Redefining genomic view of Clostridioides difficile through pangenome analysis and identification of drug targets from its core genome
DOI:
https://doi.org/10.33393/dti.2022.2469Keywords:
Clostridioides difficile, Drug target, Genome, Inhibition, PhylogenomicsAbstract
Introduction: Clostridioides difficile infection (CDI) is a leading cause of gastrointestinal infections and in the present day is a major concern for global health care system. The unavailability of specific antibiotics for CDI treatment and its emerging cases worldwide further broaden the challenge to control CDI.
Methods: The availability of a large number of genome sequences for C. difficile and many bioinformatics tools for genome analysis provides the opportunity for in silico pangenomic analysis. In the present study, 97 strains of C. difficile were used for pangenomic studies and characterized for their phylogenomic and functional analysis.
Results: Pangenome analysis reveals open pangenome of C. difficile and high genetic diversity. Sequence and interactome analysis of 1,481 core genes was done and eight potent drug targets are identified. Three drug targets, namely, aminodeoxychorismate synthase (PabB), D-alanyl-D-alanine carboxypeptidase (DD-CPase) and undecaprenyl diphospho-muramoyl pentapeptide beta-N-acetylglucosaminyl transferase (MurG transferase), have been reported as drug targets for other human pathogens, and five targets, namely, bifunctional diguanylate cyclase/phosphodiesterase (cyclic-diGMP), sporulation transcription factor (Spo0A), histidinol-phosphate transaminase (HisC), 3-deoxy-7-phosphoheptulonate synthase (DAHP synthase) and c-di-GMP phosphodiesterase (PdcA), are novel.
Conclusion: The suggested potent targets could act as broad-spectrum drug targets for C. difficile. However, further validation needs to be done before using them for lead compound discovery.
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Zhu D, Sorg JA, Sun X. Clostridioides difficile biology: sporulation, germination, and corresponding therapies for C. difficile infection. Front Cell Infect Microbiol. 2018;8:29. https://doi.org/10.3389/fcimb.2018.00029 PMID:29473021
Leffler DA, Lamont JT. Clostridium difficile infection. N Engl J Med. 2015;372(16):1539-1548. https://doi.org/10.1056/NEJMra1403772 PMID:25875259
Rupnik M, Wilcox MH, Gerding DN. Clostridium difficile infection: new developments in epidemiology and pathogenesis. Nat Rev Microbiol. 2009;7(7):526-536. https://doi.org/10.1038/nrmicro2164 PMID:19528959
Martin JS, Monaghan TM, Wilcox MH. Clostridium difficile infection: epidemiology, diagnosis and understanding transmission. Nat Rev Gastroenterol Hepatol. 2016;13(4):206-216. https://doi.org/10.1038/nrgastro.2016.25 PMID:26956066
Abt MC, McKenney PT, Pamer EG. Clostridium difficile colitis: pathogenesis and host defence. Nat Rev Microbiol. 2016;14(10):609-620. https://doi.org/10.1038/nrmicro.2016.108 PMID:27573580
Lim SC, Knight DR, Riley TV. Clostridium difficile and one health. Clin Microbiol Infect. 2020;26(7):857-863. https://doi.org/10.1016/j.cmi.2019.10.023 PMID:31682985
Lessa FC, Mu Y, Bamberg WM, et al. Burden of Clostridium difficile infection in the United States. N Engl J Med. 2015;372(9):825-834. https://doi.org/10.1056/NEJMoa1408913 PMID:25714160
Seekatz AM, Young VB. Clostridium difficile and the microbiota. J Clin Invest. 2014;124(10):4182-4189. https://doi.org/10.1172/JCI72336 PMID:25036699
Hall IC, O’Toole E. Intestinal flora in new-born infants: with a description of a new pathogenic anaerobe, Bacillus difficilis. AMA Am J Dis Child. 1935;49(2):390-402. https://doi.org/10.1001/archpedi.1935.01970020105010
Bartlett JG, Moon N, Chang TW, Taylor N, Onderdonk AB. Role of Clostridium difficile in antibiotic-associated pseudomembranous colitis. Gastroenterology. 1978;75(5):778-782. https://doi.org/10.1016/0016-5085(78)90457-2 PMID:700321
