Historically, microbial studies have concentrated on pure laboratory cultures. By contrast, metagenomics is defined as the direct analysis of genomes contained in an environmental sample, in order to dillucidate the genomic composition of the entire microbial community [1]. Consequently, the descriptions obtained from these kind of studies are more comprehensive than those provided for traditional phylogenetic studies, based on small-subunit ribosomal RNA loci (16S) [2].

Besides, shotgun metagenomics provides information about new enzymatic pathways and evolutionary relationships among non-culturable organisms [3]. They can be complemented with metatranscriptomic and metaproteomic analyses to determine expression profiles [4][5].

In this manner, environments so diverse as skin and guts of animals [6][7], marine sediments [8], rhizosphere [9] and acid mine runoff [10] have been studied.

Next Generation Sequencing edit

In the last 10 years, metagenomic studies have been impulsed by the development of next generation sequencing (NGS) platforms. Although Sanger sequencing has the lowest error rate and produces longer reads, it has been overcome for NGS technologies, because of its simplicity and low costs [11].

The most used NGS platform for metagenomics studies is 454/Roche [12][13][10,11]. This system amplifies fragments of DNA adhered to microspheres through a polymerase chain reaction in emulsion. Each microsphere is set in a well and submitted to individual and parallel pyrosequencing, which consist in the sequential addition of the four deoxinucleotide triphosphates. These are incorporated to the coding strand, if they correspond to the sequence, by a DNA polymerase. The subsequent reaction liberates one pyrophosphate, which is consumed in two enzimatic reactions to produce light. Light levels of 1.2 million parallel reactions are detected by a camera and processed to obtain the sequence of the fragments. The reads produced by 454/Roche platform have a mean length of 600 bases, which is ideal for metagenomic studies [14].

Bioinformatic analysis edit

The enormous amounts of data generated by NGS platforms are subsequently processed with bioinformatic software, in order to assess the taxonomical and functional identity of the DNA sequenced. This step is called binning and it is generally done by sequence similarity [15]. Although there are several tools to perform a metagenomic analysis, the most widely used are the MG-RAST server [16] and metAMOS [17].

References edit

  1. Eisen, J. A. (2007). Environmental Shotgun Sequencing: Its Potential and Challenges for Studying the Hidden World of Microbes. PLoS Biology, 5(3), e82. http://doi.org/10.1371/journal.pbio.0050082
  2. Thomas, T., Gilbert, J., & Meyer, F. (2012). Metagenomics - a guide from sampling to data analysis. Microbial Informatics and Experimentation, 2, 3. http://doi.org/10.1186/2042-5783-2-3
  3. Tyson G. W., Chapman J., Hugenholtz P., Allen E. E., Ram R. J., Richardson P. M., et al. . (2004). Community structure and metabolism through reconstruction of microbial genomes from the environment. Nature 428, 37–43. 10.1038/nature02340
  4. Wilmes, P., Bond P. L. Metaproteomics: studying functional gene expression in microbial ecosystems.Volume 14, Issue 2, p92–97, February 2006. http://dx.doi.org/10.1016/j.tim.2005.12.006
  5. Gilbert, J. A., Field, D., Huang, Y., Edwards, R., Li, W., Gilna, P., & Joint, I. (2008). Detection of Large Numbers of Novel Sequences in the Metatranscriptomes of Complex Marine Microbial Communities. PLoS ONE, 3(8), e3042. http://doi.org/10.1371/journal.pone.0003042
  6. Hess, M., Sczyrba, A., Egan, R., Kim, T., Chokhawala, H., Schroth, G., Luo, S., Clark, D. S., Chen, F., Zhang, T., Mackie, R. I., Pennacchio, I. A., Tringe, S. G., Visel, A., Woyke, T., Wang, Z. & Rubin, E. M. Metagenomic Discovery of Biomass-Degrading Genes and Genomes from Cow Rumen. Science: 463-467
  7. Qin, J., Li, R., Raes, J., Arumugam, M., Burgdorf, K. S., Manichanh, C., … Wang, J. (2010). A human gut microbial gene catalog established by metagenomic sequencing.Nature,464(7285), 59–65. http://doi.org/10.1038/nature08821
  8. Stein, J. L., Marsh, T. L., Wu, K. Y., Shizuya, H., & DeLong, E. F. (1996). Characterization of uncultivated prokaryotes: isolation and analysis of a 40-kilobase-pair genome fragment from a planktonic marine archaeon. Journal of Bacteriology, 178(3), 591–599.
  9. Hanin Alzubaidy, Magbubah Essack, Tareq B. Malas, Ameerah Bokhari, Olaa Motwalli, Frederick Kinyua Kamanu, Suhaiza Ahmad Jamhor, Noor Azlin Mokhtar, André Antunes, Marta Filipa Simões, Intikhab Alam, Salim Bougouffa, Feras F. Lafi, Vladimir B. Bajic, John A.C. Archer, Rhizosphere microbiome metagenomics of gray mangroves (Avicennia marina) in the Red Sea, Gene, Volume 576, Issue 2, Part 1, 1 February 2016, Pages 626-636, ISSN 0378-1119, https://doi.org/10.1016/j.gene.2015.10.032.
  10. Méndez-García, C., Peláez, A. I., Mesa, V., Sánchez, J., Golyshina, O. V., & Ferrer, M. (2015). Microbial diversity and metabolic networks in acid mine drainage habitats. Frontiers in Microbiology, 6, 475. http://doi.org/10.3389/fmicb.2015.00475
  11. Behjati, S., & Tarpey, P. S. (2013). What is next generation sequencing? Archives of Disease in Childhood. Education and Practice Edition, 98(6), 236–238. http://doi.org/10.1136/archdischild-2013-304340
  12. Wommack, K. E., Bhavsar, J., & Ravel, J. (2008). Metagenomics: Read Length Matters. Applied and Environmental Microbiology, 74(5), 1453–1463. http://doi.org/10.1128/AEM.02181-07
  13. Mardis ER. Next-generation DNA sequencing methods. Annu Rev Genomics Hum Genet. 2008;9:387–402. doi: 10.1146/annurev.genom.9.081307.164359.
  14. Wommack, K. E., Bhavsar, J., & Ravel, J. (2008). Metagenomics: Read Length Matters. Applied and Environmental Microbiology, 74(5), 1453–1463. http://doi.org/10.1128/AEM.02181-07
  15. Mande, Sharmila S.; Monzoorul Haque Mohammed; Tarini Shankar Ghosh (2012). "Classification of metagenomic sequences: methods and challenges.".Briefings in Bioinformatics. 13(6): 669–81.
  16. Meyer, F., Paarmann, D., D’Souza, M., Olson, R., Glass, E., Kubal, M., … Edwards, R. (2008). The metagenomics RAST server – a public resource for the automatic phylogenetic and functional analysis of metagenomes. BMC Bioinformatics,9, 386. http://doi.org/10.1186/1471-2105-9-386.
  17. Treangen, T. J., Koren, S., Sommer, D. D., Liu, B., Astrovskaya, I., Ondov, B., … Pop, M. (2013). MetAMOS: a modular and open source metagenomic assembly and analysis pipeline. Genome Biology, 14(1), R2. http://doi.org/10.1186/gb-2013-14-1-r2