Genome size is the total amount of DNA contained within one copy of a single genome. It is typically measured in terms of mass in picograms (trillionths (10−12) of a gram, abbreviated pg) or less frequently in Daltons or as the total number of nucleotide base pairs typically in megabases (millions of base pairs, abbreviated Mb or Mbp). One picogram equals 978 megabases. In diploid organisms, genome size is used interchangeably with the term C-value. An organism's complexity is not directly proportional to its genome size; some single cell organisms have much more DNA than humans (see Junk DNA and C-value enigma).
Origin of the term
The term "genome size" is often erroneously attributed to Hinegardner, even in discussions dealing specifically with terminology in this area of research (e.g., Greilhuber, 2005). Notably, Hinegardner used the term only once: in the title. The term actually seems to have first appeared in 1968 when Hinegardner wondered, in the last paragraph of his article, whether "cellular DNA content does, in fact, reflect genome size". In this context, "genome size" was being used in the sense of genotype to mean the number of genes. In a paper submitted only two months later (in February 1969), Wolf et al. (1969) used the term "genome size" throughout and in its present usage; therefore these authors should probably be credited with originating the term in its modern sense. By the early 1970s, "genome size" was in common usage with its present definition, probably as a result of its inclusion in Susumu Ohno's influential book Evolution by Gene Duplication, published in 1970.
Variation in genome size and gene content
The genome sizes of thousands of eukaryotes have been analyzed over the past 50 years, and these data are available in online databases for animals, plants, and fungi (see external links). Nuclear genome size is typically measured in eukaryotes using either densitometric measurements of Feulgen-stained nuclei (previously using specialized densitometers, now more commonly using computerized image analysis) or flow cytometry. In prokaryotes, pulsed field gel electrophoresis and complete genome sequencing are the predominant methods of genome size determination. Nuclear genome sizes are well known to vary enormously among eukaryotic species. In animals they range more than 3,300-fold, and in land plants they differ by a factor of about 1,000. Protist genomes have been reported to vary more than 300,000-fold in size, but the high end of this range (Amoeba) has been called into question.[by whom?] In eukaryotes (but not prokaryotes), variation in genome size is not proportional to the number of genes, an observation that was deemed wholly counterintuitive before the discovery of non-coding DNA and which became known as the C-value paradox as a result. However, although there is no longer any paradoxical aspect to the discrepancy between genome size and gene number, this term remains in common usage. For reasons of conceptual clarification, the various puzzles that remain with regard to genome size variation instead have been suggested by one author to more accurately comprise a puzzle or an enigma (the C-value enigma). Genome size correlates with a range of features at the cell and organism levels, including cell size, cell division rate, and, depending on the taxon, body size, metabolic rate, developmental rate, organ complexity, geographical distribution, or extinction risk (for recent reviews, see Bennett and Leitch 2005; Gregory 2005). Based on completely sequenced genome data currently (as of April 2009) available, log-transformed gene number forms a linear correlation with log-transformed genome size in bacteria, archea, viruses, and organelles combined whereas a nonlinear (semi-natural log) correlation in eukaryotes (Hou and Lin 2009 ). The nonlinear correlation for eukaryotes, although claim of its existence contrasts the previous view that no correlation exists for this group of organisms, reflects disproportionately fast increasing noncoding DNA in increasingly large eukaryotic genomes. Although sequenced genome data are practically biased toward small genomes, which may compromise the accuracy of the empirically derived correlation, and the ultimate proof of the correlation remains to be obtained by sequencing some of the largest eukaryotic genomes, current data do not seem to rule out a correlation.
Genome reduction, also known as Genome degradation, is the process by which a genome shrinks relative to its ancestor. Genomes fluctuate in size regularly, however, genome size reduction is most significant in bacteria.
