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Borgmästars E, Ulfenborg B, Johansson M, Jonsson P, Billing O, Franklin O, Lundin C, Jacobson S, Simm M, Lubovac-Pilav Z, Sund M. Multi-omics profiling to identify early plasma biomarkers in pre-diagnostic pancreatic ductal adenocarcinoma: a nested case-control study. Transl Oncol 2024; 48:102059. [PMID: 39018772 PMCID: PMC11301391 DOI: 10.1016/j.tranon.2024.102059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2024] [Revised: 05/20/2024] [Accepted: 07/05/2024] [Indexed: 07/19/2024] Open
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is an aggressive disease with poor survival. Novel biomarkers are urgently needed to improve the outcome through early detection. Here, we aimed to discover novel biomarkers for early PDAC detection using multi-omics profiling in pre-diagnostic plasma samples biobanked after routine health examinations. A nested case-control study within the Northern Sweden Health and Disease Study was designed. Pre-diagnostic plasma samples from 37 future PDAC patients collected within 2.3 years before diagnosis and 37 matched healthy controls were included. We analyzed metabolites using liquid chromatography mass spectrometry and gas chromatography mass spectrometry, microRNAs by HTG edgeseq, proteins by multiplex proximity extension assays, as well as three clinical biomarkers using milliplex technology. Supervised and unsupervised multi-omics integration were performed as well as univariate analyses for the different omics types and clinical biomarkers. Multiple hypothesis testing was corrected using Benjamini-Hochberg's method and a false discovery rate (FDR) below 0.1 was considered statistically significant. Carbohydrate antigen (CA) 19-9 was associated with PDAC risk (OR [95 % CI] = 3.09 [1.31-7.29], FDR = 0.03) and increased closer to PDAC diagnosis. Supervised multi-omics models resulted in poor discrimination between future PDAC cases and healthy controls with obtained accuracies between 0.429-0.500. No single metabolite, microRNA, or protein was differentially altered (FDR < 0.1) between future PDAC cases and healthy controls. CA 19-9 levels increase up to two years prior to PDAC diagnosis but extensive multi-omics analysis including metabolomics, microRNAomics and proteomics in this cohort did not identify novel early biomarkers for PDAC.
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Affiliation(s)
- Emmy Borgmästars
- Department of Diagnostics and Intervention/ Surgery, Umeå University, Umeå, Sweden.
| | - Benjamin Ulfenborg
- School of Bioscience, Department of Biology and Bioinformatics, University of Skövde, Skövde, Sweden
| | - Mattias Johansson
- Genomic Epidemiology Branch, International Agency for Research on Cancer, Lyon, France
| | - Pär Jonsson
- Department of Chemistry, Umeå University, Umeå, Sweden
| | - Ola Billing
- Department of Diagnostics and Intervention/ Surgery, Umeå University, Umeå, Sweden
| | - Oskar Franklin
- Department of Diagnostics and Intervention/ Surgery, Umeå University, Umeå, Sweden; Division of Surgical Oncology, Department of Surgery, University of Colorado School of Medicine, Aurora, CO, USA
| | - Christina Lundin
- Department of Diagnostics and Intervention/ Surgery, Umeå University, Umeå, Sweden
| | - Sara Jacobson
- Department of Diagnostics and Intervention/ Surgery, Umeå University, Umeå, Sweden
| | - Maja Simm
- Department of Diagnostics and Intervention/ Surgery, Umeå University, Umeå, Sweden; Department of Clinical Sciences/ Obstetrics and Gynecology, Umeå University, Umeå, Sweden
| | - Zelmina Lubovac-Pilav
- School of Bioscience, Department of Biology and Bioinformatics, University of Skövde, Skövde, Sweden
| | - Malin Sund
- Department of Diagnostics and Intervention/ Surgery, Umeå University, Umeå, Sweden; Department of Surgery, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
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Beutgen VM, Shinkevich V, Pörschke J, Meena C, Steitz AM, Pogge von Strandmann E, Graumann J, Gómez-Serrano M. Secretome analysis using Affinity Proteomics and Immunoassays: a focus on Tumor Biology. Mol Cell Proteomics 2024:100830. [PMID: 39147028 DOI: 10.1016/j.mcpro.2024.100830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 07/20/2024] [Accepted: 08/12/2024] [Indexed: 08/17/2024] Open
Abstract
The study of the cellular secretome using proteomic techniques continues to capture the attention of the research community across a broad range of topics in biomedical research. Due to their untargeted nature, independence from the model system employed, historically superior depth of analysis, as well as comparative affordability, mass spectrometry-based approaches traditionally dominate such analyses. More recently, however, affinity-based proteomic assays have massively gained in analytical depth, which together with their high sensitivity, dynamic range coverage as well as high throughput capabilities render them exquisitely suited to secretome analysis. In this review, we revisit the analytical challenges implied by secretomics and provide an overview of affinity-based proteomic platforms currently available for such analyses, using the study of the tumor secretome as an example for basic and translational research.
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Affiliation(s)
- Vanessa M Beutgen
- Institute of Translational Proteomics, Biochemical/Pharmacological Centre, Philipps University, Marburg, Germany; Core Facility Translational Proteomics, Biochemical/Pharmacological Centre, Philipps University, Marburg, Germany
| | - Veronika Shinkevich
- Institute of Pharmacology, Biochemical/Pharmacological Centre, Philipps University, Marburg, Germany
| | - Johanna Pörschke
- Institute for Tumor Immunology, Center for Tumor Biology and Immunology, Philipps University, Marburg, Germany
| | - Celina Meena
- Institute for Tumor Immunology, Center for Tumor Biology and Immunology, Philipps University, Marburg, Germany
| | - Anna M Steitz
- Translational Oncology Group, Center for Tumor Biology and Immunology, Philipps University, Marburg, Germany
| | - Elke Pogge von Strandmann
- Institute for Tumor Immunology, Center for Tumor Biology and Immunology, Philipps University, Marburg, Germany
| | - Johannes Graumann
- Institute of Translational Proteomics, Biochemical/Pharmacological Centre, Philipps University, Marburg, Germany; Core Facility Translational Proteomics, Biochemical/Pharmacological Centre, Philipps University, Marburg, Germany.
| | - María Gómez-Serrano
- Institute for Tumor Immunology, Center for Tumor Biology and Immunology, Philipps University, Marburg, Germany.
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3
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Quek AML, Wang S, Teng O, Shunmuganathan B, Er BGC, Mahmud NFB, Ng IXQ, Gupta R, Tan ISL, Tan NY, Qian X, Purushotorman K, Teoh HL, Ng KWP, Goh Y, Soon DTL, Tay SH, Teng GG, Ma M, Chandran NS, Hartono JL, MacAry PA, Seet RCS. Hybrid immunity augments cross-variant protection against COVID-19 among immunocompromised individuals. J Infect 2024; 89:106238. [PMID: 39121971 DOI: 10.1016/j.jinf.2024.106238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2024] [Revised: 07/24/2024] [Accepted: 07/30/2024] [Indexed: 08/12/2024]
Abstract
BACKGROUND Immunity to SARS-CoV-2 vaccination and infection differs considerably among individuals. We investigate the critical pathways that influence vaccine-induced cross-variant serological immunity among individuals at high-risk of COVID-19 complications. METHODS Neutralizing antibodies to the wild-type SARS-CoV-2 virus and its variants (Beta, Gamma, Delta and Omicron) were analyzed in patients with autoimmune diseases, chronic comorbidities (multimorbidity), and healthy controls. Antibody levels were assessed at baseline and at different intervals up to 12 months following primary and booster vaccination with either BNT162b2 or mRNA-1273. Immunity induced by vaccination with and without infection (hybrid immunity) was compared with that of unvaccinated individuals with recent SARS-CoV-2 infection. Plasma cytokines were analyzed to investigate variations in antibody production following vaccination. RESULTS Patients with autoimmune diseases (n = 137) produced lesser antibodies to the wild-type SARS-CoV-2 virus and its variants compared with those in the multimorbidity (n = 153) and healthy groups (n = 229); antibody levels were significantly lower in patients with neuromyelitis optica and those on prednisolone, mycophenolate or rituximab treatment. Multivariate logistic regression analysis identified neuromyelitis optica (odds ratio 8.20, 95% CI 1.68-39.9) and mycophenolate (13.69, 3.78-49.5) as significant predictors of a poorer antibody response to vaccination (i.e, neutralizing antibody <40%). Infected participants exhibited antibody levels that were 28.7% higher (95% CI 24.7-32.7) compared to non-infected participants six months after receiving a booster vaccination. Individuals infected during the Delta outbreak generated cross-protective neutralizing antibodies against the Omicron variant in quantities comparable to those observed after infection with the Omicron variant itself. In contrast, unvaccinated individuals recently infected with the wild-type (n = 2390) consistently displayed lower levels of neutralizing antibodies against both the wild-type virus and other variants. Pathway analyses suggested an inverse relationship between baseline T cell subsets and antibody production following vaccination. CONCLUSION Hybrid immunity confers a robust protection against COVID-19 among immunocompromised individuals.
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Affiliation(s)
- Amy May Lin Quek
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Division of Neurology, Department of Medicine, National University Hospital, Singapore
| | - Suqing Wang
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Ooiean Teng
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Bhuvaneshwari Shunmuganathan
- Antibody Engineering Programme, Life Sciences Institute, National University of Singapore, Singapore, Singapore; Department of Microbiology and Immunology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Bernadette Guek Cheng Er
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Division of Neurology, Department of Medicine, National University Hospital, Singapore
| | - Nor Fa'izah Binte Mahmud
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Division of Neurology, Department of Medicine, National University Hospital, Singapore
| | - Isabel Xue Qi Ng
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Rashi Gupta
- Department of Microbiology and Immunology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Isabelle Siang Ling Tan
- Cambridge-NUS Cell Phenotyping Center, Center for Life Sciences, National University of Singapore, Singapore
| | - Nikki Yj Tan
- Department of Microbiology and Immunology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Xinlei Qian
- Antibody Engineering Programme, Life Sciences Institute, National University of Singapore, Singapore, Singapore; Department of Microbiology and Immunology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Kiren Purushotorman
- Antibody Engineering Programme, Life Sciences Institute, National University of Singapore, Singapore, Singapore; Department of Microbiology and Immunology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Hock Luen Teoh
- Division of Neurology, Department of Medicine, National University Hospital, Singapore
| | - Kay Wei Ping Ng
- Division of Neurology, Department of Medicine, National University Hospital, Singapore
| | - Yihui Goh
- Division of Neurology, Department of Medicine, National University Hospital, Singapore
| | - Derek Tuck Loong Soon
- Division of Neurology, Department of Medicine, National University Hospital, Singapore
| | - Sen Hee Tay
- Division of Rheumatology, Department of Medicine, National University Hospital, Singapore
| | - Gim Gee Teng
- Division of Rheumatology, Department of Medicine, National University Hospital, Singapore
| | - Margaret Ma
- Division of Rheumatology, Department of Medicine, National University Hospital, Singapore
| | - Nisha Suyien Chandran
- Division of Dermatology, Department of Medicine, National University Hospital, Singapore
| | - Juanda Leo Hartono
- Division of Gastroenterology and Hepatology, Department of Medicine, National University Hospital, Singapore
| | - Paul A MacAry
- Antibody Engineering Programme, Life Sciences Institute, National University of Singapore, Singapore, Singapore; Department of Microbiology and Immunology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore; Cambridge-NUS Cell Phenotyping Center, Center for Life Sciences, National University of Singapore, Singapore
| | - Raymond Chee Seong Seet
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Division of Neurology, Department of Medicine, National University Hospital, Singapore; Healthy Longevity Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
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4
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Austin TR, Nethander M, Fink HA, Törnqvist AE, Jalal DI, Buzkova P, Barzilay JI, Carbone L, Gabrielsen ME, Grahnemo L, Lu T, Hveem K, Jonasson C, Kizer JR, Langhammer A, Mukamal KJ, Gerszten RE, Psaty BM, Robbins JA, Sun YV, Skogholt AH, Kanis JA, Johansson H, Åsvold BO, Valderrabano RJ, Zheng J, Richards JB, Coward E, Ohlsson C. A plasma protein-based risk score to predict hip fractures. NATURE AGING 2024; 4:1064-1075. [PMID: 38802582 PMCID: PMC11333168 DOI: 10.1038/s43587-024-00639-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/17/2024] [Accepted: 05/01/2024] [Indexed: 05/29/2024]
Abstract
As there are effective treatments to reduce hip fractures, identification of patients at high risk of hip fracture is important to inform efficient intervention strategies. To obtain a new tool for hip fracture prediction, we developed a protein-based risk score in the Cardiovascular Health Study using an aptamer-based proteomic platform. The proteomic risk score predicted incident hip fractures and improved hip fracture discrimination in two Trøndelag Health Study validation cohorts using the same aptamer-based platform. When transferred to an antibody-based proteomic platform in a UK Biobank validation cohort, the proteomic risk score was strongly associated with hip fractures (hazard ratio per s.d. increase, 1.64; 95% confidence interval 1.53-1.77). The proteomic risk score, but not available polygenic risk scores for fractures or bone mineral density, improved the C-index beyond the fracture risk assessment tool (FRAX), which integrates information from clinical risk factors (C-index, FRAX 0.735 versus FRAX + proteomic risk score 0.776). The developed proteomic risk score constitutes a new tool for stratifying patients according to hip fracture risk; however, its improvement in hip fracture discrimination is modest and its clinical utility beyond FRAX with information on femoral neck bone mineral density remains to be determined.
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Grants
- U01HL130114 U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute (NHLBI)
- U01 HL080295 NHLBI NIH HHS
- U01 HL130114 NHLBI NIH HHS
- HHSN268200800007C NHLBI NIH HHS
- R01HL144483 U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute (NHLBI)
- N01HC85086 NHLBI NIH HHS
- KAW 2015.0317 Knut och Alice Wallenbergs Stiftelse (Knut and Alice Wallenberg Foundation)
- LU2021-0096 IngaBritt och Arne Lundbergs Forskningsstiftelse (Ingabritt and Arne Lundberg Research Foundation)
- N01HC85083 NHLBI NIH HHS
- 2020-01392 Vetenskapsrådet (Swedish Research Council)
- N01HC85080 NHLBI NIH HHS
- N01HC85081 NHLBI NIH HHS
- N01HC55222 NHLBI NIH HHS
- U01HL080295 U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute (NHLBI)
- HHSN268201200036C NHLBI NIH HHS
- R01 HL144483 NHLBI NIH HHS
- HHSN268201800001C NHLBI NIH HHS
- 75N92021D00006 NHLBI NIH HHS
- N01HC85082 NHLBI NIH HHS
- N01HC85079 NHLBI NIH HHS
- R01 AG023629 NIA NIH HHS
- the Swedish state under the agreement between the Swedish government and the county councils, the ALF-agreement (ALFGBG-720331 and ALFGBG-965235)
- U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute (NHLBI)
- U.S. Department of Health & Human Services | U.S. Department of Health and Human Services, Administration for Community Living | National Institute on Disability, Independent Living, and Rehabilitation Research (NIDILRR)
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Affiliation(s)
- Thomas R Austin
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, US
| | - Maria Nethander
- Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, Sahlgrenska Osteoporosis Centre, Centre for Bone and Arthritis Research at the Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Bioinformatics and Data Center, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Howard A Fink
- Geriatric Research Education and Clinical Center, VA Health Care System, Minneapolis, MN, US
- Department of Medicine, University of Minnesota, Minneapolis, MN, US
| | - Anna E Törnqvist
- Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, Sahlgrenska Osteoporosis Centre, Centre for Bone and Arthritis Research at the Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Diana I Jalal
- Division of Nephrology, Department of Internal Medicine, Carver College of Medicine, Iowa City, IA, US
- Iowa City VA Medical Center, Iowa City, IA, US
| | - Petra Buzkova
- Department of Biostatistics, University of Washington, Seattle, WA, US
| | - Joshua I Barzilay
- Division of Endocrinology, Kaiser Permanente of Georgia, Atlanta, GA, US
| | - Laura Carbone
- Charlie Norwood VAMC, Augusta, GA, US
- Division of Rheumatology, Department of Medicine, Medical College of Georgia, Augusta University, Augusta, GA, US
| | - Maiken E Gabrielsen
- HUNT Center for Molecular and Clinical Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway
| | - Louise Grahnemo
- Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, Sahlgrenska Osteoporosis Centre, Centre for Bone and Arthritis Research at the Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Tianyuan Lu
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
- Quantitative Life Sciences Program, McGill University, Montreal, Quebec, Canada
- 5 Prime Sciences Inc, Montreal, Quebec, Canada
| | - Kristian Hveem
- HUNT Center for Molecular and Clinical Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway
- HUNT Research Centre, NTNU, Levanger, Norway
- Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway
| | - Christian Jonasson
- HUNT Center for Molecular and Clinical Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway
| | - Jorge R Kizer
- Cardiology Section, San Francisco VA Health Care System, San Francisco, CA, US
- Department of Medicine, Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, US
| | - Arnulf Langhammer
- HUNT Research Centre, NTNU, Levanger, Norway
- Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway
| | - Kenneth J Mukamal
- Department of Medicine, Beth Israel Deaconess Medical Center, Brookline, MA, US
| | - Robert E Gerszten
- Department of Medicine, Beth Israel Deaconess Medical Center, Brookline, MA, US
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, US
- Departments of Medicine, Epidemiology, and Health Systems and Population Health, University of Washington, Seattle, WA, US
| | - John A Robbins
- Department of Medicine, University of California, Davis, CA, US
| | - Yan V Sun
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, US
| | - Anne Heidi Skogholt
- HUNT Center for Molecular and Clinical Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway
| | - John A Kanis
- Centre for Metabolic Bone Diseases, University of Sheffield Medical School, Sheffield, UK
- Mary McKillop Institute for Health Research, Australian Catholic University, Melbourne, Victoria, Australia
| | - Helena Johansson
- Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, Sahlgrenska Osteoporosis Centre, Centre for Bone and Arthritis Research at the Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Mary McKillop Institute for Health Research, Australian Catholic University, Melbourne, Victoria, Australia
| | - Bjørn Olav Åsvold
- HUNT Center for Molecular and Clinical Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Endocrinology, Clinic of Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Rodrigo J Valderrabano
- Research Program in Men's Health, Aging and Metabolism, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, US
| | - Jie Zheng
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai National Clinical Research Center for metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, Shanghai Digital Medicine Innovation Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, UK
| | - J Brent Richards
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
- Quantitative Life Sciences Program, McGill University, Montreal, Quebec, Canada
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada
- Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Quebec, Canada
- Department of Medicine, McGill University, Montreal, Quebec, Canada
- Department of Twin Research, King's College London, London, UK
| | - Eivind Coward
- HUNT Center for Molecular and Clinical Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway
| | - Claes Ohlsson
- Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, Sahlgrenska Osteoporosis Centre, Centre for Bone and Arthritis Research at the Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
- Region Västra Götaland, Sahlgrenska University Hospital, Department of Drug Treatment, Gothenburg, Sweden.
