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Huider F, Milaneschi Y, Hottenga JJ, Bot M, Rietman ML, Kok AAL, Galesloot TE, 't Hart LM, Rutters F, Blom MT, Rhebergen D, Visser M, Brouwer I, Feskens E, Hartman CA, Oldehinkel AJ, de Geus EJC, Kiemeney LA, Huisman M, Picavet HSJ, Verschuren WMM, van Loo HM, Penninx BWJH, Boomsma DI. Genomics Research of Lifetime Depression in the Netherlands: The BIObanks Netherlands Internet Collaboration (BIONIC) Project. Twin Res Hum Genet 2024:1-11. [PMID: 38497097 DOI: 10.1017/thg.2024.4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
In this cohort profile article we describe the lifetime major depressive disorder (MDD) database that has been established as part of the BIObanks Netherlands Internet Collaboration (BIONIC). Across the Netherlands we collected data on Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) lifetime MDD diagnosis in 132,850 Dutch individuals. Currently, N = 66,684 of these also have genomewide single nucleotide polymorphism (SNP) data. We initiated this project because the complex genetic basis of MDD requires large population-wide studies with uniform in-depth phenotyping. For standardized phenotyping we developed the LIDAS (LIfetime Depression Assessment Survey), which then was used to measure MDD in 11 Dutch cohorts. Data from these cohorts were combined with diagnostic interview depression data from 5 clinical cohorts to create a dataset of N = 29,650 lifetime MDD cases (22%) meeting DSM-5 criteria and 94,300 screened controls. In addition, genomewide genotype data from the cohorts were assembled into a genomewide association study (GWAS) dataset of N = 66,684 Dutch individuals (25.3% cases). Phenotype data include DSM-5-based MDD diagnoses, sociodemographic variables, information on lifestyle and BMI, characteristics of depressive symptoms and episodes, and psychiatric diagnosis and treatment history. We describe the establishment and harmonization of the BIONIC phenotype and GWAS datasets and provide an overview of the available information and sample characteristics. Our next step is the GWAS of lifetime MDD in the Netherlands, with future plans including fine-grained genetic analyses of depression characteristics, international collaborations and multi-omics studies.
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Affiliation(s)
- Floris Huider
- Department of Biological Psychology, Faculty of Behavioral and Movement Sciences, Vrije Universiteit Amsterdam, 1081 Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, 1105 Amsterdam, the Netherlands
| | - Yuri Milaneschi
- Amsterdam Public Health Research Institute, 1105 Amsterdam, the Netherlands
- Department of Psychiatry, Amsterdam UMC location Vrije Universiteit Amsterdam, 1081 Amsterdam, the Netherlands
| | - Jouke-Jan Hottenga
- Department of Biological Psychology, Faculty of Behavioral and Movement Sciences, Vrije Universiteit Amsterdam, 1081 Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, 1105 Amsterdam, the Netherlands
| | - Mariska Bot
- Amsterdam Public Health Research Institute, 1105 Amsterdam, the Netherlands
- Department of Psychiatry, Amsterdam UMC location Vrije Universiteit Amsterdam, 1081 Amsterdam, the Netherlands
| | - M Liset Rietman
- Center for Prevention, Lifestyle and Health, Dutch National Institute for Public Health and the Environment, 3721 Bilthoven, the Netherlands
| | - Almar A L Kok
- Amsterdam Public Health Research Institute, 1105 Amsterdam, the Netherlands
- Department of Epidemiology and Data Science, Amsterdam UMC location Vrije Universiteit, 1081 Amsterdam, the Netherlands
| | | | | | | | | | - Didi Rhebergen
- Amsterdam Public Health Research Institute, 1105 Amsterdam, the Netherlands
- Department of Psychiatry, Amsterdam UMC location Vrije Universiteit Amsterdam, 1081 Amsterdam, the Netherlands
- Mental health Institute GGZ Centraal, Amersfoort, the Netherlands
| | - Marjolein Visser
- Department of Health Sciences, Faculty of Science, Vrije Universiteit Amsterdam, 1081 Amsterdam, the Netherlands
| | - Ingeborg Brouwer
- Department of Health Sciences, Faculty of Science, Vrije Universiteit Amsterdam, 1081 Amsterdam, the Netherlands
| | - Edith Feskens
- Division of Human Nutrition and Health, Wageningen University & Research, 6700 Wageningen, the Netherlands
| | - Catharina A Hartman
- Department of Psychiatry, University of Groningen, University Medical Center Groningen, 9713 Groningen, the Netherlands
