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Retallick-Townsley KG, Lee S, Cartwright S, Cohen S, Sen A, Jia M, Young H, Dobbyn L, Deans M, Fernandez-Garcia M, Huckins LM, Brennand KJ. Dynamic stress- and inflammatory-based regulation of psychiatric risk loci in human neurons. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.09.602755. [PMID: 39026810 PMCID: PMC11257632 DOI: 10.1101/2024.07.09.602755] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/20/2024]
Abstract
The prenatal environment can alter neurodevelopmental and clinical trajectories, markedly increasing risk for psychiatric disorders in childhood and adolescence. To understand if and how fetal exposures to stress and inflammation exacerbate manifestation of genetic risk for complex brain disorders, we report a large-scale context-dependent massively parallel reporter assay (MPRA) in human neurons designed to catalogue genotype x environment (GxE) interactions. Across 240 genome-wide association study (GWAS) loci linked to ten brain traits/disorders, the impact of hydrocortisone, interleukin 6, and interferon alpha on transcriptional activity is empirically evaluated in human induced pluripotent stem cell (hiPSC)-derived glutamatergic neurons. Of ~3,500 candidate regulatory risk elements (CREs), 11% of variants are active at baseline, whereas cue-specific CRE regulatory activity range from a high of 23% (hydrocortisone) to a low of 6% (IL-6). Cue-specific regulatory activity is driven, at least in part, by differences in transcription factor binding activity, the gene targets of which show unique enrichments for brain disorders as well as co-morbid metabolic and immune syndromes. The dynamic nature of genetic regulation informs the influence of environmental factors, reveals a mechanism underlying pleiotropy and variable penetrance, and identifies specific risk variants that confer greater disorder susceptibility after exposure to stress or inflammation. Understanding neurodevelopmental GxE interactions will inform mental health trajectories and uncover novel targets for therapeutic intervention.
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Affiliation(s)
- Kayla G. Retallick-Townsley
- Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - Seoyeon Lee
- Department of Psychiatry, Division of Molecular Psychiatry, Yale University School of Medicine, New Haven, CT 06511
- Department of Genetics, Wu Tsai Institute, Yale University School of Medicine, New Haven, CT 06511
| | - Sam Cartwright
- Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - Sophie Cohen
- Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - Annabel Sen
- Department of Psychiatry, Division of Molecular Psychiatry, Yale University School of Medicine, New Haven, CT 06511
- Department of Genetics, Wu Tsai Institute, Yale University School of Medicine, New Haven, CT 06511
| | - Meng Jia
- Department of Psychiatry, Division of Molecular Psychiatry, Yale University School of Medicine, New Haven, CT 06511
- Department of Genetics, Wu Tsai Institute, Yale University School of Medicine, New Haven, CT 06511
| | - Hannah Young
- Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Lee Dobbyn
- Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Michael Deans
- Department of Psychiatry, Division of Molecular Psychiatry, Yale University School of Medicine, New Haven, CT 06511
- Department of Genetics, Wu Tsai Institute, Yale University School of Medicine, New Haven, CT 06511
| | - Meilin Fernandez-Garcia
- Department of Psychiatry, Division of Molecular Psychiatry, Yale University School of Medicine, New Haven, CT 06511
- Department of Genetics, Wu Tsai Institute, Yale University School of Medicine, New Haven, CT 06511
| | - Laura M. Huckins
- Department of Psychiatry, Division of Molecular Psychiatry, Yale University School of Medicine, New Haven, CT 06511
| | - Kristen J. Brennand
- Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029
- Department of Psychiatry, Division of Molecular Psychiatry, Yale University School of Medicine, New Haven, CT 06511
- Department of Genetics, Wu Tsai Institute, Yale University School of Medicine, New Haven, CT 06511
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Zhuang X, Xu G, Amei A, Cordes D, Wang Z, Oh EC. Detecting time-varying genetic effects in Alzheimer's disease using a longitudinal genome-wide association studies model. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2024; 16:e12597. [PMID: 38855650 PMCID: PMC11157162 DOI: 10.1002/dad2.12597] [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] [Received: 01/09/2024] [Revised: 03/19/2024] [Accepted: 04/17/2024] [Indexed: 06/11/2024]
Abstract
INTRODUCTION The development and progression of Alzheimer's disease (AD) is a complex process, during which genetic influences on phenotypes may also change. Incorporating longitudinal phenotypes in genome-wide association studies (GWAS) could unmask these genetic loci. METHODS We conducted a longitudinal GWAS using a varying coefficient test to identify age-dependent single nucleotide polymorphisms (SNPs) in AD. Data from 1877 Alzheimer's Neuroimaging Data Initiative participants, including impairment status and amyloid positron emission tomography (PET) scan standardized uptake value ratio (SUVR) scores, were analyzed using a retrospective varying coefficient mixed model association test (RVMMAT). RESULTS RVMMAT identified 244 SNPs with significant time-varying effects on AD impairment status, with 12 SNPs on chromosome 19 validated using National Alzheimer's Coordinating Center data. Age-stratified analyses showed these SNPs' effects peaked between 70 and 80 years. Additionally, 73 SNPs were linked to longitudinal amyloid accumulation changes. Pathway analyses implicated immune and neuroinflammation-related disruptions. DISCUSSION Our findings demonstrate that longitudinal GWAS models can uncover time-varying genetic signals in AD. Highlights Identify time-varying genetic effects using a longitudinal GWAS model in AD.Illustrate age-dependent genetic effects on both diagnoses and amyloid accumulation.Replicate time-varying effect of APOE in a second dataset.
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Affiliation(s)
- Xiaowei Zhuang
- Interdisciplinary Neuroscience PhD ProgramUniversity of Nevada Las VegasLas VegasNevadaUSA
- Laboratory of Neurogenetics and Precision MedicineUniversity of Nevada, Las VegasLas VegasNevadaUSA
- Lou Ruvo Center for Brain HealthCleveland ClinicLas VegasNevadaUSA
| | - Gang Xu
- Department of BiostatisticsYale School of Public HealthNew HavenConnecticutUSA
- Department of Mathematical SciencesUniversity of Nevada, Las VegasLas VegasNevadaUSA
| | - Amei Amei
- Department of Mathematical SciencesUniversity of Nevada, Las VegasLas VegasNevadaUSA
| | - Dietmar Cordes
- Lou Ruvo Center for Brain HealthCleveland ClinicLas VegasNevadaUSA
- Institute of Cognitive ScienceUniversity of Colorado BoulderBoulderColoradoUSA
| | - Zuoheng Wang
- Department of BiostatisticsYale School of Public HealthNew HavenConnecticutUSA
| | - Edwin C. Oh
- Interdisciplinary Neuroscience PhD ProgramUniversity of Nevada Las VegasLas VegasNevadaUSA
- Laboratory of Neurogenetics and Precision MedicineUniversity of Nevada, Las VegasLas VegasNevadaUSA
- School of MedicineDepartment of Internal MedicineUniversity of Nevada, Las VegasLas VegasNevadaUSA
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Xu G, Amei A, Wu W, Liu Y, Shen L, Oh EC, Wang Z. RETROSPECTIVE VARYING COEFFICIENT ASSOCIATION ANALYSIS OF LONGITUDINAL BINARY TRAITS: APPLICATION TO THE IDENTIFICATION OF GENETIC LOCI ASSOCIATED WITH HYPERTENSION. Ann Appl Stat 2024; 18:487-505. [PMID: 38577266 PMCID: PMC10994004 DOI: 10.1214/23-aoas1798] [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] [Indexed: 04/06/2024]
Abstract
Many genetic studies contain rich information on longitudinal phenotypes that require powerful analytical tools for optimal analysis. Genetic analysis of longitudinal data that incorporates temporal variation is important for understanding the genetic architecture and biological variation of complex diseases. Most of the existing methods assume that the contribution of genetic variants is constant over time and fail to capture the dynamic pattern of disease progression. However, the relative influence of genetic variants on complex traits fluctuates over time. In this study, we propose a retrospective varying coefficient mixed model association test, RVMMAT, to detect time-varying genetic effect on longitudinal binary traits. We model dynamic genetic effect using smoothing splines, estimate model parameters by maximizing a double penalized quasi-likelihood function, design a joint test using a Cauchy combination method, and evaluate statistical significance via a retrospective approach to achieve robustness to model misspecification. Through simulations we illustrated that the retrospective varying-coefficient test was robust to model misspecification under different ascertainment schemes and gained power over the association methods assuming constant genetic effect. We applied RVMMAT to a genome-wide association analysis of longitudinal measure of hypertension in the Multi-Ethnic Study of Atherosclerosis. Pathway analysis identified two important pathways related to G-protein signaling and DNA damage. Our results demonstrated that RVMMAT could detect biologically relevant loci and pathways in a genome scan and provided insight into the genetic architecture of hypertension.
