Gene by environment interactions in the regulation of gene expression across primary tissues and their application to obesity and related traits
Lead Research Organisation:
King's College London
Department Name: Genetics and Molecular Medicine
Abstract
Gene expression is an important cellular phenotype under both genetic and environmental control. Changes in gene expression appear to underlie the majority of genetic associations identified in genome-wide association studies of common disease. Studying the genetic regulation of gene expression has thus been a fruitful strategy to unraveling the underlying mechanisms of common disease, both in identifying the genes and pathways that mediate the effects of disease and in characterizing the molecular mechanisms and tissues by which disease-associated genetic variants act.
Previous research into the genetics of gene expression has focused on identifying genetic variants that regulate the total expression level of a gene. This study will expand on this approach by incorporating the effects of the environment and its interactions with the genome. In the context of a cellular trait like gene expression the environment can be interpreted as exposures that operate on the organism (eg diet, medication, physical activity, or smoking) or on the physiology of tissues and cells (including obesity, insulin and cholesterol levels). This study will investigate the joint effects of the environment (lifestyle factors and biomedical measurements) and genetics on the regulation of gene expression in a deeply phenotyped, 'omics level dataset. Importantly, this study includes multiple disease-relevant tissues as both gene regulation and disease manifestation is tissue specific.
Obesity is a worldwide epidemic and is robustly associated with many co-morbidities including type 2 diabetes, cardiovascular disease, osteoarthritis, certain cancers and increased mortality. However, not all obese individuals develop the same co-morbidities and some obese individuals even appear to be metabolically healthy. Incomplete knowledge of the processes involved in the development of obesity-associated co-morbidities has limited the development of treatments or prevention strategies. While the genes driving increased body mass index (BMI) appear to act primarily in the Central Nervous System, genes underlying body fat distribution and co-morbidities such as insulin resistance and Type 2 Diabetes are active in fat tissue. Obesity has a dramatic effect on fat tissue, including cell composition, metabolism and gene expression. Obesity can thus mask, confound or modify associations between genetic variants and gene expression in fat tissue. This study will consider obesity (measured both as total adiposity and body fat distribution) as an environment in order to identify genetic variants that interact with obesity to regulate gene expression. This will allow an expanded understanding of the molecular effects of obesity on fat cells and identify genotype-dependent differences in the cellular response to obesity that could be linked to differential development of obesity co-morbidities.
Previous research into the genetics of gene expression has focused on identifying genetic variants that regulate the total expression level of a gene. This study will expand on this approach by incorporating the effects of the environment and its interactions with the genome. In the context of a cellular trait like gene expression the environment can be interpreted as exposures that operate on the organism (eg diet, medication, physical activity, or smoking) or on the physiology of tissues and cells (including obesity, insulin and cholesterol levels). This study will investigate the joint effects of the environment (lifestyle factors and biomedical measurements) and genetics on the regulation of gene expression in a deeply phenotyped, 'omics level dataset. Importantly, this study includes multiple disease-relevant tissues as both gene regulation and disease manifestation is tissue specific.
Obesity is a worldwide epidemic and is robustly associated with many co-morbidities including type 2 diabetes, cardiovascular disease, osteoarthritis, certain cancers and increased mortality. However, not all obese individuals develop the same co-morbidities and some obese individuals even appear to be metabolically healthy. Incomplete knowledge of the processes involved in the development of obesity-associated co-morbidities has limited the development of treatments or prevention strategies. While the genes driving increased body mass index (BMI) appear to act primarily in the Central Nervous System, genes underlying body fat distribution and co-morbidities such as insulin resistance and Type 2 Diabetes are active in fat tissue. Obesity has a dramatic effect on fat tissue, including cell composition, metabolism and gene expression. Obesity can thus mask, confound or modify associations between genetic variants and gene expression in fat tissue. This study will consider obesity (measured both as total adiposity and body fat distribution) as an environment in order to identify genetic variants that interact with obesity to regulate gene expression. This will allow an expanded understanding of the molecular effects of obesity on fat cells and identify genotype-dependent differences in the cellular response to obesity that could be linked to differential development of obesity co-morbidities.
Technical Summary
This study will identify gene by environment interactions (GEI) influencing gene expression. GEI effects have been difficult to identify at the disease and trait level owing to the large sample sizes needed to detect an interaction in a genome-wide search and the required careful measurement of environment for all subjects. Studying GEI effects on an expression level has many advantages. The effect size of genetic regulation of gene expression is quite large, reducing the need for huge and costly sample sizes. As each gene has a known physical position in the genome the statistical burden of multiple testing can be greatly reduced by limiting the search space to the local region around each gene. Environmental measures that operate on the organismal or cellular level can be assayed, including quantitative biomedical phenotypes.
This study will utilize gene expression data measured with next-generation sequencing technology (RNAseq) in four primary tissues (adipose, skin, whole blood, muscle) and one cell line (transformed lymphocytes) from ~800 individuals. RNAseq data allows interrogation of effects on both total gene expression and alternative splicing. A wide array of environmental exposures will be investigated, including organism level exposures such as diet, smoking and medication, and biomedical measures such as cholesterol and insulin that influence the cellular environment.
