Prediction of Human Microbe-Drug Association based on Layer Attention Graph Convolutional Network


Citar

Texto integral

Resumo

:Human microbes are closely associated with a variety of complex diseases and have emerged as drug targets. Identification of microbe-related drugs is becoming a key issue in drug development and precision medicine. It can also provide guidance for solving the increasingly serious problem of drug resistance enhancement in viruses.

Methods:In this paper, we have proposed a novel model of layer attention graph convolutional network for microbe-drug association prediction. First, multiple biological data have been integrated into a heterogeneous network. Then, the heterogeneous network has been incorporated into a graph convolutional network to determine the embedded microbe and drug. Finally, the microbe-drug association scores have been obtained by decoding the embedding of microbe and drug based on the layer attention mechanism.

Results:To evaluate the performance of our proposed model, leave-one-out crossvalidation (LOOCV) and 5-fold cross-validation have been implemented on the two datasets of aBiofilm and MDAD. As a result, based on the aBiofilm dataset, our proposed model has attained areas under the curve (AUC) of 0.9178 and 0.9022 on global LOOCV and local LOOCV, respectively. Based on aBiofilm dataset, the proposed model has attained an AUC value of 0.9018 and 0.8902 on global LOOCV and local LOOCV, respectively. In addition, the average AUC and standard deviation of the proposed model for 5- fold cross-validation on the aBiofilm and MDAD datasets were 0.9141±6.8556e-04 and 0.8982±7.5868e-04, respectively. Also, two kinds of case studies have been further conducted to evaluate the proposed models.

Conclusion:Traditional methods for microbe-drug association prediction are timeconsuming and laborious. Therefore, the computational model proposed was used to predict new microbe-drug associations. Several evaluation results have shown the proposed model to achieve satisfactory results and that it can play a role in drug development and precision medicine.

Sobre autores

Jia Qu

School of Computer Science and Artificial Intelligence & Aliyun School of Big Data, Changzhou University

Autor responsável pela correspondência
Email: info@benthamscience.net

Jie Ni

School of Computer Science and Artificial Intelligence & Aliyun School of Big Data, Changzhou University

Email: info@benthamscience.net

Tong-Guang Ni

School of Computer Science and Artificial Intelligence & Aliyun School of Big Data, Changzhou University

Email: info@benthamscience.net

Ze-Kang Bian

School of AI & Computer Science,, Jiangnan University

Email: info@benthamscience.net

Jiu-Zhen Liang

School of Computer Science and Artificial Intelligence & Aliyun School of Big Data, Changzhou University

