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Zhang R, Wu M, Xiang D, Zhu J, Zhang Q, Zhong H, Peng Y, Wang Z, Ma G, Li G, Liu F, Ye W, Shi R, Zhou X, Babarinde IA, Su H, Chen J, Zhang X, Qin D, Hutchins AP, Pei D, Li D. A primate-specific endogenous retroviral envelope protein sequesters SFRP2 to regulate human cardiomyocyte development. Cell Stem Cell 2024; 31:1298-1314.e8. [PMID: 39146934 DOI: 10.1016/j.stem.2024.07.006] [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: 01/16/2024] [Revised: 06/04/2024] [Accepted: 07/23/2024] [Indexed: 08/17/2024]
Abstract
Endogenous retroviruses (ERVs) occupy a significant part of the human genome, with some encoding proteins that influence the immune system or regulate cell-cell fusion in early extra-embryonic development. However, whether ERV-derived proteins regulate somatic development is unknown. Here, we report a somatic developmental function for the primate-specific ERVH48-1 (SUPYN/Suppressyn). ERVH48-1 encodes a fragment of a viral envelope that is expressed during early embryonic development. Loss of ERVH48-1 led to impaired mesoderm and cardiomyocyte commitment and diverted cells to an ectoderm-like fate. Mechanistically, ERVH48-1 is localized to sub-cellular membrane compartments through a functional N-terminal signal peptide and binds to the WNT antagonist SFRP2 to promote its polyubiquitination and degradation, thus limiting SFRP2 secretion and blocking repression of WNT/β-catenin signaling. Knockdown of SFRP2 or expression of a chimeric SFRP2 with the ERVH48-1 signal peptide rescued cardiomyocyte differentiation. This study demonstrates how ERVH48-1 modulates WNT/β-catenin signaling and cell type commitment in somatic development.
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Affiliation(s)
- Ran Zhang
- Key Laboratory of Biological Targeting Diagnosis, Therapy and Rehabilitation of Guangdong Higher Education Institutes, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou 510799, China; State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macao, China
| | - Menghua Wu
- Key Laboratory of Biological Targeting Diagnosis, Therapy and Rehabilitation of Guangdong Higher Education Institutes, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou 510799, China
| | - Dan Xiang
- CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, Hong Kong Institute of Science & Innovation, Guangzhou Institutes of Biomedicine and Health, Guangzhou, Guangdong 510530, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jieying Zhu
- CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, Hong Kong Institute of Science & Innovation, Guangzhou Institutes of Biomedicine and Health, Guangzhou, Guangdong 510530, China
| | - Qi Zhang
- Key Laboratory of Biological Targeting Diagnosis, Therapy and Rehabilitation of Guangdong Higher Education Institutes, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou 510799, China
| | - Hui Zhong
- CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, Hong Kong Institute of Science & Innovation, Guangzhou Institutes of Biomedicine and Health, Guangzhou, Guangdong 510530, China
| | - Yuling Peng
- Key Laboratory of Biological Targeting Diagnosis, Therapy and Rehabilitation of Guangdong Higher Education Institutes, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou 510799, China
| | - Zhenhua Wang
- Key Laboratory of Biological Targeting Diagnosis, Therapy and Rehabilitation of Guangdong Higher Education Institutes, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou 510799, China
| | - Gang Ma
- Department of Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China
| | - Guihuan Li
- Key Laboratory of Biological Targeting Diagnosis, Therapy and Rehabilitation of Guangdong Higher Education Institutes, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou 510799, China
| | - Fengping Liu
- Key Laboratory of Biological Targeting Diagnosis, Therapy and Rehabilitation of Guangdong Higher Education Institutes, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou 510799, China; Faculty of Medicine, Macau University of Science and Technology, Taipa, Macau 999078, China
| | - Weipeng Ye
- Key Laboratory of Biological Targeting Diagnosis, Therapy and Rehabilitation of Guangdong Higher Education Institutes, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou 510799, China
| | - Ruona Shi
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xuemeng Zhou
- Department of Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China
| | - Isaac A Babarinde
- Department of Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China
| | - Huanxing Su
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macao, China
| | - Jiekai Chen
- CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, Hong Kong Institute of Science & Innovation, Guangzhou Institutes of Biomedicine and Health, Guangzhou, Guangdong 510530, China
| | - Xiaofei Zhang
- Key Laboratory of Biological Targeting Diagnosis, Therapy and Rehabilitation of Guangdong Higher Education Institutes, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou 510799, China; CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, Hong Kong Institute of Science & Innovation, Guangzhou Institutes of Biomedicine and Health, Guangzhou, Guangdong 510530, China; University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Dajiang Qin
- Key Laboratory of Biological Targeting Diagnosis, Therapy and Rehabilitation of Guangdong Higher Education Institutes, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou 510799, China; Centre for Regenerative Medicine and Health, Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences, Hong Kong SAR, China.
| | - Andrew P Hutchins
- Department of Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China.
| | - Duanqing Pei
- Laboratory of Cell Fate Control, School of Life Sciences, Westlake University, Hangzhou 310024, China.
| | - Dongwei Li
- Key Laboratory of Biological Targeting Diagnosis, Therapy and Rehabilitation of Guangdong Higher Education Institutes, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou 510799, China.
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2
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Sun BB, Suhre K, Gibson BW. Promises and Challenges of populational Proteomics in Health and Disease. Mol Cell Proteomics 2024; 23:100786. [PMID: 38761890 PMCID: PMC11193116 DOI: 10.1016/j.mcpro.2024.100786] [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: 02/06/2024] [Revised: 05/13/2024] [Accepted: 05/15/2024] [Indexed: 05/20/2024] Open
Abstract
Advances in proteomic assay technologies have significantly increased coverage and throughput, enabling recent increases in the number of large-scale population-based proteomic studies of human plasma and serum. Improvements in multiplexed protein assays have facilitated the quantification of thousands of proteins over a large dynamic range, a key requirement for detecting the lowest-ranging, and potentially the most disease-relevant, blood-circulating proteins. In this perspective, we examine how populational proteomic datasets in conjunction with other concurrent omic measures can be leveraged to better understand the genomic and non-genomic correlates of the soluble proteome, constructing biomarker panels for disease prediction, among others. Mass spectrometry workflows are discussed as they are becoming increasingly competitive with affinity-based array platforms in terms of speed, cost, and proteome coverage due to advances in both instrumentation and workflows. Despite much success, there remain considerable challenges such as orthogonal validation and absolute quantification. We also highlight emergent challenges associated with study design, analytical considerations, and data integration as population-scale studies are run in batches and may involve longitudinal samples collated over many years. Lastly, we take a look at the future of what the nascent next-generation proteomic technologies might provide to the analysis of large sets of blood samples, as well as the difficulties in designing large-scale studies that will likely require participation from multiple and complex funding sources and where data sharing, study designs, and financing must be solved.
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Affiliation(s)
- Benjamin B Sun
- Human Genetics, Informatics and Predictive Sciences, Bristol-Myers Squibb, Cambridge, Massachusetts, USA.
| | - Karsten Suhre
- Bioinformatics Core, Weill Cornell Medicine-Qatar, Education City, Doha, Qatar; Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, New York, USA
| | - Bradford W Gibson
- Pharmaceutical Chemistry, University of California, San Francisco, California, USA
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3
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Jain S, Voulgaris D, Thongkorn S, Hesen R, Hägg A, Moslem M, Falk A, Herland A. On-Chip Neural Induction Boosts Neural Stem Cell Commitment: Toward a Pipeline for iPSC-Based Therapies. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2401859. [PMID: 38655836 PMCID: PMC11220685 DOI: 10.1002/advs.202401859] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Indexed: 04/26/2024]
Abstract
The clinical translation of induced pluripotent stem cells (iPSCs) holds great potential for personalized therapeutics. However, one of the main obstacles is that the current workflow to generate iPSCs is expensive, time-consuming, and requires standardization. A simplified and cost-effective microfluidic approach is presented for reprogramming fibroblasts into iPSCs and their subsequent differentiation into neural stem cells (NSCs). This method exploits microphysiological technology, providing a 100-fold reduction in reagents for reprogramming and a ninefold reduction in number of input cells. The iPSCs generated from microfluidic reprogramming of fibroblasts show upregulation of pluripotency markers and downregulation of fibroblast markers, on par with those reprogrammed in standard well-conditions. The NSCs differentiated in microfluidic chips show upregulation of neuroectodermal markers (ZIC1, PAX6, SOX1), highlighting their propensity for nervous system development. Cells obtained on conventional well plates and microfluidic chips are compared for reprogramming and neural induction by bulk RNA sequencing. Pathway enrichment analysis of NSCs from chip showed neural stem cell development enrichment and boosted commitment to neural stem cell lineage in initial phases of neural induction, attributed to a confined environment in a microfluidic chip. This method provides a cost-effective pipeline to reprogram and differentiate iPSCs for therapeutics compliant with current good manufacturing practices.
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Affiliation(s)
- Saumey Jain
- Division of Micro and NanosystemsKTH Royal Institute of TechnologyMalvinas väg 10Stockholm100 44Sweden
- Division of NanobiotechnologyScience for Life LaboratoryKTH Royal Institute of TechnologyTomtebodavägen 23aSolna171 65Sweden
| | - Dimitrios Voulgaris
- Division of Micro and NanosystemsKTH Royal Institute of TechnologyMalvinas väg 10Stockholm100 44Sweden
- Division of NanobiotechnologyScience for Life LaboratoryKTH Royal Institute of TechnologyTomtebodavägen 23aSolna171 65Sweden
- AIMESCenter for Integrated Medical and Engineering ScienceDepartment of NeuroscienceKarolinska InstitutetSolna171 65Sweden
| | - Surangrat Thongkorn
- Division of NanobiotechnologyScience for Life LaboratoryKTH Royal Institute of TechnologyTomtebodavägen 23aSolna171 65Sweden
- Chulalongkorn Autism Research and Innovation Center of Excellence (Chula ACE)Department of Clinical ChemistryFaculty of Allied Health SciencesChulalongkorn UniversityBangkok10330Thailand
| | - Rick Hesen
- Division of Micro and NanosystemsKTH Royal Institute of TechnologyMalvinas väg 10Stockholm100 44Sweden
| | - Alice Hägg
- Neural Stem CellsDepartment of Experimental Medical ScienceLund Stem Cell CenterLund UniversityLund221 84Sweden
| | - Mohsen Moslem
- Department of NeuroscienceKarolinska InstitutetSolna171 65Sweden
| | - Anna Falk
- Neural Stem CellsDepartment of Experimental Medical ScienceLund Stem Cell CenterLund UniversityLund221 84Sweden
- Department of NeuroscienceKarolinska InstitutetSolna171 65Sweden
| | - Anna Herland
- Division of Micro and NanosystemsKTH Royal Institute of TechnologyMalvinas väg 10Stockholm100 44Sweden
- Division of NanobiotechnologyScience for Life LaboratoryKTH Royal Institute of TechnologyTomtebodavägen 23aSolna171 65Sweden
- AIMESCenter for Integrated Medical and Engineering ScienceDepartment of NeuroscienceKarolinska InstitutetSolna171 65Sweden
- Department of NeuroscienceKarolinska InstitutetSolna171 65Sweden
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4
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Zhang H, Wu LZ, Liu ZY, Jin ZB. Patient-derived induced pluripotent stem cells with a MERTK mutation exhibit cell junction abnormalities and aberrant cellular differentiation potential. World J Stem Cells 2024; 16:512-524. [PMID: 38817331 PMCID: PMC11135251 DOI: 10.4252/wjsc.v16.i5.512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 01/29/2024] [Accepted: 04/01/2024] [Indexed: 05/24/2024] Open
Abstract
BACKGROUND Human induced pluripotent stem cell (hiPSC) technology is a valuable tool for generating patient-specific stem cells, facilitating disease modeling, and investigating disease mechanisms. However, iPSCs carrying specific mutations may limit their clinical applications due to certain inherent characteristics. AIM To investigate the impact of MERTK mutations on hiPSCs and determine whether hiPSC-derived extracellular vesicles (EVs) influence anomalous cell junction and differentiation potential. METHODS We employed a non-integrating reprogramming technique to generate peripheral blood-derived hiPSCs with and hiPSCs without a MERTK mutation. Chromosomal karyotype analysis, flow cytometry, and immunofluorescent staining were utilized for hiPSC identification. Transcriptomics and proteomics were employed to elucidate the expression patterns associated with cell junction abnormalities and cellular differentiation potential. Additionally, EVs were isolated from the supernatant, and their RNA and protein cargos were examined to investigate the involvement of hiPSC-derived EVs in stem cell junction and differentiation. RESULTS The generated hiPSCs, both with and without a MERTK mutation, exhibited normal karyotype and expressed pluripotency markers; however, hiPSCs with a MERTK mutation demonstrated anomalous adhesion capability and differentiation potential, as confirmed by transcriptomic and proteomic profiling. Furthermore, hiPSC-derived EVs were involved in various biological processes, including cell junction and differentiation. CONCLUSION HiPSCs with a MERTK mutation displayed altered junction characteristics and aberrant differentiation potential. Furthermore, hiPSC-derived EVs played a regulatory role in various biological processes, including cell junction and differentiation.
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Affiliation(s)
- Hang Zhang
- Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, China
| | - Ling-Zi Wu
- Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, China
| | - Zhen-Yu Liu
- Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, China
| | - Zi-Bing Jin
- Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, China.
