Analysis\u003c/h2\u003e \u003cp\u003eNucleotide diversity (π) was estimated to quantify average sequence variation [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. A sliding window analysis (100 bp window, 25 bp step) was implemented in PopGenome to detect localized regions of high and low diversity. Conserved regions with minimal diversity were highlighted on the diversity curve to identify stable genomic segments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Haplotype Analysis\u003c/h2\u003e \u003cp\u003eUnique haplotypes were inferred and their frequencies calculated using the pegas package [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. A bar plot visualizing the abundance of each haplotype provided insights into the richness and distribution of genetic types. In this study, haplotypes refer to unique 16S rRNA sequence variants across isolates rather than intraspecific haplotypes in the classical population genetic sense.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Haplotype Network\u003c/h2\u003e \u003cp\u003eA haplotype network was constructed using a minimum spanning network method in pegas to depict evolutionary relationships and mutational steps among haplotypes [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. A minimum-spanning haplotype network was constructed using the haploNet() function from the pegas package (Paradis \u003cem\u003eet al\u003c/em\u003e. 2023). This method calculates pairwise distances between haplotypes and connects them using the smallest number of mutational steps. The resulting network was visualized with node sizes representing haplotype frequency and edge lengths reflecting mutational distances. The Unweighted Pair Group Method with Arithmetic Mean (UPGMA) dendrogram was generated to explore hierarchical clustering among haplotypes. Pairwise genetic distances were computed using dist.dna() from the ape package, and the dendrogram was built using the upgma() function from the phangorn package [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Visualization was done using the ggtree package for clarity and annotation. To examine spatial patterns, a presence/absence matrix of haplotypes across sampling sites was constructed. This matrix was used to create a heatmap using the ggplot2 and reshape2 packages [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Each cell in the heatmap indicates whether a particular haplotype was found at a given location, providing a clear view of geographic distribution.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Principal Component Analysis (PCA)\u003c/h2\u003e \u003cp\u003ePrincipal Component Analysis was performed using adegenet [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] and factoextra [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. PCA facilitated visualization of genetic clustering among isolates based on binary SNP matrices derived from the aligned sequences.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Minor Allele Frequency (MAF) Distribution\u003c/h2\u003e \u003cp\u003eMinor allele frequencies were calculated for all polymorphic sites to assess allele distribution and detect dominant variants using vcfR [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] and base R functions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Genetic Distance and Heatmap\u003c/h2\u003e \u003cp\u003ePairwise genetic distances were calculated using the Kimura 2-parameter model in ape [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The resulting distance matrix was visualized using pheatmap to detect clustering and isolate similarity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10 Mismatch Distribution\u003c/h2\u003e \u003cp\u003eMismatch distribution analysis was conducted using pegas to evaluate the frequency of pairwise nucleotide differences and infer possible demographic events such as population expansion [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.11 Tajima\u0026rsquo;s D and Sliding Window Neutrality Test\u003c/h2\u003e \u003cp\u003eTajima\u0026rsquo;s D statistic was calculated across the alignment in sliding windows using PopGenome [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] to test for neutrality [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Regions exceeding\u0026thinsp;\u0026plusmn;\u0026thinsp;2 were flagged as potentially under selection or demographic influence.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.12 Software and Supplementary Materials\u003c/h2\u003e \u003cp\u003eAll analyses were performed using R version 4.3.1 (R Core Team, 2023). Full scripts used for data analysis are included in the Supplementary Materials. The following R packages were used: Biostrings [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], msa [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], DECIPHER [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], seqinr [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], ggseqlogo [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], PopGenome [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], pegas [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], adegenet [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], factoextra [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], vcfR [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], ape [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], and pheatmap [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. All downstream genetic diversity and population