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Bittremieux W, May DH, Bilmes J, Noble WS. A learned embedding for efficient joint analysis of millions of mass spectra. Nat Methods 2022; 19:675-678. [DOI: 10.1038/s41592-022-01496-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2018] [Accepted: 04/14/2022] [Indexed: 11/09/2022]
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2
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Elyanow R, Snyder TM, Dalai SC, Gittelman RM, Boonyaratanakornkit J, Wald A, Selke S, Wener MH, Morishima C, Greninger AL, Gale M, Hsiang TY, Jing L, Holbrook MR, Kaplan IM, Zahid HJ, May DH, Carlson JM, Baldo L, Manley T, Robins HS, Koelle DM. T cell receptor sequencing identifies prior SARS-CoV-2 infection and correlates with neutralizing antibodies and disease severity. JCI Insight 2022; 7:e150070. [PMID: 35439166 PMCID: PMC9220924 DOI: 10.1172/jci.insight.150070] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Accepted: 04/15/2022] [Indexed: 11/17/2022] Open
Abstract
BACKGROUNDMeasuring the immune response to SARS-CoV-2 enables assessment of past infection and protective immunity. SARS-CoV-2 infection induces humoral and T cell responses, but these responses vary with disease severity and individual characteristics.METHODSA T cell receptor (TCR) immunosequencing assay was conducted using small-volume blood samples from 302 individuals recovered from COVID-19. Correlations between the magnitude of the T cell response and neutralizing antibody (nAb) titers or indicators of disease severity were evaluated. Sensitivity of T cell testing was assessed and compared with serologic testing.RESULTSSARS-CoV-2-specific T cell responses were significantly correlated with nAb titers and clinical indicators of disease severity, including hospitalization, fever, and difficulty breathing. Despite modest declines in depth and breadth of T cell responses during convalescence, high sensitivity was observed until at least 6 months after infection, with overall sensitivity ~5% greater than serology tests for identifying prior SARS-CoV-2 infection. Improved performance of T cell testing was most apparent in recovered, nonhospitalized individuals sampled > 150 days after initial illness, suggesting greater sensitivity than serology at later time points and in individuals with less severe disease. T cell testing identified SARS-CoV-2 infection in 68% (55 of 81) of samples with undetectable nAb titers (<1:40) and in 37% (13 of 35) of samples classified as negative by 3 antibody assays.CONCLUSIONThese results support TCR-based testing as a scalable, reliable measure of past SARS-CoV-2 infection with clinical value beyond serology.TRIAL REGISTRATIONSpecimens were accrued under trial NCT04338360 accessible at clinicaltrials.gov.FUNDINGThis work was funded by Adaptive Biotechnologies, Frederick National Laboratory for Cancer Research, NIAID, Fred Hutchinson Joel Meyers Endowment, Fast Grants, and American Society for Transplantation and Cell Therapy.
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Affiliation(s)
| | | | - Sudeb C. Dalai
- Adaptive Biotechnologies, Seattle, Washington, USA
- Stanford University School of Medicine, Stanford, California, USA
| | | | - Jim Boonyaratanakornkit
- Department of Medicine, University of Washington, Seattle, Washington, USA
- Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Anna Wald
- Department of Medicine, University of Washington, Seattle, Washington, USA
- Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
- Department of Epidemiology
- Department of Laboratory Medicine and Pathology
| | - Stacy Selke
- Department of Laboratory Medicine and Pathology
| | - Mark H. Wener
- Department of Medicine, University of Washington, Seattle, Washington, USA
- Department of Laboratory Medicine and Pathology
| | | | | | - Michael Gale
- Department of Immunology
- Department of Microbiology, and
- Department of Global Health, University of Washington, Seattle, Washington, USA
| | | | - Lichen Jing
- Department of Medicine, University of Washington, Seattle, Washington, USA
| | - Michael R. Holbrook
- National Institute of Allergy and Infectious Diseases (NIAID) Integrated Research Facility, Frederick, Maryland, USA
| | | | | | - Damon H. May
- Adaptive Biotechnologies, Seattle, Washington, USA
| | | | - Lance Baldo
- Adaptive Biotechnologies, Seattle, Washington, USA
| | | | | | - David M. Koelle
- Department of Medicine, University of Washington, Seattle, Washington, USA
- Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
- Department of Laboratory Medicine and Pathology
- Department of Global Health, University of Washington, Seattle, Washington, USA
- Benaroya Research Institute, Seattle, Washington, USA
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Su Y, Yuan D, Chen DG, Ng RH, Wang K, Choi J, Li S, Hong S, Zhang R, Xie J, Kornilov SA, Scherler K, Pavlovitch-Bedzyk AJ, Dong S, Lausted C, Lee I, Fallen S, Dai CL, Baloni P, Smith B, Duvvuri VR, Anderson KG, Li J, Yang F, Duncombe CJ, McCulloch DJ, Rostomily C, Troisch P, Zhou J, Mackay S, DeGottardi Q, May DH, Taniguchi R, Gittelman RM, Klinger M, Snyder TM, Roper R, Wojciechowska G, Murray K, Edmark R, Evans S, Jones L, Zhou Y, Rowen L, Liu R, Chour W, Algren HA, Berrington WR, Wallick JA, Cochran RA, Micikas ME, Wrin T, Petropoulos CJ, Cole HR, Fischer TD, Wei W, Hoon DSB, Price ND, Subramanian N, Hill JA, Hadlock J, Magis AT, Ribas A, Lanier LL, Boyd SD, Bluestone JA, Chu H, Hood L, Gottardo R, Greenberg PD, Davis MM, Goldman JD, Heath JR. Multiple early factors anticipate post-acute COVID-19 sequelae. Cell 2022; 185:881-895.e20. [PMID: 35216672 PMCID: PMC8786632 DOI: 10.1016/j.cell.2022.01.014] [Citation(s) in RCA: 496] [Impact Index Per Article: 248.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 12/14/2021] [Accepted: 01/19/2022] [Indexed: 01/14/2023]
Abstract
Post-acute sequelae of COVID-19 (PASC) represent an emerging global crisis. However, quantifiable risk factors for PASC and their biological associations are poorly resolved. We executed a deep multi-omic, longitudinal investigation of 309 COVID-19 patients from initial diagnosis to convalescence (2-3 months later), integrated with clinical data and patient-reported symptoms. We resolved four PASC-anticipating risk factors at the time of initial COVID-19 diagnosis: type 2 diabetes, SARS-CoV-2 RNAemia, Epstein-Barr virus viremia, and specific auto-antibodies. In patients with gastrointestinal PASC, SARS-CoV-2-specific and CMV-specific CD8+ T cells exhibited unique dynamics during recovery from COVID-19. Analysis of symptom-associated immunological signatures revealed coordinated immunity polarization into four endotypes, exhibiting divergent acute severity and PASC. We find that immunological associations between PASC factors diminish over time, leading to distinct convalescent immune states. Detectability of most PASC factors at COVID-19 diagnosis emphasizes the importance of early disease measurements for understanding emergent chronic conditions and suggests PASC treatment strategies.
