Wielgoss et al, 2012 PNAS, Mutation rate dynamics in a bacterial population reflect tension between adaptation and genetic load. The authors sequenced one E. coli strain that has been evolved for 20 years in the Lenski lab. The author estimated that 20 years of E coli is about 50,000 generations. Twenty-two clones sampled at various time point during evolution were resequenced for this study. Previously, it was found that a frame-shift in mutT became 'established' in the population around 25K generations. After the fixation of this hypermutator mutT, mutation rate in two branches show reducted mutation rates (confirmed by fluctuation tests). Because the number of substitutions can be revealed by sequencing and the number of generations can be estimated, the mutations rates at different time points (hence, mutation dynamics) can be monitored (This is very nice thanks to the 20 years of in-vitro evolution experiment).
For reading, a few key concepts here are mutation rate, genetic load,
hypermutators. Mutation rate are measured using synonymous
substitutions. Genetic load
is the normalized difference between maximal and mean fitness. The
authors seem to use relative growth rate as compared to the ancestral
clone to calculate the genetic load.
Specific terms include mutT, mutY, which are the specific genes under discussion.
I noticed the use of "established" instead of 'fixed' in the population, which indicates that mutT did not reach 100% of the population.
A question: How does the author establish the causal interaction between mutT and mutY? It seems that they begin with a candidate gene approach, and used association arguments. I was hoping for a regression based causal inference like those in quantitative genetics, but this kind of analysis may not be available here.
Hi.
ReplyDeleteI am the lead author of that paper. The evoloved MutT did fix in the population and reached 100% in the population at approximately 26,500 generations (Barrick et al. 2009, Barrick & Lenski 2009).
The causal interaction between MutT and MutY is well documented in the mutation rate literature (mainly from work by Schaaper). We find that both the mutational spectrum and the mutation rates change as predicted for mutT mutY double mutators, and thus our inference appears sound. The circumstntial finding of two independent lineages carrying mutT mutY that lower the mutation rates similarly (see tree and fluction test results) is a strong indicator of parallel evolution. We think that the tension between evolvability and genetic load drives these changes.
Hope that clarified some of your quetions.
Cheers
Sébastien
Hello Sebastien,
ReplyDeleteIt makes sense to me now that MutT is fixed and then two parallel MutY mutations occurred later.
My question is about the causal relationship in the evolved populations. It seems that there hundreds of mutations in the populations, based on my understanding of table 1. My understanding is that two of these mutations occurred in mutY locus independently in two lineages. For the causal relationship between mutT and mutY hold, it requires that no other mutations occur in the same locus other than mutY. Have I missed this detail again?
Thanks for the follow up. Insights from the first author on an interesting paper is a pleasant surprise.
Hong
Hi Hong.
DeleteYou nailed it. The two mutY mutations appeared in different backgrounds independently and at different timepoints (one early, mutY-E; one late, mutY-L). While we do not present isogenic constructs to dissect the mutY mutation's exact effects, the mutT mutY double mutator's phenotypes are predicted from previous work, namely peculiar changes in the mutational spectrum (i.e., the transversion biases, Table 2) and the subtle decrease in mutation rates (Figure 1c; Figure 2a). Given the 100s of mutations in genes other than the ones known to be mutator genes and which are not part of the GO-pathway, we have a valid model that can explain our observation. For the time being the most important observation is the phenotypic one: decreases in mutation rates (and genetic load) along with a flattening (but non-zero) fitness trajectory. Our data points to mutY as the molecular mechanism (and parallel evolution strengthens this claim). More work is on the way to tackle the remaining questions, above all the trajectory over even longer time-spans.
Thanks for the interest in our work.
Nice blog concept, by the way.
Best wishes
Sébastien
Hello Sébastien,
DeleteGreat, thanks again for the comments. It was interesting to read your work and pleasant to exchange ideas with you.
Hong