Atlas Journal of Biology 2025, pp. 846–855 https://doi.org/10.5147/ajb.262 A tla s J ou rn al o f Bi ol og y - I SS N 2 15 8- 91 51 . P ub lis he d By A tla s P ub lis hi ng , L LC (w w w. at la s-p ub lis hi ng .o rg ) A tla s J ou rn al o f Bi ol og y - I SS N 2 15 8- 91 51 . P ub lis he d By A tla s P ub lis hi ng , L LC (w w w. at la s-p ub lis hi ng .o rg ) Comparative Genomics of Foodborne Pathogens: Diversity, Virulence, and Epidemiological Relevance Bradford Dreshawn1, Khalid Lodhi2, Jiazheng Yuan2, Danielle Graham2, Justin Graham2, Mohamed Maldani2, Erin White2, Afua Arhin3, and My Abdelmajid Kassem1* 1 Plant Genomics and Bioinformatics Lab, Department of Biological and Forensic Sciences, Fayetteville State University, Fayette- ville, NC 28301, USA; 2 Department of Biological and Forensic Sciences, Fayetteville State University, Fayetteville, NC 28301, USA; 3 School of Nursing, Fayetteville State University, Fayetteville, NC 28301, USA Received: June 6, 2025 / Accepted: July 4, 2025 __________________________________________________ * Corresponding author: mkassem@uncfsu.edu 846 Abstract Foodborne bacterial infections are a major global health concern, causing millions of illnesses and deaths annually. Advances in microbial genomics have improved pathogen characterization, yet the relationship between genomic traits and public health out- comes remains unclear. This study investigates 50 foodborne bacterial species by analyzing genome size, GC content, virulence gene count, and antimicrobial resistance (AMR) gene presence in relation to global infection rates and mortality. Our findings reveal substantial genomic diversity, with genome sizes ranging from 1.2 Mb to 9.0 Mb and virulence gene counts from 2 to 312. Genome size, gene number, and GC content are strongly correlated, but neither virulence nor AMR gene counts consistently predict mortality or global case numbers. These weak associations suggest that host susceptibility, ecological adaptation, and gene expres- sion contribute significantly to pathogenicity. This study also highlights the value of microbial forensics in foodborne outbreak investigations. Integrating whole-genome sequencing (WGS), comparative genomics, and phylogenetic analysis allows for tracing pathogen origins during contamination events. Bacteria such as Salmonella enterica, Escherichia coli, and Listeria monocytogenes frequently feature in forensic cases due to their high public health impact. The use of machine learning (ML) and Artificial Intel- ligence (AI) enhanced genomic surveillance holds promise for improving pathogen source attribution and biosecurity. These re- sults highlight the complexity of bacterial virulence and call for integrated approaches combining genomic, epidemiological, and forensic data. Future work should emphasize functional genomics, host-pathogen interactions, and predictive modeling to enhance foodborne disease prevention and outbreak response strategies. Keywords: Microbial forensics, Foodborne pathogens, Genomic epidemiology, Antimicrobial resistance, Virulence factors, Whole- genome sequencing. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Published in the United States of America https://www.atlas-publishing.org 1. Introduction Foodborne bacterial infections are a major global health concern, caus- ing millions of illnesses and deaths annually. According to the World Health Organization (WHO), foodborne diseases affect 600 million people each year, leading to approximately 420,000 deaths, with the highest burden observed in low- and middle-income countries (WHO, 2025). These infections are com- monly associated with contaminated food, water, improper hygiene practices, within children under five years old, immunocompromised individuals, and the elderly being the most vulnerable (Kirk et al., 2015). The economic impact is also severe, with billions of dollars lost annually due to healthcare costs, loss of productivity, and food recalls (Scallan et al., 2011). Despite decades of re- search, foodborne bacterial infections remain a persistent challenge due to the emergence of antimicrobial resistance (AMR), environmental adaptability, and complex pathogen-host interactions (Rocourt et al., 2003). The field of microbial genomics has revolutionized our understanding of bacterial pathogens, providing valuable insights into the genetic basis of viru- lence, antimicrobial resistance, and epidemiological trends (Didelot et al., 2017). The advent of whole-genome sequencing (WGS) and comparative genomics has enabled researchers to characterize the genetic diversity of bacterial spe- cies, facilitating the identification