(160ay) Investigation of Stress Response Genes in Antimicrobial Resistant Pathogens Sampled from Five Countries

Pathogens pose a significant threat to humans, causing millions of deaths worldwide each year [1]. Two of the major contributors to a pathogen’s ability to survive are its effective responses to environmental stressors and its resistance against antimicrobials. In order to successfully infect humans, pathogens must first survive stresses imposed by the environment, decontaminating processes, and humans’ immune responses [2, 3]. Therefore, in order to slow the spread of dangerous infectious diseases, it’s pertinent to identify and study genes that regulate stress-responses in pathogens. Antimicrobial resistance (AMR) is another rapidly growing threat that weakens protection against disease: as antimicrobial usage continues to increase, pathogens are evolving various mechanisms to defend themselves, including efflux pumps and enzymes that degrade the antimicrobial or change the shape of the drug target [4-6], and these antimicrobial resistant genes are easily transmitted through horizontal gene transfer [5]. Furthermore, preliminary links between more effective stress-responses and stronger AMR have been established [7-10]. Thus, to reduce the threat of dangerous infections from pathogens, it’s important to study genes controlling stress-response and AMR, as well as any connections between them.

An invaluable resource for studying pathogens is the NCBI Pathogen Detection Isolates Browser (NPDIB), which contains samples of genes related to stress-response and AMR in pathogens [11]. In the current literature, many studies have been done on AMR [12-14], but not many have looked at stress-response even though it is known that successful stress-response mechanisms can increase the survival rate of pathogens [15, 16]. Some researchers have also looked at specific genes that play a role both in stress-response and AMR [7, 10], but few have studied general relationships and patterns between stress-response and AMR genes in a large set of gene data. Our research provides the first comprehensive statistical analysis of stress response genes from five countries, as well as an analysis of their possible relationships with AMR genes.

Using NPDIB records from 2010-2020, we identified stress-response genes that were frequently found in samples and pathogens that commonly carried these genes. Since there were thousands of gene samples and around 200 dimensions (variables), we used principal component analysis (PCA) [17, 18] to visualize the high-dimensional data for each country on a 2D scale, then used hierarchical clustering to identify outliers [19, 20], which represented stress-response genes with high occurrence frequencies. We repeated this process for pathogens carrying stress-response genes. To analyze the connections between stress-response and AMR genes, we used the PCA and hierarchical clustering method to identify paired stress-response and AMR genes often found in samples together and also plotted the number of stress-response against the number of AMR genes in each sample to find the occurrence correlation. The important outlier stress-response genes frequently appeared in samples from all five countries will be shown in this work. Due to their high occurrence frequency, findings related to these genes are likely to have broader and more useful applications. The pathogens that tended to carry these genes have been identified. In addition, we discovered that samples with three stress response and three AMR genes appeared most frequently in the NPDIB data and that certain stress-response and AMR genes were often grouped together in samples. Our findings of significant stress-response genes and their relationship with certain AMR genes can inform future development of drugs that target stress-response genes to weaken deadly, antimicrobial-resistant pathogens. Effective stress-responses help pathogens survive extreme conditions, allowing more of them to successfully infect humans and transfer disease, so drugs inhibiting their stress-response could reduce their survival rate. Additionally, the rise in AMR can be partly attributed to over-reliance on antimicrobials and sometimes unnecessary or ineffective antimicrobial prescriptions [21], so drugs that attack stress-response genes may result in a decreased reliance on antimicrobials, which may reduce the prevalence of AMR. The stress-response and AMR gene pairs we identified also suggest that inhibiting stress-response genes may also weaken resistance in pathogens. Overall, targeting stress response genes with new drugs could decrease the prevalence of AMR while spurring the creation of potentially more effective treatments against harmful pathogens.

References

1. Scallan, E., et al., Foodborne Illness Acquired in the United States Response. Emerging Infectious Diseases, 2011. 17(7): p. 1339-1340.
2. Boor, K.J., Bacterial stress responses: What doesn't kill them can make them stronger. Plos Biology, 2006. 4(1): p. 18-20.
3. Dühring, S., et al., Host-pathogen interactions between the human innate immune system and Candida albicans - understanding and modeling defense and evasion strategies. Frontiers in Microbiology, 2015. 6.
4. Reygaert, W.C., An overview of the antimicrobial resistance mechanisms of bacteria. Aims Microbiology, 2018. 4(3): p. 482-501.
5. O'Brien, T.F., Emergence, spread, and environmental effect of antimicrobial resistance: How use of an antimicrobial anywhere can increase resistance to any antimicrobial anywhere else. Clinical Infectious Diseases, 2002. 34: p. S78-S84.
6. Harbottle, H., et al., Genetics of antimicrobial resistance. Animal Biotechnology, 2006. 17(2): p. 111-124.
7. Lee, S., et al., Targeting a bacterial stress response to enhance antibiotic action. Proceedings of the National Academy of Sciences of the United States of America, 2009. 106(34): p. 14570-14575.
8. Ebinesh, A., Bacterial Stress Response and Cross Resistance to Antibiotics in the Light of Natural Selection. J Infect Dis Immune Ther. 1:1.
9. Poole, K., Bacterial stress responses as determinants of antimicrobial resistance. Journal of Antimicrobial Chemotherapy, 2012. 67(9): p. 2069-2089.
10. Poole, K., Stress responses as determinants of antimicrobial resistance in Pseudomonas aeruginosa: multidrug efflux and more. Canadian Journal of Microbiology, 2014. 60(12): p. 783-791.
11. Bethesda (MD): National Library of Medicine (US), N.C.f.B.I., The NCBI Pathogen Detection Project. May 2016.
12. Li, K., et al., An Analysis of Antimicrobial Resistance of Clinical Pathogens from Historical Samples for Six Countries. Processes, 2019. 7(12).
13. Yang, K., et al., Investigation of Incidents and Trends of Antimicrobial Resistance in Foodborne Pathogens in Eight Countries from Historical Sample Data. International Journal of Environmental Research and Public Health, 2020. 17(2).
14. Hua, M.G., et al., Comparison of Antimicrobial Resistance Detected in Environmental and Clinical Isolates from Historical Data for the US.Biomed Research International, 2020. 2020.
15. Chung, H.J., W. Bang, and M.A. Drake, Stress response of Escherichia coli. Comprehensive Reviews in Food Science and Food Safety, 2006. 5(3): p. 52-64.
16. Bolton, D.J., et al., The ineffectiveness of organic acids, freezing and pulsed electric fields to control Escherichia coli O157 : H7 in beef burgers. Letters in Applied Microbiology, 2002. 34(2): p. 139-143.
17. Kourti, T., Application of latent variable methods to process control and multivariate statistical process control in industry. International Journal of Adaptive Control and Signal Processing, 2005. 19(4): p. 213-246.
18. Qin, S.J., Statistical process monitoring: basics and beyond. Journal of Chemometrics, 2003. 17(8-9): p. 480-502.
19. Bar-Joseph, Z., et al., K-ary clustering with optimal leaf ordering for gene expression data. Bioinformatics, 2003. 19(9): p. 1070-1078.
20. Wang, X.Z., K. Smith, and R. Hyndman, Characteristic-based clustering for time series data. Data Mining and Knowledge Discovery, 2006. 13(3): p. 335-364.
21. Michael, C. A., Dominey-Howes, D., and Labbate, M. The antimicrobial resistance crisis: Causes, consequences, and management. Frontiers in Public Health, 2014. 2.