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HEP_23250_sm_SupProteinTable.doc1148KWe conducted a label free quantitative shotgun proteomic approach on a Q-Tof mass spectrometer (Q-Tof Premiere, Waters Corporation), based on nano ultra performance liquid chromatography (nUPLC) coupled to MSE, to identify proteins linked to the steatosis phenotype in Timp3−/− mice that could be targets of TACE. The use of chromatographic columns with smaller particle size as well as LC pumps with higher pressure limits and nano-flow deliver capacity allowed improved chromatography performance with high reproducibility. Mass spectrometry data was acquired in data independent parallel parent and fragment ion analysis MSE (Expression mode) with no ion transmission window applied with the first mass analyzer prior to collision induced disassociation. Sequential low and high collision energy data acquisition permitted the collection of precursor ions and fragmentation data in the same chromatographic run. The processing of these two data functions, low energy and elevated energy, plus data of the reference lock mass, provides a time-aligned inventory of accurate mass-retention time components for both the low and elevated-energy (EMRT, exact mass retention time). The deconvolution and the correlation of product to precursor ions is achieved by a 3D peak detection algorithm (ProteinLynx Global Server, PLGS, Waters Corp.). The subsequent applied PLGS database searching algorithm (ion accounting) for qualitative identification of proteins is based on the measure of retention time, ion intensities, charge state and accurate masses of both precursor and product ions. The strategy is based upon hierarchical tentative peptide and protein identifications ranked and scored by their relative correlation to a number of models of known and empirically derived physicochemical attributes of proteins and peptides. In addition, it utilizes decoy database techniques for automatically determining the false positive identification rates. The following strategy for quantifying proteome profile data for differential expression analysis relies on changes in the peptide analyte signal response from each EMRT component that directly reflect their concentrations in one sample relative to another. Expression Analysis (PLGS) identifies and extracts pairs of labeled masses, computes their relative abundance, normalizes the intensity measurements of all the detected EMRT from each injection to a set of exogenous EMRT (internal standard added to samples prior to chromatographic LC-MSE runs), and indicates whether they are upregulated or downregulated (1-5).
HEP_23250_sm_SupData.doc38KSupplementary Data
HEP_23250_sm_SupMethods.doc32KSupplemental Methods
HEP_23250_sm_SupPeptideTable.doc4340KWe conducted a label free quantitative shotgun proteomic approach on a Q-Tof mass spectrometer (Q-Tof Premiere, Waters Corporation), based on nano ultra performance liquid chromatography (nUPLC) coupled to MSE, to identify proteins linked to the steatosis phenotype in Timp3−/− mice that could be targets of TACE. The use of chromatographic columns with smaller particle size as well as LC pumps with higher pressure limits and nano-flow deliver capacity allowed improved chromatography performance with high reproducibility. Mass spectrometry data was acquired in data independent parallel parent and fragment ion analysis MSE (Expression mode) with no ion transmission window applied with the first mass analyzer prior to collision induced disassociation. Sequential low and high collision energy data acquisition permitted the collection of precursor ions and fragmentation data in the same chromatographic run. The processing of these two data functions, low energy and elevated energy, plus data of the reference lock mass, provides a time-aligned inventory of accurate mass-retention time components for both the low and elevated-energy (EMRT, exact mass retention time). The deconvolution and the correlation of product to precursor ions is achieved by a 3D peak detection algorithm (ProteinLynx Global Server, PLGS, Waters Corp.). The subsequent applied PLGS database searching algorithm (ion accounting) for qualitative identification of proteins is based on the measure of retention time, ion intensities, charge state and accurate masses of both precursor and product ions. The strategy is based upon hierarchical tentative peptide and protein identifications ranked and scored by their relative correlation to a number of models of known and empirically derived physicochemical attributes of proteins and peptides. In addition, it utilizes decoy database techniques for automatically determining the false positive identification rates. The following strategy for quantifying proteome profile data for differential expression analysis relies on changes in the peptide analyte signal response from each EMRT component that directly reflect their concentrations in one sample relative to another. Expression Analysis (PLGS) identifies and extracts pairs of labeled masses, computes their relative abundance, normalizes the intensity measurements of all the detected EMRT from each injection to a set of exogenous EMRT (internal standard added to samples prior to chromatographic LC-MSE runs), and indicates whether they are upregulated or downregulated (1-5).
HEP_23250_sm_SupTab1.doc31KSupplemental Table 1. Disease processes most significant to proteins differentially expressed in WT versus Timp3 −/− mice fed a HFD. Gene products from the dataset were associated with diseases in the IPKB and were considered for the IPA Functional analysis. Fischer's exact test was used to calculate a p-value determining the probability that each disease assigned to the data set is due to chance alone.
HEP_23250_sm_SupText.doc45KSupplemental data
HEP_23250_sm_SupFig1.tif974KSupplemental Fig. 1. Insulin Receptor phosphorylation is modulated by TACE. SV40-transformed hepatocytes were infected with adenovirus encoding GFP or GFP-TACE; after 3 days from infection cells were serum starved overnight and stimulated with insulin at the indicated concentrations and time. Cell lysates were western blotted for TACE, phospho-Insrβ and Insrβ. Representative image of two independent experiments with similar results.
HEP_23250_sm_SupFig2.tif4408KSupplemental Fig. 2. IPA graphical representation of the molecular relationships between proteins differentially expressed in WT versus Timp3 −/− mice fed a HFD. The gene products are represented as nodes, and the biological relationship between two nodes is represented as an edge (line). All edges are supported by at least 1 reference from the literature, from a textbook, or from canonical information stored in the IPKB. The intensity of the node color indicates the degree of up- (red) or down- (green) regulation. Nodes are displayed using various shapes that represent the functional class of the gene product.
HEP_23250_sm_SupFig3.tif4409KSupplemental Fig. 3. Global view of biological processes and molecular functions of proteins differentially expressed in WT versus Timp3 −/− mice fed a HFD. Proteins detected as differentially expressed in WT versus Timp3 −/− mice fed a HFD were associated with biological functions using the web delivered program IPA. This approach showed significant overrepresentation of gene products involved in lipid and amino acids metabolism and hepatic system disease.
HEP_23250_sm_SupFig4.tif975KSupplemental Fig. 4. Proteomic data for FABP-1 and ADK. One typical annotated MS/MS spectrum and a representative table of the peptide ions identified by LC- MSE are shown for each protein.
HEP_23250_sm_SupFig5.tif975KSupplemental Fig. 5. Proteomic data for MATI/III and GNMT. One typical annotated MS/MS spectrum and a representative table of the peptide ions identified by LC-MSE are shown for each protein.
HEP_23250_sm_SupFig6.tif975KSupplemental Fig. 6. TACE effects on other elements of methionine metabolism (A) MAT2A, CBS and MTHFR mRNA in WT and Timp3−/− mice fed a HFD; (B) MAT2, CBS and MTHFR mRNA in SV40-transformed hepatocytes infected with adeno-GFP or adeno-GFP-TACE and then treated O/N with different concentrations of palmitic acid. (C) SAMe and SAH levels as in (B); n=3; *p<0.05,**p<0.01, ***p<0.001.

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