Literature DB >> 26645377

Personalized medicine beyond genomics: alternative futures in big data-proteomics, environtome and the social proteome.

Vural Özdemir1,2, Edward S Dove3, Ulvi K Gürsoy4, Semra Şardaş5, Arif Yıldırım6, Şenay Görücü Yılmaz7, I Ömer Barlas8, Kıvanç Güngör7,9, Alper Mete9, Sanjeeva Srivastava10.   

Abstract

No field in science and medicine today remains untouched by Big Data, and psychiatry is no exception. Proteomics is a Big Data technology and a next generation biomarker, supporting novel system diagnostics and therapeutics in psychiatry. Proteomics technology is, in fact, much older than genomics and dates to the 1970s, well before the launch of the international Human Genome Project. While the genome has long been framed as the master or "elite" executive molecule in cell biology, the proteome by contrast is humble. Yet the proteome is critical for life-it ensures the daily functioning of cells and whole organisms. In short, proteins are the blue-collar workers of biology, the down-to-earth molecules that we cannot live without. Since 2010, proteomics has found renewed meaning and international attention with the launch of the Human Proteome Project and the growing interest in Big Data technologies such as proteomics. This article presents an interdisciplinary technology foresight analysis and conceptualizes the terms "environtome" and "social proteome". We define "environtome" as the entire complement of elements external to the human host, from microbiome, ambient temperature and weather conditions to government innovation policies, stock market dynamics, human values, political power and social norms that collectively shape the human host spatially and temporally. The "social proteome" is the subset of the environtome that influences the transition of proteomics technology to innovative applications in society. The social proteome encompasses, for example, new reimbursement schemes and business innovation models for proteomics diagnostics that depart from the "once-a-life-time" genotypic tests and the anticipated hype attendant to context and time sensitive proteomics tests. Building on the "nesting principle" for governance of complex systems as discussed by Elinor Ostrom, we propose here a 3-tiered organizational architecture for Big Data science such as proteomics. The proposed nested governance structure is comprised of (a) scientists, (b) ethicists, and (c) scholars in the nascent field of "ethics-of-ethics", and aims to cultivate a robust social proteome for personalized medicine. Ostrom often noted that such nested governance designs offer assurance that political power embedded in innovation processes is distributed evenly and is not concentrated disproportionately in a single overbearing stakeholder or person. We agree with this assessment and conclude by underscoring the synergistic value of social and biological proteomes to realize the full potentials of proteomics science for personalized medicine in psychiatry in the present era of Big Data.

Entities:  

Keywords:  Big data; Futures studies; Innovation management systems; Precision medicine; Proteomics; Technology foresight

Mesh:

Substances:

Year:  2015        PMID: 26645377     DOI: 10.1007/s00702-015-1489-y

Source DB:  PubMed          Journal:  J Neural Transm (Vienna)        ISSN: 0300-9564            Impact factor:   3.575


  29 in total

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2.  Meta-Analysis of Genes in Commercially Available Nutrigenomic Tests Denotes Lack of Association with Dietary Intake and Nutrient-Related Pathologies.

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Journal:  OMICS       Date:  2015-09

Review 3.  Studying multiple protein profiles over time to assess biomarker validity.

Authors:  Raj S Kasthuri; Michael R Verneris; Hassan N Ibrahim; Bernd Jilma; Gary L Nelsestuen
Journal:  Expert Rev Proteomics       Date:  2006-08       Impact factor: 3.940

Review 4.  Proteomic studies of urinary biomarkers for prostate, bladder and kidney cancers.

Authors:  Steven L Wood; Margaret A Knowles; Douglas Thompson; Peter J Selby; Rosamonde E Banks
Journal:  Nat Rev Urol       Date:  2013-02-26       Impact factor: 14.432

5.  A quantitative analysis of the mass media coverage of genomics medicine in China: a call for science journalism in the developing world.

Authors:  Feifei Zhao; Yan Chen; Siqi Ge; Xinwei Yu; Shuang Shao; Michael Black; Youxin Wang; Jie Zhang; Manshu Song; Wei Wang
Journal:  OMICS       Date:  2014-02-10

6.  The epiknowledge of socially responsible innovation.

Authors:  Edward S Dove; Vural Ozdemir
Journal:  EMBO Rep       Date:  2014-05       Impact factor: 8.807

Review 7.  The potential of biomarkers in psychiatry: focus on proteomics.

Authors:  Izabela Sokolowska; Armand G Ngounou Wetie; Kelly Wormwood; Johannes Thome; Costel C Darie; Alisa G Woods
Journal:  J Neural Transm (Vienna)       Date:  2013-12-20       Impact factor: 3.575

8.  Multiplatform metabolome and proteome profiling identifies serum metabolite and protein signatures as prospective biomarkers for schizophrenia.

Authors:  Khaled Al Awam; Ida Sibylle Haußleiter; Ed Dudley; Rossen Donev; Martin Brüne; Georg Juckel; Johannes Thome
Journal:  J Neural Transm (Vienna)       Date:  2014-05-01       Impact factor: 3.575

9.  Towards an animal model of ovarian cancer: cataloging chicken blood proteins using combinatorial peptide ligand libraries coupled with shotgun proteomic analysis for translational research.

Authors:  Yingying Ma; Zeyu Sun; Ricardo de Matos; Jing Zhang; Kunle Odunsi; Biaoyang Lin
Journal:  OMICS       Date:  2014-03-24

10.  What Role for Law, Human Rights, and Bioethics in an Age of Big Data, Consortia Science, and Consortia Ethics? The Importance of Trustworthiness.

Authors:  Edward S Dove; Vural Özdemir
Journal:  Laws       Date:  2015-09-01
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4.  Ethical Principles, Constraints and Opportunities in Clinical Proteomics.

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Review 5.  Governing Personalized Health: A Scoping Review.

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Review 6.  Fungal secondary metabolites in food and pharmaceuticals in the era of multi-omics.

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Journal:  Appl Microbiol Biotechnol       Date:  2022-05-12       Impact factor: 5.560

7.  Innovation Management? Orienting Sepsis R&D and Technology Transfer Towards Stratified Medicine.

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Journal:  EBioMedicine       Date:  2016-04-10       Impact factor: 8.143

Review 8.  Transforming big data into computational models for personalized medicine and health care.

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  8 in total

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