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Zorin, E. M.

Publications and source records attributed to Zorin, E. M..

2 recordsLinked to original sources

Reciprocal sign epistasis and sign epistasis may not be found in multi-peaked landscapes

Fitness landscapes provide a rigorous mathematical framework for analyzing evolutionary dynamics, including the study of epistasis, the main obstacle to predicting phenotype from genotype. In 2011, Poelwijk et al. formulated a foundational theorem stating that in any multi-peaked fitness landscape, "at least two mutations exhibit reciprocal sign epistasis" (Poelwijk et al., J. Theor. Biol., 272:141). The proof relied on the implicit assumption that neutral mutations are absent, commonly accepted in theoretical studies in evolutionary biology. In this study, we extend Poelwijk et al.s analysis by incorporating genotypes with equal fitness, specifically, accounting for neutral mutations. We demonstrate that when neutral mutations are considered, conventional pairwise reciprocal sign epistasis (RSE) may be entirely absent from a multi-peaked landscape. Instead, RSE is guaranteed only when considering "distant" RSE defined through composite mutations, wherein groups of mutations are treated collectively across all their possible combinations. Applying these concepts to empirical fitness landscapes faces a practical limitation: phenotypic measurements contain experimental noise, making mutational effects statistically indistinguishable from zero. Under such conditions, statistically significant detection of RSE in multi-peaked landscapes may be impossible even when composite mutations are considered. Theoretically, our findings imply that in the presence of neutral mutations, compensatory mutations in a multi-peaked fitness landscape need not be adjacent; rather, compensation can occur following one or more neutral steps along an evolutionary path. Practically, in real-world scenarios where fitness measurements contain uncertainty, there may be a fundamental technical limitation to detecting RSE in a statistically significant manner within multi-peaked landscapes.

evolutionary biology↗

Composite mutations give an extra insight into epistasis

The intricate genotype-phenotype relationship has been a long-standing issue in biology, important both from the fundamental and applied points of view. One of the major irregularities hindering progress in establishing these links is epistasis - the complex and elusive interaction between mutations. Despite the vast accumulated genetic data and progress in this area, epistasis is still far from being completely understood. Epistasis can be studied quantitatively in combinatorially complete datasets, which form hypercubes in protein sequence space, where connected sequences are one mutation away from each other. However, this might be insufficient to portray the full picture of epistatic interactions. To extend the repertoire of the methods for exploring epistasis, we propose here to consider hyperrectangles, where some edges connect sequences being two or more mutations away from each other. The present work formalizes the theoretical knowledge about these novel structures and compares the amount of epistasis identified in hypercubes and hyperrectangles constructed from experimental datasets. A new algorithm, CuboidME, was developed for calculating hyperrectangles, which were then compared to hypercubes. In the experimental datasets, there were four orders of magnitude more hyperrectangles than hypercubes for the same sample size. Subsequently, we showed that for the studied datasets there is an increase in epistasis measured by epistatic coefficients in hyperrectangles compared to hypercubes. For the same datasets, hyperrectangles could find more sign epistasis than using hypercubes alone. We also show that there is a trend for increase in epistasis with increasing number of mutations being considered in a hyperrectangle. The results indicate that hyperrectangles can be used to reveal more information on epistasis in a fitness landscape, especially if it is combinatorially incomplete.

evolutionary biology↗