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image of Bacteriocins from Lactic Acid Bacteria Could Modulate the Wnt Pathway: A Possible Therapeutic Candidate for the Management of Colorectal Cancer- An In silico Study

Abstract

Introduction

Colorectal cancer (CRC) is a type of cancer that develops due to abnormal cell growth in the colon and rectum. Existing conventional CRC treatment strategies have side effects. Hence, exploring new and advanced techniques for bacterial CRC therapy is crucial. Bacteriocins are peptides produced by bacteria, including lactic acid bacteria (LAB), that have bactericidal effects. In the present study, we have focused on searching for effective and safe bacteriocins from LAB as alternatives to clinical therapeutics for treating CRC, leaving healthy cells unaffected.

Methods

We selected nine bacteriocin-like peptides that are effective in the human gut microbiome. These peptides were derived from LAB species using online database resources. We then conducted an phylogenetic analysis of other LAB species present in the gut microbiome using the KEGG Genome database. We established the phylogenetic relationship of these LAB species with others observed in the database to determine their closeness and similarity. Further, the bacteriocin-like peptides were modeled and refined to interact with the plausible target. The systematic network analysis was performed to find the highly interconnected targets involved in the Wnt target genes of CRC.

Results

The network analysis observed that the genes CTNNB1 and LRP5 were found as hub genes to upregulate CRC. protein-peptide docking between the target bacteriocins like peptides and the therapeutic targets of CRC was performed, significantly our findings revealed that the peptide PE4 and PE9 (Lactacin F and Lactacin B) exhibited better binding affinity with CTNNB1. In contrast, the peptides PE7 and PE9 (Doderlin and Lactacin B) revealed better binding affinity with LRP5. Furthermore, we conducted molecular dynamics (MD) simulations to confirm the stability and bonding interactions of the bacteriocins derived from the LAB species.

Conclusion

Our findings indicate that bacteriocins (Lactacin B, Lactacin F and Doderlin) may have significant potential as therapeutics for CRC.

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2025-03-12
2025-04-02

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References

  1. Sung H. Ferlay J. Siegel R.L. Laversanne M. Soerjomataram I. Jemal A. Bray F. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 2021 71 3 209 249 10.3322/caac.21660 33538338
    [Google Scholar]
  2. Xi Y. Xu P. Global colorectal cancer burden in 2020 and projections to 2040. Transl. Oncol. 2021 14 10 101174 10.1016/j.tranon.2021.101174 34243011
    [Google Scholar]
  3. Brenner H. Kloor M. Pox C.P. Colorectal cancer. Lancet 2014 383 9927 1490 1502 10.1016/S0140‑6736(13)61649‑9 24225001
    [Google Scholar]
  4. Bultman S.J. Interplay between diet, gut microbiota, epigenetic events, and colorectal cancer. Mol. Nutr. Food Res. 2017 61 1 1500902 10.1002/mnfr.201500902 27138454
    [Google Scholar]
  5. Louis P. Hold G.L. Flint H.J. The gut microbiota, bacterial metabolites and colorectal cancer. Nat. Rev. Microbiol. 2014 12 10 661 672 10.1038/nrmicro3344 25198138
    [Google Scholar]
