Identificación de Microorganismos en Muestras Ambientales: Análisis Bioinformático del Gen 16S RRNA Mediante QIIME2
Resumen
La diversidad de microorganismos en el suelo es elevada y su identificación mediante el uso de técnicas tradicionales de cultivo resulta inadecuada y limitada para un elevado porcentaje de los mismos. En la actualidad se cuentan con tecnologías de secuenciamiento masivo del ADN que ha permitido, junto a otras técnicas y herramientas, incluyendo la bioinfomática, la identificación de microrganismos sin necesidad del uso de medios de cultivos. Sin embargo, la secuenciación masiva ha generado enormes cantidades de información que requiere ser analizada y por ende demanda un esfuerzo computacional considerable. Existen diversos programas bioinformáticos, basados en uno o varios lenguajes de programación, para el análisis molecular in silico de secuencias de ADN, e.g., MOTHUR, QIIME1, DADA2 y QIIME2. De estos, QIIME2, por sus siglas en inglés “Quantitative insights into microbial ecology”, es una herramienta frecuentemente empleada para el analisis datos de secuenciamiento de marcadores moleculares o genes funcionales, e.g., 16S rRNA, 18S rRNA, ITS, COI, entre otros. Dada la importancia de éstas, y de la necesidad de acceso a este conocimiento en lenguaje español, en esta revisión se describe y detalla el flujo de trabajo para el análisis de secuencias del gen 16S rRNA provenientes de muestras ambientales empleando QIIME2.
Descargas
Citas
Alves, R. J. E., Kerou, M., Zappe, A., Bittner, R., Abby, S. S., Schmidt, H. A., Pfeifer, K., & Schleper, C. (2019). Ammonia Oxidation by the Arctic Terrestrial Thaumarchaeote Candidatus Nitrosocosmicus arcticus Is Stimulated by Increasing Temperatures. Frontiers in Microbiology, 10. https://www.frontiersin.org/articles/10.3389/fmicb.2019.01571
Baesman, S. M., Miller, L. G., Wei, J. H., Cho, Y., Matys, E. D., Summons, R. E., Welander, P. V., & Oremland, R. S. (2015). Methane Oxidation and Molecular Characterization of Methanotrophs from a Former Mercury Mine Impoundment. Microorganisms, 3(2), 290–309. https://doi.org/10.3390/microorganisms3020290
Bar-On, Y. M., Phillips, R., & Milo, R. (2018). The biomass distribution on Earth. Proceedings of the National Academy of Sciences, 115(25), 6506–6511.
https://doi.org/10.1073/pnas.1711842115
Bolyen, E., Rideout, J. R., Dillon, M. R., Bokulich, N. A., Abnet, C. C., Al-Ghalith, G. A., Alexander, H., Alm, E. J., Arumugam, M., Asnicar, F., Bai, Y., Bisanz, J. E., Bittinger, K., Brejnrod, A., Brislawn, C. J., Brown, C. T., Callahan, B. J., Caraballo-Rodríguez, A. M., Chase, J., … Caporaso, J. G. (2019). Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nature Biotechnology, 37(8), 852–857.
https://doi.org/10.1038/s41587-019-0209-9
Callahan, B. J., McMurdie, P. J., Rosen, M. J., Han, A. W., Johnson, A. J. A., & Holmes, S. P. (2016). DADA2: High-resolution sample inference from Illumina amplicon data. Nature Methods, 13(7), 581–583. https://doi.org/10.1038/nmeth.3869
Camacho, C., Coulouris, G., Avagyan, V., Ma, N., Papadopoulos, J., Bealer, K., & Madden, T. L. (2009). BLAST+: Architecture and applications. BMC Bioinformatics, 10(1), 421. https://doi.org/10.1186/1471-2105-10-421
Caporaso, J. G., Kuczynski, J., Stombaugh, J., Bittinger, K., Bushman, F. D., Costello, E. K., Fierer, N., Peña, A. G., Goodrich, J. K., Gordon, J. I., Huttley, G. A., Kelley, S. T., Knights, D., Koenig, J. E., Ley, R. E., Lozupone, C. A., McDonald, D., Muegge, B. D., Pirrung, M., … Knight, R. (2010). QIIME allows analysis of high-throughput community sequencing data. Nature Methods, 7(5), 335–336. https://doi.org/10.1038/nmeth.f.303
Delgado, E. F., Valdez, A. T., Covarrubias, S. A., Tosi, S., & Nicola, L. (2021). Soil Fungal Diversity of the Aguarongo Andean Forest (Ecuador). Biology, 10(12).