of key virulence determinants, mobile genetic elements, and resistance mechanisms (Oniciuc et al., 2018; Saini et al., 2024). However, despite these advances, the relationship between genomic features and disease severity remains poorly understood. While some pathogens exhibit large, complex genomes with numerous virulence factors, others have stream- lined genomes yet cause severe disease, suggesting that genomic complexity alone may not fully explain pathogenic potential (Merhej and Raoult, 2011). Several studies have explored genome size, GC content, and gene composi- tion as potential indicators of bacterial adaptability, virulence, and resistance capacity (Ochman and Davalos, 2006; Bobay and Ochman, 2017). Genome size has been linked to metabolic versatility and environmental adaptability, with larger genomes often associated with higher numbers of virulence and resistance genes (Toft and Andersson, 2010). However, this trend does not hold universally, as some highly virulent pathogens, such as Clostridium botulinum and Helicobacter pylori, have relatively small genomes, yet produce potent tox- ins or have evolved mechanisms to evade host immunity (Rossetto et al., 2014). Conversely, opportunistic pathogens like Pseudomonas aeruginosa and Burk- holderia species have large genomes, allowing for adaptation to diverse environ- ments but not necessarily higher virulence (Stover et al., 2000). Another important genomic feature is GC content, which varies widely among bacteria and may influence DNA stability, mutation rates, and gene reg- ulation (Hildebrand et al., 2010). Some studies suggest that higher GC content correlates with greater environmental persistence, as observed in soil-dwelling and free-living bacteria, whereas host-associated pathogens often exhibit lower GC content, possibly due to genome reduction and specialization (Moran, 2002). However, the direct impact of GC content on bacterial virulence and epidemiological success remains debated (Bentley and Parkhill, 2004). In addition to genome size and GC content, virulence gene content and antimicrobial resistance (AMR) gene presence are critical factors influencing pathogenic potential. Bacteria encode a wide array of virulence factors, includ- ing toxins, adhesins, secretion systems, and immune evasion proteins, which collectively determine their ability to infect, survive, and proliferate within hosts (Ribet and Cossart, 2015). Similarly, AMR genes allow bacteria to withstand antibiotic treatment, complicating infection management and increasing mor- tality risks (Martínez et al., 2009). The global rise of antibiotic-resistant food- borne pathogens, such as multidrug-resistant Salmonella and extended-spec- trum beta-lactamase (ESBL)-producing Escherichia coli, has been a growing concern in clinical and food safety settings (Djordjevic et al., 2024). However, while AMR genes contribute to treatment efficacy, their direct relationship with mortality rates is not well established, as some highly resistant bacteria cause mild infections, whereas others with few resistance genes can be highly lethal (Baker et al., 2018). Given these uncertainties, this study aims to systematically analyze the ge- nomic features of 50 foodborne bacterial species and assess their correlation with public health metrics, including mortality rate and annual global infection cases. By evaluating genome size, GC content, virulence gene count, and AMR gene presence, we seek to determine whether genomic complexity influences pathogenic severity. Understanding these relationships could improve risk as- sessment, surveillance, and intervention strategies for foodborne diseases. The findings from this study may also contribute to genome-based predictive mod- els, helping identify high-risk pathogens and guiding public health policies on food safety and infectious disease control. 2. Materials and Methods 2.1. Data Collection and Selection Criteria The genomic and epidemiological data for this study were obtained from well-established public databases. Bacterial genome sequences were retrieved from NCBI and RefSeq databases (O’Leary et al., 2016; Sayers et al., 2024). In parallel, epidemiological data, including global infection cases and mortal- ity rates, were collected from the Global Burden of Disease (GBD) study and World Health Organization (WHO) reports. These sources were chosen for their comprehensive surveillance of foodborne diseases across different geo- graphical regions and population groups. To ensure the reliability of our dataset, bacterial species were included only if they had well-documented genomic data and available epidemiological sta- tistics related to human infections. Species with incomplete genome sequences, low-quality assemblies, or insufficient epidemiological records were excluded from the study. 