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Lebowitz MS, Tabb K, Appelbaum PS. Asymmetrical genetic attributions for the presence and absence of health problems. Psychol Health 2024; 39:839-857. [PMID: 36067389 PMCID: PMC9986342 DOI: 10.1080/08870446.2022.2119236] [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: 12/08/2021] [Revised: 08/10/2022] [Accepted: 08/22/2022] [Indexed: 10/14/2022]
Abstract
OBJECTIVE Recent research has suggested that people more readily make genetic attributions for positively valenced or desirable traits than for negatively valenced or undesirable traits-an asymmetry that may be mediated by perceptions that positive characteristics are more 'natural' than negative ones. This research sought to examine whether a similar asymmetry in genetic attributions would emerge between positive and negative health outcomes. DESIGN Across seven experiments, participants were randomly assigned to read a short vignette describing an individual experiencing a health problem (e.g. hypertension) or a corresponding healthy state (e.g. normal blood pressure). MAIN OUTCOME MEASURES All participants provided ratings of naturalness and genetic attributions for the outcome described in their assigned vignette. RESULTS For diagnoses other than addictive disorders, participants rated the presence of a diagnosis as less genetically caused than its absence; for addictive disorders, the presence of a diagnosis was rated as more genetically caused than its absence. Participants consistently rated the presence of a health problem as less natural than its absence. CONCLUSION Even within a single domain of health, people ascribe differing degrees of 'naturalness' and genetic causation to positive versus negative health outcomes, which could impact their preferences for treatment and prevention strategies.
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Affiliation(s)
- Matthew S. Lebowitz
- Department of Psychiatry, Columbia University; NY State Psychiatric Institute, 1051 Riverside Drive, Unit 122, New York, NY 10032, USA
| | - Kathryn Tabb
- Philosophy Program, Bard College, P.O. Box 5000, Annandale-on-Hudson, NY 12504, USA
| | - Paul S. Appelbaum
- Department of Psychiatry, Columbia University; NY State Psychiatric Institute, 1051 Riverside Drive, Unit 122, New York, NY 10032, USA
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2
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Jiang J, Hiron TK, Agbaedeng TA, Malhotra Y, Drydale E, Bancroft J, Ng E, Reschen ME, Davison LJ, O’Callaghan CA. A Novel Macrophage Subpopulation Conveys Increased Genetic Risk of Coronary Artery Disease. Circ Res 2024; 135:6-25. [PMID: 38747151 PMCID: PMC11191562 DOI: 10.1161/circresaha.123.324172] [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: 12/21/2023] [Revised: 04/26/2024] [Accepted: 05/01/2024] [Indexed: 06/22/2024]
Abstract
BACKGROUND Coronary artery disease (CAD), the leading cause of death worldwide, is influenced by both environmental and genetic factors. Although over 250 genetic risk loci have been identified through genome-wide association studies, the specific causal variants and their regulatory mechanisms are still largely unknown, particularly in disease-relevant cell types such as macrophages. METHODS We utilized single-cell RNA-seq and single-cell multiomics approaches in primary human monocyte-derived macrophages to explore the transcriptional regulatory network involved in a critical pathogenic event of coronary atherosclerosis-the formation of lipid-laden foam cells. The relative genetic contribution to CAD was assessed by partitioning disease heritability across different macrophage subpopulations. Meta-analysis of single-cell RNA-seq data sets from 38 human atherosclerotic samples was conducted to provide high-resolution cross-referencing to macrophage subpopulations in vivo. RESULTS We identified 18 782 cis-regulatory elements by jointly profiling the gene expression and chromatin accessibility of >5000 macrophages. Integration with CAD genome-wide association study data prioritized 121 CAD-related genetic variants and 56 candidate causal genes. We showed that CAD heritability was not uniformly distributed and was particularly enriched in the gene programs of a novel CD52-hi lipid-handling macrophage subpopulation. These CD52-hi macrophages displayed significantly less lipoprotein accumulation and were also found in human atherosclerotic plaques. We investigated the cis-regulatory effect of a risk variant rs10488763 on FDX1, implicating the recruitment of AP-1 and C/EBP-β in the causal mechanisms at this locus. CONCLUSIONS Our results provide genetic evidence of the divergent roles of macrophage subsets in atherogenesis and highlight lipid-handling macrophages as a key subpopulation through which genetic variants operate to influence disease. These findings provide an unbiased framework for functional fine-mapping of genome-wide association study results using single-cell multiomics and offer new insights into the genotype-environment interactions underlying atherosclerotic disease.
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Affiliation(s)
- Jiahao Jiang
- Nuffield Department of Medicine, Wellcome Centre for Human Genetics (J.J., T.K.H., T.A.A., Y.M., E.D., J.B., L.J.D., C.A.O.), University of Oxford, United Kingdom
| | - Thomas K. Hiron
- Nuffield Department of Medicine, Wellcome Centre for Human Genetics (J.J., T.K.H., T.A.A., Y.M., E.D., J.B., L.J.D., C.A.O.), University of Oxford, United Kingdom
| | - Thomas A. Agbaedeng
- Nuffield Department of Medicine, Wellcome Centre for Human Genetics (J.J., T.K.H., T.A.A., Y.M., E.D., J.B., L.J.D., C.A.O.), University of Oxford, United Kingdom
| | - Yashaswat Malhotra
- Nuffield Department of Medicine, Wellcome Centre for Human Genetics (J.J., T.K.H., T.A.A., Y.M., E.D., J.B., L.J.D., C.A.O.), University of Oxford, United Kingdom
| | - Edward Drydale
- Nuffield Department of Medicine, Wellcome Centre for Human Genetics (J.J., T.K.H., T.A.A., Y.M., E.D., J.B., L.J.D., C.A.O.), University of Oxford, United Kingdom
| | - James Bancroft
- Nuffield Department of Medicine, Wellcome Centre for Human Genetics (J.J., T.K.H., T.A.A., Y.M., E.D., J.B., L.J.D., C.A.O.), University of Oxford, United Kingdom
| | - Esther Ng
- Nuffield Department of Orthopaedics, Kennedy Institute of Rheumatology, Rheumatology and Musculoskeletal Sciences (E.N.), University of Oxford, United Kingdom
| | - Michael E. Reschen
- Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, United Kingdom (M.E.R.)
| | - Lucy J. Davison
- Nuffield Department of Medicine, Wellcome Centre for Human Genetics (J.J., T.K.H., T.A.A., Y.M., E.D., J.B., L.J.D., C.A.O.), University of Oxford, United Kingdom
- Department of Clinical Science and Services, Royal Veterinary College, Hatfield, United Kingdom (L.J.D.)
| | - Chris A. O’Callaghan
- Nuffield Department of Medicine, Wellcome Centre for Human Genetics (J.J., T.K.H., T.A.A., Y.M., E.D., J.B., L.J.D., C.A.O.), University of Oxford, United Kingdom
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3
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Meng X, Zhu G, Yang YG, Sun T. Targeted delivery strategies: The interactions and applications of nanoparticles in liver diseases. Biomed Pharmacother 2024; 175:116702. [PMID: 38729052 DOI: 10.1016/j.biopha.2024.116702] [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: 02/27/2024] [Revised: 04/29/2024] [Accepted: 05/01/2024] [Indexed: 05/12/2024] Open
Abstract
In recent years, nanoparticles have been broadly utilized in various drugs delivery formulations. Nanodelivery systems have shown promise in solving problems associated with the distribution of hydrophobic drugs and have promoted the accumulation of nanomedicines in the circulation or in organs. However, the injection dose of nanoparticles (NPs) is much greater than that needed by diseased tissues or organs. In other words, most of the NPs are localized off-target and do not reach the desired tissue or organs. With the rapid development of biodegradable and biosafety nanomaterials, the nanovectors represent assurance of safety. However, the off-target effects also induce concerns about the application of NPs, especially in the delivery of gene editing tools. Therefore, a complete understanding of the biological responses to NPs in the body will clearly guide the design of targeted delivery of NPs. The different properties of various nanodelivery systems may induce diverse interactions between carriers and organs. In this review, we describe the relationship between the liver, the most influenced organ of systemic administration of NPs, and targeted delivery nanoplatforms. Various transport vehicles have adopted multiple delivery strategies for the targeted delivery to the cells in the homeostasis liver and in diseased liver. Additionally, nanodelivery systems provide a novel strategy for treating incurable diseases. The appearance of a targeted delivery has profoundly improved the application of NPs to liver diseases.
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Affiliation(s)
- Xiandi Meng
- Key Laboratory of Organ Regeneration and Transplantation of Ministry of Education, Institute of Immunology, The First Hospital, Jilin University, Changchun, Jilin, China; National-local Joint Engineering Laboratory of Animal Models for Human Diseases, Changchun, Jilin, China
| | - Ge Zhu
- Key Laboratory of Organ Regeneration and Transplantation of Ministry of Education, Institute of Immunology, The First Hospital, Jilin University, Changchun, Jilin, China; National-local Joint Engineering Laboratory of Animal Models for Human Diseases, Changchun, Jilin, China
| | - Yong-Guang Yang
- Key Laboratory of Organ Regeneration and Transplantation of Ministry of Education, Institute of Immunology, The First Hospital, Jilin University, Changchun, Jilin, China; International Center of Future Science, Jilin University, Changchun, Jilin, China; National-local Joint Engineering Laboratory of Animal Models for Human Diseases, Changchun, Jilin, China.
| | - Tianmeng Sun
- Key Laboratory of Organ Regeneration and Transplantation of Ministry of Education, Institute of Immunology, The First Hospital, Jilin University, Changchun, Jilin, China; International Center of Future Science, Jilin University, Changchun, Jilin, China; National-local Joint Engineering Laboratory of Animal Models for Human Diseases, Changchun, Jilin, China; State Key Laboratory of Supramolecular Structure and Materials, Jilin University, Changchun, Jilin, China.
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4
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García S. A, Costa M, García-Zarzoso A, Pastor O. CardioHotspots: a database of mutational hotspots for cardiac disorders. Database (Oxford) 2024; 2024:0. [PMID: 38752292 PMCID: PMC11096770 DOI: 10.1093/database/baae034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 03/21/2024] [Accepted: 04/20/2024] [Indexed: 05/19/2024]
Abstract
Mutational hotspots are DNA regions with an abnormally high frequency of genetic variants. Identifying whether a variant is located in a mutational hotspot is critical for determining the variant's role in disorder predisposition, development, and treatment response. Despite their significance, current databases on mutational hotspots are limited to the oncology domain. However, identifying mutational hotspots is critical for any disorder in which genetics plays a role. This is true for the world's leading cause of death: cardiac disorders. In this work, we present CardioHotspots, a literature-based database of manually curated hotspots for cardiac diseases. This is the only database we know of that provides high-quality and easily accessible information about hotspots associated with cardiac disorders. CardioHotspots is publicly accessible via a web-based platform (https://genomics-hub.pros.dsic.upv.es:3099/). Database URL: https://genomics-hub.pros.dsic.upv.es:3099/.
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Affiliation(s)
- Alberto García S.
- *Corresponding author: Tel: +34 96 387 70 00; Fax: +34 96 387 90 09;
| | - Mireia Costa
- PROS Research Center, VRAIN, Polytechnic University of Valencia, Camino de Vera S/N, Valencia 46022, Spain
| | - Alba García-Zarzoso
- PROS Research Center, VRAIN, Polytechnic University of Valencia, Camino de Vera S/N, Valencia 46022, Spain
| | - Oscar Pastor
- PROS Research Center, VRAIN, Polytechnic University of Valencia, Camino de Vera S/N, Valencia 46022, Spain
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5
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Yurkovich JT, Evans SJ, Rappaport N, Boore JL, Lovejoy JC, Price ND, Hood LE. The transition from genomics to phenomics in personalized population health. Nat Rev Genet 2024; 25:286-302. [PMID: 38093095 DOI: 10.1038/s41576-023-00674-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/03/2023] [Indexed: 03/21/2024]
Abstract
Modern health care faces several serious challenges, including an ageing population and its inherent burden of chronic diseases, rising costs and marginal quality metrics. By assessing and optimizing the health trajectory of each individual using a data-driven personalized approach that reflects their genetics, behaviour and environment, we can start to address these challenges. This assessment includes longitudinal phenome measures, such as the blood proteome and metabolome, gut microbiome composition and function, and lifestyle and behaviour through wearables and questionnaires. Here, we review ongoing large-scale genomics and longitudinal phenomics efforts and the powerful insights they provide into wellness. We describe our vision for the transformation of the current health care from disease-oriented to data-driven, wellness-oriented and personalized population health.
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Affiliation(s)
- James T Yurkovich
- Phenome Health, Seattle, WA, USA
- Center for Phenomic Health, The Buck Institute for Research on Aging, Novato, CA, USA
- Department of Bioengineering, University of Texas at Dallas, Richardson, TX, USA
| | - Simon J Evans
- Phenome Health, Seattle, WA, USA
- Center for Phenomic Health, The Buck Institute for Research on Aging, Novato, CA, USA
| | - Noa Rappaport
- Center for Phenomic Health, The Buck Institute for Research on Aging, Novato, CA, USA
- Institute for Systems Biology, Seattle, WA, USA
| | - Jeffrey L Boore
- Phenome Health, Seattle, WA, USA
- Center for Phenomic Health, The Buck Institute for Research on Aging, Novato, CA, USA
| | - Jennifer C Lovejoy
- Phenome Health, Seattle, WA, USA
- Center for Phenomic Health, The Buck Institute for Research on Aging, Novato, CA, USA
- Institute for Systems Biology, Seattle, WA, USA
| | - Nathan D Price
- Institute for Systems Biology, Seattle, WA, USA
- Thorne HealthTech, New York, NY, USA
- Department of Bioengineering, University of Washington, Seattle, WA, USA
- Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA, USA
| | - Leroy E Hood
- Phenome Health, Seattle, WA, USA.
- Center for Phenomic Health, The Buck Institute for Research on Aging, Novato, CA, USA.
- Institute for Systems Biology, Seattle, WA, USA.
- Department of Bioengineering, University of Washington, Seattle, WA, USA.
- Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA, USA.
- Department of Immunology, University of Washington, Seattle, WA, USA.
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6
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Chafai N, Bonizzi L, Botti S, Badaoui B. Emerging applications of machine learning in genomic medicine and healthcare. Crit Rev Clin Lab Sci 2024; 61:140-163. [PMID: 37815417 DOI: 10.1080/10408363.2023.2259466] [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: 06/19/2023] [Accepted: 09/12/2023] [Indexed: 10/11/2023]
Abstract
The integration of artificial intelligence technologies has propelled the progress of clinical and genomic medicine in recent years. The significant increase in computing power has facilitated the ability of artificial intelligence models to analyze and extract features from extensive medical data and images, thereby contributing to the advancement of intelligent diagnostic tools. Artificial intelligence (AI) models have been utilized in the field of personalized medicine to integrate clinical data and genomic information of patients. This integration allows for the identification of customized treatment recommendations, ultimately leading to enhanced patient outcomes. Notwithstanding the notable advancements, the application of artificial intelligence (AI) in the field of medicine is impeded by various obstacles such as the limited availability of clinical and genomic data, the diversity of datasets, ethical implications, and the inconclusive interpretation of AI models' results. In this review, a comprehensive evaluation of multiple machine learning algorithms utilized in the fields of clinical and genomic medicine is conducted. Furthermore, we present an overview of the implementation of artificial intelligence (AI) in the fields of clinical medicine, drug discovery, and genomic medicine. Finally, a number of constraints pertaining to the implementation of artificial intelligence within the healthcare industry are examined.
