Candida Enfeksiyonlarına Karşı Toll-benzeri Reseptörlerin ve Antimikrobiyal Peptitlerin Özelleştirilmesine Yönelik Hesaplamalı Yaklaşımlardaki Son Gelişmeler

Öz

Candida albicans'ın insan sağlığı üzerindeki kayda değer patojenik etkisine rağmen, hücresel tanıma mekanizmalarının ve ardından gelen konakçı savunma aktivasyonunun anlaşılmasındaki boşluk yeterince anlaşılmamıştır. Son bilgiler, Toll benzeri reseptörlerin (TLR'ler) patojenlere karşı doğuştan gelen bağışıklık tepkilerini düzenlemedeki önemli rolünün altını çiziyor. Özellikle, son yıllardaki ampirik araştırmalar, TLR'lerin memelilerde en önemli model tanıma reseptörleri olduğunun altını çizmiştir. Örneğin TLR2, peptidoglikanlar, lipoarabinomannan ve bakteriyel lipoproteinler için afinite sergilerken TLR4, lipopolisakkarit (LPS) ve lipo-teikoik asidin saptanmasında rol oynar. Benzer şekilde TLR5 flagellini tanır ve TLR9 bakteriyel DNA tanımayla ilişkilidir. Toll'un Drosophila'da antifungal mekanizmaların düzenleyicisi olarak ilk tanımlanması, TLR'lerin memeli antifungal savunmasında potansiyel olarak dahil olduğunu düşündürmektedir. Bununla birlikte, Drosophila'daki Toll ile antifungal mekanizmalar arasındaki evrimsel bağlantıya rağmen, insanlarda fungal patojenlerle mücadelede TLR'lerin rolünün tanımlanmasına çok az önem verilmiştir; bu, TLR'lerin memeli antifungal savunmasında makul bir rol oynadığını düşündürmektedir. Özellikle kanıtlar, Aspergillus fumigatus'a yanıt olarak proinflamatuar sitokinleri indüklemede TLR4'ü gösterir ancak TLR2'yi kapsamaz; bu arada rolünün, hücrelerin Cryptococcus neoformans ile uyarılmasından sonra TNF üretimi olmasa da hücre içi sinyalleşmeye aracılık ettiği iddia edilir. Bununla birlikte, TLR aktivasyon kurallarına ilişkin içgörüler, antimikrobiyal peptit (AMP) ile TLR etkileşimlerinin incelenmesini mümkün kılmaktadır ve çeşitli moleküllerin immünomodülatör kapasitelerine ilişkin tahminleri kolaylaştırmaktadırç Bu ilerlemelere rağmen, TLR'lerin önde gelen bir insan patojeni olan Candida albicans'ı tanımadaki spesifik rolü hala belirsizliğini koruyor ve daha fazla araştırma yapılmasını gerektiriyor. Bu hesaplamalı yaklaşım, AMP'ler ve TLR'ler arasındaki etkileşimleri aydınlatan, TLR aktivasyonunu yöneten yapısal belirleyicileri tanımlayan ve böylece çeşitli moleküler varlıkların immünomodülatör potansiyeline ilişkin öngörüler sağlayan son bulguları sentezlemektedir.

Anahtar Kelimeler

• Toll-benzeri reseptörler
• antimikrobiyal peptit
• Candida enfeksiyonlar
• hesaplamalı yaklaşımlar
• protein-protein docking
• moleküler dinamik simülasyonu
• doku mühendisliği

Recent Advancements in Computational Approaches for Tailoring Toll-like Receptors and Antimicrobial Peptides Against Candida Infections