2.2. Genomic Feature Analysis To explore the genetic diversity of foodborne bacteria, several key genomic features were extracted from assembled genome sequences. Genome size (Mb), gene number, and GC content (%) were directly obtained from RefSeq annota- tions. These fundamental metrics provide insights into bacterial genome orga- nization, metabolic potential, and evolutionary adaptation. To assess virulence potential, the number of virulence genes in each bacte- rial species was identified using the Virulence Factor Database (VFDB) (Chen et al., 2016). This database contains curated information on known bacterial virulence determinants, including toxins, adhesion proteins, and immune eva- sion mechanisms. The presence of antimicrobial resistance (AMR) genes was determined using ResFinder and the Comprehensive Antibiotic Resistance Database (CARD) (Alcock et al., 2020). These databases enable the detection of genetic determinants associated with antibiotic resistance, including genes encoding beta-lactamases, efflux pumps, and ribosomal modifications. To ensure data accuracy, we applied quality control filters during data extraction. Only complete or high-quality draft genomes were included, and duplicate entries and plasmid-only assemblies were excluded. Virulence and AMR genes were identified using standardized thresholds of >90% nucleotide identity and >80% coverage to minimize false positives and ensure consistency across species. The wide ranges observed in virulence and AMR gene counts reflect true biological variation among species and are not due to redundancy or assembly artifacts. Cases with zero gene counts were verified as true negatives, typically occurring in species with reduced genomes (e.g., Mycoplasma spp.), rather than data omissions. 2.3. Statistical Data Analysis Descriptive statistics were calculated to summarize the genomic and epi- demiological characteristics of the dataset. For each genomic feature, mean, standard deviation, quartiles, and range were computed to assess the overall distribution and variability among bacterial species. All data analyses have been conducted using Jupyter Notebook (Kluyver et al., 2016) with Python (van Ros- sum and Drake, 2009) and its libraries: NumPy (Harris et al., 2020) for numeri- cal operations, Pandas (McKinney, 2010) for data handling, Matplotlib (Hunter, 2007) for plotting, and Seaborn (Waskom, 2021) for enhanced visualization. To evaluate potential relationships between genomic traits and public health impact, Pearson and Spearman correlation coefficients were used. Pearson cor- relation was applied to assess linear relationships between continuous variables, while Spearman correlation was used to capture potential non-linear associa- tions. These analyses aimed to determine whether genome size, gene number, GC content, virulence gene count, or AMR gene presence were predictive of mortality rate and global infection cases. To facilitate interpretation, multiple data visualization techniques were 847 A tla s J ou rn al o f Bi ol og y - I SS N 2 15 8- 91 51 . P ub lis he d By A tla s P ub lis hi ng , L LC (w w w. at la s-p ub lis hi ng .o rg ) A tla s J ou rn al o f Bi ol og y - I SS N 2 15 8- 91 51 . P ub lis he d By A tla s P ub lis hi ng , L LC (w w w. at la s-p ub lis hi ng .o rg ) A tla s J ou rn al o f Bi ol og y - I SS N 2 15 8- 91 51 . P ub lis he d By A tla s P ub lis hi ng , L LC (w w w. at la s-p ub lis hi ng .o rg ) A tla s J ou rn al o f Bi ol og y - I SS N 2 15 8- 91 51 . P ub lis he d By A tla s P ub lis hi ng , L LC (w w w. at la s-p ub lis hi ng .o rg ) employed. Histograms and boxplots were generated to illustrate distribution patterns and outliers in genomic features, while heatmaps were used to depict correlation matrices. Additionally, scatterplots were created to examine potential associations between genomic complexity and pathogenic severity. These visual representations provided an intuitive means of identifying trends, clusters, and potential outliers within the dataset. 