| | - Albertine J Oldehinkel
- Department of Psychiatry, University of Groningen, University Medical Center Groningen, 9713 Groningen, the Netherlands
| | - Eco J C de Geus
- Department of Biological Psychology, Faculty of Behavioral and Movement Sciences, Vrije Universiteit Amsterdam, 1081 Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, 1105 Amsterdam, the Netherlands
| | | | - Martijn Huisman
- Amsterdam Public Health Research Institute, 1105 Amsterdam, the Netherlands
- Department of Epidemiology and Data Science, Amsterdam UMC location Vrije Universiteit, 1081 Amsterdam, the Netherlands
- Department of Sociology, Vrije Universiteit Amsterdam, 1081 Amsterdam, the Netherlands
| | - H Susan J Picavet
- Center for Prevention, Lifestyle and Health, Dutch National Institute for Public Health and the Environment, 3721 Bilthoven, the Netherlands
| | - W M Monique Verschuren
- Center for Prevention, Lifestyle and Health, Dutch National Institute for Public Health and the Environment, 3721 Bilthoven, the Netherlands
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, 3584 Utrecht, the Netherlands
| | - Hanna M van Loo
- Department of Psychiatry, University of Groningen, University Medical Center Groningen, 9713 Groningen, the Netherlands
| | - Brenda W J H Penninx
- Amsterdam Public Health Research Institute, 1105 Amsterdam, the Netherlands
- Department of Psychiatry, Amsterdam UMC location Vrije Universiteit Amsterdam, 1081 Amsterdam, the Netherlands
| | - Dorret I Boomsma
- Department of Biological Psychology, Faculty of Behavioral and Movement Sciences, Vrije Universiteit Amsterdam, 1081 Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, 1105 Amsterdam, the Netherlands
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Giacconi R, Laffon B, Costa S, Teixeira-Gomes A, Maggi F, Macera L, Spezia PG, Piacenza F, Bürkle A, Moreno-Villanueva M, Bonassi S, Valdiglesias V, Teixeira JP, Dollé ME, Rietman ML, Jansen E, Grune T, Gonos ES, Franceschi C, Capri M, Weinberger B, Sikora E, Stuetz W, Toussaint O, Debacq-Chainiaux F, Hervonen A, Hurme M, Slagboom PE, Schön C, Bernhardt J, Breusing N, Pásaro E, Maseda A, Lorenzo-López L, Millán-Calenti JC, Provinciali M, Malavolta M. Association of Torquetenovirus viremia with physical frailty and cognitive impairment in three independent European cohorts. Gerontology 2022:000528169. [DOI: 10.1159/000528169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 10/10/2022] [Indexed: 12/24/2022] Open
Abstract
Introduction: Immunosenescence and inflammaging have been implicated in the pathophysiology of frailty. Torquetenovirus (TTV), a single-stranded DNA anellovirus, the major component of the human blood virome, shows an increased replication rate with advancing age. An elevated TTV viremia has been associated with an impaired immune function and an increased risk of mortality in the older population. The objective of this study was to analyze the relation between TTV viremia, physical frailty and cognitive impairment
Methods: TTV viremia was measured in 1131 nonfrail, 45 physically frail, and 113 cognitively impaired older adults recruited in the MARK-AGE study (overall mean age 64.7±5.9 years), then the results were checked in two other independent cohorts from Spain and Portugal, including 126 frail, 252 prefrail and 141 nonfrail individuals (overall mean age: 77.5±8.3 years). Results: TTV viremia ≥4log was associated with physical frailty (OR: 4.69; 95% CI: 2.06-10.67, p<0.0001) and cognitive impairment (OR: 3.49, 95% CI : 2.14-5.69, p<0.0001) in the MARK-AGE population. The association between TTV DNA load and frailty status was confirmed in the Spanish cohort, while a slight association with cognitive impairment was observed (OR: 1.33; 95% CI: 1.000-1.773), only in the unadjusted model.
No association between TTV load and frailty or cognitive impairment was found in the Portuguese sample, although a negative association between TTV viremia and MMSE score was observed in Spanish and Portuguese females. Conclusions: These findings demonstrate an association between TTV viremia and physical frailty, while the association with cognitive impairment was observed only in the younger population from the MARK-AGE study.
Further research is necessary to clarify TTV's clinical relevance in the onset and progression of frailty and cognitive decline in older individuals.
