1
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Fredolini C, Dodig-Crnković T, Bendes A, Dahl L, Dale M, Albrecht V, Mattsson C, Thomas CE, Torinsson Naluai Å, Gisslen M, Beck O, Roxhed N, Schwenk JM. Proteome profiling of home-sampled dried blood spots reveals proteins of SARS-CoV-2 infections. COMMUNICATIONS MEDICINE 2024; 4:55. [PMID: 38565620 PMCID: PMC10987641 DOI: 10.1038/s43856-024-00480-4] [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: 10/12/2022] [Accepted: 03/07/2024] [Indexed: 04/04/2024] Open
Abstract
BACKGROUND Self-sampling of dried blood spots (DBS) offers new routes to gather valuable health-related information from the general population. Yet, the utility of using deep proteome profiling from home-sampled DBS to obtain clinically relevant insights about SARS-CoV-2 infections remains largely unexplored. METHODS Our study involved 228 individuals from the general Swedish population who used a volumetric DBS sampling device and completed questionnaires at home during spring 2020 and summer 2021. Using multi-analyte COVID-19 serology, we stratified the donors by their response phenotypes, divided them into three study sets, and analyzed 276 proteins by proximity extension assays (PEA). After normalizing the data to account for variances in layman-collected samples, we investigated the association of DBS proteomes with serology and self-reported information. RESULTS Our three studies display highly consistent variance of protein levels and share associations of proteins with sex (e.g., MMP3) and age (e.g., GDF-15). Studying seropositive (IgG+) and seronegative (IgG-) donors from the first pandemic wave reveals a network of proteins reflecting immunity, inflammation, coagulation, and stress response. A comparison of the early-infection phase (IgM+IgG-) with the post-infection phase (IgM-IgG+) indicates several proteins from the respiratory system. In DBS from the later pandemic wave, we find that levels of a virus receptor on B-cells differ between seropositive (IgG+) and seronegative (IgG-) donors. CONCLUSIONS Proteome analysis of volumetric self-sampled DBS facilitates precise analysis of clinically relevant proteins, including those secreted into the circulation or found on blood cells, augmenting previous COVID-19 reports with clinical blood collections. Our population surveys support the usefulness of DBS, underscoring the role of timing the sample collection to complement clinical and precision health monitoring initiatives.
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Affiliation(s)
- Claudia Fredolini
- Department of Protein Science, SciLifeLab, KTH Royal Institute of Technology, 171 65, Solna, Sweden
- Affinity Proteomics Unit, SciLifeLab Infrastructure, KTH Royal Institute of Technology, 171 65, Solna, Sweden
| | - Tea Dodig-Crnković
- Department of Protein Science, SciLifeLab, KTH Royal Institute of Technology, 171 65, Solna, Sweden
| | - Annika Bendes
- Department of Protein Science, SciLifeLab, KTH Royal Institute of Technology, 171 65, Solna, Sweden
| | - Leo Dahl
- Department of Protein Science, SciLifeLab, KTH Royal Institute of Technology, 171 65, Solna, Sweden
| | - Matilda Dale
- Department of Protein Science, SciLifeLab, KTH Royal Institute of Technology, 171 65, Solna, Sweden
- Affinity Proteomics Unit, SciLifeLab Infrastructure, KTH Royal Institute of Technology, 171 65, Solna, Sweden
| | - Vincent Albrecht
- Department of Protein Science, SciLifeLab, KTH Royal Institute of Technology, 171 65, Solna, Sweden
| | - Cecilia Mattsson
- Department of Protein Science, SciLifeLab, KTH Royal Institute of Technology, 171 65, Solna, Sweden
- Affinity Proteomics Unit, SciLifeLab Infrastructure, KTH Royal Institute of Technology, 171 65, Solna, Sweden
| | - Cecilia E Thomas
- Department of Protein Science, SciLifeLab, KTH Royal Institute of Technology, 171 65, Solna, Sweden
| | - Åsa Torinsson Naluai
- Institute of Biomedicine, Sahlgrenska Academy at the University of Gothenburg, 405 30, Gothenburg, Sweden
| | - Magnus Gisslen
- Department of Infectious Diseases, The Sahlgrenska Academy at University of Gothenburg, 405 30, Gothenburg, Sweden
- Sahlgrenska University Hospital, 413 45, Gothenburg, Sweden
- Public Health Agency of Sweden, 171 65, Solna, Sweden
| | - Olof Beck
- Department of Clinical Neuroscience, Karolinska Institutet, 171 77, Stockholm, Sweden
| | - Niclas Roxhed
- MedTechLabs, BioClinicum, Karolinska University Hospital, 171 64, Solna, Sweden.
- Department of Micro and Nanosystems, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology Stockholm, 100 44, Stockholm, Sweden.
| | - Jochen M Schwenk
- Department of Protein Science, SciLifeLab, KTH Royal Institute of Technology, 171 65, Solna, Sweden.
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2
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Zheng Y, Liu Y, Yang J, Dong L, Zhang R, Tian S, Yu Y, Ren L, Hou W, Zhu F, Mai Y, Han J, Zhang L, Jiang H, Lin L, Lou J, Li R, Lin J, Liu H, Kong Z, Wang D, Dai F, Bao D, Cao Z, Chen Q, Chen Q, Chen X, Gao Y, Jiang H, Li B, Li B, Li J, Liu R, Qing T, Shang E, Shang J, Sun S, Wang H, Wang X, Zhang N, Zhang P, Zhang R, Zhu S, Scherer A, Wang J, Wang J, Huo Y, Liu G, Cao C, Shao L, Xu J, Hong H, Xiao W, Liang X, Lu D, Jin L, Tong W, Ding C, Li J, Fang X, Shi L. Multi-omics data integration using ratio-based quantitative profiling with Quartet reference materials. Nat Biotechnol 2023:10.1038/s41587-023-01934-1. [PMID: 37679543 DOI: 10.1038/s41587-023-01934-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 07/31/2023] [Indexed: 09/09/2023]
Abstract
Characterization and integration of the genome, epigenome, transcriptome, proteome and metabolome of different datasets is difficult owing to a lack of ground truth. Here we develop and characterize suites of publicly available multi-omics reference materials of matched DNA, RNA, protein and metabolites derived from immortalized cell lines from a family quartet of parents and monozygotic twin daughters. These references provide built-in truth defined by relationships among the family members and the information flow from DNA to RNA to protein. We demonstrate how using a ratio-based profiling approach that scales the absolute feature values of a study sample relative to those of a concurrently measured common reference sample produces reproducible and comparable data suitable for integration across batches, labs, platforms and omics types. Our study identifies reference-free 'absolute' feature quantification as the root cause of irreproducibility in multi-omics measurement and data integration and establishes the advantages of ratio-based multi-omics profiling with common reference materials.
