GC-content

In molecular biology and genetics, GC-content (or guanine-cytosine content) is the percentage of nitrogenous bases in a DNA or RNA molecule that are either guanine (G) or cytosine (C).^[1] This measure indicates the proportion of G and C bases out of an implied four total bases, also including adenine and thymine in DNA and adenine and uracil in RNA.

Nucleotide bonds showing AT and GC pairs. Arrows point to the hydrogen bonds.

GC-content may be given for a certain fragment of DNA or RNA or for an entire genome. When it refers to a fragment, it may denote the GC-content of an individual gene or section of a gene (domain), a group of genes or gene clusters, a non-coding region, or a synthetic oligonucleotide such as a primer.

Structure

Qualitatively, guanine (G) and cytosine (C) undergo a specific hydrogen bonding with each other, whereas adenine (A) bonds specifically with thymine (T) in DNA and with uracil (U) in RNA. Quantitatively, each GC base pair is held together by three hydrogen bonds, while AT and AU base pairs are held together by two hydrogen bonds. To emphasize this difference, the base pairings are often represented as "G≡C" versus "A=T" or "A=U".

DNA with low GC-content is less stable than DNA with high GC-content; however, the hydrogen bonds themselves do not have a particularly significant impact on molecular stability, which is instead caused mainly by molecular interactions of base stacking.^[2] In spite of the higher thermostability conferred to a nucleic acid with high GC-content, it has been observed that at least some species of bacteria with DNA of high GC-content undergo autolysis more readily, thereby reducing the longevity of the cell per se.^[3] Because of the thermostability of GC pairs, it was once presumed that high GC-content was a necessary adaptation to high temperatures, but this hypothesis was refuted in 2001.^[4] Even so, it has been shown that there is a strong correlation between the optimal growth of prokaryotes at higher temperatures and the GC-content of structural RNAs such as ribosomal RNA, transfer RNA, and many other non-coding RNAs.^[4][5] The AU base pairs are less stable than the GC base pairs, making high-GC-content RNA structures more resistant to the effects of high temperatures.

More recently, it has been demonstrated that the most important factor contributing to the thermal stability of double-stranded nucleic acids is actually due to the base stackings of adjacent bases rather than the number of hydrogen bonds between the bases. There is more favorable stacking energy for GC pairs than for AT or AU pairs because of the relative positions of exocyclic groups. Additionally, there is a correlation between the order in which the bases stack and the thermal stability of the molecule as a whole.^[6]

Determination

 

Schematic karyogram of a human, showing an overview of the human genome on G banding (which includes Giemsa-staining), wherein GC rich regions are lighter and GC poor regions are darker.

Further information: Karyotype

GC-content is usually expressed as a percentage value, but sometimes as a ratio (called G+C ratio or GC-ratio). GC-content percentage is calculated as^[7]

${\displaystyle {\cfrac {G+C}{A+T+G+C}}\times 100\%}$ 

whereas the AT/GC ratio is calculated as^[8]

${\displaystyle {\cfrac {A+T}{G+C}}}$  .

The GC-content percentages as well as GC-ratio can be measured by several means, but one of the simplest methods is to measure the melting temperature of the DNA double helix using spectrophotometry. The absorbance of DNA at a wavelength of 260 nm increases fairly sharply when the double-stranded DNA molecule separates into two single strands when sufficiently heated.^[9] The most commonly used protocol for determining GC-ratios uses flow cytometry for large numbers of samples.^[10]

In an alternative manner, if the DNA or RNA molecule under investigation has been reliably sequenced, then GC-content can be accurately calculated by simple arithmetic or by using a variety of publicly available software tools, such as the free online GC calculator.

Genomic content

Within-genome variation

The GC-ratio within a genome is found to be markedly variable. These variations in GC-ratio within the genomes of more complex organisms result in a mosaic-like formation with islet regions called isochores.^[11] This results in the variations in staining intensity in chromosomes.^[12] GC-rich isochores typically include many protein-coding genes within them, and thus determination of GC-ratios of these specific regions contributes to mapping gene-rich regions of the genome.^[13][14]

Coding sequences

Within a long region of genomic sequence, genes are often characterised by having a higher GC-content in contrast to the background GC-content for the entire genome.^[15] There is evidence that the length of the coding region of a gene is directly proportional to higher G+C content.^[16] This has been pointed to the fact that the stop codon has a bias towards A and T nucleotides, and, thus, the shorter the sequence the higher the AT bias.^[17]

Comparison of more than 1,000 orthologous genes in mammals showed marked within-genome variations of the third-codon position GC content, with a range from less than 30% to more than 80%.^[18]

Among-genome variation

GC content is found to be variable with different organisms, the process of which is envisaged to be contributed to by variation in selection, mutational bias, and biased recombination-associated DNA repair.^[19]