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Affiliation(s)
- Yapeng Su
- Institute for Systems Biology, Seattle, WA 98109, USA; Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA; Clinical Research Division, Program in Immunology, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA.
| | - Dan Yuan
- Institute for Systems Biology, Seattle, WA 98109, USA; Department of Bioengineering, University of Washington, Seattle, WA 98105, USA
| | - Daniel G Chen
- Institute for Systems Biology, Seattle, WA 98109, USA; Department of Microbiology and Department of Informatics, University of Washington, Seattle, WA 98195, USA
| | - Rachel H Ng
- Institute for Systems Biology, Seattle, WA 98109, USA; Department of Bioengineering, University of Washington, Seattle, WA 98105, USA
| | - Kai Wang
- Institute for Systems Biology, Seattle, WA 98109, USA
| | - Jongchan Choi
- Institute for Systems Biology, Seattle, WA 98109, USA
| | - Sarah Li
- Institute for Systems Biology, Seattle, WA 98109, USA
| | - Sunga Hong
- Institute for Systems Biology, Seattle, WA 98109, USA
| | - Rongyu Zhang
- Institute for Systems Biology, Seattle, WA 98109, USA; Department of Bioengineering, University of Washington, Seattle, WA 98105, USA
| | - Jingyi Xie
- Institute for Systems Biology, Seattle, WA 98109, USA; Molecular Engineering & Sciences Institute, University of Washington, Seattle, WA 98105, USA
| | | | | | - Ana Jimena Pavlovitch-Bedzyk
- Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Shen Dong
- Diabetes Center, University of California, San Francisco, San Francisco, CA 94143, USA
| | | | - Inyoul Lee
- Institute for Systems Biology, Seattle, WA 98109, USA
| | | | | | | | - Brett Smith
- Institute for Systems Biology, Seattle, WA 98109, USA
| | | | - Kristin G Anderson
- Clinical Research Division, Program in Immunology, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA; Departments of Immunology and Medicine, University of Washington, Seattle, WA 98109, USA
| | - Jing Li
- Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Fan Yang
- Department of Pathology, Stanford University, Stanford, CA 94304, USA
| | | | - Denise J McCulloch
- Division of Allergy and Infectious Diseases, Department of Medicine, University of Washington, Seattle, WA 98109, USA
| | | | | | - Jing Zhou
- Isoplexis Corporation, Branford, CT 06405, USA
| | - Sean Mackay
- Isoplexis Corporation, Branford, CT 06405, USA
| | | | - Damon H May
- Adaptive Biotechnologies, Seattle, WA 98109, USA
| | | | | | - Mark Klinger
- Adaptive Biotechnologies, Seattle, WA 98109, USA
| | | | - Ryan Roper
- Institute for Systems Biology, Seattle, WA 98109, USA
| | - Gladys Wojciechowska
- Institute for Systems Biology, Seattle, WA 98109, USA; Medical University of Białystok, Białystok 15089, Poland
| | - Kim Murray
- Institute for Systems Biology, Seattle, WA 98109, USA
| | - Rick Edmark
- Institute for Systems Biology, Seattle, WA 98109, USA
| | - Simon Evans
- Institute for Systems Biology, Seattle, WA 98109, USA
| | - Lesley Jones
- Institute for Systems Biology, Seattle, WA 98109, USA
| | - Yong Zhou
- Institute for Systems Biology, Seattle, WA 98109, USA
| | - Lee Rowen
- Institute for Systems Biology, Seattle, WA 98109, USA
| | - Rachel Liu
- Institute for Systems Biology, Seattle, WA 98109, USA
| | - William Chour
- Institute for Systems Biology, Seattle, WA 98109, USA
| | - Heather A Algren
- Swedish Center for Research and Innovation, Swedish Medical Center, Seattle, WA 98109, USA; Providence St. Joseph Health, Renton, WA 98057, USA
| | - William R Berrington
- Swedish Center for Research and Innovation, Swedish Medical Center, Seattle, WA 98109, USA; Providence St. Joseph Health, Renton, WA 98057, USA
| | - Julie A Wallick
- Swedish Center for Research and Innovation, Swedish Medical Center, Seattle, WA 98109, USA; Providence St. Joseph Health, Renton, WA 98057, USA
| | - Rebecca A Cochran
- Swedish Center for Research and Innovation, Swedish Medical Center, Seattle, WA 98109, USA; Providence St. Joseph Health, Renton, WA 98057, USA
| | - Mary E Micikas
- Swedish Center for Research and Innovation, Swedish Medical Center, Seattle, WA 98109, USA; Providence St. Joseph Health, Renton, WA 98057, USA
| | - Terri Wrin
- Monogram Biosciences, South San Francisco, CA 94080, USA
| | | | - Hunter R Cole
- St. John's Cancer Institute at Saint John's Health Center, Santa Monica, CA 90404, USA
| | - Trevan D Fischer
- St. John's Cancer Institute at Saint John's Health Center, Santa Monica, CA 90404, USA
| | - Wei Wei
- Institute for Systems Biology, Seattle, WA 98109, USA
| | - Dave S B Hoon
- St. John's Cancer Institute at Saint John's Health Center, Santa Monica, CA 90404, USA
| | | | - Naeha Subramanian
- Institute for Systems Biology, Seattle, WA 98109, USA; Department of Global Heath and Department of Immunology, University of Washington, Seattle, WA 98109, USA
| | - Joshua A Hill
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA; Division of Allergy and Infectious Diseases, Department of Medicine, University of Washington, Seattle, WA 98109, USA
| | | | | | - Antoni Ribas
- Department of Medicine, University of California, Los Angeles, and Parker Institute for Cancer Immunotherapy, Los Angeles, CA 90095, USA
| | - Lewis L Lanier
- Department of Microbiology and Immunology, University of California, San Francisco, and Parker Institute for Cancer Immunotherapy, San Francisco, CA 94143, USA
| | - Scott D Boyd
- Department of Pathology, Stanford University, Stanford, CA 94304, USA
| | - Jeffrey A Bluestone
- Diabetes Center, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Helen Chu
- Division of Global Health, University of Washington, Seattle, WA 98105, USA; Division of Allergy and Infectious Diseases, Department of Medicine, University of Washington, Seattle, WA 98109, USA
| | - Leroy Hood
- Institute for Systems Biology, Seattle, WA 98109, USA; Providence St. Joseph Health, Renton, WA 98057, USA
| | - Raphael Gottardo
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA; Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA; Department of Statistics, University of Washington, Seattle, WA 98195, USA; Biomedical Data Sciences, Lausanne University Hospital, University of Lausanne, Lausanne, 1011, Switzerland
| | - Philip D Greenberg
- Clinical Research Division, Program in Immunology, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA; Departments of Immunology and Medicine, University of Washington, Seattle, WA 98109, USA
| | - Mark M Davis
- Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA; The Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Jason D Goldman
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA; Division of Allergy and Infectious Diseases, Department of Medicine, University of Washington, Seattle, WA 98109, USA; Swedish Center for Research and Innovation, Swedish Medical Center, Seattle, WA 98109, USA; Providence St. Joseph Health, Renton, WA 98057, USA.
| | - James R Heath
- Institute for Systems Biology, Seattle, WA 98109, USA; Department of Bioengineering, University of Washington, Seattle, WA 98105, USA.
