Morphological complexity increase in metazoans

Valentine, James W.; Collins, Allen G.; Meyer, Christopher

doi:10.1017/s0094837300012641

Cited by 249 publications

(212 citation statements)

References 26 publications

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“…However, a number of biological factors suggest that humans are more complex than Drosophila. Such factors include a larger genome, a larger number of genes, a larger number of proteins and protein-protein interactions (26), and likely also a larger number of cell types (27) in humans than in Drosophila. Mutational robustness models predict greater mutational robustness in humans than in Drosophila because of the higher complexity and the smaller effective population size of humans compared with Drosophila.…”

Section: Resultsmentioning

confidence: 99%

Determining the factors driving selective effects of new nonsynonymous mutations

Kim

Marsden

Lohmueller

2016

Preprint

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The distribution of fitness effects (DFE) of new mutations plays a fundamental role in evolutionary genetics. However, the extent to which the DFE differs across species has yet to be systematically investigated. Furthermore, the biological mechanisms determining the DFE in natural populations remain unclear. Here, we show that theoretical models emphasizing different biological factors at determining the DFE, such as protein stability, back-mutations, species complexity, and mutational robustness make distinct predictions about how the DFE will differ between species. Analyzing amino acid-changing variants from natural populations in a comparative population genomic framework, we find that humans have a higher proportion of strongly deleterious mutations than Drosophila melanogaster. Furthermore, when comparing the DFE across yeast, Drosophila, mice, and humans, the average selection coefficient becomes more deleterious with increasing species complexity. Last, pleiotropic genes have a DFE that is less variable than that of nonpleiotropic genes. Comparing four categories of theoretical models, only Fisher's geometrical model (FGM) is consistent with our findings. FGM assumes that multiple phenotypes are under stabilizing selection, with the number of phenotypes defining the complexity of the organism. Our results suggest that long-term population size and cost of complexity drive the evolution of the DFE, with many implications for evolutionary and medical genomics. T he distribution of fitness effects (DFE) represents the distribution of selection coefficients, s, of random mutations in the genome. Here, s quantifies the relative change in fitness due to the mutation. The DFE plays a fundamental role in evolutionary genetics because it quantifies the amount of deleterious, neutral, and adaptive mutations entering a population (1). Despite the importance and considerable study of the DFE (1-3), the extent to which the DFE in terms of s differs across species has yet to be systematically quantified. Furthermore, the biological factors determining the DFE in different species remain elusive. Several theoretical models propose different mechanisms for the evolution of the DFE (Fig. 1) (4-8). Although each of these models has a reasonable theoretical basis as well as some support from experimental evolution studies or microbial studies, which model best explains differences in the DFE between species has not yet been determined. Nor have these models been tested with genetic variation data from natural populations in higher organisms. Because experimental evolution studies in laboratory organisms often use a homogeneous environment and genetically homogeneous organisms, they may better satisfy some of the assumptions of these theoretical models. However, natural populations may provide different qualitative results due to increased resolution to measure weakly deleterious mutations and the unnatural selection pressure in the laboratory (2, 9). Importantly, five theoretical models for the evolution of the DFE predic...

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Section: Resultsmentioning

confidence: 99%

Determining the factors driving selective effects of new nonsynonymous mutations

Kim

Marsden

Lohmueller

2016

Preprint

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“…One proposed measure for organism complexity is the number of different cell types an organism possesses (45), and the ideas presented here clearly have a link to this measure. Unfortunately, numerical data describing the numbers of cell types does not appear to be readily available in the literature, so we were unable to define a numerical measure of complexity.…”

Section: Intrinsic Nucleosome Positioning Signals Correlate With Compmentioning

confidence: 87%

Genomes of Multicellular Organisms Have Evolved to Attract Nucleosomes to Promoter Regions

Tompitak

Vaillant

Schiessel

2017

Biophysical Journal

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Sequences that influence nucleosome positioning in promoter regions, and their relation to gene regulation, have been the topic of much research over the last decade. In yeast, significant nucleosome-depleted regions are found, which facilitate transcription. With the arrival of nucleosome positioning maps for the human genome, it was discovered that in our genome, unlike in that of yeast, promoters encode for high nucleosome occupancy. In this work, we look at the genomes of a range of different organisms, to provide a catalog of nucleosome positioning signals in promoters across the tree of life. We utilize a computational model of the nucleosome, based on crystallographic analyses of the structure and elasticity of the nucleosome, to predict the nucleosome positioning signals in promoter regions. To be able to apply our model to large genomic datasets, we introduce an approximative scheme that makes use of the limited range of correlations in nucleosomal sequence preferences to create a computationally efficient approximation of the full biophysical model. Our predictions show that a clear distinction between unicellular and multicellular life is visible in the intrinsically encoded nucleosome affinity. Furthermore, the strength of the nucleosome positioning signals correlates with the complexity of the organism. We conclude that encoding for high nucleosome occupancy, as in the human genome, is in fact a universal feature of multicellular life.

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“…Genome size and the number of inferred genes are from the whole-genome studies cited in the text; that of Amphimedon is from Fahey et al (2008). The number of cell types is from Valentine et al (1994), except for Trichoplax, which is from Srivastava et al (2008); and the number of miRNAs comes from Sempere et al (2006), Gimson et al (2008) and Wheeler et al (2009). Note that according to Wheeler et al (2009), the seven miRNAs in demosponges are not homologous to any eumetazoan miRNAs.…”

Section: *Erwind@siedumentioning

confidence: 99%

Early origin of the bilaterian developmental toolkit

Erwin

2009

Phil. Trans. R. Soc. B

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Whole-genome sequences from the choanoflagellate Monosiga brevicollis , the placozoan Trichoplax adhaerens and the cnidarian Nematostella vectensis have confirmed results from comparative evolutionary developmental studies that much of the developmental toolkit once thought to be characteristic of bilaterians appeared much earlier in the evolution of animals. The diversity of transcription factors and signalling pathway genes in animals with a limited number of cell types and a restricted developmental repertoire is puzzling, particularly in light of claims that such highly conserved elements among bilaterians provide evidence of a morphologically complex protostome–deuterostome ancestor. Here, I explore the early origination of elements of what became the bilaterian toolkit, and suggest that placozoans and cnidarians represent a depauperate residue of a once more diverse assemblage of early animals, some of which may be represented in the Ediacaran fauna (c. 585–542 Myr ago).

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Morphological complexity increase in metazoans

Cited by 249 publications

References 26 publications

Determining the factors driving selective effects of new nonsynonymous mutations

Determining the factors driving selective effects of new nonsynonymous mutations

Genomes of Multicellular Organisms Have Evolved to Attract Nucleosomes to Promoter Regions

Early origin of the bilaterian developmental toolkit

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