1
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Tsantilas KA, Merrihew GE, Robbins JE, Johnson RS, Park J, Plubell DL, Canterbury JD, Huang E, Riffle M, Sharma V, MacLean BX, Eckels J, Wu CC, Bereman MS, Spencer SE, Hoofnagle AN, MacCoss MJ. A Framework for Quality Control in Quantitative Proteomics. J Proteome Res 2024; 23:4392-4408. [PMID: 39248652 DOI: 10.1021/acs.jproteome.4c00363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/10/2024]
Abstract
A thorough evaluation of the quality, reproducibility, and variability of bottom-up proteomics data is necessary at every stage of a workflow, from planning to analysis. We share vignettes applying adaptable quality control (QC) measures to assess sample preparation, system function, and quantitative analysis. System suitability samples are repeatedly measured longitudinally with targeted methods, and we share examples where they are used on three instrument platforms to identify severe system failures and track function over months to years. Internal QCs incorporated at the protein and peptide levels allow our team to assess sample preparation issues and to differentiate system failures from sample-specific issues. External QC samples prepared alongside our experimental samples are used to verify the consistency and quantitative potential of our results during batch correction and normalization before assessing biological phenotypes. We combine these controls with rapid analysis (Skyline), longitudinal QC metrics (AutoQC), and server-based data deposition (PanoramaWeb). We propose that this integrated approach to QC is a useful starting point for groups to facilitate rapid quality control assessment to ensure that valuable instrument time is used to collect the best quality data possible. Data are available on Panorama Public and ProteomeXchange under the identifier PXD051318.
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Affiliation(s)
- Kristine A Tsantilas
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, United States
| | - Gennifer E Merrihew
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, United States
| | - Julia E Robbins
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, United States
| | - Richard S Johnson
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, United States
| | - Jea Park
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, United States
| | - Deanna L Plubell
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, United States
| | - Jesse D Canterbury
- Thermo Fisher Scientific, 355 River Oaks Parkway, San Jose, California 95134, United States
| | - Eric Huang
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, United States
| | - Michael Riffle
- Department of Biochemistry, University of Washington, Seattle, Washington 98195, United States
| | - Vagisha Sharma
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, United States
| | - Brendan X MacLean
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, United States
| | - Josh Eckels
- LabKey, 500 Union St #1000, Seattle, Washington 98101, United States
| | - Christine C Wu
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, United States
| | - Michael S Bereman
- Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina 27607, United States
| | - Sandra E Spencer
- Canada's Michael Smith Genome Sciences Centre (BC Cancer Research Institute), University of British Columbia, Vancouver, British Columbia V5Z 4S6, Canada
| | - Andrew N Hoofnagle
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington 98195, United States
| | - Michael J MacCoss
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, United States
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2
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Camarda ND, Lu Q, Meola DM, Man JJ, Song Z, Travers RJ, Lopez KE, Powers SN, Papanastasiou M, DeRuff KC, Mullahoo J, Egri SB, Davison D, Sebastiani P, Eblen ST, Buchsbaum R, Huggins GS, London CA, Jaffe JD, Upshaw JN, Yang VK, Jaffe IZ. Identifying mitigating strategies for endothelial cell dysfunction and hypertension in response to VEGF receptor inhibitors. Clin Sci (Lond) 2024; 138:1131-1150. [PMID: 39282930 DOI: 10.1042/cs20240537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Revised: 07/27/2024] [Accepted: 08/28/2024] [Indexed: 10/02/2024]
Abstract
Vascular endothelial growth factor receptor inhibitors (VEGFRis) improve cancer survival but are associated with treatment-limiting hypertension, often attributed to endothelial cell (EC) dysfunction. Using phosphoproteomic profiling of VEGFRi-treated ECs, drugs were screened for mitigators of VEGFRi-induced EC dysfunction and validated in primary aortic ECs, mice, and canine cancer patients. VEGFRi treatment significantly raised systolic blood pressure (SBP) and increased markers of endothelial and renal dysfunction in mice and canine cancer patients. α-Adrenergic-antagonists were identified as drugs that most oppose the VEGFRi proteomic signature. Doxazosin, one such α-antagonist, prevented EC dysfunction in murine, canine, and human aortic ECs. In mice with sorafenib-induced-hypertension, doxazosin mitigated EC dysfunction but not hypertension or glomerular endotheliosis, while lisinopril mitigated hypertension and glomerular endotheliosis without impacting EC function. Hence, reversing EC dysfunction was insufficient to mitigate VEGFRi-induced-hypertension in this mouse model. Canine cancer patients with VEGFRi-induced-hypertension were randomized to doxazosin or lisinopril and both agents significantly decreased SBP. The canine clinical trial supports safety and efficacy of doxazosin and lisinopril as antihypertensives for VEGFRi-induced-hypertension and the potential of trials in canines with spontaneous cancer to accelerate translation. The overall findings demonstrate the utility of phosphoproteomics to identify EC-protective agents to mitigate cardio-oncology side effects.
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Affiliation(s)
- Nicholas D Camarda
- Molecular Cardiology Research Institute, Tufts Medical Center, Boston, MA, U.S.A
- Genetics, Molecular, and Cellular Biology Program, Tufts Graduate School of Biomedical Sciences, Boston, MA, U.S.A
| | - Qing Lu
- Molecular Cardiology Research Institute, Tufts Medical Center, Boston, MA, U.S.A
| | - Dawn M Meola
- Tufts Cummings School of Veterinary Medicine, North Grafton, MA, U.S.A
| | - Joshua J Man
- Molecular Cardiology Research Institute, Tufts Medical Center, Boston, MA, U.S.A
- Genetics, Molecular, and Cellular Biology Program, Tufts Graduate School of Biomedical Sciences, Boston, MA, U.S.A
| | - Zeyuan Song
- Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, MA, U.S.A
| | - Richard J Travers
- Molecular Cardiology Research Institute, Tufts Medical Center, Boston, MA, U.S.A
- Division of Hematology Oncology, Department of Medicine, Tufts Medical Center, Boston, MA, U.S.A
| | - Katherine E Lopez
- Tufts Cummings School of Veterinary Medicine, North Grafton, MA, U.S.A
| | - Sarah N Powers
- Molecular Cardiology Research Institute, Tufts Medical Center, Boston, MA, U.S.A
| | | | | | | | | | | | - Paola Sebastiani
- Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, MA, U.S.A
| | - Scott T Eblen
- Department of Cell and Molecular Pharmacology, Medical University of South Carolina, Charleston, SC, U.S.A
| | - Rachel Buchsbaum
- Division of Hematology Oncology, Department of Medicine, Tufts Medical Center, Boston, MA, U.S.A
| | - Gordon S Huggins
- Molecular Cardiology Research Institute, Tufts Medical Center, Boston, MA, U.S.A
- Division of Cardiology, Tufts Medical Center, Boston, MA, U.S.A
| | - Cheryl A London
- Tufts Cummings School of Veterinary Medicine, North Grafton, MA, U.S.A
| | | | - Jenica N Upshaw
- Division of Cardiology, Tufts Medical Center, Boston, MA, U.S.A
| | - Vicky K Yang
- Tufts Cummings School of Veterinary Medicine, North Grafton, MA, U.S.A
| | - Iris Z Jaffe
- Molecular Cardiology Research Institute, Tufts Medical Center, Boston, MA, U.S.A
- Genetics, Molecular, and Cellular Biology Program, Tufts Graduate School of Biomedical Sciences, Boston, MA, U.S.A
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3
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Sun S, Shyr Z, McDaniel K, Fang Y, Tao D, Chen CZ, Zheng W, Zhu Q. Reversal Gene Expression Assessment for Drug Repurposing, a Case Study of Glioblastoma. RESEARCH SQUARE 2024:rs.3.rs-4765282. [PMID: 39315277 PMCID: PMC11419258 DOI: 10.21203/rs.3.rs-4765282/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/25/2024]
Abstract
Glioblastoma (GBM) is a rare brain cancer with an exceptionally high mortality rate, which illustrates the pressing demand for more effective therapeutic options. Despite considerable research efforts on GBM, its underlying biological mechanisms remain unclear. Furthermore, none of the United States Food and Drug Administration (FDA) approved drugs used for GBM deliver satisfactory survival improvement. This study presents a novel computational pipeline by utilizing gene expression data analysis for GBM for drug repurposing to address the challenges in rare disease drug development, particularly focusing on GBM. The GBM Gene Expression Profile (GGEP) was constructed with multi-omics data to identify drugs with reversal gene expression to GGEP from the Integrated Network-Based Cellular Signatures (iLINCS) database. We prioritized the candidates via hierarchical clustering of their expression signatures and quantification of their reversal strength by calculating two self-defined indices based on the GGEP genes' log2 foldchange (LFCs) that the drug candidates could induce. Among eight prioritized candidates, in-vitro experiments validated Clofarabine and Ciclopirox as highly efficacious in selectively targeting GBM cancer cells. The success of this study illustrated a promising avenue for accelerating drug development by uncovering underlying gene expression effect between drugs and diseases, which can be extended to other rare diseases and non-rare diseases.
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Affiliation(s)
- Shixue Sun
- NCATS: National Center for Advancing Translational Sciences
| | - Zeenat Shyr
- NCATS: National Center for Advancing Translational Sciences
| | - Kathleen McDaniel
- NCATS ETB: National Center for Advancing Translational Sciences Early Translation Branch
| | - Yuhong Fang
- NCATS: National Center for Advancing Translational Sciences
| | - Dingyin Tao
- NCATS: National Center for Advancing Translational Sciences
| | | | - Wei Zheng
- NCATS: National Center for Advancing Translational Sciences
| | - Qian Zhu
- NCATS: National Center for Advancing Translational Sciences
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4
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Tsantilas KA, Merrihew GE, Robbins JE, Johnson RS, Park J, Plubell DL, Huang E, Riffle M, Sharma V, MacLean BX, Eckels J, Wu CC, Bereman MS, Spencer SE, Hoofnagle AN, MacCoss MJ. A framework for quality control in quantitative proteomics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.12.589318. [PMID: 38645098 PMCID: PMC11030400 DOI: 10.1101/2024.04.12.589318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
A thorough evaluation of the quality, reproducibility, and variability of bottom-up proteomics data is necessary at every stage of a workflow from planning to analysis. We share vignettes applying adaptable quality control (QC) measures to assess sample preparation, system function, and quantitative analysis. System suitability samples are repeatedly measured longitudinally with targeted methods, and we share examples where they are used on three instrument platforms to identify severe system failures and track function over months to years. Internal QCs incorporated at protein and peptide-level allow our team to assess sample preparation issues and to differentiate system failures from sample-specific issues. External QC samples prepared alongside our experimental samples are used to verify the consistency and quantitative potential of our results during batch correction and normalization before assessing biological phenotypes. We combine these controls with rapid analysis (Skyline), longitudinal QC metrics (AutoQC), and server-based data deposition (PanoramaWeb). We propose that this integrated approach to QC is a useful starting point for groups to facilitate rapid quality control assessment to ensure that valuable instrument time is used to collect the best quality data possible. Data are available on Panorama Public and on ProteomeXchange under the identifier PXD051318.
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Affiliation(s)
- Kristine A. Tsantilas
- Department of Genome Sciences, University of Washington, Washington 98195, United States
| | - Gennifer E. Merrihew
- Department of Genome Sciences, University of Washington, Washington 98195, United States
| | - Julia E. Robbins
- Department of Genome Sciences, University of Washington, Washington 98195, United States
| | - Richard S. Johnson
- Department of Genome Sciences, University of Washington, Washington 98195, United States
| | - Jea Park
- Department of Genome Sciences, University of Washington, Washington 98195, United States
| | - Deanna L. Plubell
- Department of Genome Sciences, University of Washington, Washington 98195, United States
| | - Eric Huang
- Department of Genome Sciences, University of Washington, Washington 98195, United States
| | - Michael Riffle
- Department of Biochemistry, University of Washington, Washington 98195, United States
| | - Vagisha Sharma
- Department of Genome Sciences, University of Washington, Washington 98195, United States
| | - Brendan X. MacLean
- Department of Genome Sciences, University of Washington, Washington 98195, United States
| | - Josh Eckels
- LabKey, 500 Union St #1000, Seattle, Washington 98101, United States
| | - Christine C. Wu
- Department of Genome Sciences, University of Washington, Washington 98195, United States
| | - Michael S. Bereman
- Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina 27607
| | - Sandra E. Spencer
- Canada’s Michael Smith Genome Sciences Centre (BC Cancer Research Institute), University of British Columbia, Vancouver, British Columbia V5Z 4S6, Canada
| | - Andrew N. Hoofnagle
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington 98195, United States
| | - Michael J. MacCoss
- Department of Genome Sciences, University of Washington, Washington 98195, United States
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5
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Jeong E, Yoon S. Current advances in comprehensive omics data mining for oncology and cancer research. Biochim Biophys Acta Rev Cancer 2024; 1879:189030. [PMID: 38008264 DOI: 10.1016/j.bbcan.2023.189030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 09/05/2023] [Accepted: 11/19/2023] [Indexed: 11/28/2023]
Abstract
The availability of a large amount of multiomics data enables data-driven discovery studies on cancers. High-throughput data on mutations, gene/protein expression, immune scores (tumor-infiltrating cells), drug screening, and RNAi (shRNAs and CRISPRs) screening are major integrated components of patient samples and cell line datasets. Improvements in data access and user interfaces make it easy for general scientists to carry out their data mining practices on integrated multiomics data platforms without computational expertise. Here, we summarize the extent of data integration and functionality of several portals and software that provide integrated multiomics data mining platforms for all cancer studies. Recent progress includes programming interfaces (APIs) for customized data mining. Precalculated datasets assist noncomputational users in quickly browsing data associations. Furthermore, stand-alone software provides fast calculations and smart functions, guiding optimal sampling and filtering options for the easy discovery of significant data associations. These efforts improve the utility of cancer omics big data for noncomputational users at all levels of cancer research. In the present review, we aim to provide analytical information guiding general scientists to find and utilize data mining tools for their research.
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Affiliation(s)
- Euna Jeong
- Research Institute of Women's Health, Sookmyung Women's University, Seoul 04310, Republic of Korea
| | - Sukjoon Yoon
- Research Institute of Women's Health, Sookmyung Women's University, Seoul 04310, Republic of Korea; Department of Biological Sciences, Sookmyung Women's University, Seoul 04310, Republic of Korea.
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6
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Onken MD, Erdmann-Gilmore P, Zhang Q, Thapa K, King E, Kaltenbronn KM, Noda SE, Makepeace CM, Goldfarb D, Babur Ö, Townsend RR, Blumer KJ. Protein Kinase Signaling Networks Driven by Oncogenic Gq/11 in Uveal Melanoma Identified by Phosphoproteomic and Bioinformatic Analyses. Mol Cell Proteomics 2023; 22:100649. [PMID: 37730182 PMCID: PMC10616553 DOI: 10.1016/j.mcpro.2023.100649] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 08/22/2023] [Accepted: 09/17/2023] [Indexed: 09/22/2023] Open
Abstract
Metastatic uveal melanoma (UM) patients typically survive only 2 to 3 years because effective therapy does not yet exist. Here, to facilitate the discovery of therapeutic targets in UM, we have identified protein kinase signaling mechanisms elicited by the drivers in 90% of UM tumors: mutant constitutively active G protein α-subunits encoded by GNAQ (Gq) or GNA11 (G11). We used the highly specific Gq/11 inhibitor FR900359 (FR) to elucidate signaling networks that drive proliferation, metabolic reprogramming, and dedifferentiation of UM cells. We determined the effects of FR on the proteome and phosphoproteome of UM cells as indicated by bioinformatic analyses with CausalPath and site-specific gene set enrichment analysis. We found that inhibition of oncogenic Gq/11 caused deactivation of PKC, Erk, and the cyclin-dependent kinases CDK1 and CDK2 that drive proliferation. Inhibition of oncogenic Gq/11 in UM cells with low metastatic risk relieved inhibitory phosphorylation of polycomb-repressive complex subunits that regulate melanocytic redifferentiation. Site-specific gene set enrichment analysis, unsupervised analysis, and functional studies indicated that mTORC1 and 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 2 drive metabolic reprogramming in UM cells. Together, these results identified protein kinase signaling networks driven by oncogenic Gq/11 that regulate critical aspects of UM cell biology and provide targets for therapeutic investigation.
