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Molecular screening and genetic diversity analysis of anticancer Azurin-encoding and Azurin-like genes in human gut microbiome deduced through cultivation-dependent and cultivation-independent studies

  • Van Duy NguyenEmail author
  • Thanh Tra Nguyen
  • Thu Thuy Pham
  • Michael Packianather
  • Chi Hieu Le
Original Article

Abstract

Azurin, a bacteriocin produced by a human gut bacterium Pseudomonas aeruginosa, can reveal selectively cytotoxic and induce apoptosis in cancer cells. After overcoming two phase I trials, a functional region of Azurin called p28 has been approved as a drug for the treatment of brain tumor glioma by FDA. The present study aims to improve a screening procedure and assess genetic diversity of Azurin genes in P. aeruginosa and Azurin-like genes in the gut microbiome of a specific population in Vietnam and global populations. Firstly, both cultivation-dependent and cultivation-independent techniques based on genomic and metagenomic DNAs extracted from fecal samples of the healthy specific population were performed and optimized to detect Azurin genes. Secondly, the Azurin gene sequences were analyzed and compared with global populations by using bioinformatics tools. Finally, the screening procedure improved from the first step was applied for screening Azurin-like genes, followed by the protein synthesis and NCI in vitro screening for anticancer activity. As a result, this study has successfully optimized the annealing temperatures to amplify DNAs for screening Azurin genes and applying to Azurin-like genes from human gut microbiota. The novelty of this study is the first of its kind to classify Azurin genes into five different genotypes at a global scale and confirm the potential anticancer activity of three Azurin-like synthetic proteins (Cnazu1, Dlazu11, and Ruazu12). The results contribute to the procedure development applied for screening anticancer proteins from human microbiome and a comprehensive understanding of their therapeutic response at a genetic level.

Keywords

Anticancer proteins Azurin Cancer therapy Genetic diversity Microbiome NCI in vitro screening 

Notes

Acknowledgments

Authors would like to acknowledge the British Council under the frame of HAPIE and TEAM-SIE projects (www.vuheie.org) to CHL, MP, and VDN for enhancing the partnership among higher education institutions, companies, and hospitals in Vietnam and the UK. We would also like to give special thanks to Professor Arsénio M. Fialho at the Instituto Superior Técnico, University of Lisbon, Portugal, for his valuable comments and contribution to the manuscript, and Professor Simeon Keates at the Faculty of Engineering and Science, University of Greenwich, UK, for the research administration support.

Funding

This work was supported by a grant no. 106-YS.04-2014.40 from the Vietnam National Foundation for Science and Technology Development (NAFOSTED) to VDN.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the Research and Education Committee of Nha Trang University (REC-NTU) and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants or their parents as minors (less than 16 years old) included in the study.

Supplementary material

10123_2019_70_MOESM1_ESM.pdf (61 kb)
S1 Figure PCR products of Azurin genes amplified from metagenomic DNAs using a pair of primers Azu 677F/R at the annealing temperatures of 54oC. M stands for DNA Marker. Ctr is a Control, which is amplified from genomic DNA of K16P isolate. The name of fecal samples is shown above the lane designations. NOTE: This is a high quality figure for printing and publication. Number of pixels: 1641 x 2625 with Resolution 600 dpi. The original figure can be downloaded at the following link: Original image (TIF), Larger image (PNG) (PDF 60 kb)
10123_2019_70_MOESM2_ESM.pdf (196 kb)
S2 Figure Phylogenetic tree and sequence alignment of 117 Azurin genes in P. aeruginosa in the Vietnamese and global populations. The evolutionary history was inferred using the Neighbor-Joining method (Saitou and Nei 1987). The percentage of replicate trees in which the associated taxa clustered together in the bootstrap test (1000 replicates) (Felsenstein 1985). The evolutionary distances were computed using the Tamura 3-parameter method (Tamura 1992) and cut-off value of consensus tree is 50%. Evolutionary analyses were conducted in Genious program (Kearse et al. 2012). Base substitutions occur popularly in 5 positions at nucleotides 51, 282, 297, 305 and 432. The name of bacterial isolates or samples from the Vietnamese population in the present study was shown along with GenBank Accession number (% pairwise identify to P. aeruginosa strain PAO1) in global populations. NOTE: This is a high quality with number of pixels 2250 × 1984 and Resolution 600 dpi. The original figure can be downloaded at the following link: Original image (TIF), Larger image (PNG) (PDF 195 kb)