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5
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Ivansson E, Hedlund Lindberg J, Stålberg K, Sundfeldt K, Gyllensten U, Enroth S. Large-scale proteomics reveals precise biomarkers for detection of ovarian cancer in symptomatic women. Sci Rep 2024; 14:17288. [PMID: 39068297 PMCID: PMC11283551 DOI: 10.1038/s41598-024-68249-2] [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: 01/20/2024] [Accepted: 07/22/2024] [Indexed: 07/30/2024] Open
Abstract
Ovarian cancer is the 8th most common cancer among women and has a 5-year survival of only 30-50%. While the survival is close to 90% for stage I tumours it is only 20% for stage IV. Current biomarkers are not sensitive nor specific enough, and novel biomarkers are urgently needed. We used the Explore PEA technology for large-scale analysis of 2943 plasma proteins to search for new biomarkers using two independent clinical cohorts. The discovery analysis using the first cohort identified 296 proteins that had significantly different levels in malign tumours as compared to benign and for 269 (91%) of these, the association was replicated in the second cohort. Multivariate modelling, including all proteins independent of their association in the univariate analysis, identified a model for separating benign conditions from malign tumours (stage I-IV) consisting of three proteins; WFDC2, KRT19 and RBFOX3. This model achieved an AUC of 0.92 in the replication cohort and a sensitivity and specificity of 0.93 and 0.77 at a cut-off developed in the discovery cohort. There was no statistical difference of the performance in the replication cohort compared to the discovery cohort. WFDC2 and KRT19 have previously been associated with ovarian cancer but RBFOX3 has not previously been identified as a potential biomarker. Our results demonstrate the ability of using high-throughput precision proteomics for identification of novel plasma protein biomarker for ovarian cancer detection.
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Affiliation(s)
- Emma Ivansson
- Department of Immunology, Genetics, and Pathology, Biomedical Center, SciLifeLab Uppsala, Uppsala University, 75108, Uppsala, Sweden
| | - Julia Hedlund Lindberg
- Department of Immunology, Genetics, and Pathology, Biomedical Center, SciLifeLab Uppsala, Uppsala University, 75108, Uppsala, Sweden
| | - Karin Stålberg
- Department of Women's and Children's Health, Uppsala University, 75185, Uppsala, Sweden
| | - Karin Sundfeldt
- Department of Obstetrics and Gynaecology, Institute of Clinical Sciences, Sahlgrenska Academy at Gothenburg University, 41685, Gothenburg, Sweden
| | - Ulf Gyllensten
- Department of Immunology, Genetics, and Pathology, Biomedical Center, SciLifeLab Uppsala, Uppsala University, 75108, Uppsala, Sweden
| | - Stefan Enroth
- Department of Immunology, Genetics, and Pathology, Biomedical Center, SciLifeLab Uppsala, Uppsala University, 75108, Uppsala, Sweden.
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6
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Carrasco-Zanini J, Pietzner M, Davitte J, Surendran P, Croteau-Chonka DC, Robins C, Torralbo A, Tomlinson C, Grünschläger F, Fitzpatrick N, Ytsma C, Kanno T, Gade S, Freitag D, Ziebell F, Haas S, Denaxas S, Betts JC, Wareham NJ, Hemingway H, Scott RA, Langenberg C. Proteomic signatures improve risk prediction for common and rare diseases. Nat Med 2024:10.1038/s41591-024-03142-z. [PMID: 39039249 DOI: 10.1038/s41591-024-03142-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Accepted: 06/19/2024] [Indexed: 07/24/2024]
Abstract
For many diseases there are delays in diagnosis due to a lack of objective biomarkers for disease onset. Here, in 41,931 individuals from the United Kingdom Biobank Pharma Proteomics Project, we integrated measurements of ~3,000 plasma proteins with clinical information to derive sparse prediction models for the 10-year incidence of 218 common and rare diseases (81-6,038 cases). We then compared prediction models developed using proteomic data with models developed using either basic clinical information alone or clinical information combined with data from 37 clinical assays. The predictive performance of sparse models including as few as 5 to 20 proteins was superior to the performance of models developed using basic clinical information for 67 pathologically diverse diseases (median delta C-index = 0.07; range = 0.02-0.31). Sparse protein models further outperformed models developed using basic information combined with clinical assay data for 52 diseases, including multiple myeloma, non-Hodgkin lymphoma, motor neuron disease, pulmonary fibrosis and dilated cardiomyopathy. For multiple myeloma, single-cell RNA sequencing from bone marrow in newly diagnosed patients showed that four of the five predictor proteins were expressed specifically in plasma cells, consistent with the strong predictive power of these proteins. External replication of sparse protein models in the EPIC-Norfolk study showed good generalizability for prediction of the six diseases tested. These findings show that sparse plasma protein signatures, including both disease-specific proteins and protein predictors shared across several diseases, offer clinically useful prediction of common and rare diseases.
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Affiliation(s)
- Julia Carrasco-Zanini
- Human Genetics and Genomics, GSK Research and Development, Stevenage, UK.
- MRC Epidemiology Unit, School of Clinical Medicine, Institute of Metabolic Science, University of Cambridge, Cambridge, UK.
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK.
- Computational Medicine, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany.
| | - Maik Pietzner
- MRC Epidemiology Unit, School of Clinical Medicine, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK
- Computational Medicine, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Jonathan Davitte
- Human Genetics and Genomics, GSK Research and Development, Collegeville, PA, USA
| | - Praveen Surendran
- Human Genetics and Genomics, GSK Research and Development, Stevenage, UK
| | | | - Chloe Robins
- Human Genetics and Genomics, GSK Research and Development, Collegeville, PA, USA
| | - Ana Torralbo
- Institute of Health Informatics, University College London, London, UK
| | - Christopher Tomlinson
- Institute of Health Informatics, University College London, London, UK
- National Institute for Health Research, Biomedical Research Centre, University College London Hospitals NHS Trust, London, UK
| | - Florian Grünschläger
- Heidelberg Institute for Stem Cell Technology and Experimental Medicine, Heidelberg, Germany
- Division of Stem Cells and Cancer, Deutsches Krebsforschungszentrum (DKFZ) and DKFZ-ZMBH Alliance, Heidelberg, Germany
- Faculty of Biosciences, Heidelberg University, Heidelberg, Germany
| | | | - Cai Ytsma
- Institute of Health Informatics, University College London, London, UK
| | - Tokuwa Kanno
- Human Genetics and Genomics, GSK Research and Development, Collegeville, PA, USA
| | - Stephan Gade
- Genomic Sciences, Cellzome GmbH, GSK Research and Development, Heidelberg, Germany
| | - Daniel Freitag
- Human Genetics and Genomics, GSK Research and Development, Stevenage, UK
| | - Frederik Ziebell
- Genomic Sciences, Cellzome GmbH, GSK Research and Development, Heidelberg, Germany
| | - Simon Haas
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK
- Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
- Charité-Universitätsmedizin, Berlin, Germany
- Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
- German Cancer Consortium (DKTK), Heidelberg, Germany
| | - Spiros Denaxas
- Institute of Health Informatics, University College London, London, UK
- National Institute for Health Research, Biomedical Research Centre, University College London Hospitals NHS Trust, London, UK
- Health Data Research UK, London, UK
- British Heart Foundation Data Science Centre, London, UK
| | - Joanna C Betts
- Human Genetics and Genomics, GSK Research and Development, Stevenage, UK
| | - Nicholas J Wareham
- MRC Epidemiology Unit, School of Clinical Medicine, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Harry Hemingway
- Institute of Health Informatics, University College London, London, UK
- National Institute for Health Research, Biomedical Research Centre, University College London Hospitals NHS Trust, London, UK
- Health Data Research UK, London, UK
| | - Robert A Scott
- Human Genetics and Genomics, GSK Research and Development, Stevenage, UK.
| | - Claudia Langenberg
- MRC Epidemiology Unit, School of Clinical Medicine, Institute of Metabolic Science, University of Cambridge, Cambridge, UK.
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK.
- Computational Medicine, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany.
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7
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Carrasco-Zanini J, Pietzner M, Koprulu M, Wheeler E, Kerrison ND, Wareham NJ, Langenberg C. Proteomic prediction of diverse incident diseases: a machine learning-guided biomarker discovery study using data from a prospective cohort study. Lancet Digit Health 2024; 6:e470-e479. [PMID: 38906612 DOI: 10.1016/s2589-7500(24)00087-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 04/03/2024] [Accepted: 04/19/2024] [Indexed: 06/23/2024]
Abstract
BACKGROUND Broad-capture proteomic technologies have the potential to improve disease prediction, enabling targeted prevention and management, but studies have so far been limited to very few selected diseases and have not evaluated predictive performance across multiple conditions. We aimed to evaluate the potential of serum proteins to improve risk prediction over and above health-derived information and polygenic risk scores across a diverse set of 24 outcomes. METHODS We designed multiple case-cohorts nested in the EPIC-Norfolk prospective study, from participants with available serum samples and genome-wide genotype data, with more than 32 974 person-years of follow-up. Participants were middle-aged individuals (aged 40-79 years at baseline) of European ancestry who were recruited from the general population of Norfolk, England, between March, 1993 and December, 1997. We selected participants who developed one of ten less common diseases within 10 years of follow-up; we also subsampled a randomly drawn control subcohort, which also served to investigate 14 more common outcomes (n>70), including all-cause premature mortality (death before the age of 75 years; case numbers 71-437; controls 608-1556). Individuals were excluded from the current study owing to failed genotyping or proteomic quality control, relatedness, or missing information on age, sex, BMI, or smoking status. We used a machine learning framework to derive sparse predictive protein models for the onset of the the 23 individual diseases and all-cause premature mortality, and to derive a single common sparse multimorbidity signature that was predictive across multiple diseases from 2923 serum proteins. FINDINGS Participants who developed one of ten less common diseases within 10 years of follow-up included 482 women and 507 men, with a mean age at baseline of 64·56 years (8·08). The random subcohort included 990 women and 769 men, with a mean age of 58·79 years (9·31). As few as five proteins alone outperformed polygenic risk scores for 17 of 23 outcomes (median dfference in concordance index [C-index] 0·13 [0·10-0·17]) and improved predictive performance when added over basic patient-derived information models for seven outcomes, achieving a median C-index of 0·82 (IQR 0·77-0·82). This included diseases with poor prognosis such as lung cancer (C-index 0·85 [+/- cross-validation error 0·83-0·87]), for which we identified unreported biomarkers such as C-X-C motif chemokine ligand 17. A sparse multimorbidity signature of ten proteins improved prediction across seven outcomes over patient-derived information models, achieving performances (median C-index 0·81 [IQR 0·80-0·82]) similar to those of disease-specific signatures. INTERPRETATION We show the value of broad-capture proteomic biomarker discovery studies across multiple diseases of diverse causes, pointing to those that might benefit the most from proteomic approaches, and the potential to derive common sparse biomarker panels for prediction of multiple diseases at once. This framework could enable follow-up studies to explore the generalisability of proteomic models and to benchmark these against clinical assays, which are required to understand the translational potential of these findings. FUNDING Medical Research Council, Health Data Research UK, UK Research and Innovation-National Institute for Health and Care Research, Cancer Research UK, and Wellcome Trust.
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Affiliation(s)
- Julia Carrasco-Zanini
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK; Precision Healthcare University Research Institute, Queen Mary University of London, London, UK
| | - Maik Pietzner
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK; Computational Medicine, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany; Precision Healthcare University Research Institute, Queen Mary University of London, London, UK
| | - Mine Koprulu
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK
| | - Eleanor Wheeler
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK
| | - Nicola D Kerrison
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK
| | - Nicholas J Wareham
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK
| | - Claudia Langenberg
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK; Computational Medicine, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany; Precision Healthcare University Research Institute, Queen Mary University of London, London, UK.
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8
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Nilius H, Hamzeh-Cognasse H, Hastings J, Studt JD, Tsakiris DA, Greinacher A, Mendez A, Schmidt A, Wuillemin WA, Gerber B, Vishnu P, Graf L, Kremer Hovinga JA, Bakchoul T, Cognasse F, Nagler M. Proteomic profiling for biomarker discovery in heparin-induced thrombocytopenia. Blood Adv 2024; 8:2825-2834. [PMID: 38588487 PMCID: PMC11176969 DOI: 10.1182/bloodadvances.2024012782] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Revised: 03/19/2024] [Accepted: 03/19/2024] [Indexed: 04/10/2024] Open
Abstract
ABSTRACT New analytical techniques can assess hundreds of proteins simultaneously with high sensitivity, facilitating the observation of their complex interplay and role in disease mechanisms. We hypothesized that proteomic profiling targeting proteins involved in thrombus formation, inflammation, and the immune response would identify potentially new biomarkers for heparin-induced thrombocytopenia (HIT). Four existing panels of the Olink proximity extension assay covering 356 proteins involved in thrombus formation, inflammation, and immune response were applied to randomly selected patients with suspected HIT (confirmed HIT, n = 32; HIT ruled out, n = 38; and positive heparin/platelet factor 4 [H/PF4] antibodies, n = 28). The relative difference in protein concentration was analyzed using a linear regression model adjusted for sex and age. To confirm the test results, soluble P-selectin was determined using enzyme-linked immunosorbent assay (ELISA) in above mentioned patients and an additional second data set (n = 49). HIT was defined as a positive heparin-induced platelet activation assay (washed platelet assay). Among 98 patients of the primary data set, the median 4Ts score was 5 in patients with HIT, 4 in patients with positive H/PF4 antibodies, and 3 in patients without HIT. The median optical density of a polyspecific H/PF4 ELISA were 3.0, 0.9, and 0.3. Soluble P-selectin remained statistically significant after multiple test adjustments. The area under the receiver operating characteristic curve was 0.81 for Olink and 0.8 for ELISA. Future studies shall assess the diagnostic and prognostic value of soluble P-selectin in the management of HIT.
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Affiliation(s)
- Henning Nilius
- Department of Clinical Chemistry, Inselspital University Hospital Bern, Bern, Switzerland
- Graduate School for Health Sciences, University of Bern, Bern, Switzerland
| | - Hind Hamzeh-Cognasse
- French Blood Establishment Auvergne-Rhone-Alpes, Saint-Etienne, France
- University Jean Monnet, Mines Saint-Etienne, INSERM, U 1059 SAINBIOSE, Saint-Etienne, France
| | - Janna Hastings
- Institute for Implementation Science in Health Care, Faculty of Medicine, University of Zurich, Zurich, Switzerland
- School of Medicine, University of St. Gallen, St. Gallen, Switzerland
| | - Jan-Dirk Studt
- Division of Medical Oncology and Hematology, University Hospital Zurich, Zurich, Switzerland
| | | | - Andreas Greinacher
- Institut für Immunologie und Transfusionsmedizin, Universitätsmedizin Greifswald, Greifswald, Germany
| | - Adriana Mendez
- Department of Laboratory Medicine, Kantonsspital Aarau, Aarau, Switzerland
| | - Adrian Schmidt
- Institute of Laboratory Medicine and Clinic of Medical Oncology and Hematology, Municipal Hospital Zurich Triemli, Zurich, Switzerland
| | - Walter A. Wuillemin
- Division of Hematology and Central Hematology Laboratory, Cantonal Hospital of Lucerne and University of Bern, Lucerne, Switzerland
| | - Bernhard Gerber
- Clinic of Hematology, Oncology Institute of Southern Switzerland, Bellinzona, Switzerland
| | - Prakash Vishnu
- Division of Hematology, Fred Hutchinson Cancer Center, University of Washington, Seattle, WA
| | - Lukas Graf
- Cantonal Hospital of St. Gallen, Center for Laboratory Medicine, St. Gallen, Switzerland
| | - Johanna A. Kremer Hovinga
- Department of Hematology and Central Hematology Laboratory, Inselspital Bern University Hospital, Bern, Switzerland
| | - Tamam Bakchoul
- Centre for Clinical Transfusion Medicine, University Hospital of Tübingen, Tübingen, Germany
| | - Fabrice Cognasse
- French Blood Establishment Auvergne-Rhone-Alpes, Saint-Etienne, France
- University Jean Monnet, Mines Saint-Etienne, INSERM, U 1059 SAINBIOSE, Saint-Etienne, France
| | - Michael Nagler
- Department of Clinical Chemistry, Inselspital University Hospital Bern, Bern, Switzerland
- Faculty of Medicine, University of Bern, Bern, Switzerland
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9
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Pietzner M, Uluvar B, Kolnes KJ, Jeppesen PB, Frivold SV, Skattebo Ø, Johansen EI, Skålhegg BS, Wojtaszewski JFP, Kolnes AJ, Yeo GSH, O'Rahilly S, Jensen J, Langenberg C. Systemic proteome adaptions to 7-day complete caloric restriction in humans. Nat Metab 2024; 6:764-777. [PMID: 38429390 DOI: 10.1038/s42255-024-01008-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 02/01/2024] [Indexed: 03/03/2024]
Abstract
Surviving long periods without food has shaped human evolution. In ancient and modern societies, prolonged fasting was/is practiced by billions of people globally for religious purposes, used to treat diseases such as epilepsy, and recently gained popularity as weight loss intervention, but we still have a very limited understanding of the systemic adaptions in humans to extreme caloric restriction of different durations. Here we show that a 7-day water-only fast leads to an average weight loss of 5.7 kg (±0.8 kg) among 12 volunteers (5 women, 7 men). We demonstrate nine distinct proteomic response profiles, with systemic changes evident only after 3 days of complete calorie restriction based on in-depth characterization of the temporal trajectories of ~3,000 plasma proteins measured before, daily during, and after fasting. The multi-organ response to complete caloric restriction shows distinct effects of fasting duration and weight loss and is remarkably conserved across volunteers with >1,000 significantly responding proteins. The fasting signature is strongly enriched for extracellular matrix proteins from various body sites, demonstrating profound non-metabolic adaptions, including extreme changes in the brain-specific extracellular matrix protein tenascin-R. Using proteogenomic approaches, we estimate the health consequences for 212 proteins that change during fasting across ~500 outcomes and identified putative beneficial (SWAP70 and rheumatoid arthritis or HYOU1 and heart disease), as well as adverse effects. Our results advance our understanding of prolonged fasting in humans beyond a merely energy-centric adaptions towards a systemic response that can inform targeted therapeutic modulation.
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Affiliation(s)
- Maik Pietzner
- Computational Medicine, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany.
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK.