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Affiliation(s)
- Michael D Onken
- Department of Biochemistry and Molecular Biophysics, Washington University in St Louis, St Louis, Missouri, USA.
| | | | - Qiang Zhang
- Department of Medicine, Washington University in St Louis, St Louis, Missouri, USA
| | - Kisan Thapa
- Department of Computer Science, University of Massachusetts Boston, Boston, Massachusetts, USA
| | - Emily King
- Department of Cell Biology and Physiology, Washington University in St Louis, St Louis, Missouri, USA
| | - Kevin M Kaltenbronn
- Department of Cell Biology and Physiology, Washington University in St Louis, St Louis, Missouri, USA
| | - Sarah E Noda
- Department of Cell Biology and Physiology, Washington University in St Louis, St Louis, Missouri, USA
| | - Carol M Makepeace
- Department of Cell Biology and Physiology, Washington University in St Louis, St Louis, Missouri, USA
| | - Dennis Goldfarb
- Department of Cell Biology and Physiology, Washington University in St Louis, St Louis, Missouri, USA
| | - Özgün Babur
- Department of Computer Science, University of Massachusetts Boston, Boston, Massachusetts, USA
| | - R Reid Townsend
- Department of Medicine, Washington University in St Louis, St Louis, Missouri, USA
| | - Kendall J Blumer
- Department of Cell Biology and Physiology, Washington University in St Louis, St Louis, Missouri, USA.
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7
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Searle BC, Chien A, Koller A, Hawke D, Herren AW, Kim Kim J, Lee KA, Leib RD, Nelson AJ, Patel P, Ren JM, Stemmer PM, Zhu Y, Neely BA, Patel B. A Multipathway Phosphopeptide Standard for Rapid Phosphoproteomics Assay Development. Mol Cell Proteomics 2023; 22:100639. [PMID: 37657519 PMCID: PMC10561125 DOI: 10.1016/j.mcpro.2023.100639] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 08/22/2023] [Accepted: 08/24/2023] [Indexed: 09/03/2023] Open
Abstract
Recent advances in methodology have made phosphopeptide analysis a tractable problem for many proteomics researchers. There are now a wide variety of robust and accessible enrichment strategies to generate phosphoproteomes while free or inexpensive software tools for quantitation and site localization have simplified phosphoproteome analysis workflow tremendously. As a research group under the Association for Biomolecular Resource Facilities umbrella, the Proteomics Standards Research Group has worked to develop a multipathway phosphopeptide standard based on a mixture of heavy-labeled phosphopeptides designed to enable researchers to rapidly develop assays. This mixture contains 131 mass spectrometry vetted phosphopeptides specifically chosen to cover as many known biologically interesting phosphosites as possible from seven different signaling networks: AMPK signaling, death and apoptosis signaling, ErbB signaling, insulin/insulin-like growth factor-1 signaling, mTOR signaling, PI3K/AKT signaling, and stress (p38/SAPK/JNK) signaling. Here, we describe a characterization of this mixture spiked into a HeLa tryptic digest stimulated with both epidermal growth factor and insulin-like growth factor-1 to activate the MAPK and PI3K/AKT/mTOR pathways. We further demonstrate a comparison of phosphoproteomic profiling of HeLa performed independently in five labs using this phosphopeptide mixture with data-independent acquisition. Despite different experimental and instrumentation processes, we found that labs could produce reproducible, harmonized datasets by reporting measurements as ratios to the standard, while intensity measurements showed lower consistency between labs even after normalization. Our results suggest that widely available, biologically relevant phosphopeptide standards can act as a quantitative "yardstick" across laboratories and sample preparations enabling experimental designs larger than a single laboratory can perform. Raw data files are publicly available in the MassIVE dataset MSV000090564.
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Affiliation(s)
- Brian C Searle
- Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio, USA; Pelotonia Institute for Immuno-Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio, USA.
| | - Allis Chien
- Mass Spectrometry Center, Stanford University, Stanford, California, USA
| | | | | | - Anthony W Herren
- UC Davis Genome Center, Proteomics Core, University of California Davis, Davis California, USA
| | - Jenny Kim Kim
- Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, New York, USA
| | - Kimberly A Lee
- Cell Signaling Technology, Inc, Danvers, Massachusetts, USA
| | - Ryan D Leib
- Mass Spectrometry Center, Stanford University, Stanford, California, USA
| | | | - Purvi Patel
- Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, New York, USA
| | - Jian Min Ren
- Cell Signaling Technology, Inc, Danvers, Massachusetts, USA
| | - Paul M Stemmer
- Department of Pharmaceutical Sciences, Wayne State University, Detroit, Michigan, USA
| | - Yiying Zhu
- Cell Signaling Technology, Inc, Danvers, Massachusetts, USA
| | - Benjamin A Neely
- National Institute of Standards and Technology, Charleston, South Carolina, USA
| | - Bhavin Patel
- Thermo Fisher Scientific, Rockford, Illinois, USA
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8
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Park J, Wilkins C, Avtonomov D, Hong J, Back S, Kim H, Shulman N, MacLean BX, Lee SW, Kim S. Targeted proteomics data interpretation with DeepMRM. CELL REPORTS METHODS 2023; 3:100521. [PMID: 37533638 PMCID: PMC10391571 DOI: 10.1016/j.crmeth.2023.100521] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 04/18/2023] [Accepted: 06/15/2023] [Indexed: 08/04/2023]
Abstract
Targeted proteomics is widely utilized in clinical proteomics; however, researchers often devote substantial time to manual data interpretation, which hinders the transferability, reproducibility, and scalability of this approach. We introduce DeepMRM, a software package based on deep learning algorithms for object detection developed to minimize manual intervention in targeted proteomics data analysis. DeepMRM was evaluated on internal and public datasets, demonstrating superior accuracy compared with the community standard tool Skyline. To promote widespread adoption, we have incorporated a stand-alone graphical user interface for DeepMRM and integrated its algorithm into the Skyline software package as an external tool.
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Affiliation(s)
| | | | | | - Jiwon Hong
- Department of Chemistry, Center for Proteogenomic Research, Korea University, Seoul 02841, Republic of Korea
| | - Seunghoon Back
- Department of Chemistry, Center for Proteogenomic Research, Korea University, Seoul 02841, Republic of Korea
| | - Hokeun Kim
- Department of Chemistry, Center for Proteogenomic Research, Korea University, Seoul 02841, Republic of Korea
| | - Nicholas Shulman
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
| | - Brendan X. MacLean
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
| | - Sang-Won Lee
- Department of Chemistry, Center for Proteogenomic Research, Korea University, Seoul 02841, Republic of Korea
| | - Sangtae Kim
- Bertis Bioscience, Inc., San Diego, CA 92121, USA
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9
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Chang A, Leutert M, Rodriguez-Mias RA, Villén J. Automated Enrichment of Phosphotyrosine Peptides for High-Throughput Proteomics. J Proteome Res 2023; 22:1868-1880. [PMID: 37097255 PMCID: PMC10510590 DOI: 10.1021/acs.jproteome.2c00850] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/26/2023]
Abstract
Phosphotyrosine (pY) enrichment is critical for expanding the fundamental and clinical understanding of cellular signaling by mass spectrometry-based proteomics. However, current pY enrichment methods exhibit a high cost per sample and limited reproducibility due to expensive affinity reagents and manual processing. We present rapid-robotic phosphotyrosine proteomics (R2-pY), which uses a magnetic particle processor and pY superbinders or antibodies. R2-pY can handle up to 96 samples in parallel, requires 2 days to go from cell lysate to mass spectrometry injections, and results in global proteomic, phosphoproteomic, and tyrosine-specific phosphoproteomic samples. We benchmark the method on HeLa cells stimulated with pervanadate and serum and report over 4000 unique pY sites from 1 mg of peptide input, strong reproducibility between replicates, and phosphopeptide enrichment efficiencies above 99%. R2-pY extends our previously reported R2-P2 proteomic and global phosphoproteomic sample preparation framework, opening the door to large-scale studies of pY signaling in concert with global proteome and phosphoproteome profiling.
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Affiliation(s)
- Alexis Chang
- Department of Genome Sciences, University of Washington, Seattle WA 98195, USA
| | - Mario Leutert
- Department of Genome Sciences, University of Washington, Seattle WA 98195, USA
| | | | - Judit Villén
- Department of Genome Sciences, University of Washington, Seattle WA 98195, USA
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10
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Mitchell DC, Kuljanin M, Li J, Van Vranken JG, Bulloch N, Schweppe DK, Huttlin EL, Gygi SP. A proteome-wide atlas of drug mechanism of action. Nat Biotechnol 2023; 41:845-857. [PMID: 36593396 PMCID: PMC11069389 DOI: 10.1038/s41587-022-01539-0] [Citation(s) in RCA: 40] [Impact Index Per Article: 40.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Accepted: 09/30/2022] [Indexed: 01/03/2023]
Abstract
Defining the cellular response to pharmacological agents is critical for understanding the mechanism of action of small molecule perturbagens. Here, we developed a 96-well-plate-based high-throughput screening infrastructure for quantitative proteomics and profiled 875 compounds in a human cancer cell line with near-comprehensive proteome coverage. Examining the 24-h proteome changes revealed ligand-induced changes in protein expression and uncovered rules by which compounds regulate their protein targets while identifying putative dihydrofolate reductase and tankyrase inhibitors. We used protein-protein and compound-compound correlation networks to uncover mechanisms of action for several compounds, including the adrenergic receptor antagonist JP1302, which we show disrupts the FACT complex and degrades histone H1. By profiling many compounds with overlapping targets covering a broad chemical space, we linked compound structure to mechanisms of action and highlighted off-target polypharmacology for molecules within the library.
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Affiliation(s)
- Dylan C Mitchell
- Department of Cell Biology, Harvard Medical School, Boston, MA, USA
| | - Miljan Kuljanin
- Department of Cell Biology, Harvard Medical School, Boston, MA, USA
| | - Jiaming Li
- Department of Cell Biology, Harvard Medical School, Boston, MA, USA
| | | | - Nathan Bulloch
- Department of Cell Biology, Harvard Medical School, Boston, MA, USA
| | - Devin K Schweppe
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Edward L Huttlin
- Department of Cell Biology, Harvard Medical School, Boston, MA, USA
| | - Steven P Gygi
- Department of Cell Biology, Harvard Medical School, Boston, MA, USA.
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11
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Waldenmaier HE, Gorre E, Poltash ML, Gunawardena HP, Zhai XA, Li J, Zhai B, Beil EJ, Terzo JC, Lawler R, English AM, Bern M, Mahan AD, Carlson E, Nanda H. "Lab of the Future"─Today: Fully Automated System for High-Throughput Mass Spectrometry Analysis of Biotherapeutics. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2023. [PMID: 37186948 DOI: 10.1021/jasms.3c00036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
Here we describe a state-of-the-art, integrated, multi-instrument automated system designed to execute methods involved in mass spectrometry characterization of biotherapeutics. The system includes liquid and microplate handling robotics and utilities, integrated LC-MS, along with data analysis software, to perform sample purification, preparation, and analysis as a seamless integrated unit. The automated process begins with tip-based purification of target proteins from expression cell-line supernatants, which is initiated once the samples are loaded onto the automated system and the metadata are retrieved from our corporate data aggregation system. Subsequently, the purified protein samples are prepared for MS, including deglycosylation and reduction steps for intact and reduced mass analysis, and proteolytic digestions, desalting, and buffer exchange via centrifugation for peptide map analysis. The prepared samples are then loaded into the LC-MS instrumentation for data acquisition. The acquired raw data are initially stored on a local area network storage system that is monitored by watcher scripts that then upload the raw MS data to a network of cloud-based servers. The raw MS data are processed with the appropriately configured analysis workflows such as database search for peptide mapping or charge deconvolution for undigested proteins. The results are verified and formatted for expert curation directly in the cloud. Finally, the curated results are appended to sample metadata in the corporate data aggregation system to accompany the biotherapeutic cell lines in subsequent processes.
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Affiliation(s)
- Hans E Waldenmaier
- Janssen Research & Development, The Janssen Pharmaceutical Companies of Johnson & Johnson, Spring House, Pennsylvania 19477, United States
| | - Elsa Gorre
- Janssen Research & Development, The Janssen Pharmaceutical Companies of Johnson & Johnson, Spring House, Pennsylvania 19477, United States
| | - Michael L Poltash
- Janssen Research & Development, The Janssen Pharmaceutical Companies of Johnson & Johnson, Spring House, Pennsylvania 19477, United States
| | - Harsha P Gunawardena
- Janssen Research & Development, The Janssen Pharmaceutical Companies of Johnson & Johnson, Spring House, Pennsylvania 19477, United States
| | | | - Jing Li
- Protein Metrics LLC., Cupertino, California 95014, United States
| | - Bo Zhai
- Janssen Research & Development, The Janssen Pharmaceutical Companies of Johnson & Johnson, Spring House, Pennsylvania 19477, United States
| | - Eric J Beil
- Janssen Research & Development, The Janssen Pharmaceutical Companies of Johnson & Johnson, Spring House, Pennsylvania 19477, United States
| | - Joseph C Terzo
- Janssen Research & Development, The Janssen Pharmaceutical Companies of Johnson & Johnson, Spring House, Pennsylvania 19477, United States
| | - Rose Lawler
- Protein Metrics LLC., Cupertino, California 95014, United States
| | | | - Marshall Bern
- Protein Metrics LLC., Cupertino, California 95014, United States
| | - Andrew D Mahan
- Janssen Research & Development, The Janssen Pharmaceutical Companies of Johnson & Johnson, Spring House, Pennsylvania 19477, United States
| | - Eric Carlson
- Protein Metrics LLC., Cupertino, California 95014, United States
| | - Hirsh Nanda
- Janssen Research & Development, The Janssen Pharmaceutical Companies of Johnson & Johnson, Spring House, Pennsylvania 19477, United States
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12
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Abelin JG, Bergstrom EJ, Rivera KD, Taylor HB, Klaeger S, Xu C, Verzani EK, Jackson White C, Woldemichael HB, Virshup M, Olive ME, Maynard M, Vartany SA, Allen JD, Phulphagar K, Harry Kane M, Rachimi S, Mani DR, Gillette MA, Satpathy S, Clauser KR, Udeshi ND, Carr SA. Workflow enabling deepscale immunopeptidome, proteome, ubiquitylome, phosphoproteome, and acetylome analyses of sample-limited tissues. Nat Commun 2023; 14:1851. [PMID: 37012232 PMCID: PMC10070353 DOI: 10.1038/s41467-023-37547-0] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 03/20/2023] [Indexed: 04/05/2023] Open
Abstract
Serial multi-omic analysis of proteome, phosphoproteome, and acetylome provides insights into changes in protein expression, cell signaling, cross-talk and epigenetic pathways involved in disease pathology and treatment. However, ubiquitylome and HLA peptidome data collection used to understand protein degradation and antigen presentation have not together been serialized, and instead require separate samples for parallel processing using distinct protocols. Here we present MONTE, a highly sensitive multi-omic native tissue enrichment workflow, that enables serial, deep-scale analysis of HLA-I and HLA-II immunopeptidome, ubiquitylome, proteome, phosphoproteome, and acetylome from the same tissue sample. We demonstrate that the depth of coverage and quantitative precision of each 'ome is not compromised by serialization, and the addition of HLA immunopeptidomics enables the identification of peptides derived from cancer/testis antigens and patient specific neoantigens. We evaluate the technical feasibility of the MONTE workflow using a small cohort of patient lung adenocarcinoma tumors.