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5
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Teyssonnière EM, Trébulle P, Muenzner J, Loegler V, Ludwig D, Amari F, Mülleder M, Friedrich A, Hou J, Ralser M, Schacherer J. Species-wide quantitative transcriptomes and proteomes reveal distinct genetic control of gene expression variation in yeast. Proc Natl Acad Sci U S A 2024; 121:e2319211121. [PMID: 38696467 PMCID: PMC11087752 DOI: 10.1073/pnas.2319211121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Accepted: 03/25/2024] [Indexed: 05/04/2024] Open
Abstract
Gene expression varies between individuals and corresponds to a key step linking genotypes to phenotypes. However, our knowledge regarding the species-wide genetic control of protein abundance, including its dependency on transcript levels, is very limited. Here, we have determined quantitative proteomes of a large population of 942 diverse natural Saccharomyces cerevisiae yeast isolates. We found that mRNA and protein abundances are weakly correlated at the population gene level. While the protein coexpression network recapitulates major biological functions, differential expression patterns reveal proteomic signatures related to specific populations. Comprehensive genetic association analyses highlight that genetic variants associated with variation in protein (pQTL) and transcript (eQTL) levels poorly overlap (3%). Our results demonstrate that transcriptome and proteome are governed by distinct genetic bases, likely explained by protein turnover. It also highlights the importance of integrating these different levels of gene expression to better understand the genotype-phenotype relationship.
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Affiliation(s)
- Elie Marcel Teyssonnière
- UMR 7156 Génétique Moléculaire, Génomique et Microbiologie, Université de Strasbourg, CNRS, Strasbourg67000, France
| | - Pauline Trébulle
- The Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, OxfordOX3 7BN, United Kingdom
| | - Julia Muenzner
- Department of Biochemistry, Charitéplatz 1, Charité – Universitätsmedizin Berlin, Berlin10117, Germany
| | - Victor Loegler
- UMR 7156 Génétique Moléculaire, Génomique et Microbiologie, Université de Strasbourg, CNRS, Strasbourg67000, France
| | - Daniela Ludwig
- Department of Biochemistry, Charitéplatz 1, Charité – Universitätsmedizin Berlin, Berlin10117, Germany
- Core Facility High-Throughput Mass Spectrometry, Charitéplatz 1, Charité – Universitätsmedizin Berlin, Berlin10117, Germany
| | - Fatma Amari
- Department of Biochemistry, Charitéplatz 1, Charité – Universitätsmedizin Berlin, Berlin10117, Germany
- Core Facility High-Throughput Mass Spectrometry, Charitéplatz 1, Charité – Universitätsmedizin Berlin, Berlin10117, Germany
| | - Michael Mülleder
- Core Facility High-Throughput Mass Spectrometry, Charitéplatz 1, Charité – Universitätsmedizin Berlin, Berlin10117, Germany
| | - Anne Friedrich
- UMR 7156 Génétique Moléculaire, Génomique et Microbiologie, Université de Strasbourg, CNRS, Strasbourg67000, France
| | - Jing Hou
- UMR 7156 Génétique Moléculaire, Génomique et Microbiologie, Université de Strasbourg, CNRS, Strasbourg67000, France
| | - Markus Ralser
- The Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, OxfordOX3 7BN, United Kingdom
- Department of Biochemistry, Charitéplatz 1, Charité – Universitätsmedizin Berlin, Berlin10117, Germany
- Max Planck Institute for Molecular Genetics, Berlin14195, Germany
| | - Joseph Schacherer
- UMR 7156 Génétique Moléculaire, Génomique et Microbiologie, Université de Strasbourg, CNRS, Strasbourg67000, France
- Institut Universitaire de France, Paris75000, France
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6
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Kurgan N, Kjærgaard Larsen J, Deshmukh AS. Harnessing the power of proteomics in precision diabetes medicine. Diabetologia 2024; 67:783-797. [PMID: 38345659 DOI: 10.1007/s00125-024-06097-5] [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: 11/14/2023] [Accepted: 12/20/2023] [Indexed: 03/21/2024]
Abstract
Precision diabetes medicine (PDM) aims to reduce errors in prevention programmes, diagnosis thresholds, prognosis prediction and treatment strategies. However, its advancement and implementation are difficult due to the heterogeneity of complex molecular processes and environmental exposures that influence an individual's disease trajectory. To address this challenge, it is imperative to develop robust screening methods for all areas of PDM. Innovative proteomic technologies, alongside genomics, have proven effective in precision cancer medicine and are showing promise in diabetes research for potential translation. This narrative review highlights how proteomics is well-positioned to help improve PDM. Specifically, a critical assessment of widely adopted affinity-based proteomic technologies in large-scale clinical studies and evidence of the benefits and feasibility of using MS-based plasma proteomics is presented. We also present a case for the use of proteomics to identify predictive protein panels for type 2 diabetes subtyping and the development of clinical prediction models for prevention, diagnosis, prognosis and treatment strategies. Lastly, we discuss the importance of plasma and tissue proteomics and its integration with genomics (proteogenomics) for identifying unique type 2 diabetes intra- and inter-subtype aetiology. We conclude with a call for action formed on advancing proteomics technologies, benchmarking their performance and standardisation across sites, with an emphasis on data sharing and the inclusion of diverse ancestries in large cohort studies. These efforts should foster collaboration with key stakeholders and align with ongoing academic programmes such as the Precision Medicine in Diabetes Initiative consortium.
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Affiliation(s)
- Nigel Kurgan
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
| | - Jeppe Kjærgaard Larsen
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
| | - Atul S Deshmukh
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark.
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7
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Farbehi N, Neavin DR, Cuomo ASE, Studer L, MacArthur DG, Powell JE. Integrating population genetics, stem cell biology and cellular genomics to study complex human diseases. Nat Genet 2024; 56:758-766. [PMID: 38741017 DOI: 10.1038/s41588-024-01731-9] [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: 01/24/2023] [Accepted: 03/20/2024] [Indexed: 05/16/2024]
Abstract
Human pluripotent stem (hPS) cells can, in theory, be differentiated into any cell type, making them a powerful in vitro model for human biology. Recent technological advances have facilitated large-scale hPS cell studies that allow investigation of the genetic regulation of molecular phenotypes and their contribution to high-order phenotypes such as human disease. Integrating hPS cells with single-cell sequencing makes identifying context-dependent genetic effects during cell development or upon experimental manipulation possible. Here we discuss how the intersection of stem cell biology, population genetics and cellular genomics can help resolve the functional consequences of human genetic variation. We examine the critical challenges of integrating these fields and approaches to scaling them cost-effectively and practically. We highlight two areas of human biology that can particularly benefit from population-scale hPS cell studies, elucidating mechanisms underlying complex disease risk loci and evaluating relationships between common genetic variation and pharmacotherapeutic phenotypes.
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Affiliation(s)
- Nona Farbehi
- Garvan Weizmann Center for Cellular Genomics, Garvan Institute of Medical Research, Sydney, New South Wales, Australia
- Graduate School of Biomedical Engineering, University of New South Wales, Sydney, New South Wales, Australia
- Aligning Science Across Parkinson's Collaborative Research Network, Chevy Chase, MD, USA
| | - Drew R Neavin
- Garvan Weizmann Center for Cellular Genomics, Garvan Institute of Medical Research, Sydney, New South Wales, Australia
| | - Anna S E Cuomo
- Garvan Weizmann Center for Cellular Genomics, Garvan Institute of Medical Research, Sydney, New South Wales, Australia
- Centre for Population Genomics, Garvan Institute of Medical Research, University of New South Wales, Sydney, New South Wales, Australia
| | - Lorenz Studer
- Aligning Science Across Parkinson's Collaborative Research Network, Chevy Chase, MD, USA
- The Center for Stem Cell Biology and Developmental Biology Program, Sloan-Kettering Institute for Cancer Research, New York, NY, USA
| | - Daniel G MacArthur
- Centre for Population Genomics, Garvan Institute of Medical Research, University of New South Wales, Sydney, New South Wales, Australia
- Centre for Population Genomics, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
| | - Joseph E Powell
- Garvan Weizmann Center for Cellular Genomics, Garvan Institute of Medical Research, Sydney, New South Wales, Australia.
- Aligning Science Across Parkinson's Collaborative Research Network, Chevy Chase, MD, USA.
- UNSW Cellular Genomics Futures Institute, University of New South Wales, Sydney, New South Wales, Australia.
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8
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Kanchibhotla SC, Mather KA, Armstrong NJ, Ciobanu LG, Baune BT, Catts VS, Schofield PR, Trollor JN, Ames D, Sachdev PS, Thalamuthu A. Heritability of Gene Expression Measured from Peripheral Blood in Older Adults. Genes (Basel) 2024; 15:495. [PMID: 38674429 PMCID: PMC11049887 DOI: 10.3390/genes15040495] [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: 03/13/2024] [Revised: 04/08/2024] [Accepted: 04/11/2024] [Indexed: 04/28/2024] Open
Abstract
The contributions of genetic variation and the environment to gene expression may change across the lifespan. However, few studies have investigated the heritability of blood gene expression in older adults. The current study therefore aimed to investigate this question in a community sample of older adults. A total of 246 adults (71 MZ and 52 DZ twins, 69.91% females; mean age-75.79 ± 5.44) were studied. Peripheral blood gene expression was assessed using Illumina microarrays. A heritability analysis was performed using structural equation modelling. There were 5269 probes (19.9%) from 4603 unique genes (23.9%) (total 26,537 probes from 19,256 genes) that were significantly heritable (mean h2 = 0.40). A pathway analysis of the top 10% of significant genes showed enrichment for the immune response and ageing-associated genes. In a comparison with two other gene expression twin heritability studies using adults from across the lifespan, there were 38 out of 9479 overlapping genes that were significantly heritable. In conclusion, our study found ~24% of the available genes for analysis were heritable in older adults, with only a small number common across studies that used samples from across adulthood, indicating the importance of examining gene expression in older age groups.
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Affiliation(s)
- Sri C. Kanchibhotla
- Centre for Healthy Brain Ageing, Discipline of Psychiatry & Mental Health, School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW 2052, Australia
| | - Karen A. Mather
- Centre for Healthy Brain Ageing, Discipline of Psychiatry & Mental Health, School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW 2052, Australia
- Neuroscience Research Australia, Sydney, NSW 2031, Australia
| | - Nicola J. Armstrong
- Department of Mathematics and Statistics, Curtin University, Perth, WA 6845, Australia
| | - Liliana G. Ciobanu
- Discipline of Psychiatry, Adelaide Medical School, The University of Adelaide, Adelaide, SA 5005, Australia
| | - Bernhard T. Baune
- Discipline of Psychiatry, Adelaide Medical School, The University of Adelaide, Adelaide, SA 5005, Australia
- Department of Psychiatry, University of Münster, 48149 Münster, Germany
- Department of Psychiatry, Melbourne Medical School, The University of Melbourne, Melbourne, VIC 3052, Australia
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Melbourne, VIC 3052, Australia
| | - Vibeke S. Catts
- Centre for Healthy Brain Ageing, Discipline of Psychiatry & Mental Health, School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW 2052, Australia
| | - Peter R. Schofield
- Neuroscience Research Australia, Sydney, NSW 2031, Australia
- School of Medical Sciences, University of New South Wales, Sydney, NSW 2052, Australia
| | - Julian N. Trollor
- Centre for Healthy Brain Ageing, Discipline of Psychiatry & Mental Health, School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW 2052, Australia
- Department of Developmental Disability Neuropsychiatry, Discipline of Psychiatry & Mental Health, School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW 2052, Australia
| | - David Ames
- Academic Unit for Psychiatry of Old Age, University of Melbourne, St George’s Hospital, Kew, Melbourne, VIC 3010, Australia
- National Ageing Research Institute, Parkville, VIC 3052, Australia
| | - Perminder S. Sachdev
- Centre for Healthy Brain Ageing, Discipline of Psychiatry & Mental Health, School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW 2052, Australia
- Neuropsychiatric Institute, Euroa Centre, Prince of Wales Hospital, Sydney, NSW 2031, Australia
| | - Anbupalam Thalamuthu
- Centre for Healthy Brain Ageing, Discipline of Psychiatry & Mental Health, School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW 2052, Australia
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9
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Manda V, Pavelka J, Lau E. Proteomics applications in next generation induced pluripotent stem cell models. Expert Rev Proteomics 2024; 21:217-228. [PMID: 38511670 PMCID: PMC11065590 DOI: 10.1080/14789450.2024.2334033] [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: 06/14/2023] [Accepted: 03/08/2024] [Indexed: 03/22/2024]
Abstract
INTRODUCTION Induced pluripotent stem (iPS) cell technology has transformed biomedical research. New opportunities now exist to create new organoids, microtissues, and body-on-a-chip systems for basic biology investigations and clinical translations. AREAS COVERED We discuss the utility of proteomics for attaining an unbiased view into protein expression changes during iPS cell differentiation, cell maturation, and tissue generation. The ability to discover cell-type specific protein markers during the differentiation and maturation of iPS-derived cells has led to new strategies to improve cell production yield and fidelity. In parallel, proteomic characterization of iPS-derived organoids is helping to realize the goal of bridging in vitro and in vivo systems. EXPERT OPINIONS We discuss some current challenges of proteomics in iPS cell research and future directions, including the integration of proteomic and transcriptomic data for systems-level analysis.