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Affiliation(s)
- Gang Xu
- Department of Mathematical Sciences, University of Nevada
| | - Amei Amei
- Department of Mathematical Sciences, University of Nevada
| | - Weimiao Wu
- Department of Biostatistics, Yale School of Public Health
| | - Yunqing Liu
- Department of Biostatistics, Yale School of Public Health
| | - Linchuan Shen
- Department of Mathematical Sciences, University of Nevada
| | - Edwin C. Oh
- Department of Internal Medicine, University of Nevada School of Medicine
| | - Zuoheng Wang
- Department of Biostatistics, Yale School of Public Health
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Zhuang X, Xu G, Amei A, Cordes D, Wang Z, Oh EC. Detecting time-varying genetic effects in Alzheimer's disease using a longitudinal GWAS model. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.17.562756. [PMID: 37905044 PMCID: PMC10614870 DOI: 10.1101/2023.10.17.562756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/02/2023]
Abstract
Background The development and progression of Alzheimer's disease (AD) is a complex process that can change over time, during which genetic influences on phenotypes may also fluctuate. Incorporating longitudinal phenotypes in genome wide association studies (GWAS) could help unmask genetic loci with time-varying effects. In this study, we incorporated a varying coefficient test in a longitudinal GWAS model to identify single nucleotide polymorphisms (SNPs) that may have time- or age-dependent effects in AD. Methods Genotype data from 1,877 participants in the Alzheimer's Neuroimaging Data Initiative (ADNI) were imputed using the Haplotype Reference Consortium (HRC) panel, resulting in 9,573,130 SNPs. Subjects' longitudinal impairment status at each visit was considered as a binary and clinical phenotype. Participants' composite standardized uptake value ratio (SUVR) derived from each longitudinal amyloid PET scan was considered as a continuous and biological phenotype. The retrospective varying coefficient mixed model association test (RVMMAT) was used in longitudinal GWAS to detect time-varying genetic effects on the impairment status and SUVR measures. Post-hoc analyses were performed on genome-wide significant SNPs, including 1) pathway analyses; 2) age-stratified genotypic comparisons and regression analyses; and 3) replication analyses using data from the National Alzheimer's Coordinating Center (NACC). Results Our model identified 244 genome-wide significant SNPs that revealed time-varying genetic effects on the clinical impairment status in AD; among which, 12 SNPs on chromosome 19 were successfully replicated using data from NACC. Post-hoc age-stratified analyses indicated that for most of these 244 SNPs, the maximum genotypic effect on impairment status occurred between 70 to 80 years old, and then declined with age. Our model further identified 73 genome-wide significant SNPs associated with the temporal variation of amyloid accumulation. For these SNPs, an increasing genotypic effect on PET-SUVR was observed as participants' age increased. Functional pathway analyses on significant SNPs for both phenotypes highlighted the involvement and disruption of immune responses- and neuroinflammation-related pathways in AD. Conclusion We demonstrate that longitudinal GWAS models with time-varying coefficients can boost the statistical power in AD-GWAS. In addition, our analyses uncovered potential time-varying genetic variants on repeated measurements of clinical and biological phenotypes in AD.
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Culberson JW, Kopel J, Sehar U, Reddy PH. Urgent needs of caregiving in ageing populations with Alzheimer's disease and other chronic conditions: Support our loved ones. Ageing Res Rev 2023; 90:102001. [PMID: 37414157 PMCID: PMC10756323 DOI: 10.1016/j.arr.2023.102001] [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: 04/13/2023] [Revised: 06/27/2023] [Accepted: 07/03/2023] [Indexed: 07/08/2023]
Abstract
The ageing process begins at birth. It is a life-long process, and its exact origins are still unknown. Several hypotheses attempt to describe the normal ageing process, including hormonal imbalance, formation of reactive oxygen species, DNA methylation & DNA damage accumulation, loss of proteostasis, epigenetic alterations, mitochondrial dysfunction, senescence, inflammation, and stem cell depletion. With increased lifespan in elderly individuals, the prevalence of age-related diseases including, cancer, diabetes, obesity, hypertension, Alzheimer's, Alzheimer's disease and related dementias, Parkinson's, and other mental illnesses are increased. These increased age-related illnesses, put tremendous pressure & burden on caregivers, family members, and friends who are living with patients with age-related diseases. As medical needs evolve, the caregiver is expected to experience an increase in duties and challenges, which may result in stress on themselves, and impact their own family life. In the current article, we assess the biological mechanisms of ageing and its effect on body systems, exploring lifestyle and ageing, with a specific focus on age-related disorders. We also discussed the history of caregiving and specific challenges faced by caregivers in the presence of multiple comorbidities. We also assessed innovative approaches to funding caregiving, and efforts to improve the medical system to better organize chronic care efforts, while improving the skill and efficiency of both informal and formal caregivers. We also discussed the role of caregiving in end-of-life care. Our critical analysis strongly suggests that there is an urgent need for caregiving in aged populations and support from local, state, and federal agencies.
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Affiliation(s)
- John W Culberson
- Department of Family and Community Medicine, Texas Tech University Health Sciences Center, Lubbock, TX, USA
| | - Jonathan Kopel
- Department of Internal Medicine, Texas Tech University Health Sciences Center, Lubbock, TX, USA
| | - Ujala Sehar
- Department of Internal Medicine, Texas Tech University Health Sciences Center, Lubbock, TX, USA
| | - P Hemachandra Reddy
- Department of Internal Medicine, Texas Tech University Health Sciences Center, Lubbock, TX, USA; Nutritional Sciences Department, College of Human Sciences, Texas Tech University, 1301 Akron Ave, Lubbock, TX 79409, USA; Neurology, Departments of School of Medicine, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA; Public Health Department of Graduate School of Biomedical Sciences, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA; Department of Speech, Language and Hearing Sciences, School Health Professions, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA; Department of Pharmacology and Neuroscience, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA.
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Kang JB, Raveane A, Nathan A, Soranzo N, Raychaudhuri S. Methods and Insights from Single-Cell Expression Quantitative Trait Loci. Annu Rev Genomics Hum Genet 2023; 24:277-303. [PMID: 37196361 PMCID: PMC10784788 DOI: 10.1146/annurev-genom-101422-100437] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/19/2023]
Abstract
Recent advancements in single-cell technologies have enabled expression quantitative trait locus (eQTL) analysis across many individuals at single-cell resolution. Compared with bulk RNA sequencing, which averages gene expression across cell types and cell states, single-cell assays capture the transcriptional states of individual cells, including fine-grained, transient, and difficult-to-isolate populations at unprecedented scale and resolution. Single-cell eQTL (sc-eQTL) mapping can identify context-dependent eQTLs that vary with cell states, including some that colocalize with disease variants identified in genome-wide association studies. By uncovering the precise contexts in which these eQTLs act, single-cell approaches can unveil previously hidden regulatory effects and pinpoint important cell states underlying molecular mechanisms of disease. Here, we present an overview of recently deployed experimental designs in sc-eQTL studies. In the process, we consider the influence of study design choices such as cohort, cell states, and ex vivo perturbations. We then discuss current methodologies, modeling approaches, and technical challenges as well as future opportunities and applications.
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Affiliation(s)
- Joyce B Kang
- Center for Data Sciences and Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA; ,
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA;
| | | | - Aparna Nathan
- Center for Data Sciences and Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA; ,
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA;
| | - Nicole Soranzo
- Human Technopole, Milan, Italy; ,
- Department of Human Genetics, Wellcome Sanger Institute, Hinxton, United Kingdom
- British Heart Foundation Centre of Research Excellence and Department of Haematology, University of Cambridge, Cambridge, United Kingdom
| | - Soumya Raychaudhuri
- Center for Data Sciences and Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA; ,
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA;
- Centre for Genetics and Genomics Versus Arthritis, University of Manchester, Manchester, United Kingdom
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Perez BC, Bink MCAM, Svenson KL, Churchill GA, Calus MPL. Adding gene transcripts into genomic prediction improves accuracy and reveals sampling time dependence. G3 (BETHESDA, MD.) 2022; 12:jkac258. [PMID: 36161485 PMCID: PMC9635642 DOI: 10.1093/g3journal/jkac258] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Accepted: 09/07/2022] [Indexed: 06/16/2023]
Abstract
Recent developments allowed generating multiple high-quality 'omics' data that could increase the predictive performance of genomic prediction for phenotypes and genetic merit in animals and plants. Here, we have assessed the performance of parametric and nonparametric models that leverage transcriptomics in genomic prediction for 13 complex traits recorded in 478 animals from an outbred mouse population. Parametric models were implemented using the best linear unbiased prediction, while nonparametric models were implemented using the gradient boosting machine algorithm. We also propose a new model named GTCBLUP that aims to remove between-omics-layer covariance from predictors, whereas its counterpart GTBLUP does not do that. While gradient boosting machine models captured more phenotypic variation, their predictive performance did not exceed the best linear unbiased prediction models for most traits. Models leveraging gene transcripts captured higher proportions of the phenotypic variance for almost all traits when these were measured closer to the moment of measuring gene transcripts in the liver. In most cases, the combination of layers was not able to outperform the best single-omics models to predict phenotypes. Using only gene transcripts, the gradient boosting machine model was able to outperform best linear unbiased prediction for most traits except body weight, but the same pattern was not observed when using both single nucleotide polymorphism genotypes and gene transcripts. Although the GTCBLUP model was not able to produce the most accurate phenotypic predictions, it showed the highest accuracies for breeding values for 9 out of 13 traits. We recommend using the GTBLUP model for prediction of phenotypes and using the GTCBLUP for prediction of breeding values.
