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Thomas M, Mackes N, Preuss-Dodhy A, Wieland T, Bundschus M. Assessing Privacy Vulnerabilities in Genetic Data Sets: Scoping Review. JMIR BIOINFORMATICS AND BIOTECHNOLOGY 2024; 5:e54332. [PMID: 38935957 PMCID: PMC11165293 DOI: 10.2196/54332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 03/26/2024] [Accepted: 03/29/2024] [Indexed: 06/29/2024]
Abstract
BACKGROUND Genetic data are widely considered inherently identifiable. However, genetic data sets come in many shapes and sizes, and the feasibility of privacy attacks depends on their specific content. Assessing the reidentification risk of genetic data is complex, yet there is a lack of guidelines or recommendations that support data processors in performing such an evaluation. OBJECTIVE This study aims to gain a comprehensive understanding of the privacy vulnerabilities of genetic data and create a summary that can guide data processors in assessing the privacy risk of genetic data sets. METHODS We conducted a 2-step search, in which we first identified 21 reviews published between 2017 and 2023 on the topic of genomic privacy and then analyzed all references cited in the reviews (n=1645) to identify 42 unique original research studies that demonstrate a privacy attack on genetic data. We then evaluated the type and components of genetic data exploited for these attacks as well as the effort and resources needed for their implementation and their probability of success. RESULTS From our literature review, we derived 9 nonmutually exclusive features of genetic data that are both inherent to any genetic data set and informative about privacy risk: biological modality, experimental assay, data format or level of processing, germline versus somatic variation content, content of single nucleotide polymorphisms, short tandem repeats, aggregated sample measures, structural variants, and rare single nucleotide variants. CONCLUSIONS On the basis of our literature review, the evaluation of these 9 features covers the great majority of privacy-critical aspects of genetic data and thus provides a foundation and guidance for assessing genetic data risk.
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Delanote J, Correa Rojo A, Wells PM, Steves CJ, Ertaylan G. Systematic identification of the role of gut microbiota in mental disorders: a TwinsUK cohort study. Sci Rep 2024; 14:3626. [PMID: 38351227 PMCID: PMC10864280 DOI: 10.1038/s41598-024-53929-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Accepted: 02/06/2024] [Indexed: 02/16/2024] Open
Abstract
Mental disorders are complex disorders influenced by multiple genetic, environmental, and biological factors. Specific microbiota imbalances seem to affect mental health status. However, the mechanisms by which microbiota disturbances impact the presence of depression, stress, anxiety, and eating disorders remain poorly understood. Currently, there are no robust biomarkers identified. We proposed a novel pyramid-layer design to accurately identify microbial/metabolomic signatures underlying mental disorders in the TwinsUK registry. Monozygotic and dizygotic twins discordant for mental disorders were screened, in a pairwise manner, for differentially abundant bacterial genera and circulating metabolites. In addition, multivariate analyses were performed, accounting for individual-level confounders. Our pyramid-layer study design allowed us to overcome the limitations of cross-sectional study designs with significant confounder effects and resulted in an association of the abundance of genus Parabacteroides with the diagnosis of mental disorders. Future research should explore the potential role of Parabacteroides as a mediator of mental health status. Our results indicate the potential role of the microbiome as a modifier in mental disorders that might contribute to the development of novel methodologies to assess personal risk and intervention strategies.
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Affiliation(s)
- Julie Delanote
- Sustainable Health, Flemish Institute for Technological Research (VITO), Mol, Belgium
| | - Alejandro Correa Rojo
- Sustainable Health, Flemish Institute for Technological Research (VITO), Mol, Belgium
- Data Science Institute, Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Hasselt University, Diepenbeek, Belgium
| | - Philippa M Wells
- Department of Twin Research and Genetic Epidemiology, King's College London, St Thomas' Hospital, 3-4th Floor South Wing Block D, Westminster Bridge Road, London, SE1 7EH, UK
| | - Claire J Steves
- Department of Twin Research and Genetic Epidemiology, King's College London, St Thomas' Hospital, 3-4th Floor South Wing Block D, Westminster Bridge Road, London, SE1 7EH, UK
- Department of Ageing and Health, St Thomas' Hospital, 9th floor, North Wing, Westminster Bridge Road, London, SE1 7EH, UK
| | - Gökhan Ertaylan
- Sustainable Health, Flemish Institute for Technological Research (VITO), Mol, Belgium.
