Journal of Microbiology and Biotechnology

The Korean Society for Microbiology and Biotechnology (한국미생물·생명공학회)
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An Improved Approach to Identify Bacterial Pathogens to Human in Environmental Metagenome

  • Yang, Jihoon (Department of Civil and Environmental Engineering, Yonsei University) ;
  • Howe, Adina (Department of Agricultural and Biosystems Engineering, Iowa State University) ;
  • Lee, Jaejin (Department of Agricultural and Biosystems Engineering, Iowa State University) ;
  • Yoo, Keunje (Department of Environmental Engineering, Korea Maritime and Ocean University) ;
  • Park, Joonhong (Department of Civil and Environmental Engineering, Yonsei University)
  • Received : 2020.05.22
  • Accepted : 2020.06.16
  • Published : 2020.09.28
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Abstract

The identification of bacterial pathogens to humans is critical for environmental microbial risk assessment. However, current methods for identifying pathogens in environmental samples are limited in their ability to detect highly diverse bacterial communities and accurately differentiate pathogens from commensal bacteria. In the present study, we suggest an improved approach using a combination of identification results obtained from multiple databases, including the multilocus sequence typing (MLST) database, virulence factor database (VFDB), and pathosystems resource integration center (PATRIC) databases to resolve current challenges. By integrating the identification results from multiple databases, potential bacterial pathogens in metagenomes were identified and classified into eight different groups. Based on the distribution of genes in each group, we proposed an equation to calculate the metagenomic pathogen identification index (MPII) of each metagenome based on the weighted abundance of identified sequences in each database. We found that the accuracy of pathogen identification was improved by using combinations of multiple databases compared to that of individual databases. When the approach was applied to environmental metagenomes, metagenomes associated with activated sludge were estimated with higher MPII than other environments (i.e., drinking water, ocean water, ocean sediment, and freshwater sediment). The calculated MPII values were statistically distinguishable among different environments (p < 0.05). These results demonstrate that the suggested approach allows more for more accurate identification of the pathogens associated with metagenomes.

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References

  1. Furuse Y. 2019. Analysis of research intensity on infectious disease by disease burden reveals which infectious diseases are neglected by researchers. Proc. Natl. Acad. Sci. USA 116: 478-483. https://doi.org/10.1073/pnas.1814484116
  2. Hay SI, Abajobir AA, Abate KH. 2017. Global, regional, and national disability-adjusted life-years (DALYs) for 333 diseases and injuries and healthy life expectancy (HALE) for 195 countries and territories, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet 390: 1260-1344. https://doi.org/10.1016/S0140-6736(17)32130-X
  3. Rappuoli R. 2001. Reverse vaccinology, a genome-based approach to vaccine development. Vaccine 19: 2688-2691. https://doi.org/10.1016/S0264-410X(00)00554-5
  4. Perez-Losada M, Cabezas P, Castro-Nallar E, Crandall KA. 2013. Pathogen typing in the genomics era: MLST and the future of molecular epidemiology. Infect. Genet. Evol. 16: 38-53. https://doi.org/10.1016/j.meegid.2013.01.009
  5. Roche A, Hammerl JA, Appel B, Dieckmann R, Dahouk SA. 2015. FISHing for bacteria in food - A promising tool for the reliable detection of pathogenic bacteria?. Food Microbiol. 46: 395-407. https://doi.org/10.1016/j.fm.2014.09.002
  6. Li L, Mendis N, Trigui H, Oliver JD, Faucher SP. 2014. The importance of the viable but non-culturable state in human bacterial pathogens. Front. Microbiol. 5: 258. https://doi.org/10.3389/fmicb.2014.00258
  7. Stewart EJ. 2012. Growing unculturable bacteria. J. Bacteriol. 194: 4151-4160. https://doi.org/10.1128/JB.00345-12
  8. Panicker G, Call DR, Krug MJ, Bej AK. 2004. Detection of pathogenic Vibrio spp. in shellfish by using multiplex PCR and DNA microarrays. Appl. Environ. Microbiol. 70: 7436-7444. https://doi.org/10.1128/AEM.70.12.7436-7444.2004
  9. Vora GJ, Meador CE, Bird MM, Bopp CA, Andreadis JD, Stenger DA. 2005. Microarray-based detection of genetic heterogeneity, antimicrobial resistance, and the viable but nonculturable state in human pathogenic Vibrio spp. Proc. Natl. Acad. Sci. USA 102: 19109-19114. https://doi.org/10.1073/pnas.0505033102
  10. Chapela MJ, Garrido-Maestu A, Cabado AG. 2015. Detection of foodborne pathogens by qPCR: a practical approach for food industry applications. Cogent. Food Agric. 1: 1-19.