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Affiliation(s)
- Bruno C Perez
- Hendrix Genetics B.V., Research and Technology Center (RTC), 5830 AC Boxmeer, The Netherlands
| | - Marco C A M Bink
- Hendrix Genetics B.V., Research and Technology Center (RTC), 5830 AC Boxmeer, The Netherlands
| | | | | | - Mario P L Calus
- Corresponding author: Animal Breeding and Genomics, Wageningen University & Research, P.O. Box 338, 6700 AH Wageningen, The Netherlands.
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Xu J, Lan Y, Wang X, Shang K, Liu X, Wang J, Li J, Yue B, Shao M, Fan Z. Multi-omics analysis reveals the host–microbe interactions in aged rhesus macaques. Front Microbiol 2022; 13:993879. [PMID: 36238598 PMCID: PMC9551614 DOI: 10.3389/fmicb.2022.993879] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 09/08/2022] [Indexed: 11/13/2022] Open
Abstract
Aging is a complex multifactorial process that greatly affects animal health. Multi-omics analysis is widely applied in evolutionary biology and biomedical research. However, whether multi-omics can provide sufficient information to reveal comprehensive changes in aged non-human primates remains unclear. Here, we explored changes in host–microbe interactions with aging in Chinese rhesus macaques (Macaca mulatta lasiota, CRs) using multi-omics analysis. Results showed marked changes in the oral and gut microbiomes between young and aged CRs, including significantly reduced probiotic abundance and increased pathogenic bacterial abundance in aged CRs. Notably, the abundance of Lactobacillus, which can metabolize tryptophan to produce aryl hydrocarbon receptor (AhR) ligands, was decreased in aged CRs. Consistently, metabolomics detected a decrease in the plasma levels of AhR ligands. In addition, free fatty acid, acyl carnitine, heparin, 2-(4-hydroxyphenyl) propionic acid, and docosahexaenoic acid ethyl ester levels were increased in aged CRs, which may contribute to abnormal fatty acid metabolism and cardiovascular disease. Transcriptome analysis identified changes in the expression of genes associated with tryptophan metabolism and inflammation. In conclusion, many potential links among different omics were found, suggesting that aged CRs face multiple metabolic problems, immunological disorders, and oral and gut diseases. We determined that tryptophan metabolism is critical for the physiological health of aged CRs. Our findings demonstrate the value of multi-omics analyses in revealing host–microbe interactions in non-human primates and suggest that similar approaches could be applied in evolutionary and ecological research of other species.
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Affiliation(s)
- Jue Xu
- West China School of Public Health and West China Fourth Hospital, Chengdu, Sichuan, China
| | - Yue Lan
- Key Laboratory of Bioresources and Ecoenvironment (Ministry of Education), College of Life Sciences, Sichuan University, Chengdu, China
| | - Xinqi Wang
- Key Laboratory of Bioresources and Ecoenvironment (Ministry of Education), College of Life Sciences, Sichuan University, Chengdu, China
| | - Ke Shang
- Key Laboratory of Bioresources and Ecoenvironment (Ministry of Education), College of Life Sciences, Sichuan University, Chengdu, China
| | - Xu Liu
- Sichuan Key Laboratory of Conservation Biology on Endangered Wildlife, College of Life Sciences, Sichuan University, Chengdu, China
| | - Jiao Wang
- Key Laboratory of Bioresources and Ecoenvironment (Ministry of Education), College of Life Sciences, Sichuan University, Chengdu, China
| | - Jing Li
- Key Laboratory of Bioresources and Ecoenvironment (Ministry of Education), College of Life Sciences, Sichuan University, Chengdu, China
| | - Bisong Yue
- Sichuan Key Laboratory of Conservation Biology on Endangered Wildlife, College of Life Sciences, Sichuan University, Chengdu, China
| | - Meiying Shao
- West China School of Public Health and West China Fourth Hospital, Chengdu, Sichuan, China
- *Correspondence: Meiying Shao,
| | - Zhenxin Fan
- Key Laboratory of Bioresources and Ecoenvironment (Ministry of Education), College of Life Sciences, Sichuan University, Chengdu, China
- Sichuan Key Laboratory of Conservation Biology on Endangered Wildlife, College of Life Sciences, Sichuan University, Chengdu, China
- Zhenxin Fan,
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Sikder S, Arunkumar G, Melters DP, Dalal Y. Breaking the aging epigenetic barrier. Front Cell Dev Biol 2022; 10:943519. [PMID: 35966762 PMCID: PMC9366916 DOI: 10.3389/fcell.2022.943519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 07/04/2022] [Indexed: 11/13/2022] Open
Abstract
Aging is an inexorable event occurring universally for all organisms characterized by the progressive loss of cell function. However, less is known about the key events occurring inside the nucleus in the process of aging. The advent of chromosome capture techniques and extensive modern sequencing technologies have illuminated a rather dynamic structure of chromatin inside the nucleus. As cells advance along their life cycle, chromatin condensation states alter which leads to a different epigenetic landscape, correlated with modified gene expression. The exact factors mediating these changes in the chromatin structure and function remain elusive in the context of aging cells. The accumulation of DNA damage, reactive oxygen species and loss of genomic integrity as cells cease to divide can contribute to a tumor stimulating environment. In this review, we focus on genomic and epigenomic changes occurring in an aged cell which can contribute to age-related tumor formation.
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Schork NJ, Beaulieu-Jones B, Liang W, Smalley S, Goetz LH. Does Modulation of an Epigenetic Clock Define a Geroprotector? ADVANCES IN GERIATRIC MEDICINE AND RESEARCH 2022; 4:e220002. [PMID: 35466328 PMCID: PMC9022671 DOI: 10.20900/agmr20220002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
There is growing interest in the development of interventions (e.g., drugs, diets, dietary supplements, behavioral therapies, etc.) that can enhance health during the aging process, prevent or delay multiple age-related diseases, and ultimately extend lifespan. However, proving that such 'geroprotectors' do what they are hypothesized to do in relevant clinical trials is not trivial. We briefly discuss some of the more salient issues surrounding the design and interpretation of clinical trials of geroprotectors, including, importantly, how one defines a geroprotector. We also discuss whether emerging surrogate endpoints, such as epigenetic clocks, should be treated as primary or secondary endpoints in such trials. Simply put, geroprotectors should provide overt health and disease prevention benefits but the time-dependent relationships between epigenetic clocks and health-related phenomena are complex and in need of further scrutiny. Therefore, studies that enable understanding of the relationships between epigenetic clocks and disease processes while simultaneously testing the efficacy of a candidate geroprotector are crucial to move the field forward.
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Affiliation(s)
- Nicholas J. Schork
- Department of Quantitative Medicine, The Translational Genomics Research Institute (TGen), 445 North Fifth Street, Phoenix, AZ 85004, USA
- Net.bio Inc, Los Angeles, CA 90403, USA
| | - Brett Beaulieu-Jones
- Net.bio Inc, Los Angeles, CA 90403, USA
- Department of Biomedical Informatics, Harvard University, Cambridge, MA 02115, USA
| | | | - Susan Smalley
- Net.bio Inc, Los Angeles, CA 90403, USA
- Department of Psychiatry and Biobehavioral Sciences, The University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Laura H. Goetz
- Department of Quantitative Medicine, The Translational Genomics Research Institute (TGen), 445 North Fifth Street, Phoenix, AZ 85004, USA
- Net.bio Inc, Los Angeles, CA 90403, USA
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Simpson DJ, Olova NN, Chandra T. Cellular reprogramming and epigenetic rejuvenation. Clin Epigenetics 2021; 13:170. [PMID: 34488874 PMCID: PMC8419998 DOI: 10.1186/s13148-021-01158-7] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Accepted: 08/02/2021] [Indexed: 11/10/2022] Open
Abstract
Ageing is an inevitable condition that afflicts all humans. Recent achievements, such as the generation of induced pluripotent stem cells, have delivered preliminary evidence that slowing down and reversing the ageing process might be possible. However, these techniques usually involve complete dedifferentiation, i.e. somatic cell identity is lost as cells are converted to a pluripotent state. Separating the rejuvenative properties of reprogramming from dedifferentiation is a promising prospect, termed epigenetic rejuvenation. Reprogramming-induced rejuvenation strategies currently involve using Yamanaka factors (typically transiently expressed to prevent full dedifferentiation) and are promising candidates to safely reduce biological age. Here, we review the development and potential of reprogramming-induced rejuvenation as an anti-ageing strategy.