  6. Kohoutova D. Forstlova M. Moravkova P. Cyrany J. Bosak J. Smajs D. Rejchrt S. Bures J. Bacteriocin production by mucosal bacteria in current and previous colorectal neoplasia. BMC Cancer 2020 20 1 39 10.1186/s12885‑020‑6512‑5 31948419
    [Google Scholar]
  7. Saus E. Iraola-Guzmán S. Willis J.R. Brunet-Vega A. Gabaldón T. Microbiome and colorectal cancer: Roles in carcinogenesis and clinical potential. Mol. Aspects Med. 2019 69 93 106 10.1016/j.mam.2019.05.001 31082399
    [Google Scholar]
  8. Hill C. Guarner F. Reid G. Gibson G.R. Merenstein D.J. Pot B. Morelli L. Canani R.B. Flint H.J. Salminen S. Calder P.C. Sanders M.E. The International Scientific Association for Probiotics and Prebiotics consensus statement on the scope and appropriate use of the term probiotic. Nat. Rev. Gastroenterol. Hepatol. 2014 11 8 506 514 10.1038/nrgastro.2014.66 24912386
    [Google Scholar]
  9. Drago L. Probiotics, and colon cancer. Microorganisms 2019 7 3 66 10.3390/microorganisms7030066 30823471
    [Google Scholar]
  10. Trejo-González L. Gutiérrez-Carrillo A.E. Rodríguez-Hernández A.I. del Rocío López-Cuellar M. Chavarría-Hernández N. Bacteriocins produced by LAB isolated from cheeses within the period 2009-2021: A review. Probiotics Antimicrob. Proteins 2022 14 2 238 251 10.1007/s12602‑021‑09825‑0 34342858
    [Google Scholar]
  11. Lahiri D. Nag M. Dutta B. Sarkar T. Pati S. Basu D. Abdul Kari Z. Wei L.S. Smaoui S. Wen Goh K. Ray R.R. Bacteriocin: A natural approach for food safety and food security. Front. Bioeng. Biotechnol. 2022 10 1005918 10.3389/fbioe.2022.1005918 36353741
    [Google Scholar]
  12. Grande Burgos M. Pulido R. Del Carmen López Aguayo M. Gálvez A. Lucas R. The cyclic antibacterial peptide enterocin AS-48: Isolation, mode of action, and possible food applications. Int. J. Mol. Sci. 2014 15 12 22706 22727 10.3390/ijms151222706 25493478
    [Google Scholar]
  13. Alvarez-Sieiro P. Montalbán-López M. Mu D. Kuipers O.P. Bacteriocins of lactic acid bacteria: Extending the family. Appl. Microbiol. Biotechnol. 2016 100 7 2939 2951 10.1007/s00253‑016‑7343‑9 26860942
    [Google Scholar]
  14. Piper C. Hill C. Cotter P.D. Ross R.P. Bioengineering of a Nisin A‐producing Lactococcus lactis to create isogenic strains producing the natural variants Nisin F, Q and Z. Microb. Biotechnol. 2011 4 3 375 382 10.1111/j.1751‑7915.2010.00207.x 21375711
    [Google Scholar]
  15. Wang H. Jin J. Pang X. Bian Z. Zhu J. Hao Y. Zhang H. Xie Y. Plantaricin BM-1 decreases viability of SW480 human colorectal cancer cells by inducing caspase-dependent apoptosis. Front. Microbiol. 2023 13 1103600 10.3389/fmicb.2022.1103600 36687624
    [Google Scholar]
  16. Al-Madboly L.A. El-Deeb N.M. Kabbash A. Nael M.A. Kenawy A.M. Ragab A.E. Purification, characterization, identification, and anticancer activity of a circular bacteriocin from Enterococcus thailandicus. Front. Bioeng. Biotechnol. 2020 8 450 10.3389/fbioe.2020.00450 32656185
    [Google Scholar]
  17. Baindara P. Korpole S. Grover V. Bacteriocins: Perspective for the development of novel anticancer drugs. Appl. Microbiol. Biotechnol. 2018 102 24 10393 10408 10.1007/s00253‑018‑9420‑8 30338356
    [Google Scholar]
  18. Patra S. Sahu N. Saxena S. Pradhan B. Nayak S.K. Roychowdhury A. Effects of probiotics at the interface of metabolism and immunity to prevent colorectal cancer-associated gut inflammation: A systematic network and meta-analysis with molecular docking studies. Front. Microbiol. 2022 13 878297 10.3389/fmicb.2022.878297 35711771
