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. 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.
Insulin resistance (IR) is a complex metabolic disorder that underlies several human diseases, including type 2 diabetes and cardiovascular disease. Despite extensive research, the precise mechanisms underlying IR development remain poorly understood. Here, we provide new insights into the mechanistic connections between cellular alterations associated with IR, including increased ceramides, deficiency of coenzyme Q (CoQ), mitochondrial dysfunction, and oxidative stress. We demonstrate that elevated levels of ceramide in the mitochondria of skeletal muscle cells results in CoQ depletion and loss of mitochondrial respiratory chain components, leading to mitochondrial dysfunction and IR. Further, decreasing mitochondrial ceramide levels in vitro and in animal models (under chow and high fat diet) increased CoQ levels and was protective against IR. CoQ supplementation also rescued ceramide-associated IR. Examination of the mitochondrial proteome from human muscle biopsies revealed a strong correlation between the respirasome system and mitochondrial ceramide as key determinants of insulin sensitivity. Our findings highlight the mitochondrial Ceramide-CoQ-respiratory chain nexus as a potential foundation of an IR pathway that may also play a critical role in other conditions associated with ceramide accumulation and mitochondrial dysfunction, such as heart failure, cancer, and aging. These insights may have important clinical implications for the development of novel therapeutic strategies for the treatment of IR and related metabolic disorders.
Exercise is extremely beneficial to whole body health reducing the risk of a number of chronic human diseases. Some of these physiological benefits appear to be mediated via the secretion of peptide/protein hormones into the blood stream. The plasma peptidome contains the entire complement of low molecular weight endogenous peptides derived from secretion, protease activity and PTMs, and is a rich source of hormones. In the current study we have quantified the effects of intense exercise on the plasma peptidome to identify novel exercise regulated secretory factors in humans. We developed an optimized 2D-LC-MS/MS method and used multiple fragmentation methods including HCD and EThcD to analyze endogenous peptides. This resulted in quantification of 5,548 unique peptides during a time course of exercise and recovery. The plasma peptidome underwent dynamic and large changes during exercise on a time-scale of minutes with many rapidly reversible following exercise cessation. Among acutely regulated peptides, many were known hormones including insulin, glucagon, ghrelin, bradykinin, cholecystokinin and secretogranins validating the method. Prediction of bioactive peptides regulated with exercise identified C-terminal peptides from Transgelins, which were increased in plasma during exercise. In vitro experiments using synthetic peptides identified a role for transgelin peptides on the regulation of cell-cycle, extracellular matrix remodeling and cell migration. We investigated the effects of exercise on the regulation of PTMs and proteolytic processing by building a site-specific network of protease/substrate activity. Collectively, our deep peptidomic analysis of plasma revealed that exercise rapidly modulates the circulation of hundreds of bioactive peptides through a network of proteases and PTMs. These findings illustrate that peptidomics is an ideal method for quantifying changes in circulating factors on a global scale in response to physiological perturbations such as exercise. This will likely be a key method for pinpointing exercise regulated factors that generate health benefits. Exercise is extremely beneficial to whole body health reducing the risk of a number of chronic human diseases. Some of these physiological benefits appear to be mediated via the secretion of peptide/protein hormones into the blood stream. The plasma peptidome contains the entire complement of low molecular weight endogenous peptides derived from secretion, protease activity and PTMs, and is a rich source of hormones. In the current study we have quantified the effects of intense exercise on the plasma peptidome to identify novel exercise regulated secretory factors in humans. We developed an optimized 2D-LC-MS/MS method and used multiple fragmentation methods including HCD and EThcD to analyze endogenous peptides. This resulted in quantification of 5,548 unique peptides during a time course of exercise and recovery. The plasma peptidome underwent dynamic and large changes during exercise on a time-scale of minutes