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Affiliation(s)
- Narjice Chafai
- Laboratory of Biodiversity, Ecology, and Genome, Faculty of Sciences, Department of Biology, Mohammed V University in Rabat, Rabat, Morocco
| | - Luigi Bonizzi
- Department of Biomedical, Surgical and Dental Science, University of Milan, Milan, Italy
| | - Sara Botti
- PTP Science Park, Via Einstein - Loc. Cascina Codazza, Lodi, Italy
| | - Bouabid Badaoui
- Laboratory of Biodiversity, Ecology, and Genome, Faculty of Sciences, Department of Biology, Mohammed V University in Rabat, Rabat, Morocco
- African Sustainable Agriculture Research Institute (ASARI), Mohammed VI Polytechnic University (UM6P), Laâyoune, Morocco
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7
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El-Achkar TM, Eadon MT, Kretzler M, Himmelfarb J. Precision Medicine in Nephrology: An Integrative Framework of Multidimensional Data in the Kidney Precision Medicine Project. Am J Kidney Dis 2024; 83:402-410. [PMID: 37839688 PMCID: PMC10922684 DOI: 10.1053/j.ajkd.2023.08.015] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 08/20/2023] [Accepted: 08/25/2023] [Indexed: 10/17/2023]
Abstract
Chronic kidney disease (CKD) and acute kidney injury (AKI) are heterogeneous syndromes defined clinically by serial measures of kidney function. Each condition possesses strong histopathologic associations, including glomerular obsolescence or acute tubular necrosis, respectively. Despite such characterization, there remains wide variation in patient outcomes and treatment responses. Precision medicine efforts, as exemplified by the Kidney Precision Medicine Project (KPMP), have begun to establish evolving, spatially anchored, cellular and molecular atlases of the cell types, states, and niches of the kidney in health and disease. The KPMP atlas provides molecular context for CKD and AKI disease drivers and will help define subtypes of disease that are not readily apparent from canonical functional or histopathologic characterization but instead are appreciable through advanced clinical phenotyping, pathomic, transcriptomic, proteomic, epigenomic, and metabolomic interrogation of kidney biopsy samples. This perspective outlines the structure of the KPMP, its approach to the integration of these diverse datasets, and its major outputs relevant to future patient care.
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Affiliation(s)
- Tarek M El-Achkar
- Division of Nephrology, School of Medicine, Indiana University, and Richard L. Roudebush Veteran Affairs Medical Center, Indianapolis, Indiana
| | - Michael T Eadon
- Division of Nephrology, School of Medicine, Indiana University, and Richard L. Roudebush Veteran Affairs Medical Center, Indianapolis, Indiana
| | - Matthias Kretzler
- Department of Computational Medicine & Bioinformatics, and Division of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan
| | - Jonathan Himmelfarb
- Kidney Research Institute and Division of Nephrology, University of Washington, Seattle, Washington.
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Yuan X, Su J, Wang J, Dai B, Sun Y, Zhang K, Li Y, Chuan J, Tang C, Yu Y, Gong Q. Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases. Sci Rep 2024; 14:2845. [PMID: 38310124 PMCID: PMC10838329 DOI: 10.1038/s41598-024-53461-x] [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: 08/30/2023] [Accepted: 01/31/2024] [Indexed: 02/05/2024] Open
Abstract
Phenotype-guided gene prioritizers have proved a highly efficient approach to identifying causal genes for Mendelian diseases. In our previous study, we preliminarily evaluated the performance of ten prioritizers. However, all the selected software was run based on default settings and singleton mode. With a large-scale family dataset from Deciphering Developmental Disorders (DDD) project (N = 305) and an in-house trio cohort (N = 152), the four optimal performers in our prior study including Exomiser, PhenIX, AMELIE, and LIRCIAL were further assessed through parameter optimization and/or the utilization of trio mode. The in-depth assessment revealed high diagnostic yields of the four prioritizers with refined preferences, each alone or together: (1) 83.3-91.8% of the causal genes were presented among the first ten candidates in the final ranking lists of the four tools; (2) Over 97.7% of the causal genes were successfully captured within the top 50 by either of the four software. Exomiser did best in directly hitting the target (ranking the causal gene at the very top) while LIRICAL displayed a predominant overall detection capability. Besides, cases affected by low-penetrance and high-frequency pathogenic variants were found misjudged during the automated prioritization process. The discovery of the limitations shed light on the specific directions of future enhancement for causal-gene ranking tools.
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Affiliation(s)
- Xiao Yuan
- Changsha Kingmed Center for Clinical Laboratory, Lutian Road 28, Changsha, 410000, Hunan, China
| | - Jieqiong Su
- Changsha Kingmed Center for Clinical Laboratory, Lutian Road 28, Changsha, 410000, Hunan, China
| | - Jing Wang
- Changsha Kingmed Center for Clinical Laboratory, Lutian Road 28, Changsha, 410000, Hunan, China
| | - Bing Dai
- Changsha Kingmed Center for Clinical Laboratory, Lutian Road 28, Changsha, 410000, Hunan, China
| | - Yanfang Sun
- Changsha Kingmed Center for Clinical Laboratory, Lutian Road 28, Changsha, 410000, Hunan, China
| | - Keke Zhang
- Changsha Kingmed Center for Clinical Laboratory, Lutian Road 28, Changsha, 410000, Hunan, China
| | - Yinghua Li
- Guangzhou Kingmed Center for Clinical Laboratory, Guangzhou, Guangdong, China
| | - Jun Chuan
- Genetalks Biotech. Co., Ltd., Changsha, Hunan, China
| | - Chunyan Tang
- Changsha Kingmed Center for Clinical Laboratory, Lutian Road 28, Changsha, 410000, Hunan, China
| | - Yan Yu
- Changsha Kingmed Center for Clinical Laboratory, Lutian Road 28, Changsha, 410000, Hunan, China.
| | - Qiang Gong
- Changsha Kingmed Center for Clinical Laboratory, Lutian Road 28, Changsha, 410000, Hunan, China.
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Dakilah I, Harb A, Abu-Gharbieh E, El-Huneidi W, Taneera J, Hamoudi R, Semreen MH, Bustanji Y. Potential of CDC25 phosphatases in cancer research and treatment: key to precision medicine. Front Pharmacol 2024; 15:1324001. [PMID: 38313315 PMCID: PMC10834672 DOI: 10.3389/fphar.2024.1324001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 01/04/2024] [Indexed: 02/06/2024] Open
Abstract
The global burden of cancer continues to rise, underscoring the urgency of developing more effective and precisely targeted therapies. This comprehensive review explores the confluence of precision medicine and CDC25 phosphatases in the context of cancer research. Precision medicine, alternatively referred to as customized medicine, aims to customize medical interventions by taking into account the genetic, genomic, and epigenetic characteristics of individual patients. The identification of particular genetic and molecular drivers driving cancer helps both diagnostic accuracy and treatment selection. Precision medicine utilizes sophisticated technology such as genome sequencing and bioinformatics to elucidate genetic differences that underlie the proliferation of cancer cells, hence facilitating the development of customized therapeutic interventions. CDC25 phosphatases, which play a crucial role in governing the progression of the cell cycle, have garnered significant attention as potential targets for cancer treatment. The dysregulation of CDC25 is a characteristic feature observed in various types of malignancies, hence classifying them as proto-oncogenes. The proteins in question, which operate as phosphatases, play a role in the activation of Cyclin-dependent kinases (CDKs), so promoting the advancement of the cell cycle. CDC25 inhibitors demonstrate potential as therapeutic drugs for cancer treatment by specifically blocking the activity of CDKs and modulating the cell cycle in malignant cells. In brief, precision medicine presents a potentially fruitful option for augmenting cancer research, diagnosis, and treatment, with an emphasis on individualized care predicated upon patients' genetic and molecular profiles. The review highlights the significance of CDC25 phosphatases in the advancement of cancer and identifies them as promising candidates for therapeutic intervention. This statement underscores the significance of doing thorough molecular profiling in order to uncover the complex molecular characteristics of cancer cells.
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Affiliation(s)
- Ibraheem Dakilah
- Research Institute of Medical and Health Sciences, University of Sharjah, Sharjah, United Arab Emirates
| | - Amani Harb
- Department of Basic Sciences, Faculty of Arts and Sciences, Al-Ahliyya Amman University, Amman, Jordan
| | - Eman Abu-Gharbieh
- Research Institute of Medical and Health Sciences, University of Sharjah, Sharjah, United Arab Emirates
- College of Medicine, University of Sharjah, Sharjah, United Arab Emirates
| | - Waseem El-Huneidi
- Research Institute of Medical and Health Sciences, University of Sharjah, Sharjah, United Arab Emirates
- College of Medicine, University of Sharjah, Sharjah, United Arab Emirates
| | - Jalal Taneera
- Research Institute of Medical and Health Sciences, University of Sharjah, Sharjah, United Arab Emirates
- College of Medicine, University of Sharjah, Sharjah, United Arab Emirates
| | - Rifat Hamoudi
- Research Institute of Medical and Health Sciences, University of Sharjah, Sharjah, United Arab Emirates
- College of Medicine, University of Sharjah, Sharjah, United Arab Emirates
- Division of Surgery and Interventional Science, University College London, London, United Kingdom
| | - Mohammed H Semreen
- College of Pharmacy, University of Sharjah, Sharjah, United Arab Emirates
| | - Yasser Bustanji
- Research Institute of Medical and Health Sciences, University of Sharjah, Sharjah, United Arab Emirates
- College of Medicine, University of Sharjah, Sharjah, United Arab Emirates
- School of Pharmacy, The University of Jordan, Amman, Jordan
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10
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Martorella M, Kasela S, Garcia-Flores R, Gokden A, Castel SE, Lappalainen T. Evaluation of noninvasive biospecimens for transcriptome studies. BMC Genomics 2023; 24:790. [PMID: 38114913 PMCID: PMC10729488 DOI: 10.1186/s12864-023-09875-4] [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: 07/17/2023] [Accepted: 12/05/2023] [Indexed: 12/21/2023] Open
Abstract
Transcriptome studies disentangle functional mechanisms of gene expression regulation and may elucidate the underlying biology of disease processes. However, the types of tissues currently collected typically assay a single post-mortem timepoint or are limited to investigating cell types found in blood. Noninvasive tissues may improve disease-relevant discovery by enabling more complex longitudinal study designs, by capturing different and potentially more applicable cell types, and by increasing sample sizes due to reduced collection costs and possible higher enrollment from vulnerable populations. Here, we develop methods for sampling noninvasive biospecimens, investigate their performance across commercial and in-house library preparations, characterize their biology, and assess the feasibility of using noninvasive tissues in a multitude of transcriptomic applications. We collected buccal swabs, hair follicles, saliva, and urine cell pellets from 19 individuals over three to four timepoints, for a total of 300 unique biological samples, which we then prepared with replicates across three library preparations, for a final tally of 472 transcriptomes. Of the four tissues we studied, we found hair follicles and urine cell pellets to be most promising due to the consistency of sample quality, the cell types and expression profiles we observed, and their performance in disease-relevant applications. This is the first study to thoroughly delineate biological and technical features of noninvasive samples and demonstrate their use in a wide array of transcriptomic and clinical analyses. We anticipate future use of these biospecimens will facilitate discovery and development of clinical applications.
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Affiliation(s)
- Molly Martorella
- New York Genome Center, New York, NY, USA.
- Department of Systems Biology, Columbia University, New York, NY, USA.
| | - Silva Kasela
- New York Genome Center, New York, NY, USA
- Department of Systems Biology, Columbia University, New York, NY, USA
| | - Renee Garcia-Flores
- New York Genome Center, New York, NY, USA
- Department of Computer Science, Columbia University, New York, NY, USA
- Undergraduate Program On Genomic Sciences, National Autonomous University of Mexico, Cuernavaca, Morelos, Mexico
| | | | - Stephane E Castel
- New York Genome Center, New York, NY, USA.
- Department of Systems Biology, Columbia University, New York, NY, USA.
| | - Tuuli Lappalainen
- New York Genome Center, New York, NY, USA.
- Department of Systems Biology, Columbia University, New York, NY, USA.
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden.
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Lancaster T, Creese B, Escott-Price V, Driver I, Menzies G, Khan Z, Corbett A, Ballard C, Williams J, Murphy K, Chandler H. Proof-of-concept recall-by-genotype study of extremely low and high Alzheimer's polygenic risk reveals autobiographical deficits and cingulate cortex correlates. Alzheimers Res Ther 2023; 15:213. [PMID: 38087383 PMCID: PMC10714651 DOI: 10.1186/s13195-023-01362-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Accepted: 11/29/2023] [Indexed: 12/18/2023]
Abstract
BACKGROUND Genome-wide association studies demonstrate that Alzheimer's disease (AD) has a highly polygenic architecture, where thousands of independent genetic variants explain risk with high classification accuracy. This AD polygenic risk score (AD-PRS) has been previously linked to preclinical cognitive and neuroimaging features observed in asymptomatic individuals. However, shared variance between AD-PRS and neurocognitive features are small, suggesting limited preclinical utility. METHODS Here, we recruited sixteen clinically asymptomatic individuals (mean age 67; range 58-76) with either extremely low / high AD-PRS (defined as at least 2 standard deviations from the wider sample mean (N = 4504; N EFFECTIVE = 90)) with comparable age sex and education level. We assessed group differences in autobiographical memory and T1-weighted structural neuroimaging features. RESULTS We observed marked reductions in autobiographical recollection (Cohen's d = - 1.66; P FDR = 0.014) and midline structure (cingulate) thickness (Cohen's d = - 1.55, P FDR = 0.05), with no difference in hippocampal volume (P > 0.3). We further confirm the negative association between AD-PRS and cingulate thickness in a larger study with a comparable age (N = 31,966, β = - 0.002, P = 0.011), supporting the validity of our approach. CONCLUSIONS These observations conform with multiple streams of prior evidence suggesting alterations in cingulate structures may occur in individuals with higher AD genetic risk. We were able to use a genetically informed research design strategy that significantly improved the efficiency and power of the study. Thus, we further demonstrate that the recall-by-genotype of AD-PRS from wider samples is a promising approach for the detection, assessment, and intervention in specific individuals with increased AD genetic risk.
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Affiliation(s)
- Thomas Lancaster
- Department of Psychology, University of Bath, Bath, UK.
- School of Physics and Astronomy, Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, UK.
