1
|
Zelicha H, Kaplan A, Yaskolka Meir A, Rinott E, Tsaban G, Blüher M, Klöting N, Ceglarek U, Isermann B, Stumvoll M, Chassidim Y, Shelef I, Hu FB, Shai I. Altered proteome profiles related to visceral adiposity may mediate the favorable effect of green Mediterranean diet: the DIRECT-PLUS trial. Obesity (Silver Spring) 2024. [PMID: 38757229 DOI: 10.1002/oby.24036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 03/08/2024] [Accepted: 03/19/2024] [Indexed: 05/18/2024]
Abstract
OBJECTIVE The objective of this study was to explore the effects of a green Mediterranean (green-MED) diet, which is high in dietary polyphenols and green plant-based protein and low in red/processed meat, on cardiovascular disease and inflammation-related circulating proteins and their associations with cardiometabolic risk parameters. METHODS In the 18-month weight loss trial Dietary Intervention Randomized Controlled Trial Polyphenols Unprocessed Study (DIRECT-PLUS), 294 participants with abdominal obesity were randomized to basic healthy dietary guidelines, Mediterranean (MED), or green-MED diets. Both isocaloric MED diet groups consumed walnuts (28 g/day), and the green-MED diet group also consumed green tea (3-4 cups/day) and green shakes (Mankai plant shake, 500 mL/day) and avoided red/processed meat. Proteome panels were measured at three time points using Olink CVDII. RESULTS At baseline, a dominant protein cluster was significantly related to higher phenotypic cardiometabolic risk parameters, with the strongest associations attributed to magnetic resonance imaging-assessed visceral adiposity (false discovery rate of 5%). Overall, after 6 months of intervention, both the MED and green-MED diets induced improvements in cardiovascular disease and proinflammatory risk proteins (p < 0.05, vs. healthy dietary guidelines), with the green-MED diet leading to more pronounced beneficial changes, largely driven by dominant proinflammatory proteins (IL-1 receptor antagonist protein, IL-16, IL-18, thrombospondin-2, leptin, prostasin, galectin-9, and fibroblast growth factor 21; adjusted for age, sex, and weight loss; p < 0.05). After 18 months, proteomics cluster changes presented the strongest correlations with visceral adiposity reduction. CONCLUSIONS Proteomics clusters may enhance our understanding of the favorable effect of a green-MED diet that is enriched with polyphenols and low in red/processed meat on visceral adiposity and cardiometabolic risk.
Collapse
Affiliation(s)
- Hila Zelicha
- The Health and Nutrition Innovative International Research Center, Faculty of Health Sciences, Ben-Gurion University of the Negev, Be'er Sheva, Israel
| | - Alon Kaplan
- The Health and Nutrition Innovative International Research Center, Faculty of Health Sciences, Ben-Gurion University of the Negev, Be'er Sheva, Israel
| | - Anat Yaskolka Meir
- The Health and Nutrition Innovative International Research Center, Faculty of Health Sciences, Ben-Gurion University of the Negev, Be'er Sheva, Israel
| | - Ehud Rinott
- The Health and Nutrition Innovative International Research Center, Faculty of Health Sciences, Ben-Gurion University of the Negev, Be'er Sheva, Israel
| | - Gal Tsaban
- The Health and Nutrition Innovative International Research Center, Faculty of Health Sciences, Ben-Gurion University of the Negev, Be'er Sheva, Israel
| | - Matthias Blüher
- Helmholtz Institute for Metabolic, Obesity and Vascular Research (HI-MAG) of the Helmholtz Zentrum München at the University of Leipzig and University Hospital Leipzig, Leipzig, Germany
| | - Nora Klöting
- Helmholtz Institute for Metabolic, Obesity and Vascular Research (HI-MAG) of the Helmholtz Zentrum München at the University of Leipzig and University Hospital Leipzig, Leipzig, Germany
| | - Uta Ceglarek
- Department of Medicine, University of Leipzig, Leipzig, Germany
| | - Berend Isermann
- Department of Medicine, University of Leipzig, Leipzig, Germany
| | | | - Yoash Chassidim
- Department of Engineering, Sapir Academic College, Sapir, Israel
| | - Ilan Shelef
- Soroka University Medical Center, Be'er Sheva, Israel
| | - Frank B Hu
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts, USA
- Harvard Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts, USA
| | - Iris Shai
- The Health and Nutrition Innovative International Research Center, Faculty of Health Sciences, Ben-Gurion University of the Negev, Be'er Sheva, Israel
- Department of Medicine, University of Leipzig, Leipzig, Germany
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts, USA
| |
Collapse
|
2
|
Gerdle B, Dahlqvist Leinhard O, Lund E, Lundberg P, Forsgren MF, Ghafouri B. Pain and the biochemistry of fibromyalgia: patterns of peripheral cytokines and chemokines contribute to the differentiation between fibromyalgia and controls and are associated with pain, fat infiltration and content. FRONTIERS IN PAIN RESEARCH 2024; 5:1288024. [PMID: 38304854 PMCID: PMC10830731 DOI: 10.3389/fpain.2024.1288024] [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: 09/03/2023] [Accepted: 01/05/2024] [Indexed: 02/03/2024] Open
Abstract
Objectives This explorative study analyses interrelationships between peripheral compounds in saliva, plasma, and muscles together with body composition variables in healthy subjects and in fibromyalgia patients (FM). There is a need to better understand the extent cytokines and chemokines are associated with body composition and which cytokines and chemokines differentiate FM from healthy controls. Methods Here, 32 female FM patients and 30 age-matched female healthy controls underwent a clinical examination that included blood sample, saliva samples, and pain threshold tests. In addition, the subjects completed a health questionnaire. From these blood and saliva samples, a panel of 68 mainly cytokines and chemokines were determined. Microdialysis of trapezius and erector spinae muscles, phosphorus-31 magnetic resonance spectroscopy of erector spinae muscle, and whole-body magnetic resonance imaging for determination of body composition (BC)-i.e., muscle volume, fat content and infiltration-were also performed. Results After standardizing BC measurements to remove the confounding effect of Body Mass Index, fat infiltration and content are generally increased, and fat-free muscle volume is decreased in FM. Mainly saliva proteins differentiated FM from controls. When including all investigated compounds and BC variables, fat infiltration and content variables were most important, followed by muscle compounds and cytokines and chemokines from saliva and plasma. Various plasma proteins correlated positively with pain intensity in FM and negatively with pain thresholds in all subjects taken together. A mix of increased plasma cytokines and chemokines correlated with an index covering fat infiltration and content in different tissues. When muscle compounds were included in the analysis, several of these were identified as the most important regressors, although many plasma and saliva proteins remained significant. Discussion Peripheral factors were important for group differentiation between FM and controls. In saliva (but not plasma), cytokines and chemokines were significantly associated with group membership as saliva compounds were increased in FM. The importance of peripheral factors for group differentiation increased when muscle compounds and body composition variables were also included. Plasma proteins were important for pain intensity and sensitivity. Cytokines and chemokines mainly from plasma were also significantly and positively associated with a fat infiltration and content index. Conclusion Our findings of associations between cytokines and chemokines and fat infiltration and content in different tissues confirm that inflammation and immune factors are secreted from adipose tissue. FM is clearly characterized by complex interactions between peripheral tissues and the peripheral and central nervous systems, including nociceptive, immune, and neuroendocrine processes.
Collapse
Affiliation(s)
- Björn Gerdle
- Pain and Rehabilitation Centre, and Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
- Center for Medical Image Science and Visualization (CMIV), Linköping, Sweden
| | - Olof Dahlqvist Leinhard
- Center for Medical Image Science and Visualization (CMIV), Linköping, Sweden
- Radiation Physics, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
- AMRA Medical AB, Linköping, Sweden
| | - Eva Lund
- Radiation Physics, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
| | - Peter Lundberg
- Center for Medical Image Science and Visualization (CMIV), Linköping, Sweden
- Department of Radiation Physics, and Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
| | - Mikael Fredrik Forsgren
- Center for Medical Image Science and Visualization (CMIV), Linköping, Sweden
- Radiation Physics, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
- AMRA Medical AB, Linköping, Sweden
| | - Bijar Ghafouri
- Pain and Rehabilitation Centre, and Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
| |
Collapse
|
3
|
Guo CG, Sun R, Wang X, Yuan Y, Xu Y, Li S, Sun X, Wang J, Hu X, Guo T, Chen XW, Xiao RP, Zhang X. Intestinal SURF4 is essential for apolipoprotein transport and lipoprotein secretion. Mol Metab 2024; 79:101847. [PMID: 38042368 PMCID: PMC10755498 DOI: 10.1016/j.molmet.2023.101847] [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: 09/21/2023] [Revised: 11/20/2023] [Accepted: 11/27/2023] [Indexed: 12/04/2023] Open
Abstract
OBJECTIVE Lipoprotein assembly and secretion in the small intestine are critical for dietary fat absorption. Surfeit locus protein 4 (SURF4) serves as a cargo receptor, facilitating the cellular transport of multiple proteins and mediating hepatic lipid secretion in vivo. However, its involvement in intestinal lipid secretion is not fully understood. In this study, we investigated the role of SURF4 in intestinal lipid absorption. METHODS We generated intestine-specific Surf4 knockout mice and characterized the phenotypes. Additionally, we investigated the underlying mechanisms of SURF4 in intestinal lipid secretion using proteomics and cellular models. RESULTS We unveiled that SURF4 is indispensable for apolipoprotein transport and lipoprotein secretion. Intestine-specific Surf4 knockout mice exhibited ectopic lipid deposition in the small intestine and hypolipidemia. Deletion of SURF4 impeded the transport of apolipoprotein A1 (ApoA1), proline-rich acidic protein 1 (PRAP1), and apolipoprotein B48 (ApoB48) and hindered the assembly and secretion of chylomicrons and high-density lipoproteins. CONCLUSIONS SURF4 emerges as a pivotal regulator of intestinal lipid absorption via mediating the secretion of ApoA1, PRAP1 and ApoB48.
Collapse
Affiliation(s)
- Chun-Guang Guo
- Institute of Molecular Medicine, College of Future Technology, Peking University, Beijing 100871, China; Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Peking University, Beijing 100871, China
| | - Rui Sun
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou 310024, China
| | - Xiao Wang
- Institute of Molecular Medicine, College of Future Technology, Peking University, Beijing 100871, China; Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Peking University, Beijing 100871, China
| | - Ye Yuan
- Institute of Molecular Medicine, College of Future Technology, Peking University, Beijing 100871, China; Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Peking University, Beijing 100871, China
| | - Yan Xu
- Institute of Molecular Medicine, College of Future Technology, Peking University, Beijing 100871, China; Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Peking University, Beijing 100871, China
| | - Shihan Li
- Institute of Molecular Medicine, College of Future Technology, Peking University, Beijing 100871, China; Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Peking University, Beijing 100871, China
| | - Xueting Sun
- Institute of Molecular Medicine, College of Future Technology, Peking University, Beijing 100871, China; Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Peking University, Beijing 100871, China
| | - Jue Wang
- Institute of Molecular Medicine, College of Future Technology, Peking University, Beijing 100871, China; Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Peking University, Beijing 100871, China
| | - Xinli Hu
- Institute of Molecular Medicine, College of Future Technology, Peking University, Beijing 100871, China; Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Peking University, Beijing 100871, China
| | - Tiannan Guo
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou 310024, China
| | - Xiao-Wei Chen
- Institute of Molecular Medicine, College of Future Technology, Peking University, Beijing 100871, China; Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Peking University, Beijing 100871, China; Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, China.
| | - Rui-Ping Xiao
- Institute of Molecular Medicine, College of Future Technology, Peking University, Beijing 100871, China; Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Peking University, Beijing 100871, China; Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, China.
| | - Xiuqin Zhang
- Institute of Molecular Medicine, College of Future Technology, Peking University, Beijing 100871, China; Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Peking University, Beijing 100871, China.