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4
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Snyder TM, Gittelman RM, Klinger M, May DH, Osborne EJ, Taniguchi R, Jabran Zahid H, Elyanow R, Dalai SC, Kaplan IM, Dines JN, Noakes MT, Pandya R, Baldo L, Semprini S, Cerchione C, Nicolini F, Mazza M, Delmonte OM, Dobbs K, Laguna-Goya R, Carreño-Tarragona G, Barrio S, Imberti L, Sottini A, Quiros-Roldan E, Rossi C, Biondi A, Bettini LR, D’Angio M, Bonfanti P, Tompkins MF, Alba C, Dalgard C, Sambri V, Martinelli G, Goldman JD, Heath JR, Su HC, Notarangelo LD, Paz-Artal E, Martinez-Lopez J, Carlson JM, Robins HS. 126. Magnitude and Dynamics of the T-Cell Response to SARS-CoV-2 Infection and Vaccination. Open Forum Infect Dis 2021. [PMCID: PMC8690367 DOI: 10.1093/ofid/ofab466.126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Background T cells are central to the early identification and clearance of viral infections and support antibody generation by B cells, making them desirable for assessing the immune response to SARS-CoV-2 infection and vaccines. We combined 2 high-throughput immune profiling methods to create a quantitative picture of the SARS-CoV-2 T-cell response that is highly sensitive, durable, diagnostic, and discriminatory between natural infection and vaccination. Methods We deeply characterized 116 convalescent COVID-19 subjects by experimentally mapping CD8 and CD4 T-cell responses via antigen stimulation to 545 Human Leukocyte Antigen (HLA) class I and 284 class II viral peptides. We also performed T-cell receptor (TCR) repertoire sequencing on 1815 samples from 1521 PCR-confirmed SARS-CoV-2 cases and 3500 controls to identify shared public TCRs from SARS-CoV-2-associated CD8 and CD4 T cells. Combining these approaches with additional samples from vaccinated individuals, we characterized the response to natural infection as well as vaccination by separating responses to spike protein from other viral targets. Results We find that T-cell responses are often driven by a few immunodominant, HLA-restricted epitopes. As expected, the SARS-CoV-2 T-cell response peaks about 1-2 weeks after infection and is detectable at least several months after recovery. Applying these data, we trained a classifier to diagnose past SARS-CoV-2 infection based solely on TCR sequencing from blood samples and observed, at 99.8% specificity, high sensitivity soon after diagnosis (Day 3–7 = 85.1%; Day 8–14 = 94.8%) that persists after recovery (Day 29+/convalescent = 95.4%). Finally, by evaluating TCRs binding epitopes targeting all non-spike SARS-CoV-2 proteins, we were able to separate natural infection from vaccination with > 99% specificity. Conclusion TCR repertoire sequencing from whole blood reliably measures the adaptive immune response to SARS-CoV-2 soon after viral antigenic exposure (before antibodies are typically detectable) as well as at later time points, and distinguishes post-infection vs. vaccine immune responses with high specificity. This approach to characterizing the cellular immune response has applications in clinical diagnostics as well as vaccine development and monitoring. Disclosures Thomas M. Snyder, PhD, Adaptive Biotechnologies (Employee, Shareholder) Rachel M. Gittelman, PhD, Adaptive Biotechnologies (Employee, Shareholder) Mark Klinger, PhD, Adaptive Biotechnologies (Employee, Shareholder) Damon H. May, PhD, Adaptive Biotechnologies (Employee, Shareholder) Edward J. Osborne, PhD, Adaptive Biotechnologies (Employee, Shareholder) Ruth Taniguchi, PhD, Adaptive Biotechnologies (Employee, Shareholder) H. Jabran Zahid, PhD, Microsoft Research (Employee, Shareholder) Rebecca Elyanow, PhD, Adaptive Biotechnologies (Employee, Shareholder) Sudeb C. Dalai, MD, PhD, Adaptive Biotechnologies (Employee, Shareholder) Ian M. Kaplan, PhD, Adaptive Biotechnologies (Employee, Shareholder) Jennifer N. Dines, MD, Adaptive Biotechnologies (Employee, Shareholder) Matthew T. Noakes, PhD, Adaptive Biotechnologies (Employee, Shareholder) Ravi Pandya, PhD, Microsoft Research (Employee, Shareholder) Lance Baldo, MD, Adaptive Biotechnologies (Employee, Shareholder, Leadership Interest) James R. Heath, PhD, Merck (Research Grant or Support, Funding (from BARDA) for the ISB INCOV project, but had no role in planning the research or in writing the paper.) Joaquin Martinez-Lopez, MD, PhD, Adaptive Biotechnologies (Consultant) Jonathan M. Carlson, PhD, Microsoft Research (Employee, Shareholder) Harlan S. Robins, PhD, Adaptive Biotechnologies (Board Member, Employee, Shareholder)
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Affiliation(s)
| | | | | | - Damon H May
- Adaptive Biotechnologies, Seattle, Washington
| | | | | | | | | | - Sudeb C Dalai
- Adaptive Biotechnologies and Stanford University School of Medicine, Seattle, Washington
| | | | | | | | | | - Lance Baldo
- Adaptive Biotechnologies, Seattle, Washington
| | - Simona Semprini
- Unit of Microbiology - The Great Romagna Hub Laboratory, Pievesestina ITALY and DIMES, University of Bologna, Bologna, Emilia-Romagna, Italy
| | - Claudio Cerchione
- Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Medola, Lombardia, Italy
| | - Fabio Nicolini
- Immunotherapy, Cell Therapy and Biobank (ITCB), Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Medola, Lombardia, Italy
| | - Massimiliano Mazza
- Immunotherapy, Cell Therapy and Biobank (ITCB), Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Medola, Lombardia, Italy
| | - Ottavia M Delmonte
- Immune Deficiency Genetics Section, Laboratory of Clinical Immunology and Microbiology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland
| | - Kerry Dobbs
- Immune Deficiency Genetics Section, Laboratory of Clinical Immunology and Microbiology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland
| | - Rocio Laguna-Goya
- Department of Immunology, Hospital 12de Octubre, i+12, Madrid, Madrid, Spain
| | | | - Santiago Barrio
- Hematology Department, Hospital 12de Octubre, i+12, CNIO, Complutense University, Madrid, Madrid, Spain
| | - Luisa Imberti
- Laboratorio CREA, Department of Infectious and Tropical Diseases, and Medical Officer, ASST Spedali Civili di Brescia and University of Brescia, Brescia, Lombardia, Italy
| | - Alessandra Sottini
- Laboratorio CREA, Department of Infectious and Tropical Diseases, and Medical Officer, ASST Spedali Civili di Brescia and University of Brescia, Brescia, Lombardia, Italy
| | - Eugenia Quiros-Roldan
- Laboratorio CREA, Department of Infectious and Tropical Diseases, and Medical Officer, ASST Spedali Civili di Brescia and University of Brescia, Brescia, Lombardia, Italy
