Exploring the Role of Host-Pathogen Interaction in Airborne Disease Susceptibility

Abstract

The capacity of airborne infectious illnesses to generate broad epidemics and their high rates of transmission make them a constant concern to public health around the world. Serious morbidity and mortality can result from these illnesses, which are mostly transmitted by aerosols and respiratory droplets. To create efficient treatment and prevention plans, it is essential to comprehend the molecular mechanisms underpinning host-pathogen interactions. Four main airborne bacterial pathogens—Neisseria meningitidis, Yersinia pestis, Legionella pneumophila, and Streptococcus pneumoniae—and their interactions with host proteins are the subject of this study. We investigated the binding affinities of important virulence agents, including adhesins, toxins, and immune evasion proteins, with host receptors using molecular docking analyses. The docking results highlighted the molecular underpinnings of disease by revealing robust connections that promote bacterial adherence, immune system evasion, and intracellular survival. Human epithelial cell receptors and pneumococcal adhesins have high-affinity interactions, indicating possible targets for preventing bacterial colonization. Similar to this, Yersinia pestis uses its Type III Secretion System (T3SS) to control immunological responses, whereas Neisseria meningitidis uses host factor binding proteins to penetrate the blood-brain barrier. Legionella pneumophila demonstrates the pathogen's versatility in host invasion by taking advantage of the host's cellular machinery to establish a replicative niche.

These discoveries open the door for innovative therapeutic approaches by offering vital insights into the molecular underpinnings of airborne illness susceptibility. Using monoclonal antibodies, small-molecule inhibitors, or vaccine-based strategies to target these interactions may prevent bacterial colonization and illness. Furthermore, improvements in ventilation, public health initiatives, and air filtration technology all contribute to the prevention of disease. Our capacity to create efficient antibacterial methods will be improved by combining computational docking research with experimental validation. This study advances our knowledge of the dynamics between airborne pathogens and hosts and encourages the creation of novel strategies to fight infectious diseases.

Country : India

1 Palak Sachdeva2 Jyoti Prakash3 Akanksha Pandey4 Rachna Chaturvedi

  1. Amity Institute of Biotechnology, Amity University Uttar Pradesh, Lucknow Campus, India
  2. Amity Institute of Biotechnology, Amity University Uttar Pradesh, Lucknow Campus, India
  3. Queen Mary Hospital, KGMU Lucknow, India
  4. Amity Institute of Biotechnology, Amity University Uttar Pradesh, Lucknow Campus, India

