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Future Technologies

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  • Authors: Raúl J. Cano1, Gary A. Toranzos2
  • Editors: Raúl J. Cano3, Gary A. Toranzos4
  • VIEW AFFILIATIONS HIDE AFFILIATIONS
    Affiliations: 1: California Polytechnic State University, San Luis Obispo, CA 93407; 2: University of Puerto Rico, Rio Piedras Campus, San Juan, Puerto Rico 00933; 3: California Polytechnic State University, San Luis Obispo, CA; 4: University of Puerto Rico-Rio Piedras, San Juan, Puerto Rico
  • Source: microbiolspec March 2018 vol. 6 no. 2 doi:10.1128/microbiolspec.EMF-0015-2018
  • Received 10 January 2018 Accepted 17 January 2018 Published 09 March 2018
  • Gary A. Toranzos, [email protected]
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  • Abstract:

    Microbiome analysis of environmental samples may represent the next frontier in environmental microbial forensics. Next-generation sequencing technologies significantly increased the available genetic data that could be used as evidentiary material. It is not clear, however, whether the microbiome can scale across institutions using forensic-based evidence due to the data resource requirements and the associated costs of maintaining these databases. A successful microbiome study is impacted by the quality of the information gathered and the steps in sample processing and data analysis. To ascertain the validity of methods and the results obtained, there needs to be a stringent procedure to validate the methods and ensure that the results are comparable and reproducible, not only within the laboratory but also between laboratories conducting similar research. Of primary importance for meaningful microbiome studies is an experimental design that leads to carefully executed, controlled, and reproducible studies. The microbiome literature contains a fair share of anecdotal descriptions of microbial community composition and “diagnostic” relative abundance of the taxa therein. These studies are now being supplemented by experimental designs that feature repeated measurements, error estimates, correlations of microbiota with covariates, and increasingly sophisticated statistical tests that enhance the robustness of data analysis and study conclusions. It is imperative to be careful, especially when carrying out attribution studies, to be fully aware of the possible biases included in a specific sample being analyzed.

  • Citation: Cano R, Toranzos G. 2018. Future Technologies. Microbiol Spectrum 6(2):EMF-0015-2018. doi:10.1128/microbiolspec.EMF-0015-2018.