with many rapidly reversible following exercise cessation. Among acutely regulated peptides, many were known hormones including insulin, glucagon, ghrelin, bradykinin, cholecystokinin and secretogranins validating the method. Prediction of bioactive peptides regulated with exercise identified C-terminal peptides from Transgelins, which were increased in plasma during exercise. In vitro experiments using synthetic peptides identified a role for transgelin peptides on the regulation of cell-cycle, extracellular matrix remodeling and cell migration. We investigated the effects of exercise on the regulation of PTMs and proteolytic processing by building a site-specific network of protease/substrate activity. Collectively, our deep peptidomic analysis of plasma revealed that exercise rapidly modulates the circulation of hundreds of bioactive peptides through a network of proteases and PTMs. These findings illustrate that peptidomics is an ideal method for quantifying changes in circulating factors on a global scale in response to physiological perturbations such as exercise. This will likely be a key method for pinpointing exercise regulated factors that generate health benefits. Multicellular organisms have evolved sophisticated mechanisms to enable cell-cell communication. Such mechanisms are fundamental to homeostasis enabling the organism to respond appropriately to the environment. One of the most common methods of communication involves the secretion or release of proteins and peptides from one cell in response to an environmental perturbation. These signals travel via the blood to modulate physiological pathways in other cells and tissues. Thus, the comprehensive measurement of peptides in blood provides a systematic record of this complex interorgan communication system and how it changes under certain conditions. There has been an extensive effort to develop more sensitive and comprehensive methods for quantifying blood borne peptides. Traditional analysis relied on the use of antibodies to measure just one or a handful of factors. These assays have been applied to numerous hormones and are commonplace in clinical diagnostics. More recently, the ability to globally characterize the complete repertoire of endogenous peptides in blood, referred to as the peptidome, has been advanced by the field of mass spectrometry-based proteomics. This is primarily attributed to advances in isolation, separation, fragmentation, quantification and computational analysis of hundreds or thousands of peptides. A variety of methods have been used to isolate the peptidome including molecular weight separation techniques such as size-exclusion chromatography or filtration (1.Secher A. Kelstrup C.D. Conde-Frieboes K.W. Pyke C. Raun K. Wulff B.S. Olsen J.V. Analytic framework for peptidomics applied to large-scale neuropeptide identification.Nat. Commun. 2016; 7: 11436Crossref PubMed Scopus (74) Google Scholar, 2.Shen Y. Liu T. Tolic N. Petritis B.O. Zhao R. Moore R.J. Purvine S.O. Camp D.G. Smith R.D. Strategy for degradomic-peptidomic analysis of human blood plasma.J. Proteome Res. 2010; 9: 2339-2346Crossref PubMed Scopus (39) Google Scholar) or depletion of larger proteins with acid precipitation or organic solvents (3.Aristoteli L.P. Molloy M.P. Baker M.S. Evaluation of endogenous plasma peptide extraction methods for mass spectrometric biomarker discovery.J. Proteome Res. 2007; 6: 571-581Crossref PubMed Scopus (72) Google Scholar, 4.Polson C. Sarkar P. Incledon B. Raguvaran V. Grant R. Optimization of protein precipitation based upon effectiveness of protein removal and ionization effect in liquid chromatography-tandem mass spectrometry.J. Chromatogr. B Analyt. Technol. Biomed. Life Sci. 2003; 785: 263-275Crossref PubMed Scopus (508) Google Scholar). The combination of these extraction techniques with multidimensional liquid chromatography has led to the identification of hundreds of endogenous low molecular weight peptides in plasma (5.Hu L. Boos K.S. Ye M. Wu R. Zou H. Selective on-line serum peptide extraction and multidimensional separation by coupling a restricted-access material-based capillary trap column with nanoliquid chromatography-tandem mass spectrometry.J. Chromatogr. A. 2009; 1216: 5377-5384Crossref PubMed Scopus (53) Google Scholar). This has been coupled to a variety of mass spectrometry platforms and fragmentation approaches (for an extensive review see (6.Boonen K. Landuyt B. Baggerman G. Husson S.J. Huybrechts J. Schoofs L. Peptidomics: the integrated approach of MS, hyphenated techniques and bioinformatics for neuropeptide analysis.J. Sep. Sci. 2008; 31: 427-445Crossref PubMed Scopus (87) Google Scholar)). To identify differentially regulated peptides between two or more states, quantitative peptidomic analysis has been performed using label-free approaches (7.Nanni P. Levander F. Roda G. Caponi A. James P. Roda A. A label-free nano-liquid chromatography-mass spectrometry approach for quantitative serum peptidomics in Crohn's disease patients.J. Chromatogr. B Analyt. Technol. Biomed. Life Sci. 2009; 877: 3127-3136Crossref PubMed Scopus (36) Google Scholar), stable isotope labeling with chemical derivatisation (8.Che F.Y. Fricker L.D. Quantitative peptidomics of mouse pituitary: comparison of different stable isotopic tags.J. Mass Spectrom. 