Email: info@benthamscience.net

Bibliografia

  1. Graves, J.L. Principles and Applications of Antimicrobial Nanomaterials; Graves, J.L., Ed.; Elsevier, 2022, pp. 87-101. doi: 10.1016/B978-0-12-822105-1.00003-2
  2. Morozumi, S.; Ueda, M.; Okahashi, N.; Arita, M. Structures and functions of the gut microbial lipidome. Biochim. Biophys. Acta Mol. Cell Biol. Lipids, 2022, 1867(3), 159110. doi: 10.1016/j.bbalip.2021.159110 PMID: 34995792
  3. Sommer, F.; Bäckhed, F. The gut microbiota-masters of host development and physiology. Nat. Rev. Microbiol., 2013, 11(4), 227-238. doi: 10.1038/nrmicro2974 PMID: 23435359
  4. Marsland, B.J.; Gollwitzer, E.S. Host–microorganism interactions in lung diseases. Nat. Rev. Immunol., 2014, 14(12), 827-835. doi: 10.1038/nri3769 PMID: 25421702
  5. Seo, D.O.; Holtzman, D.M. Gut microbiota: From the forgotten organ to a potential key player in the pathology of Alzheimer’s Disease. J. Gerontol. A Biol. Sci. Med. Sci., 2020, 75(7), 1232-1241. doi: 10.1093/gerona/glz262 PMID: 31738402
  6. Ventura, M.; O’Flaherty, S.; Claesson, M.J.; Turroni, F.; Klaenhammer, T.R.; van Sinderen, D.; O’Toole, P.W. Genome-scale analyses of health-promoting bacteria. Probiogenomics. Nat. Rev. Microbiol., 2009, 7(1), 61-71. doi: 10.1038/nrmicro2047 PMID: 19029955
  7. Shock, T.; Badang, L.; Ferguson, B.; Martinez-Guryn, K. The interplay between diet, gut microbes, and host epigenetics in health and disease. J. Nutr. Biochem., 2021, 95, 108631. doi: 10.1016/j.jnutbio.2021.108631 PMID: 33789148
  8. Sun, J.; Chang, E.B. Exploring gut microbes in human health and disease: Pushing the envelope. Genes Dis., 2014, 1(2), 132-139. doi: 10.1016/j.gendis.2014.08.001 PMID: 25642449
  9. Zhu, W.; Romano, K.A.; Li, L.; Buffa, J.A.; Sangwan, N.; Prakash, P.; Tittle, A.N.; Li, X.S.; Fu, X.; Androjna, C.; DiDonato, A.J.; Brinson, K.; Trapp, B.D.; Fischbach, M.A.; Rey, F.E.; Hajjar, A.M.; DiDonato, J.A.; Hazen, S.L. Gut microbes impact stroke severity via the trimethylamine N-oxide pathway. Cell Host Microbe, 2021, 29(7), 1199-1208.e5. doi: 10.1016/j.chom.2021.05.002 PMID: 34139173
  10. Healey, R.D.; Saied, E.M.; Cong, X.; Karsai, G.; Gabellier, L.; Saint-Paul, J.; Del Nero, E.; Jeannot, S.; Drapeau, M.; Fontanel, S.; Maurel, D.; Basu, S.; Leyrat, C.; Golebiowski, J.; Bossis, G.; Bechara, C.; Hornemann, T.; Arenz, C.; Granier, S. Discovery and mechanism of action of small molecule inhibitors of ceramidases**. Angew. Chem. Int. Ed., 2022, 61(2), e202109967. doi: 10.1002/anie.202109967 PMID: 34668624
  11. Crunkhorn, S. Understanding PI3K inhibitor mechanism of action. Nat. Rev. Drug Discov., 2021, 20(11), 816. PMID: 34611334
  12. Viaud, S.; Saccheri, F.; Mignot, G.; Yamazaki, T.; Daillère, R.; Hannani, D.; Enot, D.P.; Pfirschke, C.; Engblom, C.; Pittet, M.J.; Schlitzer, A.; Ginhoux, F.; Apetoh, L.; Chachaty, E.; Woerther, P.L.; Eberl, G.; Bérard, M.; Ecobichon, C.; Clermont, D.; Bizet, C.; Gaboriau-Routhiau, V.; Cerf-Bensussan, N.; Opolon, P.; Yessaad, N.; Vivier, E.; Ryffel, B.; Elson, C.O.; Doré, J.; Kroemer, G.; Lepage, P.; Boneca, I.G.; Ghiringhelli, F.; Zitvogel, L. The intestinal microbiota modulates the anticancer immune effects of cyclophosphamide. Science, 2013, 342(6161), 971-976. doi: 10.1126/science.1240537 PMID: 24264990
  13. Viaud, S.; Daillère, R.; Yamazaki, T.; Lepage, P.; Boneca, I.; Goldszmid, R.; Trinchieri, G.; Zitvogel, L. Why should we need the gut microbiota to respond to cancer therapies? OncoImmunology, 2014, 3(1), e27574. doi: 10.4161/onci.27574 PMID: 24800167