References

  1. ACTION Study Group (2017) Policy and priorities for national cancer control planning in low- and middle-income countries: lessons from the Association of Southeast Asian Nations (ASEAN) costs in oncology prospective cohort study. Eur J Cancer 74:26–37CrossRefGoogle Scholar
  2. Arvidsson RH, Nordling M, Lundberg LG (1989) The Azurin gene from Pseudomonas aeruginosa. Cloning and characterization. Eur J Biochem 179:195–200CrossRefGoogle Scholar
  3. Baindara P, Korpole S, Grover V (2018) Bacteriocins: perspective for the development of novel anticancer drugs. Appl Microbiol Biotechnol 102:10393–10408CrossRefGoogle Scholar
  4. Bernardes N, Chakrabarty AM, Fialho AM (2013a) Engineering of bacterial strains and their products for cancer therapy. Appl Microbiol Biotechnol 97:5189–5199CrossRefGoogle Scholar
  5. Bernardes N, Ribeiro AS, Abreu S, Mota B, Matos RG, Arraiano CM, Seruca R, Paredes J, Fialho AM (2013b) The bacterial protein azurin impairs invasion and FAK/Src signaling in P-cadherin-overexpressing breast cancer cell models. PLoS One 8:e69023CrossRefGoogle Scholar
  6. Chakrabarty AM (2014) Microbial pathogenicity: a new approach to drug development. Adv Exp Med Biol 808:41–49CrossRefGoogle Scholar
  7. Felsenstein J (1985) Confidence limits on phylogenies: an approach using the bootstrap. Evolution 39:783–791CrossRefGoogle Scholar
  8. Fialho AM, Stevens FJ, das Gupta TK, Chakrabarty AM (2007) Beyond host-pathogen interactions: microbial defense strategy in the host environmental. Curr Opin Biotechnol 18:279–286CrossRefGoogle Scholar
  9. Fialho AM, Bernardes N, Chakrabarty AM (2012) Recent patents on live bacteria and their products as potential anticancer agents. Recent Pat Anticancer Drug Discov 7:31–55CrossRefGoogle Scholar
  10. Fialho AM, Bernardes N, Chakrabarty AM (2016) Exploring the anticancer potential of the bacterial protein Azurin. AIMS Microbiol 2:292–303CrossRefGoogle Scholar
  11. Forman D, Bray F, Brewster DH, Gombe Mbalawa C, Kohler B, Piñeros M et al (eds) (2015) Cancer incidence in five continents, vol X. International Agency for Research on Cancer, LyonGoogle Scholar
  12. Fox GE, Stackebrandt E, Hespell RB, Gibson J, Maniloff J, Dyer TA, Wolfe R, Balch W, Tanner R, Magrum L, Zablen L, Blakemore R, Gupta R, Bonen L, Lewis B, Stahl D, Luehrsen K, Chen K, Woese C (1980) The phylogeny of prokaryotes. Science 209:457–463CrossRefGoogle Scholar
  13. Gao M, Zhou J, Su Z, Huang Y (2017) Bacterial cupredoxin Azurin hijacks cellular signaling networks: protein-protein interactions and cancer therapy. Protein Sci 26:2334–2341CrossRefGoogle Scholar
  14. Giraffa G, Neviani E (2001) DNA-based, culture-independent strategies for evaluating microbial communities in food-associated ecosystems. Int J Food Microbiol 20:19–34CrossRefGoogle Scholar
  15. Gupta SS, Mohammed MH, Ghosh TS, Kanungo S, Nair GB, Mande SS (2011) Metagenome of the gut of a malnourished child. Gut Pathog 3:7CrossRefGoogle Scholar
  16. Hall TA (1999) BioEdit: a user-friendly biological sequence alignment editor and analysis program for Windows 95/98/NT. Nucleic Acids Symp Ser 41:95–98Google Scholar
  17. Ishii K, Fukui M (2001) Optimization of annealing temperature to reduce bias caused by a primer mismatch in multitemplate PCR. Appl Environ Microbiol 67:3753–3755CrossRefGoogle Scholar
