Metagenomics

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Metagenomics ( English metagenomics ) is a research area in the biosciences in which genetic material is extracted, sequenced and analyzed directly from environmental samples. This distinguishes it from classic microbiological methods in which microorganisms are cultivated before DNA extraction.

Metagenomic methods enable the identification of microorganisms regardless of their cultivability. In addition to the insights into the complexity of the physiology and ecology of microorganisms that have been made possible for the first time, metagenomic approaches with the analysis of naturally occurring biodiversity also have enormous potential for the identification and development of new biotechnological and pharmaceutical products.

Metagenomic analyzes can refer not only to microorganism communities, but also to animals and plants, for example in order to analyze their late Pleistocene occurrence in permafrost without perceiving their fossils macroscopically.

Two general types of metagenomic studies can be distinguished: functional approaches and sequence-oriented approaches.

term

In 1998, the entire genomic information of the microorganisms of a certain community ( biocenosis ) or a biotope was designated as a metagenome for the first time . The term metagenomics comes from a combination of the terms metaanalysis , a statistical process in which different results from different investigations are to be made quantitatively comparable, and genomics , the analysis of the complete genetic information ( genome ) of an organism.

Metagenomic Methods

Functional metagenomic approaches

In the functional metagenomic analysis of environmental samples, the focus is on the identification of clones with already known properties. For this purpose, DNA is extracted from an environmental sample and expressed in small fragments in a "host organism" (for example Escherichia coli ). Clones with desired properties are then selected, sequenced and biochemically characterized. The focus is usually on identifying properties that are of medical, agricultural or industrial relevance. Limitations of this approach result from the sometimes problematic expression of foreign proteins ( heterologous expression ) in the used as well as in the spatial accumulation ("clustering") of all for the expression of a particular one, which is not always guaranteed by the restriction digestion of the genomic DNA prior to cloning (see above) Property needed genes. In addition, a simple test setup that can be carried out in large numbers is required for the identification of the desired property, since the frequency of active clones is usually very low.

Sequence-oriented metagenomic approaches

While methods such as PCRs or in situ hybridizations are carried out on the basis of specific, known DNA sequences, the direct extraction , cloning and sequencing of genomic DNA offers the advantage of the potential isolation of all genes occurring in the organisms. The isolation takes place independently of the sequence or the function of the genes and thus enables the identification of previously completely unknown genes with little or no sequence homology to already existing genes. Furthermore, this sequence-oriented approach also enables the identification of so-called operons , that is, collections of genes that are spatially connected on the genomic DNA and that are functionally related and z. B. code for the components of certain signal pathways or synthesis pathways, such as. B. for enzymes for the production of antibiotics. Of course, a further goal of these sequence-oriented metagenomic approaches is also the elucidation of entire genomes, so-called Metagenome Assembled Genomes (MAGs), by combining the individual sequence sections into an overall genomic sequence with the help of bioinformatics .

The possibilities offered by today's molecular biological methods, in particular the Whole Genome Shotgun Sequencing method introduced by Craig Venter with sequencing the human genome , are impressively demonstrated by the method also developed by Venter et al. Metagenomics project carried out in 2004, Sargasso Lake , an area near the Bermuda Triangle , shown. The Sargossa Lake Project made 1,045 Gbp DNA sequences and 1.2 million entries of potentially translated proteins available to the public databases. This corresponded to a doubling of the public TrEMBL protein database in 2004. In 2020, over 180 million sequences will be available in TrEMBL. Venter et al. identified more than 69,000 new genes with no apparent homology to previously known genes. Looking at Venter's data on species diversity, at least 1800 species could be distinguished in the samples examined. However, it can be assumed that the biotope is home to many more species.

Other metagenomics projects in the pipeline include: B. aim to analyze the composition of microbial organisms in urban air (Venter et al.) Or the composition of the oral microbial community (National Institute of Dental and Craniofacial Research).