3. Results 3.1. Genomic Diversity Among Foodborne Bacteria The comprehensive dataset summarizing the species, family, genome size (Mb), gene number, GC content (%), virulence gene count, antimicrobial resis- tance (AMR) gene count, annual global infection cases, and reported mortality rates (%) for 50 foodborne bacterial species analyzed in this study are shown in Table 1. The summary statistics of the 50 foodborne bacteria (Table 2) re- veal substantial variability in genome size, gene number, GC content, virulence factors, AMR genes, and their associated public health impact. The average genome size is 3.97 Mb, with a wide range (1.2–9.0 Mb), mirroring the diversity in gene number (0–18,000, mean 3,847). GC content varies from 27% to 68% (mean 44.28%), indicating different evolutionary adaptations. The number of virulence genes also shows high dispersion (2–312, mean 47.4), as does AMR gene count (0–7,000, mean 152.4), suggesting that some pathogens possess ex- tensive resistance mechanisms while others have none (Table 1). The distribu- tion of annual global cases (1–2.8 billion, median 141,000) and mortality rates (0.1–93%) highlights the uneven burden of these bacteria, with a few species causing massive outbreaks (Table 1). The high standard deviations across most variables emphasize the heterogeneity in genome characteristics and public 848 A tla s J ou rn al o f Bi ol og y - I SS N 2 15 8- 91 51 . P ub lis he d By A tla s P ub lis hi ng , L LC (w w w. at la s-p ub lis hi ng .o rg ) A tla s J ou rn al o f Bi ol og y - I SS N 2 15 8- 91 51 . P ub lis he d By A tla s P ub lis hi ng , L LC (w w w. at la s-p ub lis hi ng .o rg ) Table 1. . Genomic, Virulence, Antimicrobial Resistance, and Epidemiological Profiles of 50 Foodborne Bacterial Species. Comprehensive dataset summa- rizing the species, family, genome size (Mb), gene number, GC content (%), virulence gene count, antimicrobial resistance (AMR) gene count, annual global infection cases, and reported mortality rates (%) for 50 foodborne bacterial species analyzed in this study. This detailed table supports comparative analyses of genomic features and their potential associations with public health impact and microbial forensic investigations. Mortality Rate (%) Annual Cases Worldwide AMR Genes Vir Genes Number GC Cont. (%) Gene Number Genome Size (Mb) FamilySpecies 17800000010505246004.6EnterobacteriaceaeSalmonella enterica 30230000005303830003ListeriaceaeListeria monocytogenes 5200000010405047005EnterobacteriaceaeEscherichia coli 0.1800000005355145004.8EnterobacteriaceaeShigella_sonnei 7010003202836003.9ClostridiaceaeClostridium botulinum 417900007605755005.5EnterobacteriaceaeKlebsiella pneumoniae 91,500,000393937004.1MorganellaceaeProteus mirabilis 305,600,000104554,0005.6EnterobacteriaceaeEnterobacter cloacae 304,0001021585,0005EnterobacteriaceaeSerratia marcescens 10290,000712525,0005EnterobacteriaceaeCitrobacter freundii 34117,0001057474,0004.6YersiniaceaeYersinia enterocolitica 590,000867482604.6YersiniaceaeYersinia pseudotuberculosis 8.610,000916573,7003.8HafniaceaeEdwardsiella tarda 2173,0001930513,5004MorganellaceaeMorganella morganii 181,000,000620675,0007PseudomonadaceaePseudomonas aeruginosa 917,000678,1009BurkholderiaceaeBurkholderia cepacia 10165,000100146686,5007.4BurkholderiaceaeBurkholderia pseudomallei 1018,0001300383,0004.8LegionellaceaeLegionella pneumophila 1540,00083126018,0005.5AeromonadaceaeAeromonas hydrophila 502,000,0004010473,9004VibrionaceaeVibrio cholerae 18200,0005300534,4005.2VibrionaceaeVibrio parahaemolyticus 331,0007160464,0007.5VibrionaceaeVibrio_vulnificus 11,500,00030158301,7001.8CampylobacteraceaeCampylobacter jejuni 11,500,0001211312,0001.8CampylobacteraceaeCampylobacter coli 41,200,000426391,2001.7HelicobacteraceaeHelicobacter pylori 160,0001311446,8006.4BacillaceaeBacillus cereus 20100,000102375,0005.3BacillaceaeBacillus anthracis 93101008396,0005.5BacillaceaeBacillus thuringiensis 61,500,00062294,0004.3ClostridiaceaeClostridium difficile 301,000,000720302,5003.3ClostridiaceaeClostridium perfringens 101,000,0003100282,7002.8ClostridiaceaeClostridium tetani 501,00013273,4003.4ClostridiaceaeClostridium septicum 101,600,000209332,0002.8StaphylococcaceaeStaphylococcus aureus 401,000863322,5002.5StaphylococcaceaeStaphylococcus epidermidis 11,500,00053332,5002.5StaphylococcaceaeStaphylococcus saprophyticus 2010014382,8002.9ListeriaceaeListeria_ivanovii 1018,000,000646391,5001.9StreptococcaceaeStreptococcus pyogenes 828,000713352,0002StreptococcaceaeStreptococcus agalactiae 5500536392,5002StreptococcaceaeStreptococcus pneumoniae 52,800,000,00049362,0002StreptococcaceaeStreptococcus mutans 7240,00065408,0002StreptococcaceaeStreptococcus suis 10100,0001010373,0003.6EnterococcaceaeEnterococcus faecalis 1350,0001073803.2EnterococcaceaeEnterococcus faecium 509,0001015654,0004.4MycobacteriaceaeMycobacterium tuberculosis 25200,00037684,0005MycobacteriaceaeMycobacterium avium 520526321,8001.8FrancisellaceaeFrancisella tularensis 110008574,2003.3BrucellaceaeBrucella abortus 2500,000410573,0003.3BrucellaceaeBrucella melitensis 