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3
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Klaproth-Andrade D, Hingerl J, Bruns Y, Smith NH, Träuble J, Wilhelm M, Gagneur J. Deep learning-driven fragment ion series classification enables highly precise and sensitive de novo peptide sequencing. Nat Commun 2024; 15:151. [PMID: 38167372 PMCID: PMC10762064 DOI: 10.1038/s41467-023-44323-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 12/08/2023] [Indexed: 01/05/2024] Open
Abstract
Unlike for DNA and RNA, accurate and high-throughput sequencing methods for proteins are lacking, hindering the utility of proteomics in applications where the sequences are unknown including variant calling, neoepitope identification, and metaproteomics. We introduce Spectralis, a de novo peptide sequencing method for tandem mass spectrometry. Spectralis leverages several innovations including a convolutional neural network layer connecting peaks in spectra spaced by amino acid masses, proposing fragment ion series classification as a pivotal task for de novo peptide sequencing, and a peptide-spectrum confidence score. On spectra for which database search provided a ground truth, Spectralis surpassed 40% sensitivity at 90% precision, nearly doubling state-of-the-art sensitivity. Application to unidentified spectra confirmed its superiority and showcased its applicability to variant calling. Altogether, these algorithmic innovations and the substantial sensitivity increase in the high-precision range constitute an important step toward broadly applicable peptide sequencing.
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Affiliation(s)
- Daniela Klaproth-Andrade
- Computational Molecular Medicine, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
- Munich Data Science Institute, Technical University of Munich, Garching, Germany
| | - Johannes Hingerl
- Computational Molecular Medicine, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Yanik Bruns
- Computational Molecular Medicine, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Nicholas H Smith
- Computational Molecular Medicine, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Jakob Träuble
- Computational Molecular Medicine, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Mathias Wilhelm
- Munich Data Science Institute, Technical University of Munich, Garching, Germany.
- Computational Mass Spectrometry, School of Life Sciences, Technical University of Munich, Freising, Germany.
| | - Julien Gagneur
- Computational Molecular Medicine, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany.
- Munich Data Science Institute, Technical University of Munich, Garching, Germany.
- Institute of Human Genetics, School of Medicine, Technical University of Munich, Munich, Germany.
- Computational Health Center, Helmholtz Center Munich, Neuherberg, Germany.
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4
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Hamzelou S, Belobrajdic D, Broadbent JA, Juhász A, Lee Chang K, Jameson I, Ralph P, Colgrave ML. Utilizing proteomics to identify and optimize microalgae strains for high-quality dietary protein: a review. Crit Rev Biotechnol 2023:1-16. [PMID: 38035669 DOI: 10.1080/07388551.2023.2283376] [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: 04/16/2023] [Accepted: 10/17/2023] [Indexed: 12/02/2023]
Abstract
Algae-derived protein has immense potential to provide high-quality protein foods for the expanding human population. To meet its potential, a broad range of scientific tools are required to identify optimal algal strains from the hundreds of thousands available and identify ideal growing conditions for strains that produce high-quality protein with functional benefits. A research pipeline that includes proteomics can provide a deeper interpretation of microalgal composition and biochemistry in the pursuit of these goals. To date, proteomic investigations have largely focused on pathways that involve lipid production in selected microalgae species. Herein, we report the current state of microalgal proteome measurement and discuss promising approaches for the development of protein-containing food products derived from algae.
