Abstract
Clostridioides difficile is the major cause of antibiotic-associated diarrhea in hospitalized patients. The low susceptibility of this pathogen to first-line antibiotics coupled with the recurrence of its infection (CDI) has become a global concern that necessitates the need to explore novel drug targets against this pathogen. In this study, in-silico approaches through pangenome and subtractive genomic analysis were used to predict putative drug targets. A total of 2556 core genes were identified after pangenome analysis of which 173 were predicted to be essential and non-homologous to human host. Further analysis such as virulence effector function, subcellular localization, involvement in metabolic pathways, gene-enrichment analysis, physicochemical properties and druggability of the proteins were done. A total of 5 cytoplasmic proteins were finally predicted as novel putative drug targets. This study contributes immensely to the search of novel drug targets against C. difficile though further experimental validation is highly imperative.
Similar content being viewed by others
References
Adasme MF, Linnemann KL, Bolz SN et al (2021) PLIP 2021: expanding the scope of the protein–ligand interaction profiler to DNA and RNA. Nucleic Acids Res 49:W530–W534. https://doi.org/10.1093/nar/gkab294
Altschul SF, Gish W, Miller W et al (1990) Basic local alignment search tool. J Mol Biol 215:403–410. https://doi.org/10.1016/S0022-2836(05)80360-2
Azam SS, Shamim A (2014) An insight into the exploration of druggable genome of Streptococcus gordonii for the identification of novel therapeutic candidates. Genomics 104:203–214
Basharat Z, Jahanzaib M, Rahman N (2021) Therapeutic target identification via differential genome analysis of antibiotic resistant Shigella sonnei and inhibitor evaluation against a selected drug target. Infect Genet Evol 94:105004. https://doi.org/10.1016/j.meegid.2021.105004
Blom J, Kreis J, Spänig S et al (2016) EDGAR 2.0: an enhanced software platform for comparative gene content analyses. Nucleic Acids Res 44:W22–W28
Boekhoud IM, Hornung BVH, Sevilla E et al (2020) Plasmid-mediated metronidazole resistance in Clostridioides difficile. Nat Commun 11:598. https://doi.org/10.1038/s41467-020-14382-1
Chawley P, Samal HB, Prava J et al (2014) Comparative genomics study for identification of drug and vaccine targets in Vibrio cholerae: MurA ligase as a case study. Genomics 103:83–93
Coggins JR, Abell C, Evans LB et al (2003) Experiences with the shikimate-pathway enzymes as targets for rational drug design. Biochem Soc Trans 31:548–552. https://doi.org/10.1042/BST0310548
Dar HA, Zaheer T, Ullah N et al (2020) Pangenome analysis of Mycobacterium tuberculosis reveals core-drug targets and screening of promising lead compounds for drug discovery. Antibiot 9(11):819
Derrer B, Macheroux P, Kappes B (2013) The shikimate pathway in apicomplexan parasites: implications for drug development. FBL 18:944–969
Du Z, Su H, Wang W et al (2021) The trRosetta server for fast and accurate protein structure prediction. Nat Protoc 16:5634–5651. https://doi.org/10.1038/s41596-021-00628-9
Fatoba AJ, Okpeku M, Adeleke MA (2021) Subtractive genomics approach for identification of novel therapeutic drug targets in Mycoplasma genitalium. Pathog 10:921. https://doi.org/10.3390/pathogens10080921
Feng J, Paparella AS, Booker GW et al (2016) Biotin protein ligase is a target for new antibacterials. Antibiotics 5:26. https://doi.org/10.3390/antibiotics5030026
Finn E, Andersson FL, Madin-Warburton M (2021) Burden of clostridioides difficile infection (CDI)—a systematic review of the epidemiology of primary and recurrent CDI. BMC Infect Dis 21:456. https://doi.org/10.1186/s12879-021-06147-y
