Differential Human Skeletal Muscle Proteome in Insulin Resistance Online Appendix 3. Supplemental Information Methods The complete methods used in these studies are described below. Subjects. Lean nondiabetic control subjects, obese nondiabetics, and patients with type 2 diabetes (n=8 each) were studied. Studies were approved by the Institutional Review Board of the University of Texas Health Science Center at San Antonio and Arizona State University. After giving informed, written consent, each volunteer underwent a medical history, physical examination, screening laboratory tests and a 75 g oral glucose tolerance test. The lean control and obese nondiabetic subjects had no family history of diabetes and had normal glucose tolerance. The participants also were instructed to not depart from their regular diet three days before study, and not to engage in exercise for two days before the study. Participants reported less than two sessions of exercise per week and were considered to be sedentary. Patients with type 2 diabetes were excluded if they were treated with metformin or a thiazolidinedione. If treated with a sulfonylurea, treatment was withheld for three days before the study. Patients treated with insulin had the last insulin dose on the previous day withheld. Following a 10 to 12- h overnight fast, subjects came to the General Clinical Research Center (GCRC) at the University of Texas Health Science Center at San Antonio or Clinical Research Unit (CRU) at Arizona State University for screening tests. Subjects returned to the GCRC or CRU at 8 A.M. after an overnight fast for a 120-min euglycemic-hyperinsulinemic (80 mU·m-2·min-1) insulin clamp study, as described (1; 2). Percutaneous biopsies of the vastus lateralis muscle were taken under local anesthesia under basal conditions, immediately frozen in liquid nitrogen, and stored frozen until they were homogenized as described (3). Gel electrophoresis and mass spectrometry analysis. Muscle lysates proteins (60 µg total protein) were separated on 4-20% linear gradient SDS polyacrylamide gels, and processed for mass spectrometry as described (4; 5) and as outlined in Supplemental Figure 1. Each lane was cut into 20 separate slices. Gel pieces were treated with trypsin to digest proteins, the resulting mixture desalted, and subjected to HPLC-MS/MS. Therefore, since 24 subjects were analyzed, a total of 480 HPLC-MS/MS runs were performed. HPLC-ESI-MS/MS was performed on a hybrid linear ion trap (LTQ)-Fourier Transform Ion Cyclotron Resonance (FTICR) mass spectrometer (LTQ FT, Thermo-Fisher, San Jose, CA) (4; 5). Protein assignment (99% confidence) and gene ontology annotation were performed as described (4; 5). Tandem mass spectra were extracted from Xcalibur “RAW” files and charge states were assigned using the Extract_MSN script (Xcalibur 2.0 SR2, Thermo-Fisher, San Jose, CA). Charge states and monoisotopic peak assignments were verified using DTA-SuperCharge, part of the MSQuant suite of software (msquant.sourceforge.net), before all “DTA” files (corresponding to each gel slice from a single gel lane) were combined into a single Mascot Generic Format (GF) file that represented the data from a single subject. The fragment mass spectra were then searched against the IPI-HUMAN_v3.28 database (68,020 entries, hhtp://www.ebi.ac.uk/IPI) using Mascot (Matrix Science, London, UK; version 2.2). The false positive discovery rate was determined by selecting the option to search the decoy randomized database, and using the two-peptide criterion for protein assignment was found to be less than 1%. The search parameters that were used were: 10 ppm mass tolerance for precursor ion masses and 0.5 Da for product ion masses; digestion with trypsin; a maximum of two missed tryptic cleavages; variable modifications of oxidation of methionine and phosphorylation of serine, threonine and tyrosine. Probability assessments of peptide and protein assignments were made through use of Scaffold (version Scaffold-01_06_19, ©2009 American Diabetes Association. Published online at http://diabetes.diabetesjournals.org/cgi/content/full/db09-00214/DC1 Differential Human Skeletal Muscle Proteome in Insulin Resistance Proteome Software Inc., Portland, OR). Only peptides with > 95% probability, based on Scaffold analysis, were considered. Criteria for protein identification included detection of at least 2 unique peptides and a probability score of > 99%. Multiple isoforms of a protein were reported only if they were differentiated by at least one unique peptide. Proteins that contained identical peptides and could not be differentiated based on MS/MS analysis alone were grouped. The spectra assigned to shared peptides were excluded from quantification using NSAF. Gene Ontology annotation of human proteins was downloaded from Gene Ontology Annotation (GOA) Databases (http://www.ebi.ac.uk/GOA, version 55.0). This GOA human database contains 33,731 distinct proteins