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Ren S, Li J, Dorado J, Sierra A, González-Díaz H, Duardo A, Shen B. From molecular mechanisms of prostate cancer to translational applications: based on multi-omics fusion analysis and intelligent medicine. Health Inf Sci Syst 2024; 12:6. [PMID: 38125666 PMCID: PMC10728428 DOI: 10.1007/s13755-023-00264-5] [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: 08/24/2023] [Accepted: 11/28/2023] [Indexed: 12/23/2023] Open
Abstract
Prostate cancer is the most common cancer in men worldwide and has a high mortality rate. The complex and heterogeneous development of prostate cancer has become a core obstacle in the treatment of prostate cancer. Simultaneously, the issues of overtreatment in early-stage diagnosis, oligometastasis and dormant tumor recognition, as well as personalized drug utilization, are also specific concerns that require attention in the clinical management of prostate cancer. Some typical genetic mutations have been proved to be associated with prostate cancer's initiation and progression. However, single-omic studies usually are not able to explain the causal relationship between molecular alterations and clinical phenotypes. Exploration from a systems genetics perspective is also lacking in this field, that is, the impact of gene network, the environmental factors, and even lifestyle behaviors on disease progression. At the meantime, current trend emphasizes the utilization of artificial intelligence (AI) and machine learning techniques to process extensive multidimensional data, including multi-omics. These technologies unveil the potential patterns, correlations, and insights related to diseases, thereby aiding the interpretable clinical decision making and applications, namely intelligent medicine. Therefore, there is a pressing need to integrate multidimensional data for identification of molecular subtypes, prediction of cancer progression and aggressiveness, along with perosonalized treatment performing. In this review, we systematically elaborated the landscape from molecular mechanism discovery of prostate cancer to clinical translational applications. We discussed the molecular profiles and clinical manifestations of prostate cancer heterogeneity, the identification of different states of prostate cancer, as well as corresponding precision medicine practices. Taking multi-omics fusion, systems genetics, and intelligence medicine as the main perspectives, the current research results and knowledge-driven research path of prostate cancer were summarized.
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Affiliation(s)
- Shumin Ren
- Department of Urology and Institutes for Systems Genetics, West China Hospital, Sichuan University, Chengdu, 610041 China
- Department of Computer Science and Information Technology, University of A Coruña, 15071 A Coruña, Spain
| | - Jiakun Li
- Department of Urology and Institutes for Systems Genetics, West China Hospital, Sichuan University, Chengdu, 610041 China
| | - Julián Dorado
- Department of Computer Science and Information Technology, University of A Coruña, 15071 A Coruña, Spain
| | - Alejandro Sierra
- Department of Computer Science and Information Technology, University of A Coruña, 15071 A Coruña, Spain
- IKERDATA S.L., ZITEK, University of Basque Country UPVEHU, Rectorate Building, 48940 Leioa, Spain
| | - Humbert González-Díaz
- Department of Computer Science and Information Technology, University of A Coruña, 15071 A Coruña, Spain
- IKERDATA S.L., ZITEK, University of Basque Country UPVEHU, Rectorate Building, 48940 Leioa, Spain
| | - Aliuska Duardo
- Department of Computer Science and Information Technology, University of A Coruña, 15071 A Coruña, Spain
- IKERDATA S.L., ZITEK, University of Basque Country UPVEHU, Rectorate Building, 48940 Leioa, Spain
| | - Bairong Shen
- Department of Urology and Institutes for Systems Genetics, West China Hospital, Sichuan University, Chengdu, 610041 China
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Reed JN, Huang J, Li Y, Ma L, Banka D, Wabitsch M, Wang T, Ding W, Björkegren JL, Civelek M. Systems genetics analysis of human body fat distribution genes identifies adipocyte processes. Life Sci Alliance 2024; 7:e202402603. [PMID: 38702075 PMCID: PMC11068934 DOI: 10.26508/lsa.202402603] [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: 01/18/2024] [Revised: 04/19/2024] [Accepted: 04/22/2024] [Indexed: 05/06/2024] Open
Abstract
Excess abdominal fat is a sexually dimorphic risk factor for cardio-metabolic disease and is approximated by the waist-to-hip ratio adjusted for body mass index (WHRadjBMI). Whereas this trait is highly heritable, few causal genes are known. We aimed to identify novel drivers of WHRadjBMI using systems genetics. We used two independent cohorts of adipose tissue gene expression and constructed sex- and depot-specific Bayesian networks to model gene-gene interactions from 8,492 genes. Using key driver analysis, we identified genes that, in silico and putatively in vitro, regulate many others. 51-119 key drivers in each network were replicated in both cohorts. In other cell types, 23 of these genes are found in crucial adipocyte pathways: Wnt signaling or mitochondrial function. We overexpressed or down-regulated seven key driver genes in human subcutaneous pre-adipocytes. Key driver genes ANAPC2 and RSPO1 inhibited adipogenesis, whereas PSME3 increased adipogenesis. RSPO1 increased Wnt signaling activity. In differentiated adipocytes, MIGA1 and UBR1 down-regulation led to mitochondrial dysfunction. These five genes regulate adipocyte function, and we hypothesize that they regulate fat distribution.
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Affiliation(s)
- Jordan N Reed
- https://ror.org/0153tk833 Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA
- https://ror.org/0153tk833 Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Jiansheng Huang
- Novo Nordisk Research Center China, Novo Nordisk A/S, Beijing, China
| | - Yong Li
- Novo Nordisk Research Center China, Novo Nordisk A/S, Beijing, China
| | - Lijiang Ma
- https://ror.org/04a9tmd77 Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Dhanush Banka
- https://ror.org/0153tk833 Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA
| | - Martin Wabitsch
- Division of Paediatric Endocrinology and Diabetes, Department of Paediatrics and Adolescent Medicine, Ulm University Medical Centre, Ulm, Germany
| | - Tianfang Wang
- Novo Nordisk Research Center China, Novo Nordisk A/S, Beijing, China
| | - Wen Ding
- Novo Nordisk Research Center China, Novo Nordisk A/S, Beijing, China
| | - Johan Lm Björkegren
- https://ror.org/04a9tmd77 Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Medicine, Karolinska Institutet, Huddinge, Stockholm, Sweden
| | - Mete Civelek
- https://ror.org/0153tk833 Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA
- https://ror.org/0153tk833 Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
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Pszczołowska M, Walczak K, Miśków W, Mroziak M, Chojdak-Łukasiewicz J, Leszek J. Mitochondrial disorders leading to Alzheimer's disease-perspectives of diagnosis and treatment. GeroScience 2024; 46:2977-2988. [PMID: 38457008 PMCID: PMC11009177 DOI: 10.1007/s11357-024-01118-y] [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: 01/15/2024] [Accepted: 02/29/2024] [Indexed: 03/09/2024] Open
Abstract
Alzheimer's disease (AD) is a neurodegenerative disorder and the most common cause of dementia globally. The pathogenesis of AD remains still unclear. The three main features of AD are extracellular deposits of amyloid beta (Aβ) plaque, accumulation of abnormal formation hyper-phosphorylated tau protein, and neuronal loss. Mitochondrial impairment plays an important role in the pathogenesis of AD. There are problems with decreased activity of multiple complexes, disturbed mitochondrial fusion, and fission or formation of reactive oxygen species (ROS). Moreover, mitochondrial transport is impaired in AD. Mouse models in many research show disruptions in anterograde and retrograde transport. Both mitochondrial transportation and network impairment have a huge impact on synapse loss and, as a result, cognitive impairment. One of the very serious problems in AD is also disruption of insulin signaling which impairs mitochondrial Aβ removal.Discovering precise mechanisms leading to AD enables us to find new treatment possibilities. Recent studies indicate the positive influence of metformin or antioxidants such as MitoQ, SS-31, SkQ, MitoApo, MitoTEMPO, and MitoVitE on mitochondrial functioning and hence prevent cognitive decline. Impairments in mitochondrial fission may be treated with mitochondrial division inhibitor-1 or ceramide.
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Affiliation(s)
| | - Kamil Walczak
- Faculty of Medicine, Wrocław Medical University, Wrocław, Poland
| | - Weronika Miśków
- Faculty of Medicine, Wrocław Medical University, Wrocław, Poland
| | | | | | - Jerzy Leszek
- Clinic of Psychiatry, Department of Psychiatry, Medical Department, Wrocław Medical University, Wrocław, Poland
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Mocci G, Sukhavasi K, Örd T, Bankier S, Singha P, Arasu UT, Agbabiaje OO, Mäkinen P, Ma L, Hodonsky CJ, Aherrahrou R, Muhl L, Liu J, Gustafsson S, Byandelger B, Wang Y, Koplev S, Lendahl U, Owens GK, Leeper NJ, Pasterkamp G, Vanlandewijck M, Michoel T, Ruusalepp A, Hao K, Ylä-Herttuala S, Väli M, Järve H, Mokry M, Civelek M, Miller CJ, Kovacic JC, Kaikkonen MU, Betsholtz C, Björkegren JL. Single-Cell Gene-Regulatory Networks of Advanced Symptomatic Atherosclerosis. Circ Res 2024; 134:1405-1423. [PMID: 38639096 PMCID: PMC11122742 DOI: 10.1161/circresaha.123.323184] [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: 06/04/2023] [Revised: 04/01/2024] [Accepted: 04/04/2024] [Indexed: 04/20/2024]
Abstract
BACKGROUND While our understanding of the single-cell gene expression patterns underlying the transformation of vascular cell types during the progression of atherosclerosis is rapidly improving, the clinical and pathophysiological relevance of these changes remains poorly understood. METHODS Single-cell RNA sequencing data generated with SmartSeq2 (≈8000 genes/cell) in 16 588 single cells isolated during atherosclerosis progression in Ldlr-/-Apob100/100 mice with human-like plasma lipoproteins and from humans with asymptomatic and symptomatic carotid plaques was clustered into multiple subtypes. For clinical and pathophysiological context, the advanced-stage and symptomatic subtype clusters were integrated with 135 tissue-specific (atherosclerotic aortic wall, mammary artery, liver, skeletal muscle, and visceral and subcutaneous, fat) gene-regulatory networks (GRNs) inferred from 600 coronary artery disease patients in the STARNET (Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task) study. RESULTS Advanced stages of atherosclerosis progression and symptomatic carotid plaques were largely characterized by 3 smooth muscle cells (SMCs), and 3 macrophage subtype clusters with extracellular matrix organization/osteogenic (SMC), and M1-type proinflammatory/Trem2-high lipid-associated (macrophage) phenotypes. Integrative analysis of these 6 clusters with STARNET revealed significant enrichments of 3 arterial wall GRNs: GRN33 (macrophage), GRN39 (SMC), and GRN122 (macrophage) with major contributions to coronary artery disease heritability and strong associations with clinical scores of coronary atherosclerosis severity. The presence and pathophysiological relevance of GRN39 were verified in 5 independent RNAseq data sets obtained from the human coronary and aortic artery, and primary SMCs and by targeting its top-key drivers, FRZB and ALCAM in cultured human coronary artery SMCs. CONCLUSIONS By identifying and integrating the most gene-rich single-cell subclusters of atherosclerosis to date with a coronary artery disease framework of GRNs, GRN39 was identified and independently validated as being critical for the transformation of contractile SMCs into an osteogenic phenotype promoting advanced, symptomatic atherosclerosis.
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MESH Headings
- Humans
- Single-Cell Analysis
- Animals
- Gene Regulatory Networks
- Atherosclerosis/genetics
- Atherosclerosis/metabolism
- Atherosclerosis/pathology
- Mice
- Myocytes, Smooth Muscle/metabolism
- Myocytes, Smooth Muscle/pathology
- Male
- Plaque, Atherosclerotic
- Disease Progression
- Female
- Macrophages/metabolism
- Macrophages/pathology
- Mice, Knockout
- Receptors, LDL/genetics
- Receptors, LDL/metabolism
- Mice, Inbred C57BL
- Muscle, Smooth, Vascular/metabolism
- Muscle, Smooth, Vascular/pathology
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Affiliation(s)
- Giuseppe Mocci
- Department of Medicine (Huddinge), Karolinska Institutet, Sweden (G.M., L. Muhl, J.L., S.G., B.B., U.L., M.V., C.B., J.L.M.B.)
| | - Katyayani Sukhavasi
- Department of Cardiac Surgery and The Heart Clinic, Tartu University Hospital and Department of Cardiology, Institute of Clinical Medicine, Tartu University, Estonia (K.S., A.R., H.J.)
| | - Tiit Örd
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio (T.O., P.S., U.T.A., O.O.A., P.M., S.Y.-H., M.U.K.)
| | - Sean Bankier
- Computational Biology Unit, Department of Informatics, University of Bergen, Norway (S.B., T.M.)
| | - Prosanta Singha
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio (T.O., P.S., U.T.A., O.O.A., P.M., S.Y.-H., M.U.K.)
| | - Uma Thanigai Arasu
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio (T.O., P.S., U.T.A., O.O.A., P.M., S.Y.-H., M.U.K.)
| | - Olayinka Oluwasegun Agbabiaje
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio (T.O., P.S., U.T.A., O.O.A., P.M., S.Y.-H., M.U.K.)
| | - Petri Mäkinen
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio (T.O., P.S., U.T.A., O.O.A., P.M., S.Y.-H., M.U.K.)
| | - Lijiang Ma
- Department of Genetics and Genomic Sciences, Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York (L. Ma, S.K., K.H., J.L.M.B.)
| | - Chani J. Hodonsky
- Robert M. Berne Cardiovascular Research Center (C.J.H., G.K.O., C.J.M.), University of Virginia, Charlottesville
- Center for Public Health Genomics (C.J.H., R.A., M.C.), University of Virginia, Charlottesville
| | - Redouane Aherrahrou
- Center for Public Health Genomics (C.J.H., R.A., M.C.), University of Virginia, Charlottesville
- Department of Biomedical Engineering (R.A., M.C.), University of Virginia, Charlottesville
| | - Lars Muhl
- Department of Medicine (Huddinge), Karolinska Institutet, Sweden (G.M., L. Muhl, J.L., S.G., B.B., U.L., M.V., C.B., J.L.M.B.)
| | - Jianping Liu
- Department of Medicine (Huddinge), Karolinska Institutet, Sweden (G.M., L. Muhl, J.L., S.G., B.B., U.L., M.V., C.B., J.L.M.B.)
| | - Sonja Gustafsson
- Department of Medicine (Huddinge), Karolinska Institutet, Sweden (G.M., L. Muhl, J.L., S.G., B.B., U.L., M.V., C.B., J.L.M.B.)
| | - Byambajav Byandelger
- Department of Medicine (Huddinge), Karolinska Institutet, Sweden (G.M., L. Muhl, J.L., S.G., B.B., U.L., M.V., C.B., J.L.M.B.)
| | - Ying Wang
- Division of Vascular Surgery, Department of Surgery, Stanford University School of Medicine, CA (Y.W., N.J.L.)
- Stanford Cardiovascular Institute, Stanford University, CA (Y.W., N.J.L.)
| | - Simon Koplev
- Department of Genetics and Genomic Sciences, Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York (L. Ma, S.K., K.H., J.L.M.B.)
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, United Kingdom (S.K.)
| | - Urban Lendahl
- Department of Medicine (Huddinge), Karolinska Institutet, Sweden (G.M., L. Muhl, J.L., S.G., B.B., U.L., M.V., C.B., J.L.M.B.)
| | - Gary K. Owens
- Robert M. Berne Cardiovascular Research Center (C.J.H., G.K.O., C.J.M.), University of Virginia, Charlottesville
| | - Nicholas J. Leeper
- Division of Vascular Surgery, Department of Surgery, Stanford University School of Medicine, CA (Y.W., N.J.L.)
- Stanford Cardiovascular Institute, Stanford University, CA (Y.W., N.J.L.)
| | - Gerard Pasterkamp
- Laboratory of Experimental Cardiology (G.P., M.M.), University Medical Center Utrecht, the Netherlands
- Central Diagnostics Laboratory (G.P., M.M.), University Medical Center Utrecht, the Netherlands
| | - Michael Vanlandewijck
- Department of Medicine (Huddinge), Karolinska Institutet, Sweden (G.M., L. Muhl, J.L., S.G., B.B., U.L., M.V., C.B., J.L.M.B.)
| | - Tom Michoel
- Computational Biology Unit, Department of Informatics, University of Bergen, Norway (S.B., T.M.)
| | - Arno Ruusalepp
- Department of Cardiac Surgery and The Heart Clinic, Tartu University Hospital and Department of Cardiology, Institute of Clinical Medicine, Tartu University, Estonia (K.S., A.R., H.J.)
| | - Ke Hao
- Department of Genetics and Genomic Sciences, Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York (L. Ma, S.K., K.H., J.L.M.B.)
| | - Seppo Ylä-Herttuala
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio (T.O., P.S., U.T.A., O.O.A., P.M., S.Y.-H., M.U.K.)
| | - Marika Väli
- Department of Immunology, Genetics, and Pathology, Rudbeck Laboratory, Uppsala University, Sweden (M.V., C.B.)
- Department of Pathological anatomy and Forensic medicine, Institute of Biomedicine and Translational Medicine, University of Tartu, Estonia (M.V.)
| | - Heli Järve
- Department of Cardiac Surgery and The Heart Clinic, Tartu University Hospital and Department of Cardiology, Institute of Clinical Medicine, Tartu University, Estonia (K.S., A.R., H.J.)
| | - Michal Mokry
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio (T.O., P.S., U.T.A., O.O.A., P.M., S.Y.-H., M.U.K.)
- Laboratory of Experimental Cardiology (G.P., M.M.), University Medical Center Utrecht, the Netherlands
| | - Mete Civelek
- Center for Public Health Genomics (C.J.H., R.A., M.C.), University of Virginia, Charlottesville
- Department of Biomedical Engineering (R.A., M.C.), University of Virginia, Charlottesville
| | - Clint J. Miller
- Robert M. Berne Cardiovascular Research Center (C.J.H., G.K.O., C.J.M.), University of Virginia, Charlottesville
| | - Jason C. Kovacic
- Cardiovascular Research Institute, Icahn School of Medicine at Mount Sinai, New York (J.C.K.)
- Victor Chang Cardiac Research Institute, Darlinghurst, Australia (J.C.K.)
- St. Vincent’s Clinical School, University of NSW, Sydney, Australia (J.C.K.)
| | - Minna U. Kaikkonen
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio (T.O., P.S., U.T.A., O.O.A., P.M., S.Y.-H., M.U.K.)
| | - Christer Betsholtz
- Department of Medicine (Huddinge), Karolinska Institutet, Sweden (G.M., L. Muhl, J.L., S.G., B.B., U.L., M.V., C.B., J.L.M.B.)
- Department of Immunology, Genetics, and Pathology, Rudbeck Laboratory, Uppsala University, Sweden (M.V., C.B.)
| | - Johan L.M. Björkegren
- Department of Medicine (Huddinge), Karolinska Institutet, Sweden (G.M., L. Muhl, J.L., S.G., B.B., U.L., M.V., C.B., J.L.M.B.)
- Department of Genetics and Genomic Sciences, Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York (L. Ma, S.K., K.H., J.L.M.B.)
- Clinical Gene Networks AB, Stockholm, Sweden (J.L.M.B.)
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Kwon JJ, Pan J, Gonzalez G, Hahn WC, Zitnik M. On knowing a gene: A distributional hypothesis of gene function. Cell Syst 2024:S2405-4712(24)00123-6. [PMID: 38810640 DOI: 10.1016/j.cels.2024.04.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 02/25/2024] [Accepted: 04/30/2024] [Indexed: 05/31/2024]
Abstract
As words can have multiple meanings that depend on sentence context, genes can have various functions that depend on the surrounding biological system. This pleiotropic nature of gene function is limited by ontologies, which annotate gene functions without considering biological contexts. We contend that the gene function problem in genetics may be informed by recent technological leaps in natural language processing, in which representations of word semantics can be automatically learned from diverse language contexts. In contrast to efforts to model semantics as "is-a" relationships in the 1990s, modern distributional semantics represents words as vectors in a learned semantic space and fuels current advances in transformer-based models such as large language models and generative pre-trained transformers. A similar shift in thinking of gene functions as distributions over cellular contexts may enable a similar breakthrough in data-driven learning from large biological datasets to inform gene function.
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Affiliation(s)
- Jason J Kwon
- Dana-Farber Cancer Institute and Harvard Medical School, Department of Medical Oncology, Boston, MA 02215, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Joshua Pan
- Dana-Farber Cancer Institute and Harvard Medical School, Department of Medical Oncology, Boston, MA 02215, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Guadalupe Gonzalez
- Department of Computing, Faculty of Engineering, Imperial College, London SW7 2AZ, UK
| | - William C Hahn
- Dana-Farber Cancer Institute and Harvard Medical School, Department of Medical Oncology, Boston, MA 02215, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
| | - Marinka Zitnik
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Harvard Medical School, Department of Biomedical Informatics, Boston, MA 02115, USA; Harvard Data Science Initiative, Harvard University, Cambridge, MA 02138, USA; Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University, Allston, MA 02134, USA.
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Rutherford HA, Candeias D, Duncan CJA, Renshaw SA, Hamilton N. Macrophage transplantation rescues RNASET2-deficient leukodystrophy by replacing deficient microglia in a zebrafish model. Proc Natl Acad Sci U S A 2024; 121:e2321496121. [PMID: 38753517 PMCID: PMC11126979 DOI: 10.1073/pnas.2321496121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Accepted: 04/05/2024] [Indexed: 05/18/2024] Open
Abstract
RNASET2-deficient leukodystrophy is a rare infantile white matter disorder mimicking a viral infection and resulting in severe psychomotor impairments. Despite its severity, there is little understanding of cellular mechanisms of pathogenesis and no treatments. Recent research using the rnaset2 mutant zebrafish model has suggested that microglia may be the drivers of the neuropathology, due to their failure to digest apoptotic debris during neurodevelopment. Therefore, we developed a strategy for microglial replacement through transplantation of adult whole kidney marrow-derived macrophages into embryonic hosts. Using live imaging, we revealed that transplant-derived macrophages can engraft within host brains and express microglia-specific markers, suggesting the adoption of a microglial phenotype. Tissue-clearing strategies revealed the persistence of transplanted cells in host brains beyond embryonic stages. We demonstrated that transplanted cells clear apoptotic cells within the brain, as well as rescue overactivation of the antiviral response otherwise seen in mutant larvae. RNA sequencing at the point of peak transplant-derived cell engraftment confirms that transplantation can reduce the brain-wide immune response and particularly, the antiviral response, in rnaset2-deficient brains. Crucially, this reduction in neuroinflammation resulted in behavioral rescue-restoring rnaset2 mutant motor activity to wild-type (WT) levels in embryonic and juvenile stages. Together, these findings demonstrate the role of microglia as the cellular drivers of neuropathology in rnaset2 mutants and that macrophage transplantation is a viable strategy for microglial replacement in the zebrafish. Therefore, microglia-targeted interventions may have therapeutic benefits in RNASET2-deficient leukodystrophy.
