Variation of gene regulatory network robustness.

<p>(<b>a</b>) Percentage of networks whose output does not change upon mutation (D_mutsmall). Fraction of dimers (F_dim) equals 0.0, 0.3 or 0.6, and fraction regulatory interactions (F_regint) equals 2.0 or 4.0. <i>Upper panel</i>, protein interaction mutations. <i>Lower panel</i>, regulatory mutations. (<b>b</b>) Percentage of compensatory mutations after a first large effect (D_mut>D_large) protein interaction mutation. From left to right, the fraction activating interactions equals 0.25, 0.5 or 0.75. Blue symbols indicate average percentage (and standard deviation) for secondary protein interaction mutations, red for secondary regulatory mutations. (<b>c</b>) Dependence of robustness on number of autoregulatory interactions. Average D_mut is shown (legend at the bottom) depending on the number of activating (x-axis) and repressing (y-axis) autoregulatory interactions, for protein interaction mutations (<i>upper panel</i>) and regulatory mutations (<i>lower panel</i>). Note that the higher the average D_mut, the lower the robustness.</p

Mutational Robustness of Gene Regulatory Networks

<div><p>Mutational robustness of gene regulatory networks refers to their ability to generate constant biological output upon mutations that change network structure. Such networks contain regulatory interactions (transcription factor – target gene interactions) but often also protein-protein interactions between transcription factors. Using computational modeling, we study factors that influence robustness and we infer several network properties governing it. These include the type of mutation, i.e. whether a regulatory interaction or a protein-protein interaction is mutated, and in the case of mutation of a regulatory interaction, the sign of the interaction (activating vs. repressive). In addition, we analyze the effect of combinations of mutations and we compare networks containing monomeric with those containing dimeric transcription factors. Our results are consistent with available data on biological networks, for example based on evolutionary conservation of network features. As a novel and remarkable property, we predict that networks are more robust against mutations in monomer than in dimer transcription factors, a prediction for which analysis of conservation of DNA binding residues in monomeric vs. dimeric transcription factors provides indirect evidence.</p> </div

Cumulative histogram of sequence conservation of DNA contacting residues in human TFs.

<p>Sequence entropy was calculated for dimeric (black) and monomeric TFs (red). Lower values indicate more conservation.</p

Network model parameters and network topology parameters.

a<p>F_regint is calculated as the number of regulatory interactions per protein.</p>b<p>F_act is calculated as the ratio of the number of activating regulatory interactions over the total number of regulatory interactions.</p

Predicted effects of various network characteristics on robustness^a.

a<p>Simulations were performed with models of gene regulatory networks, both for wildtype and mutated versions of the network. This enabled to find characteristics of networks with low robustness (large changes in expression patterns upon mutations) vs. those with high robustness (small changes in expression patterns upon mutations).</p

Histogram of D_mut for experimental datasets.

<p>D_mut quantifies expression differences between orthologs (interspecies sets) or the same gene in various conditions (intraspecies sets). Interspecies analysis was performed using wine yeast strains (red) and <i>Drosophila</i> species (green), and intraspecies analysis for <i>Arabidopsis thaliana</i> (blue) and human (purple). The observed 1th and 99th percentile were used to obtain cutoffs for D_mut in order to describe ‘small’ and ‘large’ changes (D_small and D_large).</p

Overview of our study of evolutionary robustness.

<p>(<b>a</b>) Models for gene regulatory networks containing transcription factor – transcription factor (protein-protein) interactions and transcription factor – target gene interactions were simulated. Mutations changing the network topology were applied, and the resulting change in expression patterns was described using a metric D_mut. By comparing networks with various properties, network features were found that influence network robustness. Finally, the observed trends were compared with available data about biological networks. (<b>b</b>) Two types of mutations were applied, either targeting a protein-protein interaction (top panel), or targeting a regulatory interaction (bottom panel). In the case of a mutation changing a protein-protein interaction, a dimer is changed into another dimer; all its regulatory interactions remain. In the case of a mutation changing a regulatory interaction, for one specific regulator (either a dimer as shown in the figure, or a monomer in case of monomeric networks), one regulatory interaction is changed; all other regulatory interactions remain, as do all protein-protein interactions.</p

Predicting the Impact of Alternative Splicing on Plant MADS Domain Protein Function

<div><p>Several genome-wide studies demonstrated that alternative splicing (AS) significantly increases the transcriptome complexity in plants. However, the impact of AS on the functional diversity of proteins is difficult to assess using genome-wide approaches. The availability of detailed sequence annotations for specific genes and gene families allows for a more detailed assessment of the potential effect of AS on their function. One example is the plant MADS-domain transcription factor family, members of which interact to form protein complexes that function in transcription regulation. Here, we perform an <em>in silico</em> analysis of the potential impact of AS on the protein-protein interaction capabilities of MIKC-type MADS-domain proteins. We first confirmed the expression of transcript isoforms resulting from predicted AS events. Expressed transcript isoforms were considered functional if they were likely to be translated and if their corresponding AS events either had an effect on predicted dimerisation motifs or occurred in regions known to be involved in multimeric complex formation, or otherwise, if their effect was conserved in different species. Nine out of twelve MIKC MADS-box genes predicted to produce multiple protein isoforms harbored putative functional AS events according to those criteria. AS events with conserved effects were only found at the borders of or within the K-box domain. We illustrate how AS can contribute to the evolution of interaction networks through an example of selective inclusion of a recently evolved interaction motif in the MADS AFFECTING FLOWERING1-3 (MAF1–3) subclade. Furthermore, we demonstrate the potential effect of an AS event in SHORT VEGETATIVE PHASE (SVP), resulting in the deletion of a short sequence stretch including a predicted interaction motif, by overexpression of the fully spliced and the alternatively spliced <em>SVP</em> transcripts. For most of the AS events we were able to formulate hypotheses about the potential impact on the interaction capabilities of the encoded MIKC proteins.</p> </div

Interaction motif architecture of <i>MADS AFFECTING FLOWERING1 (MAF1)</i> isoforms.

<p>Two <i>MADS AFFECTING FLOWERING1</i> (<i>MAF1</i>) isoforms differ as the result of the selective inclusion of either one of the two mutually exclusive exons (named 5′- and 3′-exon). Only the 5′-exon of the mutually exclusive pair that is included in isoform 1 contains motif <b>A</b>. Both the 5′- and 3′-exons of the mutually exclusive pair have residues at their 3′-boundary that can form interaction motifs (<b>BD</b> or <b>CD</b>) together with the first residues of the downstream constitutively spliced exon. Introns are indicated by horizontal lines.</p

Functional analysis of two <i>SHORT VEGETATIVE PHASE (SVP)</i> isoforms.

<p><b>A</b>) Graphical representation of protein-protein interaction capacity of the SVP1 (black) and SVP3 (grey) splicing variants as determined by matrix-based yeast two-hybrid studies (Van Dijk et al, 2010). <b>B</b>) and <b>C</b>) Effect of ectopic expression of <i>SVP1</i> (CZN094) and <i>SVP3</i> (CZN756) on flowering-time under short day conditions. Flowering-time was assessed using days until bolting (<b>B</b>) as well as the number of rosette leaves (<b>C</b>). For both constructs, flowering-time of three segregating lines was analyzed and compared to flowering-time of wild type control plants. * denotes statistical significance with p<0.01 (<i>t</i>-test). <b>D</b>) Floral phenotypes upon ectopic expression of <i>SVP1</i>. First and second whorl organs are greenish and leaf-like and flowers are partially sterile due to reduced anther filament elongation.</p