and 172661 GO associations. In addition, GO hierarchy information (version: 52) was downloaded from www.geneontology.com. Human GO associations and GO hierarchy information were assembled into a new database by an in-house program written using MATLAB (The Mathworks, Natick, MA). IPI IDs, gene names, UniProt and SwissProt IDs of identified proteins were input into the database to obtain GO associations and GO hierarchy information. Normalized Spectral Abundance Factors. To quantify protein abundance, normalized spectral abundance factors (NSAF) were used (6-8). MS/MS spectra assigned to a protein were normalized to the length of the protein (number of amino acids), resulting in a Spectral Abundance Factor, or SAF, SAF = SpectrumCount NumberAA. Each SAF was normalized against the sum of all SAFs in one sample, resulting in the NSAF value. For a protein, i, the N normalized spectral abundance factor, NSAF, is calculated by NSAFi = SAFi ∑ SAFi , where N i=1 is the total number of proteins detected in a sample. Thus, NSAF values allow for direct comparison of a protein’s abundance between individual runs in a fashion similar to microarray data analysis. Reproducibility and linearity of NSAF method. To determine linearity, varying amounts of BSA tryptic digests (62 to 250 fmoles) were added to 125 fmoles of cytochrome c digest. Mixtures were analyzed by MS/MS. To assess linearity in a “real life”, complex mixture of proteins, muscle biopsy lysates were used. If NSAF provides an accurate reflection of the proportion any particular protein in a complex protein sample, such as a muscle lysate, then the NSAF for any given protein should be equivalent, no matter how much total protein is analyzed. To test this 30, 60 and 120 µg protein from the same muscle lysate were loaded onto a gel in three separate lanes. Proteins were assigned and NSAF values were calculated as described above. NSAF values for each of the proteins that were assigned in common to each of three lanes lane were plotted against each other. If the values were equal, it would be expected that a linear regression performed on these points would have a slope of 1.0 and a Y intercept of 0. To test reproducibility, a muscle biopsy from one subject was divided into three sections. The three biopsy specimens, which theoretically should contain the same proteins in the same proportions, were homogenized, 60 µg of lysate proteins were run in three separate lanes on a 4-20% linear gradient SDS gel, the lanes were cut into slices, the slices were digested with trypsin, digests were analyzed by HPLC-MS/MS, and spectra thus obtained were used to assign proteins and calculate NSAF values. Single protein analysis. Although a large number of proteins were assigned in at least one of 24 subjects who were studied, a series of filters were used to narrow the number of proteins that were used in comparisons among groups. This approach is diagrammed in Figure 1. First, NSAF values for each assigned protein were determined for each member of each group of subjects. In individuals for whom no spectra for a protein were observed, the NSAF value for ©2009 American Diabetes Association. Published online at http://diabetes.diabetesjournals.org/cgi/content/full/db09-00214/DC1 Differential Human Skeletal Muscle Proteome in Insulin Resistance that protein for that subject was set to zero, and zeroes were included in all analysis of single protein abundance differences. The first filter applied was to exclude all proteins from further analysis that were not detected in at least half (12 of 24) of the subjects. For example, a protein found in 8 lean, 3 obese, and 1 diabetic subjects (12 of 24) would be included in the analysis. The next step was to calculate average NSAF values for each group for each protein that was present in at least half the subjects. The second filter applied was for proteins that differed in abundance by a factor of two (increased by two-fold or decreased by half) between the averages of at least one pair-wise comparison (obese vs. lean, diabetic vs. lean, and obese vs. diabetic). Finally, the proteins that were assigned in at least 12 of 24 subjects and were increased in abundance by a factor of two or decreased by one half were subjected to one-way analysis of variance with post hoc t-tests using StatView (SAS Institute, 1998). Because about half of these proteins did not meet the assumption of analysis of variance of homogeneity of variances, a Kruskal-Wallis nonparametric equivalent of analysis of variance also was performed using StatView. The correlation coefficient between P values for the two tests was 0.76 (P < 0.01). Differential patterns of protein abundance among groups (protein sets). Differences in NSAF values for a set of proteins among groups were assessed as follows (Figure 2). First, for each protein in a set, an average NSAF value was calculated using all 24 values (including zeroes) from the three groups of eight.