Öz

Despite the considerable pathogenic impact of Candida albicans in human health, the gap in understanding the cellular recognition mechanisms and subsequent host defence activation remain insufficiently understood. Recent insights underscore the pivotal role of Toll-like receptors (TLRs) in organising innate immune responses against pathogens. Notably, empirical investigations over recent years have underscored TLRs as paramount pattern-recognition receptors in mammals. TLR2, for examples, exhibits affinity for peptidoglycans, lipoarabinomannan, and bacterial lipoproteins, while TLR4 implicated in detecting lipopolysaccharide (LPS) and lipo-teichoic acid. Similarly, TLR5 recognizes flagellin, and TLR9 is associated with bacterial DNA recognition. The initial identification of Toll in Drosophila as a regulator of antifungal mechanisms suggests the potential involvement of TLRs in mammalian antifungal defence. However, scant attention has been devoted to delineating the role of TLRs in combating fungal pathogens in humans, despite the evolutionary link between Toll in Drosophila and antifungal mechanisms, suggesting a plausible involvement of TLRs in mammalian antifungal defense. Notably, evidence implicates TLR4, but not TLR2, in inducing proinflammatory cytokines in response to Aspergillus fumigatus, while its role is purported to mediate intracellular signaling, albeit not TNF production, after stimulation of cells with Cryptococcus neoformans. However, insights into TLR activation rules have enabled the examination of antimicrobial peptide (AMP) interactions with TLRs, facilitating predictions regarding the immunomodulatory capacities of diverse molecules. Despite these advancements, the specific role of TLRs in recognizing Candida albicans, a prominent human pathogen, remains elusive, warranting further investigation. This computational approach synthesizes recent findings elucidating the interactions between AMPs and TLRs, delineating the structural determinants governing TLR activation, thus enabling predictive insights into the immunomodulatory potential of diverse molecular entities

Anahtar Kelimeler

• Toll-like receptors
• antimicrobial peptide
• candida infections
• computational approaches
• protein-protein docking
• molecular dynamics simulation
• tissue engineering

Kaynakça

1. [1]. Medici, N. P., Del Poeta, M. 2015. New insights on the development of fungal vaccines: from immunity to recent challenges. Mem Inst Oswaldo Cruz, 110, 966-73.
2. [2]. Bongomin, F., Gago, S., Oladele, R. O., Denning, D. W. 2017. Global and Multi-National Prevalence of Fungal Diseases-Estimate Precision. J Fungi (Basel), 3.
3. [3]. Benedict, K., Richardson, M., Vallabhaneni, S., Jackson, B. R., Chiller, T. 2017. Emerging issues, challenges, and changing epidemiology of fungal disease outbreaks. Lancet Infect Dis, 17, e403-e411.
4. [4]. Quindós, G., Marcos-Arias, C., San-Millán, R., Mateo, E., Eraso, E. 2018. The continuous changes in the aetiology and epidemiology of invasive candidiasis: from familiar Candida albicans to multiresistant Candida auris. Int Microbiol, 21, 107-119.
5. [5]. Robbins, N., Wright, G. D., Cowen, L. E. 2016. Antifungal Drugs: The Current Armamentarium and Development of New Agents. Microbiol Spectr, 4.
6. [6]. Freitas, C. G., Felipe, M. S. 2023. Candida albicans and Antifungal Peptides. Infectious Diseases and Therapy, 12, 2631-2648.
7. [7]. Osset-Trénor, P., Pascual-Ahuir, A., Proft, M. 2023. Fungal Drug Response and Antimicrobial Resistance. J Fungi (Basel), 9.
8. [8]. Pappas, P. G., Kauffman, C. A., Andes, D., Benjamin, D. K. Jr., Calandra, T. F., Edwards, J. E. Jr., Filler, S. G., Fisher, J. F., Kullberg, B. J., Ostrosky-Zeichner, L., Reboli, A. C., Rex, J. H., Walsh, T. J., Sobel, J. D. 2009. Clinical practice guidelines for the management of candidiasis: 2009 update by the Infectious Diseases Society of America. Clin Infect Dis, 48, 503-535.

