Systems Biology

Fin biospies were obtained from 152 individuals of the short-lived teleost fish Nothobranchius furzeri (maximum lifespan ~ 60 weeks), at the age of 10 weeks and 20 weeks. The biopsies were taken without sacrificing the fish, so that lifespan data were available for each individual. Transcriptome data have been generated for these samples using RNA-Seq on the Illumina platform. In order to identify genes which are predictive for lifespan, a Random Forest analysis has been made, considering the expression at both time points as well as the change of the expression between the two time points. Based on this analysis, we can conclude that differences in lifespan manifest in gene expression already at young age. On the other hand, the analysis reveals that batch effects hamper the identification of generally applicable biomarkers for the prediction of lifespan.

A system of non-linear ordinary differential equations (ODE) has been established in order to describe the response of a gene regulatory network to environmental signals. It is shown that a double-negative feedback loop in the mTOR pathway can lead to a hormesis-like signal-response characteristic of gene expression levels. Signals with moderate intensity or duration trigger the system to switch to a distinguished stable state that can be related to a putative defense loop. If a certain pulse duration or intensity is exceeded, the defense loop cannot be maintained by the system. The parameters of the model were chosen in such a way that calculated stationary-state values fit to gene expression data measured by RNA-Seq. Using the model, we speculate about processes that possibly contribute to ageing.

Description The package provides functions to carry out a Goodness-of-fit test for discrete multivariate data. It is tested if a given observation is likely to have occurred under the assumption of an ab-initio model. A p-value can be calculated using different distance measures between observed and expected frequencies. A Monte Carlo method is provided to make the package capable of solving high-dimensional problems.

The package RMThreshold attempts to determine an objective threshold which separates signal from noise in large real-valued, symmetric matrices. Such matrices can for instance describe correlation or mutual information between data of various origin, or might represent the set of edges in undirected networks. RMThreshold takes advantage of the predictions of Random Matrix Theory (RMT) for the distribution of the spacing between the eigenvalues of such matrices. That distribution is usually called Nearest Neighbor Spacing Distribution (NNSD). The predictions of RMT are valid in the limit of large matrix dimensions. RMT was initiated by Eugene Wigner in the context of nuclear physics in 1955 (Wigner E. P., Annals of Mathematics, 1955). RMT predicts two extreme scenarios for the NNSD of eigenvalues: 1.) If the matrix elements are completely random, the NNSD is characterized by Gaussian Orthogonal Ensemble (GOE) statistics, and the shape of the NNSD resembles the Wigner-Dyson distribution (" Wigner surmise "): , where s is the eigenvalue spacing and P(s) it's distribution. This distribution approaches zero for s = 0 which can be imagined as if there was some sort of " repulsion " between the eigenvalues. 2.) If the matrix has a non-random, modular structure (associated with block-like composition), the NNSD comes close to an Exponential distribution: Both functions differ most at s = 0, where PGOE = 0 and Pexp = 1. An imaginary " repulsion " does not occur in the modular case, and zero-spacings between the eigenvalues frequently occur. This case might apply to the adjacency matrix of a large undirected network consisting of relatively independent clusters with weak connections between them. The connections might possibly just being noise by their nature. By identifying an appropriate threshold for such matrices, it should be possible to reveal the underlying modular structure of the network, i.e. to identify the clusters. Now, if we assume that a matrix or a network actually has a modular structure which is hidden by noise, it should be possible to identify a signal-noise separating threshold by finding the threshold at which the NNSD changes from the Wigner-Dyson case to the Exponential case. Consequentially, the main function of the package (rm.get.threshold) increments a suppositional threshold monotonically, thereby recording the eigenvalue spacing distribution of the thresholded matrix. A typical procedure to infer a signal-noise separating threshold by using the package RMThreshold may consist of the following steps: 1.) checking the conformity of the input matrix using the function rm.matrix.validation, 2.) running the main function rm.get.threshold in order to find a candidate threshold, 3.) optionally repeat running rm.get.threshold on a smaller interval of thresholds, and 4.) applying the identified threshold to the matrix. The thresholded matrix created by the latter step should then represent the real signal. Some important steps of this procedure are described in more detail in the following text.

Glucose restriction mimicked by feeding the roundworm Caenorhabditis elegans with 2-deoxy-D-glucose (DOG) -a glucose molecule that lacks the ability to undergo glycolysis -has been found to increase the life span of the nematodes considerably. To facilitate understanding of the molecular mechanisms behind this life extension, we analyzed transcriptomes of DOG-treated and untreated roundworms obtained by RNA-seq at different ages. We found that, depending on age, DOG changes the magnitude of the expression values of about 2 to 24 percent of the genes significantly, although our results reveal that the gross changes introduced by DOG are small compared to the age-induced changes. We found that 27 genes are constantly either up-or down-regulated by DOG over the whole life span, among them several members of the cytochrome P450 family. The monotonic change with age of the temporal expression patterns of the genes was investigated, leading to the result that 21 genes reverse their monotonic behaviour under impaired glycolysis. Put simply, the DOG-treatment reduces the gross transcriptional activity but increases the interconnectedness of gene expression. However, a detailed analysis of network parameters discloses that the introduced changes differ remarkably between individual signalling pathways. We found a reorganization of the hubs of the mTOR pathway when standard diet is replaced by DOG feeding. By constructing correlation based difference networks, we identified those signalling pathways that are most vigorously changed by impaired glycolysis. Taken together, we have found a number of genes and pathways that are potentially involved in the DOG-driven extension of life span of C. elegans. Furthermore, our results demonstrate how the network structure of ageing-relevant signalling pathways is reorganised under impaired glycolysis.