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK.
| | - Burulça Uluvar
- Computational Medicine, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Kristoffer J Kolnes
- Department of Physical Performance, Norwegian School of Sport Sciences, Oslo, Norway
- Steno Diabetes Center Odense, Odense University Hospital, Odense, Denmark
| | - Per B Jeppesen
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - S Victoria Frivold
- Institute of Health and Society, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Øyvind Skattebo
- Department of Physical Performance, Norwegian School of Sport Sciences, Oslo, Norway
| | - Egil I Johansen
- Department of Physical Performance, Norwegian School of Sport Sciences, Oslo, Norway
| | - Bjørn S Skålhegg
- Department of Nutrition, Division for Molecular Nutrition, University of Oslo, Oslo, Norway
| | - Jørgen F P Wojtaszewski
- August Krogh Section for Molecular Physiology, Department of Nutrition, Exercise and Sports, University of Copenhagen, Copenhagen, Denmark
| | - Anders J Kolnes
- Section of Specialized Endocrinology, Department of Endocrinology, Oslo University Hospital, Oslo, Norway
| | - Giles S H Yeo
- Metabolic Research Laboratory, Wellcome-MRC Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Stephen O'Rahilly
- Metabolic Research Laboratory, Wellcome-MRC Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Jørgen Jensen
- Department of Physical Performance, Norwegian School of Sport Sciences, Oslo, Norway
| | - Claudia Langenberg
- Computational Medicine, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany.
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK.
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK.
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10
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Liu C, Lin H, Yu H, Mai X, Pan W, Guo J, Liao T, Feng J, Zhang Y, Situ B, Zheng L, Li B. Isolation and Enrichment of Extracellular Vesicles with Double-Positive Membrane Protein for Subsequent Biological Studies. Adv Healthc Mater 2024; 13:e2303430. [PMID: 37942845 DOI: 10.1002/adhm.202303430] [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: 10/08/2023] [Indexed: 11/10/2023]
Abstract
The isolation and enrichment of specific extracellular vesicle (EV) subpopulations are essential in the context of precision medicine. However, the current methods predominantly rely on a single-positive marker and are susceptible to interference from soluble proteins or impurities. This limitation represents a significant obstacle to the widespread application of EVs in biological research. Herein, a novel approach that utilizes proximity ligation assay (PLA) and DNA-RNA hybridization are proposed to facilitate the binding of two proteins on the EV membrane in advance enabling the isolation and enrichment of intact EVs with double-positive membrane proteins followed by using functionalized magnetic beads for capture and enzymatic cleavage for isolated EVs release. The isolated subpopulations of EVs can be further utilized for cellular uptake studies, high-throughput small RNA sequencing, and breast cancer diagnosis. Hence, developing and implementing a specialized system for isolating and enriching a specific subpopulation of EVs can enhance basic and clinical research in this field.
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Affiliation(s)
- Chunchen Liu
- Department of Laboratory Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
- Guangdong Engineering and Technology Research Center for Rapid Diagnostic Biosensors, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Huixian Lin
- Department of Laboratory Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
- Guangdong Engineering and Technology Research Center for Rapid Diagnostic Biosensors, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Haiyang Yu
- Department of Laboratory Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
- Guangdong Engineering and Technology Research Center for Rapid Diagnostic Biosensors, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Xueying Mai
- Department of Laboratory Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
- Guangdong Engineering and Technology Research Center for Rapid Diagnostic Biosensors, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Weilun Pan
- Department of Laboratory Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
- Guangdong Engineering and Technology Research Center for Rapid Diagnostic Biosensors, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Jingyun Guo
- Breast Center, Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Tong Liao
- Department of Laboratory Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
- Guangdong Engineering and Technology Research Center for Rapid Diagnostic Biosensors, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Junjie Feng
- Department of Laboratory Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
- Guangdong Engineering and Technology Research Center for Rapid Diagnostic Biosensors, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Ye Zhang
- Department of Laboratory Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
- Guangdong Engineering and Technology Research Center for Rapid Diagnostic Biosensors, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Bo Situ
- Department of Laboratory Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
- Guangdong Engineering and Technology Research Center for Rapid Diagnostic Biosensors, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Lei Zheng
- Department of Laboratory Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
- Guangdong Engineering and Technology Research Center for Rapid Diagnostic Biosensors, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Bo Li
- Department of Laboratory Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
- Guangdong Engineering and Technology Research Center for Rapid Diagnostic Biosensors, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
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11
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Venegas-Solis F, Staliunaite L, Rudolph E, Münch CCS, Yu P, Freibert SA, Maeda T, Zimmer CL, Möbs C, Keller C, Kaufmann A, Bauer S. A type I interferon regulatory network for human plasmacytoid dendritic cells based on heparin, membrane-bound and soluble BDCA-2. Proc Natl Acad Sci U S A 2024; 121:e2312404121. [PMID: 38478694 PMCID: PMC10963015 DOI: 10.1073/pnas.2312404121] [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: 07/20/2023] [Accepted: 01/10/2024] [Indexed: 03/27/2024] Open
Abstract
Plasmacytoid dendritic cells (pDCs) produce type I interferons (IFNs) after sensing viral/bacterial RNA or DNA by toll-like receptor (TLR) 7 or TLR9, respectively. However, aberrant pDCs activation can cause adverse effects on the host and contributes to the pathogenesis of type I IFN-related autoimmune diseases. Here, we show that heparin interacts with the human pDCs-specific blood dendritic cell antigen 2 (BDCA-2) but not with related lectins such as DCIR or dectin-2. Importantly, BDCA-2-heparin interaction depends on heparin sulfation and receptor glycosylation and results in inhibition of TLR9-driven type I IFN production in primary human pDCs and the pDC-like cell line CAL-1. This inhibition is mediated by unfractionated and low-molecular-weight heparin, as well as endogenous heparin from plasma, suggesting that the local blood environment controls the production of IFN-α in pDCs. Additionally, we identified an activation-dependent soluble form of BDCA-2 (solBDCA-2) in human plasma that functions as heparin antagonist and thereby increases TLR9-driven IFN-α production in pDCs. Of importance, solBDCA-2 levels in the serum were increased in patients with scrub typhus (an acute infectious disease caused by Orientia tsutsugamushi) compared to healthy control subjects and correlated with anti-dsDNA antibodies titers. In contrast, solBDCA-2 levels in plasma from patients with bullous pemphigoid or psoriasis were reduced. In summary, this work identifies a regulatory network consisting of heparin, membrane-bound and solBDCA-2 modulating TLR9-driven IFN-α production in pDCs. This insight into pDCs function and regulation may have implications for the treatment of pDCs-related autoimmune diseases.
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Affiliation(s)
- Francisco Venegas-Solis
- Institute for Immunology, Philipps-Universität Marburg, Biomedizinisches Forschungszentrum Marburg, Marburg35043, Germany
| | - Laura Staliunaite
- Institute for Immunology, Philipps-Universität Marburg, Biomedizinisches Forschungszentrum Marburg, Marburg35043, Germany
| | - Elisa Rudolph
- Institute for Immunology, Philipps-Universität Marburg, Biomedizinisches Forschungszentrum Marburg, Marburg35043, Germany
| | - Carina Chan-Song Münch
- Institute of Virology, Philipps-Universität Marburg, Biomedizinisches Forschungszemtrum Marburg, Marburg35043, Germany
| | - Philipp Yu
- Institute for Immunology, Philipps-Universität Marburg, Biomedizinisches Forschungszentrum Marburg, Marburg35043, Germany
| | - Sven-A. Freibert
- Institute for Cytobiology, Center for Synthetic Microbiology, Philipps-Universität Marburg, Marburg35032, Germany
- Core Facility “Protein Biochemistry and Spectroscopy”, Philipps-Universität Marburg, Marburg35032, Germany
| | - Takahiro Maeda
- Department of Island and Community Medicine, Island Medical Research Institute, Nagasaki University Graduate School of Biomedical Science, Nagasaki852-8523, Japan
| | - Christine L. Zimmer
- Department of Dermatology and Allergology, Philipps-Universität Marburg, Marburg35043, Germany
| | - Christian Möbs
- Department of Dermatology and Allergology, Philipps-Universität Marburg, Marburg35043, Germany
| | - Christian Keller
- Institute of Virology, Philipps-Universität Marburg, Biomedizinisches Forschungszemtrum Marburg, Marburg35043, Germany
| | - Andreas Kaufmann
- Institute for Immunology, Philipps-Universität Marburg, Biomedizinisches Forschungszentrum Marburg, Marburg35043, Germany
| | - Stefan Bauer
- Institute for Immunology, Philipps-Universität Marburg, Biomedizinisches Forschungszentrum Marburg, Marburg35043, Germany
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12
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Zhou Y, Zheng H, Tan Z, Kang E, Xue P, Li X, Guan F. Optimizing and integrating depletion and precipitation methods for plasma proteomics through data-independent acquisition-mass spectrometry. J Chromatogr B Analyt Technol Biomed Life Sci 2024; 1235:124046. [PMID: 38382157 DOI: 10.1016/j.jchromb.2024.124046] [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: 12/09/2023] [Revised: 01/29/2024] [Accepted: 02/10/2024] [Indexed: 02/23/2024]
Abstract
The application of plasma proteomics is a reliable approach for the discovery of biomarkers. However, the utilization of mass spectrometry-based proteomics in plasma encounters limitations due to the presence of high-abundant proteins (HAPs) and the vast dynamic range. To address this issue, we conducted an optimization and integration of depletion and precipitation strategies eliminating interference from HAPs. The optimized procedure involved utilizing 40 µL of beads for the removal of 1 µL of plasma, and maintaining a ratio of 1:1:1 between plasma, urea, and trichloroacetic acid for the precipitation of 50 µL of plasma. To facilitate high-throughput processing, experimental procedures were carried out utilizing 96-well plates. The depletion method identified a total of 1510 proteins, whereas the precipitated method yielded a total of 802 proteins. The integration of these methods yielded a total of 1794 proteins, including a wide concentration range spanning over 8 orders of magnitude. Furthermore, these approaches exhibited a commendable level of reproducibility, as indicated by median coefficients of variation of 14.7 % and 21.1 % for protein intensities, respectively. The integrative method was found to be effective in precisely quantifying yeast proteins that were intentionally spiked in plasma at predetermined rations of 5, 2, 0.5, and 0.2 with a high genuine positive recovery with a range of 71 % to 91 % of all yeast proteins. The use of a complementary and finely tuned approach involving depletion and precipitation demonstrates tremendous potential in the field of discovering protein biomarkers from large-scale cohort studies.
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Affiliation(s)
- Yue Zhou
- College of Life Science, Northwest University, Xi'an, Shaanxi, China
| | - Helong Zheng
- College of Life Science, Northwest University, Xi'an, Shaanxi, China
| | - Zengqi Tan
- College of Life Science, Northwest University, Xi'an, Shaanxi, China
| | - Enci Kang
- Xi'an Gaoxin No.1 High School International Division, Xi'an, Shaanxi, China
| | - Peng Xue
- Guangzhou National Laboratory, Guangzhou, Guangdong, China
| | - Xiang Li
- College of Life Science, Northwest University, Xi'an, Shaanxi, China
| | - Feng Guan
- College of Life Science, Northwest University, Xi'an, Shaanxi, China.
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13
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Nakajima D, Konno R, Miyashita Y, Ishikawa M, Ohara O, Kawashima Y. Proteome Analysis of Serum Purified Using Solanum tuberosum and Lycopersicon esculentum Lectins. Int J Mol Sci 2024; 25:1315. [PMID: 38279312 PMCID: PMC10816257 DOI: 10.3390/ijms25021315] [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: 12/27/2023] [Revised: 01/17/2024] [Accepted: 01/19/2024] [Indexed: 01/28/2024] Open
Abstract
Serum and plasma exhibit a broad dynamic range of protein concentrations, posing challenges for proteome analysis. Various technologies have been developed to reduce this complexity, including high-abundance depletion methods utilizing antibody columns, extracellular vesicle enrichment techniques, and trace protein enrichment using nanobead cocktails. Here, we employed lectins to address this, thereby extending the scope of biomarker discovery in serum or plasma using a novel approach. We enriched serum proteins using 37 different lectins and subjected them to LC-MS/MS analysis with data-independent acquisition. Solanum tuberosum lectin (STL) and Lycopersicon esculentum lectin (LEL) enabled the detection of more serum proteins than the other lectins. STL and LEL bind to N-acetylglucosamine oligomers, emphasizing the significance of capturing these oligomer-binding proteins when analyzing serum trace proteins. Combining STL and LEL proved more effective than using them separately, allowing us to identify over 3000 proteins from serum through single-shot proteome analysis. We applied the STL/LEL trace-protein enrichment method to the sera of systemic lupus erythematosus model mice. This revealed differences in >1300 proteins between the systemic lupus erythematosus model and control mouse sera, underscoring the utility of this method for biomarker discovery.
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Affiliation(s)
- Daisuke Nakajima
- Department of Applied Genomics, Kazusa DNA Research Institute, 2-5-23 Kazusa Kamatari, Kisarazu 292-0818, Chiba, Japan; (D.N.); (R.K.); (Y.M.); (M.I.); (O.O.)
| | - Ryo Konno
- Department of Applied Genomics, Kazusa DNA Research Institute, 2-5-23 Kazusa Kamatari, Kisarazu 292-0818, Chiba, Japan; (D.N.); (R.K.); (Y.M.); (M.I.); (O.O.)
| | - Yasuomi Miyashita
- Department of Applied Genomics, Kazusa DNA Research Institute, 2-5-23 Kazusa Kamatari, Kisarazu 292-0818, Chiba, Japan; (D.N.); (R.K.); (Y.M.); (M.I.); (O.O.)
- Department of Developmental Biology, Graduate School of Medicine, Chiba University, 1-8-1 Inohana, Chuo-ku, Chiba 260-8670, Chiba, Japan
| | - Masaki Ishikawa
- Department of Applied Genomics, Kazusa DNA Research Institute, 2-5-23 Kazusa Kamatari, Kisarazu 292-0818, Chiba, Japan; (D.N.); (R.K.); (Y.M.); (M.I.); (O.O.)
| | - Osamu Ohara
- Department of Applied Genomics, Kazusa DNA Research Institute, 2-5-23 Kazusa Kamatari, Kisarazu 292-0818, Chiba, Japan; (D.N.); (R.K.); (Y.M.); (M.I.); (O.O.)
| | - Yusuke Kawashima
- Department of Applied Genomics, Kazusa DNA Research Institute, 2-5-23 Kazusa Kamatari, Kisarazu 292-0818, Chiba, Japan; (D.N.); (R.K.); (Y.M.); (M.I.); (O.O.)
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14
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Michaud SA, Pětrošová H, Sinclair NJ, Kinnear AL, Jackson AM, McGuire JC, Hardie DB, Bhowmick P, Ganguly M, Flenniken AM, Nutter LMJ, McKerlie C, Smith D, Mohammed Y, Schibli D, Sickmann A, Borchers CH. Multiple reaction monitoring assays for large-scale quantitation of proteins from 20 mouse organs and tissues. Commun Biol 2024; 7:6. [PMID: 38168632 PMCID: PMC10762018 DOI: 10.1038/s42003-023-05687-0] [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: 09/16/2020] [Accepted: 12/07/2023] [Indexed: 01/05/2024] Open
Abstract
Mouse is the mammalian model of choice to study human health and disease due to its size, ease of breeding and the natural occurrence of conditions mimicking human pathology. Here we design and validate multiple reaction monitoring mass spectrometry (MRM-MS) assays for quantitation of 2118 unique proteins in 20 murine tissues and organs. We provide open access to technical aspects of these assays to enable their implementation in other laboratories, and demonstrate their suitability for proteomic profiling in mice by measuring normal protein abundances in tissues from three mouse strains: C57BL/6NCrl, NOD/SCID, and BALB/cAnNCrl. Sex- and strain-specific differences in protein abundances are identified and described, and the measured values are freely accessible via our MouseQuaPro database: http://mousequapro.proteincentre.com . Together, this large library of quantitative MRM-MS assays established in mice and the measured baseline protein abundances represent an important resource for research involving mouse models.
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Affiliation(s)
- Sarah A Michaud
- University of Victoria-Genome British Columbia Proteomics Centre, Victoria, BC, Canada.
| | - Helena Pětrošová
- University of Victoria-Genome British Columbia Proteomics Centre, Victoria, BC, Canada
| | - Nicholas J Sinclair
- University of Victoria-Genome British Columbia Proteomics Centre, Victoria, BC, Canada
| | - Andrea L Kinnear
- University of Victoria-Genome British Columbia Proteomics Centre, Victoria, BC, Canada
| | - Angela M Jackson
- University of Victoria-Genome British Columbia Proteomics Centre, Victoria, BC, Canada
| | - Jamie C McGuire
- University of Victoria-Genome British Columbia Proteomics Centre, Victoria, BC, Canada
| | - Darryl B Hardie
- University of Victoria-Genome British Columbia Proteomics Centre, Victoria, BC, Canada
| | - Pallab Bhowmick
- University of Victoria-Genome British Columbia Proteomics Centre, Victoria, BC, Canada
| | - Milan Ganguly
- The Center for Phenogenomics, Toronto, ON, Canada
- The Hospital for Sick Children, Toronto, ON, Canada
| | - Ann M Flenniken
- The Center for Phenogenomics, Toronto, ON, Canada
- Sinai Health Lunenfeld-Tanenbaum Research Institute, Toronto, ON, Canada
| | - Lauryl M J Nutter
- The Center for Phenogenomics, Toronto, ON, Canada
- The Hospital for Sick Children, Toronto, ON, Canada
| | | | - Derek Smith
- University of Victoria-Genome British Columbia Proteomics Centre, Victoria, BC, Canada
| | - Yassene Mohammed
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, the Netherlands
- Leibniz-Institut für Analytische Wissenschaften-ISAS-e.V, Dortmund, 44139, Germany
- Segal Cancer Proteomics Centre, Lady Davis Institute, Jewish General Hospital, McGill University, Montreal, QC, Canada
| | - David Schibli
- University of Victoria-Genome British Columbia Proteomics Centre, Victoria, BC, Canada
| | - Albert Sickmann
- Leibniz-Institut für Analytische Wissenschaften-ISAS-e.V, Dortmund, 44139, Germany
| | - Christoph H Borchers
- Segal Cancer Proteomics Centre, Lady Davis Institute, Jewish General Hospital, McGill University, Montreal, QC, Canada.
- Gerald Bronfman Department of Oncology, Jewish General Hospital, Montreal, QC, Canada.
- Department of Experimental Medicine, McGill University, Montreal, QC, Canada.
- Department of Pathology, McGill University, Montreal, QC, Canada.
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15
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Uhlen M, Quake SR. Sequential sequencing by synthesis and the next-generation sequencing revolution. Trends Biotechnol 2023; 41:1565-1572. [PMID: 37482467 DOI: 10.1016/j.tibtech.2023.06.007] [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: 05/08/2023] [Revised: 06/11/2023] [Accepted: 06/15/2023] [Indexed: 07/25/2023]
Abstract
The impact of next-generation sequencing (NGS) cannot be overestimated. The technology has transformed the field of life science, contributing to a dramatic expansion in our understanding of human health and disease and our understanding of biology and ecology. The vast majority of the major NGS systems today are based on the concept of 'sequencing by synthesis' (SBS) with sequential detection of nucleotide incorporation using an engineered DNA polymerase. Based on this strategy, various alternative platforms have been developed, including the use of either native nucleotides or reversible terminators and different strategies for the attachment of DNA to a solid support. In this review, some of the key concepts leading to this remarkable development are discussed.
