International Journal of Social Science & Economic Research
Submit Paper

Title:
Antimicrobial Resistance Prediction in Neisseria Gonorrhoeae via Multilayer Genomic Analysis

Authors:
Rohit Kamath

|| ||

Rohit Kamath
James Madison High School

MLA 8
Kamath, Rohit. "Antimicrobial Resistance Prediction in Neisseria Gonorrhoeae via Multilayer Genomic Analysis." Int. j. of Social Science and Economic Research, vol. 9, no. 11, Nov. 2024, pp. 5568-5580, doi.org/10.46609/IJSSER.2024.v09i11.038. Accessed Nov. 2024.
APA 6
Kamath, R. (2024, November). Antimicrobial Resistance Prediction in Neisseria Gonorrhoeae via Multilayer Genomic Analysis. Int. j. of Social Science and Economic Research, 9(11), 5568-5580. Retrieved from https://doi.org/10.46609/IJSSER.2024.v09i11.038
Chicago
Kamath, Rohit. "Antimicrobial Resistance Prediction in Neisseria Gonorrhoeae via Multilayer Genomic Analysis." Int. j. of Social Science and Economic Research 9, no. 11 (November 2024), 5568-5580. Accessed November, 2024. https://doi.org/10.46609/IJSSER.2024.v09i11.038.