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Affiliation(s)
| | | | | | - Angéla Juhász
- School of Science, Australian Research Council Centre of Excellence for Innovations in Peptide and Protein Science, Edith Cowan University, Joondalup, Australia
| | | | - Ian Jameson
- CSIRO Ocean and Atmosphere, Hobart, Australia
| | - Peter Ralph
- Climate Change Cluster, University of Technology Sydney, Ultimo, Australia
| | - Michelle L Colgrave
- CSIRO Agriculture and Food, St Lucia, Australia
- School of Science, Australian Research Council Centre of Excellence for Innovations in Peptide and Protein Science, Edith Cowan University, Joondalup, Australia
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5
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Hill AC, Guo C, Litkowski EM, Manichaikul AW, Yu B, Konigsberg IR, Gorbet BA, Lange LA, Pratte KA, Kechris KJ, DeCamp M, Coors M, Ortega VE, Rich SS, Rotter JI, Gerzsten RE, Clish CB, Curtis JL, Hu X, Obeidat ME, Morris M, Loureiro J, Ngo D, O'Neal WK, Meyers DA, Bleecker ER, Hobbs BD, Cho MH, Banaei-Kashani F, Bowler RP. Large scale proteomic studies create novel privacy considerations. Sci Rep 2023; 13:9254. [PMID: 37286633 PMCID: PMC10247808 DOI: 10.1038/s41598-023-34866-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 05/09/2023] [Indexed: 06/09/2023] Open
Abstract
Privacy protection is a core principle of genomic but not proteomic research. We identified independent single nucleotide polymorphism (SNP) quantitative trait loci (pQTL) from COPDGene and Jackson Heart Study (JHS), calculated continuous protein level genotype probabilities, and then applied a naïve Bayesian approach to link SomaScan 1.3K proteomes to genomes for 2812 independent subjects from COPDGene, JHS, SubPopulations and InteRmediate Outcome Measures In COPD Study (SPIROMICS) and Multi-Ethnic Study of Atherosclerosis (MESA). We correctly linked 90-95% of proteomes to their correct genome and for 95-99% we identify the 1% most likely links. The linking accuracy in subjects with African ancestry was lower (~ 60%) unless training included diverse subjects. With larger profiling (SomaScan 5K) in the Atherosclerosis Risk Communities (ARIC) correct identification was > 99% even in mixed ancestry populations. We also linked proteomes-to-proteomes and used the proteome only to determine features such as sex, ancestry, and first-degree relatives. When serial proteomes are available, the linking algorithm can be used to identify and correct mislabeled samples. This work also demonstrates the importance of including diverse populations in omics research and that large proteomic datasets (> 1000 proteins) can be accurately linked to a specific genome through pQTL knowledge and should not be considered unidentifiable.
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Affiliation(s)
| | | | | | - Ani W Manichaikul
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Bing Yu
- Department of Epidemiology and Human Genetics Center, UTHealth School of Public Health, Houston, TX, USA
| | | | - Betty A Gorbet
- Department of Epidemiology and Human Genetics Center, UTHealth School of Public Health, Houston, TX, USA
| | - Leslie A Lange
- University of Colorado - Anschutz Medical Campus, Aurora, CO, USA
| | | | | | - Matthew DeCamp
- University of Colorado - Anschutz Medical Campus, Aurora, CO, USA
| | - Marilyn Coors
- University of Colorado - Anschutz Medical Campus, Aurora, CO, USA
| | | | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Jerome I Rotter
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Robert E Gerzsten
- Division of Cardiovascular Medicine, Cardiovascular Research Center, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Clary B Clish
- Metabolomics Platform, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA
| | | | - Xiaowei Hu
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | | | | | | | | | - Wanda K O'Neal
- University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | | | | | - Brian D Hobbs
- Harvard Medical School, Boston, MA, USA
- Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Michael H Cho
- Harvard Medical School, Boston, MA, USA
- Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA
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6
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Dief EM, Low PJ, Díez-Pérez I, Darwish N. Advances in single-molecule junctions as tools for chemical and biochemical analysis. Nat Chem 2023; 15:600-614. [PMID: 37106094 DOI: 10.1038/s41557-023-01178-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Accepted: 03/02/2023] [Indexed: 04/29/2023]
Abstract
The development of miniaturized electronics has led to the design and construction of powerful experimental platforms capable of measuring electronic properties to the level of single molecules, along with new theoretical concepts to aid in the interpretation of the data. A new area of activity is now emerging concerned with repurposing the tools of molecular electronics for applications in chemical and biological analysis. Single-molecule junction techniques, such as the scanning tunnelling microscope break junction and related single-molecule circuit approaches have a remarkable capacity to transduce chemical information from individual molecules, sampled in real time, to electrical signals. In this Review, we discuss single-molecule junction approaches as emerging analytical tools for the chemical and biological sciences. We demonstrate how these analytical techniques are being extended to systems capable of probing chemical reaction mechanisms. We also examine how molecular junctions enable the detection of RNA, DNA, and traces of proteins in solution with limits of detection at the zeptomole level.