The most evolutionary significant cases of genome reduction may be the eukaryotic organelles that are derived from bacteria: the mitochondrion and plastid. These organelles are descended from endosymbionts, which can only survive within the host cell and which the host cell likewise needs for survival. Many mitochondria have less than 20 genes in their entire genome, whereas a free-living bacterium generally has at least 1000 genes. Many genes have been transferred to the host nucleus, while others have simply been lost and their function replaced by host processes.
Other bacteria have become endosymbionts or obligate intracellular pathogens and experienced extensive genome reduction as a result. This process seems to be dominated by genetic drift resulting from small population size, low recombination rates, and high mutation rates, as opposed to selection for smaller genomes.
Genome reduction in obligate endosymbiotic species
Obligate endosymbiotic species are characterized by a complete inability to survive external to their host environment. These species have become a considerable threat to human health, as they are often highly capable of evading human immune systems and manipulating the host environment to acquire nutrients. A common explanation for these keen manipulative abilities is the compact and efficient genomic structure consistently found in obligate endosymbionts. This compact genome structure is the result of massive losses of extraneous DNA - an occurrence that is exclusively associated with the loss of a free-living stage. In fact, as much as 90% of the genetic material can be lost when a species makes the evolutionary transition from a free-living to obligate intracellular lifestyle. Common examples of species with reduced genomes include: Buchnera aphidicola, Rickettsia prowazekii and Mycobacterium leprae. One obligate endosymbiont of leafhoppers, Nasuia deltocephalinicola, has the smallest genome currently known among cellular organisms at 112kb. It is important to note, however, that some obligate intracellular species have positive fitness effects on their hosts. (See also mutualists and parasites.)
The reductive evolution model has been proposed as an effort to define the genomic commonalities seen in all obligate endosymbionts. This model illustrates four general features of reduced genomes and obligate intracellular species:
- ‘genome streamlining’ resulting from relaxed selection on genes that are superfluous in the intracellular environment;
- a bias towards deletions (rather than insertions), which heavily affects genes that have been disrupted by accumulation of mutations (pseudogenes);
- very little or no capability for acquiring new DNA; and
- considerable reduction of effective population size in endosymbiotic populations, particularly in species that rely on vertical transmission.
Based on this model, it is clear that endosymbionts face different adaptive challenges than free-living species.
Conversion from picograms (pg) to base pairs (bp)
In 1991 Drake proposed a rule: that the mutation rate within a genome and its size were inversely correlated. This rule has been found to be approximately correct for DNA viruses and unicellular organisms. Its basis is unknown.
The small size of RNA viruses has been proposed to be locked into a three part relation between replication fidelity, genome size and genetic complexity. The majority of RNA viruses lack an RNA proofreading facility which limits their replication fidelity and hence the genome size. This has also been described as the Eigen paradox.
An exception to the rule of small genome sizes in RNA viruses is found the Nidoviruses. These viruses appear to have acquired a 3′-to-5′ exoribonuclease (ExoN) which has allowed for an increase in genome size.
- Dolezel J, Bartoš J, Voglmayr H, Greilhuber J; Bartos; Voglmayr; Greilhuber (2003). "Nuclear DNA content and genome size of trout and human". Cytometry A 51 (2): 127–128. doi:10.1002/cyto.a.10013. PMID 12541287.
- Hinegardner R (1976). "Evolution of genome size". In F.J. Ayala. Molecular Evolution. Sinauer Associates, Inc., Sunderland. pp. 179–199.
- Greilhuber J, Doležel J, Lysák M, Bennett MD; Dolezel; Lysák; Bennett (2005). "The origin, evolution and proposed stabilization of the terms 'genome size' and 'C-value' to describe nuclear DNA contents". Annals of Botany 95 (1): 255–260. doi:10.1093/aob/mci019. PMID 15596473.
- Hinegardner R (1968). "Evolution of cellular DNA content in teleost fishes". American Naturalist 102 (928): 517–523. doi:10.1086/282564.