    [Google Scholar]
  19. Bian J. Dannappel M. Wan C. Firestein R. Transcriptional regulation of Wnt/β-catenin pathway in colorectal cancer. Cells 2020 9 9 2125 10.3390/cells9092125 32961708
    [Google Scholar]
  20. MacDonald B.T. He X. Frizzled and LRP5/6 receptors for Wnt/β-catenin signaling. Cold Spring Harb. Perspect. Biol. 2012 4 12 a007880 10.1101/cshperspect.a007880 23209147
    [Google Scholar]
  21. Chen Y. Chen M. Deng K. Blocking the Wnt/β‑catenin signaling pathway to treat colorectal cancer: Strategies to improve current therapies (Review). Int. J. Oncol. 2022 62 2 24 10.3892/ijo.2022.5472 36579676
    [Google Scholar]
  22. Raisch J. Côté-Biron A. Langlois M.J. Leblanc C. Rivard N. Unveiling the roles of low-density lipoprotein receptor-related protein 6 in intestinal homeostasis, regeneration and oncogenesis. Cells 2021 10 7 1792 10.3390/cells10071792 34359960
    [Google Scholar]
  23. Wang G. Li X. Wang Z. APD3: The antimicrobial peptide database as a tool for research and education. Nucleic Acids Res. 2016 44 D1 D1087 D1093 10.1093/nar/gkv1278 26602694
    [Google Scholar]
  24. Kanehisa M. Furumichi M. Sato Y. Kawashima M. Ishiguro-Watanabe M. KEGG for taxonomy-based analysis of pathways and genomes. Nucleic Acids Res. 2023 51 D1 D587 D592 10.1093/nar/gkac963 36300620
    [Google Scholar]
  25. Meier-Kolthoff J.P. Carbasse J.S. Peinado-Olarte R.L. Göker M. TYGS and LPSN: A database tandem for fast and reliable genome-based classification and nomenclature of prokaryotes. Nucleic Acids Res. 2022 50 D1 D801 D807 10.1093/nar/gkab902 34634793
    [Google Scholar]
  26. Henz S.R. Huson D.H. Auch A.F. Nieselt-Struwe K. Schuster S.C. Whole-genome prokaryotic phylogeny. Bioinformatics 2005 21 10 2329 2335 10.1093/bioinformatics/bth324 15166018
    [Google Scholar]
  27. Meier-Kolthoff J.P. Auch A.F. Klenk H.P. Göker M. Genome sequence-based species delimitation with confidence intervals and improved distance functions. BMC Bioinformatics 2013 14 1 60 10.1186/1471‑2105‑14‑60 23432962
    [Google Scholar]
  28. Kumar S. Stecher G. Li M. Knyaz C. Tamura K. MEGA X: Molecular evolutionary genetics analysis across computing platforms. Mol. Biol. Evol. 2018 35 6 1547 1549 10.1093/molbev/msy096 29722887
    [Google Scholar]
  29. Dempsey E. Corr S.C. Lactobacillus spp. for gastrointestinal health: Current and future perspectives. Front. Immunol. 2022 13 840245 10.3389/fimmu.2022.840245 35464397
    [Google Scholar]
  30. Piñero J. Ramírez-Anguita J.M. Saüch-Pitarch J. Ronzano F. Centeno E. Sanz F. Furlong L.I. The DisGeNET knowledge platform for disease genomics: 2019 update. Nucleic Acids Res. 2020 48 D1 D845 D855 31680165
    [Google Scholar]
  31. Stelzer G. Rosen N. Plaschkes I. Zimmerman S. Twik M. Fishilevich S. Stein T.I. Nudel R. Lieder I. Mazor Y. Kaplan S. Dahary D. Warshawsky D. Guan-Golan Y. Kohn A. Rappaport N. Safran M. Lancet D. The GeneCards suite: From gene data mining to disease genome sequence analyses. Curr Protoc Bioinformatics 2016 54 1.30.1 1.30.33 10.1002/cpbi.5 31680165
    [Google Scholar]
  32. Masuda M. Sawa M. Yamada T. Therapeutic targets in the Wnt signaling pathway: Feasibility of targeting TNIK in colorectal cancer. Pharmacol. Ther. 2015 156 1 9 10.1016/j.pharmthera.2015.10.009 26542362