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Affiliation(s)
- Jennifer G Abelin
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, 02142, USA.
| | - Erik J Bergstrom
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, 02142, USA
| | - Keith D Rivera
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, 02142, USA
| | - Hannah B Taylor
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, 02142, USA
| | - Susan Klaeger
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, 02142, USA
| | - Charles Xu
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, 02142, USA
| | - Eva K Verzani
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, 02142, USA
| | - C Jackson White
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, 02142, USA
| | - Hilina B Woldemichael
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, 02142, USA
| | - Maya Virshup
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, 02142, USA
| | - Meagan E Olive
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, 02142, USA
| | - Myranda Maynard
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, 02142, USA
| | - Stephanie A Vartany
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, 02142, USA
| | - Joseph D Allen
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, 02142, USA
| | - Kshiti Phulphagar
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, 02142, USA
| | - M Harry Kane
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, 02142, USA
| | - Suzanna Rachimi
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, 02142, USA
| | - D R Mani
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, 02142, USA
| | - Michael A Gillette
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, 02142, USA
- Massachusetts General Hospital, Boston, MA, 02114, USA
| | - Shankha Satpathy
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, 02142, USA
| | - Karl R Clauser
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, 02142, USA
| | - Namrata D Udeshi
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, 02142, USA.
| | - Steven A Carr
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, 02142, USA.
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13
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Messner CB, Demichev V, Wang Z, Hartl J, Kustatscher G, Mülleder M, Ralser M. Mass spectrometry-based high-throughput proteomics and its role in biomedical studies and systems biology. Proteomics 2023; 23:e2200013. [PMID: 36349817 DOI: 10.1002/pmic.202200013] [Citation(s) in RCA: 30] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 10/13/2022] [Accepted: 10/13/2022] [Indexed: 11/11/2022]
Abstract
There are multiple reasons why the next generation of biological and medical studies require increasing numbers of samples. Biological systems are dynamic, and the effect of a perturbation depends on the genetic background and environment. As a consequence, many conditions need to be considered to reach generalizable conclusions. Moreover, human population and clinical studies only reach sufficient statistical power if conducted at scale and with precise measurement methods. Finally, many proteins remain without sufficient functional annotations, because they have not been systematically studied under a broad range of conditions. In this review, we discuss the latest technical developments in mass spectrometry (MS)-based proteomics that facilitate large-scale studies by fast and efficient chromatography, fast scanning mass spectrometers, data-independent acquisition (DIA), and new software. We further highlight recent studies which demonstrate how high-throughput (HT) proteomics can be applied to capture biological diversity, to annotate gene functions or to generate predictive and prognostic models for human diseases.
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Affiliation(s)
- Christoph B Messner
- Precision Proteomics Center, Swiss Institute of Allergy and Asthma Research (SIAF), University of Zurich, Davos, Switzerland
| | - Vadim Demichev
- Institute of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Ziyue Wang
- Institute of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Johannes Hartl
- Institute of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Georg Kustatscher
- Wellcome Centre for Cell Biology, University of Edinburgh, Max Born Crescent, Edinburgh, Scotland, UK
| | - Michael Mülleder
- Core Facility High Throughput Mass Spectrometry, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Markus Ralser
- Institute of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
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14
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Ishiguro H, Mizuno T, Uchida Y, Sato R, Sasaki H, Nemoto S, Terasaki T, Kusuhara H. Characterization of proteome profile data of chemicals based on data-independent acquisition MS with SWATH method. NAR Genom Bioinform 2023; 5:lqad022. [PMID: 36915410 PMCID: PMC10006730 DOI: 10.1093/nargab/lqad022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 02/06/2023] [Accepted: 02/20/2023] [Indexed: 03/16/2023] Open
Abstract
Transcriptomic data of cultured cells treated with a chemical are widely recognized as useful numeric information that describes the effects of the chemical. This property is due to the high coverage and low arbitrariness of the transcriptomic data as profiles of chemicals. Considering the importance of posttranslational regulation, proteomic profiles could provide insights into the unrecognized aspects of the effects of chemicals. Therefore, this study aimed to address the question of how well the proteomic profiles obtained using data-independent acquisition (DIA) with the sequential window acquisition of all theoretical mass spectra, which can achieve comprehensive and arbitrariness-free protein quantification, can describe chemical effects. We demonstrated that the proteomic data obtained using DIA-MS exhibited favorable properties as profile data, such as being able to discriminate chemicals like the transcriptomic profiles. Furthermore, we revealed a new mode of action of a natural compound, harmine, through profile data analysis using the proteomic profile data. To our knowledge, this is the first study to investigate the properties of proteomic data obtained using DIA-MS as the profiles of chemicals. Our 54 (samples) × 2831 (proteins) data matrix would be an important source for further analyses to understand the effects of chemicals in a data-driven manner.
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Affiliation(s)
- Hiromu Ishiguro
- Graduate School of Pharmaceutical Sciences, the University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Tadahaya Mizuno
- Graduate School of Pharmaceutical Sciences, the University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Yasuo Uchida
- Graduate School of Pharmaceutical Sciences, Tohoku University, Aoba, Aramaki, Aoba-ku, Sendai 980-8578, Japan
| | - Risa Sato
- Graduate School of Pharmaceutical Sciences, Tohoku University, Aoba, Aramaki, Aoba-ku, Sendai 980-8578, Japan
| | - Hayate Sasaki
- Graduate School of Pharmaceutical Sciences, Tohoku University, Aoba, Aramaki, Aoba-ku, Sendai 980-8578, Japan
| | - Shumpei Nemoto
- Graduate School of Pharmaceutical Sciences, the University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Tetsuya Terasaki
- Graduate School of Pharmaceutical Sciences, Tohoku University, Aoba, Aramaki, Aoba-ku, Sendai 980-8578, Japan
| | - Hiroyuki Kusuhara
- Graduate School of Pharmaceutical Sciences, the University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan
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15
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Salovska B, Gao E, Müller‐Dott S, Li W, Cordon CC, Wang S, Dugourd A, Rosenberger G, Saez‐Rodriguez J, Liu Y. Phosphoproteomic analysis of metformin signaling in colorectal cancer cells elucidates mechanism of action and potential therapeutic opportunities. Clin Transl Med 2023; 13:e1179. [PMID: 36781298 PMCID: PMC9925373 DOI: 10.1002/ctm2.1179] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 12/30/2022] [Accepted: 01/05/2023] [Indexed: 02/15/2023] Open
Abstract
BACKGROUND The biguanide drug metformin is a safe and widely prescribed drug for type 2 diabetes. Interestingly, hundreds of clinical trials have been set to evaluate the potential role of metformin in the prevention and treatment of cancer including colorectal cancer (CRC). However, the "metformin signaling" remains controversial. AIMS AND METHODS To interrogate cell signaling induced by metformin in CRC and explore the druggability of the metformin-rewired phosphorylation network, we performed integrative analysis of phosphoproteomics, bioinformatics, and cell proliferation assays on a panel of 12 molecularly heterogeneous CRC cell lines. Using the high-resolute data-independent analysis mass spectrometry (DIA-MS), we monitored a total of 10,142 proteins and 56,080 phosphosites (P-sites) in CRC cells upon a short- and a long-term metformin treatment. RESULTS AND CONCLUSIONS We found that metformin tended to primarily remodel cell signaling in the long-term and only minimally regulated the total proteome expression levels. Strikingly, the phosphorylation signaling response to metformin was highly heterogeneous in the CRC panel, based on a network analysis inferring kinase/phosphatase activities and cell signaling reconstruction. A "MetScore" was determined to assign the metformin relevance of each P-site, revealing new and robust phosphorylation nodes and pathways in metformin signaling. Finally, we leveraged the metformin P-site signature to identify pharmacodynamic interactions and confirmed a number of candidate metformin-interacting drugs, including navitoclax, a BCL-2/BCL-xL inhibitor. Together, we provide a comprehensive phosphoproteomic resource to explore the metformin-induced cell signaling for potential cancer therapeutics. This resource can be accessed at https://yslproteomics.shinyapps.io/Metformin/.
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Affiliation(s)
- Barbora Salovska
- Yale Cancer Biology InstituteYale UniversityWest HavenConnecticutUSA
| | - Erli Gao
- Yale Cancer Biology InstituteYale UniversityWest HavenConnecticutUSA
| | - Sophia Müller‐Dott
- Institute for Computational BiomedicineFaculty of MedicineHeidelberg University HospitalBioquant, Heidelberg UniversityHeidelbergGermany
| | - Wenxue Li
- Yale Cancer Biology InstituteYale UniversityWest HavenConnecticutUSA
| | | | - Shisheng Wang
- West China‐Washington Mitochondria and Metabolism Research CenterWest China HospitalSichuan UniversityChengduChina
| | - Aurelien Dugourd
- Institute for Computational BiomedicineFaculty of MedicineHeidelberg University HospitalBioquant, Heidelberg UniversityHeidelbergGermany
| | | | - Julio Saez‐Rodriguez
- Institute for Computational BiomedicineFaculty of MedicineHeidelberg University HospitalBioquant, Heidelberg UniversityHeidelbergGermany
| | - Yansheng Liu
- Yale Cancer Biology InstituteYale UniversityWest HavenConnecticutUSA
- Department of PharmacologyYale University School of MedicineNew HavenConnecticutUSA
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16
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Targeted Quantification of Protein Phosphorylation and Its Contributions towards Mathematical Modeling of Signaling Pathways. Molecules 2023; 28:molecules28031143. [PMID: 36770810 PMCID: PMC9919559 DOI: 10.3390/molecules28031143] [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: 11/18/2022] [Revised: 01/12/2023] [Accepted: 01/18/2023] [Indexed: 01/26/2023] Open
Abstract
Post-translational modifications (PTMs) are key regulatory mechanisms that can control protein function. Of these, phosphorylation is the most common and widely studied. Because of its importance in regulating cell signaling, precise and accurate measurements of protein phosphorylation across wide dynamic ranges are crucial to understanding how signaling pathways function. Although immunological assays are commonly used to detect phosphoproteins, their lack of sensitivity, specificity, and selectivity often make them unreliable for quantitative measurements of complex biological samples. Recent advances in Mass Spectrometry (MS)-based targeted proteomics have made it a more useful approach than immunoassays for studying the dynamics of protein phosphorylation. Selected reaction monitoring (SRM)-also known as multiple reaction monitoring (MRM)-and parallel reaction monitoring (PRM) can quantify relative and absolute abundances of protein phosphorylation in multiplexed fashions targeting specific pathways. In addition, the refinement of these tools by enrichment and fractionation strategies has improved measurement of phosphorylation of low-abundance proteins. The quantitative data generated are particularly useful for building and parameterizing mathematical models of complex phospho-signaling pathways. Potentially, these models can provide a framework for linking analytical measurements of clinical samples to better diagnosis and treatment of disease.
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17
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Chang A, Leutert M, Rodriguez-Mias RA, Villén J. Automated Enrichment of Phosphotyrosine Peptides for High-Throughput Proteomics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.05.522335. [PMID: 36711935 PMCID: PMC9881991 DOI: 10.1101/2023.01.05.522335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Phosphotyrosine (pY) enrichment is critical for expanding fundamental and clinical understanding of cellular signaling by mass spectrometry-based proteomics. However, current pY enrichment methods exhibit a high cost per sample and limited reproducibility due to expensive affinity reagents and manual processing. We present rapid-robotic phosphotyrosine proteomics (R2-pY), which uses a magnetic particle processor and pY superbinders or antibodies. R2-pY handles 96 samples in parallel, requires 2 days to go from cell lysate to mass spectrometry injections, and results in global proteomic, phosphoproteomic and tyrosine specific phosphoproteomic samples. We benchmark the method on HeLa cells stimulated with pervanadate and serum and report over 4000 unique pY sites from 1 mg of peptide input, strong reproducibility between replicates, and phosphopeptide enrichment efficiencies above 99%. R2-pY extends our previously reported R2-P2 proteomic and global phosphoproteomic sample preparation framework, opening the door to large-scale studies of pY signaling in concert with global proteome and phosphoproteome profiling.
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Affiliation(s)
- Alexis Chang
- Department of Genome Sciences, University of Washington, Seattle WA 98195, USA
| | - Mario Leutert
- Department of Genome Sciences, University of Washington, Seattle WA 98195, USA
| | | | - Judit Villén
- Department of Genome Sciences, University of Washington, Seattle WA 98195, USA
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18
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Materials, workflows and applications of IMAC for phosphoproteome profiling in the recent decade: A review. Trends Analyt Chem 2022. [DOI: 10.1016/j.trac.2022.116862] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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19
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Cifani P, Kentsis A. Quantitative Cell Proteomic Atlas: Pathway-Scale Targeted Mass Spectrometry for High-Resolution Functional Profiling of Cell Signaling. J Proteome Res 2022; 21:2535-2544. [PMID: 36154077 PMCID: PMC10494574 DOI: 10.1021/acs.jproteome.2c00223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
In spite of extensive studies of cellular signaling, many fundamental processes such as pathway integration, cross-talk, and feedback remain poorly understood. To enable integrated and quantitative measurements of cellular biochemical activities, we have developed the Quantitative Cell Proteomics Atlas (QCPA). QCPA consists of panels of targeted mass spectrometry assays to determine the abundance and stoichiometry of regulatory post-translational modifications of sentinel proteins from most known physiologic and pathogenic signaling pathways in human cells. QCPA currently profiles 1 913 peptides from 469 effectors of cell surface signaling, apoptosis, stress response, gene expression, quiescence, and proliferation. For each protein, QCPA includes triplets of isotopically labeled peptides covering known post-translational regulatory sites to determine their stoichiometries and unmodified protein regions to measure total protein abundance. The QCPA framework incorporates analytes to control for technical variability of sample preparation and mass spectrometric analysis, including TrypQuant, a synthetic substrate for accurate quantification of proteolysis efficiency for proteins containing chemically modified residues. The ability to precisely and accurately quantify most known signaling pathways should enable improved chemoproteomic approaches for the comprehensive analysis of cell signaling and clinical proteomics of diagnostic specimens. QCPA is openly available at https://qcpa.mskcc.org.
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Affiliation(s)
- Paolo Cifani
- Molecular Pharmacology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, 10065 USA
| | - Alex Kentsis
- Molecular Pharmacology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, 10065 USA
- Tow Center for Developmental Oncology, Department of Pediatrics, Memorial Sloan Kettering Cancer Center, NY, 10065 USA
- Departments of Pediatrics, Pharmacology, and Physiology & Biophysics, Weill Medical College of Cornell University, NY, 10065 USA
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20
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Pilarczyk M, Fazel-Najafabadi M, Kouril M, Shamsaei B, Vasiliauskas J, Niu W, Mahi N, Zhang L, Clark NA, Ren Y, White S, Karim R, Xu H, Biesiada J, Bennett MF, Davidson SE, Reichard JF, Roberts K, Stathias V, Koleti A, Vidovic D, Clarke DJB, Schürer SC, Ma'ayan A, Meller J, Medvedovic M. Connecting omics signatures and revealing biological mechanisms with iLINCS. Nat Commun 2022; 13:4678. [PMID: 35945222 PMCID: PMC9362980 DOI: 10.1038/s41467-022-32205-3] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Accepted: 07/21/2022] [Indexed: 11/21/2022] Open
Abstract
There are only a few platforms that integrate multiple omics data types, bioinformatics tools, and interfaces for integrative analyses and visualization that do not require programming skills. Here we present iLINCS ( http://ilincs.org ), an integrative web-based platform for analysis of omics data and signatures of cellular perturbations. The platform facilitates mining and re-analysis of the large collection of omics datasets (>34,000), pre-computed signatures (>200,000), and their connections, as well as the analysis of user-submitted omics signatures of diseases and cellular perturbations. iLINCS analysis workflows integrate vast omics data resources and a range of analytics and interactive visualization tools into a comprehensive platform for analysis of omics signatures. iLINCS user-friendly interfaces enable execution of sophisticated analyses of omics signatures, mechanism of action analysis, and signature-driven drug repositioning. We illustrate the utility of iLINCS with three use cases involving analysis of cancer proteogenomic signatures, COVID 19 transcriptomic signatures and mTOR signaling.
