Developing an evolutionary model for metabolic syndrome: understanding energy sparing adaptations in geothermal populations of stickleback

Find Similar History 16 Claim Ownership Request Data Change Add Favourite

Title
Developing an evolutionary model for metabolic syndrome: understanding energy sparing adaptations in geothermal populations of stickleback

CoPED ID
13a84def-b521-4dcd-a45d-715404b2e055

Status
Active


Value
£3,872,665

Start Date
March 14, 2022

End Date
March 13, 2026

Description

More Like This


Most human disorders are complex and involve a multitude of genes, environmental inputs, and change in prevalence with age. To understand such disorders clinical researchers often use a specific set of lab animals (model organisms) that display similarity to human disease. However, while this approach has provided major advances for understanding some of the causal mutations underlying human disease there can be limitations in terms of how translatable results are. This is often attributed to differences between humans and other animals but it may also be the result of the approach used.

Normally, lab-based research on such model organisms standardizes environmental conditions, as well as genetic variation within a lab line. This can simplify comparisons with an 'all else being equal' approach that allows for single mutations to be compared (i.e. this is often referred to as the mutant model approach). However, as mentioned above, many diseases manifest themselves from a multitude of genes, environments, and age. An emerging alternative to the traditional mutant model approach is now arriving from nature where in some cases evolutionary adaptations can actually resemble human disease. What is particularly exciting in that such populations can be more in line with how humans live, in that they experience environmental variation, and often have a diversity of genetic variation. Thus, they could be particularly effective for understanding complex human disease.

This project will take advantage of a natural set of populations of fish, specifically threespine sticklebacks experiencing warmed habitats as a result of geothermal activity in Iceland. Such warmed habitats present a unique challenge to these fish as the higher temperature raises their metabolism, including in winter when prey are limited. This appears to have caused 'energy-sparing' adaptations to evolve in these fish, such as increased fat deposition, indications of glucose tolerance, and higher appetites. This can be considered to resemble metabolic syndrome in humans, but it is unclear what underlying mechanisms determine these changes, and whether a wider suite of traits are involved. To determine the utility of this system as a model for human disease will require deeper investigation, but could be especially valuable if these fish have also evolved the mitigate the negative effects of these traits.

Therefore, we will aim to ascertain how the body composition of these fish is determined using a comprehensive approach that accounts for genetic, epigenetic, and environmental cues. This approach should more closely match how traits are determined in nature and lead to accurate insights about their mechanisms. We will also assess further aspects of the metabolic divergence between geothermal and ambient populations of stickleback, including glucose and insulin tolerance. Lastly, we will examine whether the expected negative effects of metabolic syndrome occur with associated traits in sticklebacks. Specifically, we will look for signs of non-alcoholic fatty liver disease, and test whether blood concentrations of triglycerides and cholesterol occur in geothermal fish (which would be expected to occur with higher levels of body fat). We predict that geothermal fish will show signs of mitigating these negative effects, and if supported it could provide the basis for further insight and even therapies for humans.


More Information


Technical Abstract:
A suitable model for understanding disease should emulate characteristics of the target population. In this sense, a natural population that experiences environmental variation, is free-breeding, but faces natural selection can offer insights for the understanding of complex (polygenic) human disease. In this proposal we will test for the presence and underlying mechanisms of metabolic adaptations that resemble human metabolic syndrome using sticklebacks from populations facing energy-limitations in response to geothermal warming.

This project will use two main experiments. First, we will rear F2 hybrid (geothermal x ambient fish) pedigrees of sticklebacks under different temperatures to determine the genetic, environmental, and epigenetic mechanisms of body composition and how they change with age. This will make use of current sequencing techniques including genotyping-by-sequencing, while reduced-representation- bisulphite sequencing will be used to assess variation in methylation (epigenetic variation). Our assessment of body composition will involve the quantification of muscle, bone, and fat ratios through specialized micro-CT based approaches. Body composition traits will serve as phenotypes for QTL models incorporating environmental and epigenetic variation going well beyond current standards.

Second, we will perform a factorial experiment using sticklebacks derived from geothermal and ambient populations. It is predicted that geothermal populations will display aspects of metabolic syndrome, but without the negative effects. This part of the project will test the effects of temperature and food level on resting blood glucose levels, glucose tolerance, fat levels (and gene expression associated with adipogenesis), and insulin tolerance. However, we will also examine the associated negative effects of obesity, including investigation of inflammation-associated genes, liver steastosis, and blood triglyceride/cholesterol levels.

Kevin Parsons PI_PER
Colin Selman COI_PER

Subjects by relevance
  1. Genetic variation
  2. Mutations
  3. Genes
  4. Environmental factors
  5. Fishes
  6. Population genetics
  7. Populations

Extracted key phrases
  1. Human metabolic syndrome
  2. Complex human disease
  3. Traditional mutant model approach
  4. Evolutionary model
  5. Energy sparing adaptation
  6. Metabolic adaptation
  7. Geothermal population
  8. Case evolutionary adaptation
  9. Human disorder
  10. Model organism
  11. Suitable model
  12. Geothermal fish
  13. QTL model
  14. Metabolic divergence
  15. Environmental variation

Related Pages

UKRI project entry

UK Project Locations