    [Google Scholar]
  33. Szklarczyk D. Kirsch R. Koutrouli M. Nastou K. Mehryary F. Hachilif R. Gable A.L. Fang T. Doncheva N.T. Pyysalo S. Bork P. Jensen L.J. von Mering C. The STRING database in 2023: Protein–protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Res. 2023 51 D1 D638 D646 10.1093/nar/gkac1000 36370105
    [Google Scholar]
  34. Shannon P. Markiel A. Ozier O. Baliga N.S. Wang J.T. Ramage D. Amin N. Schwikowski B. Ideker T. Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res. 2003 13 11 2498 2504 10.1101/gr.1239303 14597658
    [Google Scholar]
  35. Doncheva N.T. Assenov Y. Domingues F.S. Albrecht M. Topological analysis and interactive visualization of biological networks and protein structures. Nat. Protoc. 2012 7 4 670 685 10.1038/nprot.2012.004 22422314
    [Google Scholar]
  36. Chin C.H. Chen S.H. Wu H.H. Ho C.W. Ko M.T. Lin C.Y. CytoHubba: Identifying hub objects and sub-networks from complex interactome. BMC Syst Biol. 2014 8 Suppl 4 S11 10.1186/1752‑0509‑8‑S4‑S11 25521941
    [Google Scholar]
  37. Janani B. Vijayakumar M. Priya K. Kim J.H. Geddawy A. Shahid M. El-Bidawy M.H. Al-Ghamdi S. Alsaidan M. Abdelzaher M.H. Mohideen A.P. Ramesh T. A network-based pharmacological investigation to identify the mechanistic regulatory pathway of andrographolide against colorectal cancer. Front. Pharmacol. 2022 13 967262 10.3389/fphar.2022.967262 36110531
    [Google Scholar]
  38. Dennis G. Jr Sherman B.T. Hosack D.A. Yang J. Gao W. Lane H.C. Lempicki R.A. DAVID: Database for annotation, visualization, and integrated discovery. Genome Biol. 2003 4 5 P3 10.1186/gb‑2003‑4‑5‑p3 12734009
    [Google Scholar]
  39. Bateman A. Martin M-J. Orchard S. Magrane M. Agivetova R. Ahmad S. Alpi E. Bowler-Barnett E.H. Britto R. Bursteinas B. Bye-A-Jee H. Coetzee R. Cukura A. Da Silva A. Denny P. Dogan T. Ebenezer T.G. Fan J. Castro L.G. Garmiri P. Georghiou G. Gonzales L. Hatton-Ellis E. Hussein A. Ignatchenko A. Insana G. Ishtiaq R. Jokinen P. Joshi V. Jyothi D. Lock A. Lopez R. Luciani A. Luo J. Lussi Y. MacDougall A. Madeira F. Mahmoudy M. Menchi M. Mishra A. Moulang K. Nightingale A. Oliveira C.S. Pundir S. Qi G. Raj S. Rice D. Lopez M.R. Saidi R. Sampson J. Sawford T. Speretta E. Turner E. Tyagi N. Vasudev P. Volynkin V. Warner K. Watkins X. Zaru R. Zellner H. Bridge A. Poux S. Redaschi N. Aimo L. Argoud-Puy G. Auchincloss A. Axelsen K. Bansal P. Baratin D. Blatter M-C. Bolleman J. Boutet E. Breuza L. Casals-Casas C. de Castro E. Echioukh K.C. Coudert E. Cuche B. Doche M. Dornevil D. Estreicher A. Famiglietti M.L. Feuermann M. Gasteiger E. Gehant S. Gerritsen V. Gos A. Gruaz-Gumowski N. Hinz U. Hulo C. Hyka-Nouspikel N. Jungo F. Keller G. Kerhornou A. Lara V. Le Mercier P. Lieberherr D. Lombardot T. Martin X. Masson P. Morgat A. Neto T.B. Paesano S. Pedruzzi I. Pilbout S. Pourcel L. Pozzato M. Pruess M. Rivoire C. Sigrist C. Sonesson K. Stutz A. Sundaram S. Tognolli M. Verbregue L. Wu C.H. Arighi C.N. Arminski L. Chen C. Chen Y. Garavelli J.S. Huang H. Laiho K. McGarvey P. Natale D.A. Ross K. Vinayaka C.R. Wang Q. Wang Y. Yeh L-S. Zhang J. Ruch P. Teodoro D. UniProt Consortium UniProt: The universal protein knowledgebase in 2021. Nucleic Acids Res. 2021 49 D1 D480 D489 10.1093/nar/gkaa1100 33237286
    [Google Scholar]