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Affiliation(s)
- Yuanting Zheng
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China.
| | - Yaqing Liu
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Jingcheng Yang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
- Greater Bay Area Institute of Precision Medicine, Guangzhou, China
| | | | - Rui Zhang
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital, Beijing, China
| | - Sha Tian
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Ying Yu
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Luyao Ren
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Wanwan Hou
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Feng Zhu
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Yuanbang Mai
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | | | | | | | - Ling Lin
- Zhangjiang Center for Translational Medicine, Shanghai Biotecan Medical Diagnostics Co. Ltd., Shanghai, China
| | - Jingwei Lou
- Zhangjiang Center for Translational Medicine, Shanghai Biotecan Medical Diagnostics Co. Ltd., Shanghai, China
| | - Ruiqiang Li
- Novogene Bioinformatics Institute, Beijing, China
| | - Jingchao Lin
- Metabo-Profile Biotechnology (Shanghai) Co. Ltd., Shanghai, China
| | | | | | - Depeng Wang
- Nextomics Biosciences Institute, Wuhan, China
| | | | - Ding Bao
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Zehui Cao
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Qiaochu Chen
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Qingwang Chen
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Xingdong Chen
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Yuechen Gao
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - He Jiang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Bin Li
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Bingying Li
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Jingjing Li
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
- Nextomics Biosciences Institute, Wuhan, China
| | - Ruimei Liu
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Tao Qing
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Erfei Shang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Jun Shang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Shanyue Sun
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Haiyan Wang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Xiaolin Wang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Naixin Zhang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Peipei Zhang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Ruolan Zhang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Sibo Zhu
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Andreas Scherer
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- EATRIS ERIC-European Infrastructure for Translational Medicine, Amsterdam, the Netherlands
| | - Jiucun Wang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Jing Wang
- National Institute of Metrology, Beijing, China
| | - Yinbo Huo
- Key Laboratory of Bioanalysis and Metrology for State Market Regulation, Shanghai Institute of Measurement and Testing Technology, Shanghai, China
| | - Gang Liu
- Key Laboratory of Bioanalysis and Metrology for State Market Regulation, Shanghai Institute of Measurement and Testing Technology, Shanghai, China
| | - Chengming Cao
- Key Laboratory of Bioanalysis and Metrology for State Market Regulation, Shanghai Institute of Measurement and Testing Technology, Shanghai, China
| | - Li Shao
- Key Laboratory of Bioanalysis and Metrology for State Market Regulation, Shanghai Institute of Measurement and Testing Technology, Shanghai, China
| | - Joshua Xu
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA
| | - Huixiao Hong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA
| | - Wenming Xiao
- Office of Oncologic Diseases, Office of New Drugs, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA
| | - Xiaozhen Liang
- Shanghai Institute of Immunity and Infection, Chinese Academy of Sciences, Shanghai, China
| | - Daru Lu
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Li Jin
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Weida Tong
- Key Laboratory of Bioanalysis and Metrology for State Market Regulation, Shanghai Institute of Measurement and Testing Technology, Shanghai, China
| | - Chen Ding
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China.
| | - Jinming Li
- National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital, Beijing, China.
| | - Xiang Fang
- National Institute of Metrology, Beijing, China.
| | - Leming Shi
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute and Shanghai Cancer Center, Fudan University, Shanghai, China.
- International Human Phenome Institutes (Shanghai), Shanghai, China.
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3
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Zenlander R, Fredolini C, Schwenk JM, Rydén I, Påhlsson P, Löwbeer C, Eggertsen G, Stål P. A wide scan of plasma proteins demonstrates thioredoxin reductase 1 as a potential new diagnostic biomarker for hepatocellular carcinoma. Scand J Gastroenterol 2023; 58:998-1008. [PMID: 37017178 DOI: 10.1080/00365521.2023.2194008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 03/12/2023] [Accepted: 03/17/2023] [Indexed: 04/06/2023]
Abstract
BACKGROUND Patients with liver cirrhosis are recommended ultrasonography screening for early detection of hepatocellular carcinoma to increase the chances of curative treatment. However, ultrasonography alone lacks in sensitivity. Adding plasma biomarkers may increase the detection rate. We performed a broad exploratory analysis to find new plasma proteins with potential applicability for HCC screening in patients with cirrhosis. METHODS In a protein discovery cohort of 172 patients with cirrhosis or HCC, we screened for 481 proteins with suspension bead array or proximity extension assay. From these, 24 proteins were selected for further analysis in a protein verification cohort (n = 160), using ELISA, Luminex or an electrochemiluminescence platform. A cut-off model and a stepwise logistic regression model were used to find combinations of proteins with the best discriminatory performance between HCC and cirrhosis. RESULTS Stepwise logistic regression revealed alpha-fetoprotein (AFP), decarboxy-prothrombin (DCP), thioredoxin reductase 1 (TXNRD1), and fibroblast growth factor 21 (FGF21) as the proteins with the best discriminatory performance between HCC and cirrhosis. Adding TXNRD1 to DCP and AFP increased the AUC from 0.844 to 0.878, and combining AFP, DCP and TXNRD1 with age and sex resulted in an AUC of 0.920. FGF21, however, did not further increase the performance when including age and sex. CONCLUSION In the present study, TXNRD1 improves the sensitivity and specificity of AFP and DCP as HCC screening tools in patients with cirrhosis. We suggest that TXNRD1 should be validated in prospective settings as a new complementary HCC biomarker together with AFP and DCP.
