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Raphael I, Mahesula S, Kalsaria K, Kotagiri V, Purkar AB, Anjanappa M, Shah D, Pericherla V, Jadhav YLA, Raghunathan R, Vaynberg M, Noriega D, Grimaldo NH, Wenk C, Gelfond JAL, Forsthuber TG, Haskins WE. Microwave and magnetic (M(2) ) proteomics of the experimental autoimmune encephalomyelitis animal model of multiple sclerosis. Electrophoresis 2013; 33:3810-9. [PMID: 23161666 DOI: 10.1002/elps.201200200] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2012] [Revised: 06/20/2012] [Accepted: 06/21/2012] [Indexed: 11/12/2022]
Abstract
We hypothesized that quantitative MS/MS-based proteomics at multiple time points, incorporating rapid microwave and magnetic (M(2) ) sample preparation, could enable relative protein expression to be correlated to disease progression in the experimental autoimmune encephalomyelitis (EAE) animal model of multiple sclerosis. To test our hypothesis, microwave-assisted reduction/alkylation/digestion of proteins from brain tissue lysates bound to C8 magnetic beads and microwave-assisted isobaric chemical labeling were performed of released peptides, in 90 s prior to unbiased proteomic analysis. Disease progression in EAE was assessed by scoring clinical EAE disease severity and confirmed by histopathologic evaluation for central nervous system inflammation. Decoding the expression of 283 top-ranked proteins (p <0.05) at each time point relative to their expression at the peak of disease, from a total of 1191 proteins observed in four technical replicates, revealed a strong statistical correlation to EAE disease score, particularly for the following four proteins that closely mirror disease progression: 14-3-3ε (p = 3.4E-6); GPI (p = 2.1E-5); PLP1 (p = 8.0E-4); PRX1 (p = 1.7E-4). These results were confirmed by Western blotting, signaling pathway analysis, and hierarchical clustering of EAE risk groups. While validation in a larger cohort is underway, we conclude that M(2) proteomics is a rapid method to quantify putative prognostic/predictive protein biomarkers and therapeutic targets of disease progression in the EAE animal model of multiple sclerosis.
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Affiliation(s)
- Itay Raphael
- Department of Biology, University of Texas at San Antonio, San Antonio, TX, USA
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Mahesula S, Raphael I, Raghunathan R, Kalsaria K, Kotagiri V, Purkar AB, Anjanappa M, Shah D, Pericherla V, Jadhav YLA, Gelfond JA, Forsthuber TG, Haskins WE. Immunoenrichment microwave and magnetic proteomics for quantifying CD47 in the experimental autoimmune encephalomyelitis model of multiple sclerosis. Electrophoresis 2012; 33:3820-9. [PMID: 23160929 PMCID: PMC3724470 DOI: 10.1002/elps.201200515] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2012] [Revised: 09/30/2012] [Accepted: 09/30/2012] [Indexed: 01/21/2023]
Abstract
We hypothesized that quantitative MS/MS-based proteomics at multiple time points, incorporating immunoenrichment prior to rapid microwave and magnetic (IM(2) ) sample preparation, might enable correlation of the relative expression of CD47 and other low abundance proteins to disease progression in the experimental autoimmune encephalomyelitis (EAE) animal model of multiple sclerosis. To test our hypothesis, anti-CD47 antibodies were used to enrich for low abundance CD47 prior to microwave and magnetic proteomics in EAE. Decoding protein expression at each time point, with CD47-immunoenriched samples and targeted proteomic analysis, enabled peptides from the low abundance proteins to be precisely quantified throughout disease progression, including: CD47: 86-99, corresponding to the "marker of self" overexpressed by myelin that prevents phagocytosis, or "cellular devouring," by microglia and macrophages; myelin basic protein: 223-228, corresponding to myelin basic protein; and migration inhibitory factor: 79-87, corresponding to a proinflammatory cytokine that inhibits macrophage migration. While validation in a larger cohort is underway, we conclude that IM(2) proteomics is a rapid method to precisely quantify peptides from CD47 and other low abundance proteins throughout disease progression in EAE. This is likely due to improvements in selectivity and sensitivity, necessary to partially overcome masking of low abundance proteins by high abundance proteins and improve dynamic range.