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Affiliation(s)
- Vyshnavi Manda
- Department of Medicine, Division of Cardiology, University of Colorado School of Medicine, Aurora, Colorado, USA
- Consortium for Fibrosis Research and Translation, University of Colorado School of Medicine, Aurora, Colorado, USA
| | - Jay Pavelka
- Department of Medicine, Division of Cardiology, University of Colorado School of Medicine, Aurora, Colorado, USA
- Consortium for Fibrosis Research and Translation, University of Colorado School of Medicine, Aurora, Colorado, USA
| | - Edward Lau
- Department of Medicine, Division of Cardiology, University of Colorado School of Medicine, Aurora, Colorado, USA
- Consortium for Fibrosis Research and Translation, University of Colorado School of Medicine, Aurora, Colorado, USA
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10
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Smullen M, Olson MN, Reichert JM, Dawes P, Murray LF, Baer CE, Wang Q, Readhead B, Church GM, Lim ET, Chan Y. Reliable multiplex generation of pooled induced pluripotent stem cells. CELL REPORTS METHODS 2023; 3:100570. [PMID: 37751688 PMCID: PMC10545906 DOI: 10.1016/j.crmeth.2023.100570] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 06/23/2023] [Accepted: 08/04/2023] [Indexed: 09/28/2023]
Abstract
Reprogramming somatic cells into pluripotent stem cells (iPSCs) enables the study of systems in vitro. To increase the throughput of reprogramming, we present induction of pluripotency from pooled cells (iPPC)-an efficient, scalable, and reliable reprogramming procedure. Using our deconvolution algorithm that employs pooled sequencing of single-nucleotide polymorphisms (SNPs), we accurately estimated individual donor proportions of the pooled iPSCs. With iPPC, we concurrently reprogrammed over one hundred donor lymphoblastoid cell lines (LCLs) into iPSCs and found strong correlations of individual donors' reprogramming ability across multiple experiments. Individual donors' reprogramming ability remains consistent across both same-day replicates and multiple experimental runs, and the expression of certain immunoglobulin precursor genes may impact reprogramming ability. The pooled iPSCs were also able to differentiate into cerebral organoids. Our procedure enables a multiplex framework of using pooled libraries of donor iPSCs for downstream research and investigation of in vitro phenotypes.
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Affiliation(s)
- Molly Smullen
- Department of Neurology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA; Program in Bioinformatics and Integrative Biology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA; NeuroNexus Institute, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA; Department of Molecular, Cell and Cancer Biology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA
| | - Meagan N Olson
- Department of Neurology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA; Program in Bioinformatics and Integrative Biology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA; NeuroNexus Institute, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA; Department of Molecular, Cell and Cancer Biology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA
| | - Julia M Reichert
- Department of Neurology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA; Program in Bioinformatics and Integrative Biology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA; NeuroNexus Institute, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA
| | - Pepper Dawes
- Department of Neurology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA; Program in Bioinformatics and Integrative Biology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA; NeuroNexus Institute, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA; Department of Molecular, Cell and Cancer Biology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA
| | - Liam F Murray
- Department of Neurology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA; Program in Bioinformatics and Integrative Biology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA; NeuroNexus Institute, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA; Department of Molecular, Cell and Cancer Biology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA
| | - Christina E Baer
- Department of Microbiology and Physiological Systems, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA
| | - Qi Wang
- ASU-Banner Neurodegenerative Disease Research Center, Arizona State University, Tempe, AZ 85281, USA
| | - Benjamin Readhead
- ASU-Banner Neurodegenerative Disease Research Center, Arizona State University, Tempe, AZ 85281, USA
| | - George M Church
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA; Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA
| | - Elaine T Lim
- Department of Neurology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA; Program in Bioinformatics and Integrative Biology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA; NeuroNexus Institute, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA; Department of Molecular, Cell and Cancer Biology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA
| | - Yingleong Chan
- Department of Neurology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA; Program in Bioinformatics and Integrative Biology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA; NeuroNexus Institute, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA.
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11
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Teyssonnière E, Trébulle P, Muenzner J, Loegler V, Ludwig D, Amari F, Mülleder M, Friedrich A, Hou J, Ralser M, Schacherer J. Species-wide quantitative transcriptomes and proteomes reveal distinct genetic control of gene expression variation in yeast. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.18.558197. [PMID: 37781592 PMCID: PMC10541136 DOI: 10.1101/2023.09.18.558197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/03/2023]
Abstract
Gene expression varies between individuals and corresponds to a key step linking genotypes to phenotypes. However, our knowledge regarding the species-wide genetic control of protein abundance, including its dependency on transcript levels, is very limited. Here, we have determined quantitative proteomes of a large population of 942 diverse natural Saccharomyces cerevisiae yeast isolates. We found that mRNA and protein abundances are weakly correlated at the population gene level. While the protein co-expression network recapitulates major biological functions, differential expression patterns reveal proteomic signatures related to specific populations. Comprehensive genetic association analyses highlight that genetic variants associated with variation in protein (pQTL) and transcript (eQTL) levels poorly overlap (3.6%). Our results demonstrate that transcriptome and proteome are governed by distinct genetic bases, likely explained by protein turnover. It also highlights the importance of integrating these different levels of gene expression to better understand the genotype-phenotype relationship. Highlights At the level of individual genes, the abundance of transcripts and proteins is weakly correlated within a species ( ρ = 0.165). While the proteome is not imprinted by population structure, co-expression patterns recapitulate the cellular functional landscapeWild populations exhibit a higher abundance of respiration-related proteins compared to domesticated populationsLoci that influence protein abundance differ from those that impact transcript levels, likely because of protein turnover.
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12
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Brown AA, Fernandez-Tajes JJ, Hong MG, Brorsson CA, Koivula RW, Davtian D, Dupuis T, Sartori A, Michalettou TD, Forgie IM, Adam J, Allin KH, Caiazzo R, Cederberg H, De Masi F, Elders PJM, Giordano GN, Haid M, Hansen T, Hansen TH, Hattersley AT, Heggie AJ, Howald C, Jones AG, Kokkola T, Laakso M, Mahajan A, Mari A, McDonald TJ, McEvoy D, Mourby M, Musholt PB, Nilsson B, Pattou F, Penet D, Raverdy V, Ridderstråle M, Romano L, Rutters F, Sharma S, Teare H, 't Hart L, Tsirigos KD, Vangipurapu J, Vestergaard H, Brunak S, Franks PW, Frost G, Grallert H, Jablonka B, McCarthy MI, Pavo I, Pedersen O, Ruetten H, Walker M, Adamski J, Schwenk JM, Pearson ER, Dermitzakis ET, Viñuela A. Genetic analysis of blood molecular phenotypes reveals common properties in the regulatory networks affecting complex traits. Nat Commun 2023; 14:5062. [PMID: 37604891 PMCID: PMC10442420 DOI: 10.1038/s41467-023-40569-3] [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: 05/12/2021] [Accepted: 08/02/2023] [Indexed: 08/23/2023] Open
Abstract
We evaluate the shared genetic regulation of mRNA molecules, proteins and metabolites derived from whole blood from 3029 human donors. We find abundant allelic heterogeneity, where multiple variants regulate a particular molecular phenotype, and pleiotropy, where a single variant associates with multiple molecular phenotypes over multiple genomic regions. The highest proportion of share genetic regulation is detected between gene expression and proteins (66.6%), with a further median shared genetic associations across 49 different tissues of 78.3% and 62.4% between plasma proteins and gene expression. We represent the genetic and molecular associations in networks including 2828 known GWAS variants, showing that GWAS variants are more often connected to gene expression in trans than other molecular phenotypes in the network. Our work provides a roadmap to understanding molecular networks and deriving the underlying mechanism of action of GWAS variants using different molecular phenotypes in an accessible tissue.
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Affiliation(s)
- Andrew A Brown
- Population Health and Genomics, Ninewells Hospital and Medical School, University of Dundee, Dundee, DD1 9SY, United Kingdom
| | - Juan J Fernandez-Tajes
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, United Kingdom
| | - Mun-Gwan Hong
- Science for Life Laboratory, School of Biotechnology, KTH - Royal Institute of Technology, Solna, SE-171 21, Sweden
| | - Caroline A Brorsson
- Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, DK-2100, Denmark
| | - Robert W Koivula
- Oxford Centre for Diabetes Endocrinology and Metabolism, University of Oxford, Oxford, OX3 7LJ, United Kingdom
| | - David Davtian
- Population Health and Genomics, Ninewells Hospital and Medical School, University of Dundee, Dundee, DD1 9SY, United Kingdom
| | - Théo Dupuis
- Population Health and Genomics, Ninewells Hospital and Medical School, University of Dundee, Dundee, DD1 9SY, United Kingdom
| | - Ambra Sartori
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, 1211, Switzerland
- Institute for Genetics and Genomics in Geneva (iGE3), University of Geneva, Geneva, 1211, Switzerland
- Swiss Institute of Bioinformatics, Geneva, 1211, Switzerland
| | - Theodora-Dafni Michalettou
- Biosciences Institute, Faculty of Medical Sciences, University of Newcastle, Newcastle upon Tyne, NE1 4EP, United Kingdom
| | - Ian M Forgie
- Population Health and Genomics, Ninewells Hospital and Medical School, University of Dundee, Dundee, DD1 9SY, United Kingdom
| | - Jonathan Adam
- German Center for Diabetes Research (DZD), Neuherberg, 85764, Germany
- Research Unit of Molecular Epidemiology, Institute of Epidemiology, German Research Center for Environmental Health, Helmholtz Zentrum München, Neuherberg, 85764, Germany
| | - Kristine H Allin
- The Novo Nordisk Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, DK-2100, Denmark
| | - Robert Caiazzo
- University of Lille, Inserm, Lille Pasteur Institute, Lille, France
| | - Henna Cederberg
- Internal Medicine, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland
| | - Federico De Masi
- Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Petra J M Elders
- Department of General Practice, Amsterdam UMC- location Vumc, Amsterdam Public Health research institute, Amsterdam, The Netherlands
| | - Giuseppe N Giordano
- Department of Clinical Science, Genetic and Molecular Epidemiology, Lund University Diabetes Centre, Malmö, Sweden
| | - Mark Haid
- Metabolomics and Proteomics Core, German Research Center for Environmental Health, Helmholtz Zentrum München, Neuherberg, 85764, Germany
| | - Torben Hansen
- The Novo Nordisk Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, DK-2100, Denmark
| | - Tue H Hansen
- The Novo Nordisk Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, DK-2100, Denmark
| | - Andrew T Hattersley
- Department of Clinical and Biomedical Sciences, University of Exeter College of Medicine & Health, Exeter, EX25DW, United Kingdom
| | - Alison J Heggie
- Institute of Cellular Medicine, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Cédric Howald
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, 1211, Switzerland
- Institute for Genetics and Genomics in Geneva (iGE3), University of Geneva, Geneva, 1211, Switzerland
- Swiss Institute of Bioinformatics, Geneva, 1211, Switzerland
| | - Angus G Jones
- Department of Clinical and Biomedical Sciences, University of Exeter College of Medicine & Health, Exeter, EX25DW, United Kingdom
| | - Tarja Kokkola
- Internal Medicine, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland
| | - Markku Laakso
- Internal Medicine, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland
| | - Anubha Mahajan
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, United Kingdom
| | - Andrea Mari
- Institute of Neuroscience, National Research Council, Padova, 35127, Italy
| | - Timothy J McDonald
- Blood Sciences, Royal Devon and Exeter NHS Foundation Trust, Exeter, EX2 5DW, United Kingdom
| | - Donna McEvoy
- Diabetes Research Network, Royal Victoria Infirmary, Newcastle upon Tyne, United Kingdom
| | - Miranda Mourby
- Nuffield Department of Population Health, Centre for Health, Law and Emerging Technologies (HeLEX), University of Oxford, Oxford, OX2 7DD, United Kingdom
| | - Petra B Musholt
- Global Development, Sanofi-Aventis Deutschland GmbH, Hoechst Industrial Park, Frankfurt am Main, 65926, Germany
| | - Birgitte Nilsson
- Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Francois Pattou
- University of Lille, Inserm, Lille Pasteur Institute, Lille, France
| | - Deborah Penet
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, 1211, Switzerland
- Institute for Genetics and Genomics in Geneva (iGE3), University of Geneva, Geneva, 1211, Switzerland
- Swiss Institute of Bioinformatics, Geneva, 1211, Switzerland
| | - Violeta Raverdy
- University of Lille, Inserm, Lille Pasteur Institute, Lille, France
| | | | - Luciana Romano
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, 1211, Switzerland
- Institute for Genetics and Genomics in Geneva (iGE3), University of Geneva, Geneva, 1211, Switzerland
- Swiss Institute of Bioinformatics, Geneva, 1211, Switzerland
| | - Femke Rutters
- Epidemiology and Data Science, VUMC, Amsterdam, The Netherlands
| | - Sapna Sharma
- Research Unit of Molecular Epidemiology, Institute of Epidemiology, German Research Center for Environmental Health, Helmholtz Zentrum München, Neuherberg, 85764, Germany
- Food Chemistry and Molecular and Sensory Science, Technical University of Munich, München, Germany
| | - Harriet Teare
- Centre for Health Law and Emerging Technologies, Department of Population Health, University of Oxford, Old Road Campus, Oxford, OX3 7DQ, United Kingdom
| | - Leen 't Hart
- Epidemiology and Data Science, VUMC, Amsterdam, The Netherlands
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, The Netherlands
- Department of Biomedical Data Sciences, Molecular Epidemiology section, Leiden University Medical Center, Leiden, The Netherlands
| | | | - Jagadish Vangipurapu
- Internal Medicine, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland
| | - Henrik Vestergaard
- The Novo Nordisk Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, DK-2100, Denmark
- Steno Diabetes Center Copenhagen, Copenhagen, Denmark
| | - Søren Brunak
- Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, DK-2100, Denmark
| | - Paul W Franks
- Department of Clinical Science, Genetic and Molecular Epidemiology, Lund University Diabetes Centre, Malmö, Sweden
| | - Gary Frost
- Nutrition and Dietetics Research Group, Imperial College London, London, SW7 2AZ, United Kingdom
| | - Harald Grallert
- German Center for Diabetes Research (DZD), Neuherberg, 85764, Germany
- Research Unit of Molecular Epidemiology, Institute of Epidemiology, German Research Center for Environmental Health, Helmholtz Zentrum München, Neuherberg, 85764, Germany
| | - Bernd Jablonka
- Sanofi Partnering, Sanofi-Aventis Deutschland GmbH, Frankfurt am Main, 65926, Germany
| | - Mark I McCarthy
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, United Kingdom
- GENENTECH, 1 DNA Way, San Francisco, CA, 94080, USA
| | - Imre Pavo
- Eli Lilly Regional Operations Ges.m.b.H, Vienna, 1030, Austria
| | - Oluf Pedersen
- Center for Clinical Metabolic Research, Herlev and Gentofte University Hospital, Copenhagen, Denmark
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, DK-2100, Denmark
| | - Hartmut Ruetten
- Sanofi Partnering, Sanofi-Aventis Deutschland GmbH, Frankfurt am Main, 65926, Germany
| | - Mark Walker
- Translational and Clinical Research Institute, Faculty of Medical Sciences, University of Newcastle, Newcastle upon Tyne, United Kingdom
| | - Jerzy Adamski
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117597, Singapore
- Institute of Experimental Genetics, German Research Center for Environmental Health, Helmholtz Zentrum München, Neuherberg, 85764, Germany
- Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Jochen M Schwenk
- Science for Life Laboratory, School of Biotechnology, KTH - Royal Institute of Technology, Solna, SE-171 21, Sweden
| | - Ewan R Pearson
- Population Health and Genomics, Ninewells Hospital and Medical School, University of Dundee, Dundee, DD1 9SY, United Kingdom
| | - Emmanouil T Dermitzakis
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, 1211, Switzerland.