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Affiliation(s)
- Robin Zenlander
- Department of Clinical Chemistry, Karolinska University Hospital, Stockholm, Sweden
- Department of Laboratory Medicine, Karolinska Institutet, Stockholm, Sweden
- Department of Medicine, Huddinge, Karolinska Institutet, Stockholm, Sweden
| | - Claudia Fredolini
- Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Solna, Sweden
| | - Jochen M Schwenk
- Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Solna, Sweden
| | - Ingvar Rydén
- Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden
| | - Peter Påhlsson
- Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden
| | - Christian Löwbeer
- Department of Laboratory Medicine, Karolinska Institutet, Stockholm, Sweden
- Department of Clinical Chemistry, SYNLAB Sverige, Täby, Sweden
| | - Gösta Eggertsen
- Department of Clinical Chemistry, Karolinska University Hospital, Stockholm, Sweden
- Department of Medicine, Huddinge, Karolinska Institutet, Stockholm, Sweden
| | - Per Stål
- Department of Medicine, Huddinge, Karolinska Institutet, Stockholm, Sweden
- Division of Hepatology, Department of Upper GI diseases, Karolinska University Hospital, Stockholm, Sweden
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4
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Oraki Kohshour M, Kannaiyan NR, Falk AJ, Papiol S, Heilbronner U, Budde M, Kalman JL, Schulte EC, Rietschel M, Witt S, Forstner AJ, Heilmann-Heimbach S, Nöthen MM, Spitzer C, Malchow B, Müller T, Wiltfang J, Falkai P, Schmitt A, Rossner MJ, Nilsson P, Schulze TG. Comparative serum proteomic analysis of a selected protein panel in individuals with schizophrenia and bipolar disorder and the impact of genetic risk burden on serum proteomic profiles. Transl Psychiatry 2022; 12:471. [PMID: 36351892 PMCID: PMC9646817 DOI: 10.1038/s41398-022-02228-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 10/15/2022] [Accepted: 10/20/2022] [Indexed: 11/11/2022] Open
Abstract
The diagnostic criteria for schizophrenia (SCZ) and bipolar disorder (BD) are based on clinical assessments of symptoms. In this pilot study, we applied high-throughput antibody-based protein profiling to serum samples of healthy controls and individuals with SCZ and BD with the aim of identifying differentially expressed proteins in these disorders. Moreover, we explored the influence of polygenic burden for SCZ and BD on the serum levels of these proteins. Serum samples from 113 individuals with SCZ and 125 with BD from the PsyCourse Study and from 44 healthy controls were analyzed by using a set of 155 antibodies in an antibody-based assay targeting a selected panel of 95 proteins. For the cases, genotyping and imputation were conducted for DNA samples and SCZ and BD polygenic risk scores (PRS) were calculated. Univariate linear and logistic models were used for association analyses. The comparison between SCZ and BD revealed two serum proteins that were significantly elevated in BD after multiple testing adjustment: "complement C9" and "Interleukin 1 Receptor Accessory Protein". Moreover, the first principal component of variance in the proteomics dataset differed significantly between SCZ and BD. After multiple testing correction, SCZ-PRS, BD-PRS, and SCZ-vs-BD-PRS were not significantly associated with the levels of the individual proteins or the values of the proteome principal components indicating no detectable genetic effects. Overall, our findings contribute to the evidence suggesting that the analysis of circulating proteins could lead to the identification of distinctive biomarkers for SCZ and BD. Our investigation warrants replication in large-scale studies to confirm these findings.
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Affiliation(s)
- Mojtaba Oraki Kohshour
- grid.5252.00000 0004 1936 973XInstitute of Psychiatric Phenomics and Genomics (IPPG), University Hospital, LMU Munich, Munich, Germany ,grid.411230.50000 0000 9296 6873Department of Immunology, Faculty of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Nirmal R. Kannaiyan
- grid.5252.00000 0004 1936 973XDepartment of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
| | - August Jernbom Falk
- grid.5037.10000000121581746Department of Protein Science, SciLifeLab, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Sergi Papiol
- grid.5252.00000 0004 1936 973XInstitute of Psychiatric Phenomics and Genomics (IPPG), University Hospital, LMU Munich, Munich, Germany ,grid.5252.00000 0004 1936 973XDepartment of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
| | - Urs Heilbronner
- grid.5252.00000 0004 1936 973XInstitute of Psychiatric Phenomics and Genomics (IPPG), University Hospital, LMU Munich, Munich, Germany
| | - Monika Budde
- grid.5252.00000 0004 1936 973XInstitute of Psychiatric Phenomics and Genomics (IPPG), University Hospital, LMU Munich, Munich, Germany
| | - Janos L. Kalman
- grid.5252.00000 0004 1936 973XInstitute of Psychiatric Phenomics and Genomics (IPPG), University Hospital, LMU Munich, Munich, Germany ,grid.5252.00000 0004 1936 973XDepartment of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany ,grid.419548.50000 0000 9497 5095International Max Planck Research School for Translational Psychiatry (IMPRS-TP), Max Planck Institute of Psychiatry, Munich, Germany
| | - Eva C. Schulte
- grid.5252.00000 0004 1936 973XInstitute of Psychiatric Phenomics and Genomics (IPPG), University Hospital, LMU Munich, Munich, Germany ,grid.5252.00000 0004 1936 973XDepartment of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
| | - Marcella Rietschel
- grid.7700.00000 0001 2190 4373Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Stephanie Witt
- grid.7700.00000 0001 2190 4373Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Andreas J. Forstner
- grid.10388.320000 0001 2240 3300Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany
| | - Stefanie Heilmann-Heimbach
- grid.10388.320000 0001 2240 3300Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany
| | - Markus M. Nöthen
- grid.10388.320000 0001 2240 3300Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany
| | - Carsten Spitzer
- grid.413108.f0000 0000 9737 0454Department of Psychosomatic Medicine and Psychotherapy, University Medical Center Rostock, Rostock, Germany
| | - Berend Malchow
- grid.411984.10000 0001 0482 5331Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, Göttingen, Germany
| | - Thorsten Müller
- grid.5252.00000 0004 1936 973XInstitute of Psychiatric Phenomics and Genomics (IPPG), University Hospital, LMU Munich, Munich, Germany
| | - Jens Wiltfang
- grid.411984.10000 0001 0482 5331Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, Göttingen, Germany ,grid.424247.30000 0004 0438 0426German Center for Neurodegenerative Diseases (DZNE), Göttingen, Germany ,grid.7311.40000000123236065iBiMED, Medical Sciences Department, University of Aveiro, Aveiro, Portugal
| | - Peter Falkai
- grid.5252.00000 0004 1936 973XDepartment of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
| | - Andrea Schmitt