structure analyses were conducted in R using validated population genetics and visualization packages to ensure reproducibility.lina\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results and Discussion","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Nucleotide Diversity\u003c/h2\u003e \u003cp\u003eNucleotide diversity (π) shows the average number of nucleotide differences per site between any two sequences in your alignment [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The mean nucleotide diversity (π) across the aligned microbial sequences was 0.7006, with values ranging from 0.0582 to 0.7498 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The high π value largely reflects sequence divergence among multiple bacterial taxa rather than within-species polymorphism. These findings demonstrate substantial genetic diversity among bacterial 16S rRNA isolates recovered from beef tripe, reflecting heterogeneous microbial populations rather than direct evidence of pathogenicity. No positions along the aligned 16S rRNA gene region exceeded a diversity threshold of 0.8, suggesting that while diversity is elevated across the alignment, extreme hypervariable sites are absent. High nucleotide diversity (π) is often indicative of a large, genetically heterogeneous population, potentially arising from varied environmental sources or long-standing microbial colonization within the beef tripe ecosystem [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The heterogeneous nature of the tripe microbiota reflects possible exposure to multiple contamination points from the animal gut flora, processing environments, or water sources used during preparation. From a food safety perspective, high genetic diversity may complicate pathogen tracking and outbreak source attribution [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Moreover, it increases the likelihood of encountering strains with unique virulence factors or antimicrobial resistance genes [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], which emphasizes the need for comprehensive molecular surveillance. While the observed genetic diversity highlights potential food safety concerns, pathogenicity and antimicrobial resistance cannot be inferred from 16S rRNA data alone.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSliding window analysis is a commonly used method for studying the properties of molecular sequences: data are plotted as moving averages of a particular criterion, such as the number of nucleotide changes, for a window of a certain length slid along a sequence or sequence alignment [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The sliding window analysis revealed moderate fluctuations in nucleotide diversity along the alignment (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Some windows showed dips in diversity, interpreted as conserved regions possibly associated with housekeeping genes or essential structural loci. These conserved zones were marked in red on the diversity curve, helping to distinguish them from more variable intergenic or mobile element-associated regions. Such insights are essential for identifying candidate genes for typing or functional assays, as conserved regions may serve as PCR targets in diagnostic applications, while highly variable regions could be used in strain differentiation protocols [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Haplotype Structure and Distribution\u003c/h2\u003e \u003cp\u003eA haplotype (short for haploid genotype) is a group of alleles (or DNA sequence variants) that are inherited together from a single parent and are located close to each other on the same chromosome or region of DNA [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Haplotypes help identify genetic variation and track evolutionary relationships while a haplotype network shows how these different sequence types are connected, may represent more centrally connected sequence variants within the network. A total of 99 unique haplotypes were identified among the sequences analyzed, with the most frequent haplotype found only twice (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This implies that nearly all isolates carry unique nucleotide signatures, further reinforcing the idea of a non-clonal, highly diverse population structure in the beef tripe microbial diversity. The haplotype frequency distribution was right-skewed, suggesting a population in which most genotypes are rare, a pattern typical of microbial communities in complex environments, where niche specialization and local adaptations drive diversification [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The haplotype network revealed minimal reticulation, consistent with neutral evolutionary dynamics and limited recombination. Isolates formed loosely connected nodes, reflecting micro-evolutionary divergence rather than tight clonal expansion, a hallmark of environments not under strong selective pressure [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe minimum-spanning haplotype network graphically illustrates the genetic relationships and mutational steps among haplotypes. Central haplotypes such as H5 (\u003cem\u003eLysinibacillus sphaericus\u003c/em\u003e), H43 (\u003cem\u003eAeromonas caviae\u003c/em\u003e), H54 (\u003cem\u003eAeromonas allosaccharophila\u003c/em\u003e), and H61 (\u003cem\u003eMoellerella wisconsensis\u003c/em\u003e) appear as hubs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), each connected to several others by short