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Affiliation(s)
- Marcin Pilarczyk
- Department of Environmental and Public Health Sciences, University of Cincinnati, Cincinnati, OH, 45220, USA
- LINCS Data Coordination and Integration Center (DCIC), Cincinnati, USA
- LINCS Data Coordination and Integration Center (DCIC), New York, USA
- LINCS Data Coordination and Integration Center (DCIC), Miami, USA
| | - Mehdi Fazel-Najafabadi
- Department of Environmental and Public Health Sciences, University of Cincinnati, Cincinnati, OH, 45220, USA
- LINCS Data Coordination and Integration Center (DCIC), Cincinnati, USA
- LINCS Data Coordination and Integration Center (DCIC), New York, USA
- LINCS Data Coordination and Integration Center (DCIC), Miami, USA
| | - Michal Kouril
- LINCS Data Coordination and Integration Center (DCIC), Cincinnati, USA
- LINCS Data Coordination and Integration Center (DCIC), New York, USA
- LINCS Data Coordination and Integration Center (DCIC), Miami, USA
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, 45229, USA
| | - Behrouz Shamsaei
- Department of Environmental and Public Health Sciences, University of Cincinnati, Cincinnati, OH, 45220, USA
- LINCS Data Coordination and Integration Center (DCIC), Cincinnati, USA
- LINCS Data Coordination and Integration Center (DCIC), New York, USA
- LINCS Data Coordination and Integration Center (DCIC), Miami, USA
| | - Juozas Vasiliauskas
- Department of Environmental and Public Health Sciences, University of Cincinnati, Cincinnati, OH, 45220, USA
- LINCS Data Coordination and Integration Center (DCIC), Cincinnati, USA
- LINCS Data Coordination and Integration Center (DCIC), New York, USA
- LINCS Data Coordination and Integration Center (DCIC), Miami, USA
| | - Wen Niu
- Department of Environmental and Public Health Sciences, University of Cincinnati, Cincinnati, OH, 45220, USA
- LINCS Data Coordination and Integration Center (DCIC), Cincinnati, USA
- LINCS Data Coordination and Integration Center (DCIC), New York, USA
- LINCS Data Coordination and Integration Center (DCIC), Miami, USA
| | - Naim Mahi
- Department of Environmental and Public Health Sciences, University of Cincinnati, Cincinnati, OH, 45220, USA
- LINCS Data Coordination and Integration Center (DCIC), Cincinnati, USA
- LINCS Data Coordination and Integration Center (DCIC), New York, USA
- LINCS Data Coordination and Integration Center (DCIC), Miami, USA
| | - Lixia Zhang
- Department of Environmental and Public Health Sciences, University of Cincinnati, Cincinnati, OH, 45220, USA
- LINCS Data Coordination and Integration Center (DCIC), Cincinnati, USA
- LINCS Data Coordination and Integration Center (DCIC), New York, USA
- LINCS Data Coordination and Integration Center (DCIC), Miami, USA
| | - Nicholas A Clark
- Department of Environmental and Public Health Sciences, University of Cincinnati, Cincinnati, OH, 45220, USA
- LINCS Data Coordination and Integration Center (DCIC), Cincinnati, USA
- LINCS Data Coordination and Integration Center (DCIC), New York, USA
- LINCS Data Coordination and Integration Center (DCIC), Miami, USA
| | - Yan Ren
- Department of Environmental and Public Health Sciences, University of Cincinnati, Cincinnati, OH, 45220, USA
- LINCS Data Coordination and Integration Center (DCIC), Cincinnati, USA
- LINCS Data Coordination and Integration Center (DCIC), New York, USA
- LINCS Data Coordination and Integration Center (DCIC), Miami, USA
| | - Shana White
- Department of Environmental and Public Health Sciences, University of Cincinnati, Cincinnati, OH, 45220, USA
- LINCS Data Coordination and Integration Center (DCIC), Cincinnati, USA
- LINCS Data Coordination and Integration Center (DCIC), New York, USA
- LINCS Data Coordination and Integration Center (DCIC), Miami, USA
| | - Rashid Karim
- Department of Environmental and Public Health Sciences, University of Cincinnati, Cincinnati, OH, 45220, USA
- Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, OH, 45220, USA
| | - Huan Xu
- Department of Environmental and Public Health Sciences, University of Cincinnati, Cincinnati, OH, 45220, USA
- LINCS Data Coordination and Integration Center (DCIC), Cincinnati, USA
- LINCS Data Coordination and Integration Center (DCIC), New York, USA
- LINCS Data Coordination and Integration Center (DCIC), Miami, USA
| | - Jacek Biesiada
- Department of Environmental and Public Health Sciences, University of Cincinnati, Cincinnati, OH, 45220, USA
| | - Mark F Bennett
- Department of Environmental and Public Health Sciences, University of Cincinnati, Cincinnati, OH, 45220, USA
- LINCS Data Coordination and Integration Center (DCIC), Cincinnati, USA
- LINCS Data Coordination and Integration Center (DCIC), New York, USA
- LINCS Data Coordination and Integration Center (DCIC), Miami, USA
| | - Sarah E Davidson
- Department of Environmental and Public Health Sciences, University of Cincinnati, Cincinnati, OH, 45220, USA
| | - John F Reichard
- Department of Environmental and Public Health Sciences, University of Cincinnati, Cincinnati, OH, 45220, USA
- LINCS Data Coordination and Integration Center (DCIC), Cincinnati, USA
- LINCS Data Coordination and Integration Center (DCIC), New York, USA
- LINCS Data Coordination and Integration Center (DCIC), Miami, USA
| | - Kurt Roberts
- Department of Environmental and Public Health Sciences, University of Cincinnati, Cincinnati, OH, 45220, USA
| | - Vasileios Stathias
- LINCS Data Coordination and Integration Center (DCIC), Cincinnati, USA
- LINCS Data Coordination and Integration Center (DCIC), New York, USA
- LINCS Data Coordination and Integration Center (DCIC), Miami, USA
- Department of Molecular and Cellular Pharmacology, Miller School of Medicine and Center for Computational Science, University of Miami, Miami, FL 33136, USA
| | - Amar Koleti
- LINCS Data Coordination and Integration Center (DCIC), Cincinnati, USA
- LINCS Data Coordination and Integration Center (DCIC), New York, USA
- LINCS Data Coordination and Integration Center (DCIC), Miami, USA
- Department of Molecular and Cellular Pharmacology, Miller School of Medicine and Center for Computational Science, University of Miami, Miami, FL 33136, USA
| | - Dusica Vidovic
- LINCS Data Coordination and Integration Center (DCIC), Cincinnati, USA
- LINCS Data Coordination and Integration Center (DCIC), New York, USA
- LINCS Data Coordination and Integration Center (DCIC), Miami, USA
- Department of Molecular and Cellular Pharmacology, Miller School of Medicine and Center for Computational Science, University of Miami, Miami, FL 33136, USA
| | - Daniel J B Clarke
- LINCS Data Coordination and Integration Center (DCIC), Cincinnati, USA
- LINCS Data Coordination and Integration Center (DCIC), New York, USA
- LINCS Data Coordination and Integration Center (DCIC), Miami, USA
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Stephan C Schürer
- LINCS Data Coordination and Integration Center (DCIC), Cincinnati, USA
- LINCS Data Coordination and Integration Center (DCIC), New York, USA
- LINCS Data Coordination and Integration Center (DCIC), Miami, USA
- Department of Molecular and Cellular Pharmacology, Miller School of Medicine and Center for Computational Science, University of Miami, Miami, FL 33136, USA
| | - Avi Ma'ayan
- LINCS Data Coordination and Integration Center (DCIC), Cincinnati, USA
- LINCS Data Coordination and Integration Center (DCIC), New York, USA
- LINCS Data Coordination and Integration Center (DCIC), Miami, USA
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Jarek Meller
- Department of Environmental and Public Health Sciences, University of Cincinnati, Cincinnati, OH, 45220, USA
- LINCS Data Coordination and Integration Center (DCIC), Cincinnati, USA
- LINCS Data Coordination and Integration Center (DCIC), New York, USA
- LINCS Data Coordination and Integration Center (DCIC), Miami, USA
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, 45229, USA
- Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, OH, 45220, USA
| | - Mario Medvedovic
- Department of Environmental and Public Health Sciences, University of Cincinnati, Cincinnati, OH, 45220, USA.
- LINCS Data Coordination and Integration Center (DCIC), Cincinnati, USA.
- LINCS Data Coordination and Integration Center (DCIC), New York, USA.
- LINCS Data Coordination and Integration Center (DCIC), Miami, USA.
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21
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Functional stratification of cancer drugs through integrated network similarity. NPJ Syst Biol Appl 2022; 8:11. [PMID: 35440787 PMCID: PMC9018743 DOI: 10.1038/s41540-022-00219-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Accepted: 01/21/2022] [Indexed: 11/30/2022] Open
Abstract
Drugs not only perturb their immediate protein targets but also modulate multiple signaling pathways. In this study, we explored networks modulated by several drugs across multiple cancer cell lines by integrating their targets with transcriptomic and phosphoproteomic data. As a result, we obtained 236 reconstructed networks covering five cell lines and 70 drugs. A rigorous topological and pathway analysis showed that chemically and functionally different drugs may modulate overlapping networks. Additionally, we revealed a set of tumor-specific hidden pathways with the help of drug network models that are not detectable from the initial data. The difference in the target selectivity of the drugs leads to disjoint networks despite sharing a similar mechanism of action, e.g., HDAC inhibitors. We also used the reconstructed network models to study potential drug combinations based on the topological separation and found literature evidence for a set of drug pairs. Overall, network-level exploration of drug-modulated pathways and their deep comparison may potentially help optimize treatment strategies and suggest new drug combinations.
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22
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Urban J. A review on recent trends in the phosphoproteomics workflow. From sample preparation to data analysis. Anal Chim Acta 2022; 1199:338857. [DOI: 10.1016/j.aca.2021.338857] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2021] [Revised: 07/14/2021] [Accepted: 07/15/2021] [Indexed: 12/12/2022]
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23
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Trapotsi MA, Hosseini-Gerami L, Bender A. Computational analyses of mechanism of action (MoA): data, methods and integration. RSC Chem Biol 2022; 3:170-200. [PMID: 35360890 PMCID: PMC8827085 DOI: 10.1039/d1cb00069a] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 12/09/2021] [Indexed: 12/15/2022] Open
Abstract
The elucidation of a compound's Mechanism of Action (MoA) is a challenging task in the drug discovery process, but it is important in order to rationalise phenotypic findings and to anticipate potential side-effects. Bioinformatic approaches, advances in machine learning techniques and the increasing deposition of high-throughput data in public databases have significantly contributed to recent advances in the field, but it is not straightforward to decide which data and methods are most suitable to use in a given case. In this review, we focus on these methods and data and their applications in generating MoA hypotheses for subsequent experimental validation. We discuss compound-specific data such as -omics, cell morphology and bioactivity data, as well as commonly used supplementary prior knowledge such as network and pathway data, and provide information on databases where this data can be accessed. In terms of methodologies, we discuss both well-established methods (connectivity mapping, pathway enrichment) as well as more developing methods (neural networks and multi-omics integration). Finally, we review case studies where the MoA of a compound was successfully suggested from computational analysis by incorporating multiple data modalities and/or methodologies. Our aim for this review is to provide researchers with insights into the benefits and drawbacks of both the data and methods in terms of level of understanding, biases and interpretation - and to highlight future avenues of investigation which we foresee will improve the field of MoA elucidation, including greater public access to -omics data and methodologies which are capable of data integration.
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Affiliation(s)
- Maria-Anna Trapotsi
- Centre for Molecular Informatics, Yusuf Hamied Department of Chemistry, University of Cambridge UK
| | - Layla Hosseini-Gerami
- Centre for Molecular Informatics, Yusuf Hamied Department of Chemistry, University of Cambridge UK
| | - Andreas Bender
- Centre for Molecular Informatics, Yusuf Hamied Department of Chemistry, University of Cambridge UK
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24
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Zhang W, Lai CK, Huang W, Li W, Wu S, Kong Q, Hopkinson AC, Fernie AR, Siu KWM, Yan S. An eco-friendly, low-cost, and automated strategy for phosphoproteome profiling. GREEN CHEMISTRY 2022; 24:9697-9708. [DOI: 10.1039/d2gc02345h] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/05/2024]
Abstract
An automated, online analysis platform using a reusable phos-trap column helps reduce organic solvent, plastic consumables, waste, and labor costs in phosphoproteomic studies.
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Affiliation(s)
- Wenyang Zhang
- Guangdong Key Laboratory for Crop Germplasm Resources Preservation and Utilization, Agro-biological Gene Research Center, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China
| | - Cheuk-Kuen Lai
- Department of Chemistry and Centre for Research in Mass Spectrometry, York University, Toronto, Ontario, M3J 1P3, Canada
| | - Wenjie Huang
- Guangdong Key Laboratory for Crop Germplasm Resources Preservation and Utilization, Agro-biological Gene Research Center, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China
| | - Wenyan Li
- Guangdong Key Laboratory for Crop Germplasm Resources Preservation and Utilization, Agro-biological Gene Research Center, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China
| | - Shaowen Wu
- Guangdong Key Laboratory for Crop Germplasm Resources Preservation and Utilization, Agro-biological Gene Research Center, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China
| | - Qian Kong
- Guangdong Key Laboratory for Crop Germplasm Resources Preservation and Utilization, Agro-biological Gene Research Center, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China
| | - Alan C. Hopkinson
- Department of Chemistry and Centre for Research in Mass Spectrometry, York University, Toronto, Ontario, M3J 1P3, Canada
| | - Alisdair R. Fernie
- Max Planck Institute of Molecular Plant Physiology, Am Muhlenberg 1, 14476, Potsdam-Golm, Germany
| | - K. W. Michael Siu
- Department of Chemistry and Centre for Research in Mass Spectrometry, York University, Toronto, Ontario, M3J 1P3, Canada
- Department of Chemistry and Biochemistry, University of Windsor, Windsor, Ontario, N9B 3P4, Canada
| | - Shijuan Yan
- Guangdong Key Laboratory for Crop Germplasm Resources Preservation and Utilization, Agro-biological Gene Research Center, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China
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25
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King J, Patel M, Chandrasekaran S. Metabolism, HDACs, and HDAC Inhibitors: A Systems Biology Perspective. Metabolites 2021; 11:792. [PMID: 34822450 PMCID: PMC8620738 DOI: 10.3390/metabo11110792] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2021] [Revised: 11/15/2021] [Accepted: 11/17/2021] [Indexed: 01/15/2023] Open
Abstract
Histone deacetylases (HDACs) are epigenetic enzymes that play a central role in gene regulation and are sensitive to the metabolic state of the cell. The cross talk between metabolism and histone acetylation impacts numerous biological processes including development and immune function. HDAC inhibitors are being explored for treating cancers, viral infections, inflammation, neurodegenerative diseases, and metabolic disorders. However, how HDAC inhibitors impact cellular metabolism and how metabolism influences their potency is unclear. Discussed herein are recent applications and future potential of systems biology methods such as high throughput drug screens, cancer cell line profiling, single cell sequencing, proteomics, metabolomics, and computational modeling to uncover the interplay between metabolism, HDACs, and HDAC inhibitors. The synthesis of new systems technologies can ultimately help identify epigenomic and metabolic biomarkers for patient stratification and the design of effective therapeutics.
