Even Saccharomyces cerevisiae has 400 proteins associated with chromatin structure and function, as well as histones and polymerases, to control ~5300 genes the majority of which have only core promoters and no regulatory elements, compared to E.coli’s ~285 proteins to control 4300 genes. are presumed to have selectable function unrelated to coding). By contrast over 90% of the human genome is non-coding, and conservative estimates are that 10 times as many non-coding bases as coding bases are evolutionarily conserved (i.e. coli genome has little non-coding DNA, and ~285 proteins are involved in gene control, ~7% of the genome. The size difference between prokaryotic and eukaryotic genomes is primarily due to non-coding DNA that is related in part to gene control. ) and autotrophic protists (10,000–20,000 CDS ) and approaches that of Drosophila melanogaster (~16,000 CDS ). The coding capacity of some of the larger prokaryotic genomes such as those of some cyanobacteria (~12,000 coding sequences (CDS) ), Ktedonobacter racemifer (~11,500 CDS ), Sorangium cellulosum (~9000 CDS ), Magnetobacterium bavaricum (~8500 CDS ) overlaps with coding capacity of multicellular fungi (5000–15,000 (e.g.
There is substantial overlap in coding capacity between the larger prokaryotic genomes and eukaryotic genomes. Data from !/overview/, accessed 15 th June 2020 based on 27308 bacteria, 1769 Archaea, 5300 Eukarya and 19536 viruses. X axis: Genome size in megabases, Y axis: Fraction of each of the four classes of organism that have genome of that size. Sizes of completed genome sequences, showing distinct size distributions for prokaryotes (bacteria and archaea), eukaryotes and viruses. We discuss how this might relate to the evolution of the complex eukaryotic genome, which operates in a ‘default off’ mode. More complex genomes correlated with the evolution of genetic controls in which genes were active (‘default on’), and a low fraction of genes being expressed correlated with a genetic logic in which genes were biased to being inactive unless positively activated (‘default off’ logic). Genetic logic was strongly correlated with genome complexity and with the fraction of genes active in the cell at any one time. Unexpectedly, the control logic that evolved was not significantly correlated to the complexity of the environment. The model was run 118 times to an average of 1.4∙10 6 ‘generations’ each with a range of starting parameters probed the conditions under which genomes evolved a ‘default style’ of control. Control and coding regions evolve to maximize a fitness function between expressed coding sequences and the environment. Genes contain control elements which respond to the internal state of the cell as well as the environment to control expression of a coding region. The model conceptually follows the Jacob and Monod model of gene control. We present a model of the evolution of control systems in a genome under environmental constraints.
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