mutational paths. This suggests they may represent ancestral or more common haplotypes within the population [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Peripheral haplotypes like H8 (\u003cem\u003eKurthia gibsonii\u003c/em\u003e), H28 (\u003cem\u003eMacrococcus caseolyticus\u003c/em\u003e) and H84 (\u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e) show fewer connections, indicating more recent divergence or lower frequency. Notably, H72 (\u003cem\u003eEscherichia fergusonii\u003c/em\u003e), H74 (\u003cem\u003eEscherichia\u003c/em\u003e spp.), and H93 (\u003cem\u003eEnterobacter hormaechei\u003c/em\u003e) create branching structures, suggesting sub-lineages or potential site-specific evolutionary events (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Such star-like patterns around central haplotypes often imply recent population expansions which indicates heterogeneous sequence variation among isolates [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe UPGMA (Unweighted Pair Group Method with Arithmetic Mean) dendrogram further supports the relationships seen in the network but emphasizes clustering based on genetic distance (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows localized variation in nucleotide diversity along the aligned sequences. Haplotypes such as H5 (\u003cem\u003eLysinibacillus sphaericus\u003c/em\u003e), H10 (\u003cem\u003eKurthia gibsonii\u003c/em\u003e) and H18 (\u003cem\u003eMacrococcus caseolyticus\u003c/em\u003e) cluster together, suggesting close genetic affinity, potentially due to geographic proximity or shared evolutionary pressures (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Interestingly, H3 (\u003cem\u003eLysinibacillus sphaericus\u003c/em\u003e) and H27 (\u003cem\u003eMacrococcus caseolyticus\u003c/em\u003e) diverge early, indicating significant genetic separation from the rest. This could imply ancient divergence or isolated mutation events. These dendrogram results can inform hypotheses about microevolution and even help predict phenotypic traits or pathogenic potential in microbial studies [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe UPGMA dendrogram based on genetic distance (0.0\u0026ndash;0.7) revealed four major clades (I\u0026ndash;IV), reflecting phylogenetic relationships among microbial haplotypes isolated from beef tripe. Clade I (0.0\u0026ndash;0.2) was dominated by \u003cem\u003eEscherichia coli\u003c/em\u003e (H1, H3, H5) and \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e (H7), suggesting higher sequence similarity that may reflect shared ecological sources or taxonomic relatedness, possibly linked to shared environmental sources such as slaughterhouse contamination [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Clade II (0.2\u0026ndash;0.4) consisted primarily of \u003cem\u003eAeromonas veronii\u003c/em\u003e (H12, H15) and \u003cem\u003eA. caviae\u003c/em\u003e (H18), with sub-clustering indicative of strain-level diversity potentially related to adaptive traits such as antimicrobial resistance [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Clade III (0.4\u0026ndash;0.6), featuring \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e (H22) and \u003cem\u003eMacrococcus caseolyticus\u003c/em\u003e (H25), showed higher divergence, which may reflect ecological specialization. For instance, \u003cem\u003eP. aeruginosa\u003c/em\u003e's known ability to form biofilms on meat surfaces [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Finally, Clade IV (distance\u0026thinsp;\u0026gt;\u0026thinsp;0.6) included the psychrotrophic \u003cem\u003eExiguobacterium profundum\u003c/em\u003e (H30), an outlier whose substantial divergence may be attributed to its adaptation to low-temperature environments such as cold storage [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. The dominance of Clades I and II highlights potential hygiene lapses during tripe processing, whereas Clades III and IV underscore the survival of niche-adapted taxa even after post-processing interventions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThis heatmap reveals the geographic occurrence of haplotypes across the sampled locations in Gaborone, Botswana. Haplotypes like H88 (\u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e) and H60 (\u003cem\u003eAeromonas veronii\u003c/em\u003e) are found in multiple locations (e.g., Block 5, G-west Industrial), suggesting possible environmental stability or widespread dissemination mechanisms, potentially via shared food handling practices or market routes. These patterns should be interpreted cautiously, as 16S rRNA data alone cannot resolve fine-scale spatial adaptation. In contrast, unique haplotypes like H3 (\u003cem\u003eLysinibacillus sphaericus\u003c/em\u003e from Old Naledi) and H12 (\u003cem\u003eKurthia gibsonii\u003c/em\u003e from Block 3) suggest local adaptations or micro-environmental influences that limit gene flow (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The distribution of H27 (\u003cem\u003eMacrococcus caseolyticus\u003c/em\u003e) in multiple unrelated locations could indicate a more ubiquitous presence or historical spread. Spatial structuring of haplotypes highlights the importance of geography in shaping genetic diversity and can provide insights into contamination sources, especially in food safety research [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Principal Component Analysis (PCA)\u003c/h2\u003e \u003cp\u003ePrincipal Component Analysis is about the creation of new set