Fong A, Ross M, Boudreau J et al (2021) Raja 42, a novel gamma lactam compound, is effective against Clostridioides difficile. PLoS ONE 16:e0257143
Ford CA, Hurford IM, Cassat JE (2021) Antivirulence strategies for the treatment of Staphylococcus aureus infections: a mini review. Front Microbiol 11:632706
Ge SX, Jung D, Yao R (2020) ShinyGO: a graphical gene-set enrichment tool for animals and plants. Bioinformatics 36:2628–2629. https://doi.org/10.1093/bioinformatics/btz931
Hamilton DJ, Ábrányi-Balogh P, Keeley A et al (2020) Bromo-cyclobutenaminones as new covalent UDP-N-acetylglucosamine enolpyruvyl transferase (MurA) inhibitors. Pharm 13(11):362
Han X, Chen C, Yan Q et al (2019) Action of dicumarol on glucosamine-1-phosphate acetyltransferase of GlmU and mycobacterium tuberculosis. Front Microbiol 10:1799
Hart LR, Lebedenko CG, Mitchell SM et al (2022) In silico studies of tumor targeted peptide-conjugated natural products for targeting over-expressed receptors in breast cancer cells using molecular docking, molecular dynamics and MMGBSA calculations. Appl Sci 12:515
Herrmann KM, Weaver LM (1999) The shikimate pathway. Annu Rev Plant Biol 50:473–503
Hutton CA, Perugini MA, Gerrard JA (2007) Inhibition of lysine biosynthesis: an evolving antibiotic strategy. Mol Biosyst 3:458–465. https://doi.org/10.1039/B705624A
Larson EC, Lim AL, Pond CD et al (2020) Pyrrolocin C and equisetin inhibit bacterial acetyl-CoA carboxylase. PLoS One 15:e0233485
Lessa FC, Mu Y, Bamberg WM et al (2015) Burden of Clostridium difficile infection in the United States. N Engl J Med 372:825–834
Liu B, Zheng D, Jin Q et al (2019) VFDB 2019: a comparative pathogenomic platform with an interactive web interface. Nucleic Acids Res 47:D687–D692. https://doi.org/10.1093/nar/gky1080
Mochalkin I, Lightle S, Zhu Y et al (2007) Characterization of substrate binding and catalysis in the potential antibacterial target N-acetylglucosamine-1-phosphate uridyltransferase (GlmU). Protein Sci 16:2657–2666. https://doi.org/10.1110/ps.073135107
Moriya Y, Itoh M, Okuda S et al (2007) KAAS: an automatic genome annotation and pathway reconstruction server. Nucleic Acids Res 35:W182–W185. https://doi.org/10.1093/nar/gkm321
Mutai WC, Mureithi MW, Anzala O et al (2021) High prevalence of multidrug-resistant clostridioides difficile following extensive use of antimicrobials in hospitalized patients in Kenya. Front Cell Infect Microbiol 10:604986
Naorem RS, Pangabam BD, Bora SS et al (2022) Identification of putative vaccine and drug targets against the methicillin-resistant Staphylococcus aureus by reverse vaccinology and subtractive genomics approaches. Mol 27(7):2083
Naqvi KF, Patin D, Wheatley MS et al (2016) Identification and partial characterization of a novel UDP-N-Acetylenolpyruvoylglucosamine reductase/UDP-N-Acetylmuramate:l-Alanine Ligase fusion enzyme from Verrucomicrobium spinosum DSM 4136T. Front Microbiol 7:362
Nunes JES, Duque MA, de Freitas TF et al (2020) mycobacterium tuberculosis shikimate pathway enzymes as targets for the rational design of anti-tuberculosis drugs. Mol 25(6):1259
O’Boyle NM, Banck M, James CA et al (2011) Open babel: an open chemical toolbox. J Cheminform 3:33. https://doi.org/10.1186/1758-2946-3-33
Pal R, Dai M, Seleem MN (2021) High-throughput screening identifies a novel natural product-inspired scaffold capable of inhibiting Clostridioides difficile in vitro. Sci Rep 11:10913. https://doi.org/10.1038/s41598-021-90314-3
Pujari I, Sengupta R, Babu VS (2021) Docking and ADMET studies for investigating the anticancer potency of Moscatilin on APC10/DOC1 and PKM2 against five clinical drugs. J Genet Eng Biotechnol 19:161. https://doi.org/10.1186/s43141-021-00256-6
Qureshi NA, Bakhtiar SM, Faheem M et al (2021) Genome-based drug target identification in human pathogen Streptococcus gallolyticus. Front Genet 12:564056