9. [9]. Barantsevich, N., Barantsevich, E. 2022. Diagnosis and Treatment of Invasive Candidiasis. Antibiotics (Basel), 11.
10. [10]. Andes, D. R., Safdar, N., Baddley, J. W., Playford, G., Reboli, A. C., Rex, J. H., Sobel, J. D., Pappas, P. G., Kullberg, B. J. 2012. Impact of treatment strategy on outcomes in patients with candidemia and other forms of invasive candidiasis: a patient-level quantitative review of randomized trials. Clin Infect Dis, 54, 1110-1122.
11. [11]. Roy, M., Karhana, S., Shamsuzzaman, M., Khan, M. A. 2023. Recent drug development and treatments for fungal infections. Braz J Microbiol, 54, 1695-1716.
12. [12]. Sarkar, S., Uppuluri, P., Pierce, C. G., Lopez-Ribot, J. L. 2014. In vitro study of sequential fluconazole and caspofungin treatment against Candida albicans biofilms. Antimicrob Agents Chemother, 58, 1183-1186.
13. [13]. McKeny, P. T., Nessel, T. A., Zito, P. M. 2024. Antifungal Antibiotics. StatPearls, StatPearls Publishing Copyright © 2024, StatPearls Publishing LLC., Treasure Island (FL) ineligible companies. Disclosure: Trevor Nessel declares no relevant financial relationships with ineligible companies. Disclosure: Patrick Zito declares no relevant financial relationships with ineligible companies.
14. [14]. Haney, E. F., Straus, S. K., Hancock, R. E. W. 2019. Reassessing the Host Defense Peptide Landscape. Frontiers in Chemistry, 7.
15. [15]. Haney, E. F., Hancock, R. E. 2013. Peptide design for antimicrobial and immunomodulatory applications. Biopolymers, 100, 572-583.
16. [16]. Nijnik, A., Hancock, R. 2009. Host defence peptides: antimicrobial and immunomodulatory activity and potential applications for tackling antibiotic-resistant infections. Emerg Health Threats J, 2, e1.
17. [17]. Anwar, M. A., Shah, M., Kim, J., Choi, S. 2019. Recent clinical trends in Toll-like receptor targeting therapeutics. Medicinal Research Reviews, 39, 1053-1090.
18. [18]. Baek, M., DiMaio, F., Anishchenko, I., Dauparas, J., Ovchinnikov, S., Lee, G. R., Wang, J., Cong, Q., Kinch, L. N., Schaeffer, R. D., Millán, C., Park, H., Adams, C., Glassman, C. R., DeGiovanni, A., Pereira, J. H., Rodrigues, A. V., van Dijk, A. A., Ebrecht, A. C., Opperman, D. J., Sagmeister, T., Buhlheller, C., Pavkov-Keller T, Rathinaswamy MK, Dalwadi U, Yip CK, Burke JE, Garcia KC, Grishin NV, Adams PD, Read RJ, Baker D. 2021. Accurate prediction of protein structures and interactions using a three-track neural network. Science, 373, 871-876.
19. [19]. Sartorius, R., Trovato, M., Manco, R., D’Apice, L., De Berardinis, P. 2021. Exploiting viral sensing mediated by Toll-like receptors to design innovative vaccines. npj Vaccines, 6, 127.
20. [20]. Murgueitio, M. S., Rakers, C., Frank, A., Wolber, G. 2017. Balancing Inflammation: Computational Design of Small-Molecule Toll-like Receptor Modulators. Trends in Pharmacological Sciences, 38, 155-168.
21. [21]. Billod, J-M., Lacetera, A., Guzmán-Caldentey, J., Martín-Santamaría, S. 2016. Computational Approaches to Toll-Like Receptor 4 Modulation. Molecules, 21, 994.
22. [22]. Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., Bridgland, A., Meyer, C., Kohl, S. A. A., Ballard, A. J., Cowie, A., Romera-Paredes, B., Nikolov, S., Jain, R., Adler, J., Back, T., Petersen, S., Reiman, D., Clancy, E., Zielinski, M., Steinegger, M., Pacholska, M., Berghammer, T., Bodenstein, S., Silver, D., Vinyals, O., Senior, A. W., Kavukcuoglu, K., Kohli, P., Hassabis, D. 2021. Highly accurate protein structure prediction with AlphaFold. Nature, 596, 583-589.