of uncorrelated variables from a set of possibly correlated variables [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. PCA revealed a diffuse clustering pattern, with no clearly defined groups among isolates. This indicates that genetic variation is broadly distributed rather than structured into distinct subpopulations (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The lack of major clustering implies no dominant source of contamination, supporting the hypothesis of diverse microbial inputs during tripe handling and processing. In similar food matrices, PCA has successfully discriminated between strains based on geographic origin or processing method [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. However, in this case, the absence of structure may reflect random colonization events, frequent microbial turnover, and a lack of ecological filtering in the tripe environment.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Minor Allele Frequencies (MAF)\u003c/h2\u003e \u003cp\u003eThe Minor Allele Frequency (MAF) refers to the frequency at which the less common allele occurs at a specific genetic locus in a population. At any given single nucleotide polymorphism (SNP) site, there are typically two possible nucleotide variants, known as alleles [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. For example, if the alleles at a particular site are adenine (A) and guanine (G), and within a population, allele A is observed in 80% of the sequences while allele G is present in 20%, then allele G is considered the minor allele because it is less frequent. The MAF, which quantifies the proportion of the less common allele at that site, would be 0.20 or 20% in this case. MAF is a key measure used in population genetics to assess the level of genetic variation at specific loci.\u003c/p\u003e \u003cp\u003eAnalysis of MAF showed that most alleles were rare, consistent with a recent population expansion or mutation accumulation without purging (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Rare alleles are especially important in microbial ecology as they may represent low-frequency sequence variants, some of which could warrant further functional investigation [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. For food safety, this has direct implications. Rare but functional variants can go undetected in routine surveillance but may pose significant risks if positively selected under stress conditions, such as exposure to antibiotics or sanitizers.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Pairwise Genetic Distance\u003c/h2\u003e \u003cp\u003eGenetic distance refers to a measure of evolutionary divergence between DNA sequences from different individuals or isolates. It quantifies how different two sequences are based on the number of nucleotide substitutions between them [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. In this study, a pairwise genetic distance matrix was generated from the aligned sequences of microbial isolates obtained from beef tripe. The results revealed generally high levels of divergence among the sequences, consistent with the high nucleotide and haplotype diversity observed. A heatmap visualization of the distance matrix showed no clear clustering, indicating the absence of dominant or closely related groups among the isolates (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). As seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, E. \u003cem\u003ecoli\u003c/em\u003e isolate TB87 clusters distantly from other enteric bacteria, consistent with its unique haplotype. This suggests that the microbial population is highly heterogeneous, likely due to multiple contamination sources, such as animal gut flora, water, handling surfaces, or post-slaughter environments [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The lack of genetic clustering supports the hypothesis that the microbial population is shaped more by random colonization and microevolution than by clonal expansion or selective sweeps. In food microbiology, high genetic distance among isolates is a red flag, as it may reflect the presence of multiple strains, some of which could harbor novel antimicrobial resistance or virulence factors [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Mismatch Distribution\u003c/h2\u003e \u003cp\u003eMismatch distribution analysis examines the number of nucleotide differences (mismatches) between all pairs of sequences in a population. This distribution provides insight into the demographic history of the population, such as expansion, contraction, or bottlenecks [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. In this study, the mismatch distribution produced a unimodal (single peak) curve, which is typically consistent with a unimodal pattern of sequence divergence across isolates (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). This pattern suggests that the microbial community has undergone a recent increase in size, possibly due to favorable environmental conditions, such as nutrient availability in beef tripe or lack of effective cleaning and preservation during post-slaughter processing. A unimodal mismatch distribution is also consistent with neutral evolution, where mutations accumulate randomly rather than being driven by strong selective pressures. In contrast, multimodal or irregular distributions are often associated with population structure, bottlenecks, or balancing selection [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. The genetic heterogeneity observed may influence bacterial persistence and adaptability within the food chain, but direct health risk requires confirmation through virulence and resistance profiling.