11,00002422,0001.9CoxiellaceaeCoxiella burnetii 525005301,3001.2RickettsiaceaeRickettsia rickettsii Mortality Rate (%) Annual Cases World AMR Genes Virulence Genes GC Content (%) Gene Number Genome Size (Mb) 50.0050.0050.0048.0050.0050.0050.00Count 17.8360499803.32152.4047.4044.283847.203.97Mean 19.40395649413.95988.3677.3012.062679.741.73Std 0.101.000.002.0027.000.001.20Min 5.005250.005.007.7535.252500.002.5825% 10.00141000.007.0014.0039.503650.003.9550% 28.751500000.0010.0047.0052.754575.005.0075% 93.002800000000.007000.00312.0068.0018000.009.00Max Table 2. Summary statistics of genome size, gene number, GC content, virulence genes, AMR genes, annual cases worldwide, and mortality rates among 50 foodborne bacteria. 849 health risks associated with these pathogens. The genomic features of the 50 foodborne bacterial species exhibit sub- stantial variability, as seen in the distribution plots and boxplots. Genome size ranges from 1.2 Mb to 9.0 Mb (mean: 3.97 Mb), with most bacteria clustering around 2–5 Mb (Figure 1). The gene count follows a similar trend, varying from 0 to 18,000 genes (mean: 3,847), with a few species having exceptionally large genomes and gene content (Figure 2). GC content shows a wide distribution (27%–68%) (Figure 3), indicating diverse evolutionary strategies across species. Boxplots reveal that some species exhibit outliers in genome size, gene number, and GC content (Figure 4), suggesting the influence of horizontal gene transfer, environmental adaptation, or pathogenic specialization. A barplot ranking bacterial species by genome size (Figure 5) highlights Burkholderia cepacia, Vibrio vulnificus, and Burkholderia pseudomallei as the species with the largest genomes (>7 Mb). In contrast, Rickettsia rickettsii, Heli- cobacter pylori, and Campylobacter jejuni have the smallest genomes (<2 Mb), suggesting a more specialized or host-dependent lifestyle. A tla s J ou rn al o f Bi ol og y - I SS N 2 15 8- 91 51 . P ub lis he d By A tla s P ub lis hi ng , L LC (w w w. at la s-p ub lis hi ng .o rg ) A tla s J ou rn al o f Bi ol og y - I SS N 2 15 8- 91 51 . P ub lis he d By A tla s P ub lis hi ng , L LC (w w w. at la s-p ub lis hi ng .o rg ) A tla s J ou rn al o f Bi ol og y - I SS N 2 15 8- 91 51 . P ub lis he d By A tla s P ub lis hi ng , L LC (w w w. at la s-p ub lis hi ng .o rg ) A tla s J ou rn al o f Bi ol og y - I SS N 2 15 8- 91 51 . P ub lis he d By A tla s P ub lis hi ng , L LC (w w w. at la s-p ub lis hi ng .o rg ) 3.2. Virulence and Antimicrobial Resistance (AMR) Gene Distribu- tions The number of virulence genes varies widely across species, ranging from 2 to 312 (mean: 47.4) (Figure 6). Most bacteria harbor relatively few virulence genes, but a few species possess over 100, suggesting enhanced pathogenic po- tential. The boxplot (Figure 7) confirms this trend, with several outliers exhibit- ing exceptionally high virulence gene counts. Similarly, AMR gene distribution is highly skewed, with most bacteria con- taining few resistance genes (mean: 152.4), while a select few harbor thousands of AMR genes (Figure 8). The presence of extreme outliers in the boxplot (Fig- ure 9) suggests that some species have undergone extensive resistance acquisi- tion, likely due to antibiotic pressure or horizontal gene transfer. 3.3. Correlation Between Genomic Features and Public Health Im- pact A heatmap (Figure 10) shows strong positive correlations between genome size, gene number (0.55), and GC content (0.67), indicating that larger genomes tend to have higher GC content and more genes. Virulence gene count exhibits moderate correlations with genome size (0.32) and gene number (0.42), sug- gesting that more complex genomes may contain more virulence factors. AMR gene count, annual cases, and mortality rates show weak or no correlation with genomic traits, implying that pathogenicity and public health burden are influ- enced by additional ecological or host factors. 3.4. Scatterplot Analysis of Mortality Rate vs. Genomic Traits Scatterplots comparing mortality rates with genomic features (Figure 11) indicate that genome size, gene number, and GC content do not strongly corre- late with mortality, suggesting that larger genomes or higher gene counts do not predict increased lethality. Virulence gene count shows a slight upward trend, with some species possessing >100 virulence genes exhibiting higher mortality; however, the overall pattern remains dispersed, indicating the influence of ad- ditional factors. Similarly, AMR gene counts and annual global case numbers do not show clear relationships with mortality, as bacteria with high resistance gene counts or widespread prevalence often exhibit low mortality rates. These patterns empha- size the complexity of bacterial pathogenicity and the role of host, ecological, and environmental factors beyond genomic content. 