https://doi.org/10.3390/biology10121289
Demko, A. M., Patin, N. V., & Jensen, P. R. 2021. Microbial diversity in tropical marine sediments assessed using culture-dependent and culture-independent techniques. Environmental Microbiology, 23(11), 6859–6875. https://doi.org/10.1111/1462-2920.15798
Furutani, S., Furutani, N., Kawai, Y., Nakayama, A., & Nagai, H. (2022). Rapid DNA Sequencing Technology Based on the Sanger Method for Bacterial Identification. Sensors (Basel, Switzerland), 22(6). https://doi.org/10.3390/s22062130
Gil-Martínez, M., López-García, Á., Domínguez, M. T., Kjøller, R., Navarro-Fernández, C. M., Rosendahl, S., & Marañón, T. (2021). Soil fungal diversity and functionality are driven by plant species used in phytoremediation. Soil Biology and Biochemistry, 153, 108102. https://doi.org/10.1016/j.soilbio.2020.108102
Heather, J. M., & Chain, B. (2016). The sequence of sequencers: The history of sequencing DNA. Genomics, 107(1), 1–8. https://doi.org/10.1016/j.ygeno.2015.11.003
Hernández-Guzmán, M., Pérez-Hernández, V., Navarro-Noya, Y. E., Luna-Guido, M. L., Verhulst, N., Govaerts, B., & Dendooven, L. (2022). Application of ammonium to a N limited arable soil enriches a succession of bacteria typically found in the rhizosphere. Scientific Reports, 12(1), 4110. https://doi.org/10.1038/s41598-022-07623-4
Illumina. (2022). Specification Sheet: MiSeq System.
Illumina. (2023). Cost of NGS | Comparisons and budget guidance. 2023.
https://www.illumina.com/science/technology/next-generation-sequencing/beginners/ngs-cost.html. Consultado 18 de septiembre de 2023
Jing, Z., Lu, Z., Mao, T., Cao, W., Wang, W., Ke, Y., Zhao, Z., Wang, X., & Sun, W. (2021). Microbial composition and diversity of drinking water: A full scale spatial-temporal investigation of a city in northern China. Science of The Total Environment, 776, 145986.
https://doi.org/10.1016/j.scitotenv.2021.145986
Kanzi, A. M., San, J. E., Chimukangara, B., Wilkinson, E., Fish, M., Ramsuran, V., & de Oliveira, T. (2020). Next Generation Sequencing and Bioinformatics Analysis of Family Genetic Inheritance. Frontiers in Genetics, 11.
https://www.frontiersin.org/articles/10.3389/fgene.2020.544162
Liu, S., Sun, Y., Shi, F., Liu, Y., Wang, F., Dong, S., & Li, M. (2022). Composition and Diversity of Soil Microbial Community Associated With Land Use Types in the Agro–Pastoral Area in the Upper Yellow River Basin. Frontiers in Plant Science, 13.
https://www.frontiersin.org/articles/10.3389/fpls.2022.819661
McDonald, D., Price, M. N., Goodrich, J., Nawrocki, E. P., DeSantis, T. Z., Probst, A., Andersen, G. L., Knight, R., & Hugenholtz, P. (2012). An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea. The ISME Journal, 6(3), 610–618. https://doi.org/10.1038/ismej.2011.139
McDonald, D., Jiang, Y., Balaban, M., Cantrell, K., Zhu, Q., Gonzalez, A., Morton, J. T., Nicolaou, G., Parks, D. H., Karst, S. M., Albertsen, M., Hugenholtz, P., DeSantis, T., Song, S. J., Bartko, A., Havulinna, A. S., Jousilahti, P., Cheng, S., Inouye, M., … Knight, R. (2023). Greengenes2 unifies microbial data in a single reference tree. Nature Biotechnology. https://doi.org/10.1038/s41587-023-01845-1
McMurdie, P. J., & Holmes, S. (2013). phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data. PLoS ONE, 8(4), e61217. https://doi.org/10.1371/journal.pone.0061217
Martin, M. (2011). Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.Journal, 17(1), 10. https://doi.org/10.14806/ej.17.1.200
Martiny, A. C. (2019). High proportions of bacteria are culturable across major biomes. The ISME Journal, 13(8), 2125–2128. https://doi.org/10.1038/s41396-019-0410-3
Sanger, F., Air, G. M., Barrell, B., Brown, N. L., Coulson, A. R., Fiddes, J. C., Hutchison, C. A., Slocombe, P. M., & Smith, M. (1977). Nucleotide sequence of bacteriophage φX174 DNA. Nature, 265, 687–695.