  40. Abramson J. Adler J. Dunger J. Evans R. Green T. Pritzel A. Ronneberger O. Willmore L. Ballard A.J. Bambrick J. Bodenstein S.W. Evans D.A. Hung C.C. O’Neill M. Reiman D. Tunyasuvunakool K. Wu Z. Žemgulytė A. Arvaniti E. Beattie C. Bertolli O. Bridgland A. Cherepanov A. Congreve M. Cowen-Rivers A.I. Cowie A. Figurnov M. Fuchs F.B. Gladman H. Jain R. Khan Y.A. Low C.M.R. Perlin K. Potapenko A. Savy P. Singh S. Stecula A. Thillaisundaram A. Tong C. Yakneen S. Zhong E.D. Zielinski M. Žídek A. Bapst V. Kohli P. Jaderberg M. Hassabis D. Jumper J.M. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 2024 630 8016 493 500 10.1038/s41586‑024‑07487‑w 38718835
    [Google Scholar]
  41. Laskowski R. Rullmann J.A.C. MacArthur M. Kaptein R. Thornton J. AQUA and PROCHECK-NMR: Programs for checking the quality of protein structures solved by NMR. J. Biomol. NMR 1996 8 4 477 486 10.1007/BF00228148 9008363
    [Google Scholar]
  42. Sahay A. Shakya M. In silico analysis and homology modelling of antioxidant proteins of spinach. J. Proteomics Bioinform. 2010 3 5 148 154 10.4172/jpb.1000134
    [Google Scholar]
  43. Ambrosetti F. Jandova Z. Bonvin A.M.J.J. Information-driven antibody–antigen modelling with HADDOCK. Methods Mol. Biol. 2023 2552 267 282 10.1007/978‑1‑0716‑2609‑2_14 36346597
    [Google Scholar]
  44. van Zundert G.C.P. Rodrigues J.P.G.L.M. Trellet M. Schmitz C. Kastritis P.L. Karaca E. Melquiond A.S.J. van Dijk M. de Vries S.J. Bonvin A.M.J.J. The HADDOCK2. 2 web server: User-friendly integrative modeling of biomolecular complexes. J. Mol. Biol. 2016 428 4 720 725 10.1016/j.jmb.2015.09.014 26410586
    [Google Scholar]
  45. Stroet M. Caron B. Visscher K.M. Geerke D.P. Malde A.K. Mark A.E. Automated topology builder version 3.0: Prediction of solvation free enthalpies in water and hexane. J. Chem. Theory Comput. 2018 14 11 5834 5845 10.1021/acs.jctc.8b00768 30289710
    [Google Scholar]
  46. Berendsen H.J.C. Postma J.P.M. van Gunsteren W.F. DiNola A. Haak J.R. Molecular dynamics with coupling to an external bath. J. Chem. Phys. 1984 81 8 3684 3690 10.1063/1.448118
    [Google Scholar]
  47. Jayaraj J.M. Krishnasamy G. Lee J.K. Muthusamy K. In silico identification and screening of CYP24A1 inhibitors: 3D QSAR pharmacophore mapping and molecular dynamics analysis. J. Biomol. Struct. Dyn. 2019 37 7 1700 1714 10.1080/07391102.2018.1464958 29658431
    [Google Scholar]
  48. Chatterjee S. Debenedetti P.G. Stillinger F.H. Lynden-Bell R.M. A computational investigation of thermodynamics, structure, dynamics and solvation behavior in modified water models. J. Chem. Phys. 2008 128 12 124511 10.1063/1.2841127 18376947
    [Google Scholar]
  49. Armstrong J.A. Bresme F. Water polarization induced by thermal gradients: The extended simple point charge model (SPC/E). J. Chem. Phys. 2013 139 1 014504 10.1063/1.4811291 23822311
    [Google Scholar]
  50. Huang W. Lin Z. van Gunsteren W.F. Validation of the GROMOS 54A7 force field with respect to β-peptide folding. J. Chem. Theory Comput. 2011 7 5 1237 1243 10.1021/ct100747y 26610119
    [Google Scholar]
  51. Gangadharappa B.S. Sharath R. Revanasiddappa P.D. Chandramohan V. Balasubramaniam M. Vardhineni T.P. Structural insights of metallo-beta-lactamase revealed an effective way of inhibition of enzyme by natural inhibitors. J. Biomol. Struct. Dyn. 2020 38 13 3757 3771 10.1080/07391102.2019.1667265 31514687
