Wikipedia

Microbial dark matter

Microbial dark matter[1][2] (MDM) comprises the vast majority of microbial organisms (usually bacteria and archaea) that microbiologists are unable to culture in the laboratory, due to lack of knowledge or ability to supply the required growth conditions. Microbial dark matter is analogous to the dark matter of physics and cosmology due to its elusiveness in research and importance to our understanding of biological diversity. Microbial dark matter can be found ubiquitously and abundantly across multiple ecosystems, but remains difficult to study due to difficulties in detecting and culturing these species, posing challenges to research efforts.[3] It is difficult to estimate its relative magnitude, but the accepted gross estimate is that as little as one percent of microbial species in a given ecological niche are culturable. In recent years, more effort has been directed towards deciphering microbial dark matter by means of recovering genome DNA sequences from environmental samples via culture independent methods such as single cell genomics[4] and metagenomics.[5] These studies have enabled insights into the evolutionary history and the metabolism of the sequenced genomes,[6][7] providing valuable knowledge required for the cultivation of microbial dark matter lineages. However, microbial dark matter research remains comparatively undeveloped and is hypothesized to provide insight into processes radically different from known biology, new understandings of microbial communities, and increasing understanding of how life survives in extreme environments.[8]

History of the term

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Our contemporary understanding of microbial dark matter was born from a field that still faced constraints with the cultivation of traditional microbes. One of the main constraints of this time was an over dependence on the use of culturing methods. This over reliance meant that a large amount of microbial diversity remained yet to be discovered.  However in the late 20th century new developments in molecular techniques led to a surge in discovery of uncultured microbes. Despite this newfound diversity, a large majority of microbial species remain uncharacterized.[9] This fact was further proven by the development of advanced genomic sequencing techniques in the early 21st century which uncovered a larger amount of microbial diversity than previously thought.[8]

Methods for studying microbial dark matter

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Metagenomics

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Metagenomics is a technique in the field of microbial studies that enables us to sequence DNA directly from samples of microbial environments. This innovative technique allows us to identify the genetic material of unknown microbes and avoid overreliance on the use of culturing. The use of metagenomics differs from other microbial methods in that it uses a broad description through its use of bulk samples. This technique has expanded our understanding of microbial functions in ecosystems through the discovery of new genes and metabolic pathways.[10]

Single-cell genomics

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Methods of single-cell genomics have shown promise in supporting metagenomics approaches by allowing the study of individual microbial cells isolated from their natural environments, a method which has been employed to uncover the genomic and functional diversity within microbial communities, particularly those that cannot be cultured. Single-cell techniques have also successfully identified numerous new branches on the tree of life, providing insight into the gaps of current phylogenetic understanding and metabolic potential of these organisms.[11]

Improved culturing techniques

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Despite the rise of culture-independent methods as successful methods for dark matter research, improvements in culturing techniques remain both relevant and necessary to further current understanding of MRM microbes. To this point, developments in methods such as highly specific growth media to mimic natural microbial environments and co-culturing of synergistic microbial species have shown success in studying previously unculturable microbes. These advancements also serve to facilitate the application of MRM research into biotechnological and physiological uses.[12]

Computational tools

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Genomic studies produce vast amounts of data to be analyzed. This analysis requires the use of advanced computational components. The scientific subdiscipline of bioinformatics used computational technology to collect genomes and conduct analysis on metabolic pathways. In recent years, research on artificial intelligence and machine learning has produced new ways to increase our ability to predict the behavior of microbial species using their genetic data.[13] These new developments in the world of computational tools have allowed us to further understand the structure and dynamics present in microbial communities.

Microbes with highly unusual DNA

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It has been suggested certain microbial dark matter genetic material could belong to a new (i.e., fourth) domain of life,[14][15] although other explanations (e.g., viral origin) are also possible, which has ties with the issue of a hypothetical shadow biosphere.[16]