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Affiliation(s)
- Essam M Dief
- School of Molecular and Life Sciences, Curtin University, Bentley, Western Australia, Australia
| | - Paul J Low
- School of Molecular Sciences, University of Western Australia, Crawley, Western Australia, Australia
| | - Ismael Díez-Pérez
- Department of Chemistry, Faculty of Natural & Mathematical Sciences, King's College London, London, UK
| | - Nadim Darwish
- School of Molecular and Life Sciences, Curtin University, Bentley, Western Australia, Australia.
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7
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Safarlou CW, Jongsma KR, Vermeulen R, Bredenoord AL. The ethical aspects of exposome research: a systematic review. EXPOSOME 2023; 3:osad004. [PMID: 37745046 PMCID: PMC7615114 DOI: 10.1093/exposome/osad004] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
In recent years, exposome research has been put forward as the next frontier for the study of human health and disease. Exposome research entails the analysis of the totality of environmental exposures and their corresponding biological responses within the human body. Increasingly, this is operationalized by big-data approaches to map the effects of internal as well as external exposures using smart sensors and multiomics technologies. However, the ethical implications of exposome research are still only rarely discussed in the literature. Therefore, we conducted a systematic review of the academic literature regarding both the exposome and underlying research fields and approaches, to map the ethical aspects that are relevant to exposome research. We identify five ethical themes that are prominent in ethics discussions: the goals of exposome research, its standards, its tools, how it relates to study participants, and the consequences of its products. Furthermore, we provide a number of general principles for how future ethics research can best make use of our comprehensive overview of the ethical aspects of exposome research. Lastly, we highlight three aspects of exposome research that are most in need of ethical reflection: the actionability of its findings, the epidemiological or clinical norms applicable to exposome research, and the meaning and action-implications of bias.
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Affiliation(s)
- Caspar W. Safarlou
- Department of Global Public Health and Bioethics, Julius Center for
Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The
Netherlands
| | - Karin R. Jongsma
- Department of Global Public Health and Bioethics, Julius Center for
Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The
Netherlands
| | - Roel Vermeulen
- Department of Global Public Health and Bioethics, Julius Center for
Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The
Netherlands
- Department of Population Health Sciences, Utrecht University,
Utrecht, The Netherlands
| | - Annelien L. Bredenoord
- Department of Global Public Health and Bioethics, Julius Center for
Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The
Netherlands
- Erasmus School of Philosophy, Erasmus University Rotterdam,
Rotterdam, The Netherlands
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8
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Fierro-Monti I, Wright JC, Choudhary JS, Vizcaíno JA. Identifying individuals using proteomics: are we there yet? Front Mol Biosci 2022; 9:1062031. [PMID: 36523653 PMCID: PMC9744771 DOI: 10.3389/fmolb.2022.1062031] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Accepted: 11/16/2022] [Indexed: 08/31/2023] Open
Abstract
Multi-omics approaches including proteomics analyses are becoming an integral component of precision medicine. As clinical proteomics studies gain momentum and their sensitivity increases, research on identifying individuals based on their proteomics data is here examined for risks and ethics-related issues. A great deal of work has already been done on this topic for DNA/RNA sequencing data, but it has yet to be widely studied in other omics fields. The current state-of-the-art for the identification of individuals based solely on proteomics data is explained. Protein sequence variation analysis approaches are covered in more detail, including the available analysis workflows and their limitations. We also outline some previous forensic and omics proteomics studies that are relevant for the identification of individuals. Following that, we discuss the risks of patient reidentification using other proteomics data types such as protein expression abundance and post-translational modification (PTM) profiles. In light of the potential identification of individuals through proteomics data, possible legal and ethical implications are becoming increasingly important in the field.
