High throughput protein-protein interaction data: clues for the architecture of protein complexes

<p>Abstract</p> <p>Background</p> <p>High-throughput techniques are becoming widely used to study protein-protein interactions and protein complexes on a proteome-wide scale. Here we have explored the potential of these techniques to accurately determine the constituent proteins of complexes and their architecture within the complex.</p> <p>Results</p> <p>Two-dimensional representations of the 19S and 20S proteasome, mediator, and SAGA complexes were generated and overlaid with high quality pairwise interaction data, core-module-attachment classifications from affinity purifications of complexes and predicted domain-domain interactions. Pairwise interaction data could accurately determine the members of each complex, but was unexpectedly poor at deciphering the topology of proteins in complexes. Core and module data from affinity purification studies were less useful for accurately defining the member proteins of these complexes. However, these data gave strong information on the spatial proximity of many proteins. Predicted domain-domain interactions provided some insight into the topology of proteins within complexes, but was affected by a lack of available structural data for the co-activator complexes and the presence of shared domains in paralogous proteins.</p> <p>Conclusion</p> <p>The constituent proteins of complexes are likely to be determined with accuracy by combining data from high-throughput techniques. The topology of some proteins in the complexes will be able to be clearly inferred. We finally suggest strategies that can be employed to use high throughput interaction data to define the membership and understand the architecture of proteins in novel complexes.</p

A modified gas-trapping method for high-throughput metabolic experiments in Drosophila melanogaster

Metabolism is often studied in animal models, with the Drosophila melanogaster fruit fly model offering ease of genetic manipulation and high-throughput studies. Fly metabolism is typically studied using end-point assays that are simple but destructive, and do not provide information on the utilization of specific nutrients. To address these limitations, we adapted existing gas-trapping protocols to measure the oxidation of radiolabeled substrates (such as glucose) in multi-well plates. This protocol is cost effective, simple, and offers precise control over experimental diet and measurement time, thus being amenable to high-throughput studies. Furthermore, it is nondestructive, enabling time-course experiments and multiplexing with other parameters. Overall, this protocol is useful for merging fly genetics with metabolic studies to understand whole organism responses to different macronutrients

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

The role of the Niemann-Pick disease, type C1 protein in adipocyte insulin action.

The Niemann-Pick disease, type C1 (NPC1) gene encodes a transmembrane protein involved in cholesterol efflux from the lysosome. SNPs within NPC1 have been associated with obesity and type 2 diabetes, and mice heterozygous or null for NPC1 are insulin resistant. However, the molecular mechanism underpinning this association is currently undefined. This study aimed to investigate the effects of inhibiting NPC1 function on insulin action in adipocytes. Both pharmacological and genetic inhibition of NPC1 impaired insulin action. This impairment was evident at the level of insulin signalling and insulin-mediated glucose transport in the short term and decreased GLUT4 expression due to reduced liver X receptor (LXR) transcriptional activity in the long-term. These data show that cholesterol homeostasis through NPC1 plays a crucial role in maintaining insulin action at multiple levels in adipocytes

Sterol regulatory element binding protein-1 (SREBP1) gene expression is similarly increased in polycystic ovary syndrome and endometrial cancer

Introduction: Women with polycystic ovary syndrome (PCOS) have a 3-fold higher risk of endometrial cancer (EC). Insulin resistance and hyperlipidaemia may be pertinent factors in the pathogenesis of both conditions. The aim of this study was to investigate endometrial Sterol Regulatory Element Binding Protein-1 gene (SREBP1) expression in PCOS and EC endometrium, and to correlate endometrial SREBP1 expression with serum lipid profiles. Material and methods: A cross-sectional study was performed at Nottingham University Hospital, United Kingdom. A total of 102 women (PCOS, EC and controls; 34 participants in each group) were recruited. Clinical and biochemical assessments were performed before endometrial biopsies were obtained from all participants. Taqman real-time PCR for endometrial SREBP1 and its systemic protein expression were analysed. Results: The BMI of women with PCOS (29.28 (±2.91) kg/m2) and controls (28.58 (±2.62) kg/m2) was not significantly different. Women with EC had a higher mean BMI (32.22 (±5.70) kg/m2). SREBP1 gene expression was significantly increased in PCOS and EC endometrium compared to controls (p<0.0001). SREBP1 gene expression was positively correlated with BMI (r=0.017, p=0.921) and waist-hip ratio (r=0.023, p=0.544) in PCOS, but this was not statistically significant. Similarly, statistically insignificant positive correlations were found between endometrial SREBP1 gene expression and BMI in EC (r=0.643, p=0.06) and waist-hip ratio (r=0.096, p=0.073). SREBP1 expression was significantly positively correlated with triglyceride in both PCOS and EC (p= 0.028 and p=0.027). Quantitative serum SREBP1 correlated with endometrial gene expression (p<0.05). Conclusions: SREBP1 gene expression is significantly increased in the endometrium of PCOS and EC women compared with controls and positively correlates with serum triglyceride in both PCOS and EC

The transcriptional response to oxidative stress is part of, but not sufficient for, insulin resistance in adipocytes.

