LoTo: a graphlet based method for the comparison of local topology between gene regulatory networks

One of the main challenges of the post-genomic era is the understanding of how gene expression is controlled. Changes in gene expression lay behind diverse biological phenomena such as development, disease and the adaptation to different environmental conditions. Despite the availability of well-established methods to identify these changes, tools to discern how gene regulation is orchestrated are still required. The regulation of gene expression is usually depicted as a Gene Regulatory Network (GRN) where changes in the network structure (i.e., network topology) represent adjustments of gene regulation. Like other networks, GRNs are composed of basic building blocks; small induced subgraphs called graphlets. Here we present LoTo, a novel method that using Graphlet Based Metrics (GBMs) identifies topological variations between different states of a GRN. Under our approach, different states of a GRN are analyzed to determine the types of graphlet formed by all triplets of nodes in the network. Subsequently, graphlets occurring in a state of the network are compared to those formed by the same three nodes in another version of the network. Once the comparisons are performed, LoTo applies metrics from binary classification problems calculated on the existence and absence of graphlets to assess the topological similarity between both network states. Experiments performed on randomized networks demonstrate that GBMs are more sensitive to topological variation than the same metrics calculated on single edges. Additional comparisons with other common metrics demonstrate that our GBMs are capable to identify nodes whose local topology changes between different states of the network. Notably, due to the explicit use of graphlets, LoTo captures topological variations that are disregarded by other approaches. LoTo is freely available as an online web server at http://dlab.cl/loto

Graphlet Based Metrics for the Comparison of Gene Regulatory Networks.

Understanding the control of gene expression remains one of the main challenges in the post-genomic era. Accordingly, a plethora of methods exists to identify variations in gene expression levels. These variations underlay almost all relevant biological phenomena, including disease and adaptation to environmental conditions. However, computational tools to identify how regulation changes are scarce. Regulation of gene expression is usually depicted in the form of a gene regulatory network (GRN). Structural changes in a GRN over time and conditions represent variations in the regulation of gene expression. Like other biological networks, GRNs are composed of basic building blocks called graphlets. As a consequence, two new metrics based on graphlets are proposed in this work: REConstruction Rate (REC) and REC Graphlet Degree (RGD). REC determines the rate of graphlet similarity between different states of a network and RGD identifies the subset of nodes with the highest topological variation. In other words, RGD discerns how th GRN was rewired. REC and RGD were used to compare the local structure of nodes in condition-specific GRNs obtained from gene expression data of Escherichia coli, forming biofilms and cultured in suspension. According to our results, most of the network local structure remains unaltered in the two compared conditions. Nevertheless, changes reported by RGD necessarily imply that a different cohort of regulators (i.e. transcription factors (TFs)) appear on the scene, shedding light on how the regulation of gene expression occurs when E. coli transits from suspension to biofilm. Consequently, we propose that both metrics REC and RGD should be adopted as a quantitative approach to conduct differential analyses of GRNs. A tool that implements both metrics is available as an on-line web server (http://dlab.cl/loto)

Merged sub-network of the TFs with the lowest RGD using suspension network at 15 hours as reference.

<p>The TF encoding genes identified using their RGD are colored in red; other TFs are colored in orange and they are named TP because their expression was detected in the two compared networks; effector genes, those that do not code TFs are colored in purple if their expression was detected only in the reference network (FN nodes) and in blue if their expression was detected in both networks; with respect to the edges, they are colored in black if they were present in both networks (TP edges), light purple if they were found only in the reference network (FN edges) and yellow when they were detected only in the compared network (FP edges). The same image but with TP edges removed is shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0163497#pone.0163497.s005" target="_blank">S5 Fig</a>.</p

Statistical significance of RGD variation.

<p>Fraction of genes with RGD < 1.0 for genes participating in at least one graphlet comparing different conditions and time points. BS: biofilm versus suspension, for all four time points analyzed. B: biofilm, at consecutive time points. S: suspension, at consecutive time points. TF: genes coding for TFs. NTF: genes non coding for TFs. Statistically significant P-values obtained from a one way ANOVA test (0.05 threshold) are marked with an ∗ .</p

RGD versus absolute value of the change in CLustering Coefficient (CLC) at 15 hours.

<p>Left panel shows values of the metrics calculated using the suspension network as reference and right panel using the biofilm condition as reference in the comparison. Regression line calculated with R using as dependent variable RGD, regression coefficients between brackets.</p

Graphlets composed by three nodes.

<p>The direction of edges indicate the direction of the transcription regulation. Straight black edges denote true interactions, and dashed edges depict false ones. Grey nodes represent genes that are required to be TF encoding genes, white nodes represent genes that do not require to code for TF. Adapted from [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0163497#pone.0163497.ref017" target="_blank">17</a>].</p

Similarities between the Binding Sites of SB-206553 at Serotonin Type 2 and Alpha7 Acetylcholine Nicotinic Receptors: Rationale for Its Polypharmacological Profile

<div><p>Evidence from systems biology indicates that promiscuous drugs, i.e. those that act simultaneously at various protein targets, are clinically better in terms of efficacy, than those that act in a more selective fashion. This has generated a new trend in drug development called polypharmacology. However, the rational design of promiscuous compounds is a difficult task, particularly when the drugs are aimed to act at receptors with diverse structure, function and endogenous ligand. In the present work, using docking and molecular dynamics methodologies, we established the most probable binding sites of SB-206553, a drug originally described as a competitive antagonist of serotonin type 2B/2C metabotropic receptors (5-HT_2B/2CRs) and more recently as a positive allosteric modulator of the ionotropic α7 nicotinic acetylcholine receptor (nAChR). To this end, we employed the crystal structures of the 5-HT_2BR and acetylcholine binding protein as templates to build homology models of the 5-HT_2CR and α7 nAChR, respectively. Then, using a statistical algorithm, the similarity between these binding sites was determined. Our analysis showed that the most plausible binding sites for SB-206553 at 5-HT₂Rs and α7 nAChR are remarkably similar, both in size and chemical nature of the amino acid residues lining these pockets, thus providing a rationale to explain its affinity towards both receptor types. Finally, using a computational tool for multiple binding site alignment, we determined a consensus binding site, which should be useful for the rational design of novel compounds acting simultaneously at these two types of highly different protein targets.</p></div

Alignment of the extracellular (ECD) α7, α4 and β2 nAChR subunits and AChBP sequences using ClustalW.

<p>Conserved residues are highlighted in yellow and partially conserved residues highlighted in red. Secondary structures are shown schematically above the sequences; alpha helices and beta sheets are represented by cylinders and arrows respectively.</p

Alignment of 5-HT_2BR and 5-HT_2CR sequences using ClustalW.

<p>Conserved residues are highlighted in yellow.</p

Structural determinants of the SB-206553 binding site at the 5-HT₂Rs.

<p>(A) Ribbon diagram of the superimposed structures of the 5-HT_2BR (silver) and 5-HT_2CR (purple), showing the putative binding site for SB-206553 (red or blue, respectively) at each protein. For comparative purposes, the binding site for ergotamine (yellow) in the crystal structure of the 5-HT_2BR (PDB code 4IB4) is also depicted. (B-C) Close ups of the docking poses of SB-206553 at 5-HT_2BR and 5-HT_2CR, respectively. Main active site amino acid residues (cyan) are rendered as stick models.</p