  18. Jia L, Gorman GS, Coward LU, Noker PE, McCormick D, Horn TL et al (2011) Preclinical pharmacokinetics, metabolism, and toxicity of Azurin-p28 (NSC 745104) a peptide inhibitor of p53 ubiquitination. Cancer Chemother Pharmacol 28:513–524CrossRefGoogle Scholar
  19. Kearse M, Moir R, Wilson A, Stones-Havas S, Cheung M, Sturrock S, Buxton S, Cooper A, Markowitz S, Duran C, Thierer T, Ashton B, Meintjes P, Drummond A (2012) Geneious basic: an integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics 28:1647–1649CrossRefGoogle Scholar
  20. Krieger E, Vriend G (2014) YASARA View - molecular graphics for all devices - from smartphones to workstations. Bioinformatics 30:2981–2982CrossRefGoogle Scholar
  21. Kroes I, Lepp PW, Relman DA (1999) Bacterial diversity within the human subgingival crevice. Proc Natl Acad Sci 96:14547–14552CrossRefGoogle Scholar
  22. Kumar S, Stecher G, Tamura K (2016) MEGA7: molecular evolutionary genetics analysis version 7.0 for bigger datasets. Mol Biol Evol 33:1870–1874CrossRefGoogle Scholar
  23. Larkin M, Blackshields G, Brown N, Chenna R, McGettigan PA, McWilliam H et al (2007) ClustalW and ClustalX version 2. Bioinformatics 23:2947–2948CrossRefGoogle Scholar
  24. Lulla RR, Goldman S, Yamada T, Beattie CW, Bressler L, Pacini M et al (2016) Phase 1 trial of p28 (NSC745104), a non-HDM2mediated peptide inhibitor of p53 ubiquitination in pediatric patients with recurrent or progressive central nervous system tumors: a pediatric brain tumor consortium study. Neuro Oncol 18(9):1319–1325CrossRefGoogle Scholar
  25. McGuire S (2016) World cancer report 2014. World Health Organization, International Agency for Research on Cancer, WHO Press, Geneva, 2015. Adv Nutr 7:418–419CrossRefGoogle Scholar
  26. Mehta N, Lyon JG, Patil K, Mokarram N, Kim C, Bellamkonda RV (2016) Bacterial carriers for glioblastoma therapy. Mol Ther Oncolytics 14:1–17Google Scholar
  27. Milletti F (2012) Cell-penetrating peptides: classes, origin, and current landscape. Drug Discov Today 17:850–860CrossRefGoogle Scholar
  28. Nguyen VD, Nguyen HHC (2015) Molecular screening of Azurin-like anticancer bacteriocins from human gut microflora using bioinformatics. Adv Intell Syst Comput 358:219–229Google Scholar
  29. Nguyen C, Nguyen VD (2016) Discovery of Azurin-like anticancer Bacteriocins from human gut microbiome through homology modeling and molecular docking against the tumor suppressor p53. Biomed Res Int 2016:8490482Google Scholar
  30. Nguyen VD, Pham TT, Nguyen THT, Nguyen TTX, Lone H (2014) Screening of marine bacteria with bacteriocin-like activities and probiotic potential for ornate spiny lobster (Panulirus ornatus) juvenile. Fish Shellfish Immunol 40(1):49–60CrossRefGoogle Scholar
  31. Nocker A, Burr M, Camper AK (2007) Genotypic microbial community profiling: a critical technical review. Microb Ecol 54:276–893CrossRefGoogle Scholar
  32. Pham TT, Ho THN, Nguyen VD (2014) Screening for bacteriocin-like antimicrobial activity against shrimp pathogenic vibrios and molecular identification of marine bacteria from otter clam Lutraria philippinarum. Thai J Vet Med 44(3):345–353Google Scholar
  33. Pisal DS, Kosloski MP, Balu-Iyer SV (2010) Delivery of therapeutic proteins. J Pharm Sci 99:2557–2575CrossRefGoogle Scholar
  34. Punj V, Bhattacharyya S, Saint-Dic D, Vasu C, Cunningham EA, Graves J, Yamada T, Constantinou AI, Christov K, White B, Li G, Majumdar D, Chakrabarty AM, Das Gupta TK (2004) Bacterial cupredoxin Azurin as an inducer of apoptosis and regression in human breast cancer. Oncogene 23:2367–2378CrossRefGoogle Scholar