The average GC-content in human genomes ranges from 35% to 60% across 100-Kb fragments, with a mean of 41%.^[20] The GC-content of Yeast (Saccharomyces cerevisiae) is 38%,^[21] and that of another common model organism, thale cress (Arabidopsis thaliana), is 36%.^[22] Because of the nature of the genetic code, it is virtually impossible for an organism to have a genome with a GC-content approaching either 0% or 100%. However, a species with an extremely low GC-content is Plasmodium falciparum (GC% = ~20%),^[23] and it is usually common to refer to such examples as being AT-rich instead of GC-poor.^[24]

Several mammalian species (e.g., shrew, microbat, tenrec, rabbit) have independently undergone a marked increase in the GC-content of their genes. These GC-content changes are correlated with species life-history traits (e.g., body mass or longevity) and genome size,^[18] and might be linked to a molecular phenomenon called the GC-biased gene conversion.^[25]

Applications

Molecular biology

In polymerase chain reaction (PCR) experiments, the GC-content of short oligonucleotides known as primers is often used to predict their annealing temperature to the template DNA. A higher GC-content level indicates a relatively higher melting temperature.

Many sequencing technologies, such as Illumina sequencing, have trouble reading high-GC-content sequences. Bird genomes are known to have many such parts, causing the problem of "missing genes" expected to be present from evolution and phenotype but never sequenced — until improved methods were used.^[26]

Systematics

The species problem in non-eukaryotic taxonomy has led to various suggestions in classifying bacteria, and the ad hoc committee on reconciliation of approaches to bacterial systematics of 1987 has recommended use of GC-ratios in higher-level hierarchical classification.^[27] For example, the Actinomycetota are characterised as "high GC-content bacteria".^[28] In Streptomyces coelicolor A3(2), GC-content is 72%.^[29] With the use of more reliable, modern methods of molecular systematics, the GC-content definition of Actinomycetota has been abolished and low-GC bacteria of this clade have been found.^[30]

Software tools

GCSpeciesSorter^[31] and TopSort^[32] are software tools for classifying species based on their GC-contents.

