About Me:
#MeetTheScientist
Hello! My name is Tam Tran, and I am a student at Georgia State University pursuing a Bachelor of Interdisciplinary Studies in Biomedical Sciences and Enterprise. I am passionate about science and healthcare and enjoy learning how biomedical research can improve human health and advance medicine.
Through my academic coursework and healthcare experiences, I have developed a strong interest in the translation of science to real-world experiences. This portfolio highlights my growth, skills, and experiences as I continue my journey in the biomedical sciences.
Beyond a common Bacterium: What Genome Analysis reveals about E. Coli's Ability to cause Disease
Introduction: Why Study E. Coli?
Most people have heard of Escherichia coli (E. coli), a bacterium commonly associated with food poisoning. However, many E. coli strains naturally live in the intestines of humans and animals without causing harm. The difference between harmless and harmful strains often comes down to their genetic makeup.
In this project, I analyzed the genome of an unknown E. coli strain to uncover clues about its evolutionary background, antibiotic resistance abilities, and potential to cause disease.
Where does this E. coli strain fit within its evolutionary family?
Using Clermont Typing, the assigned genome was classified as phylogroup B2, a group of E. coli strains that is often associated with infections outside of the intestinal tract (Clermont et al., 2019). To further understand its genetic background, Multi-Locus Sequence Typing (MLST) was performed using the Center for Genomic Epidemiology (CGE) online platform. The genome was identified as sequence type 636 (ST636), which represents a specific combination of genetic markers shared through related bacterial strains. Identifying sequence types helps researchers track bacterial populations, study their evolution, and monitor the spread of potentially harmful strains.
After determining the identity of this E. coli strain, the next step was investigating whether its genome contained traits that could make infections more difficult to treat. Using the Comprehensive Antibiotic Resistance Database (CARD), several antibiotic resistance mechanisms were identified, including antibiotic target alteration, reduced permeability, antibiotic efflux, and antibiotic inactivation (Alcock et al., 2023). The genome contained two important beta-lactamase genes, CTX-M-15 and TEM-1, which produce enzymes capable of breaking down beta-lactam antibiotics (Bonomo, 2017). Additionally, a soxS mutation was identified as a concerning feature because it can contribute to increased antibiotic efflux, allowing bacteria to remove certain antibiotics from the cell and potentially survive exposure to multiple antibiotic classes (Alcock et al., 2023).
Beyond antibiotic resistance, the genome was also examined for genetic features that may contribute to infection. Using VirulenceFinder, the genome showed characteristics consistent with uropathogenic Escherichia coli (UPEC), a group of E. coli strains commonly associated with urinary tract infections. This classification was supported by the presence of multiple afa genes, including afaA, afaB, afaC, and afaD (Whelan et al., 2023). These genes are involved in producing adhesion factors that help bacteria attach to host cells. This ability to adhere to tissues is an important early step in colonization and infection.
These findings reveal how genome analysis can uncover important information about bacterial behavior. By examining evolutionary relationships, antibiotic resistance genes, and virulence factors, scientists can better understand how specific bacterial strains develop the ability to survive and cause disease. This genome serves as an example of why genomic surveillance is valuable for tracking emerging threats and improving strategies for preventing and treating bacterial infections.
(Centers for Disease Control and Prevention, 2024).
(Bonomo, 2017).
Methodology:
Exploring the Genome Through Bioinformatics
To investigate the assigned Escherichia coli genome, several bioinformatics tools were used to analyze different genetic features of the bacterium. Genome analysis allows researchers to study bacterial DNA and identify characteristics that may influence evolution, survival, and interactions with hosts.
First, Clermont Typing was used to determine the evolutionary group of the E. coli genome. This classification helps researchers understand how bacterial strains are related and provides information on their genetic background.
Next, Multi-Locus Sequence Typing (MLST) was performed using the Center for Genomic Epidemiology (CGE) platform. MLST compares specific conserved genes within bacterial genomes to assign sequence types, allowing scientists to identify and track related bacterial strains.
The genome was then analyzed using the Comprehensive Antibiotic Resistance Database (CARD) through the Resistance Gene Identifier (RGI) tool. This analysis searches for known genetic features associated with antibiotic resistance and helps identify potential mechanisms bacteria use to survive antibiotic exposure.