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Ibi D, Rietman ML, Picavet HSJ, van Klinken JB, van Dijk KW, Dollé MET, Verschuren WM. Adverse generational changes in obesity development converge at midlife without increased cardiometabolic risk. Obesity (Silver Spring) 2021; 29:1925-1938. [PMID: 34514749 PMCID: PMC8597017 DOI: 10.1002/oby.23260] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/25/2021] [Revised: 06/30/2021] [Accepted: 07/01/2021] [Indexed: 01/01/2023]
Abstract
OBJECTIVE Obesity is becoming a global public health problem, but it is unclear how it impacts different generations over the life course. Here, a descriptive analysis of the age-related changes in anthropometric measures and related cardiometabolic risk factors across different generations was performed. METHODS The development of anthropometric measures and related cardiometabolic risk factors was studied during 26 years of follow-up in the Doetinchem Cohort Study (N = 6,314 at baseline). All analyses were stratified by sex and generation, i.e., 10-year age groups (20-29, 30-39, 40-49, and 50-59 years) at baseline. Generalized estimating equations were used to test for generational differences. RESULTS Weight, BMI, waist circumference, and prevalence of overweight and obesity were higher, in general, in the younger generations during the first 10 to 15 years of follow-up. From age 50 to 59 years onward, these measures converged in all generations of men and women. Among cardiometabolic risk factors, only type 2 diabetes showed an unfavorable shift between the two oldest generations of men. CONCLUSIONS It was observed that, compared with the older generations, the younger generations had obesity at an earlier age but did not reach higher levels at midlife and beyond. This increased exposure to obesity was not (yet) associated with increased prevalence of cardiometabolic risk factors.
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Affiliation(s)
- Dorina Ibi
- Leiden University Medical CenterLeidenthe Netherlands
- National Institute for Public Health and the EnvironmentBilthoventhe Netherlands
| | - M. Liset Rietman
- National Institute for Public Health and the EnvironmentBilthoventhe Netherlands
| | - H. S. J. Picavet
- National Institute for Public Health and the EnvironmentBilthoventhe Netherlands
| | | | | | - Martijn E. T. Dollé
- Leiden University Medical CenterLeidenthe Netherlands
- National Institute for Public Health and the EnvironmentBilthoventhe Netherlands
| | - W.M. Monique Verschuren
- National Institute for Public Health and the EnvironmentBilthoventhe Netherlands
- Julius Center for Health Sciences and Primary CareUniversity Medical Center UtrechtUtrecht UniversityUtrechtthe Netherlands
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Rietman ML, Hulsegge G, Nooyens ACJ, Dollé MET, Picavet HSJ, Bakker SJL, Gansevoort RT, Spijkerman AMW, Verschuren WMM. Trajectories of (Bio)markers During the Development of Cognitive Frailty in the Doetinchem Cohort Study. Front Neurol 2019; 10:497. [PMID: 31214102 PMCID: PMC6555275 DOI: 10.3389/fneur.2019.00497] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2018] [Accepted: 04/24/2019] [Indexed: 01/31/2023] Open
Abstract
Background: Long-term changes in (bio)markers for cognitive frailty are not well characterized. Therefore, our aim is to explore (bio)marker trajectories in adults who became cognitively frail compared to age- and sex-matched controls who did not become cognitively frail over a 15 year follow-up. We hypothesize that those who become cognitively frail have more unfavorable trajectories of (bio)markers compared to controls. Methods: The Doetinchem Cohort Study is a longitudinal population-based study that started in 1987-1991 in men and women aged 20-59 years, with follow-up examinations every 5 years. For the current analyses, we used data of 17 potentially relevant (bio)markers (e.g., body mass index (BMI), urea) from rounds 2 to 5 (1993-2012). A global cognitive functioning score (based on memory, speed, and flexibility) was calculated for each round and transformed into education and examination round-adjusted z-scores. The z-score that corresponded to the 10th percentile in round 5 (z-score = -0.77) was applied as cut-off point for incident cognitive frailty in rounds 2-5. In total, 455 incident cognitively frail cases were identified retrospectively and were compared with 910 age- and sex-matched controls. Trajectories up to 15 years before and 10 years after incident cognitive frailty were analyzed using generalized estimating equations with stratification for sex and adjustment for age and, if appropriate, medication use. Results were further adjusted for level of education, depressive symptoms, BMI, and lifestyle factors. Results: In men, (bio)marker trajectories did not differ as they ran parallel and the difference in levels was not statistically significant between those who became cognitively frail compared to controls. In women, total cholesterol trajectories first increased and thereafter decreased in cognitively frail women and steadily increased in controls, gamma-glutamyltransferase trajectories were more or less stable in cognitively frail women and increased in controls, and urea trajectories increased in cognitively frail women and remained more or less stable in controls. Results were similar after additional adjustment for potential confounders. Conclusions: Out of the 17 (bio)markers included in this explorative study, differential trajectories for three biomarkers were observed in women. We do not yet consider any of the studied (bio)markers as promising biomarkers for cognitive frailty.