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Affiliation(s)
- Ivo Fierro-Monti
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, United Kingdom
| | | | | | - Juan Antonio Vizcaíno
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, United Kingdom
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9
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Pusparum M, Ertaylan G, Thas O. Individual Reference Intervals for Personalised Interpretation of Clinical and Metabolomics Measurements. J Biomed Inform 2022; 131:104111. [PMID: 35671939 DOI: 10.1016/j.jbi.2022.104111] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 04/22/2022] [Accepted: 06/01/2022] [Indexed: 11/16/2022]
Abstract
The Population Reference Interval (PRI) refers to the range of outcomes that are expected in a healthy population for a clinical or a diagnostic measurement. It is widely used in daily clinical practice and is essential for assisting clinical decision-making in diagnostics and treatment. In this manuscript, we start from the observation that each healthy individual has its own range for a given variable, depending on personal biological traits. This Individual Reference Interval (IRI) can be calculated and be utilised in clinical practice, in combination with the PRI for improved decision making. Nonparametric estimation of IRIs would require quite long time series. To circumvent this problem, we propose methods based on quantile models in combination with penalised parameter estimation methods that allow for information-sharing among the subjects. Our approach considers the calculation of an IRI as a prediction problem rather than an estimation problem. We perform a simulation study designed to benchmark the methods under different assumptions. From the simulation study we conclude that the new methods are robust and provide empirical coverages close to the nominal level. Finally, we evaluate the methods on real-life data consisting of eleven clinical tests and metabolomics measurements from the VITO IAM Frontier study.
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Affiliation(s)
- Murih Pusparum
- Data Science Institute, I-Biostat, Hasselt University, Hasselt 3500, Belgium; Health, Flemish Institute for Technological Research (VITO), Mol 2400, Belgium.
| | - Gökhan Ertaylan
- Health, Flemish Institute for Technological Research (VITO), Mol 2400, Belgium.
| | - Olivier Thas
- Data Science Institute, I-Biostat, Hasselt University, Hasselt 3500, Belgium; Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent 9000, Belgium; National Institute for Applied Statistics Research Australia (NIASRA), University of Wollongong, Wollongong 2500, NSW, Australia.
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10
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Pawlikowski J, Wiechetek M, Majchrowska A. Associations between the Willingness to Donate Samples to Biobanks and Selected Psychological Variables. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19052552. [PMID: 35270246 PMCID: PMC8910049 DOI: 10.3390/ijerph19052552] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 02/10/2022] [Accepted: 02/17/2022] [Indexed: 12/10/2022]
Abstract
Over the past few decades, there has been a dynamic development of biobanks collecting human biological material and data. Advances in biomedical research based on biobanks, however, are highly dependent on the successful enrolment and participation of human subjects. Therefore, it is crucial to recognise those factors affecting the willingness of individuals to participate in biomedical research. There are very few studies pointing to the role of trust, preferred values and specific psychological factors. The aim of our study was the analysis of the most significant relationships between selected moral and psychological variables (i.e., preferred values, types of trust and personality) and willingness to donate biological material to biobanks. The research was carried out on a Polish representative national sample of 1100 people over 18 years of age. Statistical methods with regression models were used during the analyses. The willingness to donate samples to a biobank was associated with different types of trust and specific values. Based on regression analysis, the most important factors related to the willingness to donate material to biobanks seemed to be (1) trust towards scientists and doctors and (2) selected preferred values such as knowledge, self-development and tradition. Other values or personality traits did not seem to be as important in this context. The obtained results can be useful in building the social responsibility of biobankers and scientists, issuing more appropriate opinions by research ethics committees and planning better communication strategies between participants and biobanks.
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Affiliation(s)
- Jakub Pawlikowski
- Department of Humanities and Social Medicine, Medical University of Lublin, 20-093 Lublin, Poland;
- Biobanking and Biomolecular Resources Research Infrastructure Poland, BBMRI.pl Consortium, 54-066 Wrocław, Poland
- Correspondence:
| | - Michał Wiechetek
- Institute of Psychology, The John Paul II Catholic University of Lublin, 20-950 Lublin, Poland;
| | - Anita Majchrowska
- Department of Humanities and Social Medicine, Medical University of Lublin, 20-093 Lublin, Poland;
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11
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Krekora-Zając D, Marciniak B, Pawlikowski J. Recommendations for Creating Codes of Conduct for Processing Personal Data in Biobanking Based on the GDPR art.40. Front Genet 2021; 12:711614. [PMID: 34868197 PMCID: PMC8633112 DOI: 10.3389/fgene.2021.711614] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Accepted: 10/18/2021] [Indexed: 12/02/2022] Open
Abstract
Personal data protection has become a fundamental normative challenge for biobankers and scientists researching human biological samples and associated data. The General Data Protection Regulation (GDPR) harmonises the law on protecting personal data throughout Europe and allows developing codes of conduct for processing personal data based on GDPR art. 40. Codes of conduct are a soft law measure to create protective standards for data processing adapted to the specific area, among others, to biobanking of human biological material. Challenges in this area were noticed by the European Data Protection Supervisor on data protection and Biobanking and BioMolecular Resources Research Infrastructure-European Research Infrastructure Consortium (BBMRI.ERIC). They concern mainly the specification of the definitions of the GDPR and the determination of the appropriate legal basis for data processing, particularly for transferring data to other European countries. Recommendations indicated in the article, which are based on the GDPR, guidelines published by the authority and expert bodies, and our experiences regarding the creation of the Polish code of conduct, should help develop how a code of conduct for processing personal data in biobanks should be developed.