2005; 40: 238-249Crossref PubMed Scopus (98) Google Scholar, 9.Hardt M. Witkowska H.E. Webb S. Thomas L.R. Dixon S.E. Hall S.C. Fisher S.J. Assessing the effects of diurnal variation on the composition of human parotid saliva: quantitative analysis of native peptides using iTRAQ reagents.Anal. Chem. 2005; 77: 4947-4954Crossref PubMed Scopus (145) Google Scholar), or stable isotope labeling with metabolic incorporation (10.Bourdetsky D. Schmelzer C.E. Admon A. The nature and extent of contributions by defective ribosome products to the HLA peptidome.Proc. Natl. Acad. Sci. U.S.A. 2014; 111: E1591-E1599Crossref PubMed Scopus (75) Google Scholar). These quantification strategies have been applied to identify biomarkers for prognosis or diagnosis of disease. This includes analysis of urine for the identification of biomarkers in chronic kidney disease (11.Good D.M. Zurbig P. Argiles A. Bauer H.W. Behrens G. Coon J.J. Dakna M. Decramer S. Delles C. Dominiczak A.F. Ehrich J.H. Eitner F. Fliser D. Frommberger M. Ganser A. Girolami M.A. Golovko I. Gwinner W. Haubitz M. Herget-Rosenthal S. Jankowski J. Jahn H. Jerums G. Julian B.A. Kellmann M. Kliem V. Kolch W. Krolewski A.S. Luppi M. Massy Z. Melter M. Neususs C. Novak J. Peter K. Rossing K. Rupprecht H. Schanstra J.P. Schiffer E. Stolzenburg J.U. Tarnow L. Theodorescu D. Thongboonkerd V. Vanholder R. Weissinger E.M. Mischak H. Schmitt-Kopplin P. Naturally occurring human urinary peptides for use in diagnosis of chronic kidney disease.Mol. Cell. Proteomics. 2010; 9: 2424-2437Abstract Full Text Full Text PDF PubMed Scopus (361) Google Scholar), transplant rejection (12.Ling X.B. Sigdel T.K. Lau K. Ying L. Lau I. Schilling J. Sarwal M.M. Integrative urinary peptidomics in renal transplantation identifies biomarkers for acute rejection.J. Am. Soc. Nephrol. 2010; 21: 646-653Crossref PubMed Scopus (104) Google Scholar), and cardiovascular disease (13.Zimmerli L.U. Schiffer E. Zurbig P. Good D.M. Kellmann M. Mouls L. Pitt A.R. Coon J.J. Schmieder R.E. Peter K.H. Mischak H. Kolch W. Delles C. Dominiczak A.F. Urinary proteomic biomarkers in coronary artery disease.Mol. Cell. Proteomics. 2008; 7: 290-298Abstract Full Text Full Text PDF PubMed Scopus (189) Google Scholar), and validated peptide biomarkers have also been identified for cancer (14.Antwi K. Hostetter G. Demeure M.J. Katchman B.A. Decker G.A. Ruiz Y. Sielaff T.D. Koep L.J. Lake D.F. Analysis of the plasma peptidome from pancreas cancer patients connects a peptide in plasma to overexpression of the parent protein in tumors.J. Proteome Res. 2009; 8: 4722-4731Crossref PubMed Scopus (53) Google Scholar, 15.Bassani-Sternberg M. Barnea E. Beer I. Avivi I. Katz T. Admon A. Soluble plasma HLA peptidome as a potential source for cancer biomarkers.Proc. Natl. Acad. Sci. U.S.A. 2010; 107: 18769-18776Crossref PubMed Scopus (101) Google Scholar) and diabetes (16.Liu F. Zhao C. Liu L. Ding H. Huo R. Shi Z. Peptidome profiling of umbilical cord plasma associated with gestational diabetes-induced fetal macrosomia.J. Proteomics. 2016; 139: 38-44Crossref PubMed Scopus (20) Google Scholar). Despite these advances in peptidomic technologies, further developments are required to increase throughput for clinical analysis to overcome the large dynamic range in plasma. Overcoming these hurdles will facilitate the identification of new peptides with bioactive properties and reveal the temporal regulation and stability of the peptidome. A key feature of the peptidome is it will undergo dynamic and detectable change in response to physiological perturbations and/or disease. The goal of the present study was to determine the influence of exercise on the peptidome. This is important because exercise is a common physiological perturbation that is likely to have a profound impact on the peptidome. Exercise also has many health benefits including improvements in heart function, neurological function and insulin sensitivity and, these effects are thought to be mediated at least in part by the regulation of circulating plasma factors (17.Hawley J.A. Hargreaves M. Joyner