  14. Larrosa, M.; Yañéz-Gascón, M.J.; Selma, M.V.; González-Sarrías, A.; Toti, S.; Cerón, J.J.; Tomás-Barberán, F.; Dolara, P.; Espín, J.C. Effect of a low dose of dietary resveratrol on colon microbiota, inflammation and tissue damage in a DSS-induced colitis rat model. J. Agric. Food Chem., 2009, 57(6), 2211-2220. doi: 10.1021/jf803638d PMID: 19228061
  15. van Sorge, N.M.; Cole, J.N.; Kuipers, K.; Henningham, A.; Aziz, R.K.; Kasirer-Friede, A.; Lin, L.; Berends, E.T.M.; Davies, M.R.; Dougan, G.; Zhang, F.; Dahesh, S.; Shaw, L.; Gin, J.; Cunningham, M.; Merriman, J.A.; Hütter, J.; Lepenies, B.; Rooijakkers, S.H.M.; Malley, R.; Walker, M.J.; Shattil, S.J.; Schlievert, P.M.; Choudhury, B.; Nizet, V. The classical lancefield antigen of group a Streptococcus is a virulence determinant with implications for vaccine design. Cell Host Microbe, 2014, 15(6), 729-740. doi: 10.1016/j.chom.2014.05.009 PMID: 24922575
  16. Jackson, M.A.; Goodrich, J.K.; Maxan, M.E.; Freedberg, D.E.; Abrams, J.A.; Poole, A.C.; Sutter, J.L.; Welter, D.; Ley, R.E.; Bell, J.T.; Spector, T.D.; Steves, C.J. Proton pump inhibitors alter the composition of the gut microbiota. Gut, 2016, 65(5), 749-756. doi: 10.1136/gutjnl-2015-310861 PMID: 26719299
  17. Bauer, P.V.; Duca, F.A.; Waise, T.M.Z.; Dranse, H.J.; Rasmussen, B.A.; Puri, A.; Rasti, M.; O’Brien, C.A.; Lam, T.K.T. Lactobacillus gasseri in the upper small intestine impacts an ACSL3-dependent fatty acid-sensing pathway regulating whole-body glucose homeostasis. Cell Metab., 2018, 27(3), 572-587.e6. doi: 10.1016/j.cmet.2018.01.013 PMID: 29514066
  18. Kurita, A.; Kado, S.; Matsumoto, T.; Asakawa, N.; Kaneda, N.; Kato, I.; Uchida, K.; Onoue, M.; Yokokura, T. Streptomycin alleviates irinotecan-induced delayed-onset diarrhea in rats by a mechanism other than inhibition of β-glucuronidase activity in intestinal lumen. Cancer Chemother. Pharmacol., 2011, 67(1), 201-213. doi: 10.1007/s00280-010-1310-4 PMID: 20354702
  19. Stringer, A.M.; Gibson, R.J.; Logan, R.M.; Bowen, J.M.; Yeoh, A.S.J.; Keefe, D.M.K. Faecal microflora and β-glucuronidase expression are altered in an irinotecan-induced diarrhea model in rats. Cancer Biol. Ther., 2008, 7(12), 1919-1925. doi: 10.4161/cbt.7.12.6940 PMID: 18927500
  20. Lee, H.J.; Zhang, H.; Orlovich, D.A.; Fawcett, J.P. The influence of probiotic treatment on sulfasalazine metabolism in rat. Xenobiotica, 2012, 42(8), 791-797. doi: 10.3109/00498254.2012.660508 PMID: 22348441
  21. Lam, K.N.; Alexander, M.; Turnbaugh, P.J. Precision medicine goes microscopic: Engineering the microbiome to improve drug outcomes. Cell Host Microbe, 2019, 26(1), 22-34. doi: 10.1016/j.chom.2019.06.011 PMID: 31295421
  22. Long, Y.; Wu, M.; Kwoh, C.K.; Luo, J.; Li, X. Predicting human microbe–drug associations via graph convolutional network with conditional random field. Bioinformatics, 2020, 36(19), 4918-4927. doi: 10.1093/bioinformatics/btaa598 PMID: 32597948
  23. Torsvik, V.; Øvreås, L. Microbial diversity and function in soil: From genes to ecosystems. Curr. Opin. Microbiol., 2002, 5(3), 240-245. doi: 10.1016/S1369-5274(02)00324-7 PMID: 12057676
  24. Yelin, I.; Snitser, O.; Novich, G.; Katz, R.; Tal, O.; Parizade, M.; Chodick, G.; Koren, G.; Shalev, V.; Kishony, R. Personal clinical history predicts antibiotic resistance of urinary tract infections. Nat. Med., 2019, 25(7), 1143-1152. doi: 10.1038/s41591-019-0503-6 PMID: 31273328
  25. Long, Y.; Luo, J. Association mining to identify microbe drug interactions based on heterogeneous network embedding representation. IEEE J. Biomed. Health Inform., 2021, 25(1), 266-275. doi: 10.1109/JBHI.2020.2998906 PMID: 32750918
  26. Long, Y.; Wu, M.; Liu, Y.; Kwoh, C.K.; Luo, J.; Li, X. Ensembling graph attention networks for human microbe–drug association prediction. Bioinformatics, 2020, 36(Suppl. 2), i779-i786. doi: 10.1093/bioinformatics/btaa891 PMID: 33381844
