IKAP: A heuristic framework for inference of kinase activities from Phosphoproteomics data.

MOTIVATION: Phosphoproteomics measurements are widely applied in cellular biology to detect changes in signalling dynamics. However, due to the inherent complexity of phosphorylation patterns and the lack of knowledge on how phosphorylations are related to functions, it is often not possible to directly deduce protein activities from those measurements. Here, we present a heuristic machine learning algorithm that infers the activities of kinases from Phosphoproteomics data using kinase-target information from the PhosphoSitePlus database. By comparing the estimated kinase activity profiles to the measured phosphosite profiles, it is furthermore possible to derive the kinases that are most likely to phosphorylate the respective phosphosite. RESULTS: We apply our approach to published datasets of the human cell cycle generated from HeLaS3 cells, and insulin signalling dynamics in mouse hepatocytes. In the first case, we estimate the activities of 118 at six cell cycle stages and derive 94 new kinase-phosphosite links that can be validated through either database or motif information. In the second case, the activities of 143 kinases at eight time points are estimated and 49 new kinase-target links are derived. AVAILABILITY AND IMPLEMENTATION: The algorithm is implemented in Matlab and be downloaded from github. It makes use of the Optimization and Statistics toolboxes. https://github.com/marcel-mischnik/IKAP.git. CONTACT: marcel.mischnik@gmail.com SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Minimal modeling of insulin secretion in the perfused rat pancreas: a drug effect case study.

The experimental protocol of the perfused rat pancreas is commonly used to evaluate ß-cell function. In this context, mathematical models become useful tools through the determination of indexes that allow the assessment of ß-cell function in different experimental groups and the quantification of the effects of antidiabetic drugs, secretagogues, or treatments. However, a minimal model applicable to the isolated perfused rat pancreas has so far been unavailable. In this work, we adapt the C-peptide minimal model applied previously to the intravenous glucose tolerance test to obtain a specific model for the experimental settings of the perfused pancreas. Using the model, it is possible to estimate indexes describing ß-cell responsivity for first (ΦD) and second phase (ΦS, T) of insulin secretion. The model was initially applied to untreated pancreata and afterward used for the assessment of pharmacologically relevant agents (the gut hormone GLP-1, the potent GLP-1 receptor agonist lixisenatide, and a GPR40/FFAR1 agonist, SAR1) to quantify and differentiate their effect on insulin secretion. Model fit was satisfactory, and parameters were estimated with good precision for both untreated and treated pancreata. Model application showed that lixisenatide reaches improvement of ß-cell function similarly to GLP-1 (11.7- vs. 13.1-fold increase in ΦD and 2.3- vs. 2.8-fold increase in ΦS) and demonstrated that SAR1 leads to an additional improvement of ß-cell function in the presence of postprandial GLP-1 levels.

Understanding drugs and diseases by systems biology?

Systems biology aims to provide a holistic and in many cases dynamic picture of biological function and malfunction, in case of disease. Technology developments in the generation of genome-wide datasets and massive improvements in computer power now allow to obtain new insights into complex biological networks and to copy nature by computing these interactions and their kinetics and by generating in silico models of cells, tissues and organs. The expectations are high that systems biology will pave the way to the identification of novel disease genes, to the selection of successful drug candidates--that do not fail in clinical studies due to toxicity or lack of human efficacy--and finally to a more successful discovery of novel therapeutics. However, further research is necessary to fully unleash the potential of systems biology. Within this review we aim to highlight the most important and promising top-down and bottom-up systems biology applications in drug discovery.

Correlational analysis for identifying genes whose regulation contributes to chronic neuropathic pain.

BACKGROUND: Nerve injury-triggered hyperexcitability in primary sensory neurons is considered a major source of chronic neuropathic pain. The hyperexcitability, in turn, is thought to be related to transcriptional switching in afferent cell somata. Analysis using expression microarrays has revealed that many genes are regulated in the dorsal root ganglion (DRG) following axotomy. But which contribute to pain phenotype versus other nerve injury-evoked processes such as nerve regeneration? Using the L5 spinal nerve ligation model of neuropathy we examined differential changes in gene expression in the L5 (and L4) DRGs in five mouse strains with contrasting susceptibility to neuropathic pain. We sought genes for which the degree of regulation correlates with strain-specific pain phenotype. RESULTS: In an initial experiment six candidate genes previously identified as important in pain physiology were selected for in situ hybridization to DRG sections. Among these, regulation of the Na+ channel alpha subunit Scn11a correlated with levels of spontaneous pain behavior, and regulation of the cool receptor Trpm8 correlated with heat hypersensibility. In a larger scale experiment, mRNA extracted from individual mouse DRGs was processed on Affymetrix whole-genome expression microarrays. Overall, 2552 +/- 477 transcripts were significantly regulated in the axotomized L5DRG 3 days postoperatively. However, in only a small fraction of these was the degree of regulation correlated with pain behavior across strains. Very few genes in the "uninjured" L4DRG showed altered expression (24 +/- 28). CONCLUSION: Correlational analysis based on in situ hybridization provided evidence that differential regulation of Scn11a and Trpm8 contributes to across-strain variability in pain phenotype. This does not, of course, constitute evidence that the others are unrelated to pain. Correlational analysis based on microarray data yielded a larger "look-up table" of genes whose regulation likely contributes to pain variability. While this list is enriched in genes of potential importance for pain physiology, and is relatively free of the bias inherent in the candidate gene approach, additional steps are required to clarify which transcripts on the list are in fact of functional importance.

Mechanistic interplay between ceramide and insulin resistance.

Recent research adds to a growing body of literature on the essential role of ceramides in glucose homeostasis and insulin signaling, while the mechanistic interplay between various components of ceramide metabolism remains to be quantified. We present an extended model of C16:0 ceramide production through both the de novo synthesis and the salvage pathways. We verify our model with a combination of published models and independent experimental data. In silico experiments of the behavior of ceramide and related bioactive lipids in accordance with the observed transcriptomic changes in obese/diabetic murine macrophages at 5 and 16 weeks support the observation of insulin resistance only at the later phase. Our analysis suggests the pivotal role of ceramide synthase, serine palmitoyltransferase and dihydroceramide desaturase involved in the de novo synthesis and the salvage pathways in influencing insulin resistance versus its regulation.