- Dementia Research Institute (UKDRI), Cardiff University, Cardiff, UK.
| | - Byron Creese
- Department of Clinical and Biomedical Science, Faculty of Health and Life Sciences, University of Exeter, Exeter, UK
- Department of Life Sciences, Brunel University London, Uxbridge, west London, UK
| | - Valentina Escott-Price
- Division of Neuroscience and Mental Health, School of Medicine, Cardiff University, Cardiff, UK
| | - Ian Driver
- School of Physics and Astronomy, Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, UK
| | - Georgina Menzies
- Dementia Research Institute (UKDRI), Cardiff University, Cardiff, UK
- School of Biosciences, Cardiff University, Cardiff, UK
| | - Zunera Khan
- Institute of Psychiatry, King's College London, Psychology & Neuroscience, London, UK
| | - Anne Corbett
- Deptartment of Health & Community Sciences, University of Exeter, Exeter, UK
| | - Clive Ballard
- Department of Clinical and Biomedical Science, Faculty of Health and Life Sciences, University of Exeter, Exeter, UK
| | - Julie Williams
- Dementia Research Institute (UKDRI), Cardiff University, Cardiff, UK
| | - Kevin Murphy
- School of Physics and Astronomy, Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, UK
| | - Hannah Chandler
- School of Physics and Astronomy, Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, UK
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Chen YT, Nyam TTE, Tsai LC, Chang CH, Su CL, Ho CH, Chio CC, Gean PW, Kuo JR. Pretreatment with Lovastatin Improves Depression-Like Behavior After Traumatic Brain Injury Through Activation of the AMPK Pathway. World Neurosurg 2023; 180:e350-e363. [PMID: 37757945 DOI: 10.1016/j.wneu.2023.09.071] [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: 06/02/2023] [Accepted: 09/16/2023] [Indexed: 09/29/2023]
Abstract
BACKGROUND The beneficial effect of pretreatment with statins on traumatic brain injury (TBI)-induced depression and anxiety and its mechanism of action remain unclear. In this study, we combined epidemiological and experimental animal data to clarify this issue. METHODS We used the Taiwan National Health Insurance database to identify patients who were diagnosed with TBI from 2000 to 2013 and compared patients with and without statin treatment matched by age, sex, and underlying comorbidities in a 1:1 ratio. The risk of developing depression and/or anxiety was compared between patients with and without a statin using Cox proportional hazards regression. We also used a rat model to assess the effect of lovastatin pretreatment on neurobehavioral and neuropathological changes following TBI. RESULTS The risk of developing depression was lower in the 41,803 patients in the statin cohort than nonstatin cohort (adjusted hazard ratio, 0.91 [95% confidence interval, 0.83-0.99]). In animal models, the lovastatin group had significantly reduced infarct volume, decreased immobility time and latency to eat, a reduced number of Fluoro- Jade-positive cells and levels of glial fibrillary acidic protein and tumor necrosis factor-alpha, and increased adenosine monophosphate -activated protein kinase (AMPK) and its upstream kinase liver kinase B1 in the hippocampal dentate gyrus. These effects were blocked in AMPK inhibitor-pretreated TBI rats. CONCLUSIONS Our epidemiological data showed that a decreased risk of depression was associated with statin pretreatment, which was supported by an animal study. The underlying mechanism for this appears to involve AMPK activation in the statin pretreatment-induced alleviation of TBI.
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Affiliation(s)
- Yu-Ting Chen
- Department of Neurosurgery, Chi Mei Medical Center, Tainan, Taiwan
| | | | - Li-Chen Tsai
- Department of Pharmacology, College of Medicine, National Cheng-Kung University, Tainan, Taiwan
| | - Chih-Hua Chang
- Department of Pharmacology, College of Medicine, National Cheng-Kung University, Tainan, Taiwan
| | - Chun-Lin Su
- Department of Pharmacology, College of Medicine, National Cheng-Kung University, Tainan, Taiwan
| | - Chung-Han Ho
- Department of Medical Research, Chi-Mei Medical Center, Tainan, Taiwan; Department of Information Management, Southern Taiwan University of Science and Technology, Tainan, Taiwan
| | - Chung-Ching Chio
- Department of Neurosurgery, Chi Mei Medical Center, Tainan, Taiwan
| | - Po-Wu Gean
- Department of Pharmacology, College of Medicine, National Cheng-Kung University, Tainan, Taiwan
| | - Jinn-Rung Kuo
- Department of Neurosurgery, Chi Mei Medical Center, Tainan, Taiwan; Department of Medical Research, Chi-Mei Medical Center, Tainan, Taiwan; Department of Post-Baccalaureate Medicine, National Sun Yat-sen University, Kaohsiung, Taiwan.
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13
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Zhang T, Xu Y. Update on Surgical Management of FAP. Clin Colon Rectal Surg 2023; 36:385-390. [PMID: 37795461 PMCID: PMC10547540 DOI: 10.1055/s-0043-1767707] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/06/2023]
Abstract
Familial adenomatous polyposis (FAP) is an autosomal dominant disease caused by pathogenic germline adenomatous polyposis coli mutation, and characterized with multiple adenomas in the colon and the rectum. Various genetic variants have been confirmed to be associated with corresponding FAP phenotypes, which play important roles in the diagnosis and surgical treatment of FAP. Generally, proctocolectomy is recommended for FAP patients at the age of 20s. Exceptionally, for patients with attenuated FAP, high-risk of desmoid, chemoprevention therapy, or other circumstances, surgery can be postponed. With the wide application of minimal invasive surgery in colorectal cancer, laparoscopic, robotic surgery, and natural orifice specimen extraction are proved to be feasible for FAP patients, but high-level evidences are needed to confirm their safety and advantages. In the times of precise medicine, the surgical management of FAP should vary with individuals based on genotype, phenotype, and clinical practice. Therefore, in addition to innovation in surgical procedures, investigation in links between genetic features and phenotypes will be helpful to optimize the surgical management of FAP in the future.
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Affiliation(s)
- Tianqi Zhang
- Department of Colorectal Surgery, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China
| | - Ye Xu
- Department of Colorectal Surgery, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China
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14
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Zushin PJH, Mukherjee S, Wu JC. FDA Modernization Act 2.0: transitioning beyond animal models with human cells, organoids, and AI/ML-based approaches. J Clin Invest 2023; 133:e175824. [PMID: 37909337 PMCID: PMC10617761 DOI: 10.1172/jci175824] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2023] Open
Affiliation(s)
- Peter-James H. Zushin
- Stanford Cardiovascular Institute and
- Department of Medicine (Division of Cardiology), Stanford University, Stanford, California, USA
| | | | - Joseph C. Wu
- Stanford Cardiovascular Institute and
- Department of Medicine (Division of Cardiology), Stanford University, Stanford, California, USA
- Greenstone Biosciences, Palo Alto, California, USA
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15
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Cai M, Wang Z, Xiao J, Hu X, Chen G, Yang C. XMAP: Cross-population fine-mapping by leveraging genetic diversity and accounting for confounding bias. Nat Commun 2023; 14:6870. [PMID: 37898663 PMCID: PMC10613261 DOI: 10.1038/s41467-023-42614-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Accepted: 10/17/2023] [Indexed: 10/30/2023] Open
Abstract
Fine-mapping prioritizes risk variants identified by genome-wide association studies (GWASs), serving as a critical step to uncover biological mechanisms underlying complex traits. However, several major challenges still remain for existing fine-mapping methods. First, the strong linkage disequilibrium among variants can limit the statistical power and resolution of fine-mapping. Second, it is computationally expensive to simultaneously search for multiple causal variants. Third, the confounding bias hidden in GWAS summary statistics can produce spurious signals. To address these challenges, we develop a statistical method for cross-population fine-mapping (XMAP) by leveraging genetic diversity and accounting for confounding bias. By using cross-population GWAS summary statistics from global biobanks and genomic consortia, we show that XMAP can achieve greater statistical power, better control of false positive rate, and substantially higher computational efficiency for identifying multiple causal signals, compared to existing methods. Importantly, we show that the output of XMAP can be integrated with single-cell datasets, which greatly improves the interpretation of putative causal variants in their cellular context at single-cell resolution.
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Affiliation(s)
- Mingxuan Cai
- Department of Biostatistics, City University of Hong Kong, Hong Kong SAR, China.
| | - Zhiwei Wang
- Guangzhou HKUST Fok Ying Tung Research Institute, Guangzhou, 511458, China
- Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong SAR, China
| | - Jiashun Xiao
- Shenzhen Research Institute of Big Data, Shenzhen, 518172, China
| | - Xianghong Hu
- Guangzhou HKUST Fok Ying Tung Research Institute, Guangzhou, 511458, China
- Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong SAR, China
| | - Gang Chen
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, 410083, China
- WeGene, Shenzhen Zaozhidao Technology Co., Ltd, Shenzhen, 518040, China
- Graduate Affairs, Faculty of Medicine, Chulalongkorn University, 10330, Bangkok, Thailand
| | - Can Yang
- Guangzhou HKUST Fok Ying Tung Research Institute, Guangzhou, 511458, China.
- Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong SAR, China.
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16
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Lakiotaki K, Papadovasilakis Z, Lagani V, Fafalios S, Charonyktakis P, Tsagris M, Tsamardinos I. Automated machine learning for genome wide association studies. Bioinformatics 2023; 39:btad545. [PMID: 37672022 PMCID: PMC10562960 DOI: 10.1093/bioinformatics/btad545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 06/29/2023] [Accepted: 09/05/2023] [Indexed: 09/07/2023] Open
Abstract
MOTIVATION Genome-wide association studies (GWAS) present several computational and statistical challenges for their data analysis, including knowledge discovery, interpretability, and translation to clinical practice. RESULTS We develop, apply, and comparatively evaluate an automated machine learning (AutoML) approach, customized for genomic data that delivers reliable predictive and diagnostic models, the set of genetic variants that are important for predictions (called a biosignature), and an estimate of the out-of-sample predictive power. This AutoML approach discovers variants with higher predictive performance compared to standard GWAS methods, computes an individual risk prediction score, generalizes to new, unseen data, is shown to better differentiate causal variants from other highly correlated variants, and enhances knowledge discovery and interpretability by reporting multiple equivalent biosignatures. AVAILABILITY AND IMPLEMENTATION Code for this study is available at: https://github.com/mensxmachina/autoML-GWAS. JADBio offers a free version at: https://jadbio.com/sign-up/. SNP data can be downloaded from the EGA repository (https://ega-archive.org/). PRS data are found at: https://www.aicrowd.com/challenges/opensnp-height-prediction. Simulation data to study population structure can be found at: https://easygwas.ethz.ch/data/public/dataset/view/1/.
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Affiliation(s)
| | - Zaharias Papadovasilakis
- Department of Computer Science, University of Crete, Heraklion, Greece
- JADBio Gnosis DA S.A., Science and Technology Park of Crete, GR-70013 Heraklion, Greece
- Laboratory of Immune Regulation and Tolerance, School of Medicine, University of Crete, Heraklion, Greece
| | - Vincenzo Lagani
- Biological and Environmental Sciences and Engineering Division (BESE), King Abdullah University of Science and Technology KAUST, Thuwal 23952, Saudi Arabia
- SDAIA-KAUST Center of Excellence in Data Science and Artificial Intelligence, Thuwal 23952, Saudi Arabia
- Institute of Chemical Biology, Ilia State University, Tbilisi, Georgia
| | - Stefanos Fafalios
- Department of Computer Science, University of Crete, Heraklion, Greece
- JADBio Gnosis DA S.A., Science and Technology Park of Crete, GR-70013 Heraklion, Greece
| | - Paulos Charonyktakis
- JADBio Gnosis DA S.A., Science and Technology Park of Crete, GR-70013 Heraklion, Greece
| | - Michail Tsagris
- Department of Computer Science, University of Crete, Heraklion, Greece
- Department of Economics, University of Crete, Heraklion, Greece
| | - Ioannis Tsamardinos
- Department of Computer Science, University of Crete, Heraklion, Greece
- JADBio Gnosis DA S.A., Science and Technology Park of Crete, GR-70013 Heraklion, Greece
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Zheng R, Zhang L, Parvin R, Su L, Chi J, Shi K, Ye F, Huang X. Progress and Perspective of CRISPR-Cas9 Technology in Translational Medicine. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2300195. [PMID: 37356052 PMCID: PMC10477906 DOI: 10.1002/advs.202300195] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 03/29/2023] [Indexed: 06/27/2023]
Abstract
Translational medicine aims to improve human health by exploring potential treatment methods developed during basic scientific research and applying them to the treatment of patients in clinical settings. The advanced perceptions of gene functions have remarkably revolutionized clinical treatment strategies for target agents. However, the progress in gene editing therapy has been hindered due to the severe off-target effects and limited editing sites. Fortunately, the development in the clustered regularly interspaced short palindromic repeats associated protein 9 (CRISPR-Cas9) system has renewed hope for gene therapy field. The CRISPR-Cas9 system can fulfill various simple or complex purposes, including gene knockout, knock-in, activation, interference, base editing, and sequence detection. Accordingly, the CRISPR-Cas9 system is adaptable to translational medicine, which calls for the alteration of genomic sequences. This review aims to present the latest CRISPR-Cas9 technology achievements and prospect to translational medicine advances. The principle and characterization of the CRISPR-Cas9 system are firstly introduced. The authors then focus on recent pre-clinical and clinical research directions, including the construction of disease models, disease-related gene screening and regulation, and disease treatment and diagnosis for multiple refractory diseases. Finally, some clinical challenges including off-target effects, in vivo vectors, and ethical problems, and future perspective are also discussed.
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Affiliation(s)
- Ruixuan Zheng
- Joint Centre of Translational MedicineThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhouZhejiang325000P. R. China
- Division of Pulmonary MedicineThe First Affiliated HospitalWenzhou Medical UniversityWenzhouZhejiang325000P. R. China
- Wenzhou Key Laboratory of Interdiscipline and Translational MedicineThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhouZhejiang325000P. R. China
| | - Lexiang Zhang
- Joint Centre of Translational MedicineThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhouZhejiang325000P. R. China
- Wenzhou Key Laboratory of Interdiscipline and Translational MedicineThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhouZhejiang325000P. R. China
- Oujiang Laboratory (Zhejiang Lab for Regenerative MedicineVision and Brain Health); Wenzhou InstituteUniversity of Chinese Academy of SciencesWenzhouZhejiang325000P. R. China
| | - Rokshana Parvin
- Oujiang Laboratory (Zhejiang Lab for Regenerative MedicineVision and Brain Health); Wenzhou InstituteUniversity of Chinese Academy of SciencesWenzhouZhejiang325000P. R. China
| | - Lihuang Su
- Joint Centre of Translational MedicineThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhouZhejiang325000P. R. China
- Division of Pulmonary MedicineThe First Affiliated HospitalWenzhou Medical UniversityWenzhouZhejiang325000P. R. China
- Wenzhou Key Laboratory of Interdiscipline and Translational MedicineThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhouZhejiang325000P. R. China
| | - Junjie Chi
- Joint Centre of Translational MedicineThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhouZhejiang325000P. R. China
- Wenzhou Key Laboratory of Interdiscipline and Translational MedicineThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhouZhejiang325000P. R. China
| | - Keqing Shi
- Joint Centre of Translational MedicineThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhouZhejiang325000P. R. China
- Wenzhou Key Laboratory of Interdiscipline and Translational MedicineThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhouZhejiang325000P. R. China
| | - Fangfu Ye
- Joint Centre of Translational MedicineThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhouZhejiang325000P. R. China
- Oujiang Laboratory (Zhejiang Lab for Regenerative MedicineVision and Brain Health); Wenzhou InstituteUniversity of Chinese Academy of SciencesWenzhouZhejiang325000P. R. China
- Beijing National Laboratory for Condensed Matter PhysicsInstitute of PhysicsChinese Academy of SciencesBeijing100190P. R. China
| | - Xiaoying Huang
- Joint Centre of Translational MedicineThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhouZhejiang325000P. R. China
- Division of Pulmonary MedicineThe First Affiliated HospitalWenzhou Medical UniversityWenzhouZhejiang325000P. R. China
- Wenzhou Key Laboratory of Interdiscipline and Translational MedicineThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhouZhejiang325000P. R. China
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18
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Chung CCY, Hue SPY, Ng NYT, Doong PHL, Chu ATW, Chung BHY. Meta-analysis of the diagnostic and clinical utility of exome and genome sequencing in pediatric and adult patients with rare diseases across diverse populations. Genet Med 2023; 25:100896. [PMID: 37191093 DOI: 10.1016/j.gim.2023.100896] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 05/07/2023] [Accepted: 05/10/2023] [Indexed: 05/17/2023] Open
Abstract
PURPOSE This meta-analysis aims to compare the diagnostic and clinical utility of exome sequencing (ES) vs genome sequencing (GS) in pediatric and adult patients with rare diseases across diverse populations. METHODS A meta-analysis was conducted to identify studies from 2011 to 2021. RESULTS One hundred sixty-one studies across 31 countries/regions were eligible, featuring 50,417 probands of diverse populations. Diagnostic rates of ES (0.38, 95% CI 0.36-0.40) and GS (0.34, 95% CI 0.30-0.38) were similar (P = .1). Within-cohort comparison illustrated 1.2-times odds of diagnosis by GS over ES (95% CI 0.79-1.83, P = .38). GS studies discovered a higher range of novel genes than ES studies; yet, the rate of variant of unknown significance did not differ (P = .78). Among high-quality studies, clinical utility of GS (0.77, 95% CI 0.64-0.90) was higher than that of ES (0.44, 95% CI 0.30-0.58) (P < .01). CONCLUSION This meta-analysis provides an important update to demonstrate the similar diagnostic rates between ES and GS and the higher clinical utility of GS over ES. With the newly published recommendations for clinical interpretation of variants found in noncoding regions of the genome and the trend of decreasing variant of unknown significance and GS cost, it is expected that GS will be more widely used in clinical settings.