  11. Yang X, Noyes NR, Doster E, Martin JN, Linke LM, Magnuson RJ, et al. 2016. Use of metagenomic shotgun sequencing technology to detect foodborne pathogens within the microbiome of the beef production chain. Appl. Environ. Microbiol. 82: 2433-2443. https://doi.org/10.1128/AEM.00078-16
  12. Mohiuddin MM, Salama Y, Schellhorn HE, Golding GB. 2017. Shotgun metagenomic sequencing reveals freshwater beach sands as reservoir of bacterial pathogens. Water Res. 115: 360-369. https://doi.org/10.1016/j.watres.2017.02.057
  13. Iseki H, Alhassan A, Ohta N, Thekisoe OMM, Yokoyama N, et al. 2007. Development of a multiplex loop-mediated isothermal amplification (mLAMP) method for the simulttaneous detection of bovine Babesia parasites. J. Microbiol. Methods 71: 281-287. https://doi.org/10.1016/j.mimet.2007.09.019
  14. Wylezich C, Papa A, Beer M, et al. 2018. A versatile sample processing workflow for metagenomic pathogen detection. Sci. Rep. 8: 13108. https://doi.org/10.1038/s41598-018-31496-1
  15. Zolfo M, Tett A, Jousson O, Donati C, Segata N. 2017. MetaMLST: multi-locus strain-level bacterial typing from metagenomic samples. Nucleic. Acids Res. 45: e7. https://doi.org/10.1093/nar/gkw837
  16. Wattam AR, Abraham D, Dalay O, Disz TL, Driscoll T, Gabbard JL, et al. 2014 PATRIC, the bacterial bioinformatics database and analysis resource. Nucleic Acids Res. 42: D581-D591. https://doi.org/10.1093/nar/gkt1099
  17. Chen L, Yang J, Yu J, Yao Z, Sun L, Shen Y, Jin Q. 2005. VFDB: a reference database for bacterial virulence factors. Nucleic Acids Res. 33: D325-D328. https://doi.org/10.1093/nar/gki008
  18. Chan MS, Maiden MCJ, Spratt BG. 2001. Database-driven Multi Locus Sequence Typing (MLST) of bacterial pathogens. Bioinformatics 17: 1077-1083. https://doi.org/10.1093/bioinformatics/17.11.1077
  19. Larsen MV, Cosentino S, Rasmussen S, Friis C, Hasman H, Marvig RL, et al. 2012. Multilocus sequence typing of total-genome-sequenced bacteria. J. Clin. Microbiol. 50: 1355-1361. https://doi.org/10.1128/JCM.06094-11
  20. Cai L, Zhang T. 2013. Detecting human bacterial pathogens in wastewater treatment plants by a high-throughput shotgun sequencing technique. Environ. Sci. Technol. 47: 5433-5441. https://doi.org/10.1021/es400275r
  21. Waller AS, Yamada T, Kristensen DM, Kultima JR, Sunagawa S, Koonin E V, et al. 2014. Classification and quantification of bacteriophage taxa in human gut metagenomes. ISME J. 8: 1391-1402. https://doi.org/10.1038/ismej.2014.30
  22. Gillespie JJ, Wattam AR, Cammer SA, Gabbard JL, Shukla MP, Dalay O, et al. 2011. PATRIC: the comprehensive bacterial bioinformatics resource with a focus on human pathogenic species. Infect. Immun. 79: 4286-4298. https://doi.org/10.1128/IAI.00207-11
  23. Comas I, Homolka S, Niemann S, Gagneux S. 2009. Genotyping of genetically monomorphic bacteria: DNA sequencing in Mycobacterium tuberculosis highlights the limitations of current methodologies. PLoS One 4: e7815. https://doi.org/10.1371/journal.pone.0007815
  24. Jolley KA, Maiden MC. 2013. Automated extraction of typing information for bacterial pathogens from whole genome sequence data: neisseria meningitidis as an exemplar. Euro Surveill. 18: 20379.