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Affiliation(s)
- Daniel J Simpson
- MRC Human Genetics Unit, MRC Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XU, UK.
| | - Nelly N Olova
- MRC Human Genetics Unit, MRC Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XU, UK.
| | - Tamir Chandra
- MRC Human Genetics Unit, MRC Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XU, UK.
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12
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Simpson DJ, Chandra T. Epigenetic age prediction. Aging Cell 2021; 20:e13452. [PMID: 34415665 PMCID: PMC8441394 DOI: 10.1111/acel.13452] [Citation(s) in RCA: 59] [Impact Index Per Article: 19.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 07/21/2021] [Accepted: 07/27/2021] [Indexed: 12/14/2022] Open
Abstract
Advanced age is the main common risk factor for cancer, cardiovascular disease and neurodegeneration. Yet, more is known about the molecular basis of any of these groups of diseases than the changes that accompany ageing itself. Progress in molecular ageing research was slow because the tools predicting whether someone aged slowly or fast (biological age) were unreliable. To understand ageing as a risk factor for disease and to develop interventions, the molecular ageing field needed a quantitative measure; a clock for biological age. Over the past decade, a number of age predictors utilising DNA methylation have been developed, referred to as epigenetic clocks. While they appear to estimate biological age, it remains unclear whether the methylation changes used to train the clocks are a reflection of other underlying cellular or molecular processes, or whether methylation itself is involved in the ageing process. The precise aspects of ageing that the epigenetic clocks capture remain hidden and seem to vary between predictors. Nonetheless, the use of epigenetic clocks has opened the door towards studying biological ageing quantitatively, and new clocks and applications, such as forensics, appear frequently. In this review, we will discuss the range of epigenetic clocks available, their strengths and weaknesses, and their applicability to various scientific queries.
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Affiliation(s)
- Daniel J. Simpson
- MRC Human Genetics UnitMRC Institute of Genetics and Molecular MedicineUniversity of EdinburghEdinburghUK
| | - Tamir Chandra
- MRC Human Genetics UnitMRC Institute of Genetics and Molecular MedicineUniversity of EdinburghEdinburghUK
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13
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Szymczak S, Dose J, Torres GG, Heinsen FA, Venkatesh G, Datlinger P, Nygaard M, Mengel-From J, Flachsbart F, Klapper W, Christensen K, Lieb W, Schreiber S, Häsler R, Bock C, Franke A, Nebel A. DNA methylation QTL analysis identifies new regulators of human longevity. Hum Mol Genet 2021; 29:1154-1167. [PMID: 32160291 PMCID: PMC7206852 DOI: 10.1093/hmg/ddaa033] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2019] [Revised: 01/01/2020] [Accepted: 02/11/2020] [Indexed: 12/14/2022] Open
Abstract
Human longevity is a complex trait influenced by both genetic and environmental factors, whose interaction is mediated by epigenetic mechanisms like DNA methylation. Here, we generated genome-wide whole-blood methylome data from 267 individuals, of which 71 were long-lived (90–104 years), by applying reduced representation bisulfite sequencing. We followed a stringent two-stage analysis procedure using discovery and replication samples to detect differentially methylated sites (DMSs) between young and long-lived study participants. Additionally, we performed a DNA methylation quantitative trait loci analysis to identify DMSs that underlie the longevity phenotype. We combined the DMSs results with gene expression data as an indicator of functional relevance. This approach yielded 21 new candidate genes, the majority of which are involved in neurophysiological processes or cancer. Notably, two candidates (PVRL2, ERCC1) are located on chromosome 19q, in close proximity to the well-known longevity- and Alzheimer’s disease-associated loci APOE and TOMM40. We propose this region as a longevity hub, operating on both a genetic (APOE, TOMM40) and an epigenetic (PVRL2, ERCC1) level. We hypothesize that the heritable methylation and associated gene expression changes reported here are overall advantageous for the LLI and may prevent/postpone age-related diseases and facilitate survival into very old age.
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Affiliation(s)
- Silke Szymczak
- Institute of Medical Informatics and Statistics, Kiel University, University Hospital Schleswig-Holstein, Campus Kiel, D-24105 Kiel, Germany
| | - Janina Dose
- Institute of Clinical Molecular Biology, Kiel University, University Hospital Schleswig-Holstein, Campus Kiel, D-24105 Kiel, Germany
| | - Guillermo G Torres
- Institute of Clinical Molecular Biology, Kiel University, University Hospital Schleswig-Holstein, Campus Kiel, D-24105 Kiel, Germany
| | - Femke-Anouska Heinsen
- Institute of Clinical Molecular Biology, Kiel University, University Hospital Schleswig-Holstein, Campus Kiel, D-24105 Kiel, Germany
| | - Geetha Venkatesh
- Institute of Clinical Molecular Biology, Kiel University, University Hospital Schleswig-Holstein, Campus Kiel, D-24105 Kiel, Germany
| | - Paul Datlinger
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, A-1090 Vienna, Austria
| | - Marianne Nygaard
- Research Unit of Epidemiology, Biostatistics and Biodemography, Department of Public Health, University of Southern Denmark, DK-5000 Odense C, Denmark.,Department of Clinical Genetics, Odense University Hospital, DK-5000 Odense C, Denmark
| | - Jonas Mengel-From
- Research Unit of Epidemiology, Biostatistics and Biodemography, Department of Public Health, University of Southern Denmark, DK-5000 Odense C, Denmark.,Department of Clinical Genetics, Odense University Hospital, DK-5000 Odense C, Denmark
| | - Friederike Flachsbart
- Institute of Clinical Molecular Biology, Kiel University, University Hospital Schleswig-Holstein, Campus Kiel, D-24105 Kiel, Germany
| | - Wolfram Klapper
- Institute of Pathology, Kiel University, University Hospital Schleswig-Holstein, Campus Kiel, D-24105 Kiel, Germany
| | - Kaare Christensen
- Research Unit of Epidemiology, Biostatistics and Biodemography, Department of Public Health, University of Southern Denmark, DK-5000 Odense C, Denmark.,Department of Clinical Genetics, Odense University Hospital, DK-5000 Odense C, Denmark.,Department of Clinical Biochemistry and Pharmacology, Odense University Hospital, DK-5000 Odense C, Denmark
| | - Wolfgang Lieb
- Institute of Epidemiology, Kiel University, University Hospital Schleswig-Holstein, Campus Kiel, D-24105 Kiel, Germany
| | - Stefan Schreiber
- Institute of Clinical Molecular Biology, Kiel University, University Hospital Schleswig-Holstein, Campus Kiel, D-24105 Kiel, Germany
| | - Robert Häsler
- Institute of Clinical Molecular Biology, Kiel University, University Hospital Schleswig-Holstein, Campus Kiel, D-24105 Kiel, Germany
| | - Christoph Bock
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, A-1090 Vienna, Austria.,Department of Laboratory Medicine, Medical University of Vienna, A-1090 Vienna, Austria.,Max Planck Institute for Informatics, Saarland Informatics Campus, D-66123 Saarbrücken, Germany
| | - Andre Franke
- Institute of Clinical Molecular Biology, Kiel University, University Hospital Schleswig-Holstein, Campus Kiel, D-24105 Kiel, Germany
| | - Almut Nebel
- Institute of Clinical Molecular Biology, Kiel University, University Hospital Schleswig-Holstein, Campus Kiel, D-24105 Kiel, Germany
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14
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Hoffman GE, Roussos P. Dream: powerful differential expression analysis for repeated measures designs. Bioinformatics 2021; 37:192-201. [PMID: 32730587 DOI: 10.1093/bioinformatics/btaa687] [Citation(s) in RCA: 103] [Impact Index Per Article: 34.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Revised: 07/13/2020] [Accepted: 07/23/2020] [Indexed: 01/08/2023] Open
Abstract
SUMMARY Large-scale transcriptome studies with multiple samples per individual are widely used to study disease biology. Yet, current methods for differential expression are inadequate for cross-individual testing for these repeated measures designs. Most problematic, we observe across multiple datasets that current methods can give reproducible false-positive findings that are driven by genetic regulation of gene expression, yet are unrelated to the trait of interest. Here, we introduce a statistical software package, dream, that increases power, controls the false positive rate, enables multiple types of hypothesis tests, and integrates with standard workflows. In 12 analyses in 6 independent datasets, dream yields biological insight not found with existing software while addressing the issue of reproducible false-positive findings. AVAILABILITY AND IMPLEMENTATION Dream is available within the variancePartition Bioconductor package at http://bioconductor.org/packages/variancePartition. CONTACT gabriel.hoffman@mssm.edu. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Gabriel E Hoffman
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.,Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Panos Roussos
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.,Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.,Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.,Mental Illness Research, Education, and Clinical Center (VISN 2 South), James J. Peters VA Medical Center, Bronx, NY 10468, USA
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15
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Vaughan D, Mitchell R, Kretz O, Chambers D, Lalowski M, Amthor H, Ritvos O, Pasternack A, Matsakas A, Vaiyapuri S, Huber TB, Denecke B, Mukherjee A, Widera D, Patel K. A muscle growth-promoting treatment based on the attenuation of activin/myostatin signalling results in long-term testicular abnormalities. Dis Model Mech 2021; 14:dmm.047555. [PMID: 33408083 PMCID: PMC7903914 DOI: 10.1242/dmm.047555] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Accepted: 12/22/2020] [Indexed: 12/19/2022] Open
Abstract
Activin/myostatin signalling acts to induce skeletal muscle atrophy in adult mammals by inhibiting protein synthesis as well as promoting protein and organelle turnover. Numerous strategies have been successfully developed to attenuate the signalling properties of these molecules, which result in augmenting muscle growth. However, these molecules, in particular activin, play major roles in tissue homeostasis in numerous organs of the mammalian body. We have recently shown that although the attenuation of activin/myostatin results in robust muscle growth, it also has a detrimental impact on the testis. Here, we aimed to discover the long-term consequences of a brief period of exposure to muscle growth-promoting molecules in the testis. We demonstrate that muscle hypertrophy promoted by a soluble activin type IIB ligand trap (sActRIIB) is a short-lived phenomenon. In stark contrast, short-term treatment with sActRIIB results in immediate impact on the testis, which persists after the sessions of the intervention. Gene array analysis identified an expansion in aberrant gene expression over time in the testis, initiated by a brief exposure to muscle growth-promoting molecules. The impact on the testis results in decreased organ size as well as quantitative and qualitative impact on sperm. Finally, we have used a drug-repurposing strategy to exploit the gene expression data to identify a compound - N 6-methyladenosine - that may protect the testis from the impact of the muscle growth-promoting regime. This work indicates the potential long-term harmful effects of strategies aimed at promoting muscle growth by attenuating activin/myostatin signalling. Furthermore, we have identified a molecule that could, in the future, be used to overcome the detrimental impact of sActRIIB treatment on the testis.