  27. Deng, L.; Huang, Y.; Liu, X.; Liu, H. Graph2MDA: A multi-modal variational graph embedding model for predicting microbe-drug associations. Bioinformatics, 2022, 38(4), 1118-1125.
  28. Meng, Y.; Jin, M.; Tang, X.; Xu, J. Drug repositioning based on similarity constrained probabilistic matrix factorization: COVID-19 as a case study. Appl. Soft Comput., 2021, 103, 107135. doi: 10.1016/j.asoc.2021.107135 PMID: 33519322
  29. K, D.; A S, J.; Liu, Y. A deep learning ensemble approach to prioritize antiviral drugs against novel coronavirus SARS-CoV-2 for COVID-19 drug repurposing. Appl. Soft Comput., 2021, 113, 107945. doi: 10.1016/j.asoc.2021.107945 PMID: 34630000
  30. Rajput, A.; Thakur, A.; Sharma, S.; Kumar, M. aBiofilm: A resource of anti-biofilm agents and their potential implications in targeting antibiotic drug resistance. Nucleic Acids Res., 2018, 46(D1), D894-D900. doi: 10.1093/nar/gkx1157 PMID: 29156005
  31. Sun, Y.Z.; Zhang, D.H.; Cai, S.B.; Ming, Z.; Li, J.Q.; Chen, X. MDAD: A special resource for microbe-drug associations. Front. Cell. Infect. Microbiol., 2018, 8, 424. doi: 10.3389/fcimb.2018.00424 PMID: 30581775
  32. Hattori, M.; Tanaka, N.; Kanehisa, M.; Goto, S. Simcomp/Subcomp: Chemical structure search servers for network analyses. Nucleic Acids Res., 2010, 38(Web Server issue), W652-656.
  33. Kuhn, M.; Campillos, M.; Letunic, I.; Jensen, L.J.; Bork, P. A side effect resource to capture phenotypic effects of drugs. Mol. Syst. Biol., 2010, 6(1), 343. doi: 10.1038/msb.2009.98 PMID: 20087340
  34. Gottlieb, A.; Stein, G.Y.; Ruppin, E.; Sharan, R. PREDICT: A method for inferring novel drug indications with application to personalized medicine. Mol. Syst. Biol., 2011, 7(1), 496. doi: 10.1038/msb.2011.26 PMID: 21654673
  35. van Laarhoven, T.; Nabuurs, S.B.; Marchiori, E. Gaussian interaction profile kernels for predicting drug–target interaction. Bioinformatics, 2011, 27(21), 3036-3043. doi: 10.1093/bioinformatics/btr500 PMID: 21893517
  36. Chen, X.; Huang, Y.A.; You, Z.H.; Yan, G.Y.; Wang, X.S. A novel approach based on KATZ measure to predict associations of human microbiota with non-infectious diseases. Bioinformatics, 2017, 33(5), 733-739. doi: 10.1093/bioinformatics/btw715 PMID: 28025197
  37. Chen, X.; Yan, C.C.; Zhang, X.; You, Z.H.; Deng, L.; Liu, Y.; Zhang, Y.; Dai, Q. WBSMDA: Within and between score for MiRNA-disease association prediction. Sci. Rep., 2016, 6(1), 21106. doi: 10.1038/srep21106 PMID: 26880032
  38. Chen, X.; Yan, G.Y. Novel human lncRNA–disease association inference based on lncRNA expression profiles. Bioinformatics, 2013, 29(20), 2617-2624. doi: 10.1093/bioinformatics/btt426 PMID: 24002109
  39. Huang, Y.; Hu, P.; Chan, K.C.C.; You, Z.H. Graph convolution for predicting associations between miRNA and drug resistance. Bioinformatics, 2020, 36(3), 851-858. doi: 10.1093/bioinformatics/btz621 PMID: 31397851
  40. Chen, X.; Yin, J.; Qu, J.; Huang, L. MDHGI: Matrix Decomposition and Heterogeneous Graph Inference for miRNA-disease association prediction. PLOS Comput. Biol., 2018, 14(8), e1006418. doi: 10.1371/journal.pcbi.1006418 PMID: 30142158
  41. Chen, X.; Yan, C.C.; Zhang, X.; You, Z.H.; Huang, Y.A.; Yan, G.Y. HGIMDA: Heterogeneous graph inference for miRNA-disease association prediction. Oncotarget, 2016, 7(40), 65257-65269. doi: 10.18632/oncotarget.11251 PMID: 27533456
  42. Chen, X.; Wang, L.; Qu, J.; Guan, N.N.; Li, J.Q. Predicting miRNA–disease association based on inductive matrix completion. Bioinformatics, 2018, 34(24), 4256-4265. doi: 10.1093/bioinformatics/bty503 PMID: 29939227