Particular attention will be paid to obesity-related exposures and their effects on adipose expression. Obesity has a dramatic impact on the physiology of adipose tissue. In this study measures of adiposity will be utilized to identify GEI regulatory effects, where adiposity is the exposure and expression the outcome variable. This will identify downstream molecular consequences of obesity, the genetic variants that mediate them, and potentially why different individuals develop different obesity associated co-morbidities.
This study will utilize gene expression data measured with next-generation sequencing technology (RNAseq) in four primary tissues (adipose, skin, whole blood, muscle) and one cell line (transformed lymphocytes) from ~800 individuals. RNAseq data allows interrogation of effects on both total gene expression and alternative splicing. A wide array of environmental exposures will be investigated, including organism level exposures such as diet, smoking and medication, and biomedical measures such as cholesterol and insulin that influence the cellular environment.
Particular attention will be paid to obesity-related exposures and their effects on adipose expression. Obesity has a dramatic impact on the physiology of adipose tissue. In this study measures of adiposity will be utilized to identify GEI regulatory effects, where adiposity is the exposure and expression the outcome variable. This will identify downstream molecular consequences of obesity, the genetic variants that mediate them, and potentially why different individuals develop different obesity associated co-morbidities.
Planned Impact
The principal beneficiaries of the research will be:
1) Academics in the fields outlined in the "academic beneficiaries" section
2) Clinicians
3) Pharmaceutical industry
4) The wider public if the translational potential is realized and results in better therapies and/or counseling for treatment or prevention of obesity related co-morbidities
The major impacts will include.
1) Improved ability to stratify risk and response to treatment and interventions.
2) Targeted treatments could help target individuals responsive to diet or physical activity intervention
3) As not all obese individuals develop the same downstream morbidities, identification of BMI-based GEI interactions could help predict which co-morbidity an individual is more likely to develop based on their genotype.
4) Identifying the early consequences of obesity will allow for early monitoring, its use in clinical trials and potentially interventional strategies.
5) Pharma are particularly interested in the downstream affects of obesity and how the adverse consequences could be prevented therapeutically. Data on gene expression and networks will be of great value to this effort.
6) The catalogue of genes and genetic variants with regulatory GEI effects will be useful to study designs, particularly interventions, which wish to target genes without GEI effects to ensure a more homogenous response.
7) GWAS genes frequently are related to therapeutic targets and this study will improve the functional understanding of those targets
1) Academics in the fields outlined in the "academic beneficiaries" section
2) Clinicians
3) Pharmaceutical industry
4) The wider public if the translational potential is realized and results in better therapies and/or counseling for treatment or prevention of obesity related co-morbidities
The major impacts will include.
1) Improved ability to stratify risk and response to treatment and interventions.
2) Targeted treatments could help target individuals responsive to diet or physical activity intervention
3) As not all obese individuals develop the same downstream morbidities, identification of BMI-based GEI interactions could help predict which co-morbidity an individual is more likely to develop based on their genotype.
4) Identifying the early consequences of obesity will allow for early monitoring, its use in clinical trials and potentially interventional strategies.
5) Pharma are particularly interested in the downstream affects of obesity and how the adverse consequences could be prevented therapeutically. Data on gene expression and networks will be of great value to this effort.
6) The catalogue of genes and genetic variants with regulatory GEI effects will be useful to study designs, particularly interventions, which wish to target genes without GEI effects to ensure a more homogenous response.
7) GWAS genes frequently are related to therapeutic targets and this study will improve the functional understanding of those targets
Organisations
Publications
Zhou Z
(2020)
Estrogen receptor a controls metabolism in white and brown adipocytes by regulating Polg1 and mitochondrial remodeling.
in Science translational medicine
Viñuela A
(2018)
Age-dependent changes in mean and variance of gene expression across tissues in a twin cohort.
in Human molecular genetics
Tsai PC
(2018)
Smoking induces coordinated DNA methylation and gene expression changes in adipose tissue with consequences for metabolic health.
in Clinical epigenetics
Small KS
(2018)
Regulatory variants at KLF14 influence type 2 diabetes risk via a female-specific effect on adipocyte size and body composition.
in Nature genetics
Pan DZ
(2018)
Integration of human adipocyte chromosomal interactions with adipose gene expression prioritizes obesity-related genes from GWAS.
in Nature communications
Pan DZ
(2018)
Author Correction: Integration of human adipocyte chromosomal interactions with adipose gene expression prioritizes obesity-related genes from GWAS.
in Nature communications
Pallister T
(2017)
Untangling the relationship between diet and visceral fat mass through blood metabolomics and gut microbiome profiling.
in International journal of obesity (2005)
Odhams CA
(2017)
Mapping eQTLs with RNA-seq reveals novel susceptibility genes, non-coding RNAs and alternative-splicing events in systemic lupus erythematosus.
in Human molecular genetics
Morales LD
(2021)
Further evidence supporting a potential role for ADH1B in obesity.
in Scientific reports
Menni C
(2016)
Metabolomic profiling to dissect the role of visceral fat in cardiometabolic health.
in Obesity (Silver Spring, Md.)
