48 research outputs found
Universals of genome and molecular phenome evolution.
<p>The figure shows idealized versions of universal dependencies and distributions. The scattered points show the range of characteristic variance. (A) Log-normal distribution of evolutionary rates of orthologous genes. (B) Anticorrelation between gene expression level (protein abundance) and sequence evolution rate. (C) Power law–like distribution of paralogous family size. (D) Differential scaling of functional classes of genes with the total number of genes in a genome. Three fundamental exponents are thought to exist: 0 – no dependence, typical of translation system component; 1 – linear dependence, characteristic of metabolic enzymes; 2 – quadratic dependence, characteristic of regulatory and signal transduction system components.</p
Universals of genome and molecular phenome evolution and underlying physical/mathematical models.
<p>Arrows connect each model with the universals it accounts for.</p
Ranking of the strength of dependency of each pair of editing sites in human 5-HT<sub>2C</sub>R mRNA.
<p>The lower the rank of an edge, and the higher its support, the stronger is the dependency between the pair of editing sites (see text for details). The third column is the same as the second column, except that either A or B are marked as F. The rightmost column lists the models in which the given edge appears.</p
BIC scores (dots) and pDAGs of rat models <b> through </b><b>.</b>
<p>The designations are as in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002663#pcbi-1002663-g002" target="_blank">Figure 2</a>. The number of parameters required to describe each of the models is 5 (), 6 (), 7 (), 9 (), 13 (), 15 (), 23 (), 20 (), 26 (), 30 (), and 31 ().</p
Clustering of the five editing sites using Jaccard distance in (a) human and (b) rat.
<p>Each edge is assigned with a confidence level according to the fraction of times by which it was supported by the different individuals.</p
A hypothetical mechanistic model of concerted action of ADAR1 and ADAR2 in 5-HT<sub>2C</sub>R mRNA editing.
<p>The squares denote the 5 distinct editing sites and the stars denote editing. The figure is not to scale.</p
Level of support of each edge in all individual best-fitting models with fixed number of edges.
<p>The results are for BIC scores in rat.</p
BIC scores (dots) and pDAGs of human models <b> through </b><b>.</b>
<p>The BIC scores of the models are shown as dots, and the pDAGs of the models themselves are plotted next to each dot. These models represent relationship between sites rather than true causality, as indicated by the fact that some edges reverse their direction in different models. The number of parameters required to describe each of the models is 5 (), 6 (), 7 (), 8 (), 10 (), 14 (), 13 (), 17 (), 21 (), 29 (), and 31 ().</p
Dependencies among Editing Sites in Serotonin 2C Receptor mRNA
<div><p>The serotonin 2C receptor (5-HT<sub>2C</sub>R)–a key regulator of diverse neurological processes–exhibits functional variability derived from editing of its pre-mRNA by site-specific adenosine deamination (A-to-I pre-mRNA editing) in five distinct sites. Here we describe a statistical technique that was developed for analysis of the dependencies among the editing states of the five sites. The statistical significance of the observed correlations was estimated by comparing editing patterns in multiple individuals. For both human and rat 5-HT<sub>2C</sub>R, the editing states of the physically proximal sites A and B were found to be strongly dependent. In contrast, the editing states of sites C and D, which are also physically close, seem not to be directly dependent but instead are linked through the dependencies on sites A and B, respectively. We observed pronounced differences between the editing patterns in humans and rats: in humans site A is the key determinant of the editing state of the other sites, whereas in rats this role belongs to site B. The structure of the dependencies among the editing sites is notably simpler in rats than it is in humans implying more complex regulation of 5-HT<sub>2C</sub>R editing and, by inference, function in the human brain. Thus, exhaustive statistical analysis of the 5-HT<sub>2C</sub>R editing patterns indicates that the editing state of sites A and B is the primary determinant of the editing states of the other three sites, and hence the overall editing pattern. Taken together, these findings allow us to propose a mechanistic model of concerted action of ADAR1 and ADAR2 in 5-HT<sub>2C</sub>R editing. Statistical approach developed here can be applied to other cases of interdependencies among modification sites in RNA and proteins.</p> </div
Edges present in the best-fitting models in human (BIC scores).
<p>For each fixed number of <b>edges </b><b>, we report</b> the basic set of edges (the most supported edges), as well as additional edges that are not significantly less supported (at Bonferroni-corrected significance level 0.05).</p