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Affiliation(s)
- Holly A. Rutherford
- Department of Infection and Immunity, School of Medicine and Population Health, University of Sheffield, SheffieldS10 2RX, United Kingdom
- Bateson Centre, University of Sheffield, SheffieldS10 2TN, United Kingdom
| | - Diogo Candeias
- Department of Biology, University of York, YorkYO10 5DD, United Kingdom
- York Biomedical research Institute, University of York, YorkYO10 5DD, United Kingdom
| | - Christopher J. A. Duncan
- Immunology and Inflammation Theme, Translational and Clinical Research Institute, Newcastle University, NewcastleNE2 4HH, United Kingdom
- Department of Infection and Tropical Medicine, Royal Victoria Infirmary, Newcastle upon Tyne Hospitals National Health Services Foundation Trust, NewcastleNE2 4HH, United Kingdom
| | - Stephen A. Renshaw
- Department of Infection and Immunity, School of Medicine and Population Health, University of Sheffield, SheffieldS10 2RX, United Kingdom
- Bateson Centre, University of Sheffield, SheffieldS10 2TN, United Kingdom
| | - Noémie Hamilton
- Department of Biology, University of York, YorkYO10 5DD, United Kingdom
- York Biomedical research Institute, University of York, YorkYO10 5DD, United Kingdom
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7
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Talari NK, Mattam U, Kaminska D, Sotomayor-Rodriguez I, Rahman AP, Péterfy M, Pajukanta P, Pihlajamäki J, Chella Krishnan K. Hepatokine ITIH3 protects against hepatic steatosis by downregulating mitochondrial bioenergetics and de novo lipogenesis. iScience 2024; 27:109709. [PMID: 38689636 PMCID: PMC11059128 DOI: 10.1016/j.isci.2024.109709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 02/16/2024] [Accepted: 04/06/2024] [Indexed: 05/02/2024] Open
Abstract
Recent studies demonstrate that liver secretory proteins, also known as hepatokines, regulate normal development, obesity, and simple steatosis to non-alcoholic steatohepatitis (NASH) progression. Using a panel of ∼100 diverse inbred strains of mice and a cohort of bariatric surgery patients, we found that one such hepatokine, inter-trypsin inhibitor heavy chain 3 (ITIH3), was progressively lower in severe non-alcoholic fatty liver disease (NAFLD) disease states highlighting an inverse relationship between Itih3/ITIH3 expression and NAFLD severity. Follow-up animal and cell culture models demonstrated that hepatic ITIH3 overexpression lowered liver triglyceride and lipid droplet accumulation, respectively. Conversely, ITIH3 knockdown in mice increased the liver triglyceride in two independent NAFLD models. Mechanistically, ITIH3 reduced mitochondrial respiration and this, in turn, reduced liver triglycerides, via downregulated de novo lipogenesis. This was accompanied by increased STAT1 signaling and Stat3 expression, both of which are known to protect against NAFLD/NASH. Our findings indicate hepatokine ITIH3 as a potential biomarker and/or treatment for NAFLD.
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Affiliation(s)
- Noble Kumar Talari
- Department of Pharmacology and Systems Physiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Ushodaya Mattam
- Department of Pharmacology and Systems Physiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Dorota Kaminska
- Department of Medicine, Division of Cardiology, University of California Los Angeles, Los Angeles, CA, USA
- Institute of Public Health and Clinical Nutrition, Department of Clinical Nutrition, University of Eastern Finland, Kuopio, Finland
| | - Irene Sotomayor-Rodriguez
- Medical Sciences Baccalaureate Program, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Afra P. Rahman
- Medical Sciences Baccalaureate Program, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Miklós Péterfy
- Department of Basic Medical Sciences, Western University of Health Sciences, Pomona, CA, USA
| | - Päivi Pajukanta
- Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California Los Angeles, Los Angeles, CA, USA
- Institute for Precision Health, David Geffen School of Medicine at UCLA, University of California Los Angeles, Los Angeles, CA, USA
| | - Jussi Pihlajamäki
- Institute of Public Health and Clinical Nutrition, Department of Clinical Nutrition, University of Eastern Finland, Kuopio, Finland
- Department of Medicine, Endocrinology and Clinical Nutrition, Kuopio University Hospital, Kuopio, Finland
| | - Karthickeyan Chella Krishnan
- Department of Pharmacology and Systems Physiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
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8
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Lundy DJ, Szomolay B, Liao CT. Systems Approaches to Cell Culture-Derived Extracellular Vesicles for Acute Kidney Injury Therapy: Prospects and Challenges. FUNCTION 2024; 5:zqae012. [PMID: 38706963 PMCID: PMC11065115 DOI: 10.1093/function/zqae012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Revised: 03/02/2024] [Accepted: 03/05/2024] [Indexed: 05/07/2024] Open
Abstract
Acute kidney injury (AKI) is a heterogeneous syndrome, comprising diverse etiologies of kidney insults that result in high mortality and morbidity if not well managed. Although great efforts have been made to investigate underlying pathogenic mechanisms of AKI, there are limited therapeutic strategies available. Extracellular vesicles (EV) are membrane-bound vesicles secreted by various cell types, which can serve as cell-free therapy through transfer of bioactive molecules. In this review, we first overview the AKI syndrome and EV biology, with a particular focus on the technical aspects and therapeutic application of cell culture-derived EVs. Second, we illustrate how multi-omic approaches to EV miRNA, protein, and genomic cargo analysis can yield new insights into their mechanisms of action and address unresolved questions in the field. We then summarize major experimental evidence regarding the therapeutic potential of EVs in AKI, which we subdivide into stem cell and non-stem cell-derived EVs. Finally, we highlight the challenges and opportunities related to the clinical translation of animal studies into human patients.
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Affiliation(s)
- David J Lundy
- Graduate Institute of Biomedical Materials & Tissue Engineering, Taipei Medical University, Taipei 235603, Taiwan
- International PhD Program in Biomedical Engineering, Taipei Medical University, Taipei 235603, Taiwan
- Center for Cell Therapy, Taipei Medical University Hospital, Taipei 110301, Taiwan
| | - Barbara Szomolay
- Systems Immunity Research Institute, Cardiff University School of Medicine, Cardiff CF14 4XN, UK
- Division of Infection and Immunity, Cardiff University School of Medicine, Cardiff CF14 4XN, UK
| | - Chia-Te Liao
- Division of Nephrology, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City 23561, Taiwan
- Division of Nephrology, Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan
- Research Center of Urology and Kidney, Taipei Medical University, Taipei 110, Taiwan
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9
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Li Y, Wu X, Fang D, Luo Y. Informing immunotherapy with multi-omics driven machine learning. NPJ Digit Med 2024; 7:67. [PMID: 38486092 PMCID: PMC10940614 DOI: 10.1038/s41746-024-01043-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2023] [Accepted: 02/14/2024] [Indexed: 03/18/2024] Open
Abstract
Progress in sequencing technologies and clinical experiments has revolutionized immunotherapy on solid and hematologic malignancies. However, the benefits of immunotherapy are limited to specific patient subsets, posing challenges for broader application. To improve its effectiveness, identifying biomarkers that can predict patient response is crucial. Machine learning (ML) play a pivotal role in harnessing multi-omic cancer datasets and unlocking new insights into immunotherapy. This review provides an overview of cutting-edge ML models applied in omics data for immunotherapy analysis, including immunotherapy response prediction and immunotherapy-relevant tumor microenvironment identification. We elucidate how ML leverages diverse data types to identify significant biomarkers, enhance our understanding of immunotherapy mechanisms, and optimize decision-making process. Additionally, we discuss current limitations and challenges of ML in this rapidly evolving field. Finally, we outline future directions aimed at overcoming these barriers and improving the efficiency of ML in immunotherapy research.
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Affiliation(s)
- Yawei Li
- Department of Preventive Medicine, Northwestern University, Feinberg School of Medicine, Chicago, IL, 60611, USA
- Center for Collaborative AI in Healthcare, Northwestern University, Feinberg School of Medicine, Chicago, IL, 60611, USA
| | - Xin Wu
- Department of Medicine, University of Illinois at Chicago, Chicago, IL, 60612, USA
| | - Deyu Fang
- Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA
| | - Yuan Luo
- Department of Preventive Medicine, Northwestern University, Feinberg School of Medicine, Chicago, IL, 60611, USA.
- Center for Collaborative AI in Healthcare, Northwestern University, Feinberg School of Medicine, Chicago, IL, 60611, USA.
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10
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Hebert JD, Tang YJ, Andrejka L, Lopez SS, Petrov DA, Boross G, Winslow MM. Combinatorial in vivo genome editing identifies widespread epistasis during lung tumorigenesis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.07.583981. [PMID: 38496564 PMCID: PMC10942407 DOI: 10.1101/2024.03.07.583981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Lung adenocarcinoma, the most common subtype of lung cancer, is genomically complex, with tumors containing tens to hundreds of non-synonymous mutations. However, little is understood about how genes interact with each other to enable tumorigenesis in vivo , largely due to a lack of methods for investigating genetic interactions in a high-throughput and multiplexed manner. Here, we employed a novel platform to generate tumors with all pairwise inactivation of ten tumor suppressor genes within an autochthonous mouse model of oncogenic KRAS-driven lung cancer. By quantifying the fitness of tumors with every single and double mutant genotype, we show that most tumor suppressor genetic interactions exhibited negative epistasis, with diminishing returns on tumor fitness. In contrast, Apc inactivation showed positive epistasis with the inactivation of several other genes, including dramatically synergistic effects on tumor fitness in combination with Lkb1 or Nf1 inactivation. This approach has the potential to expand the scope of genetic interactions that may be functionally characterized in vivo , which could lead to a better understanding of how complex tumor genotypes impact each step of carcinogenesis.
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11
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Cerván-Martín M, González-Muñoz S, Guzmán-Jiménez A, Higueras-Serrano I, Castilla JA, Garrido N, Luján S, Bassas L, Seixas S, Gonçalves J, Lopes AM, Larriba S, Palomino-Morales RJ, Bossini-Castillo L, Carmona FD. Changes in environmental exposures over decades may influence the genetic architecture of severe spermatogenic failure. Hum Reprod 2024; 39:612-622. [PMID: 38305414 DOI: 10.1093/humrep/deae007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 01/08/2024] [Indexed: 02/03/2024] Open
Abstract
STUDY QUESTION Do the genetic determinants of idiopathic severe spermatogenic failure (SPGF) differ between generations? SUMMARY ANSWER Our data support that the genetic component of idiopathic SPGF is impacted by dynamic changes in environmental exposures over decades. WHAT IS KNOWN ALREADY The idiopathic form of SPGF has a multifactorial etiology wherein an interaction between genetic, epigenetic, and environmental factors leads to the disease onset and progression. At the genetic level, genome-wide association studies (GWASs) allow the analysis of millions of genetic variants across the genome in a hypothesis-free manner, as a valuable tool for identifying susceptibility risk loci. However, little is known about the specific role of non-genetic factors and their influence on the genetic determinants in this type of conditions. STUDY DESIGN, SIZE, DURATION Case-control genetic association analyses were performed including a total of 912 SPGF cases and 1360 unaffected controls. PARTICIPANTS/MATERIALS, SETTING, METHODS All participants had European ancestry (Iberian and German). SPGF cases were diagnosed during the last decade either with idiopathic non-obstructive azoospermia (n = 547) or with idiopathic non-obstructive oligozoospermia (n = 365). Case-control genetic association analyses were performed by logistic regression models considering the generation as a covariate and by in silico functional characterization of the susceptibility genomic regions. MAIN RESULTS AND THE ROLE OF CHANCE This analysis revealed 13 novel genetic association signals with SPGF, with eight of them being independent. The observed associations were mostly explained by the interaction between each lead variant and the age-group. Additionally, we established links between these loci and diverse non-genetic factors, such as toxic or dietary habits, respiratory disorders, and autoimmune diseases, which might potentially influence the genetic architecture of idiopathic SPGF. LARGE SCALE DATA GWAS data are available from the authors upon reasonable request. LIMITATIONS, REASONS FOR CAUTION Additional independent studies involving large cohorts in ethnically diverse populations are warranted to confirm our findings. WIDER IMPLICATIONS OF THE FINDINGS Overall, this study proposes an innovative strategy to achieve a more precise understanding of conditions such as SPGF by considering the interactions between a variable exposome through different generations and genetic predisposition to complex diseases. STUDY FUNDING/COMPETING INTEREST(S) This work was supported by the "Plan Andaluz de Investigación, Desarrollo e Innovación (PAIDI 2020)" (ref. PY20_00212, P20_00583), the Spanish Ministry of Economy and Competitiveness through the Spanish National Plan for Scientific and Technical Research and Innovation (ref. PID2020-120157RB-I00 funded by MCIN/ AEI/10.13039/501100011033), and the 'Proyectos I+D+i del Programa Operativo FEDER 2020' (ref. B-CTS-584-UGR20). ToxOmics-Centre for Toxicogenomics and Human Health, Genetics, Oncology and Human Toxicology, is also partially supported by the Portuguese Foundation for Science and Technology (Projects: UIDB/00009/2020; UIDP/00009/2020). The authors declare no competing interests. TRIAL REGISTRATION NUMBER N/A.
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Affiliation(s)
- Miriam Cerván-Martín
- Departamento de Genética e Instituto de Biotecnología, Centro de Investigación Biomédica (CIBM), Universidad de Granada, Granada, Spain
- Instituto de Investigación Biosanitaria ibs.GRANADA, Granada, Spain
| | - Sara González-Muñoz
- Departamento de Genética e Instituto de Biotecnología, Centro de Investigación Biomédica (CIBM), Universidad de Granada, Granada, Spain
- Instituto de Investigación Biosanitaria ibs.GRANADA, Granada, Spain
| | - Andrea Guzmán-Jiménez
- Departamento de Genética e Instituto de Biotecnología, Centro de Investigación Biomédica (CIBM), Universidad de Granada, Granada, Spain
- Instituto de Investigación Biosanitaria ibs.GRANADA, Granada, Spain
| | - Inmaculada Higueras-Serrano
- Departamento de Genética e Instituto de Biotecnología, Centro de Investigación Biomédica (CIBM), Universidad de Granada, Granada, Spain
| | - José A Castilla
- Instituto de Investigación Biosanitaria ibs.GRANADA, Granada, Spain
- Unidad de Reproducción, UGC Obstetricia y Ginecología, HU Virgen de las Nieves, Granada, Spain
| | - Nicolás Garrido
- IVI Foundation, Health Research Institute La Fe, Valencia, Spain
- Servicio de Urología, Hospital Universitari i Politecnic La Fe e Instituto de Investigación Sanitaria La Fe (IIS La Fe), Valencia, Spain
| | - Saturnino Luján
- Servicio de Urología, Hospital Universitari i Politecnic La Fe e Instituto de Investigación Sanitaria La Fe (IIS La Fe), Valencia, Spain
| | - Lluís Bassas
- Laboratory of Seminology and Embryology, Andrology Service, Fundació Puigvert, Barcelona, Spain
| | - Susana Seixas
- Instituto de Investigação e Inovação em Saúde, Universidade do Porto (I3S), Porto, Portugal
- Institute of Molecular Pathology and Immunology of the University of Porto (IPATIMUP), Porto, Portugal
| | - João Gonçalves
- Departamento de Genética Humana, Instituto Nacional de Saúde Dr. Ricardo Jorge, Lisbon, Portugal
- ToxOmics-Centro de Toxicogenómica e Saúde Humana, Nova Medical School, Lisbon, Portugal
| | - Alexandra M Lopes
- Instituto de Investigação e Inovação em Saúde, Universidade do Porto (I3S), Porto, Portugal
- Institute of Molecular Pathology and Immunology of the University of Porto (IPATIMUP), Porto, Portugal
- Center for Predictive and Preventive Genetics, Institute for Cell and Molecular Biology, University of Porto, Porto, Portugal
| | - Sara Larriba
- Human Molecular Genetics Group, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain
| | - Rogelio J Palomino-Morales
- Instituto de Investigación Biosanitaria ibs.GRANADA, Granada, Spain
- Departamento de Bioquímica y Biología Molecular I, Universidad de Granada, Granada, Spain
| | - Lara Bossini-Castillo
- Departamento de Genética e Instituto de Biotecnología, Centro de Investigación Biomédica (CIBM), Universidad de Granada, Granada, Spain
- Instituto de Investigación Biosanitaria ibs.GRANADA, Granada, Spain
| | - F David Carmona
- Departamento de Genética e Instituto de Biotecnología, Centro de Investigación Biomédica (CIBM), Universidad de Granada, Granada, Spain
- Instituto de Investigación Biosanitaria ibs.GRANADA, Granada, Spain
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12
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van Gerwen J, Masson SWC, Cutler HB, Vegas AD, Potter M, Stöckli J, Madsen S, Nelson ME, Humphrey SJ, James DE. The genetic and dietary landscape of the muscle insulin signalling network. eLife 2024; 12:RP89212. [PMID: 38329473 PMCID: PMC10942587 DOI: 10.7554/elife.89212] [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] [Indexed: 02/09/2024] Open
Abstract
Metabolic disease is caused by a combination of genetic and environmental factors, yet few studies have examined how these factors influence signal transduction, a key mediator of metabolism. Using mass spectrometry-based phosphoproteomics, we quantified 23,126 phosphosites in skeletal muscle of five genetically distinct mouse strains in two dietary environments, with and without acute in vivo insulin stimulation. Almost half of the insulin-regulated phosphoproteome was modified by genetic background on an ordinary diet, and high-fat high-sugar feeding affected insulin signalling in a strain-dependent manner. Our data revealed coregulated subnetworks within the insulin signalling pathway, expanding our understanding of the pathway's organisation. Furthermore, associating diverse signalling responses with insulin-stimulated glucose uptake uncovered regulators of muscle insulin responsiveness, including the regulatory phosphosite S469 on Pfkfb2, a key activator of glycolysis. Finally, we confirmed the role of glycolysis in modulating insulin action in insulin resistance. Our results underscore the significance of genetics in shaping global signalling responses and their adaptability to environmental changes, emphasising the utility of studying biological diversity with phosphoproteomics to discover key regulatory mechanisms of complex traits.
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Affiliation(s)
- Julian van Gerwen
- Charles Perkins Centre, School of Life and Environmental Sciences, University of SydneySydneyAustralia
| | - Stewart WC Masson
- Charles Perkins Centre, School of Life and Environmental Sciences, University of SydneySydneyAustralia
| | - Harry B Cutler
- Charles Perkins Centre, School of Life and Environmental Sciences, University of SydneySydneyAustralia
| | - Alexis Diaz Vegas
- Charles Perkins Centre, School of Life and Environmental Sciences, University of SydneySydneyAustralia
| | - Meg Potter
- Charles Perkins Centre, School of Life and Environmental Sciences, University of SydneySydneyAustralia
| | - Jacqueline Stöckli
- Charles Perkins Centre, School of Life and Environmental Sciences, University of SydneySydneyAustralia
| | - Søren Madsen
- Charles Perkins Centre, School of Life and Environmental Sciences, University of SydneySydneyAustralia
| | - Marin E Nelson
- Charles Perkins Centre, School of Life and Environmental Sciences, University of SydneySydneyAustralia
| | - Sean J Humphrey
- Charles Perkins Centre, School of Life and Environmental Sciences, University of SydneySydneyAustralia
| | - David E James
- Charles Perkins Centre, School of Life and Environmental Sciences, University of SydneySydneyAustralia
- Faculty of Medicine and Health, University of SydneySydneyAustralia
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13
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Abbas M, Diallo A, Goodney G, Gaye A. Leveraging the transcriptome to further our understanding of GWAS findings: eQTLs associated with genes related to LDL and LDL subclasses, in a cohort of African Americans. Front Genet 2024; 15:1345541. [PMID: 38384714 PMCID: PMC10879560 DOI: 10.3389/fgene.2024.1345541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 01/16/2024] [Indexed: 02/23/2024] Open
Abstract
Background: GWAS discoveries often pose a significant challenge in terms of understanding their underlying mechanisms. Further research, such as an integration with expression quantitative trait locus (eQTL) analyses, are required to decipher the mechanisms connecting GWAS variants to phenotypes. An eQTL analysis was conducted on genes associated with low-density lipoprotein (LDL) cholesterol and its subclasses, with the aim of pinpointing genetic variants previously implicated in GWAS studies focused on lipid-related traits. Notably, the study cohort consisted of African Americans, a population characterized by a heightened prevalence of hypercholesterolemia. Methods: A comprehensive differential expression (DE) analysis was undertaken, with a dataset of 17,948 protein-coding mRNA transcripts extracted from the whole-blood transcriptomes of 416 samples to identify mRNA transcripts associated with LDL, with further granularity delineated between small LDL and large LDL subclasses. Subsequently, eQTL analysis was conducted with a subset of 242 samples for which whole-genome sequencing data were available to identify single-nucleotide polymorphisms (SNPs) associated with the LDL-related mRNA transcripts. Lastly, plausible functional connections were established between the identified eQTLs and genetic variants reported in the GWAS catalogue. Results: DE analysis revealed 1,048, 284, and 94 mRNA transcripts that exhibited differential expression in response to LDL, small LDL, and large LDL, respectively. The eQTL analysis identified a total of 9,950 significant SNP-mRNA associations involving 6,955 SNPs including a subset 101 SNPs previously documented in GWAS of LDL and LDL-related traits. Conclusion: Through comprehensive differential expression analysis, we identified numerous mRNA transcripts responsive to LDL, small LDL, and large LDL. Subsequent eQTL analysis revealed a rich landscape of eQTL-mRNA associations, including a subset of eQTL reported in GWAS studies of LDL and related traits. The study serves as a testament to the important role of integrative genomics in unraveling the enigmatic GWAS relationships between genetic variants and the complex fabric of human traits and diseases.
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Affiliation(s)
- Malak Abbas
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, United States
| | - Ana Diallo
- School of Nursing, Virginia Commonwealth University, Richmond, VA, United States
| | - Gabriel Goodney
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, United States
| | - Amadou Gaye
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, United States
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14
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Huang Y, Swarge BN, Roseboom W, Bleeker JD, Brul S, Setlow P, Kramer G. Integrative Metabolomics and Proteomics Allow the Global Intracellular Characterization of Bacillus subtilis Cells and Spores. J Proteome Res 2024; 23:596-608. [PMID: 38190553 PMCID: PMC10845140 DOI: 10.1021/acs.jproteome.3c00386] [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: 06/29/2023] [Revised: 12/01/2023] [Accepted: 12/08/2023] [Indexed: 01/10/2024]
Abstract
Reliable and comprehensive multi-omics analysis is essential for researchers to understand and explore complex biological systems more completely. Bacillus subtilis (B. subtilis) is a model organism for Gram-positive spore-forming bacteria, and in-depth insight into the physiology and molecular basis of spore formation and germination in this organism requires advanced multilayer molecular data sets generated from the same sample. In this study, we evaluated two monophasic methods for polar and nonpolar compound extraction (acetonitrile/methanol/water; isopropanol/water, and 60% ethanol) and two biphasic methods (chloroform/methanol/water, and methyl tert-butyl ether/methanol/water) on coefficients of variation of analytes, identified metabolite composition, and the quality of proteomics profiles. The 60% EtOH protocol proved to be the easiest in sample processing and was more amenable to automation. Collectively, we annotated 505 and 484 metabolites and identified 1665 and 1562 proteins in B. subtilis vegetative cells and spores, respectively. We also show differences between vegetative cells and spores from a multi-omics perspective and demonstrate that an integrative multi-omics analysis can be implemented from one sample using the 60% EtOH protocol. The results obtained by the 60% EtOH protocol provide comprehensive insight into differences in the metabolic and protein makeup of B. subtilis vegetative cells and spores.
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Affiliation(s)
- Yixuan Huang
- Laboratory
for Mass Spectrometry of Biomolecules, Swammerdam Institute for Life
Sciences, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands
- Molecular
Biology and Microbial Food Safety, Swammerdam Institute for Life Sciences, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands
| | - Bhagyashree N. Swarge
- Laboratory
for Mass Spectrometry of Biomolecules, Swammerdam Institute for Life
Sciences, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands
- Molecular
Biology and Microbial Food Safety, Swammerdam Institute for Life Sciences, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands
| | - Winfried Roseboom
- Laboratory
for Mass Spectrometry of Biomolecules, Swammerdam Institute for Life
Sciences, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands
| | - Jurre D. Bleeker
- Laboratory
for Mass Spectrometry of Biomolecules, Swammerdam Institute for Life
Sciences, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands
| | - Stanley Brul
- Molecular
Biology and Microbial Food Safety, Swammerdam Institute for Life Sciences, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands
| | - Peter Setlow
- Department
of Molecular Biology and Biophysics, UConn
Health, Farmington, Connecticut 06030-3305, United States
| | - Gertjan Kramer
- Laboratory
for Mass Spectrometry of Biomolecules, Swammerdam Institute for Life
Sciences, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands
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15
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Mersha TB. From Mendel to multi-omics: shifting paradigms. Eur J Hum Genet 2024; 32:139-142. [PMID: 37468578 PMCID: PMC10853174 DOI: 10.1038/s41431-023-01420-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2023] [Revised: 05/24/2023] [Accepted: 06/22/2023] [Indexed: 07/21/2023] Open
Affiliation(s)
- Tesfaye B Mersha
- Cincinnati Children's Hospital Medical Center, Department of Pediatrics, College of Medicine, University of Cincinnati, Cincinnati, OH, USA.