| |
Collapse
|
4
|
Drouard G, Hagenbeek FA, Whipp AM, Pool R, Hottenga JJ, Jansen R, Hubers N, Afonin A, Willemsen G, de Geus EJC, Ripatti S, Pirinen M, Kanninen KM, Boomsma DI, van Dongen J, Kaprio J. Longitudinal multi-omics study reveals common etiology underlying association between plasma proteome and BMI trajectories in adolescent and young adult twins. BMC Med 2023; 21:508. [PMID: 38129841 PMCID: PMC10740308 DOI: 10.1186/s12916-023-03198-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 11/27/2023] [Indexed: 12/23/2023] Open
Abstract
BACKGROUND The influence of genetics and environment on the association of the plasma proteome with body mass index (BMI) and changes in BMI remains underexplored, and the links to other omics in these associations remain to be investigated. We characterized protein-BMI trajectory associations in adolescents and adults and how these connect to other omics layers. METHODS Our study included two cohorts of longitudinally followed twins: FinnTwin12 (N = 651) and the Netherlands Twin Register (NTR) (N = 665). Follow-up comprised 4 BMI measurements over approximately 6 (NTR: 23-27 years old) to 10 years (FinnTwin12: 12-22 years old), with omics data collected at the last BMI measurement. BMI changes were calculated in latent growth curve models. Mixed-effects models were used to quantify the associations between the abundance of 439 plasma proteins with BMI at blood sampling and changes in BMI. In FinnTwin12, the sources of genetic and environmental variation underlying the protein abundances were quantified by twin models, as were the associations of proteins with BMI and BMI changes. In NTR, we investigated the association of gene expression of genes encoding proteins identified in FinnTwin12 with BMI and changes in BMI. We linked identified proteins and their coding genes to plasma metabolites and polygenic risk scores (PRS) applying mixed-effects models and correlation networks. RESULTS We identified 66 and 14 proteins associated with BMI at blood sampling and changes in BMI, respectively. The average heritability of these proteins was 35%. Of the 66 BMI-protein associations, 43 and 12 showed genetic and environmental correlations, respectively, including 8 proteins showing both. Similarly, we observed 7 and 3 genetic and environmental correlations between changes in BMI and protein abundance, respectively. S100A8 gene expression was associated with BMI at blood sampling, and the PRG4 and CFI genes were associated with BMI changes. Proteins showed strong connections with metabolites and PRSs, but we observed no multi-omics connections among gene expression and other omics layers. CONCLUSIONS Associations between the proteome and BMI trajectories are characterized by shared genetic, environmental, and metabolic etiologies. We observed few gene-protein pairs associated with BMI or changes in BMI at the proteome and transcriptome levels.
Collapse
Affiliation(s)
- Gabin Drouard
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland.
| | - Fiona A Hagenbeek
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Alyce M Whipp
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - René Pool
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Jouke Jan Hottenga
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Rick Jansen
- Department of Psychiatry, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Mood, Anxiety, Psychosis, Sleep & Stress Program, Amsterdam, The Netherlands
| | - Nikki Hubers
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
- Amsterdam Reproduction & Development (AR&D) Research Institute, Amsterdam, The Netherlands
| | - Aleksei Afonin
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Gonneke Willemsen
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Eco J C de Geus
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Department of Public Health, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Matti Pirinen
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Department of Public Health, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland
| | - Katja M Kanninen
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
- Amsterdam Reproduction & Development (AR&D) Research Institute, Amsterdam, The Netherlands
| | - Jenny van Dongen
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
- Amsterdam Reproduction & Development (AR&D) Research Institute, Amsterdam, The Netherlands
| | - Jaakko Kaprio
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland.
| |
Collapse
|
5
|
Kussmann M. Mass spectrometry as a lens into molecular human nutrition and health. EUROPEAN JOURNAL OF MASS SPECTROMETRY (CHICHESTER, ENGLAND) 2023; 29:370-379. [PMID: 37587732 DOI: 10.1177/14690667231193555] [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: 08/18/2023]
Abstract
Mass spectrometry (MS) has developed over the last decades into the most informative and versatile analytical technology in molecular and structural biology (). The platform enables discovery, identification, and characterisation of non-volatile biomolecules, such as proteins, peptides, DNA, RNA, nutrients, metabolites, and lipids at both speed and scale and can elucidate their interactions and effects. The versatility, robustness, and throughput have rendered MS a major research and development platform in molecular human health and biomedical science. More recently, MS has also been established as the central tool for 'Molecular Nutrition', enabling comprehensive and rapid identification and characterisation of macro- and micronutrients, bioactives, and other food compounds. 'Molecular Nutrition' thereby helps understand bioaccessibility, bioavailability, and bioefficacy of macro- and micronutrients and related health effects. Hence, MS provides a lens through which the fate of nutrients can be monitored along digestion via absorption to metabolism. This in turn provides the bioanalytical foundation for 'Personalised Nutrition' or 'Precision Nutrition' in which design and development of diets and nutritional products is tailored towards consumer and patient groups sharing similar genetic and environmental predisposition, health/disease conditions and lifestyles, and/or objectives of performance and wellbeing. The next level of integrated nutrition science is now being built as 'Systems Nutrition' where public and personal health data are correlated with life condition and lifestyle factors, to establish directional relationships between nutrition, lifestyle, environment, and health, eventually translating into science-based public and personal heath recommendations and actions. This account provides a condensed summary of the contributions of MS to a precise, quantitative, and comprehensive nutrition and health science and sketches an outlook on its future role in this fascinating and relevant field.
Collapse
Affiliation(s)
- Martin Kussmann
- Abteilung Wissenschaft, Kompetenzzentrum für Ernährung (KErn), Germany
- Kussmann Biotech GmbH, Germany
| |
Collapse
|
6
|
Drouard G, Hagenbeek FA, Whipp A, Pool R, Hottenga JJ, Jansen R, Hubers N, Afonin A, Willemsen G, de Geus EJC, Ripatti S, Pirinen M, Kanninen KM, Boomsma DI, van Dongen J, Kaprio J. Longitudinal multi-omics study reveals common etiology underlying association between plasma proteome and BMI trajectories in adolescent and young adult twins. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.06.28.23291995. [PMID: 37425750 PMCID: PMC10327285 DOI: 10.1101/2023.06.28.23291995] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
Background The influence of genetics and environment on the association of the plasma proteome with body mass index (BMI) and changes in BMI remain underexplored, and the links to other omics in these associations remain to be investigated. We characterized protein-BMI trajectory associations in adolescents and adults and how these connect to other omics layers. Methods Our study included two cohorts of longitudinally followed twins: FinnTwin12 (N=651) and the Netherlands Twin Register (NTR) (N=665). Follow-up comprised four BMI measurements over approximately 6 (NTR: 23-27 years old) to 10 years (FinnTwin12: 12-22 years old), with omics data collected at the last BMI measurement. BMI changes were calculated using latent growth curve models. Mixed-effects models were used to quantify the associations between the abundance of 439 plasma proteins with BMI at blood sampling and changes in BMI. The sources of genetic and environmental variation underlying the protein abundances were quantified using twin models, as were the associations of proteins with BMI and BMI changes. In NTR, we investigated the association of gene expression of genes encoding proteins identified in FinnTwin12 with BMI and changes in BMI. We linked identified proteins and their coding genes to plasma metabolites and polygenic risk scores (PRS) using mixed-effect models and correlation networks. Results We identified 66 and 14 proteins associated with BMI at blood sampling and changes in BMI, respectively. The average heritability of these proteins was 35%. Of the 66 BMI-protein associations, 43 and 12 showed genetic and environmental correlations, respectively, including 8 proteins showing both. Similarly, we observed 6 and 4 genetic and environmental correlations between changes in BMI and protein abundance, respectively. S100A8 gene expression was associated with BMI at blood sampling, and the PRG4 and CFI genes were associated with BMI changes. Proteins showed strong connections with many metabolites and PRSs, but we observed no multi-omics connections among gene expression and other omics layers. Conclusions Associations between the proteome and BMI trajectories are characterized by shared genetic, environmental, and metabolic etiologies. We observed few gene-protein pairs associated with BMI or changes in BMI at the proteome and transcriptome levels.
Collapse
Affiliation(s)
- Gabin Drouard
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Fiona A. Hagenbeek
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Alyce Whipp
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - René Pool
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Jouke Jan Hottenga
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Rick Jansen
- Amsterdam UMC location Vrije Universiteit Amsterdam, Department of Psychiatry, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Mood, Anxiety, Psychosis, Sleep & Stress Program, Amsterdam, The Netherlands
| | - Nikki Hubers
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Reproduction & Development (AR&D) Research Institute, Amsterdam, The Netherlands
| | - Aleksei Afonin
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - BIOS Consortium
- Biobank-based Integrative Omics Study Consortium. Lists of authors and their affiliations appear in the supplementary material (see Additional file 1)
| | | | - Gonneke Willemsen
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Eco J. C. de Geus
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Department of Public Health, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Matti Pirinen
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Department of Public Health, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland
| | - Katja M. Kanninen
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Dorret I. Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
- Amsterdam Reproduction & Development (AR&D) Research Institute, Amsterdam, The Netherlands
| | - Jenny van Dongen
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
- Amsterdam Reproduction & Development (AR&D) Research Institute, Amsterdam, The Netherlands
| | - Jaakko Kaprio
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| |
Collapse
|
7
|
Woldemariam S, Dorner TE, Wiesinger T, Stein KV. Multi-omics approaches for precision obesity management : Potentials and limitations of omics in precision prevention, treatment and risk reduction of obesity. Wien Klin Wochenschr 2023; 135:113-124. [PMID: 36717394 PMCID: PMC10020295 DOI: 10.1007/s00508-022-02146-4] [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: 04/29/2022] [Accepted: 12/12/2022] [Indexed: 02/01/2023]
Abstract
INTRODUCTION Obesity is a multifactorial chronic disease that cannot be addressed by simply promoting better diets and more physical activity. To date, not a single country has successfully been able to curb the accumulating burden of obesity. One explanation for the lack of progress is that lifestyle intervention programs are traditionally implemented without a comprehensive evaluation of an individual's diagnostic biomarkers. Evidence from genome-wide association studies highlight the importance of genetic and epigenetic factors in the development of obesity and how they in turn affect the transcriptome, metabolites, microbiomes, and proteomes. OBJECTIVE The purpose of this review is to provide an overview of the different types of omics data: genomics, epigenomics, transcriptomics, proteomics, metabolomics and illustrate how a multi-omics approach can be fundamental for the implementation of precision obesity management. RESULTS The different types of omics designs are grouped into two categories, the genotype approach and the phenotype approach. When applied to obesity prevention and management, each omics type could potentially help to detect specific biomarkers in people with risk profiles and guide healthcare professionals and decision makers in developing individualized treatment plans according to the needs of the individual before the onset of obesity. CONCLUSION Integrating multi-omics approaches will enable a paradigm shift from the one size fits all approach towards precision obesity management, i.e. (1) precision prevention of the onset of obesity, (2) precision medicine and tailored treatment of obesity, and (3) precision risk reduction and prevention of secondary diseases related to obesity.
Collapse
Affiliation(s)
- Selam Woldemariam
- Karl Landsteiner Institute for Health Promotion Research, 3062, Kirchstetten, Austria
| | - Thomas E Dorner
- Karl Landsteiner Institute for Health Promotion Research, 3062, Kirchstetten, Austria
- Academy for Ageing Research, House of Mercy, 1160, Vienna, Austria
| | - Thomas Wiesinger
- Karl Landsteiner Institute for Health Promotion Research, 3062, Kirchstetten, Austria
| | - Katharina Viktoria Stein
- Karl Landsteiner Institute for Health Promotion Research, 3062, Kirchstetten, Austria.
- Department of Public Health and Primary Care, Leiden University Medical Centre, 2511 DP, The Hague, The Netherlands.
| |
Collapse
|
8
|
Gerdle B, Wåhlén K, Gordh T, Bäckryd E, Carlsson A, Ghafouri B. Plasma proteins from several components of the immune system differentiate chronic widespread pain patients from healthy controls - an exploratory case-control study combining targeted and non-targeted protein identification. Medicine (Baltimore) 2022; 101:e31013. [PMID: 36401429 PMCID: PMC9678582 DOI: 10.1097/md.0000000000031013] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/02/2022] Open
Abstract
Chronic widespread pain (CWP), including fibromyalgia (FM), is characterized by generalized musculoskeletal pain and hyperalgesia. Plasma proteins from proteomics (non-targeted) and from targeted inflammatory panels (cytokines/chemokines) differentiate CWP/FM from controls. The importance of proteins obtained from these two sources, the protein-protein association network, and the biological processes involved were investigated. Plasma proteins from women with CWP (n = 15) and CON (n = 23) were analyzed using two-dimensional gel electrophoresis analysis and a multiplex proximity extension assay for analysis of cytokines/chemokines. Associations between the proteins and group were multivarietly analyzed. The protein-protein association network and the biological processes according to the Gene Ontology were investigated. Proteins from both sources were important for group differentiation; the majority from the two-dimensional gel electrophoresis analysis. 58 proteins significantly differentiated the two groups (R2 = 0.83). A significantly enriched network was found; biological processes were acute phase response, complement activation, and innate immune response. As with other studies, this study shows that plasma proteins can differentiate CWP from healthy subjects. Focusing on cytokines/chemokines is not sufficient to grasp the peripheral biological processes that maintain CWP/FM since our results show that other components of the immune and inflammation systems are also highly significant.