- Institute for Genetics and Genomics in Geneva (iGE3), University of Geneva, Geneva, 1211, Switzerland.
- Swiss Institute of Bioinformatics, Geneva, 1211, Switzerland.
| | - Ana Viñuela
- Biosciences Institute, Faculty of Medical Sciences, University of Newcastle, Newcastle upon Tyne, NE1 4EP, United Kingdom.
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13
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Eade KT, Ansell BRE, Giles S, Fallon R, Harkins-Perry S, Nagasaki T, Tzaridis S, Wallace M, Mills EA, Farashi S, Johnson A, Sauer L, Hart B, Diaz-Rubio ME, Bahlo M, Metallo C, Allikmets R, Gantner ML, Bernstein PS, Friedlander M. iPSC-derived retinal pigmented epithelial cells from patients with macular telangiectasia show decreased mitochondrial function. J Clin Invest 2023; 133:e163771. [PMID: 37115691 PMCID: PMC10145939 DOI: 10.1172/jci163771] [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: 07/21/2022] [Accepted: 03/14/2023] [Indexed: 04/29/2023] Open
Abstract
Patient-derived induced pluripotent stem cells (iPSCs) provide a powerful tool for identifying cellular and molecular mechanisms of disease. Macular telangiectasia type 2 (MacTel) is a rare, late-onset degenerative retinal disease with an extremely heterogeneous genetic architecture, lending itself to the use of iPSCs. Whole-exome sequencing screens and pedigree analyses have identified rare causative mutations that account for less than 5% of cases. Metabolomic surveys of patient populations and GWAS have linked MacTel to decreased circulating levels of serine and elevated levels of neurotoxic 1-deoxysphingolipids (1-dSLs). However, retina-specific, disease-contributing factors have yet to be identified. Here, we used iPSC-differentiated retinal pigmented epithelial (iRPE) cells derived from donors with or without MacTel to screen for novel cell-intrinsic pathological mechanisms. We show that MacTel iRPE cells mimicked the low serine levels observed in serum from patients with MacTel. Through RNA-Seq and gene set enrichment pathway analysis, we determined that MacTel iRPE cells are enriched in cellular stress pathways and dysregulation of central carbon metabolism. Using respirometry and mitochondrial stress testing, we functionally validated that MacTel iRPE cells had a reduction in mitochondrial function that was independent of defects in serine biosynthesis and 1-dSL accumulation. Thus, we identified phenotypes that may constitute alternative disease mechanisms beyond the known serine/sphingolipid pathway.
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Affiliation(s)
- Kevin T. Eade
- The Lowy Medical Research Institute, La Jolla, California, USA
- Department of Molecular Medicine, The Scripps Research Institute (TSRI), La Jolla, California, USA
| | - Brendan Robert E. Ansell
- Population Health and Immunity Division, Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia
- Department of Medical Biology, The University of Melbourne, Parkville, Victoria, Australia
| | - Sarah Giles
- The Lowy Medical Research Institute, La Jolla, California, USA
- Department of Molecular Medicine, The Scripps Research Institute (TSRI), La Jolla, California, USA
| | - Regis Fallon
- The Lowy Medical Research Institute, La Jolla, California, USA
- Department of Molecular Medicine, The Scripps Research Institute (TSRI), La Jolla, California, USA
| | - Sarah Harkins-Perry
- The Lowy Medical Research Institute, La Jolla, California, USA
- Department of Molecular Medicine, The Scripps Research Institute (TSRI), La Jolla, California, USA
| | - Takayuki Nagasaki
- Department of Ophthalmology and
- Department of Pathology and Cell Biology, Columbia University, New York, New York, USA
| | - Simone Tzaridis
- The Lowy Medical Research Institute, La Jolla, California, USA
- Department of Molecular Medicine, The Scripps Research Institute (TSRI), La Jolla, California, USA
| | - Martina Wallace
- Institute of Food and Health, School of Agriculture and Food Science, University College Dublin, Dublin, Ireland
| | - Elizabeth A. Mills
- The Lowy Medical Research Institute, La Jolla, California, USA
- Department of Molecular Medicine, The Scripps Research Institute (TSRI), La Jolla, California, USA
| | - Samaneh Farashi
- Population Health and Immunity Division, Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia
- Department of Medical Biology, The University of Melbourne, Parkville, Victoria, Australia
| | - Alec Johnson
- The Lowy Medical Research Institute, La Jolla, California, USA
| | - Lydia Sauer
- Moran Eye Center, University of Utah School of Medicine, Salt Lake City, Utah, USA
| | - Barbara Hart
- Moran Eye Center, University of Utah School of Medicine, Salt Lake City, Utah, USA
| | - M. Elena Diaz-Rubio
- Molecular and Cell Biology Laboratory, Salk Institute for Biological Studies, La Jolla, California, USA
| | - Melanie Bahlo
- Population Health and Immunity Division, Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia
- Department of Medical Biology, The University of Melbourne, Parkville, Victoria, Australia
| | - Christian Metallo
- Molecular and Cell Biology Laboratory, Salk Institute for Biological Studies, La Jolla, California, USA
- Department of Bioengineering, University of California San Diego, La Jolla, California, USA
| | - Rando Allikmets
- Department of Ophthalmology and
- Department of Pathology and Cell Biology, Columbia University, New York, New York, USA
| | - Marin L. Gantner
- The Lowy Medical Research Institute, La Jolla, California, USA
- Department of Molecular Medicine, The Scripps Research Institute (TSRI), La Jolla, California, USA
| | - Paul S. Bernstein
- Moran Eye Center, University of Utah School of Medicine, Salt Lake City, Utah, USA
| | - Martin Friedlander
- The Lowy Medical Research Institute, La Jolla, California, USA
- Department of Molecular Medicine, The Scripps Research Institute (TSRI), La Jolla, California, USA
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14
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Zhao Q, Han B, Xu Q, Wang T, Fang C, Li R, Zhang L, Pei Y. Proteome and genome integration analysis of obesity. Chin Med J (Engl) 2023; 136:910-921. [PMID: 37000968 PMCID: PMC10278747 DOI: 10.1097/cm9.0000000000002644] [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: 11/07/2022] [Indexed: 04/03/2023] Open
Abstract
ABSTRACT The prevalence of obesity has increased worldwide in recent decades. Genetic factors are now known to play a substantial role in the predisposition to obesity and may contribute up to 70% of the risk for obesity. Technological advancements during the last decades have allowed the identification of many hundreds of genetic markers associated with obesity. However, the transformation of current genetic variant-obesity associations into biological knowledge has been proven challenging. Genomics and proteomics are complementary fields, as proteomics extends functional analyses. Integrating genomic and proteomic data can help to bridge a gap in knowledge regarding genetic variant-obesity associations and to identify new drug targets for the treatment of obesity. We provide an overview of the published papers on the integrated analysis of proteomic and genomic data in obesity and summarize four mainstream strategies: overlap, colocalization, Mendelian randomization, and proteome-wide association studies. The integrated analyses identified many obesity-associated proteins, such as leptin, follistatin, and adenylate cyclase 3. Despite great progress, integrative studies focusing on obesity are still limited. There is an increased demand for large prospective cohort studies to identify and validate findings, and further apply these findings to the prevention, intervention, and treatment of obesity. In addition, we also discuss several other potential integration methods.
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Affiliation(s)
- Qigang Zhao
- Department of Epidemiology and Biostatistics, School of Public Health, Suzhou Medical College of Soochow University, Suzhou, Jiangsu 215123, China
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Suzhou Medical College of Soochow University, Suzhou, Jiangsu 215213, China
| | - Baixue Han
- Department of Epidemiology and Biostatistics, School of Public Health, Suzhou Medical College of Soochow University, Suzhou, Jiangsu 215123, China
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Suzhou Medical College of Soochow University, Suzhou, Jiangsu 215213, China
| | - Qian Xu
- Department of Epidemiology and Biostatistics, School of Public Health, Suzhou Medical College of Soochow University, Suzhou, Jiangsu 215123, China
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Suzhou Medical College of Soochow University, Suzhou, Jiangsu 215213, China
| | - Tao Wang
- Department of Endocrinology, The Second Affiliated Hospital, Soochow University, Suzhou, Jiangsu 215004, China
| | - Chen Fang
- Department of Endocrinology, The Second Affiliated Hospital, Soochow University, Suzhou, Jiangsu 215004, China
| | - Rui Li
- Department of Gastroenterology, The First Affiliated Hospital, Soochow University, Suzhou, Jiangsu 215006, China
| | - Lei Zhang
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Suzhou Medical College of Soochow University, Suzhou, Jiangsu 215213, China
- Center for Genetic Epidemiology and Genomics, School of Public Health, Suzhou Medical College of Soochow University, Suzhou, Jiangsu 215123, China
| | - Yufang Pei
- Department of Epidemiology and Biostatistics, School of Public Health, Suzhou Medical College of Soochow University, Suzhou, Jiangsu 215123, China
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Suzhou Medical College of Soochow University, Suzhou, Jiangsu 215213, China
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15
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Aydin S, Pham DT, Zhang T, Keele GR, Skelly DA, Paulo JA, Pankratz M, Choi T, Gygi SP, Reinholdt LG, Baker CL, Churchill GA, Munger SC. Genetic dissection of the pluripotent proteome through multi-omics data integration. CELL GENOMICS 2023; 3:100283. [PMID: 37082146 PMCID: PMC10112288 DOI: 10.1016/j.xgen.2023.100283] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 09/12/2022] [Accepted: 02/27/2023] [Indexed: 04/22/2023]
Abstract
Genetic background drives phenotypic variability in pluripotent stem cells (PSCs). Most studies to date have used transcript abundance as the primary molecular readout of cell state in PSCs. We performed a comprehensive proteogenomics analysis of 190 genetically diverse mouse embryonic stem cell (mESC) lines. The quantitative proteome is highly variable across lines, and we identified pluripotency-associated pathways that were differentially activated in the proteomics data that were not evident in transcriptome data from the same lines. Integration of protein abundance to transcript levels and chromatin accessibility revealed broad co-variation across molecular layers as well as shared and unique drivers of quantitative variation in pluripotency-associated pathways. Quantitative trait locus (QTL) mapping localized the drivers of these multi-omic signatures to genomic hotspots. This study reveals post-transcriptional mechanisms and genetic interactions that underlie quantitative variability in the pluripotent proteome and provides a regulatory map for mESCs that can provide a basis for future mechanistic studies.