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Affiliation(s)
| | - Shirley P Y Hue
- Hong Kong Genome Institute, Hong Kong Special Administrative Region
| | - Nicole Y T Ng
- Department of Paediatrics and Adolescent Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region
| | - Phoenix H L Doong
- Department of Paediatrics and Adolescent Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region
| | - Annie T W Chu
- Hong Kong Genome Institute, Hong Kong Special Administrative Region.
| | - Brian H Y Chung
- Hong Kong Genome Institute, Hong Kong Special Administrative Region; Department of Paediatrics and Adolescent Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region.
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Vallano A, Pontes C, Agustí A. The challenges of access to innovative medicines with limited evidence in the European Union. Front Pharmacol 2023; 14:1215431. [PMID: 37719853 PMCID: PMC10500193 DOI: 10.3389/fphar.2023.1215431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Accepted: 08/21/2023] [Indexed: 09/19/2023] Open
Abstract
The European Medicines Agency (EMA) fosters access to innovative medicines through accelerated procedures and flexibility in the authorization requirements for diseases with unmet medical needs, such as many rare diseases as well as oncological diseases. However, the resulting increase of medicines being marketed with conditional authorizations and in exceptional circumstances has lead to higher clinical uncertainty about their efficacy and safety than when the standard authorizations are applied. This uncertainty has significant implications for clinical practice and the negotiation of pricing and reimbursement, particularly as high prices are based on assumptions of high value, supported by regulatory prioritization. The burden of clinical development is often shifted towards public healthcare systems, resulting in increased spending budgets and opportunity costs. Effective management of uncertainty, through appropriate testing and evaluation, and fair reflection of costs and risks in prices, is crucial. However, it is important not to sacrifice essential elements of evidence-based healthcare for the sake of access to new treatments. Balancing sensitive and rational access to new treatments, ensuring their safety, efficacy, and affordability to healthcare systems requires thoughtful decision-making. Ultimately, a responsible approach to timely access to innovative medicines that balances the needs of patients with healthcare systems' concerns is necessary. This approach emphasizes the importance of evidence-based decision-making and fair pricing and reimbursement.
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Affiliation(s)
- Antonio Vallano
- Medicines Department, Catalan Healthcare Service, Barcelona, Spain
- Department of Pharmacology, Therapeutics and Toxicology, Universitat Autònoma de Barcelona, Barcelona, Spain
- Healthcare Management of Hospitals, Catalan Institute of Health, Barcelona, Spain
| | - Caridad Pontes
- Medicines Department, Catalan Healthcare Service, Barcelona, Spain
- Department of Pharmacology, Therapeutics and Toxicology, Universitat Autònoma de Barcelona, Barcelona, Spain
- Digitalization for the Sustainability of the Healthcare System DS3-IDIBEL, L’Hospitalet de Llobregat, Spain
| | - Antònia Agustí
- Department of Pharmacology, Therapeutics and Toxicology, Universitat Autònoma de Barcelona, Barcelona, Spain
- Clinical Pharmacology Service, Vall d’Hebron University Hospital, Barcelona, Spain
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20
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Julkunen V, Schwarz C, Kalapudas J, Hallikainen M, Piironen AK, Mannermaa A, Kujala H, Laitinen T, Kosma VM, Paajanen TI, Kälviäinen R, Hiltunen M, Herukka SK, Kärkkäinen S, Kokkola T, Urjansson M, Perola M, Palotie A, Vuoksimaa E, Runz H. A FinnGen pilot clinical recall study for Alzheimer's disease. Sci Rep 2023; 13:12641. [PMID: 37537264 PMCID: PMC10400697 DOI: 10.1038/s41598-023-39835-7] [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: 03/27/2023] [Accepted: 07/31/2023] [Indexed: 08/05/2023] Open
Abstract
Successful development of novel therapies requires that clinical trials are conducted in patient cohorts with the highest benefit-to-risk ratio. Population-based biobanks with comprehensive health and genetic data from large numbers of individuals hold promise to facilitate identification of trial participants, particularly when interventions need to start while symptoms are still mild, such as for Alzheimer's disease (AD). This study describes a process for clinical recall studies from FinnGen. We demonstrate the feasibility to systematically ascertain customized clinical data from FinnGen participants with ICD10 diagnosis of AD or mild cognitive disorder (MCD) in a single-center cross-sectional study testing blood-based biomarkers and cognitive functioning in-person, computer-based and remote. As a result, 19% (27/140) of a pre-specified FinnGen subcohort were successfully recalled and completed the study. Hospital records largely validated registry entries. For 8/12 MCD patients, other reasons than AD were identified as underlying diagnosis. Cognitive measures correlated across platforms, with highest consistencies for dementia screening (r = 0.818) and semantic fluency (r = 0.764), respectively, for in-person versus telephone-administered tests. Glial fibrillary acidic protein (GFAP) (p < 0.002) and phosphorylated-tau 181 (pTau-181) (p < 0.020) most reliably differentiated AD from MCD participants. We conclude that informative, customized clinical recall studies from FinnGen are feasible.
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Affiliation(s)
- Valtteri Julkunen
- Institute of Clinical Medicine/Neurology, University of Eastern Finland, Kuopio, Finland.
- Department of Neurology, Neurocenter, Kuopio University Hospital, Kuopio, Finland.
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland.
| | - Claudia Schwarz
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Department of Neurology, University Medicine Greifswald, Greifswald, Germany
| | - Juho Kalapudas
- Institute of Clinical Medicine/Neurology, University of Eastern Finland, Kuopio, Finland
| | - Merja Hallikainen
- Institute of Clinical Medicine/Neurology, University of Eastern Finland, Kuopio, Finland
| | | | | | | | | | | | - Teemu I Paajanen
- Work Ability and Working Careers, Finnish Institute of Occupational Health, Helsinki, Finland
| | - Reetta Kälviäinen
- Institute of Clinical Medicine/Neurology, University of Eastern Finland, Kuopio, Finland
| | - Mikko Hiltunen
- Institute of Clinical Medicine/Neurology, University of Eastern Finland, Kuopio, Finland
| | - Sanna-Kaisa Herukka
- Institute of Clinical Medicine/Neurology, University of Eastern Finland, Kuopio, Finland
| | - Sari Kärkkäinen
- Institute of Clinical Medicine/Neurology, University of Eastern Finland, Kuopio, Finland
| | - Tarja Kokkola
- Institute of Clinical Medicine/Neurology, University of Eastern Finland, Kuopio, Finland
| | - Mia Urjansson
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Markus Perola
- Finnish Institute for Health and Welfare (THL), Helsinki, Finland
| | - Aarno Palotie
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Analytic and Translational Genetics Unit, Department of Medicine, Department of Neurology and Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
- The Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Eero Vuoksimaa
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland.
| | - Heiko Runz
- Translational Sciences, Biogen, Cambridge, MA, USA.
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21
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Chen C, Wang J, Pan D, Wang X, Xu Y, Yan J, Wang L, Yang X, Yang M, Liu G. Applications of multi-omics analysis in human diseases. MedComm (Beijing) 2023; 4:e315. [PMID: 37533767 PMCID: PMC10390758 DOI: 10.1002/mco2.315] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 05/25/2023] [Accepted: 05/31/2023] [Indexed: 08/04/2023] Open
Abstract
Multi-omics usually refers to the crossover application of multiple high-throughput screening technologies represented by genomics, transcriptomics, single-cell transcriptomics, proteomics and metabolomics, spatial transcriptomics, and so on, which play a great role in promoting the study of human diseases. Most of the current reviews focus on describing the development of multi-omics technologies, data integration, and application to a particular disease; however, few of them provide a comprehensive and systematic introduction of multi-omics. This review outlines the existing technical categories of multi-omics, cautions for experimental design, focuses on the integrated analysis methods of multi-omics, especially the approach of machine learning and deep learning in multi-omics data integration and the corresponding tools, and the application of multi-omics in medical researches (e.g., cancer, neurodegenerative diseases, aging, and drug target discovery) as well as the corresponding open-source analysis tools and databases, and finally, discusses the challenges and future directions of multi-omics integration and application in precision medicine. With the development of high-throughput technologies and data integration algorithms, as important directions of multi-omics for future disease research, single-cell multi-omics and spatial multi-omics also provided a detailed introduction. This review will provide important guidance for researchers, especially who are just entering into multi-omics medical research.
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Affiliation(s)
- Chongyang Chen
- Key Laboratory of Nuclear MedicineMinistry of HealthJiangsu Key Laboratory of Molecular Nuclear MedicineJiangsu Institute of Nuclear MedicineWuxiChina
- Co‐innovation Center of NeurodegenerationNantong UniversityNantongChina
| | - Jing Wang
- Shenzhen Key Laboratory of Modern ToxicologyShenzhen Medical Key Discipline of Health Toxicology (2020–2024)Shenzhen Center for Disease Control and PreventionShenzhenChina
| | - Donghui Pan
- Key Laboratory of Nuclear MedicineMinistry of HealthJiangsu Key Laboratory of Molecular Nuclear MedicineJiangsu Institute of Nuclear MedicineWuxiChina
| | - Xinyu Wang
- Key Laboratory of Nuclear MedicineMinistry of HealthJiangsu Key Laboratory of Molecular Nuclear MedicineJiangsu Institute of Nuclear MedicineWuxiChina
| | - Yuping Xu
- Key Laboratory of Nuclear MedicineMinistry of HealthJiangsu Key Laboratory of Molecular Nuclear MedicineJiangsu Institute of Nuclear MedicineWuxiChina
| | - Junjie Yan
- Key Laboratory of Nuclear MedicineMinistry of HealthJiangsu Key Laboratory of Molecular Nuclear MedicineJiangsu Institute of Nuclear MedicineWuxiChina
| | - Lizhen Wang
- Key Laboratory of Nuclear MedicineMinistry of HealthJiangsu Key Laboratory of Molecular Nuclear MedicineJiangsu Institute of Nuclear MedicineWuxiChina
| | - Xifei Yang
- Shenzhen Key Laboratory of Modern ToxicologyShenzhen Medical Key Discipline of Health Toxicology (2020–2024)Shenzhen Center for Disease Control and PreventionShenzhenChina
| | - Min Yang
- Key Laboratory of Nuclear MedicineMinistry of HealthJiangsu Key Laboratory of Molecular Nuclear MedicineJiangsu Institute of Nuclear MedicineWuxiChina
| | - Gong‐Ping Liu
- Co‐innovation Center of NeurodegenerationNantong UniversityNantongChina
- Department of PathophysiologySchool of Basic MedicineKey Laboratory of Ministry of Education of China and Hubei Province for Neurological DisordersTongji Medical CollegeHuazhong University of Science and TechnologyWuhanChina
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22
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Sood T, Perrot N, Chong M, Mohammadi-Shemirani P, Mushtaha M, Leong D, Rangarajan S, Hess S, Yusuf S, Gerstein HC, Paré G, Pigeyre M. Biomarkers Associated With Severe COVID-19 Among Populations With High Cardiometabolic Risk: A 2-Sample Mendelian Randomization Study. JAMA Netw Open 2023; 6:e2325914. [PMID: 37498601 PMCID: PMC10375306 DOI: 10.1001/jamanetworkopen.2023.25914] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/28/2023] Open
Abstract
Importance Cardiometabolic parameters are established risk factors for COVID-19 severity. The identification of causal or protective biomarkers for COVID-19 severity may facilitate the development of novel therapies. Objective To identify protein biomarkers that promote or reduce COVID-19 severity and that mediate the association of cardiometabolic risk factors with COVID-19 severity. Design, Setting, and Participants This genetic association study using 2-sample mendelian randomization (MR) was conducted in 2022 to investigate associations among cardiometabolic risk factors, circulating biomarkers, and COVID-19 hospitalization. Inputs for MR included genetic and proteomic data from 4147 participants with dysglycemia and cardiovascular risk factors collected through the Outcome Reduction With Initial Glargine Intervention (ORIGIN) trial. Genome-wide association study summary statistics were obtained from (1) 3 additional independent plasma proteome studies, (2) genetic consortia for selected cardiometabolic risk factors (including body mass index [BMI], type 2 diabetes, type 1 diabetes, and systolic blood pressure; all n >10 000), and (3) the COVID-19 Host Genetics Initiative (n = 5773 hospitalized and 15 497 nonhospitalized case participants with COVID-19). Data analysis was performed in July 2022. Exposures Genetically determined concentrations of 235 circulating proteins assayed with a multiplex biomarker panel from the ORIGIN trial for the initial analysis. Main Outcomes and Measures Hospitalization status of individuals from the COVID-19 Host Genetics Initiative with a positive COVID-19 test result. Results Among 235 biomarkers tested in samples totaling 22 101 individuals, MR analysis showed that higher kidney injury molecule-1 (KIM-1) levels reduced the likelihood of COVID-19 hospitalization (odds ratio [OR] per SD increase in KIM-1 levels, 0.86 [95% CI, 0.79-0.93]). A meta-analysis validated the protective association with no observed directional pleiotropy (OR per SD increase in KIM-1 levels, 0.91 [95% CI, 0.88-0.95]). Of the cardiometabolic risk factors studied, only BMI was associated with KIM-1 levels (0.17 SD increase in biomarker level per 1 kg/m2 [95% CI, 0.08-0.26]) and COVID-19 hospitalization (OR per 1-SD biomarker level, 1.33 [95% CI, 1.18-1.50]). Multivariable MR analysis also revealed that KIM-1 partially mitigated the association of BMI with COVID-19 hospitalization, reducing it by 10 percentage points (OR adjusted for KIM-1 level per 1 kg/m2, 1.23 [95% CI, 1.06-1.43]). Conclusions and Relevance In this genetic association study, KIM-1 was identified as a potential mitigator of COVID-19 severity, possibly attenuating the increased risk of COVID-19 hospitalization among individuals with high BMI. Further studies are required to better understand the underlying biological mechanisms.