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16
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Varshney A, Manickam N, Orchard P, Tovar A, Zhang Z, Feng F, Erdos MR, Narisu N, Ventresca C, Nishino K, Rai V, Stringham HM, Jackson AU, Tamsen T, Gao C, Yang M, Koues OI, Welch JD, Burant CF, Williams LK, Jenkinson C, DeFronzo RA, Norton L, Saramies J, Lakka TA, Laakso M, Tuomilehto J, Mohlke KL, Kitzman JO, Koistinen HA, Liu J, Boehnke M, Collins FS, Scott LJ, Parker SCJ. Population-scale skeletal muscle single-nucleus multi-omic profiling reveals extensive context specific genetic regulation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.15.571696. [PMID: 38168419 PMCID: PMC10760134 DOI: 10.1101/2023.12.15.571696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
Skeletal muscle, the largest human organ by weight, is relevant to several polygenic metabolic traits and diseases including type 2 diabetes (T2D). Identifying genetic mechanisms underlying these traits requires pinpointing the relevant cell types, regulatory elements, target genes, and causal variants. Here, we used genetic multiplexing to generate population-scale single nucleus (sn) chromatin accessibility (snATAC-seq) and transcriptome (snRNA-seq) maps across 287 frozen human skeletal muscle biopsies representing 456,880 nuclei. We identified 13 cell types that collectively represented 983,155 ATAC summits. We integrated genetic variation to discover 6,866 expression quantitative trait loci (eQTL) and 100,928 chromatin accessibility QTL (caQTL) (5% FDR) across the five most abundant cell types, cataloging caQTL peaks that atlas-level snATAC maps often miss. We identified 1,973 eGenes colocalized with caQTL and used mediation analyses to construct causal directional maps for chromatin accessibility and gene expression. 3,378 genome-wide association study (GWAS) signals across 43 relevant traits colocalized with sn-e/caQTL, 52% in a cell-specific manner. 77% of GWAS signals colocalized with caQTL and not eQTL, highlighting the critical importance of population-scale chromatin profiling for GWAS functional studies. GWAS-caQTL colocalization showed distinct cell-specific regulatory paradigms. For example, a C2CD4A/B T2D GWAS signal colocalized with caQTL in muscle fibers and multiple chromatin loop models nominated VPS13C, a glucose uptake gene. Sequence of the caQTL peak overlapping caSNP rs7163757 showed allelic regulatory activity differences in a human myocyte cell line massively parallel reporter assay. These results illuminate the genetic regulatory architecture of human skeletal muscle at high-resolution epigenomic, transcriptomic, and cell state scales and serve as a template for population-scale multi-omic mapping in complex tissues and traits.
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Affiliation(s)
- Arushi Varshney
- Dept. of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Nandini Manickam
- Dept. of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Peter Orchard
- Dept. of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Adelaide Tovar
- Dept. of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Zhenhao Zhang
- Dept. of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Fan Feng
- Dept. of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Michael R Erdos
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Narisu Narisu
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Christa Ventresca
- Dept. of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
- Dept. of Human Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Kirsten Nishino
- Dept. of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Vivek Rai
- Dept. of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Heather M Stringham
- Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Anne U Jackson
- Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Tricia Tamsen
- Biomedical Research Core Facilities Advanced Genomics Core, University of Michigan, Ann Arbor, MI, USA
| | - Chao Gao
- Dept. of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Mao Yang
- Department of Internal Medicine, Center for Individualized and Genomic Medicine Research, Henry Ford Hospital, Detroit, MI, USA
| | - Olivia I Koues
- Biomedical Research Core Facilities Advanced Genomics Core, University of Michigan, Ann Arbor, MI, USA
| | - Joshua D Welch
- Dept. of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Charles F Burant
- Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - L Keoki Williams
- Department of Internal Medicine, Center for Individualized and Genomic Medicine Research, Henry Ford Hospital, Detroit, MI, USA
| | - Chris Jenkinson
- South Texas Diabetes and Obesity Research Institute, School of Medicine, University of Texas, Rio Grande Valley, TX, USA
| | - Ralph A DeFronzo
- Department of Medicine/Diabetes Division, University of Texas Health, San Antonio, TX, USA
| | - Luke Norton
- Department of Medicine/Diabetes Division, University of Texas Health, San Antonio, TX, USA
| | - Jouko Saramies
- Savitaipale Health Center, South Karelia Central Hospital, Lappeenranta, Finland
| | - Timo A Lakka
- Institute of Biomedicine, University of Eastern Finland, Kuopio, Finland
| | - Markku Laakso
- Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland
| | - Jaakko Tuomilehto
- Dept. of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
- Dept. of Public Health, University of Helsinki, Helsinki, Finland
- Diabetes Research Group, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Karen L Mohlke
- Dept. of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Jacob O Kitzman
- Dept. of Human Genetics, University of Michigan, Ann Arbor, MI, USA
- Dept. of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Heikki A Koistinen
- Dept. of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
- Department of Medicine, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Minerva Foundation Institute for Medical Research, Helsinki, Finland
| | - Jie Liu
- Dept. of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Michael Boehnke
- Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Francis S Collins
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Laura J Scott
- Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Stephen C J Parker
- Dept. of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
- Dept. of Human Genetics, University of Michigan, Ann Arbor, MI, USA
- Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
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17
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Biswas S, Hilser JR, Woodward NC, Wang Z, Gukasyan J, Nemet I, Schwartzman WS, Huang P, Han Y, Fouladian Z, Charugundla S, Spencer NJ, Pan C, Tang WW, Lusis AJ, Hazen SL, Hartiala JA, Allayee H. Effect of Genetic and Dietary Perturbation of Glycine Metabolism on Atherosclerosis in Humans and Mice. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.12.08.23299748. [PMID: 38168321 PMCID: PMC10760269 DOI: 10.1101/2023.12.08.23299748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
Objective Epidemiological and genetic studies have reported inverse associations between circulating glycine levels and risk of coronary artery disease (CAD). However, these findings have not been consistently observed in all studies. We sought to evaluate the causal relationship between circulating glycine levels and atherosclerosis using large-scale genetic analyses in humans and dietary supplementation experiments in mice. Methods Serum glycine levels were evaluated for association with prevalent and incident CAD in the UK Biobank. A multi-ancestry genome-wide association study (GWAS) meta-analysis was carried out to identify genetic determinants for circulating glycine levels, which were then used to evaluate the causal relationship between glycine and risk of CAD by Mendelian randomization (MR). A glycine feeding study was carried out with atherosclerosis-prone apolipoprotein E deficient (ApoE-/-) mice to determine the effects of increased circulating glycine levels on amino acid metabolism, metabolic traits, and aortic lesion formation. Results Among 105,718 subjects from the UK Biobank, elevated serum glycine levels were associated with significantly reduced risk of prevalent CAD (Quintile 5 vs. Quintile 1 OR=0.76, 95% CI 0.67-0.87; P<0.0001) and incident CAD (Quintile 5 vs. Quintile 1 HR=0.70, 95% CI 0.65-0.77; P<0.0001) in models adjusted for age, sex, ethnicity, anti-hypertensive and lipid-lowering medications, blood pressure, kidney function, and diabetes. A meta-analysis of 13 GWAS datasets (total n=230,947) identified 61 loci for circulating glycine levels, of which 26 were novel. MR analyses provided modest evidence that genetically elevated glycine levels were causally associated with reduced systolic blood pressure and risk of type 2 diabetes, but did provide evidence for an association with risk of CAD. Furthermore, glycine-supplementation in ApoE-/- mice did not alter cardiometabolic traits, inflammatory biomarkers, or development of atherosclerotic lesions. Conclusions Circulating glycine levels were inversely associated with risk of prevalent and incident CAD in a large population-based cohort. While substantially expanding the genetic architecture of circulating glycine levels, MR analyses and in vivo feeding studies in humans and mice, respectively, did not provide evidence that the clinical association of this amino acid with CAD represents a causal relationship, despite being associated with two correlated risk factors.
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Affiliation(s)
- Subarna Biswas
- Department of Surgery, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033
- Department of Population & Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033
| | - James R. Hilser
- Department of Population & Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033
- Department of Biochemistry & Molecular Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033
| | - Nicholas C. Woodward
- Department of Population & Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033
- Department of Biochemistry & Molecular Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033
| | - Zeneng Wang
- Department of Cardiovascular & Metabolic Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195
- Department of Center for Microbiome and Human Health, Cleveland Clinic, Cleveland, OH 44195
- Department of Cardiovascular Medicine, Heart, Vascular and Thoracic Institute, Cleveland Clinic, Cleveland, OH 44195
| | - Janet Gukasyan
- Department of Population & Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033
- Department of Biochemistry & Molecular Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033
| | - Ina Nemet
- Department of Cardiovascular & Metabolic Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195
- Department of Center for Microbiome and Human Health, Cleveland Clinic, Cleveland, OH 44195
| | - William S. Schwartzman
- Department of Population & Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033
- Department of Biochemistry & Molecular Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033
| | - Pin Huang
- Department of Population & Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033
- Department of Biochemistry & Molecular Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033
| | - Yi Han
- Department of Population & Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033
- Department of Biochemistry & Molecular Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033
| | - Zachary Fouladian
- Department of Medicine, Immunology, & Molecular Genetics, David Geffen School of Medicine of UCLA, Los Angeles, CA 90095
| | - Sarada Charugundla
- Department of Medicine, Immunology, & Molecular Genetics, David Geffen School of Medicine of UCLA, Los Angeles, CA 90095
| | - Neal J. Spencer
- Department of Population & Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033
- Department of Biochemistry & Molecular Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033
| | - Calvin Pan
- Department of Human Genetics, Immunology, & Molecular Genetics, David Geffen School of Medicine of UCLA, Los Angeles, CA 90095
| | - W.H. Wilson Tang
- Department of Cardiovascular & Metabolic Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195
- Department of Center for Microbiome and Human Health, Cleveland Clinic, Cleveland, OH 44195
- Department of Cardiovascular Medicine, Heart, Vascular and Thoracic Institute, Cleveland Clinic, Cleveland, OH 44195
| | - Aldons J. Lusis
- Department of Medicine, Immunology, & Molecular Genetics, David Geffen School of Medicine of UCLA, Los Angeles, CA 90095
- Department of Human Genetics, Immunology, & Molecular Genetics, David Geffen School of Medicine of UCLA, Los Angeles, CA 90095
- Department of Microbiology, Immunology, & Molecular Genetics, David Geffen School of Medicine of UCLA, Los Angeles, CA 90095
| | - Stanley L. Hazen
- Department of Cardiovascular & Metabolic Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195
- Department of Center for Microbiome and Human Health, Cleveland Clinic, Cleveland, OH 44195
- Department of Cardiovascular Medicine, Heart, Vascular and Thoracic Institute, Cleveland Clinic, Cleveland, OH 44195
| | - Jaana A. Hartiala
- Department of Population & Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033
| | - Hooman Allayee
- Department of Population & Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033
- Department of Biochemistry & Molecular Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033
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18
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Allayee H, Farber CR, Seldin MM, Williams EG, James DE, Lusis AJ. Systems genetics approaches for understanding complex traits with relevance for human disease. eLife 2023; 12:e91004. [PMID: 37962168 PMCID: PMC10645424 DOI: 10.7554/elife.91004] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 10/16/2023] [Indexed: 11/15/2023] Open
Abstract
Quantitative traits are often complex because of the contribution of many loci, with further complexity added by environmental factors. In medical research, systems genetics is a powerful approach for the study of complex traits, as it integrates intermediate phenotypes, such as RNA, protein, and metabolite levels, to understand molecular and physiological phenotypes linking discrete DNA sequence variation to complex clinical and physiological traits. The primary purpose of this review is to describe some of the resources and tools of systems genetics in humans and rodent models, so that researchers in many areas of biology and medicine can make use of the data.
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Affiliation(s)
- Hooman Allayee
- Departments of Population & Public Health Sciences, University of Southern CaliforniaLos AngelesUnited States
- Biochemistry & Molecular Medicine, Keck School of Medicine, University of Southern CaliforniaLos AngelesUnited States
| | - Charles R Farber
- Center for Public Health Genomics, University of Virginia School of MedicineCharlottesvilleUnited States
- Departments of Biochemistry & Molecular Genetics, University of Virginia School of MedicineCharlottesvilleUnited States
- Public Health Sciences, University of Virginia School of MedicineCharlottesvilleUnited States
| | - Marcus M Seldin
- Department of Biological Chemistry, University of California, IrvineIrvineUnited States
| | - Evan Graehl Williams
- Luxembourg Centre for Systems Biomedicine, University of LuxembourgLuxembourgLuxembourg
| | - David E James
- School of Life and Environmental Sciences, University of SydneyCamperdownAustralia
- Faculty of Medicine and Health, University of SydneyCamperdownAustralia
- Charles Perkins Centre, University of SydneyCamperdownAustralia
| | - Aldons J Lusis
- Departments of Human Genetics, University of California, Los AngelesLos AngelesUnited States
- Medicine, University of California, Los AngelesLos AngelesUnited States
- Microbiology, Immunology, & Molecular Genetics, David Geffen School of Medicine of UCLALos AngelesUnited States
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19
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Sabesan V, Dawoud M, Al-Mansoori A, Stephens BJ, Lavin AC, Lozano JM, Fomunung CK. Factors influencing physical therapy utilization after shoulder surgery: a retrospective review. JSES REVIEWS, REPORTS, AND TECHNIQUES 2023; 3:511-518. [PMID: 37928991 PMCID: PMC10625012 DOI: 10.1016/j.xrrt.2023.05.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/07/2023]
Abstract
Background Postoperative physical therapy (PT) is a cornerstone to achieve optimal patient outcomes. Access to postoperative PT can be limited by insurance type, coverage, and cost. With copayments (CP) for PT as high as $75 per visit, PT can be costprohibitive for patients. The purpose of this study was to evaluate factors affecting PT utilization among patients that underwent shoulder surgery. Methods A retrospective analysis was performed of 80 shoulder surgery patients with postoperative PT sessions attended at a single institution from 2017 to 2019. Patients were divided based on insurance type: private insurance (PI), and Medicare with or without supplemental insurance (MI), and CP or no copayment. Demographics, CP, total, and postoperative number of PT sessions utilized was collected and analyzed. Results The cohort had 53 females and an average age of 62. There was no significant difference between PI and MI at baseline other than surgery performed (P = .03), older MI group (69 years vs. 56 years: P < .01), and more females in PI group (76% vs. 55%; P = .05). There was no significant difference in the number of PT sessions between groups. The PI group was more likely to have a CP (P < .01). The CP group more often had PI and significantly more total PT visits (P = .05), while the no copayment group more often had Medicare (P < .01). CP was not independently associated with a change in the number of PT visits or total PT visits. Conclusions The utilization of PT after shoulder surgery was found to not be influenced by insurance type or CP as determined by the number of PT sessions attended. Further investigations are necessary to better understand the relationship between CP and different insurance types and develop effective strategies to increase access to PT for postoperative shoulder patients.
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Affiliation(s)
- Vani Sabesan
- HCA Florida JFK Hospital, Palm Beach Shoulder Service – Atlantis Orthopaedics, Palm Beach, FL, USA
- Cleveland Clinic Florida, Levitetz Department of Orthopedic Surgery, Weston, FL, USA
| | - Mirelle Dawoud
- Cleveland Clinic Florida, Levitetz Department of Orthopedic Surgery, Weston, FL, USA
| | - Ahmed Al-Mansoori
- Cleveland Clinic Florida, Levitetz Department of Orthopedic Surgery, Weston, FL, USA
| | - B. Joshua Stephens
- Nova Southeastern University, Dr. Kiran C. Patel College of Osteopathic Medicine, Davie, FL, USA
| | - Alessia C. Lavin
- HCA Florida JFK Hospital, Palm Beach Shoulder Service – Atlantis Orthopaedics, Palm Beach, FL, USA
| | - Juan Manuel Lozano
- Herbert Wertheim College of Medicine, Florida International University, Miami, FL, USA
| | - Clyde K. Fomunung
- HCA Florida JFK Hospital, Palm Beach Shoulder Service – Atlantis Orthopaedics, Palm Beach, FL, USA
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20
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Astrologo NCN, Gaudillo JD, Albia JR, Roxas-Villanueva RML. Genetic risk assessment based on association and prediction studies. Sci Rep 2023; 13:15230. [PMID: 37709797 PMCID: PMC10502006 DOI: 10.1038/s41598-023-41862-3] [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: 06/01/2023] [Accepted: 09/01/2023] [Indexed: 09/16/2023] Open
Abstract
The genetic basis of phenotypic emergence provides valuable information for assessing individual risk. While association studies have been pivotal in identifying genetic risk factors within a population, complementing it with insights derived from predictions studies that assess individual-level risk offers a more comprehensive approach to understanding phenotypic expression. In this study, we established personalized risk assessment models using single-nucleotide polymorphism (SNP) data from 200 Korean patients, of which 100 experienced hepatitis B surface antigen (HBsAg) seroclearance and 100 patients demonstrated high levels of HBsAg. The risk assessment models determined the predictive power of the following: (1) genome-wide association study (GWAS)-identified candidate biomarkers considered significant in a reference study and (2) machine learning (ML)-identified candidate biomarkers with the highest feature importance scores obtained by using random forest (RF). While utilizing all features yielded 64% model accuracy, using relevant biomarkers achieved higher model accuracies: 82% for 52 GWAS-identified candidate biomarkers, 71% for three GWAS-identified biomarkers, and 80% for 150 ML-identified candidate biomarkers. Findings highlight that the joint contributions of relevant biomarkers significantly influence phenotypic emergence. On the other hand, combining ML-identified candidate biomarkers into the pool of GWAS-identified candidate biomarkers resulted in the improved predictive accuracy of 90%, demonstrating the capability of ML as an auxiliary analysis to GWAS. Furthermore, some of the ML-identified candidate biomarkers were found to be linked with hepatocellular carcinoma (HCC), reinforcing previous claims that HCC can still occur despite the absence of HBsAg.
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Affiliation(s)
- Nicole Cathlene N Astrologo
- Data Analytics Research Laboratory (DARELab), Institute of Mathematical Sciences and Physics, University of the Philippines Los Baños, 4031, Los Baños, Laguna, Philippines
- Computational Interdisciplinary Research Laboratory (CINTERLabs), University of the Philippines Los Baños, 4031, Los Baños, Laguna, Philippines
| | - Joverlyn D Gaudillo
- Data Analytics Research Laboratory (DARELab), Institute of Mathematical Sciences and Physics, University of the Philippines Los Baños, 4031, Los Baños, Laguna, Philippines.
- Computational Interdisciplinary Research Laboratory (CINTERLabs), University of the Philippines Los Baños, 4031, Los Baños, Laguna, Philippines.
- Domingo AI Research Center (DARC Labs), 1606, Pasig, Philippines.
| | - Jason R Albia
- Domingo AI Research Center (DARC Labs), 1606, Pasig, Philippines
- Venn Biosciences Corporation Dba InterVenn Biosciences, Metro Manila, Pasig, Philippines
- Graduate School, University of the Philippines Los Baños, 4031, Los Baños, Laguna, Philippines
| | - Ranzivelle Marianne L Roxas-Villanueva
- Data Analytics Research Laboratory (DARELab), Institute of Mathematical Sciences and Physics, University of the Philippines Los Baños, 4031, Los Baños, Laguna, Philippines
- Computational Interdisciplinary Research Laboratory (CINTERLabs), University of the Philippines Los Baños, 4031, Los Baños, Laguna, Philippines
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21
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Liu Q, Peng Q, Zhang B, Tan Y. X-ray cross-complementing family: the bridge linking DNA damage repair and cancer. J Transl Med 2023; 21:602. [PMID: 37679817 PMCID: PMC10483876 DOI: 10.1186/s12967-023-04447-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 08/17/2023] [Indexed: 09/09/2023] Open
Abstract
Genomic instability is a common hallmark of human tumours. As a carrier of genetic information, DNA is constantly threatened by various damaging factors that, if not repaired in time, can affect the transmission of genetic information and lead to cellular carcinogenesis. In response to these threats, cells have evolved a range of DNA damage response mechanisms, including DNA damage repair, to maintain genomic stability. The X-ray repair cross-complementary gene family (XRCC) comprises an important class of DNA damage repair genes that encode proteins that play important roles in DNA single-strand breakage and DNA base damage repair. The dysfunction of the XRCC gene family is associated with the development of various tumours. In the context of tumours, mutations in XRCC and its aberrant expression, result in abnormal DNA damage repair, thus contributing to the malignant progression of tumour cells. In this review, we summarise the significant roles played by XRCC in diverse tumour types. In addition, we discuss the correlation between the XRCC family members and tumour therapeutic sensitivity.
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Affiliation(s)
- Qiang Liu
- Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, 283 Tongzipo Road, Changsha, 410013, Hunan, China
- NHC Key Laboratory of Human Stem Cell and Reproductive Engineering, School of Basic Medical Sciences, Institute of Reproductive and Stem Cell Engineering, Central South University, Changsha, 410078, Hunan, China
- Clinical Research Center for Reproduction and Genetics in Hunan Province, Reproductive and Genetic Hospital of CITIC-Xiangya, Changsha, 410008, Hunan, China
| | - Qiu Peng
- Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, 283 Tongzipo Road, Changsha, 410013, Hunan, China
| | - Bin Zhang
- Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, 283 Tongzipo Road, Changsha, 410013, Hunan, China.
- Department of Histology and Embryology, Xiangya School of Medicine, Central South University, Changsha, 410013, Hunan, China.
| | - Yueqiu Tan
- Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, 283 Tongzipo Road, Changsha, 410013, Hunan, China.
- NHC Key Laboratory of Human Stem Cell and Reproductive Engineering, School of Basic Medical Sciences, Institute of Reproductive and Stem Cell Engineering, Central South University, Changsha, 410078, Hunan, China.
- Clinical Research Center for Reproduction and Genetics in Hunan Province, Reproductive and Genetic Hospital of CITIC-Xiangya, Changsha, 410008, Hunan, China.
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22
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Reed JN, Huang J, Li Y, Ma L, Banka D, Wabitsch M, Wang T, Ding W, Björkegren JLM, Civelek M. Systems genetics analysis of human body fat distribution genes identifies Wnt signaling and mitochondrial activity in adipocytes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.06.556534. [PMID: 37732278 PMCID: PMC10508754 DOI: 10.1101/2023.09.06.556534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/22/2023]
Abstract
BACKGROUND Excess fat in the abdomen is a sexually dimorphic risk factor for cardio-metabolic disease. The relative storage between abdominal and lower-body subcutaneous adipose tissue depots is approximated by the waist-to-hip ratio adjusted for body mass index (WHRadjBMI). Genome-wide association studies (GWAS) identified 346 loci near 495 genes associated with WHRadjBMI. Most of these genes have unknown roles in fat distribution, but many are expressed and putatively act in adipose tissue. We aimed to identify novel sex- and depot-specific drivers of WHRadjBMI using a systems genetics approach. METHODS We used two independent cohorts of adipose tissue gene expression with 362 - 444 males and 147 - 219 females, primarily of European ancestry. We constructed sex- and depot- specific Bayesian networks to model the gene-gene interactions from 8,492 adipose tissue genes. Key driver analysis identified genes that, in silico and putatively in vitro, regulate many others, including the 495 WHRadjBMI GWAS genes. Key driver gene function was determined by perturbing their expression in human subcutaneous pre-adipocytes using lenti-virus or siRNA. RESULTS 51 - 119 key drivers in each network were replicated in both cohorts. We used single-cell expression data to select replicated key drivers expressed in adipocyte precursors and mature adipocytes, prioritized genes which have not been previously studied in adipose tissue, and used public human and mouse data to nominate 53 novel key driver genes (10 - 21 from each network) that may regulate fat distribution by altering adipocyte function. In other cell types, 23 of these genes are found in crucial adipocyte pathways: Wnt signaling or mitochondrial function. We selected seven genes whose expression is highly correlated with WHRadjBMI to further study their effects on adipogenesis/Wnt signaling (ANAPC2, PSME3, RSPO1, TYRO3) or mitochondrial function (C1QTNF3, MIGA1, PSME3, UBR1).Adipogenesis was inhibited in cells overexpressing ANAPC2 and RSPO1 compared to controls. RSPO1 results are consistent with a positive correlation between gene expression in the subcutaneous depot and WHRadjBMI, therefore lower relative storage in the subcutaneous depot. RSPO1 inhibited adipogenesis by increasing β-catenin activation and Wnt-related transcription, thus repressing PPARG and CEBPA. PSME3 overexpression led to more adipogenesis than controls. In differentiated adipocytes, MIGA1 and UBR1 downregulation led to mitochondrial dysfunction, with lower oxygen consumption than controls; MIGA1 knockdown also lowered UCP1 expression. SUMMARY ANAPC2, MIGA1, PSME3, RSPO1, and UBR1 affect adipocyte function and may drive body fat distribution.