Collapse
Affiliation(s)
- Björn Gerdle
- Pain and Rehabilitation Centre, and Department of Health, Medicine, and Caring Sciences, Linköping University, Linköping, Sweden
- *Correspondence: Björn Gerdle, Pain and Rehabilitation Centre and Department of Health, Medicine and Caring Sciences, Linköping University, SE-581 85 Linköping, Sweden (e-mail: )
| | - Karin Wåhlén
- Pain and Rehabilitation Centre, and Department of Health, Medicine, and Caring Sciences, Linköping University, Linköping, Sweden
| | - Torsten Gordh
- Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
| | - Emmanuel Bäckryd
- Pain and Rehabilitation Centre, and Department of Health, Medicine, and Caring Sciences, Linköping University, Linköping, Sweden
| | - Anders Carlsson
- Pain and Rehabilitation Centre, and Department of Health, Medicine, and Caring Sciences, Linköping University, Linköping, Sweden
| | - Bijar Ghafouri
- Pain and Rehabilitation Centre, and Department of Health, Medicine, and Caring Sciences, Linköping University, Linköping, Sweden
| |
Collapse
|
9
|
Caliskan A, Crouch SAW, Giddins S, Dandekar T, Dangwal S. Progeria and Aging-Omics Based Comparative Analysis. Biomedicines 2022; 10:2440. [PMID: 36289702 PMCID: PMC9599154 DOI: 10.3390/biomedicines10102440] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 09/09/2022] [Accepted: 09/21/2022] [Indexed: 10/21/2023] Open
Abstract
Since ancient times aging has also been regarded as a disease, and humankind has always strived to extend the natural lifespan. Analyzing the genes involved in aging and disease allows for finding important indicators and biological markers for pathologies and possible therapeutic targets. An example of the use of omics technologies is the research regarding aging and the rare and fatal premature aging syndrome progeria (Hutchinson-Gilford progeria syndrome, HGPS). In our study, we focused on the in silico analysis of differentially expressed genes (DEGs) in progeria and aging, using a publicly available RNA-Seq dataset (GEO dataset GSE113957) and a variety of bioinformatics tools. Despite the GSE113957 RNA-Seq dataset being well-known and frequently analyzed, the RNA-Seq data shared by Fleischer et al. is far from exhausted and reusing and repurposing the data still reveals new insights. By analyzing the literature citing the use of the dataset and subsequently conducting a comparative analysis comparing the RNA-Seq data analyses of different subsets of the dataset (healthy children, nonagenarians and progeria patients), we identified several genes involved in both natural aging and progeria (KRT8, KRT18, ACKR4, CCL2, UCP2, ADAMTS15, ACTN4P1, WNT16, IGFBP2). Further analyzing these genes and the pathways involved indicated their possible roles in aging, suggesting the need for further in vitro and in vivo research. In this paper, we (1) compare "normal aging" (nonagenarians vs. healthy children) and progeria (HGPS patients vs. healthy children), (2) enlist genes possibly involved in both the natural aging process and progeria, including the first mention of IGFBP2 in progeria, (3) predict miRNAs and interactomes for WNT16 (hsa-mir-181a-5p), UCP2 (hsa-mir-26a-5p and hsa-mir-124-3p), and IGFBP2 (hsa-mir-124-3p, hsa-mir-126-3p, and hsa-mir-27b-3p), (4) demonstrate the compatibility of well-established R packages for RNA-Seq analysis for researchers interested but not yet familiar with this kind of analysis, and (5) present comparative proteomics analyses to show an association between our RNA-Seq data analyses and corresponding changes in protein expression.
Collapse
Affiliation(s)
- Aylin Caliskan
- Department of Bioinformatics, Biocenter, University of Würzburg, 97074 Würzburg, Germany
| | - Samantha A. W. Crouch
- Department of Bioinformatics, Biocenter, University of Würzburg, 97074 Würzburg, Germany
| | - Sara Giddins
- Department of Bioinformatics, Biocenter, University of Würzburg, 97074 Würzburg, Germany
| | - Thomas Dandekar
- Department of Bioinformatics, Biocenter, University of Würzburg, 97074 Würzburg, Germany
| | - Seema Dangwal
- Stanford Cardiovascular Institute, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| |
Collapse
|
10
|
Lilley LM, Sanche S, Moore SC, Salemi MR, Vu D, Iyer S, Hengartner NW, Mukundan H. Methods to capture proteomic and metabolomic signatures from cerebrospinal fluid and serum of healthy individuals. Sci Rep 2022; 12:13339. [PMID: 35922450 PMCID: PMC9349260 DOI: 10.1038/s41598-022-16598-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Accepted: 05/17/2022] [Indexed: 11/20/2022] Open
Abstract
Discovery of reliable signatures for the empirical diagnosis of neurological diseases-both infectious and non-infectious-remains unrealized. One of the primary challenges encountered in such studies is the lack of a comprehensive database representative of a signature background that exists in healthy individuals, and against which an aberrant event can be assessed. For neurological insults and injuries, it is important to understand the normal profile in the neuronal (cerebrospinal fluid) and systemic fluids (e.g., blood). Here, we present the first comparative multi-omic human database of signatures derived from a population of 30 individuals (15 males, 15 females, 23-74 years) of serum and cerebrospinal fluid. In addition to empirical signatures, we also assigned common pathways between serum and CSF. Together, our findings provide a cohort against which aberrant signature profiles in individuals with neurological injuries/disease can be assessed-providing a pathway for comprehensive diagnostics and therapeutics discovery.
Collapse
Affiliation(s)
- Laura M Lilley
- Los Alamos National Laboratory, P.O. Box 1663, Los Alamos, NM, 87545, USA
| | - Steven Sanche
- Los Alamos National Laboratory, P.O. Box 1663, Los Alamos, NM, 87545, USA
| | - Shepard C Moore
- Los Alamos National Laboratory, P.O. Box 1663, Los Alamos, NM, 87545, USA
| | - Michelle R Salemi
- Genome Center, Proteomics Core Facility, University of California, Davis, CA, 95616, USA
| | - Dung Vu
- Los Alamos National Laboratory, P.O. Box 1663, Los Alamos, NM, 87545, USA
| | - Srinivas Iyer
- Los Alamos National Laboratory, P.O. Box 1663, Los Alamos, NM, 87545, USA
| | | | - Harshini Mukundan
- Los Alamos National Laboratory, P.O. Box 1663, Los Alamos, NM, 87545, USA.
| |
Collapse
|
11
|
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.
Collapse
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
| |
Collapse
|
12
|
Metabolomic Analysis of Serum and Tear Samples from Patients with Obesity and Type 2 Diabetes Mellitus. Int J Mol Sci 2022; 23:ijms23094534. [PMID: 35562924 PMCID: PMC9105607 DOI: 10.3390/ijms23094534] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 04/13/2022] [Accepted: 04/14/2022] [Indexed: 12/14/2022] Open
Abstract
Metabolomics strategies are widely used to examine obesity and type 2 diabetes (T2D). Patients with obesity (n = 31) or T2D (n = 26) and sex- and age-matched controls (n = 28) were recruited, and serum and tear samples were collected. The concentration of 23 amino acids and 10 biogenic amines in serum and tear samples was analyzed. Statistical analysis and Pearson correlation analysis along with network analysis were carried out. Compared to controls, changes in the level of 6 analytes in the obese group and of 10 analytes in the T2D group were statistically significant. For obesity, the energy generation, while for T2D, the involvement of NO synthesis and its relation to insulin signaling and inflammation, were characteristic. We found that BCAA and glutamine metabolism, urea cycle, and beta-oxidation make up crucial parts of the metabolic changes in T2D. According to our data, the retromer-mediated retrograde transport, the ethanolamine metabolism, and, consequently, the endocannabinoid signaling and phospholipid metabolism were characteristic of both conditions and can be relevant pathways to understanding and treating insulin resistance. By providing potential therapeutic targets and new starting points for mechanistic studies, our results emphasize the importance of complex data analysis procedures to better understand the pathomechanism of obesity and diabetes.
Collapse
|
13
|
Dubois E, Galindo AN, Dayon L, Cominetti O. Assessing normalization methods in mass spectrometry-based proteome profiling of clinical samples. Biosystems 2022; 215-216:104661. [PMID: 35247480 DOI: 10.1016/j.biosystems.2022.104661] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 02/21/2022] [Accepted: 02/28/2022] [Indexed: 12/31/2022]
Abstract
BACKGROUND Large-scale proteomic studies have to deal with unwanted variability, especially when samples originate from different centers and multiple analytical batches are needed. Such variability is typically added throughout all the steps of a clinical research study, from human biological sample collection and storage, sample preparation, spectral data acquisition, to peptide and protein quantification. In order to remove such diverse and unwanted variability, normalization of the protein data is performed. There have been already several published reviews comparing normalization methods in the -omics field, but reports focusing on proteomic data generated with mass spectrometry (MS) are much fewer. Additionally, most of these reports have only dealt with small datasets. RESULTS As a case study, here we focused on the normalization of a large MS-based proteomic dataset obtained from an overweight and obese pan-European cohort, where different normalization methods were evaluated, namely: center standardize, quantile protein, quantile sample, global standardization, ComBat, median centering, mean centering, single standard and removal of unwanted variation (RUV); some of these are generic normalization methods while others have been specifically created to deal with genomic or metabolomic data. We checked how relationships between proteins and clinical variables (e.g., gender, levels of triglycerides or cholesterol) were improved after normalizing the data with the different methods. CONCLUSIONS Some normalization methods were better adapted for this particular large-scale shotgun proteomic dataset of human plasma samples labeled with isobaric tags and analyzed with liquid chromatography-tandem MS. In particular, quantile sample normalization, RUV, mean and median centering showed very good performance, while quantile protein normalization provided worse results than those obtained with unnormalized data.
Collapse
Affiliation(s)
- Etienne Dubois
- Nestlé Institute of Food Safety & Analytical Sciences, Nestlé Research, EPFL Innovation Park, 1015, Lausanne, Switzerland
| | - Antonio Núñez Galindo
- Nestlé Institute of Food Safety & Analytical Sciences, Nestlé Research, EPFL Innovation Park, 1015, Lausanne, Switzerland
| | - Loïc Dayon
- Nestlé Institute of Food Safety & Analytical Sciences, Nestlé Research, EPFL Innovation Park, 1015, Lausanne, Switzerland; Chemistry and Chemical Engineering Section, School of Basic Sciences, Ecole Polytechnique Fédérale de Lausanne, 1015, Lausanne, Switzerland
| | - Ornella Cominetti
- Nestlé Institute of Food Safety & Analytical Sciences, Nestlé Research, EPFL Innovation Park, 1015, Lausanne, Switzerland.
| |
Collapse
|
14
|
Liu K, Salvati A, Sabirsh A. Physiology, pathology and the biomolecular corona: the confounding factors in nanomedicine design. NANOSCALE 2022; 14:2136-2154. [PMID: 35103268 DOI: 10.1039/d1nr08101b] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The biomolecular corona that forms on nanomedicines in different physiological and pathological environments confers a new biological identity. How the recipient biological system's state can potentially affect nanomedicine corona formation, and how this can be modulated, remains obscure. With this perspective, this review summarizes the current knowledge about the content of biological fluids in various compartments and how they can be affected by pathological states, thus impacting biomolecular corona formation. The content of representative biological fluids is explored, and the urgency of integrating corona formation, as an essential component of nanomedicine designs for effective cargo delivery, is highlighted.
