| Metadata |
| datasetIdentifier | PASS01066 |
| datasetType | MSMS |
| submitter | Vidya Venkatraman <vidya.venkatraman@cshs.org> |
| submitter_organization | Cedars-Sinai Medical Center |
| lab_head_full_name | Dr. Jennifer Van Eyk |
| lab_head_email | jennifer.vaneyk@cshs.org |
| lab_head_organization | Cedars-Sinai Medical Center |
| lab_head_country | United States |
| datasetTag | HumanAtherosclerosis |
| datasetTitle | Proteomic Architecture of Human Coronary and Aortic Atherosclerosis |
| publicReleaseDate | 2018-12-01 00:00:00 |
| finalizedDate | 2018-03-19 21:40:45 |
| summary | The inability to detect premature atherosclerosis significantly hinders implementation of personalized therapy to prevent coronary heart disease. A comprehensive understanding of arterial protein networks and how they change in early atherosclerosis could identify new biomarkers for disease detection and improved therapeutic targets. Here we describe the human arterial proteome and the proteomic features strongly associated with early atherosclerosis based on mass-spectrometry analysis of coronary artery and aortic specimens from 100 autopsied young adults (200 arterial specimens). Convex analysis of mixtures, differential dependent network modeling and bioinformatic analyses defined the composition, network re-wiring and likely regulatory features of the protein networks associated with early atherosclerosis. Among other things the results reveal major differences in mitochondrial protein mass between the coronary artery and distal aorta in both normal and atherosclerotic samples – highlighting the importance of anatomic specificity and dynamic network structures in in the study of arterial proteomics. The publicly available data resource and the description of the analysis pipeline establish a new foundation for understanding the proteomic architecture of atherosclerosis and provide a template for similar investigations of other chronic diseases characterized by multi-cellular tissue phenotypes. |
| contributors | Herrington David M1*, Mao Chunhong2, Parker Sarah3, Fu Zongming4, Yu Guoqiang5, Chen Lulu5, Venkatraman Vidya3, Fu Yi5, Wang Yizhi5, Howard Tim6, Goo Jun7, Zhao CF1, Liu Yongming8, Saylor Georgia1, Athas Grace9, Troxclair Dana9, Hixson James7*, Vander Heide Richard9*, Wang Yue4*,Van Eyk Jennifer3* |
| publication | Herrington et al, Proteomic Architecture of Human Coronary and Aortic Atherosclerosis,Circulation,Submitted |
| growth | |
| treatment | Male and female cases of any race, aged 18-50 years (men) or 18-60 years (women) with no ante mortem clinical suspicion of coronary disease autopsied < 24 hours of death were eligible for inclusion. This report includes data from the first 100 adult coroner cases included in the study (age range:15-55 yrs., 75% males, 67% White, 26% Black, 7% Other). The Medico-Legal Death Investigators obtain signed family consent for retrieval of anatomic specimens prior to the autopsy. During the autopsy the pathologist dissects the aorta (ligamentum arteriosum to aortic bifurcation) and LAD and removes branching arteries and adventitial or epicardial adipose tissue. Both the aorta and LAD are opened longitudinally, cleansed of blood and photographed (Supplemental Fig. 9a and b). In each of the regions to be sampled, the pathologist inspects the intimal surface and categorizes the type and percent involvement of the following atherosclerotic changes: a) fatty streaks (FS); b) fibrous plaques (FP); c) complicated lesions (CL); and d) calcified lesions (CO), and records the data on a data collection form. Consistent with the age and cause of death (trauma), the fibrous plaques were almost exclusively early lesions - only one specimen had any macroscopic evidence of calcification and <3% of samples had any evidence of plaque hemorrhage, ulceration or thrombosis. Next the pathologist collects up to 1 gram of tissue from each of three standardized sections of the thoracic and abdominal aorta and two sections from the coronary artery 34 (Supplemental Fig. 9c and d). The specimens are snap frozen in cryotubes with liquid nitrogen and remain in a 34L VWR Cryogenic Dewar until transferred to a -80 freezer equipped with temperature alarms and automated generator back-up systems in the Department of Pathology at LSU. Additionally, targeted “pure” samples of grossly normal (non-lesion) and, if available, grossly atherosclerotic (lesion) are taken and divided into three 100mg portions and 1.) snap frozen, 2.) placed in RNALater solution and frozen, and 3.) placed in formalin and stored at room temperature for possible future immunohistochemistry analyses. A 50 gram sample of liver is also collected and frozen for future studies. All samples received at LSU are checked for label accuracy and entered in a database using an unlinked anonymous code for further processing, analyses and storage. |
