Dynamic Metabolomics Reveals that Insulin Primes the Adipocyte for Glucose Metabolism

Insulin triggers an extensive signaling cascade to coordinate adipocyte glucose metabolism. It is considered that the major role of insulin is to provide anabolic substrates by activating GLUT4-dependent glucose uptake. However, insulin stimulates phosphorylation of many metabolic proteins. To examine the implications of this on glucose metabolism, we performed dynamic tracer metabolomics in cultured adipocytes treated with insulin. Temporal analysis of metabolite concentrations and tracer labeling revealed rapid and distinct changes in glucose metabolism, favoring specific glycolytic branch points and pyruvate anaplerosis. Integrating dynamic metabolomics and phosphoproteomics data revealed that insulin-dependent phosphorylation of anabolic enzymes occurred prior to substrate accumulation. Indeed, glycogen synthesis was activated independently of glucose supply. We refer to this phenomenon as metabolic priming, whereby insulin signaling creates a demand-driven system to "pull" glucose into specific anabolic pathways. This complements the supply-driven regulation of anabolism by substrate accumulation and highlights an additional role for insulin action in adipocyte glucose metabolism.

Unraveling Kinase Activation Dynamics Using Kinase-Substrate Relationships from Temporal Large-Scale Phosphoproteomics Studies

In response to stimuli, biological processes are tightly controlled by dynamic cellular signaling mechanisms. Reversible protein phosphorylation occurs on rapid time-scales (milliseconds to seconds), making it an ideal carrier of these signals. Advances in mass spectrometry-based proteomics have led to the identification of many tens of thousands of phosphorylation sites, yet for the majority of these the kinase is unknown and the underlying network topology of signaling networks therefore remains obscured. Identifying kinase substrate relationships (KSRs) is therefore an important goal in cell signaling research. Existing consensus sequence motif based prediction algorithms do not consider the biological context of KSRs, and are therefore insensitive to many other mechanisms guiding kinase-substrate recognition in cellular contexts. Here, we use temporal information to identify biologically relevant KSRs from Large-scale In Vivo Experiments (KSR-LIVE) in a data-dependent and automated fashion. First, we used available phosphorylation databases to construct a repository of existing experimentally-predicted KSRs. For each kinase in this database, we used time-resolved phosphoproteomics data to examine how its substrates changed in phosphorylation over time. Although substrates for a particular kinase clustered together, they often exhibited a different temporal pattern to the phosphorylation of the kinase. Therefore, although phosphorylation regulates kinase activity, our findings imply that substrate phosphorylation likely serve as a better proxy for kinase activity than kinase phosphorylation. KSR-LIVE can thereby infer which kinases are regulated within a biological context. Moreover, KSR-LIVE can also be used to automatically generate positive training sets for the subsequent prediction of novel KSRs using machine learning approaches. We demonstrate that this approach can distinguish between Akt and Rps6kb1, two kinases that share the same linear consensus motif, and provide evidence suggesting IRS-1 S265 as a novel Akt site. KSR-LIVE is an open-access algorithm that allows users to dissect phosphorylation signaling within a specific biological context, with the potential to be included in the standard analysis workflow for studying temporal high-throughput signal transduction data.

Highlights from the 11th ISCB Student Council Symposium 2015. Dublin, Ireland. 10 July 2015

A1 Highlights from the eleventh ISCB Student Council Symposium 2015

Katie Wilkins, Mehedi Hassan, Margherita Francescatto, Jakob Jespersen, R. Gonzalo Parra, Bart Cuypers, Dan DeBlasio, Alexander Junge, Anupama Jigisha, Farzana Rahman

O1 Prioritizing a drug’s targets using both gene expression and structural similarity

Griet Laenen, Sander Willems, Lieven Thorrez, Yves Moreau

O2 Organism specific protein-RNA recognition: A computational analysis of protein-RNA complex structures from different organisms

Nagarajan Raju, Sonia Pankaj Chothani, C. Ramakrishnan, Masakazu Sekijima; M. Michael Gromiha

O3 Detection of Heterogeneity in Single Particle Tracking Trajectories

Paddy J Slator, Nigel J Burroughs

O4 3D-NOME: 3D NucleOme Multiscale Engine for data-driven modeling of three-dimensional genome architecture

Przemysław Szałaj, Zhonghui Tang, Paul Michalski, Oskar Luo, Xingwang Li, Yijun Ruan, Dariusz Plewczynski

O5 A novel feature selection method to extract multiple adjacent solutions for viral genomic sequences classification

Giulia Fiscon, Emanuel Weitschek, Massimo Ciccozzi, Paola Bertolazzi, Giovanni Felici

O6 A Systems Biology Compendium for Leishmania donovani

Bart Cuypers, Pieter Meysman, Manu Vanaerschot, Maya Berg, Hideo Imamura, Jean-Claude Dujardin, Kris Laukens

O7 Unravelling signal coordination from large scale phosphorylation kinetic data

Westa Domanova, James R. Krycer, Rima Chaudhuri, Pengyi Yang, Fatemeh Vafaee, Daniel J. Fazakerley, Sean J. Humphrey, David E. James, Zdenka Kuncic

The cytoplasm as a radiation target: an in silico study of microbeam cell irradiation

We performed in silico microbeam cell irradiation modelling to quantitatively investigate ionisations resulting from soft x-ray and alpha particle microbeams targeting the cytoplasm of a realistic cell model. Our results on the spatial distribution of ionisations show that as x-rays are susceptible to scatter within a cell that can lead to ionisations in the nucleus, soft x-ray microbeams may not be suitable for investigating the DNA damage response to radiation targeting the cytoplasm alone. In contrast, ionisations from an ideal alpha microbeam are tightly confined to the cytoplasm, but a realistic alpha microbeam degrades upon interaction with components upstream of the cellular target. Thus it is difficult to completely rule out a contribution from alpha particle hits to the nucleus when investigating DNA damage response to cytoplasmic irradiation. We find that although the cytoplasm targeting efficiency of an alpha microbeam is better than that of a soft x-ray microbeam (the probability of stray alphas hitting the nucleus is 0.2% compared to 3.6% for x-rays), stray alphas produce more ionisations in the nucleus and thus have greater potential for initiating damage responses therein. Our results suggest that observed biological responses to cytoplasmic irradiation include a small component that can be attributed to stray ionisations in the nucleus resulting from the stochastic nature of particle interactions that cause out-of-beam scatter. This contribution is difficult to isolate experimentally, thus demonstrating the value of the in silico approach.