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Affiliation(s)
- Jacob King
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA; (J.K.); (M.P.)
| | - Maya Patel
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA; (J.K.); (M.P.)
| | - Sriram Chandrasekaran
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA; (J.K.); (M.P.)
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
- Program in Chemical Biology, University of Michigan, Ann Arbor, MI 48109, USA
- Center for Bioinformatics and Computational Medicine, University of Michigan, Ann Arbor, MI 48109, USA
- Rogel Cancer Center, University of Michigan Medical School, Ann Arbor, MI 48109, USA
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26
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Abstract
![]()
Protein phosphorylation
in prokaryotes has gained more
attention in recent years as several
studies linked it to regulatory and signaling functions, indicating
importance similar to protein phosphorylation in eukaryotes. Studies
on bacterial phosphorylation have so far been conducted using manual
or HPLC-supported phosphopeptide enrichment, whereas automation of
phosphopeptide enrichment has been established in eukaryotes, allowing
for high-throughput sampling. To facilitate the prospect of studying
bacterial phosphorylation on a systems level, we here established
an automated Ser/Thr/Tyr phosphopeptide enrichment workflow on the
Agilent AssayMap platform. We present optimized buffer conditions
for TiO2 and Fe(III)-NTA-IMAC cartridge-based enrichment
and the most advantageous, species-specific loading amounts for Streptococcus pyogenes, Listeria monocytogenes, and Bacillus subtilis. For higher
sample amounts (≥250 μg), we observed superior performance
of the Fe(III)-NTA cartridges, whereas for lower sample amounts (≤100
μg), TiO2-based enrichment is equally efficient.
Both cartridges largely enriched the same set of phosphopeptides,
suggesting no improvement of peptide yield by the complementary use
of the two cartridges. Our data represent, to the best of our knowledge,
the largest phosphoproteome identified in a single study for each
of these bacteria.
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Affiliation(s)
- Marlène S Birk
- Max Planck Unit for the Science of Pathogens, 10117 Berlin, Germany
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27
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Keshishian H, McDonald ER, Mundt F, Melanson R, Krug K, Porter DA, Wallace L, Forestier D, Rabasha B, Marlow SE, Jane‐Valbuena J, Todres E, Specht H, Robinson ML, Jean Beltran PM, Babur O, Olive ME, Golji J, Kuhn E, Burgess M, MacMullan MA, Rejtar T, Wang K, Mani DR, Satpathy S, Gillette MA, Sellers WR, Carr SA. A highly multiplexed quantitative phosphosite assay for biology and preclinical studies. Mol Syst Biol 2021; 17:e10156. [PMID: 34569154 PMCID: PMC8474009 DOI: 10.15252/msb.202010156] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Revised: 08/19/2021] [Accepted: 08/20/2021] [Indexed: 12/14/2022] Open
Abstract
Reliable methods to quantify dynamic signaling changes across diverse pathways are needed to better understand the effects of disease and drug treatment in cells and tissues but are presently lacking. Here, we present SigPath, a targeted mass spectrometry (MS) assay that measures 284 phosphosites in 200 phosphoproteins of biological interest. SigPath probes a broad swath of signaling biology with high throughput and quantitative precision. We applied the assay to investigate changes in phospho-signaling in drug-treated cancer cell lines, breast cancer preclinical models, and human medulloblastoma tumors. In addition to validating previous findings, SigPath detected and quantified a large number of differentially regulated phosphosites newly associated with disease models and human tumors at baseline or with drug perturbation. Our results highlight the potential of SigPath to monitor phosphoproteomic signaling events and to nominate mechanistic hypotheses regarding oncogenesis, response, and resistance to therapy.
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Affiliation(s)
- Hasmik Keshishian
- Broad Institute of Massachusetts Institute of Technology and HarvardCambridgeMAUSA
| | | | - Filip Mundt
- Broad Institute of Massachusetts Institute of Technology and HarvardCambridgeMAUSA
- Present address:
Novo Nordisk Foundation Center for Protein ResearchFaculty of Health SciencesUniversity of CopenhagenCopenhagenDenmark
- Present address:
Department of Oncology and PathologyScience for Life LaboratoryKarolinska InstitutetStockholmSweden
| | - Randy Melanson
- Broad Institute of Massachusetts Institute of Technology and HarvardCambridgeMAUSA
| | - Karsten Krug
- Broad Institute of Massachusetts Institute of Technology and HarvardCambridgeMAUSA
| | - Dale A Porter
- Novartis Institute of Biomedical ResearchCambridgeMAUSA
- Present address:
Cedilla TherapeuticsCambridgeMAUSA
| | - Luke Wallace
- Broad Institute of Massachusetts Institute of Technology and HarvardCambridgeMAUSA
| | - Dominique Forestier
- Broad Institute of Massachusetts Institute of Technology and HarvardCambridgeMAUSA
| | - Bokang Rabasha
- Broad Institute of Massachusetts Institute of Technology and HarvardCambridgeMAUSA
| | - Sara E Marlow
- Broad Institute of Massachusetts Institute of Technology and HarvardCambridgeMAUSA
| | - Judit Jane‐Valbuena
- Broad Institute of Massachusetts Institute of Technology and HarvardCambridgeMAUSA
| | - Ellen Todres
- Broad Institute of Massachusetts Institute of Technology and HarvardCambridgeMAUSA
| | - Harrison Specht
- Broad Institute of Massachusetts Institute of Technology and HarvardCambridgeMAUSA
| | | | | | - Ozgun Babur
- Computer Science DepartmentUniversity of Massachusetts BostonBostonMAUSA
| | - Meagan E Olive
- Broad Institute of Massachusetts Institute of Technology and HarvardCambridgeMAUSA
| | - Javad Golji
- Novartis Institute of Biomedical ResearchCambridgeMAUSA
| | - Eric Kuhn
- Broad Institute of Massachusetts Institute of Technology and HarvardCambridgeMAUSA
| | - Michael Burgess
- Broad Institute of Massachusetts Institute of Technology and HarvardCambridgeMAUSA
| | - Melanie A MacMullan
- Broad Institute of Massachusetts Institute of Technology and HarvardCambridgeMAUSA
| | - Tomas Rejtar
- Novartis Institute of Biomedical ResearchCambridgeMAUSA
| | - Karen Wang
- Novartis Institute of Biomedical ResearchCambridgeMAUSA
| | - DR Mani
- Broad Institute of Massachusetts Institute of Technology and HarvardCambridgeMAUSA
| | - Shankha Satpathy
- Broad Institute of Massachusetts Institute of Technology and HarvardCambridgeMAUSA
| | - Michael A Gillette
- Broad Institute of Massachusetts Institute of Technology and HarvardCambridgeMAUSA
- Division of Pulmonary and Critical Care MedicineMassachusetts General HospitalBostonMAUSA
| | - William R Sellers
- Broad Institute of Massachusetts Institute of Technology and HarvardCambridgeMAUSA
- Department of Medical OncologyDana‐Farber Cancer Institute and Harvard Medical SchoolBostonMAUSA
| | - Steven A Carr
- Broad Institute of Massachusetts Institute of Technology and HarvardCambridgeMAUSA
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28
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Dele-Oni DO, Christianson KE, Egri SB, Vaca Jacome AS, DeRuff KC, Mullahoo J, Sharma V, Davison D, Ko T, Bula M, Blanchard J, Young JZ, Litichevskiy L, Lu X, Lam D, Asiedu JK, Toder C, Officer A, Peckner R, MacCoss MJ, Tsai LH, Carr SA, Papanastasiou M, Jaffe JD. Proteomic profiling dataset of chemical perturbations in multiple biological backgrounds. Sci Data 2021; 8:226. [PMID: 34433823 PMCID: PMC8387426 DOI: 10.1038/s41597-021-01008-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Accepted: 05/07/2021] [Indexed: 02/07/2023] Open
Abstract
While gene expression profiling has traditionally been the method of choice for large-scale perturbational profiling studies, proteomics has emerged as an effective tool in this context for directly monitoring cellular responses to perturbations. We previously reported a pilot library containing 3400 profiles of multiple perturbations across diverse cellular backgrounds in the reduced-representation phosphoproteome (P100) and chromatin space (Global Chromatin Profiling, GCP). Here, we expand our original dataset to include profiles from a new set of cardiotoxic compounds and from astrocytes, an additional neural cell model, totaling 5300 proteomic signatures. We describe filtering criteria and quality control metrics used to assess and validate the technical quality and reproducibility of our data. To demonstrate the power of the library, we present two case studies where data is queried using the concept of "connectivity" to obtain biological insight. All data presented in this study have been deposited to the ProteomeXchange Consortium with identifiers PXD017458 (P100) and PXD017459 (GCP) and can be queried at https://clue.io/proteomics .
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Affiliation(s)
| | | | - Shawn B Egri
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, United States
| | | | | | - James Mullahoo
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, United States
| | - Vagisha Sharma
- Department of Genome Sciences, University of Washington, Seattle, WA, 98195, United States
| | - Desiree Davison
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, United States
| | - Tak Ko
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, 02139, United States
| | - Michael Bula
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, 02139, United States
| | - Joel Blanchard
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, 02139, United States
| | - Jennie Z Young
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, 02139, United States
| | - Lev Litichevskiy
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, United States
| | - Xiaodong Lu
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, United States
| | - Daniel Lam
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, United States
| | - Jacob K Asiedu
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, United States
| | - Caidin Toder
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, United States
| | - Adam Officer
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, United States
| | - Ryan Peckner
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, United States
| | - Michael J MacCoss
- Department of Genome Sciences, University of Washington, Seattle, WA, 98195, United States
| | - Li-Huei Tsai
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, 02139, United States
| | - Steven A Carr
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, United States
| | | | - Jacob D Jaffe
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, United States.
- Inzen Therapeutics, Cambridge, MA, 02139, United States.
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29
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Bryan L, Henry M, Barron N, Gallagher C, Kelly RM, Frye CC, Osborne MD, Clynes M, Meleady P. Differential expression of miRNAs and functional role of mir-200a in high and low productivity CHO cells expressing an Fc fusion protein. Biotechnol Lett 2021; 43:1551-1563. [PMID: 34131805 PMCID: PMC8254715 DOI: 10.1007/s10529-021-03153-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Accepted: 06/05/2021] [Indexed: 12/27/2022]
Abstract
Objectives We used miRNA and proteomic profiling to understand intracellular pathways that contribute to high and low specific productivity (Qp) phenotypes in CHO clonally derived cell lines (CDCLs) from the same cell line generation project. Results Differentially expressed (DE) miRNAs were identified which are predicted to target several proteins associated with protein folding. MiR-200a was found to have a number of predicted targets associated with the unfolded protein response (UPR) which were shown to have decreased expression in high Qp CDCLs and have no detected change at the mRNA level. MiR-200a overexpression in a CHO CDCL was found to increase recombinant protein titer by 1.2 fold and Qp by 1.8 fold. Conclusion These results may suggest a role for miR-200a in post-transcriptional regulation of the UPR, presenting miR-200a as a potential target for engineering industrially attractive CHO cell phenotypes. Supplementary Information The online version contains supplementary material available at 10.1007/s10529-021-03153-7.
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Affiliation(s)
- Laura Bryan
- National Institute for Cellular Biotechnology, Dublin City University, Glasnevin, Dublin 9, Ireland.
| | - Michael Henry
- National Institute for Cellular Biotechnology, Dublin City University, Glasnevin, Dublin 9, Ireland
| | - Niall Barron
- National Institute for Bioprocessing Research and Training, Dublin 4, Ireland.,School of Chemical and Bioprocess Engineering, University College Dublin, Belfield, Dublin 4, Ireland
| | - Clair Gallagher
- National Institute for Cellular Biotechnology, Dublin City University, Glasnevin, Dublin 9, Ireland
| | - Ronan M Kelly
- Eli Lilly and Company, LTC-North, 1200 Kentucky Avenue, Indianapolis, IN, 46225, USA
| | - Christopher C Frye
- Eli Lilly and Company, LTC-North, 1200 Kentucky Avenue, Indianapolis, IN, 46225, USA
| | | | - Martin Clynes
- National Institute for Cellular Biotechnology, Dublin City University, Glasnevin, Dublin 9, Ireland
| | - Paula Meleady
- National Institute for Cellular Biotechnology, Dublin City University, Glasnevin, Dublin 9, Ireland
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30
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Zhang X, Maity TK, Ross KE, Qi Y, Cultraro CM, Bahta M, Pitts S, Keswani M, Gao S, Nguyen KDP, Cowart J, Kirkali F, Wu C, Guha U. Alterations in the Global Proteome and Phosphoproteome in Third Generation EGFR TKI Resistance Reveal Drug Targets to Circumvent Resistance. Cancer Res 2021; 81:3051-3066. [PMID: 33727228 PMCID: PMC8182571 DOI: 10.1158/0008-5472.can-20-2435] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Revised: 02/08/2021] [Accepted: 03/10/2021] [Indexed: 11/16/2022]
Abstract
Lung cancer is the leading cause of cancer mortality worldwide. The treatment of patients with lung cancer harboring mutant EGFR with orally administered EGFR tyrosine kinase inhibitors (TKI) has been a paradigm shift. Osimertinib and rociletinib are third-generation irreversible EGFR TKIs targeting the EGFR T790M mutation. Osimertinib is the current standard of care for patients with EGFR mutations due to increased efficacy, lower side effects, and enhanced brain penetrance. Unfortunately, all patients develop resistance. Genomic approaches have primarily been used to interrogate resistance mechanisms. Here we characterized the proteome and phosphoproteome of a series of isogenic EGFR-mutant lung adenocarcinoma cell lines that are either sensitive or resistant to these drugs, comprising the most comprehensive proteomic dataset resource to date to investigate third generation EGFR TKI resistance in lung adenocarcinoma. Unbiased global quantitative mass spectrometry uncovered alterations in signaling pathways, revealed a proteomic signature of epithelial-mesenchymal transition, and identified kinases and phosphatases with altered expression and phosphorylation in TKI-resistant cells. Decreased tyrosine phosphorylation of key sites in the phosphatase SHP2 suggests its inhibition, resulting in subsequent inhibition of RAS/MAPK and activation of PI3K/AKT pathways. Anticorrelation analyses of this phosphoproteomic dataset with published drug-induced P100 phosphoproteomic datasets from the Library of Integrated Network-Based Cellular Signatures program predicted drugs with the potential to overcome EGFR TKI resistance. The PI3K/MTOR inhibitor dactolisib in combination with osimertinib overcame resistance both in vitro and in vivo. Taken together, this study reveals global proteomic alterations upon third generation EGFR TKI resistance and highlights potential novel approaches to overcome resistance. SIGNIFICANCE: Global quantitative proteomics reveals changes in the proteome and phosphoproteome in lung cancer cells resistant to third generation EGFR TKIs, identifying the PI3K/mTOR inhibitor dactolisib as a potential approach to overcome resistance.
