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/

Differential predictors for alcohol use in adolescents as a function of familial risk

Abstract: Traditional models of future alcohol use in adolescents have used variable-centered approaches, predicting alcohol use from a set of variables across entire samples or populations. Following the proposition that predictive factors may vary in adolescents as a function of family history, we used a two-pronged approach by first defining clusters of familial risk, followed by prediction analyses within each cluster. Thus, for the first time in adolescents, we tested whether adolescents with a family history of drug abuse exhibit a set of predictors different from adolescents without a family history. We apply this approach to a genetic risk score and individual differences in personality, cognition, behavior (risk-taking and discounting) substance use behavior at age 14, life events, and functional brain imaging, to predict scores on the alcohol use disorders identification test (AUDIT) at age 14 and 16 in a sample of adolescents (N = 1659 at baseline, N = 1327 at follow-up) from the IMAGEN cohort, a longitudinal community-based cohort of adolescents. In the absence of familial risk (n = 616), individual differences in baseline drinking, personality measures (extraversion, negative thinking), discounting behaviors, life events, and ventral striatal activation during reward anticipation were significantly associated with future AUDIT scores, while the overall model explained 22% of the variance in future AUDIT. In the presence of familial risk (n = 711), drinking behavior at age 14, personality measures (extraversion, impulsivity), behavioral risk-taking, and life events were significantly associated with future AUDIT scores, explaining 20.1% of the overall variance. Results suggest that individual differences in personality, cognition, life events, brain function, and drinking behavior contribute differentially to the prediction of future alcohol misuse. This approach may inform more individualized preventive interventions

Differential predictors for alcohol use in adolescents as a function of familial risk

Abstract: Traditional models of future alcohol use in adolescents have used variable-centered approaches, predicting alcohol use from a set of variables across entire samples or populations. Following the proposition that predictive factors may vary in adolescents as a function of family history, we used a two-pronged approach by first defining clusters of familial risk, followed by prediction analyses within each cluster. Thus, for the first time in adolescents, we tested whether adolescents with a family history of drug abuse exhibit a set of predictors different from adolescents without a family history. We apply this approach to a genetic risk score and individual differences in personality, cognition, behavior (risk-taking and discounting) substance use behavior at age 14, life events, and functional brain imaging, to predict scores on the alcohol use disorders identification test (AUDIT) at age 14 and 16 in a sample of adolescents (N = 1659 at baseline, N = 1327 at follow-up) from the IMAGEN cohort, a longitudinal community-based cohort of adolescents. In the absence of familial risk (n = 616), individual differences in baseline drinking, personality measures (extraversion, negative thinking), discounting behaviors, life events, and ventral striatal activation during reward anticipation were significantly associated with future AUDIT scores, while the overall model explained 22% of the variance in future AUDIT. In the presence of familial risk (n = 711), drinking behavior at age 14, personality measures (extraversion, impulsivity), behavioral risk-taking, and life events were significantly associated with future AUDIT scores, explaining 20.1% of the overall variance. Results suggest that individual differences in personality, cognition, life events, brain function, and drinking behavior contribute differentially to the prediction of future alcohol misuse. This approach may inform more individualized preventive interventions

Pseudocode of the algorithm.

<p>Pseudocode for extracting the hierarchical organization of directed networks.</p

From the gene regulatory network (GRN) to the hierarchical structure.

<p>Illustration of (<b>A</b>) the GRN of E. coli with 1095 nodes and 2044 edges and (<b>D</b>) the GRN of S. cerevisiae with 3458 nodes and 8371 edges. Red nodes indicate transcription factors, green nodes genes. For extracting the hierarchical structure we deduced transcriptional regulation only. The transcriptional regulatory networks (TRNs) are shown in (<b>B</b>) for E. coli and (<b>E</b>) S. cerevisiae only. A snapshot of the hierarchical structure of the TRNs is shown for E. coli in (<b>C</b>) and for S. cerevisiae in (<b>F</b>). The TRN of E. coli has a four-layered pyramidal-shaped hierarchical structure, whereas the TRN of S. cerevisiae consists only of three layers. The different colors represent the distinct hierarchical levels: level 4– yellow; level 3– red; level 2– green; level 1– blue.</p

Transcriptional regulatory networks.

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

Comparison BFS-method vs. HiNO using the TRN of S. cerevisiae.

<p>A snapshot of the results (the comparison of the BFS-approach and HiNO) using the TRN of S. cerevisiae is shown. Node colors indicate the level assignment in HiNO: blue - level 1; green - level 2; red - level 3; yellow - level 4. Dashed edges represent additional regulatory interdependencies (further up- and/or down-stream factors) that are not shown. The results of the BFS method are shown in (<b>A</b>). The node coloring indicates conflicts in level assignment. In (<b>B</b>) the result of the “downgrade” step of HiNO is shown. In (<b>C</b>) the result of the “upgrade” step is shown. This is also the final hierarchical assignment of the elements by HiNO.</p