The Boggarts of biology: how non-genetic changes influence the genotype

Abstract

The notion that there is a one-one mapping from genotype to phenotype was overturned a long time ago. Along with genotype and environment, 'non-genetic changes' orchestrated by altered RNA and protein molecules also guide the development of phenotype. The idea that there is a route through which changes in phenotype can lead to changes in genotype impinges on several phenomena of molecular, developmental, evolutionary and applied interest. Phenotypic changes that do not alter the underlying DNA sequence have been studied across model systems (eg: DNA and histone modifications, RNA editing, prion formation) and are known to play an important role in short-term adaptation. However, because of their transient nature and unstable inheritance, the role of such changes in long-term evolution has remained controversial. I classify and review three ways in which non-genetic changes can influence genotype and impact cellular fitness across generations, with an emphasis on the enticing idea that they may act as stepping stones for genetic adaptation. I focus on work from microbial systems and attempt to highlight recent experiments and models that bear on this idea. Overall, I review evidence which suggests that non-genetic changes can impact phenotype via their influence on the genotype, and thus play a role in evolutionary change.

The impact of mistranslation on phenotypic variability and fitness

“…2008; Evans et al. 2018; reviewed in Samhita 2020). However, these two perspectives have only rarely been connected, resulting in poor empirical understanding of the possible role of mistranslation for survival and adaptation in new environments.…”

“…Since our single‐cell phenotype distributions were non‐parametric, accurately estimating and comparing mean and variance of the distributions was challenging. Hence, we used bootstrap analysis to estimate the error in the mean X of each dataset as follows (Efron and Tibshirani 1994): for a dataset D = (Samhita 2020) with N entries, a large number of realizations of the dataset is generated by randomly sampling entries with replacement. A given realisation is thus produced by picking N randomly selected entries from the dataset; and a given entry may be picked multiple times.…”

“…Such continuous variation in protein quality or quantity can reliably generate large phenotypic variability, as seen with the heat shock protein Hsp90 (Cowen and Lindquist 2005) and prions (Halfmann et al. 2012), which in turn can determine survival in a new environment (Novick and Weiner 1957) (reviewed in Samhita 2020). Therefore, it is speculated that mistranslation‐induced non‐genetic variation may generate substantial phenotypic variability, potentially altering the outcome of natural selection (Miranda et al.…”