- grid.5252.00000 0004 1936 973XDepartment of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany ,grid.11899.380000 0004 1937 0722Laboratory of Neuroscience (LIM27), Institute of Psychiatry, University of Sao Paulo, São Paulo, SP Brazil
| | - Moritz J. Rossner
- grid.5252.00000 0004 1936 973XDepartment of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
| | - Peter Nilsson
- grid.5037.10000000121581746Department of Protein Science, SciLifeLab, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Thomas G. Schulze
- grid.5252.00000 0004 1936 973XInstitute of Psychiatric Phenomics and Genomics (IPPG), University Hospital, LMU Munich, Munich, Germany ,grid.411023.50000 0000 9159 4457Department of Psychiatry and Behavioral Sciences, SUNY Upstate Medical University, Syracuse, NY USA ,grid.21107.350000 0001 2171 9311Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD USA
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5
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Thomas CE, Dahl L, Byström S, Chen Y, Uhlén M, Mälarstig A, Czene K, Hall P, Schwenk JM, Gabrielson M. Circulating proteins reveal prior use of menopausal hormonal therapy and increased risk of breast cancer. Transl Oncol 2022; 17:101339. [PMID: 35033985 PMCID: PMC8760550 DOI: 10.1016/j.tranon.2022.101339] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 12/18/2021] [Accepted: 12/31/2021] [Indexed: 11/15/2022] Open
Abstract
Accessible risk predictors are crucial for improving the early detection and prognosis of breast cancer. Blood samples are widely available and contain proteins that provide important information about human health and disease, however, little is still known about the contribution of circulating proteins to breast cancer risk prediction. We profiled EDTA plasma samples collected before diagnosis from the Swedish KARMA breast cancer cohort to evaluate circulating proteins as molecular predictors. A data-driven analysis strategy was applied to the molecular phenotypes built on 700 circulating proteins to identify and annotate clusters of women. The unsupervised analysis of 183 future breast cancer cases and 366 age-matched controls revealed five stable clusters with distinct proteomic plasma profiles. Among these women, those in the most stable cluster (N = 19; mean Jaccard index: 0.70 ± 0.29) were significantly more likely to have used menopausal hormonal therapy (MHT), get a breast cancer diagnosis, and were older compared to the remaining clusters. The circulating proteins associated with this cluster (FDR < 0.001) represented physiological processes related to cell junctions (F11R, CLDN15, ITGAL), DNA repair (RBBP8), cell replication (TJP3), and included proteins found in female reproductive tissue (PTCH1, ZP4). Using a data-driven approach on plasma proteomics data revealed the potential long-lasting molecular effects of menopausal hormonal therapy (MHT) on the circulating proteome, even after women had ended their treatment. This provides valuable insights concerning proteomics efforts to identify molecular markers for breast cancer risk prediction.
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Affiliation(s)
- Cecilia E Thomas
- Science for Life Laboratory, Department of Protein Science School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Tomtebodavägen 23, Solna, Stockholm 171 65, Sweden
| | - Leo Dahl
- Science for Life Laboratory, Department of Protein Science School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Tomtebodavägen 23, Solna, Stockholm 171 65, Sweden
| | - Sanna Byström
- Science for Life Laboratory, Department of Protein Science School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Tomtebodavägen 23, Solna, Stockholm 171 65, Sweden
| | - Yan Chen
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet Nobels väg 12A, Stockholm SE-171 77, Sweden; Department of Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Mathias Uhlén
- Science for Life Laboratory, Department of Protein Science School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Tomtebodavägen 23, Solna, Stockholm 171 65, Sweden
| | - Anders Mälarstig
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet Nobels väg 12A, Stockholm SE-171 77, Sweden; Department of Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet Nobels väg 12A, Stockholm SE-171 77, Sweden
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet Nobels väg 12A, Stockholm SE-171 77, Sweden; Department of Oncology, Södersjukhuset, Stockholm, Sweden
| | - Jochen M Schwenk
- Science for Life Laboratory, Department of Protein Science School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Tomtebodavägen 23, Solna, Stockholm 171 65, Sweden.
| | - Marike Gabrielson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet Nobels väg 12A, Stockholm SE-171 77, Sweden.
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6
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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: 65] [Impact Index Per Article: 21.7] [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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7
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Mann M, Kumar C, Zeng WF, Strauss MT. Artificial intelligence for proteomics and biomarker discovery. Cell Syst 2021; 12:759-770. [PMID: 34411543 DOI: 10.1016/j.cels.2021.06.006] [Citation(s) in RCA: 71] [Impact Index Per Article: 23.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 05/07/2021] [Accepted: 06/28/2021] [Indexed: 12/14/2022]
Abstract
There is an avalanche of biomedical data generation and a parallel expansion in computational capabilities to analyze and make sense of these data. Starting with genome sequencing and widely employed deep sequencing technologies, these trends have now taken hold in all omics disciplines and increasingly call for multi-omics integration as well as data interpretation by artificial intelligence technologies. Here, we focus on mass spectrometry (MS)-based proteomics and describe how machine learning and, in particular, deep learning now predicts experimental peptide measurements from amino acid sequences alone. This will dramatically improve the quality and reliability of analytical workflows because experimental results should agree with predictions in a multi-dimensional data landscape. Machine learning has also become central to biomarker discovery from proteomics data, which now starts to outperform existing best-in-class assays. Finally, we discuss model transparency and explainability and data privacy that are required to deploy MS-based biomarkers in clinical settings.
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Affiliation(s)
- Matthias Mann
- Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany.
| | - Chanchal Kumar
- Translational Science & Experimental Medicine, Research and Early Development, Cardiovascular, Renal and Metabolism (CVRM), BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden.
| | - Wen-Feng Zeng
- Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany.