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23
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Zhou Y, Li H, Liu X, Chi X, Gu Z, Cui B, Bergquist J, Wang B, Tian G, Yang C, Xu F, Mi J. The Combination of Quantitative Proteomics and Systems Genetics Analysis Reveals that PTN Is Associated with Sleep-Loss-Induced Cognitive Impairment. J Proteome Res 2023; 22:2936-2949. [PMID: 37611228 DOI: 10.1021/acs.jproteome.3c00269] [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] [Indexed: 08/25/2023]
Abstract
Sleep loss is associated with cognitive dysfunction. However, the detailed mechanisms remain unclear. In this study, we established a para-chlorophenylalanine (PCPA)-induced insomniac mouse model with impaired cognitive function. Mass-spectrometry-based proteomics showed that the expression of 164 proteins was significantly altered in the hippocampus of the PCPA mice. To identify critical regulators among the potential markers, a transcriptome-wide association screening was performed in the BXD mice panel. Among the candidates, the expression of pleiotrophin (Ptn) was significantly associated with cognitive functions, indicating that Ptn-mediates sleep-loss-induced cognitive impairment. Gene co-expression analysis further revealed the potential mechanism by which Ptn mediates insomnia-induced cognitive impairment via the MAPK signaling pathway; that is, the decreased secretion of Ptn induced by insomnia leads to reduced binding to Ptprz1 on the postsynaptic membrane with the activation of the MAPK pathway via Fos and Nr4a1, further leading to the apoptosis of neurons. In addition, Ptn is genetically trans-regulated in the mouse hippocampus and implicated in neurodegenerative diseases in human genome-wide association studies. Our study provides a novel biomarker for insomnia-induced cognitive impairment and a new strategy for seeking neurological biomarkers by the integration of proteomics and systems genetics.
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Affiliation(s)
- Yutong Zhou
- Shandong Technology Innovation Center of Molecular Targeting and Intelligent Diagnosis and Treatment, Binzhou Medical University, Yantai, Shandong 264003, China
| | - Hui Li
- Shandong Technology Innovation Center of Molecular Targeting and Intelligent Diagnosis and Treatment, Binzhou Medical University, Yantai, Shandong 264003, China
| | - Xiaoya Liu
- Shandong Technology Innovation Center of Molecular Targeting and Intelligent Diagnosis and Treatment, Binzhou Medical University, Yantai, Shandong 264003, China
| | - Xiaodong Chi
- Shandong Technology Innovation Center of Molecular Targeting and Intelligent Diagnosis and Treatment, Binzhou Medical University, Yantai, Shandong 264003, China
| | - Zhaoxi Gu
- Shandong Technology Innovation Center of Molecular Targeting and Intelligent Diagnosis and Treatment, Binzhou Medical University, Yantai, Shandong 264003, China
| | - Binsen Cui
- Shandong Technology Innovation Center of Molecular Targeting and Intelligent Diagnosis and Treatment, Binzhou Medical University, Yantai, Shandong 264003, China
| | - Jonas Bergquist
- Shandong Technology Innovation Center of Molecular Targeting and Intelligent Diagnosis and Treatment, Binzhou Medical University, Yantai, Shandong 264003, China
- Department of Chemistry-BMC, Analytical Chemistry and Neurochemistry, Uppsala University, Uppsala 75124, Sweden
| | - Binsheng Wang
- Shandong Technology Innovation Center of Molecular Targeting and Intelligent Diagnosis and Treatment, Binzhou Medical University, Yantai, Shandong 264003, China
| | - Geng Tian
- Shandong Technology Innovation Center of Molecular Targeting and Intelligent Diagnosis and Treatment, Binzhou Medical University, Yantai, Shandong 264003, China
| | - Chunhua Yang
- Shandong Technology Innovation Center of Molecular Targeting and Intelligent Diagnosis and Treatment, Binzhou Medical University, Yantai, Shandong 264003, China
| | - Fuyi Xu
- Shandong Technology Innovation Center of Molecular Targeting and Intelligent Diagnosis and Treatment, Binzhou Medical University, Yantai, Shandong 264003, China
| | - Jia Mi
- Shandong Technology Innovation Center of Molecular Targeting and Intelligent Diagnosis and Treatment, Binzhou Medical University, Yantai, Shandong 264003, China
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24
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Holterhus PM, Kulle A, Busch H, Spielmann M. Classic genetic and hormonal switches during fetal sex development and beyond. MED GENET-BERLIN 2023; 35:163-171. [PMID: 38840820 PMCID: PMC10842585 DOI: 10.1515/medgen-2023-2036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/07/2024]
Abstract
Critical genetic and hormonal switches characterize fetal sex development in humans. They are decisive for gonadal sex determination and subsequent differentiation of the genital and somatic sex phenotype. Only at the first glace these switches seem to behave like the dual 0 and 1 system in computer sciences and lead invariably to either typically male or female phenotypes. More recent data indicate that this model is insufficient. In addition, in case of distinct mutations, many of these switches may act variably, causing a functional continuum of alterations of gene functions and -dosages, enzymatic activities, sex hormone levels, and sex hormone sensitivity, giving rise to a broad clinical spectrum of biological differences of sex development (DSD) and potentially diversity of genital and somatic sex phenotypes. The gonadal anlage is initially a bipotential organ that can develop either into a testis or an ovary. Sex-determining region Y (SRY) is the most important upstream switch of gonadal sex determination inducing SOX9 further downstream, leading to testicular Sertoli cell differentiation and the repression of ovarian pathways. If SRY is absent (virtually "switched off"), e. g., in 46,XX females, RSPO1, WNT4, FOXL2, and other factors repress the male pathway and promote ovarian development. Testosterone and its more potent derivative, dihydrotestosterone (DHT) as well as AMH, are the most important upstream hormonal switches in phenotypic sex differentiation. Masculinization of the genitalia, i. e., external genital midline fusion forming the scrotum, growth of the genital tubercle, and Wolffian duct development, occurs in response to testosterone synthesized by steroidogenic cells in the testis. Müllerian ducts will not develop into a uterus and fallopian tubes in males due to Anti-Müllerian-Hormone (AMH) produced by the Sertoli cells. The functionality of these two hormone-dependent switches is ensured by their corresponding receptors, the intracellular androgen receptor (AR) and the transmembrane AMH type II receptor. The absence of high testosterone and high AMH is crucial for anatomically female genital development during fetal life. Recent technological advances, including single-cell and spatial transcriptomics, will likely shed more light on the nature of these molecular switches.
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Affiliation(s)
- Paul-Martin Holterhus
- Christian-Albrechts University of Kiel (CAU)Pediatric Endocrinology and Diabetes, Department of Pediatrics IKielGermany
| | - Alexandra Kulle
- Christian-Albrechts University of Kiel (CAU)Pediatric Endocrinology and Diabetes, Department of Pediatrics IKielGermany
| | - Hauke Busch
- University of LübeckMedical Systems Biology Group, Lübeck Institute of Experimental Dermatology (LIED)Ratzeburger Allee 16023562LübeckGermany
| | - Malte Spielmann
- University of LübeckInstitute of Human GeneticsLübeckGermany
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Fang Y, Wang D, Xiao L, Quan M, Qi W, Song F, Zhou J, Liu X, Qin S, Du Q, Liu Q, El-Kassaby YA, Zhang D. Allelic variation in transcription factor PtoWRKY68 contributes to drought tolerance in Populus. PLANT PHYSIOLOGY 2023; 193:736-755. [PMID: 37247391 PMCID: PMC10469405 DOI: 10.1093/plphys/kiad315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 04/21/2023] [Accepted: 04/30/2023] [Indexed: 05/31/2023]
Abstract
Drought stress limits woody species productivity and influences tree distribution. However, dissecting the molecular mechanisms that underpin drought responses in forest trees can be challenging due to trait complexity. Here, using a panel of 300 Chinese white poplar (Populus tomentosa) accessions collected from different geographical climatic regions in China, we performed a genome-wide association study (GWAS) on seven drought-related traits and identified PtoWRKY68 as a candidate gene involved in the response to drought stress. A 12-bp insertion and/or deletion and three nonsynonymous variants in the PtoWRKY68 coding sequence categorized natural populations of P. tomentosa into two haplotype groups, PtoWRKY68hap1 and PtoWRKY68hap2. The allelic variation in these two PtoWRKY68 haplotypes conferred differential transcriptional regulatory activities and binding to the promoters of downstream abscisic acid (ABA) efflux and signaling genes. Overexpression of PtoWRKY68hap1 and PtoWRKY68hap2 in Arabidopsis (Arabidopsis thaliana) ameliorated the drought tolerance of two transgenic lines and increased ABA content by 42.7% and 14.3% compared to wild-type plants, respectively. Notably, PtoWRKY68hap1 (associated with drought tolerance) is ubiquitous in accessions in water-deficient environments, whereas the drought-sensitive allele PtoWRKY68hap2 is widely distributed in well-watered regions, consistent with the trends in local precipitation, suggesting that these alleles correspond to geographical adaptation in Populus. Moreover, quantitative trait loci analysis and an electrophoretic mobility shift assay showed that SHORT VEGETATIVE PHASE (PtoSVP.3) positively regulates the expression of PtoWRKY68 under drought stress. We propose a drought tolerance regulatory module in which PtoWRKY68 modulates ABA signaling and accumulation, providing insight into the genetic basis of drought tolerance in trees. Our findings will facilitate molecular breeding to improve the drought tolerance of forest trees.
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Affiliation(s)
- Yuanyuan Fang
- National Engineering Research Center of Tree Breeding and Ecological Restoration, College of Biological Sciences and Technology, Beijing Forestry University, No. 35, Qinghua East Road, Beijing 100083, People’s Republic of China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, No. 35, Qinghua East Road, Beijing 100083, People’s Republic of China
| | - Dan Wang
- National Engineering Research Center of Tree Breeding and Ecological Restoration, College of Biological Sciences and Technology, Beijing Forestry University, No. 35, Qinghua East Road, Beijing 100083, People’s Republic of China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, No. 35, Qinghua East Road, Beijing 100083, People’s Republic of China
| | - Liang Xiao
- National Engineering Research Center of Tree Breeding and Ecological Restoration, College of Biological Sciences and Technology, Beijing Forestry University, No. 35, Qinghua East Road, Beijing 100083, People’s Republic of China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, No. 35, Qinghua East Road, Beijing 100083, People’s Republic of China
| | - Mingyang Quan
- National Engineering Research Center of Tree Breeding and Ecological Restoration, College of Biological Sciences and Technology, Beijing Forestry University, No. 35, Qinghua East Road, Beijing 100083, People’s Republic of China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, No. 35, Qinghua East Road, Beijing 100083, People’s Republic of China
| | - Weina Qi
- National Engineering Research Center of Tree Breeding and Ecological Restoration, College of Biological Sciences and Technology, Beijing Forestry University, No. 35, Qinghua East Road, Beijing 100083, People’s Republic of China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, No. 35, Qinghua East Road, Beijing 100083, People’s Republic of China
| | - Fangyuan Song
- National Engineering Research Center of Tree Breeding and Ecological Restoration, College of Biological Sciences and Technology, Beijing Forestry University, No. 35, Qinghua East Road, Beijing 100083, People’s Republic of China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, No. 35, Qinghua East Road, Beijing 100083, People’s Republic of China
| | - Jiaxuan Zhou
- National Engineering Research Center of Tree Breeding and Ecological Restoration, College of Biological Sciences and Technology, Beijing Forestry University, No. 35, Qinghua East Road, Beijing 100083, People’s Republic of China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, No. 35, Qinghua East Road, Beijing 100083, People’s Republic of China
| | - Xin Liu
- Institute of Forestry and Pomology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100093, People’s Republic of China
| | - Shitong Qin
- National Engineering Research Center of Tree Breeding and Ecological Restoration, College of Biological Sciences and Technology, Beijing Forestry University, No. 35, Qinghua East Road, Beijing 100083, People’s Republic of China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, No. 35, Qinghua East Road, Beijing 100083, People’s Republic of China
| | - Qingzhang Du
- National Engineering Research Center of Tree Breeding and Ecological Restoration, College of Biological Sciences and Technology, Beijing Forestry University, No. 35, Qinghua East Road, Beijing 100083, People’s Republic of China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, No. 35, Qinghua East Road, Beijing 100083, People’s Republic of China
| | - Qing Liu
- The Institute of Agriculture and Food Research, CSIRO Agriculture and Food, Black Mountain, Canberra ACT 2601, Australia
| | - Yousry A El-Kassaby
- Department of Forest and Conservation Sciences, Faculty of Forestry, Forest Sciences Centre, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
| | - Deqiang Zhang
- National Engineering Research Center of Tree Breeding and Ecological Restoration, College of Biological Sciences and Technology, Beijing Forestry University, No. 35, Qinghua East Road, Beijing 100083, People’s Republic of China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, No. 35, Qinghua East Road, Beijing 100083, People’s Republic of China
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26
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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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27
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Lin KZ, Zhang NR. Quantifying common and distinct information in single-cell multimodal data with Tilted Canonical Correlation Analysis. Proc Natl Acad Sci U S A 2023; 120:e2303647120. [PMID: 37523521 PMCID: PMC10410705 DOI: 10.1073/pnas.2303647120] [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: 03/06/2023] [Accepted: 06/24/2023] [Indexed: 08/02/2023] Open
Abstract
Multimodal single-cell technologies profile multiple modalities for each cell simultaneously, enabling a more thorough characterization of cell populations. Existing dimension-reduction methods for multimodal data capture the "union of information," producing a lower-dimensional embedding that combines the information across modalities. While these tools are useful, we focus on a fundamentally different task of separating and quantifying the information among cells that is shared between the two modalities as well as unique to only one modality. Hence, we develop Tilted Canonical Correlation Analysis (Tilted-CCA), a method that decomposes a paired multimodal dataset into three lower-dimensional embeddings-one embedding captures the "intersection of information," representing the geometric relations among the cells that is common to both modalities, while the remaining two embeddings capture the "distinct information for a modality," representing the modality-specific geometric relations. We analyze single-cell multimodal datasets sequencing RNA along surface antibodies (i.e., CITE-seq) as well as RNA alongside chromatin accessibility (i.e., 10x) for blood cells and developing neurons via Tilted-CCA. These analyses show that Tilted-CCA enables meaningful visualization and quantification of the cross-modal information. Finally, Tilted-CCA's framework allows us to perform two specific downstream analyses. First, for single-cell datasets that simultaneously profile transcriptome and surface antibody markers, we show that Tilted-CCA helps design the target antibody panel to complement the transcriptome best. Second, for developmental single-cell datasets that simultaneously profile transcriptome and chromatin accessibility, we show that Tilted-CCA helps identify development-informative genes and distinguish between transient versus terminal cell types.
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Affiliation(s)
- Kevin Z. Lin
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA19104
| | - Nancy R. Zhang
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA19104
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28
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Gómez-Vecino A, Corchado-Cobos R, Blanco-Gómez A, García-Sancha N, Castillo-Lluva S, Martín-García A, Mendiburu-Eliçabe M, Prieto C, Ruiz-Pinto S, Pita G, Velasco-Ruiz A, Patino-Alonso C, Galindo-Villardón P, Vera-Pedrosa ML, Jalife J, Mao JH, Macías de Plasencia G, Castellanos-Martín A, Sáez-Freire MDM, Fraile-Martín S, Rodrigues-Teixeira T, García-Macías C, Galvis-Jiménez JM, García-Sánchez A, Isidoro-García M, Fuentes M, García-Cenador MB, García-Criado FJ, García-Hernández JL, Hernández-García MÁ, Cruz-Hernández JJ, Rodríguez-Sánchez CA, García-Sancho AM, Pérez-López E, Pérez-Martínez A, Gutiérrez-Larraya F, Cartón AJ, García-Sáenz JÁ, Patiño-García A, Martín M, Alonso-Gordoa T, Vulsteke C, Croes L, Hatse S, Van Brussel T, Lambrechts D, Wildiers H, Chang H, Holgado-Madruga M, González-Neira A, Sánchez PL, Pérez Losada J. Intermediate Molecular Phenotypes to Identify Genetic Markers of Anthracycline-Induced Cardiotoxicity Risk. Cells 2023; 12:1956. [PMID: 37566035 PMCID: PMC10417374 DOI: 10.3390/cells12151956] [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: 04/17/2023] [Revised: 07/14/2023] [Accepted: 07/21/2023] [Indexed: 08/12/2023] Open
Abstract
Cardiotoxicity due to anthracyclines (CDA) affects cancer patients, but we cannot predict who may suffer from this complication. CDA is a complex trait with a polygenic component that is mainly unidentified. We propose that levels of intermediate molecular phenotypes (IMPs) in the myocardium associated with histopathological damage could explain CDA susceptibility, so variants of genes encoding these IMPs could identify patients susceptible to this complication. Thus, a genetically heterogeneous cohort of mice (n = 165) generated by backcrossing were treated with doxorubicin and docetaxel. We quantified heart fibrosis using an Ariol slide scanner and intramyocardial levels of IMPs using multiplex bead arrays and QPCR. We identified quantitative trait loci linked to IMPs (ipQTLs) and cdaQTLs via linkage analysis. In three cancer patient cohorts, CDA was quantified using echocardiography or Cardiac Magnetic Resonance. CDA behaves as a complex trait in the mouse cohort. IMP levels in the myocardium were associated with CDA. ipQTLs integrated into genetic models with cdaQTLs account for more CDA phenotypic variation than that explained by cda-QTLs alone. Allelic forms of genes encoding IMPs associated with CDA in mice, including AKT1, MAPK14, MAPK8, STAT3, CAS3, and TP53, are genetic determinants of CDA in patients. Two genetic risk scores for pediatric patients (n = 71) and women with breast cancer (n = 420) were generated using machine-learning Least Absolute Shrinkage and Selection Operator (LASSO) regression. Thus, IMPs associated with heart damage identify genetic markers of CDA risk, thereby allowing more personalized patient management.
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Affiliation(s)
- Aurora Gómez-Vecino
- Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca/CSIC, 37007 Salamanca, Spain; (A.G.-V.); (R.C.-C.); (A.B.-G.); (N.G.-S.); (M.M.-E.); (A.C.-M.); (M.d.M.S.-F.); (J.M.G.-J.); (M.F.); (J.L.G.-H.); (J.J.C.-H.); (C.A.R.-S.); (A.M.G.-S.); (E.P.-L.)
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), 37007 Salamanca, Spain; (A.M.-G.); (C.P.-A.); (P.G.-V.); (G.M.d.P.); (A.G.-S.); (M.I.-G.); (M.B.G.-C.); (F.J.G.-C.)
| | - Roberto Corchado-Cobos
- Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca/CSIC, 37007 Salamanca, Spain; (A.G.-V.); (R.C.-C.); (A.B.-G.); (N.G.-S.); (M.M.-E.); (A.C.-M.); (M.d.M.S.-F.); (J.M.G.-J.); (M.F.); (J.L.G.-H.); (J.J.C.-H.); (C.A.R.-S.); (A.M.G.-S.); (E.P.-L.)
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), 37007 Salamanca, Spain; (A.M.-G.); (C.P.-A.); (P.G.-V.); (G.M.d.P.); (A.G.-S.); (M.I.-G.); (M.B.G.-C.); (F.J.G.-C.)
| | - Adrián Blanco-Gómez
- Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca/CSIC, 37007 Salamanca, Spain; (A.G.-V.); (R.C.-C.); (A.B.-G.); (N.G.-S.); (M.M.-E.); (A.C.-M.); (M.d.M.S.-F.); (J.M.G.-J.); (M.F.); (J.L.G.-H.); (J.J.C.-H.); (C.A.R.-S.); (A.M.G.-S.); (E.P.-L.)
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), 37007 Salamanca, Spain; (A.M.-G.); (C.P.-A.); (P.G.-V.); (G.M.d.P.); (A.G.-S.); (M.I.-G.); (M.B.G.-C.); (F.J.G.-C.)
| | - Natalia García-Sancha
- Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca/CSIC, 37007 Salamanca, Spain; (A.G.-V.); (R.C.-C.); (A.B.-G.); (N.G.-S.); (M.M.-E.); (A.C.-M.); (M.d.M.S.-F.); (J.M.G.-J.); (M.F.); (J.L.G.-H.); (J.J.C.-H.); (C.A.R.-S.); (A.M.G.-S.); (E.P.-L.)
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), 37007 Salamanca, Spain; (A.M.-G.); (C.P.-A.); (P.G.-V.); (G.M.d.P.); (A.G.-S.); (M.I.-G.); (M.B.G.-C.); (F.J.G.-C.)
| | - Sonia Castillo-Lluva
- Departamento de Bioquímica y Biología Molecular, Facultad de Ciencias Químicas, Universidad Complutense, 28040 Madrid, Spain;
- Instituto de Investigaciones Sanitarias San Carlos (IdISSC), 24040 Madrid, Spain
| | - Ana Martín-García
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), 37007 Salamanca, Spain; (A.M.-G.); (C.P.-A.); (P.G.-V.); (G.M.d.P.); (A.G.-S.); (M.I.-G.); (M.B.G.-C.); (F.J.G.-C.)
- Servicio de Cardiología, Hospital Universitario de Salamanca, Universidad de Salamanca (CIBER.CV), 37007 Salamanca, Spain
| | - Marina Mendiburu-Eliçabe
- Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca/CSIC, 37007 Salamanca, Spain; (A.G.-V.); (R.C.-C.); (A.B.-G.); (N.G.-S.); (M.M.-E.); (A.C.-M.); (M.d.M.S.-F.); (J.M.G.-J.); (M.F.); (J.L.G.-H.); (J.J.C.-H.); (C.A.R.-S.); (A.M.G.-S.); (E.P.-L.)
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), 37007 Salamanca, Spain; (A.M.-G.); (C.P.-A.); (P.G.-V.); (G.M.d.P.); (A.G.-S.); (M.I.-G.); (M.B.G.-C.); (F.J.G.-C.)
| | - Carlos Prieto
- Servicio de Bioinformática, Nucleus, Universidad de Salamanca, 37007 Salamanca, Spain;
| | - Sara Ruiz-Pinto
- Human Genotyping Unit-CeGen, Human Cancer Genetics Programme, Spanish National Cancer Research Centre (CNIO), 28029 Madrid, Spain; (S.R.-P.); (G.P.); (A.V.-R.)
| | - Guillermo Pita
- Human Genotyping Unit-CeGen, Human Cancer Genetics Programme, Spanish National Cancer Research Centre (CNIO), 28029 Madrid, Spain; (S.R.-P.); (G.P.); (A.V.-R.)
| | - Alejandro Velasco-Ruiz
- Human Genotyping Unit-CeGen, Human Cancer Genetics Programme, Spanish National Cancer Research Centre (CNIO), 28029 Madrid, Spain; (S.R.-P.); (G.P.); (A.V.-R.)
| | - Carmen Patino-Alonso
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), 37007 Salamanca, Spain; (A.M.-G.); (C.P.-A.); (P.G.-V.); (G.M.d.P.); (A.G.-S.); (M.I.-G.); (M.B.G.-C.); (F.J.G.-C.)