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e: \u003cb\u003eThe mismatch distribution, showing the frequency of pairwise differences among all sequence pairs.\u003c/b\u003e This distribution is often used to infer demographic events such as population expansion or bottlenecks. A unimodal distribution typically suggests a recent population expansion, while a multimodal or ragged distribution may indicate demographic stability or structure. Given the multi-species nature of the dataset, population genetic statistics are interpreted here as indicators of overall sequence variability rather than evidence of demographic history.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Tajima\u0026rsquo;s D Analysis\u003c/h2\u003e \u003cp\u003eTajima\u0026rsquo;s D is a widely used statistical test in population genetics that compares two measures of genetic variation: the number of segregating sites (S) and the average number of nucleotide differences (π) between sequences. It is used to detect departures from the neutral theory of evolution, where genetic variation is assumed to arise purely from random mutation and genetic drift, without the influence of selection [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In this study, Tajima\u0026rsquo;s D was calculated across the aligned sequences of microbial isolates from beef tripe using a sliding window approach (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). This method enables the identification of localized regions of the genome that may be under different evolutionary pressures. The window size and step parameters (e.g., 100 bp window with 25 bp step) allowed for fine-scale resolution across the alignment. The results showed that Tajima\u0026rsquo;s D values did not exceed\u0026thinsp;+\u0026thinsp;2 or drop below \u0026minus;\u0026thinsp;2, which indicates that no strong evidence of positive or purifying selection was observed in any region of the genome. Most Tajima\u0026rsquo;s D values hovered around zero, suggesting no strong deviation from neutral-like patterns of sequence variation across the aligned region.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Intensity of genetic variability and distribution\u003c/h2\u003e \u003cp\u003ePolymorphism refers to the occurrence of multiple genetic variants at particular loci within a population and is a key indicator of evolutionary potential and adaptation [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The heatmap of polymorphic sites across bacterial isolates from beef tripe reveals a broad distribution of sequence variation, with diverse bacterial taxa exhibiting extensive nucleotide polymorphism across the alignment. Each vertical band represents a nucleotide position, and the variability in color intensity reflects base substitutions among isolates. Notably, isolates such as \u003cem\u003eAeromonas veronii\u003c/em\u003e (e.g., TB1, TB9, TB35), \u003cem\u003eEscherichia coli\u003c/em\u003e (e.g., TB2, TB42, TB87), and \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e (e.g., TB34, TB44, TB88) show continuous and dense polymorphic patterns, suggesting that these species harbor high intra-species genetic diversity (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). In contrast, isolates such as \u003cem\u003eKurthia gibsonii\u003c/em\u003e (TB27, TB84) and \u003cem\u003eMacrococcus caseolyticus\u003c/em\u003e (TB33, TB40, TB85) display patchier and more conserved profiles, implying either a more recent evolutionary origin, lower mutation rates, or more limited genetic input. Interestingly, some species like \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e (TB46, TB61) and \u003cem\u003eEnterobacter cloacae\u003c/em\u003e (TB63) demonstrate mixed profiles, with regions of both high and low variability, which may point to recombination events or selective pressures acting differentially across the genome [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe overall heatmap does not show dominant conserved blocks, indicating no universal loci under purifying selection, further supporting the results of Tajima\u0026rsquo;s D analysis that showed near-neutral evolution. The wide variation across both Gram-negative (e.g., \u003cem\u003eKlebsiella\u003c/em\u003e, \u003cem\u003ePseudomonas\u003c/em\u003e, \u003cem\u003eAeromonas\u003c/em\u003e) and Gram-positive genera (e.g., \u003cem\u003eMacrococcus\u003c/em\u003e, \u003cem\u003eKurthia\u003c/em\u003e) suggests that the microbial community inhabiting beef tripe is shaped more by ecological diversity and environmental exposure than by vertical transmission or clonal expansion [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. From a food safety perspective, such diversity is a double-edged sword. On one hand, it reduces the dominance of any single pathogenic strain, but on the other hand, it increases the likelihood that rare but high-risk variants, such as those carrying resistance or virulence determinants, may be overlooked by culture-dependent or marker-based surveillance systems. The heatmap reinforces the need for whole-genome or metagenomic surveillance to capture the full spectrum of microbial diversity in food products, particularly in under-regulated environments like informal butcheries.