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Affiliation(s)
- Xu Zhang
- Thoracic and GI Malignancies Branch, Center for Cancer Research, NCI, NIH, Bethesda, Maryland.
| | - Tapan K Maity
- Thoracic and GI Malignancies Branch, Center for Cancer Research, NCI, NIH, Bethesda, Maryland
| | - Karen E Ross
- Dept. of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, Washington, D.C
| | - Yue Qi
- Thoracic and GI Malignancies Branch, Center for Cancer Research, NCI, NIH, Bethesda, Maryland
| | - Constance M Cultraro
- Thoracic and GI Malignancies Branch, Center for Cancer Research, NCI, NIH, Bethesda, Maryland
| | - Meriam Bahta
- Thoracic and GI Malignancies Branch, Center for Cancer Research, NCI, NIH, Bethesda, Maryland
| | - Stephanie Pitts
- Thoracic and GI Malignancies Branch, Center for Cancer Research, NCI, NIH, Bethesda, Maryland
| | - Meghana Keswani
- Thoracic and GI Malignancies Branch, Center for Cancer Research, NCI, NIH, Bethesda, Maryland
| | - Shaojian Gao
- Thoracic and GI Malignancies Branch, Center for Cancer Research, NCI, NIH, Bethesda, Maryland
| | - Khoa Dang P Nguyen
- Thoracic and GI Malignancies Branch, Center for Cancer Research, NCI, NIH, Bethesda, Maryland
| | - Julie Cowart
- Center for Bioinformatics and Computational Biology, University of Delaware, Newark, Delaware
| | - Fatos Kirkali
- Thoracic and GI Malignancies Branch, Center for Cancer Research, NCI, NIH, Bethesda, Maryland
| | - Cathy Wu
- Center for Bioinformatics and Computational Biology, University of Delaware, Newark, Delaware
| | - Udayan Guha
- Thoracic and GI Malignancies Branch, Center for Cancer Research, NCI, NIH, Bethesda, Maryland.
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31
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Bryan L, Henry M, Kelly RM, Lloyd M, Frye CC, Osborne MD, Clynes M, Meleady P. Global phosphoproteomic study of high/low specific productivity industrially relevant mAb producing recombinant CHO cell lines. CURRENT RESEARCH IN BIOTECHNOLOGY 2021. [DOI: 10.1016/j.crbiot.2021.02.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
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32
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Vaca Jacome AS, Peckner R, Shulman N, Krug K, DeRuff KC, Officer A, Christianson KE, MacLean B, MacCoss MJ, Carr SA, Jaffe JD. Avant-garde: an automated data-driven DIA data curation tool. Nat Methods 2020; 17:1237-1244. [PMID: 33199889 PMCID: PMC7723322 DOI: 10.1038/s41592-020-00986-4] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2019] [Accepted: 09/25/2020] [Indexed: 12/03/2022]
Abstract
Several challenges remain in data-independent acquisition (DIA) data analysis, such as to confidently identify peptides, define integration boundaries, remove interferences, and control false discovery rates. In practice, a visual inspection of the signals is still required, which is impractical with large datasets. We present Avant-garde as a tool to refine DIA (and parallel reaction monitoring) data. Avant-garde uses a novel data-driven scoring strategy: signals are refined by learning from the dataset itself, using all measurements in all samples to achieve the best optimization. We evaluate the performance of Avant-garde using benchmark DIA datasets and show that it can determine the quantitative suitability of a peptide peak, and reach the same levels of selectivity, accuracy, and reproducibility as manual validation. Avant-garde is complementary to existing DIA analysis engines and aims to establish a strong foundation for subsequent analysis of quantitative mass spectrometry data.
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Affiliation(s)
| | - Ryan Peckner
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cogen Therapeutics, Cambridge, MA, USA
| | | | - Karsten Krug
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Adam Officer
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | | | | | - Steven A Carr
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jacob D Jaffe
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Inzen Therapeutics, Cambridge, MA, USA.
- Inzen Therapeutics, Cambridge, MA, USA.
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33
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Shamsaei B, Chojnacki S, Pilarczyk M, Najafabadi M, Niu W, Chen C, Ross K, Matlock A, Muhlich J, Chutipongtanate S, Zheng J, Turner J, Vidović D, Jaffe J, MacCoss M, Wu C, Pillai A, Ma'ayan A, Schürer S, Kouril M, Medvedovic M, Meller J. piNET: a versatile web platform for downstream analysis and visualization of proteomics data. Nucleic Acids Res 2020; 48:W85-W93. [PMID: 32469073 PMCID: PMC7319557 DOI: 10.1093/nar/gkaa436] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2020] [Revised: 04/29/2020] [Accepted: 05/27/2020] [Indexed: 02/03/2023] Open
Abstract
Rapid progress in proteomics and large-scale profiling of biological systems at the protein level necessitates the continued development of efficient computational tools for the analysis and interpretation of proteomics data. Here, we present the piNET server that facilitates integrated annotation, analysis and visualization of quantitative proteomics data, with emphasis on PTM networks and integration with the LINCS library of chemical and genetic perturbation signatures in order to provide further mechanistic and functional insights. The primary input for the server consists of a set of peptides or proteins, optionally with PTM sites, and their corresponding abundance values. Several interconnected workflows can be used to generate: (i) interactive graphs and tables providing comprehensive annotation and mapping between peptides and proteins with PTM sites; (ii) high resolution and interactive visualization for enzyme-substrate networks, including kinases and their phospho-peptide targets; (iii) mapping and visualization of LINCS signature connectivity for chemical inhibitors or genetic knockdown of enzymes upstream of their target PTM sites. piNET has been built using a modular Spring-Boot JAVA platform as a fast, versatile and easy to use tool. The Apache Lucene indexing is used for fast mapping of peptides into UniProt entries for the human, mouse and other commonly used model organism proteomes. PTM-centric network analyses combine PhosphoSitePlus, iPTMnet and SIGNOR databases of validated enzyme-substrate relationships, for kinase networks augmented by DeepPhos predictions and sequence-based mapping of PhosphoSitePlus consensus motifs. Concordant LINCS signatures are mapped using iLINCS. For each workflow, a RESTful API counterpart can be used to generate the results programmatically in the json format. The server is available at http://pinet-server.org, and it is free and open to all users without login requirement.
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Affiliation(s)
- Behrouz Shamsaei
- Department of Environmental and Public Health Sciences, University of Cincinnati College of Medicine, USA
| | - Szymon Chojnacki
- Department of Environmental and Public Health Sciences, University of Cincinnati College of Medicine, USA
| | - Marcin Pilarczyk
- Department of Environmental and Public Health Sciences, University of Cincinnati College of Medicine, USA
| | - Mehdi Najafabadi
- Department of Environmental and Public Health Sciences, University of Cincinnati College of Medicine, USA
| | - Wen Niu
- Department of Environmental and Public Health Sciences, University of Cincinnati College of Medicine, USA
| | - Chuming Chen
- Center for Bioinformatics & Computational Biology; University of Delaware, USA
| | - Karen Ross
- Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, USA
| | - Andrea Matlock
- Department of Biomedical Sciences, Cedars-Sinai Medical Center, USA
| | - Jeremy Muhlich
- Department of Systems Biology, Harvard Medical School, USA
| | - Somchai Chutipongtanate
- Department of Cancer Biology, University of Cincinnati College of Medicine, USA.,Department of Pediatrics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Thailand
| | - Jie Zheng
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, USA
| | - John Turner
- Department of Pharmacology, Miller School of Medicine, Sylvester Comprehensive Cancer Center, Center for Computational Science, University of Miami, Miami, USA
| | - Dušica Vidović
- Department of Pharmacology, Miller School of Medicine, Sylvester Comprehensive Cancer Center, Center for Computational Science, University of Miami, Miami, USA
| | - Jake Jaffe
- Broad Institute of MIT and Harvard & Inzen Therapeutics, USA
| | - Michael MacCoss
- Department of Genome Sciences, University of Washington, USA
| | - Cathy Wu
- Center for Bioinformatics & Computational Biology; University of Delaware, USA.,Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, USA
| | - Ajay Pillai
- Human Genome Research Institute, National Institutes of Health, Bethesda, USA
| | - Avi Ma'ayan
- Mount Sinai Center for Bioinformatics, Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, USA
| | - Stephan Schürer
- Department of Pharmacology, Miller School of Medicine, Sylvester Comprehensive Cancer Center, Center for Computational Science, University of Miami, Miami, USA
| | - Michal Kouril
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, USA
| | - Mario Medvedovic
- Department of Environmental and Public Health Sciences, University of Cincinnati College of Medicine, USA.,Department of Biomedical Informatics, University of Cincinnati College of Medicine, USA
| | - Jarek Meller
- Department of Environmental and Public Health Sciences, University of Cincinnati College of Medicine, USA.,Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, USA.,Department of Electrical Engineering and Computer Science, University of Cincinnati, USA
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34
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A mass spectrometry-based proteome map of drug action in lung cancer cell lines. Nat Chem Biol 2020; 16:1111-1119. [PMID: 32690943 DOI: 10.1038/s41589-020-0572-3] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2019] [Accepted: 05/22/2020] [Indexed: 11/08/2022]
Abstract
Mass spectrometry-based discovery proteomics is an essential tool for the proximal readout of cellular drug action. Here, we apply a robust proteomic workflow to rapidly profile the proteomes of five lung cancer cell lines in response to more than 50 drugs. Integration of millions of quantitative protein-drug associations substantially improved the mechanism of action (MoA) deconvolution of single compounds. For example, MoA specificity increased after removal of proteins that frequently responded to drugs and the aggregation of proteome changes across cell lines resolved compound effects on proteostasis. We leveraged these findings to demonstrate efficient target identification of chemical protein degraders. Aggregating drug response across cell lines also revealed that one-quarter of compounds modulated the abundance of one of their known protein targets. Finally, the proteomic data led us to discover that inhibition of mitochondrial function is an off-target mechanism of the MAP2K1/2 inhibitor PD184352 and that the ALK inhibitor ceritinib modulates autophagy.
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35
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LC-MS/MS-based quantitative proteomic and phosphoproteomic analysis of CHO-K1 cells adapted to growth in glutamine-free media. Biotechnol Lett 2020; 42:2523-2536. [DOI: 10.1007/s10529-020-02953-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2020] [Accepted: 06/28/2020] [Indexed: 12/24/2022]
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36
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Silva MC, Haggarty SJ. Human pluripotent stem cell-derived models and drug screening in CNS precision medicine. Ann N Y Acad Sci 2020; 1471:18-56. [PMID: 30875083 PMCID: PMC8193821 DOI: 10.1111/nyas.14012] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2018] [Revised: 01/02/2019] [Accepted: 01/07/2019] [Indexed: 12/12/2022]
Abstract
Development of effective therapeutics for neurological disorders has historically been challenging partly because of lack of accurate model systems in which to investigate disease etiology and test new therapeutics at the preclinical stage. Human stem cells, particularly patient-derived induced pluripotent stem cells (iPSCs) upon differentiation, have the ability to recapitulate aspects of disease pathophysiology and are increasingly recognized as robust scalable systems for drug discovery. We review advances in deriving cellular models of human central nervous system (CNS) disorders using iPSCs along with strategies for investigating disease-relevant phenotypes, translatable biomarkers, and therapeutic targets. Given their potential to identify novel therapeutic targets and leads, we focus on phenotype-based, small-molecule screens employing human stem cell-derived models. Integrated efforts to assemble patient iPSC-derived cell models with deeply annotated clinicopathological data, along with molecular and drug-response signatures, may aid in the stratification of patients, diagnostics, and clinical trial success, shifting translational science and precision medicine approaches. A number of remaining challenges, including the optimization of cost-effective, large-scale culture of iPSC-derived cell types, incorporation of aging into neuronal models, as well as robustness and automation of phenotypic assays to support quantitative drug efficacy, toxicity, and metabolism testing workflows, are covered. Continued advancement of the field is expected to help fully humanize the process of CNS drug discovery.
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Affiliation(s)
- M. Catarina Silva
- Chemical Neurobiology Laboratory, Departments of Neurology and Psychiatry, Massachusetts General Hospital, Center for Genomic Medicine, Harvard Medical School, Boston MA, USA
| | - Stephen J. Haggarty
- Chemical Neurobiology Laboratory, Departments of Neurology and Psychiatry, Massachusetts General Hospital, Center for Genomic Medicine, Harvard Medical School, Boston MA, USA
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37
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Pino LK, Searle BC, Bollinger JG, Nunn B, MacLean B, MacCoss MJ. The Skyline ecosystem: Informatics for quantitative mass spectrometry proteomics. MASS SPECTROMETRY REVIEWS 2020; 39:229-244. [PMID: 28691345 PMCID: PMC5799042 DOI: 10.1002/mas.21540] [Citation(s) in RCA: 432] [Impact Index Per Article: 108.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/19/2016] [Accepted: 06/01/2017] [Indexed: 05/03/2023]
Abstract
Skyline is a freely available, open-source Windows client application for accelerating targeted proteomics experimentation, with an emphasis on the proteomics and mass spectrometry community as users and as contributors. This review covers the informatics encompassed by the Skyline ecosystem, from computationally assisted targeted mass spectrometry method development, to raw acquisition file data processing, and quantitative analysis and results sharing.
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Affiliation(s)
- Lindsay K Pino
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, Washington
| | - Brian C Searle
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, Washington
| | - James G Bollinger
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, Washington
| | - Brook Nunn
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, Washington
| | - Brendan MacLean
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, Washington
| | - Michael J MacCoss
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, Washington
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38
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Enache OM, Lahr DL, Natoli TE, Litichevskiy L, Wadden D, Flynn C, Gould J, Asiedu JK, Narayan R, Subramanian A. The GCTx format and cmap{Py, R, M, J} packages: resources for optimized storage and integrated traversal of annotated dense matrices. Bioinformatics 2020; 35:1427-1429. [PMID: 30203022 PMCID: PMC6477971 DOI: 10.1093/bioinformatics/bty784] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2018] [Revised: 07/16/2018] [Accepted: 09/07/2018] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION Facilitated by technological improvements, pharmacologic and genetic perturbational datasets have grown in recent years to include millions of experiments. Sharing and publicly distributing these diverse data creates many opportunities for discovery, but in recent years the unprecedented size of data generated and its complex associated metadata have also created data storage and integration challenges. RESULTS We present the GCTx file format and a suite of open-source packages for the efficient storage, serialization and analysis of dense two-dimensional matrices. We have extensively used the format in the Connectivity Map to assemble and share massive datasets currently comprising 1.3 million experiments, and we anticipate that the format's generalizability, paired with code libraries that we provide, will lower barriers for integrated cross-assay analysis and algorithm development. AVAILABILITY AND IMPLEMENTATION Software packages (available in Python, R, Matlab and Java) are freely available at https://github.com/cmap. Additional instructions, tutorials and datasets are available at clue.io/code. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | | | | | | | - David Wadden
- Department of Computer Science and Engineering, University of Washington, Seattle, WA, USA
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39
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Mollah SA, Subramaniam S. Histone Signatures Predict Therapeutic Efficacy in Breast Cancer. IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY 2020; 1:74-82. [PMID: 32412527 PMCID: PMC7207876 DOI: 10.1109/ojemb.2020.2967105] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2019] [Revised: 01/08/2020] [Accepted: 01/08/2020] [Indexed: 01/01/2023] Open
Abstract
Objective: Regulatory abnormalities caused by chromatin modifications are being increasingly recognized as contributors to cancer. While many molecularly targeted drugs have the potential to revert these modifications, their precise mechanism of action in cellular reprogramming is not known. Methods: To address this, we introduce an integrated phosphoprotein-histone-drug network (iPhDNet) approach to generate “global chromatin fingerprints of histone signatures.” The method integrates proteomic/phosphoproteomic, transcriptomic and regulatory genomic data to provide a causal mechanistic network and histone signatures of drug response. Results: We demonstrate the utility of iPhDNet in identifying H3K27me3K36me3 histone mark as a key fingerprint of response, mediated by chromatin remodelers BRD4, NSD3, EZH2, and a proto-oncogene MYC when treated with CDK inhibitors. Conclusions: We construct a regulatory network of breast cancer response to treatment and show that histone H3K27me3K36me3 status changes, driven by the BRD4/MYC pathway, upon treatment with drugs are hallmarks of response to treatment.