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Affiliation(s)
- Selcan Aydin
- The Jackson Laboratory, Bar Harbor, ME 04609, USA
| | - Duy T. Pham
- The Jackson Laboratory, Bar Harbor, ME 04609, USA
| | - Tian Zhang
- Harvard Medical School, Boston, MA 02115, USA
| | | | | | | | | | - Ted Choi
- Predictive Biology, Inc., Carlsbad, CA 92010, USA
| | | | - Laura G. Reinholdt
- The Jackson Laboratory, Bar Harbor, ME 04609, USA
- Graduate School of Biomedical Sciences, Tufts University, Boston, MA 02111, USA
| | - Christopher L. Baker
- The Jackson Laboratory, Bar Harbor, ME 04609, USA
- Graduate School of Biomedical Sciences, Tufts University, Boston, MA 02111, USA
| | - Gary A. Churchill
- The Jackson Laboratory, Bar Harbor, ME 04609, USA
- Graduate School of Biomedical Sciences, Tufts University, Boston, MA 02111, USA
| | - Steven C. Munger
- The Jackson Laboratory, Bar Harbor, ME 04609, USA
- Graduate School of Biomedical Sciences, Tufts University, Boston, MA 02111, USA
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16
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Gedik H, Peterson RE, Riley BP, Vladimirov VI, Bacanu SA. Integrative Post-Genome-Wide Association Study Analyses Relevant to Psychiatric Disorders: Imputing Transcriptome and Proteome Signals. Complex Psychiatry 2023; 9:130-144. [PMID: 37588130 PMCID: PMC10425719 DOI: 10.1159/000530223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 03/09/2023] [Indexed: 08/18/2023] Open
Abstract
Background The genome-wide association study (GWAS) is a common tool to identify genetic variants associated with complex traits, including psychiatric disorders (PDs). However, post-GWAS analyses are needed to extend the statistical inference to biologically relevant entities, e.g., genes, proteins, and pathways. To achieve this goal, researchers developed methods that incorporate biologically relevant intermediate molecular phenotypes, such as gene expression and protein abundance, which are posited to mediate the variant-trait association. Transcriptome-wide association study (TWAS) and proteome-wide association study (PWAS) are commonly used methods to test the association between these molecular mediators and the trait. Summary In this review, we discuss the most recent developments in TWAS and PWAS. These methods integrate existing "omic" information with the GWAS summary statistics for trait(s) of interest. Specifically, they impute transcript/protein data and test the association between imputed gene expression/protein level with phenotype of interest by using (i) GWAS summary statistics and (ii) reference transcriptomic/proteomic/genomic datasets. TWAS and PWAS are suitable as analysis tools for (i) primary association scan and (ii) fine-mapping to identify potentially causal genes for PDs. Key Messages As post-GWAS analyses, TWAS and PWAS have the potential to highlight causal genes for PDs. These prioritized genes could indicate targets for the development of novel drug therapies. For researchers attempting such analyses, we recommend Mendelian randomization tools that use GWAS statistics for both trait and reference datasets, e.g., summary Mendelian randomization (SMR). We base our recommendation on (i) being able to use the same tool for both TWAS and PWAS, (ii) not requiring the pre-computed weights (and thus easier to update for larger reference datasets), and (iii) most larger transcriptome reference datasets are publicly available and easy to transform into a compatible format for SMR analysis.
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Affiliation(s)
- Huseyin Gedik
- Integrative Life Sciences, Virginia Institute of Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA
| | - Roseann E. Peterson
- Institute for Genomics in Health, SUNY Downstate Health Sciences University, Brooklyn, NY, USA
| | - Brien P. Riley
- Institute for Genomics in Health, SUNY Downstate Health Sciences University, Brooklyn, NY, USA
| | - Vladimir I. Vladimirov
- Department of Psychiatry, College of Medicine-Phoenix, University of Arizona, Phoenix, AZ, USA
| | - Silviu-Alin Bacanu
- Institute for Genomics in Health, SUNY Downstate Health Sciences University, Brooklyn, NY, USA
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17
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Shared regulation and functional relevance of local gene co-expression revealed by single cell analysis. Commun Biol 2022; 5:876. [PMID: 36028576 PMCID: PMC9418141 DOI: 10.1038/s42003-022-03831-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Accepted: 08/10/2022] [Indexed: 02/01/2023] Open
Abstract
Most human genes are co-expressed with a nearby gene. Previous studies have revealed this local gene co-expression to be widespread across chromosomes and across dozens of tissues. Yet, so far these studies used bulk RNA-seq, averaging gene expression measurements across millions of cells, thus being unclear if this co-expression stems from transcription events in single cells. Here, we leverage single cell datasets in >85 individuals to identify gene co-expression across cells, unbiased by cell-type heterogeneity and benefiting from the co-occurrence of transcription events in single cells. We discover >3800 co-expressed gene pairs in two human cell types, induced pluripotent stem cells (iPSCs) and lymphoblastoid cell lines (LCLs) and (i) compare single cell to bulk RNA-seq in identifying local gene co-expression, (ii) show that many co-expressed genes – but not the majority – are composed of functionally related genes and (iii) using proteomics data, provide evidence that their co-expression is maintained up to the protein level. Finally, using single cell RNA-sequencing (scRNA-seq) and single cell ATAC-sequencing (scATAC-seq) data for the same single cells, we identify gene-enhancer associations and reveal that >95% of co-expressed gene pairs share regulatory elements. These results elucidate the potential reasons for co-expression in single cell gene regulatory networks and warrant a deeper study of shared regulatory elements, in view of explaining disease comorbidity due to affecting several genes. Our in-depth view of local gene co-expression and regulatory element co-activity advances our understanding of the shared regulatory architecture between genes. Using single-cell data from cell lines, the co-expression of genes and co-activity of regulatory elements is analyzed, providing insight into shared architecture and regulation between genes.
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18
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Plant proteostasis: a proven and promising target for crop improvement. Essays Biochem 2022; 66:75-85. [PMID: 35929615 DOI: 10.1042/ebc20210078] [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: 07/21/2022] [Revised: 08/02/2022] [Accepted: 08/02/2022] [Indexed: 11/17/2022]
Abstract
The Green Revolution of the 1960s accomplished dramatic increases in crop yields through genetic improvement, chemical fertilisers, irrigation, and mechanisation. However, the current trajectory of population growth, against a backdrop of climate change and geopolitical unrest, predicts that agricultural production will be insufficient to ensure global food security in the next three decades. Improvements to crops that go beyond incremental gains are urgently needed. Plant biology has also undergone a revolution in recent years, through the development and application of powerful technologies including genome sequencing, a pantheon of 'omics techniques, precise genome editing, and step changes in structural biology and microscopy. Proteostasis - the collective processes that control the protein complement of the cell, comprising synthesis, modification, localisation, and degradation - is a field that has benefitted from these advances. This special issue presents a selection of the latest research in this vibrant field, with a particular focus on protein degradation. In the current article, we highlight the diverse and widespread contributions of plant proteostasis to agronomic traits, suggest opportunities and strategies to manipulate different elements of proteostatic mechanisms for crop improvement, and discuss the challenges involved in bringing these ideas into practice.
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19
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Senabouth A, Daniszewski M, Lidgerwood GE, Liang HH, Hernández D, Mirzaei M, Keenan SN, Zhang R, Han X, Neavin D, Rooney L, Lopez Sanchez MIG, Gulluyan L, Paulo JA, Clarke L, Kearns LS, Gnanasambandapillai V, Chan CL, Nguyen U, Steinmann AM, McCloy RA, Farbehi N, Gupta VK, Mackey DA, Bylsma G, Verma N, MacGregor S, Watt MJ, Guymer RH, Powell JE, Hewitt AW, Pébay A. Transcriptomic and proteomic retinal pigment epithelium signatures of age-related macular degeneration. Nat Commun 2022; 13:4233. [PMID: 35882847 PMCID: PMC9325891 DOI: 10.1038/s41467-022-31707-4] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Accepted: 06/29/2022] [Indexed: 11/08/2022] Open
Abstract
There are currently no treatments for geographic atrophy, the advanced form of age-related macular degeneration. Hence, innovative studies are needed to model this condition and prevent or delay its progression. Induced pluripotent stem cells generated from patients with geographic atrophy and healthy individuals were differentiated to retinal pigment epithelium. Integrating transcriptional profiles of 127,659 retinal pigment epithelium cells generated from 43 individuals with geographic atrophy and 36 controls with genotype data, we identify 445 expression quantitative trait loci in cis that are asssociated with disease status and specific to retinal pigment epithelium subpopulations. Transcriptomics and proteomics approaches identify molecular pathways significantly upregulated in geographic atrophy, including in mitochondrial functions, metabolic pathways and extracellular cellular matrix reorganization. Five significant protein quantitative trait loci that regulate protein expression in the retinal pigment epithelium and in geographic atrophy are identified - two of which share variants with cis- expression quantitative trait loci, including proteins involved in mitochondrial biology and neurodegeneration. Investigation of mitochondrial metabolism confirms mitochondrial dysfunction as a core constitutive difference of the retinal pigment epithelium from patients with geographic atrophy. This study uncovers important differences in retinal pigment epithelium homeostasis associated with geographic atrophy.
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Affiliation(s)
- Anne Senabouth
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Sydney, NSW, 2010, Australia
| | - Maciej Daniszewski
- Department of Anatomy and Physiology, The University of Melbourne, Parkville, VIC, 3010, Australia
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, VIC, 3002, Australia
| | - Grace E Lidgerwood
- Department of Anatomy and Physiology, The University of Melbourne, Parkville, VIC, 3010, Australia
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, VIC, 3002, Australia
| | - Helena H Liang
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, VIC, 3002, Australia
| | - Damián Hernández
- Department of Anatomy and Physiology, The University of Melbourne, Parkville, VIC, 3010, Australia
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, VIC, 3002, Australia
| | - Mehdi Mirzaei
- Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, NSW, 2109, Australia
| | - Stacey N Keenan
- Department of Anatomy and Physiology, The University of Melbourne, Parkville, VIC, 3010, Australia
| | - Ran Zhang
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Sydney, NSW, 2010, Australia
| | - Xikun Han
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, 4006, Australia
| | - Drew Neavin
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Sydney, NSW, 2010, Australia
| | - Louise Rooney
- Department of Anatomy and Physiology, The University of Melbourne, Parkville, VIC, 3010, Australia
| | | | - Lerna Gulluyan
- Department of Anatomy and Physiology, The University of Melbourne, Parkville, VIC, 3010, Australia
| | - Joao A Paulo
- Department of Cell Biology, Harvard Medical School, Boston, MA, 02115, USA
| | - Linda Clarke
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, VIC, 3002, Australia
| | - Lisa S Kearns
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, VIC, 3002, Australia
| | | | - Chia-Ling Chan
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Sydney, NSW, 2010, Australia
| | - Uyen Nguyen
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Sydney, NSW, 2010, Australia
| | - Angela M Steinmann
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Sydney, NSW, 2010, Australia
| | - Rachael A McCloy
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Sydney, NSW, 2010, Australia
| | - Nona Farbehi
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Sydney, NSW, 2010, Australia
| | - Vivek K Gupta
- Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, NSW, 2109, Australia
| | - David A Mackey
- Lions Eye Institute, Centre for Vision Sciences, University of Western Australia, Perth, WA, 6009, Australia
- School of Medicine, University of Tasmania, Hobart, TAS, 7005, Australia
| | - Guy Bylsma
- Lions Eye Institute, Centre for Vision Sciences, University of Western Australia, Perth, WA, 6009, Australia
| | - Nitin Verma
- School of Medicine, University of Tasmania, Hobart, TAS, 7005, Australia
| | - Stuart MacGregor
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, 4006, Australia
| | - Matthew J Watt
- Department of Anatomy and Physiology, The University of Melbourne, Parkville, VIC, 3010, Australia
| | - Robyn H Guymer
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, VIC, 3002, Australia
- Department of Surgery, Ophthalmology, Royal Victorian Eye and Ear Hospital, The University of Melbourne, East Melbourne, VIC, 3002, Australia
| | - Joseph E Powell
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Sydney, NSW, 2010, Australia.
- UNSW Cellular Genomics Futures Institute, University of New South Wales, Sydney, NSW, 2052, Australia.
| | - Alex W Hewitt
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, VIC, 3002, Australia.
- School of Medicine, University of Tasmania, Hobart, TAS, 7005, Australia.
- Department of Surgery, Ophthalmology, Royal Victorian Eye and Ear Hospital, The University of Melbourne, East Melbourne, VIC, 3002, Australia.
- Menzies Institute for Medical Research, University of Tasmania, Hobart, TAS, 7000, Australia.
| | - Alice Pébay
- Department of Anatomy and Physiology, The University of Melbourne, Parkville, VIC, 3010, Australia.
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, VIC, 3002, Australia.
- Department of Surgery, Royal Melbourne Hospital, The University of Melbourne, Parkville, VIC, 3010, Australia.
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20
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Abstract
An ensemble of in vitro cardiac tissue models has been developed over the past several decades to aid our understanding of complex cardiovascular disorders using a reductionist approach. These approaches often rely on recapitulating single or multiple clinically relevant end points in a dish indicative of the cardiac pathophysiology. The possibility to generate disease-relevant and patient-specific human induced pluripotent stem cells has further leveraged the utility of the cardiac models as screening tools at a large scale. To elucidate biological mechanisms in the cardiac models, it is critical to integrate physiological cues in form of biochemical, biophysical, and electromechanical stimuli to achieve desired tissue-like maturity for a robust phenotyping. Here, we review the latest advances in the directed stem cell differentiation approaches to derive a wide gamut of cardiovascular cell types, to allow customization in cardiac model systems, and to study diseased states in multiple cell types. We also highlight the recent progress in the development of several cardiovascular models, such as cardiac organoids, microtissues, engineered heart tissues, and microphysiological systems. We further expand our discussion on defining the context of use for the selection of currently available cardiac tissue models. Last, we discuss the limitations and challenges with the current state-of-the-art cardiac models and highlight future directions.