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Affiliation(s)
- Danielle Vaughan
- School of Biological Sciences, University of Reading, Reading NW1 0TU, UK
| | - Robert Mitchell
- School of Biological Sciences, University of Reading, Reading NW1 0TU, UK
| | - Oliver Kretz
- III. Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg 20251, Germany
| | - David Chambers
- Functional Genomics, King's College London, London SE1 1UL, UK
| | - Maciej Lalowski
- Department of Biochemistry and Developmental Biology, HiLIFE, Meilahti Clinical Proteomics Core Facility, University of Helsinki, Helsinki 00014, Finland
| | - Helge Amthor
- Versailles Saint-Quentin-en-Yvelines University, INSERM U1179, LIA BAHN CSM, Montigny-le-Bretonneux 78180, France
| | - Olli Ritvos
- Department of Bacteriology and Immunology, University of Helsinki, Helsinki 00014, Finland
| | - Arja Pasternack
- Department of Bacteriology and Immunology, University of Helsinki, Helsinki 00014, Finland
| | - Antonios Matsakas
- Molecular Physiology Laboratory, Centre for Atherothrombosis and Metabolic Disease, Hull York Medical School, Hull HU6 7RX, UK
| | | | - Tobias B Huber
- III. Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg 20251, Germany
| | | | | | - Darius Widera
- School of Pharmacy, University of Reading, Reading RG6 6UB, UK
| | - Ketan Patel
- School of Biological Sciences, University of Reading, Reading NW1 0TU, UK
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16
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Principles of the Molecular and Cellular Mechanisms of Aging. J Invest Dermatol 2021; 141:951-960. [PMID: 33518357 DOI: 10.1016/j.jid.2020.11.018] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Revised: 10/23/2020] [Accepted: 11/02/2020] [Indexed: 12/13/2022]
Abstract
Aging can be defined as a state of progressive functional decline accompanied by an increase in mortality. Time-dependent accumulation of cellular damage, namely lesions and mutations in the DNA and misfolded proteins, impair organellar and cellular function. Ensuing cell fate alterations lead to the accumulation of dysfunctional cells and hamper homeostatic processes, thus limiting regenerative potential; trigger low-grade inflammation; and alter intercellular and intertissue communication. The accumulation of molecular damage together with modifications in the epigenetic landscape, dysregulation of gene expression, and altered endocrine communication, drive the aging process and establish age as the main risk factor for age-associated diseases and multimorbidity.
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17
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Arjas A, Hauptmann A, Sillanpää MJ. Estimation of dynamic SNP-heritability with Bayesian Gaussian process models. Bioinformatics 2020; 36:3795-3802. [PMID: 32186692 PMCID: PMC7672693 DOI: 10.1093/bioinformatics/btaa199] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Revised: 03/10/2020] [Accepted: 03/17/2020] [Indexed: 11/23/2022] Open
Abstract
Motivation Improved DNA technology has made it practical to estimate single-nucleotide polymorphism (SNP)-heritability among distantly related individuals with unknown relationships. For growth- and development-related traits, it is meaningful to base SNP-heritability estimation on longitudinal data due to the time-dependency of the process. However, only few statistical methods have been developed so far for estimating dynamic SNP-heritability and quantifying its full uncertainty. Results We introduce a completely tuning-free Bayesian Gaussian process (GP)-based approach for estimating dynamic variance components and heritability as their function. For parameter estimation, we use a modern Markov Chain Monte Carlo method which allows full uncertainty quantification. Several datasets are analysed and our results clearly illustrate that the 95% credible intervals of the proposed joint estimation method (which ‘borrows strength’ from adjacent time points) are significantly narrower than of a two-stage baseline method that first estimates the variance components at each time point independently and then performs smoothing. We compare the method with a random regression model using MTG2 and BLUPF90 software and quantitative measures indicate superior performance of our method. Results are presented for simulated and real data with up to 1000 time points. Finally, we demonstrate scalability of the proposed method for simulated data with tens of thousands of individuals. Availability and implementation The C++ implementation dynBGP and simulated data are available in GitHub: https://github.com/aarjas/dynBGP. The programmes can be run in R. Real datasets are available in QTL archive: https://phenome.jax.org/centers/QTLA. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Arttu Arjas
- Research Unit of Mathematical Sciences, University of Oulu, Oulu FI-90014, Finland
| | - Andreas Hauptmann
- Research Unit of Mathematical Sciences, University of Oulu, Oulu FI-90014, Finland.,Department of Computer Science, University College London, London WC1E 6BT, UK
| | - Mikko J Sillanpää
- Research Unit of Mathematical Sciences, University of Oulu, Oulu FI-90014, Finland.,Infotech Oulu, University of Oulu, Oulu FI-90014, Finland
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18
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Lee JR, Choe SH, Kim YH, Cho HM, Park HR, Lee HE, Jin YB, Kim JS, Jeong KJ, Park SJ, Huh JW. Longitudinal profiling of the blood transcriptome in an African green monkey aging model. Aging (Albany NY) 2020; 13:846-864. [PMID: 33290253 PMCID: PMC7834999 DOI: 10.18632/aging.202190] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Accepted: 10/22/2020] [Indexed: 12/14/2022]
Abstract
African green monkeys (AGMs, Chlorocebus aethiops) are Old World monkeys which are used as experimental models in biomedical research. Recent technological advances in next generation sequencing are useful for unraveling the genetic mechanisms underlying senescence, aging, and age-related disease. To elucidate the normal aging mechanisms in older age, the blood transcriptomes of nine healthy, aged AGMs (15‒23 years old), were analyzed over two years. We identified 910‒1399 accumulated differentially expressed genes (DEGs) in each individual, which increased with age. Aging-related DEGs were sorted across the three time points. A major proportion of the aging-related DEGs belonged to gene ontology (GO) categories involved in translation and rRNA metabolic processes. Next, we sorted common aging-related DEGs across three time points over two years. Common aging-related DEGs belonged to GO categories involved in translation, cellular component biogenesis, rRNA metabolic processes, cellular component organization, biogenesis, and RNA metabolic processes. Furthermore, we identified 29 candidate aging genes that were upregulated across the time series analysis. These candidate aging genes were linked to protein synthesis. This study describes a changing gene expression pattern in AGMs during aging using longitudinal transcriptome sequencing. The candidate aging genes identified here may be potential targets for the treatment of aging.