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Affiliation(s)
- Mathias Uhlen
- Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden; Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden.
| | - Stephen R Quake
- Departments of Bioengineering and Applied Physics, Stanford University, Stanford, CA, USA; Chan Zuckerberg Initiative, Redwood City, California, USA, Stanford, CA, USA
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16
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Abyadeh M, Alikhani M, Mirzaei M, Gupta V, Shekari F, Salekdeh GH. Proteomics provides insights into the theranostic potential of extracellular vesicles. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2023; 138:101-133. [PMID: 38220422 DOI: 10.1016/bs.apcsb.2023.08.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/16/2024]
Abstract
Extracellular vesicles (EVs) encompass a diverse range of membranous structures derived from cells, including exosomes and microvesicles. These vesicles are present in biological fluids and play vital roles in various physiological and pathological processes. They facilitate intercellular communication by enabling the exchange of proteins, lipids, and genetic material between cells. Understanding the cellular processes that govern EV biology is essential for unraveling their physiological and pathological functions and their potential clinical applications. Despite significant advancements in EV research in recent years, there is still much to learn about these vesicles. The advent of improved mass spectrometry (MS)-based techniques has allowed for a deeper characterization of EV protein composition, providing valuable insights into their roles in different physiological and pathological conditions. In this chapter, we provide an overview of proteomics studies conducted to identify the protein contents of EVs, which contribute to their therapeutic and pathological features. We also provided evidence on the potential of EV proteome contents as biomarkers for early disease diagnosis, progression, and treatment response, as well as factors that influence their composition. Additionally, we discuss the available databases containing information on EV proteome contents, and finally, we highlight the need for further research to pave the way toward their utilization in clinical settings.
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Affiliation(s)
- Morteza Abyadeh
- Department of Stem Cells and Developmental Biology, Cell Science Research Center, Royan Institute for Stem Cell Biology and Technology, ACECR, Tehran, Iran
| | - Mehdi Alikhani
- Department of Stem Cells and Developmental Biology, Cell Science Research Center, Royan Institute for Stem Cell Biology and Technology, ACECR, Tehran, Iran
| | - Mehdi Mirzaei
- Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, North Ryde, Sydney, NSW, Australia
| | - Vivek Gupta
- Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, North Ryde, Sydney, NSW, Australia
| | - Faezeh Shekari
- Department of Stem Cells and Developmental Biology, Cell Science Research Center, Royan Institute for Stem Cell Biology and Technology, ACECR, Tehran, Iran
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17
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Åkesson J, Hojjati S, Hellberg S, Raffetseder J, Khademi M, Rynkowski R, Kockum I, Altafini C, Lubovac-Pilav Z, Mellergård J, Jenmalm MC, Piehl F, Olsson T, Ernerudh J, Gustafsson M. Proteomics reveal biomarkers for diagnosis, disease activity and long-term disability outcomes in multiple sclerosis. Nat Commun 2023; 14:6903. [PMID: 37903821 PMCID: PMC10616092 DOI: 10.1038/s41467-023-42682-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Accepted: 10/17/2023] [Indexed: 11/01/2023] Open
Abstract
Sensitive and reliable protein biomarkers are needed to predict disease trajectory and personalize treatment strategies for multiple sclerosis (MS). Here, we use the highly sensitive proximity-extension assay combined with next-generation sequencing (Olink Explore) to quantify 1463 proteins in cerebrospinal fluid (CSF) and plasma from 143 people with early-stage MS and 43 healthy controls. With longitudinally followed discovery and replication cohorts, we identify CSF proteins that consistently predicted both short- and long-term disease progression. Lower levels of neurofilament light chain (NfL) in CSF is superior in predicting the absence of disease activity two years after sampling (replication AUC = 0.77) compared to all other tested proteins. Importantly, we also identify a combination of 11 CSF proteins (CXCL13, LTA, FCN2, ICAM3, LY9, SLAMF7, TYMP, CHI3L1, FYB1, TNFRSF1B and NfL) that predict the severity of disability worsening according to the normalized age-related MS severity score (replication AUC = 0.90). The identification of these proteins may help elucidate pathogenetic processes and might aid decisions on treatment strategies for persons with MS.
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Affiliation(s)
- Julia Åkesson
- Bioinformatics, Department of Physics, Chemistry and Biology, Linköping University, 581 83, Linköping, Sweden
- Systems Biology Research Centre, School of Bioscience, University of Skövde, 541 28, Skövde, Sweden
| | - Sara Hojjati
- Division of Inflammation and Infection, Department of Biomedical and Clinical Sciences, Linköping University, 581 83, Linköping, Sweden
| | - Sandra Hellberg
- Bioinformatics, Department of Physics, Chemistry and Biology, Linköping University, 581 83, Linköping, Sweden
- Division of Inflammation and Infection, Department of Biomedical and Clinical Sciences, Linköping University, 581 83, Linköping, Sweden
| | - Johanna Raffetseder
- Division of Inflammation and Infection, Department of Biomedical and Clinical Sciences, Linköping University, 581 83, Linköping, Sweden
| | - Mohsen Khademi
- Neuroimmunology Unit, Department of Clinical Neuroscience, Center for Molecular Medicine, Karolinska University Hospital, Karolinska Institute, 171 76, Stockholm, Sweden
| | - Robert Rynkowski
- Department of Neurology, and Department of Biomedical and Clinical Sciences, Linköping University, 581 83, Linköping, Sweden
| | - Ingrid Kockum
- Neuroimmunology Unit, Department of Clinical Neuroscience, Center for Molecular Medicine, Karolinska University Hospital, Karolinska Institute, 171 76, Stockholm, Sweden
| | - Claudio Altafini
- Division of Automatic Control, Department of Electrical Engineering, Linköping University, 581 83, Linköping, Sweden
| | - Zelmina Lubovac-Pilav
- Systems Biology Research Centre, School of Bioscience, University of Skövde, 541 28, Skövde, Sweden
| | - Johan Mellergård
- Department of Neurology, and Department of Biomedical and Clinical Sciences, Linköping University, 581 83, Linköping, Sweden
| | - Maria C Jenmalm
- Division of Inflammation and Infection, Department of Biomedical and Clinical Sciences, Linköping University, 581 83, Linköping, Sweden
| | - Fredrik Piehl
- Neuroimmunology Unit, Department of Clinical Neuroscience, Center for Molecular Medicine, Karolinska University Hospital, Karolinska Institute, 171 76, Stockholm, Sweden
| | - Tomas Olsson
- Neuroimmunology Unit, Department of Clinical Neuroscience, Center for Molecular Medicine, Karolinska University Hospital, Karolinska Institute, 171 76, Stockholm, Sweden
| | - Jan Ernerudh
- Department of Clinical Immunology and Transfusion Medicine, and Department of Biomedical and Clinical Sciences, Linköping University, 581 83, Linköping, Sweden
| | - Mika Gustafsson
- Bioinformatics, Department of Physics, Chemistry and Biology, Linköping University, 581 83, Linköping, Sweden.
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18
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Elzinga SE, Eid SA, McGregor BA, Jang DG, Hinder LM, Dauch JR, Hayes JM, Zhang H, Guo K, Pennathur S, Kretzler M, Brosius FC, Koubek EJ, Feldman EL, Hur J. Transcriptomic analysis of diabetic kidney disease and neuropathy in mouse models of type 1 and type 2 diabetes. Dis Model Mech 2023; 16:dmm050080. [PMID: 37791586 PMCID: PMC10565109 DOI: 10.1242/dmm.050080] [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: 01/13/2023] [Accepted: 04/26/2023] [Indexed: 10/05/2023] Open
Abstract
Diabetic kidney disease (DKD) and diabetic peripheral neuropathy (DPN) are common complications of type 1 (T1D) and type 2 (T2D) diabetes. However, the mechanisms underlying pathogenesis of these complications are unclear. In this study, we optimized a streptozotocin-induced db/+ murine model of T1D and compared it to our established db/db T2D mouse model of the same C57BLKS/J background. Glomeruli and sciatic nerve transcriptomic data from T1D and T2D mice were analyzed by self-organizing map and differential gene expression analysis. Consistent with prior literature, pathways related to immune function and inflammation were dysregulated in both complications in T1D and T2D mice. Gene-level analysis identified a high degree of concordance in shared differentially expressed genes (DEGs) in both complications and across diabetes type when using mice from the same cohort and genetic background. As we have previously shown a low concordance of shared DEGs in DPN when using mice from different cohorts and genetic backgrounds, this suggests that genetic background may influence diabetic complications. Collectively, these findings support the role of inflammation and indicate that genetic background is important in complications of both T1D and T2D.
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Affiliation(s)
- Sarah E. Elzinga
- Department of Neurology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Stephanie A. Eid
- Department of Neurology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Brett A. McGregor
- Department of Biomedical Sciences, University of North Dakota School of Medicine and Health Sciences, Grand Forks, ND 58202, USA
| | - Dae-Gyu Jang
- Department of Neurology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Lucy M. Hinder
- Department of Neurology, University of Michigan, Ann Arbor, MI 48109, USA
| | | | - John M. Hayes
- Department of Neurology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Hongyu Zhang
- Division of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109, USA
| | - Kai Guo
- Department of Neurology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Subramaniam Pennathur
- Division of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Matthias Kretzler
- Division of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Frank C. Brosius
- Division of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Medicine, University of Arizona, Tucson, AZ 85721, USA
| | - Emily J. Koubek
- Department of Neurology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Eva L. Feldman
- Department of Neurology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Junguk Hur
- Department of Biomedical Sciences, University of North Dakota School of Medicine and Health Sciences, Grand Forks, ND 58202, USA
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19
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Benson MD, Eisman AS, Tahir UA, Katz DH, Deng S, Ngo D, Robbins JM, Hofmann A, Shi X, Zheng S, Keyes M, Yu Z, Gao Y, Farrell L, Shen D, Chen ZZ, Cruz DE, Sims M, Correa A, Tracy RP, Durda P, Taylor KD, Liu Y, Johnson WC, Guo X, Yao J, Chen YDI, Manichaikul AW, Jain D, Yang Q, Bouchard C, Sarzynski MA, Rich SS, Rotter JI, Wang TJ, Wilson JG, Clish CB, Sarkar IN, Natarajan P, Gerszten RE. Protein-metabolite association studies identify novel proteomic determinants of metabolite levels in human plasma. Cell Metab 2023; 35:1646-1660.e3. [PMID: 37582364 PMCID: PMC11118091 DOI: 10.1016/j.cmet.2023.07.012] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 04/12/2023] [Accepted: 07/24/2023] [Indexed: 08/17/2023]
Abstract
Although many novel gene-metabolite and gene-protein associations have been identified using high-throughput biochemical profiling, systematic studies that leverage human genetics to illuminate causal relationships between circulating proteins and metabolites are lacking. Here, we performed protein-metabolite association studies in 3,626 plasma samples from three human cohorts. We detected 171,800 significant protein-metabolite pairwise correlations between 1,265 proteins and 365 metabolites, including established relationships in metabolic and signaling pathways such as the protein thyroxine-binding globulin and the metabolite thyroxine, as well as thousands of new findings. In Mendelian randomization (MR) analyses, we identified putative causal protein-to-metabolite associations. We experimentally validated top MR associations in proof-of-concept plasma metabolomics studies in three murine knockout strains of key protein regulators. These analyses identified previously unrecognized associations between bioactive proteins and metabolites in human plasma. We provide publicly available data to be leveraged for studies in human metabolism and disease.
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Affiliation(s)
- Mark D Benson
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Aaron S Eisman
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA; Center for Biomedical Informatics, Brown University, Providence, RI, USA
| | - Usman A Tahir
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Daniel H Katz
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Shuliang Deng
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Debby Ngo
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Jeremy M Robbins
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Alissa Hofmann
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Xu Shi
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Shuning Zheng
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Michelle Keyes
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Zhi Yu
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Yan Gao
- University of Mississippi Medical Center, Jackson, MS, USA
| | - Laurie Farrell
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Dongxiao Shen
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Zsu-Zsu Chen
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Daniel E Cruz
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Mario Sims
- University of Mississippi Medical Center, Jackson, MS, USA
| | - Adolfo Correa
- University of Mississippi Medical Center, Jackson, MS, USA
| | - Russell P Tracy
- Department of Pathology Laboratory Medicine, Larner College of Medicine, University of Vermont, Burlington, VT, USA
| | - Peter Durda
- Department of Pathology Laboratory Medicine, Larner College of Medicine, University of Vermont, Burlington, VT, USA
| | - Kent D Taylor
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Yongmei Liu
- Department of Medicine, Division of Cardiology, Duke Molecular Physiology Institute, Duke University Medical Center, Durham, NC, USA
| | - W Craig Johnson
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Jie Yao
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Yii-Der Ida Chen
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Ani W Manichaikul
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA; Division of Biostatistics and Epidemiology, Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA
| | | | - Qiong Yang
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Claude Bouchard
- Human Genomic Laboratory, Pennington Biomedical Research Center, Baton Rouge, LA, USA
| | - Mark A Sarzynski
- Department of Exercise Science, University of South Carolina, Columbia, Columbia, SC, USA
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Thomas J Wang
- Department of Medicine, UT Southwestern Medical Center, Dallas, TX, USA
| | - James G Wilson
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Clary B Clish
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Indra Neil Sarkar
- Center for Biomedical Informatics, Brown University, Providence, RI, USA
| | - Pradeep Natarajan
- Broad Institute of Harvard and MIT, Cambridge, MA, USA; Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA; Department of Medicine Harvard Medical School, Boston, MA, USA
| | - Robert E Gerszten
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA; Broad Institute of Harvard and MIT, Cambridge, MA, USA.
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20
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Lou N, Wang G, Wang Y, Xu M, Zhou Y, Tan Q, Zhong Q, Zhang L, Zhang X, Liu S, Luo R, Wang S, Tang L, Yao J, Zhang Z, Shi Y, Yu X, Han X. Proteomics Identifies Circulating TIMP-1 as a Prognostic Biomarker for Diffuse Large B-Cell Lymphoma. Mol Cell Proteomics 2023; 22:100625. [PMID: 37500057 PMCID: PMC10470290 DOI: 10.1016/j.mcpro.2023.100625] [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: 01/28/2023] [Revised: 06/24/2023] [Accepted: 07/24/2023] [Indexed: 07/29/2023] Open
Abstract
Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous disease, although disease stratification using in-depth plasma proteomics has not been performed to date. By measuring more than 1000 proteins in the plasma of 147 DLBCL patients using data-independent acquisition mass spectrometry and antibody array, DLBCL patients were classified into four proteomic subtypes (PS-I-IV). Patients with the PS-IV subtype and worst prognosis had increased levels of proteins involved in inflammation, including a high expression of metalloproteinase inhibitor-1 (TIMP-1) that was associated with poor survival across two validation cohorts (n = 180). Notably, the combination of TIMP-1 with the international prognostic index (IPI) identified 64.00% to 88.24% of relapsed and 65.00% to 80.49% of deceased patients in the discovery and two validation cohorts, which represents a 24.00% to 41.67% and 20.00% to 31.70% improvement compared to the IPI score alone, respectively. Taken together, we demonstrate that DLBCL heterogeneity is reflected in the plasma proteome and that TIMP-1, together with the IPI, could improve the prognostic stratification of patients.
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Affiliation(s)
- Ning Lou
- Department of Clinical Laboratory, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing, China
| | - Guibin Wang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences-Beijing (PHOENIX Center), Beijing Institute of Lifeomics, Beijing, China
| | - Yanrong Wang
- Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing, China
| | - Meng Xu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences-Beijing (PHOENIX Center), Beijing Institute of Lifeomics, Beijing, China
| | - Yu Zhou
- Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing, China
| | - Qiaoyun Tan
- Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing, China
| | - Qiaofeng Zhong
- Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing, China
| | - Lei Zhang
- Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing, China
| | - Xiaomei Zhang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences-Beijing (PHOENIX Center), Beijing Institute of Lifeomics, Beijing, China
| | - Shuxia Liu
- Department of Clinical Laboratory, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing, China
| | - Rongrong Luo
- Department of Clinical Laboratory, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing, China
| | - Shasha Wang
- Department of Clinical Laboratory, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing, China
| | - Le Tang
- Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing, China
| | - Jiarui Yao
- Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing, China
| | - Zhishang Zhang
- Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing, China
| | - Yuankai Shi
- Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing, China.
| | - Xiaobo Yu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences-Beijing (PHOENIX Center), Beijing Institute of Lifeomics, Beijing, China.
| | - Xiaohong Han
- Clinical Pharmacology Research Center, Peking Union Medical College Hospital, State Key Laboratory of Complex Severe and Rare Diseases, NMPA Key Laboratory for Clinical Research and Evaluation of Drug, Beijing Key Laboratory of Clinical PK & PD Investigation for Innovative Drugs, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
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21
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Zhong W, Danielsson H, Brusselaers N, Wackernagel D, Sjöbom U, Sävman K, Hansen Pupp I, Ley D, Nilsson AK, Fagerberg L, Uhlén M, Hellström A. The development of blood protein profiles in extremely preterm infants follows a stereotypic evolution pattern. COMMUNICATIONS MEDICINE 2023; 3:107. [PMID: 37532738 PMCID: PMC10397184 DOI: 10.1038/s43856-023-00338-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 07/25/2023] [Indexed: 08/04/2023] Open
Abstract
BACKGROUND Preterm birth is the leading cause of neonatal mortality and morbidity. Early diagnosis and interventions are critical to improving the clinical outcomes of extremely premature infants. Blood protein profiling during the first months of life in preterm infants can shed light on the role of early extrauterine development and provide an increased understanding of maturation after extremely preterm birth and the underlying mechanisms of prematurity-related disorders. METHODS We have investigated the blood protein profiles during the first months of life in preterm infants on the role of early extrauterine development. The blood protein levels were analyzed using next generation blood profiling on 1335 serum samples, collected longitudinally at nine time points from birth to full-term from 182 extremely preterm infants. RESULTS The protein analysis reveals evident predestined serum evolution patterns common for all included infants. The majority of the variations in blood protein expression are associated with the postnatal age of the preterm infants rather than any other factors. There is a uniform protein pattern on postnatal day 1 and after 30 weeks postmenstrual age (PMA), independent of gestational age (GA). However, during the first month of life, GA had a significant impact on protein variability. CONCLUSIONS The unified pattern of protein development for all included infants suggests an age-dependent stereotypic development of blood proteins after birth. This knowledge should be considered in neonatal settings and might alter the clinical approach within neonatology, where PMA is today the most dominant age variable.
