Semantic Multi-Classifier Systems Identify Predictive Processes in Heart Failure Models across Species

Genetic model organisms have the potential of removing blind spots from the underlying gene regulatory networks of human diseases. Allowing analyses under experimental conditions they complement the insights gained from observational data. An inevitable requirement for a successful trans-species transfer is an abstract but precise high-level characterization of experimental findings. In this work, we provide a large-scale analysis of seven weak contractility/heart failure genotypes of the model organism zebrafish which all share a weak contractility phenotype. In supervised classification experiments, we screen for discriminative patterns that distinguish between observable phenotypes (homozygous mutant individuals) as well as wild-type (homozygous wild-types) and carriers (heterozygous individuals). As the method of choice we use semantic multi-classifier systems, a knowledge-based approach which constructs hypotheses from a predefined vocabulary of high-level terms (e.g., Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways or Gene Ontology (GO) terms). Evaluating these models leads to a compact description of the underlying processes and guides the screening for new molecular markers of heart failure. Furthermore, we were able to independently corroborate the identified processes in Wistar rats

Stability of Signaling Pathways during Aging—A Boolean Network Approach

Biological pathways are thought to be robust against a variety of internal and external perturbations. Fail-safe mechanisms allow for compensation of perturbations to maintain the characteristic function of a pathway. Pathways can undergo changes during aging, which may lead to changes in their stability. Less stable or less robust pathways may be consequential to or increase the susceptibility of the development of diseases. Among others, NF- κ B signaling is a crucial pathway in the process of aging. The NF- κ B system is involved in the immune response and dealing with various internal and external stresses. Boolean networks as models of biological pathways allow for simulation of signaling behavior. They can help to identify which proposed mechanisms are biologically representative and which ones function but do not mirror physical processes—for instance, changes of signaling pathways during the aging process. Boolean networks can be inferred from time-series of gene expression data. This allows us to get insights into the changes of behavior of pathways such as NF- κ B signaling in aged organisms in comparison to young ones

A Boolean network of the crosstalk between IGF and Wnt signaling in aging satellite cells

<div><p>Aging is a complex biological process, which determines the life span of an organism. Insulin-like growth factor (IGF) and Wnt signaling pathways govern the process of aging. Both pathways share common downstream targets that allow competitive crosstalk between these branches. Of note, a shift from IGF to Wnt signaling has been observed during aging of satellite cells. Biological regulatory networks necessary to recreate aging have not yet been discovered. Here, we established a mathematical <i>in silico</i> model that robustly recapitulates the crosstalk between IGF and Wnt signaling. Strikingly, it predicts critical nodes following a shift from IGF to Wnt signaling. These findings indicate that this shift might cause age-related diseases.</p></div

Attractors of the sub-networks.

<p>(A) Simulation of the IGF sub-network lead to attractors A, B and C, the first of which could be matched to attractors 3 and 5 of the complete crosstalk model (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0195126#pone.0195126.g002" target="_blank">Fig 2</a>). (B) Attractors D and E were found while simulating the Wnt sub-network. Here, attractor D could be matched to attractor 1. Each block represents an attractor. The regulatory factors are listed on the y-axis. Each rectangle symbolizes the state of such a factor: red stands for inactive, green for active.</p

Effects of input factors in signaling cascade.

<p>(A) Based on an initial state where all nodes are inactive, a simulation of a signaling cascade was performed. The model results in an attractor representing an un-stimulated cell. (B) Simulation from an initial state with IGF as single active node results in an attractor representing the young phenotype. (C) In contrast, a simulation of signaling cascade with IGF and Wnt as single active nodes results in an attractor representing a mid-aged phenotype. (D) Simulation of the signaling cascade with Wnt as single active node results in an attractor representing an aged phenotype. Nodes are listed on the y-axis. Time is plotted on the x-axis. Every rectangle represents the state of a node at a specific time: red stands for inactive, green for active.</p

Age-related shift from IGF to Wnt signaling.

<p>(A) The age-related shift from IGF to Wnt happens stepwise. At the beginning of this shift both signals are active and the temporal sequence simulation results in a single state attractor. (B) Passing the life span of an organism, initially IGF as external signal is active, resulting in a three-state attractor. Then, a slow shift from IGF to Wnt takes place. At the beginning both input factors are active, whereas at the end Wnt as single external input is active, resulting in a single-state attractor.</p

Crosstalk of IGF and Wnt signaling.

<p>IGF and Wnt signaling are simplified and reduced to their most important nodes. Signaling pathways are highlighted in different colors and the IGF and Wnt sub-networks are depicted by the dashed boxes. Interactions between two molecules are symbolized as black lines. Activation is represented by arrowheads, inhibition by bar-headed arrows. Cellular compartments are separated by grey bars.</p

Table of the Boolean functions of the IGF/Wnt crosstalk model.

<p>Table of the Boolean functions of the IGF/Wnt crosstalk model.</p

Attractors of the IGF/Wnt crosstalk model.

<p>Exhaustive attractor search of the IGF/Wnt crosstalk model yielded four single state attractors and one three-states attractor. The frequency of occurrence of each attractor is given as percentage below each column. Each block represents an attractor. The nodes are listed on the y-axis. Each rectangle symbolizes the state of a node: red stands for inactive, green for active.</p