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8
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Geyer PE, Arend FM, Doll S, Louiset M, Virreira Winter S, Müller‐Reif JB, Torun FM, Weigand M, Eichhorn P, Bruegel M, Strauss MT, Holdt LM, Mann M, Teupser D. High-resolution serum proteome trajectories in COVID-19 reveal patient-specific seroconversion. EMBO Mol Med 2021; 13:e14167. [PMID: 34232570 PMCID: PMC8687121 DOI: 10.15252/emmm.202114167] [Citation(s) in RCA: 74] [Impact Index Per Article: 24.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 05/16/2021] [Accepted: 05/25/2021] [Indexed: 12/12/2022] Open
Abstract
A deeper understanding of COVID-19 on human molecular pathophysiology is urgently needed as a foundation for the discovery of new biomarkers and therapeutic targets. Here we applied mass spectrometry (MS)-based proteomics to measure serum proteomes of COVID-19 patients and symptomatic, but PCR-negative controls, in a time-resolved manner. In 262 controls and 458 longitudinal samples of 31 patients, hospitalized for COVID-19, a remarkable 26% of proteins changed significantly. Bioinformatics analyses revealed co-regulated groups and shared biological functions. Proteins of the innate immune system such as CRP, SAA1, CD14, LBP, and LGALS3BP decreased early in the time course. Regulators of coagulation (APOH, FN1, HRG, KNG1, PLG) and lipid homeostasis (APOA1, APOC1, APOC2, APOC3, PON1) increased over the course of the disease. A global correlation map provides a system-wide functional association between proteins, biological processes, and clinical chemistry parameters. Importantly, five SARS-CoV-2 immunoassays against antibodies revealed excellent correlations with an extensive range of immunoglobulin regions, which were quantified by MS-based proteomics. The high-resolution profile of all immunoglobulin regions showed individual-specific differences and commonalities of potential pathophysiological relevance.
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Affiliation(s)
| | - Florian M Arend
- Institute of Laboratory MedicineUniversity HospitalLMU MunichMunichGermany
| | | | | | | | | | | | - Michael Weigand
- Institute of Laboratory MedicineUniversity HospitalLMU MunichMunichGermany
| | - Peter Eichhorn
- Institute of Laboratory MedicineUniversity HospitalLMU MunichMunichGermany
| | - Mathias Bruegel
- Institute of Laboratory MedicineUniversity HospitalLMU MunichMunichGermany
| | | | - Lesca M Holdt
- Institute of Laboratory MedicineUniversity HospitalLMU MunichMunichGermany
| | - Matthias Mann
- NNF Center for Protein ResearchFaculty of Health SciencesUniversity of CopenhagenCopenhagenDenmark
| | - Daniel Teupser
- Institute of Laboratory MedicineUniversity HospitalLMU MunichMunichGermany
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9
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Babačić H, Eriksson H, Pernemalm M. Plasma proteome alterations by MAPK inhibitors in BRAF V600-mutated metastatic cutaneous melanoma. Neoplasia 2021; 23:783-791. [PMID: 34246984 PMCID: PMC8274243 DOI: 10.1016/j.neo.2021.06.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 06/01/2021] [Accepted: 06/02/2021] [Indexed: 11/22/2022]
Abstract
Approximately half of metastatic cutaneous melanomas (CM) harbor a mutation in the BRAF protooncogene, upregulating the mitogen-activated protein kinase (MAPK)-pathway. The development of inhibitors targeting the MAPK pathway (MAPKi), i.e., BRAF- and MEK-inhibitors (BRAFi and MEKi), have substantially improved the survival in BRAFV600E/K-mutated stage IV metastatic CM. However, most patients develop resistance to treatment and no predictive biomarkers exist in practice. This study aimed at discovering plasma proteome changes during treatment MAPKi in patients with metastatic (stage IV) CM. Matched plasma samples before (pre) and during treatment (trm) from 23 patients with stage IV CM, treated with BRAF-inhibitors (BRAFi) alone or BRAF- and MEK- inhibitors combined (BRAFi and MEKi), were collected and analyzed with targeted proteomics by proximity extension assays. Additionally, plasma from 9 patients treated with BRAFi and MEKi was analyzed with in-depth high-resolution isoelectric focusing liquid-chromatography mass-spectrometry proteomics. Alterations of plasma proteins involved in granzyme and interferon gamma pathways were detected in patients treated with BRAFi, and cell adhesion-, neutrophil degranulation-, and proteolysis pathways in patients treated with BRAFi and MEKi. Several proteins were associated with progression-free survival after MAPKi treatment. We show that the majority of the altered plasma proteins were traceable to BRAFV600E-mutant metastatic CM tissue at mRNA level in 154 patients from the TCGA, further strengthening their involvement in tumoral response to treatment. This wide screen of plasma proteins unravels proteins that may serve as predictive and/or prognostic biomarkers of MAPKi treatment, opening a window of opportunity for plasma biomarker discovery in MAPKi-treatment of BRAFV600-mutant metastatic CM.
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Affiliation(s)
- Haris Babačić
- Department of Oncology-Pathology, Science for Life Laboratory, Karolinska Institute, Stockholm, Sweden
| | - Hanna Eriksson
- Theme Cancer / Department of Oncology, Karolinska University Hospital, Stockholm, Sweden.
| | - Maria Pernemalm
- Department of Oncology-Pathology, Science for Life Laboratory, Karolinska Institute, Stockholm, Sweden
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10
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Huang HQ, Chen G, Xiong DD, Lai ZF, Liu LM, Fang YY, Shen JH, Gan XY, Liao LF, Dang YW. Down-regulation of microRNA-125b-2-3p is a risk factor for a poor prognosis in hepatocellular carcinoma. Bioengineered 2021; 12:1627-1641. [PMID: 33949293 PMCID: PMC8806266 DOI: 10.1080/21655979.2021.1921549] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Hepatocellular carcinoma (HCC) is a leading cause of mortality in cancer patients, but the association between miR-125b-2-3p and the onset and prognosis of HCC has not been reported in previous studies; thus, the clinicopathological implications of miR-125b-2-3p in HCC require elaboration. To examine the expression of miR-125b-2-3p in HCC, both in-house RT-qPCR and public datasets were used to calculate the standard mean difference (SMD) and the summary receiver operating characteristic (sROC). MiR-125b-2-3p was markedly lower in HCC than in non-tumor tissue as assessed by the in-house RT-qPCR which was confirmed by the integrative analysis showing the SMD being -0.69 and the area under the curve (AUC) being 0.84 based on 1,233 cases of HCC and 630 cases of non-HCC controls. To gain a overview of the clinical value of miR-125b-2-3p in HCC, all possible datasets were integrated, and lower miR-125b-2-3p levels could lead to poorer differentiation and a more advanced clinical stage of HCC. The hazard ratio (HR) of miR-125b-2-3p was also calculated using a Cox proportional hazards model, and the miR-125b-2-3p level could act as an protective indication for the survival with the HR being 0.74 based on 586 cases of HCC. Furthermore, the effect of nitidine chloride (NC), a natural bioactive phytochemical alkaloid, on the regulation of miR-125b-2-3p and its potential targets was also investigated. The miR-125b-2-3p level was increased after NC treatment, while the expression of its potential target PRKCA was reduced. Above all, a low-expressed level of miR-125b-2-3p plays a tumor suppressive role in HCC.