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Affiliation(s)
- Wen Zhong
- Science for Life Laboratory, Department of Biomedical and Clinical Sciences (BKV), Linköping University, Linköping, Sweden
- Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Hanna Danielsson
- Centre for Translational Microbiome Research, Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden
- Sach's Children's and Youth Hospital, Södersjukhuset, Stockholm, Sweden
| | - Nele Brusselaers
- Centre for Translational Microbiome Research, Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden
- Global Health Institute, Antwerp University, Antwerp, Belgium
| | - Dirk Wackernagel
- Department of Neonatology, Karolinska University Hospital and Institute, Astrid Lindgrens Children's Hospital, Stockholm, Sweden
| | - Ulrika Sjöbom
- Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Learning and Leadership for Health Care Professionals At the Institute of Health and Care Science at Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
| | - Karin Sävman
- Department of Pediatrics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Region Västra Götaland, Dept of Neonatology, The Queen Silvia Children's Hospital, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Ingrid Hansen Pupp
- Department of Pediatrics, Institute of Clinical Sciences Lund, Lund University and Skane University Hospital, Lund, Sweden
| | - David Ley
- Department of Pediatrics, Institute of Clinical Sciences Lund, Lund University and Skane University Hospital, Lund, Sweden
| | - Anders K Nilsson
- Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Linn Fagerberg
- Science for Life Laboratory, Department of Protein Science, KTH-Royal Institute of Technology, Stockholm, Sweden
| | - Mathias Uhlén
- Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Science for Life Laboratory, Department of Protein Science, KTH-Royal Institute of Technology, Stockholm, Sweden
| | - Ann Hellström
- Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
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22
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Álvez MB, Edfors F, von Feilitzen K, Zwahlen M, Mardinoglu A, Edqvist PH, Sjöblom T, Lundin E, Rameika N, Enblad G, Lindman H, Höglund M, Hesselager G, Stålberg K, Enblad M, Simonson OE, Häggman M, Axelsson T, Åberg M, Nordlund J, Zhong W, Karlsson M, Gyllensten U, Ponten F, Fagerberg L, Uhlén M. Next generation pan-cancer blood proteome profiling using proximity extension assay. Nat Commun 2023; 14:4308. [PMID: 37463882 DOI: 10.1038/s41467-023-39765-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Accepted: 06/27/2023] [Indexed: 07/20/2023] Open
Abstract
A comprehensive characterization of blood proteome profiles in cancer patients can contribute to a better understanding of the disease etiology, resulting in earlier diagnosis, risk stratification and better monitoring of the different cancer subtypes. Here, we describe the use of next generation protein profiling to explore the proteome signature in blood across patients representing many of the major cancer types. Plasma profiles of 1463 proteins from more than 1400 cancer patients are measured in minute amounts of blood collected at the time of diagnosis and before treatment. An open access Disease Blood Atlas resource allows the exploration of the individual protein profiles in blood collected from the individual cancer patients. We also present studies in which classification models based on machine learning have been used for the identification of a set of proteins associated with each of the analyzed cancers. The implication for cancer precision medicine of next generation plasma profiling is discussed.
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Affiliation(s)
- María Bueno Álvez
- Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Fredrik Edfors
- Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Kalle von Feilitzen
- Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Martin Zwahlen
- Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Adil Mardinoglu
- Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, SE1 9RT, UK
| | - Per-Henrik Edqvist
- Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Tobias Sjöblom
- Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Emma Lundin
- Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Natallia Rameika
- Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Gunilla Enblad
- Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Henrik Lindman
- Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Martin Höglund
- Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Göran Hesselager
- Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Karin Stålberg
- Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden
| | - Malin Enblad
- Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
| | - Oscar E Simonson
- Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
| | - Michael Häggman
- Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
| | - Tomas Axelsson
- Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Mikael Åberg
- Department of Medical Sciences, Clinical Chemistry and SciLifeLab Affinity Proteomics, Uppsala University, Uppsala, Sweden
| | - Jessica Nordlund
- Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Wen Zhong
- Science for Life Laboratory, Department of Biomedical and Clinical Sciences (BKV), Linköping University, Linköping, Sweden
| | - Max Karlsson
- Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Ulf Gyllensten
- Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Fredrik Ponten
- Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Linn Fagerberg
- Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Mathias Uhlén
- Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden.
- Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden.
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23
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Nimer RM, Abdel Rahman AM. Recent advances in proteomic-based diagnostics of cystic fibrosis. Expert Rev Proteomics 2023; 20:151-169. [PMID: 37766616 DOI: 10.1080/14789450.2023.2258282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Accepted: 07/06/2023] [Indexed: 09/29/2023]
Abstract
INTRODUCTION Cystic fibrosis (CF) is a genetic disease characterized by thick and sticky mucus accumulation, which may harm numerous internal organs. Various variables such as gene modifiers, environmental factors, age of diagnosis, and CF transmembrane conductance regulator (CFTR) gene mutations influence phenotypic disease diversity. Biomarkers that are based on genomic information may not accurately represent the underlying mechanism of the disease as well as its lethal complications. Therefore, recent advancements in mass spectrometry (MS)-based proteomics may provide deep insights into CF mechanisms and cellular functions by examining alterations in the protein expression patterns from various samples of individuals with CF. AREAS COVERED We present current developments in MS-based proteomics, its application, and findings in CF. In addition, the future roles of proteomics in finding diagnostic and prognostic novel biomarkers. EXPERT OPINION Despite significant advances in MS-based proteomics, extensive research in a large cohort for identifying and validating diagnostic, prognostic, predictive, and therapeutic biomarkers for CF disease is highly needed.
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Affiliation(s)
- Refat M Nimer
- Department of Medical Laboratory Sciences, Jordan University of Science and Technology, Irbid, Jordan
| | - Anas M Abdel Rahman
- Metabolomics Section, Department of Clinical Genomics, Center for Genome Medicine, King Faisal Specialist Hospital and Research Centre (KFSHRC), Riyadh, Saudi Arabia
- Department of Biochemistry and Molecular Medicine, College of Medicine, Alfaisal University, Riyadh, Saudi Arabia
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24
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Lu S, Fowler CR, Ream B, Waugh SM, Russell TM, Rohloff JC, Gold L, Cleveland JP, Stoll S. Magnetically Detected Protein Binding Using Spin-Labeled Slow Off-Rate Modified Aptamers. ACS Sens 2023; 8:2219-2227. [PMID: 37300508 DOI: 10.1021/acssensors.3c00112] [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: 06/12/2023]
Abstract
Recent developments in aptamer chemistry open up opportunities for new tools for protein biosensing. In this work, we present an approach to use immobilized slow off-rate modified aptamers (SOMAmers) site-specifically labeled with a nitroxide radical via azide-alkyne click chemistry as a means for detecting protein binding. Protein binding induces a change in rotational mobility of the spin label, which is detected via solution-state electron paramagnetic resonance (EPR) spectroscopy. We demonstrate the workflow and test the protocol using the SOMAmer SL5 and its protein target, platelet-derived growth factor B (PDGF-BB). In a complete site scan of the nitroxide over the SOMAmer, we determine the rotational mobility of the spin label in the absence and presence of target protein. Several sites with sufficiently tight affinity and large rotational mobility change upon protein binding are identified. We then model a system where the spin-labeled SOMAmer assay is combined with fluorescence detection via diamond nitrogen-vacancy (NV) center relaxometry. The NV center spin-lattice relaxation time is modulated by the rotational mobility of a proximal spin label and thus responsive to SOMAmer-protein binding. The spin label-mediated assay provides a general approach for transducing protein binding events into magnetically detectable signals.
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Affiliation(s)
- Shutian Lu
- Department of Chemistry, University of Washington, Seattle, Washington 98195, United States
| | | | - Brian Ream
- SomaLogic, Boulder, Colorado 80301, United States
| | | | | | | | - Larry Gold
- SomaLogic, Boulder, Colorado 80301, United States
| | | | - Stefan Stoll
- Department of Chemistry, University of Washington, Seattle, Washington 98195, United States
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25
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Lövfors W, Magnusson R, Jönsson C, Gustafsson M, Olofsson CS, Cedersund G, Nyman E. A comprehensive mechanistic model of adipocyte signaling with layers of confidence. NPJ Syst Biol Appl 2023; 9:24. [PMID: 37286693 DOI: 10.1038/s41540-023-00282-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Accepted: 05/17/2023] [Indexed: 06/09/2023] Open
Abstract
Adipocyte signaling, normally and in type 2 diabetes, is far from fully understood. We have earlier developed detailed dynamic mathematical models for several well-studied, partially overlapping, signaling pathways in adipocytes. Still, these models only cover a fraction of the total cellular response. For a broader coverage of the response, large-scale phosphoproteomic data and systems level knowledge on protein interactions are key. However, methods to combine detailed dynamic models with large-scale data, using information about the confidence of included interactions, are lacking. We have developed a method to first establish a core model by connecting existing models of adipocyte cellular signaling for: (1) lipolysis and fatty acid release, (2) glucose uptake, and (3) the release of adiponectin. Next, we use publicly available phosphoproteome data for the insulin response in adipocytes together with prior knowledge on protein interactions, to identify phosphosites downstream of the core model. In a parallel pairwise approach with low computation time, we test whether identified phosphosites can be added to the model. We iteratively collect accepted additions into layers and continue the search for phosphosites downstream of these added layers. For the first 30 layers with the highest confidence (311 added phosphosites), the model predicts independent data well (70-90% correct), and the predictive capability gradually decreases when we add layers of decreasing confidence. In total, 57 layers (3059 phosphosites) can be added to the model with predictive ability kept. Finally, our large-scale, layered model enables dynamic simulations of systems-wide alterations in adipocytes in type 2 diabetes.
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Affiliation(s)
- William Lövfors
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden.
- Department of Mathematics, Linköping University, Linköping, Sweden.
- School of Medical Sciences and Inflammatory Response and Infection Susceptibility Centre (iRiSC), Faculty of Medicine and Health, Örebro University, Örebro, Sweden.
| | - Rasmus Magnusson
- School of Bioscience, Systems Biology Research Center, University of Skövde, Skövde, Sweden
| | - Cecilia Jönsson
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden
- Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
| | - Mika Gustafsson
- Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden
| | - Charlotta S Olofsson
- Department of Physiology/Metabolic Physiology, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
| | - Gunnar Cedersund
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden.
- School of Medical Sciences and Inflammatory Response and Infection Susceptibility Centre (iRiSC), Faculty of Medicine and Health, Örebro University, Örebro, Sweden.
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.
| | - Elin Nyman
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden.
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26
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Bader JM, Albrecht V, Mann M. MS-based proteomics of body fluids: The end of the beginning. Mol Cell Proteomics 2023:100577. [PMID: 37209816 PMCID: PMC10388585 DOI: 10.1016/j.mcpro.2023.100577] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 05/07/2023] [Accepted: 05/11/2023] [Indexed: 05/22/2023] Open
Abstract
Accurate biomarkers are a crucial and necessary precondition for precision medicine, yet existing ones are often unspecific and new ones have been very slow to enter the clinic. Mass spectrometry (MS)-based proteomics excels by its untargeted nature, specificity of identification and quantification making it an ideal technology for biomarker discovery and routine measurement. It has unique attributes compared to affinity binder technologies, such as OLINK Proximity Extension Assay and SOMAscan. In a previous review we described technological and conceptual limitations that had held back success (Geyer et al., 2017). We proposed a 'rectangular strategy' to better separate true biomarkers by minimizing cohort-specific effects. Today, this has converged with advances in MS-based proteomics technology, such as increased sample throughput, depth of identification and quantification. As a result, biomarker discovery studies have become more successful, producing biomarker candidates that withstand independent verification and, in some cases, already outperform state-of-the-art clinical assays. We summarize developments over the last years, including the benefits of large and independent cohorts, which are necessary for clinical acceptance. They are also required for machine learning or deep learning. Shorter gradients, new scan modes and multiplexing are about to drastically increase throughput, cross-study integration, and quantification, including proxies for absolute levels. We have found that multi-protein panels are inherently more robust than current single analyte tests and better capture the complexity of human phenotypes. Routine MS measurement in the clinic is fast becoming a viable option. The full set of proteins in a body fluid (global proteome) is the most important reference and the best process control. Additionally, it increasingly has all the information that could be obtained from targeted analysis although the latter may be the most straightforward way to enter into regular use. Many challenges remain, not least of a regulatory and ethical nature, but the outlook for MS-based clinical applications has never been brighter.
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Affiliation(s)
- Jakob M Bader
- Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, 82152 Martinsried, Germany
| | - Vincent Albrecht
- Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, 82152 Martinsried, Germany
| | - Matthias Mann
- Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, 82152 Martinsried, Germany; Novo Nordisk Foundation Center for Protein Research, Faculty of Health Sciences, University of Copenhagen, 2200 Copenhagen, Denmark.
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27
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van der Burgt Y, Wuhrer M. The role of clinical glyco(proteo)mics in precision medicine. Mol Cell Proteomics 2023:100565. [PMID: 37169080 DOI: 10.1016/j.mcpro.2023.100565] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 04/12/2023] [Accepted: 05/02/2023] [Indexed: 05/13/2023] Open
Abstract
Glycoproteomics reveals site-specific O- and N-glycosylation that may influence protein properties including binding, activity and half-life. The increasingly mature toolbox with glycomic- and glycoproteomic strategies is applied for the development of biopharmaceuticals and discovery and clinical evaluation of glycobiomarkers in various disease fields. Notwithstanding the contributions of glycoscience in identifying new drug targets, the current report is focused on the biomarker modality that is of interest for diagnostic and monitoring purposes. To this end it is noted that the identification of biomarkers has received more attention than corresponding quantification. Most analytical methods are very efficient in detecting large numbers of analytes but developments to accurately quantify these have so far been limited. In this perspective a parallel is made with earlier proposed tiers for protein quantification using mass spectrometry. Moreover, the foreseen reporting of multimarker readouts is discussed to describe an individual's health or disease state and their role in clinical decision-making. The potential of longitudinal sampling and monitoring of glycomic features for diagnosis and treatment monitoring is emphasized. Finally, different strategies that address quantification of a multimarker panel will be discussed.
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Affiliation(s)
- Yuri van der Burgt
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, The Netherlands.
| | - Manfred Wuhrer
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, The Netherlands
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28
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Urbiola-Salvador V, Jabłońska A, Miroszewska D, Huang Q, Duzowska K, Drężek-Chyła K, Zdrenka M, Śrutek E, Szylberg Ł, Jankowski M, Bała D, Zegarski W, Nowikiewicz T, Makarewicz W, Adamczyk A, Ambicka A, Przewoźnik M, Harazin-Lechowicz A, Ryś J, Filipowicz N, Piotrowski A, Dumanski JP, Li B, Chen Z. Plasma protein changes reflect colorectal cancer development and associated inflammation. Front Oncol 2023; 13:1158261. [PMID: 37228491 PMCID: PMC10203952 DOI: 10.3389/fonc.2023.1158261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Accepted: 04/05/2023] [Indexed: 05/27/2023] Open
Abstract
Introduction Colorectal cancer (CRC) is the third most common malignancy and the second leading cause of death worldwide. Efficient non-invasive blood-based biomarkers for CRC early detection and prognosis are urgently needed. Methods To identify novel potential plasma biomarkers, we applied a proximity extension assay (PEA), an antibody-based proteomics strategy to quantify the abundance of plasma proteins in CRC development and cancer-associated inflammation from few μL of plasma sample. Results Among the 690 quantified proteins, levels of 202 plasma proteins were significantly changed in CRC patients compared to age-and-sex-matched healthy subjects. We identified novel protein changes involved in Th17 activity, oncogenic pathways, and cancer-related inflammation with potential implications in the CRC diagnosis. Moreover, the interferon γ (IFNG), interleukin (IL) 32, and IL17C were identified as associated with the early stages of CRC, whereas lysophosphatidic acid phosphatase type 6 (ACP6), Fms-related tyrosine kinase 4 (FLT4), and MANSC domain-containing protein 1 (MANSC1) were correlated with the late-stages of CRC. Discussion Further study to characterize the newly identified plasma protein changes from larger cohorts will facilitate the identification of potential novel diagnostic, prognostic biomarkers for CRC.