Loh NY
(2020)
RSPO3 impacts body fat distribution and regulates adipose cell biology in vitro.
in Nature communications
Kurushima Y
(2019)
Epigenetic findings in periodontitis in UK twins: a cross-sectional study.
in Clinical epigenetics
Koprulu M
(2022)
Identification of Rare Loss-of-Function Genetic Variation Regulating Body Fat Distribution.
in The Journal of clinical endocrinology and metabolism
Hore V
(2016)
Tensor decomposition for multiple-tissue gene expression experiments.
in Nature genetics
Glastonbury CA
(2016)
Adiposity-Dependent Regulatory Effects on Multi-tissue Transcriptomes.
in American journal of human genetics
Glastonbury CA
(2019)
Cell-Type Heterogeneity in Adipose Tissue Is Associated with Complex Traits and Reveals Disease-Relevant Cell-Specific eQTLs.
in American journal of human genetics
El-Sayed Moustafa JS
(2022)
ACE2 expression in adipose tissue is associated with cardio-metabolic risk factors and cell type composition-implications for COVID-19.
in International journal of obesity (2005)
Couto Alves A
(2018)
Fasting and time of day independently modulate circadian rhythm relevant gene expression in adipose and skin tissue.
in BMC genomics
Civelek M
(2017)
Genetic Regulation of Adipose Gene Expression and Cardio-Metabolic Traits.
in American journal of human genetics
Brown AA
(2017)
Predicting causal variants affecting expression by using whole-genome sequencing and RNA-seq from multiple human tissues.
in Nature genetics
Description | Adipose Multi-omics in TwinsUK |
Amount | $250,000 (USD) |
Organisation | Foundation for the National Institutes of Health (FNIH) |
Sector | Charity/Non Profit |
Country | United States |
Start | 11/2022 |
End | 03/2024 |
Description | Exploring host-gut microbial genetic and immune interactions using twins |
Amount | £803,748 (GBP) |
Funding ID | MR/N01183X/1 |
Organisation | Medical Research Council (MRC) |
Sector | Public |
Country | United Kingdom |
Start | 03/2016 |
End | 03/2019 |
Description | Genetics Society Conference Grant for Phd Students |
Amount | £750 (GBP) |
Organisation | The Genetics Society |
Sector | Charity/Non Profit |
Country | United Kingdom |
Start | 07/2016 |
End | 12/2016 |
Description | Identification and functional evaluation of genetic and epigenetic determinants of human fat distribution; investigations to understand the cardio-protective effect of lower body adiposity. |
Amount | £779,060 (GBP) |
Funding ID | RG/17/1/32663 |
Organisation | British Heart Foundation (BHF) |
Sector | Charity/Non Profit |
Country | United Kingdom |
Start | 06/2017 |
End | 06/2023 |
Description | MRC Project Grant "Genetic and environmental determinants of age-acquired skewed X-inactivation and escape from X-inactivation" |
Amount | £775,000 (GBP) |
Funding ID | MR/R023131/1 |
Organisation | Medical Research Council (MRC) |
Sector | Public |
Country | United Kingdom |
Start | 09/2018 |
End | 09/2021 |
Description | NIH Type 2 Diabetes Accelerating Medicine Partnership, Sub-Award |
Amount | £56,000 (GBP) |
Organisation | National Institutes of Health (NIH) |
Sector | Public |
Country | United States |
Start | 03/2019 |
End | 03/2020 |
Description | Supplement to existing NIH RO1 award to Dr. K Mohlke |
Amount | $27,796 (USD) |
Organisation | National Institutes of Health (NIH) |
Sector | Public |
Country | United States |
Start | 01/2016 |
End | 05/2016 |
Description | Wellcome Longitudinal Population Studes Award "TwinsUK (2019-2022) - An Epidemiological and Genomic Resource" |
Amount | £3,000,000 (GBP) |
Organisation | Wellcome Trust |
Sector | Charity/Non Profit |
Country | United Kingdom |
Start | 05/2019 |
End | 12/2022 |
Title | Pheno Express |
Description | We have generated a database of our transcriptome-wide results and developed a website to allow external researchers to freely query the entire database. |
Type Of Material | Database/Collection of data |
Year Produced | 2016 |
Provided To Others? | Yes |
Impact | Our database was accessed by external researchers on average X times per month in 2016. |
URL | http://expression.kcl.ac.uk/phenoexpress/1/ |
Description | American Society of Human Genetics Conference |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Other audiences |
Results and Impact | Selected to give talk at largest annual meeting of human geneticists. |
Year(s) Of Engagement Activity | 2016 |