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Affiliation(s)
- Tushar Sood
- Population Health Research Institute, Hamilton, Ontario, Canada
- Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Nicolas Perrot
- Population Health Research Institute, Hamilton, Ontario, Canada
- Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada
- Thrombosis and Atherosclerosis Research Institute, Hamilton, Ontario, Canada
| | - Michael Chong
- Population Health Research Institute, Hamilton, Ontario, Canada
- Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada
- Thrombosis and Atherosclerosis Research Institute, Hamilton, Ontario, Canada
| | - Pedrum Mohammadi-Shemirani
- Population Health Research Institute, Hamilton, Ontario, Canada
- Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada
- Thrombosis and Atherosclerosis Research Institute, Hamilton, Ontario, Canada
- Deep Genomics Inc, Toronto, Ontario, Canada
| | - Maha Mushtaha
- Population Health Research Institute, Hamilton, Ontario, Canada
| | - Darryl Leong
- Population Health Research Institute, Hamilton, Ontario, Canada
- Department of Medicine, McMaster University, Hamilton, Ontario, Canada
| | | | - Sibylle Hess
- Global Medical Diabetes, Sanofi, Frankfurt, Germany
| | - Salim Yusuf
- Population Health Research Institute, Hamilton, Ontario, Canada
- Department of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Hertzel C Gerstein
- Population Health Research Institute, Hamilton, Ontario, Canada
- Department of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Guillaume Paré
- Population Health Research Institute, Hamilton, Ontario, Canada
- Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada
- Thrombosis and Atherosclerosis Research Institute, Hamilton, Ontario, Canada
| | - Marie Pigeyre
- Population Health Research Institute, Hamilton, Ontario, Canada
- Department of Medicine, McMaster University, Hamilton, Ontario, Canada
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23
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Li C, Pan Y, Zhang R, Huang Z, Li D, Han Y, Larkin C, Rao V, Sun X, Kelly TN. Genomic Innovation in Early Life Cardiovascular Disease Prevention and Treatment. Circ Res 2023; 132:1628-1647. [PMID: 37289909 PMCID: PMC10328558 DOI: 10.1161/circresaha.123.321999] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Cardiovascular disease (CVD) is a leading cause of morbidity and mortality globally. Although CVD events do not typically manifest until older adulthood, CVD develops gradually across the life-course, beginning with the elevation of risk factors observed as early as childhood or adolescence and the emergence of subclinical disease that can occur in young adulthood or midlife. Genomic background, which is determined at zygote formation, is among the earliest risk factors for CVD. With major advances in molecular technology, including the emergence of gene-editing techniques, along with deep whole-genome sequencing and high-throughput array-based genotyping, scientists now have the opportunity to not only discover genomic mechanisms underlying CVD but use this knowledge for the life-course prevention and treatment of these conditions. The current review focuses on innovations in the field of genomics and their applications to monogenic and polygenic CVD prevention and treatment. With respect to monogenic CVD, we discuss how the emergence of whole-genome sequencing technology has accelerated the discovery of disease-causing variants, allowing comprehensive screening and early, aggressive CVD mitigation strategies in patients and their families. We further describe advances in gene editing technology, which might soon make possible cures for CVD conditions once thought untreatable. In relation to polygenic CVD, we focus on recent innovations that leverage findings of genome-wide association studies to identify druggable gene targets and develop predictive genomic models of disease, which are already facilitating breakthroughs in the life-course treatment and prevention of CVD. Gaps in current research and future directions of genomics studies are also discussed. In aggregate, we hope to underline the value of leveraging genomics and broader multiomics information for characterizing CVD conditions, work which promises to expand precision approaches for the life-course prevention and treatment of CVD.
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Affiliation(s)
- Changwei Li
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA (C. Li, R.Z., Z.H., X.S.)
| | - Yang Pan
- Division of Nephrology, Department of Medicine, College of Medicine, University of Illinois Chicago (Y.P., D.L., Y.H., C.L., V.R., T.N.K.)
| | - Ruiyuan Zhang
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA (C. Li, R.Z., Z.H., X.S.)
| | - Zhijie Huang
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA (C. Li, R.Z., Z.H., X.S.)
| | - Davey Li
- Division of Nephrology, Department of Medicine, College of Medicine, University of Illinois Chicago (Y.P., D.L., Y.H., C.L., V.R., T.N.K.)
| | - Yunan Han
- Division of Nephrology, Department of Medicine, College of Medicine, University of Illinois Chicago (Y.P., D.L., Y.H., C.L., V.R., T.N.K.)
| | - Claire Larkin
- Division of Nephrology, Department of Medicine, College of Medicine, University of Illinois Chicago (Y.P., D.L., Y.H., C.L., V.R., T.N.K.)
| | - Varun Rao
- Division of Nephrology, Department of Medicine, College of Medicine, University of Illinois Chicago (Y.P., D.L., Y.H., C.L., V.R., T.N.K.)
| | - Xiao Sun
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA (C. Li, R.Z., Z.H., X.S.)
| | - Tanika N Kelly
- Division of Nephrology, Department of Medicine, College of Medicine, University of Illinois Chicago (Y.P., D.L., Y.H., C.L., V.R., T.N.K.)
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24
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Feng L, Yang W, Ding M, Hou L, Gragnoli C, Griffin C, Wu R. A personalized pharmaco-epistatic network model of precision medicine. Drug Discov Today 2023; 28:103608. [PMID: 37149282 DOI: 10.1016/j.drudis.2023.103608] [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: 01/17/2023] [Revised: 04/12/2023] [Accepted: 04/28/2023] [Indexed: 05/08/2023]
Abstract
Precision medicine, the utilization of targeted treatments to address an individual's disease, relies on knowledge about the genetic cause of that individual's drug response. Here, we present a functional graph (FunGraph) theory to chart comprehensive pharmacogenetic architecture for each and every patient. FunGraph is the combination of functional mapping - a dynamic model for genetic mapping and evolutionary game theory guiding interactive strategies. It coalesces all pharmacogenetic factors into multilayer and multiplex networks that fully capture bidirectional, signed and weighted epistasis. It can visualize and interrogate how epistasis moves in the cell and how this movement leads to patient- and context-specific genetic architecture in response to organismic physiology. We discuss the future implementation of FunGraph to achieve precision medicine. Teaser: We present a functional graph (FunGraph) theory to draw a complete picture of pharmacogenetic architecture underlying interindividual variability in drug response. FunGraph can characterize how each gene acts and interacts with every other gene to mediate therapeutic response.
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Affiliation(s)
- Li Feng
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Wuyue Yang
- Beijing Yanqi Lake Institute of Mathematical Sciences and Applications, Beijing 101408, China
| | - Mengdong Ding
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Luke Hou
- Ward Melville High School, East Setauket, NY 11733, USA
| | - Claudia Gragnoli
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA 17033, USA; Division of Endocrinology, Department of Medicine, Creighton University School of Medicine, Omaha, NE 68124, USA; Molecular Biology Laboratory, Bios Biotech Multi-Diagnostic Health Center, Rome 00197, Italy
| | - Christipher Griffin
- Applied Research Laboratory, The Pennsylvania State University, University Park, PA 16802, USA
| | - Rongling Wu
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China; Beijing Yanqi Lake Institute of Mathematical Sciences and Applications, Beijing 101408, China; Yau Mathematical Sciences Center, Tsinghua University, Beijing 100084, China.
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25
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Liu J, Zhang C, Song J, Zhang Q, Zhang R, Zhang M, Han D, Tan W. Unlocking Genetic Profiles with a Programmable DNA-Powered Decoding Circuit. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023:e2206343. [PMID: 37116171 PMCID: PMC10369254 DOI: 10.1002/advs.202206343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Revised: 04/12/2023] [Indexed: 06/19/2023]
Abstract
Human genetic architecture provides remarkable insights into disease risk prediction and personalized medication. Advances in genomics have boosted the fine-mapping of disease-associated genetic variants across human genome. In healthcare practice, interpreting intricate genetic profiles into actionable medical decisions can improve health outcomes but remains challenging. Here an intelligent genetic decoder is engineered with programmable DNA computation to automate clinical analyses and interpretations. The DNA-based decoder recognizes multiplex genetic information by one-pot ligase-dependent reactions and interprets implicit genetic profiles into explicit decision reports. It is shown that the DNA decoder implements intended computation on genetic profiles and outputs a corresponding answer within hours. Effectiveness in 30 human genomic samples is validated and it is shown that it achieves desirable performance on the interpretation of CYP2C19 genetic profiles into drug responses, with accuracy equivalent to that of Sanger sequencing. Circuit modules of the DNA decoder can also be readily reprogrammed to interpret another pharmacogenetics genes, provide drug dosing recommendations, and implement reliable molecular calculation of polygenic risk score (PRS) and PRS-informed cancer risk assessment. The DNA-powered intelligent decoder provides a general solution to the translation of complex genetic profiles into actionable healthcare decisions and will facilitate personalized healthcare in primary care.
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Affiliation(s)
- Junlan Liu
- Institute of Molecular Medicine (IMM), Renji Hospital, School of Medicine, and College of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Chao Zhang
- Institute of Molecular Medicine (IMM), Renji Hospital, School of Medicine, and College of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Jinxing Song
- Institute of Molecular Medicine (IMM), Renji Hospital, School of Medicine, and College of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Qing Zhang
- Institute of Molecular Medicine (IMM), Renji Hospital, School of Medicine, and College of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Rongjun Zhang
- Institute of Molecular Medicine (IMM), Renji Hospital, School of Medicine, and College of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Mingzhi Zhang
- Institute of Molecular Medicine (IMM), Renji Hospital, School of Medicine, and College of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Da Han
- Institute of Molecular Medicine (IMM), Renji Hospital, School of Medicine, and College of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
- The Key Laboratory of Zhejiang Province for Aptamers and Theranostics, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, 310022, China
| | - Weihong Tan
- Institute of Molecular Medicine (IMM), Renji Hospital, School of Medicine, and College of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
- The Key Laboratory of Zhejiang Province for Aptamers and Theranostics, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, 310022, China
- Molecular Science and Biomedicine Laboratory (MBL), State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, College of Biology, Aptamer Engineering Center of Hunan Province, Hunan University, Changsha, Hunan, 410082, China
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26
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Körner A, Stein R, Landgraf K. Beyond genetic screening-functionality-based precision medicine in monogenic obesity. Lancet Diabetes Endocrinol 2023; 11:143-144. [PMID: 36822741 DOI: 10.1016/s2213-8587(23)00031-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 01/24/2023] [Indexed: 02/25/2023]
Affiliation(s)
- Antje Körner
- Helmholtz Institute for Metabolic, Obesity, and Vascular Research, Helmholtz Zentrum München at the University of Leipzig and University Hospital Leipzig, Leipzig, Germany; University of Leipzig, Medical Faculty, Leipzig University Hospital for Children and Adolescents, Center for Pediatric Research, Leipzig, Germany.
| | - Robert Stein
- Helmholtz Institute for Metabolic, Obesity, and Vascular Research, Helmholtz Zentrum München at the University of Leipzig and University Hospital Leipzig, Leipzig, Germany; University of Leipzig, Medical Faculty, Leipzig University Hospital for Children and Adolescents, Center for Pediatric Research, Leipzig, Germany
| | - Kathrin Landgraf
- University of Leipzig, Medical Faculty, Leipzig University Hospital for Children and Adolescents, Center for Pediatric Research, Leipzig, Germany
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27
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Wu Q, Jung J. Genome-wide polygenic risk score for major osteoporotic fractures in postmenopausal women using associated single nucleotide polymorphisms. J Transl Med 2023; 21:127. [PMID: 36797788 PMCID: PMC9933300 DOI: 10.1186/s12967-023-03974-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Accepted: 02/07/2023] [Indexed: 02/18/2023] Open
Abstract
BACKGROUND Osteoporosis is highly polygenic and heritable, with heritability ranging from 50 to 80%; most inherited susceptibility is associated with the cumulative effect of many common genetic variants. However, existing genetic risk scores (GRS) only provide a few percent predictive power for osteoporotic fracture. METHODS We derived and validated a novel genome-wide polygenic score (GPS) comprised of 103,155 common genetic variants to quantify this susceptibility and tested this GPS prediction ability in an independent dataset (n = 15,776). RESULTS Among postmenopausal women, we found a fivefold gradient in the risk of major osteoporotic fracture (MOF) (p < 0.001) and a 15.25-fold increased risk of severe osteoporosis (p < 0.001) across the GPS deciles. Compared with the remainder of the GPS distribution, the top GPS decile was associated with a 3.59-, 2.48-, 1.92-, and 1.58-fold increased risk of any fracture, MOF, hip fracture, and spine fracture, respectively. The top GPS decile also identified nearly twofold more high-risk osteoporotic patients than the top decile of conventional GRS based on 1103 conditionally independent genome-wide significant SNPs. Although the relative risk of severe osteoporosis for postmenopausal women at around 50 is relatively similar, the cumulative incident at 20-year follow-up is significantly different between the top GPS decile (13.7%) and the bottom decile (< 1%). In the subgroup analysis, the GPS transferability in non-Hispanic White is better than in other racial/ethnic groups. CONCLUSIONS This new method to quantify inherited susceptibility to osteoporosis and osteoporotic fracture affords new opportunities for clinical prevention and risk assessment.
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Affiliation(s)
- Qing Wu
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, 250 Lincoln Tower, 1800 Cannon Drive, Columbus, OH, 43210, USA.
| | - Jongyun Jung
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, 250 Lincoln Tower, 1800 Cannon Drive, Columbus, OH, 43210, USA
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28
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Schork NJ, Beaulieu-Jones B, Liang WS, Smalley S, Goetz LH. Exploring human biology with N-of-1 clinical trials. CAMBRIDGE PRISMS. PRECISION MEDICINE 2023; 1:e12. [PMID: 37255593 PMCID: PMC10228692 DOI: 10.1017/pcm.2022.15] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 12/12/2022] [Accepted: 12/26/2022] [Indexed: 06/01/2023]
Abstract
Studies on humans that exploit contemporary data-intensive, high-throughput 'omic' assay technologies, such as genomics, transcriptomics, proteomics and metabolomics, have unequivocally revealed that humans differ greatly at the molecular level. These differences, which are compounded by each individual's distinct behavioral and environmental exposures, impact individual responses to health interventions such as diet and drugs. Questions about the best way to tailor health interventions to individuals based on their nuanced genomic, physiologic, behavioral, etc. profiles have motivated the current emphasis on 'precision' medicine. This review's purpose is to describe how the design and execution of N-of-1 (or personalized) multivariate clinical trials can advance the field. Such trials focus on individual responses to health interventions from a whole-person perspective, leverage emerging health monitoring technologies, and can be used to address the most relevant questions in the precision medicine era. This includes how to validate biomarkers that may indicate appropriate activity of an intervention as well as how to identify likely beneficial interventions for an individual. We also argue that multivariate N-of-1 and aggregated N-of-1 trials are ideal vehicles for advancing biomedical and translational science in the precision medicine era since the insights gained from them can not only shed light on how to treat or prevent diseases generally, but also provide insight into how to provide real-time care to the very individuals who are seeking attention for their health concerns in the first place.