- Departamento de Estadística, Universidad de Salamanca, 37007 Salamanca, Spain
| | - Purificación Galindo-Villardón
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), 37007 Salamanca, Spain; (A.M.-G.); (C.P.-A.); (P.G.-V.); (G.M.d.P.); (A.G.-S.); (M.I.-G.); (M.B.G.-C.); (F.J.G.-C.)
- Departamento de Estadística, Universidad de Salamanca, 37007 Salamanca, Spain
- Escuela Superior Politécnica del Litoral, ESPOL, Centro de Estudios e Investigaciones Estadísticas, Campus Gustavo Galindo, Km. 30.5 Via Perimetral, Guayaquil P.O. Box 09-01-5863, Ecuador
| | | | - José Jalife
- Centro Nacional de Investigaciones Cardiovasculares (CNIC) Carlos III, 28029 Madrid, Spain; (M.L.V.-P.); (J.J.)
| | - Jian-Hua Mao
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA;
- Berkeley Biomedical Data Science Center, Lawrence Berkeley National Laboratory, Berkeley, CA 92720, USA
| | - Guillermo Macías de Plasencia
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), 37007 Salamanca, Spain; (A.M.-G.); (C.P.-A.); (P.G.-V.); (G.M.d.P.); (A.G.-S.); (M.I.-G.); (M.B.G.-C.); (F.J.G.-C.)
- Servicio de Cardiología, Hospital Universitario de Salamanca, Universidad de Salamanca (CIBER.CV), 37007 Salamanca, Spain
| | - Andrés Castellanos-Martín
- Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca/CSIC, 37007 Salamanca, Spain; (A.G.-V.); (R.C.-C.); (A.B.-G.); (N.G.-S.); (M.M.-E.); (A.C.-M.); (M.d.M.S.-F.); (J.M.G.-J.); (M.F.); (J.L.G.-H.); (J.J.C.-H.); (C.A.R.-S.); (A.M.G.-S.); (E.P.-L.)
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), 37007 Salamanca, Spain; (A.M.-G.); (C.P.-A.); (P.G.-V.); (G.M.d.P.); (A.G.-S.); (M.I.-G.); (M.B.G.-C.); (F.J.G.-C.)
| | - María del Mar Sáez-Freire
- Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca/CSIC, 37007 Salamanca, Spain; (A.G.-V.); (R.C.-C.); (A.B.-G.); (N.G.-S.); (M.M.-E.); (A.C.-M.); (M.d.M.S.-F.); (J.M.G.-J.); (M.F.); (J.L.G.-H.); (J.J.C.-H.); (C.A.R.-S.); (A.M.G.-S.); (E.P.-L.)
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), 37007 Salamanca, Spain; (A.M.-G.); (C.P.-A.); (P.G.-V.); (G.M.d.P.); (A.G.-S.); (M.I.-G.); (M.B.G.-C.); (F.J.G.-C.)
| | - Susana Fraile-Martín
- Servicio de Patología Molecular Comparada, Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca, 37007 Salamanca, Spain; (S.F.-M.); (T.R.-T.); (C.G.-M.)
| | - Telmo Rodrigues-Teixeira
- Servicio de Patología Molecular Comparada, Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca, 37007 Salamanca, Spain; (S.F.-M.); (T.R.-T.); (C.G.-M.)
| | - Carmen García-Macías
- Servicio de Patología Molecular Comparada, Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca, 37007 Salamanca, Spain; (S.F.-M.); (T.R.-T.); (C.G.-M.)
| | - Julie Milena Galvis-Jiménez
- Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca/CSIC, 37007 Salamanca, Spain; (A.G.-V.); (R.C.-C.); (A.B.-G.); (N.G.-S.); (M.M.-E.); (A.C.-M.); (M.d.M.S.-F.); (J.M.G.-J.); (M.F.); (J.L.G.-H.); (J.J.C.-H.); (C.A.R.-S.); (A.M.G.-S.); (E.P.-L.)
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), 37007 Salamanca, Spain; (A.M.-G.); (C.P.-A.); (P.G.-V.); (G.M.d.P.); (A.G.-S.); (M.I.-G.); (M.B.G.-C.); (F.J.G.-C.)
- Instituto Nacional de Cancerología de Colombia, Bogotá 111511-110411001, Colombia
| | - Asunción García-Sánchez
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), 37007 Salamanca, Spain; (A.M.-G.); (C.P.-A.); (P.G.-V.); (G.M.d.P.); (A.G.-S.); (M.I.-G.); (M.B.G.-C.); (F.J.G.-C.)
- Servicio de Bioquímica Clínica, Hospital Universitario de Salamanca, 37007 Salamanca, Spain
| | - María Isidoro-García
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), 37007 Salamanca, Spain; (A.M.-G.); (C.P.-A.); (P.G.-V.); (G.M.d.P.); (A.G.-S.); (M.I.-G.); (M.B.G.-C.); (F.J.G.-C.)
- Servicio de Bioquímica Clínica, Hospital Universitario de Salamanca, 37007 Salamanca, Spain
- Departamento de Medicina, Universidad de Salamanca, 37007 Salamanca, Spain
| | - Manuel Fuentes
- Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca/CSIC, 37007 Salamanca, Spain; (A.G.-V.); (R.C.-C.); (A.B.-G.); (N.G.-S.); (M.M.-E.); (A.C.-M.); (M.d.M.S.-F.); (J.M.G.-J.); (M.F.); (J.L.G.-H.); (J.J.C.-H.); (C.A.R.-S.); (A.M.G.-S.); (E.P.-L.)
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), 37007 Salamanca, Spain; (A.M.-G.); (C.P.-A.); (P.G.-V.); (G.M.d.P.); (A.G.-S.); (M.I.-G.); (M.B.G.-C.); (F.J.G.-C.)
- Departamento de Medicina, Universidad de Salamanca, 37007 Salamanca, Spain
- Unidad de Proteómica y Servicio General de Citometría de Flujo, Nucleus, Universidad de Salamanca, 37007 Salamanca, Spain
| | - María Begoña García-Cenador
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), 37007 Salamanca, Spain; (A.M.-G.); (C.P.-A.); (P.G.-V.); (G.M.d.P.); (A.G.-S.); (M.I.-G.); (M.B.G.-C.); (F.J.G.-C.)
- Departamento de Cirugía, Universidad de Salamanca, 37007 Salamanca, Spain
| | - Francisco Javier García-Criado
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), 37007 Salamanca, Spain; (A.M.-G.); (C.P.-A.); (P.G.-V.); (G.M.d.P.); (A.G.-S.); (M.I.-G.); (M.B.G.-C.); (F.J.G.-C.)
- Departamento de Cirugía, Universidad de Salamanca, 37007 Salamanca, Spain
| | - Juan Luis García-Hernández
- Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca/CSIC, 37007 Salamanca, Spain; (A.G.-V.); (R.C.-C.); (A.B.-G.); (N.G.-S.); (M.M.-E.); (A.C.-M.); (M.d.M.S.-F.); (J.M.G.-J.); (M.F.); (J.L.G.-H.); (J.J.C.-H.); (C.A.R.-S.); (A.M.G.-S.); (E.P.-L.)
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), 37007 Salamanca, Spain; (A.M.-G.); (C.P.-A.); (P.G.-V.); (G.M.d.P.); (A.G.-S.); (M.I.-G.); (M.B.G.-C.); (F.J.G.-C.)
| | | | - Juan Jesús Cruz-Hernández
- Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca/CSIC, 37007 Salamanca, Spain; (A.G.-V.); (R.C.-C.); (A.B.-G.); (N.G.-S.); (M.M.-E.); (A.C.-M.); (M.d.M.S.-F.); (J.M.G.-J.); (M.F.); (J.L.G.-H.); (J.J.C.-H.); (C.A.R.-S.); (A.M.G.-S.); (E.P.-L.)
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), 37007 Salamanca, Spain; (A.M.-G.); (C.P.-A.); (P.G.-V.); (G.M.d.P.); (A.G.-S.); (M.I.-G.); (M.B.G.-C.); (F.J.G.-C.)
- Departamento de Medicina, Universidad de Salamanca, 37007 Salamanca, Spain
- Servicio de Oncología, Hospital Universitario de Salamanca, 37007 Salamanca, Spain
| | - César Augusto Rodríguez-Sánchez
- Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca/CSIC, 37007 Salamanca, Spain; (A.G.-V.); (R.C.-C.); (A.B.-G.); (N.G.-S.); (M.M.-E.); (A.C.-M.); (M.d.M.S.-F.); (J.M.G.-J.); (M.F.); (J.L.G.-H.); (J.J.C.-H.); (C.A.R.-S.); (A.M.G.-S.); (E.P.-L.)
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), 37007 Salamanca, Spain; (A.M.-G.); (C.P.-A.); (P.G.-V.); (G.M.d.P.); (A.G.-S.); (M.I.-G.); (M.B.G.-C.); (F.J.G.-C.)
- Departamento de Medicina, Universidad de Salamanca, 37007 Salamanca, Spain
- Servicio de Oncología, Hospital Universitario de Salamanca, 37007 Salamanca, Spain
| | - Alejandro Martín García-Sancho
- Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca/CSIC, 37007 Salamanca, Spain; (A.G.-V.); (R.C.-C.); (A.B.-G.); (N.G.-S.); (M.M.-E.); (A.C.-M.); (M.d.M.S.-F.); (J.M.G.-J.); (M.F.); (J.L.G.-H.); (J.J.C.-H.); (C.A.R.-S.); (A.M.G.-S.); (E.P.-L.)
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), 37007 Salamanca, Spain; (A.M.-G.); (C.P.-A.); (P.G.-V.); (G.M.d.P.); (A.G.-S.); (M.I.-G.); (M.B.G.-C.); (F.J.G.-C.)
- Servicio de Hematología, Hospital Universitario de Salamanca, CIBERONC, 37007 Salamanca, Spain;
| | - Estefanía Pérez-López
- Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca/CSIC, 37007 Salamanca, Spain; (A.G.-V.); (R.C.-C.); (A.B.-G.); (N.G.-S.); (M.M.-E.); (A.C.-M.); (M.d.M.S.-F.); (J.M.G.-J.); (M.F.); (J.L.G.-H.); (J.J.C.-H.); (C.A.R.-S.); (A.M.G.-S.); (E.P.-L.)
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), 37007 Salamanca, Spain; (A.M.-G.); (C.P.-A.); (P.G.-V.); (G.M.d.P.); (A.G.-S.); (M.I.-G.); (M.B.G.-C.); (F.J.G.-C.)
- Servicio de Hematología, Hospital Universitario de Salamanca, CIBERONC, 37007 Salamanca, Spain;
| | - Antonio Pérez-Martínez
- Department of Paediatric Hemato-Oncology, Hospital Universitario La Paz, 28046 Madrid, Spain;
| | - Federico Gutiérrez-Larraya
- Department of Paediatric Cardiology, Hospital Universitario La Paz, 28046 Madrid, Spain; (F.G.-L.); (A.J.C.)
| | - Antonio J. Cartón
- Department of Paediatric Cardiology, Hospital Universitario La Paz, 28046 Madrid, Spain; (F.G.-L.); (A.J.C.)
| | - José Ángel García-Sáenz
- Medical Oncology Service, Instituto de Investigación Sanitaria del Hospital Clínico San Carlos (IdISSC), Hospital Clínico San Carlos, 28040 Madrid, Spain;
| | - Ana Patiño-García
- Department of Pediatrics, Solid Tumor Program, Centro de Investigación Médica Aplicada (CIMA), Universidad de Navarra, IdisNA, 31008 Pamplona, Spain;
| | - Miguel Martín
- Department of Medicine, Gregorio Marañón Health Research Institute (IISGM), Centro de Investigación Biomédica en Red Oncológica (CIBERONC), Universidad Complutense, 28007 Madrid, Spain;
| | - Teresa Alonso-Gordoa
- Department of Medical Oncology, Hospital Universitario Ramón y Cajal, 28034 Madrid, Spain;
| | - Christof Vulsteke
- Department of Molecular Imaging, Pathology, Radiotherapy and Oncology (MIPRO), Center for Oncological Research (CORE), Antwerp University, 2610 Antwerp, Belgium; (C.V.); (L.C.)
- Department of Oncology, Integrated Cancer Center in Ghent, AZ Maria Middelares, 9000 Ghent, Belgium
| | - Lieselot Croes
- Department of Molecular Imaging, Pathology, Radiotherapy and Oncology (MIPRO), Center for Oncological Research (CORE), Antwerp University, 2610 Antwerp, Belgium; (C.V.); (L.C.)
- Department of Oncology, Integrated Cancer Center in Ghent, AZ Maria Middelares, 9000 Ghent, Belgium
| | - Sigrid Hatse
- Laboratory of Experimental Oncology (LEO), Department of Oncology, Department of General Medical Oncology, University Hospitals Leuven, Leuven Cancer Institute, Katholieke Universiteit (KU) Leuven, 3000 Leuven, Belgium;
| | - Thomas Van Brussel
- VIB Center for Cancer Biology, VIB, 3000 Leuven, Belgium; (T.V.B.); (D.L.)
- Laboratory of Translational Genetics, Department of Human Genetics, Katholieke Universiteit (KU) Leuven, 3000 Leuven, Belgium
| | - Diether Lambrechts
- VIB Center for Cancer Biology, VIB, 3000 Leuven, Belgium; (T.V.B.); (D.L.)
- Laboratory of Translational Genetics, Department of Human Genetics, Katholieke Universiteit (KU) Leuven, 3000 Leuven, Belgium
| | - Hans Wildiers
- Department of General Medical Oncology and Multidisciplinary Breast Unit, Leuven Cancer Institute, and Laboratory of Experimental Oncology (LEO), Department of Oncology, Leuven Cancer Institute and University Hospital Leuven, Katholieke Universiteit (KU) Leuven, 3000 Leuven, Belgium;
| | - Hang Chang
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA;
- Berkeley Biomedical Data Science Center, Lawrence Berkeley National Laboratory, Berkeley, CA 92720, USA
| | - Marina Holgado-Madruga
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), 37007 Salamanca, Spain; (A.M.-G.); (C.P.-A.); (P.G.-V.); (G.M.d.P.); (A.G.-S.); (M.I.-G.); (M.B.G.-C.); (F.J.G.-C.)
- Departamento de Fisiología y Farmacología, Universidad de Salamanca, 37007 Salamanca, Spain
- Instituto de Neurociencias de Castilla y León (INCyL), 37007 Salamanca, Spain
| | - Anna González-Neira
- Human Genotyping Unit-CeGen, Human Cancer Genetics Programme, Spanish National Cancer Research Centre (CNIO), 28029 Madrid, Spain; (S.R.-P.); (G.P.); (A.V.-R.)
| | - Pedro L. Sánchez
- Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca/CSIC, 37007 Salamanca, Spain; (A.G.-V.); (R.C.-C.); (A.B.-G.); (N.G.-S.); (M.M.-E.); (A.C.-M.); (M.d.M.S.-F.); (J.M.G.-J.); (M.F.); (J.L.G.-H.); (J.J.C.-H.); (C.A.R.-S.); (A.M.G.-S.); (E.P.-L.)
- Servicio de Cardiología, Hospital Universitario de Salamanca, Universidad de Salamanca (CIBER.CV), 37007 Salamanca, Spain
- Departamento de Medicina, Universidad de Salamanca, 37007 Salamanca, Spain
| | - Jesús Pérez Losada
- Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca/CSIC, 37007 Salamanca, Spain; (A.G.-V.); (R.C.-C.); (A.B.-G.); (N.G.-S.); (M.M.-E.); (A.C.-M.); (M.d.M.S.-F.); (J.M.G.-J.); (M.F.); (J.L.G.-H.); (J.J.C.-H.); (C.A.R.-S.); (A.M.G.-S.); (E.P.-L.)
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), 37007 Salamanca, Spain; (A.M.-G.); (C.P.-A.); (P.G.-V.); (G.M.d.P.); (A.G.-S.); (M.I.-G.); (M.B.G.-C.); (F.J.G.-C.)
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Cao S, Wei Y, Xu H, Weng J, Qi T, Yu F, Liu S, Xiong A, Liu P, Zeng H. Crosstalk between ferroptosis and chondrocytes in osteoarthritis: a systematic review of in vivo and in vitro studies. Front Immunol 2023; 14:1202436. [PMID: 37520558 PMCID: PMC10376718 DOI: 10.3389/fimmu.2023.1202436] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2023] [Accepted: 06/27/2023] [Indexed: 08/01/2023] Open
Abstract
Purpose Recent scientific reports have revealed a close association between ferroptosis and the occurrence and development of osteoarthritis (OA). Nevertheless, the precise mechanisms by which ferroptosis influences OA and how to hobble OA progression by inhibiting chondrocyte ferroptosis have not yet been fully elucidated. This study aims to conduct a comprehensive systematic review (SR) to address these gaps. Methods Following the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020, we conducted a comprehensive search of the Embase, Ovid, ProQuest, PubMed, Scopus, the Cochrane Library, and Web of Science databases to identify relevant studies that investigate the association between ferroptosis and chondrocytes in OA. Our search included studies published from the inception of these databases until January 31st, 2023. Only studies that met the predetermined quality criteria were included in this SR. Results In this comprehensive SR, a total of 21 studies that met the specified criteria were considered suitable and included in the current updated synthesis. The mechanisms underlying chondrocyte ferroptosis and its association with OA progression involve various biological phenomena, including mitochondrial dysfunction, dysregulated iron metabolism, oxidative stress, and crucial signaling pathways. Conclusion Ferroptosis in chondrocytes has opened an entirely new chapter for the investigation of OA, and targeted regulation of it is springing up as an attractive and promising therapeutic tactic for OA. Systematic review registration https://inplasy.com/inplasy-2023-3-0044/, identifier INPLASY202330044.
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Affiliation(s)
- Siyang Cao
- Department of Bone & Joint Surgery, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
- National & Local Joint Engineering Research Centre of Orthopaedic Biomaterials, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
- Shenzhen Key Laboratory of Orthopaedic Diseases and Biomaterials Research, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
| | - Yihao Wei
- Department of Bone & Joint Surgery, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
- National & Local Joint Engineering Research Centre of Orthopaedic Biomaterials, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
- Shenzhen Key Laboratory of Orthopaedic Diseases and Biomaterials Research, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
| | - Huihui Xu
- Department of Bone & Joint Surgery, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
- National & Local Joint Engineering Research Centre of Orthopaedic Biomaterials, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
- Shenzhen Key Laboratory of Orthopaedic Diseases and Biomaterials Research, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
| | - Jian Weng
- Department of Bone & Joint Surgery, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
- National & Local Joint Engineering Research Centre of Orthopaedic Biomaterials, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
- Shenzhen Key Laboratory of Orthopaedic Diseases and Biomaterials Research, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
| | - Tiantian Qi
- Department of Bone & Joint Surgery, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
- National & Local Joint Engineering Research Centre of Orthopaedic Biomaterials, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
- Shenzhen Key Laboratory of Orthopaedic Diseases and Biomaterials Research, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
| | - Fei Yu
- Department of Bone & Joint Surgery, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
- National & Local Joint Engineering Research Centre of Orthopaedic Biomaterials, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
- Shenzhen Key Laboratory of Orthopaedic Diseases and Biomaterials Research, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
| | - Su Liu
- Department of Bone & Joint Surgery, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
- National & Local Joint Engineering Research Centre of Orthopaedic Biomaterials, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
- Shenzhen Key Laboratory of Orthopaedic Diseases and Biomaterials Research, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
| | - Ao Xiong
- Department of Bone & Joint Surgery, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
- National & Local Joint Engineering Research Centre of Orthopaedic Biomaterials, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
- Shenzhen Key Laboratory of Orthopaedic Diseases and Biomaterials Research, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
| | - Peng Liu
- Department of Bone & Joint Surgery, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
- National & Local Joint Engineering Research Centre of Orthopaedic Biomaterials, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
- Shenzhen Key Laboratory of Orthopaedic Diseases and Biomaterials Research, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
| | - Hui Zeng
- Department of Bone & Joint Surgery, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
- National & Local Joint Engineering Research Centre of Orthopaedic Biomaterials, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
- Shenzhen Key Laboratory of Orthopaedic Diseases and Biomaterials Research, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
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Aberra YT, Ma L, Björkegren JLM, Civelek M. Predicting mechanisms of action at genetic loci associated with discordant effects on type 2 diabetes and abdominal fat accumulation. eLife 2023; 12:e79834. [PMID: 37326626 PMCID: PMC10275637 DOI: 10.7554/elife.79834] [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: 04/28/2022] [Accepted: 05/31/2023] [Indexed: 06/17/2023] Open
Abstract
Obesity is a major risk factor for cardiovascular disease, stroke, and type 2 diabetes (T2D). Excessive accumulation of fat in the abdomen further increases T2D risk. Abdominal obesity is measured by calculating the ratio of waist-to-hip circumference adjusted for the body-mass index (WHRadjBMI), a trait with a significant genetic inheritance. Genetic loci associated with WHRadjBMI identified in genome-wide association studies are predicted to act through adipose tissues, but many of the exact molecular mechanisms underlying fat distribution and its consequences for T2D risk are poorly understood. Further, mechanisms that uncouple the genetic inheritance of abdominal obesity from T2D risk have not yet been described. Here we utilize multi-omic data to predict mechanisms of action at loci associated with discordant effects on abdominal obesity and T2D risk. We find six genetic signals in five loci associated with protection from T2D but also with increased abdominal obesity. We predict the tissues of action at these discordant loci and the likely effector Genes (eGenes) at three discordant loci, from which we predict significant involvement of adipose biology. We then evaluate the relationship between adipose gene expression of eGenes with adipogenesis, obesity, and diabetic physiological phenotypes. By integrating these analyses with prior literature, we propose models that resolve the discordant associations at two of the five loci. While experimental validation is required to validate predictions, these hypotheses provide potential mechanisms underlying T2D risk stratification within abdominal obesity.
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Affiliation(s)
- Yonathan Tamrat Aberra
- Department of Biomedical Engineering, University of VirginiaCharlottesvilleUnited States
- Center for Public Health Genomics, University of VirginiaCharlottesvilleUnited States
| | - Lijiang Ma
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount SinaiNew YorkUnited States
| | - Johan LM Björkegren
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount SinaiNew YorkUnited States
- Department of Medicine, Karolinska Institutet, HuddingeStockholmSweden
| | - Mete Civelek
- Department of Biomedical Engineering, University of VirginiaCharlottesvilleUnited States
- Center for Public Health Genomics, University of VirginiaCharlottesvilleUnited States
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31
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Chella Krishnan K, El Hachem EJ, Keller MP, Patel SG, Carroll L, Vegas AD, Gerdes Gyuricza I, Light C, Cao Y, Pan C, Kaczor-Urbanowicz KE, Shravah V, Anum D, Pellegrini M, Lee CF, Seldin MM, Rosenthal NA, Churchill GA, Attie AD, Parker B, James DE, Lusis AJ. Genetic architecture of heart mitochondrial proteome influencing cardiac hypertrophy. eLife 2023; 12:e82619. [PMID: 37276142 PMCID: PMC10241513 DOI: 10.7554/elife.82619] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 05/18/2023] [Indexed: 06/07/2023] Open
Abstract
Mitochondria play an important role in both normal heart function and disease etiology. We report analysis of common genetic variations contributing to mitochondrial and heart functions using an integrative proteomics approach in a panel of inbred mouse strains called the Hybrid Mouse Diversity Panel (HMDP). We performed a whole heart proteome study in the HMDP (72 strains, n=2-3 mice) and retrieved 848 mitochondrial proteins (quantified in ≥50 strains). High-resolution association mapping on their relative abundance levels revealed three trans-acting genetic loci on chromosomes (chr) 7, 13 and 17 that regulate distinct classes of mitochondrial proteins as well as cardiac hypertrophy. DAVID enrichment analyses of genes regulated by each of the loci revealed that the chr13 locus was highly enriched for complex-I proteins (24 proteins, P=2.2E-61), the chr17 locus for mitochondrial ribonucleoprotein complex (17 proteins, P=3.1E-25) and the chr7 locus for ubiquinone biosynthesis (3 proteins, P=6.9E-05). Follow-up high resolution regional mapping identified NDUFS4, LRPPRC and COQ7 as the candidate genes for chr13, chr17 and chr7 loci, respectively, and both experimental and statistical analyses supported their causal roles. Furthermore, a large cohort of Diversity Outbred mice was used to corroborate Lrpprc gene as a driver of mitochondrial DNA (mtDNA)-encoded gene regulation, and to show that the chr17 locus is specific to heart. Variations in all three loci were associated with heart mass in at least one of two independent heart stress models, namely, isoproterenol-induced heart failure and diet-induced obesity. These findings suggest that common variations in certain mitochondrial proteins can act in trans to influence tissue-specific mitochondrial functions and contribute to heart hypertrophy, elucidating mechanisms that may underlie genetic susceptibility to heart failure in human populations.