M.J. Zierath J.R. Integrative biology of exercise.Cell. 2014; 159: 738-749Abstract Full Text Full Text PDF PubMed Scopus (581) Google Scholar). These factors, including peptide hormones, interleukins and growth factors have profound effects throughout the body. For example, to meet the large amounts of energy essential for exercise, increased oxygen delivery is required. This is achieved by increasing blood flow through the action of a variety of vasodilators. Exercise increases the activity of the kinin-kallikrein system, a protease cascade producing the bradykinin peptide, a potent endogenous vasodilator (18.Hellsten Y. Nyberg M. Jensen L.G. Mortensen S.P. Vasodilator interactions in skeletal muscle blood flow regulation.J. Physiol. 2012; 590: 6297-6305Crossref PubMed Scopus (132) Google Scholar). Therefore, exercise and the inhibition of enzymes that degrade vasodilators are important therapies for the treatment of hypertensive patients. We hypothesize that characterizing the plasma peptidome in response to exercise will allow an investigation of protease regulation and simultaneously identify new signaling factors that contribute to physiological adaptions. Moreover, such an analysis will provide another step forward in validating the use of peptidomics as a useful tool for discovery of both novel regulatory factors and clinical diagnostics. In the present study, we performed a temporal peptidomic analysis of human plasma in response to high-intensity exercise. Using isobaric tagging, multidimensional liquid chromatography and tandem mass spectrometry with complementary fragmentation techniques, we identified 6,652 unique endogenous plasma peptides. Our data reveal the peptidome is rapidly modulated by exercise and involves the coordinated regulation of a network of proteases. The unexplored complexity of the exercise peptidome may reveal important signaling molecules mediating the beneficial effects of exercise. Four healthy male volunteers (age: 26–28; BMI: 23.3–25.8 kg/m2, VO2 max: 41.6–47.3 ml/kg/min, Wmax: 280–295 W) abstained from strenuous exercise for 2 days before the experiment. They reported to the laboratory in the overnight fasted state and rested in the supine position for 30 min. A venous catheter was inserted in a forearm vein and a blood sample was obtained. Venous blood was collected from the forearm and obtained in heparinized syringes and quickly transferred to Eppendorf tubes containing 30 μl 200 mm EDTA/1500 μl blood. Following warm up for 2 min, subjects underwent cycle exercise for 6 min at 77% of individual Wmax and then to exhaustion at 87–88% of Wmax, which occurred after 9–11 min total exercise time following warm up. Blood samples were collected in the last minute of exercise followed by three additional samples taken at 1, 2, and 5 h postexercise. Postexercise, subjects lay fasted in the supine position with access only to water. The study was approved by the regional ethics committee in Denmark (Journal number: H-1-2012-006) and carried out in accordance with the Declaration of Helsinki II. Written informed consent was obtained from each subject. For plasma peptidome isolation comparisons, 100 μl aliquots of the identical plasma was mixed 1:1 with either PBS or Urea Buffer (8 M urea, 20 mm dithiothreitol (DTT) in 50 mm 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acids (HEPES), pH 8.0), and incubated at room temperature for 5 min. The diluted plasma aliquots were mixed with either; (1) 1 volume of 20% trichloroacetic (TCA), (2) 5 volumes of 100% acetonitrile (AcN), or (3) 5 volumes of 100% acetone and incubated for 60 min at 4 °C to precipitate proteins. The precipitate was centrifuged at 16,000 × g for 10 min and the supernatant containing peptides collected. Supernatants from the AcN and acetone precipitations were dried by vacuum centrifugation and resuspended in 5% AcN, 0.1% TFA. Additional 100 μl aliquots of the same plasma sample were diluted with either PBS or Urea Buffer and applied to 10 kDa molecular weight cut-off (MWCO) 1The abbreviations used are: MWCO; molecular weight cut-off; TFA, trifluoracetic acid; FA, formic acid; TMT, tandem mass tags; HILIC, hydrophilic interaction liquid chromatography. filters and centrifuged at 16,000 × g for 30 min at 4 °C. The filters were washed once with either PBS or Urea Buffer by a second centrifugation at 16,000 × g for 30 min and filtrates adjusted to 5% AcN, 0.1% TFA. All peptide preparations were desalted with hydrophilic-lipophilic balance solid-phase extraction (HLB-SPE) columns (Waters, Milford, MA). Peptides were eluted with 50% acetonitrile, 0.1% trifluoroacetic acid (TFA) and dried by vacuum centrifugation. For quantification of