Collapse
Affiliation(s)
- Kai Liu
- Advanced Drug Delivery, Pharmaceutical Sciences, R&D, AstraZeneca, Gothenburg, Sweden.
| | - Anna Salvati
- Department of Nanomedicine & Drug Targeting, Groningen Research Institute of Pharmacy, University of Groningen, Groningen 9713AV, The Netherlands
| | - Alan Sabirsh
- Advanced Drug Delivery, Pharmaceutical Sciences, R&D, AstraZeneca, Gothenburg, Sweden.
| |
Collapse
|
15
|
Minamijima Y, Tozaki T, Kuroda T, Urayama S, Nomura M, Yamamoto K. A comprehensive and comparative proteomic analysis of horse serum proteins in colitis. Equine Vet J 2022; 54:1039-1046. [PMID: 35000251 DOI: 10.1111/evj.13554] [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: 09/11/2020] [Revised: 12/14/2021] [Accepted: 12/30/2021] [Indexed: 11/29/2022]
Abstract
BACKGROUND Equine colitis is a diarrhoeal disease caused by inflammation of the large bowel and can potentially be life-threatening due to its rapid progression. Pathogenesis is multifactorial and pathophysiology is highly complicated, therefore, reliable diagnostic biomarkers are needed in the veterinary field. OBJECTIVE Serum is one of the most commonly used diagnostic tools in equine clinical investigation. To discover diagnostic or prognostic protein markers for colitis in horse serum, comprehensive and comparative proteomic analysis was conducted using liquid chromatography-tandem mass spectrometry (LC-MS/MS). STUDY DESIGN Case-control study. METHODS Serum samples were collected from 36 healthy Thoroughbreds and 12 Thoroughbreds with colitis. Serum from each horse suffering from colitis was collected daily until death or recovery. Collected sera were digested with trypsin. Peptides obtained from serum proteins were measured by Q-Exactive HF Orbitrap mass spectrometer. The identification and quantification of peptides were performed using Proteome Discoverer version 2.2. RESULTS On day 1 of treatment, eight proteins in the colitis group were upregulated (P < .05, more than a twofold change) compared with the healthy group. Among the eight proteins, biliverdin reductase B was significantly upregulated (P < .05) in the non-survivor group (n = 5) compared with the survivor group (n = 7). On the last day of the treatment, haemoglobin subunit alpha, clusterin, glyceraldehyde-3-phosphate dehydrogenase, lipopolysaccharide-binding protein, and biliverdin reductase B showed significant increases (P < .05) in the non-survivor group. MAIN LIMITATIONS The number of the identified proteins is limited due to the existence of abundant proteins. CONCLUSIONS Measuring the changes of these proteins together may enable a potential prognosis or early diagnosis of horses suffering from colitis.
Collapse
Affiliation(s)
- Yohei Minamijima
- Laboratory of Racing Chemistry, Utsunomiya, Japan.,Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Japan
| | | | - Taisuke Kuroda
- Equine Research Institute, Japan Racing Association, Shimotsuke, Japan
| | - Shuntaro Urayama
- Racehorse Hospital, Miho Training Center, Japan Racing Association, Inashiki, Japan
| | - Motoi Nomura
- Equine Hospital, Horseracing School, Japan Racing Association, Shiroi, Japan
| | - Kazuo Yamamoto
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Japan
| |
Collapse
|
16
|
Yousri NA, Engelke R, Sarwath H, McKinlay RD, Simper SC, Adams TD, Schmidt F, Suhre K, Hunt SC. Proteome-wide associations with short- and long-term weight loss and regain after Roux-en-Y gastric bypass surgery. Obesity (Silver Spring) 2022; 30:129-141. [PMID: 34796696 PMCID: PMC8692443 DOI: 10.1002/oby.23303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 07/26/2021] [Accepted: 08/25/2021] [Indexed: 12/05/2022]
Abstract
OBJECTIVE Gastric bypass surgery results in long-term weight loss. Small studies have examined protein changes during rapid weight loss (up to 1 or 2 years post surgery). This study tested whether short-term changes were maintained after 12 years. METHODS A 12-year follow-up, protein-wide association study of 1,297 SomaLogic aptamer-based plasma proteins compared short- (2-year) and long-term (12-year) protein changes in 234 individuals who had gastric bypass surgery with 144 nonintervened individuals with severe obesity. RESULTS There were 51 replicated 12-year protein changes that differed between the surgery and nonsurgery groups. Adjusting for change in BMI, only 12 proteins remained significant, suggesting that BMI change was the primary reason for most protein changes and not non-BMI-related surgical effects. Protein changes were related to BMI changes during both weight-loss and weight-regain periods. The significant proteins were associated primarily with lipid, uric acid, or resting energy expenditure clinical variables and metabolic pathways. Eight protein changes were associated with 12-year diabetes remission, including apolipoprotein M, sex hormone binding globulin, and adiponectin (p < 3.5 × 10-5 ). CONCLUSIONS This study showed that most short-term postsurgical changes in proteins were maintained at 12 years. Systemic protection pathways, including inflammation, complement, lipid, and adipocyte pathways, were related to the long-term benefits of gastric bypass surgery.
Collapse
Affiliation(s)
- Noha A. Yousri
- Department of Genetic MedicineWeill Cornell MedicineDohaQatar
- Computer and Systems EngineeringAlexandria UniversityAlexandriaEgypt
| | | | | | | | | | - Ted D. Adams
- Intermountain Live Well CenterIntermountain HealthcareSalt Lake CityUtahUSA
- Department of Internal MedicineUniversity of UtahSalt Lake CityUtahUSA
| | - Frank Schmidt
- Proteomics CoreWeill Cornell MedicineDohaQatar
- Department of BiochemistryWeill Cornell MedicineDohaQatar
| | - Karsten Suhre
- Department of Physiology and BiophysicsWeill Cornell MedicineDohaQatar
| | - Steven C. Hunt
- Department of Genetic MedicineWeill Cornell MedicineDohaQatar
- Department of Internal MedicineUniversity of UtahSalt Lake CityUtahUSA
| |
Collapse
|
17
|
Goudswaard LJ, Bell JA, Hughes DA, Corbin LJ, Walter K, Davey Smith G, Soranzo N, Danesh J, Di Angelantonio E, Ouwehand WH, Watkins NA, Roberts DJ, Butterworth AS, Hers I, Timpson NJ. Effects of adiposity on the human plasma proteome: observational and Mendelian randomisation estimates. Int J Obes (Lond) 2021; 45:2221-2229. [PMID: 34226637 PMCID: PMC8455324 DOI: 10.1038/s41366-021-00896-1] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 06/24/2021] [Accepted: 06/24/2021] [Indexed: 02/06/2023]
Abstract
BACKGROUND Variation in adiposity is associated with cardiometabolic disease outcomes, but mechanisms leading from this exposure to disease are unclear. This study aimed to estimate effects of body mass index (BMI) on an extensive set of circulating proteins. METHODS We used SomaLogic proteomic data from up to 2737 healthy participants from the INTERVAL study. Associations between self-reported BMI and 3622 unique plasma proteins were explored using linear regression. These were complemented by Mendelian randomisation (MR) analyses using a genetic risk score (GRS) comprised of 654 BMI-associated polymorphisms from a recent genome-wide association study (GWAS) of adult BMI. A disease enrichment analysis was performed using DAVID Bioinformatics 6.8 for proteins which were altered by BMI. RESULTS Observationally, BMI was associated with 1576 proteins (P < 1.4 × 10-5), with particularly strong evidence for a positive association with leptin and fatty acid-binding protein-4 (FABP4), and a negative association with sex hormone-binding globulin (SHBG). Observational estimates were likely confounded, but the GRS for BMI did not associate with measured confounders. MR analyses provided evidence for a causal relationship between BMI and eight proteins including leptin (0.63 standard deviation (SD) per SD BMI, 95% CI 0.48-0.79, P = 1.6 × 10-15), FABP4 (0.64 SD per SD BMI, 95% CI 0.46-0.83, P = 6.7 × 10-12) and SHBG (-0.45 SD per SD BMI, 95% CI -0.65 to -0.25, P = 1.4 × 10-5). There was agreement in the magnitude of observational and MR estimates (R2 = 0.33) and evidence that proteins most strongly altered by BMI were enriched for genes involved in cardiovascular disease. CONCLUSIONS This study provides evidence for a broad impact of adiposity on the human proteome. Proteins strongly altered by BMI include those involved in regulating appetite, sex hormones and inflammation; such proteins are also enriched for cardiovascular disease-related genes. Altogether, results help focus attention onto new proteomic signatures of obesity-related disease.
Collapse
Affiliation(s)
- Lucy J Goudswaard
- Medical Research Council (MRC) Integrative Epidemiology Unit at the University of Bristol, Bristol, UK.
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
- School of Physiology, Pharmacology and Neuroscience, University of Bristol, Bristol, UK.
- Bristol Heart Institute, Bristol, UK.
| | - Joshua A Bell
- Medical Research Council (MRC) Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - David A Hughes
- Medical Research Council (MRC) Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Laura J Corbin
- Medical Research Council (MRC) Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | | | - George Davey Smith
- Medical Research Council (MRC) Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Nicole Soranzo
- Wellcome Sanger Institute, Hinxton, UK
- Department of Haematology, School of Clinical Medicine, University of Cambridge, Cambridge, UK
- NIHR Blood and Transplant Research Unit in Donor Health and Genomics, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - John Danesh
- Wellcome Sanger Institute, Hinxton, UK
- NIHR Blood and Transplant Research Unit in Donor Health and Genomics, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- NIHR Cambridge Biomedical Research Centre, University of Cambridge and Cambridge University Hospitals, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
| | - Emanuele Di Angelantonio
- NIHR Blood and Transplant Research Unit in Donor Health and Genomics, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- NIHR Cambridge Biomedical Research Centre, University of Cambridge and Cambridge University Hospitals, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
| | - Willem H Ouwehand
- Wellcome Sanger Institute, Hinxton, UK
- Department of Haematology, School of Clinical Medicine, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- NHS Blood and Transplant, Cambridge Biomedical Campus, Cambridge, UK
| | | | - David J Roberts
- NIHR Blood and Transplant Research Unit in Donor Health and Genomics, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- NHS Blood and Transplant-Oxford Centre, Level 2, John Radcliffe Hospital, Oxford, UK
- Radcliffe Department of Medicine, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - Adam S Butterworth
- NIHR Blood and Transplant Research Unit in Donor Health and Genomics, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- NIHR Cambridge Biomedical Research Centre, University of Cambridge and Cambridge University Hospitals, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
| | - Ingeborg Hers
- School of Physiology, Pharmacology and Neuroscience, University of Bristol, Bristol, UK
- Bristol Heart Institute, Bristol, UK
| | - Nicholas J Timpson
- Medical Research Council (MRC) Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| |
Collapse
|
18
|
Zheng R, Govorukhina N, Arrey TN, Pynn C, van der Zee A, Marko-Varga G, Bischoff R, Boychenko A. Online-2D NanoLC-MS for Crude Serum Proteome Profiling: Assessing Sample Preparation Impact on Proteome Composition. Anal Chem 2021; 93:9663-9668. [PMID: 34236853 DOI: 10.1021/acs.analchem.1c01291] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Although current LC-MS technology permits scientists to efficiently screen clinical samples in translational research, e.g., steroids, biogenic amines, and even plasma or serum proteomes, in a daily routine, maintaining the balance between throughput and analytical depth is still a limiting factor. A typical approach to enhance the proteome depth is employing offline two-dimensional (2D) fractionation techniques before reversed-phase nanoLC-MS/MS analysis (1D-nanoLC-MS). These additional sample preparation steps usually require extensive sample manipulation, which could result in sample alteration and sample loss. Here, we present and compare 1D-nanoLC-MS with an automated online-2D high-pH RP × low pH RP separation method for deep proteome profiling using a nanoLC system coupled to a high-resolution accurate-mass mass spectrometer. The proof-of-principle study permitted the identification of ca. 500 proteins with ∼10,000 peptides in 15 enzymatically digested crude serum samples collected from healthy donors in 3 laboratories across Europe. The developed method identified 60% more peptides in comparison with conventional 1D nanoLC-MS/MS analysis with ca. 4 times lower throughput while retaining the quantitative information. Serum sample preparation related changes were revealed by applying unsupervised classification techniques and, therefore, must be taken into account while planning multicentric biomarker discovery and validation studies. Overall, this novel method reduces sample complexity and boosts the number of peptide and protein identifications without the need for extra sample handling procedures for samples equivalent to less than 1 μL of blood, which expands the space for potential biomarker discovery by looking deeper into the composition of biofluids.