Finally, VirulenceFinder was used to screen the genome for genes associated with bacterial infection. This tool identifies genetic factors that may contribute to bacterial colonization and disease development.
These approaches provided a comprehensive analysis of the genome by examining its evolutionary relationships, antibiotic resistance potential, and possible disease-associated characteristics.
In Conclusion,
Analyzing the assigned Escherichia coli genome demonstrated how bioinformatics can reveal important information about a bacterium from its DNA alone. Through identifying its phylogroup, sequence type, antibiotic resistance mechanisms, and virulence-associated genes, this project provided information to the strain's evolutionary background and characteristics that may contribute to its ability to survive antibiotic treatment and cause disease. While genome analysis cannot predict how a bacterium will behave in every situation, it provides evidence that can guide future research and clinical investigations.
A potential next step would be to validate these findings through laboratory experiments to determine whether the predicted resistance genes and virulence factors are actively expressed. Expanding this analysis by comparing similar E. coli genomes could also improve our understanding of how antibiotic resistance and pathogenic traits evolve and spread.
As antibiotic-resistant bacteria continue to pose a global public health challenge, genomic analysis has become an essential tool for disease surveillance and research. This project highlights how combining genomics and bioinformatics can transform DNA sequence data into meaningful biological insights, helping researchers better understand bacterial pathogens and supporting future efforts to improve infection prevention and treatment.
Reflection,
This Signature Experience strengthened both my technical knowledge and professional skills by introducing me to the field of bacterial genomics and bioinformatics. Throughout this project, I learned how to use bioinformatics tools to analyze a bacterial genome, identify its evolutionary relationships, and investigate genes associated with antibiotic resistance and virulence. In addition to collecting results, I developed a deeper understanding of how genomic data can be used to answer meaningful biological questions and support public health research.
One of the most valuable skills I gained was the ability to interpret complex scientific data and communicate my findings in a way that is understandable to different audiences. Translating the information into clear explanations challenged me to think critically about the significance of my results rather than simply reporting them. This experience also strengthened my problem-solving skills, attention to detail, and ability to evaluate scientific evidence, all of which are essential competencies in biomedical sciences.
This project reinforced the importance of continuous learning in a rapidly evolving field. Bioinformatics integrates biology, computer science, and data analysis, demonstrating how interdisciplinary approaches are becoming increasingly important in modern healthcare and biomedical research. Learning to navigate genomic databases and online analysis tools has given me the confidence in applying computational methods to biological questions.
Overall, this Signature Experience has prepared me for future opportunities in biomedical sciences by strengthening my analytical thinking, scientific communication, and data interpretation skills. Whether pursuing research, clinical laboratory science, or other healthcare-related careers, I will be able to apply these transferable skills to evaluate scientific evidence, solve complex problems, and contribute to improving patient care and public health.
Citations
Alcock, Brian P, et al. “RGI Resistance Gene Identifier.” CARD, card.mcmaster.ca/analyze/rgi. Accessed July 2026.
Bonomo, Robert A. “Β-Lactamases: A Focus on Current Challenges - PMC.” National Library of Medicine, Jan. 2017, pmc.ncbi.nlm.nih.gov/articles/PMC5204326/.
Center for Genomic Epidemiology, genepi.dk/mlstfinder. Accessed July 2026.
Clermont O, Dixit OVA, Vangchhia B, Condamine B, Dion S, Bridier-Nahmias A, Denamur E, Gordon D. Characterization and rapid identification of phylogroup G in Escherichia coli, a lineage with high virulence and antibiotic resistance potential. Environ Microbiol. 2019 Jun 12. [PMID: 31188527] [DOI: 10.11111/1462-2920.14713]
Whelan, Shane, et al. “Uropathogenic Escherichia Coli (Upec)-Associated Urinary ...” Uropathogenic Escherichia Coli (UPEC)-Associated Urinary Tract Infections: The Molecular Basis for Challenges to Effective Treatment, 28 Aug. 2023, pmc.ncbi.nlm.nih.gov/articles/PMC10537683/.