Knight DR, Imwattana K, Kullin B, et al. Major genetic discontinuity and novel toxigenic species in Clostridioides difficile taxonomy. eLife. 2021;10:e64325. https://doi.org/10.7554/eLife.64325 PMID:34114561
Vernikos G, Medini D, Riley DR, Tettelin H. Ten years of pan-genome analyses. Curr Opin Microbiol. 2015;23:148-154. https://doi.org/10.1016/j.mib.2014.11.016 PMID:25483351
Tettelin H, Masignani V, Cieslewicz MJ, et al. Genome analysis of multiple pathogenic isolates of Streptococcus agalactiae: implications for the microbial “pan-genome”. Proc Natl Acad Sci USA. 2005;102(39):13950-13955. https://doi.org/10.1073/pnas.0506758102 PMID:16172379
Croll D, McDonald BA. The accessory genome as a cradle for adaptive evolution in pathogens. PLoS Pathog. 2012;8(4):e1002608. https://doi.org/10.1371/journal.ppat.1002608 PMID:22570606
Katsila T, Spyroulias GA, Patrinos GP, Matsoukas MT. Computational approaches in target identification and drug discovery. Comput Struct Biotechnol J. 2016;14:177-184. https://doi.org/10.1016/j.csbj.2016.04.004 PMID:27293534
Sayers EW, Beck J, Bolton EE, et al. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 2021;49(D1):D10-D17. https://doi.org/10.1093/nar/gkaa892 PMID:33095870
Chaudhari NM, Gupta VK, Dutta C. BPGA – an ultra-fast pan-genome analysis pipeline. Sci Rep. 2016;6(1):24373. https://doi.org/10.1038/srep24373 PMID:27071527
Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics. 2010;26(19):2460-2461. https://doi.org/10.1093/bioinformatics/btq461 PMID:20709691
Kanehisa M, Goto S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 2000;28(1):27-30. https://doi.org/10.1093/nar/28.1.27 PMID:10592173
Tatusov RL, Fedorova ND, Jackson JD, et al. The COG database: an updated version includes eukaryotes. BMC Bioinformatics. 2003;4(1):41. https://doi.org/10.1186/1471-2105-4-41 PMID:12969510
Ye J, McGinnis S, Madden TL. BLAST: improvements for better sequence analysis. Nucleic Acids Res. 2006;34(Web Server issue):W6-9. PMID:16845079
Luo H, Lin Y, Liu T, et al. DEG 15, an update of the Database of Essential Genes that includes built-in analysis tools. Nucleic Acids Res. 2021;49(D1):D677-D686. https://doi.org/10.1093/nar/gkaa917 PMID:33095861
Liu B, Zheng D, Jin Q, Chen L, Yang J. VFDB 2019: a comparative pathogenomic platform with an interactive web interface. Nucleic Acids Res. 2019;47(D1):D687-D692. https://doi.org/10.1093/nar/gky1080 PMID:30395255
Gasteiger E, Hoogland C, Gattiker A, et al. Protein identification and analysis tools on the ExPASy server. The proteomics protocols handbook 2005; 571-607. https://doi.org/10.1385/1-59259-890-0:571
Yu CS, Chen YC, Lu CH, Hwang JK. Prediction of protein subcellular localization. Proteins. 2006;64(3):643-651. https://doi.org/10.1002/prot.21018 PMID:16752418
Gashaw I, Ellinghaus P, Sommer A, Asadullah K. What makes a good drug target? Drug Discov Today. 2011;16(23-24):1037-1043. https://doi.org/10.1016/j.drudis.2011.09.007 PMID:21945861
Bakheet TM, Doig AJ. Properties and identification of human protein drug targets. Bioinformatics. 2009;25(4):451-457. https://doi.org/10.1093/bioinformatics/btp002 PMID:19164304
Solanki V, Tiwari V. Subtractive proteomics to identify novel drug targets and reverse vaccinology for the development of chimeric vaccine against Acinetobacter baumannii. Sci Rep. 2018;8(1):9044. https://doi.org/10.1038/s41598-018-26689-7 PMID:29899345
Chang KY, Yang JR. Analysis and prediction of highly effective antiviral peptides based on random forests. PLoS One. 2013;8(8):e70166. https://doi.org/10.1371/journal.pone.0070166 PMID:23940542
Kumar A, Ahmad A, Vyawahare A, Khan R. Membrane trafficking and subcellular drug targeting pathways. Front Pharmacol. 2020;11:629. https://doi.org/10.3389/fphar.2020.00629 PMID:32536862