    [Google Scholar]
  52. Kumari R. Kumar R. Lynn A. Open-source drug discovery consortium, & Lynn, A. g_mmpbsa A GROMACS tool for high-throughput MM-PBSA calculations. J. Chem. Inf. Model. 2014 54 7 1951 1962 10.1021/ci500020m 24850022
    [Google Scholar]
  53. Thoda C. Touraki M. Probiotic-derived bioactive compounds in colorectal cancer treatment. Microorganisms 2023 11 8 1898 10.3390/microorganisms11081898 37630458
    [Google Scholar]
  54. Huang F. Li S. Chen W. Han Y. Yao Y. Yang L. Li Q. Xiao Q. Wei J. Liu Z. Chen T. Deng X. Postoperative probiotics administration attenuates gastrointestinal complications and gut microbiota dysbiosis caused by chemotherapy in colorectal cancer patients. Nutrients 2023 15 2 356 10.3390/nu15020356 36678227
    [Google Scholar]
  55. Kaeid Sharaf L. Shukla G. Probiotics (Lactobacillus acidophilus and Lactobacillus rhamnosus Gg) in conjunction with celecoxib (selective cox-2 inhibitor) modulated DMH-induced early experimental colon carcinogenesis. Nutr. Cancer 2018 70 6 946 955 10.1080/01635581.2018.1490783 30183370
    [Google Scholar]
  56. Walia S. Kamal R. Dhawan D.K. Kanwar S.S. Chemoprevention by probiotics during 1,2-dimethylhydrazine-induced colon carcinogenesis in rats. Dig. Dis. Sci. 2018 63 4 900 909 10.1007/s10620‑018‑4949‑z 29427224
    [Google Scholar]
  57. Bajramagic S. Hodzic E. Mulabdic A. Holjan S. Smajlovic S. Rovcanin A. Usage of probiotics and its clinical significance at surgically treated patients sufferig from colorectal carcinoma. Med. Arh. 2019 73 5 316 320 10.5455/medarh.2019.73.316‑320 31819304
    [Google Scholar]
  58. Polakowski C.B. Kato M. Preti V.B. Schieferdecker M.E.M. Ligocki Campos A.C. Impact of the preoperative use of synbiotics in colorectal cancer patients: A prospective, randomized, double-blind, placebo-controlled study. Nutrition 2019 58 40 46 10.1016/j.nut.2018.06.004 30278428
    [Google Scholar]
  59. Oh N.S. Joung J.Y. Lee J.Y. Kim Y.J. Kim Y. Kim S.H. A synbiotic combination of Lactobacillus gasseri 505 and Cudrania tricuspidata leaf extract prevents hepatic toxicity induced by colorectal cancer in mice. J. Dairy Sci. 2020 103 4 2947 2955 10.3168/jds.2019‑17411 32008775
    [Google Scholar]
  60. Dubey V. Ghosh A.R. Probiotics cross-talk with multi-cell signaling in colon carcinogenesis. J. Probiotics Health 2013 1 3 2 5 10.4172/2329‑8901.1000109
    [Google Scholar]
  61. Kahouli I. Tomaro-Duchesneau C. Prakash S. Probiotics in colorectal cancer (CRC) with emphasis on mechanisms of action and current perspectives. J. Med. Microbiol. 2013 62 8 1107 1123 10.1099/jmm.0.048975‑0 23558140
    [Google Scholar]
  62. Lee H.A. Kim H. Lee K.W. Park K.Y. Dead nano-sized Lactobacillus plantarum inhibits azoxymethane/dextran sulfate sodium-induced colon cancer in Balb/c mice. J. Med. Food 2015 18 12 1400 1405 10.1089/jmf.2015.3577 26595186
    [Google Scholar]
  63. Chang J.H. Shim Y.Y. Cha S.K. Reaney M.J.T. Chee K.M. Effect of Lactobacillus acidophilus KFRI342 on the development of chemically induced precancerous growths in the rat colon. J. Med. Microbiol. 2012 61 3 361 368 10.1099/jmm.0.035154‑0 22034161
    [Google Scholar]
  64. Zhu J. Zhu C. Ge S. Zhang M. Jiang L. Cui J. Ren F. Lactobacillus salivarius Ren prevent the early colorectal carcinogenesis in 1, 2-dimethylhydrazine-induced rat model. J. Appl. Microbiol. 2014 117 1 208 216 10.1111/jam.12499 24754742