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThis study provides a comprehensive molecular characterization of microbial populations isolated from beef tripe sold in butcheries in Gaborone, Botswana, using a suite of population genetic tools. The analyses revealed high levels of nucleotide and haplotype diversity, with 99 unique haplotypes and a mean nucleotide diversity (π) of 0.7006, indicating a genetically diverse and heterogeneous microbial community. The polymorphism heatmap and genetic distance matrices further illustrated this diversity, with no clear clustering or dominant lineages among isolates such as \u003cem\u003eAeromonas veronii\u003c/em\u003e, \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e, and \u003cem\u003eEscherichia coli\u003c/em\u003e. These findings reflect microbiota shaped by multiple sources of contamination and neutral evolutionary dynamics, as supported by near-zero Tajima\u0026rsquo;s D values and a unimodal mismatch distribution, both of which suggest recent population expansion without strong selective pressures. Haplotypes like H5, H43, and H54 emerge as genetically central and geographically widespread, potentially serving as molecular markers for monitoring foodborne pathogens in beef tripe. The presence of unique, location-specific haplotypes such as H3, H12, and H84 underscores the importance of localized surveillance to detect emergent strains. While the lack of evidence for positive or purifying selection may appear reassuring from a food safety standpoint, the presence of low-frequency variants (as shown by minor allele frequency analysis) highlights the potential risk posed by rare but potentially harmful strains, including those with antimicrobial resistance or virulent genes. These variants may persist and expand under changing environmental or handling conditions. The diffuse clustering pattern observed in PCA, coupled with widespread polymorphism across the alignment, further supports the hypothesis of non-clonal, environment-driven population dynamics.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e3.9 Study Limitations\u003c/h2\u003e \u003cp\u003eThis study has several limitations that should be considered when interpreting the findings. First, the analyses were conducted on a multi-species dataset comprising bacterial isolates from diverse genera and families. While this approach provides a broad overview of sequence heterogeneity associated with beef tripe, it limits the application of classical population genetic frameworks, which typically assume a single, panmictic population with a shared evolutionary history. As a result, metrics such as nucleotide diversity, haplotype structure, Tajima\u0026rsquo;s D, and mismatch distributions are best interpreted as descriptive indicators of overall genetic variability rather than definitive evidence of demographic processes or selective pressures operating within individual species.\u003c/p\u003e \u003cp\u003eSecond, the study relied on partial 16S rRNA gene sequences. Although this marker is widely used for bacterial identification and comparative diversity analyses, it represents a relatively conserved genomic region subject to functional constraint. This limits resolution for fine-scale evolutionary inference, strain-level discrimination, and the detection of recombination or adaptive selection. Consequently, signals of neutrality or population expansion inferred from 16S rRNA data should be interpreted cautiously, as they may reflect taxonomic breadth and interspecific divergence rather than true within-species evolutionary dynamics.\u003c/p\u003e \u003cp\u003eThird, the absence of functional gene analyses, including antimicrobial resistance and virulence determinants, restricts the ability to directly link observed genetic diversity to phenotypic risk. While the presence of rare variants and high overall heterogeneity raises important food safety considerations, functional validation through whole-genome sequencing or targeted gene screening is required to confirm their public health relevance. Despite these limitations, the study provides valuable baseline molecular data on beef tripe\u0026ndash;associated microbiota in Botswana and highlights the importance of integrative genetic surveillance as a foundation for future high-resolution investigations.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eThese findings underscore the importance of integrating molecular surveillance into food safety protocols, particularly in informal meat supply chains where regulatory oversight may be limited. Monitoring microbial diversity at the genomic level allows for early detection of emerging threats, enhances epidemiological tracing, and informs risk management strategies. Because analyses were based on partial 16S rRNA gene sequences from multiple bacterial taxa, evolutionary interpretations should be viewed as descriptive indicators of diversity rather than definitive population genetic inferences. In conclusion, while beef tripe remains a culturally and nutritionally important food source, it also represents a complex and dynamic reservoir of microbial diversity that warrants continued attention, regulation, and molecular risk assessment.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eFunds for sequencing were provided by the Botswana University of Agriculture and Natural Resources Graduate Office.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eK.M. - Conceptualization, Methodology, Investigation, Formal analysis, Writing \u0026ndash; Original DraftM.D.S - Investigation, Data curation, Writing \u0026ndash; Review \u0026amp; EditingN.Z. - Writing \u0026ndash; Review \u0026amp; EditingE.M. - Writing \u0026ndash; Review \u0026amp; EditingJ.M.