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Affiliation(s)
- Karthickeyan Chella Krishnan
- Department of Pharmacology and Systems Physiology, University of Cincinnati College of MedicineCincinnatiUnited States
| | - Elie-Julien El Hachem
- Department of Integrative Biology and Physiology, Field Systems Biology, Sciences Sorbonne UniversitéParisFrance
| | - Mark P Keller
- Biochemistry Department, University of Wisconsin-MadisonMadisonUnited States
| | - Sanjeet G Patel
- Department of Surgery/Division of Cardiac Surgery, University of Southern California Keck School of MedicineLos AngelesUnited States
| | - Luke Carroll
- Metabolic Systems Biology Laboratory, Charles Perkins Centre, School of Life and Environmental Sciences, University of SydneySydneyAustralia
| | - Alexis Diaz Vegas
- Metabolic Systems Biology Laboratory, Charles Perkins Centre, School of Life and Environmental Sciences, University of SydneySydneyAustralia
| | | | - Christine Light
- Cardiovascular Biology Research Program, Oklahoma Medical Research FoundationOklahoma CityUnited States
| | - Yang Cao
- Department of Medicine/Division of Cardiology, University of California, Los AngelesLos AngelesUnited States
| | - Calvin Pan
- Department of Medicine/Division of Cardiology, University of California, Los AngelesLos AngelesUnited States
| | - Karolina Elżbieta Kaczor-Urbanowicz
- Division of Oral Biology and Medicine, UCLA School of DentistryLos AngelesUnited States
- UCLA Institute for Quantitative and Computational BiosciencesLos AngelesUnited States
| | - Varun Shravah
- Department of Chemistry, University of CaliforniaLos AngelesUnited States
| | - Diana Anum
- Department of Integrative Biology and Physiology, University of CaliforniaLos AngelesUnited States
| | - Matteo Pellegrini
- UCLA Institute for Quantitative and Computational BiosciencesLos AngelesUnited States
| | - Chi Fung Lee
- Cardiovascular Biology Research Program, Oklahoma Medical Research FoundationOklahoma CityUnited States
- Department of Physiology, University of Oklahoma Health Sciences CenterOklahoma CityUnited States
| | - Marcus M Seldin
- Center for Epigenetics and MetabolismIrvineUnited States
- Department of Biological Chemistry, University of CaliforniaIrvineUnited States
| | | | | | - Alan D Attie
- Biochemistry Department, University of Wisconsin-MadisonMadisonUnited States
| | - Benjamin Parker
- Department of Anatomy and Physiology, University of MelbourneMelbourneAustralia
| | - David E James
- Metabolic Systems Biology Laboratory, Charles Perkins Centre, School of Life and Environmental Sciences, University of SydneySydneyAustralia
| | - Aldons J Lusis
- Department of Medicine/Division of Cardiology, University of California, Los AngelesLos AngelesUnited States
- Department of Human Genetics, University of CaliforniaLos AngelesUnited States
- Department of Microbiology, Immunology and Molecular Genetics, University of CaliforniaLos AngelesUnited States
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32
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Ghnaim A, Lone IM, Nun NB, Iraqi FA. Unraveling the Host Genetic Background Effect on Internal Organ Weight Influenced by Obesity and Diabetes Using Collaborative Cross Mice. Int J Mol Sci 2023; 24:ijms24098201. [PMID: 37175908 PMCID: PMC10179483 DOI: 10.3390/ijms24098201] [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: 02/25/2023] [Revised: 04/09/2023] [Accepted: 04/28/2023] [Indexed: 05/15/2023] Open
Abstract
Type 2 diabetes mellitus (T2DM) is a severe chronic epidemic that results from the body's improper usage of the hormone insulin. Globally, 700 million people are expected to have received a diabetes diagnosis by 2045, according to the International Diabetes Federation (IDF). Cancer and macro- and microvascular illnesses are only a few immediate and long-term issues it could lead to. T2DM accelerates the effect of organ weights by triggering a hyperinflammatory response in the body's organs, inhibiting tissue repair and resolving inflammation. Understanding how genetic variation translates into different clinical presentations may highlight the mechanisms through which dietary elements may initiate or accelerate inflammatory disease processes and suggest potential disease-prevention techniques. To address the host genetic background effect on the organ weight by utilizing the newly developed mouse model, the Collaborative Cross mice (CC). The study was conducted on 207 genetically different CC mice from 8 CC lines of both sexes. The experiment started with 8-week-old mice for 12 weeks. During this period, one group maintained a standard chow diet (CHD), while the other group maintained a high-fat diet (HFD). In addition, body weight was recorded bi-weekly, and at the end of the study, a glucose tolerance test, as well as tissue collection (liver, spleen, heart), were conducted. Our study observed a strong effect of HFD on blood glucose clearance among different CC lines. The HFD decreased the blood glucose clearance displayed by the significant Area Under Curve (AUC) values in both populations. In addition, variation in body weight changes among the different CC lines in response to HFD. The female liver weight significantly increased compared to males in the overall population when exposed to HFD. Moreover, males showed higher heritability values than females on the same diet. Regardless of the dietary challenge, the liver weight in the overall male population correlated positively with the final body weight. The liver weight results revealed that three different CC lines perform well under classification models. The regression results also varied among organs. Accordingly, the differences among these lines correspond to the genetic variance, and we suspect that some genetic factors invoke different body responses to HFD. Further investigations, such as quantitative trait loci (QTL) analysis and genomic studies, could find these genetic elements. These findings would prove critical factors for developing personalized medicine, as they could indicate future body responses to numerous situations early, thus preventing the development of complex diseases.
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Affiliation(s)
- Aya Ghnaim
- Department of Clinical Microbiology and Immunology, Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv 69978, Israel
| | - Iqbal M Lone
- Department of Clinical Microbiology and Immunology, Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv 69978, Israel
| | - Nadav Ben Nun
- Department of Clinical Microbiology and Immunology, Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv 69978, Israel
| | - Fuad A Iraqi
- Department of Clinical Microbiology and Immunology, Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv 69978, Israel
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33
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Jurrjens AW, Seldin MM, Giles C, Meikle PJ, Drew BG, Calkin AC. The potential of integrating human and mouse discovery platforms to advance our understanding of cardiometabolic diseases. eLife 2023; 12:e86139. [PMID: 37000167 PMCID: PMC10065800 DOI: 10.7554/elife.86139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 03/15/2023] [Indexed: 04/01/2023] Open
Abstract
Cardiometabolic diseases encompass a range of interrelated conditions that arise from underlying metabolic perturbations precipitated by genetic, environmental, and lifestyle factors. While obesity, dyslipidaemia, smoking, and insulin resistance are major risk factors for cardiometabolic diseases, individuals still present in the absence of such traditional risk factors, making it difficult to determine those at greatest risk of disease. Thus, it is crucial to elucidate the genetic, environmental, and molecular underpinnings to better understand, diagnose, and treat cardiometabolic diseases. Much of this information can be garnered using systems genetics, which takes population-based approaches to investigate how genetic variance contributes to complex traits. Despite the important advances made by human genome-wide association studies (GWAS) in this space, corroboration of these findings has been hampered by limitations including the inability to control environmental influence, limited access to pertinent metabolic tissues, and often, poor classification of diseases or phenotypes. A complementary approach to human GWAS is the utilisation of model systems such as genetically diverse mouse panels to study natural genetic and phenotypic variation in a controlled environment. Here, we review mouse genetic reference panels and the opportunities they provide for the study of cardiometabolic diseases and related traits. We discuss how the post-GWAS era has prompted a shift in focus from discovery of novel genetic variants to understanding gene function. Finally, we highlight key advantages and challenges of integrating complementary genetic and multi-omics data from human and mouse populations to advance biological discovery.
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Affiliation(s)
- Aaron W Jurrjens
- Baker Heart and Diabetes Institute, Melbourne, Australia
- Central Clinical School, Monash University, Melbourne, Australia
| | - Marcus M Seldin
- Department of Biological Chemistry and Center for Epigenetics and Metabolism, University of California, Irvine, Irvine, United States
| | - Corey Giles
- Baker Heart and Diabetes Institute, Melbourne, Australia
- Baker Department of Cardiometabolic Health, University of Melbourne, Melbourne, Australia
- Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Bundoora, Australia
| | - Peter J Meikle
- Baker Heart and Diabetes Institute, Melbourne, Australia
- Central Clinical School, Monash University, Melbourne, Australia
- Baker Department of Cardiometabolic Health, University of Melbourne, Melbourne, Australia
- Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Bundoora, Australia
| | - Brian G Drew
- Baker Heart and Diabetes Institute, Melbourne, Australia
- Central Clinical School, Monash University, Melbourne, Australia
- Baker Department of Cardiometabolic Health, University of Melbourne, Melbourne, Australia
| | - Anna C Calkin
- Baker Heart and Diabetes Institute, Melbourne, Australia
- Central Clinical School, Monash University, Melbourne, Australia
- Baker Department of Cardiometabolic Health, University of Melbourne, Melbourne, Australia
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34
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Rozowsky J, Gao J, Borsari B, Yang YT, Galeev T, Gürsoy G, Epstein CB, Xiong K, Xu J, Li T, Liu J, Yu K, Berthel A, Chen Z, Navarro F, Sun MS, Wright J, Chang J, Cameron CJF, Shoresh N, Gaskell E, Drenkow J, Adrian J, Aganezov S, Aguet F, Balderrama-Gutierrez G, Banskota S, Corona GB, Chee S, Chhetri SB, Cortez Martins GC, Danyko C, Davis CA, Farid D, Farrell NP, Gabdank I, Gofin Y, Gorkin DU, Gu M, Hecht V, Hitz BC, Issner R, Jiang Y, Kirsche M, Kong X, Lam BR, Li S, Li B, Li X, Lin KZ, Luo R, Mackiewicz M, Meng R, Moore JE, Mudge J, Nelson N, Nusbaum C, Popov I, Pratt HE, Qiu Y, Ramakrishnan S, Raymond J, Salichos L, Scavelli A, Schreiber JM, Sedlazeck FJ, See LH, Sherman RM, Shi X, Shi M, Sloan CA, Strattan JS, Tan Z, Tanaka FY, Vlasova A, Wang J, Werner J, Williams B, Xu M, Yan C, Yu L, Zaleski C, Zhang J, Ardlie K, Cherry JM, Mendenhall EM, Noble WS, Weng Z, Levine ME, Dobin A, Wold B, Mortazavi A, Ren B, Gillis J, Myers RM, Snyder MP, Choudhary J, Milosavljevic A, Schatz MC, Bernstein BE, Guigó R, Gingeras TR, Gerstein M. The EN-TEx resource of multi-tissue personal epigenomes & variant-impact models. Cell 2023; 186:1493-1511.e40. [PMID: 37001506 PMCID: PMC10074325 DOI: 10.1016/j.cell.2023.02.018] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Revised: 10/16/2022] [Accepted: 02/10/2023] [Indexed: 04/03/2023]
Abstract
Understanding how genetic variants impact molecular phenotypes is a key goal of functional genomics, currently hindered by reliance on a single haploid reference genome. Here, we present the EN-TEx resource of 1,635 open-access datasets from four donors (∼30 tissues × ∼15 assays). The datasets are mapped to matched, diploid genomes with long-read phasing and structural variants, instantiating a catalog of >1 million allele-specific loci. These loci exhibit coordinated activity along haplotypes and are less conserved than corresponding, non-allele-specific ones. Surprisingly, a deep-learning transformer model can predict the allele-specific activity based only on local nucleotide-sequence context, highlighting the importance of transcription-factor-binding motifs particularly sensitive to variants. Furthermore, combining EN-TEx with existing genome annotations reveals strong associations between allele-specific and GWAS loci. It also enables models for transferring known eQTLs to difficult-to-profile tissues (e.g., from skin to heart). Overall, EN-TEx provides rich data and generalizable models for more accurate personal functional genomics.
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Affiliation(s)
- Joel Rozowsky
- Section on Biomedical Informatics and Data Science, Yale University, New Haven, CT, USA; Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Jiahao Gao
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Beatrice Borsari
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA; Centre for Genomic Regulation, The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain
| | - Yucheng T Yang
- Institute of Science and Technology for Brain-Inspired Intelligence; MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence; MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200433, China; Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Timur Galeev
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Gamze Gürsoy
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | | | - Kun Xiong
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Jinrui Xu
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Tianxiao Li
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Jason Liu
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Keyang Yu
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Ana Berthel
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Zhanlin Chen
- Department of Statistics and Data Science, Yale University, New Haven, CT, USA
| | - Fabio Navarro
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Maxwell S Sun
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | | | - Justin Chang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Christopher J F Cameron
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Noam Shoresh
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Jorg Drenkow
- Functional Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Jessika Adrian
- Department of Genetics, School of Medicine, Stanford University, Palo Alto, CA, USA
| | - Sergey Aganezov
- Departments of Computer Science and Biology, Johns Hopkins University, Baltimore, MD, USA
| | | | | | | | | | - Sora Chee
- Ludwig Institute for Cancer Research, University of California, San Diego, La Jolla, CA, USA
| | - Surya B Chhetri
- HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA
| | - Gabriel Conte Cortez Martins
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Cassidy Danyko
- Functional Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Carrie A Davis
- Functional Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Daniel Farid
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | | | - Idan Gabdank
- Department of Genetics, School of Medicine, Stanford University, Palo Alto, CA, USA
| | - Yoel Gofin
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - David U Gorkin
- Ludwig Institute for Cancer Research, University of California, San Diego, La Jolla, CA, USA
| | - Mengting Gu
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Vivian Hecht
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Benjamin C Hitz
- Department of Genetics, School of Medicine, Stanford University, Palo Alto, CA, USA
| | - Robbyn Issner
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Yunzhe Jiang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Melanie Kirsche
- Departments of Computer Science and Biology, Johns Hopkins University, Baltimore, MD, USA
| | - Xiangmeng Kong
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Bonita R Lam
- Department of Genetics, School of Medicine, Stanford University, Palo Alto, CA, USA
| | - Shantao Li
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Bian Li
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Xiqi Li
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Khine Zin Lin
- Department of Genetics, School of Medicine, Stanford University, Palo Alto, CA, USA
| | - Ruibang Luo
- Department of Computer Science, The University of Hong Kong, Hong Kong, CHN
| | - Mark Mackiewicz
- HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA
| | - Ran Meng
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Jill E Moore
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA, USA
| | - Jonathan Mudge
- European Bioinformatics Institute, Cambridge, Cambridgeshire, GB
| | | | - Chad Nusbaum
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Ioann Popov
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Henry E Pratt
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA, USA
| | - Yunjiang Qiu
- Ludwig Institute for Cancer Research, University of California, San Diego, La Jolla, CA, USA
| | - Srividya Ramakrishnan
- Departments of Computer Science and Biology, Johns Hopkins University, Baltimore, MD, USA
| | - Joe Raymond
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Leonidas Salichos
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA; Department of Biological and Chemical Sciences, New York Institute of Technology, Old Westbury, NY, USA
| | - Alexandra Scavelli
- Functional Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Jacob M Schreiber
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Fritz J Sedlazeck
- Departments of Computer Science and Biology, Johns Hopkins University, Baltimore, MD, USA; Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA; Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Lei Hoon See
- Functional Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Rachel M Sherman
- Departments of Computer Science and Biology, Johns Hopkins University, Baltimore, MD, USA
| | - Xu Shi
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Minyi Shi
- Department of Genetics, School of Medicine, Stanford University, Palo Alto, CA, USA
| | - Cricket Alicia Sloan
- Department of Genetics, School of Medicine, Stanford University, Palo Alto, CA, USA
| | - J Seth Strattan
- Department of Genetics, School of Medicine, Stanford University, Palo Alto, CA, USA
| | - Zhen Tan
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Forrest Y Tanaka
- Department of Genetics, School of Medicine, Stanford University, Palo Alto, CA, USA
| | - Anna Vlasova
- Centre for Genomic Regulation, The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain; Comparative Genomics Group, Life Science Programme, Barcelona Supercomputing Centre, Barcelona, Spain; Institute of Research in Biomedicine, Barcelona, Spain
| | - Jun Wang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Jonathan Werner
- Functional Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Brian Williams
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Min Xu
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Chengfei Yan
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Lu Yu
- Institute of Cancer Research, London, UK
| | - Christopher Zaleski
- Functional Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Jing Zhang
- Department of Computer Science, University of California, Irvine, Irvine, CA, USA
| | | | - J Michael Cherry
- Department of Genetics, School of Medicine, Stanford University, Palo Alto, CA, USA
| | | | - William S Noble
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Zhiping Weng
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA, USA
| | - Morgan E Levine
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Pathology, Yale University School of Medicine, New Haven, CT, USA
| | - Alexander Dobin
- Functional Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Barbara Wold
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Ali Mortazavi
- Department of Developmental and Cell Biology, University of California, Irvine, Irvine, CA, USA
| | - Bing Ren
- Ludwig Institute for Cancer Research, University of California, San Diego, La Jolla, CA, USA
| | - Jesse Gillis
- Functional Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA; Department of Physiology, University of Toronto, Toronto, ON, Canada
| | - Richard M Myers
- HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA
| | - Michael P Snyder
- Department of Genetics, School of Medicine, Stanford University, Palo Alto, CA, USA
| | | | | | - Michael C Schatz
- Departments of Computer Science and Biology, Johns Hopkins University, Baltimore, MD, USA; Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
| | - Bradley E Bernstein
- Broad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA.
| | - Roderic Guigó
- Centre for Genomic Regulation, The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain; Universitat Pompeu Fabra, Barcelona, Catalonia, Spain.
| | - Thomas R Gingeras
- Functional Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
| | - Mark Gerstein
- Section on Biomedical Informatics and Data Science, Yale University, New Haven, CT, USA; Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA; Department of Statistics and Data Science, Yale University, New Haven, CT, USA; Department of Computer Science, Yale University, New Haven, CT, USA.
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35
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Yang X, Lin C, Liu J, Zhang Y, Deng T, Wei M, Pan S, Lu L, Li X, Tian G, Mi J, Xu F, Yang C. Identification of the regulatory mechanism of ACE2 in COVID-19-induced kidney damage with systems genetics approach. J Mol Med (Berl) 2023; 101:449-460. [PMID: 36951969 PMCID: PMC10034233 DOI: 10.1007/s00109-023-02304-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 02/16/2023] [Accepted: 03/06/2023] [Indexed: 03/24/2023]
Abstract
Studies showed that SARS-CoV-2 can directly target the kidney and induce renal damage. As the cell surface receptor for SARS-CoV-2 infection, the angiotensin-converting enzyme 2 (ACE2) plays a pivotal role for renal physiology and function. Thus, it is important to understand ACE2 through which pathway influences the pathogenesis of renal damage induced by COVID-19. In this study, we first performed an eQTL mapping for Ace2 in kidney tissues in 53 BXD mice strains. Results demonstrated that Ace2 is highly expressed and strongly controlled by a genetic locus on chromosome 16 in the kidney, with six genes (Dnase1, Vasn, Usp7, Abat, Mgrn1, and Rbfox1) dominated as the upstream modulator, as they are highly correlated with Ace2 expression. Gene co-expression analysis showed that Ace2 co-variates are significantly involved in the renin-angiotensin system (RAS) pathway which acts as a reno-protector. Importantly, we also found that Ace2 is positively correlated with Pdgf family members, particularly Pdgfc, which showed the most association among the 76 investigated growth factors. Mammalian Phenotype Ontology enrichment indicated that the cognate transcripts for both Ace2 and Pdgfc were mainly involved in regulating renal physiology and morphology. Among which, Cd44, Egfr, Met, Smad3, and Stat3 were identified as hub genes through protein-protein interaction analysis. Finally, in aligning with our systems genetics findings, we found ACE2, pdgf family members, and RAS genes decreased significantly in the CAKI-1 kidney cancer cells treated with S protein and receptor binding domain structural protein. Collectively, our data suggested that ACE2 work with RAS, PDGFC, as well as their cognate hub genes to regulate renal function, which could guide for future clinical prevention and targeted treatment for COVID-19-induced renal damage outcomes. KEY MESSAGES: • Ace2 is highly expressed and strongly controlled by a genetic locus on chromosome 16 in the kidney. • Ace2 co-variates are enriched in the RAS pathway. • Ace2 is strongly correlated with the growth factor Pdgfc. • Ace2 and Pdgfc co-expressed genes involved in the regulation of renal physiology and morphology. • SARS-CoV-2 spike glycoprotein induces down-regulation of Ace2, RAS, and Pdgfc.
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Affiliation(s)
- Xueling Yang
- Shandong Technology Innovation Center of Molecular Targeting and Intelligent Diagnosis and Treatment, Binzhou Medical University, Shandong, Yantai, 264003, China
| | - Chunhua Lin
- Department of Urology, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, Shandong, 264008, China
| | - Jian Liu
- Department of Plastic Surgery, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, Shandong, 250014, China
| | - Ya Zhang
- Shandong Technology Innovation Center of Molecular Targeting and Intelligent Diagnosis and Treatment, Binzhou Medical University, Shandong, Yantai, 264003, China
| | - Tingzhi Deng
- Shandong Technology Innovation Center of Molecular Targeting and Intelligent Diagnosis and Treatment, Binzhou Medical University, Shandong, Yantai, 264003, China
| | - Mengna Wei
- Shandong Technology Innovation Center of Molecular Targeting and Intelligent Diagnosis and Treatment, Binzhou Medical University, Shandong, Yantai, 264003, China
| | - Shuijing Pan
- Shandong Technology Innovation Center of Molecular Targeting and Intelligent Diagnosis and Treatment, Binzhou Medical University, Shandong, Yantai, 264003, China
| | - Lu Lu
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, 38163, USA
| | - Xuri Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangzhou, Guangdong, 510060, China
| | - Geng Tian
- Shandong Technology Innovation Center of Molecular Targeting and Intelligent Diagnosis and Treatment, Binzhou Medical University, Shandong, Yantai, 264003, China
| | - Jia Mi
- Shandong Technology Innovation Center of Molecular Targeting and Intelligent Diagnosis and Treatment, Binzhou Medical University, Shandong, Yantai, 264003, China.
| | - Fuyi Xu
- Shandong Technology Innovation Center of Molecular Targeting and Intelligent Diagnosis and Treatment, Binzhou Medical University, Shandong, Yantai, 264003, China.
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, 38163, USA.
| | - Chunhua Yang
- Shandong Technology Innovation Center of Molecular Targeting and Intelligent Diagnosis and Treatment, Binzhou Medical University, Shandong, Yantai, 264003, China.