See also

• Codon usage bias

References

1. Definition of GC – content on CancerWeb of Newcastle University,UK
2. Yakovchuk P, Protozanova E, Frank-Kamenetskii MD (2006). "Base-stacking and base-pairing contributions into thermal stability of the DNA double helix". Nucleic Acids Res. 34 (2): 564–74. doi:10.1093/nar/gkj454. PMC 1360284. PMID 16449200.
3. Levin RE, Van Sickle C (1976). "Autolysis of high-GC isolates of Pseudomonas putrefaciens". Antonie van Leeuwenhoek. 42 (1–2): 145–55. doi:10.1007/BF00399459. PMID 7999. S2CID 9960732.
4. Hurst LD, Merchant AR (March 2001). "High guanine-cytosine content is not an adaptation to high temperature: a comparative analysis amongst prokaryotes". Proc. Biol. Sci. 268 (1466): 493–7. doi:10.1098/rspb.2000.1397. PMC 1088632. PMID 11296861.
5. Galtier, N.; Lobry, J.R. (1997). "Relationships between genomic G+C content, RNA secondary structures, and optimal growth temperature in Prokaryotes". Journal of Molecular Evolution. 44 (6): 632–636. Bibcode:1997JMolE..44..632G. doi:10.1007/PL00006186. PMID 9169555. S2CID 19054315.
6. Yakovchuk, Peter; Protozanova, Ekaterina; Frank-Kamenetskii, Maxim D. (2006). "Base-stacking and base-pairing contributions into thermal stability of the DNA double helix". Nucleic Acids Research. 34 (2): 564–574. doi:10.1093/nar/gkj454. ISSN 0305-1048. PMC 1360284. PMID 16449200.
7. Madigan,MT. and Martinko JM. (2003). Brock biology of microorganisms (10th ed.). Pearson-Prentice Hall. ISBN 978-84-205-3679-8.
8. . Archived from the original on 20 June 2010. Retrieved 11 June 2007.
9. Wilhelm J, Pingoud A, Hahn M (May 2003). "Real-time PCR-based method for the estimation of genome sizes". Nucleic Acids Res. 31 (10): e56. doi:10.1093/nar/gng056. PMC 156059. PMID 12736322.
10. Vinogradov AE (May 1994). "Measurement by flow cytometry of genomic AT/GC ratio and genome size". Cytometry. 16 (1): 34–40. doi:10.1002/cyto.990160106. PMID 7518377.
11. Bernardi G (January 2000). "Isochores and the evolutionary genomics of vertebrates". Gene. 241 (1): 3–17. doi:10.1016/S0378-1119(99)00485-0. PMID 10607893.
12. Furey TS, Haussler D (May 2003). "Integration of the cytogenetic map with the draft human genome sequence". Hum. Mol. Genet. 12 (9): 1037–44. doi:10.1093/hmg/ddg113. PMID 12700172.
13. Sumner AT, de la Torre J, Stuppia L (August 1993). "The distribution of genes on chromosomes: a cytological approach". J. Mol. Evol. 37 (2): 117–22. Bibcode:1993JMolE..37..117S. doi:10.1007/BF02407346. PMID 8411200. S2CID 24677431.
14. Aïssani B, Bernardi G (October 1991). "CpG islands, genes and isochores in the genomes of vertebrates". Gene. 106 (2): 185–95. doi:10.1016/0378-1119(91)90198-K. PMID 1937049.
15. Romiguier J, Roux C (2017). "Analytical Biases Associated with GC-Content in Molecular Evolution". Front Genet. 8: 16. doi:10.3389/fgene.2017.00016. PMC 5309256. PMID 28261263.
16. Pozzoli U, Menozzi G, Fumagalli M, et al. (2008). "Both selective and neutral processes drive GC content evolution in the human genome". BMC Evol. Biol. 8 (1): 99. Bibcode:2008BMCEE...8...99P. doi:10.1186/1471-2148-8-99. PMC 2292697. PMID 18371205.
17. Wuitschick JD, Karrer KM (1999). "Analysis of genomic G + C content, codon usage, initiator codon context and translation termination sites in Tetrahymena thermophila". J. Eukaryot. Microbiol. 46 (3): 239–47. doi:10.1111/j.1550-7408.1999.tb05120.x. PMID 10377985. S2CID 28836138.
18. Romiguier, Jonathan; Ranwez, Vincent; Douzery, Emmanuel J. P.; Galtier, Nicolas (1 August 2010). "Contrasting GC-content dynamics across 33 mammalian genomes: Relationship with life-history traits and chromosome sizes". Genome Research. 20 (8): 1001–1009. doi:10.1101/gr.104372.109. ISSN 1088-9051. PMC 2909565. PMID 20530252.
19. Birdsell JA (1 July 2002). "Integrating genomics, bioinformatics, and classical genetics to study the effects of recombination on genome evolution". Mol. Biol. Evol. 19 (7): 1181–97. CiteSeerX 10.1.1.337.1535. doi:10.1093/oxfordjournals.molbev.a004176. PMID 12082137.
20. International Human Genome Sequencing Consortium (February 2001). "Initial sequencing and analysis of the human genome". Nature. 409 (6822): 860–921. Bibcode:2001Natur.409..860L. doi:10.1038/35057062. hdl:2027.42/62798. PMID 11237011. (page 876)
21. Whole genome data of Saccharomyces cerevisiae on NCBI
22. Whole genome data of Arabidopsis thaliana on NCBI
23. Whole genome data of Plasmodium falciparum on NCBI
24. Musto H, Cacciò S, Rodríguez-Maseda H, Bernardi G (1997). "Compositional constraints in the extremely GC-poor genome of Plasmodium falciparum" (PDF). Mem. Inst. Oswaldo Cruz. 92 (6): 835–41. doi:10.1590/S0074-02761997000600020. PMID 9566216.
25. Duret L, Galtier N (2009). "Biased gene conversion and the evolution of mammalian genomic landscapes". Annu Rev Genom Hum Genet. 10: 285–311. doi:10.1146/annurev-genom-082908-150001. PMID 19630562. S2CID 9126286.
26. Huttener R, Thorrez L, Veld TI, et al. (2021). "Sequencing refractory regions in bird genomes are hotspots for accelerated protein evolution". BMC Ecol Evol. 21 (176): 176. doi:10.1186/s12862-021-01905-7. PMC 8449477. PMID 34537008.
27. Wayne LG; et al. (1987). "Report of the ad hoc committee on reconciliation of approaches to bacterial systematic". International Journal of Systematic Bacteriology. 37 (4): 463–4. doi:10.1099/00207713-37-4-463.
28. Taxonomy browser on NCBI
29. Whole genome data of Streptomyces coelicolor A3(2) on NCBI
30. Ghai R, McMahon KD, Rodriguez-Valera F (2012). "Breaking a paradigm: Cosmopolitan and abundant freshwater actinobacteria are low GC". Environmental Microbiology Reports. 4 (1): 29–35. Bibcode:2012EnvMR...4...29G. doi:10.1111/j.1758-2229.2011.00274.x. PMID 23757226.
31. Karimi K, Wuitchik D, Oldach M, Vize P (2018). "Distinguishing Species Using GC Contents in Mixed DNA or RNA Sequences". Evol Bioinform Online. 14 (January 1, 2018): 1176934318788866. doi:10.1177/1176934318788866. PMC 6052495. PMID 30038485.
32. Lehnert E, Mouchka M, Burriesci M, Gallo N, Schwarz J, Pringle J (2014). "Extensive differences in gene expression between symbiotic and aposymbiotic cnidarians". G3 (Bethesda). 4 (2): 277–95. doi:10.1534/g3.113.009084. PMC 3931562. PMID 24368779.

External links

1. Table with GC-content of all sequenced prokaryotes
2. Taxonomic browser of bacteria based on GC ratio on NCBI website.
3. .