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Affiliation(s)
- He-Qing Huang
- Department of Pathology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, P. R. China
| | - Gang Chen
- Department of Pathology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, P. R. China
| | - Dan-Dan Xiong
- Department of Pathology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, P. R. China
| | - Ze-Feng Lai
- Center for Pharmaceutical Research, Pharmaceutical College, Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, P. R. China
| | - Li-Min Liu
- Department of Drug Toxicology, Pharmaceutical College, Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, P. R. China
| | - Ye-Ying Fang
- Department of Radiotherapy, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, P. R. China
| | - Jin-Hai Shen
- Department of Pathology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, P. R. China
| | - Xiang-Yu Gan
- Department of Pathology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, P. R. China
| | - Liu-Feng Liao
- Department of Pharmacy, Guangxi Medical University Cancer Hospital, Nanning, Guangxi Zhuang Autonomous Region, P.R. China
| | - Yi-Wu Dang
- Department of Pathology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, P. R. China
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11
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Zhong W, Edfors F, Gummesson A, Bergström G, Fagerberg L, Uhlén M. Next generation plasma proteome profiling to monitor health and disease. Nat Commun 2021; 12:2493. [PMID: 33941778 PMCID: PMC8093230 DOI: 10.1038/s41467-021-22767-z] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Accepted: 03/09/2021] [Indexed: 12/24/2022] Open
Abstract
The need for precision medicine approaches to monitor health and disease makes it important to develop sensitive and accurate assays for proteome profiles in blood. Here, we describe an approach for plasma profiling based on proximity extension assay combined with next generation sequencing. First, we analyze the variability of plasma profiles between and within healthy individuals in a longitudinal wellness study, including the influence of genetic variations on plasma levels. Second, we follow patients newly diagnosed with type 2 diabetes before and during therapeutic intervention using plasma proteome profiling. The studies show that healthy individuals have a unique and stable proteome profile and indicate that a panel of proteins could potentially be used for early diagnosis of diabetes, including stratification of patients with regards to response to metformin treatment. Although validation in larger cohorts is needed, the analysis demonstrates the usefulness of comprehensive plasma profiling for precision medicine efforts.
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Affiliation(s)
- Wen Zhong
- 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
| | - Anders Gummesson
- Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, Gothenburg University, Gothenburg, Sweden.,Region Västra Götaland, Department of Clinical Genetics and Genomics, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Göran Bergström
- Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, Gothenburg University, Gothenburg, Sweden.,Region Västra Götaland, Department of Clinical Physiology, Sahlgrenska University Hospital, Gothenburg, 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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12
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Stockfelt M, Hong MG, Hesselmar B, Adlerberth I, Wold AE, Schwenk JM, Lundell AC, Rudin A. Circulating proteins associated with allergy development in infants-an exploratory analysis. Clin Proteomics 2021; 18:11. [PMID: 33722194 PMCID: PMC7958444 DOI: 10.1186/s12014-021-09318-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Accepted: 03/07/2021] [Indexed: 12/15/2022] Open
Abstract
Background Protein profiles that can predict allergy development in children are lacking and the ideal sampling age is unknown. By applying an exploratory proteomics approach in the prospective FARMFLORA birth cohort, we sought to identify previously unknown circulating proteins in early life that associate to protection or risk for development of allergy up to 8 years of age. Methods We analyzed plasma prepared from umbilical cord blood (n = 38) and blood collected at 1 month (n = 42), 4 months (n = 39), 18 months (n = 42), 36 months (n = 42) and 8 years (n = 44) of age. We profiled 230 proteins with a multiplexed assay and evaluated the global structure of the data with principal component analysis (PCA). Protein profiles informative to allergic disease at 18 months, 36 months and/or 8 years were evaluated using Lasso logistic regression and random forest. Results Two clusters emerged in the PCA analysis that separated samples obtained at birth and at 1 month of age from samples obtained later. Differences between the clusters were mostly driven by abundant plasma proteins. For the prediction of allergy, both Lasso logistic regression and random forest were most informative with samples collected at 1 month of age. A Lasso model with 27 proteins together with farm environment differentiated children who remained healthy from those developing allergy. This protein panel was primarily composed of antigen-presenting MHC class I molecules, interleukins and chemokines. Conclusion Sampled at one month of age, circulating proteins that reflect processes of the immune system may predict the development of allergic disease later in childhood. Supplementary Information The online version contains supplementary material available at 10.1186/s12014-021-09318-w.