    [Google Scholar]
  65. Dronkers T.M.G. Ouwehand A.C. Rijkers G.T. Global analysis of clinical trials with probiotics. Heliyon 2020 6 7 e04467 10.1016/j.heliyon.2020.e04467 32715136
    [Google Scholar]
  66. Surachat K. Sangket U. Deachamag P. Chotigeat W. In silico analysis of protein toxin and bacteriocins from Lactobacillus paracasei SD1 genome and available online databases. PLoS One 2017 12 8 e0183548 10.1371/journal.pone.0183548 28837656
    [Google Scholar]
  67. Ondov B.D. Treangen T.J. Melsted P. Mallonee A.B. Bergman N.H. Koren S. Phillippy A.M. Mash: Fast genome and metagenome distance estimation using MinHash. Genome Biol. 2016 17 1 132 10.1186/s13059‑016‑0997‑x 27323842
    [Google Scholar]
  68. Suresh N.T. Ashok S. Comparative strategy for the statistical & network-based analysis of biological networks. Procedia Comput. Sci. 2018 143 165 180 10.1016/j.procs.2018.10.373
    [Google Scholar]
  69. Chen B.S. Li C.W. Constructing an integrated genetic and epigenetic cellular network for whole cellular mechanism using high-throughput next-generation sequencing data. BMC Syst. Biol. 2016 10 1 18 10.1186/s12918‑016‑0256‑5 26897165
    [Google Scholar]
  70. Hasannejad Bibalan M. Eshaghi M. Rohani M. Esghaei M. Darban-Sarokhalil D. Pourshafie M.R. Talebi M. Corrigendum: Isolates of Lactobacillus plantarum and L. reuteri display greater antiproliferative and antipathogenic activity than other Lactobacillus isolates. J Med Microbiol 2017 66 11 1703 10.1099/jmm.0.000614 29106349
    [Google Scholar]
  71. Zhan T. Rindtorff N. Boutros M. Wnt signaling in cancer. Oncogene 2017 36 11 1461 1473 10.1038/onc.2016.304 27617575
    [Google Scholar]
  72. Ghanavati R. Akbari A. Mohammadi F. Asadollahi P. Javadi A. Talebi M. Rohani M. Lactobacillus species inhibitory effect on colorectal cancer progression through modulating the Wnt/β-catenin signaling pathway. Mol. Cell. Biochem. 2020 470 1-2 1 13 10.1007/s11010‑020‑03740‑8 32419125
    [Google Scholar]
  73. Manoharan M. Ragothaman P. Balasubramanian T.S. Initiation of apoptotic pathway by the cell-free supernatant synthesized from Weissella cibaria through in-silico and in-vitro methods. Appl. Biochem. Biotechnol. 2023 10.1007/s12010‑023‑04688‑3 37751008
    [Google Scholar]
  74. He K. Gan W.J. Wnt/β-catenin signaling pathway in the development and progression of colorectal cancer. Cancer Manag. Res. 2023 15 435 448 10.2147/CMAR.S411168 37250384
    [Google Scholar]
  75. Nie X. Liu H. Ye W. Wei X. Fan L. Ma H. Li L. Xue W. Qi W. Wang Y.D. Chen W.D. LRP5 promotes cancer stem cell traits and chemoresistance in colorectal cancer. J. Cell. Mol. Med. 2022 26 4 1095 1112 10.1111/jcmm.17164 34997691
    [Google Scholar]
  76. MacDonald B.T. Tamai K. He X. Wnt/beta-catenin signaling: Components, mechanisms, and diseases. Dev. Cell 2009 17 1 9 26 10.1016/j.devcel.2009.06.016 19619488
    [Google Scholar]
  77. Li V.S.W. Ng S.S. Boersema P.J. Low T.Y. Karthaus W.R. Gerlach J.P. Mohammed S. Heck A.J.R. Maurice M.M. Mahmoudi T. Clevers H. Wnt signaling through inhibition of β-catenin degradation in an intact Axin1 complex. Cell 2012 149 6 1245 1256 10.1016/j.cell.2012.05.002 22682247
    [Google Scholar]