exercise plasma samples, 200 μl of plasma was processed with the Urea Buffer and TCA precipitation approach described above. Desalted peptides were resuspended in 40 μl of 100 mm HEPES and adjusted to pH 8.0. Peptides were labeled with 10-plex tandem mass tags (TMT; Thermo Scentific, CA) for 90 min at room temperature, quenched with 2 μl of 5% hydroxylamine and acidified to 2% formic acid (FA). To cover the 20 samples in total (4 subjects x 5 time-points) two TMT 10-plex experiments were performed. Each 10-plex experiment contained two subjects with the following labeling of channels; 126/129N: preexercise, 127N/129C: exercise, 127C/130N: 1h postexercise, 128N/130C: 2h postexercise, 128C/131: 5h postexercise. The peptides from each TMT 10-plex experiment were combined and concentrated by HLB-SPE followed by removal of excess unreacted TMT reagent and fractionation with hydrophilic interaction liquid chromatography (HILIC), as previously described (19.Palmisano G. Lendal S.E. Engholm-Keller K. Leth-Larsen R. Parker B.L. Larsen M.R. Selective enrichment of sialic acid-containing glycopeptides using titanium dioxide chromatography with analysis by HILIC and mass spectrometry.Nat. Protoc. 2010; 5: 1974-1982Crossref PubMed Scopus (203) Google Scholar). L6 myoblasts were harvested by scraping in 8 m guanidine containing 10 mm (Tris(2-carboxyethyl)phosphine) and 40 mm chloroacetamide in 100 mm Tris, pH 7.5 and tip-probe sonicated for 30 s. Lysates were heated to 95 °C for 5 min followed by centrifugation and 20,000 × g for 15 min at 4 °C. Protein containing supernatant was precipitated with 4 volumes of acetone overnight at −30 °C and protein pellets washed with 80% acetone. Protein pellets were resuspension in 100 mm Tris containing 10% trifluoroethanol and digested with trypsin (1:50 enzyme/substrate) overnight at 37 °C with vortexing. Peptides were acidified to a final concentration of 1% TFA and desalted using SDB-RPS microcolumns (3 m Empore, Sigma). Peptides were eluted in 80% acetonitrile, 1% ammonium hydroxide and dried by vacuum centrifugation. For peptidomics analysis, peptides were analyzed on a Dionex 3500RS nanoUHPLC coupled to an Orbitrap Fusion mass spectrometer in positive mode. Peptides were separated using an in-house packed 75 μm × 40 cm pulled column (1.9 μm particle size, C18AQ; Dr Maisch, Germany) with a gradient of 2–30% acetonitrile containing 0.1% FA over 120 min at 250 nl/min at 55 °C. An MS1 scan was acquired from 350–1550 (120,000 resolution, 5e5 AGC, 100 ms injection time) followed by MS/MS data-dependent acquisition with HCD and detection in the Orbitrap (60,000 resolution, 2e5 AGC, 120 ms injection time, 40 NCE, 2.0 m/z quadrupole isolation width) and, EThcD (20.Frese C.K. Altelaar A.F. van den Toorn H. Nolting D. Griep-Raming J. Heck A.J. Mohammed S. Toward full peptide sequence coverage by dual fragmentation combining electron-transfer and higher-energy collision dissociation tandem mass spectrometry.Anal. Chem. 2012; 84: 9668-9673Crossref PubMed Scopus (217) Google Scholar) and detection in the Orbitrap (60,000 resolution, 2e5 AGC, 120 ms injection time, calibrated charge-dependent ETD reaction times (2 + 121 ms; 3 + 54 ms; 4 + 30 ms; 5 + 20ms; 6 + 13 ms; 7+; 10 ms), 25 NCE for HCD supplemental activation, 2.0 m/z quadrupole isolation width). For proteomic analysis, peptides were analyzed on a Easy-nLC 1200 nanoUHPLC coupled to a Q Exactive HF mass spectrometer in positive mode. Peptides were separated using an in-house packed 75 μm × 50 cm pulled column (1.9 μm particle size, C18AQ; Dr Maisch, Germany) with a gradient of 2–30% acetonitrile containing 0.1% FA over 120 min at 300 nl/min at 60 °C. An MS1 scan was acquired from 300–1650 (60,000 resolution, 3e6 AGC, 50 ms injection time) followed by MS/MS data-dependent acquisition with HCD and detection in the Orbitrap (15,000 resolution, 2e5 AGC, 25 ms injection time, 27 NCE, 1.4 m/z quadrupole isolation width). Data were processed with Proteome Discoverer (v2.1) using the Byonic node (v1.0.334) (21.Bern M. Kil Y.J. Becker C. Byonic: advanced peptide and protein identification software.Curr. Protoc. Bioinformatics. 2012; (Chapter 13, Unit 13.20)Crossref PubMed Scopus (389) Google Scholar) or MaxQuant (v1.5.3.30) using Andromeda (22.Tyanova S. Temu T. Cox J. The MaxQuant computational platform for mass spectrometry-based shotgun proteomics.Nat. Protocols. 