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Affiliation(s)
- Víctor Urbiola-Salvador
- Intercollegiate Faculty of Biotechnology of University of Gdańsk and Medical University of Gdańsk, University of Gdańsk, Gdańsk, Poland
| | - Agnieszka Jabłońska
- Intercollegiate Faculty of Biotechnology of University of Gdańsk and Medical University of Gdańsk, University of Gdańsk, Gdańsk, Poland
| | - Dominika Miroszewska
- Intercollegiate Faculty of Biotechnology of University of Gdańsk and Medical University of Gdańsk, University of Gdańsk, Gdańsk, Poland
| | - Qianru Huang
- Center for Immune-Related Diseases at Shanghai Institute of Immunology, Department of Respiratory and Critical Care Medicine of Ruijin Hospital, Department of Immunology and Microbiology, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | | | | | - Marek Zdrenka
- Department of Tumor Pathology and Pathomorphology, Oncology Center−Prof. Franciszek Łukaszczyk Memorial Hospital, Bydgoszcz, Poland
| | - Ewa Śrutek
- Department of Tumor Pathology and Pathomorphology, Oncology Center−Prof. Franciszek Łukaszczyk Memorial Hospital, Bydgoszcz, Poland
| | - Łukasz Szylberg
- Department of Tumor Pathology and Pathomorphology, Oncology Center−Prof. Franciszek Łukaszczyk Memorial Hospital, Bydgoszcz, Poland
- Department of Obstetrics, Gynaecology and Oncology, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Torun, Bydgoszcz, Poland
| | - Michał Jankowski
- Surgical Oncology, Ludwik Rydygier’s Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in ToruńSurgical Oncology, Ludwik Rydygier’s Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, Toruń, Poland
- Department of Surgical Oncology, Oncology Center−Prof. Franciszek Łukaszczyk Memorial Hospital, Bydgoszcz, Poland
| | - Dariusz Bała
- Surgical Oncology, Ludwik Rydygier’s Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in ToruńSurgical Oncology, Ludwik Rydygier’s Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, Toruń, Poland
- Department of Surgical Oncology, Oncology Center−Prof. Franciszek Łukaszczyk Memorial Hospital, Bydgoszcz, Poland
| | - Wojciech Zegarski
- Surgical Oncology, Ludwik Rydygier’s Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in ToruńSurgical Oncology, Ludwik Rydygier’s Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, Toruń, Poland
- Department of Surgical Oncology, Oncology Center−Prof. Franciszek Łukaszczyk Memorial Hospital, Bydgoszcz, Poland
| | - Tomasz Nowikiewicz
- Surgical Oncology, Ludwik Rydygier’s Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in ToruńSurgical Oncology, Ludwik Rydygier’s Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, Toruń, Poland
- Department of Breast Cancer and Reconstructive Surgery, Oncology Center−Prof. Franciszek Łukaszczyk Memorial Hospital, Bydgoszcz, Poland
| | - Wojciech Makarewicz
- Clinic of General and Oncological Surgery, Specialist Hospital of Kościerzyna, Kościerzyna, Poland
| | - Agnieszka Adamczyk
- Department of Tumor Pathology, Maria Skłodowska-Curie National Research Institute of Oncology, Kraków, Poland
| | - Aleksandra Ambicka
- Department of Tumor Pathology, Maria Skłodowska-Curie National Research Institute of Oncology, Kraków, Poland
| | - Marcin Przewoźnik
- Department of Tumor Pathology, Maria Skłodowska-Curie National Research Institute of Oncology, Kraków, Poland
| | - Agnieszka Harazin-Lechowicz
- Department of Tumor Pathology, Maria Skłodowska-Curie National Research Institute of Oncology, Kraków, Poland
| | - Janusz Ryś
- Department of Tumor Pathology, Maria Skłodowska-Curie National Research Institute of Oncology, Kraków, Poland
| | | | | | - Jan P. Dumanski
- 3P-Medicine Laboratory, Medical University of Gdańsk, Gdańsk, Poland
- Department of Immunology, Genetics and Pathology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
- Department of Biology and Pharmaceutical Botany, Medical University of Gdańsk, Gdańsk, Poland
| | - Bin Li
- Center for Immune-Related Diseases at Shanghai Institute of Immunology, Department of Respiratory and Critical Care Medicine of Ruijin Hospital, Department of Immunology and Microbiology, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhi Chen
- Intercollegiate Faculty of Biotechnology of University of Gdańsk and Medical University of Gdańsk, University of Gdańsk, Gdańsk, Poland
- Faculty of Biochemistry and Molecular Medicine, University of Oulu, Oulu, Finland
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29
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Omenn GS, Lane L, Overall CM, Pineau C, Packer NH, Cristea IM, Lindskog C, Weintraub ST, Orchard S, Roehrl MH, Nice E, Liu S, Bandeira N, Chen YJ, Guo T, Aebersold R, Moritz RL, Deutsch EW. The 2022 Report on the Human Proteome from the HUPO Human Proteome Project. J Proteome Res 2023; 22:1024-1042. [PMID: 36318223 PMCID: PMC10081950 DOI: 10.1021/acs.jproteome.2c00498] [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] [Indexed: 11/07/2022]
Abstract
The 2022 Metrics of the Human Proteome from the HUPO Human Proteome Project (HPP) show that protein expression has now been credibly detected (neXtProt PE1 level) for 18 407 (93.2%) of the 19 750 predicted proteins coded in the human genome, a net gain of 50 since 2021 from data sets generated around the world and reanalyzed by the HPP. Conversely, the number of neXtProt PE2, PE3, and PE4 missing proteins has been reduced by 78 from 1421 to 1343. This represents continuing experimental progress on the human proteome parts list across all the chromosomes, as well as significant reclassifications. Meanwhile, applying proteomics in a vast array of biological and clinical studies continues to yield significant findings and growing integration with other omics platforms. We present highlights from the Chromosome-Centric HPP, Biology and Disease-driven HPP, and HPP Resource Pillars, compare features of mass spectrometry and Olink and Somalogic platforms, note the emergence of translation products from ribosome profiling of small open reading frames, and discuss the launch of the initial HPP Grand Challenge Project, "A Function for Each Protein".
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Affiliation(s)
- Gilbert S. Omenn
- University of Michigan, Ann Arbor, Michigan 48109, United States
- Institute for Systems Biology, Seattle, Washington 98109, United States
| | - Lydie Lane
- CALIPHO Group, SIB Swiss Institute of Bioinformatics and University of Geneva, 1015 Lausanne, Switzerland
| | | | - Charles Pineau
- French Institute of Health and Medical Research, 35042 RENNES Cedex, France
| | - Nicolle H. Packer
- Macquarie University, Sydney, NSW 2109, Australia
- Griffith University’s Institute for Glycomics, Sydney, NSW 2109, Australia
| | | | | | - Susan T. Weintraub
- University of Texas Health Science Center-San Antonio, San Antonio, Texas 78229-3900, United States
| | - Sandra Orchard
- EMBL-EBI, Hinxton, Cambridgeshire, CB10 1SD, United Kingdom
| | - Michael H.A. Roehrl
- Memorial Sloan Kettering Cancer Center, New York, New York, 10065, United States
| | | | - Siqi Liu
- BGI Group, Shenzhen 518083, China
| | - Nuno Bandeira
- University of California, San Diego, La Jolla, California 92093, United States
| | - Yu-Ju Chen
- National Taiwan University, Academia Sinica, Nankang, Taipei 11529, Taiwan
| | - Tiannan Guo
- Westlake University Guomics Laboratory of Big Proteomic Data, Hangzhou 310024, Zhejiang Province, China
| | - Ruedi Aebersold
- Institute of Molecular Systems Biology in ETH Zurich, 8092 Zurich, Switzerland
| | - Robert L. Moritz
- Institute for Systems Biology, Seattle, Washington 98109, United States
| | - Eric W. Deutsch
- Institute for Systems Biology, Seattle, Washington 98109, United States
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Kalinina AA, Ziganshin RK, Silaeva YY, Sharova NI, Nikonova MF, Persiyantseva NA, Gorkova TG, Antoshina EE, Trukhanova LS, Donetskova AD, Komogorova VV, Litvina MM, Mitin AN, Zamkova MA, Bruter AV, Khromykh LM, Kazansky DB. Physiological and Functional Effects of Dominant Active TCRα Expression in Transgenic Mice. Int J Mol Sci 2023; 24:ijms24076527. [PMID: 37047500 PMCID: PMC10094918 DOI: 10.3390/ijms24076527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 03/28/2023] [Accepted: 03/29/2023] [Indexed: 04/03/2023] Open
Abstract
A T cell receptor (TCR) consists of α- and β-chains. Accumulating evidence suggests that some TCRs possess chain centricity, i.e., either of the hemi-chains can dominate in antigen recognition and dictate the TCR’s specificity. The introduction of TCRα/β into naive lymphocytes generates antigen-specific T cells that are ready to perform their functions. Transgenesis of the dominant active TCRα creates transgenic animals with improved anti-tumor immune control, and adoptive immunotherapy with TCRα-transduced T cells provides resistance to infections. However, the potential detrimental effects of the dominant hemi-chain TCR’s expression in transgenic animals have not been well investigated. Here, we analyzed, in detail, the functional status of the immune system of recently generated 1D1a transgenic mice expressing the dominant active TCRα specific to the H2-Kb molecule. In their age dynamics, neither autoimmunity due to the random pairing of transgenic TCRα with endogenous TCRβ variants nor significant disturbances in systemic homeostasis were detected in these mice. Although the specific immune response was considerably enhanced in 1D1a mice, responses to third-party alloantigens were not compromised, indicating that the expression of dominant active TCRα did not limit immune reactivity in transgenic mice. Our data suggest that TCRα transgene expression could delay thymic involution and maintain TCRβ repertoire diversity in old transgenic mice. The detected changes in the systemic homeostasis in 1D1a transgenic mice, which are minor and primarily transient, may indicate variations in the ontogeny of wild-type and transgenic mouse lines.
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Affiliation(s)
- Anastasiia A. Kalinina
- N.N. Blokhin National Medical Research Center of Oncology, Ministry of Health of the Russian Federation, Kashirskoe sh., 24, 115478 Moscow, Russia
| | - Rustam Kh. Ziganshin
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Miklukho-Maklaya st. 16/10, 117997 Moscow, Russia
| | - Yulia Yu. Silaeva
- Institute of Gene Biology, Russian Academy of Sciences, Vavilova st. 34/5, 119334 Moscow, Russia
| | - Nina I. Sharova
- National Research Center, Institute of Immunology Federal Medical-Biological Agency of Russia, Kashirskoe sh., 24, 115522 Moscow, Russia
| | - Margarita F. Nikonova
- National Research Center, Institute of Immunology Federal Medical-Biological Agency of Russia, Kashirskoe sh., 24, 115522 Moscow, Russia
| | - Nadezda A. Persiyantseva
- N.N. Blokhin National Medical Research Center of Oncology, Ministry of Health of the Russian Federation, Kashirskoe sh., 24, 115478 Moscow, Russia
| | - Tatiana G. Gorkova
- N.N. Blokhin National Medical Research Center of Oncology, Ministry of Health of the Russian Federation, Kashirskoe sh., 24, 115478 Moscow, Russia
| | - Elena E. Antoshina
- N.N. Blokhin National Medical Research Center of Oncology, Ministry of Health of the Russian Federation, Kashirskoe sh., 24, 115478 Moscow, Russia
| | - Lubov S. Trukhanova
- N.N. Blokhin National Medical Research Center of Oncology, Ministry of Health of the Russian Federation, Kashirskoe sh., 24, 115478 Moscow, Russia
| | - Almira D. Donetskova
- National Research Center, Institute of Immunology Federal Medical-Biological Agency of Russia, Kashirskoe sh., 24, 115522 Moscow, Russia
| | - Victoria V. Komogorova
- National Research Center, Institute of Immunology Federal Medical-Biological Agency of Russia, Kashirskoe sh., 24, 115522 Moscow, Russia
| | - Marina M. Litvina
- National Research Center, Institute of Immunology Federal Medical-Biological Agency of Russia, Kashirskoe sh., 24, 115522 Moscow, Russia
| | - Alexander N. Mitin
- National Research Center, Institute of Immunology Federal Medical-Biological Agency of Russia, Kashirskoe sh., 24, 115522 Moscow, Russia
| | - Maria A. Zamkova
- N.N. Blokhin National Medical Research Center of Oncology, Ministry of Health of the Russian Federation, Kashirskoe sh., 24, 115478 Moscow, Russia
- Institute of Gene Biology, Russian Academy of Sciences, Vavilova st. 34/5, 119334 Moscow, Russia
| | - Alexandra V. Bruter
- N.N. Blokhin National Medical Research Center of Oncology, Ministry of Health of the Russian Federation, Kashirskoe sh., 24, 115478 Moscow, Russia
- Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Institute of Gene Biology, Russian Academy of Sciences, 34/5 Vavilov St., 119334 Moscow, Russia
| | - Ludmila M. Khromykh
- N.N. Blokhin National Medical Research Center of Oncology, Ministry of Health of the Russian Federation, Kashirskoe sh., 24, 115478 Moscow, Russia
| | - Dmitry B. Kazansky
- N.N. Blokhin National Medical Research Center of Oncology, Ministry of Health of the Russian Federation, Kashirskoe sh., 24, 115478 Moscow, Russia
- Correspondence:
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31
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Urbiola-Salvador V, Lima de Souza S, Grešner P, Qureshi T, Chen Z. Plasma Proteomics Unveil Novel Immune Signatures and Biomarkers upon SARS-CoV-2 Infection. Int J Mol Sci 2023; 24:ijms24076276. [PMID: 37047248 PMCID: PMC10093853 DOI: 10.3390/ijms24076276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Revised: 03/07/2023] [Accepted: 03/24/2023] [Indexed: 03/29/2023] Open
Abstract
Several elements have an impact on COVID-19, including comorbidities, age and sex. To determine the protein profile changes in peripheral blood caused by a SARS-CoV-2 infection, a proximity extension assay was used to quantify 1387 proteins in plasma samples among 28 Finnish patients with COVID-19 with and without comorbidities and their controls. Key immune signatures, including CD4 and CD28, were changed in patients with comorbidities. Importantly, several unreported elevated proteins in patients with COVID-19, such as RBP2 and BST2, which show anti-microbial activity, along with proteins involved in extracellular matrix remodeling, including MATN2 and COL6A3, were identified. RNF41 was downregulated in patients compared to healthy controls. Our study demonstrates that SARS-CoV-2 infection causes distinct plasma protein changes in the presence of comorbidities despite the interpatient heterogeneity, and several novel potential biomarkers associated with a SARS-CoV-2 infection alone and in the presence of comorbidities were identified. Protein changes linked to the generation of SARS-CoV-2-specific antibodies, long-term effects and potential association with post-COVID-19 condition were revealed. Further study to characterize the identified plasma protein changes from larger cohorts with more diverse ethnicities of patients with COVID-19 combined with functional studies will facilitate the identification of novel diagnostic, prognostic biomarkers and potential therapeutic targets for patients with COVID-19.
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Affiliation(s)
- Víctor Urbiola-Salvador
- Intercollegiate Faculty of Biotechnology of University of Gdańsk and Medical University of Gdańsk, University of Gdańsk, 80-307 Gdańsk, Pomerania, Poland
| | - Suiane Lima de Souza
- Faculty of Biochemistry and Molecular Medicine, University of Oulu, 90220 Oulu, North Ostrobothnia, Finland
| | - Peter Grešner
- Department of Translational Oncology, Intercollegiate Faculty of Biotechnology of University of Gdańsk and Medical University of Gdańsk, Medical University of Gdańsk, 80-211 Gdańsk, Pomerania, Poland
| | - Talha Qureshi
- Faculty of Biochemistry and Molecular Medicine, University of Oulu, 90220 Oulu, North Ostrobothnia, Finland
| | - Zhi Chen
- Faculty of Biochemistry and Molecular Medicine, University of Oulu, 90220 Oulu, North Ostrobothnia, Finland
- Correspondence:
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Gajula SNR, Khairnar AS, Jock P, Kumari N, Pratima K, Munjal V, Kalan P, Sonti R. LC-MS/MS: A sensitive and selective analytical technique to detect COVID-19 protein biomarkers in the early disease stage. Expert Rev Proteomics 2023; 20:5-18. [PMID: 36919634 DOI: 10.1080/14789450.2023.2191845] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/16/2023]
Abstract
INTRODUCTION The COVID-19 outbreak has put enormous pressure on the scientific community to detect infection rapidly, identify the status of disease severity, and provide an immediate vaccine/drug for the treatment. Relying on immunoassay and a real-time reverse transcription polymerase chain reaction (rRT-PCR) led to many false-negative and false-positive reports. Therefore, detecting biomarkers is an alternative and reliable approach for determining the infection, its severity, and disease progression. Recent advances in liquid chromatography and mass spectrometry (LC-MS/MS) enable the protein biomarkers even at low concentrations, thus facilitating clinicians to monitor the treatment in hospitals. AREAS COVERED This review highlights the role of LC-MS/MS in identifying protein biomarkers and discusses the clinically significant protein biomarkers such as Serum amyloid A, Interleukin-6, C-Reactive Protein, Lactate dehydrogenase, D-dimer, cardiac troponin, ferritin, Alanine transaminase, Aspartate transaminase, gelsolin and galectin-3-binding protein in COVID-19, and their analysis by LC-MS/MS in the early stage. EXPERT OPINION Clinical doctors monitor significant biomarkers to understand, stratify, and treat patients according to disease severity. Knowledge of clinically significant COVID-19 protein biomarkers is critical not only for COVID-19 caused by the coronavirus but also to prepare us for future pandemics of other diseases in detecting by LC-MS/MS at the early stages.
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Affiliation(s)
- Siva Nageswara Rao Gajula
- Department of Pharmaceutical Analysis, National Institute of Pharmaceutical Education and Research (NIPER), Hyderabad, Balanagar, India
| | - Ankita Sahebrao Khairnar
- Department of Pharmaceutical Analysis, National Institute of Pharmaceutical Education and Research (NIPER), Hyderabad, Balanagar, India
| | - Pallavi Jock
- Department of Pharmaceutical Analysis, National Institute of Pharmaceutical Education and Research (NIPER), Hyderabad, Balanagar, India
| | - Nikita Kumari
- Department of Pharmaceutical Analysis, National Institute of Pharmaceutical Education and Research (NIPER), Hyderabad, Balanagar, India
| | - Kendre Pratima
- Department of Pharmaceutical Analysis, National Institute of Pharmaceutical Education and Research (NIPER), Hyderabad, Balanagar, India
| | - Vijay Munjal
- Department of Pharmaceutical Analysis, National Institute of Pharmaceutical Education and Research (NIPER), Hyderabad, Balanagar, India
| | - Pavan Kalan
- Department of Pharmaceutical Analysis, National Institute of Pharmaceutical Education and Research (NIPER), Hyderabad, Balanagar, India
| | - Rajesh Sonti
- Department of Pharmaceutical Analysis, National Institute of Pharmaceutical Education and Research (NIPER), Hyderabad, Balanagar, India
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Koprulu M, Carrasco-Zanini J, Wheeler E, Lockhart S, Kerrison ND, Wareham NJ, Pietzner M, Langenberg C. Proteogenomic links to human metabolic diseases. Nat Metab 2023; 5:516-528. [PMID: 36823471 PMCID: PMC7614946 DOI: 10.1038/s42255-023-00753-7] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Accepted: 02/01/2023] [Indexed: 02/25/2023]
Abstract
Studying the plasma proteome as the intermediate layer between the genome and the phenome has the potential to identify new disease processes. Here, we conducted a cis-focused proteogenomic analysis of 2,923 plasma proteins measured in 1,180 individuals using antibody-based assays. We (1) identify 256 unreported protein quantitative trait loci (pQTL); (2) demonstrate shared genetic regulation of 224 cis-pQTLs with 575 specific health outcomes, revealing examples for notable metabolic diseases (such as gastrin-releasing peptide as a potential therapeutic target for type 2 diabetes); (3) improve causal gene assignment at 40% (n = 192) of overlapping risk loci; and (4) observe convergence of phenotypic consequences of cis-pQTLs and rare loss-of-function gene burden for 12 proteins, such as TIMD4 for lipoprotein metabolism. Our findings demonstrate the value of integrating complementary proteomic technologies with genomics even at moderate scale to identify new mediators of metabolic diseases with the potential for therapeutic interventions.
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Affiliation(s)
- Mine Koprulu
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK
| | - Julia Carrasco-Zanini
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK
| | - Eleanor Wheeler
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK
| | - Sam Lockhart
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK
- MRC Metabolic Diseases Unit, Wellcome-MRC Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Nicola D Kerrison
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK
| | - Nicholas J Wareham
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK
| | - Maik Pietzner
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK
- Computational Medicine, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK
| | - Claudia Langenberg
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK.
- Computational Medicine, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany.
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK.