  43. Davis, R.; Markham, A.; Balfour, J.A. Ciprofloxacin. Drugs, 1996, 51(6), 1019-1074. doi: 10.2165/00003495-199651060-00010 PMID: 8736621
  44. Zhang, G.F.; Liu, X.; Zhang, S.; Pan, B.; Liu, M.L. Ciprofloxacin derivatives and their antibacterial activities. Eur. J. Med. Chem., 2018, 146, 599-612. doi: 10.1016/j.ejmech.2018.01.078 PMID: 29407984
  45. Maheshwari, M.; Yaser, N.H.; Naz, S.; Fatima, M.; Ahmad, I. Emergence of ciprofloxacin-resistant extended-spectrum β-lactamase-producing enteric bacteria in hospital wastewater and clinical sources. J. Glob. Antimicrob. Resist., 2016, 5, 22-25. doi: 10.1016/j.jgar.2016.01.008 PMID: 27436461
  46. Price, L.B.; Vogler, A.; Pearson, T.; Busch, J.D.; Schupp, J.M.; Keim, P. In vitro selection and characterization of Bacillus anthracis mutants with high-level resistance to ciprofloxacin. Antimicrob. Agents Chemother., 2003, 47(7), 2362-2365. doi: 10.1128/AAC.47.7.2362-2365.2003 PMID: 12821500
  47. Keating, G.M.; Scott, L.J. Moxifloxacin. Drugs, 2004, 64(20), 2347-2377. doi: 10.2165/00003495-200464200-00006 PMID: 15456331
  48. Tulkens, P.M.; Arvis, P.; Kruesmann, F. Moxifloxacin safety. Drugs R D., 2012, 12(2), 71-100. doi: 10.2165/11634300-000000000-00000 PMID: 22715866
  49. Nguyen, T.K.; Argudín, M.A.; Deplano, A.; Nhung, P.H.; Nguyen, H.A.; Tulkens, P.M.; Dodemont, M.; Van Bambeke, F. Antibiotic resistance, biofilm formation, and intracellular survival as possible determinants of persistent or recurrent infections by Staphylococcus aureus in a vietnamese tertiary hospital: Focus on bacterial response to moxifloxacin. Microb. Drug Resist., 2020, 26(6), 537-544. doi: 10.1089/mdr.2019.0282 PMID: 31825276
  50. Tapal, A.; Tiku, P.K. Complexation of curcumin with soy protein isolate and its implications on solubility and stability of curcumin. Food Chem., 2012, 130(4), 960-965. doi: 10.1016/j.foodchem.2011.08.025
  51. Lestari, M.L.A.D.; Indrayanto, G. Curcumin. Profiles Drug Subst. Excip. Relat. Methodol., 2014, 39, 113-204. doi: 10.1016/B978-0-12-800173-8.00003-9 PMID: 24794906
  52. Koboziev, I.; Scoggin, S.; Gong, X.; Mirzaei, P.; Zabet-Moghaddam, M.; Yosofvand, M.; Moussa, H.; Jones-Hall, Y.; Moustaid-Moussa, N. Effects of curcumin in a mouse model of very high fat diet-induced obesity. Biomolecules, 2020, 10(10), 1368. doi: 10.3390/biom10101368 PMID: 32992936
  53. Singh, B.N.; Shankar, S.; Srivastava, R.K. Green tea catechin, epigallocatechin-3-gallate (EGCG): Mechanisms, perspectives and clinical applications. Biochem. Pharmacol., 2011, 82(12), 1807-1821. doi: 10.1016/j.bcp.2011.07.093 PMID: 21827739
  54. Wang, X.; Ye, T.; Chen, W.J.; Lv, Y.; Hao, Z.; Chen, J.; Zhao, J.Y.; Wang, H.P.; Cai, Y.K. Structural shift of gut microbiota during chemo-preventive effects of epigallocatechin gallate on colorectal carcinogenesis in mice. World J. Gastroenterol., 2017, 23(46), 8128-8139. doi: 10.3748/wjg.v23.i46.8128 PMID: 29290650
  55. Wan, M.L.Y.; Ling, K.H.; Wang, M.F.; El-Nezami, H. Green tea polyphenol epigallocatechin‐3‐gallate improves epithelial barrier function by inducing the production of antimicrobial peptide pBD‐1 and pBD‐2 in monolayers of porcine intestinal epithelial IPEC‐J2 cells. Mol. Nutr. Food Res., 2016, 60(5), 1048-1058. doi: 10.1002/mnfr.201500992 PMID: 26991948
  56. Cai, S.; Xie, L.W.; Xu, J.Y.; Zhou, H.; Yang, C.; Tang, L.F.; Tian, Y.; Li, M. (-)-Epigallocatechin-3-Gallate (EGCG) modulates the composition of the gut microbiota to protect against radiation-induced intestinal injury in mice. Front. Oncol., 2022, 12, 848107. doi: 10.3389/fonc.2022.848107 PMID: 35480105

Arquivos suplementares

Arquivos suplementares
Ação
1. JATS XML
Baixar

Declaração de direitos autorais © Bentham Science Publishers, 2024