Schadt, E. E., Turner, S., & Kasarskis, A. (2010). A window into third-generation sequencing. Human Molecular Genetics, 19(R2), R227–R240. https://doi.org/10.1093/hmg/ddq416
Schloss, J. A., Gibbs, R. A., Makhijani, V. B., & Marziali, A. (2020). Cultivating DNA Sequencing Technology After the Human Genome Project. Annual Review of Genomics and Human Genetics, 21(1), 117–138. https://doi.org/10.1146/annurev-genom-111919-082433
Schloss, P. D., Westcott, S. L., Ryabin, T., Hall, J. R., Hartmann, M., Hollister, E. B., Lesniewski, R. A., Oakley, B. B., Parks, D. H., Robinson, C. J., Sahl, J. W., Stres, B., Thallinger, G. G., Van Horn, D. J., & Weber, C. F. (2009). Introducing mothur: Open-Source, Platform-Independent, Community-Supported Software for Describing and Comparing Microbial Communities. Applied and Environmental Microbiology, 75(23), 7537–7541.
https://doi.org/10.1128/AEM.01541-09
Singh, B., Yeasmin, S., & Sparks, D. L. (2023). Mineral-organic-microbial interactions. In M. J. Goss & M. Oliver (Eds.), Encyclopedia of Soils in the Environment (Second Edition) (pp. 387–406). Academic Press. https://doi.org/10.1016/B978-0-12-822974-3.00128-2
Siniscalchi, L. A. B., Siqueira, J. C., Batista, A. M. M., & Araújo, J. C. (2022). Detection of methanotrophic microorganisms in sludge and sediment samples from sewage treatment systems. Water Practice and Technology, 17(1), 329–335.
https://doi.org/10.2166/wpt.2021.101
Slatko, B. E., Gardner, A. F., & Ausubel, F. M. (2018). Overview of Next-Generation Sequencing Technologies. Current Protocols in Molecular Biology, 122(1), e59.
https://doi.org/10.1002/cpmb.59
Pedregosa F., Varoquaux G., Gramfort A., Michel V., Thirion B., Grisel O., Blondel M., Prettenhofer P., Weiss R., Dubourg V., Vanderplas J., Passos A., Cournapeau D., Brucher M., Perrot M., and Duchesnay E. (2011). Scikit-learn: machine learning in python. Journal of machine learning research, 12:2825–2830.
Posit team (2023). RStudio: Integrated Development Environment for R. Posit Software, PBC, Boston, MA. URL http://www.posit.co/
Quast, C., Pruesse, E., Yilmaz, P., Gerken, J., Schweer, T., Yarza, P., Peplies, J., & Glöckner, F. O. (2012). The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Research, 41(D1), D590–D596.
https://doi.org/10.1093/nar/gks1219
R Core Team. (2023). R: A Language and Environment for Statistical Computing, Vienna, Austria. https://www.R-project.org/.
Rim, S. O., Roy, M., Jeon, J., Montecillo, J. A. V., Park, S.-C., & Bae, H. (2021). Diversity and Communities of Fungal Endophytes from Four Pinus Species in Korea. Forests, 12(3). https://doi.org/10.3390/f12030302
Rudkjøbing, V. B., Thomsen, T. R., Xu, Y., Melton-Kreft, R., Ahmed, A., Eickhardt, S., Bjarnsholt, T., Poulsen, S. S., Nielsen, P. H., Earl, J. P., Ehrlich, G. D., & Moser, C. (2016). Comparing culture and molecular methods for the identification of microorganisms involved in necrotizing soft tissue infections. BMC Infectious Diseases, 16(1), 652. https://doi.org/10.1186/s12879-016-1976-2
Teixeira, H., & Rodríguez-Echeverría, S. (2016). Identification of symbiotic nitrogen-fixing bacteria from three African leguminous trees in Gorongosa National Park. Systematic and Applied Microbiology, 39(5), 350–358. https://doi.org/10.1016/j.syapm.2016.05.004
Derechos de autor 2023 Valetín Pérez Hernández , Mario Hernández Guzmán
Esta obra está bajo licencia internacional Creative Commons Reconocimiento 4.0.
.jpg)
.png)
.png)
1.png)