| | - Camillo Rossi
- Department of Pediatrics and Centro Tettamanti-European Reference Network PaedCan, EuroBloodNet, MetabERN-University of Milano-Bicocca-Fondazione MBBM-Ospedale San Gerardo, Monza, Lombardia, Italy
| | - Andrea Biondi
- Hematology Department, Hospital 12de Octubre, i+12, CNIO, Complutense University, Madrid, Madrid, Spain
| | - Laura Rachele Bettini
- Department of Pediatrics and Centro Tettamanti-European Reference Network PaedCan, EuroBloodNet, MetabERN-University of Milano-Bicocca-Fondazione MBBM-Ospedale San Gerardo, Monza, Lombardia, Italy
| | - Mariella D’Angio
- Department of Pediatrics and Centro Tettamanti-European Reference Network PaedCan, EuroBloodNet, MetabERN-University of Milano-Bicocca-Fondazione MBBM-Ospedale San Gerardo, Monza, Lombardia, Italy
| | - Paolo Bonfanti
- Department of Infectious Diseases, University of Milano-Bicocca-Ospedale San Gerardo, Monza, Lombardia, Italy
| | - Miranda F Tompkins
- The American Genome Center, Uniformed Services University of the Health Sciences, Bethesda, Maryland
| | - Camille Alba
- The American Genome Center, Uniformed Services University of the Health Sciences, Bethesda, Maryland
| | - Clifton Dalgard
- The American Genome Center, Uniformed Services University of the Health Sciences, Bethesda, Maryland
| | - Vittorio Sambri
- Unit of Microbiology - The Great Romagna Hub Laboratory, Pievesestina ITALY and DIMES, University of Bologna, Bologna, Emilia-Romagna, Italy
| | - Giovanni Martinelli
- Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Medola, Lombardia, Italy
| | - Jason D Goldman
- Swedish Medical Center, Seattle, WA, USA, and Division of Allergy and Infectious Diseases, University of Washington, Seattle, Washington
| | - James R Heath
- Institute for Systems Biology, Seattle, WA, USA, Seattle, Washington
| | - Helen C Su
- Immune Deficiency Genetics Section, Laboratory of Clinical Immunology and Microbiology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland
| | - Luigi D Notarangelo
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD
| | - Estela Paz-Artal
- Department of Immunology, Hospital 12de Octubre, i+12, Madrid, Madrid, Spain
| | - Joaquin Martinez-Lopez
- Hematology Department, Hospital 12de Octubre, i+12, CNIO, Complutense University, Madrid, Madrid, Spain
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5
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Snyder TM, Gittelman RM, Klinger M, May DH, Osborne EJ, Taniguchi R, Zahid HJ, Kaplan IM, Dines JN, Noakes MT, Pandya R, Chen X, Elasady S, Svejnoha E, Ebert P, Pesesky MW, De Almeida P, O'Donnell H, DeGottardi Q, Keitany G, Lu J, Vong A, Elyanow R, Fields P, Greissl J, Baldo L, Semprini S, Cerchione C, Nicolini F, Mazza M, Delmonte OM, Dobbs K, Laguna-Goya R, Carreño-Tarragona G, Barrio S, Imberti L, Sottini A, Quiros-Roldan E, Rossi C, Biondi A, Bettini LR, D'Angio M, Bonfanti P, Tompkins MF, Alba C, Dalgard C, Sambri V, Martinelli G, Goldman JD, Heath JR, Su HC, Notarangelo LD, Paz-Artal E, Martinez-Lopez J, Carlson JM, Robins HS. Magnitude and Dynamics of the T-Cell Response to SARS-CoV-2 Infection at Both Individual and Population Levels. medRxiv 2020:2020.07.31.20165647. [PMID: 32793919 PMCID: PMC7418734 DOI: 10.1101/2020.07.31.20165647] [Citation(s) in RCA: 94] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
T cells are involved in the early identification and clearance of viral infections and also support the development of antibodies by B cells. This central role for T cells makes them a desirable target for assessing the immune response to SARS-CoV-2 infection. Here, we combined two high-throughput immune profiling methods to create a quantitative picture of the T-cell response to SARS-CoV-2. First, at the individual level, we deeply characterized 3 acutely infected and 58 recovered COVID-19 subjects by experimentally mapping their CD8 T-cell response through antigen stimulation to 545 Human Leukocyte Antigen (HLA) class I presented viral peptides (class II data in a forthcoming study). Then, at the population level, we performed T-cell repertoire sequencing on 1,815 samples (from 1,521 COVID-19 subjects) as well as 3,500 controls to identify shared "public" T-cell receptors (TCRs) associated with SARS-CoV-2 infection from both CD8 and CD4 T cells. Collectively, our data reveal that CD8 T-cell responses are often driven by a few immunodominant, HLA-restricted epitopes. As expected, the T-cell response to SARS-CoV-2 peaks about one to two weeks after infection and is detectable for at least several months after recovery. As an application of these data, we trained a classifier to diagnose SARS-CoV-2 infection based solely on TCR sequencing from blood samples, and observed, at 99.8% specificity, high early sensitivity soon after diagnosis (Day 3-7 = 85.1% [95% CI = 79.9-89.7]; Day 8-14 = 94.8% [90.7-98.4]) as well as lasting sensitivity after recovery (Day 29+/convalescent = 95.4% [92.1-98.3]). These results demonstrate an approach to reliably assess the adaptive immune response both soon after viral antigenic exposure (before antibodies are typically detectable) as well as at later time points. This blood-based molecular approach to characterizing the cellular immune response has applications in clinical diagnostics as well as in vaccine development and monitoring.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Allen Vong
- Adaptive Biotechnologies, Seattle, WA, USA
| | | | | | | | | | - Simona Semprini
- Unit of Microbiology - The Great Romagna Hub Laboratory, Pievesestina ITALY and DIMES, University of Bologna, Bologna, Italy
| | - Claudio Cerchione
- Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Meldola, Italy
| | - Fabio Nicolini
- Immunotherapy, Cell Therapy and Biobank (ITCB), Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Meldola, FC, Italy
| | - Massimiliano Mazza
- Immunotherapy, Cell Therapy and Biobank (ITCB), Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Meldola, FC, Italy
| | - Ottavia M Delmonte
- Immune Deficiency Genetics Section, Laboratory of Clinical Immunology and Microbiology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Kerry Dobbs
- Immune Deficiency Genetics Section, Laboratory of Clinical Immunology and Microbiology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Rocio Laguna-Goya
- Department of Immunology, Hospital 12 de Octubre, i+12, CNIO, Complutense University, Madrid, Spain
| | | | - Santiago Barrio
- Hematology Department, Hospital 12 de Octubre, i+12, CNIO, Complutense University, Madrid, Spain
| | - Luisa Imberti