2016; 11: 2301-2319Crossref PubMed Scopus (1899) Google Scholar) against the UniProt human database containing only the primary accession of an open reading frame without isoforms (January 2016, 20,955 entries). For Byonic analysis, the precursor MS, HCD MS/MS and EThcD MS/MS tolerance were set to 20 ppm with nonspecific enzyme searching. The peptides were searched with oxidation of methionine, C terminus amidation and asparagine N-glycan modification in the NxS/T motif (48 N-glycan monosaccharide compositions) set as variable modification, and TMT tags on peptide N terminus/lysine set as a fixed modification. A precursor isotope off set was enabled (narrow) to account for incorrect precursor monoisotopic reporting (± 1.0 Da). All data were searched as a single batch with PSM and protein FDR set to 1% using a target decoy approach in Byonic. For MaxQuant analysis of the peptidomics data, all settings were default with precursor-ion and product-ion tolerance set to 20 ppm and 0.02 Da, respectively. No enzyme specificity was employed and peptides searched with oxidation of methionine and C terminus amidation set as variable modifications, and TMT tags on peptide N terminus/lysine set as a fixed modification. All data were searched as a single batch with PSM and protein FDR set to 1% using a target decoy approach. For MaxQuant analysis of proteomics data, all settings were default with precursor-ion and product-ion tolerance set to 20 ppm and 0.02 Da, respectively. Full trypsin specificity was employed with a maximum of 2-missed cleavages and peptides searched with oxidation of methionine and acetylation on protein N terminus set as variable modifications, and carbamidomethylation of cysteine set as fixed modification. All data was searched as a single batch with PSM and protein FDR set to 1% using a target decoy approach. The match between runs and label-free quantification (MaxLFQ) options were selected (23.Cox J. Hein M.Y. Luber C.A. Paron I. Nagaraj N. Mann M. Accurate proteome-wide label-free quantification by delayed normalization and maximal peptide ratio extraction, termed MaxLFQ.Mol. Cell. Proteomics. 2014; 13: 2513-2526Abstract Full Text Full Text PDF PubMed Scopus (2710) Google Scholar). Quantification of peptidomics data was performed with either the precursor area detector node for LFQ, or reporter ion quantification node for TMT quantification in Proteome Discoverer. For LFQ, extracted ion chromatograms were generated at 2 ppm, and for TMT precision was set to 10 ppm and corrected for isotopic impurities. Only spectra with <50% coisolation interference were used for quantification with an average signal-to-noise filter of >10. Statistical analysis including the determination of differentially regulated peptides and enrichment analysis, and visualization including heat maps were performed in Perseus (v1.5.3.0) (24.Tyanova S. Temu T. Sinitcyn P. Carlson A. Hein M.Y. Geiger T. Mann M. Cox J. The Perseus computational platform for comprehensive analysis of (prote)omics data.Nat. Methods. 2016; 13: 731-740Crossref PubMed Scopus (3574) Google Scholar). Fuzzy c-means clustering (five to nine clusters) was performed in GPROX (25.Rigbolt K.T. Vanselow J.T. Blagoev B. GProX, a user-friendly platform for bioinformatics analysis and visualization of quantitative proteomics data.Mol. Cell. Proteomics. 2011; 10Abstract Full Text Full Text PDF PubMed Scopus (123) Google Scholar) using 100 iterations and a fuzzification factor of 2.0. Transgelin 1: MGSNRGASQAGMTGYGRPRQIIS (TAGLN), Transgelin 2: MGTNRGASQAGMTGYGMPRQIL (TAGLN2), and scramble Transgelin 1 sequence as a negative control: RGMINGRMIQSTGSYPSARGQAG (control) were prepared at 0.1 mmol scale using Fmoc solid-phase peptide synthesis (SPPS) on Wang resin using standard side chain protecting groups. The negative control for the cell migration experiments was based on the Transgelin 1 sequence and randomized using the ExPASy RandSeq tool (Random protein sequence generator, http://web.expasy.org/randseq/). The first residue was loaded to the Wang resin using a 3-fold molar excess of Fmoc-amino acid and HBTU (113.7 mg, 0.3 mmol) with a catalytic amount of DMAP (0.1 equiv, 1.2 mg, 0.01 mmol) in DMF (0.6 ml). After overnight loading the resin was capped with a 1vol.% acetic anhydride, 2vol.% N,N,-diisopropylethylamine solution in DMF (1 ml) for 10 min, then the extent of the first residue loading was determined from the combined pool (8 ml) of 2 × 15 min treatments with 20% piperidine in DMF; 50 μl of the pool was diluted to 1 ml and the UV absorbance of the piperidine-fulvine (λ = 301 nm, ε = 7800 m-1 cm-1) adduct determined. Elongation of the target peptides was performed on a CEM Liberty Blue automated microwave peptide synthesizer (USA, NC) using a 4 min coupling cycle (2 min coupling (90 °C), 1 min deprotection (90 °C), with the additional 1 min for associated washes and liquid handling), and a 5-fold excess of Fmoc amino acid, Oxyma and DIC as recommended by the manufacturer. Peptides were cleaved