3.5. Pairwise Comparisons of Genomic and Epidemiological Fea- tures A comprehensive pairplot (Figure 12) visualizes relationships among nu- merical features across species. Genome size, gene number, and GC content exhibit strong correlations, consistent with observations in the correlation heat- map, reflecting genome structural relationships across foodborne bacteria. In contrast, relationships between genomic features and public health metrics Figure 1. Distribution of genome size among 50 foodborne bacteria species. Figure 2. Distribution of gene number among 50 foodborne bacteria species. Figure 3. Distribution of GC content among 50 foodborne bacteria species. Figure 4. Boxplots of genome size, gene number, and GC content among 50 food- borne bacteria species. 850 Figure 5. Ranked barplot of genome size by species among 50 foodborne bacteria species. A tla s J ou rn al o f Bi ol og y - I SS N 2 15 8- 91 51 . P ub lis he d By A tla s P ub lis hi ng , L LC (w w w. at la s-p ub lis hi ng .o rg ) A tla s J ou rn al o f Bi ol og y - I SS N 2 15 8- 91 51 . P ub lis he d By A tla s P ub lis hi ng , L LC (w w w. at la s-p ub lis hi ng .o rg ) Figure 6. Distribution of virulence gene count among 50 foodborne bacteria species. Figure 7. Boxplot of virulence gene count among 50 foodborne bacteria species. Figure 8. Distribution of AMR gene count among 50 foodborne bacteria species. Figure 9. Boxplot of AMR gene count among 50 foodborne bacteria species. 851 A tla s J ou rn al o f Bi ol og y - I SS N 2 15 8- 91 51 . P ub lis he d By A tla s P ub lis hi ng , L LC (w w w. at la s-p ub lis hi ng .o rg ) A tla s J ou rn al o f Bi ol og y - I SS N 2 15 8- 91 51 . P ub lis he d By A tla s P ub lis hi ng , L LC (w w w. at la s-p ub lis hi ng .o rg ) A tla s J ou rn al o f Bi ol og y - I SS N 2 15 8- 91 51 . P ub lis he d By A tla s P ub lis hi ng , L LC (w w w. at la s-p ub lis hi ng .o rg ) A tla s J ou rn al o f Bi ol og y - I SS N 2 15 8- 91 51 . P ub lis he d By A tla s P ub lis hi ng , L LC (w w w. at la s-p ub lis hi ng .o rg ) Figure 10. Correlation heatmap of genomic features among 50 foodborne bacteria species. Figure 11. Scatterplots of mortality rate vs. genomic traits among 50 foodborne bacteria species. 852 A tla s J ou rn al o f Bi ol og y - I SS N 2 15 8- 91 51 . P ub lis he d By A tla s P ub lis hi ng , L LC (w w w. at la s-p ub lis hi ng .o rg ) A tla s J ou rn al o f Bi ol og y - I SS N 2 15 8- 91 51 . P ub lis he d By A tla s P ub lis hi ng , L LC (w w w. at la s-p ub lis hi ng .o rg ) Figure 12. Pairplot of all numerical features among 50 foodborne bacteria species. (mortality rates, annual cases) remain weak or absent, highlighting that genome characteristics alone are insufficient to predict disease severity or burden. Virulence and AMR gene counts display skewed distributions, with a few spe- cies harboring high counts while most remain low, without clear clustering pat- terns based on pathogenic severity. These findings underscore the multifactorial nature of foodborne bacterial pathogenicity and the necessity of integrating genomic, epidemiological, and ecological data to improve risk assessment, sur- veillance, and disease prevention strategies. 4. Discussion This study provides a comprehensive analysis of the genomic diversity, viru- lence potential, and antimicrobial resistance (AMR) profiles of 50 foodborne bacterial species, linking these features to their public health impact. Our find- ings reveal substantial variability in genome size, gene content, virulence fac- tors, and resistance genes, but weak correlations between genomic traits and epidemiological severity. These results highlight the multifactorial nature of bacterial pathogenicity, emphasizing the need for integrated genomic, clinical, ecological, and host-related assessments to better predict and mitigate food- borne disease risks (Pightling et al., 2021; Hendriksen et al., 2019). 