Karp PD, Paley S, Romero P. The pathway tools software. Bioinformatics. 2002;18(suppl 1):S225-S232. https://doi.org/10.1093/bioinformatics/18.suppl_1.S225 PMID:12169551
Szklarczyk D, Morris JH, Cook H, et al. The STRING database in 2017: quality-controlled protein–protein association networks, made broadly accessible. Nucleic Acids Res. 2017 Jan 4;45(D1):D362-D368. https://doi.org/10.1093/nar/gkw937 PMID:27924014
Demchak B, Hull T, Reich M, et al. Cytoscape: the network visualization tool for GenomeSpace workflows. F1000 Res. 2014;3:151. https://doi.org/10.12688/f1000research.4492.2 PMID:25165537
Kulecka M, Waker E, Ambrozkiewicz F, et al. Higher genome variability within metabolism genes associates with recurrent Clostridium difficile infection. BMC Microbiol. 2021;21(1):36. https://doi.org/10.1186/s12866-021-02090-9 PMID:33509087
Sahr T, Ravanel S, Basset G, Nichols BP, Hanson AD, Rébeillé F. Folate synthesis in plants: purification, kinetic properties, and inhibition of aminodeoxychorismate synthase. Biochem J. 2006;396(1):157-162. https://doi.org/10.1042/BJ20051851 PMID:16466344
Nesbitt NM, Arora DP, Johnson RA, Boon EM. Modification of a bi-functional diguanylate cyclase-phosphodiesterase to efficiently produce cyclic diguanylate monophosphate. Biotechnol Rep (Amst). 2015;7:30-37. https://doi.org/10.1016/j.btre.2015.04.008 PMID:28626712
Phippen CW, Mikolajek H, Schlaefli HG, Keevil CW, Webb JS, Tews I. Formation and dimerization of the phosphodiesterase active site of the Pseudomonas aeruginosa MorA, a bi-functional c-di-GMP regulator. FEBS Lett. 2014;588(24):4631-4636. https://doi.org/10.1016/j.febslet.2014.11.002 PMID:25447517
Fujita M, Losick R. The master regulator for entry into sporulation in Bacillus subtilis becomes a cell-specific transcription factor after asymmetric division. Genes Dev. 2003;17(9):1166-1174. https://doi.org/10.1101/gad.1078303 PMID:12730135
Sivaraman J, Li Y, Larocque R, Schrag JD, Cygler M, Matte A. Crystal structure of histidinol phosphate aminotransferase (HisC) from Escherichia coli, and its covalent complex with pyridoxal-5′-phosphate and l-histidinol phosphate. J Mol Biol. 2001;311(4):761-776. https://doi.org/10.1006/jmbi.2001.4882 PMID:11518529
de Oliveira MD, Araújo JO, Galúcio JMP, Santana K, Lima AH. Targeting shikimate pathway: in silico analysis of phosphoenolpyruvate derivatives as inhibitors of EPSP synthase and DAHP synthase. J Mol Graph Model. 2020;101:107735. https://doi.org/10.1016/j.jmgm.2020.107735 PMID:32947107
Tripathi P, Tripathi V. Determination of murG transferase as a potential drug target in Neisseria meningitides by spectral graph theory approach. In: Kesar KK. Perspectives in Environmental Toxicology 2017; 147-160. https://doi.org/10.1007/978-3-319-46248-6_7
Amera GM, Khan RJ, Pathak A, Jha RK, Muthukumaran J, Singh AK. Screening of promising molecules against MurG as drug target in multi-drug-resistant-Acinetobacter baumannii – insights from comparative protein modeling, molecular docking and molecular dynamics simulation. J Biomol Struct Dyn. 2020;38(17):5230-5252. https://doi.org/10.1080/07391102.2019.1700167 PMID:31787065
Konyariková Z, Savková K, Kozmon S, Mikušová K. Biosynthesis of galactan in Mycobacterium tuberculosis as a viable TB drug target? Antibiotics (Basel). 2020;9(1):20. https://doi.org/10.3390/antibiotics9010020 PMID:31935842
Rioseras B, Yagüe P, López-García MT, et al. Characterization of SCO4439, a D-alanyl-D-alanine carboxypeptidase involved in spore cell wall maturation, resistance, and germination in Streptomyces coelicolor. Sci Rep. 2016;6(1):21659. https://doi.org/10.1038/srep21659 PMID:26867711
Tamayo R. Cyclic diguanylate riboswitches control bacterial pathogenesis mechanisms. PLoS Pathog. 2019;15(2):e1007529. https://doi.org/10.1371/journal.ppat.1007529 PMID:30730989
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Accepted 2022-10-10
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