Prominent Strain-Condition Interactions in Yeast Transcripts

Gene-environment interactions occur when the effect of a genetic variant differs in multiple environments. Representing an intermediate between the “nature and nurture” sides of genetic variation, these interactions are important contributors to the development of complex phenotypes. There have been many studies performed on humans and model organisms that have attempted to observe the effects of these interactions, most of them involving the use of techniques such as genome-wide association studies (GWAS) and candidate gene association studies (CGAS). These methods, however, have proven ineffective in elucidating the molecular mechanisms of gene-environment interactions.

Expression quantitative trait loci (eQTL) mapping is a powerful extension of standard quantitative mapping techniques that has shown promise in studying these interactions. As its name suggests, eQTL mapping involves the association of variances in gene expression and genetic polymorphisms. With modern technology, thousands of genetic transcripts can be simultaneously measured under varying genetic and environmental conditions. This allows for the application of eQTL mapping techniques when observing difficult-to-study phenomena such as gene-environment interactions.

Based on existing conclusions surrounding gene-environment interactions in different organisms, Smith and Kruglyak intended to characterize the overall genetic architecture of the gene-environment interactions in yeast. They particularly focused on the metabolism of two sugar sources: glucose and ethanol. These conditions provide an interesting environmental contrast, as yeast metabolizes both compounds through reverse metabolic pathways (fermentation for glucose and anaerobic respiration for ethanol). The researchers also narrowed down the genetic component by focusing on two particular strains: BY and RM. Due to the molecular differences between the conditions and the strains tested, most of the measured variance was hypothesized to be due to strain and condition effects. However, based on the literature evidence for gene-environment interactions, they also expected to observe a significant contribution of this component in phenotypic variance.

The two parental strains BY and RM, as well as 109 segregant strains derived from a cross between the parental strains, were grown in glucose and ethanol. Each strain was expression profiled by using DNA microarrays to hybridize mRNA that was extracted from the cells. A hybridization standard was created by mixing equal amounts of mRNA from both parents grown in both conditions.

Smith and Kruglyak’s experiments produced two major conclusions. First, from their analyses of variance (ANOVA), they determined that yeast transcripts are influenced by genetic components, environmental components, and by the interactions between both of these components (Figure 1). The presence of all three components demonstrates that even though genetic and environmental contributions to the phenotypic variance are dominant, the interactions between the two are still significant. Another major conclusion from this experiment was the characterization of local and distant linkages. Local linkages were described as being more stable and less dependent on the environment. They also usually affected both environmental conditions in the same manner. On the other hand, distant linkages were described as being more volatile and environmentally-dependent. In addition, distant linkages usually affected only one condition. In general, the majority of the interactions between genes and the environment occurred at distant linkages.

Figure 1. The relative proportion of strain-condition, strain, and condition variance for all transcripts where these three components accounted for more than 50% of total variance. Insets illustrate two-factor plots for representative transcripts. The averages of the BY and RM strains are in orange and purple, respectively, with error bars indicating standard deviation.

Figure 1. The relative proportion of strain-condition, strain, and condition variance for all transcripts where these three components accounted for more than 50% of total variance. Insets illustrate two-factor plots for representative transcripts. The averages of the BY and RM strains are in orange and purple, respectively, with error bars indicating standard deviation.

The results of this experiment highlight the importance of considering gene-environment interactions when performing linkage studies, as ignoring this component produces bias towards certain loci and compromises the data’s validity. More specifically, even though gene-environment interactions “play a dominant role in a minority of traits” (Smith & Kruglyak, 2008), these traits have the potential to play a significant role in determining the overall phenotypic variance. This is particularly relevant for creating lifestyle choices in relation to human diseases that have already been proven to possess a significant contribution from these interactions, like in the cases of heart disease, depression, and cancer.

Reference

Smith, E. N., & Kruglyak, L. (2008). Gene-Environment Interaction in Yeast Gene Expression. PLoS Biology, 6(4), 0812-0824.

4 Responses to Prominent Strain-Condition Interactions in Yeast Transcripts

  1. As implied by GxE phenotypic variation, it is clear that interactions between genetic information and environmental favors effect phenotypic outcomes in organisms. Like Rinaldys and Sahil mentioned at the end of journal club, this tells us that lifestyle can effect organisms in many ways. This is interesting to me because genetic testing is becoming so much more prominent in today’s society and it is possible that in the future everyone will have their DNA genotyped and have their risk calculated for various diseases or characteristics, much like the project I am doing for lab. If genetic testing does become a norm, how can scientists ensure that people understand that their genome cannot tell them with 100% accuracy what will happen to them but instead only probabilities can be calculated and that lifestyle can be as important as predisposition through your genes? Could regular genome sequencing and risk assessment make some people make poor decisions about the way they live due to their lowered genetic risk factors or has lifestyle emphasized enough in the recent past/present that a majority of people understand the health risks that result from poor lifestyle? Likewise, could further eQTL studies that uncover more GxE interactions influence lifestyle advice given by scientists in order to fend against specific conditions?

  2. At first, it was hard for me to wrap my head around the idea that gene-expression is the phenotype in this experiment– which reflects on the yeast’s ability to metabolize either ethanol or glucose. If the experimenters did not study the gene expression however, would they get the same results? I have never taken a biochemistry course, but i find that studying the pathways involved is the next step for QTL mapping, but many studies must be conducted before being able to study the pathways of complex traits.

    I also found the linkage study interesting. The fact that they were able to distinguish local and distant linkages, and show how each was affected by the environment (local=less, distant=more) it would be interesting to take multiple strains of yeasts with eQTL traits and see if it always follows this patter.

  3. I found it very interesting that there is a correlation between linkage and gene-environment interactions on phenotype expression. When we previously studied linkage, it was due to recombination. It is not, however, surprising to learn that even linkage, a factor that already “defies” Mendelian genetics, is under the influence of yet another factor that defies simple inheritance.

    I also found it interesting that it seems as though only one gene, IRA2, is responsible for the shift between glucose and ethanol metabolism. A complicated pathway like metabolism doesn’t seem like it would come down to one gene, especially given the common, inconsequential traits such as eye color and height that have multiple loci acting on them, but according to the study, it just might. Perhaps the fact that ethanol and glucose processing can be controlled by genotype, environment and gene-environmental interactions makes up for this.

  4. In class we’ve learned that GxE interaction is typically a difficult factor to determine in a population due to the necessity for controlled conditions when making measurements. To test these interactions a model organism is needed that can be exposed to binary environments like glucose or ethanol based metabolism. This is where a model organism like yeast excels, and JC7 demonstrates the principle of GxE interactions as well explaining how they’re discovered through relevant graphs that pertain to what we’re learning in class with eQTL mapping.
    Kruglyak et al concluded that GxE interactions “play a dominant role for a minority of traits” indicating that while they certainly play a role in determining an organism’s phenotype based on environment, it’s rarely the driving force behind variation within a population. This note struck a chord as I’ve always been of the belief that personal genomics will be a game changer, and that sequencing an individuals genome will tell more about disease predisposition than anything else, however this article says otherwise. While certain alleles may be more efficient based on environment, lifestyle choice and behavior remain one of the largest contributors to our health. I’m interested in seeing what we can learn from further GxE interaction studies, possibly looking at more complex traits, both from an evolutionary and medical perspective.

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