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The Roles of Exosomes in the Diagnose, Development and Therapeutic Resistance of Oral Squamous Cell Carcinoma. Int J Mol Sci 2023; 24:ijms24031968. [PMID: 36768288 PMCID: PMC9916286 DOI: 10.3390/ijms24031968] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 01/11/2023] [Accepted: 01/16/2023] [Indexed: 01/20/2023] Open
Abstract
Oral cancer is one of the most common cancers worldwide, of which more than half of patients are diagnosed at a locally advanced stage with poor prognosis due to recurrence, metastasis and resistant to treatment. Thus, it is imperative to further explore the potential mechanism of development and drug resistance of oral cancer. Exosomes are small endosome-derived lipid nanoparticles that are released by cells. Since the cargoes of exosomes were inherited from their donor cells, the cargo profiles of exosomes can well recapitulate that of their donor cells. This is the theoretical basis of exosome-based liquid biopsy, providing a tool for early diagnosis of oral cancer. As an important intracellular bioactive cargo delivery vector, exosomes play a critical role in the development of oral cancer by transferring their cargoes to receipt cells. More importantly, recent studies have revealed that exosomes could induce therapy-resistance in oral cancer through multiple ways, including exosome-mediated drug efflux. In this review, we summarize and compare the role of exosomes in the diagnosis, development and therapy-resistant of oral cancer. We also highlight the clinical application of exosomes, and discuss the advantages and challenges of exosomes serving as predictive biomarker, therapy target and therapy vector in oral cancer.
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35
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Campbell AE, Arjomand J, King OD, Tawil R, Jagannathan S. A Targeted Approach for Evaluating DUX4-Regulated Proteins as Potential Serum Biomarkers for Facioscapulohumeral Muscular Dystrophy Using Immunoassay Proteomics. J Neuromuscul Dis 2023; 10:1031-1040. [PMID: 37899061 PMCID: PMC10657687 DOI: 10.3233/jnd-221636] [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] [Accepted: 09/28/2023] [Indexed: 10/31/2023]
Abstract
BACKGROUND Facioscapulohumeral muscular dystrophy (FSHD) is a progressive myopathy caused by misexpression of the double homeobox 4 (DUX4) embryonic transcription factor in skeletal muscle. Identifying quantitative and minimally invasive FSHD biomarkers to report on DUX4 activity will significantly accelerate therapeutic development. OBJECTIVE The goal of this study was to analyze secreted proteins known to be induced by DUX4 using the commercially available Olink Proteomics platform in order to identify potential blood-based molecular FSHD biomarkers. METHODS We used high-throughput, multiplex immunoassays from Olink Proteomics to measure the levels of several known DUX4-induced genes in a cellular myoblast model of FSHD, in FSHD patient-derived myotube cell cultures, and in serum from individuals with FSHD. Levels of other proteins on the Olink Proteomics panels containing these DUX4 targets were also examined in secondary exploratory analysis. RESULTS Placental alkaline phosphatase (ALPP) levels correlated with DUX4 expression in both cell-based FSHD systems but did not distinguish FSHD patient serum from unaffected controls. CONCLUSIONS ALPP, as measured with the Olink Proteomics platform, is not a promising FSHD serum biomarker candidate but could be utilized to evaluate DUX4 activity in discovery research efforts.
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Affiliation(s)
- Amy E. Campbell
- Department of Biochemistry and Molecular Genetics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | | | - Oliver D. King
- Department of Neurology, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Rabi Tawil
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| | - Sujatha Jagannathan
- Department of Biochemistry and Molecular Genetics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
- RNA Bioscience Initiative, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
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36
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He B, Huang Z, Huang C, Nice EC. Clinical applications of plasma proteomics and peptidomics: Towards precision medicine. Proteomics Clin Appl 2022; 16:e2100097. [PMID: 35490333 DOI: 10.1002/prca.202100097] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 04/16/2022] [Accepted: 04/28/2022] [Indexed: 02/05/2023]
Abstract
In the context of precision medicine, disease treatment requires individualized strategies based on the underlying molecular characteristics to overcome therapeutic challenges posed by heterogeneity. For this purpose, it is essential to develop new biomarkers to diagnose, stratify, or possibly prevent diseases. Plasma is an available source of biomarkers that greatly reflects the physiological and pathological conditions of the body. An increasing number of studies are focusing on proteins and peptides, including many involving the Human Proteome Project (HPP) of the Human Proteome Organization (HUPO), and proteomics and peptidomics techniques are emerging as critical tools for developing novel precision medicine preventative measures. Excitingly, the emerging plasma proteomics and peptidomics toolbox exhibits a huge potential for studying pathogenesis of diseases (e.g., COVID-19 and cancer), identifying valuable biomarkers and improving clinical management. However, the enormous complexity and wide dynamic range of plasma proteins makes plasma proteome profiling challenging. Herein, we summarize the recent advances in plasma proteomics and peptidomics with a focus on their emerging roles in COVID-19 and cancer research, aiming to emphasize the significance of plasma proteomics and peptidomics in clinical applications and precision medicine.
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Affiliation(s)
- Bo He
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, and West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University and Collaborative Innovation Center for Biotherapy, Chengdu, P. R. China
| | - Zhao Huang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, and West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University and Collaborative Innovation Center for Biotherapy, Chengdu, P. R. China
| | - Canhua Huang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, and West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University and Collaborative Innovation Center for Biotherapy, Chengdu, P. R. China.,Department of Pharmacology, and Provincial Key Laboratory of Pathophysiology in Ningbo University School of Medicine, Ningbo, Zhejiang, China
| | - Edouard C Nice
- Department of Biochemistry and Molecular Biology, Monash University, Clayton, Victoria, Australia
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37
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del Campo M, Zetterberg H, Gandy S, Onyike CU, Oliveira F, Udeh‐Momoh C, Lleó A, Teunissen CE, Pijnenburg Y. New developments of biofluid-based biomarkers for routine diagnosis and disease trajectories in frontotemporal dementia. Alzheimers Dement 2022; 18:2292-2307. [PMID: 35235699 PMCID: PMC9790674 DOI: 10.1002/alz.12643] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 02/04/2022] [Accepted: 02/04/2022] [Indexed: 01/31/2023]
Abstract
Frontotemporal dementia (FTD) covers a spectrum of neurodegenerative disorders with different phenotypes, genetic backgrounds, and pathological states. Its clinicopathological diversity challenges the diagnostic process and the execution of clinical trials, calling for specific diagnostic biomarkers of pathologic FTD types. There is also a need for biomarkers that facilitate disease staging, quantification of severity, monitoring in clinics and observational studies, and for evaluation of target engagement and treatment response in clinical trials. This review discusses current FTD biofluid-based biomarker knowledge taking into account the differing applications. The limitations, knowledge gaps, and challenges for the development and implementation of such markers are also examined. Strategies to overcome these hurdles are proposed, including the technologies available, patient cohorts, and collaborative research initiatives. Access to robust and reliable biomarkers that define the exact underlying pathophysiological FTD process will meet the needs for specific diagnosis, disease quantitation, clinical monitoring, and treatment development.
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Affiliation(s)
- Marta del Campo
- Departamento de Ciencias Farmacéuticas y de la SaludFacultad de FarmaciaUniversidad San Pablo‐CEUCEU UniversitiesMadridSpain
| | - Henrik Zetterberg
- Institute of Neuroscience and PhysiologyThe Sahlgrenska Academy at the University of GothenburgGothenburgSweden,Clinical Neurochemistry LaboratorySahlgrenska University HospitalMölndalSweden,UK Dementia Research Institute at UCLLondonUK,Department of Neurodegenerative DiseaseUCL Institute of NeurologyLondonUK,Hong Kong Center for Neurodegenerative DiseasesHong KongChina
| | - Sam Gandy
- Department of NeurologyIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Chiadi U Onyike
- Division of Geriatric Psychiatry and NeuropsychiatryThe Johns Hopkins University School of MedicineBaltimoreMarylandUSA
| | - Fabricio Oliveira
- Department of Neurology and NeurosurgeryEscola Paulista de MedicinaFederal University of São Paulo (UNIFESP)São PauloSão PauloBrazil
| | - Chi Udeh‐Momoh
- Ageing Epidemiology Research UnitSchool of Public HealthFaculty of MedicineImperial College LondonLondonUK,Translational Health SciencesFaculty of MedicineUniversity of BristolBristolUK
| | - Alberto Lleó
- Neurology DepartmentHospital de la Santa Creu I Sant PauBarcelonaSpain
| | - Charlotte E. Teunissen
- Neurochemistry LaboratoryDepartment of Clinical ChemistryAmsterdam NeuroscienceAmsterdam University Medical CentersVrije UniversiteitAmsterdamthe Netherlands
| | - Yolande Pijnenburg
- Alzheimer Center AmsterdamDepartment of NeurologyAmsterdam NeuroscienceVrije Universiteit AmsterdamAmsterdam UMCAmsterdamthe Netherlands
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Carrasco-Zanini J, Pietzner M, Lindbohm JV, Wheeler E, Oerton E, Kerrison N, Simpson M, Westacott M, Drolet D, Kivimaki M, Ostroff R, Williams SA, Wareham NJ, Langenberg C. Proteomic signatures for identification of impaired glucose tolerance. Nat Med 2022; 28:2293-2300. [PMID: 36357677 PMCID: PMC7614638 DOI: 10.1038/s41591-022-02055-z] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 09/27/2022] [Indexed: 11/12/2022]
Abstract
The implementation of recommendations for type 2 diabetes (T2D) screening and diagnosis focuses on the measurement of glycated hemoglobin (HbA1c) and fasting glucose. This approach leaves a large number of individuals with isolated impaired glucose tolerance (iIGT), who are only detectable through oral glucose tolerance tests (OGTTs), at risk of diabetes and its severe complications. We applied machine learning to the proteomic profiles of a single fasted sample from 11,546 participants of the Fenland study to test discrimination of iIGT defined using the gold-standard OGTTs. We observed significantly improved discriminative performance by adding only three proteins (RTN4R, CBPM and GHR) to the best clinical model (AUROC = 0.80 (95% confidence interval: 0.79-0.86), P = 0.004), which we validated in an external cohort. Increased plasma levels of these candidate proteins were associated with an increased risk for future T2D in an independent cohort and were also increased in individuals genetically susceptible to impaired glucose homeostasis and T2D. Assessment of a limited number of proteins can identify individuals likely to be missed by current diagnostic strategies and at high risk of T2D and its complications.
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Affiliation(s)
- Julia Carrasco-Zanini
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Maik Pietzner
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
- Computational Medicine, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Joni V Lindbohm
- Clinicum, Department of Public Health, University of Helsinki, Helsinki, Finland
- Department of Epidemiology and Public Health, University College London, London, UK
- The Klarman Cell Observatory, Broad Institute of the Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA
| | - Eleanor Wheeler
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Erin Oerton
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Nicola Kerrison
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | | | | | | | - Mika Kivimaki
- Clinicum, Department of Public Health, University of Helsinki, Helsinki, Finland
- Department of Epidemiology and Public Health, University College London, London, UK
| | | | | | - Nicholas J Wareham
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Claudia Langenberg
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK.
- Computational Medicine, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany.
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK.
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Leukotriene A4 Hydrolase and Hepatocyte Growth Factor Are Risk Factors of Sudden Cardiac Death Due to First-Ever Myocardial Infarction. Int J Mol Sci 2022; 23:ijms231810251. [PMID: 36142157 PMCID: PMC9499415 DOI: 10.3390/ijms231810251] [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: 08/16/2022] [Accepted: 09/03/2022] [Indexed: 11/16/2022] Open
Abstract
Patients at a high risk for sudden cardiac death (SCD) without previous history of cardiovascular disease remain a challenge to identify. Atherosclerosis and prothrombotic states involve inflammation and non-cardiac tissue damage that may play active roles in SCD development. Therefore, we hypothesized that circulating proteins implicated in inflammation and tissue damage are linked to the future risk of SCD. We conducted a prospective nested case–control study of SCD cases with verified myocardial infarction (N = 224) and matched controls without myocardial infarction (N = 224), aged 60 ± 10 years time and median time to event was 8 years. Protein concentrations (N = 122) were measured using a proximity extension immunoassay. The analyses revealed 14 proteins significantly associated with an increased risk of SCD, from which two remained significant after adjusting for smoking status, systolic blood pressure, BMI, cholesterol, and glucose levels. We identified leukotriene A4 hydrolase (LTA4H, odds ratio 1.80, corrected confidence interval (CIcorr) 1.02–3.17) and hepatocyte growth factor (HGF; odds ratio 1.81, CIcorr 1.06–3.11) as independent risk markers of SCD. Elevated LTA4H may reflect increased systemic and pulmonary neutrophilic inflammatory processes that can contribute to atherosclerotic plaque instability. Increased HGF levels are linked to obesity-related metabolic disturbances that are more prevalent in SCD cases than the controls.
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Urbiola-Salvador V, Miroszewska D, Jabłońska A, Qureshi T, Chen Z. Proteomics approaches to characterize the immune responses in cancer. BIOCHIMICA ET BIOPHYSICA ACTA. MOLECULAR CELL RESEARCH 2022; 1869:119266. [PMID: 35390423 DOI: 10.1016/j.bbamcr.2022.119266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Revised: 03/01/2022] [Accepted: 03/28/2022] [Indexed: 06/14/2023]
Abstract
Despite the dynamic development of cancer research, annually millions of people die of cancer. The human immune system is the major 'guard' against tumor development. Unfortunately, cancer cells have the ability to evade the immune system and continue to grow. The proper understanding of the intricate immune response in tumorigenesis remains the holy grail of cancer immunology and designing effective immunotherapy. To decode the immune responses in cancer, in recent years, proteomics studies have received considerable attention. Proteomics studies focus on the detection and quantification of proteins, which are the effectors of biological functions, and as such, are proven to reflect the cell state more accurately, in comparison to genomic or transcriptomic studies. In this review, we discuss the proteomics studies applied to characterize the immune responses in cancer and tumor immune microenvironment heterogeneity. Further, we describe emerging single-cell proteomics approaches that have the potential to be applied in cancer immunity studies.
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Affiliation(s)
- Víctor Urbiola-Salvador
- Intercollegiate Faculty of Biotechnology of University of Gdańsk and Medical University of Gdańsk, University of Gdańsk, Poland.
| | - Dominika Miroszewska
- Intercollegiate Faculty of Biotechnology of University of Gdańsk and Medical University of Gdańsk, University of Gdańsk, Poland.
| | - Agnieszka Jabłońska
- Intercollegiate Faculty of Biotechnology of University of Gdańsk and Medical University of Gdańsk, University of Gdańsk, Poland.
| | - Talha Qureshi
- Faculty of Biochemistry and Molecular Medicine, University of Oulu, Oulu, Finland.
| | - Zhi Chen
- Intercollegiate Faculty of Biotechnology of University of Gdańsk and Medical University of Gdańsk, University of Gdańsk, Poland; Faculty of Biochemistry and Molecular Medicine, University of Oulu, Oulu, Finland.
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Oxidative Stress in Type 2 Diabetes: The Case for Future Pediatric Redoxomics Studies. Antioxidants (Basel) 2022; 11:antiox11071336. [PMID: 35883827 PMCID: PMC9312244 DOI: 10.3390/antiox11071336] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 06/28/2022] [Accepted: 06/30/2022] [Indexed: 01/27/2023] Open
Abstract
Considerable evidence supports the role of oxidative stress in adult type 2 diabetes (T2D). Due to increasing rates of pediatric obesity, lack of physical activity, and consumption of excess food calories, it is projected that the number of children living with insulin resistance, prediabetes, and T2D will markedly increase with enormous worldwide economic costs. Understanding the factors contributing to oxidative stress and T2D risk may help develop optimal early intervention strategies. Evidence suggests that oxidative stress, triggered by excess dietary fat consumption, causes excess mitochondrial hydrogen peroxide emission in skeletal muscle, alters redox status, and promotes insulin resistance leading to T2D. The pathophysiological events arising from excess calorie-induced mitochondrial reactive oxygen species production are complex and not yet investigated in children. Systems medicine is an integrative approach leveraging conventional medical information and environmental factors with data obtained from “omics” technologies such as genomics, proteomics, and metabolomics. In adults with T2D, systems medicine shows promise in risk assessment and predicting drug response. Redoxomics is a branch of systems medicine focusing on “omics” data related to redox status. Systems medicine with a complementary emphasis on redoxomics can potentially optimize future healthcare strategies for adults and children with T2D.
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Nishizawa K, Shinohara N, Cadotte MW, Mori AS. The latitudinal gradient in plant community assembly processes: A meta-analysis. Ecol Lett 2022; 25:1711-1724. [PMID: 35616424 DOI: 10.1111/ele.14019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 03/03/2022] [Accepted: 04/13/2022] [Indexed: 11/28/2022]
Abstract
Beta(β)-diversity, or site-to-site variation in species composition, generally decreases with increasing latitude, and the underlying processes driving this pattern have been challenging to elucidate because the signals of community assembly processes are scale-dependent. In this meta-analysis, by synthesising the results of 103 studies that were distributed globally and conducted at various spatial scales, we revealed a latitudinal gradient in the detectable assembly processes of vascular plant communities. Variations in plant community composition at low and high latitudes were mainly explained by geographic variables, suggesting that distance decay and dispersal limitations causing spatial aggregation are influential in these regions. In contrast, variation in species composition correlated most strongly with environmental variables at mid-latitudes (20-30°), reflecting the importance of environmental filtering, although this unimodal pattern was not statistically significant. Importantly, our analysis revealed the effects of different spatial scales, such that the correlation with spatial variables was stronger at smaller sampling extents, and environmental variables were more influential at larger sampling extents. We concluded that plant communities are driven by different community assembly processes in distinct biogeographical regions, suggesting that the latitudinal gradient of biodiversity is created by a combination of multiple processes that vary with environmental and species size differences.
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Affiliation(s)
- Keita Nishizawa
- The University of Tokyo, Tokyo, Japan.,Yokohama National University, Yokohama, Japan
| | | | - Marc W Cadotte
- Biological Sciences, University of Toronto Scarborough, Toronto, Canada
| | - Akira S Mori
- The University of Tokyo, Tokyo, Japan.,Yokohama National University, Yokohama, Japan
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Edfors F, Iglesias MJ, Butler LM, Odeberg J. Proteomics in thrombosis research. Res Pract Thromb Haemost 2022; 6:e12706. [PMID: 35494505 PMCID: PMC9039028 DOI: 10.1002/rth2.12706] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Revised: 03/23/2022] [Accepted: 03/24/2022] [Indexed: 11/24/2022] Open
Abstract
A State of the Art lecture titled “Proteomics in Thrombosis Research” was presented at the ISTH Congress in 2021. In clinical practice, there is a need for improved plasma biomarker‐based tools for diagnosis and risk prediction of venous thromboembolism (VTE). Analysis of blood, to identify plasma proteins with potential utility for such tools, could enable an individualized approach to treatment and prevention. Technological advances to study the plasma proteome on a large scale allows broad screening for the identification of novel plasma biomarkers, both by targeted and nontargeted proteomics methods. However, assay limitations need to be considered when interpreting results, with orthogonal validation required before conclusions are drawn. Here, we review and provide perspectives on the application of affinity‐ and mass spectrometry‐based methods for the identification and analysis of plasma protein biomarkers, with potential application in the field of VTE. We also provide a future perspective on discovery strategies and emerging technologies for targeted proteomics in thrombosis research. Finally, we summarize relevant new data on this topic, presented during the 2021 ISTH Congress.