4.1. Genomic Complexity and its Relationship to Pathogenic Poten- tial Our results confirm that genome size is strongly correlated with gene num- ber (r = 0.55) and GC content (r = 0.67), consistent with previous findings that larger bacterial genomes often encode more genes, allowing for greater metabolic versatility and adaptability (Ochman and Davalos, 2006). However, genome size alone does not dictate pathogenicity, as some highly virulent bacte- ria, such as Helicobacter pylori and Campylobacter jejuni, have small genomes (<2 Mb), while others with larger genomes, such as Burkholderia species, are opportunistic rather than obligate pathogens. This suggests that pathogenicity is more reliant on specific virulence factors than on genome size alone. Furthermore, virulence gene count exhibits only moderate correlations with genome size (r = 0.32) and gene number (r = 0.42), reinforcing that pathogenic potential is not solely determined by genomic expansion. Some pathogens with relatively low gene counts, such as Clostridium botulinum, produce highly po- tent toxins, making them lethal despite their small genomes (Rossetto et al., 2014). Conversely, some species with large genomes, such as Bacillus cereus, are not always highly virulent, highlighting the role of regulatory mechanisms, environmental conditions, and host interactions in determining disease severity (Ehling-Schulz et al., 2019). 4.2. Antimicrobial Resistance and Public Health Risk The distribution of AMR genes across bacterial species is highly skewed, with some species possessing thousands of resistance genes, while others have none. This variability aligns with prior studies showing that horizontal gene transfer (HGT) plays a crucial role in the acquisition of resistance determinants, particularly in species frequently exposed to antibiotic pressure (Frost et al., 2005; von Wintersdorff et al., 2016). Global surveillance efforts using metage- nomics and WGS have highlighted the spread of AMR determinants across environmental and clinical settings, underscoring the need for integrated moni- toring frameworks (Hendriksen et al., 2019; Collignon and McEwen, 2019). Despite the presence of high AMR gene counts in some bacteria, no strong correlation was found between AMR genes and mortality rate, suggesting that antibiotic resistance alone does not directly predict disease severity. While re- sistance may contribute to treatment failure and prolonged infections, it does not necessarily increase intrinsic virulence (Martinez et al., 2009). Additionally, some highly resistant bacteria, such as Enterococcus faecium, typically cause chronic but non-lethal infections, whereas low-resistance pathogens like Listeria monocytogenes can be fatal, particularly in immunocompromised individuals (Radoshevich and Cossart, 2018). 4.3. Weak Correlations Between Genomic Features and Mortality Rate A key finding of this study is the lack of strong correlations between genome size, gene number, GC content, virulence genes, and mortality rate. Scatterplots show that some highly lethal bacteria have relatively simple genomes, while others with large genomes and numerous virulence factors cause only mild in- fections. This reinforces the idea that mortality rate is shaped by a complex interplay of factors, including host susceptibility, transmission dynamics, and immune evasion strategies (Casadevall and Pirofski, 2018). For instance, pathogens such as Vibrio vulnificus and Yersinia pseudotu- berculosis exhibit high fatality rates despite moderate genome sizes, whereas Escherichia coli and Salmonella enterica, which have relatively large genomes, cause a spectrum of diseases ranging from mild gastroenteritis to severe sys- temic infections (Jolley and Maiden, 2010; Baker et al., 2018). This suggests that key virulence determinants, such as toxin production, immune evasion mecha- nisms, and host-pathogen interactions, may be better predictors of disease se- verity than genome size alone (Pightling et al., 2021). 4.5. Evolutionary and Ecological Influences on Pathogenicity The pairwise comparison of genomic and epidemiological features indi- cates that certain bacterial species cluster together based on genome size and GC content, but not necessarily on virulence or resistance patterns. This aligns with previous research showing that bacterial genome evolution is driven by niche adaptation, environmental pressures, and host interactions rather than a simple accumulation of virulence factors (Merhej and Raoult, 2011). For example, intracellular pathogens like Rickettsia and Mycobacterium exhibit genome reduction, reflecting their reliance on host cellular machinery, while environmentally persistent bacteria like Pseudomonas aeruginosa main- tain large, flexible genomes to survive diverse conditions (Toft and Andersson, 2010). Machine learning and AI approaches are increasingly being used to analyze these complex genomic and ecological patterns, enhancing our under- standing of pathogen evolution and spread (Jiang et al., 2022; Danko et al., 2021; Libbrecht and Noble, 2015). This suggests that pathogen success is dic- tated by ecological fitness rather than genome complexity alone, underscoring the need for contextual analysis of bacterial pathogenicity. 