from the resin and deprotected with a TFA cleavage solution (TFA/TIPS/H2O/DODT, 92.5:2.5:2.5:2.5, 5 ml per 100 mg of resin) for 2 h at room temperature. A mobile phase of 0.1% TFA in water (Solvent A) and 0.1% TFA in acetonitrile (Solvent B) was used in all cases. Preparative reverse-phase HPLC was performed using a Waters 2535 Quaternary gradient Module with a Waters 2489 UV/Vis detector operating at 230 and 254 nm. Transgelin peptides were purified on a 19 mm x 150 mm Waters Sunfire column (C18 OBD, 5 μm particle size) at a flow rate of 20 ml/min. Transgelin 1 was purified using a linear 0–30% buffer B gradient over 30 min whereas transgelin 2 was purified using a linear 3–35% buffer B gradient over 30 min, and for the negative control peptide a linear 10–40% buffer B gradient over 40 min. The identity of the peptides was confirmed by LC-MS on a Shimadzu UPLC-MS 2020 instrument consisting of LC-M20A pumps and a SPD-M30A diode array detector with a Shimadzu 2020 mass spectrometer (ESI) operating in positive mode (0.1% formic acid); Transgelin 1, Calculated Mass (M+2H)2+: 1198.1 (100%), (M+3H)3+: 799.1 (100%), (M+4H)4+: 599.6; Observed Mass (ESI+) (M+2H)2+: 1199.4 (100%), (M+3H)3+: 799.8 (100%), (M+4H)4+: 600.0 (100%). Transgelin 2, Calculated Mass (M+2H)2+: 1149.1 (100%), (M+3H)3+: 766.4 (100%), (M+4H)4+: 575.0; Observed Mass (ESI+) (M+2H)2+: 1150.3 (100%), (M+3H)3+: 767.0 (100%), (M+4H)4+: 575.4 (100%), negative control peptide, calculated Mass (M+2H)2+: 1198.1 (100%), (M+3H)3+: 799.1 (100%), (M+4H)4+: 599.6; Observed Mass (ESI+) (M+2H)2+: 1198.6 (100%), (M+3H)3+: 799.5 (100%), (M+4H)4+: 599.8 (100%). The purity of the purified peptides was assessed on a Waters Acquity UPLC system and on a Waters Acquity C18 BEH 1.7 μm 2.1 × 50 mm column (0–50% B, 5 min at 0.6 ml/min). Yields for the final peptides were: Transgelin 1, 4.6 mg, overall yield 11% (purity ≥98%); Transgelin 2, 1.6 mg, overall yield 4% (purity ≥98%); negative control peptide, 7.2 mg, overall yield 24% (purity ≥98%). L6 myoblasts were cultured in alpha-MEM containing 10% fetal calf serum (FCS) to a maximum of 80% confluency before being split every 2–3 days. Cells were treated for 7 d with either a synthetic C-terminal peptide from TAGLN spanning amino acids 179–200, or a synthetic C-terminal peptide from TAGLN2 spanning amino acids 178–199. The peptides were prepared at 10 mg/ml in PBS and diluted 1:10,000 to a final concentration of 1 μg/ml in alpha-MEM containing 10% FCS. Peptides were present for the duration of the experiment with media replaced every 2–3 d. For proteomics experiments, additional control cells were cultured throughout the duration of the experiment and treated only with PBS at a 1:10,000 dilution. For microscopy experiments, additional control cells were treated with a synthetic scramble control peptide prepared at 10 mg/ml in PBS and diluted 1:10,000 to a final concentration of (1 μg/ml in alpha-MEM containing 10% FCS). Cells were trypsinized and seeded into each well of an ibidi Culture-Insert 2 Well silicone insert in a 35-mm tissue culture treated μ-dish. Following a 4 h recovery, the inserts were removed and the dishes placed into an incubation chamber (37 °C, 10% CO2; Okolabs) on a Nikon TiE inverted microscope. The 500 μm wide wound created by removal of the insert was then
For the body to work properly, cells must constantly ‘talk’ to each other using signalling molecules. Receiving a chemical signal triggers a series of molecular events in a cell, a so-called ‘signal transduction pathway’ that connects a signal with a precise outcome. Disturbing cell signalling can trigger disease, and strict control mechanisms are therefore in place to ensure that communication does not break down or become erratic. For instance, just as a thermostat turns off the heater once the right temperature is reached, negative feedback mechanisms in cells switch off signal transduction pathways when the desired outcome has been achieved. The hormone insulin is a signal for growth that increases in the body following a meal to promote the storage of excess blood glucose (sugar) in muscle and fat cells. The hormone binds to insulin receptors at the cell surface and switches on a signal transduction pathway that makes the cell take up glucose from the bloodstream. If the signal is not engaged diseases such as diabetes develop. Conversely, if the signal cannot be adequately switched of cancer can develop. Determining exactly how insulin works would help to understand these diseases better and to develop new treatments. Kearney et al. therefore set out to examine the biochemical ‘fail-safes’ that control insulin signalling. Experiments using computer simulations of the insulin