Cis-Acting Polymorphisms Affect Complex Traits through Modifications of MicroRNA Regulation Pathways

Genome-wide association studies (GWAS) have become an effective tool to map genes and regions contributing to multifactorial human diseases and traits. A comparably small number of variants identified by GWAS are known to have a direct effect on protein structure whereas the majority of variants is thought to exert their moderate influences on the phenotype through regulatory changes in mRNA expression. MicroRNAs (miRNAs) have been identified as powerful posttranscriptional regulators of mRNAs. Binding to their target sites, which are mostly located within the 3′-untranslated region (3′-UTR) of mRNA transcripts, they modulate mRNA expression and stability. Until today almost all human mRNA transcripts are known to harbor at least one miRNA target site with an average of over 20 miRNA target sites per transcript. Among 5,101 GWAS-identified sentinel single nucleotide polymorphisms (SNPs) that correspond to 18,884 SNPs in linkage disequilibrium (LD) with the sentinels () we identified a significant overrepresentation of SNPs that affect the 3′-UTR of genes (OR = 2.33, 95% CI = 2.12–2.57, ). This effect was even stronger considering all SNPs in one LD bin a single signal (OR = 4.27, 95% CI = 3.84–4.74, ). Based on crosslinking immunoprecipitation data we identified four mechanisms affecting miRNA regulation by 3′-UTR mutations: (i) deletion or (ii) creation of miRNA recognition elements within validated RNA-induced silencing complex binding sites, (iii) alteration of 3′-UTR splicing leading to a loss of binding sites, and (iv) change of binding affinity due to modifications of 3′-UTR folding. We annotated 53 SNPs of a total of 288 trait-associated 3′-UTR SNPs as mediating at least one of these mechanisms. Using a qualitative systems biology approach, we demonstrate how our findings can be used to support biological interpretation of GWAS results as well as to provide new experimentally testable hypotheses

HiNO: An Approach for Inferring Hierarchical Organization from Regulatory Networks

BACKGROUND: Gene expression as governed by the interplay of the components of regulatory networks is indeed one of the most complex fundamental processes in biological systems. Although several methods have been published to unravel the hierarchical structure of regulatory networks, weaknesses such as the incorrect or inconsistent assignment of elements to their hierarchical levels, the incapability to cope with cyclic dependencies within the networks or the need for a manual curation to retrieve non-overlapping levels remain unsolved. METHODOLOGY/RESULTS: We developed HiNO as a significant improvement of the so-called breadth-first-search (BFS) method. While BFS is capable of determining the overall hierarchical structures from gene regulatory networks, it especially has problems solving feed-forward type of loops leading to conflicts within the level assignments. We resolved these problems by adding a recursive correction approach consisting of two steps. First each vertex is placed on the lowest level that this vertex and its regulating vertices are assigned to (downgrade procedure). Second, vertices are assigned to the next higher level (upgrade procedure) if they have successors with the same level assignment and have themselves no regulators. We evaluated HiNO by comparing it with the BFS method by applying them to the regulatory networks from Saccharomyces cerevisiae and Escherichia coli, respectively. The comparison shows clearly how conflicts in level assignment are resolved in HiNO in order to produce correct hierarchical structures even on the local levels in an automated fashion. CONCLUSIONS: We showed that the resolution of conflicting assignments clearly improves the BFS-method. While we restricted our analysis to gene regulatory networks, our approach is suitable to deal with any directed hierarchical networks structure such as the interaction of microRNAs or the action of non-coding RNAs in general. Furthermore we provide a user-friendly web-interface for HiNO that enables the extraction of the hierarchical structure of any directed regulatory network. AVAILABILITY: HiNO is freely accessible at http://mips.helmholtz-muenchen.de/hino/

Structuring heterogeneous biological information using fuzzy clustering of k-partite graphs

<p>Abstract</p> <p>Background</p> <p>Extensive and automated data integration in bioinformatics facilitates the construction of large, complex biological networks. However, the challenge lies in the interpretation of these networks. While most research focuses on the unipartite or bipartite case, we address the more general but common situation of <it>k</it>-partite graphs. These graphs contain <it>k </it>different node types and links are only allowed between nodes of different types. In order to reveal their structural organization and describe the contained information in a more coarse-grained fashion, we ask how to detect clusters within each node type.</p> <p>Results</p> <p>Since entities in biological networks regularly have more than one function and hence participate in more than one cluster, we developed a <it>k</it>-partite graph partitioning algorithm that allows for overlapping (fuzzy) clusters. It determines for each node a degree of membership to each cluster. Moreover, the algorithm estimates a weighted <it>k</it>-partite graph that connects the extracted clusters. Our method is fast and efficient, mimicking the multiplicative update rules commonly employed in algorithms for non-negative matrix factorization. It facilitates the decomposition of networks on a chosen scale and therefore allows for analysis and interpretation of structures on various resolution levels. Applying our algorithm to a tripartite disease-gene-protein complex network, we were able to structure this graph on a large scale into clusters that are functionally correlated and biologically meaningful. Locally, smaller clusters enabled reclassification or annotation of the clusters' elements. We exemplified this for the transcription factor MECP2.</p> <p>Conclusions</p> <p>In order to cope with the overwhelming amount of information available from biomedical literature, we need to tackle the challenge of finding structures in large networks with nodes of multiple types. To this end, we presented a novel fuzzy <it>k</it>-partite graph partitioning algorithm that allows the decomposition of these objects in a comprehensive fashion. We validated our approach both on artificial and real-world data. It is readily applicable to any further problem.</p

Level distribution of the TRN in E. coli.

<p>Comparison of the level distribution of the TRN in E. coli retrieved by applying the BFS method <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0013698#pone.0013698-Yu1" target="_blank">[4]</a> and HiNO.</p

Hierarchical structure clarifies regulatory interdependencies.

<p>An abstract example of a directed regulatory network is shown (<b>A</b>) unstructured and (<b>B</b>) hierarchically structured. We schematically show in (<b>C</b>) and (<b>D</b>) that for master regulators like <i>HOXA10</i> significant downstream effects in postnatal hematopoietic development depending on different concentrations can be observed. In contrast to master regulators so called mid-level regulators can not only be mediators of different incoming regulatory signals but also influence multiple downstream components or pathways. We schematically display that <i>TP53</i> is influenced by various upstream signals such as (<b>E</b>) DNA damage or (<b>F</b>) oncogenes leading to different downstream effects such as apoptosis or development of cancer. The detailed understanding of the hierarchical topology is necessary to comprehend the dynamic behavior and the possible malfunction of regulatory networks.</p

Transcriptional regulatory networks.

<p>Number of nodes and edges for the extracted TF-networks.</p

Web interface.

<p>An illustration of the user-friendly web-interface for HiNO is shown. The user can upload (<b>A</b>) a text file containing the representation of any directed network. Afterwards (<b>B</b>) the user retrieves information about node and edge types within the uploaded network and can decide which types should be used for the hierarchy extraction. Additionally, TF annotation can be added for human or mouse networks. (<b>C</b>) Information about the extracted hierarchical structure can be downloaded either as text file or in a graphical representation.</p

Regulatory networks.

<p>Number of nodes and edges for the gene regulatory networks in E. coli (ERN) and S. cerevisiae (YRN).</p

Level distribution of the TRN in S. cerevisiae.

<p>Comparison of the level distribution of the TRN in S. cerevisiae retrieved by applying the BFS method <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0013698#pone.0013698-Yu1" target="_blank">[4]</a> and HiNO.</p