23. [23]. Burley, S. K., Bhikadiya, C., Bi, C., Bittrich, S., Chao, H., Chen, L., Craig, P. A., Crichlow, G. V., Dalenberg, K., Duarte, J. M., Dutta, S., Fayazi, M., Feng, Z., Flatt, J. W., Ganesan, S., Ghosh, S., Goodsell, D. S., Green, R. K., Guranovic, V., Henry, J., Hudson, B. P., Khokhriakov, I., Lawson, C. L., Liang, Y., Lowe, R., Peisach, E., Persikova, I., Piehl, D. W., Rose, Y., Sali, A., Segura, J., Sekharan, M., Shao, C., Vallat, B., Voigt, M., Webb, B., Westbrook, J. D., Whetstone, S., Young, J. Y., Zalevsky, A., Zardecki, C. 2022. RCSB Protein Data Bank (RCSB.org): delivery of experimentally-determined PDB structures alongside one million computed structure models of proteins from artificial intelligence/machine learning. Nucleic Acids Research, 51, D488-D508.
24. [24]. Mirdita, M., Schütze, K., Moriwaki, Y., Heo, L., Ovchinnikov, S., Steinegger, M. 2022. ColabFold: making protein folding accessible to all. Nature Methods, 19, 679-682.
25. [25]. Kozakov, D., Hall, D. R., Xia, B., Porter, K. A., Padhorny, D., Yueh, C., Beglov, D., Vajda, S. 2017. The ClusPro web server for protein–protein docking. Nature Protocols, 12, 255-278.
26. [26]. Jo, S., Kim, T., Iyer, V. G., Im, W. 2008. CHARMM-GUI: A web-based graphical user interface for CHARMM. Journal of Computational Chemistry, 29, 1859-1865.
27. [27]. Lee, J., Cheng, X., Swails, J. M., Yeom, M. S., Eastman, P. K., Lemkul, J. A., Wei, S., Buckner, J., Jeong, J. C., Qi, Y., Jo, S., Pande, V. S., Case, D. A., Brooks, C. L., 3rd, MacKerell, A. D., Klauda, J. B., Im, W. 2016. CHARMM-GUI Input Generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM Simulations Using the CHARMM36 Additive Force Field. J Chem Theory Comput, 12, 405-413.
28. [28]. Huang, J., Rauscher, S., Nawrocki, G., Ran, T., Feig, M., de Groot, B. L., Grubmüller, H., MacKerell, A. D. 2017. CHARMM36m: an improved force field for folded and intrinsically disordered proteins. Nature Methods, 14, 71-73.
29. [29]. Abraham, M. J., Murtola, T., Schulz, R., Páll, S., Smith, J. C., Hess, B., Lindahl, E. 2015. GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX, 1(2):19-25.
30. [30]. Humphrey, W., Dalke, A., Schulten, K. 1996. VMD: Visual molecular dynamics. Journal of Molecular Graphics, 14, 33-38.
31. [31]. Bakan, A., Meireles, L. M., Bahar, I. 2011. ProDy: Protein Dynamics Inferred from Theory and Experiments. Bioinformatics, 27, 1575-1577.
32. [32]. Miller, B. R. III., McGee, T. D., Swails, J. M., Homeyer, N., Gohlke, H., Roitberg, A. E. 2012. MMPBSA.py: An Efficient Program for End-State Free Energy Calculations. Journal of Chemical Theory and Computation, 8, 3314-3321.
33. [33]. Valdés-Tresanco, M. S, Valdés-Tresanco, M. E., Valiente, P. A., Moreno, E. 2021. gmx_MMPBSA: A New Tool to Perform End-State Free Energy Calculations with GROMACS. J Chem Theory Comput, 17, 6281-6291.
34. [34]. Sameer, A. S., Nissar, S. 2021. Toll-Like Receptors (TLRs): Structure, Functions, Signaling, and Role of Their Polymorphisms in Colorectal Cancer Susceptibility. Biomed Res Int, 2021, 1157023.
35. [35]. Lee EY, Lee MW, Wong GCL (2019) Modulation of toll-like receptor signaling by antimicrobial peptides. Semin Cell Dev Biol 88:173-184.