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Affiliation(s)
- Fredrik Edfors
- Science for Life Laboratory Department of Protein Science CBH KTH Royal Institute of Technology Stockholm Sweden
- Karolinska University Laboratory Karolinska University Hospital Stockholm Sweden
| | - Maria Jesus Iglesias
- Science for Life Laboratory Department of Protein Science CBH KTH Royal Institute of Technology Stockholm Sweden
| | - Lynn M. Butler
- Science for Life Laboratory Department of Protein Science CBH KTH Royal Institute of Technology Stockholm Sweden
- Clinical Chemistry and Blood Coagulation Research Department of Molecular Medicine and Surgery Karolinska Institute Stockholm Sweden
- Clinical Chemistry Karolinska University Laboratory Karolinska University Hospital Stockholm Sweden
- Department of Clinical Medicine The Arctic University of Norway Tromsø Norway
| | - Jacob Odeberg
- Science for Life Laboratory Department of Protein Science CBH KTH Royal Institute of Technology Stockholm Sweden
- Department of Clinical Medicine The Arctic University of Norway Tromsø Norway
- Division of Internal Medicine University Hospital of North Norway Tromsø Norway
- Coagulation Unit Department of Hematology Karolinska University Hospital Stockholm Sweden
- Department of Medicine Solna Karolinska Institute Stockholm Sweden
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Dayon L, Cominetti O, Affolter M. Proteomics of Human Biological Fluids for Biomarker Discoveries: Technical Advances and Recent Applications. Expert Rev Proteomics 2022; 19:131-151. [PMID: 35466824 DOI: 10.1080/14789450.2022.2070477] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
INTRODUCTION Biological fluids are routine samples for diagnostic testing and monitoring. Blood samples are typically measured because of their moderate collection invasiveness and high information content on health and disease. Several body fluids, such as cerebrospinal fluid (CSF), are also studied and suited to specific pathologies. Over the last two decades proteomics has quested to identify protein biomarkers but with limited success. Recent technologies and refined pipelines have accelerated the profiling of human biological fluids. AREAS COVERED We review proteomic technologies for the identification of biomarkers. Those are based on antibodies/aptamers arrays or mass spectrometry (MS), but new ones are emerging. Advances in scalability and throughput have allowed to better design studies and cope with the limited sample size that had until now prevailed due to technological constraints. With these enablers, plasma/serum, CSF, saliva, tears, urine, and milk proteomes have been further profiled; we provide a non-exhaustive picture of some recent highlights (mainly covering literature from last five years in the Scopus database) using MS-based proteomics. EXPERT OPINION While proteomics has been in the shadow of genomics for years, proteomic tools and methodologies have reached a certain maturity. They are better suited to discover innovative and robust biofluid biomarkers.
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Affiliation(s)
- Loïc Dayon
- Proteomics, Nestlé Institute of Food Safety & Analytical Sciences, Nestlé Research, CH-1015 Lausanne, Switzerland.,Institut des Sciences et Ingénierie Chimiques, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
| | - Ornella Cominetti
- Proteomics, Nestlé Institute of Food Safety & Analytical Sciences, Nestlé Research, CH-1015 Lausanne, Switzerland
| | - Michael Affolter
- Proteomics, Nestlé Institute of Food Safety & Analytical Sciences, Nestlé Research, CH-1015 Lausanne, Switzerland
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Miller MR, Robinson M, Fischer L, DiBattista A, Patel MA, Daley M, Bartha R, Dekaban GA, Menon RS, Shoemaker JK, Diamandis EP, Prassas I, Fraser DD. Putative Concussion Biomarkers Identified in Adolescent Male Athletes Using Targeted Plasma Proteomics. Front Neurol 2022; 12:787480. [PMID: 34987469 PMCID: PMC8721148 DOI: 10.3389/fneur.2021.787480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 11/17/2021] [Indexed: 11/13/2022] Open
Abstract
Sport concussions can be difficult to diagnose and if missed, they can expose athletes to greater injury risk and long-lasting neurological disabilities. Discovery of objective biomarkers to aid concussion diagnosis is critical to protecting athlete brain health. To this end, we performed targeted proteomics on plasma obtained from adolescent athletes suffering a sports concussion. A total of 11 concussed male athletes were enrolled at our academic Sport Medicine Concussion Clinic, as well as 24 sex-, age- and activity-matched healthy control subjects. Clinical evaluation was performed and blood was drawn within 72 h of injury. Proximity extension assays were performed for 1,472 plasma proteins; a total of six proteins were considered significantly different between cohorts (P < 0.01; five proteins decreased and one protein increased). Receiver operating characteristic curves on the six individual protein biomarkers identified had areas-under-the-curves (AUCs) for concussion diagnosis ≥0.78; antioxidant 1 copper chaperone (ATOX1; AUC 0.81, P = 0.003), secreted protein acidic and rich in cysteine (SPARC; AUC 0.81, P = 0.004), cluster of differentiation 34 (CD34; AUC 0.79, P = 0.006), polyglutamine binding protein 1 (PQBP1; AUC 0.78, P = 0.008), insulin-like growth factor-binding protein-like 1 (IGFBPL1; AUC 0.78, P = 0.008) and cytosolic 5'-nucleotidase 3A (NT5C3A; AUC 0.78, P = 0.009). Combining three of the protein biomarkers (ATOX1, SPARC and NT5C3A), produced an AUC of 0.98 for concussion diagnoses (P < 0.001; 95% CI: 0.95, 1.00). Despite a paucity of studies on these three identified proteins, the available evidence points to their roles in modulating tissue inflammation and regulating integrity of the cerebral microvasculature. Taken together, our exploratory data suggest that three or less novel proteins, which are amenable to a point-of-care immunoassay, may be future candidate biomarkers for screening adolescent sport concussion. Validation with protein assays is required in larger cohorts.
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Affiliation(s)
- Michael R Miller
- Department of Pediatrics, Western University, London, ON, Canada.,Children's Health Research Institute, London, ON, Canada
| | - Michael Robinson
- School of Health Studies, Western University, London, ON, Canada.,School of Kinesiology, Western University, London, ON, Canada.,Department of Family Medicine, Western University, London, ON, Canada
| | - Lisa Fischer
- Department of Family Medicine, Western University, London, ON, Canada
| | - Alicia DiBattista
- Children's Hospital of Eastern Ontario Research Institute, Ottawa, ON, Canada.,Neurolytixs Inc., Toronto, ON, Canada
| | - Maitray A Patel
- Department of Epidemiology, Western University, London, ON, Canada
| | - Mark Daley
- Department of Epidemiology, Western University, London, ON, Canada.,Department of Computer Science, Western University, London, ON, Canada
| | - Robert Bartha
- Department of Medical Biophysics, Western University, London, ON, Canada.,Robarts Research Institute, London, ON, Canada
| | - Gregory A Dekaban
- Robarts Research Institute, London, ON, Canada.,Department of Microbiology and Immunology, Western University, London, ON, Canada
| | - Ravi S Menon
- Department of Medical Biophysics, Western University, London, ON, Canada.,Robarts Research Institute, London, ON, Canada
| | | | | | - Ioannis Prassas
- Department of Pathology and Laboratory Medicine, University of Toronto, Toronto, ON, Canada
| | - Douglas D Fraser
- Department of Pediatrics, Western University, London, ON, Canada.,Children's Health Research Institute, London, ON, Canada.,Neurolytixs Inc., Toronto, ON, Canada.,Department of Physiology and Pharmacology, Western University, London, ON, Canada.,Depatment of Clinical Neurological Sciences, Western University, London, ON, Canada
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Next generation plasma proteome profiling of COVID-19 patients with mild to moderate symptoms. EBioMedicine 2021; 74:103723. [PMID: 34844191 PMCID: PMC8626206 DOI: 10.1016/j.ebiom.2021.103723] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Revised: 11/04/2021] [Accepted: 11/16/2021] [Indexed: 12/15/2022] Open
Abstract
Background COVID-19 has caused millions of deaths globally, yet the cellular mechanisms underlying the various effects of the disease remain poorly understood. Recently, a new analytical platform for comprehensive analysis of plasma protein profiles using proximity extension assays combined with next generation sequencing has been developed, which allows for multiple proteins to be analyzed simultaneously without sacrifice on accuracy or sensitivity. Methods We analyzed the plasma protein profiles of COVID-19 patients (n = 50) with mild and moderate symptoms by comparing the protein levels in newly diagnosed patients with the protein levels in the same individuals after 14 days. Findings The study has identified more than 200 proteins that are significantly elevated during infection and many of these are related to cytokine response and other immune-related functions. In addition, several other proteins are shown to be elevated, including SCARB2, a host cell receptor protein involved in virus entry. A comparison with the plasma protein response in patients with severe symptoms shows a highly similar pattern, but with some interesting differences. Interpretation The study presented here demonstrates the usefulness of “next generation plasma protein profiling” to identify molecular signatures of importance for disease progression and to allow monitoring of disease during recovery from the infection. The results will facilitate further studies to understand the molecular mechanism of the immune-related response of the SARS-CoV-2 virus. Funding This work was financially supported by Knut and Alice Wallenberg Foundation.
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Shi J, Xiao J, Wang X, Jung SM, Bleske BE, Markowitz JS, Patrick KS, Zhu HJ. Plasma Carboxylesterase 1 Predicts Methylphenidate Exposure: A Proof-of-Concept Study Using Plasma Protein Biomarker for Hepatic Drug Metabolism. Clin Pharmacol Ther 2021; 111:878-885. [PMID: 34743324 DOI: 10.1002/cpt.2486] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Accepted: 10/27/2021] [Indexed: 12/11/2022]
Abstract
Hepatic drug-metabolizing enzymes (DMEs) play critical roles in determining the pharmacokinetics and pharmacodynamics of numerous therapeutic agents. As such, noninvasive biomarkers capable of predicting DME expression in the liver have the potential to be used to personalize pharmacotherapy and improve drug treatment outcomes. In the present study, we quantified carboxylesterase 1 (CES1) protein concentrations in plasma samples collected during a methylphenidate pharmacokinetics study. CES1 is a prominent hepatic enzyme responsible for the metabolism of many medications containing small ester moieties, including methylphenidate. The results revealed a significant inverse correlation between plasma CES1 protein concentrations and the area under the concentration-time curves (AUCs) of plasma d-methylphenidate (P = 0.014, r = -0.617). In addition, when plasma CES1 protein levels were normalized to the plasma concentrations of 24 liver-enriched proteins to account for potential interindividual differences in hepatic protein release rate, the correlation was further improved (P = 0.003, r = -0.703), suggesting that plasma CES1 protein could explain ~ 50% of the variability in d-methylphenidate AUCs in the study participants. A physiologically-based pharmacokinetic modeling simulation revealed that the CES1-based individualized dosing strategy might significantly reduce d-methylphenidate exposure variability in pediatric patients relative to conventional trial and error fixed dosing regimens. This proof-of-concept study indicates that the plasma protein of a hepatic DME may serve as a biomarker for predicting its metabolic function and the pharmacokinetics of its substrate drugs.
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Affiliation(s)
- Jian Shi
- Department of Clinical Pharmacy, University of Michigan, Ann Arbor, Michigan, USA
| | - Jingcheng Xiao
- Department of Pharmaceutical Sciences, University of Michigan, Ann Arbor, Michigan, USA
| | - Xinwen Wang
- Department of Pharmaceutical Sciences, Northeast Ohio Medical University, Rootstown, Ohio, USA
| | - Sun Min Jung
- Department of Pharmaceutical Sciences, University of Michigan, Ann Arbor, Michigan, USA
| | - Barry E Bleske
- Department of Pharmacy Practice and Administrative Sciences, The University of New Mexico, Albuquerque, New Mexico, USA
| | - John S Markowitz
- Department of Pharmacotherapy and Translational Research, University of Florida, Gainesville, Florida, USA
| | - Kennerly S Patrick
- Department of Drug Discovery and Biomedical Sciences, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Hao-Jie Zhu
- Department of Clinical Pharmacy, University of Michigan, Ann Arbor, Michigan, USA
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Deutsch EW, Omenn GS, Sun Z, Maes M, Pernemalm M, Palaniappan KK, Letunica N, Vandenbrouck Y, Brun V, Tao SC, Yu X, Geyer PE, Ignjatovic V, Moritz RL, Schwenk JM. Advances and Utility of the Human Plasma Proteome. J Proteome Res 2021; 20:5241-5263. [PMID: 34672606 PMCID: PMC9469506 DOI: 10.1021/acs.jproteome.1c00657] [Citation(s) in RCA: 83] [Impact Index Per Article: 27.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
The study of proteins circulating in blood offers tremendous opportunities to diagnose, stratify, or possibly prevent diseases. With recent technological advances and the urgent need to understand the effects of COVID-19, the proteomic analysis of blood-derived serum and plasma has become even more important for studying human biology and pathophysiology. Here we provide views and perspectives about technological developments and possible clinical applications that use mass-spectrometry(MS)- or affinity-based methods. We discuss examples where plasma proteomics contributed valuable insights into SARS-CoV-2 infections, aging, and hemostasis and the opportunities offered by combining proteomics with genetic data. As a contribution to the Human Proteome Organization (HUPO) Human Plasma Proteome Project (HPPP), we present the Human Plasma PeptideAtlas build 2021-07 that comprises 4395 canonical and 1482 additional nonredundant human proteins detected in 240 MS-based experiments. In addition, we report the new Human Extracellular Vesicle PeptideAtlas 2021-06, which comprises five studies and 2757 canonical proteins detected in extracellular vesicles circulating in blood, of which 74% (2047) are in common with the plasma PeptideAtlas. Our overview summarizes the recent advances, impactful applications, and ongoing challenges for translating plasma proteomics into utility for precision medicine.
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Affiliation(s)
- Eric W Deutsch
- Institute for Systems Biology, Seattle, Washington 98109, United States
| | - Gilbert S Omenn
- Institute for Systems Biology, Seattle, Washington 98109, United States.,Departments of Computational Medicine & Bioinformatics, Internal Medicine, and Human Genetics and School of Public Health, University of Michigan, Ann Arbor, Michigan 48109-2218, United States
| | - Zhi Sun
- Institute for Systems Biology, Seattle, Washington 98109, United States
| | - Michal Maes
- Institute for Systems Biology, Seattle, Washington 98109, United States
| | - Maria Pernemalm
- Department of Oncology and Pathology/Science for Life Laboratory, Karolinska Institutet, 171 65 Stockholm, Sweden
| | | | - Natasha Letunica
- Murdoch Children's Research Institute, 50 Flemington Road, Parkville 3052, Victoria, Australia
| | - Yves Vandenbrouck
- Université Grenoble Alpes, CEA, Inserm U1292, Grenoble 38000, France
| | - Virginie Brun
- Université Grenoble Alpes, CEA, Inserm U1292, Grenoble 38000, France
| | - Sheng-Ce Tao
- Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, B207 SCSB Building, 800 Dongchuan Road, Shanghai 200240, China
| | - Xiaobo Yu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences-Beijing (PHOENIX Center), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Philipp E Geyer
- OmicEra Diagnostics GmbH, Behringstr. 6, 82152 Planegg, Germany
| | - Vera Ignjatovic
- Murdoch Children's Research Institute, 50 Flemington Road, Parkville 3052, Victoria, Australia.,Department of Paediatrics, The University of Melbourne, 50 Flemington Road, Parkville 3052, Victoria, Australia
| | - Robert L Moritz
- Institute for Systems Biology, Seattle, Washington 98109, United States
| | - Jochen M Schwenk
- Affinity Proteomics, Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Tomtebodavägen 23, SE-171 65 Solna, Sweden
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Keane TM, O'Donovan C, Vizcaíno JA. The growing need for controlled data access models in clinical proteomics and metabolomics. Nat Commun 2021; 12:5787. [PMID: 34599180 PMCID: PMC8486822 DOI: 10.1038/s41467-021-26110-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 09/17/2021] [Indexed: 01/25/2023] Open
Abstract
More and more clinical studies include potentially sensitive human proteomics or metabolomics datasets, but bioinformatics resources for managing the access to these data are not yet available. This commentary discusses current best practices and future perspectives for the responsible handling of clinical proteomics and metabolomics data.
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Affiliation(s)
- Thomas M Keane
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Claire O'Donovan
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Juan Antonio Vizcaíno
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.
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50
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Correa Rojo A, Heylen D, Aerts J, Thas O, Hooyberghs J, Ertaylan G, Valkenborg D. Towards Building a Quantitative Proteomics Toolbox in Precision Medicine: A Mini-Review. Front Physiol 2021; 12:723510. [PMID: 34512391 PMCID: PMC8427610 DOI: 10.3389/fphys.2021.723510] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 08/05/2021] [Indexed: 12/26/2022] Open
Abstract
Precision medicine as a framework for disease diagnosis, treatment, and prevention at the molecular level has entered clinical practice. From the start, genetics has been an indispensable tool to understand and stratify the biology of chronic and complex diseases in precision medicine. However, with the advances in biomedical and omics technologies, quantitative proteomics is emerging as a powerful technology complementing genetics. Quantitative proteomics provide insight about the dynamic behaviour of proteins as they represent intermediate phenotypes. They provide direct biological insights into physiological patterns, while genetics accounting for baseline characteristics. Additionally, it opens a wide range of applications in clinical diagnostics, treatment stratification, and drug discovery. In this mini-review, we discuss the current status of quantitative proteomics in precision medicine including the available technologies and common methods to analyze quantitative proteomics data. Furthermore, we highlight the current challenges to put quantitative proteomics into clinical settings and provide a perspective to integrate proteomics data with genomics data for future applications in precision medicine.
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Affiliation(s)
- Alejandro Correa Rojo
- Data Science Institute, Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Hasselt University, Diepenbeek, Belgium.,Flemish Institute for Technological Research (VITO), Mol, Belgium
| | - Dries Heylen
- Data Science Institute, Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Hasselt University, Diepenbeek, Belgium.,Flemish Institute for Technological Research (VITO), Mol, Belgium
| | - Jan Aerts
- Data Science Institute, Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Hasselt University, Diepenbeek, Belgium
| | - Olivier Thas
- Data Science Institute, Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Hasselt University, Diepenbeek, Belgium.,Department of Applied Mathematics, Computer Science and Statistics, Faculty of Sciences, Ghent University, Ghent, Belgium.,National Institute for Applied Statistics Research Australia (NIASRA), Wollongong, NSW, Australia
| | - Jef Hooyberghs
- Flemish Institute for Technological Research (VITO), Mol, Belgium.,Theoretical Physics, Data Science Institute, Hasselt University, Diepenbeek, Belgium
| | - Gökhan Ertaylan
- Flemish Institute for Technological Research (VITO), Mol, Belgium
| | - Dirk Valkenborg
- Data Science Institute, Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Hasselt University, Diepenbeek, Belgium
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