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Affiliation(s)
- Marit Stockfelt
- Institute of Medicine, Department of Rheumatology and Inflammation Research, Sahlgrenska Academy, University of Gothenburg, Box 480, 405 30, Göteborg, Sweden.
| | - Mun-Gwan Hong
- Affinity Proteomics, SciLifeLab, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Bill Hesselmar
- Institute of Clinical Sciences, Department of Pediatrics, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Ingegerd Adlerberth
- Institute of Biomedicine, Department of Infectious Diseases, University of Gothenburg, Gothenburg, Sweden
| | - Agnes E Wold
- Institute of Biomedicine, Department of Infectious Diseases, University of Gothenburg, Gothenburg, Sweden
| | - Jochen M Schwenk
- Affinity Proteomics, SciLifeLab, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Anna-Carin Lundell
- Institute of Medicine, Department of Rheumatology and Inflammation Research, Sahlgrenska Academy, University of Gothenburg, Box 480, 405 30, Göteborg, Sweden
| | - Anna Rudin
- Institute of Medicine, Department of Rheumatology and Inflammation Research, Sahlgrenska Academy, University of Gothenburg, Box 480, 405 30, Göteborg, Sweden
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13
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Geyer PE, Mann SP, Treit PV, Mann M. Plasma Proteomes Can Be Reidentifiable and Potentially Contain Personally Sensitive and Incidental Findings. Mol Cell Proteomics 2021; 20:100035. [PMID: 33444735 PMCID: PMC7950134 DOI: 10.1074/mcp.ra120.002359] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Revised: 12/02/2020] [Accepted: 12/14/2020] [Indexed: 12/12/2022] Open
Abstract
The goal of clinical proteomics is to identify, quantify, and characterize proteins in body fluids or tissue to assist diagnosis, prognosis, and treatment of patients. In this way, it is similar to more mature omics technologies, such as genomics, that are increasingly applied in biomedicine. We argue that, similar to those fields, proteomics also faces ethical issues related to the kinds of information that is inherently obtained through sample measurement, although their acquisition was not the primary purpose. Specifically, we demonstrate the potential to identify individuals both by their characteristic, individual-specific protein levels and by variant peptides reporting on coding single nucleotide polymorphisms. Furthermore, it is in the nature of blood plasma proteomics profiling that it broadly reports on the health status of an individual-beyond the disease under investigation. Finally, we show that private and potentially sensitive information, such as ethnicity and pregnancy status, can increasingly be derived from proteomics data. Although this is potentially valuable not only to the individual, but also for biomedical research, it raises ethical questions similar to the incidental findings obtained through other omics technologies. We here introduce the necessity of-and argue for the desirability for-ethical and human-rights-related issues to be discussed within the proteomics community. Those thoughts are more fully developed in our accompanying manuscript. Appreciation and discussion of ethical aspects of proteomic research will allow for deeper, better-informed, more diverse, and, most importantly, wiser guidelines for clinical proteomics.
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Affiliation(s)
- Philipp E Geyer
- Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany; Faculty of Health Sciences, NNF Center for Protein Research, University of Copenhagen, Copenhagen, Denmark; OmicEra Diagnostics GmbH, Planegg, Germany.
| | - Sebastian Porsdam Mann
- Department of Media, Cognition and Communication, University of Copenhagen, Copenhagen, Denmark; Uehiro Center for Practical Ethics, Oxford University, Oxford, UK
| | - Peter V Treit
- Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Matthias Mann
- Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany; Faculty of Health Sciences, NNF Center for Protein Research, University of Copenhagen, Copenhagen, Denmark.
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14
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Bendes A, Dale M, Mattsson C, Dodig-Crnković T, Iglesias MJ, Schwenk JM, Fredolini C. Bead-Based Assays for Validating Proteomic Profiles in Body Fluids. Methods Mol Biol 2021; 2344:65-78. [PMID: 34115352 DOI: 10.1007/978-1-0716-1562-1_5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Protein biomarkers in biological fluids represent an important resource for improving the clinical management of diseases. Current proteomics technologies are capable of performing high-throughput and multiplex profiling in different types of fluids, often leading to the shortlisting of tens of candidate biomarkers per study. However, before reaching any clinical setting, these discoveries require thorough validation and an assay that would be suitable for routine analyses. In the path from biomarker discovery to validation, the performance of the assay implemented for the intended protein quantification is extremely critical toward achieving reliable and reproducible results. Development of robust sandwich immunoassays for individual candidates is challenging and labor and resource intensive, and multiplies when evaluating a panel of interesting candidates at the same time. Here we describe a versatile pipeline that facilitates the systematic and parallel development of multiple sandwich immunoassays using a bead-based technology.
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Affiliation(s)
- Annika Bendes
- Science for Life Laboratory, Department of Protein Science, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Matilda Dale
- Science for Life Laboratory, Department of Protein Science, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Cecilia Mattsson
- Science for Life Laboratory, Department of Protein Science, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Tea Dodig-Crnković
- Science for Life Laboratory, Department of Protein Science, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Maria Jesus Iglesias
- Science for Life Laboratory, Department of Protein Science, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Stockholm, Sweden.,Department of Clinical Medicine, Faculty of Health Science, The Arctic University of Tromsö, Tromsö, Norway
| | - Jochen M Schwenk
- Science for Life Laboratory, Department of Protein Science, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Claudia Fredolini
- Science for Life Laboratory, Department of Protein Science, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Stockholm, Sweden.
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15
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Longitudinal plasma protein profiling of newly diagnosed type 2 diabetes. EBioMedicine 2020; 63:103147. [PMID: 33279861 PMCID: PMC7718461 DOI: 10.1016/j.ebiom.2020.103147] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Revised: 10/05/2020] [Accepted: 11/10/2020] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Comprehensive proteomics profiling may offer new insights into the dysregulated metabolic milieu of type 2 diabetes, and in the future, serve as a useful tool for personalized medicine. This calls for a better understanding of circulating protein patterns at the early stage of type 2 diabetes as well as the dynamics of protein patterns during changes in metabolic status. METHODS To elucidate the systemic alterations in early-stage diabetes and to investigate the effects on the proteome during metabolic improvement, we measured 974 circulating proteins in 52 newly diagnosed, treatment-naïve type 2 diabetes subjects at baseline and after 1 and 3 months of guideline-based diabetes treatment, while comparing their protein profiles to that of 94 subjects without diabetes. FINDINGS Early stage type 2 diabetes was associated with distinct protein patterns, reflecting key metabolic syndrome features including insulin resistance, adiposity, hyperglycemia and liver steatosis. The protein profiles at baseline were attenuated during guideline-based diabetes treatment and several plasma proteins associated with metformin medication independently of metabolic variables, such as circulating EPCAM. INTERPRETATION The results advance our knowledge about the biochemical manifestations of type 2 diabetes and suggest that comprehensive protein profiling may serve as a useful tool for metabolic phenotyping and for elucidating the biological effects of diabetes treatments. FUNDING This work was supported by the Swedish Heart and Lung Foundation, the Swedish Research Council, the Erling Persson Foundation, the Knut and Alice Wallenberg Foundation, and the Swedish state under the agreement between the Swedish government and the county councils (ALF-agreement).