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Affiliation(s)
- Ja-Rang Lee
- Primate Resource Center, Korea Research Institute of Bioscience and Biotechnology, Jeongeup 56216, Republic of Korea
| | - Se-Hee Choe
- National Primate Research Center, Korea Research Institute of Bioscience and Biotechnology, Cheongju 28116, Republic of Korea.,Department of Functional Genomics, KRIBB School of Bioscience, Korea University of Science and Technology (UST), Daejeon 34113, Republic of Korea
| | - Young-Hyun Kim
- National Primate Research Center, Korea Research Institute of Bioscience and Biotechnology, Cheongju 28116, Republic of Korea
| | - Hyeon-Mu Cho
- National Primate Research Center, Korea Research Institute of Bioscience and Biotechnology, Cheongju 28116, Republic of Korea.,Department of Functional Genomics, KRIBB School of Bioscience, Korea University of Science and Technology (UST), Daejeon 34113, Republic of Korea
| | - Hye-Ri Park
- National Primate Research Center, Korea Research Institute of Bioscience and Biotechnology, Cheongju 28116, Republic of Korea.,Department of Functional Genomics, KRIBB School of Bioscience, Korea University of Science and Technology (UST), Daejeon 34113, Republic of Korea
| | - Hee-Eun Lee
- National Primate Research Center, Korea Research Institute of Bioscience and Biotechnology, Cheongju 28116, Republic of Korea
| | - Yeung Bae Jin
- National Primate Research Center, Korea Research Institute of Bioscience and Biotechnology, Cheongju 28116, Republic of Korea
| | - Ji-Su Kim
- Primate Resource Center, Korea Research Institute of Bioscience and Biotechnology, Jeongeup 56216, Republic of Korea
| | - Kang Jin Jeong
- National Primate Research Center, Korea Research Institute of Bioscience and Biotechnology, Cheongju 28116, Republic of Korea
| | - Sang-Je Park
- National Primate Research Center, Korea Research Institute of Bioscience and Biotechnology, Cheongju 28116, Republic of Korea
| | - Jae-Won Huh
- National Primate Research Center, Korea Research Institute of Bioscience and Biotechnology, Cheongju 28116, Republic of Korea.,Department of Functional Genomics, KRIBB School of Bioscience, Korea University of Science and Technology (UST), Daejeon 34113, Republic of Korea
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19
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Zhou M, Zhang L, Yang Q, Yan C, Jiang P, Lan Y, Wang J, Tang R, He M, Lei G, Sun P, Su N, Price M, Li J, Lin F, Yue B, Fan Z. Age-related gene expression and DNA methylation changes in rhesus macaque. Genomics 2020; 112:5147-5156. [DOI: 10.1016/j.ygeno.2020.09.021] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Revised: 08/23/2020] [Accepted: 09/08/2020] [Indexed: 02/06/2023]
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20
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Wu Y, Broadaway KA, Raulerson CK, Scott LJ, Pan C, Ko A, He A, Tilford C, Fuchsberger C, Locke AE, Stringham HM, Jackson AU, Narisu N, Kuusisto J, Pajukanta P, Collins FS, Boehnke M, Laakso M, Lusis AJ, Civelek M, Mohlke KL. Colocalization of GWAS and eQTL signals at loci with multiple signals identifies additional candidate genes for body fat distribution. Hum Mol Genet 2020; 28:4161-4172. [PMID: 31691812 DOI: 10.1093/hmg/ddz263] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2019] [Revised: 10/23/2019] [Accepted: 10/30/2019] [Indexed: 12/22/2022] Open
Abstract
Integration of genome-wide association study (GWAS) signals with expression quantitative trait loci (eQTL) studies enables identification of candidate genes. However, evaluating whether nearby signals may share causal variants, termed colocalization, is affected by the presence of allelic heterogeneity, different variants at the same locus impacting the same phenotype. We previously identified eQTL in subcutaneous adipose tissue from 770 participants in the Metabolic Syndrome in Men (METSIM) study and detected 15 eQTL signals that colocalized with GWAS signals for waist-hip ratio adjusted for body mass index (WHRadjBMI) from the Genetic Investigation of Anthropometric Traits consortium. Here, we reevaluated evidence of colocalization using two approaches, conditional analysis and the Bayesian test COLOC, and show that providing COLOC with approximate conditional summary statistics at multi-signal GWAS loci can reconcile disagreements in colocalization classification between the two tests. Next, we performed conditional analysis on the METSIM subcutaneous adipose tissue data to identify conditionally distinct or secondary eQTL signals. We used the two approaches to test for colocalization with WHRadjBMI GWAS signals and evaluated the differences in colocalization classification between the two tests. Through these analyses, we identified four GWAS signals colocalized with secondary eQTL signals for FAM13A, SSR3, GRB14 and FMO1. Thus, at loci with multiple eQTL and/or GWAS signals, analyzing each signal independently enabled additional candidate genes to be identified.
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Affiliation(s)
- Ying Wu
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA
| | - K Alaine Broadaway
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Chelsea K Raulerson
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Laura J Scott
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Calvin Pan
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA
| | - Arthur Ko
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA
| | - Aiqing He
- Bristol-Myers Squibb, Pennington, NJ 08534, USA
| | | | - Christian Fuchsberger
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA.,Center for Biomedicine, European Academy of Bolzano/Bozen, University of Lübeck, Bolzano/Bozen, Italy
| | - Adam E Locke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA.,McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO 63108, USA
| | - Heather M Stringham
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Anne U Jackson
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Narisu Narisu
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Johanna Kuusisto
- Department of Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio 70210, Finland
| | - Päivi Pajukanta
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA
| | - Francis S Collins
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Markku Laakso
- Department of Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio 70210, Finland
| | - Aldons J Lusis
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA
| | - Mete Civelek
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA.,Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22904, USA
| | - Karen L Mohlke
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA
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21
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Rondina MT, Voora D, Simon LM, Schwertz H, Harper JF, Lee O, Bhatlekar SC, Li Q, Eustes AS, Montenont E, Campbell RA, Tolley ND, Kosaka Y, Weyrich AS, Bray PF, Rowley JW. Longitudinal RNA-Seq Analysis of the Repeatability of Gene Expression and Splicing in Human Platelets Identifies a Platelet SELP Splice QTL. Circ Res 2019; 126:501-516. [PMID: 31852401 DOI: 10.1161/circresaha.119.315215] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
RATIONALE Longitudinal studies are required to distinguish within versus between-individual variation and repeatability of gene expression. They are uniquely positioned to decipher genetic signal from environmental noise, with potential application to gene variant and expression studies. However, longitudinal analyses of gene expression in healthy individuals-especially with regards to alternative splicing-are lacking for most primary cell types, including platelets. OBJECTIVE To assess repeatability of gene expression and splicing in platelets and use repeatability to identify novel platelet expression quantitative trait loci (QTLs) and splice QTLs. METHODS AND RESULTS We sequenced the transcriptome of platelets isolated repeatedly up to 4 years from healthy individuals. We examined within and between individual variation and repeatability of platelet RNA expression and exon skipping, a readily measured alternative splicing event. We find that platelet gene expression is generally stable between and within-individuals over time-with the exception of a subset of genes enriched for the inflammation gene ontology. We show an enrichment among repeatable genes for associations with heritable traits, including known and novel platelet expression QTLs. Several exon skipping events were also highly repeatable, suggesting heritable patterns of splicing in platelets. One of the most repeatable was exon 14 skipping of SELP. Accordingly, we identify rs6128 as a platelet splice QTL and define an rs6128-dependent association between SELP exon 14 skipping and race. In vitro experiments demonstrate that this single nucleotide variant directly affects exon 14 skipping and changes the ratio of transmembrane versus soluble P-selectin protein production. CONCLUSIONS We conclude that the platelet transcriptome is generally stable over 4 years. We demonstrate the use of repeatability of gene expression and splicing to identify novel platelet expression QTLs and splice QTLs. rs6128 is a platelet splice QTL that alters SELP exon 14 skipping and soluble versus transmembrane P-selectin protein production.
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Affiliation(s)
- Matthew T Rondina
- From the Molecular Medicine Program (M.T.R., H.S., J.F.H., O.L., S.C.B., A.S.E., E.M., R.A.C., N.D.T., Y.K., A.S.W., P.F.B., J.W.R.)
- Department of Internal Medicine (M.T.R., H.S., R.A.C., A.S.W., P.F.B., J.W.R.)
- George E. Wahlen VAMC Geriatric Research and Education Clinical Center (M.T.R.)
| | - Deepak Voora
- Duke Center for Applied Genomics & Precision Medicine, Durham, NC (D.V.)
| | - Lukas M Simon
- Helmholtz Zentrum München, German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Germany (L.M.S.)
| | - Hansjörg Schwertz
- From the Molecular Medicine Program (M.T.R., H.S., J.F.H., O.L., S.C.B., A.S.E., E.M., R.A.C., N.D.T., Y.K., A.S.W., P.F.B., J.W.R.)
- Department of Internal Medicine (M.T.R., H.S., R.A.C., A.S.W., P.F.B., J.W.R.)