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Affiliation(s)
- Dorota Krekora-Zając
- Department of Comparative Civil Law, Faculty of Law and Administration, University of Warsaw, Warsaw, Poland
- BBMRI.pl Consortium, Wroclaw, Poland
| | - Błażej Marciniak
- BBMRI.pl Consortium, Wroclaw, Poland
- Biobank Laboratory, Department of Molecular Biophysics, Faculty of Biology and Environmental Protection, University of Lodz, Łódź, Poland
| | - Jakub Pawlikowski
- BBMRI.pl Consortium, Wroclaw, Poland
- Department of Humanities and Social Medicine, Medical University of Lublin, Lublin, Poland
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12
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Correa Rojo A, Heylen D, Aerts J, Thas O, Hooyberghs J, Ertaylan G, Valkenborg D. Towards Building a Quantitative Proteomics Toolbox in Precision Medicine: A Mini-Review. Front Physiol 2021; 12:723510. [PMID: 34512391 PMCID: PMC8427610 DOI: 10.3389/fphys.2021.723510] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 08/05/2021] [Indexed: 12/26/2022] Open
Abstract
Precision medicine as a framework for disease diagnosis, treatment, and prevention at the molecular level has entered clinical practice. From the start, genetics has been an indispensable tool to understand and stratify the biology of chronic and complex diseases in precision medicine. However, with the advances in biomedical and omics technologies, quantitative proteomics is emerging as a powerful technology complementing genetics. Quantitative proteomics provide insight about the dynamic behaviour of proteins as they represent intermediate phenotypes. They provide direct biological insights into physiological patterns, while genetics accounting for baseline characteristics. Additionally, it opens a wide range of applications in clinical diagnostics, treatment stratification, and drug discovery. In this mini-review, we discuss the current status of quantitative proteomics in precision medicine including the available technologies and common methods to analyze quantitative proteomics data. Furthermore, we highlight the current challenges to put quantitative proteomics into clinical settings and provide a perspective to integrate proteomics data with genomics data for future applications in precision medicine.
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Affiliation(s)
- Alejandro Correa Rojo
- Data Science Institute, Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Hasselt University, Diepenbeek, Belgium.,Flemish Institute for Technological Research (VITO), Mol, Belgium
| | - Dries Heylen
- Data Science Institute, Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Hasselt University, Diepenbeek, Belgium.,Flemish Institute for Technological Research (VITO), Mol, Belgium
| | - Jan Aerts
- Data Science Institute, Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Hasselt University, Diepenbeek, Belgium
| | - Olivier Thas
- Data Science Institute, Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Hasselt University, Diepenbeek, Belgium.,Department of Applied Mathematics, Computer Science and Statistics, Faculty of Sciences, Ghent University, Ghent, Belgium.,National Institute for Applied Statistics Research Australia (NIASRA), Wollongong, NSW, Australia
| | - Jef Hooyberghs
- Flemish Institute for Technological Research (VITO), Mol, Belgium.,Theoretical Physics, Data Science Institute, Hasselt University, Diepenbeek, Belgium
| | - Gökhan Ertaylan
- Flemish Institute for Technological Research (VITO), Mol, Belgium
| | - Dirk Valkenborg
- Data Science Institute, Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Hasselt University, Diepenbeek, Belgium
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Bandeira N, Deutsch EW, Kohlbacher O, Martens L, Vizcaíno JA. Data Management of Sensitive Human Proteomics Data: Current Practices, Recommendations, and Perspectives for the Future. Mol Cell Proteomics 2021; 20:100071. [PMID: 33711481 PMCID: PMC8056256 DOI: 10.1016/j.mcpro.2021.100071] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 03/01/2021] [Accepted: 03/02/2021] [Indexed: 12/12/2022] Open
Abstract