Roberts F, Roberts CW, Johnson JJ et al (1998) Evidence for the shikimate pathway in apicomplexan parasites. Nature 393:801–805. https://doi.org/10.1038/31723
Salaemae W, Azhar A, Booker GW, Polyak SW (2011) Biotin biosynthesis in Mycobacterium tuberculosis: physiology, biochemistry and molecular intervention. Protein Cell 2:691–695. https://doi.org/10.1007/s13238-011-1100-8
Saleem H, Ashfaq UA, Nadeem H et al (2021) Subtractive genomics and molecular docking approach to identify drug targets against Stenotrophomonas maltophilia. PLoS One 16:e0261111
Sapkota M, Marreddy RKR, Wu X et al (2020) The early stage peptidoglycan biosynthesis Mur enzymes are antibacterial and antisporulation drug targets for recurrent Clostridioides difficile infection. Anaerobe 61:102129. https://doi.org/10.1016/j.anaerobe.2019.102129
Schnell R, Oehlmann W, Sandalova T et al (2012) Tetrahydrodipicolinate N-succinyltransferase and dihydrodipicolinate synthase from Pseudomonas aeruginosa: structure analysis and gene deletion. PLoS One 7:e31133
Shahid F, Ashfaq UA, Saeed S et al (2020) In silico subtractive proteomics approach for identification of potential drug targets in Staphylococcus saprophyticus. Int J Environ Res Public Health 17:1–10. https://doi.org/10.3390/ijerph17103644
Silvério-Machado R, Couto BRGM, dos Santos MA (2015) Retrieval of Enterobacteriaceae drug targets using singular value decomposition. Bioinformatics 31:1267–1273. https://doi.org/10.1093/bioinformatics/btu792
Smits WK, Lyras D, Lacy DB et al (2016) Clostridium difficile infection. Nat Rev Dis Prim 2:1–20
Spigaglia P (2016) Recent advances in the understanding of antibiotic resistance in Clostridium difficile infection. Ther Adv Infect Dis 3:23–42. https://doi.org/10.1177/2049936115622891
Tillery LM, Barrett KF, Dranow DM et al (2020) Toward a structome of Acinetobacter baumannii drug targets. Protein Sci 29:789–802. https://doi.org/10.1002/pro.3826
Uddin R, Siraj B, Rashid M et al (2020) Genome subtraction and comparison for the identification of novel drug targets against Mycobacterium avium subsp. Hominissuis Pathogens 9(5):368. https://doi.org/10.3390/pathogens9050368
Wen QF, Liu S, Dong C et al (2019) Geptop 2.0: an updated, more precise, and faster Geptop server for identification of prokaryotic essential genes. Front Microbiol 10:1–6. https://doi.org/10.3389/fmicb.2019.01236
Wishart DS, Knox C, Guo AC et al (2006) DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res 34:D668–D672. https://doi.org/10.1093/nar/gkj067
Yu C-S, Lin C-J, Hwang J-K (2004) Predicting subcellular localization of proteins for Gram-negative bacteria by support vector machines based on n-peptide compositions. Protein Sci 13:1402–1406. https://doi.org/10.1110/ps.03479604
Yu C-S, Cheng C-W, Su W-C et al (2014) CELLO2GO: a web server for protein subCELlular LOcalization prediction with functional gene ontology annotation. PLoS ONE 9:e99368
Author information
Authors and Affiliations
Contributions
Methodology: AJ Fatoba, DO Fatoba; conceptualization: AJ Fatoba, DO Fatoba and SO Babalola; formal analysis: AJ Fatoba, DO Fatoba; writing original draft: AJ Fatoba; review and editing: AJ Fatoba, DO Fatoba and SO Babalola.
Corresponding author
Ethics declarations
Conflict of interest
The authors declare no conflict of interest of any kind.
Additional information
Publisher's Note
Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Fatoba, A.J., Fatoba, D.O. & Babalola, S.O. Pangenome and subtractive genomic analysis of Clostridioides difficile reveals putative drug targets. J Proteins Proteom 13, 247–256 (2022). https://doi.org/10.1007/s42485-022-00097-y
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s42485-022-00097-y