José I. Rojas Echenique

My graduate work consists of two main projects, both concerned with the effects of epistatic interactions on the course of evolution. The first is a systematic study of the epistatic relationships between gene deletions and their compensatory mutations. The second is a new technology that makes it possible to observe the effects of epistasis on fine-scale evolutionary dynamics.

Modular epistasis and compensatory evolution

Recent research suggests that during laboratory evolution, the evolutionary effects of epistasis are mediated through fitness effects [kryazhimskiy2014][jerison2017][wunsche2017][wang2016][szamecz2014]. The emerging consensus is that this fitness mediated epistasis causes beneficial mutations to have smaller effects in more fit backgrounds. As a result, given a common pool of adaptive mutations, we can predict that populations with lower initial fitness will adapt more rapidly and that as any population adapts the rate of adaptation will decrease.

The idea of fitness mediated epistasis looks strange juxtaposed with the classical view of epistasis as an indicator of functional relationships. In this view, often called modular epistasis, epistasis between gene deletion mutations, for example, is caused by a functional interaction between those genes: positive epistasis can arise when the genes are both essential in maintaining the same functional module, and negative epistasis can arise when the genes are each essential for maintaining two redundant functional modules [segre2005]. This organization of epistasis into a hierarchy of functional modules would, in principle, allow one to predict that the adaptive trajectories of two populations with different perturbations to the same functional module will be more similar to each other than those of two populations with perturbations to different functional modules.


Figure 1: (A) A schematic describing the genotypes of our experimental populations and our subsequent manipulations. (B) The relationship between the initial fitness of the gene deletion mutants (Δ) and the fitness increase in their evolved descendants at 500 generations (Δ, Evo - Δ). Transparent points show individual populations and solid points show the mean of all the populations decendent from the same deletion mutant, colored according to assigned functional module. Lines show the hierarchical regression fits for each functional module (C) The relationship between the fitness effects of evolved mutations in the background they evolved in (Δ, Evo - Δ) and the fitness effects of those mutations in the wild-type background (Evo). Colors highlight the two deletions which always evolved entrenching compensatory mutations.

In this study, we used the yeast genetic interaction map [costanzo2016] to assign genes to functional modules. We evolved groups of gene deletion mutants from several tightly clustered functional modules in replicate (figure 1A) and found that functional relatedness, as revealed by genetic interaction data, predicts the rate of compensatory adaptation (figure 1B). For example, strains with deletions of genes in the elongator complex consistently adapt more slowly than would be predicted by their initial fitness alone. This suggests that the dependence of adaptation on initial genotype is, at least partially, mediated by the functional state of those initial genotypes. Furthermore, we found that this was true in spite of the fact that most evolved mutations were also beneficial in the wild type background (figure 1C) suggesting that the effect of functionally mediated epistasis is not due entirely to the availability of functionally specific compensatory mutations.

Long-term lineage tracking reveals dynamics of second order lineage competition

In an evolving asexual population, the fate of a new mutant lineage is determined both by its intrinsic capacity to leave more offspring than other lineages and by its offspring’s potential to acquire further beneficial mutations. Thus, it’s possible that mutations could be selected for their effects on the evolvability rather than their direct effects on fitness.

DNA barcoding technology has made it possible to resolve the lineage dynamics of individual adaptive mutations in experimental microbial populations [levy2015]. Unfortunately, the technology is limited to the first adaptive mutations that arise in previously clonal populations. The barcodes are blind to subsequent beneficial mutations which arise as lineages with initial mutations increase in frequency.

In collaboration with Alex N. Nguyen Ba, I’ve developed a lineage tracking technique that allows us to directly observe the dynamics of both of these facets of lineage competition: first order selection and the second order effects of additional beneficial mutations. Our technique records the lineage history of every cell in an evolving population genetically, by the sequential addition of random DNA barcodes into a time sorted barcode array; earlier barcodes identifying more distant ancestors. Deep sequencing of this growing array allows us to infer the frequency and evolutionary history of each lineage in the population.


Figure 2: Stacked frequency trajectories for a long term lineage tracking population. Gray bars indicated the addition of new barcodes every 100 generations. Colors indicate nested lineage ancestry, where more similar colors have more recent common ancestors. (A The entire population. (B) and (C) Sublineages highlighted in (A) scaled to total lineage frequency.

We’ve applied our technique to several controlled laboratory populations of budding yeast, for over one thousand generations, in several different environments, in both haploid and diploid lines. The stacked frequency plot of abundant barcodes for one of these populations is shown in figure 2. We’re working with Ivanna Cvijović to analyze these data to infer the fitness history of each lineage and to detect the effects of epistatic interactions between initial and subsequent mutations.


[costanzo2016]Costanzo, M., B. VanderSluis, E. N. Koch, et al. (2016). A global genetic interaction network maps a wiring diagram of cellular function. Science 353(6306).
[jerison2017]Jerison, E. R., S. Kryazhimskiy, J. K. Mitchell, J. S. Bloom, L. Kruglyak, and M. M. Desai (2017). Genetic variation in adaptability and pleiotropy in budding yeast. eLife 6.
[kryazhimskiy2014]Kryazhimskiy, S., D. P. Rice, E. R. Jerison, and M. M. Desai (2014). Global epistasis makes adaptation predictable despite sequence-level stochasticity. Science 344(6191), 1519–1522.
[levy2015]Levy, S. F., J. R. Blundell, S. Venkataram, D. A. Petrov, D. S. Fisher, and G. Sherlock (2015). Quantitative evolutionary dynamics using high-resolution lineage tracking. Nature 519(7542), 181.
[segre2005]Segre, D., A. DeLuna, G. M. Church, and R. Kishony (2005). Modular epistasis in yeast metabolism. Nature genetics 37 (1), 77–83.
[szamecz2014]Szamecz, B., G. Boross, D. Kalapis, K. Kovács, G. Fekete, Z. Farkas, V. Lázár, M. Hrtyan, P. Kemmeren, G. K. MJ, et al. (2014). The genomic landscape of compensatory evolution. PLoS biology 12(8), e1001935–e1001935.
[wang2016]Wang, Y., C. D. Arenas, D. M. Stoebel, K. Flynn, E. Knapp, M. M. Dillon, A. Wünsche, P. J. Hatcher, F. B.-G. Moore, V. S. Cooper, et al. (2016). Benefit of transferred mutations is better predicted by the fittness of recipients than by their ecological or genetic relatedness. Proceedings of the National Academy of Sciences 113(18), 5047–5052.
[wunsche2017]Wünsche, A., D. M. Dinh, R. S. Satterwhite, C. D. Arenas, D. M. Stoebel, and T. F. Cooper (2017). Diminishing-returns epistasis decreases adaptability along an evolutionary trajectory. Nature Ecology & Evolution 1, 0061.