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Affiliation(s)
- M Liset Rietman
- National Institute for Public Health and the Environment, Bilthoven, Netherlands.,Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Gerben Hulsegge
- Department of Public and Occupational Health, Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Astrid C J Nooyens
- National Institute for Public Health and the Environment, Bilthoven, Netherlands
| | - Martijn E T Dollé
- National Institute for Public Health and the Environment, Bilthoven, Netherlands.,Department of Molecular Medicine, University of Texas Health Science Center at San Antonio, San Antonio, TX, United States
| | - H Susan J Picavet
- National Institute for Public Health and the Environment, Bilthoven, Netherlands
| | - Stephan J L Bakker
- Department of Internal Medicine, University Medical Center Groningen and University of Groningen, Groningen, Netherlands
| | - Ron T Gansevoort
- Department of Internal Medicine, University Medical Center Groningen and University of Groningen, Groningen, Netherlands
| | | | - W M Monique Verschuren
- National Institute for Public Health and the Environment, Bilthoven, Netherlands.,Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
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Rietman ML, van der A DL, van Oostrom SH, Picavet HSJ, Dollé MET, van Steeg H, Verschuren WMM, Spijkerman AMW. The Association between BMI and Different Frailty Domains: A U-Shaped Curve? J Nutr Health Aging 2018; 22:8-15. [PMID: 29300416 DOI: 10.1007/s12603-016-0854-3] [Citation(s) in RCA: 42] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
OBJECTIVES Previous studies showed a U-shaped association between BMI and (physical) frailty. We studied the association between BMI and physical, cognitive, psychological, and social frailty. Furthermore, the overlap between and prevalence of these frailty domains was examined. DESIGN Cross-sectional study. SETTING The Doetinchem Cohort Study is a longitudinal population-based study starting in 1987-1991 examining men and women aged 20-59 with follow-up examinations every 5 yrs. PARTICIPANTS For the current analyses, we used data from round 5 (2008-2012) with 4019 participants aged 41-81 yrs. MEASUREMENTS Physical frailty was defined as having ≥ 2 of 4 frailty criteria from the Frailty Phenotype (unintentional weight loss, exhaustion, physical activity, handgrip strength). Cognitive frailty was defined as the < 10th percentile on global cognitive functioning (based on memory, speed, flexibility). Psychological frailty was defined as having 2 out of 2 criteria (depression, mental health). Social frailty was defined as having ≥ 2 of 3 criteria (loneliness, social support, social participation). BMI was divided into four classes. Analyses were adjusted for sex, age, level of education, and smoking. RESULTS A U-shaped association was observed between BMI and physical frailty, a small linear association for BMI and cognitive frailty and no association between BMI and psychological and social frailty. The four frailty domains showed only a small proportion of overlap. The prevalence of physical, cognitive and social frailty increased with age, whereas psychological frailty did not. CONCLUSION We confirm that not only underweight but also obesity is associated with physical frailty. Obesity also seems to be associated with cognitive frailty. Further, frailty prevention should focus on multiple domains and target individuals at a younger age (<65yrs).