  35. Ranjard L, Poly F, Nazaret S (2000) Monitoring complex bacterial communities using culture-independent molecular techniques: application to soil environment. Res Microbiol 51:167–177CrossRefGoogle Scholar
  36. Sacoman JL, Monteiro KM, Possenti A, Figueira GM, Foglio MA, Carvalho JE (2008) Cytotoxicity and antitumoral activity of dichloromethane extract and its fractions from Pothomorphe umbellata. Braz J Med Biol Res 41:411–415CrossRefGoogle Scholar
  37. Saitou N, Nei M (1987) The neighbor-joining method: a new method for reconstructing phylogenetic trees. Mol Biol Evol 4:406–425Google Scholar
  38. Samarakoon SR, Ediriweera MK, Nwokwu CDU, Bandara CJ, Tennekoon KH, Piyathilaka P, Karunaratne DN, Karunaratne V (2017) A study on cytotoxic and apoptotic potential of a triterpenoid saponin (3-O-α-L-arabinosyl oleanolic acid) isolated from Schumacheria castaneifolia Vahl in human non-small-cell lung cancer (NCI-H292) cells. Biomed Res Int 2017:9854083CrossRefGoogle Scholar
  39. Shoemaker RH (2006) The NCI60 human tumour cell line anticancer drug screen. Nat Rev Cancer 6:813–823CrossRefGoogle Scholar
  40. Stewart BW, Wild CP (eds) (2014) World cancer report 2014. International Agency for Research on Cancer, LyonGoogle Scholar
  41. Tamura K (1992) Estimation of the number of nucleotide substitutions when there are strong transition-transversion and G + C-content biases. Mol Biol Evol 9:678–687Google Scholar
  42. Vargas-Casanova Y, Rodríguez-Guerra JA, Umaña-Pérez YA et al (2017) Antibacterial synthetic peptides derived from bovine lactoferricin exhibit cytotoxic effect against MDA-MB-468 and MDA-MB-231 breast cancer cell lines. Molecules 22(10)Google Scholar
  43. Wang GC, Wang Y (1997) Frequency of formation of chimeric molecules as a consequence of PCR coamplification of 16S rRNA genes from mixed bacterial genomes. Appl Environ Microbiol 63:4645–4650Google Scholar
  44. Warso MA, Richards JM, Mehta D, Christov K, Schaeffer C, Rae Bressler L, Yamada T, Majumdar D, Kennedy SA, Beattie CW, Das Gupta TK (2013) A first-in-class, first-in-human, phase I trial of p28, a non-HDM2-mediated peptide inhibitor of p53 ubiquitination in patients with advanced solid tumours. Br J Cancer 108:1061–1070CrossRefGoogle Scholar
  45. Wu SY, Leu YL, Chang YL, Wu TS, Kuo PC, Liao YR, Teng CM, Pan SL (2012) Physalin F induces cell apoptosis in human renal carcinoma cells by targeting NF-kappaB and generating reactive oxygen species. PLoS One 7(7):e40727CrossRefGoogle Scholar
  46. Yamada T, Hiraoka Y, Ikehata M, Kimbara K, Avner BS, Gupta TK et al (2004) Apoptosis or growth arrest: modulation of tumor suppressor p53’s specificity by bacterial redox protein Azurin. PNAS 101:4770–4775CrossRefGoogle Scholar
  47. Yamada T, Christov K, Das Gupta TK, Beattie CW (2011) Mechanism of action of p28, a first-in-class, non-HDM2 mediated peptide inhibitor of p53 ubiquitination. J Clin Oncol 29:e13513–e13513CrossRefGoogle Scholar
  48. Zhang Z, Luo Z, Min W, Zhang L, Wu Y, Hu X (2017) An anti-cancer WxxxE-containing Azurin polypeptide inhibits Rac1-dependent STAT3 and ERK/GSK-3β signaling in breast cancer cells. Oncotarget 8:43091–43103Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Institute of Biotechnology and EnvironmentNha Trang UniversityNha TrangVietnam
  2. 2.School of EngineeringCardiff UniversityCardiffUK
  3. 3.Faculty of Engineering and ScienceUniversity of GreenwichChathamUK

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