Reverse ecology

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Reverse ecology refers to the use of genomics to study or predict an organism's ecology. [1] [2] The term was suggested in 2007 by Matthew Rockman during a conference on ecological genomics in Christchurch, New Zealand. [3] Rockman was drawing an analogy to the term reverse genetics in which gene function is studied by comparing the phenotypic effects of different genetic sequences of that gene.

Contents

Most researchers employing reverse ecology make use of some sort of population genomics and computational biology method, including BioPython and R. [4] [5] This requires that a genome scan is performed on multiple individuals from at least two populations in order to identify genomic regions or sites that show signs of selection. These genome scans can utilize single nucleotide polymorphism (SNP) markers, microsatellites can work as well.[ citation needed ]

Methodology

Reverse ecology has been used by researchers to understand environments and other ecological traits of organisms on Earth using genomic approaches. By examining the genes of bacteria, scientists are able to reconstruct what the organisms' native environment, either today or even from millions of years ago. These predictions can include growth temperature [6] [4] [7] [8] , pH [8] , metabolism [9] , and other growth characteristics. The data could help us understand key events in the history of life on Earth.[ citation needed ]

In 2011, researchers at the University of California, Berkeley were able to demonstrate that one can determine an organism's adaptive traits by looking first at its genome and checking for variations across a population. [10]

See also

References

  1. ↑ Levy, Roie; Borenstein, Elhanan (2012). Reverse Ecology: From Systems to Environments and Back. Advances in Experimental Medicine and Biology. Vol. 751. pp. 329–345. doi:10.1007/978-1-4614-3567-9_15. ISBN   978-1-4614-3566-2. PMID   22821465.{{cite book}}: |journal= ignored (help)
  2. ↑ Arevalo, Philip; VanInsberghe, David; Polz, Martin F. (2018). "A Reverse Ecology Framework for Bacteria and Archaea". Population Genomics: Microorganisms. Population Genomics: 77–96. doi:10.1007/13836_2018_46. ISBN   978-3-030-04755-9.
  3. ↑ Li, YF; et al. (2008). ""Reverse ecology" and the power of population genomics". Evolution. 62 (12): 2984–2994. doi:10.1111/j.1558-5646.2008.00486.x. PMC   2626434 . PMID   18752601.
  4. 1 2 Sauer, David B; Wang, Da-Neng (15 September 2019). "Predicting the optimal growth temperatures of prokaryotes using only genome derived features". Bioinformatics. 35 (18): 3224–3231. doi:10.1093/bioinformatics/btz059. PMC   6748728 . PMID   30689741.
  5. ↑ Cao, Yang; Wang, Yuanyuan; Zheng, Xiaofei; Li, Fei; Bo, Xiaochen (29 July 2016). "RevEcoR: an R package for the reverse ecology analysis of microbiomes". BMC Bioinformatics. 17 (1) 294. doi: 10.1186/s12859-016-1088-4 . PMID   27473172.
  6. ↑ Zheng H, Wu H (December 2010). "Gene-centric association analysis for the correlation between the guanine-cytosine content levels and temperature range conditions of prokaryotic species". BMC Bioinformatics. 11 (Suppl 11) S7. doi: 10.1186/1471-2105-11-S11-S7 . PMC   3024870 . PMID   21172057.
  7. ↑ Li, Gang; Rabe, Kersten S.; Nielsen, Jens; Engqvist, Martin K. M. (21 June 2019). "Machine Learning Applied to Predicting Microorganism Growth Temperatures and Enzyme Catalytic Optima". ACS Synthetic Biology. 8 (6): 1411–1420. doi:10.1021/acssynbio.9b00099. PMID   31117361.
  8. 1 2 Zhu, Mingming; Song, Yidong; Yuan, Qianmu; Yang, Yuedong (29 December 2024). "Accurately predicting optimal conditions for microorganism proteins through geometric graph learning and language model". Communications Biology. 7 (1) 1709. doi:10.1038/s42003-024-07436-3.
  9. ↑ Carr, Rogan; Borenstein, Elhanan (1 March 2012). "NetSeed: a network-based reverse-ecology tool for calculating the metabolic interface of an organism with its environment". Bioinformatics. 28 (5): 734–735. doi:10.1093/bioinformatics/btr721.
  10. ↑ Ellison C, et al. (2011). "Population genomics and local adaptation in wild isolates of a model microbial eukaryote". Proceedings of the National Academy of Sciences. 108 (7): 2831–2836. Bibcode:2011PNAS..108.2831E. doi: 10.1073/pnas.1014971108 . PMC   3041088 . PMID   21282627.
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