References

[1] . Baker, M. (2019). 1.5 million researchers to lose access to Springer journals. Nature. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7584619/
[2] . Brown, J. K., & Smith, A. L. (2024). Antibiotic resistance in microbial communities: Implications for
[3] . public health. Journal of Medical Microbiology, 73(3), 123-130. https://pubmed.ncbi.nlm.nih.gov/38219758/
[4] . Centers for Disease Control and Prevention. (2021). Gonorrhea - STD treatment guidelines. U.S. Department of Health & Human Services. https://www.cdc.gov/std/treatment-guidelines/gonorrhea-adults.htm
[5] . Doshi, J., Erus, G., Ou, Y., Resnick, S. M., & Davatzikos, C. (2020). Automated brain extraction of multisequence MRI using artificial neural networks. Human Brain Mapping, 41(13), 3696-3709. https://doi.org/10.1002/hbm.24750
[6] . Gehring, J., & Ranzato, M. (2015). Convolutional sequence to sequence learning. PLoS One, 10(8), e0134419. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4534515/
[7] . Graham, S., & Knight, D. (2003). Genomic research: Ethical considerations and future directions. Ethics in Science, 22(2), 101-115. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11041453/
[8] . Hu, F., Guo, Y., Yang, Y., & Huang, M. (2020). Standard antimicrobial susceptibility testing (AST) for Haemophilus influenzae: Limitations and future directions. Journal of Global Antimicrobial Resistance, 21, 25-30. https://www.sciencedirect.com/science/article/pii/S2352396417302244
[9] . Javvadi, Y., & Mohan s. V. (2024). Temporal dynamics and persistence of resistance genes to broad spectrum antibiotics in an urban community. Npj Clean Water, 7(56). https://doi.org/10.1038/s41545-024-00349-y
[10] . Kim, D., & Park, H. (2021). Advances in the molecular diagnosis of antibiotic resistance. Journal of Clinical Microbiology, 59(6), e01234-20. https://pubmed.ncbi.nlm.nih.gov/34135355/
[11] . Koser, C. U., Ellington, M. J., Cartwright, E. J., Gillespie, S. H., Brown, N. M., & Farrington, M. (2014).
[12] . Routine use of microbial whole genome sequencing in diagnostic and public health microbiology. PLoS Pathogens, 10(8), e1004206. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2730337/
[13] . Li, X., & Zhou, Y. (2022). Application of deep learning in antibiotic resistance prediction. Journal of Clinical Microbiology, 60(7), e01678-21. https://pubmed.ncbi.nlm.nih.gov/35616713/
[14] . Tzelves, L., Lazarou, L., Feretzakis, G., Kalles, D., Mourmouris, P., Loupelis, E., Basourakos, S., Berdempes, M., Manolitsis, I., Mitsogiannis, I., Skolarikos, A., & Varkarakis, I. (2022). Using machine learning techniques to predict antimicrobial resistance in stone disease patients. World journal of urology, 40(7), 1731–1736. https://doi.org/10.1007/s00345-022-04043-x
[15] . Ma, Z., & Ma, J. (2018). Genomic predictors of the evolution of E. coli strains. Cancer Genomics & Proteomics, 15(1), 41-56. https://cgp.iiarjournals.org/content/15/1/41
[16] . Mo, Z., Du, P., Wang, G., & Zhang, Y. (2022). Machine learning techniques in antibiotic resistance prediction: Current state and future directions. Frontiers in Microbiology, 13, 841232. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9491192/
[17] . Nashaat, M. (2021, July 12). Hyperparameter tuning with GridSearchCV. Medium. https://medium.com/@mohammednashaat29/hyperparameter-tuning-with-gridsearchcv-8724f215a383
[18] . Rosenbloom, K. R., Armstrong, J., Barber, G. P., Casper, J., Clawson, H., & Diekhans, M. (2021). The UCSC Genome Browser database: 2021 update. Genome Biology, 22(1), 1-26. https://genomebiology.biomedcentral.com/articles/10.1186/s13059-021-02473-1
[19] . Schmutz, T., & Henikoff, S. (2021). Dynamic nucleosome positioning by remodelers. Nature Reviews Molecular Cell Biology, 22(9), 532-550. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10044642/
[20] . Steele, E., Radivojac, P., & Jou, J. D. (2017). Modeling protein sequence evolution with probabilistic grammars and neural networks. PLoS One, 12(5), e0176867. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6258240/
[21] . Tan, Q., & Li, W. (2022). AI-driven approaches in the study of bacterial resistance mechanisms. Frontiers in Microbiology, 13, 835674. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9041565/
[22] . Van Schaik, W., & Willems, R. J. L. (2010). Genome-based insights into the evolution of enterococci. Genome Biology, 11(4), 203. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4378521/
[23] . World Health Organization. (2018). Multi-drug-resistant gonorrhoea. World Health Organization. https://www.who.int/news-room/fact-sheets/detail/multi-drug-resistant-gonorrhoea
[24] . Yang, M.R., & Wu, Y.W. (2022). Enhancing predictions of antimicrobial resistance of pathogens by expanding the potential resistance gene repertoire using a pan-genome-based feature selection approach. BMC bioinformatics, 23(Suppl 4), 131. https://doi.org/10.1186/s12859-022-04666-2
[25] . Zou, K. H., O'Malley, A. J., & Mauri, L. (2007). Receiver-operating characteristic analysis for evaluating diagnostic tests and predictive models. Circulation, 115(5), 654-657. https://pubmed.ncbi.nlm.nih.gov/27890457/

ABSTRACT:
Neisseria gonorrhoeae, designated by the CDC as a critical priority pathogen, is responsible for over 1 million new infections annually (CDC, 2021). The rapid proliferation of antimicrobial resistance (AMR) within N. gonorrhoeae, driven by horizontal gene transfer and point mutations in key loci such as penA, 23S rRNA, and mtrR, has necessitated continuous revisions to antibiotic regimens. This study investigates genomic mutations within bacterial DNA contigs to identify molecular biomarkers associated with resistance phenotypes. A Support Vector Machine (SVM) model was trained on 9967 patient-derived genomic contigs, focusing on resistance mechanisms against azithromycin, ciprofloxacin, and cefixime—the most commonly prescribed antibiotics targeting N. gonorrhoeae. The SVM model achieved a classification accuracy of 90.3%, underscoring the efficacy of machine learning in the characterization of resistance mechanisms at the molecular level. These findings support the integration of genomic data and machine learning approaches for biomarker discovery in the context of precision antimicrobial therapy.

IJSSER is Member of