  25. Jordan K, McAuliffe O. 2018. Chapter Seven - Listeria monocytogenes in foods. Adv. Food. Nutr. Res. 86: 181-213. https://doi.org/10.1016/bs.afnr.2018.02.006
  26. Zheng LL, Li YX, Ding J, Guo XK, Feng KY, Wang YJ, et al. 2012. A comparison of computational methods for identifying virulence factors. PLoS One 7: e42517. https://doi.org/10.1371/journal.pone.0042517
  27. Niu C, Yu D, Wang Y, Ren H, Jin Y, Zhou W, et al. 2013. Common and pathogen-specific virulence factors are different in function and structure. Virulence 4: 473-482. https://doi.org/10.4161/viru.25730
  28. Yang X, Noyes NR, Doster E, Martin JN, Linke LM, Magnuson RJ, et al. 2016. Use of metagenomic shotgun sequencing technology to detect foodborne pathogens within the microbiome of the beef production Chain. Appl. Environ. Microbiol. 82: 2433-2443. https://doi.org/10.1128/AEM.00078-16
  29. Schnoes AM, Brown SD, Dodevski I, Babbitt PC. 2009. Annotation error in public databases: misannotation of molecular function in enzyme superfamilies. PLoS Comput. Biol. 5: e1000605. https://doi.org/10.1371/journal.pcbi.1000605
  30. Richardson EJ, Watson M. 2013. The automatic annotation of bacterial genomes. Brief. Bioinform. 14: 1-12. https://doi.org/10.1093/bib/bbs007
  31. Brettin T, Davis JJ, Disz T, Edwards RA, Gerdes S, Olsen GJ, et al. 2015. RASTtk: a modular and extensible implementation of the RAST algorithm for building custom annotation pipelines and annotating batches of genomes. Sci. Rep. 5: 8365. https://doi.org/10.1038/srep08365
  32. Styles D, O'Brien P, O'Boyle S, Cunningham P, Donlon B, Jones MB. 2009. Measuring the environmental performance of IPPC industry: I. Devising a quantitative science-based and policy-weighted Environmental Emissions Index. Environ. Sci. Policy 12: 226-242. https://doi.org/10.1016/j.envsci.2009.02.003
  33. Behnken S, Hertweck C. 2012. Cryptic polyketide synthase genes in non-pathogenic clostridium SPP. PLoS One 7: e29609. https://doi.org/10.1371/journal.pone.0029609
  34. Thiel T, Pratte BS, Zhong J, Goodwin L, Copeland A, Lucas S, et al. 2013. Complete genome sequence of Anabaena variabilis ATCC 29413. Stand. Genomic. Sci. 9: 562-573.
  35. Turroni F, Bottacini F, Foroni E, Mulder I, Kim JH, Zomer A, Sanchez B, Bidossi A, Ferrarini A, Giubellini V, et al. 2010. Genome analysis of Bifidobacterium bifidum PRL2010 reveals metabolic pathways for host-derived glycan foraging. Proc. Natl. Acad. Sci. USA 107: 19514-19519. https://doi.org/10.1073/pnas.1011100107
  36. Hall TA. 1999. BioEdit: a user-friendly biological sequence alignment editor and analysis program for Windows 95/98/ NT. Nucleic. Acids. Symp. 41: 95-98.
  37. Kumar S, Stecher G, Li M, Knyaz C, Tamura K. 2018. MEGA X: molecular evolutionary genetics analysis across computing platforms. Mol. Biol. Evol. 35: 1547-1549. https://doi.org/10.1093/molbev/msy096
  38. Richter DC, Ott F, Auch AF, Schmid R, Huson DH. 2008. MetaSim-A sequencing simulator for genomics and metagenomics. PLoS One 3: e3373. https://doi.org/10.1371/journal.pone.0003373
  39. Li B, Ju F, Cai L, Zhang T. 2015. Profile and fate of bacterial pathogens in sewage treatment plants revealed by high-throughput metagenomic approach. Environ. Sci. Technol. 49: 10492-10502. https://doi.org/10.1021/acs.est.5b02345
  40. Tang J, Bu Y, Zhang XX, Huang K, He X, Ye L, et al. 2016. Metagenomic analysis of bacterial community composition and antibiotic resistance genes in a wastewater treatment plant and its receiving surface water. Ecotoxicol. Environ. Saf. 132: 260-269. https://doi.org/10.1016/j.ecoenv.2016.06.016
  41. Fawcett T. 2006. An introduction to ROC analysis. Pattern. Recognit. Lett. 27: 861-874. https://doi.org/10.1016/j.patrec.2005.10.010
  42. Florkowski CM. 2008. Sensitivity, specificity, Receiver-Operating Characteristic (ROC) curves and likelihood ratios: communicating the performance of diagnostic tests. Clin. Biochem. Rev. 29: S83-S87.
