9
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Facets of individual-specific health signatures determined from longitudinal plasma proteome profiling

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Background

          Precision medicine approaches aim to tackle diseases on an individual level through molecular profiling. Despite the growing knowledge about diseases and the reported diversity of molecular phenotypes, the descriptions of human health on an individual level have been far less elaborate.

          Methods

          To provide insights into the longitudinal protein signatures of well-being, we profiled blood plasma collected over one year from 101 clinically healthy individuals using multiplexed antibody assays. After applying an antibody validation scheme, we utilized > 700 protein profiles for in-depth analyses of the individuals’ short-term health trajectories.

          Findings

          We found signatures of circulating proteomes to be highly individual-specific. Considering technical and longitudinal variability, we observed that 49% of the protein profiles were stable over one year. We also identified eight networks of proteins in which 11–242 proteins covaried over time. For each participant, there were unique protein profiles of which some could be explained by associations to genetic variants.

          Interpretation

          This observational and non-interventional study identifyed noticeable diversity among clinically healthy subjects, and facets of individual-specific signatures emerged by monitoring the variability of the circulating proteomes over time. To enable more personal hence precise assessments of health states, longitudinal profiling of circulating proteomes can provide a valuable component for precision medicine approaches.

          Funding

          This work was supported by the Erling Persson Foundation, the Swedish Heart and Lung Foundation, the Knut and Alice Wallenberg Foundation, Science for Life Laboratory, and the Swedish Research Council.

          Related collections

          Most cited references18

          • Record: found
          • Abstract: found
          • Article: not found

          A general framework for weighted gene co-expression network analysis.

          Gene co-expression networks are increasingly used to explore the system-level functionality of genes. The network construction is conceptually straightforward: nodes represent genes and nodes are connected if the corresponding genes are significantly co-expressed across appropriately chosen tissue samples. In reality, it is tricky to define the connections between the nodes in such networks. An important question is whether it is biologically meaningful to encode gene co-expression using binary information (connected=1, unconnected=0). We describe a general framework for ;soft' thresholding that assigns a connection weight to each gene pair. This leads us to define the notion of a weighted gene co-expression network. For soft thresholding we propose several adjacency functions that convert the co-expression measure to a connection weight. For determining the parameters of the adjacency function, we propose a biologically motivated criterion (referred to as the scale-free topology criterion). We generalize the following important network concepts to the case of weighted networks. First, we introduce several node connectivity measures and provide empirical evidence that they can be important for predicting the biological significance of a gene. Second, we provide theoretical and empirical evidence that the ;weighted' topological overlap measure (used to define gene modules) leads to more cohesive modules than its ;unweighted' counterpart. Third, we generalize the clustering coefficient to weighted networks. Unlike the unweighted clustering coefficient, the weighted clustering coefficient is not inversely related to the connectivity. We provide a model that shows how an inverse relationship between clustering coefficient and connectivity arises from hard thresholding. We apply our methods to simulated data, a cancer microarray data set, and a yeast microarray data set.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Co-regulatory networks of human serum proteins link genetics to disease

            Proteins circulating in the blood are critical for age-related disease processes, however the serum proteome has remained largely unexplored. To this end, 4,137 proteins covering most predicted extracellular proteins, were measured in the serum of 5,457 Icelanders over 65 years of age. Pair-wise correlation between proteins as they varied across individuals, revealed 27 different network modules of serum proteins, many of which were associated with cardiovascular and metabolic disease states as well as overall survival. The protein modules were controlled by cis and trans acting genetic variants; which in many cases were also associated with complex disease. This revealed co-regulated groups of circulating proteins that incorporated regulatory control between tissues and demonstrated close relationships to past, current and future disease states.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              The human secretome

              The proteins secreted by human cells (collectively referred to as the secretome) are important not only for the basic understanding of human biology but also for the identification of potential targets for future diagnostics and therapies. Here, we present a comprehensive analysis of proteins predicted to be secreted in human cells, which provides information about their final localization in the human body, including the proteins actively secreted to peripheral blood. The analysis suggests that a large number of the proteins of the secretome are not secreted out of the cell, but instead are retained intracellularly, whereas another large group of proteins were identified that are predicted to be retained locally at the tissue of expression and not secreted into the blood. Proteins detected in the human blood by mass spectrometry–based proteomics and antibody-based immunoassays are also presented with estimates of their concentrations in the blood. The results are presented in an updated version 19 of the Human Protein Atlas in which each gene encoding a secretome protein is annotated to provide an open-access knowledge resource of the human secretome, including body-wide expression data, spatial localization data down to the single-cell and subcellular levels, and data about the presence of proteins that are detectable in the blood.
                Bookmark

                Author and article information

                Contributors
                Journal
                EBioMedicine
                EBioMedicine
                EBioMedicine
                Elsevier
                2352-3964
                03 July 2020
                July 2020
                03 July 2020
                : 57
                : 102854
                Affiliations
                [a ]Science for Life Laboratory, Department of Protein Science, KTH-Royal Institute of Technology, Tomtebodavägen 23, Stockholm 171 65, Sweden
                [b ]Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
                [c ]Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels väg 12A, Stockholm 171 77, Sweden
                [d ]Center for Translational Microbiome Research, Department of Microbiology, Tumor and Cell Biology, Karolinska Institute, Stockholm 171 77, Sweden
                [e ]Department of Clinical Medicine, K.G. Jebsen Thrombosis Research and Expertise Center (TREC), UiT the Arctic University of Norway, Tromsø 9010, Norway
                [f ]Coagulation unit, Department of Hematology, Karolinska University Hospital, Stockholm 171 76, Sweden
                [g ]Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, Gothenburg University, Gothenburg 413 45, Sweden
                [h ]Region Västra Götaland, Department of Clinical Genetics and Genomics, Sahlgrenska University Hospital, Gothenburg 413 45, Sweden
                [i ]Region Västra Götaland, Department of Clinical Physiology, Sahlgrenska University Hospital, Gothenburg 413 45, Sweden
                [j ]Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby 2800, Denmark
                Author notes
                [* ]Corresponding author. jochen.schwenk@ 123456scilifelab.se
                Article
                S2352-3964(20)30229-2 102854
                10.1016/j.ebiom.2020.102854
                7334812
                32629387
                7ccffcf6-47cc-45c7-97f9-ad84e4519058
                © 2020 The Authors

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 6 March 2020
                : 1 June 2020
                : 9 June 2020
                Categories
                Research paper

                affinity proteomics,longitudinal profiling,plasma proteomics,pqtls,precision medicine

                Comments

                Comment on this article