IRJIET, Volume 9, Issue 5, May 2025 pp. 131-138

doi.org/10.47001/IRJIET/2025.905017

References

  1. R. L. Y. C. B. J. & T. J. W. Tellier, “Recognition of aerosol transmission of infectious agents: A commentary.” BMC Infectious Diseases, vol. 19(1), p. 101, 2019.
  2. L. & C. J. Morawska, “Airborne transmission of SARS-CoV-2: The world should face the reality.” Environmental International, vol. 139, p. 105730, 2020.
  3. B. B. &. M. G. Finlay, “Anti-Immunology: Evasion of the host immune system by bacterial and viral pathogens,” Cell, vol. 124(4), pp. 767-782, 2006.
  4. A. W. J. N. P. J. C. & A. P. W. Kadioglu, “The role of Streptococcus pneumoniae virulence factors in host respiratory colonization and disease.” Nature Reviews Microbiology, vol. 6(4), pp. 288-301, 2008.
  5. S. &. P. Jones, “Computational approaches in drug discovery and the role of docking.” Bioinformatics Reviews, vol. 15(3), pp. 225-242, 2019.
  6. H. R. & O. A. J. Morris G. M., “Using AutoDock for ligand-receptor docking.” Current protocols in Bioinformatics, vol. 8(14), pp. 1-30, 2009.
  7. D. M. F. G. K. & F. A. S. Morens, “Emerging infectious diseases: Threats to human health and global stability.” Cell, vol. 131(4), pp. 693-703, 2008.
  8. K. P. Fennelly, “Particle sizes of infectious aerosols: implications for infection control.” The Lancet Respiratory Medicine, vol. 8(9), pp. 914-924, 2020.
  9. S. J. & H. A. V. S. Chapman, “Human genetic susceptibility to infectious disease.” Nature Reviews Genetics, vol. 13(3), pp. 175-188, 2012.
  10. D. M. & B. R. J. Altmann, “SARS-CoV-2 T cell immunity: Specificity, function, durability, and role in protection,” Science Immunology, vol. 5(49), p. eabd6160, 2020.
  11. J. K. & K. J. C. Taubenberger, “Influenza virus evolution, host adaptation, and pandemic formation.” Cell Host & Microbe, vol. 7(6), pp. 440-451, 2010.
  12. C. J. F. S. & R. L. Cambier, “Host evasion and exploitation schemes of Mycobacterium tuberculosis,” Cell, vol. 159(7), pp. 1497-1509, 2014.
  13. J. &. L. R. Ostrop, “Contact, communication, and cooperation of myeloid cells in the lung immune response,” Frontiers in Immunology, vol. 8, p. 1026, 2017.
  14. E.A. Brown. J., “Molecular Mechanisms of Bacterial Pathogenesis,” Microbial Pathogenesis, vol. 142, p. 104047, 2020.
  15. H. E. A. Berman, “The Protein Data Bank,” Nucleic Acids Research, vol. 28.1, pp. 235-242, 2000.
  16. A. E. A. Waterhouse, “SWISS-MODEL: homology modelling of protein structures and complexes,” Nucleic Acids Research, vol. 46, W1, pp. W296-W303, 2018.
  17. M. E. A. Abraham, “GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers.” SoftwareX 1-2, pp. 19-25, 2015.
  18. O. &. O. A. Trott, “AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading.” Journal of Computational Chemistry, vol. 31.2, pp. 455-461, 2010.
  19. S. T. P. A. B. E. E. C. J. F. G. G. A. & B. S. H. Kim, “PubChem substance and compound databases.” Nucleic Acids Research, vol. 44(D1), pp. D1202-1213, 2016.
  20. Y. X. J. S. T. O. Z. J. B. S. H. Wang, “PubChem: A public information system for maintaining, indexing, and searching compund data.,” Nucleic Acids Research, vol. 37(suppl_2), pp. W623-633, 2009.
  21. G. M. H. R. L. W. S. M. F. B. R. K. G. D. S. & O. A. J. Morris, “AutoDock4 and AutoDockTools4: Automated Docking with Selective receptor flexibility.” Journal of Computational Chemistry, vol. 30(16), pp. 2785-2791, 2009.
  22. K. A. S. A. G. S. &. J. T. Ghosh, “Chemical-informatics approach to COVID-19 drug discovery: Identifying promising anti-SARS-CoV-2 inhibitors.,” Briefings in Bioinformatics, vol. 22(2), pp. 1-12, 2020.
  23. S. S. S. P. & R. G. P. S. Mishra, “Exploring chemical space of potential inhibitors for Tuberculosis Drug Discovery,” Briefings in Bioinformatics, vol. 20(1), pp. 109-124, 2019.
  24. W. C. A. J. H. A. R. A. V. A. M. H. A. P. & A. V. M. Van Voorhis, “Open-source drug discovery with the malaria box compound collection for neglected diseases and beyond.” PLoS Pathogens, vol. 12(7), p. e1005763, 2016.
  25. G. M. H. R. L. W. e. a. Morris, “AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility,” Journal of Computational Chemistry, vol. 30(16), pp. 2785-2791, 2009.
  26. O. &. O. A. J. Trott, “AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading.” Journal of Computational Chemistry, vol. 31(2), pp. 455-461, 2010.
  27. Y. G. M. D. W. T. H. M. C. X. Z. X. &. C. Y. Liu, “CB-Dock2: Improved protein-ligand blind docking by cavity detection and flexible docking.” Bioinformatics, vol. 38(6), pp. 1628-1630, 2022.
  28. J. S.-M. D. T. A. F. & F. S. Eberhardt, “AutoDock Vina 1.2.0: New docking methods, expanded force field, and Python bindings.” Journal of Chemical Information and Modeling, vol. 61(8), pp. 3891-3898, 2021.
  29. J. R. A. & Z. Y. Yang, “Protein-ligand docking with an implicit solvent model.” Nature Protocols, vol. 16(2), pp. 435-457, 2021.
  30. J. H. et al. Beigel, “Remdesivir for the treatment of COVID-19.” New England Journal of Medicine, vol. 383(19), pp. 1813-1826, 2020.
  31. L. e. a. Guglielmetti, “Bedaquiline- and delamanid-containing regimens in tuberculosis treatment.” European Respiratory Journal, vol. 57(3), p. 2001796, 2021.
  32. P. C. e. a. Taylor, “Neutralizing monoclonal antibodies for treatment of COVID-19,” Nature Reviews Immunology, vol. 21(6), pp. 382-393, 2021.
  33. J. e. a. Wang, “Nanotechnology-based drug delivery systems for tuberculosis treatment.,” Advanced Drug Delivery Reviews, vol. 178, p. 113920, 2022.