  43. Harvey R, McBean E. 2015. A Data Mining Tool for Planning Sanitary Sewer Condition Inspection, pp. 181-199. In Hipel K, Fang L, Cullmann J, Bristow M (eds.), Conflict Resolution in Water Resources and Environmental Management, Springer, Cham.
  44. Youngstrom EA. 2014. A primer on receiver operating characteristic analysis and diagnostic efficiency statistics for pediatric psychology: we are ready to ROC. J. Pediatr. Psychol. 39: 204-221. https://doi.org/10.1093/jpepsy/jst062
  45. Ibarbalz FM, Orellana E, Figuerola ELM, Erijman L. 2016. Shotgun metagenomic profiles have a high capacity to discriminate samples of activated sludge according to wastewater type. Appl. Environ. Microbiol. 82: 5186-5196. https://doi.org/10.1128/AEM.00916-16
  46. Ma L, Li B, Jiang XT, Wang YL, Xia Y, Li AD, et al. 2017. Catalogue of antibiotic resistome and host-tracking in drinking water deciphered by a large scale survey. Microbiome. 5: 154. https://doi.org/10.1186/s40168-017-0369-0
  47. Pinto AJ, Marcus DN, Ijaz UZ, Bautista-de lose Santos QM, Dick GJ, Raskin L. 2016. Metagenomic evidence for the presence of comammox nitrospira -like bacteria in a drinking water system. mSphere. 1: e00054-15.
  48. Ma L, Li B, Zhang T. 2014. Abundant rifampin resistance genes and significant correlations of antibiotic resistance genes and plasmids in various environments revealed by metagenomic analysis. Appl. Microbiol. Biotechnol. 98: 5195-5204. https://doi.org/10.1007/s00253-014-5511-3
  49. Kopf A, Bicak M, Kottmann R, Schnetzer J, Kostadinov I, Lehmann K, et al. 2015. The ocean sampling day consortium. Gigascience 4: 27. https://doi.org/10.1186/s13742-015-0066-5
  50. Wommack KE, Bhavsar J, Ravel J. 2008. Metagenomics: read length matters. Appl. Environ. Microbiol. 74: 1453-1463. https://doi.org/10.1128/AEM.02181-07
  51. Nayfach S, Bradley PH, Wyman SK, Laurent TJ, Williams A, Eisen JA, et al. 2015. Automated and accurate estimation of gene family abundance from shotgun metagenomics. PLoS Comput. Bio. 11: e1004573. https://doi.org/10.1371/journal.pcbi.1004573
  52. Bibby K, Viau E, Peccia J. 2011. Viral metagenome analysis to guide human pathogen monitoring in environmental samples. Lett. Appl. Microbiol. 52: 386-392. https://doi.org/10.1111/j.1472-765X.2011.03014.x
  53. Shapiro-Ilan DI, Fuxa JR, Lacey LA, Onstad DW, Kaya HK. 2005. Definitions of pathogenicity and virulence in invertebrate pathology. J. Invertebr. Pathol. 88: 1-7. https://doi.org/10.1016/j.jip.2004.10.003
  54. Yoo K., Yoo H., Lee JM, Shukla SK, Park J. 2018. Classification and regression tree approach for prediction of potential hazards of urban airborne bacteria during Asian dust events. Sci. Rep. 8: 11823. https://doi.org/10.1038/s41598-018-29796-7
  55. Aertsen W, Kint V, Van Orshoven J, Ozkan K, Muys B. 2010. Comparison and ranking of different modelling techniques for prediction of site index in Mediterranean mountain forests Ecol. Model. 221: 1119-1130. https://doi.org/10.1016/j.ecolmodel.2010.01.007
  56. Ul-Saufie AZ, Yahya AS, Ramli NA, Hamid HA. 2011. Comparison between multiple linear regression and feed forward back propagation neural network models for predicting PM10 concentration level based on gaseous and meteorological parameters. Int. J. Res. Appl. Sci. Eng. Technol. 1: 42-49.
  57. Roy K, Ambure P. 2016. The "double cross-validation" software tool for MLR QSAR model development. Chemom. Intell. Lab. Syst. 159: 108-126. https://doi.org/10.1016/j.chemolab.2016.10.009
  58. Jarraud S, Mougel C, Thioulouse J, Lina G, Meugnier H, Forey F, et al. 2002. Relationships between Staphylococcus aureus genetic background, virulence factors, agr groups (alleles), and human disease. Infect. Immun. 70: 631-641. https://doi.org/10.1128/IAI.70.2.631-641.2002