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Gómez-Vecino A, Corchado-Cobos R, Blanco-Gómez A, García-Sancha N, Castillo-Lluva S, Martín-García A, Mendiburu-Eliçabe M, Prieto C, Ruiz-Pinto S, Pita G, Velasco-Ruiz A, Patino-Alonso C, Galindo-Villardón P, Vera-Pedrosa ML, Jalife J, Mao JH, de Plasencia GM, Castellanos-Martín A, Freire MDMS, Fraile-Martín S, Rodrigues-Teixeira T, García-Macías C, Galvis-Jiménez JM, García-Sánchez A, Isidoro-García M, Fuentes M, García-Cenador MB, García-Criado FJ, García JL, Hernández-García MÁ, Hernández JJC, Rodríguez-Sánchez CA, Martín-Ruiz A, Pérez-López E, Pérez-Martínez A, Gutiérrez-Larraya F, Cartón AJ, García-Sáenz JÁ, Patiño-García A, Martín M, Gordoa TA, Vulsteke C, Croes L, Hatse S, Brussel TV, Lambrechts D, Wildiers H, Hang C, Holgado-Madruga M, González-Neira A, Sánchez PL, Losada JP. Intermediate molecular phenotypes to identify genetic markers of anthracycline-induced cardiotoxicity risk. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.05.522844. [PMID: 36712139 PMCID: PMC9881971 DOI: 10.1101/2023.01.05.522844] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Cardiotoxicity due to anthracyclines (CDA) affects cancer patients, but we cannot predict who may suffer from this complication. CDA is a complex disease whose polygenic component is mainly unidentified. We propose that levels of intermediate molecular phenotypes in the myocardium associated with histopathological damage could explain CDA susceptibility; so that variants of genes encoding these intermediate molecular phenotypes could identify patients susceptible to this complication. A genetically heterogeneous cohort of mice generated by backcrossing (N = 165) was treated with doxorubicin and docetaxel. Cardiac histopathological damage was measured by fibrosis and cardiomyocyte size by an Ariol slide scanner. We determine intramyocardial levels of intermediate molecular phenotypes of CDA associated with histopathological damage and quantitative trait loci (ipQTLs) linked to them. These ipQTLs seem to contribute to the missing heritability of CDA because they improve the heritability explained by QTL directly linked to CDA (cda-QTLs) through genetic models. Genes encoding these molecular subphenotypes were evaluated as genetic markers of CDA in three cancer patient cohorts (N = 517) whose cardiac damage was quantified by echocardiography or Cardiac Magnetic Resonance. Many SNPs associated with CDA were found using genetic models. LASSO multivariate regression identified two risk score models, one for pediatric cancer patients and the other for women with breast cancer. Molecular intermediate phenotypes associated with heart damage can identify genetic markers of CDA risk, thereby allowing a more personalized patient management. A similar strategy could be applied to identify genetic markers of other complex trait diseases.
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Affiliation(s)
- Aurora Gómez-Vecino
- Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca/CSIC, Salamanca, 37007, Spain
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), Salamanca, 37007, Spain
| | - Roberto Corchado-Cobos
- Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca/CSIC, Salamanca, 37007, Spain
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), Salamanca, 37007, Spain
| | - Adrián Blanco-Gómez
- Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca/CSIC, Salamanca, 37007, Spain
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), Salamanca, 37007, Spain
| | - Natalia García-Sancha
- Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca/CSIC, Salamanca, 37007, Spain
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), Salamanca, 37007, Spain
| | - Sonia Castillo-Lluva
- Departamento de Bioquímica y Biología Molecular, Facultad de Ciencias Químicas, Universidad Complutense, Madrid, 28040, Spain
- Instituto de Investigaciones Sanitarias San Carlos (IdISSC), Madrid, Spain
| | - Ana Martín-García
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), Salamanca, 37007, Spain
- Servicio de Cardiología, Hospital Universitario de Salamanca, Universidad de Salamanca, and CIBER.CV, Salamanca, 37007, Spain
| | - Marina Mendiburu-Eliçabe
- Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca/CSIC, Salamanca, 37007, Spain
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), Salamanca, 37007, Spain
| | - Carlos Prieto
- Servicio de Bioinformática, Nucleus, Universidad de Salamanca, Salamanca, 37007, Spain
| | - Sara Ruiz-Pinto
- Human Genotyping Unit-CeGen, Human Cancer Genetics Programme, Spanish National Cancer Research Centre (CNIO), 28029, Spain
| | - Guillermo Pita
- Human Genotyping Unit-CeGen, Human Cancer Genetics Programme, Spanish National Cancer Research Centre (CNIO), 28029, Spain
| | - Alejandro Velasco-Ruiz
- Human Genotyping Unit-CeGen, Human Cancer Genetics Programme, Spanish National Cancer Research Centre (CNIO), 28029, Spain
| | - Carmen Patino-Alonso
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), Salamanca, 37007, Spain
- Departamento de Estadística, Universidad de Salamanca, Salamanca, 37007, Spain; and Centro de Investigación Institucional (CII). Universidad Bernardo O’Higgins, 1497. Santiago, Chile
| | - Purificación Galindo-Villardón
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), Salamanca, 37007, Spain
- Departamento de Estadística, Universidad de Salamanca, Salamanca, 37007, Spain; and Centro de Investigación Institucional (CII). Universidad Bernardo O’Higgins, 1497. Santiago, Chile
| | | | - José Jalife
- Centro Nacional de Investigaciones Cardiovasculares (CNIC) Carlos III, Madrid, 28029, Spain
| | - Jian-Hua Mao
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
- Berkeley Biomedical Data Science Center, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Guillermo Macías de Plasencia
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), Salamanca, 37007, Spain
- Servicio de Cardiología, Hospital Universitario de Salamanca, Universidad de Salamanca, and CIBER.CV, Salamanca, 37007, Spain
| | - Andrés Castellanos-Martín
- Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca/CSIC, Salamanca, 37007, Spain
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), Salamanca, 37007, Spain
| | - María del Mar Sáez Freire
- Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca/CSIC, Salamanca, 37007, Spain
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), Salamanca, 37007, Spain
| | - Susana Fraile-Martín
- Servicio de Patología Molecular Comparada, Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca, Salamanca, 37007, Spain
| | - Telmo Rodrigues-Teixeira
- Servicio de Patología Molecular Comparada, Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca, Salamanca, 37007, Spain
| | - Carmen García-Macías
- Servicio de Patología Molecular Comparada, Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca, Salamanca, 37007, Spain
| | - Julie Milena Galvis-Jiménez
- Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca/CSIC, Salamanca, 37007, Spain
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), Salamanca, 37007, Spain
- Instituto Nacional de Cancerología de Colombia, Bogotá D. C., Colombia
| | - Asunción García-Sánchez
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), Salamanca, 37007, Spain
- Servicio de Bioquímica Clínica, Hospital Universitario de Salamanca, Salamanca, 37007, Spain
| | - María Isidoro-García
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), Salamanca, 37007, Spain
- Servicio de Bioquímica Clínica, Hospital Universitario de Salamanca, Salamanca, 37007, Spain
- Departamento de Medicina, Universidad de Salamanca, Salamanca, 37007, Spain
| | - Manuel Fuentes
- Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca/CSIC, Salamanca, 37007, Spain
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), Salamanca, 37007, Spain
- Departamento de Medicina, Universidad de Salamanca, Salamanca, 37007, Spain
- Unidad de Proteómica y Servicio General de Citometría de Flujo, Nucleus, Universidad de Salamanca, 37007, Spain
| | - María Begoña García-Cenador
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), Salamanca, 37007, Spain
- Departamento de Cirugía, Universidad de Salamanca. Salamanca, 37007, Spain
| | - Francisco Javier García-Criado
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), Salamanca, 37007, Spain
- Departamento de Cirugía, Universidad de Salamanca. Salamanca, 37007, Spain
| | - Juan Luis García
- Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca/CSIC, Salamanca, 37007, Spain
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), Salamanca, 37007, Spain
| | | | - Juan Jesús Cruz Hernández
- Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca/CSIC, Salamanca, 37007, Spain
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), Salamanca, 37007, Spain
- Departamento de Medicina, Universidad de Salamanca, Salamanca, 37007, Spain
- Servicio de Oncología, Hospital Universitario de Salamanca, Salamanca, 37007, Spain
| | - César Augusto Rodríguez-Sánchez
- Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca/CSIC, Salamanca, 37007, Spain
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), Salamanca, 37007, Spain
- Departamento de Medicina, Universidad de Salamanca, Salamanca, 37007, Spain
- Servicio de Oncología, Hospital Universitario de Salamanca, Salamanca, 37007, Spain
| | - Alejandro Martín-Ruiz
- Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca/CSIC, Salamanca, 37007, Spain
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), Salamanca, 37007, Spain
- Servicio de Hematología, Hospital Universitario de Salamanca, CIBERONC, Salamanca, 37007, Spain
| | - Estefanía Pérez-López
- Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca/CSIC, Salamanca, 37007, Spain
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), Salamanca, 37007, Spain
- Servicio de Hematología, Hospital Universitario de Salamanca, CIBERONC, Salamanca, 37007, Spain
| | - Antonio Pérez-Martínez
- Department of Paediatric Hemato-Oncology, Hospital Universitario La Paz, Madrid, 28046, Spain
| | | | - Antonio J. Cartón
- Department of Paediatric Hemato-Oncology, Hospital Universitario La Paz, Madrid, 28046, Spain
| | - José Ángel García-Sáenz
- Medical Oncology Service, Instituto de Investigación Sanitaria del Hospital Clínico San Carlos (IdISSC), Hospital Clínico San Carlos, Madrid, 28040, Spain
| | - Ana Patiño-García
- Department of Pediatrics, University Clinic of Navarra, Solid Tumor Program, CIMA, Universidad de Navarra, IdisNA, Pamplona, 31008, Spain
| | - Miguel Martín
- Gregorio Marañón Health Research Institute (IISGM), CIBERONC, Department of Medicine, Universidad Complutense, Madrid, 28007, Spain
| | - Teresa Alonso Gordoa
- Department of Medical Oncology, Hospital Universitario Ramón y Cajal, Madrid, 28034, Spain
| | - Christof Vulsteke
- Department of Molecular Imaging, Pathology, Radiotherapy and Oncology (MIPRO), Center for Oncological Research (CORE), Antwerp University, Antwerp, Belgium
- Department of Oncology, Integrated Cancer Center in Ghent, AZ Maria Middelares, Ghent, Belgium
| | - Lieselot Croes
- Department of Molecular Imaging, Pathology, Radiotherapy and Oncology (MIPRO), Center for Oncological Research (CORE), Antwerp University, Antwerp, Belgium
- Department of Oncology, Integrated Cancer Center in Ghent, AZ Maria Middelares, Ghent, Belgium
| | - Sigrid Hatse
- Laboratory of Experimental Oncology (LEO), Department of Oncology, KU Leuven, and Department of General Medical Oncology, University Hospitals Leuven, Leuven Cancer Institute, Leuven, Belgium
| | - Thomas Van Brussel
- VIB Center for Cancer Biology, VIB, Leuven, Belgium
- Laboratory of Translational Genetics, Department of Human Genetics, KU Leuven, 3000 Leuven, Belgium
| | - Diether Lambrechts
- VIB Center for Cancer Biology, VIB, Leuven, Belgium
- Laboratory of Translational Genetics, Department of Human Genetics, KU Leuven, 3000 Leuven, Belgium
| | - Hans Wildiers
- Department of General Medical Oncology and Multidisciplinary Breast Centre, University Hospitals Leuven, Leuven Cancer Institute, and Laboratory of Experimental Oncology (LEO), Department of Oncology, KU Leuven, Leuven, Belgium
| | - Chang Hang
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
- Berkeley Biomedical Data Science Center, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Marina Holgado-Madruga
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), Salamanca, 37007, Spain
- Departamento de Fisiología y Farmacología, Universidad de Salamanca, 37007, Salamanca. Spain
- Instituto de Neurociencias de Castilla y León (INCyL), Salamanca, 37007, Spain
| | - Anna González-Neira
- Human Genotyping Unit-CeGen, Human Cancer Genetics Programme, Spanish National Cancer Research Centre (CNIO), 28029, Spain
| | - Pedro L Sánchez
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), Salamanca, 37007, Spain
- Servicio de Cardiología, Hospital Universitario de Salamanca, Universidad de Salamanca, and CIBER.CV, Salamanca, 37007, Spain
- Departamento de Medicina, Universidad de Salamanca, Salamanca, 37007, Spain
| | - Jesús Pérez Losada
- Instituto de Biología Molecular y Celular del Cáncer (IBMCC-CIC), Universidad de Salamanca/CSIC, Salamanca, 37007, Spain
- Instituto de Investigación Biosanitaria de Salamanca (IBSAL), Salamanca, 37007, Spain
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Mondal P. A Critical Perspective on the (Neuro)biological Foundations of Language and Linguistic Cognition. Integr Psychol Behav Sci 2022:10.1007/s12124-022-09741-0. [PMID: 36562960 DOI: 10.1007/s12124-022-09741-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/12/2022] [Indexed: 12/24/2022]
Abstract
The biological foundations of language reflect assumptions about the way language and biology relate to one another, and with the rise of biological studies of language, we appear to have come closer to a deep understanding of linguistic cognition-the part of cognition constituted by language. This article argues that relations of neurobiological and genetic instantiation between linguistic cognition and the underlying biological substrate are ultimately irrelevant to understanding the higher-level structure and form of language. Linguistic patterns and those that make up the character of cognition constituted by language do not simply arise from the biological substrate because higher-level structures typically assume forms based on constraints that only emerge once these new levels are constructed. The goal is not to show how the mapping problem between linguistic cognition and neurobiology can be solved. Rather, the goal is to show the mapping problem ceases to exist once a different understanding of language-(neuro)biology relations is embraced. With this goal, this article first uncovers a number of logical and conceptual fallacies in strategies deployed in understanding language-(neuro)biology relations. After having shown these flaws, the article offers an alternative view of language-biology relations that shows how biological constraints shape language (nature and form), making it what it is.
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Affiliation(s)
- Prakash Mondal
- Indian Institute of Technology Hyderabad, Kandi, Sangareddy, Telangana, 502284, India.
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Genome-Wide Association Studies across Environmental and Genetic Contexts Reveal Complex Genetic Architecture of Symbiotic Extended Phenotypes. mBio 2022; 13:e0182322. [PMID: 36286519 PMCID: PMC9765617 DOI: 10.1128/mbio.01823-22] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
A goal of modern biology is to develop the genotype-phenotype (G→P) map, a predictive understanding of how genomic information generates trait variation that forms the basis of both natural and managed communities. As microbiome research advances, however, it has become clear that many of these traits are symbiotic extended phenotypes, being governed by genetic variation encoded not only by the host's own genome, but also by the genomes of myriad cryptic symbionts. Building a reliable G→P map therefore requires accounting for the multitude of interacting genes and even genomes involved in symbiosis. Here, we use naturally occurring genetic variation in 191 strains of the model microbial symbiont Sinorhizobium meliloti paired with two genotypes of the host Medicago truncatula in four genome-wide association studies (GWAS) to determine the genomic architecture of a key symbiotic extended phenotype-partner quality, or the fitness benefit conferred to a host by a particular symbiont genotype, within and across environmental contexts and host genotypes. We define three novel categories of loci in rhizobium genomes that must be accounted for if we want to build a reliable G→P map of partner quality; namely, (i) loci whose identities depend on the environment, (ii) those that depend on the host genotype with which rhizobia interact, and (iii) universal loci that are likely important in all or most environments. IMPORTANCE Given the rapid rise of research on how microbiomes can be harnessed to improve host health, understanding the contribution of microbial genetic variation to host phenotypic variation is pressing, and will better enable us to predict the evolution of (and select more precisely for) symbiotic extended phenotypes that impact host health. We uncover extensive context-dependency in both the identity and functions of symbiont loci that control host growth, which makes predicting the genes and pathways important for determining symbiotic outcomes under different conditions more challenging. Despite this context-dependency, we also resolve a core set of universal loci that are likely important in all or most environments, and thus, serve as excellent targets both for genetic engineering and future coevolutionary studies of symbiosis.
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Malinowska M, Ruud AK, Jensen J, Svane SF, Smith AG, Bellucci A, Lenk I, Nagy I, Fois M, Didion T, Thorup-Kristensen K, Jensen CS, Asp T. Relative importance of genotype, gene expression, and DNA methylation on complex traits in perennial ryegrass. THE PLANT GENOME 2022; 15:e20253. [PMID: 35975565 DOI: 10.1002/tpg2.20253] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 06/30/2022] [Indexed: 06/15/2023]
Abstract
The growing demand for food and feed crops in the world because of growing population and more extreme weather events requires high-yielding and resilient crops. Many agriculturally important traits are polygenic, controlled by multiple regulatory layers, and with a strong interaction with the environment. In this study, 120 F2 families of perennial ryegrass (Lolium perenne L.) were grown across a water gradient in a semifield facility with subsoil irrigation. Genomic (single-nucleotide polymorphism [SNP]), transcriptomic (gene expression [GE]), and DNA methylomic (MET) data were integrated with feed quality trait data collected from control and drought sections in the semifield facility, providing a treatment effect. Deep root length (DRL) below 110 cm was assessed with convolutional neural network image analysis. Bayesian prediction models were used to partition phenotypic variance into its components and evaluated the proportion of phenotypic variance in all traits captured by different regulatory layers (SNP, GE, and MET). The spatial effects and effects of SNP, GE, MET, the interaction between GE and MET (GE × MET) and GE × treatment (GEControl and GEDrought ) interaction were investigated. Gene expression explained a substantial part of the genetic and spatial variance for all the investigated phenotypes, whereas MET explained residual variance not accounted for by SNPs or GE. For DRL, MET also contributed to explaining spatial variance. The study provides a statistically elegant analytical paradigm that integrates genomic, transcriptomic, and MET information to understand the regulatory mechanisms of polygenic effects for complex traits.
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Affiliation(s)
- Marta Malinowska
- Center for Quantitative Genetics and Genomics, Aarhus Univ., Slagelse, Denmark
| | - Anja Karine Ruud
- Center for Quantitative Genetics and Genomics, Aarhus Univ., Slagelse, Denmark
| | - Just Jensen
- Center for Quantitative Genetics and Genomics, Aarhus Univ., Slagelse, Denmark
| | - Simon Fiil Svane
- Dep. of Plant and Environmental Sciences, Univ. of Copenhagen, Taastrup, Denmark
| | | | - Andrea Bellucci
- Center for Quantitative Genetics and Genomics, Aarhus Univ., Slagelse, Denmark
| | - Ingo Lenk
- Research Division, DLF Seeds A/S, Store Heddinge, Denmark
| | - Istvan Nagy
- Center for Quantitative Genetics and Genomics, Aarhus Univ., Slagelse, Denmark
| | - Mattia Fois
- Center for Quantitative Genetics and Genomics, Aarhus Univ., Slagelse, Denmark
| | - Thomas Didion
- Research Division, DLF Seeds A/S, Store Heddinge, Denmark
| | | | | | - Torben Asp
- Center for Quantitative Genetics and Genomics, Aarhus Univ., Slagelse, Denmark
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40
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Hawe JS, Saha A, Waldenberger M, Kunze S, Wahl S, Müller-Nurasyid M, Prokisch H, Grallert H, Herder C, Peters A, Strauch K, Theis FJ, Gieger C, Chambers J, Battle A, Heinig M. Network reconstruction for trans acting genetic loci using multi-omics data and prior information. Genome Med 2022; 14:125. [PMID: 36344995 PMCID: PMC9641770 DOI: 10.1186/s13073-022-01124-9] [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: 06/15/2022] [Accepted: 10/11/2022] [Indexed: 11/09/2022] Open
Abstract
BACKGROUND Molecular measurements of the genome, the transcriptome, and the epigenome, often termed multi-omics data, provide an in-depth view on biological systems and their integration is crucial for gaining insights in complex regulatory processes. These data can be used to explain disease related genetic variants by linking them to intermediate molecular traits (quantitative trait loci, QTL). Molecular networks regulating cellular processes leave footprints in QTL results as so-called trans-QTL hotspots. Reconstructing these networks is a complex endeavor and use of biological prior information can improve network inference. However, previous efforts were limited in the types of priors used or have only been applied to model systems. In this study, we reconstruct the regulatory networks underlying trans-QTL hotspots using human cohort data and data-driven prior information. METHODS We devised a new strategy to integrate QTL with human population scale multi-omics data. State-of-the art network inference methods including BDgraph and glasso were applied to these data. Comprehensive prior information to guide network inference was manually curated from large-scale biological databases. The inference approach was extensively benchmarked using simulated data and cross-cohort replication analyses. Best performing methods were subsequently applied to real-world human cohort data. RESULTS Our benchmarks showed that prior-based strategies outperform methods without prior information in simulated data and show better replication across datasets. Application of our approach to human cohort data highlighted two novel regulatory networks related to schizophrenia and lean body mass for which we generated novel functional hypotheses. CONCLUSIONS We demonstrate that existing biological knowledge can improve the integrative analysis of networks underlying trans associations and generate novel hypotheses about regulatory mechanisms.
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Affiliation(s)
- Johann S Hawe
- Institute of Computational Biology, German Research Center for Environmental Health, HelmholtzZentrum München, Neuherberg, Germany.,German Heart Centre Munich, Department of Cardiology, Technical University Munich, Munich, Germany.,Department of Informatics, Technical University of Munich, Garching, Germany
| | - Ashis Saha
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Melanie Waldenberger
- Research Unit of Molecular Epidemiology, German Research Center for Environmental Health, HelmholtzZentrum München, Neuherberg, Germany
| | - Sonja Kunze
- Research Unit of Molecular Epidemiology, German Research Center for Environmental Health, HelmholtzZentrum München, Neuherberg, Germany
| | - Simone Wahl
- Research Unit of Molecular Epidemiology, German Research Center for Environmental Health, HelmholtzZentrum München, Neuherberg, Germany
| | - Martina Müller-Nurasyid
- Institute of Genetic Epidemiology, German Research Center for Environmental Health, HelmholtzZentrum München, Neuherberg, Germany.,IBE, Faculty of Medicine, LMU Munich, 81377, Munich, Germany.,Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center, Johannes Gutenberg University, Mainz, Germany.,Department of Internal Medicine I (Cardiology), Hospital of the Ludwig-Maximilians-University (LMU) Munich, Munich, Germany
| | - Holger Prokisch
- Institute of Human Genetics, School of Medicine, Technische Universität München, Munich, Germany
| | - Harald Grallert
- Research Unit of Molecular Epidemiology, German Research Center for Environmental Health, HelmholtzZentrum München, Neuherberg, Germany.,Institute of Epidemiology, German Research Center for Environmental Health, HelmholtzZentrum München, Neuherberg, Germany.,German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Christian Herder
- German Center for Diabetes Research (DZD), Neuherberg, Germany.,Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University, Düsseldorf, Germany.,Division of Endocrinology and Diabetology, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
| | - Annette Peters
- Institute of Epidemiology, German Research Center for Environmental Health, HelmholtzZentrum München, Neuherberg, Germany
| | - Konstantin Strauch
- Institute of Genetic Epidemiology, German Research Center for Environmental Health, HelmholtzZentrum München, Neuherberg, Germany.,Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center, Johannes Gutenberg University, Mainz, Germany.,Chair of Genetic Epidemiology, IBE, Faculty of Medicine, LMU Munich, Munich, Germany
| | - Fabian J Theis
- Department of Informatics, Technical University of Munich, Garching, Germany.,Department of Mathematics, Technical University of Munich, Garching, Germany
| | - Christian Gieger
- Research Unit of Molecular Epidemiology, German Research Center for Environmental Health, HelmholtzZentrum München, Neuherberg, Germany.,Institute of Epidemiology, German Research Center for Environmental Health, HelmholtzZentrum München, Neuherberg, Germany.,German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - John Chambers
- Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK.,Lee Kong Chian School of Medicine, Nanyang Technological University, 308232, Singapore, Singapore
| | - Alexis Battle
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.,Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Matthias Heinig
- Institute of Computational Biology, German Research Center for Environmental Health, HelmholtzZentrum München, Neuherberg, Germany. .,Department of Informatics, Technical University of Munich, Garching, Germany. .,Munich Heart Association, Partner Site Munich, DZHK (German Centre for Cardiovascular Research), 10785, Berlin, Germany.