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Affiliation(s)
- N. J. Schork
- Department of Quantitative Medicine, The Translational Genomics Research Institute (TGen), Phoenix, AZ, USA
- Net.bio Inc., Los Angeles, CA, USA
| | - B. Beaulieu-Jones
- Net.bio Inc., Los Angeles, CA, USA
- University of Chicago, Chicago, IL, USA
| | | | - S. Smalley
- Net.bio Inc., Los Angeles, CA, USA
- The University of California Los Angeles, Los Angeles, CA, USA
| | - L. H. Goetz
- Department of Quantitative Medicine, The Translational Genomics Research Institute (TGen), Phoenix, AZ, USA
- Net.bio Inc., Los Angeles, CA, USA
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29
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Oluwole OG, Henry M. Genomic medicine in Africa: a need for molecular genetics and pharmacogenomics experts. Curr Med Res Opin 2023; 39:141-147. [PMID: 36094413 DOI: 10.1080/03007995.2022.2124072] [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] [Indexed: 01/03/2023]
Abstract
The large-scale implementation of genomic medicine in Africa has not been actualized. This overview describes how routine molecular genetics and advanced protein engineering/structural biotechnology could accelerate the implementation of genomic medicine. By using data-mining and analysis approaches, we analyzed relevant information obtained from public genomic databases on pharmacogenomics biomarkers and reviewed published studies to discuss the ideas. The results showed that only 68 very important pharmacogenes currently exist, while 867 drug label annotations, 201 curated functional pathways, and 746 annotated drugs have been catalogued on the largest pharmacogenomics database (PharmGKB). Only about 5009 variants of the reported ∼25,000 have been clinically annotated. Predominantly, the genetic variants were derived from 43 genes that contribute to 2318 clinically relevant variations in 57 diseases. Majority (∼60%) of the clinically relevant genetic variations in the pharmacogenes are missense variants (1390). The enrichment analysis showed that 15 pharmacogenes are connected biologically and are involved in the metabolism of cardiovascular and cancer drugs. The review of studies showed that cardiovascular diseases are the most frequent non-communicable diseases responsible for approximately 13% of all deaths in Africa. Also, warfarin pharmacogenomics is the most studied drug on the continent, while CYP2D6, CYP2C9, DPD, and TPMT are the most investigated pharmacogenes with allele activities indicated in African and considered to be intermediate metaboliser for DPD and TPMT (8.4% and 11%). In summary, we highlighted a framework for implementing genomic medicine starting from the available resources on ground.
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Affiliation(s)
- Oluwafemi G Oluwole
- Division of Human Genetics, Department of Pathology, University of Cape Town, Cape Town, South Africa
| | - Marc Henry
- Medical Biotechnology and Immunotherapy Unit, Department of Integrative Biomedical Sciences Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa
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30
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Insights from multi-omics integration in complex disease primary tissues. Trends Genet 2023; 39:46-58. [PMID: 36137835 DOI: 10.1016/j.tig.2022.08.005] [Citation(s) in RCA: 27] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 08/11/2022] [Accepted: 08/12/2022] [Indexed: 11/23/2022]
Abstract
Genome-wide association studies (GWAS) have provided insights into the genetic basis of complex diseases. In the next step, integrative multi-omics approaches can characterize molecular profiles in relevant primary tissues to reveal the mechanisms that underlie disease development. Here, we highlight recent progress in four examples of complex diseases generated by integrative studies: type 2 diabetes (T2D), osteoarthritis, Alzheimer's disease (AD), and systemic lupus erythematosus (SLE). High-resolution methodologies such as single-cell and spatial omics techniques will become even more important in the future. Furthermore, we emphasize the urgent need to include as yet understudied cell types and increase the diversity of studied populations.
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31
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Framework and baseline examination of the German National Cohort (NAKO). Eur J Epidemiol 2022; 37:1107-1124. [PMID: 36260190 PMCID: PMC9581448 DOI: 10.1007/s10654-022-00890-5] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 06/14/2022] [Indexed: 11/25/2022]
Abstract
The German National Cohort (NAKO) is a multidisciplinary, population-based prospective cohort study that aims to investigate the causes of widespread diseases, identify risk factors and improve early detection and prevention of disease. Specifically, NAKO is designed to identify novel and better characterize established risk and protection factors for the development of cardiovascular diseases, cancer, diabetes, neurodegenerative and psychiatric diseases, musculoskeletal diseases, respiratory and infectious diseases in a random sample of the general population. Between 2014 and 2019, a total of 205,415 men and women aged 19–74 years were recruited and examined in 18 study centres in Germany. The baseline assessment included a face-to-face interview, self-administered questionnaires and a wide range of biomedical examinations. Biomaterials were collected from all participants including serum, EDTA plasma, buffy coats, RNA and erythrocytes, urine, saliva, nasal swabs and stool. In 56,971 participants, an intensified examination programme was implemented. Whole-body 3T magnetic resonance imaging was performed in 30,861 participants on dedicated scanners. NAKO collects follow-up information on incident diseases through a combination of active follow-up using self-report via written questionnaires at 2–3 year intervals and passive follow-up via record linkages. All study participants are invited for re-examinations at the study centres in 4–5 year intervals. Thereby, longitudinal information on changes in risk factor profiles and in vascular, cardiac, metabolic, neurocognitive, pulmonary and sensory function is collected. NAKO is a major resource for population-based epidemiology to identify new and tailored strategies for early detection, prediction, prevention and treatment of major diseases for the next 30 years.
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Hinney A, Körner A, Fischer-Posovszky P. The promise of new anti-obesity therapies arising from knowledge of genetic obesity traits. Nat Rev Endocrinol 2022; 18:623-637. [PMID: 35902734 PMCID: PMC9330928 DOI: 10.1038/s41574-022-00716-0] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 06/17/2022] [Indexed: 02/07/2023]
Abstract
Obesity is a multifactorial and complex disease that often manifests in early childhood with a lifelong burden. Polygenic and monogenic obesity are driven by the interaction between genetic predisposition and environmental factors. Polygenic variants are frequent and confer small effect sizes. Rare monogenic obesity syndromes are caused by defined pathogenic variants in single genes with large effect sizes. Most of these genes are involved in the central nervous regulation of body weight; for example, genes of the leptin-melanocortin pathway. Clinically, patients with monogenic obesity present with impaired satiety, hyperphagia and pronounced food-seeking behaviour in early childhood, which leads to severe early-onset obesity. With the advent of novel pharmacological treatment options emerging for monogenic obesity syndromes that target the central melanocortin pathway, genetic testing is recommended for patients with rapid weight gain in infancy and additional clinical suggestive features. Likewise, patients with obesity associated with hypothalamic damage or other forms of syndromic obesity involving energy regulatory circuits could benefit from these novel pharmacological treatment options. Early identification of patients affected by syndromic obesity will lead to appropriate treatment, thereby preventing the development of obesity sequelae, avoiding failure of conservative treatment approaches and alleviating stigmatization of patients and their families.
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Affiliation(s)
- Anke Hinney
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy and University Hospital Essen, University of Duisburg-Essen, Essen, Germany.
- Center for Translational Neuro- and Behavioral Sciences, University Hospital Essen, University of Duisburg-Essen, Essen, Germany.
| | - Antje Körner
- Leipzig University, Medical Faculty, Hospital for Children and Adolescents, Centre of Paediatric Research (CPL), Leipzig, Germany
- LIFE Child, Leipzig Research Centre for Civilization Diseases, Leipzig, Germany
- 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
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Identifying disease-critical cell types and cellular processes by integrating single-cell RNA-sequencing and human genetics. Nat Genet 2022; 54:1479-1492. [PMID: 36175791 PMCID: PMC9910198 DOI: 10.1038/s41588-022-01187-9] [Citation(s) in RCA: 59] [Impact Index Per Article: 29.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Accepted: 08/18/2022] [Indexed: 12/13/2022]
Abstract
Genome-wide association studies provide a powerful means of identifying loci and genes contributing to disease, but in many cases, the related cell types/states through which genes confer disease risk remain unknown. Deciphering such relationships is important for identifying pathogenic processes and developing therapeutics. In the present study, we introduce sc-linker, a framework for integrating single-cell RNA-sequencing, epigenomic SNP-to-gene maps and genome-wide association study summary statistics to infer the underlying cell types and processes by which genetic variants influence disease. The inferred disease enrichments recapitulated known biology and highlighted notable cell-disease relationships, including γ-aminobutyric acid-ergic neurons in major depressive disorder, a disease-dependent M-cell program in ulcerative colitis and a disease-specific complement cascade process in multiple sclerosis. In autoimmune disease, both healthy and disease-dependent immune cell-type programs were associated, whereas only disease-dependent epithelial cell programs were prominent, suggesting a role in disease response rather than initiation. Our framework provides a powerful approach for identifying the cell types and cellular processes by which genetic variants influence disease.
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Transcriptomics and RNA-Based Therapeutics as Potential Approaches to Manage SARS-CoV-2 Infection. Int J Mol Sci 2022; 23:ijms231911058. [PMID: 36232363 PMCID: PMC9570475 DOI: 10.3390/ijms231911058] [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: 08/01/2022] [Revised: 09/13/2022] [Accepted: 09/14/2022] [Indexed: 11/24/2022] Open
Abstract
SARS-CoV-2 is a coronavirus family member that appeared in China in December 2019 and caused the disease called COVID-19, which was declared a pandemic in 2020 by the World Health Organization. In recent months, great efforts have been made in the field of basic and clinical research to understand the biology and infection processes of SARS-CoV-2. In particular, transcriptome analysis has contributed to generating new knowledge of the viral sequences and intracellular signaling pathways that regulate the infection and pathogenesis of SARS-CoV-2, generating new information about its biology. Furthermore, transcriptomics approaches including spatial transcriptomics, single-cell transcriptomics and direct RNA sequencing have been used for clinical applications in monitoring, detection, diagnosis, and treatment to generate new clinical predictive models for SARS-CoV-2. Consequently, RNA-based therapeutics and their relationship with SARS-CoV-2 have emerged as promising strategies to battle the SARS-CoV-2 pandemic with the assistance of novel approaches such as CRISPR-CAS, ASOs, and siRNA systems. Lastly, we discuss the importance of precision public health in the management of patients infected with SARS-CoV-2 and establish that the fusion of transcriptomics, RNA-based therapeutics, and precision public health will allow a linkage for developing health systems that facilitate the acquisition of relevant clinical strategies for rapid decision making to assist in the management and treatment of the SARS-CoV-2-infected population to combat this global public health problem.
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Siemens A, Anderson SJ, Rassekh SR, Ross CJD, Carleton BC. A Systematic Review of Polygenic Models for Predicting Drug Outcomes. J Pers Med 2022; 12:jpm12091394. [PMID: 36143179 PMCID: PMC9505711 DOI: 10.3390/jpm12091394] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 08/21/2022] [Accepted: 08/25/2022] [Indexed: 11/16/2022] Open
Abstract
Polygenic models have emerged as promising prediction tools for the prediction of complex traits. Currently, the majority of polygenic models are developed in the context of predicting disease risk, but polygenic models may also prove useful in predicting drug outcomes. This study sought to understand how polygenic models incorporating pharmacogenetic variants are being used in the prediction of drug outcomes. A systematic review was conducted with the aim of gaining insights into the methods used to construct polygenic models, as well as their performance in drug outcome prediction. The search uncovered 89 papers that incorporated pharmacogenetic variants in the development of polygenic models. It was found that the most common polygenic models were constructed for drug dosing predictions in anticoagulant therapies (n = 27). While nearly all studies found a significant association with their polygenic model and the investigated drug outcome (93.3%), less than half (47.2%) compared the performance of the polygenic model against clinical predictors, and even fewer (40.4%) sought to validate model predictions in an independent cohort. Additionally, the heterogeneity of reported performance measures makes the comparison of models across studies challenging. These findings highlight key considerations for future work in developing polygenic models in pharmacogenomic research.
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Affiliation(s)
- Angela Siemens
- Department of Medical Genetics, Faculty of Medicine, University of British Columbia, Vancouver, BC V6H 3N1, Canada
- BC Children’s Hospital Research Institute, Vancouver, BC V5Z 4H4, Canada
| | - Spencer J. Anderson
- Department of Medical Genetics, Faculty of Medicine, University of British Columbia, Vancouver, BC V6H 3N1, Canada
- BC Children’s Hospital Research Institute, Vancouver, BC V5Z 4H4, Canada
| | - S. Rod Rassekh
- Division of Translational Therapeutics, Department of Pediatrics, Faculty of Medicine, University of British Columbia, Vancouver, BC V6H 3V4, Canada
- Division of Oncology, Hematology and Bone Marrow Transplant, University of British Columbia, Vancouver, BC V6H 3V4, Canada
| | - Colin J. D. Ross
- Department of Medical Genetics, Faculty of Medicine, University of British Columbia, Vancouver, BC V6H 3N1, Canada
- BC Children’s Hospital Research Institute, Vancouver, BC V5Z 4H4, Canada
- Faculty of Pharmaceutical Sciences, The University of British Columbia, Vancouver, BC V6T 1Z3, Canada
| | - Bruce C. Carleton
- Department of Medical Genetics, Faculty of Medicine, University of British Columbia, Vancouver, BC V6H 3N1, Canada
- Division of Translational Therapeutics, Department of Pediatrics, Faculty of Medicine, University of British Columbia, Vancouver, BC V6H 3V4, Canada
- Pharmaceutical Outcomes Programme, British Columbia Children’s Hospital, Vancouver, BC V5Z 4H4, Canada
- Correspondence:
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Pitini E, Migliara G, Baccolini V, Isonne C, Mazzalai E, Turatto F, Salerno C, Pagano F, Menzano MT, De Vito C, Marzuillo C, Villari P. Managing the introduction of genomic applications into the National Health Service: A special challenge for health technology assessment in Italy. Front Public Health 2022; 10:932093. [PMID: 36033790 PMCID: PMC9399489 DOI: 10.3389/fpubh.2022.932093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 07/04/2022] [Indexed: 01/25/2023] Open
Abstract
In recent years, the rapid proliferation of genomic tests for use in clinical practice has prompted healthcare systems to use a health technology assessment (HTA) approach to distinguish valuable from unwarranted applications. In this study, we narratively review the Italian HTA mechanisms for medical devices (MDs), both at the national and regional levels, and discuss the opportunity and benefits of extending them to genomic technologies, for which a dedicated assessment path was advocated by the National Plan for Public Health Genomics in 2017. We found that the National Health Technology Assessment Program for MDs, completed in 2019, had developed a structured pathway for the HTA of MDs; it established a hub-and-spoke structure, run by a governmental institution, and put in place transparent methodological procedures to cover all four HTA phases (i.e., proposal and prioritization, assessment, appraisal, and dissemination). However, several factors have hindered its adoption, and the regions are at different stages of its implementation. For these reasons, efforts should be made to ensure its effective deployment, both at national and regional levels. In addition, we argue that to harmonize the institutional roles and methodological procedures adopted, the time has come to concentrate resources on a single pathway for the assessment of all technologies that include both MDs and genomic applications.
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Affiliation(s)
- Erica Pitini
- Department of Public Health and Infectious Diseases, Sapienza University of Rome, Rome, Italy,*Correspondence: Erica Pitini
| | - Giuseppe Migliara
- Department of Public Health and Infectious Diseases, Sapienza University of Rome, Rome, Italy
| | - Valentina Baccolini
- Department of Public Health and Infectious Diseases, Sapienza University of Rome, Rome, Italy
| | - Claudia Isonne
- Department of Public Health and Infectious Diseases, Sapienza University of Rome, Rome, Italy
| | - Elena Mazzalai
- Department of Public Health and Infectious Diseases, Sapienza University of Rome, Rome, Italy
| | - Federica Turatto
- Department of Public Health and Infectious Diseases, Sapienza University of Rome, Rome, Italy
| | - Carla Salerno
- Department of Public Health and Infectious Diseases, Sapienza University of Rome, Rome, Italy
| | - Federica Pagano
- Department of Public Health and Infectious Diseases, Sapienza University of Rome, Rome, Italy
| | - Maria Teresa Menzano
- Italian Ministry of Health, General Directorate for Health Prevention, Rome, Italy
| | - Corrado De Vito
- Department of Public Health and Infectious Diseases, Sapienza University of Rome, Rome, Italy
| | - Carolina Marzuillo
- Department of Public Health and Infectious Diseases, Sapienza University of Rome, Rome, Italy
| | - Paolo Villari
- Department of Public Health and Infectious Diseases, Sapienza University of Rome, Rome, Italy
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Genomic instability genes in lung and colon adenocarcinoma indicate organ specificity of transcriptomic impact on Copy Number Alterations. Sci Rep 2022; 12:11739. [PMID: 35817785 PMCID: PMC9273645 DOI: 10.1038/s41598-022-15692-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 06/28/2022] [Indexed: 11/10/2022] Open
Abstract
Genomic instability (GI) in cancer facilitates cancer evolution and is an exploitable target for therapy purposes. However, specific genes involved in cancer GI remain elusive. Causal genes for GI via expressions have not been comprehensively identified in colorectal cancers (CRCs). To fill the gap in knowledge, we developed a data mining strategy (Gene Expression to Copy Number Alterations; "GE-CNA"). Here we applied the GE-CNA approach to 592 TCGA CRC datasets, and identified 500 genes whose expression levels associate with CNA. Among these, 18 were survival-critical (i.e., expression levels correlate with significant differences in patients' survival). Comparison with previous results indicated striking differences between lung adenocarcinoma and CRC: (a) less involvement of overexpression of mitotic genes in generating genomic instability in the colon and (b) the presence of CNA-suppressing pathways, including immune-surveillance, was only partly similar to those in the lung. Following 13 genes (TIGD6, TMED6, APOBEC3D, EP400NL, B3GNT4, ZNF683, FOXD4, FOXD4L1, PKIB, DDB2, MT1G, CLCN3, CAPS) were evaluated as potential drug development targets (hazard ratio [> 1.3 or < 0.5]). Identification of specific CRC genomic instability genes enables researchers to develop GI targeting approach. The new results suggest that the "targeting genomic instability and/or aneuploidy" approach must be tailored for specific organs.