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Affiliation(s)
- Swetha Mahesula
- Pediatric Biochemistry Laboratory, University of Texas at San Antonio, San Antonio, TX, 78249
- Department of Biology, University of Texas at San Antonio, San Antonio, TX, 78249
- Department of Chemistry, University of Texas at San Antonio, San Antonio, TX, 78249
- Center for Interdisciplinary Health Research, University of Texas at San Antonio, San Antonio, TX, 78249
- Center for Research & Training in the Sciences, University of Texas at San Antonio, San Antonio, TX, 78249
| | - Itay Raphael
- Department of Biology, University of Texas at San Antonio, San Antonio, TX, 78249
| | - Rekha Raghunathan
- Pediatric Biochemistry Laboratory, University of Texas at San Antonio, San Antonio, TX, 78249
- Department of Biology, University of Texas at San Antonio, San Antonio, TX, 78249
- Center for Interdisciplinary Health Research, University of Texas at San Antonio, San Antonio, TX, 78249
- Center for Research & Training in the Sciences, University of Texas at San Antonio, San Antonio, TX, 78249
| | - Karan Kalsaria
- Pediatric Biochemistry Laboratory, University of Texas at San Antonio, San Antonio, TX, 78249
- Department of Biology, University of Texas at San Antonio, San Antonio, TX, 78249
- Center for Interdisciplinary Health Research, University of Texas at San Antonio, San Antonio, TX, 78249
- Center for Research & Training in the Sciences, University of Texas at San Antonio, San Antonio, TX, 78249
| | - Venkat Kotagiri
- Pediatric Biochemistry Laboratory, University of Texas at San Antonio, San Antonio, TX, 78249
- Department of Biology, University of Texas at San Antonio, San Antonio, TX, 78249
- Center for Interdisciplinary Health Research, University of Texas at San Antonio, San Antonio, TX, 78249
- Center for Research & Training in the Sciences, University of Texas at San Antonio, San Antonio, TX, 78249
| | - Anjali B. Purkar
- Pediatric Biochemistry Laboratory, University of Texas at San Antonio, San Antonio, TX, 78249
- Department of Biology, University of Texas at San Antonio, San Antonio, TX, 78249
- Center for Interdisciplinary Health Research, University of Texas at San Antonio, San Antonio, TX, 78249
- Center for Research & Training in the Sciences, University of Texas at San Antonio, San Antonio, TX, 78249
| | - Manjushree Anjanappa
- Pediatric Biochemistry Laboratory, University of Texas at San Antonio, San Antonio, TX, 78249
- Department of Biology, University of Texas at San Antonio, San Antonio, TX, 78249
- Center for Interdisciplinary Health Research, University of Texas at San Antonio, San Antonio, TX, 78249
- Center for Research & Training in the Sciences, University of Texas at San Antonio, San Antonio, TX, 78249
| | - Darshit Shah
- Pediatric Biochemistry Laboratory, University of Texas at San Antonio, San Antonio, TX, 78249
- Department of Biology, University of Texas at San Antonio, San Antonio, TX, 78249
- Center for Interdisciplinary Health Research, University of Texas at San Antonio, San Antonio, TX, 78249
- Center for Research & Training in the Sciences, University of Texas at San Antonio, San Antonio, TX, 78249
| | - Vidya Pericherla
- Pediatric Biochemistry Laboratory, University of Texas at San Antonio, San Antonio, TX, 78249
- Department of Biology, University of Texas at San Antonio, San Antonio, TX, 78249
- Center for Interdisciplinary Health Research, University of Texas at San Antonio, San Antonio, TX, 78249
- Center for Research & Training in the Sciences, University of Texas at San Antonio, San Antonio, TX, 78249
| | - Yeshwant Lal Avinash Jadhav
- Pediatric Biochemistry Laboratory, University of Texas at San Antonio, San Antonio, TX, 78249
- Department of Biology, University of Texas at San Antonio, San Antonio, TX, 78249
- Center for Interdisciplinary Health Research, University of Texas at San Antonio, San Antonio, TX, 78249
- Center for Research & Training in the Sciences, University of Texas at San Antonio, San Antonio, TX, 78249
| | - Jonathan A.L. Gelfond
- Department of Epidemiology & Biostatistics, University of Texas Health Science Center at San Antonio, San Antonio, TX, 78229
| | - Thomas G. Forsthuber
- Department of Biology, University of Texas at San Antonio, San Antonio, TX, 78249
- Center for Interdisciplinary Health Research, University of Texas at San Antonio, San Antonio, TX, 78249
| | - William E. Haskins
- Pediatric Biochemistry Laboratory, University of Texas at San Antonio, San Antonio, TX, 78249
- Department of Biology, University of Texas at San Antonio, San Antonio, TX, 78249
- Department of Chemistry, University of Texas at San Antonio, San Antonio, TX, 78249
- RCMI Proteomics, University of Texas at San Antonio, San Antonio, TX, 78249
- Protein Biomarkers Cores, University of Texas at San Antonio, San Antonio, TX, 78249
- Center for Interdisciplinary Health Research, University of Texas at San Antonio, San Antonio, TX, 78249
- Center for Research & Training in the Sciences, University of Texas at San Antonio, San Antonio, TX, 78249
- Department of Medicine, Division of Hematology & Medical Oncology, University of Texas Health Science Center at San Antonio, San Antonio, TX, 78229
- Cancer Therapy & Research Center, University of Texas Health Science Center at San Antonio, San Antonio, TX, 78229
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