Collapse
Affiliation(s)
- Runsheng Zheng
- Thermo Fisher Scientific, Dornierstrasse 4, 82110 Germering, Germany
| | - Natalia Govorukhina
- Department of Analytical Biochemistry, University of Groningen, 9713 AV Groningen, The Netherlands
| | - Tabiwang N Arrey
- Thermo Fisher Scientific, Hanna-Kunath-Straße 11, 28199 Bremen, Germany
| | - Christopher Pynn
- Thermo Fisher Scientific, Dornierstrasse 4, 82110 Germering, Germany
| | - Ate van der Zee
- University Medical Centre Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands
| | - György Marko-Varga
- Clinical Protein Science and Imaging, Lund University, Box 117, S-22100 Lund, Sweden
| | - Rainer Bischoff
- Department of Analytical Biochemistry, University of Groningen, 9713 AV Groningen, The Netherlands
| | | |
Collapse
|
19
|
Dayon L, Macron C, Lahrichi S, Núñez Galindo A, Affolter M. Proteomics of Human Milk: Definition of a Discovery Workflow for Clinical Research Studies. J Proteome Res 2021; 20:2283-2290. [PMID: 33769819 DOI: 10.1021/acs.jproteome.0c00816] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Milk is a complex biological fluid composed mainly of water, carbohydrates, lipids, proteins, and diverse bioactive factors. Human milk represents a unique tailored source of nutrients that adapts during lactation to the specific needs of the developing infant. Proteins in milk have been studied for decades, and proteomics, peptidomics, and glycoproteomics are the main approaches previously deployed to decipher the proteome of human milk. In the present work, we aimed at implementing a highly automated pipeline for the proteomic analysis of human milk with liquid chromatography mass spectrometry (MS). Commercial human milk samples were used to evaluate and optimize workflows. Centrifugation for defatting milk samples was assessed before and after reduction, alkylation, and enzymatic digestion of proteins, without and with presence of surfactants. Skimmed milk samples were analyzed using isobaric labeling-based quantitative MS on an Orbitrap Tribrid mass spectrometer. Sample fractionation using isoelectric focusing was also evaluated to more deeply profile the human milk proteome. Finally, the most appropriate workflow was transferred to a liquid handling workstation for automated sample preparation. In conclusion, we have defined and describe herein an efficient highly automated proteomic workflow for human milk sample analysis. It is compatible with clinical research, possibly allowing the analysis of sufficiently large cohorts of samples.
Collapse
Affiliation(s)
- Loïc Dayon
- Proteomics, Nestlé Institute of Food Safety & Analytical Sciences, Nestlé Research, Lausanne 1015, Switzerland.,Institut des Sciences et Ingénierie Chimiques, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne 1015, Switzerland
| | - Charlotte Macron
- Proteomics, Nestlé Institute of Food Safety & Analytical Sciences, Nestlé Research, Lausanne 1015, Switzerland
| | - Sabine Lahrichi
- Proteomics, Nestlé Institute of Food Safety & Analytical Sciences, Nestlé Research, Lausanne 1015, Switzerland
| | - Antonio Núñez Galindo
- Proteomics, Nestlé Institute of Food Safety & Analytical Sciences, Nestlé Research, Lausanne 1015, Switzerland
| | - Michael Affolter
- Proteomics, Nestlé Institute of Food Safety & Analytical Sciences, Nestlé Research, Lausanne 1015, Switzerland
| |
Collapse
|
20
|
Does Proteomic Mirror Reflect Clinical Characteristics of Obesity? J Pers Med 2021; 11:jpm11020064. [PMID: 33494491 PMCID: PMC7912072 DOI: 10.3390/jpm11020064] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Revised: 01/14/2021] [Accepted: 01/15/2021] [Indexed: 12/14/2022] Open
Abstract
Obesity is a frightening chronic disease, which has tripled since 1975. It is not expected to slow down staying one of the leading cases of preventable death and resulting in an increased clinical and economic burden. Poor lifestyle choices and excessive intake of “cheap calories” are major contributors to obesity, triggering type 2 diabetes, cardiovascular diseases, and other comorbidities. Understanding the molecular mechanisms responsible for development of obesity is essential as it might result in the introducing of anti-obesity targets and early-stage obesity biomarkers, allowing the distinction between metabolic syndromes. The complex nature of this disease, coupled with the phenomenon of metabolically healthy obesity, inspired us to perform data-centric, hypothesis-generating pilot research, aimed to find correlations between parameters of classic clinical blood tests and proteomic profiles of 104 lean and obese subjects. As the result, we assembled patterns of proteins, which presence or absence allows predicting the weight of the patient fairly well. We believe that such proteomic patterns with high prediction power should facilitate the translation of potential candidates into biomarkers of clinical use for early-stage stratification of obesity therapy.
Collapse
|
21
|
Aleksandrova K, Egea Rodrigues C, Floegel A, Ahrens W. Omics Biomarkers in Obesity: Novel Etiological Insights and Targets for Precision Prevention. Curr Obes Rep 2020; 9:219-230. [PMID: 32594318 PMCID: PMC7447658 DOI: 10.1007/s13679-020-00393-y] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
PURPOSE OF REVIEW Omics-based technologies were suggested to provide an advanced understanding of obesity etiology and its metabolic consequences. This review highlights the recent developments in "omics"-based research aimed to identify obesity-related biomarkers. RECENT FINDINGS Recent advances in obesity and metabolism research increasingly rely on new technologies to identify mechanisms in the development of obesity using various "omics" platforms. Genetic and epigenetic biomarkers that translate into changes in transcriptome, proteome, and metabolome could serve as targets for obesity prevention. Despite a number of promising candidate biomarkers, there is an increased demand for larger prospective cohort studies to validate findings and determine biomarker reproducibility before they can find applications in primary care and public health. "Omics" biomarkers have advanced our knowledge on the etiology of obesity and its links with chronic diseases. They bring substantial promise in identifying effective public health strategies that pave the way towards patient stratification and precision prevention.
Collapse
Affiliation(s)
- Krasimira Aleksandrova
- Nutrition, Immunity and Metabolism Senior Scientist Group, Department of Nutrition and Gerontology, German Institute of Human Nutrition Potsdam-Rehbruecke (DIfE), Nuthetal, Germany.
- Institute of Nutritional Science, University of Potsdam, Potsdam, Germany.
| | - Caue Egea Rodrigues
- Nutrition, Immunity and Metabolism Senior Scientist Group, Department of Nutrition and Gerontology, German Institute of Human Nutrition Potsdam-Rehbruecke (DIfE), Nuthetal, Germany
- Institute of Nutritional Science, University of Potsdam, Potsdam, Germany
| | - Anna Floegel
- Department of Epidemiological Methods and Etiological Research, Leibniz Institute for Prevention Research and Epidemiology-BIPS, Bremen, Germany
| | - Wolfgang Ahrens
- Department of Epidemiological Methods and Etiological Research, Leibniz Institute for Prevention Research and Epidemiology-BIPS, Bremen, Germany
- Faculty of Mathematics and Computer Science, University of Bremen, Bremen, Germany
| |
Collapse
|
22
|
Zhu T, Zhu Y, Xuan Y, Gao H, Cai X, Piersma SR, Pham TV, Schelfhorst T, Haas RRGD, Bijnsdorp IV, Sun R, Yue L, Ruan G, Zhang Q, Hu M, Zhou Y, Van Houdt WJ, Le Large TYS, Cloos J, Wojtuszkiewicz A, Koppers-Lalic D, Böttger F, Scheepbouwer C, Brakenhoff RH, van Leenders GJLH, Ijzermans JNM, Martens JWM, Steenbergen RDM, Grieken NC, Selvarajan S, Mantoo S, Lee SS, Yeow SJY, Alkaff SMF, Xiang N, Sun Y, Yi X, Dai S, Liu W, Lu T, Wu Z, Liang X, Wang M, Shao Y, Zheng X, Xu K, Yang Q, Meng Y, Lu C, Zhu J, Zheng J, Wang B, Lou S, Dai Y, Xu C, Yu C, Ying H, Lim TK, Wu J, Gao X, Luan Z, Teng X, Wu P, Huang S, Tao Z, Iyer NG, Zhou S, Shao W, Lam H, Ma D, Ji J, Kon OL, Zheng S, Aebersold R, Jimenez CR, Guo T. DPHL: A DIA Pan-human Protein Mass Spectrometry Library for Robust Biomarker Discovery. GENOMICS PROTEOMICS & BIOINFORMATICS 2020; 18:104-119. [PMID: 32795611 PMCID: PMC7646093 DOI: 10.1016/j.gpb.2019.11.008] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/08/2019] [Revised: 09/03/2019] [Accepted: 11/08/2019] [Indexed: 12/21/2022]
Abstract
To address the increasing need for detecting and validating protein biomarkers in clinical specimens, mass spectrometry (MS)-based targeted proteomic techniques, including the selected reaction monitoring (SRM), parallel reaction monitoring (PRM), and massively parallel data-independent acquisition (DIA), have been developed. For optimal performance, they require the fragment ion spectra of targeted peptides as prior knowledge. In this report, we describe a MS pipeline and spectral resource to support targeted proteomics studies for human tissue samples. To build the spectral resource, we integrated common open-source MS computational tools to assemble a freely accessible computational workflow based on Docker. We then applied the workflow to generate DPHL, a comprehensive DIA pan-human library, from 1096 data-dependent acquisition (DDA) MS raw files for 16 types of cancer samples. This extensive spectral resource was then applied to a proteomic study of 17 prostate cancer (PCa) patients. Thereafter, PRM validation was applied to a larger study of 57 PCa patients and the differential expression of three proteins in prostate tumor was validated. As a second application, the DPHL spectral resource was applied to a study consisting of plasma samples from 19 diffuse large B cell lymphoma (DLBCL) patients and 18 healthy control subjects. Differentially expressed proteins between DLBCL patients and healthy control subjects were detected by DIA-MS and confirmed by PRM. These data demonstrate that the DPHL supports DIA and PRM MS pipelines for robust protein biomarker discovery. DPHL is freely accessible at https://www.iprox.org/page/project.html?id=IPX0001400000.
Collapse
Affiliation(s)
- Tiansheng Zhu
- Zhejiang Provincial Laboratory of Life Sciences and Biomedicine, Westlake University, Hangzhou 310024, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou 310024, China; School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai 200433, China
| | - Yi Zhu
- Zhejiang Provincial Laboratory of Life Sciences and Biomedicine, Westlake University, Hangzhou 310024, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou 310024, China.