These numbers impressively underline the dimension of the still unfounded part of the world of microorganisms and show that we are just beginning to scratch the surface of microbial diversity. They affirm that we are still a long way from a complete understanding of the ecological relationships in the microbial world, which, although mostly not perceived, nonetheless represent the basis for all life and are indispensable for the organic and inorganic material cycles that are essential in nature.

See also

literature

  • WR Streit and RA Schmitz: Metagenomics - the key to the uncultured microbes. In: Curr Opin Microbiol. 7. 2004, pp. 492-498
  • JC Venter et al .: Environmental Genome Shotgun Sequencing of the Sargasso Sea. In: Science. 304. 2004, pp. 66-74

Web links

Individual evidence

  1. ↑ Elizaveta Rivkina, Lada Petrovskaya, Tatiana Vishnivetskaya, Kirill Krivushin, Lyubov Shmakova, Maria Tutukina, Arthur Meyers, Fyodor Kondrashov: Metagenomic analyzes of the Late Pleistocene permafrost - additional tools for reconstruction of environmental conditions. In: Biogeosciences , Vol. 13, No. 7, 1. 2016, pp. 2207-2219, (PDF) , doi: 10.5194 / bg-13-2207-2016 .
  2. ↑ Susannah Green Tringe, Edward M. Rubin: metagenomics: DNA sequencing of environmental samples. In: Nature Reviews Genetics , Vol. 6, No. 11, Nov. 2005, pp. 805-814, (PDF) .
  3. ↑ Tara Sadoway: A metagenomic Analysis of Ancient DNA Sedimentary Across the Pleistocene-Holocene transition. Thesis, 10th 2014, (PDF) .
  4. ^ Naomi Ward: New directions and interactions in metagenomics research. In: FEMS Microbiology Ecology , Vol. 55, No. 3, March 2006, pp. 331-338, (PDF) .
  5. ↑ J. Handelsman, MR Rondon, SF Brady, J. Clardy, RM Goodman: Molecular biological access to the chemistry of unknown soil microbes: a new frontier for natural products . In: Chemistry & Biology . tape 5 , no. October 10 , 1998, ISSN  1074-5521 , pp. R245-249 , PMID 9818143 . .
  6. ↑ J. Johnson, Kunal Jain, D. Madamwar: 2 - Functional Metagenomics: Exploring Nature's Gold Mine . In: Current Developments in Biotechnology and Bioengineering . Elsevier, 2017, ISBN 978-0-444-63667-6 , pp. 27-43 ( sciencedirect.com [accessed May 27, 2019]). .
  7. ↑ Christopher Quince, Alan W. Walker, Jared T. Simpson, Nicholas J. Loman, Nicola Segata: Shotgun metagenomics, from sampling to analysis . In: Nature Biotechnology . tape 35 , no. 9 , September 2017, ISSN  1546-1696 , p. 833–844 , doi : 10.1038 / nbt.3935 ( nature.com [accessed July 9, 2020]).
  8. ↑ Naseer Sangwan, Fangfang Xia, Jack A. Gilbert: Recovering complete and draft population genomes from metagenome datasets . In: Microbiome . tape 4 , no. 1 , December 2016, ISSN  2049-2618 , p. 8 , doi : 10.1186 / s40168-016-0154-5 , PMID 26951112 , PMC 4782286 (free full text) - ( microbiomejournal.com [accessed July 9, 2020]).
  9. ^ J. Craig Venter, Karin Remington, John F. Heidelberg, Aaron L. Halpern, Doug Rusch: Environmental genome shotgun sequencing of the Sargasso Sea . In: Science (New York, NY) . tape 304 , no. 5667 , April 2, 2004, ISSN  1095-9203 , p. 66-74 , doi : 10.1126 / science.1093857 , PMID 15001713 . .
  10. ↑ UniProt: Release 3.0 of the UniProt Knowledgebase. UniProt, October 25, 2004, accessed July 9, 2020 .
  11. ↑ UniProt release statistics 2020_02. UniProt, April 22, 2020, accessed on July 9, 2020 .