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Creamer DR, Beynon RJ, Hubbard SJ, Ashe MP, Grant CM. Isoform-specific sequestration of protein kinase A fine-tunes intracellular signaling during heat stress. Cell Rep 2024; 43:114360. [PMID: 38865242 DOI: 10.1016/j.celrep.2024.114360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 04/24/2024] [Accepted: 05/30/2024] [Indexed: 06/14/2024] Open
Abstract
Protein kinase A (PKA) is a conserved kinase crucial for fundamental biological processes linked to growth, development, and metabolism. The PKA catalytic subunit is expressed as multiple isoforms in diverse eukaryotes; however, their contribution to ensuring signaling specificity in response to environmental cues remains poorly defined. Catalytic subunit activity is classically moderated via interaction with an inhibitory regulatory subunit. Here, a quantitative mass spectrometry approach is used to examine heat-stress-induced changes in the binding of yeast Tpk1-3 catalytic subunits to the Bcy1 regulatory subunit. We show that Tpk3 is not regulated by Bcy1 binding but, instead, is deactivated upon heat stress via reversible sequestration into cytoplasmic granules. These "Tpk3 granules" are enriched for multiple PKA substrates involved in various metabolic processes, with the Hsp42 sequestrase required for their formation. Hence, regulated sequestration of Tpk3 provides a mechanism to control isoform-specific kinase signaling activity during stress conditions.
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Affiliation(s)
- Declan R Creamer
- Faculty of Biology, Medicine and Health, The University of Manchester, Michael Smith Building, Oxford Road, Manchester M13 9PT, UK
| | - Robert J Beynon
- Centre for Proteome Research, Institute of Systems and Integrative Biology, University of Liverpool, Crown Street, Liverpool L69 7ZB, UK
| | - Simon J Hubbard
- Faculty of Biology, Medicine and Health, The University of Manchester, Michael Smith Building, Oxford Road, Manchester M13 9PT, UK
| | - Mark P Ashe
- Faculty of Biology, Medicine and Health, The University of Manchester, Michael Smith Building, Oxford Road, Manchester M13 9PT, UK
| | - Chris M Grant
- Faculty of Biology, Medicine and Health, The University of Manchester, Michael Smith Building, Oxford Road, Manchester M13 9PT, UK.
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Williams G, Couchman L, Taylor DR, Sandhu JK, Slingsby OC, Ng LL, Moniz CF, Jones DJL, Maxwell CB. Use of Nonhuman Sera as a Highly Cost-Effective Internal Standard for Quantitation of Multiple Human Proteins Using Species-Specific Tryptic Peptides: Applicability in Clinical LC-MS Analyses. J Proteome Res 2024. [PMID: 38533909 DOI: 10.1021/acs.jproteome.3c00762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/28/2024]
Abstract
Quantitation of proteins using liquid chromatography-tandem mass spectrometry (LC-MS/MS) is complex, with a multiplicity of options ranging from label-free techniques to chemically and metabolically labeling proteins. Increasingly, for clinically relevant analyses, stable isotope-labeled (SIL) internal standards (ISs) represent the "gold standard" for quantitation due to their similar physiochemical properties to the analyte, wide availability, and ability to multiplex to several peptides. However, the purchase of SIL-ISs is a resource-intensive step in terms of cost and time, particularly for screening putative biomarker panels of hundreds of proteins. We demonstrate an alternative strategy utilizing nonhuman sera as the IS for quantitation of multiple human proteins. We demonstrate the effectiveness of this strategy using two high abundance clinically relevant analytes, vitamin D binding protein [Gc globulin] (DBP) and albumin (ALB). We extend this to three putative risk markers for cardiovascular disease: plasma protease C1 inhibitor (SERPING1), annexin A1 (ANXA1), and protein kinase, DNA-activated catalytic subunit (PRKDC). The results show highly specific, reproducible, and linear measurement of the proteins of interest with comparable precision and accuracy to the gold standard SIL-IS technique. This approach may not be applicable to every protein, but for many proteins it can offer a cost-effective solution to LC-MS/MS protein quantitation.