36. [36]. Kumar, N., Sood, D., Tomar, R., Chandra, R. 2019. Antimicrobial Peptide Designing and Optimization Employing Large-Scale Flexibility Analysis of Protein-Peptide Fragments. ACS Omega, 4, 21370-21380.
37. [37]. Zhang, Y., Liang, X., Bao, X., Xiao, W., Chen, G. 2022. Toll-like receptor 4 (TLR4) inhibitors: Current research and prospective. European Journal of Medicinal Chemistry, 235, 114291.
38. [38]. Konstantinidis, K., Karakasiliotis, I., Anagnostopoulos, K., Boulougouris, G. C. 2021. On the estimation of the molecular inaccessible volume and the molecular accessible surface of a ligand in protein–ligand systems. Molecular Systems Design & Engineering, 6, 946-963.
39. [39]. Chaieb, K., Kouidhi, B., Hosawi, S. B., Baothman, O. A. S., Zamzami, M. A., Altayeb, H. N. 2022. Computational screening of natural compounds as putative quorum sensing inhibitors targeting drug resistance bacteria: Molecular docking and molecular dynamics simulations. Computers in Biology and Medicine, 145, 105517.
40. [40]. Agarwal, S. M., Nandekar, P., Saini, R. 2022. Computational identification of natural product inhibitors against EGFR double mutant (T790M/L858R) by integrating ADMET, machine learning, molecular docking and a dynamics approach. RSC Advances, 12, 16779-16789.
41. [41]. Bellocchio, S., Gaziano, R., Bozza, S., Rossi, G., Montagnoli, C., Perruccio, K., Calvitti, M., Pitzurra, L., Romani, L. 2005. Liposomal amphotericin B activates antifungal resistance with reduced toxicity by diverting Toll-like receptor signalling from TLR-2 to TLR-4. J Antimicrob Chemother, 55, 214-222.
42. [42]. van de Veerdonk, F. L., Netea, M. G., Jansen, T. J., Jacobs, L., Verschueren, I., van der Meer, J. W., Kullberg, B. J. 2008. Redundant role of TLR9 for anti-Candida host defense. Immunobiology, 213, 613-620.
43. [43]. Naglik, J. R., Richardson, J. P., Moyes, D. L. 2014. Candida albicans pathogenicity and epithelial immunity. PLoS Pathog, 10, e1004257.
44. [44]. Adase, C. A., Borkowski, A. W., Zhang, L. J., Williams, M. R., Sato, E., Sanford, J. A., Gallo, R. L. 2016. Non-coding Double-stranded RNA and Antimicrobial Peptide LL-37 Induce Growth Factor Expression from Keratinocytes and Endothelial Cells. J Biol Chem, 291, 11635-11646.
45. [45]. Zhang, L. J., Sen, G. L., Ward, N. L., Johnston, A., Chun, K., Chen, Y., Adase, C., Sanford, J. A., Gao, N., Chensee, M., Sato, E., Fritz, Y., Baliwag, J., Williams, M. R., Hata, T., Gallo, R. L. 2016. Antimicrobial Peptide LL37 and MAVS Signaling Drive Interferon-β Production by Epidermal Keratinocytes during Skin Injury. Immunity, 45,119-130.
46. [46]. Heil, F., Hemmi, H., Hochrein, H., Ampenberger, F., Kirschning, C., Akira, S., Lipford, G., Wagner, H., Bauer, S. 2004. Species-specific recognition of single-stranded RNA via toll-like receptor 7 and 8. Science, 303, 1526-1529.
47. [47]. Lund, J. M., Alexopoulou, L., Sato, A., Karow, M., Adams, N. C., Gale, N. W., Iwasaki, A., Flavell, R. A. 2004. Recognition of single-stranded RNA viruses by Toll-like receptor 7. Proc Natl Acad Sci U S A, 101, 5598-5603.
48. [48]. Sun, H., Li, Y., Zhang, P., Xing, H., Zhao, S., Song, Y., Wan, D., Yu, J. 2022. Targeting toll-like receptor 7/8 for immunotherapy: recent advances and prospectives. Biomarker Research, 10, 89.
49. [49]. Hanten, J. A., Vasilakos, J. P., Riter, C. L., Neys, L., Lipson, K. E., Alkan, S. S., Birmachu, W. 2008. Comparison of human B cell activation by TLR7 and TLR9 agonists. BMC Immunol, 9, 39.