- Laboratorio CREA, Department of Infectious and Tropical Diseases, and Medical Officer, ASST Spedali Civili di Brescia and University of Brescia, Brescia, Italy
| | - Alessandra Sottini
- Laboratorio CREA, Department of Infectious and Tropical Diseases, and Medical Officer, ASST Spedali Civili di Brescia and University of Brescia, Brescia, Italy
| | - Eugenia Quiros-Roldan
- Laboratorio CREA, Department of Infectious and Tropical Diseases, and Medical Officer, ASST Spedali Civili di Brescia and University of Brescia, Brescia, Italy
| | - Camillo Rossi
- Laboratorio CREA, Department of Infectious and Tropical Diseases, and Medical Officer, ASST Spedali Civili di Brescia and University of Brescia, Brescia, Italy
| | - Andrea Biondi
- Department of Pediatrics and Centro Tettamanti-European Reference Network PaedCan, EuroBloodNet, MetabERN-University of Milano-Bicocca-Fondazione MBBM-Ospedale San Gerardo, Monza, IT
| | - Laura Rachele Bettini
- Department of Pediatrics and Centro Tettamanti-European Reference Network PaedCan, EuroBloodNet, MetabERN-University of Milano-Bicocca-Fondazione MBBM-Ospedale San Gerardo, Monza, IT
| | - Mariella D'Angio
- Department of Pediatrics and Centro Tettamanti-European Reference Network PaedCan, EuroBloodNet, MetabERN-University of Milano-Bicocca-Fondazione MBBM-Ospedale San Gerardo, Monza, IT
| | - Paolo Bonfanti
- Department of Infectious Diseases, University of Milano-Bicocca-Ospedale San Gerardo, Monza, IT
| | - Miranda F Tompkins
- The American Genome Center, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
| | - Camille Alba
- The American Genome Center, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
| | - Clifton Dalgard
- Department of Anatomy, Physiology & Genetics, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
| | - Vittorio Sambri
- Unit of Microbiology - The Great Romagna Hub Laboratory, Pievesestina ITALY and DIMES, University of Bologna, Bologna, Italy
| | - Giovanni Martinelli
- Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Meldola, Italy
| | - Jason D Goldman
- Swedish Medical Center, Seattle, WA, USA, and Division of Allergy and Infectious Diseases, University of Washington, Seattle, WA, USA
| | | | - Helen C Su
- Immune Deficiency Genetics Section, Laboratory of Clinical Immunology and Microbiology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Luigi D Notarangelo
- Immune Deficiency Genetics Section, Laboratory of Clinical Immunology and Microbiology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Estela Paz-Artal
- Department of Immunology, Hospital 12 de Octubre, i+12, CNIO, Complutense University, Madrid, Spain
| | - Joaquin Martinez-Lopez
- Hematology Department, Hospital 12 de Octubre, i+12, CNIO, Complutense University, Madrid, Spain
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6
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Pan S, Hullar MAJ, Lai LA, Peng H, May DH, Noble WS, Raftery D, Navarro SL, Neuhouser ML, Lampe PD, Lampe JW, Chen R. Gut Microbial Protein Expression in Response to Dietary Patterns in a Controlled Feeding Study: A Metaproteomic Approach. Microorganisms 2020; 8:E379. [PMID: 32156071 PMCID: PMC7143255 DOI: 10.3390/microorganisms8030379] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2020] [Revised: 03/02/2020] [Accepted: 03/04/2020] [Indexed: 12/11/2022] Open
Abstract
Although the gut microbiome has been associated with dietary patterns linked to health, microbial metabolism is not well characterized. This ancillary study was a proof of principle analysis for a novel application of metaproteomics to study microbial protein expression in a controlled dietary intervention. We measured the response of the microbiome to diet in a randomized crossover dietary intervention of a whole-grain, low glycemic load diet (WG) and a refined-grain, high glycemic load diet (RG). Total proteins in stools from 9 participants at the end of each diet period (n = 18) were analyzed by LC MS/MS and proteins were identified using the Human Microbiome Project (HMP) human gut microbiome database and UniProt human protein databases. T-tests, controlling for false discovery rate (FDR) <10%, were used to compare the Gene Ontology (GO) biological processes and bacterial enzymes between the two interventions. Using shotgun proteomics, more than 53,000 unique peptides were identified including microbial (89%) and human peptides (11%). Forty-eight bacterial enzymes were statistically different between the diets, including those implicated in SCFA production and degradation of fatty acids. Enzymes associated with degradation of human mucin were significantly enriched in the RG diet. These results illustrate that the metaproteomic approach is a valuable tool to study the microbial metabolism of diets that may influence host health.
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Affiliation(s)
- Sheng Pan
- Institute of Molecular Medicine, the University of Texas Health Science Center at Houston, Houston, TX 77030, USA; (S.P.); (H.P.)
| | - Meredith A. J. Hullar
- Fred Hutchinson Cancer Research Center, Division of Public Health Sciences, Seattle, WA 98109, USA; (D.R.); (S.L.N.); (M.L.N.); (P.D.L.); (J.W.L.)
| | - Lisa A. Lai
- Department of Medicine, University of Washington, Seattle, WA 98105, USA;
| | - Hong Peng
- Institute of Molecular Medicine, the University of Texas Health Science Center at Houston, Houston, TX 77030, USA; (S.P.); (H.P.)
| | - Damon H. May
- Department of Genome Sciences, University of Washington, Seattle, WA 98105, USA; (D.H.M.)
| | - William S. Noble
- Department of Genome Sciences, University of Washington, Seattle, WA 98105, USA; (D.H.M.)
| | - Daniel Raftery
- Fred Hutchinson Cancer Research Center, Division of Public Health Sciences, Seattle, WA 98109, USA; (D.R.); (S.L.N.); (M.L.N.); (P.D.L.); (J.W.L.)
- Department of Anesthesiology and Pain Medicine, Northwest Metabolomics Research Center, University of Washington, Seattle, WA 98109 USA
| | - Sandi L. Navarro
- Fred Hutchinson Cancer Research Center, Division of Public Health Sciences, Seattle, WA 98109, USA; (D.R.); (S.L.N.); (M.L.N.); (P.D.L.); (J.W.L.)
| | - Marian L. Neuhouser
- Fred Hutchinson Cancer Research Center, Division of Public Health Sciences, Seattle, WA 98109, USA; (D.R.); (S.L.N.); (M.L.N.); (P.D.L.); (J.W.L.)
| | - Paul D. Lampe
- Fred Hutchinson Cancer Research Center, Division of Public Health Sciences, Seattle, WA 98109, USA; (D.R.); (S.L.N.); (M.L.N.); (P.D.L.); (J.W.L.)
| | - Johanna W. Lampe
- Fred Hutchinson Cancer Research Center, Division of Public Health Sciences, Seattle, WA 98109, USA; (D.R.); (S.L.N.); (M.L.N.); (P.D.L.); (J.W.L.)