| | - Yue Xuan
- Thermo Fisher Scientific (BREMEN) GmbH, Bremen 28195, Germany
| | - Huanhuan Gao
- Zhejiang Provincial Laboratory of Life Sciences and Biomedicine, Westlake University, Hangzhou 310024, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou 310024, China
| | - Xue Cai
- Zhejiang Provincial Laboratory of Life Sciences and Biomedicine, Westlake University, Hangzhou 310024, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou 310024, China
| | - Sander R Piersma
- OncoProteomics Laboratory, Department of Medical Oncology, VU University Medical Center, VU University, Amsterdam 1011, The Netherlands
| | - Thang V Pham
- OncoProteomics Laboratory, Department of Medical Oncology, VU University Medical Center, VU University, Amsterdam 1011, The Netherlands
| | - Tim Schelfhorst
- OncoProteomics Laboratory, Department of Medical Oncology, VU University Medical Center, VU University, Amsterdam 1011, The Netherlands
| | - Richard R G D Haas
- OncoProteomics Laboratory, Department of Medical Oncology, VU University Medical Center, VU University, Amsterdam 1011, The Netherlands
| | - Irene V Bijnsdorp
- OncoProteomics Laboratory, Department of Medical Oncology, VU University Medical Center, VU University, Amsterdam 1011, The Netherlands; Amsterdam UMC, Vrije Universiteit Amsterdam, Urology, Cancer Center Amsterdam, Amsterdam 1011, The Netherlands
| | - Rui Sun
- Zhejiang Provincial Laboratory of Life Sciences and Biomedicine, Westlake University, Hangzhou 310024, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou 310024, China
| | - Liang Yue
- Zhejiang Provincial Laboratory of Life Sciences and Biomedicine, Westlake University, Hangzhou 310024, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou 310024, China
| | - Guan Ruan
- Zhejiang Provincial Laboratory of Life Sciences and Biomedicine, Westlake University, Hangzhou 310024, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou 310024, China
| | - Qiushi Zhang
- Zhejiang Provincial Laboratory of Life Sciences and Biomedicine, Westlake University, Hangzhou 310024, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou 310024, China
| | - Mo Hu
- Thermo Fisher Scientific, Shanghai 201206, China
| | - Yue Zhou
- Thermo Fisher Scientific, Shanghai 201206, China
| | - Winan J Van Houdt
- The Netherlands Cancer Institute, Surgical Oncology, Amsterdam 1011, The Netherlands
| | - Tessa Y S Le Large
- Amsterdam UMC, Vrije Universiteit Amsterdam, Surgery, Cancer Center Amsterdam, Amsterdam 1011, The Netherlands
| | - Jacqueline Cloos
- Amsterdam UMC, Vrije Universiteit Amsterdam, Pediatric Oncology/Hematology, Cancer Center Amsterdam, Amsterdam 1011, The Netherlands
| | - Anna Wojtuszkiewicz
- Amsterdam UMC, Vrije Universiteit Amsterdam, Pediatric Oncology/Hematology, Cancer Center Amsterdam, Amsterdam 1011, The Netherlands
| | - Danijela Koppers-Lalic
- Amsterdam UMC, Vrije Universiteit Amsterdam, Hematology, Cancer Center Amsterdam, Amsterdam 1011, The Netherlands
| | - Franziska Böttger
- Amsterdam UMC, Vrije Universiteit Amsterdam, Medical Oncology, Cancer Center Amsterdam, Amsterdam 1011, The Netherlands
| | - Chantal Scheepbouwer
- Amsterdam UMC, Vrije Universiteit Amsterdam, Neurosurgery, Cancer Center Amsterdam, Amsterdam 1011, The Netherlands; Amsterdam UMC, Vrije Universiteit Amsterdam, Pathology, Cancer Center Amsterdam, Amsterdam 1011, The Netherlands
| | - Ruud H Brakenhoff
- Amsterdam UMC, Vrije Universiteit Amsterdam, Otolaryngology/Head and Neck Surgery, Cancer Center Amsterdam, Amsterdam 1011, The Netherlands
| | | | - Jan N M Ijzermans
- Erasmus MC University Medical Center, Surgery, Rotterdam 1016LV, The Netherlands
| | - John W M Martens
- Erasmus MC University Medical Center, Medical Oncology, Rotterdam 1016LV, The Netherlands
| | - Renske D M Steenbergen
- Amsterdam UMC, Vrije Universiteit Amsterdam, Pathology, Cancer Center Amsterdam, Amsterdam 1011, The Netherlands
| | - Nicole C Grieken
- Amsterdam UMC, Vrije Universiteit Amsterdam, Pathology, Cancer Center Amsterdam, Amsterdam 1011, The Netherlands
| | | | - Sangeeta Mantoo
- Department of Anatomical Pathology, Singapore General Hospital, Singapore 169608, Singapore
| | - Sze S Lee
- Division of Medical Sciences, National Cancer Centre Singapore, Singapore 169608, Singapore
| | - Serene J Y Yeow
- Division of Medical Sciences, National Cancer Centre Singapore, Singapore 169608, Singapore
| | - Syed M F Alkaff
- Department of Anatomical Pathology, Singapore General Hospital, Singapore 169608, Singapore
| | - Nan Xiang
- Zhejiang Provincial Laboratory of Life Sciences and Biomedicine, Westlake University, Hangzhou 310024, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou 310024, China
| | - Yaoting Sun
- Zhejiang Provincial Laboratory of Life Sciences and Biomedicine, Westlake University, Hangzhou 310024, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou 310024, China
| | - Xiao Yi
- Zhejiang Provincial Laboratory of Life Sciences and Biomedicine, Westlake University, Hangzhou 310024, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou 310024, China
| | - Shaozheng Dai
- School of Computer Science and Engineering, Beihang University, Beijing 100191, China
| | - Wei Liu
- Zhejiang Provincial Laboratory of Life Sciences and Biomedicine, Westlake University, Hangzhou 310024, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou 310024, China
| | - Tian Lu
- Zhejiang Provincial Laboratory of Life Sciences and Biomedicine, Westlake University, Hangzhou 310024, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou 310024, China
| | - Zhicheng Wu
- Zhejiang Provincial Laboratory of Life Sciences and Biomedicine, Westlake University, Hangzhou 310024, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou 310024, China; School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai 200433, China
| | - Xiao Liang
- Zhejiang Provincial Laboratory of Life Sciences and Biomedicine, Westlake University, Hangzhou 310024, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou 310024, China
| | - Man Wang
- MOE Key Laboratory of Carcinogenesis and Translational Research, Department of Gastrointestinal Translational Research, Peking University Cancer Hospital, Beijing 100142, China
| | - Yingkuan Shao
- Cancer Institute (MOE Key Laboratory of Cancer Prevention and Intervention, Zhejiang Provincial Key Laboratory of Molecular Biology in Medical Sciences), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China
| | - Xi Zheng
- Cancer Institute (MOE Key Laboratory of Cancer Prevention and Intervention, Zhejiang Provincial Key Laboratory of Molecular Biology in Medical Sciences), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China
| | - Kailun Xu
- Cancer Institute (MOE Key Laboratory of Cancer Prevention and Intervention, Zhejiang Provincial Key Laboratory of Molecular Biology in Medical Sciences), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China
| | - Qin Yang
- Cancer Biology Research Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Yifan Meng
- Cancer Biology Research Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Cong Lu
- Center for Stem Cell Research and Application, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Jiang Zhu
- Center for Stem Cell Research and Application, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Jin'e Zheng
- Center for Stem Cell Research and Application, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Bo Wang
- Department of Pathology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China
| | - Sai Lou
- Phase I Clinical Research Center, Zhejiang Provincial People's Hospital, Hangzhou 310014, China
| | - Yibei Dai
- Department of Laboratory Medicine, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China
| | - Chao Xu
- College of Mathematics and Informatics, Digital Fujian Institute of Big Data Security Technology, Fujian Normal University, Fuzhou 350108, China
| | - Chenhuan Yu
- Zhejiang Provincial Key Laboratory of Experimental Animal and Safety Evaluation, Zhejiang Academy of Medical Sciences, Hangzhou 310015, China
| | - Huazhong Ying
- Zhejiang Provincial Key Laboratory of Experimental Animal and Safety Evaluation, Zhejiang Academy of Medical Sciences, Hangzhou 310015, China
| | - Tony K Lim
- Department of Anatomical Pathology, Singapore General Hospital, Singapore 169608, Singapore
| | - Jianmin Wu
- MOE Key Laboratory of Carcinogenesis and Translational Research, Department of Gastrointestinal Translational Research, Peking University Cancer Hospital, Beijing 100142, China
| | - Xiaofei Gao
- Zhejiang Provincial Laboratory of Life Sciences and Biomedicine, Westlake University, Hangzhou 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou 310024, China; Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou 310024, China
| | - Zhongzhi Luan
- School of Computer Science and Engineering, Beihang University, Beijing 100191, China
| | - Xiaodong Teng
- Department of Pathology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China
| | - Peng Wu
- Cancer Biology Research Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Shi'ang Huang
- Center for Stem Cell Research and Application, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Zhihua Tao
- Department of Laboratory Medicine, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China
| | - Narayanan G Iyer
- Division of Medical Sciences, National Cancer Centre Singapore, Singapore 169608, Singapore
| | - Shuigeng Zhou
- School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai 200433, China
| | - Wenguang Shao
- Department of Biology, Institute for Molecular Systems Biology, ETH Zurich, Zurich 8092, Switzerland
| | - Henry Lam
- Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong Special Administrative Region, China
| | - Ding Ma
- Cancer Biology Research Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Jiafu Ji
- MOE Key Laboratory of Carcinogenesis and Translational Research, Department of Gastrointestinal Translational Research, Peking University Cancer Hospital, Beijing 100142, China
| | - Oi L Kon
- Division of Medical Sciences, National Cancer Centre Singapore, Singapore 169608, Singapore
| | - Shu Zheng
- Cancer Institute (MOE Key Laboratory of Cancer Prevention and Intervention, Zhejiang Provincial Key Laboratory of Molecular Biology in Medical Sciences), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China
| | - Ruedi Aebersold
- Department of Biology, Institute for Molecular Systems Biology, ETH Zurich, Zurich 8092, Switzerland; Faculty of Science, University of Zurich, Zurich 8092, Switzerland
| | - Connie R Jimenez
- OncoProteomics Laboratory, Department of Medical Oncology, VU University Medical Center, VU University, Amsterdam 1011, The Netherlands
| | - Tiannan Guo
- Zhejiang Provincial Laboratory of Life Sciences and Biomedicine, Westlake University, Hangzhou 310024, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou 310024, China.
| |
Collapse
|
23
|
Gerdle B, Ghafouri B. Proteomic studies of common chronic pain conditions - a systematic review and associated network analyses. Expert Rev Proteomics 2020; 17:483-505. [DOI: 10.1080/14789450.2020.1797499] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Affiliation(s)
- Björn Gerdle
- Pain and Rehabilitation Centre, and Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
| | - Bijar Ghafouri
- Pain and Rehabilitation Centre, and Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
| |
Collapse
|
24
|
Mongan D, Sabherwal S, Susai SR, Föcking M, Cannon M, Cotter DR. Peripheral complement proteins in schizophrenia: A systematic review and meta-analysis of serological studies. Schizophr Res 2020; 222:58-72. [PMID: 32456884 PMCID: PMC7594643 DOI: 10.1016/j.schres.2020.05.036] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Revised: 04/30/2020] [Accepted: 05/15/2020] [Indexed: 01/01/2023]
Abstract
BACKGROUND There is renewed focus on the complement system in the pathogenesis of schizophrenia. In addition to providing aetiological insights, consistently dysregulated complement proteins in serum or plasma may have clinical utility as biomarkers. METHODS We performed a systematic literature review searching PubMed, Embase and PsycINFO for studies measuring complement system activity or complement protein concentrations in serum or plasma from patients with schizophrenia compared to controls. Random-effects meta-analyses were performed to calculate pooled effect estimates (Hedges' g standardised mean difference [SMD]) for complement proteins whose concentrations were measured in three or more studies. The review was pre-registered on the PROSPERO database (CRD42018109012). RESULTS Database searching identified 1146 records. Fifty-eight full-text articles were assessed for eligibility and 24 studies included. Seven studies measured complement system activity. Activity of the classical pathway did not differ between cases and controls in four of six studies, and conflicting results were noted in two studies of alternative pathway activity. Twenty studies quantified complement protein concentrations of which complement components 3 (C3) and 4 (C4) were measured in more than three studies. Meta-analyses showed no evidence of significant differences between cases and controls for 11 studies of C3 (SMD 0.04, 95% confidence interval [CI] -0.29-0.36) and 10 studies of C4 (SMD 0.10, 95% CI -0.21-0.41). CONCLUSIONS Serological studies provide mixed evidence regarding dysregulation of the complement system in schizophrenia. Larger studies of a longitudinal nature, focusing on early phenotypes, could provide further insights regarding the potential role of the complement system in psychotic disorders.
Collapse
Affiliation(s)
- David Mongan
- Department of Psychiatry, Royal College of Surgeons in Ireland, Dublin, Ireland.
| | | | | | | | | | | |
Collapse
|
25
|
Gabuza KB, Sibuyi NRS, Mobo MP, Madiehe AM. Differentially expressed serum proteins from obese Wistar rats as a risk factor for obesity-induced diseases. Sci Rep 2020; 10:12415. [PMID: 32709962 PMCID: PMC7381623 DOI: 10.1038/s41598-020-69198-2] [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: 03/03/2020] [Accepted: 07/07/2020] [Indexed: 11/09/2022] Open
Abstract
Obesity is a chronic disease that negatively affects life expectancy through its association with life-threatening diseases such as cancer and cardiovascular diseases. Expression proteomics combined with in silico interaction studies are used to uncover potential biomarkers and the pathways that promote obesity-related complications. These biomarkers can either aid in the development of personalized therapies or identify individuals at risk of developing obesity-related diseases. To determine the serum protein changes, Wistar rats were fed standard chow (low fat, LF), or chow formulated high fat (HF) diets (HF1, HF2 and HF3) for 8 and 42 weeks to induce obesity. Serum samples were collected from lean and obese rats at these time points. The serum samples were precipitated using trichloroacetic acid (TCA)/acetone and analyzed by 2-Dimensional SDS-PAGE. Serum protein profiles were examined using mass spectrometry (MS)-based proteomics and validated by western blotting. Protein-protein interactions among the selected proteins were studied in silico using bioinformatics tools. Several proteins showed differences in expression among the three HF diets when compared to the LF diet, and only proteins with ≥ twofold expression levels were considered differentially expressed. Apolipoprotein-AIV (APOA4), C-reactive protein (CRP), and alpha 2-HS glycoprotein (AHSG) showed differential expression at both 8 and 42 weeks, whereas alpha 1 macroglobulin (AMBP) was differentially expressed only at 8 weeks. Network analysis revealed some interactions among the proteins, an indication that these proteins might interactively play a crucial role in development of obesity-induced diseases. These data show the variation in the expression of serum proteins during acute and chronic exposure to high fat diet. Based on the expression and the in-silico interaction these proteins warrant further investigation for their role in obesity development.