References

1. Metcalf JL, Xu ZZ, Bouslimani A, Dorrestein P, Carter DO, Knight R. 2017. Microbiome tools for forensic science. Trends Biotechnol 35:814–823. [PubMed]
2. Stämmler F, Gläsner J, Hiergeist A, Holler E, Weber D, Oefner PJ, Gessner A, Spang R. 2016. Adjusting microbiome profiles for differences in microbial load by spike-in bacteria. Microbiome 4:28. [PubMed]
3. Gorzelak MA, Gill SK, Tasnim N, Ahmadi-Vand Z, Jay M, Gibson DL. 2015. Methods for improving human gut microbiome data by reducing variability through sample processing and storage of stool. PLoS One 10:e0134802. [PubMed]
4. Goodrich JK, Di Rienzi SC, Poole AC, Koren O, Walters WA, Caporaso JG, Knight R, Ley RE. 2014. Conducting a microbiome study. Cell 158:250–262. [PubMed]
5. La Rosa PS, Brooks JP, Deych E, Boone EL, Edwards DJ, Wang Q, Sodergren E, Weinstock G, Shannon WD. 2012. Hypothesis testing and power calculations for taxonomic-based human microbiome data. PLoS One 7:e52078. [PubMed]
6. Sinha R, Abnet CC, White O, Knight R, Huttenhower C. 2015. The microbiome quality control project: baseline study design and future directions. Genome Biol 16:276. [PubMed]
7. Hiergeist A, Reischl U, Gessner A, Priority Program 1656 Intestinal Microbiota Consortium/Quality Assessment Participants. 2016. Multicenter quality assessment of 16S ribosomal DNA-sequencing for microbiome analyses reveals high inter-center variability. Int J Med Microbiol 306:334–342. [PubMed]
8. Kim D, Hofstaedter CE, Zhao C, Mattei L, Tanes C, Clarke E, Lauder A, Sherrill-Mix S, Chehoud C, Kelsen J, Conrad M, Collman RG, Baldassano R, Bushman FD, Bittinger K. 2017. Optimizing methods and dodging pitfalls in microbiome research. Microbiome 5:52. [PubMed]
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11. Castillo-Peinado LS, Luque de Castro MD. 2016. Present and foreseeable future of metabolomics in forensic analysis. Anal Chim Acta 925:1–15. [PubMed]
12. Alvarez AJ, Khanna M, Toranzos GA, Stotzky G. 1998. Amplification of DNA bound on clay minerals. Mol Ecol 7:775–778.
13. Alvarez AJ, Yumet GM, Santiago CL, Toranzos GA. 1996. Stability of manipulated plasmid DNA in aquatic environments. Environ Toxicol Water Qual 11:129–135.
14. Bohmann K, Evans A, Gilbert MT, Carvalho GR, Creer S, Knapp M, Yu DW, de Bruyn M. 2014. Environmental DNA for wildlife biology and biodiversity monitoring. Trends Ecol Evol 29:358–367. (Erratum, doi:10.1016/j.tree.2014.05.012.)
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16. Budowle B. 2003. Defining a new forensic discipline: microbial forensics. Profiles DNA 6:7–10.
17. Cano RJ, Rivera-Perez J, Toranzos GA, Santiago-Rodriguez TM, Narganes-Storde YM, Chanlatte-Baik L, García-Roldán E, Bunkley-Williams L, Massey SE. 2014. Paleomicrobiology: revealing fecal microbiomes of ancient indigenous cultures. PLoS One 9:e106833. [PubMed]
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19. Patrício AR, Herbst LH, Duarte A, Vélez-Zuazo X, Santos Loureiro N, Pereira N, Tavares L, Toranzos GA. 2012. Global phylogeography and evolution of chelonid fibropapilloma-associated herpesvirus 1. J Gen Virol 93:1035–1045. [PubMed]
20. Piñar G, Piombino-Mascali D, Maixner F, Zink A, Sterflinger K. 2013. Microbial survey of the mummies from the Capuchin Catacombs of Palermo, Italy: biodeterioration risk and contamination of the indoor air. FEMS Microbiol Ecol 86:341–356. [PubMed]
21. von Wintzingerode F, Göbel UB, Stackebrandt E. 1997. Determination of microbial diversity in environmental samples: pitfalls of PCR-based rRNA analysis. FEMS Microbiol Rev 21:213–229. [PubMed]
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/content/journal/microbiolspec/10.1128/microbiolspec.EMF-0015-2018
2018-03-09
2018-10-18

Abstract:

Microbiome analysis of environmental samples may represent the next frontier in environmental microbial forensics. Next-generation sequencing technologies significantly increased the available genetic data that could be used as evidentiary material. It is not clear, however, whether the microbiome can scale across institutions using forensic-based evidence due to the data resource requirements and the associated costs of maintaining these databases. A successful microbiome study is impacted by the quality of the information gathered and the steps in sample processing and data analysis. To ascertain the validity of methods and the results obtained, there needs to be a stringent procedure to validate the methods and ensure that the results are comparable and reproducible, not only within the laboratory but also between laboratories conducting similar research. Of primary importance for meaningful microbiome studies is an experimental design that leads to carefully executed, controlled, and reproducible studies. The microbiome literature contains a fair share of anecdotal descriptions of microbial community composition and “diagnostic” relative abundance of the taxa therein. These studies are now being supplemented by experimental designs that feature repeated measurements, error estimates, correlations of microbiota with covariates, and increasingly sophisticated statistical tests that enhance the robustness of data analysis and study conclusions. It is imperative to be careful, especially when carrying out attribution studies, to be fully aware of the possible biases included in a specific sample being analyzed.

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FIGURE 1

Technical factors in microbiome research that influence results and conclusions.

Source: microbiolspec March 2018 vol. 6 no. 2 doi:10.1128/microbiolspec.EMF-0015-2018
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