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Affiliation(s)
- Shamim A Mollah
- 11Bioinformatics & Systems Biology ProgramThe University of California San DiegoLa JollaCA92093USA
| | - Shankar Subramaniam
- 33Departments of Bioengineering, Cellular & Molecular Medicine and Computer Science & EngineeringThe University of California San DiegoLa JollaCA92093USA
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40
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Leutert M, Rodríguez‐Mias RA, Fukuda NK, Villén J. R2-P2 rapid-robotic phosphoproteomics enables multidimensional cell signaling studies. Mol Syst Biol 2019; 15:e9021. [PMID: 31885202 PMCID: PMC6920700 DOI: 10.15252/msb.20199021] [Citation(s) in RCA: 91] [Impact Index Per Article: 18.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Revised: 11/14/2019] [Accepted: 11/18/2019] [Indexed: 01/17/2023] Open
Abstract
Recent developments in proteomics have enabled signaling studies where > 10,000 phosphosites can be routinely identified and quantified. Yet, current analyses are limited in throughput, reproducibility, and robustness, hampering experiments that involve multiple perturbations, such as those needed to map kinase-substrate relationships, capture pathway crosstalks, and network inference analysis. To address these challenges, we introduce rapid-robotic phosphoproteomics (R2-P2), an end-to-end automated method that uses magnetic particles to process protein extracts to deliver mass spectrometry-ready phosphopeptides. R2-P2 is rapid, robust, versatile, and high-throughput. To showcase the method, we applied it, in combination with data-independent acquisition mass spectrometry, to study signaling dynamics in the mitogen-activated protein kinase (MAPK) pathway in yeast. Our results reveal broad and specific signaling events along the mating, the high-osmolarity glycerol, and the invasive growth branches of the MAPK pathway, with robust phosphorylation of downstream regulatory proteins and transcription factors. Our method facilitates large-scale signaling studies involving hundreds of perturbations opening the door to systems-level studies aiming to capture signaling complexity.
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Affiliation(s)
- Mario Leutert
- Department of Genome SciencesUniversity of WashingtonSeattleWAUSA
| | | | - Noelle K Fukuda
- Department of Genome SciencesUniversity of WashingtonSeattleWAUSA
| | - Judit Villén
- Department of Genome SciencesUniversity of WashingtonSeattleWAUSA
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41
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High-Throughput Assessment of Kinome-wide Activation States. Cell Syst 2019; 9:366-374.e5. [PMID: 31521607 PMCID: PMC6838672 DOI: 10.1016/j.cels.2019.08.005] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2018] [Revised: 06/13/2019] [Accepted: 08/13/2019] [Indexed: 02/02/2023]
Abstract
Aberrant kinase activity has been linked to a variety of disorders; however, methods to probe kinase activation states in cells have been lacking. Until now, kinase activity has mainly been deduced from either protein expression or substrate phosphorylation levels. Here, we describe a strategy to directly infer kinase activation through targeted quantification of T-loop phosphorylation, which serves as a critical activation switch in a majority of protein kinases. Combining selective phosphopeptide enrichment with robust targeted mass spectrometry, we provide highly specific assays for 248 peptides, covering 221 phosphosites in the T-loop region of 178 human kinases. Using these assays, we monitored the activation of 63 kinases through 73 T-loop phosphosites across different cell types, primary cells, and patient-derived tissue material. The sensitivity of our assays is highlighted by the reproducible detection of TNF-α-induced RIPK1 activation and the detection of 46 T-loop phosphorylation sites from a breast tumor needle biopsy. Robust targeted MS assays permit observation of conserved kinome activation sites 178 human kinases are characterized in high-throughput assays Kinase activation states are observed in human primary cells and needle biopsy Specific kinase activation states are induced during cell death and drug resistance
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42
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Gil J, Betancourt LH, Pla I, Sanchez A, Appelqvist R, Miliotis T, Kuras M, Oskolas H, Kim Y, Horvath Z, Eriksson J, Berge E, Burestedt E, Jönsson G, Baldetorp B, Ingvar C, Olsson H, Lundgren L, Horvatovich P, Murillo JR, Sugihara Y, Welinder C, Wieslander E, Lee B, Lindberg H, Pawłowski K, Kwon HJ, Doma V, Timar J, Karpati S, Szasz AM, Németh IB, Nishimura T, Corthals G, Rezeli M, Knudsen B, Malm J, Marko-Varga G. Clinical protein science in translational medicine targeting malignant melanoma. Cell Biol Toxicol 2019; 35:293-332. [PMID: 30900145 PMCID: PMC6757020 DOI: 10.1007/s10565-019-09468-6] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2018] [Accepted: 02/13/2019] [Indexed: 02/06/2023]
Abstract
Melanoma of the skin is the sixth most common type of cancer in Europe and accounts for 3.4% of all diagnosed cancers. More alarming is the degree of recurrence that occurs with approximately 20% of patients lethally relapsing following treatment. Malignant melanoma is a highly aggressive skin cancer and metastases rapidly extend to the regional lymph nodes (stage 3) and to distal organs (stage 4). Targeted oncotherapy is one of the standard treatment for progressive stage 4 melanoma, and BRAF inhibitors (e.g. vemurafenib, dabrafenib) combined with MEK inhibitor (e.g. trametinib) can effectively counter BRAFV600E-mutated melanomas. Compared to conventional chemotherapy, targeted BRAFV600E inhibition achieves a significantly higher response rate. After a period of cancer control, however, most responsive patients develop resistance to the therapy and lethal progression. The many underlying factors potentially causing resistance to BRAF inhibitors have been extensively studied. Nevertheless, the remaining unsolved clinical questions necessitate alternative research approaches to address the molecular mechanisms underlying metastatic and treatment-resistant melanoma. In broader terms, proteomics can address clinical questions far beyond the reach of genomics, by measuring, i.e. the relative abundance of protein products, post-translational modifications (PTMs), protein localisation, turnover, protein interactions and protein function. More specifically, proteomic analysis of body fluids and tissues in a given medical and clinical setting can aid in the identification of cancer biomarkers and novel therapeutic targets. Achieving this goal requires the development of a robust and reproducible clinical proteomic platform that encompasses automated biobanking of patient samples, tissue sectioning and histological examination, efficient protein extraction, enzymatic digestion, mass spectrometry-based quantitative protein analysis by label-free or labelling technologies and/or enrichment of peptides with specific PTMs. By combining data from, e.g. phosphoproteomics and acetylomics, the protein expression profiles of different melanoma stages can provide a solid framework for understanding the biology and progression of the disease. When complemented by proteogenomics, customised protein sequence databases generated from patient-specific genomic and transcriptomic data aid in interpreting clinical proteomic biomarker data to provide a deeper and more comprehensive molecular characterisation of cellular functions underlying disease progression. In parallel to a streamlined, patient-centric, clinical proteomic pipeline, mass spectrometry-based imaging can aid in interrogating the spatial distribution of drugs and drug metabolites within tissues at single-cell resolution. These developments are an important advancement in studying drug action and efficacy in vivo and will aid in the development of more effective and safer strategies for the treatment of melanoma. A collaborative effort of gargantuan proportions between academia and healthcare professionals has led to the initiation, establishment and development of a cutting-edge cancer research centre with a specialisation in melanoma and lung cancer. The primary research focus of the European Cancer Moonshot Lund Center is to understand the impact that drugs have on cancer at an individualised and personalised level. Simultaneously, the centre increases awareness of the relentless battle against cancer and attracts global interest in the exceptional research performed at the centre.
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Affiliation(s)
- Jeovanis Gil
- Clinical Protein Science & Imaging, Biomedical Centre, Department of Biomedical Engineering, Lund University, BMC D13, 221 84, Lund, Sweden.
| | - Lazaro Hiram Betancourt
- Clinical Protein Science & Imaging, Biomedical Centre, Department of Biomedical Engineering, Lund University, BMC D13, 221 84, Lund, Sweden.
| | - Indira Pla
- Clinical Protein Science & Imaging, Biomedical Centre, Department of Biomedical Engineering, Lund University, BMC D13, 221 84, Lund, Sweden
- Section for Clinical Chemistry, Department of Translational Medicine, Lund University, Skåne University Hospital Malmö, 205 02, Malmö, Sweden
| | - Aniel Sanchez
- Clinical Protein Science & Imaging, Biomedical Centre, Department of Biomedical Engineering, Lund University, BMC D13, 221 84, Lund, Sweden
- Section for Clinical Chemistry, Department of Translational Medicine, Lund University, Skåne University Hospital Malmö, 205 02, Malmö, Sweden
| | - Roger Appelqvist
- Clinical Protein Science & Imaging, Biomedical Centre, Department of Biomedical Engineering, Lund University, BMC D13, 221 84, Lund, Sweden
| | - Tasso Miliotis
- Clinical Protein Science & Imaging, Biomedical Centre, Department of Biomedical Engineering, Lund University, BMC D13, 221 84, Lund, Sweden
- Translational Science, Cardiovascular Renal and Metabolism, IMED Biotech Unit, AstraZeneca, Gothenburg, Sweden
| | - Magdalena Kuras
- Clinical Protein Science & Imaging, Biomedical Centre, Department of Biomedical Engineering, Lund University, BMC D13, 221 84, Lund, Sweden
| | - Henriette Oskolas
- Clinical Protein Science & Imaging, Biomedical Centre, Department of Biomedical Engineering, Lund University, BMC D13, 221 84, Lund, Sweden
| | - Yonghyo Kim
- Clinical Protein Science & Imaging, Biomedical Centre, Department of Biomedical Engineering, Lund University, BMC D13, 221 84, Lund, Sweden
| | - Zsolt Horvath
- Clinical Protein Science & Imaging, Biomedical Centre, Department of Biomedical Engineering, Lund University, BMC D13, 221 84, Lund, Sweden
| | - Jonatan Eriksson
- Clinical Protein Science & Imaging, Biomedical Centre, Department of Biomedical Engineering, Lund University, BMC D13, 221 84, Lund, Sweden
| | - Ethan Berge
- Clinical Protein Science & Imaging, Biomedical Centre, Department of Biomedical Engineering, Lund University, BMC D13, 221 84, Lund, Sweden
| | - Elisabeth Burestedt
- Clinical Protein Science & Imaging, Biomedical Centre, Department of Biomedical Engineering, Lund University, BMC D13, 221 84, Lund, Sweden
| | - Göran Jönsson
- Division of Oncology and Pathology, Department of Clinical Sciences Lund, Lund University, 221 85, Lund, Sweden
| | - Bo Baldetorp
- Division of Oncology and Pathology, Department of Clinical Sciences Lund, Lund University, 221 85, Lund, Sweden
| | - Christian Ingvar
- Department of Surgery, Clinical Sciences, Lund University, SUS, Lund, Sweden
| | - Håkan Olsson
- Division of Oncology and Pathology, Department of Clinical Sciences Lund, Lund University, 221 85, Lund, Sweden
| | - Lotta Lundgren
- Division of Oncology and Pathology, Department of Clinical Sciences Lund, Lund University, 221 85, Lund, Sweden
- Department of Haematology, Oncology and Radiation Physics, Skåne University Hospital, Lund, Sweden
| | - Peter Horvatovich
- Department of Analytical Biochemistry, Faculty of Science and Engineering, University of Groningen, Groningen, The Netherlands
| | - Jimmy Rodriguez Murillo
- Clinical Protein Science & Imaging, Biomedical Centre, Department of Biomedical Engineering, Lund University, BMC D13, 221 84, Lund, Sweden
| | - Yutaka Sugihara
- Clinical Protein Science & Imaging, Biomedical Centre, Department of Biomedical Engineering, Lund University, BMC D13, 221 84, Lund, Sweden
| | - Charlotte Welinder
- Division of Oncology and Pathology, Department of Clinical Sciences Lund, Lund University, 221 85, Lund, Sweden
| | - Elisabet Wieslander
- Division of Oncology and Pathology, Department of Clinical Sciences Lund, Lund University, 221 85, Lund, Sweden
| | - Boram Lee
- Clinical Protein Science & Imaging, Biomedical Centre, Department of Biomedical Engineering, Lund University, BMC D13, 221 84, Lund, Sweden
| | - Henrik Lindberg
- Clinical Protein Science & Imaging, Biomedical Centre, Department of Biomedical Engineering, Lund University, BMC D13, 221 84, Lund, Sweden
| | - Krzysztof Pawłowski
- Clinical Protein Science & Imaging, Biomedical Centre, Department of Biomedical Engineering, Lund University, BMC D13, 221 84, Lund, Sweden
- Department of Experimental Design and Bioinformatics, Faculty of Agriculture and Biology, Warsaw University of Life Sciences, Warsaw, Poland
| | - Ho Jeong Kwon
- Clinical Protein Science & Imaging, Biomedical Centre, Department of Biomedical Engineering, Lund University, BMC D13, 221 84, Lund, Sweden
- Chemical Genomics Global Research Lab, Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, Republic of Korea
| | - Viktoria Doma
- Second Department of Pathology, Semmelweis University, Budapest, Hungary
| | - Jozsef Timar
- Second Department of Pathology, Semmelweis University, Budapest, Hungary
| | - Sarolta Karpati
- Department of Dermatology, Semmelweis University, Budapest, Hungary
| | - A Marcell Szasz
- Clinical Protein Science & Imaging, Biomedical Centre, Department of Biomedical Engineering, Lund University, BMC D13, 221 84, Lund, Sweden
- Division of Oncology and Pathology, Department of Clinical Sciences Lund, Lund University, 221 85, Lund, Sweden
- Cancer Center, Semmelweis University, Budapest, 1083, Hungary
- MTA-TTK Momentum Oncology Biomarker Research Group, Hungarian Academy of Sciences, Budapest, 1117, Hungary
| | - István Balázs Németh
- Department of Dermatology and Allergology, University of Szeged, Szeged, H-6720, Hungary
| | - Toshihide Nishimura
- Clinical Translational Medicine Informatics, St. Marianna University School of Medicine, Kawasaki, Kanagawa, Japan
- Department of Surgery, Tokyo Medical University, 6-7-1 Nishishinjiku Shinjiku-ku, Tokyo, Japan
| | - Garry Corthals
- Van't Hoff Institute of Molecular Sciences, 1090 GS, Amsterdam, The Netherlands
| | - Melinda Rezeli
- Clinical Protein Science & Imaging, Biomedical Centre, Department of Biomedical Engineering, Lund University, BMC D13, 221 84, Lund, Sweden
| | - Beatrice Knudsen
- Biomedical Sciences and Pathology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Johan Malm
- Section for Clinical Chemistry, Department of Translational Medicine, Lund University, Skåne University Hospital Malmö, 205 02, Malmö, Sweden
| | - György Marko-Varga
- Clinical Protein Science & Imaging, Biomedical Centre, Department of Biomedical Engineering, Lund University, BMC D13, 221 84, Lund, Sweden
- Chemical Genomics Global Research Lab, Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, Republic of Korea
- Department of Surgery, Tokyo Medical University, 6-7-1 Nishishinjiku Shinjiku-ku, Tokyo, Japan
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43
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Keenan AB, Wojciechowicz ML, Wang Z, Jagodnik KM, Jenkins SL, Lachmann A, Ma'ayan A. Connectivity Mapping: Methods and Applications. Annu Rev Biomed Data Sci 2019. [DOI: 10.1146/annurev-biodatasci-072018-021211] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Connectivity mapping resources consist of signatures representing changes in cellular state following systematic small-molecule, disease, gene, or other form of perturbations. Such resources enable the characterization of signatures from novel perturbations based on similarity; provide a global view of the space of many themed perturbations; and allow the ability to predict cellular, tissue, and organismal phenotypes for perturbagens. A signature search engine enables hypothesis generation by finding connections between query signatures and the database of signatures. This framework has been used to identify connections between small molecules and their targets, to discover cell-specific responses to perturbations and ways to reverse disease expression states with small molecules, and to predict small-molecule mimickers for existing drugs. This review provides a historical perspective and the current state of connectivity mapping resources with a focus on both methodology and community implementations.
