Reverse ecology

  1. Home
  2. Wiki

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.

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

[edit]

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

[edit]

References

[edit]
  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. ^ a b 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. ^ a b 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. PMC 11683147.
  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.