Collapse
Affiliation(s)
| | | | - Mmabatho Peggy Mobo
- Department of Biotechnology, University of the Western Cape, Bellville, Cape Town, 7535, South Africa
| | - Abram Madimabe Madiehe
- Department of Biotechnology, University of the Western Cape, Bellville, Cape Town, 7535, South Africa.
| |
Collapse
|
26
|
Johnson AA, Shokhirev MN, Wyss-Coray T, Lehallier B. Systematic review and analysis of human proteomics aging studies unveils a novel proteomic aging clock and identifies key processes that change with age. Ageing Res Rev 2020; 60:101070. [PMID: 32311500 DOI: 10.1016/j.arr.2020.101070] [Citation(s) in RCA: 66] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Revised: 03/23/2020] [Accepted: 04/07/2020] [Indexed: 12/14/2022]
Abstract
The development of clinical interventions that significantly improve human healthspan requires robust markers of biological age as well as thoughtful therapeutic targets. To promote these goals, we performed a systematic review and analysis of human aging and proteomics studies. The systematic review includes 36 different proteomics analyses, each of which identified proteins that significantly changed with age. We discovered 1,128 proteins that had been reported by at least two or more analyses and 32 proteins that had been reported by five or more analyses. Each of these 32 proteins has known connections relevant to aging and age-related disease. GDF15, for example, extends both lifespan and healthspan when overexpressed in mice and is additionally required for the anti-diabetic drug metformin to exert beneficial effects on body weight and energy balance. Bioinformatic enrichment analyses of our 1,128 commonly identified proteins heavily implicated processes relevant to inflammation, the extracellular matrix, and gene regulation. We additionally propose a novel proteomic aging clock comprised of proteins that were reported to change with age in plasma in three or more different studies. Using a large patient cohort comprised of 3,301 subjects (aged 18-76 years), we demonstrate that this clock is able to accurately predict human age.
Collapse
|
27
|
A fully joint Bayesian quantitative trait locus mapping of human protein abundance in plasma. PLoS Comput Biol 2020; 16:e1007882. [PMID: 32492067 PMCID: PMC7295243 DOI: 10.1371/journal.pcbi.1007882] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2019] [Revised: 06/15/2020] [Accepted: 04/16/2020] [Indexed: 11/19/2022] Open
Abstract
Molecular quantitative trait locus (QTL) analyses are increasingly popular to explore the genetic architecture of complex traits, but existing studies do not leverage shared regulatory patterns and suffer from a large multiplicity burden, which hampers the detection of weak signals such as trans associations. Here, we present a fully multivariate proteomic QTL (pQTL) analysis performed with our recently proposed Bayesian method LOCUS on data from two clinical cohorts, with plasma protein levels quantified by mass-spectrometry and aptamer-based assays. Our two-stage study identifies 136 pQTL associations in the first cohort, of which >80% replicate in the second independent cohort and have significant enrichment with functional genomic elements and disease risk loci. Moreover, 78% of the pQTLs whose protein abundance was quantified by both proteomic techniques are confirmed across assays. Our thorough comparisons with standard univariate QTL mapping on (1) these data and (2) synthetic data emulating the real data show how LOCUS borrows strength across correlated protein levels and markers on a genome-wide scale to effectively increase statistical power. Notably, 15% of the pQTLs uncovered by LOCUS would be missed by the univariate approach, including several trans and pleiotropic hits with successful independent validation. Finally, the analysis of extensive clinical data from the two cohorts indicates that the genetically-driven proteins identified by LOCUS are enriched in associations with low-grade inflammation, insulin resistance and dyslipidemia and might therefore act as endophenotypes for metabolic diseases. While considerations on the clinical role of the pQTLs are beyond the scope of our work, these findings generate useful hypotheses to be explored in future research; all results are accessible online from our searchable database. Thanks to its efficient variational Bayes implementation, LOCUS can analyze jointly thousands of traits and millions of markers. Its applicability goes beyond pQTL studies, opening new perspectives for large-scale genome-wide association and QTL analyses. Diet, Obesity and Genes (DiOGenes) trial registration number: NCT00390637. Exploring the functional mechanisms between the genotype and disease endpoints in view of identifying innovative therapeutic targets has prompted molecular quantitative trait locus studies, which assess how genetic variants (single nucleotide polymorphisms, SNPs) affect intermediate gene (eQTL), protein (pQTL) or metabolite (mQTL) levels. However, conventional univariate screening approaches do not account for local dependencies and association structures shared by multiple molecular levels and markers. Conversely, the current joint modelling approaches are restricted to small datasets by computational constraints. We illustrate and exploit the advantages of our recently introduced Bayesian framework LOCUS in a fully multivariate pQTL study, with ≈300K tag SNPs (capturing information from 4M markers) and 100 − 1, 000 plasma protein levels measured by two distinct technologies. LOCUS identifies novel pQTLs that replicate in an independent cohort, confirms signals documented in studies 2 − 18 times larger, and detects more pQTLs than a conventional two-stage univariate analysis of our datasets. Moreover, some of these pQTLs might be of biomedical relevance and would therefore deserve dedicated investigation. Our extensive numerical experiments on these data and on simulated data demonstrate that the increased statistical power of LOCUS over standard approaches is largely attributable to its ability to exploit shared information across outcomes while efficiently accounting for the genetic correlation structures at a genome-wide level.
Collapse
|
28
|
Proteomic profiles before and during weight loss: Results from randomized trial of dietary intervention. Sci Rep 2020; 10:7913. [PMID: 32404980 PMCID: PMC7220904 DOI: 10.1038/s41598-020-64636-7] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Accepted: 04/02/2020] [Indexed: 12/15/2022] Open
Abstract
Inflammatory and cardiovascular biomarkers have been associated with obesity, but little is known about how they change upon dietary intervention and concomitant weight loss. Further, protein biomarkers might be useful for predicting weight loss in overweight and obese individuals. We performed secondary analyses in the Diet Intervention Examining The Factors Interacting with Treatment Success (DIETFITS) randomized intervention trial that included healthy 609 adults (18–50 years old) with BMI 28–40 kg/m2, to evaluate associations between circulating protein biomarkers and BMI at baseline, during a weight loss diet intervention, and to assess predictive potential of baseline blood proteins on weight loss. We analyzed 263 plasma proteins at baseline and 6 months into the intervention using the Olink Proteomics CVD II, CVD III and Inflammation arrays. BMI was assessed at baseline, after 3 and 6 months of dietary intervention. At baseline, 102 of the examined inflammatory and cardiovascular biomarkers were associated with BMI (>90% with successful replication in 1,584 overweight/obese individuals from a community-based cohort study) and 130 tracked with weight loss shedding light into the pathophysiology of obesity. However, out of 263 proteins analyzed at baseline, only fibroblast growth factor 21 (FGF-21) predicted weight loss, and none helped individualize dietary assignment.
Collapse
|
29
|
Dayon L, Affolter M. Progress and pitfalls of using isobaric mass tags for proteome profiling. Expert Rev Proteomics 2020; 17:149-161. [PMID: 32067523 DOI: 10.1080/14789450.2020.1731309] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Introduction: Quantitative proteomics using mass spectrometry is performed via label-free or label-based approaches. Labeling strategies rely on the incorporation of stable heavy isotopes by metabolic, enzymatic, or chemical routes. Isobaric labeling uses chemical labels of identical masses but of different fragmentation behaviors to allow the relative quantitative comparison of peptide/protein abundances between biological samples.Areas covered: We have carried out a systematic review on the use of isobaric mass tags in proteomic research since their inception in 2003. We focused on their quantitative performances, their multiplexing evolution, as well as their broad use for relative quantification of proteins in pre-clinical models and clinical studies. Current limitations, primarily linked to the quantitative ratio distortion, as well as state-of-the-art and emerging solutions to improve their quantitative readouts are discussed.Expert opinion: The isobaric mass tag technology offers a unique opportunity to compare multiple protein samples simultaneously, allowing higher sample throughput and internal relative quantification for improved trueness and precision. Large studies can be performed when shared reference samples are introduced in multiple experiments. The technology is well suited for proteome profiling in the context of proteomic discovery studies.
Collapse
Affiliation(s)
- Loïc Dayon
- Proteomics, Nestlé Institute of Food Safety & Analytical Sciences, Nestlé Research, Lausanne, Switzerland.,Institut des Sciences et Ingénierie Chimiques, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Michael Affolter
- Proteomics, Nestlé Institute of Food Safety & Analytical Sciences, Nestlé Research, Lausanne, Switzerland
| |
Collapse
|
30
|
Sobsey CA, Ibrahim S, Richard VR, Gaspar V, Mitsa G, Lacasse V, Zahedi RP, Batist G, Borchers CH. Targeted and Untargeted Proteomics Approaches in Biomarker Development. Proteomics 2020; 20:e1900029. [DOI: 10.1002/pmic.201900029] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2019] [Revised: 10/10/2019] [Indexed: 01/24/2023]
Affiliation(s)
- Constance A. Sobsey
- Segal Cancer Proteomics CentreLady Davis InstituteJewish General HospitalMcGill University Montreal Quebec H3T 1E2 Canada
| | - Sahar Ibrahim
- Segal Cancer Proteomics CentreLady Davis InstituteJewish General HospitalMcGill University Montreal Quebec H3T 1E2 Canada
| | - Vincent R. Richard
- Segal Cancer Proteomics CentreLady Davis InstituteJewish General HospitalMcGill University Montreal Quebec H3T 1E2 Canada
| | - Vanessa Gaspar
- Segal Cancer Proteomics CentreLady Davis InstituteJewish General HospitalMcGill University Montreal Quebec H3T 1E2 Canada
| | - Georgia Mitsa
- Segal Cancer Proteomics CentreLady Davis InstituteJewish General HospitalMcGill University Montreal Quebec H3T 1E2 Canada
| | - Vincent Lacasse
- Segal Cancer Proteomics CentreLady Davis InstituteJewish General HospitalMcGill University Montreal Quebec H3T 1E2 Canada
| | - René P. Zahedi
- Segal Cancer Proteomics CentreLady Davis InstituteJewish General HospitalMcGill University Montreal Quebec H3T 1E2 Canada
| | - Gerald Batist
- Gerald Bronfman Department of OncologyJewish General HospitalMcGill University Montreal Quebec H4A 3T2 Canada
| | - Christoph H. Borchers
- Segal Cancer Proteomics CentreLady Davis InstituteJewish General HospitalMcGill University Montreal Quebec H3T 1E2 Canada
- Gerald Bronfman Department of OncologyJewish General HospitalMcGill University Montreal Quebec H4A 3T2 Canada
- Department of Data Intensive Science and EngineeringSkolkovo Institute of Science and TechnologySkolkovo Innovation Center Moscow 143026 Russia
| |
Collapse
|
31
|
Rajan MR, Sotak M, Barrenäs F, Shen T, Borkowski K, Ashton NJ, Biörserud C, Lindahl TL, Ramström S, Schöll M, Lindahl P, Fiehn O, Newman JW, Perkins R, Wallenius V, Lange S, Börgeson E. Comparative analysis of obesity-related cardiometabolic and renal biomarkers in human plasma and serum. Sci Rep 2019; 9:15385. [PMID: 31659186 PMCID: PMC6817872 DOI: 10.1038/s41598-019-51673-0] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2019] [Accepted: 10/02/2019] [Indexed: 12/19/2022] Open
Abstract
The search for biomarkers associated with obesity-related diseases is ongoing, but it is not clear whether plasma and serum can be used interchangeably in this process. Here we used high-throughput screening to analyze 358 proteins and 76 lipids, selected because of their relevance to obesity-associated diseases, in plasma and serum from age- and sex-matched lean and obese humans. Most of the proteins/lipids had similar concentrations in plasma and serum, but a subset showed significant differences. Notably, a key marker of cardiovascular disease PAI-1 showed a difference in concentration between the obese and lean groups only in plasma. Furthermore, some biomarkers showed poor correlations between plasma and serum, including PCSK9, an important regulator of cholesterol homeostasis. Collectively, our results show that the choice of biofluid may impact study outcome when screening for obesity-related biomarkers and we identify several markers where this will be the case.