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Affiliation(s)
- Alexandra B. Keenan
- Department of Pharmacological Sciences and Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Megan L. Wojciechowicz
- Department of Pharmacological Sciences and Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Zichen Wang
- Department of Pharmacological Sciences and Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Kathleen M. Jagodnik
- Department of Pharmacological Sciences and Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Sherry L. Jenkins
- Department of Pharmacological Sciences and Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Alexander Lachmann
- Department of Pharmacological Sciences and Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Avi Ma'ayan
- Department of Pharmacological Sciences and Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
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44
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Roewenstrunk J, Di Vona C, Chen J, Borras E, Dong C, Arató K, Sabidó E, Huen MSY, de la Luna S. A comprehensive proteomics-based interaction screen that links DYRK1A to RNF169 and to the DNA damage response. Sci Rep 2019; 9:6014. [PMID: 30979931 PMCID: PMC6461666 DOI: 10.1038/s41598-019-42445-x] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2019] [Accepted: 03/29/2019] [Indexed: 12/15/2022] Open
Abstract
Dysregulation of the DYRK1A protein kinase has been associated with human disease. On the one hand, its overexpression in trisomy 21 has been linked to certain pathological traits of Down syndrome, while on the other, inactivating mutations in just one allele are responsible for a distinct yet rare clinical syndrome, DYRK1A haploinsufficiency. Moreover, altered expression of this kinase may also provoke other human pathologies, including cancer and diabetes. Although a few DYRK1A substrates have been described, its upstream regulators and downstream targets are still poorly understood, an information that could shed light on the functions of DYRK1A in the cell. Here, we carried out a proteomic screen using antibody-based affinity purification coupled to mass spectrometry to identify proteins that directly or indirectly bind to endogenous DYRK1A. We show that the use of a cell line not expressing DYRK1A, generated by CRISPR/Cas9 technology, was needed in order to discriminate between true positives and non-specific interactions. Most of the proteins identified in the screen are novel candidate DYRK1A interactors linked to a variety of activities in the cell. The in-depth characterization of DYRK1A's functional interaction with one of them, the E3 ubiquitin ligase RNF169, revealed a role for this kinase in the DNA damage response. We found that RNF169 is a DYRK1A substrate and we identified several of its phosphorylation sites. In particular, one of these sites appears to modify the ability of RNF169 to displace 53BP1 from sites of DNA damage. Indeed, DYRK1A depletion increases cell sensitivity to ionizing irradiation. Therefore, our unbiased proteomic screen has revealed a novel activity of DYRK1A, expanding the complex role of this kinase in controlling cell homeostasis.
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Affiliation(s)
- Julia Roewenstrunk
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology (BIST), 08003, Barcelona, Spain
- Centro de Investigación Biomédica en Red en Enfermedades Raras (CIBERER), Barcelona, Spain
| | - Chiara Di Vona
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology (BIST), 08003, Barcelona, Spain
- Centro de Investigación Biomédica en Red en Enfermedades Raras (CIBERER), Barcelona, Spain
| | - Jie Chen
- School of Biomedical Sciences, LKS Faculty of Medicine, The University of Hong Kong, S.A.R., Hong Kong, China
| | - Eva Borras
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology (BIST), 08003, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), 08003, Barcelona, Spain
| | - Chao Dong
- School of Biomedical Sciences, LKS Faculty of Medicine, The University of Hong Kong, S.A.R., Hong Kong, China
| | - Krisztina Arató
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology (BIST), 08003, Barcelona, Spain
- Centro de Investigación Biomédica en Red en Enfermedades Raras (CIBERER), Barcelona, Spain
| | - Eduard Sabidó
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology (BIST), 08003, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), 08003, Barcelona, Spain
| | - Michael S Y Huen
- School of Biomedical Sciences, LKS Faculty of Medicine, The University of Hong Kong, S.A.R., Hong Kong, China
- State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, S.A.R., Hong Kong, China
| | - Susana de la Luna
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology (BIST), 08003, Barcelona, Spain.
- Centro de Investigación Biomédica en Red en Enfermedades Raras (CIBERER), Barcelona, Spain.
- Universitat Pompeu Fabra (UPF), 08003, Barcelona, Spain.
- Institució Catalana de Recerca i Estudis Avançats (ICREA), 08010, Barcelona, Spain.
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45
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A phosphoproteomic signature in endothelial cells predicts vascular toxicity of tyrosine kinase inhibitors used in CML. Blood Adv 2019; 2:1680-1684. [PMID: 30021779 DOI: 10.1182/bloodadvances.2018020396] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2018] [Accepted: 06/26/2018] [Indexed: 12/11/2022] Open
Abstract
Key Points
Newer CML kinase inhibitors increase ischemia risk and are toxic to endothelial cells where they produce a proteomic toxicity signature. This phosphoproteomic EC toxicity signature predicts bosutinib to be safe, providing a potential screening tool for safer drug development.
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46
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Zhang B, Whiteaker JR, Hoofnagle AN, Baird GS, Rodland KD, Paulovich AG. Clinical potential of mass spectrometry-based proteogenomics. Nat Rev Clin Oncol 2019; 16:256-268. [PMID: 30487530 PMCID: PMC6448780 DOI: 10.1038/s41571-018-0135-7] [Citation(s) in RCA: 130] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Cancer genomics research aims to advance personalized oncology by finding and targeting specific genetic alterations associated with cancers. In genome-driven oncology, treatments are selected for individual patients on the basis of the findings of tumour genome sequencing. This personalized approach has prolonged the survival of subsets of patients with cancer. However, many patients do not respond to the predicted therapies based on the genomic profiles of their tumours. Furthermore, studies pairing genomic and proteomic analyses of samples from the same tumours have shown that the proteome contains novel information that cannot be discerned through genomic analysis alone. This observation has led to the concept of proteogenomics, in which both types of data are leveraged for a more complete view of tumour biology that might enable patients to be more successfully matched to effective treatments than they would using genomics alone. In this Perspective, we discuss the added value of proteogenomics over the current genome-driven approach to the clinical characterization of cancers and summarize current efforts to incorporate targeted proteomic measurements based on selected/multiple reaction monitoring (SRM/MRM) mass spectrometry into the clinical laboratory to facilitate clinical proteogenomics.
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Affiliation(s)
- Bing Zhang
- Department of Molecular and Human Genetics, Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX, USA
| | - Jeffrey R Whiteaker
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Andrew N Hoofnagle
- Department of Medicine, University of Washington, Seattle, WA, USA
- Department of Laboratory Medicine, University of Washington, Seattle, WA, USA
| | - Geoffrey S Baird
- Department of Laboratory Medicine, University of Washington, Seattle, WA, USA
- Department of Pathology, University of Washington, Seattle, WA, USA
| | - Karin D Rodland
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
- Department of Cell, Development and Cancer Biology, Oregon Health & Sciences University, Portland, OR, USA
| | - Amanda G Paulovich
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
- Division of Medical Oncology, University of Washington School of Medicine, Seattle, WA, USA.
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47
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Krug K, Mertins P, Zhang B, Hornbeck P, Raju R, Ahmad R, Szucs M, Mundt F, Forestier D, Jane-Valbuena J, Keshishian H, Gillette MA, Tamayo P, Mesirov JP, Jaffe JD, Carr SA, Mani DR. A Curated Resource for Phosphosite-specific Signature Analysis. Mol Cell Proteomics 2019; 18:576-593. [PMID: 30563849 PMCID: PMC6398202 DOI: 10.1074/mcp.tir118.000943] [Citation(s) in RCA: 164] [Impact Index Per Article: 32.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2018] [Revised: 11/13/2018] [Indexed: 12/28/2022] Open
Abstract
Signaling pathways are orchestrated by post-translational modifications (PTMs) such as phosphorylation. However, pathway analysis of PTM data sets generated by mass spectrometry (MS)-based proteomics is typically performed at a gene-centric level because of the lack of appropriately curated PTM signature databases and bioinformatic tools that leverage PTM site-specific information. Here we present the first version of PTMsigDB, a database of modification site-specific signatures of perturbations, kinase activities and signaling pathways curated from more than 2,500 publications. We adapted the widely used single sample Gene Set Enrichment Analysis approach to utilize PTMsigDB, enabling PTMSignature Enrichment Analysis (PTM-SEA) of quantitative MS data. We used a well-characterized data set of epidermal growth factor (EGF)-perturbed cancer cells to evaluate our approach and demonstrated better representation of signaling events compared with gene-centric methods. We then applied PTM-SEA to analyze the phosphoproteomes of cancer cells treated with cell-cycle inhibitors and detected mechanism-of-action specific signatures of cell cycle kinases. We also applied our methods to analyze the phosphoproteomes of PI3K-inhibited human breast cancer cells and detected signatures of compounds inhibiting PI3K as well as targets downstream of PI3K (AKT, MAPK/ERK) covering a substantial fraction of the PI3K pathway. PTMsigDB and PTM-SEA can be freely accessed at https://github.com/broadinstitute/ssGSEA2.0.
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Affiliation(s)
- Karsten Krug
- From the ‡Broad Institute of MIT and Harvard, Cambridge Massachusetts 02142
| | - Philipp Mertins
- From the ‡Broad Institute of MIT and Harvard, Cambridge Massachusetts 02142
- §Proteomics Platform, Max Delbrück Center for Molecular Medicine, Berlin, Germany 13092
- ¶Berlin Institute of Health, Berlin, Germany 10178
| | - Bin Zhang
- ‖Cell Signaling Technology, Danvers Massachusetts 01923
| | | | - Rajesh Raju
- **Rajiv Gandhi Centre for Biotechnology, Thiruvananthapuram, India 695014
| | - Rushdy Ahmad
- From the ‡Broad Institute of MIT and Harvard, Cambridge Massachusetts 02142
| | - Matthew Szucs
- From the ‡Broad Institute of MIT and Harvard, Cambridge Massachusetts 02142
- ‡‡University of Colorado, Denver Colorado 80204
| | - Filip Mundt
- From the ‡Broad Institute of MIT and Harvard, Cambridge Massachusetts 02142
| | - Dominique Forestier
- §§Department of Oncology, Novartis Institute of Biomedical Research, Cambridge Massachusetts 02139
| | | | - Hasmik Keshishian
- From the ‡Broad Institute of MIT and Harvard, Cambridge Massachusetts 02142
| | - Michael A Gillette
- From the ‡Broad Institute of MIT and Harvard, Cambridge Massachusetts 02142
- ¶¶Massachusetts General Hospital, Boston Massachusetts 02114
| | - Pablo Tamayo
- From the ‡Broad Institute of MIT and Harvard, Cambridge Massachusetts 02142
- ‖‖Department of Medicine, UCSD, La Jolla Califorrnia 92093
- ***Moores Cancer Center, UCSD, La Jolla California 92093
| | - Jill P Mesirov
- From the ‡Broad Institute of MIT and Harvard, Cambridge Massachusetts 02142
- ‖‖Department of Medicine, UCSD, La Jolla Califorrnia 92093
- ***Moores Cancer Center, UCSD, La Jolla California 92093
| | - Jacob D Jaffe
- From the ‡Broad Institute of MIT and Harvard, Cambridge Massachusetts 02142
| | - Steven A Carr
- From the ‡Broad Institute of MIT and Harvard, Cambridge Massachusetts 02142
| | - D R Mani
- From the ‡Broad Institute of MIT and Harvard, Cambridge Massachusetts 02142;
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48
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Enhanced Molecular Appreciation of Psychiatric Disorders Through High-Dimensionality Data Acquisition and Analytics. Methods Mol Biol 2019; 2011:671-723. [PMID: 31273728 DOI: 10.1007/978-1-4939-9554-7_39] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
The initial diagnosis, molecular investigation, treatment, and posttreatment care of major psychiatric disorders (schizophrenia and bipolar depression) are all still significantly hindered by the current inability to define these disorders in an explicit molecular signaling manner. High-dimensionality data analytics, using large datastreams from transcriptomic, proteomic, or metabolomic investigations, will likely advance both the appreciation of the molecular nature of major psychiatric disorders and simultaneously enhance our ability to more efficiently diagnose and treat these debilitating conditions. High-dimensionality data analysis in psychiatric research has been heterogeneous in aims and methods and limited by insufficient sample sizes, poorly defined case definitions, methodological inhomogeneity, and confounding results. All of these issues combine to constrain the conclusions that can be extracted from them. Here, we discuss possibilities for overcoming methodological challenges through the implementation of transcriptomic, proteomic, or metabolomics signatures in psychiatric diagnosis and offer an outlook for future investigations. To fulfill the promise of intelligent high-dimensionality data-based differential diagnosis in mental disease diagnosis and treatment, future research will need large, well-defined cohorts in combination with state-of-the-art technologies.
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49
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Automated phosphopeptide enrichment from minute quantities of frozen malignant melanoma tissue. PLoS One 2018; 13:e0208562. [PMID: 30532160 PMCID: PMC6287822 DOI: 10.1371/journal.pone.0208562] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2018] [Accepted: 11/19/2018] [Indexed: 11/19/2022] Open
Abstract
To acquire a deeper understanding of malignant melanoma (MM), it is essential to study the proteome of patient tissues. In particular, phosphoproteomics of MM has become of significant importance because of the central role that phosphorylation plays in the development of MM. Investigating clinical samples, however, is an extremely challenging task as there is usually only very limited quantities of material available to perform targeted enrichment approaches. Here, an automated phosphopeptide enrichment protocol using the AssayMap Bravo platform was applied to MM tissues and assessed for performance. The strategy proved to be highly-sensitive, less prone to variability, less laborious than existing techniques and adequate for starting quantities at the microgram level. An Fe(III)-NTA-IMAC-based enrichment workflow was applied to a dilution series of MM tissue lysates. The workflow was efficient in terms of sensitivity, reproducibility and phosphosite localization; and from only 12.5 μg of sample, more than 1,000 phosphopeptides were identified. In addition, from 60 μg of protein material the number of identified phosphoproteins from individual MM samples was comparable to previous reports that used extensive fractionation methods. Our data set included key pathways that are involved in MM progression; such as MAPK, melanocyte development and integrin signaling. Moreover, tissue-specific immunological proteins were identified, that have not been previously observed in the proteome of MM-derived cell lines. In conclusion, this workflow is suitable to study large cohorts of clinical samples that demand automatic and careful handling.
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50
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Reconstructing phosphorylation signalling networks from quantitative phosphoproteomic data. Essays Biochem 2018; 62:525-534. [PMID: 30072490 PMCID: PMC6204553 DOI: 10.1042/ebc20180019] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2018] [Revised: 06/25/2018] [Accepted: 06/26/2018] [Indexed: 12/25/2022]
Abstract
Cascades of phosphorylation between protein kinases comprise a core mechanism in the integration and propagation of intracellular signals. Although we have accumulated a wealth of knowledge around some such pathways, this is subject to study biases and much remains to be uncovered. Phosphoproteomics, the identification and quantification of phosphorylated proteins on a proteomic scale, provides a high-throughput means of interrogating the state of intracellular phosphorylation, both at the pathway level and at the whole-cell level. In this review, we discuss methods for using human quantitative phosphoproteomic data to reconstruct the underlying signalling networks that generated it. We address several challenges imposed by the data on such analyses and we consider promising advances towards reconstructing unbiased, kinome-scale signalling networks.
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