- Rocky Mountain Center for Occupational and Environmental Health, The University of Utah, Salt Lake City (H.S.)
| | - Julie F Harper
- From the Molecular Medicine Program (M.T.R., H.S., J.F.H., O.L., S.C.B., A.S.E., E.M., R.A.C., N.D.T., Y.K., A.S.W., P.F.B., J.W.R.)
| | - Olivia Lee
- From the Molecular Medicine Program (M.T.R., H.S., J.F.H., O.L., S.C.B., A.S.E., E.M., R.A.C., N.D.T., Y.K., A.S.W., P.F.B., J.W.R.)
| | - Seema C Bhatlekar
- From the Molecular Medicine Program (M.T.R., H.S., J.F.H., O.L., S.C.B., A.S.E., E.M., R.A.C., N.D.T., Y.K., A.S.W., P.F.B., J.W.R.)
| | - Qing Li
- Huntsman Cancer Institute, Salt Lake City, Utah (Q.L.)
| | - Alicia S Eustes
- From the Molecular Medicine Program (M.T.R., H.S., J.F.H., O.L., S.C.B., A.S.E., E.M., R.A.C., N.D.T., Y.K., A.S.W., P.F.B., J.W.R.)
| | - Emilie Montenont
- From the Molecular Medicine Program (M.T.R., H.S., J.F.H., O.L., S.C.B., A.S.E., E.M., R.A.C., N.D.T., Y.K., A.S.W., P.F.B., J.W.R.)
| | - Robert A Campbell
- From the Molecular Medicine Program (M.T.R., H.S., J.F.H., O.L., S.C.B., A.S.E., E.M., R.A.C., N.D.T., Y.K., A.S.W., P.F.B., J.W.R.)
- Department of Internal Medicine (M.T.R., H.S., R.A.C., A.S.W., P.F.B., J.W.R.)
| | - Neal D Tolley
- From the Molecular Medicine Program (M.T.R., H.S., J.F.H., O.L., S.C.B., A.S.E., E.M., R.A.C., N.D.T., Y.K., A.S.W., P.F.B., J.W.R.)
| | - Yasuhiro Kosaka
- From the Molecular Medicine Program (M.T.R., H.S., J.F.H., O.L., S.C.B., A.S.E., E.M., R.A.C., N.D.T., Y.K., A.S.W., P.F.B., J.W.R.)
| | - Andrew S Weyrich
- From the Molecular Medicine Program (M.T.R., H.S., J.F.H., O.L., S.C.B., A.S.E., E.M., R.A.C., N.D.T., Y.K., A.S.W., P.F.B., J.W.R.)
- Department of Internal Medicine (M.T.R., H.S., R.A.C., A.S.W., P.F.B., J.W.R.)
| | - Paul F Bray
- From the Molecular Medicine Program (M.T.R., H.S., J.F.H., O.L., S.C.B., A.S.E., E.M., R.A.C., N.D.T., Y.K., A.S.W., P.F.B., J.W.R.)
- Department of Internal Medicine (M.T.R., H.S., R.A.C., A.S.W., P.F.B., J.W.R.)
| | - Jesse W Rowley
- From the Molecular Medicine Program (M.T.R., H.S., J.F.H., O.L., S.C.B., A.S.E., E.M., R.A.C., N.D.T., Y.K., A.S.W., P.F.B., J.W.R.)
- Department of Internal Medicine (M.T.R., H.S., R.A.C., A.S.W., P.F.B., J.W.R.)
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22
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Heritability of skewed X-inactivation in female twins is tissue-specific and associated with age. Nat Commun 2019; 10:5339. [PMID: 31767861 PMCID: PMC6877649 DOI: 10.1038/s41467-019-13340-w] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2019] [Accepted: 11/04/2019] [Indexed: 12/16/2022] Open
Abstract
Female somatic X-chromosome inactivation (XCI) balances the X-linked transcriptional dosages between the sexes. Skewed XCI toward one parental X has been observed in several complex human traits, but the extent to which genetics and environment influence skewed XCI is largely unexplored. To address this, we quantify XCI-skew in multiple tissues and immune cell types in a twin cohort. Within an individual, XCI-skew differs between blood, fat and skin tissue, but is shared across immune cell types. XCI skew increases with age in blood, but not other tissues, and is associated with smoking. XCI-skew is increased in twins with Rheumatoid Arthritis compared to unaffected identical co-twins. XCI-skew is heritable in blood of females >55 years old (h2 = 0.34), but not in younger individuals or other tissues. This results in a Gene x Age interaction that shifts the functional dosage of all X-linked heterozygous loci in a tissue-restricted manner. Skewing of X chromosome inactivation (XCI) occurs when the silencing of one parental X chromosome is non-random. Here, Zito et al. report XCI patterns in lymphoblastoid cell lines, blood, subcutaneous adipose tissue samples and skin samples of monozygotic and dizygotic twins and find XCI skew to associate with tissue and age.
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23
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Balliu B, Durrant M, Goede OD, Abell N, Li X, Liu B, Gloudemans MJ, Cook NL, Smith KS, Knowles DA, Pala M, Cucca F, Schlessinger D, Jaiswal S, Sabatti C, Lind L, Ingelsson E, Montgomery SB. Genetic regulation of gene expression and splicing during a 10-year period of human aging. Genome Biol 2019; 20:230. [PMID: 31684996 PMCID: PMC6827221 DOI: 10.1186/s13059-019-1840-y] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2019] [Accepted: 09/27/2019] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Molecular and cellular changes are intrinsic to aging and age-related diseases. Prior cross-sectional studies have investigated the combined effects of age and genetics on gene expression and alternative splicing; however, there has been no long-term, longitudinal characterization of these molecular changes, especially in older age. RESULTS We perform RNA sequencing in whole blood from the same individuals at ages 70 and 80 to quantify how gene expression, alternative splicing, and their genetic regulation are altered during this 10-year period of advanced aging at a population and individual level. We observe that individuals are more similar to their own expression profiles later in life than profiles of other individuals their own age. We identify 1291 and 294 genes differentially expressed and alternatively spliced with age, as well as 529 genes with outlying individual trajectories. Further, we observe a strong correlation of genetic effects on expression and splicing between the two ages, with a small subset of tested genes showing a reduction in genetic associations with expression and splicing in older age. CONCLUSIONS These findings demonstrate that, although the transcriptome and its genetic regulation is mostly stable late in life, a small subset of genes is dynamic and is characterized by a reduction in genetic regulation, most likely due to increasing environmental variance with age.
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Affiliation(s)
- Brunilda Balliu
- Department of Pathology, Stanford University School of Medicine, Stanford, USA.
| | - Matthew Durrant
- Department of Genetics, Stanford University School of Medicine, Stanford, USA
| | - Olivia de Goede
- Department of Genetics, Stanford University School of Medicine, Stanford, USA
| | - Nathan Abell
- Department of Genetics, Stanford University School of Medicine, Stanford, USA
| | - Xin Li
- Department of Pathology, Stanford University School of Medicine, Stanford, USA
| | - Boxiang Liu
- Department of Biology, Stanford University School of Medicine, Stanford, USA
| | | | - Naomi L Cook
- Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Kevin S Smith
- Department of Pathology, Stanford University School of Medicine, Stanford, USA
| | | | - Mauro Pala
- Dipartimento di Scienze Biomediche, Universita di Sassari, Sassari, Italy
| | - Francesco Cucca
- Dipartimento di Scienze Biomediche, Universita di Sassari, Sassari, Italy
| | | | - Siddhartha Jaiswal
- Department of Pathology, Stanford University School of Medicine, Stanford, USA
| | - Chiara Sabatti
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, USA
| | - Lars Lind
- Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Erik Ingelsson
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, USA.
- Stanford Cardiovascular Institute, Stanford University, Stanford, USA.
- Stanford Diabetes Research Center, Stanford University, Stanford, USA.
| | - Stephen B Montgomery
- Department of Pathology, Stanford University School of Medicine, Stanford, USA.
- Department of Genetics, Stanford University School of Medicine, Stanford, USA.
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24
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Ouwens KG, Jansen R, Nivard MG, van Dongen J, Frieser MJ, Hottenga JJ, Arindrarto W, Claringbould A, van Iterson M, Mei H, Franke L, Heijmans BT, A C 't Hoen P, van Meurs J, Brooks AI, Penninx BWJH, Boomsma DI. A characterization of cis- and trans-heritability of RNA-Seq-based gene expression. Eur J Hum Genet 2019; 28:253-263. [PMID: 31558840 DOI: 10.1038/s41431-019-0511-5] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2018] [Revised: 07/21/2019] [Accepted: 08/12/2019] [Indexed: 01/19/2023] Open
Abstract
Insights into individual differences in gene expression and its heritability (h2) can help in understanding pathways from DNA to phenotype. We estimated the heritability of gene expression of 52,844 genes measured in whole blood in the largest twin RNA-Seq sample to date (1497 individuals including 459 monozygotic twin pairs and 150 dizygotic twin pairs) from classical twin modeling and identity-by-state-based approaches. We estimated for each gene h2total, composed of cis-heritability (h2cis, the variance explained by single nucleotide polymorphisms in the cis-window of the gene), and trans-heritability (h2res, the residual variance explained by all other genome-wide variants). Mean h2total was 0.26, which was significantly higher than heritability estimates earlier found in a microarray-based study using largely overlapping (>60%) RNA samples (mean h2 = 0.14, p = 6.15 × 10-258). Mean h2cis was 0.06 and strongly correlated with beta of the top cis expression quantitative loci (eQTL, ρ = 0.76, p < 10-308) and with estimates from earlier RNA-Seq-based studies. Mean h2res was 0.20 and correlated with the beta of the corresponding trans-eQTL (ρ = 0.04, p < 1.89 × 10-3) and was significantly higher for genes involved in cytokine-cytokine interactions (p = 4.22 × 10-15), many other immune system pathways, and genes identified in genome-wide association studies for various traits including behavioral disorders and cancer. This study provides a thorough characterization of cis- and trans-h2 estimates of gene expression, which is of value for interpretation of GWAS and gene expression studies.