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Gilly A, Klaric L, Park YC, Png G, Barysenka A, Marsh JA, Tsafantakis E, Karaleftheri M, Dedoussis G, Wilson JF, Zeggini E. Gene-based whole genome sequencing meta-analysis of 250 circulating proteins in three isolated European populations. Mol Metab 2022; 61:101509. [PMID: 35504531 PMCID: PMC9118462 DOI: 10.1016/j.molmet.2022.101509] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 04/26/2022] [Accepted: 04/26/2022] [Indexed: 11/24/2022] Open
Abstract
OBJECTIVE Deep sequencing offers unparalleled access to rare variants in human populations. Understanding their role in disease is a priority, yet prohibitive sequencing costs mean that many cohorts lack the sample size to discover these effects on their own. Meta-analysis of individual variant scores allows the combination of rare variants across cohorts and study of their aggregated effect at the gene level, boosting discovery power. However, the methods involved have largely not been field-tested. In this study, we aim to perform the first meta-analysis of gene-based rare variant aggregation optimal tests, applied to the human cardiometabolic proteome. METHODS Here, we carry out this analysis across MANOLIS, Pomak and ORCADES, three isolated European cohorts with whole-genome sequencing (total N = 4,422). We examine the genetic architecture of 250 proteomic traits of cardiometabolic relevance. We use a containerised pipeline to harmonise variant lists across cohorts and define four sets of qualifying variants. For every gene, we interrogate protein-damaging variants, exonic variants, exonic and regulatory variants, and regulatory only variants, using the CADD and Eigen scores to weigh variants according to their predicted functional consequence. We perform single-cohort rare variant analysis and meta-analyse variant scores using the SMMAT package. RESULTS We describe 5 rare variant pQTLs (RV-pQTL) which pass our stringent significance threshold (7.45 × 10-11) and quality control procedure. These were split between four cis signals for MARCO, TEK, MMP2 and MPO, and one trans association for GDF2 in the SERPINA11 gene. We show that the cis-MPO association, which was not detectable using the single-point data alone, is driven by 5 missense and frameshift variants. These include rs140636390 and rs119468010, which are specific to MANOLIS and ORCADES, respectively. We show how this kind of signal could improve the predictive accuracy of genetic factors in common complex disease such as stroke and cardiovascular disease. CONCLUSIONS Our proof-of-concept study demonstrates the power of gene-based meta-analyses for discovering disease-relevant associations complementing common-variant signals by incorporating population-specific rare variation.
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Affiliation(s)
- Arthur Gilly
- Institute of Translational Genomics, Helmholtz Zentrum München - German Research Center for Environmental Health, Ingolstaedter Landstr. 1, 85764 Neuherberg, Germany
| | - Lucija Klaric
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Western General Hospital, Edinburgh, EH4 2XU, UK
| | - Young-Chan Park
- Institute of Translational Genomics, Helmholtz Zentrum München - German Research Center for Environmental Health, Ingolstaedter Landstr. 1, 85764 Neuherberg, Germany
| | - Grace Png
- Institute of Translational Genomics, Helmholtz Zentrum München - German Research Center for Environmental Health, Ingolstaedter Landstr. 1, 85764 Neuherberg, Germany; TUM School of Medicine, Technical University of Munich and Klinikum Rechts der Isar, Ismaninger Straße 22, 8167 Munich, Germany
| | - Andrei Barysenka
- Institute of Translational Genomics, Helmholtz Zentrum München - German Research Center for Environmental Health, Ingolstaedter Landstr. 1, 85764 Neuherberg, Germany
| | - Joseph A Marsh
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Western General Hospital, Edinburgh, EH4 2XU, UK
| | | | | | - George Dedoussis
- Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University of Athens, 70, El. Venizelou ave. 17671, Kallithea, Greece
| | - James F Wilson
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Western General Hospital, Edinburgh, EH4 2XU, UK; Centre for Global Health Research, Usher Institute, University of Edinburgh, Teviot Place, Edinburgh, EH8 9AG, UK
| | - Eleftheria Zeggini
- Institute of Translational Genomics, Helmholtz Zentrum München - German Research Center for Environmental Health, Ingolstaedter Landstr. 1, 85764 Neuherberg, Germany; TUM School of Medicine, Technical University of Munich and Klinikum Rechts der Isar, Ismaninger Straße 22, 8167 Munich, Germany.
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Fang Z, Peltz G. An automated multi-modal graph-based pipeline for mouse genetic discovery. Bioinformatics 2022; 38:3385-3394. [PMID: 35608290 PMCID: PMC9992076 DOI: 10.1093/bioinformatics/btac356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 04/18/2022] [Accepted: 05/19/2022] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Our ability to identify causative genetic factors for mouse genetic models of human diseases and biomedical traits has been limited by the difficulties associated with identifying true causative factors, which are often obscured by the many false positive genetic associations produced by a GWAS. RESULTS To accelerate the pace of genetic discovery, we developed a graph neural network (GNN)-based automated pipeline (GNNHap) that could rapidly analyze mouse genetic model data and identify high probability causal genetic factors for analyzed traits. After assessing the strength of allelic associations with the strain response pattern; this pipeline analyzes 29M published papers to assess candidate gene-phenotype relationships; and incorporates the information obtained from a protein-protein interaction network and protein sequence features into the analysis. The GNN model produces markedly improved results relative to that of a simple linear neural network. We demonstrate that GNNHap can identify novel causative genetic factors for murine models of diabetes/obesity and for cataract formation, which were validated by the phenotypes appearing in previously analyzed gene knockout mice. The diabetes/obesity results indicate how characterization of the underlying genetic architecture enables new therapies to be discovered and tested by applying 'precision medicine' principles to murine models. AVAILABILITY AND IMPLEMENTATION The GNNHap source code is freely available at https://github.com/zqfang/gnnhap, and the new version of the HBCGM program is available at https://github.com/zqfang/haplomap. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Zhuoqing Fang
- Department of Anesthesia, Pain and Perioperative Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Gary Peltz
- Department of Anesthesia, Pain and Perioperative Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
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Tschigg K, Consoli L, Biasiotto R, Mascalzoni D. Ethical, legal and social/societal implications (ELSI) of recall-by-genotype (RbG) and genotype-driven-research (GDR) approaches: a scoping review. Eur J Hum Genet 2022; 30:1000-1010. [PMID: 35705790 PMCID: PMC9437022 DOI: 10.1038/s41431-022-01120-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Revised: 03/17/2022] [Accepted: 05/05/2022] [Indexed: 11/29/2022] Open
Abstract
Recall by Genotype (RbG), Genotype-driven-recall (GDR), and Genotype-based-recall (GBR) strategies are increasingly used to conduct genomic or biobanking sub-studies that single out participants as eligible because of their specific individual genotypic information. However, existing regulatory and governance frameworks do not apply to all aspects of genotype-driven research approaches. The recall strategies disclose or withhold personal genotypic information with uncertain clinical utility. Accordingly, this scoping review aims to identify peculiar, explicit and implicit ethical, legal, and societal/social implications (ELSI) of RbG study designs. We conducted a systematic literature search of three electronic databases from November 2020 to February 2021. We investigated qualitative and quantitative research methods used to report ELSI aspects in RbG research. Congruent with other research findings, we identified a lack of qualitative research investigating the particular ELSI challenges with RbG. We included and analysed the content of twenty-five publications. We found a consensus on RbG posing significant ethical issues, dilemmas, barriers, concerns and societal challenges. However, we found that the approaches to disclosure and study-specific recall and communication strategies employed consent models and Return of Research Results (RoRR) policies varied considerably. Furthermore, we identified a high heterogeneity in perspectives of participants and experts about ELSI of study-specific RbG policies. Therefore, further fine-mapping through qualitative and empirical research is needed to draw conclusions and re-fine ELSI frameworks.
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Affiliation(s)
- Katharina Tschigg
- Department of Cellular, Computational, and Integrative Biology, University of Trento, Trento, Italy. .,Institute for Biomedicine & Affiliated Institute of the University of Lübeck, Eurac Research, Bolzano, Italy, Bozen, Italy.
| | - Luca Consoli
- Institute for Science in Society, Radboud University, Nijmegen, Netherlands
| | - Roberta Biasiotto
- Institute for Biomedicine & Affiliated Institute of the University of Lübeck, Eurac Research, Bolzano, Italy, Bozen, Italy.,Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy
| | - Deborah Mascalzoni
- Institute for Biomedicine & Affiliated Institute of the University of Lübeck, Eurac Research, Bolzano, Italy, Bozen, Italy.,Department of Public Health and Caring Sciences, Center for Research Ethics and Bioethics, Uppsala University, Uppsala, Sweden
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Hasanzad M, Sarhangi N, Naghavi A, Ghavimehr E, Khatami F, Ehsani Chimeh S, Larijani B, Aghaei Meybodi HR. Genomic medicine on the frontier of precision medicine. J Diabetes Metab Disord 2022; 21:853-861. [PMID: 35673457 PMCID: PMC9167337 DOI: 10.1007/s40200-021-00880-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Accepted: 08/11/2021] [Indexed: 10/20/2022]
Abstract
Genomic medicine has created a great deal of hope since the completion of the Human Genome Project (HGP). Genomic medicine promises disease prevention and early diagnosis in the context of precision medicine. Precision medicine as a scientific discipline has introduced as an evolution in medicine. The rapid growth of high-development technologies permits the assessment of biological systems. Study of the integrated profiles of omics, such as genome, transcriptome, proteome and other omics information lead to significant advances in personalized and precision medicine. In the context of precision medicine, pharmacogenomics can play an important role in order to discriminate responders and non-responders to medications and avoiding toxicity and achieving the optimum dose. So precision medicine in accordance with genomic medicine will transform medicine from conventional evidence-based medicine in the diagnosis and treatment towards precision based-medicine. In this review, we have summarized the related issues for genomic medicine and precision medicine.
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Affiliation(s)
- Mandana Hasanzad
- Medical Genomics Research Center, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
- Personalized Medicine Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, No.10- Jalal -e-Ale-Ahmad Street, Chamran Highway, 1411713119 Tehran, Iran
| | - Negar Sarhangi
- Personalized Medicine Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, No.10- Jalal -e-Ale-Ahmad Street, Chamran Highway, 1411713119 Tehran, Iran
| | - Anoosh Naghavi
- Cellular and Molecular Research Center, Zahedan University of Medical Sciences, Zahedan, Iran
| | - Ehsan Ghavimehr
- Medical Genomics Research Center, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | - Fatemeh Khatami
- Urology Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | | | - Bagher Larijani
- Endocrinology and Metabolism Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Hamid Reza Aghaei Meybodi
- Personalized Medicine Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, No.10- Jalal -e-Ale-Ahmad Street, Chamran Highway, 1411713119 Tehran, Iran
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Aubourg G, Rice SJ, Bruce-Wootton P, Loughlin J. Genetics of osteoarthritis. Osteoarthritis Cartilage 2022; 30:636-649. [PMID: 33722698 PMCID: PMC9067452 DOI: 10.1016/j.joca.2021.03.002] [Citation(s) in RCA: 51] [Impact Index Per Article: 25.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Revised: 02/17/2021] [Accepted: 03/06/2021] [Indexed: 02/02/2023]
Abstract
Osteoarthritis genetics has been transformed in the past decade through the application of large-scale genome-wide association scans. So far, over 100 polymorphic DNA variants have been associated with this common and complex disease. These genetic risk variants account for over 20% of osteoarthritis heritability and the vast majority map to non-protein coding regions of the genome where they are presumed to act by regulating the expression of target genes. Statistical fine mapping, in silico analyses of genomics data, and laboratory-based functional studies have enabled the identification of some of these targets, which encode proteins with diverse roles, including extracellular signaling molecules, intracellular enzymes, transcription factors, and cytoskeletal proteins. A large number of the risk variants correlate with epigenetic factors, in particular cartilage DNA methylation changes in cis, implying that epigenetics may be a conduit through which genetic effects on gene expression are mediated. Some of the variants also appear to have been selected as humans adapted to bipedalism, suggesting that a proportion of osteoarthritis genetic susceptibility results from antagonistic pleiotropy, with risk variants having a positive role in joint formation but a negative role in the long-term health of the joint. Although data from an osteoarthritis genetic study has not yet directly led to a novel treatment, some of the osteoarthritis associated genes code for proteins that have available therapeutics. Genetic investigations are therefore revealing fascinating fundamental insights into osteoarthritis and can expose options for translational intervention.
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Affiliation(s)
- G Aubourg
- Biosciences Institute, Newcastle University, Newcastle Upon Tyne, UK
| | - S J Rice
- Biosciences Institute, Newcastle University, Newcastle Upon Tyne, UK
| | - P Bruce-Wootton
- Biosciences Institute, Newcastle University, Newcastle Upon Tyne, UK
| | - J Loughlin
- Biosciences Institute, Newcastle University, Newcastle Upon Tyne, UK.