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16
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Tebani A, Gummesson A, Zhong W, Koistinen IS, Lakshmikanth T, Olsson LM, Boulund F, Neiman M, Stenlund H, Hellström C, Karlsson MJ, Arif M, Dodig-Crnković T, Mardinoglu A, Lee S, Zhang C, Chen Y, Olin A, Mikes J, Danielsson H, von Feilitzen K, Jansson PA, Angerås O, Huss M, Kjellqvist S, Odeberg J, Edfors F, Tremaroli V, Forsström B, Schwenk JM, Nilsson P, Moritz T, Bäckhed F, Engstrand L, Brodin P, Bergström G, Uhlen M, Fagerberg L. Integration of molecular profiles in a longitudinal wellness profiling cohort. Nat Commun 2020; 11:4487. [PMID: 32900998 PMCID: PMC7479148 DOI: 10.1038/s41467-020-18148-7] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Accepted: 08/03/2020] [Indexed: 12/19/2022] Open
Abstract
An important aspect of precision medicine is to probe the stability in molecular profiles among healthy individuals over time. Here, we sample a longitudinal wellness cohort with 100 healthy individuals and analyze blood molecular profiles including proteomics, transcriptomics, lipidomics, metabolomics, autoantibodies and immune cell profiling, complemented with gut microbiota composition and routine clinical chemistry. Overall, our results show high variation between individuals across different molecular readouts, while the intra-individual baseline variation is low. The analyses show that each individual has a unique and stable plasma protein profile throughout the study period and that many individuals also show distinct profiles with regards to the other omics datasets, with strong underlying connections between the blood proteome and the clinical chemistry parameters. In conclusion, the results support an individual-based definition of health and show that comprehensive omics profiling in a longitudinal manner is a path forward for precision medicine. An important aspect of precision medicine is to probe the stability in molecular profiles among healthy individuals over time. Here, the authors sample a longitudinal wellness cohort and analyse blood molecular profiles as well as gut microbiota composition.
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Affiliation(s)
- Abdellah Tebani
- Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Anders Gummesson
- Wallenberg Laboratory and Sahlgrenska Center for Cardiovascular and Metabolic Research, Department of Molecular and Clinical Medicine, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden.,Region Västra Götaland, Sahlgrenska University Hospital, Department of Clinical Genetics and Genomics, Gothenburg, Sweden
| | - Wen Zhong
- Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Ina Schuppe Koistinen
- Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden.,Center for Translational Microbiome Research, Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden
| | - Tadepally Lakshmikanth
- Science for Life Laboratory, Department of Women's and Children's Health, Karolinska Institutet, Stockholm, Sweden
| | - Lisa M Olsson
- Wallenberg Laboratory and Sahlgrenska Center for Cardiovascular and Metabolic Research, Department of Molecular and Clinical Medicine, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden
| | - Fredrik Boulund
- Center for Translational Microbiome Research, Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden
| | - Maja Neiman
- Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Hans Stenlund
- Swedish Metabolomics Centre, Department of Molecular Biology, Umeå University, 901 87, Umeå, Sweden
| | - Cecilia Hellström
- Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Max J Karlsson
- Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Muhammad Arif
- Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Tea Dodig-Crnković
- 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, UK
| | - Sunjae Lee
- Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Cheng Zhang
- Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Yang Chen
- Science for Life Laboratory, Department of Women's and Children's Health, Karolinska Institutet, Stockholm, Sweden
| | - Axel Olin
- Science for Life Laboratory, Department of Women's and Children's Health, Karolinska Institutet, Stockholm, Sweden
| | - Jaromir Mikes
- Science for Life Laboratory, Department of Women's and Children's Health, Karolinska Institutet, Stockholm, Sweden
| | - Hanna Danielsson
- Center for Translational Microbiome Research, Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden
| | - Kalle von Feilitzen
- Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Per-Anders Jansson
- Wallenberg Laboratory and Sahlgrenska Center for Cardiovascular and Metabolic Research, Department of Molecular and Clinical Medicine, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden.,Region Västra Götaland, Sahlgrenska University Hospital, Department of Internal Medicine, Gothenburg, Sweden
| | - Oskar Angerås
- Department of Molecular and Clinical Medicine, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden.,Region Västra Götaland, Sahlgrenska University Hospital, Department of Cardiology, Gothenburg, Sweden
| | - Mikael Huss
- Codon Consulting, 118 26, Stockholm, Sweden.,Department of Learning, Informatics, Management and Ethics, Karolinska Institutet, Stockholm, Sweden
| | - Sanela Kjellqvist
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Jacob Odeberg
- 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
| | - Valentina Tremaroli
- Wallenberg Laboratory and Sahlgrenska Center for Cardiovascular and Metabolic Research, Department of Molecular and Clinical Medicine, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden
| | - Björn Forsström
- Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Jochen M Schwenk
- Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Peter Nilsson
- Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Thomas Moritz
- Swedish Metabolomics Centre, Department of Forest Genetics and Plant Physiology, Swedish University of Agricultural Sciences, 907 36, Umeå, Sweden
| | - Fredrik Bäckhed
- Wallenberg Laboratory and Sahlgrenska Center for Cardiovascular and Metabolic Research, Department of Molecular and Clinical Medicine, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden.,Region Västra Götaland, Sahlgrenska University Hospital, Department of Clinical Physiology, Gothenburg, Sweden.,Novo Nordisk Foundation Center for Basic Metabolic Research, Section for Metabolic Receptology and Enteroendocrinology, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Lars Engstrand
- Center for Translational Microbiome Research, Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden
| | - Petter Brodin
- Science for Life Laboratory, Department of Women's and Children's Health, Karolinska Institutet, Stockholm, Sweden
| | - Göran Bergström
- Wallenberg Laboratory and Sahlgrenska Center for Cardiovascular and Metabolic Research, Department of Molecular and Clinical Medicine, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden.,Region Västra Götaland, Sahlgrenska University Hospital, Department of Clinical Physiology, Gothenburg, Sweden
| | - Mathias Uhlen
- Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden.,Center for Biosustainability, Danish Technical University, Copenhagen, Denmark
| | - Linn Fagerberg
- Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden.
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