  78. Erfanian N. Nasseri S. Miraki Feriz A. Safarpour H. Namaei M.H. Characterization of Wnt signaling pathway under treatment of Lactobacillus acidophilus postbiotic in colorectal cancer using an integrated in silico and in vitro analysis. Sci. Rep. 2023 13 1 22988 10.1038/s41598‑023‑50047‑x 38151510
    [Google Scholar]
  79. Hanafiah A. Abd Aziz S.N.A. Md Nesran Z.N. Wezen X.C. Ahmad M.F. Molecular investigation of antimicrobial peptides against Helicobacter pylori proteins using a peptide-protein docking approach. Heliyon 2024 10 6 e28128 10.1016/j.heliyon.2024.e28128 38533069
    [Google Scholar]
  80. Aier I. Varadwaj P.K. Raj U. Structural insights into conformational stability of both wild-type and mutant EZH2 receptor. Sci. Rep. 2016 6 1 34984 10.1038/srep34984 27713574
    [Google Scholar]
  81. Oyewusi H.A. Wahab R.A. Akinyede K.A. Albadrani G.M. Al-Ghadi M.Q. Abdel-Daim M.M. Ajiboye B.O. Huyop F. Bioinformatics analysis and molecular dynamics simulations of azoreductases (AzrBmH2) from Bacillus megaterium H2 for the decolorization of commercial dyes. Environ. Sci. Eur. 2024 a 36 1 31 10.1186/s12302‑024‑00853‑5
    [Google Scholar]
  82. Zakary S. Mashal H. Osmani A. Oyewus H. Huyop F. Nasim M. In silico molecular characterization of a putative haloacid dehalogenase type II from genomic of Mesorhizobium loti strain TONO. J. Trop. Life Sci. 2022 12 2 241 252 10.11594/jtls.12.02.10
    [Google Scholar]
  83. Wahhab B.H. Oyewusi H.A. Wahab R.A. Mohammad Hood M.H. Abdul Hamid A.A. Al-Nimer M.S. Edbeib M.F. Kaya Y. Huyop F. Comparative modeling and enzymatic affinity of novel haloacid dehalogenase from Bacillus megaterium strain BHS1 isolated from alkaline Blue Lake in Turkey. J. Biomol. Struct. Dyn. 2024 42 3 1429 1442 10.1080/07391102.2023.2199870 37038649
    [Google Scholar]
  84. Oyewusi H.A. Huyop F. Wahab R.A. Hamid A.A.A. In silico assessment of dehalogenase from Bacillus thuringiensis H2 in relation to its salinity-stability and pollutants degradation. J. Biomol. Struct. Dyn. 2022 40 19 9332 9346 10.1080/07391102.2021.1927846 34014147
    [Google Scholar]
  85. Oyewusi H.A. Adedamola Akinyede K. Wahab R.A. Susanti E. Syed Yaacob S.N. Huyop F. Biological and molecular approaches of the degradation or decolorization potential of the hypersaline Lake Tuz Bacillus megaterium H2 isolate. J. Biomol. Struct. Dyn. 2024 b 42 12 6228 6244 10.1080/07391102.2023.2234040 37455463
    [Google Scholar]
  86. Borjian Boroujeni M. Shahbazi Dastjerdeh M. Shokrgozar M. Rahimi H. Omidinia E. Computational driven molecular dynamics simulation of keratinocyte growth factor behavior at different pH conditions. Inform. Med. Unlocked 2021 23 100514 10.1016/j.imu.2021.100514
    [Google Scholar]
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Bacteriocins from Lactic Acid Bacteria Could Modulate the Wnt Pathway: A Possible Therapeutic Candidate for the Management of Colorectal Cancer- An In silico Study
Bentham Science Publishers ; https://doi.org/10.2174/0118715206367950250228100833
/content/journals/acamc/10.2174/0118715206367950250228100833
/content/journals/acamc/10.2174/0118715206367950250228100833
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  • Article Type:     Research Article
Keywords: colorectal cancer ; lactic acid bacteria ; protein-peptide docking ; molecular dynamics ; Bacteriocins

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