- Writing \u0026ndash; Review \u0026amp; EditingM.H.D.M - Writing \u0026ndash; Review \u0026amp; EditingG.M. - Writing \u0026ndash; Review \u0026amp; Editing \u0026amp; Supervision\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eA special thank you to Botswana University of Agriculture and Natural Resources (BUAN) for the sequence\u0026rsquo;s funds and Inqaba Biotech (Pretoria, South Africa) for sequencing the data.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll 16S rRNA gene sequences generated during this study have been deposited and published in the NCBI GenBank database under accession numbers PV686902\u0026ndash;PV686997.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBerrada H, Font G. \u003cem\u003eHANDBOOK OF Analysis of Edible Animal By-Products\u003c/em\u003e; NOLLET, L.M.L., TOLDR\u0026Aacute;, F., Eds.; CRC Press, 2011; ISBN 9781439803608.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBensink JC, Dobrenov B, Mulenga MP, Bensink ZS, McKee JJ. 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Population Growth Makes Waves in the Distribution of Pairwise Genetic Differences \u0026rsquo;. 1992, \u003cem\u003e9\u003c/em\u003e, 552\u0026ndash;69.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gics","sideBox":"Learn more about [BMC Genomics](http://bmcgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/gics","title":"BMC Genomics","twitterHandle":"#BMCGenomics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Beef tripe, nucleotide diversity, Tajima’s D, haplotypes, microbial genetics","lastPublishedDoi":"10.21203/rs.3.rs-8590956/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8590956/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBeef tripe is a commonly consumed organ meat in many parts of the world, including Botswana, but its porous texture and microbial richness pose potential food safety risks. Despite its widespread consumption, limited studies have characterized the genetic diversity of tripe-associated microbial populations at the molecular level. This study aimed to assess the genetic polymorphism and evolutionary dynamics of microbial isolates recovered from beef tripe sold in butcheries in Gaborone, Botswana, using nucleotide sequence data and population genetic tools implemented in R. Population genetic statistics were applied as descriptive measures of sequence heterogeneity across multiple bacterial taxa rather than as strict intraspecific evolutionary tests. A total of 99 aligned nucleotide sequences were analyzed for nucleotide composition, polymorphism, haplotype structure, and genetic diversity. Sliding window analyses of nucleotide diversity (π) and Tajima\u0026rsquo;s D were conducted. Additional analyses included haplotype network construction, PCA, minor allele frequency profiling, mismatch distribution, and genetic distance heatmaps. High nucleotide diversity (mean π\u0026thinsp;=\u0026thinsp;0.7006) as well as 99 unique haplotypes were observed, indicating a highly diverse microbial population. Polymorphism was widely distributed across the genome, with no hyper-conserved or hypervariable regions. PCA and genetic distance heatmaps revealed diffuse clustering, consistent with a non-clonal structure. Tajima\u0026rsquo;s D values hovered around zero, suggesting neutral evolution. A unimodal mismatch distribution indicated recent population expansion. Notably, rare alleles with low minor allele frequencies were present across multiple isolates. The findings reveal a genetically diverse, neutrally evolving microbial community in beef tripe, shaped by environmental exposure and stochastic colonization. This underscores the need for genomic surveillance in food safety systems to detect emerging antimicrobial-resistant or pathogenic variants. However, pathogenic potential cannot be inferred from 16S rRNA sequence data alone. These findings describe genetic diversity patterns among bacterial isolates but do not directly infer pathogenicity or public health risk.\u003c/p\u003e","manuscriptTitle":"Genetic diversity and sequence polymorphism of bacterial 16S rRNA isolates from beef tripe from Gaborone, Botswana","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-20 07:54:54","doi":"10.21203/rs.3.rs-8590956/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"246460047857279191375384672429589177526","date":"2026-04-06T17:41:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"282316574281626304808370073745502142143","date":"2026-02-23T20:10:14+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-17T16:49:28+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-27T19:05:13+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-27T06:59:13+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Genomics","date":"2026-01-27T06:47:16+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gics","sideBox":"Learn more about [BMC Genomics](http://bmcgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/gics","title":"BMC Genomics","twitterHandle":"#BMCGenomics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"dac7fc7d-cc18-4468-9599-42d34cebc058","owner":[],"postedDate":"February 20th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-20T07:54:54+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-20 07:54:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8590956","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8590956","identity":"rs-8590956","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}