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Affiliation(s)
- Klaasjan G Ouwens
- Department of Biological Psychology, Amsterdam Public Health research institute, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
| | - Rick Jansen
- Department of Psychiatry, Amsterdam Public Health and Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | - Michel G Nivard
- Department of Biological Psychology, Amsterdam Public Health research institute, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Jenny van Dongen
- Department of Biological Psychology, Amsterdam Public Health research institute, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Maia J Frieser
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, USA.,Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, USA
| | - Jouke-Jan Hottenga
- Department of Biological Psychology, Amsterdam Public Health research institute, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Wibowo Arindrarto
- Sequencing Analysis Support Core, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - Annique Claringbould
- Department of Genetics, University of Groningen, University Medical Centre Groningen, Groningen, The Netherlands
| | - Maarten van Iterson
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - Hailiang Mei
- Sequencing Analysis Support Core, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - Lude Franke
- Department of Genetics, University of Groningen, University Medical Centre Groningen, Groningen, The Netherlands
| | - Bastiaan T Heijmans
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - Peter A C 't Hoen
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands.,Center for Molecular and Biomolecular Informatics, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center Nijmegen, Nijmegen, the Netherlands
| | - Joyce van Meurs
- Department of Internal Medicine, ErasmusMC, Rotterdam, The Netherlands
| | - Andrew I Brooks
- Department of Genetics and the Human Genetics Institute, RUCDR Infinite Biologics, Rutgers University, New Brunswick, NJ, USA
| | | | - Brenda W J H Penninx
- Department of Psychiatry, Amsterdam Public Health and Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | - Dorret I Boomsma
- Department of Biological Psychology, Amsterdam Public Health research institute, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
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25
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Payán-Gómez C, Rodríguez D, Amador-Muñoz D, Ramírez-Clavijo S. Integrative Analysis of Global Gene Expression Identifies Opposite Patterns of Reactive Astrogliosis in Aged Human Prefrontal Cortex. Brain Sci 2018; 8:brainsci8120227. [PMID: 30572619 PMCID: PMC6317157 DOI: 10.3390/brainsci8120227] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2018] [Revised: 11/28/2018] [Accepted: 12/04/2018] [Indexed: 12/14/2022] Open
Abstract
The prefrontal cortex (PFC) is one of the brain regions with more prominent changes in human aging. The molecular processes related to the cognitive decline and mood changes during aging are not completely understood. To improve our knowledge, we integrated transcriptomic data of four studies of human PFC from elderly people (58–80 years old) compared with younger people (20–40 years old) using a meta-analytic approximation combined with molecular signature analysis. We identified 1817 differentially expressed genes, 561 up-regulated and 1256 down-regulated. Pathway analysis revealed down-regulation of synaptic genes with conservation of gene expression of other neuronal regions. Additionally, we identified up-regulation of markers of astrogliosis with transcriptomic signature compatible with A1 neurotoxic astrocytes and A2 neuroprotective astrocytes. Response to interferon is related to A1 astrocytes and the A2 phenotype is mediated in aging by activation of sonic hedgehog (SHH) pathway and up-regulation of metallothioneins I and genes of the family ERM (ezrin, radixin, and moesin). The main conclusions of our study are the confirmation of a global dysfunction of the synapses in the aged PFC and the evidence of opposite phenotypes of astrogliosis in the aging brain, which we report for the first time in the present article.
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Affiliation(s)
- César Payán-Gómez
- Facultad de Ciencias Naturales y Matemáticas, Universidad del Rosario, Bogotá 111221, Colombia.
| | - Diego Rodríguez
- Neuroscience (NEUROS) Research Group, School of Medicine and Health Sciences, Universidad del Rosario, Carrera 24 No. 63C-69, Bogotá 111221, Colombia.
| | - Diana Amador-Muñoz
- Neuroscience (NEUROS) Research Group, School of Medicine and Health Sciences, Universidad del Rosario, Carrera 24 No. 63C-69, Bogotá 111221, Colombia.
| | - Sandra Ramírez-Clavijo
- Facultad de Ciencias Naturales y Matemáticas, Universidad del Rosario, Bogotá 111221, Colombia.
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26
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Tremblay BL, Guénard F, Lamarche B, Pérusse L, Vohl MC. Familial resemblances in human whole blood transcriptome. BMC Genomics 2018; 19:300. [PMID: 29703154 PMCID: PMC5921553 DOI: 10.1186/s12864-018-4698-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2017] [Accepted: 04/18/2018] [Indexed: 12/31/2022] Open
Abstract
Background Considering the implication of gene expression in the susceptibility of chronic diseases and the familial clustering of chronic diseases, the study of familial resemblances in gene expression levels is then highly relevant. Few studies have considered the contribution of both genetic and common environmental effects to familial resemblances in whole blood gene expression levels. The objective is to quantify the contribution of genetic and common environmental effects in the familial resemblances of whole blood genome-wide gene expression levels. We also make comparisons with familial resemblances in blood leukocytes genome-wide DNA methylation levels in the same cohort in order to further investigate biological mechanisms. Results Maximal heritability, genetic heritability, and common environmental effect were computed for all probes (20.6%, 15.6%, and 5.0% respectively) and for probes showing a significant familial effect (78.1%, 60.1%, and 18.0% respectively). Pairwise phenotypic correlations between gene expression and DNA methylation levels adjusted for blood cell heterogeneity were computed for probes showing significant familial effect. A total of 78 probe pairs among the 7,618,401 possible pairs passed Bonferroni correction (corrected P-value = 6.56 × 10− 9). Significant genetic correlations between gene expression and DNA methylation levels were found for 25 probe pairs (absolute genetic correlation of 0.97). Conclusions Familial resemblances in gene expression levels were mainly attributable to genetic factors, but common environmental effect also played a role especially in probes showing a significant familial effect. Probes and CpG sites with familial effect seem to be under a strong shared genetic control. Electronic supplementary material The online version of this article (10.1186/s12864-018-4698-6) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Bénédicte L Tremblay
- Institute of Nutrition and Functional Foods (INAF), Laval University, Pavillon des Services, 2440 Hochelaga Blvd, Quebec City, QC G1V 0A6, Canada
| | - Frédéric Guénard
- Institute of Nutrition and Functional Foods (INAF), Laval University, Pavillon des Services, 2440 Hochelaga Blvd, Quebec City, QC G1V 0A6, Canada
| | - Benoît Lamarche
- Institute of Nutrition and Functional Foods (INAF), Laval University, Pavillon des Services, 2440 Hochelaga Blvd, Quebec City, QC G1V 0A6, Canada
| | - Louis Pérusse
- CHU de Québec Research Center - Endocrinology and Nephrology, 2705 Laurier Blvd, Quebec City, QC G1V 4G2, Canada
| | - Marie-Claude Vohl
- Institute of Nutrition and Functional Foods (INAF), Laval University, Pavillon des Services, 2440 Hochelaga Blvd, Quebec City, QC G1V 0A6, Canada. .,CHU de Québec Research Center - Endocrinology and Nephrology, 2705 Laurier Blvd, Quebec City, QC G1V 4G2, Canada.
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27
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Abstract
Ageing leads to dramatic changes in the physiology of many different tissues resulting in a spectrum of pathology. Nonetheless, many lines of evidence suggest that ageing is driven by highly conserved cell intrinsic processes, and a set of unifying hallmarks of ageing has been defined. Here, we survey reports of age-linked changes in basal gene expression across eukaryotes from yeast to human and identify six gene expression hallmarks of cellular ageing: downregulation of genes encoding mitochondrial proteins; downregulation of the protein synthesis machinery; dysregulation of immune system genes; reduced growth factor signalling; constitutive responses to stress and DNA damage; dysregulation of gene expression and mRNA processing. These encompass widely reported features of ageing such as increased senescence and inflammation, reduced electron transport chain activity and reduced ribosome synthesis, but also reveal a surprising lack of gene expression responses to known age-linked cellular stresses. We discuss how the existence of conserved transcriptomic hallmarks relates to genome-wide epigenetic differences underlying ageing clocks, and how the changing transcriptome results in proteomic alterations where data is available and to variations in cell physiology characteristic of ageing. Identification of gene expression events that occur during ageing across distant organisms should be informative as to conserved underlying mechanisms of ageing, and provide additional biomarkers to assess the effects of diet and other environmental factors on the rate of ageing.
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Affiliation(s)
- Stephen Frenk
- Department of Genetics, University of North Carolina, Chapel Hill, NC, 27599-3280, USA
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