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43
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Chen L, Tian B, Liu W, Liang H, You Y, Liu W. Molecular Biomarker of Drug Resistance Developed From Patient-Derived Organoids Predicts Survival of Colorectal Cancer Patients. Front Oncol 2022; 12:855674. [PMID: 35425715 PMCID: PMC9004628 DOI: 10.3389/fonc.2022.855674] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Accepted: 02/10/2022] [Indexed: 12/24/2022] Open
Abstract
The drug 5-fluorouracil (5-Fu) is the critical composition of colorectal cancer (CRC) treatments. Prognostic and predictive molecular biomarkers for CRC patients (CRCpts) treated with 5-Fu-based chemotherapy can provide assistance for tailoring treatment approach. Here, we established a molecular biomarker of 5-Fu resistance derived from colorectal cancer organoids (CRCOs) for predicting the survival of CRCpts. Forty-one CRCO cultures were generated from 50 CRC tumor tissues after surgery (82%). The following experiments revealed a great diversity in drug sensitivity for 10 μM 5-Fu treatment tested by using organoid size change. Fourteen cases (34.1%) were 5-Fu sensitive and the other 27 (65.9%) were resistant. Then, differentially expressed genes (DEGs) associated with 5-Fu resistance were outputted by transcriptome sequencing. In particular, DEGs were generated in two comparison groups: 1) 5-Fu sensitive and resistant untreated CRCOs; 2) CRCOs before 5-Fu treatment and surviving CRCOs after 5-Fu treatment. Some molecules and most of the pathways that have been reported to be involved in 5-Fu resistance were identified in the current research. By using DEGs correlated with 5-Fu resistance and survival of CRCpts, the gene signature and drug-resistant score model (DRSM) containing five molecules were established in The Cancer Genome Atlas (TCGA)-CRC cohort by least absolute shrinkage and selection operator (LASSO) regression analysis and 5-fold cross-validation. Multivariate analysis revealed that drug-resistant score (DRS) was an independent prognostic factor for overall survival (OS) in CRCpts in TCGA-CRC cohort (P < 0.001). Further validation results from four Gene Expression Omnibus (GEO) cohorts elucidated that the DRSM based on five genes related to 5-Fu chemosensitivity and developed from patient-derived organoids can predict survival of CRCpts. Meanwhile, our model could predict the survival of CRCpts in different subgroups. Besides, the difference of molecular pathways, tumor mutational burden (TMB), immune response-related pathways, immune score, stromal score, and immune cell proportion were dissected between DRS-high and DRS-low patients in TCGA-CRC cohort.
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Affiliation(s)
- Lifeng Chen
- Department of Hematology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Bo Tian
- Department of Orthopaedic Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Wen Liu
- Department of Hematology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Haitao Liang
- Shenzhen Second People's Hospital (The First Hospital Affiliated to Shenzhen University, Health Science Center), Shenzhen, China
| | - Yong You
- Department of Hematology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Weizhen Liu
- Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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44
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Blobel B, Oemig F, Ruotsalainen P, Lopez DM. Transformation of Health and Social Care Systems-An Interdisciplinary Approach Toward a Foundational Architecture. Front Med (Lausanne) 2022; 9:802487. [PMID: 35402446 PMCID: PMC8992002 DOI: 10.3389/fmed.2022.802487] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Accepted: 01/11/2022] [Indexed: 11/23/2022] Open
Abstract
Objective For realizing pervasive and ubiquitous health and social care services in a safe and high quality as well as efficient and effective way, health and social care systems have to meet new organizational, methodological, and technological paradigms. The resulting ecosystems are highly complex, highly distributed, and highly dynamic, following inter-organizational and even international approaches. Even though based on international, but domain-specific models and standards, achieving interoperability between such systems integrating multiple domains managed by multiple disciplines and their individually skilled actors is cumbersome. Methods Using the abstract presentation of any system by the universal type theory as well as universal logics and combining the resulting Barendregt Cube with parameters and the engineering approach of cognitive theories, systems theory, and good modeling best practices, this study argues for a generic reference architecture model moderating between the different perspectives and disciplines involved provide on that system. To represent architectural elements consistently, an aligned system of ontologies is used. Results The system-oriented, architecture-centric, and ontology-based generic reference model allows for re-engineering the existing and emerging knowledge representations, models, and standards, also considering the real-world business processes and the related development process of supporting IT systems for the sake of comprehensive systems integration and interoperability. The solution enables the analysis, design, and implementation of dynamic, interoperable multi-domain systems without requesting continuous revision of existing specifications.
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Affiliation(s)
- Bernd Blobel
- Medical Faculty, University of Regensburg, Regensburg, Germany.,eHealth Competence Center Bavaria, Deggendorf Institute of Technology, Deggendorf, Germany.,First Medical Faculty, Charles University Prague, Prague, Czechia
| | - Frank Oemig
- IT-Consulting in Healthcare, Mülheim, Germany
| | - Pekka Ruotsalainen
- Faculty of Information Technology and Communication Sciences (ITC), Tampere University, Tampere, Finland
| | - Diego M Lopez
- Telematics Engineering Research Group, University of Cauca, Popayan, Colombia
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45
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Griese M. Etiologic Classification of Diffuse Parenchymal (Interstitial) Lung Diseases. J Clin Med 2022; 11:jcm11061747. [PMID: 35330072 PMCID: PMC8950114 DOI: 10.3390/jcm11061747] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 03/15/2022] [Accepted: 03/18/2022] [Indexed: 12/15/2022] Open
Abstract
Interstitial lung diseases (ILD) or diffuse parenchymal lung diseases (DPLD) comprise a large number of disorders. Disease definition and classification allow advanced and personalized judgements on clinical disease, risks for genetic or environmental transmissions, and precision medicine treatments. Registers collect specific rare entities and use ontologies for a precise description of complex phenotypes. Here we present a brief history of ILD classification systems from adult and pediatric pneumology. We center on an etiologic classification, with four main categories: lung-only (native parenchymal) disorders, systemic disease-related disorders, exposure-related disorders, and vascular disorders. Splitting diseases into molecularly defined entities is key for precision medicine and the identification of novel entities. Lumping diseases targeted by similar diagnostic or therapeutic principles is key for clinical practice and register work, as our experience with the European children’s ILD register (chILD-EU) demonstrates. The etiologic classification favored combines pediatric and adult lung diseases in a single system and considers genomics and other -omics as central steps towards the solution of “idiopathic” lung diseases. Future tasks focus on a systems’ medicine approach integrating all data and bringing precision medicine closer to the patients.
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Affiliation(s)
- Matthias Griese
- Department of Pediatric Pneumology, Dr. von Haunersches Kinderspital, University of Munich, German Center for Lung Research, Lindwurmstr. 4a, D-80337 Munich, Germany
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46
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Alsaffar MM, Hasan M, McStay GP, Sedky M. Digital DNA lifecycle security and privacy: an overview. Brief Bioinform 2022; 23:6518049. [PMID: 35106557 DOI: 10.1093/bib/bbab607] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 12/29/2021] [Accepted: 12/30/2021] [Indexed: 11/14/2022] Open
Abstract
DNA sequencing technologies have advanced significantly in the last few years leading to advancements in biomedical research which has improved personalised medicine and the discovery of new treatments for diseases. Sequencing technology advancement has also reduced the cost of DNA sequencing, which has led to the rise of direct-to-consumer (DTC) sequencing, e.g. 23andme.com, ancestry.co.uk, etc. In the meantime, concerns have emerged over privacy and security in collecting, handling, analysing and sharing DNA and genomic data. DNA data are unique and can be used to identify individuals. Moreover, those data provide information on people's current disease status and disposition, e.g. mental health or susceptibility for developing cancer. DNA privacy violation does not only affect the owner but also affects their close consanguinity due to its hereditary nature. This article introduces and defines the term 'digital DNA life cycle' and presents an overview of privacy and security threats and their mitigation techniques for predigital DNA and throughout the digital DNA life cycle. It covers DNA sequencing hardware, software and DNA sequence pipeline in addition to common privacy attacks and their countermeasures when DNA digital data are stored, queried or shared. Likewise, the article examines DTC genomic sequencing privacy and security.
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Affiliation(s)
- Muhalb M Alsaffar
- Department of Computing, AI and Robotics, School of Digital, Technologies and Arts, Staffordshire University, College Road, ST4 2DE, Staffordshire, United Kingdom
| | | | - Gavin P McStay
- Department of Biological Sciences, School of Health, Science and Wellbeing, Staffordshire University, College Road, Stoke-on-Trent, Staffordshire, ST4 2DE, United Kingdom
| | - Mohamed Sedky
- Department of Computing, AI and Robotics, School of Digital, Technologies and Arts, Staffordshire University, College Road, ST4 2DE, Staffordshire, United Kingdom
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47
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Yuan K, Zeng T, Chen L. Interpreting Functional Impact of Genetic Variations by Network QTL for Genotype–Phenotype Association Study. Front Cell Dev Biol 2022; 9:720321. [PMID: 35155440 PMCID: PMC8826544 DOI: 10.3389/fcell.2021.720321] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Accepted: 12/13/2021] [Indexed: 12/18/2022] Open
Abstract
An enormous challenge in the post-genome era is to annotate and resolve the consequences of genetic variation on diverse phenotypes. The genome-wide association study (GWAS) is a well-known method to identify potential genetic loci for complex traits from huge genetic variations, following which it is crucial to identify expression quantitative trait loci (eQTL). However, the conventional eQTL methods usually disregard the systematical role of single-nucleotide polymorphisms (SNPs) or genes, thereby overlooking many network-associated phenotypic determinates. Such a problem motivates us to recognize the network-based quantitative trait loci (QTL), i.e., network QTL (nQTL), which is to detect the cascade association as genotype → network → phenotype rather than conventional genotype → expression → phenotype in eQTL. Specifically, we develop the nQTL framework on the theory and approach of single-sample networks, which can identify not only network traits (e.g., the gene subnetwork associated with genotype) for analyzing complex biological processes but also network signatures (e.g., the interactive gene biomarker candidates screened from network traits) for characterizing targeted phenotype and corresponding subtypes. Our results show that the nQTL framework can efficiently capture associations between SNPs and network traits (i.e., edge traits) in various simulated data scenarios, compared with traditional eQTL methods. Furthermore, we have carried out nQTL analysis on diverse biological and biomedical datasets. Our analysis is effective in detecting network traits for various biological problems and can discover many network signatures for discriminating phenotypes, which can help interpret the influence of nQTL on disease subtyping, disease prognosis, drug response, and pathogen factor association. Particularly, in contrast to the conventional approaches, the nQTL framework could also identify many network traits from human bulk expression data, validated by matched single-cell RNA-seq data in an independent or unsupervised manner. All these results strongly support that nQTL and its detection framework can simultaneously explore the global genotype–network–phenotype associations and the underlying network traits or network signatures with functional impact and importance.
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Affiliation(s)
- Kai Yuan
- Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, China
| | - Tao Zeng
- Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, China
- Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
- Guangzhou Laboratory, Guangzhou, China
- *Correspondence: Tao Zeng, ; Luonan Chen,
| | - Luonan Chen
- Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, China
- Key Laboratory of Systems Health Science of Zhejiang Province, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China
- *Correspondence: Tao Zeng, ; Luonan Chen,
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48
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Orozco G. Fine mapping with epigenetic information and 3D structure. Semin Immunopathol 2022; 44:115-125. [PMID: 35022890 PMCID: PMC8837508 DOI: 10.1007/s00281-021-00906-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Accepted: 12/13/2021] [Indexed: 12/12/2022]
Abstract
Since 2005, thousands of genome-wide association studies (GWAS) have been published, identifying hundreds of thousands of genetic variants that increase risk of complex traits such as autoimmune diseases. This wealth of data has the potential to improve patient care, through personalized medicine and the identification of novel drug targets. However, the potential of GWAS for clinical translation has not been fully achieved yet, due to the fact that the functional interpretation of risk variants and the identification of causal variants and genes are challenging. The past decade has seen the development of great advances that are facilitating the overcoming of these limitations, by utilizing a plethora of genomics and epigenomics tools to map and characterize regulatory elements and chromatin interactions, which can be used to fine map GWAS loci, and advance our understanding of the biological mechanisms that cause disease.
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Affiliation(s)
- Gisela Orozco
- Centre for Genetics and Genomics Versus Arthritis, Division of Musculoskeletal and Dermatological Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, AV Hill Building, Oxford Road, Manchester, M13 9LJ, UK. .,NIHR Manchester Biomedical Research Centre, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK.
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49
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Orozco G, Schoenfelder S, Walker N, Eyre S, Fraser P. 3D genome organization links non-coding disease-associated variants to genes. Front Cell Dev Biol 2022; 10:995388. [PMID: 36340032 PMCID: PMC9631826 DOI: 10.3389/fcell.2022.995388] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 09/27/2022] [Indexed: 11/13/2022] Open
Abstract
Genome sequencing has revealed over 300 million genetic variations in human populations. Over 90% of variants are single nucleotide polymorphisms (SNPs), the remainder include short deletions or insertions, and small numbers of structural variants. Hundreds of thousands of these variants have been associated with specific phenotypic traits and diseases through genome wide association studies which link significant differences in variant frequencies with specific phenotypes among large groups of individuals. Only 5% of disease-associated SNPs are located in gene coding sequences, with the potential to disrupt gene expression or alter of the function of encoded proteins. The remaining 95% of disease-associated SNPs are located in non-coding DNA sequences which make up 98% of the genome. The role of non-coding, disease-associated SNPs, many of which are located at considerable distances from any gene, was at first a mystery until the discovery that gene promoters regularly interact with distal regulatory elements to control gene expression. Disease-associated SNPs are enriched at the millions of gene regulatory elements that are dispersed throughout the non-coding sequences of the genome, suggesting they function as gene regulation variants. Assigning specific regulatory elements to the genes they control is not straightforward since they can be millions of base pairs apart. In this review we describe how understanding 3D genome organization can identify specific interactions between gene promoters and distal regulatory elements and how 3D genomics can link disease-associated SNPs to their target genes. Understanding which gene or genes contribute to a specific disease is the first step in designing rational therapeutic interventions.
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Affiliation(s)
- Gisela Orozco
- Centre for Genetics and Genomics Versus Arthritis, Division of Musculoskeletal and Dermatological Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester Academic Health Science Centre, Manchester, United Kingdom.,NIHR Manchester Biomedical Research Centre, Manchester University Foundation Trust, Manchester, United Kingdom
| | - Stefan Schoenfelder
- Enhanc3D Genomics Ltd., Cambridge, United Kingdom.,Epigenetics Programme, The Babraham Institute, Babraham Research Campus, CB22 3AT Cambridge, Cambridge, United Kingdom
| | | | - Stephan Eyre
- Centre for Genetics and Genomics Versus Arthritis, Division of Musculoskeletal and Dermatological Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester Academic Health Science Centre, Manchester, United Kingdom.,NIHR Manchester Biomedical Research Centre, Manchester University Foundation Trust, Manchester, United Kingdom
| | - Peter Fraser
- Enhanc3D Genomics Ltd., Cambridge, United Kingdom.,Department of Biological Science, Florida State University, Tallahassee, FL, United States
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50
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Karlsson Linnér R, Koellinger PD. Genetic risk scores in life insurance underwriting. JOURNAL OF HEALTH ECONOMICS 2022; 81:102556. [PMID: 34847443 DOI: 10.1016/j.jhealeco.2021.102556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Revised: 11/03/2021] [Accepted: 11/12/2021] [Indexed: 06/13/2023]
Abstract
Genetic tests that predict the lifetime risk of common medical conditions are fast becoming more accurate and affordable. The life insurance industry is interested in using predictive genetic tests in the underwriting process, but more research is needed to establish whether this nascent form of genetic testing can refine the process over conventional underwriting factors. Here, we perform Cox regression of survival on a battery of genetic risk scores for common medical conditions and mortality risks in the Health and Retirement Study, without returning results to participants. Adjusted for covariates in a relevant insurance scenario, the scores could improve mortality risk classification by identifying 2.6 years shorter median lifespan in the highest decile of total genetic liability. We conclude that existing genetic risk scores can already improve life insurance underwriting, which stresses the urgency of policymakers to balance competing interests between stakeholders as this technology develops.
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Affiliation(s)
- Richard Karlsson Linnér
- School of Business and Economics, Vrije Universiteit Amsterdam, De Boelelaan 1105, Amsterdam 1081HV, the Netherlands; Department of Economics, Leiden University, Steenschuur 25, Leiden 2531ES, the Netherlands.
| | - Philipp D Koellinger
- School of Business and Economics, Vrije Universiteit Amsterdam, De Boelelaan 1105, Amsterdam 1081HV, the Netherlands; La Follette School of Public Affairs, University of Wisconsin-Madison, 1225 Observatory Dr., Madison, WI 53706, USA..
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