| | - Ru Chen
- Division of Gastroenterology and Hepatology, Department of Medicine, Baylor College of Medicine, Houston, TX 77030, USA
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7
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Krayushkina D, Timmins-Schiffman E, Faux J, May DH, Riffle M, Harvey HR, Nunn BL. Growth phase proteomics of the heterotrophic marine bacterium Ruegeria pomeroyi. Sci Data 2019; 6:303. [PMID: 31796751 PMCID: PMC6890736 DOI: 10.1038/s41597-019-0308-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2019] [Accepted: 11/07/2019] [Indexed: 11/14/2022] Open
Abstract
The heterotrophic marine bacterium, Ruegeria pomeroyi, was experimentally cultured under environmentally realistic carbon conditions and with a tracer-level addition of 13C-labeled leucine to track bacterial protein biosynthesis through growth phases. A combination of methods allowed observation of real-time bacterial protein production to understand metabolic priorities through the different growth phases. Over 2000 proteins were identified in each experimental culture from exponential and stationary growth phases. Within two hours of the 13C-labeled leucine addition, R. pomeroyi significantly assimilated the newly encountered substrate into new proteins. This dataset provides a fundamental baseline for understanding growth phase differences in molecular physiology of a cosmopolitan marine bacterium.
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Affiliation(s)
- Dasha Krayushkina
- University of Washington, Department of Psychiatry and Behavioral Sciences, Seattle, WA, 98195, USA
| | | | - Jessica Faux
- Old Dominion University, Department of Ocean, Earth & Atmospheric Sciences, Norfolk, VA, 23529, USA
| | - Damon H May
- University of Washington, Department of Genome Sciences, Seattle, WA, 98195, USA
| | - Michael Riffle
- University of Washington, Department of Biochemistry, Seattle, WA, 98195, USA
| | - H Rodger Harvey
- Old Dominion University, Department of Ocean, Earth & Atmospheric Sciences, Norfolk, VA, 23529, USA
| | - Brook L Nunn
- University of Washington, Department of Genome Sciences, Seattle, WA, 98195, USA.
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8
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Abstract
Searching tandem mass spectra against a peptide database requires accurate knowledge of various experimental parameters, including machine settings and details of the sample preparation protocol. In some cases, such as in reanalysis of public data sets, this experimental metadata may be missing or inaccurate. We describe a method for automatically inferring the presence of various types of modifications, including stable-isotope and isobaric labeling and tandem mass tags as well as the enrichment of phosphorylated peptides, directly from a given set of mass spectra. We demonstrate the sensitivity and specificity of the proposed approach, and we provide open-source Python and C++ implementations in a new version of the software tool Param-Medic.
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Affiliation(s)
- Damon H May
- Department of Genome Sciences , University of Washington , Seattle , Washington 98195 , United States
| | - Kaipo Tamura
- Department of Genome Sciences , University of Washington , Seattle , Washington 98195 , United States
| | - William S Noble
- Department of Genome Sciences , University of Washington , Seattle , Washington 98195 , United States.,Paul G. Allen School of Computer Science and Engineering , University of Washington , Seattle , Washington 98195 , United States
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9
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Abstract
In shotgun proteomics analysis, user-specified parameters are critical to database search performance and therefore to the yield of confident peptide-spectrum matches (PSMs). Two of the most important parameters are related to the accuracy of the mass spectrometer. Precursor mass tolerance defines the peptide candidates considered for each spectrum. Fragment mass tolerance or bin size determines how close observed and theoretical fragments must be to be considered a match. For either of these two parameters, too wide a setting yields randomly high-scoring false PSMs, whereas too narrow a setting erroneously excludes true PSMs, in both cases, lowering the yield of peptides detected at a given false discovery rate. We describe a strategy for inferring optimal search parameters by assembling and analyzing pairs of spectra that are likely to have been generated by the same peptide ion to infer precursor and fragment mass error. This strategy does not rely on a database search, making it usable in a wide variety of settings. In our experiments on data from a variety of instruments including Orbitrap and Q-TOF acquisitions, this strategy yields more high-confidence PSMs than using settings based on instrument defaults or determined by experts. Param-Medic is open-source and cross-platform. It is available as a standalone tool ( http://noble.gs.washington.edu/proj/param-medic/ ) and has been integrated into the Crux proteomics toolkit ( http://crux.ms ), providing automatic parameter selection for the Comet and Tide search engines.
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Affiliation(s)
- Damon H May
- Department of Genome Sciences, University of Washington , Seattle, Washington 98195, United States
| | - Kaipo Tamura
- Department of Genome Sciences, University of Washington , Seattle, Washington 98195, United States
| | - William S Noble
- Department of Genome Sciences, University of Washington , Seattle, Washington 98195, United States
- Department of Computer Science and Engineering, University of Washington , Seattle, Washington 98195, United States
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10
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May DH, Timmins-Schiffman E, Mikan MP, Harvey HR, Borenstein E, Nunn BL, Noble WS. An Alignment-Free "Metapeptide" Strategy for Metaproteomic Characterization of Microbiome Samples Using Shotgun Metagenomic Sequencing. J Proteome Res 2016; 15:2697-705. [PMID: 27396978 PMCID: PMC5116374 DOI: 10.1021/acs.jproteome.6b00239] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
In principle, tandem mass spectrometry can be used to detect and quantify the peptides present in a microbiome sample, enabling functional and taxonomic insight into microbiome metabolic activity. However, the phylogenetic diversity constituting a particular microbiome is often unknown, and many of the organisms present may not have assembled genomes. In ocean microbiome samples, with particularly diverse and uncultured bacterial communities, it is difficult to construct protein databases that contain the bulk of the peptides in the sample without losing detection sensitivity due to the overwhelming number of candidate peptides for each tandem mass spectrum. We describe a method for deriving "metapeptides" (short amino acid sequences that may be represented in multiple organisms) from shotgun metagenomic sequencing of microbiome samples. In two ocean microbiome samples, we constructed site-specific metapeptide databases to detect more than one and a half times as many peptides as by searching against predicted genes from an assembled metagenome and roughly three times as many peptides as by searching against the NCBI environmental proteome database. The increased peptide yield has the potential to enrich the taxonomic and functional characterization of sample metaproteomes.