4.6. Implications for Food Safety and Disease Prevention These findings have important implications for foodborne disease surveil- lance and risk assessment. Given the weak correlations between genomic traits and public health burden, a multifactorial approach incorporating genomic, epidemiological, and host-pathogen interaction data is essential for improving predictive models of foodborne disease risk (Scallan et al., 2011; Collignon and McEwen, 2019). Genomic screening alone may not be sufficient to assess virulence risk; func- tional studies on toxin production, immune evasion, and transmission dynam- ics are critical. AMR monitoring should focus on clinically relevant resistance genes and their impact on treatment outcomes rather than total gene counts. Surveillance strategies should prioritize high-risk species with both high viru- lence and frequent foodborne transmission, such as Listeria monocytogenes, Vibrio vulnificus, and Salmonella enterica. Future research should explore machine learning approaches to integrate genomic, epidemiological, and clinical data for more accurate pathogenicity risk assessments (Jiang et al., 2022; Libbrecht and Noble, 2015). Additionally, functional genomics studies could help determine which virulence and resis- tance factors are most predictive of severe disease outcomes. 4.7. Foodborne Bacteria and Their Role in Microbial Forensics The genomic analysis of foodborne bacteria not only enhances our un- derstanding of pathogenicity and public health risks but also plays a crucial role in microbial forensics—a field dedicated to identifying and tracking micro- bial agents in criminal, bioterrorism, and foodborne outbreak investigations. By leveraging whole-genome sequencing (WGS), phylogenetic analysis, and comparative genomics, microbial forensics can trace the origin, evolution, and transmission routes of foodborne pathogens, providing critical evidence in cases of food contamination, bioterrorism, and intentional adulteration (Thirunavuk- karasu et al., 2018). Today, advancements in microbial forensics enable real-time genomic sur- veillance, allowing authorities to rapidly identify specific bacterial strains, their 853 A tla s J ou rn al o f Bi ol og y - I SS N 2 15 8- 91 51 . P ub lis he d By A tla s P ub lis hi ng , L LC (w w w. at la s-p ub lis hi ng .o rg ) A tla s J ou rn al o f Bi ol og y - I SS N 2 15 8- 91 51 . P ub lis he d By A tla s P ub lis hi ng , L LC (w w w. at la s-p ub lis hi ng .o rg ) A tla s J ou rn al o f Bi ol og y - I SS N 2 15 8- 91 51 . P ub lis he d By A tla s P ub lis hi ng , L LC (w w w. at la s-p ub lis hi ng .o rg ) A tla s J ou rn al o f Bi ol og y - I SS N 2 15 8- 91 51 . P ub lis he d By A tla s P ub lis hi ng , L LC (w w w. at la s-p ub lis hi ng .o rg ) virulence factors, and antimicrobial resistance genes, aiding in forensic investi- gations and outbreak control (Oliveira et al., 2024; Pightling et al., 2021). The integration of ML and AI models with WGS data can enhance source attribu- tion, evolutionary tracking, and prediction of outbreak dynamics in microbial forensics (Jiang et al., 2022; Danko et al., 2021). The bacterial species analyzed in this study, including Salmonella enteri- ca, Escherichia coli, Listeria monocytogenes, and Vibrio cholerae, are among the most common culprits in foodborne outbreaks and forensic investigations (Todd, 2017; Baliyan et al., 2025). As global food supply chains become in- creasingly complex, the combination of pathogen genomics and forensic tools will be essential for ensuring food safety, tracing sources of contamination, and mitigating biosecurity threats. 4.8. Limitations and Future Directions While this study provides valuable insights into the genomic and epidemio- logical diversity of foodborne bacteria, several limitations remain. Key factors such as host susceptibility, infection dose, and environmental conditions were not included, yet they play crucial roles in disease severity and transmission. Future research should incorporate functional genomics approaches to analyze how gene expression influences virulence and antimicrobial resistance, rather than relying solely on gene presence. Additionally, ML techniques could signifi- cantly improve predictive modeling by identifying genomic signatures linked to high-risk pathogens, enabling more precise risk assessment and targeted public health interventions. Funding This research received no external funding. Conflict of Interest The authors declare no conflicts of interest. 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