See also

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References

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  1. ^ Filee, J.; Tetart, F.; Suttle, C. A.; Krisch, H. M. (2005). "Marine T4-type bacteriophages, a ubiquitous component of the dark matter of the biosphere". Proceedings of the National Academy of Sciences. 102 (35): 12471–12476. Bibcode:2005PNAS..10212471F. doi:10.1073/pnas.0503404102. ISSN 0027-8424. PMC 1194919. PMID 16116082.
  2. ^ University of Tennessee at Knoxville (25 September 2018). "Study: Microbial dark matter dominates Earth's environments". Eurekalert! (Press release). Retrieved 26 September 2018.
  3. ^ Dorminey, Bruce. "Microbial 'Dark Matter' Still Eludes Earth's Astrobiologists". Forbes. Retrieved 2024-05-06.
  4. ^ Rinke, Christian (2018). "Single-Cell Genomics of Microbial Dark Matter". In Robert G. Beiko; Will Hsiao; John Parkinson (eds.). Microbiome Analysis: Methods and Protocols. Methods in Molecular Biology. Vol. 1849. New York: Springer New York. pp. 99–111. doi:10.1007/978-1-4939-8728-3_7. ISBN 978-1-4939-8728-3. PMID 30298250.
  5. ^ Jiao, Jian-Yu; Liu, Lan; Hua, Zheng-Shuang; Fang, Bao-Zhu; Zhou, En-Min; Salam, Nimaichand; Hedlund, Brian P; Li, Wen-Jun (2021-03-01). "Microbial dark matter coming to light: challenges and opportunities". National Science Review. 8 (3): –280. doi:10.1093/nsr/nwaa280. ISSN 2095-5138. PMC 8288357. PMID 34691599.
  6. ^ Hedlund, Brian P.; Dodsworth, Jeremy A.; Murugapiran, Senthil K.; Rinke, Christian; Woyke, Tanja (2014). "Impact of single-cell genomics and metagenomics on the emerging view of extremophile "microbial dark matter"". Extremophiles. 18 (5): 865–875. doi:10.1007/s00792-014-0664-7. ISSN 1431-0651. PMID 25113821. S2CID 16888890.
  7. ^ Rinke, Christian; et, al. (2013). "Insights into the phylogeny and coding potential of microbial dark matter". Nature. 499 (7459): 431–437. Bibcode:2013Natur.499..431R. doi:10.1038/nature12352. hdl:10453/27467. PMID 23851394. S2CID 4394530.
  8. ^ a b Bernard, Guillaume; Pathmanathan, Jananan S; Lannes, Romain; Lopez, Philippe; Bapteste, Eric (2018). "Microbial Dark Matter Investigations: How Microbial Studies Transform Biological Knowledge and Empirically Sketch a Logic of Scientific Discovery". Genome Biology and Evolution. 10 (3): 707–715. doi:10.1093/gbe/evy031. PMC 5830969. PMID 29420719.
  9. ^ Amann, R. I., Ludwig, W., & Schleifer, K. H. (1995). Phylogenetic identification and in situ detection of individual microbial cells without cultivation. Microbiological Reviews, 59(1), 143-169.
  10. ^ Quince, Christopher; Walker, Alan W.; Simpson, Jared T.; Loman, Nicholas J.; Segata, Nicola (2017). "Shotgun metagenomics, from sampling to analysis". Nature Biotechnology. 35 (9): 833–844. doi:10.1038/nbt.3935. PMID 28898207.
  11. ^ Stepanauskas, Ramunas (2012). "Single cell genomics: An individual look at microbes". Current Opinion in Microbiology. 15 (5): 613–620. doi:10.1016/j.mib.2012.09.001. PMID 23026140.
  12. ^ Zengler, Karsten; Toledo, Gerardo; Rappé, Michael; Elkins, James; Mathur, Eric J.; Short, Jay M.; Keller, Martin (2002). "Cultivating the uncultured". Proceedings of the National Academy of Sciences. 99 (24): 15681–15686. Bibcode:2002PNAS...9915681Z. doi:10.1073/pnas.252630999. PMC 137776. PMID 12438682.
  13. ^ Nayfach, Stephen; Roux, Simon; Seshadri, Rekha; Udwary, Daniel; Varghese, Neha; Schulz, Frederik; Wu, Dongying; Paez-Espino, David; Chen, I.-Min; Huntemann, Marcel; Palaniappan, Krishna; Ladau, Joshua; Mukherjee, Supratim; Reddy, T. B. K.; Nielsen, Torben (2021). "A genomic catalog of Earth's microbiomes". Nature Biotechnology. 39 (4): 499–509. doi:10.1038/s41587-020-0718-6. PMC 8041624. PMID 33169036.
  14. ^ Wu D, Wu M, Halpern A, Rusch DB, Yooseph S, Frazier M, Venter JC, Eisen JA (March 2011). "Stalking the fourth domain in metagenomic data: searching for, discovering, and interpreting novel, deep branches in marker gene phylogenetic trees". PLOS ONE. 6 (3): e18011. Bibcode:2011PLoSO...618011W. doi:10.1371/journal.pone.0018011. PMC 3060911. PMID 21437252.
  15. ^ Lopez P, Halary S, Bapteste E (October 2015). "Highly divergent ancient gene families in metagenomic samples are compatible with additional divisions of life". Biology Direct. 10: 64. doi:10.1186/s13062-015-0092-3. PMC 4624368. PMID 26502935.
  16. ^ Schulze-Makuch, Dirk (Feb 28, 2017). "Could Alien Life Be Hidden All Around Us?". Smithsonian Magazine. Smithsonian Institution. Retrieved Nov 30, 2023.

[1]

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  1. ^ Quince, C., et al. (2017). Shotgun metagenomics, from sampling to analysis. Nature Biotechnology, 35(9), 833-844. https://doi.org/10.1038/nbt.3935
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