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Affiliation(s)
- Dilip Thomas
- Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA (D.T., C.A., J.C.W.).,Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA (D.T., C.A., J.C.W.)
| | - Suji Choi
- Disease Biophysics Group, John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA (S.C., K.K.P.)
| | - Christina Alamana
- Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA (D.T., C.A., J.C.W.).,Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA (D.T., C.A., J.C.W.)
| | - Kevin Kit Parker
- Disease Biophysics Group, John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA (S.C., K.K.P.).,Harvard Stem Cell Institute, Harvard University, Cambridge, MA, Wyss Institute for Biologically Inspired Engineering, Boston, MA (K.K.P.)
| | - Joseph C Wu
- Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA (D.T., C.A., J.C.W.).,Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA (D.T., C.A., J.C.W.).,Greenstone Biosciences, Palo Alto, CA (J.C.W.)
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21
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Wołosowicz M, Dajnowicz-Brzezik P, Łukaszuk B, Żebrowska E, Maciejczyk M, Zalewska A, Kasacka I, Chabowski A. Diverse impact of N-acetylcysteine or alpha-lipoic acid supplementation during high-fat diet regime on fatty acid transporters in visceral and subcutaneous adipose tissue. Adv Med Sci 2022; 67:216-228. [PMID: 35594763 DOI: 10.1016/j.advms.2022.05.001] [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: 12/14/2021] [Revised: 03/16/2022] [Accepted: 05/08/2022] [Indexed: 11/20/2022]
Abstract
PURPOSE Adipose tissue's (AT) structural changes accompanying obesity may alter lipid transport protein expression and, thus, the fatty acids (FAs) transport and lipid balance of the body. Metabolic abnormalities within AT contribute to the elevated production of reactive oxygen species and increased oxidative/nitrosative stress. Although compounds such as N-acetylcysteine (NAC) and α-lipoic acid (ALA), which restore redox homeostasis, may improve lipid metabolism in AT, the mechanism of action of these antioxidants on lipid metabolism in AT is still unknown. This study aimed to examine the impact of NAC and ALA on the level and FA composition of the lipid fractions, and the expression of FA transporters in the visceral and subcutaneous AT of high-fat diet-fed rats. MATERIALS AND METHODS Male Wistar rats were randomly divided into four groups. The mRNA levels and protein expression of FA transporters were assessed using real-time PCR and Western Blot analyses. The collected samples were subjected to histological evaluation. The level of lipids (FFA, DAG, and TAG) was measured using gas-liquid chromatography. RESULTS We found that antioxidants affect FA transporter expressions at both the transcript and protein levels, and, therefore, they promote changes in AT's lipid pools. One of the most remarkable findings of our research is that different antioxidant molecules may have a varying impact on AT phenotype. CONCLUSION NAC and ALA exert different influences on AT, which is reflected in histopathological images, FA transport proteins expression patterns, or even the lipid storage capacity of adipocytes.
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Affiliation(s)
- Marta Wołosowicz
- Department of Physiology, Medical University of Bialystok, Bialystok, Poland.
| | | | - Bartłomiej Łukaszuk
- Department of Physiology, Medical University of Bialystok, Bialystok, Poland
| | - Ewa Żebrowska
- Department of Physiology, Medical University of Bialystok, Bialystok, Poland
| | - Mateusz Maciejczyk
- Department of Hygiene, Epidemiology and Ergonomics, Medical University of Bialystok, Bialystok, Poland
| | - Anna Zalewska
- Experimental Dentistry Laboratory, Medical University of Bialystok, Bialystok, Poland
| | - Irena Kasacka
- Department of Histology and Cytophysiology, Medical University of Bialystok, Bialystok, Poland
| | - Adrian Chabowski
- Department of Physiology, Medical University of Bialystok, Bialystok, Poland
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22
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Flynn E, Lappalainen T. Functional Characterization of Genetic Variant Effects on Expression. Annu Rev Biomed Data Sci 2022; 5:119-139. [PMID: 35483347 DOI: 10.1146/annurev-biodatasci-122120-010010] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Thousands of common genetic variants in the human population have been associated with disease risk and phenotypic variation by genome-wide association studies (GWAS). However, the majority of GWAS variants fall into noncoding regions of the genome, complicating our understanding of their regulatory functions, and few molecular mechanisms of GWAS variant effects have been clearly elucidated. Here, we set out to review genetic variant effects, focusing on expression quantitative trait loci (eQTLs), including their utility in interpreting GWAS variant mechanisms. We discuss the interrelated challenges and opportunities for eQTL analysis, covering determining causal variants, elucidating molecular mechanisms of action, and understanding context variability. Addressing these questions can enable better functional characterization of disease-associated loci and provide insights into fundamental biological questions of the noncoding genetic regulatory code and its control of gene expression. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 5 is August 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
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Affiliation(s)
- Elise Flynn
- 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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23
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Kunowska N, Stelzl U. Decoding the cellular effects of genetic variation through interaction proteomics. Curr Opin Chem Biol 2022; 66:102100. [PMID: 34801969 DOI: 10.1016/j.cbpa.2021.102100] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 10/07/2021] [Accepted: 10/14/2021] [Indexed: 12/24/2022]
Abstract
It is often unclear how genetic variation translates into cellular phenotypes, including how much of the coding variation can be recovered in the proteome. Proteogenomic analyses of heterogenous cell lines revealed that the genetic differences impact mostly the abundance and stoichiometry of protein complexes, with the effects propagating post-transcriptionally via protein interactions onto other subunits. Conversely, large scale binary interaction analyses of missense variants revealed that loss of interaction is widespread and caused by about 50% disease-associated mutations, while deep scanning mutagenesis of binary interactions identified thousands of interaction-deficient variants per interaction. The idea that phenotypes arise from genetic variation through protein-protein interaction is therefore substantiated by both forward and reverse interaction proteomics. With improved methodologies, these two approaches combined can close the knowledge gap between nucleotide sequence variation and its functional consequences on the cellular proteome.
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Affiliation(s)
- Natalia Kunowska
- Institute of Pharmaceutical Sciences, Pharmaceutical Chemistry, University of Graz, Austria
| | - Ulrich Stelzl
- Institute of Pharmaceutical Sciences, Pharmaceutical Chemistry, University of Graz, Austria; BioTechMed-Graz, Austria; Field of Excellence BioHealth - University of Graz, Austria.
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24
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Emilsson V, Gudmundsdottir V, Gudjonsson A, Jonmundsson T, Jonsson BG, Karim MA, Ilkov M, Staley JR, Gudmundsson EF, Launer LJ, Lindeman JH, Morton NM, Aspelund T, Lamb JR, Jennings LL, Gudnason V. Coding and regulatory variants are associated with serum protein levels and disease. Nat Commun 2022; 13:481. [PMID: 35079000 PMCID: PMC8789809 DOI: 10.1038/s41467-022-28081-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2020] [Accepted: 01/07/2022] [Indexed: 12/20/2022] Open
Abstract
Circulating proteins can be used to diagnose and predict disease-related outcomes. A deep serum proteome survey recently revealed close associations between serum protein networks and common disease. In the current study, 54,469 low-frequency and common exome-array variants were compared to 4782 protein measurements in the serum of 5343 individuals from the AGES Reykjavik cohort. This analysis identifies a large number of serum proteins with genetic signatures overlapping those of many diseases. More specifically, using a study-wide significance threshold, we find that 2021 independent exome array variants are associated with serum levels of 1942 proteins. These variants reside in genetic loci shared by hundreds of complex disease traits, highlighting serum proteins' emerging role as biomarkers and potential causative agents of a wide range of diseases.
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Affiliation(s)
- Valur Emilsson
- Icelandic Heart Association, Holtasmari 1, IS-201 Kopavogur, Kopavogur, Iceland.
- Faculty of Medicine, University of Iceland, 101 Reykjavik, Reykjavík, Iceland.
| | | | | | | | | | - Mohd A Karim
- Wellcome Trust Sanger Institute, Welcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SA, UK
- Open Targets, Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Marjan Ilkov
- Icelandic Heart Association, Holtasmari 1, IS-201 Kopavogur, Kopavogur, Iceland
| | - James R Staley
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Elias F Gudmundsson
- Icelandic Heart Association, Holtasmari 1, IS-201 Kopavogur, Kopavogur, Iceland
| | - Lenore J Launer
- Laboratory of Epidemiology and Population Sciences, Intramural Research Program, National Institute on Aging, Bethesda, MD, 20892-9205, USA
| | - Jan H Lindeman
- Department of Surgery, Leiden University Medical Center, Leiden, Netherlands
| | - Nicholas M Morton
- Centre for Cardiovascular Science, Queen's Medical Research Institute, University of Edinburgh, Edinburgh, EH16 4TJ, UK
| | - Thor Aspelund
- Icelandic Heart Association, Holtasmari 1, IS-201 Kopavogur, Kopavogur, Iceland
| | - John R Lamb
- GNF Novartis, 10675 John Jay Hopkins Drive, San Diego, CA, 92121, USA
| | - Lori L Jennings
- Novartis Institutes for Biomedical Research, 22 Windsor Street, Cambridge, MA, 02139, USA
| | - Vilmundur Gudnason
- Icelandic Heart Association, Holtasmari 1, IS-201 Kopavogur, Kopavogur, Iceland.
- Faculty of Medicine, University of Iceland, 101 Reykjavik, Reykjavík, Iceland.
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25
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Hu B, Ge W, Wang Y, Zhang X, Li T, Cui H, Qian Y, Zhang Y, Li Z. Metabolomic and Proteomic Analyses of Persistent Valvular Atrial Fibrillation and Non-Valvular Atrial Fibrillation. Front Genet 2021; 12:789485. [PMID: 34917134 PMCID: PMC8669813 DOI: 10.3389/fgene.2021.789485] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Accepted: 11/08/2021] [Indexed: 02/05/2023] Open
Abstract
Atrial fibrillation (AF) is an abnormal heart rhythm related to an increased risk of heart failure, dementia, and stroke. The distinction between valvular and non-valvular AF remains a debate. In this study, proteomics and metabolomics were integrated to describe the dysregulated metabolites and proteins of AF patients relative to sinus rhythm (SR) patients. Totally 47 up-regulated and 41 down-regulated proteins in valvular AF, and 59 up-regulated and 149 down-regulated proteins in non-valvular AF were recognized in comparison to SR patients. Moreover, 58 up-regulated and 49 significantly down-regulated metabolites in valvular AF, and 47 up-regulated and 122 down-regulated metabolites in persistent non-valvular AF patients were identified in comparison to SR patients. Based on analysis of differential levels of metabolites and proteins, 15 up-regulated and 22 down-regulated proteins, and 13 up-regulated and 122 down-regulated metabolites in persistent non-valvular AF were identified relative to valvular AF. KEGG pathway enrichment analysis showed the altered proteins and metabolites were significantly related to multiple metabolic pathways, such as Glycolysis/Gluconeogenesis. Interestingly, the enrichment pathways related to non-valvular AF were obviously different from those in valvular AF. For example, valvular AF was significantly related to Glycolysis/Gluconeogenesis, but non-valvular AF was more related to Citrate cycle (TCA cycle). Correlation analysis between the differentially expressed proteins and metabolites was also performed. Several hub proteins with metabolites were identified in valvular AF and non-valvular AF. For example, Taurine, D-Threitol, L-Rhamnose, and DL-lactate played crucial roles in valvular AF, while Glycerol-3-phosphate dehydrogenase, Inorganic pyrophosphatase 2, Hydroxymethylglutaryl-CoAlyase, and Deoxyuridine 5-triphosphate nucleotidohydrolase were crucial in non-valvular AF. Then two hub networks were recognized as potential biomarkers, which can effectively distinguish valvular AF and non-valvular persistent AF from SR samples, with areas under curve of 0.75 and 0.707, respectively. Hence, these metabolites and proteins can be used as potential clinical molecular markers to discriminate two types of AF from SR samples. In summary, this study provides novel insights to understanding the mechanisms of AF progression and identifying novel biomarkers for prognosis of non-valvular AF and valvular AF by using metabolomics and proteomics analyses.
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Affiliation(s)
- Bo Hu
- Department of Cardiology, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Wen Ge
- Department of Cardiothoracic Surgery, Shuguang Hospital, Affiliated to Shanghai University of TCM, Shanghai, China
| | - Yuliang Wang
- Department of Immunology, Nanjing Medical University, Nanjing, China
| | - Xiaobin Zhang
- Department of Cardiovascular Surgery, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Tao Li
- Department of Cardiovascular Surgery, National Clinical Research Center for Geriatric, West China Hospital, Sichuan University, Chengdu, China
| | - Hui Cui
- School of Life Science and Technology, Shanghai Tech University, Shanghai, China
| | - Yongjun Qian
- Department of Cardiovascular Surgery, National Clinical Research Center for Geriatric, West China Hospital, Sichuan University, Chengdu, China
| | - Yangyang Zhang
- Department of Cardiovascular Surgery, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Zhi Li
- Department of Cardiovascular Surgery, Jiangsu Province Hospital, the First Affiliated Hospital of Nanjing Medical University, Nanjing, China
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26
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Vickers A, Tewary M, Laddach A, Poletti M, Salameti V, Fraternali F, Danovi D, Watt FM. Plating human iPSC lines on micropatterned substrates reveals role for ITGB1 nsSNV in endoderm formation. Stem Cell Reports 2021; 16:2628-2641. [PMID: 34678211 PMCID: PMC8581167 DOI: 10.1016/j.stemcr.2021.09.017] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Revised: 09/22/2021] [Accepted: 09/23/2021] [Indexed: 12/03/2022] Open
Abstract
Quantitative analysis of human induced pluripotent stem cell (iPSC) lines from healthy donors is a powerful tool for uncovering the relationship between genetic variants and cellular behavior. We previously identified rare, deleterious non-synonymous single nucleotide variants (nsSNVs) in cell adhesion genes that are associated with outlier iPSC phenotypes in the pluripotent state. Here, we generated micropatterned colonies of iPSCs to test whether nsSNVs influence patterning of radially ordered germ layers. Using a custom-built image analysis pipeline, we quantified the differentiation phenotypes of 13 iPSC lines that harbor nsSNVs in genes related to cell adhesion or germ layer development. All iPSC lines differentiated into the three germ layers; however, there was donor-specific variation in germ layer patterning. We identified one line that presented an outlier phenotype of expanded endodermal differentiation, which was associated with a nsSNV in ITGB1. Our study establishes a platform for investigating the impact of nsSNVs on differentiation.