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Perlo V, Furtado A, Botha FC, Margarido GRA, Hodgson‐Kratky K, Choudhary H, Gladden J, Simmons B, Henry RJ. Transcriptome and metabolome integration in sugarcane through culm development. Food Energy Secur 2022. [DOI: 10.1002/fes3.421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Affiliation(s)
- Virginie Perlo
- Queensland Alliance for Agriculture and Food Innovation University of Queensland Brisbane Australia
| | - Agnelo Furtado
- Queensland Alliance for Agriculture and Food Innovation University of Queensland Brisbane Australia
| | - Frederik C. Botha
- Queensland Alliance for Agriculture and Food Innovation University of Queensland Brisbane Australia
| | - Gabriel R. A. Margarido
- Departamento de Genética, Escola Superior de Agricultura “Luiz de Queiroz” Universidade de São Paulo São Paulo Brazil
| | - Katrina Hodgson‐Kratky
- Queensland Alliance for Agriculture and Food Innovation University of Queensland Brisbane Australia
| | - Hemant Choudhary
- Joint BioEnergy Institute Emeryville CA USA
- Sandia National Laboratories Livermore CA USA
| | - John Gladden
- Joint BioEnergy Institute Emeryville CA USA
- Sandia National Laboratories Livermore CA USA
| | | | - Robert J. Henry
- Queensland Alliance for Agriculture and Food Innovation University of Queensland Brisbane Australia
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Identification of Cardiovascular Disease-Related Genes Based on the Co-Expression Network Analysis of Genome-Wide Blood Transcriptome. Cells 2022; 11:cells11182867. [PMID: 36139449 PMCID: PMC9496853 DOI: 10.3390/cells11182867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 08/26/2022] [Accepted: 09/10/2022] [Indexed: 12/02/2022] Open
Abstract
Inference of co-expression network and identification of disease-related modules and gene sets can help us understand disease-related molecular pathophysiology. We aimed to identify a cardiovascular disease (CVD)-related transcriptomic signature, specifically, in peripheral blood tissue, based on differential expression (DE) and differential co-expression (DcoE) analyses. Publicly available blood sample datasets for coronary artery disease (CAD) and acute coronary syndrome (ACS) statuses were integrated to establish a co-expression network. A weighted gene co-expression network analysis was used to construct modules that include genes with highly correlated expression values. The DE criterion is a linear regression with module eigengenes for module-specific genes calculated from principal component analysis and disease status as the dependent and independent variables, respectively. The DcoE criterion is a paired t-test for intramodular connectivity between disease and matched control statuses. A total of 21 and 23 modules were established from CAD status- and ACS-related datasets, respectively, of which six modules per disease status (i.e., obstructive CAD and ACS) were selected based on the DE and DcoE criteria. For each module, gene–gene interactions with extremely high correlation coefficients were individually selected under the two conditions. Genes displaying a significant change in the number of edges (gene–gene interaction) were selected. A total of 6, 10, and 7 genes in each of the three modules were identified as potential CAD status-related genes, and 14 and 8 genes in each of the two modules were selected as ACS-related genes. Our study identified gene sets and genes that were dysregulated in CVD blood samples. These findings may contribute to the understanding of CVD pathophysiology.
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Park YJ, Rahman MS, Pang WK, Ryu DY, Jung MJ, Amjad S, Kim JM, Pang MG. Systematic multi-omics reveals the overactivation of T cell receptor signaling in immune system following bisphenol A exposure. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2022; 308:119590. [PMID: 35752395 DOI: 10.1016/j.envpol.2022.119590] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 05/17/2022] [Accepted: 06/04/2022] [Indexed: 06/15/2023]
Abstract
Bisphenol A (BPA) is pervasive in the environment, and exposure to BPA may increase the incidence of noncommunicable diseases like autoimmune diseases and cancer. Although BPA causes immunological problems at the cellular level, no system-level research has been conducted on this. Hence, in this study, we aimed to gain a better understanding of the biological response to BPA exposure and its association with immunological disorders. For that, we explored the transcriptome and the proteomic modifications at the systems and cellular levels following BPA exposure. Our integrated multi-omics data showed the alteration of the T cell receptor (TCR) signaling pathway at both levels. The proportion of enlarged T cells increased with upregulation of CD69, a surface marker of early T cell activation, even though the number of T cells reduced after BPA exposure. Additionally, on BPA exposure, the levels of pLCK and pSRC increased in T cells, while that of pLAT decreased. Following BPA exposure, we investigated cytokine profiles and discovered that chitinase 3 Like 1 and matrix metalloproteinase 9 were enriched in T cells. These results indicated that T cells were hyperactivated by CD69 stimulation, and phosphorylation of SRC accelerated on BPA exposure. Hence, alteration in the TCR signaling pathway during development and differentiation due to BPA exposure could lead to insufficient and hasty activation of TCR signaling in T cells, which could modify cytokine profiles, leading to increased environmental susceptibility to chronic inflammation or diseases, increasing the chance of autoimmune diseases and cancer. This study enhances our understanding of the effects of environmental perturbations on immunosuppression at molecular, cellular, and systematic levels following pubertal BPA exposure, and may help develop better predictive, preventative, and therapeutic techniques.
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Affiliation(s)
- Yoo-Jin Park
- Department of Animal Science & Technology and BET Research Institute, Chung-Ang University, Anseong, Gyeonggi-do 17546, South Korea
| | - Md Saidur Rahman
- Department of Animal Science & Technology and BET Research Institute, Chung-Ang University, Anseong, Gyeonggi-do 17546, South Korea
| | - Won-Ki Pang
- Department of Animal Science & Technology and BET Research Institute, Chung-Ang University, Anseong, Gyeonggi-do 17546, South Korea
| | - Do-Yeal Ryu
- Department of Animal Science & Technology and BET Research Institute, Chung-Ang University, Anseong, Gyeonggi-do 17546, South Korea
| | - Min-Ji Jung
- Department of Animal Science & Technology and BET Research Institute, Chung-Ang University, Anseong, Gyeonggi-do 17546, South Korea
| | - Shehreen Amjad
- Department of Animal Science & Technology and BET Research Institute, Chung-Ang University, Anseong, Gyeonggi-do 17546, South Korea
| | - Jun-Mo Kim
- Department of Animal Science & Technology and BET Research Institute, Chung-Ang University, Anseong, Gyeonggi-do 17546, South Korea
| | - Myung-Geol Pang
- Department of Animal Science & Technology and BET Research Institute, Chung-Ang University, Anseong, Gyeonggi-do 17546, South Korea.
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The Future of Biomarkers in Veterinary Medicine: Emerging Approaches and Associated Challenges. Animals (Basel) 2022; 12:ani12172194. [PMID: 36077913 PMCID: PMC9454634 DOI: 10.3390/ani12172194] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 08/20/2022] [Accepted: 08/22/2022] [Indexed: 11/30/2022] Open
Abstract
Simple Summary In this review we seek to outline the role of new technologies in biomarker discovery, particularly within the veterinary field and with an emphasis on ‘omics’, as well as to examine why many biomarkers-despite much excitement-have not yet made it to clinical practice. Further we emphasise the critical need for close collaboration between clinicians, researchers and funding bodies and the need to set clear goals for biomarker requirements and realistic application in the clinical setting, ensuring that biomarker type, method of detection and clinical utility are compatible, and adequate funding, time and sample size are available for all phases of development. Abstract New biomarkers promise to transform veterinary practice through rapid diagnosis of diseases, effective monitoring of animal health and improved welfare and production efficiency. However, the road from biomarker discovery to translation is not always straightforward. This review focuses on molecular biomarkers under development in the veterinary field, introduces the emerging technological approaches transforming this space and the role of ‘omics platforms in novel biomarker discovery. The vast majority of veterinary biomarkers are at preliminary stages of development and not yet ready to be deployed into clinical translation. Hence, we examine the major challenges encountered in the process of biomarker development from discovery, through validation and translation to clinical practice, including the hurdles specific to veterinary practice and to each of the ‘omics platforms–transcriptomics, proteomics, lipidomics and metabolomics. Finally, recommendations are made for the planning and execution of biomarker studies with a view to assisting the success of novel biomarkers in reaching their full potential.
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45
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Parker CC, Philip VM, Gatti DM, Kasparek S, Kreuzman AM, Kuffler L, Mansky B, Masneuf S, Sharif K, Sluys E, Taterra D, Taylor WM, Thomas M, Polesskaya O, Palmer AA, Holmes A, Chesler EJ. Genome-wide association mapping of ethanol sensitivity in the Diversity Outbred mouse population. Alcohol Clin Exp Res 2022; 46:941-960. [PMID: 35383961 DOI: 10.1111/acer.14825] [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: 09/01/2021] [Revised: 03/04/2022] [Accepted: 03/30/2022] [Indexed: 12/01/2022]
Abstract
BACKGROUND A strong predictor for the development of alcohol use disorder (AUD) is altered sensitivity to the intoxicating effects of alcohol. Individual differences in the initial sensitivity to alcohol are controlled in part by genetic factors. Mice offer a powerful tool to elucidate the genetic basis of behavioral and physiological traits relevant to AUD, but conventional experimental crosses have only been able to identify large chromosomal regions rather than specific genes. Genetically diverse, highly recombinant mouse populations make it possible to observe a wider range of phenotypic variation, offer greater mapping precision, and thus increase the potential for efficient gene identification. METHODS We have taken advantage of the Diversity Outbred (DO) mouse population to identify and precisely map quantitative trait loci (QTL) associated with ethanol sensitivity. We phenotyped 798 male J:DO mice for three measures of ethanol sensitivity: ataxia, hypothermia, and loss of the righting response. We used high-density MegaMUGA and GigaMUGA to obtain genotypes ranging from 77,808 to 143,259 SNPs. We also performed RNA sequencing in striatum to map expression QTLs and identify gene expression-trait correlations. We then applied a systems genetic strategy to identify narrow QTLs and construct the network of correlations that exists between DNA sequence, gene expression values, and ethanol-related phenotypes to prioritize our list of positional candidate genes. RESULTS We observed large amounts of phenotypic variation with the DO population and identified suggestive and significant QTLs associated with ethanol sensitivity on chromosomes 1, 2, and 16. The implicated regions were narrow (4.5-6.9 Mb in size) and each QTL explained ~4-5% of the variance. CONCLUSIONS Our results can be used to identify alleles that contribute to AUD in humans, elucidate causative biological mechanisms, or assist in the development of novel therapeutic interventions.
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Affiliation(s)
- Clarissa C Parker
- Department of Psychology and Program in Neuroscience, Middlebury College, Middlebury, Vermont, USA
| | - Vivek M Philip
- Center for Computational Sciences, The Jackson Laboratory, Bar Harbor, Maine, USA
| | - Daniel M Gatti
- Center for Computational Sciences, The Jackson Laboratory, Bar Harbor, Maine, USA
| | - Steven Kasparek
- Department of Psychology and Program in Neuroscience, Middlebury College, Middlebury, Vermont, USA
| | - Andrew M Kreuzman
- Department of Psychology and Program in Neuroscience, Middlebury College, Middlebury, Vermont, USA
| | - Lauren Kuffler
- Center for Mammalian Genetics, The Jackson Laboratory, Bar Harbor, Maine, USA
| | - Benjamin Mansky
- Department of Psychology and Program in Neuroscience, Middlebury College, Middlebury, Vermont, USA
| | - Sophie Masneuf
- Laboratory of Behavioral and Genomic Neuroscience, NIAAA, NIH, Rockville, MD, USA
| | - Kayvon Sharif
- Department of Psychology and Program in Neuroscience, Middlebury College, Middlebury, Vermont, USA
| | - Erica Sluys
- Laboratory of Behavioral and Genomic Neuroscience, NIAAA, NIH, Rockville, MD, USA
| | - Dominik Taterra
- Department of Psychology and Program in Neuroscience, Middlebury College, Middlebury, Vermont, USA
| | - Walter M Taylor
- Department of Psychology and Program in Neuroscience, Middlebury College, Middlebury, Vermont, USA
| | - Mary Thomas
- Department of Psychology and Program in Neuroscience, Middlebury College, Middlebury, Vermont, USA
| | - Oksana Polesskaya
- Department of Psychiatry, University of California San Diego, La Jolla, California, USA.,Institute for Genomic Medicine, University of California San Diego, La Jolla, California, USA
| | - Abraham A Palmer
- Department of Psychiatry, University of California San Diego, La Jolla, California, USA.,Institute for Genomic Medicine, University of California San Diego, La Jolla, California, USA
| | - Andrew Holmes
- Laboratory of Behavioral and Genomic Neuroscience, NIAAA, NIH, Rockville, MD, USA
| | - Elissa J Chesler
- Center for Mammalian Genetics, The Jackson Laboratory, Bar Harbor, Maine, USA
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46
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Grossbach J, Gillet L, Clément‐Ziza M, Schmalohr CL, Schubert OT, Schütter M, Mawer JSP, Barnes CA, Bludau I, Weith M, Tessarz P, Graef M, Aebersold R, Beyer A. The impact of genomic variation on protein phosphorylation states and regulatory networks. Mol Syst Biol 2022; 18:e10712. [PMID: 35574625 PMCID: PMC9109056 DOI: 10.15252/msb.202110712] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 04/08/2022] [Accepted: 04/12/2022] [Indexed: 12/11/2022] Open
Abstract
Genomic variation impacts on cellular networks by affecting the abundance (e.g., protein levels) and the functional states (e.g., protein phosphorylation) of their components. Previous work has focused on the former, while in this context, the functional states of proteins have largely remained neglected. Here, we generated high‐quality transcriptome, proteome, and phosphoproteome data for a panel of 112 genomically well‐defined yeast strains. Genetic effects on transcripts were generally transmitted to the protein layer, but specific gene groups, such as ribosomal proteins, showed diverging effects on protein levels compared with RNA levels. Phosphorylation states proved crucial to unravel genetic effects on signaling networks. Correspondingly, genetic variants that cause phosphorylation changes were mostly different from those causing abundance changes in the respective proteins. Underscoring their relevance for cell physiology, phosphorylation traits were more strongly correlated with cell physiological traits such as chemical compound resistance or cell morphology, compared with transcript or protein abundance. This study demonstrates how molecular networks mediate the effects of genomic variants to cellular traits and highlights the particular importance of protein phosphorylation.
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Affiliation(s)
- Jan Grossbach
- Excellence Cluster on Cellular Stress Responses in Aging Associated Diseases University of Cologne Cologne Germany
| | - Ludovic Gillet
- Department of Biology Institute of Molecular Systems Biology ETH Zurich Zurich Switzerland
| | - Mathieu Clément‐Ziza
- Center for Molecular Medicine Cologne (CMMC) Medical Faculty, University of Cologne Cologne Germany
- Lesaffre International Marcq‐en‐Barœul France
| | - Corinna L Schmalohr
- Excellence Cluster on Cellular Stress Responses in Aging Associated Diseases University of Cologne Cologne Germany
| | - Olga T Schubert
- Department of Human Genetics University of California, Los Angeles Los Angeles CA USA
| | | | | | | | - Isabell Bludau
- Department of Biology Institute of Molecular Systems Biology ETH Zurich Zurich Switzerland
- Department of Proteomics and Signal Transduction Max Planck Institute of Biochemistry Martinsried Germany
| | - Matthias Weith
- Excellence Cluster on Cellular Stress Responses in Aging Associated Diseases University of Cologne Cologne Germany
| | - Peter Tessarz
- Excellence Cluster on Cellular Stress Responses in Aging Associated Diseases University of Cologne Cologne Germany
- Max Planck Institute for Biology of Ageing Cologne Germany
| | - Martin Graef
- Excellence Cluster on Cellular Stress Responses in Aging Associated Diseases University of Cologne Cologne Germany
- Max Planck Institute for Biology of Ageing Cologne Germany
| | - Ruedi Aebersold
- Department of Biology Institute of Molecular Systems Biology ETH Zurich Zurich Switzerland
- Faculty of Science University of Zurich Zurich Switzerland
| | - Andreas Beyer
- Excellence Cluster on Cellular Stress Responses in Aging Associated Diseases University of Cologne Cologne Germany
- Center for Molecular Medicine Cologne (CMMC) Medical Faculty, University of Cologne Cologne Germany
- Institute for Genetics Faculty of Mathematics and Natural Sciences University of Cologne Cologne Germany
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Powerful and robust inference of complex phenotypes' causal genes with dependent expression quantitative loci by a median-based Mendelian randomization. Am J Hum Genet 2022; 109:838-856. [PMID: 35460606 DOI: 10.1016/j.ajhg.2022.04.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Accepted: 04/04/2022] [Indexed: 11/22/2022] Open
Abstract
Isolating the causal genes from numerous genetic association signals in genome-wide association studies (GWASs) of complex phenotypes remains an open and challenging question. In the present study, we proposed a statistical approach, the effective-median-based Mendelian randomization (MR) framework, for inferring the causal genes of complex phenotypes with the GWAS summary statistics (named EMIC). The effective-median method solved the high false-positive issue in the existing MR methods due to either correlation among instrumental variables or noises in approximated linkage disequilibrium (LD). EMIC can further perform a pleiotropy fine-mapping analysis to remove possible false-positive estimates. With the usage of multiple cis-expression quantitative trait loci (eQTLs), EMIC was also more powerful than the alternative methods for the causal gene inference in the simulated datasets. Furthermore, EMIC rediscovered many known causal genes of complex phenotypes (schizophrenia, bipolar disorder, and total cholesterol) and reported many new and promising candidate causal genes. In sum, this study provided an efficient solution to discriminate the candidate causal genes from vast amounts of GWAS signals with eQTLs. EMIC has been implemented in our integrative software platform KGGSEE.
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48
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Elkjaer ML, Röttger R, Baumbach J, Illes Z. A Systematic Review of Tissue and Single Cell Transcriptome/Proteome Studies of the Brain in Multiple Sclerosis. Front Immunol 2022; 13:761225. [PMID: 35309325 PMCID: PMC8924618 DOI: 10.3389/fimmu.2022.761225] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 01/28/2022] [Indexed: 11/27/2022] Open
Abstract
Multiple sclerosis (MS) is an inflammatory demyelinating and degenerative disease of the central nervous system (CNS). Although inflammatory responses are efficiently treated, therapies for progression are scarce and suboptimal, and biomarkers to predict the disease course are insufficient. Cure or preventive measures for MS require knowledge of core pathological events at the site of the tissue damage. Novelties in systems biology have emerged and paved the way for a more fine-grained understanding of key pathological pathways within the CNS, but they have also raised questions still without answers. Here, we systemically review the power of tissue and single-cell/nucleus CNS omics and discuss major gaps of integration into the clinical practice. Systemic search identified 49 transcriptome and 11 proteome studies of the CNS from 1997 till October 2021. Pioneering molecular discoveries indicate that MS affects the whole brain and all resident cell types. Despite inconsistency of results, studies imply increase in transcripts/proteins of semaphorins, heat shock proteins, myelin proteins, apolipoproteins and HLAs. Different lesions are characterized by distinct astrocytic and microglial polarization, altered oligodendrogenesis, and changes in specific neuronal subtypes. In all white matter lesion types, CXCL12, SCD, CD163 are highly expressed, and STAT6- and TGFβ-signaling are increased. In the grey matter lesions, TNF-signaling seems to drive cell death, and especially CUX2-expressing neurons may be susceptible to neurodegeneration. The vast heterogeneity at both cellular and lesional levels may underlie the clinical heterogeneity of MS, and it may be more complex than the current disease phenotyping in the clinical practice. Systems biology has not solved the mystery of MS, but it has discovered multiple molecules and networks potentially contributing to the pathogenesis. However, these results are mostly descriptive; focused functional studies of the molecular changes may open up for a better interpretation. Guidelines for acceptable quality or awareness of results from low quality data, and standardized computational and biological pipelines may help to overcome limited tissue availability and the “snap shot” problem of omics. These may help in identifying core pathological events and point in directions for focus in clinical prevention.
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Affiliation(s)
- Maria L Elkjaer
- Department of Neurology, Odense University Hospital, Odense, Denmark.,Institute of Clinical Research, University of Southern Denmark, Odense, Denmark.,Institute of Molecular Medicine, University of Southern Denmark, Odense, Denmark
| | - Richard Röttger
- Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
| | - Jan Baumbach
- Chair of Computational Systems Biology, University of Hamburg, Hamburg, Germany
| | - Zsolt Illes
- Department of Neurology, Odense University Hospital, Odense, Denmark.,Institute of Clinical Research, University of Southern Denmark, Odense, Denmark.,Institute of Molecular Medicine, University of Southern Denmark, Odense, Denmark
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Yang Z, Liang C, Wei L, Wang S, Yin F, Liu D, Guo L, Zhou Y, Yang QY. BnVIR: bridging the genotype-phenotype gap to accelerate mining of candidate variations underlying agronomic traits in Brassica napus. MOLECULAR PLANT 2022; 15:779-782. [PMID: 35144025 DOI: 10.1016/j.molp.2022.02.002] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 12/08/2021] [Accepted: 02/03/2022] [Indexed: 06/14/2023]
Affiliation(s)
- Zhiquan Yang
- National Key Laboratory of Crop Genetic Improvement, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China; Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Congyuan Liang
- National Key Laboratory of Crop Genetic Improvement, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China; Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - LuLu Wei
- National Key Laboratory of Crop Genetic Improvement, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China; Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Shengbo Wang
- National Key Laboratory of Crop Genetic Improvement, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China; Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Feifan Yin
- National Key Laboratory of Crop Genetic Improvement, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China; Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Dongxu Liu
- National Key Laboratory of Crop Genetic Improvement, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China; Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Liang Guo
- National Key Laboratory of Crop Genetic Improvement, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Yongming Zhou
- National Key Laboratory of Crop Genetic Improvement, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Qing-Yong Yang
- National Key Laboratory of Crop Genetic Improvement, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China; Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.
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Muhtaseb AW, Duan J. Modeling common and rare genetic risk factors of neuropsychiatric disorders in human induced pluripotent stem cells. Schizophr Res 2022:S0920-9964(22)00156-6. [PMID: 35459617 PMCID: PMC9735430 DOI: 10.1016/j.schres.2022.04.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 04/05/2022] [Accepted: 04/07/2022] [Indexed: 12/13/2022]
Abstract
Recent genome-wide association studies (GWAS) and whole-exome sequencing of neuropsychiatric disorders, especially schizophrenia, have identified a plethora of common and rare disease risk variants/genes. Translating the mounting human genetic discoveries into novel disease biology and more tailored clinical treatments is tied to our ability to causally connect genetic risk variants to molecular and cellular phenotypes. When combined with the Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/CRISPR-associated (Cas) nuclease-mediated genome editing system, human induced pluripotent stem cell (hiPSC)-derived neural cultures (both 2D and 3D organoids) provide a promising tractable cellular model for bridging the gap between genetic findings and disease biology. In this review, we first conceptualize the advances in understanding the disease polygenicity and convergence from the past decade of iPSC modeling of different types of genetic risk factors of neuropsychiatric disorders. We then discuss the major cell types and cellular phenotypes that are most relevant to neuropsychiatric disorders in iPSC modeling. Finally, we critically review the limitations of iPSC modeling of neuropsychiatric disorders and outline the need for implementing and developing novel methods to scale up the number of iPSC lines and disease risk variants in a systematic manner. Sufficiently scaled-up iPSC modeling and a better functional interpretation of genetic risk variants, in combination with cutting-edge CRISPR/Cas9 gene editing and single-cell multi-omics methods, will enable the field to identify the specific and convergent molecular and cellular phenotypes in precision for neuropsychiatric disorders.
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Affiliation(s)
- Abdurrahman W Muhtaseb
- Center for Psychiatric Genetics, NorthShore University HealthSystem, Evanston, IL 60201, United States of America; Department of Human Genetics, The University of Chicago, Chicago, IL 60637, United States of America
| | - Jubao Duan
- Center for Psychiatric Genetics, NorthShore University HealthSystem, Evanston, IL 60201, United States of America; Department of Psychiatry and Behavioral Neuroscience, The University of Chicago, Chicago, IL 60637, United States of America.
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