In Silico Nanodosimetry: New Insights into Nontargeted Biological Responses to Radiation

The long-held view that radiation-induced biological damage must be initiated in the cell nucleus, either on or near DNA itself, is being confronted by mounting evidence to suggest otherwise. While the efficacy of cell death may be determined by radiation damage to nuclear DNA, a plethora of less deterministic biological responses has been observed when DNA is not targeted. These so-called nontargeted responses cannot be understood in the framework of DNA-centric radiobiological models; what is needed are new physically motivated models that address the damage-sensing signalling pathways triggered by the production of reactive free radicals. To this end, we have conducted a series of in silico experiments aimed at elucidating the underlying physical processes responsible for nontargeted biological responses to radiation. Our simulation studies implement new results on very low-energy electromagnetic interactions in liquid water (applicable down to nanoscales) and we also consider a realistic simulation of extranuclear microbeam irradiation of a cell. Our results support the idea that organelles with important functional roles, such as mitochondria and lysosomes, as well as membranes, are viable targets for ionizations and excitations, and their chemical composition and density are critical to determining the free radical yield and ensuing biological responses

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

Unraveling the regulation of phosphorylation in insulin singaling from temporal large-scale phosphoproteomics

Homeostasis is essential for normal function of the mammalian body. On a cellular scale biological processes are tightly controlled by dynamic intracellular signalling mechanisms. Cells use intricate signalling networks to respond to environmental cues and appropriately regulate functions such as differentiation, metabolism and proliferation. Deregulated signalling can result in complex diseases such as cancer, neurodegenerative diseases and cancer. Signals inside cells are transmitted via protein phosphorylation. Protein phosphorylation is a reversible chemical modification where a phosphate group is attached to a protein by enzymes (kinases). Phosphatases are responsible for the reverse reaction. 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. The improvement of the technique has also recently allowed us to measure phosphorylation over time on a large scale. The analysis on these temporal datasets did not differ from the analysis applied to static datasets. However, temporal data offers more possibilities for knowledge discovery and more intricate analysis methods can be applied to interrogate the time course data. This work focuses on the development and application of analysis techniques for large-scale temporal phosphoproteomics data. Using these techniques it is possible to identify kinases that are involved in a signalling process, it is possible to predict which phosphorylation sites are important and it is possible to find gaps in the knowledge of a signalling network. The results from this work will direct further experimental studies. Taken together, the analysis techniques applied in this work can help our understanding of intracellular signalling and this in turn can facilitate drug discover

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

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

[This corrects the article DOI: 10.1371/journal.pone.0157763.]

mTORC1 Is a Major Regulatory Node in the FGF21 Signaling Network in Adipocytes

FGF21 improves the metabolic profile of obese animals through its actions on adipocytes. To elucidate the signaling network responsible for mediating these effects, we quantified dynamic changes in the adipocyte phosphoproteome following acute exposure to FGF21. FGF21 regulated a network of 821 phosphosites on 542 proteins. A major FGF21-regulated signaling node was mTORC1/S6K. In contrast to insulin, FGF21 activated mTORC1 via MAPK rather than through the canonical PI3K/AKT pathway. Activation of mTORC1/S6K by FGF21 was surprising because this is thought to contribute to deleterious metabolic effects such as obesity and insulin resistance. Rather, mTORC1 mediated many of the beneficial actions of FGF21 in vitro, including UCP1 and FGF21 induction, increased adiponectin secretion, and enhanced glucose uptake without any adverse effects on insulin action. This study provides a global view of FGF21 signaling and suggests that mTORC1 may act to facilitate FGF21-mediated health benefits in vivo

Overview of KSR-LIVE.

<p>A) Flowchart of clustering procedure. Substrates for a kinase (for example Akt) are extracted from the KSR knowledgebase and can either be exclusive (blue) or not (pink). In the first step tight clustering is performed on exclusive substrates and core substrates (purple) identified. In the second step tight clustering is performed using all substrates and the characteristic temporal activity of a kinase is identified. B) Heatmap of scaled log fold change of the characteristic temporal activity of 9 kinases over time. High log fold change is represented in red, low log fold change is shown in blue C) Table showing the time points included in the accuracy analysis and the accuracy of using a database or KSR-LIVE for Akt and mTOR.</p

Validation of IRS1 S265 as an AKT substrate.

<p>A) Comparison of AKT and RPS6KB1 consensus motif and IRS1 S265 site. B) CTA of AKT (green) and RPS6KB1 (purple) and time profile of IRS1 S265 (blue). (CTA is depicted with mean ± SD) C) Scatter plot of RPS6KB1 prediction scores (y-axis) against RPS6KB1 prediction score—AKT prediction score (x-axis). AKT training substrates are shown in red and RPS6KB1 training substrates are shown in blue. IRS1 S265 is shown in green. D) Insulin signaling via AKT and RPS6KB1. See main text for details. E) 3T3-L1 adipocytes were stimulated with insulin alone or in the presence of inhibitors of AKT (MK, GDC) or mTORC1 (Rapa), after which AKT and RPS6KB1 signaling were assessed by Western blotting. Blots shown are representative of 3 separate experiments. F) Quantification of IRS1 S265 phosphorylation from (E), depicted as mean ± SEM.</p

Scaled log fold change over time of kinase (shown in blue) and the corresponding CTA (shown in red, mean ± SD) for multiple kinases.

<p>Scaled log fold change over time of kinase (shown in blue) and the corresponding CTA (shown in red, mean ± SD) for multiple kinases.</p

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\ua0specific glycolytic branch points and pyruvate anaplerosis. Integrating dynamic metabolomics and\ua0phosphoproteomics data revealed that insulin-dependent phosphorylation of anabolic enzymes occurred prior to substrate accumulation. Indeed, glycogen synthesis was activated independently of\ua0glucose 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