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Affiliation(s)
- M L Rietman
- M. Liset Rietman, MSc, National Institute for Public Health and the Environment, Bilthoven, the Netherlands, E-mail address: , Telephone number: +31302742709
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van Oostrom SH, van der A DL, Rietman ML, Picavet HSJ, Lette M, Verschuren WMM, de Bruin SR, Spijkerman AMW. A four-domain approach of frailty explored in the Doetinchem Cohort Study. BMC Geriatr 2017; 17:196. [PMID: 28854882 PMCID: PMC5577839 DOI: 10.1186/s12877-017-0595-0] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2017] [Accepted: 08/22/2017] [Indexed: 11/24/2022] Open
Abstract
Background Accumulation of problems in physical, psychological, cognitive, or social functioning is characteristic for frail individuals. Using a four-domain approach of frailty, this study explored how sociodemographic and lifestyle factors, life events and health are associated with frailty. Methods The study sample included 4019 men and women (aged 40–81 years) examined during the fifth round (2008–2012) of the Doetinchem Cohort Study. Four domains of frailty were considered: physical (≥4 of 8 criteria: unintentional weight loss, exhaustion, strength, perceived health, walking, balance, hearing and vision impairments), psychological (2 criteria: depressive symptoms, mental health), cognitive (<10th percentile on global cognitive functioning), and social frailty (≥2 of 3 criteria: loneliness, social support, social participation). Logistic regression was used to study the cross-sectional association of sociodemographic factors, lifestyle, life events and chronic diseases with frailty domains. Results About 17% of the population was frail on one or more domains. Overlap between the frailty domains was limited since 82% of the frail population was frail on one domain only. Low educated respondents were at higher risk of being psychologically and socially frail. Having multiple diseases was associated with a higher risk of being physically and psychologically frail. Being physically active was consistently associated with a lower risk of frailty on each of the four domains. Short or long sleep duration was associated with a higher risk of being physically, psychologically, and socially frail. Conclusions Sociodemographic factors, lifestyle and multimorbidity contributed differently to the four frailty domains. It is important to consider multiple frailty domains since this helps to identify different groups of frail people, and as such to provide tailored care and support. Lifestyle factors including physical activity, smoking and sleep duration were associated with multiple domains of frailty. Electronic supplementary material The online version of this article doi: 10.1186/s12877-017-0595-0) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Sandra H van Oostrom
- Centre for Nutrition, Prevention and Health Services, National Institute of Public Health and the Environment, P.O. Box 1, 3720, Bilthoven, BA, The Netherlands.
| | - Daphne L van der A
- Centre for Nutrition, Prevention and Health Services, National Institute of Public Health and the Environment, P.O. Box 1, 3720, Bilthoven, BA, The Netherlands
| | - M Liset Rietman
- Centre for Nutrition, Prevention and Health Services, National Institute of Public Health and the Environment, P.O. Box 1, 3720, Bilthoven, BA, The Netherlands.,Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands
| | - H Susan J Picavet
- Centre for Nutrition, Prevention and Health Services, National Institute of Public Health and the Environment, P.O. Box 1, 3720, Bilthoven, BA, The Netherlands
| | - Manon Lette
- Centre for Nutrition, Prevention and Health Services, National Institute of Public Health and the Environment, P.O. Box 1, 3720, Bilthoven, BA, The Netherlands
| | - W M Monique Verschuren
- Centre for Nutrition, Prevention and Health Services, National Institute of Public Health and the Environment, P.O. Box 1, 3720, Bilthoven, BA, The Netherlands.,Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Simone R de Bruin
- Centre for Nutrition, Prevention and Health Services, National Institute of Public Health and the Environment, P.O. Box 1, 3720, Bilthoven, BA, The Netherlands
| | - Annemieke M W Spijkerman
- Centre for Nutrition, Prevention and Health Services, National Institute of Public Health and the Environment, P.O. Box 1, 3720, Bilthoven, BA, The Netherlands
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Abstract
Many studies have been devoted to the identification of genes involved in experience-dependent plasticity in the visual cortex. To discover new candidate genes, we have reexamined data from one such study on ocular dominance (OD) plasticity in recombinant inbred BXD mouse strains. We have correlated the level of plasticity with the gene expression data in the neocortex that have become available for these same strains. We propose that genes with a high correlation are likely to play a role in OD plasticity. We have tested this hypothesis for genes whose inactivation is known to affect OD plasticity. The expression levels of these genes indeed correlated with OD plasticity if their levels showed strong differences between the BXD strains. To narrow down our candidate list of correlated genes, we have selected only those genes that were previously found to be regulated by visual experience and associated with pathways implicated in OD plasticity. This resulted in a list of 32 candidate genes. The list contained unproven, but not unexpected candidates such as the genes for IGF-1, NCAM1, NOGO-A, the gamma2 subunit of the GABA(A) receptor, acetylcholine esterase, and the catalytic subunit of cAMP-dependent protein kinase A. This demonstrates the viability of our approach. More interestingly, the following novel candidate genes were identified: Akap7, Akt1, Camk2d, Cckbr, Cd44, Crim1, Ctdsp2, Dnajc5, Gnai1, Itpka, Mapk8, Nbea, Nfatc3, Nlk, Npy5r, Phf21a, Phip, Ppm1l, Ppp1r1b, Rbbp4, Slc1a3, Slit2, Socs2, Spock3, St8sia1, Zfp207. Whether all these novel candidates indeed function in OD plasticity remains to be established, but possible roles of some of them are discussed in the article.
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Affiliation(s)
- M Liset Rietman
- Department of Molecular Visual Plasticity, Netherlands Institute for Neuroscience, An Institute of the Royal Netherlands Academy of Arts and Sciences Amsterdam, Netherlands
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