Drift barriers to quality control when genes are expressed at different levels

Abstract

Gene expression is imperfect, sometimes leading to toxic products. Solutions take two forms: globally reducing error rates, or ensuring that the consequences of erroneous expression are relatively harmless. The latter is optimal, but because it must evolve independently at so many loci, it is subject to a stringent "drift barrier"-a limit to how weak the effects of a deleterious mutation s can be, while still being effectively purged by selection, expressed in terms of the population size N of an idealized population such that purging requires s , 21/N. In previous work, only large populations evolved the optimal local solution, small populations instead evolved globally low error rates, and intermediate populations were bistable, with either solution possible. Here, we take into consideration the fact that the effectiveness of purging varies among loci, because of variation in gene expression level, and variation in the intrinsic vulnerabilities of different gene products to error. The previously found dichotomy between the two kinds of solution breaks down, replaced by a gradual transition as a function of population size. In the extreme case of a small enough population, selection fails to maintain even the global solution against deleterious mutations, explaining the nonmonotonic relationship between effective population size and transcriptional error rate that was recently observed in experiments on Escherichia coli, Caenorhabditis elegans, and Buchnera aphidicola.KEYWORDS cryptic genetic variation; stop codon readthrough; robustness; evolvability; transcriptional errors; proofreading I N classical population genetic models of idealized populations, the probability of fixation of a new mutant depends sharply on the product of the selection coefficient, s, and the population size, N. As s falls below 21/N, fixation probabilities drop exponentially, corresponding to efficient selective purging of deleterious mutations. For s . 21/N, random genetic drift makes the fate of new mutants less certain. This nonlinear dependence of fixation probability on sN has given rise to the "drift barrier" hypothesis (Lynch 2007), which holds that populations are characterized by a threshold or "barrier" value of the selection coefficient, s, corresponding to the tipping point at which the removal of deleterious mutations switches between effective and ineffective. In idealized populations, described by Wright-Fisher or Moran models, the drift barrier is positioned at s = 21/N. Drift barriers also exist, albeit sometimes with less abrupt threshold behavior, in more complex models of evolution in which some assumptions of an idealized population are relaxed (Good and Desai 2014).The drift barrier theory argues that variation among species in their characteristic threshold values for s, thresholds that are equal by definition to the inverse of the selection effective population size, N e , can explain why different species have different characteristics, e.g., streamlined vs. bloated genomes (Lynch 2007). The simplest i...

Random genetic drift sets an upper limit on mRNA splicing accuracy in metazoans

“…Notably, there is a growing body of evidence that the complexity of transcripts produced by eukaryotic genes (resulting from alternative transcription initiation, polyadenylation, splicing or back-splicing, RNA editing) often does not correspond to fine-tuned adaptations but simply to the accumulation of errors (Pickrell et al ., 2010; Saudemont et al ., 2017; Xu et al ., 2019; Xu and Zhang, 2018; Liu and Zhang, 2018b,a; Xu and Zhang, 2014, 2020; Gout et al ., 2013). This does not necessarily imply that species with low N e should systematically have higher error rates than species with high N e (Xiong et al ., 2017; Rajon and Masel, 2011). However, this highlights the importance of considering the contribution of non-adaptive evolutionary processes when trying to understand the origin of the complexity of eukaryotic gene expression.…”

“…As a control (blue), we selected AG or GT dinucleotides that are unlikely to correspond to alternative splice sites, namely: AG dinucleotides located toward the end of the upstream exon or the beginning of the intron (unlikely to correspond to a genuine acceptor site), and GT dinucleotides located toward the beginning of the downstream exon or the end of the intron (unlikely to correspond to a donor site). To mobilization of cellular machineries) increases with gene expression level (Saudemont et al, 2017;Xiong et al, 2017). Thus, the selection-mutation-drift balance should lead to a negative correlation between gene expression level and the rate of splicing errors.…”

Evolutionary Capacitance Emerges Spontaneously during Adaptation to Environmental Changes

“…Mutations in benign cryptic sequences convert the sequence to a deleterious state with probability p del = 0.4, and those in deleterious sequences convert the sequence to benign with probability p ben = 0.1. Therefore, in the absence of selection, a cryptic sequence has a probability p del /(p del + p ben ) = 0.8 of being deleterious, a ratio compatible with mutational effects on protein folding (Bloom et al, 2005); previous work has found that results are qualitatively insensitive to the exact value of this mutation bias (Xiong et al, 2017). Assuming that the readthrough rate is determined by relatively few loci, we set the mutation rate in r to m r = 1,000m, with each mutation changing log 10 (r) by an amount sampled from a normal distribution with mean zero and variance 1.…”

“…Stop-codon readthrough occurs with frequency r, with negative consequences driven by L del deleterious sequences out of the L tot = 500 cryptic loci. Deleterious sequences have an additive fitness cost g = 20 estimated from Geiler-Samerotte et al (2011) by Xiong et al (2017), yielding…”

“…Using d = 10 À2.5 , a biologically reasonable decrease in growth rate of 0.72% ensues when readthrough rates decrease from 10 À2 to 10 À3 (Rajon and Masel, 2011). To ensure that errors do not become the new normal when all cryptic sequences are benign (i.e., to avoid r evolving values > 0.5) (Xiong et al, 2017), we use a phenomenological fitness term that has little impact on fitness for low values of r but causes fitness to decline rapidly as the probability of a stop-codon readthrough becomes large. The parameter q determines the value of r at which fitness is zero; here, we set q = 0.5.…”