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Affiliation(s)
- Geraldine Williams
- Leicester van Geest MS-OMICS Facility, Hodgkin Building, University of Leicester, Leicester LE1 9HN, United Kingdom
- Department of Cardiovascular Sciences and NIHR Leicester Cardiovascular Biomedical Research Unit, Glenfield Hospital, Leicester LE3 9QP, United Kingdom
| | - Lewis Couchman
- Leicester Cancer Research Centre, RKCSB, University of Leicester, Leicester LE2 7LX, United Kingdom
- Viapath Analytics, King's College Hospital, Denmark Hill, London SE5 9RS, United Kingdom
- Department of Clinical Biochemistry, King's College Hospital, Denmark Hill, London SE5 9RS, United Kingdom
| | - David R Taylor
- Viapath Analytics, King's College Hospital, Denmark Hill, London SE5 9RS, United Kingdom
| | - Jatinderpal K Sandhu
- Leicester van Geest MS-OMICS Facility, Hodgkin Building, University of Leicester, Leicester LE1 9HN, United Kingdom
- Department of Cardiovascular Sciences and NIHR Leicester Cardiovascular Biomedical Research Unit, Glenfield Hospital, Leicester LE3 9QP, United Kingdom
| | - Oliver C Slingsby
- Leicester van Geest MS-OMICS Facility, Hodgkin Building, University of Leicester, Leicester LE1 9HN, United Kingdom
- Department of Cardiovascular Sciences and NIHR Leicester Cardiovascular Biomedical Research Unit, Glenfield Hospital, Leicester LE3 9QP, United Kingdom
| | - Leong L Ng
- Leicester van Geest MS-OMICS Facility, Hodgkin Building, University of Leicester, Leicester LE1 9HN, United Kingdom
- Department of Cardiovascular Sciences and NIHR Leicester Cardiovascular Biomedical Research Unit, Glenfield Hospital, Leicester LE3 9QP, United Kingdom
| | - Cajetan F Moniz
- Department of Clinical Biochemistry, King's College Hospital, Denmark Hill, London SE5 9RS, United Kingdom
| | - Donald J L Jones
- Leicester van Geest MS-OMICS Facility, Hodgkin Building, University of Leicester, Leicester LE1 9HN, United Kingdom
- Leicester Cancer Research Centre, RKCSB, University of Leicester, Leicester LE2 7LX, United Kingdom
- Department of Cardiovascular Sciences and NIHR Leicester Cardiovascular Biomedical Research Unit, Glenfield Hospital, Leicester LE3 9QP, United Kingdom
| | - Colleen B Maxwell
- Leicester van Geest MS-OMICS Facility, Hodgkin Building, University of Leicester, Leicester LE1 9HN, United Kingdom
- Department of Cardiovascular Sciences and NIHR Leicester Cardiovascular Biomedical Research Unit, Glenfield Hospital, Leicester LE3 9QP, United Kingdom
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Abdul-Khalek N, Wimmer R, Overgaard MT, Gregersen Echers S. Insight on physicochemical properties governing peptide MS1 response in HPLC-ESI-MS/MS: A deep learning approach. Comput Struct Biotechnol J 2023; 21:3715-3727. [PMID: 37560124 PMCID: PMC10407266 DOI: 10.1016/j.csbj.2023.07.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 07/13/2023] [Accepted: 07/19/2023] [Indexed: 08/11/2023] Open
Abstract
Accurate and absolute quantification of peptides in complex mixtures using quantitative mass spectrometry (MS)-based methods requires foreground knowledge and isotopically labeled standards, thereby increasing analytical expenses, time consumption, and labor, thus limiting the number of peptides that can be accurately quantified. This originates from differential ionization efficiency between peptides and thus, understanding the physicochemical properties that influence the ionization and response in MS analysis is essential for developing less restrictive label-free quantitative methods. Here, we used equimolar peptide pool repository data to develop a deep learning model capable of identifying amino acids influencing the MS1 response. By using an encoder-decoder with an attention mechanism and correlating attention weights with amino acid physicochemical properties, we obtain insight on properties governing the peptide-level MS1 response within the datasets. While the problem cannot be described by one single set of amino acids and properties, distinct patterns were reproducibly obtained. Properties are grouped in three main categories related to peptide hydrophobicity, charge, and structural propensities. Moreover, our model can predict MS1 intensity output under defined conditions based solely on peptide sequence input. Using a refined training dataset, the model predicted log-transformed peptide MS1 intensities with an average error of 9.7 ± 0.5% based on 5-fold cross validation, and outperformed random forest and ridge regression models on both log-transformed and real scale data. This work demonstrates how deep learning can facilitate identification of physicochemical properties influencing peptide MS1 responses, but also illustrates how sequence-based response prediction and label-free peptide-level quantification may impact future workflows within quantitative proteomics.
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Affiliation(s)
- Naim Abdul-Khalek
- Department of Chemistry and Bioscience, Aalborg University, Aalborg 9220, Denmark
| | - Reinhard Wimmer
- Department of Chemistry and Bioscience, Aalborg University, Aalborg 9220, Denmark
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