- Wolf U, Ritter H, Atkin NB, Ohno S; Ritter; Atkin; Ohno (1969). "Polyploidization in the fish family Cyprinidae, Order Cypriniformes. I. DNA-content and chromosome sets in various species of Cyprinidae". Humangenetik 7 (3): 240–244. doi:10.1007/BF00273173. PMID 5800705.
- Ohno S (1970). Evolution by Gene Duplication. New York: Springer-Verlag. ISBN 0-04-575015-7.
- Hardie DC, Gregory TR, Hebert PDN; Gregory; Hebert (2002). "From pixels to picograms: a beginners' guide to genome quantification by Feulgen image analysis densitometry". Journal of Histochemistry and Cytochemistry 50 (6): 735–749. doi:10.1177/002215540205000601. PMID 12019291.
- Bennett MD, Leitch IJ (2005). "Genome size evolution in plants". In T.R. Gregory. The Evolution of the Genome. San Diego: Elsevier. pp. 89–162.
- Gregory TR (2005). "Genome size evolution in animals". In T.R. Gregory. The Evolution of the Genome. San Diego: Elsevier. pp. 3–87.
- Hou Y, Lin S; Lin (2009). Redfield, Rosemary Jeanne, ed. "Distinct Gene Number- Genome Size Relationships for Eukaryotes and Non-Eukaryotes: Gene Content Estimation for Dinoflagellate Genomes". PLoS ONE 4 (9): e6978. doi:10.1371/journal.pone.0006978. PMC 2737104. PMID 19750009.
- Koonin, Eugene V. (2011). The Logic of Chance: The Nature and Origin of Biological Evolution. FT Press. ISBN 9780132542494.
- Dufresne A, Garczarek L, Partensky F; Garczarek; Partensky (2005). "Accelerated evolution associated with genome reduction in a free-living prokaryote". Genome Biol 6 (2): R14. doi:10.1186/gb-2005-6-2-r14. PMC 551534. PMID 15693943.
- Giovannoni SJ; et al. (2005). "Genome streamlining in a cosmopolitan oceanic bacterium". Science 309 (5738): 1242–1245. doi:10.1126/science.1114057. PMID 16109880.
- Giovannoni SJ; et al. (2008). "The small genome of an abundant coastal ocean methylotroph". Environmental Microbiology 10 (7): 1771–1782. doi:10.1111/j.1462-2920.2008.01598.x. PMID 18393994.
- And the Genomes Keep Shrinking…
- Wernegreen J (2005). "For better or worse: Genomic consequences of genomic mutualism and parasitism" (PDF). Current Opinion in Genetics & Development 15 (6): 1–12. doi:10.1016/j.gde.2005.09.013. PMID 16230003.
- Moran NA, Plague GR; Plague (2004). "Genomic changes following host restriction in bacteria". Current Opinion in Genetics & Development 14 (6): 627–633. doi:10.1016/j.gde.2004.09.003.
- Drake, J W (1991). "A constant rate of spontaneous mutation in DNA-based microbes". Proc Natl Acad Sci USA 88: 7160–7164. doi:10.1073/pnas.88.16.7160. PMC 52253. PMID 1831267.
- Kun, A; Santos, M; Szathmary, E (2005). "Real ribozymes suggest a relaxed error threshold". Nat Genet 37: 1008–1011. doi:10.1038/ng1621. PMID 16127452.
- Lauber, C; Goeman, JJ; Parquet Mdel, C; Thi Nga, P; Snijder, EJ; Morita, K; Gorbalenya, AE (Jul 2013). "The footprint of genome architecture in the largest genome expansion in RNA viruses". PLoS Pathog 9 (7): e1003500. doi:10.1371/journal.ppat.1003500.
- Evolution of Chlamydiaceae
- Andersson JO Andersson SG; Andersson (1999). "Genome degradation is an ongoing process in Rickettsia". Molecular Biology and Evolution 16 (9): 1178–1191. doi:10.1093/oxfordjournals.molbev.a026208. PMID 10486973.