Today it is the norm that all relevant proteomics data that support the conclusions in scientific publications are made available in public proteomics data repositories. However, given the increase in the number of clinical proteomics studies, an important emerging topic is the management and dissemination of clinical, and thus potentially sensitive, human proteomics data. Both in the United States and in the European Union, there are legal frameworks protecting the privacy of individuals. Implementing privacy standards for publicly released research data in genomics and transcriptomics has led to processes to control who may access the data, so-called "controlled access" data. In parallel with the technological developments in the field, it is clear that the privacy risks of sharing proteomics data need to be properly assessed and managed. In our view, the proteomics community must be proactive in addressing these issues. Yet a careful balance must be kept. On the one hand, neglecting to address the potential of identifiability in human proteomics data could lead to reputational damage of the field, while on the other hand, erecting barriers to open access to clinical proteomics data will inevitably reduce reuse of proteomics data and could substantially delay critical discoveries in biomedical research. In order to balance these apparently conflicting requirements for data privacy and efficient use and reuse of research efforts through the sharing of clinical proteomics data, development efforts will be needed at different levels including bioinformatics infrastructure, policymaking, and mechanisms of oversight.
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Affiliation(s)
- Nuno Bandeira
- Center for Computational Mass Spectrometry, University of California, San Diego (UCSD), La Jolla, California, USA; Department Computer Science and Engineering, University of California, San Diego (UCSD), La Jolla, California, USA; Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego (UCSD), La Jolla, California, USA
| | | | - Oliver Kohlbacher
- Institute for Bioinformatics and Medical Informatics, University of Tübingen, Tübingen, Germany; Quantitative Biology Center, University of Tübingen, Tübingen, Germany; Biomolecular Interactions, Max Planck Institute for Developmental Biology, Tübingen, Germany; Institute for Translational Bioinformatics, University Hospital Tübingen, Tübingen, Germany
| | - Lennart Martens
- VIB-UGent Center for Medical Biotechnology, VIB, Ghent, Belgium; Department of Biomolecular Medicine, Ghent University, Ghent, Belgium
| | - Juan Antonio Vizcaíno
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom.
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Mann SP, Treit PV, Geyer PE, Omenn GS, Mann M. Ethical Principles, Constraints and Opportunities in Clinical Proteomics. Mol Cell Proteomics 2021; 20:100046. [PMID: 33453411 PMCID: PMC7950205 DOI: 10.1016/j.mcpro.2021.100046] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Accepted: 01/04/2021] [Indexed: 12/11/2022] Open
Abstract
Recent advances in mass spectrometry (MS)-based proteomics have vastly increased the quality and scope of biological information that can be derived from human samples. These advances have rendered current workflows increasingly applicable in biomedical and clinical contexts. As proteomics is poised to take an important role in the clinic, associated ethical responsibilities increase in tandem with impacts on the health, privacy, and wellbeing of individuals. We conducted and here report a systematic literature review of ethical issues in clinical proteomics. We add our perspectives from a background of bioethics, the results of our accompanying paper extracting individual-sensitive results from patient samples, and the literature addressing similar issues in genomics. The spectrum of potential issues ranges from patient re-identification to incidental findings of clinical significance. The latter can be divided into actionable and unactionable findings. Some of these have the potential to be employed in discriminatory or privacy-infringing ways. However, incidental findings may also have great positive potential. A plasma proteome profile, for instance, could inform on the general health or disease status of an individual regardless of the narrow diagnostic question that prompted it. We suggest that early discussion of ethical issues in clinical proteomics can ensure that eventual healthcare practices and regulations reflect the considered judgment of the community and anticipate opportunities and problems that may arise as the technology matures.