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Affiliation(s)
- Alice Vickers
- Centre for Stem Cells and Regenerative Medicine, King's College London, Guy's Hospital, Floor 28, Tower Wing, Great Maze Pond, London SE1 9RT, UK
| | - Mukul Tewary
- Centre for Stem Cells and Regenerative Medicine, King's College London, Guy's Hospital, Floor 28, Tower Wing, Great Maze Pond, London SE1 9RT, UK
| | - Anna Laddach
- Randall Centre for Cell and Molecular Biophysics, King's College London, New Hunt's House, Great Maze Pond, London SE1 9RT, UK; Development and Homeostasis of the Nervous System Laboratory, The Francis Crick Institute, London NW1 1AT, UK
| | - Martina Poletti
- Earlham Institute, Norwich Research Park, Norwich NR4 7UZ, UK; Quadram Institute, Norwich Research Park, Norwich NR4 7UZ, UK
| | - Vasiliki Salameti
- Centre for Stem Cells and Regenerative Medicine, King's College London, Guy's Hospital, Floor 28, Tower Wing, Great Maze Pond, London SE1 9RT, UK
| | - Franca Fraternali
- Randall Centre for Cell and Molecular Biophysics, King's College London, New Hunt's House, Great Maze Pond, London SE1 9RT, UK
| | - Davide Danovi
- Centre for Stem Cells and Regenerative Medicine, King's College London, Guy's Hospital, Floor 28, Tower Wing, Great Maze Pond, London SE1 9RT, UK; bit.bio, Babraham Research Campus, The Dorothy Hodgkin Building, Cambridge CB22 3FH, UK
| | - Fiona M Watt
- Centre for Stem Cells and Regenerative Medicine, King's College London, Guy's Hospital, Floor 28, Tower Wing, Great Maze Pond, London SE1 9RT, UK.
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27
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Babarinde IA, Ma G, Li Y, Deng B, Luo Z, Liu H, Abdul MM, Ward C, Chen M, Fu X, Shi L, Duttlinger M, He J, Sun L, Li W, Zhuang Q, Tong G, Frampton J, Cazier JB, Chen J, Jauch R, Esteban MA, Hutchins AP. Transposable element sequence fragments incorporated into coding and noncoding transcripts modulate the transcriptome of human pluripotent stem cells. Nucleic Acids Res 2021; 49:9132-9153. [PMID: 34390351 PMCID: PMC8450112 DOI: 10.1093/nar/gkab710] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2020] [Revised: 07/29/2021] [Accepted: 08/02/2021] [Indexed: 12/12/2022] Open
Abstract
Transposable elements (TEs) occupy nearly 40% of mammalian genomes and, whilst most are fragmentary and no longer capable of transposition, they can nevertheless contribute to cell function. TEs within genes transcribed by RNA polymerase II can be copied as parts of primary transcripts; however, their full contribution to mature transcript sequences remains unresolved. Here, using long and short read (LR and SR) RNA sequencing data, we show that 26% of coding and 65% of noncoding transcripts in human pluripotent stem cells (hPSCs) contain TE-derived sequences. Different TE families are incorporated into RNAs in unique patterns, with consequences to transcript structure and function. The presence of TE sequences within a transcript is correlated with TE-type specific changes in its subcellular distribution, alterations in steady-state levels and half-life, and differential association with RNA Binding Proteins (RBPs). We identify hPSC-specific incorporation of endogenous retroviruses (ERVs) and LINE:L1 into protein-coding mRNAs, which generate TE sequence-derived peptides. Finally, single cell RNA-seq reveals that hPSCs express ERV-containing transcripts, whilst differentiating subpopulations lack ERVs and express SINE and LINE-containing transcripts. Overall, our comprehensive analysis demonstrates that the incorporation of TE sequences into the RNAs of hPSCs is more widespread and has a greater impact than previously appreciated.
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Affiliation(s)
- Isaac A Babarinde
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China.,Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China
| | - Gang Ma
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China.,Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China
| | - Yuhao Li
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China.,Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China
| | - Boping Deng
- Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China.,Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham B15 2TT, UK
| | - Zhiwei Luo
- Laboratory of Integrative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China.,Key Laboratory of Regenerative Biology of the Chinese Academy of Sciences and Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China
| | - Hao Liu
- Laboratory of Integrative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China.,Key Laboratory of Regenerative Biology of the Chinese Academy of Sciences and Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China
| | - Mazid Md Abdul
- Laboratory of Integrative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China.,Key Laboratory of Regenerative Biology of the Chinese Academy of Sciences and Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China
| | - Carl Ward
- Laboratory of Integrative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China.,Key Laboratory of Regenerative Biology of the Chinese Academy of Sciences and Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China
| | - Minchun Chen
- Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China
| | - Xiuling Fu
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China.,Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China
| | - Liyang Shi
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China.,Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China
| | - Martha Duttlinger
- Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China
| | - Jiangping He
- Center for Cell Lineage and Atlas (CCLA), Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou 510005, China
| | - Li Sun
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China.,Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China
| | - Wenjuan Li
- Laboratory of Integrative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China.,Key Laboratory of Regenerative Biology of the Chinese Academy of Sciences and Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China
| | - Qiang Zhuang
- Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China
| | - Guoqing Tong
- Center for Reproductive Medicine, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai 200120, China
| | - Jon Frampton
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham B15 2TT, UK
| | - Jean-Baptiste Cazier
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham B15 2TT, UK.,Centre for Computational Biology, University of Birmingham, Birmingham, UK
| | - Jiekai Chen
- Key Laboratory of Regenerative Biology of the Chinese Academy of Sciences and Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China.,Center for Cell Lineage and Atlas (CCLA), Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou 510005, China.,Joint School of Life Sciences, Guangzhou Medical University and Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
| | - Ralf Jauch
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Miguel A Esteban
- Laboratory of Integrative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China.,Key Laboratory of Regenerative Biology of the Chinese Academy of Sciences and Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China.,Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou 510005, China
| | - Andrew P Hutchins
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China.,Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China
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28
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Lu H, Zhang J, Chen YE, Garcia-Barrio MT. Integration of Transformative Platforms for the Discovery of Causative Genes in Cardiovascular Diseases. Cardiovasc Drugs Ther 2021; 35:637-654. [PMID: 33856594 PMCID: PMC8216854 DOI: 10.1007/s10557-021-07175-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/18/2021] [Indexed: 12/11/2022]
Abstract
Cardiovascular diseases are the leading cause of morbidity and mortality worldwide. Genome-wide association studies (GWAS) are powerful epidemiological tools to find genes and variants associated with cardiovascular diseases while follow-up biological studies allow to better understand the etiology and mechanisms of disease and assign causality. Improved methodologies and reduced costs have allowed wider use of bulk and single-cell RNA sequencing, human-induced pluripotent stem cells, organoids, metabolomics, epigenomics, and novel animal models in conjunction with GWAS. In this review, we feature recent advancements relevant to cardiovascular diseases arising from the integration of genetic findings with multiple enabling technologies within multidisciplinary teams to highlight the solidifying transformative potential of this approach. Well-designed workflows integrating different platforms are greatly improving and accelerating the unraveling and understanding of complex disease processes while promoting an effective way to find better drug targets, improve drug design and repurposing, and provide insight towards a more personalized clinical practice.
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Affiliation(s)
- Haocheng Lu
- Department of Internal Medicine, University of Michigan Medical Center, 2800 Plymouth Rd, Ann Arbor, MI, 48109-2800, USA
| | - Jifeng Zhang
- Department of Internal Medicine, University of Michigan Medical Center, 2800 Plymouth Rd, Ann Arbor, MI, 48109-2800, USA
- Center for Advanced Models for Translational Sciences and Therapeutics, University of Michigan Medical Center, Ann Arbor, MI, 48109, USA
| | - Y Eugene Chen
- Department of Internal Medicine, University of Michigan Medical Center, 2800 Plymouth Rd, Ann Arbor, MI, 48109-2800, USA.
- Center for Advanced Models for Translational Sciences and Therapeutics, University of Michigan Medical Center, Ann Arbor, MI, 48109, USA.
| | - Minerva T Garcia-Barrio
- Department of Internal Medicine, University of Michigan Medical Center, 2800 Plymouth Rd, Ann Arbor, MI, 48109-2800, USA.
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29
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Pathogenic missense protein variants affect different functional pathways and proteomic features than healthy population variants. PLoS Biol 2021; 19:e3001207. [PMID: 33909605 PMCID: PMC8110273 DOI: 10.1371/journal.pbio.3001207] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2020] [Revised: 05/10/2021] [Accepted: 03/26/2021] [Indexed: 12/27/2022] Open
Abstract
Missense variants are present amongst the healthy population, but some of them are causative of human diseases. A classification of variants associated with “healthy” or “diseased” states is therefore not always straightforward. A deeper understanding of the nature of missense variants in health and disease, the cellular processes they may affect, and the general molecular principles which underlie these differences is essential to offer mechanistic explanations of the true impact of pathogenic variants. Here, we have formalised a statistical framework which enables robust probabilistic quantification of variant enrichment across full-length proteins, their domains, and 3D structure-defined regions. Using this framework, we validate and extend previously reported trends of variant enrichment in different protein structural regions (surface/core/interface). By examining the association of variant enrichment with available functional pathways and transcriptomic and proteomic (protein half-life, thermal stability, abundance) data, we have mined a rich set of molecular features which distinguish between pathogenic and population variants: Pathogenic variants mainly affect proteins involved in cell proliferation and nucleotide processing and are enriched in more abundant proteins. Additionally, rare population variants display features closer to common than pathogenic variants. We validate the association between these molecular features and variant pathogenicity by comparing against existing in silico variant impact annotations. This study provides molecular details into how different proteins exhibit resilience and/or sensitivity towards missense variants and provides the rationale to prioritise variant-enriched proteins and protein domains for therapeutic targeting and development. The ZoomVar database, which we created for this study, is available at fraternalilab.kcl.ac.uk/ZoomVar. It allows users to programmatically annotate missense variants with protein structural information and to calculate variant enrichment in different protein structural regions. How do can one improve the classification of genetic variants as harmful or harmless? This study uses a robust statistical analysis to exploit the interplay between protein structure, proteomic measurements and functional pathways to enable better discrimination between missense variants in health and disease.
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30
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Brenes AJ, Yoshikawa H, Bensaddek D, Mirauta B, Seaton D, Hukelmann JL, Jiang H, Stegle O, Lamond AI. Erosion of human X chromosome inactivation causes major remodeling of the iPSC proteome. Cell Rep 2021; 35:109032. [PMID: 33910018 PMCID: PMC8097692 DOI: 10.1016/j.celrep.2021.109032] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Revised: 02/26/2021] [Accepted: 04/02/2021] [Indexed: 12/13/2022] Open
Abstract
X chromosome inactivation (XCI) is a dosage compensation mechanism in female mammals whereby transcription from one X chromosome is repressed. Analysis of human induced pluripotent stem cells (iPSCs) derived from female donors identified that low levels of XIST RNA correlated strongly with erosion of XCI. Proteomic analysis, RNA sequencing (RNA-seq), and polysome profiling showed that XCI erosion resulted in amplified RNA and protein expression from X-linked genes, providing a proteomic characterization of skewed dosage compensation. Increased protein expression was also detected from autosomal genes without an mRNA increase, thus altering the protein-RNA correlation between the X chromosome and autosomes. XCI-eroded lines display an ∼13% increase in total cell protein content, with increased ribosomal proteins, ribosome biogenesis and translation factors, and polysome levels. We conclude that XCI erosion in iPSCs causes a remodeling of the proteome, affecting the expression of a much wider range of proteins and disease-linked loci than previously realized.
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Affiliation(s)
- Alejandro J Brenes
- Centre for Gene Regulation and Expression, School of Life Sciences, University of Dundee, Dow St., Dundee DD1 5EH, UK; Cell Signalling & Immunology, School of Life Sciences, University of Dundee, Dow St., Dundee DD1 5EH, UK.
| | - Harunori Yoshikawa
- Centre for Gene Regulation and Expression, School of Life Sciences, University of Dundee, Dow St., Dundee DD1 5EH, UK; Division of Cell Signalling, Fujii Memorial Institute of Medical Sciences, Institute of Advanced Medical Sciences, Tokushima University, 3-18-15 Kuramoto, Tokushima 770-8503, Japan
| | - Dalila Bensaddek
- Centre for Gene Regulation and Expression, School of Life Sciences, University of Dundee, Dow St., Dundee DD1 5EH, UK; Biosciences Core Labs, Proteomics, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia
| | - Bogdan Mirauta
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton CB10 1SD, UK
| | - Daniel Seaton
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton CB10 1SD, UK
| | - Jens L Hukelmann
- Centre for Gene Regulation and Expression, School of Life Sciences, University of Dundee, Dow St., Dundee DD1 5EH, UK; Immatics Biotechnologies, Paul-Ehrlich-Str. 15, Tuebingen 72076, Germany
| | - Hao Jiang
- Centre for Gene Regulation and Expression, School of Life Sciences, University of Dundee, Dow St., Dundee DD1 5EH, UK
| | - Oliver Stegle
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton CB10 1SD, UK; European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany; Division of Computational Genomics and Systems Genetic, German Cancer Research Center, Heidelberg, Germany
| | - Angus I Lamond
- Centre for Gene Regulation and Expression, School of Life Sciences, University of Dundee, Dow St., Dundee DD1 5EH, UK.