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Affiliation(s)
- Damon H May
- Department of Genome Sciences and ‡Department of Computer Science and Engineering, University of Washington , Seattle, Washington 98195-5065, United States
| | - Emma Timmins-Schiffman
- Department of Genome Sciences and ‡Department of Computer Science and Engineering, University of Washington , Seattle, Washington 98195-5065, United States
| | - Molly P Mikan
- Department of Ocean, Earth & Atmospheric Sciences, Old Dominion University , Norfolk, Virginia 23529, United States
| | - H Rodger Harvey
- Department of Ocean, Earth & Atmospheric Sciences, Old Dominion University , Norfolk, Virginia 23529, United States
| | - Elhanan Borenstein
- Department of Genome Sciences and ‡Department of Computer Science and Engineering, University of Washington , Seattle, Washington 98195-5065, United States
- Santa Fe Institute , Santa Fe, New Mexico 87501, United States
| | - Brook L Nunn
- Department of Genome Sciences and ‡Department of Computer Science and Engineering, University of Washington , Seattle, Washington 98195-5065, United States
| | - William S Noble
- Department of Genome Sciences and ‡Department of Computer Science and Engineering, University of Washington , Seattle, Washington 98195-5065, United States
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11
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Chen R, Dawson DW, Pan S, Ottenhof NA, de Wilde RF, Wolfgang CL, May DH, Crispin DA, Lai LA, Lay AR, Waghray M, Wang S, McIntosh MW, Simeone DM, Maitra A, Brentnall TA. Proteins associated with pancreatic cancer survival in patients with resectable pancreatic ductal adenocarcinoma. J Transl Med 2015; 95:43-55. [PMID: 25347153 PMCID: PMC4281293 DOI: 10.1038/labinvest.2014.128] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2014] [Revised: 08/06/2014] [Accepted: 08/30/2014] [Indexed: 12/14/2022] Open
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal disease with a dismal prognosis. However, while most patients die within the first year of diagnosis, very rarely, a few patients can survive for >10 years. Better understanding the molecular characteristics of the pancreatic adenocarcinomas from these very-long-term survivors (VLTS) may provide clues for personalized medicine and improve current pancreatic cancer treatment. To extend our previous investigation, we examined the proteomes of individual pancreas tumor tissues from a group of VLTS patients (survival ≥10 years) and short-term survival patients (STS, survival <14 months). With a given analytical sensitivity, the protein profile of each pancreatic tumor tissue was compared to reveal the proteome alterations that may be associated with pancreatic cancer survival. Pathway analysis of the differential proteins identified suggested that MYC, IGF1R and p53 were the top three upstream regulators for the STS-associated proteins, and VEGFA, APOE and TGFβ-1 were the top three upstream regulators for the VLTS-associated proteins. Immunohistochemistry analysis using an independent cohort of 145 PDAC confirmed that the higher abundance of ribosomal protein S8 (RPS8) and prolargin (PRELP) were correlated with STS and VLTS, respectively. Multivariate Cox analysis indicated that 'High-RPS8 and Low-PRELP' was significantly associated with shorter survival time (HR=2.69, 95% CI 1.46-4.92, P=0.001). In addition, galectin-1, a previously identified protein with its abundance aversely associated with pancreatic cancer survival, was further evaluated for its significance in cancer-associated fibroblasts. Knockdown of galectin-1 in pancreatic cancer-associated fibroblasts dramatically reduced cell migration and invasion. The results from our study suggested that PRELP, LGALS1 and RPS8 might be significant prognostic factors, and RPS8 and LGALS1 could be potential therapeutic targets to improve pancreatic cancer survival if further validated.
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Affiliation(s)
- Ru Chen
- Department of Medicine, University of Washington, Seattle, WA, USA
| | - David W Dawson
- 1] Department of Pathology and Laboratory Medicine, UCLA, Los Angeles, CA, USA [2] Jonsson Comprehensive Cancer Center, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
| | - Sheng Pan
- Department of Medicine, University of Washington, Seattle, WA, USA
| | - Niki A Ottenhof
- Department of Pathology, Sol Goldman Pancreatic Cancer Research Center, The Johns Hopkins Medical Institutions, Baltimore, MD, USA
| | - Roeland F de Wilde
- Department of Pathology, Sol Goldman Pancreatic Cancer Research Center, The Johns Hopkins Medical Institutions, Baltimore, MD, USA
| | - Christopher L Wolfgang
- Department of Surgery, Sol Goldman Pancreatic Cancer Research Center, The Johns Hopkins Medical Institutions, Baltimore, MD, USA
| | - Damon H May
- Fred Hutchinson Cancer Research Center, Molecular Diagnostics Program, Seattle, WA, USA
| | - David A Crispin
- Department of Medicine, University of Washington, Seattle, WA, USA
| | - Lisa A Lai
- Department of Medicine, University of Washington, Seattle, WA, USA
| | - Anna R Lay
- Department of Pathology and Laboratory Medicine, UCLA, Los Angeles, CA, USA
| | - Meghna Waghray
- Department of Surgery, University of Michigan, Ann Arbor, MI, USA
| | - Shouli Wang
- Department of Pathology, Soochow University School of Medicine, Suzhou, China
| | - Martin W McIntosh
- Fred Hutchinson Cancer Research Center, Molecular Diagnostics Program, Seattle, WA, USA
| | - Diane M Simeone
- Department of Surgery, University of Michigan, Ann Arbor, MI, USA
| | - Anirban Maitra
- Department of Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
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12
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Pan S, Chen R, Tamura Y, Crispin DA, Lai LA, May DH, McIntosh MW, Goodlett DR, Brentnall TA. Quantitative glycoproteomics analysis reveals changes in N-glycosylation level associated with pancreatic ductal adenocarcinoma. J Proteome Res 2014; 13:1293-306. [PMID: 24471499 PMCID: PMC3993895 DOI: 10.1021/pr4010184] [Citation(s) in RCA: 76] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Glycosylation plays an important role in epithelial cancers, including pancreatic ductal adenocarcinoma. However, little is known about the glycoproteome of the human pancreas or its alterations associated with pancreatic tumorigenesis. Using quantitative glycoproteomics approach, we investigated protein N-glycosylation in pancreatic tumor tissue in comparison with normal pancreas and chronic pancreatitis tissue. The study lead to the discovery of a roster of glycoproteins with aberrant N-glycosylation level associated with pancreatic cancer, including mucin-5AC (MUC5AC), carcinoembryonic antigen-related cell adhesion molecule 5 (CEACAM5), insulin-like growth factor binding protein (IGFBP3), and galectin-3-binding protein (LGALS3BP). Pathway analysis of cancer-associated aberrant glycoproteins revealed an emerging phenomenon that increased activity of N-glycosylation was implicated in several pancreatic cancer pathways, including TGF-β, TNF, NF-kappa-B, and TFEB-related lysosomal changes. In addition, the study provided evidence that specific N-glycosylation sites within certain individual proteins can have significantly altered glycosylation occupancy in pancreatic cancer, reflecting the complexity of the molecular mechanisms underlying cancer-associated glycosylation events.