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Affiliation(s)
- Sebastian Porsdam Mann
- Department of Media, Cognition and Communication, University of Copenhagen, Copenhagen, Denmark; Uehiro Center for Practical Ethics, University of Oxford, Oxford, UK; New address: Faculty of Law, University of Oxford, Oxford, UK.
| | - Peter V Treit
- Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Philipp E Geyer
- Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany; NNF Center for Protein Research, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark; New address: OmicEra Diagnostics GmbH, Planegg, Germany
| | - Gilbert S Omenn
- Departments of Computational Medicine & Bioinformatics, Internal Medicine, Human Genetics, and School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Matthias Mann
- Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany; NNF Center for Protein Research, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark.
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Peeters MKR, Menschaert G. The hunt for sORFs: A multidisciplinary strategy. Exp Cell Res 2020; 391:111923. [PMID: 32135166 DOI: 10.1016/j.yexcr.2020.111923] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Revised: 02/21/2020] [Accepted: 02/23/2020] [Indexed: 11/28/2022]
Abstract
Growing evidence illustrates the shortcomings on the current understanding of the full complexity of the proteome. Previously overlooked small open reading frames (sORFs) and their encoded microproteins have filled important gaps, exerting their function as biologically relevant regulators. The characterization of the full small proteome has potential applications in many fields. Continuous development of techniques and tools led to an improved sORF discovery, where these can originate from bioinformatics analyses, from sequencing routines or proteomics approaches. In this mini review, we discuss the ongoing trends in the three fields and suggest some strategies for further characterization of high potential candidates.
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Affiliation(s)
- Marlies K R Peeters
- BioBix, Department of Data Analysis and Mathematical Modelling, Ghent University, Coupure Links 653, 900, Gent, Belgium
| | - Gerben Menschaert
- BioBix, Department of Data Analysis and Mathematical Modelling, Ghent University, Coupure Links 653, 900, Gent, Belgium.
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Tan Z, Zhu J, Stemmer PM, Sun L, Yang Z, Schultz K, Gaffrey MJ, Cesnik AJ, Yi X, Hao X, Shortreed MR, Shi T, Lubman DM. Comprehensive Detection of Single Amino Acid Variants and Evaluation of Their Deleterious Potential in a PANC-1 Cell Line. J Proteome Res 2020; 19:1635-1646. [PMID: 32058723 DOI: 10.1021/acs.jproteome.9b00840] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Identifying single amino acid variants (SAAVs) in cancer is critical for precision oncology. Several advanced algorithms are now available to identify SAAVs, but attempts to combine different algorithms and optimize them on large data sets to achieve a more comprehensive coverage of SAAVs have not been implemented. Herein, we report an expanded detection of SAAVs in the PANC-1 cell line using three different strategies, which results in the identification of 540 SAAVs in the mass spectrometry data. Among the set of 540 SAAVs, 79 are evaluated as deleterious SAAVs based on analysis using the novel AssVar software in which one of the driver mutations found in each protein of KRAS, TP53, and SLC37A4 is further validated using independent selected reaction monitoring (SRM) analysis. Our study represents the most comprehensive discovery of SAAVs to date and the first large-scale detection of deleterious SAAVs in the PANC-1 cell line. This work may serve as the basis for future research in pancreatic cancer and personal immunotherapy and treatment.
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Affiliation(s)
- Zhijing Tan
- Department of Surgery, The University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Jianhui Zhu
- Department of Surgery, The University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Paul M Stemmer
- Institute of Environmental Health Sciences, Wayne State University, Detroit, Michigan 48202, United States
| | - Liangliang Sun
- Department of Chemistry, Michigan State University, 578 South Shaw Lane, East Lansing, Michigan 48824, United States
| | - Zhichang Yang
- Department of Chemistry, Michigan State University, 578 South Shaw Lane, East Lansing, Michigan 48824, United States
| | - Kendall Schultz
- Integrative Omics Group, Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Matthew J Gaffrey
- Integrative Omics Group, Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Anthony J Cesnik
- Department of Genetics, Stanford University, Stanford, California 94305, United States
| | - Xinpei Yi
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, Texas 77030, United States
| | - Xiaohu Hao
- Shanghai Institutes for Biological Science, Chinese Academy of Science, Shanghai 200031, China
| | - Michael R Shortreed
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
| | - Tujin Shi
- Integrative Omics Group, Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - David M Lubman
- Department of Surgery, The University of Michigan, Ann Arbor, Michigan 48109, United States
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