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31
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Ebert P, Audano PA, Zhu Q, Rodriguez-Martin B, Porubsky D, Bonder MJ, Sulovari A, Ebler J, Zhou W, Serra Mari R, Yilmaz F, Zhao X, Hsieh P, Lee J, Kumar S, Lin J, Rausch T, Chen Y, Ren J, Santamarina M, Höps W, Ashraf H, Chuang NT, Yang X, Munson KM, Lewis AP, Fairley S, Tallon LJ, Clarke WE, Basile AO, Byrska-Bishop M, Corvelo A, Evani US, Lu TY, Chaisson MJP, Chen J, Li C, Brand H, Wenger AM, Ghareghani M, Harvey WT, Raeder B, Hasenfeld P, Regier AA, Abel HJ, Hall IM, Flicek P, Stegle O, Gerstein MB, Tubio JMC, Mu Z, Li YI, Shi X, Hastie AR, Ye K, Chong Z, Sanders AD, Zody MC, Talkowski ME, Mills RE, Devine SE, Lee C, Korbel JO, Marschall T, Eichler EE. Haplotype-resolved diverse human genomes and integrated analysis of structural variation. Science 2021; 372:eabf7117. [PMID: 33632895 PMCID: PMC8026704 DOI: 10.1126/science.abf7117] [Citation(s) in RCA: 316] [Impact Index Per Article: 105.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Accepted: 02/09/2021] [Indexed: 12/14/2022]
Abstract
Long-read and strand-specific sequencing technologies together facilitate the de novo assembly of high-quality haplotype-resolved human genomes without parent-child trio data. We present 64 assembled haplotypes from 32 diverse human genomes. These highly contiguous haplotype assemblies (average minimum contig length needed to cover 50% of the genome: 26 million base pairs) integrate all forms of genetic variation, even across complex loci. We identified 107,590 structural variants (SVs), of which 68% were not discovered with short-read sequencing, and 278 SV hotspots (spanning megabases of gene-rich sequence). We characterized 130 of the most active mobile element source elements and found that 63% of all SVs arise through homology-mediated mechanisms. This resource enables reliable graph-based genotyping from short reads of up to 50,340 SVs, resulting in the identification of 1526 expression quantitative trait loci as well as SV candidates for adaptive selection within the human population.
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Affiliation(s)
- Peter Ebert
- Heinrich Heine University, Medical Faculty, Institute for Medical Biometry and Bioinformatics, Moorenstraße 20, 40225 Düsseldorf, Germany
| | - Peter A Audano
- Department of Genome Sciences, University of Washington School of Medicine, 3720 15th Avenue NE, Seattle, WA 98195-5065, USA
| | - Qihui Zhu
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT 06032, USA
| | - Bernardo Rodriguez-Martin
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Meyerhofstraße 1, 69117 Heidelberg, Germany
| | - David Porubsky
- Department of Genome Sciences, University of Washington School of Medicine, 3720 15th Avenue NE, Seattle, WA 98195-5065, USA
| | - Marc Jan Bonder
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Meyerhofstraße 1, 69117 Heidelberg, Germany
- Division of Computational Genomics and Systems Genetics, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Arvis Sulovari
- Department of Genome Sciences, University of Washington School of Medicine, 3720 15th Avenue NE, Seattle, WA 98195-5065, USA
| | - Jana Ebler
- Heinrich Heine University, Medical Faculty, Institute for Medical Biometry and Bioinformatics, Moorenstraße 20, 40225 Düsseldorf, Germany
| | - Weichen Zhou
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, 100 Washtenaw Avenue, Ann Arbor, MI 48109, USA
| | - Rebecca Serra Mari
- Heinrich Heine University, Medical Faculty, Institute for Medical Biometry and Bioinformatics, Moorenstraße 20, 40225 Düsseldorf, Germany
| | - Feyza Yilmaz
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT 06032, USA
| | - Xuefang Zhao
- Center for Genomic Medicine, Massachusetts General Hospital, Department of Neurology, Harvard Medical School, Boston, MA 02114, USA
- Program in Medical and Population Genetics and Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - PingHsun Hsieh
- Department of Genome Sciences, University of Washington School of Medicine, 3720 15th Avenue NE, Seattle, WA 98195-5065, USA
| | - Joyce Lee
- Bionano Genomics, San Diego, CA 92121, USA
| | - Sushant Kumar
- Program in Computational Biology and Bioinformatics, Yale University, BASS 432 and 437, 266 Whitney Avenue, New Haven, CT 06520, USA
| | - Jiadong Lin
- School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
| | - Tobias Rausch
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Meyerhofstraße 1, 69117 Heidelberg, Germany
| | - Yu Chen
- Department of Genetics and Informatics Institute, School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Jingwen Ren
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA
| | - Martin Santamarina
- Genomes and Disease, Centre for Research in Molecular Medicine and Chronic Diseases (CIMUS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain
- Department of Zoology, Genetics, and Physical Anthropology, Universidade de Santiago de Compostela, Santiago de Compostela, Spain
| | - Wolfram Höps
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Meyerhofstraße 1, 69117 Heidelberg, Germany
| | - Hufsah Ashraf
- Heinrich Heine University, Medical Faculty, Institute for Medical Biometry and Bioinformatics, Moorenstraße 20, 40225 Düsseldorf, Germany
| | - Nelson T Chuang
- Institute for Genome Sciences, University of Maryland School of Medicine, 670 W Baltimore Street, Baltimore, MD 21201, USA
| | - Xiaofei Yang
- School of Computer Science and Technology, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
| | - Katherine M Munson
- Department of Genome Sciences, University of Washington School of Medicine, 3720 15th Avenue NE, Seattle, WA 98195-5065, USA
| | - Alexandra P Lewis
- Department of Genome Sciences, University of Washington School of Medicine, 3720 15th Avenue NE, Seattle, WA 98195-5065, USA
| | - Susan Fairley
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Luke J Tallon
- Institute for Genome Sciences, University of Maryland School of Medicine, 670 W Baltimore Street, Baltimore, MD 21201, USA
| | | | | | | | | | | | - Tsung-Yu Lu
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA
| | - Mark J P Chaisson
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA
| | - Junjie Chen
- Department of Computer and Information Sciences, Temple University, Philadelphia, PA 19122, USA
| | - Chong Li
- Department of Computer and Information Sciences, Temple University, Philadelphia, PA 19122, USA
| | - Harrison Brand
- Center for Genomic Medicine, Massachusetts General Hospital, Department of Neurology, Harvard Medical School, Boston, MA 02114, USA
- Program in Medical and Population Genetics and Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Aaron M Wenger
- Pacific Biosciences of California, Menlo Park, CA 94025, USA
| | - Maryam Ghareghani
- Max Planck Institute for Informatics, Saarland Informatics Campus E1.4, 66123 Saarbrücken, Germany
- Saarbrücken Graduate School of Computer Science, Saarland University, Saarland Informatics Campus E1.3, 66123 Saarbrücken, Germany
- Heinrich Heine University, Medical Faculty, Institute for Medical Biometry and Bioinformatics, Moorenstraße 20, 40225 Düsseldorf, Germany
| | - William T Harvey
- Department of Genome Sciences, University of Washington School of Medicine, 3720 15th Avenue NE, Seattle, WA 98195-5065, USA
| | - Benjamin Raeder
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Meyerhofstraße 1, 69117 Heidelberg, Germany
| | - Patrick Hasenfeld
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Meyerhofstraße 1, 69117 Heidelberg, Germany
| | - Allison A Regier
- Department of Medicine, Washington University, St. Louis, MO 63108, USA
| | - Haley J Abel
- Department of Medicine, Washington University, St. Louis, MO 63108, USA
| | - Ira M Hall
- Department of Genetics, Yale School of Medicine, 333 Cedar Street, New Haven, CT 06510, USA
| | - Paul Flicek
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Oliver Stegle
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Meyerhofstraße 1, 69117 Heidelberg, Germany
- Division of Computational Genomics and Systems Genetics, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Mark B Gerstein
- Program in Computational Biology and Bioinformatics, Yale University, BASS 432 and 437, 266 Whitney Avenue, New Haven, CT 06520, USA
| | - Jose M C Tubio
- Genomes and Disease, Centre for Research in Molecular Medicine and Chronic Diseases (CIMUS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain
- Department of Zoology, Genetics, and Physical Anthropology, Universidade de Santiago de Compostela, Santiago de Compostela, Spain
| | - Zepeng Mu
- Genetics, Genomics, and Systems Biology, University of Chicago, Chicago, IL 60637, USA
| | - Yang I Li
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL 60637, USA
| | - Xinghua Shi
- Department of Computer and Information Sciences, Temple University, Philadelphia, PA 19122, USA
| | | | - Kai Ye
- School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
- Department of Human Genetics, University of Michigan, 1241 E. Catherine Street, Ann Arbor, MI 48109, USA
| | - Zechen Chong
- Department of Genetics and Informatics Institute, School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Ashley D Sanders
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Meyerhofstraße 1, 69117 Heidelberg, Germany
| | | | - Michael E Talkowski
- Center for Genomic Medicine, Massachusetts General Hospital, Department of Neurology, Harvard Medical School, Boston, MA 02114, USA
- Program in Medical and Population Genetics and Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Ryan E Mills
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, 100 Washtenaw Avenue, Ann Arbor, MI 48109, USA
- Department of Human Genetics, University of Michigan, 1241 E. Catherine Street, Ann Arbor, MI 48109, USA
| | - Scott E Devine
- Institute for Genome Sciences, University of Maryland School of Medicine, 670 W Baltimore Street, Baltimore, MD 21201, USA
| | - Charles Lee
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT 06032, USA.
- Precision Medicine Center, The First Affiliated Hospital of Xi'an Jiaotong University, 277 West Yanta Road, Xi'an, 710061, Shaanxi, China
- Department of Graduate Studies-Life Sciences, Ewha Womans University, Ewhayeodae-gil, Seodaemun-gu, Seoul 120-750, South Korea
| | - Jan O Korbel
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Meyerhofstraße 1, 69117 Heidelberg, Germany.
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Tobias Marschall
- Heinrich Heine University, Medical Faculty, Institute for Medical Biometry and Bioinformatics, Moorenstraße 20, 40225 Düsseldorf, Germany.
| | - Evan E Eichler
- Department of Genome Sciences, University of Washington School of Medicine, 3720 15th Avenue NE, Seattle, WA 98195-5065, USA.
- Howard Hughes Medical Institute, University of Washington, Seattle, WA 98195, USA
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Proteome-wide Systems Genetics to Identify Functional Regulators of Complex Traits. Cell Syst 2021; 12:5-22. [PMID: 33476553 DOI: 10.1016/j.cels.2020.10.005] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Revised: 09/15/2020] [Accepted: 10/07/2020] [Indexed: 02/08/2023]
Abstract
Proteomic technologies now enable the rapid quantification of thousands of proteins across genetically diverse samples. Integration of these data with systems-genetics analyses is a powerful approach to identify new regulators of economically important or disease-relevant phenotypes in various populations. In this review, we summarize the latest proteomic technologies and discuss technical challenges for their use in population studies. We demonstrate how the analysis of correlation structure and loci mapping can be used to identify genetic factors regulating functional protein networks and complex traits. Finally, we provide an extensive summary of the use of proteome-wide systems genetics throughout fungi, plant, and animal kingdoms and discuss the power of this approach to identify candidate regulators and drug targets in large human consortium studies.
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Brion C, Lutz SM, Albert FW. Simultaneous quantification of mRNA and protein in single cells reveals post-transcriptional effects of genetic variation. eLife 2020; 9:60645. [PMID: 33191917 PMCID: PMC7707838 DOI: 10.7554/elife.60645] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Accepted: 11/14/2020] [Indexed: 01/27/2023] Open
Abstract
Trans-acting DNA variants may specifically affect mRNA or protein levels of genes located throughout the genome. However, prior work compared trans-acting loci mapped in separate studies, many of which had limited statistical power. Here, we developed a CRISPR-based system for simultaneous quantification of mRNA and protein of a given gene via dual fluorescent reporters in single, live cells of the yeast Saccharomyces cerevisiae. In large populations of recombinant cells from a cross between two genetically divergent strains, we mapped 86 trans-acting loci affecting the expression of ten genes. Less than 20% of these loci had concordant effects on mRNA and protein of the same gene. Most loci influenced protein but not mRNA of a given gene. One locus harbored a premature stop variant in the YAK1 kinase gene that had specific effects on protein or mRNA of dozens of genes. These results demonstrate complex, post-transcriptional genetic effects on gene expression.
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Affiliation(s)
- Christian Brion
- Department of Genetics, Cell Biology and Development, University of Minnesota, Minneapolis, United States
| | - Sheila M Lutz
- Department of Genetics, Cell Biology and Development, University of Minnesota, Minneapolis, United States
| | - Frank Wolfgang Albert
- Department of Genetics, Cell Biology and Development, University of Minnesota, Minneapolis, United States
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