Collapse
Affiliation(s)
- Meenu Rohini Rajan
- Department of Molecular and Clinical Medicine, Wallenberg Laboratory, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden
- Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden
| | - Matus Sotak
- Department of Molecular and Clinical Medicine, Wallenberg Laboratory, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden
- Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden
| | - Fredrik Barrenäs
- Department of Molecular and Clinical Medicine, Wallenberg Laboratory, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden
- Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden
- Department of Cell & Molecular Biology, Uppsala University, Uppsala, Sweden
| | - Tong Shen
- NIH West Coast Metabolomics Center, Genome Center, University of California Davis, Davis, USA
| | - Kamil Borkowski
- NIH West Coast Metabolomics Center, Genome Center, University of California Davis, Davis, USA
| | - Nicholas J Ashton
- Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden
- Department of Psychiatry and Neurochemistry, Institute of Physiology and Neuroscience, University of Gothenburg, Gothenburg, Sweden
- King's College London, Institute of Psychiatry, Psychology & Neuroscience, Maurice Wohl Clinical Neuroscience Institute, London, UK
- NIHR Biomedical Research Centre for Mental Health & Biomedical Research Unit for Dementia at South London & Maudsley NHS Foundation, London, UK
| | - Christina Biörserud
- Department of Gastrosurgical Research and Education, Institute of Clinical Sciences, Sahlgrenska University Hospital, University of Gothenburg, Gothenburg, Sweden
| | - Tomas L Lindahl
- Department of Clinical Chemistry and Department of Clinical and Experimental Medicine, Linköping University, Linköping, Sweden
| | - Sofia Ramström
- Department of Clinical Chemistry and Department of Clinical and Experimental Medicine, Linköping University, Linköping, Sweden
- Cardiovascular Research Centre, School of Medical Sciences, Örebro University, Örebro, Sweden
| | - Michael Schöll
- Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden
- Department of Psychiatry and Neurochemistry, Institute of Physiology and Neuroscience, University of Gothenburg, Gothenburg, Sweden
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Per Lindahl
- Department of Molecular and Clinical Medicine, Wallenberg Laboratory, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden
| | - Oliver Fiehn
- NIH West Coast Metabolomics Center, Genome Center, University of California Davis, Davis, USA
| | - John W Newman
- NIH West Coast Metabolomics Center, Genome Center, University of California Davis, Davis, USA
- Department of Nutrition, University of California Davis, Davis, USA
- USDA, ARS, Western Human Nutrition Research Center, Davis, USA
| | - Rosie Perkins
- Department of Molecular and Clinical Medicine, Wallenberg Laboratory, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden
| | - Ville Wallenius
- Department of Gastrosurgical Research and Education, Institute of Clinical Sciences, Sahlgrenska University Hospital, University of Gothenburg, Gothenburg, Sweden
| | - Stephan Lange
- Department of Molecular and Clinical Medicine, Wallenberg Laboratory, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden
- Division of Cardiology, School of Medicine, University of California San Diego, San Diego, USA
| | - Emma Börgeson
- Department of Molecular and Clinical Medicine, Wallenberg Laboratory, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden.
- Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden.
- Department of Clinical Physiology, Sahlgrenska University Hospital, Gothenburg, Sweden.
| |
Collapse
|
32
|
Ignjatovic V, Geyer PE, Palaniappan KK, Chaaban JE, Omenn GS, Baker MS, Deutsch EW, Schwenk JM. Mass Spectrometry-Based Plasma Proteomics: Considerations from Sample Collection to Achieving Translational Data. J Proteome Res 2019; 18:4085-4097. [PMID: 31573204 DOI: 10.1021/acs.jproteome.9b00503] [Citation(s) in RCA: 104] [Impact Index Per Article: 20.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
The proteomic analysis of human blood and blood-derived products (e.g., plasma) offers an attractive avenue to translate research progress from the laboratory into the clinic. However, due to its unique protein composition, performing proteomics assays with plasma is challenging. Plasma proteomics has regained interest due to recent technological advances, but challenges imposed by both complications inherent to studying human biology (e.g., interindividual variability) and analysis of biospecimens (e.g., sample variability), as well as technological limitations remain. As part of the Human Proteome Project (HPP), the Human Plasma Proteome Project (HPPP) brings together key aspects of the plasma proteomics pipeline. Here, we provide considerations and recommendations concerning study design, plasma collection, quality metrics, plasma processing workflows, mass spectrometry (MS) data acquisition, data processing, and bioinformatic analysis. With exciting opportunities in studying human health and disease though this plasma proteomics pipeline, a more informed analysis of human plasma will accelerate interest while enhancing possibilities for the incorporation of proteomics-scaled assays into clinical practice.
Collapse
Affiliation(s)
- Vera Ignjatovic
- Haematology Research , Murdoch Children's Research Institute , Parkville , VIC 3052 , Australia.,Department of Paediatrics , The University of Melbourne , Parkville , VIC 3052 , Australia
| | - Philipp E Geyer
- NNF Center for Protein Research, Faculty of Health Sciences , University of Copenhagen , 2200 Copenhagen , Denmark.,Department of Proteomics and Signal Transduction , Max Planck Institute of Biochemistry , 82152 Martinsried , Germany
| | - Krishnan K Palaniappan
- Freenome , 259 East Grand Avenue , South San Francisco , California 94080 , United States
| | - Jessica E Chaaban
- Haematology Research , Murdoch Children's Research Institute , Parkville , VIC 3052 , Australia
| | - Gilbert S Omenn
- Departments of Computational Medicine & Bioinformatics, Human Genetics, and Internal Medicine and School of Public Health , University of Michigan , 100 Washtenaw Avenue , Ann Arbor , Michigan 48109-2218 , United States
| | - Mark S Baker
- Department of Biomedical Sciences, Faculty of Medicine & Health Sciences , Macquarie University , 75 Talavera Road , North Ryde , NSW 2109 , Australia
| | - Eric W Deutsch
- Institute for Systems Biology , 401 Terry Avenue North , Seattle , Washington 98109 , United States
| | - Jochen M Schwenk
- Affinity Proteomics, SciLifeLab , KTH Royal Institute of Technology , 171 65 Stockholm , Sweden
| |
Collapse
|
33
|
Bruderer R, Muntel J, Müller S, Bernhardt OM, Gandhi T, Cominetti O, Macron C, Carayol J, Rinner O, Astrup A, Saris WHM, Hager J, Valsesia A, Dayon L, Reiter L. Analysis of 1508 Plasma Samples by Capillary-Flow Data-Independent Acquisition Profiles Proteomics of Weight Loss and Maintenance. Mol Cell Proteomics 2019; 18:1242-1254. [PMID: 30948622 PMCID: PMC6553938 DOI: 10.1074/mcp.ra118.001288] [Citation(s) in RCA: 117] [Impact Index Per Article: 23.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2018] [Revised: 03/26/2019] [Indexed: 12/14/2022] Open
Abstract
Comprehensive, high throughput analysis of the plasma proteome has the potential to enable holistic analysis of the health state of an individual. Based on our own experience and the evaluation of recent large-scale plasma mass spectrometry (MS) based proteomic studies, we identified two outstanding challenges: slow and delicate nano-flow liquid chromatography (LC) and irreproducibility of identification of data-dependent acquisition (DDA). We determined an optimal solution reducing these limitations with robust capillary-flow data-independent acquisition (DIA) MS. This platform can measure 31 plasma proteomes per day. Using this setup, we acquired a large-scale plasma study of the diet, obesity and genes dietary (DiOGenes) comprising 1508 samples. Proving the robustness, the complete acquisition was achieved on a single analytical column. Totally, 565 proteins (459 identified with two or more peptide sequences) were profiled with 74% data set completeness. On average 408 proteins (5246 peptides) were identified per acquisition (319 proteins in 90% of all acquisitions). The workflow reproducibility was assessed using 34 quality control pools acquired at regular intervals, resulting in 92% data set completeness with CVs for protein measurements of 10.9%.The profiles of 20 apolipoproteins could be profiled revealing distinct changes. The weight loss and weight maintenance resulted in sustained effects on low-grade inflammation, as well as steroid hormone and lipid metabolism, indicating beneficial effects. Comparison to other large-scale plasma weight loss studies demonstrated high robustness and quality of biomarker candidates identified. Tracking of nonenzymatic glycation indicated a delayed, slight reduction of glycation in the weight maintenance phase. Using stable-isotope-references, we could directly and absolutely quantify 60 proteins in the DIA.In conclusion, we present herein the first large-scale plasma DIA study and one of the largest clinical research proteomic studies to date. Application of this fast and robust workflow has great potential to advance biomarker discovery in plasma.
Collapse
Affiliation(s)
| | - Jan Muntel
- From the ‡Biognosys, 8952 Zurich-Schlieren, Switzerland
| | | | | | - Tejas Gandhi
- From the ‡Biognosys, 8952 Zurich-Schlieren, Switzerland
| | | | - Charlotte Macron
- §Nestlé Institute of Health Sciences, 1015 Lausanne, Switzerland
| | - Jérôme Carayol
- §Nestlé Institute of Health Sciences, 1015 Lausanne, Switzerland
| | - Oliver Rinner
- From the ‡Biognosys, 8952 Zurich-Schlieren, Switzerland
| | - Arne Astrup
- ¶Department of Nutrition, Exercise and Sports, Faculty of Science, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Wim H M Saris
- ‖NUTRIM, School for Nutrition, Toxicology and Metabolism, Department of Human Biology, Maastricht University Medical Centre, 6200 MD Maastricht, The Netherlands
| | - Jörg Hager
- §Nestlé Institute of Health Sciences, 1015 Lausanne, Switzerland
| | - Armand Valsesia
- §Nestlé Institute of Health Sciences, 1015 Lausanne, Switzerland
| | - Loïc Dayon
- §Nestlé Institute of Health Sciences, 1015 Lausanne, Switzerland
| | - Lukas Reiter
- From the ‡Biognosys, 8952 Zurich-Schlieren, Switzerland;
| |
Collapse
|
34
|
Savedoroudi P, Bennike TB, Kastaniegaard K, Talebpour M, Ghassempour A, Stensballe A. Serum proteome changes and accelerated reduction of fat mass after laparoscopic gastric plication in morbidly obese patients. J Proteomics 2019; 203:103373. [PMID: 31054967 DOI: 10.1016/j.jprot.2019.05.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2019] [Revised: 04/29/2019] [Accepted: 05/01/2019] [Indexed: 02/06/2023]
Abstract
Laparoscopic Gastric Plication (LGP) is a relatively new bariatric surgical procedure which no part of the stomach is removed. It is not clearly understood how LGP leads to fatty tissue reduction. We aimed to investigate the impact of LGP on serum proteome and understand molecular mechanisms of LGP-induced weight loss post-surgery. A Prospective observational study of 16 obese individuals who underwent LGP was performed. A Label-free quantitative shotgun proteomics approach was used to compare serum proteome of subjects before surgery with serum of the same individuals 1 to 2 months post-surgery (T1) and 4 to 5 months post-surgery (T2). The proteome analysis revealed that 48 proteins were differentially regulated between pre-surgery and T1, and seven proteins between pre-surgery and T2 of which six proteins were shared between the two timepoints. Among differentially regulated proteins, four proteins (SRGN, FETUB, LCP1 and CFP) have not previously been described in the context of BMI/weight loss. Despite few differences following LGP, most regulated serum proteins are in accordance with alternative weight loss procedures. Pathway analysis revealed changes to lipid- and inflammatory pathways, including PPARα/RXRα, LXR/RXR and FXR/RXR activation, especially at T1. At T2, the pathways related to inflammation and immune system are most affected. SIGNIFICANCE: Among the available clinical therapies for morbid obesity, bariatric surgery is considered as the most effective approach to achieve long-term weight loss, alongside a significant improvement in metabolic syndrome. However, very little is known about the underlying mechanism associated with significant weight loss post-surgery. Understanding such mechanisms could lead to development of safer non-surgical weight loss approaches. We here present the first analysis of the impact of LGP on the serum proteome, to bring new insights into the underlying molecular mechanism. Our findings indicate that LGP has a comprehensive systemic effect based on the blood serum proteome profile which might account for accelerated reduction of fat mass after surgery, thus, food restriction is not the only reason for weight loss following this unique surgical approach. As secretory regions of the stomach are preserved in LGP and it is associated with minimal physiological and anatomical changes, the findings are of high importance in the field of bariatric surgery and weight loss.
Collapse
Affiliation(s)
- Parisa Savedoroudi
- Medicinal Plants and Drugs Research Institute, Shahid Beheshti University, Tehran, Iran; Department of Health Science and Technology, Aalborg University, Denmark.
| | - Tue Bjerg Bennike
- Department of Health Science and Technology, Aalborg University, Denmark.
| | | | - Mohammad Talebpour
- Laparoscopic Surgery Ward, Sina Hospital, Tehran University of Medical Sciences, Tehran, Iran.
| | - Alireza Ghassempour
- Medicinal Plants and Drugs Research Institute, Shahid Beheshti University, Tehran, Iran.
| | - Allan Stensballe
- Department of Health Science and Technology, Aalborg University, Denmark.
| |
Collapse
|