| extraction | Aorta and LAD tissues were pulverized in liquid nitrogen and homogenized in 8M urea, 2M thiourea, 4% CHAPs and 1% DTT using Dounce homogenizer for 100 strokes, centrifuged at 16000 rpm for 20 mins . Protein concentration of the supernatant was assessed by CB-X assay kit (G-Biosciences MO, USA). 100 μg of protein was precipitated using 2-D clean-up kit (GE Healthcare MA, USA) and then reconstitute in 6M urea, 50mM ammonium bicarbonate. |
| separation | |
| digestion | The protein was reduced, alkylated and digested with trypsin (1:20). Peptides were desalted using Oasis HLB 96-well Plate (Waters MA, USA). |
| acquisition | A total of 2.0 μg of peptides per sample were then analyzed using label-free quantification on a reversed-phase liquid chromatography tandem mass spectrometry (RPLC–MS/MS) online with an Orbitrap Elite mass spectrometer (Thermo Scientific, USA) coupled to an Easy-nLC 1000 system (Thermo Scientific, USA). Peptides concentrated on a C18 trap column (Acclaim PepMap 100, 300 μm × 5 mm, C18, 5 μm, 100 Å; maximum pressure 800bar) in 0.1% TFA, then separated on a C18 analytical column (Acclaim PepMap RSLC, 75 μm × 15 cm, nano Viper, C18, 2 μm, 100 Å) using a linear gradient from 5% to 35% solvent B over 155 mins (solvent A: 0.1% aqueous formic acid and solvent B: 0.1% formic acid in acetonitrile; flow rate 350 nL/min; column oven temperature 45 °C). The analysis was operated in a data-dependent mode with full scan MS spectra acquired at a resolution of 60,000 in the Orbitrap analyzer, followed by tandem mass spectra of the 20 most abundant peaks in the linear ion trap after peptide fragmentation by collision-induced dissociation (CID).
To eliminate batch effect, the same parameters were used for mass spectra acquisition and the peptides from each individual were analyzed randomly in one batch. To minimize cross contamination, a blank run was performed between each sample. To monitor column performance, 200fmol BSA digested peptides were analyzed to make sure elution time of same peptide were within 0.2 minutes and signal intensity and total spectra counts variation were less than 10%. One LAD and one AA sample (from different subjects) were excluded because of poor protein yield leaving n=99 samples from each territory for analysis. |
| informatics | The MS/MS data obtained from the Orbitrap Elite were converted to mzXML and mgf format using Msconvert version 3.0.3858 from ProteoWizard 35 for peaklist generation. All data were searched using the X!Tandem 36 algorithm version 2009.10.01.1 and OMSSA 37 algorithm version 2.1.9. The dataset was searched against the concatenated target/decoy 38 Human Uniprot 39 database as of July 24, 2015, with only reviewed and canonical sequences used. The search parameters were as follows: Fixed modification of Carbamidomethyl (C) and variable modifications of Oxidation (M), Phosphorylation (STY); Enzyme: Trypsin with 2 maximum missed cleavages; Parent Tolerance: 0.050.08 Da; Fragment tolerance: 1.00 Da. Post-search analysis was performed using Trans Proteomic Pipeline 40 version v4.6, rev 1 with protein group and peptide probability thresholds set to 90% and 90%, respectively, and one or more distinct peptide required for identification. PeptideProphet 41 was used for peptide validation from each individual search algorithm and iProphet 42 was used to merge results from the separate algorithms and further refine the identification probabilities. Lastly, ProteinProphet 43 was then used to infer protein identifications from the resulting combined peptide list and perform grouping of ambiguous hits. Protein Group and Peptide False Discovery Rates were calculated automatically using a target-decoy method for the above probability thresholds (0.72% and 0.06% respectively). Protein isoforms were only reported if a peptide comprising an amino acid sequence that was unique to the isoform was identified. Label-free quantification of each protein was performed using weighted spectral counting 44. The distribution of protein spectral counts for each data file was inspected to verify equivalence in sample loading and instrument performance between files. Based on this analysis, a median normalization (adjustment of the raw spectral counts for each protein of a given file to the median of all spectral counts observed for the same file) was performed (Supplemental Fig. 10). The final non-redundant protein groups were analyzed through the use of IPA (Ingenuity® Systems, www.ingenuity.com) to generate network, functional and pathway analyses. |
| instruments | Thermo Scientific LTQ Orbitrap Elite, Sciex QTRAP 6500, Sciex TRIPLETOF 6600 |
| species | Human |
| massModifications | static: C+57.021464, variable: M+15, STY+79 |