signalling pathway revealed a potential new mechanism for negative feedback, which centred on a molecule known as Akt. The models predicted that if the negative feedback were removed, then Akt would become hyperactive and accumulate at the cell’s surface after stimulation with insulin. Further manipulation of the ‘virtual’ insulin signalling pathway and studies of live cells in culture confirmed that this was indeed the case. The cell biology experiments also showed how Akt, once at the cell surface, was able to engage the negative feedback and shut down further insulin signalling. Akt did this by inactivating a protein required to pass the signal from the insulin receptor to the rest of the cell. Overall, this work helps to understand cell communication by revealing a previously unknown, and critical component of the insulin signalling pathway.
Regulated GLUT4 trafficking is a key action of insulin. Quantitative stepwise analysis of this process provides a powerful tool for pinpointing regulatory nodes that contribute to insulin regulation and insulin resistance. We describe a novel GLUT4 construct and workflow for the streamlined dissection of GLUT4 trafficking; from simple high throughput screens to high resolution analyses of individual vesicles. We reveal single cell heterogeneity in insulin action highlighting the utility of this approach - each cell displayed a unique and highly reproducible insulin response, implying that each cell is hard-wired to produce a specific output in response to a given stimulus. These data highlight that the response of a cell population to insulin is underpinned by extensive heterogeneity at the single cell level. This heterogeneity is pre-programmed within each cell and is not the result of intracellular stochastic events.
Abstract The failure of metabolic tissues to appropriately respond to insulin (“insulin resistance”) is an early marker in the pathogenesis of type 2 diabetes. Protein phosphorylation is central to the adipocyte insulin response, but how adipocyte signaling networks are dysregulated upon insulin resistance is unknown. Here we employed phosphoproteomics to delineate insulin signal transduction in adipocyte cells and adipose tissue. Across a range of insults triggering insulin resistance, we observed marked rewiring of the insulin signaling network. This included both attenuated insulin-responsive phosphorylation, and the emergence of phosphorylation uniquely insulin-regulated in insulin resistance. Identifying signaling changes common to multiple insults revealed subnetworks likely containing causal drivers of insulin resistance. Focusing on defective GSK3 signaling initially observed in a relatively small subset of well-characterized substrates, we employed a pipeline for identifying context-specific kinase substrates. This facilitated robust identification of widespread dysregulated GSK3 signaling. Pharmacological inhibition of GSK3 partially reversed insulin resistance in cells and tissue explants. These data highlight that insulin resistance is a multi-nodal signaling defect that encompasses dysregulated GSK3 activity.
Insulin resistance (IR) is a complex metabolic disorder that underlies several human diseases, including type 2 diabetes and cardiovascular disease. Despite extensive research, the precise mechanisms underlying IR development remain poorly understood. Here, we provide new insights into the mechanistic connections between cellular alterations associated with IR, including increased ceramides, deficiency of coenzyme Q (CoQ), mitochondrial dysfunction, and oxidative stress. We demonstrate that elevated levels of ceramide in the mitochondria of skeletal muscle cells results in CoQ depletion and loss of mitochondrial respiratory chain components, leading to mitochondrial dysfunction and IR. Further, decreasing mitochondrial ceramide levels in vitro and in animal models increased CoQ levels and was protective against IR. CoQ supplementation also rescued ceramide-associated IR. Examination of the mitochondrial proteome from human muscle biopsies revealed a strong correlation between the respirasome system and mitochondrial ceramide as key determinants of insulin sensitivity. Our findings highlight the mitochondrial Ceramide-CoQ-respiratory chain nexus as a potential foundation of an IR pathway that may also play a critical role in other conditions associated with ceramide accumulation and mitochondrial dysfunction, such as heart failure, cancer, and aging. These insights may have important clinical implications for the development of novel therapeutic strategies for the treatment of IR and related metabolic disorders.
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