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Affiliation(s)
- Sheng Pan
- The Division of Gastroenterology, Department of Medicine, University of Washington , 1959 North East Pacific Street, Seattle, Washington 98195, United States
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13
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May DH, Navarro SL, Ruczinski I, Hogan J, Ogata Y, Schwarz Y, Levy L, Holzman T, McIntosh MW, Lampe JW. Metabolomic profiling of urine: response to a randomised, controlled feeding study of select fruits and vegetables, and application to an observational study. Br J Nutr 2013; 110:1760-70. [PMID: 23657156 PMCID: PMC3818452 DOI: 10.1017/s000711451300127x] [Citation(s) in RCA: 51] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Metabolomic profiles were used to characterise the effects of consuming a high-phytochemical diet compared with a diet devoid of fruits and vegetables (F&V) in a randomised trial and cross-sectional study. In the trial, 8 h fasting urine from healthy men (n 5) and women (n 5) was collected after a 2-week randomised, controlled trial of two diet periods: a diet rich in cruciferous vegetables, citrus and soya (F&V), and a fruit- and vegetable-free (basal) diet. Among the ions found to differentiate the diets, 176 were putatively annotated with compound identifications, with forty-six supported by MS/MS fragment evidence. Metabolites more abundant in the F&V diet included markers of the dietary intervention (e.g. crucifers, citrus and soya), fatty acids and niacin metabolites. Ions more abundant in the basal diet included riboflavin, several acylcarnitines and amino acid metabolites. In the cross-sectional study, we compared the participants based on the tertiles of crucifers, citrus and soya from 3 d food records (n 36) and FFQ (n 57); intake was separately divided into the tertiles of total fruit and vegetable intake for FFQ. As a group, ions individually differential between the experimental diets differentiated the observational study participants. However, only four ions were significant individually, differentiating the third v. first tertile of crucifer, citrus and soya intake based on 3 d food records. One of these ions was putatively annotated: proline betaine, a marker of citrus consumption. There were no ions significantly distinguishing tertiles by FFQ. The metabolomic assessment of controlled dietary interventions provides a more accurate and stronger characterisation of the diet than observational data.
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Affiliation(s)
- Damon H. May
- Fred Hutchinson Cancer Research Center, Division of Public Health Sciences, Seattle, WA, 98109
| | - Sandi L. Navarro
- Fred Hutchinson Cancer Research Center, Division of Public Health Sciences, Seattle, WA, 98109
| | - Ingo Ruczinski
- Johns Hopkins Bloomberg School of Public Health, Department of Biostatistics
| | - Jason Hogan
- Fred Hutchinson Cancer Research Center, Proteomics Facility
| | - Yuko Ogata
- Fred Hutchinson Cancer Research Center, Proteomics Facility
| | - Yvonne Schwarz
- Fred Hutchinson Cancer Research Center, Division of Public Health Sciences, Seattle, WA, 98109
| | - Lisa Levy
- Fred Hutchinson Cancer Research Center, Division of Public Health Sciences, Seattle, WA, 98109
| | - Ted Holzman
- Fred Hutchinson Cancer Research Center, Division of Public Health Sciences, Seattle, WA, 98109
| | - Martin W. McIntosh
- Fred Hutchinson Cancer Research Center, Division of Public Health Sciences, Seattle, WA, 98109
| | - Johanna W. Lampe
- Fred Hutchinson Cancer Research Center, Division of Public Health Sciences, Seattle, WA, 98109
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14
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Herbrich SM, Cole RN, West KP, Schulze K, Yager JD, Groopman JD, Christian P, Wu L, O'Meally RN, May DH, McIntosh MW, Ruczinski I. Statistical inference from multiple iTRAQ experiments without using common reference standards. J Proteome Res 2013; 12:594-604. [PMID: 23270375 DOI: 10.1021/pr300624g] [Citation(s) in RCA: 104] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Isobaric tags for relative and absolute quantitation (iTRAQ) is a prominent mass spectrometry technology for protein identification and quantification that is capable of analyzing multiple samples in a single experiment. Frequently, iTRAQ experiments are carried out using an aliquot from a pool of all samples, or "masterpool", in one of the channels as a reference sample standard to estimate protein relative abundances in the biological samples and to combine abundance estimates from multiple experiments. In this manuscript, we show that using a masterpool is counterproductive. We obtain more precise estimates of protein relative abundance by using the available biological data instead of the masterpool and do not need to occupy a channel that could otherwise be used for another biological sample. In addition, we introduce a simple statistical method to associate proteomic data from multiple iTRAQ experiments with a numeric response and show that this approach is more powerful than the conventionally employed masterpool-based approach. We illustrate our methods using data from four replicate iTRAQ experiments on aliquots of the same pool of plasma samples and from a 406-sample project designed to identify plasma proteins that covary with nutrient concentrations in chronically undernourished children from South Asia.
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Affiliation(s)
- Shelley M Herbrich
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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15
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Reimel BA, Pan S, May DH, Shaffer SA, Goodlett DR, McIntosh MW, Yerian LM, Bronner MP, Chen R, Brentnall TA. Proteomics on Fixed Tissue Specimens - A Review. CURR PROTEOMICS 2009; 6:63-69. [PMID: 19829741 DOI: 10.2174/157016409787847420] [Citation(s) in RCA: 29] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
The vast majority of clinical tissue samples are formalin-fixed and paraffin-preserved. This type of preservation has been considered an obstacle to protein extraction from these tissues. However, these are the very tissue samples that have associated patient histories, diagnoses and outcomes - ideal samples in the quest to translate bench research into clinical applications. Thus, until recently, these valuable specimens have been unavailable for proteomic analysis.Over the last decade, researchers have been exploring efficient methods to undo protein cross-linking caused by standard tissue fixatives and extract proteins from archived tissue specimens. These methods have been applied in different clinical proteomic studies. In this report, we attempt to review the development of these techniques, summarize the proteomic findings, and discuss the impact on future clinical proteomics.
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Affiliation(s)
- Beth Ann Reimel
- Departments of Gastroenterology and Surgery, University of Washington, Seattle, Washington 98195
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16
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Fang Q, Strand A, Law W, Faca VM, Fitzgibbon MP, Hamel N, Houle B, Liu X, May DH, Poschmann G, Roy L, Stühler K, Ying W, Zhang J, Zheng Z, Bergeron JJM, Hanash S, He F, Leavitt BR, Meyer HE, Qian X, McIntosh MW. Brain-specific proteins decline in the cerebrospinal fluid of humans with Huntington disease. Mol Cell Proteomics 2008; 8:451-66. [PMID: 18984577 PMCID: PMC2649809 DOI: 10.1074/mcp.m800231-mcp200] [Citation(s) in RCA: 65] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
We integrated five sets of proteomics data profiling the constituents of cerebrospinal fluid (CSF) derived from Huntington disease (HD)-affected and -unaffected individuals with genomics data profiling various human and mouse tissues, including the human HD brain. Based on an integrated analysis, we found that brain-specific proteins are 1.8 times more likely to be observed in CSF than in plasma, that brain-specific proteins tend to decrease in HD CSF compared with unaffected CSF, and that 81% of brain-specific proteins have quantitative changes concordant with transcriptional changes identified in different regions of HD brain. The proteins found to increase in HD CSF tend to be liver-associated. These protein changes are consistent with neurodegeneration, microgliosis, and astrocytosis known to occur in HD. We also discuss concordance between laboratories and find that ratios of individual proteins can vary greatly, but the overall trends with respect to brain or liver specificity were consistent. Concordance is highest between the two laboratories observing the largest numbers of proteins.
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Affiliation(s)
- Qiaojun Fang
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, USA
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