Insulin resistance is a major risk factor for metabolic diseases such as Type 2 diabetes. Although the underlying mechanisms of insulin resistance remain elusive, oxidative stress is a unifying driver by which numerous extrinsic signals and cellular stresses trigger insulin resistance. Consequently, we sought to understand the cellular response to oxidative stress and its role in insulin resistance. Using cultured 3T3-L1 adipocytes, we established a model of physiologically-derived oxidative stress by inhibiting the cycling of glutathione and thioredoxin, which induced insulin resistance as measured by impaired insulin-stimulated 2-deoxyglucose uptake. Using time-resolved transcriptomics, we found > 2000 genes differentially-expressed over 24 hours, with specific metabolic and signalling pathways enriched at different times. We explored this coordination using a knowledge-based hierarchical-clustering approach to generate a temporal transcriptional cascade and identify key transcription factors responding to oxidative stress. This response shared many similarities with changes observed in distinct insulin resistance models. However, an anti-oxidant reversed insulin resistance phenotypically but not transcriptionally, implying that the transcriptional response to oxidative stress is insufficient for insulin resistance. This suggests that the primary site by which oxidative stress impairs insulin action occurs post-transcriptionally, warranting a multi-level 'trans-omic' approach when studying time-resolved responses to cellular perturbations

Mitochondrial oxidative stress causes insulin resistance without disrupting oxidative phosphorylation.

Mitochondrial oxidative stress, mitochondrial dysfunction, or both have been implicated in insulin resistance. However, disentangling the individual roles of these processes in insulin resistance has been difficult because they often occur in tandem, and tools that selectively increase oxidant production without impairing mitochondrial respiration have been lacking. Using the dimer/monomer status of peroxiredoxin isoforms as an indicator of compartmental hydrogen peroxide burden, we provide evidence that oxidative stress is localized to mitochondria in insulin-resistant 3T3-L1 adipocytes and adipose tissue from mice. To dissociate oxidative stress from impaired oxidative phosphorylation and study whether mitochondrial oxidative stress per se can cause insulin resistance, we used mitochondria-targeted paraquat (MitoPQ) to generate superoxide within mitochondria without directly disrupting the respiratory chain. At ≤10 μm, MitoPQ specifically increased mitochondrial superoxide and hydrogen peroxide without altering mitochondrial respiration in intact cells. Under these conditions, MitoPQ impaired insulin-stimulated glucose uptake and glucose transporter 4 (GLUT4) translocation to the plasma membrane in both adipocytes and myotubes. MitoPQ recapitulated many features of insulin resistance found in other experimental models, including increased oxidants in mitochondria but not cytosol; a more profound effect on glucose transport than on other insulin-regulated processes, such as protein synthesis and lipolysis; an absence of overt defects in insulin signaling; and defective insulin- but not AMP-activated protein kinase (AMPK)-regulated GLUT4 translocation. We conclude that elevated mitochondrial oxidants rapidly impair insulin-regulated GLUT4 translocation and significantly contribute to insulin resistance and that MitoPQ is an ideal tool for studying the link between mitochondrial oxidative stress and regulated GLUT4 trafficking

Acute mTOR inhibition induces insulin resistance and alters substrate utilization in vivo.

The effect of acute inhibition of both mTORC1 and mTORC2 on metabolism is unknown. A single injection of the mTOR kinase inhibitor, AZD8055, induced a transient, yet marked increase in fat oxidation and insulin resistance in mice, whereas the mTORC1 inhibitor rapamycin had no effect. AZD8055, but not rapamycin reduced insulin-stimulated glucose uptake into incubated muscles, despite normal GLUT4 translocation in muscle cells. AZD8055 inhibited glycolysis in MEF cells. Abrogation of mTORC2 activity by SIN1 deletion impaired glycolysis and AZD8055 had no effect in SIN1 KO MEFs. Re-expression of wildtype SIN1 rescued glycolysis. Glucose intolerance following AZD8055 administration was absent in mice lacking the mTORC2 subunit Rictor in muscle, and in vivo glucose uptake into Rictor-deficient muscle was reduced despite normal Akt activity. Taken together, acute mTOR inhibition is detrimental to glucose homeostasis in part by blocking muscle mTORC2, indicating its importance in muscle metabolism in vivo

Proteomic Analysis of GLUT4 Storage Vesicles Reveals Tumor Suppressor Candidate 5 (TUSC5) as a Novel Regulator of Insulin Action in Adipocytes.

Insulin signaling augments glucose transport by regulating glucose transporter 4 (GLUT4) trafficking from specialized intracellular compartments, termed GLUT4 storage vesicles (GSVs), to the plasma membrane. Proteomic analysis of GSVs by mass spectrometry revealed enrichment of 59 proteins in these vesicles. We measured reduced abundance of 23 of these proteins following insulin stimulation and assigned these as high confidence GSV proteins. These included established GSV proteins such as GLUT4 and insulin-responsive aminopeptidase, as well as six proteins not previously reported to be localized to GSVs. Tumor suppressor candidate 5 (TUSC5) was shown to be a novel GSV protein that underwent a 3.7-fold increase in abundance at the plasma membrane in response to insulin. siRNA-mediated knockdown of TUSC5 decreased insulin-stimulated glucose uptake, although overexpression of TUSC5 had the opposite effect, implicating TUSC5 as a positive regulator of insulin-stimulated glucose transport in adipocytes. Incubation of adipocytes with TNFα caused insulin resistance and a concomitant reduction in TUSC5. Consistent with previous studies, peroxisome proliferator-activated receptor (PPAR) γ agonism reversed TNFα-induced insulin resistance. TUSC5 expression was necessary but insufficient for PPARγ-mediated reversal of insulin resistance. These findings functionally link TUSC5 to GLUT4 trafficking, insulin action, insulin resistance, and PPARγ action in the adipocyte. Further studies are required to establish the exact role of TUSC5 in adipocytes