Lineage‐specific sequence evolution and exon edge conservation partially explain the relationship between evolutionary rate and expression level in A. thaliana

Abstract

Rapidly evolving proteins can aid the identification of genes underlying phenotypic adaptation across taxa, but functional and structural elements of genes can also affect evolutionary rates. In plants, the ‘edges’ of exons, flanking intron junctions, are known to contain splice enhancers and to have a higher degree of conservation compared to the remainder of the coding region. However, the extent to which these regions may be masking indicators of positive selection or account for the relationship between dN/dS and other genomic parameters is unclear. We investigate the effects of exon edge conservation on the relationship of dN/dS to various sequence characteristics and gene expression parameters in the model plant Arabidopsis thaliana. We also obtain lineage-specific dN/dS estimates, making use of the recently sequenced genome of Thellungiella parvula, the second closest sequenced relative after the sister species Arabidopsis lyrata. Overall, we find that the effect of exon edge conservation, as well as the use of lineage-specific substitution estimates, upon dN/dS ratios partly explains the relationship between the rates of protein evolution and expression level. Furthermore, the removal of exon edges shifts dN/dS estimates upwards, increasing the proportion of genes potentially under adaptive selection. We conclude that lineage-specific substitutions and exon edge conservation have an important effect on dN/dS ratios and should be considered when assessing their relationship with other genomic parameters.

Weaker selection on genes with treatment-specific expression consistent with a limit on plasticity evolution inArabidopsis thaliana

“…This study disentangles several processes that were often difficult to resolve in previous research. First, many previous studies focus mainly on explaining trends in dN/dS [69,27,7], but both relaxed negative selection and increased positive selection can lead to increases in dN/dS. To tease apart these two processes, we additionally investigated treatment specificity's relationship with Tajima's D and DoS.…”

“…A protein’s expression level is often considered the best predictor of its evolutionary rate [65] - a result observed across all domains of life [91] and sometimes considered a “law” of genome evolution [37]. Among multicellular organisms, the degree of tissue specificity in expression is also generally predictive of evolutionary rates [22, 42, 82, 92, 69, 7, 52, 29, 30]. Additional factors that also influence evolutionary rates include exon edge conservation [7], mutational bias [80, 58], gene length [52], gene age [51], GC content [93, 52], expression stochasticity [29], involvement in general vs specialized metabolism [52], identity as a regulatory or structural gene [81], recombination rate [41], codon-bias [6], mating system [83, 28, 61], gene compactness [42, 52], co-expression or protein-protein interaction network connectivity [3, 48, 54, 4, 34], gene body methylation [74], metabolic flux [14], protein structure [46], essentiallity [56, 87, 19], and even plant height [40].…”

“…Among multicellular organisms, the degree of tissue specificity in expression is also generally predictive of evolutionary rates [22, 42, 82, 92, 69, 7, 52, 29, 30]. Additional factors that also influence evolutionary rates include exon edge conservation [7], mutational bias [80, 58], gene length [52], gene age [51], GC content [93, 52], expression stochasticity [29], involvement in general vs specialized metabolism [52], identity as a regulatory or structural gene [81], recombination rate [41], codon-bias [6], mating system [83, 28, 61], gene compactness [42, 52], co-expression or protein-protein interaction network connectivity [3, 48, 54, 4, 34], gene body methylation [74], metabolic flux [14], protein structure [46], essentiallity [56, 87, 19], and even plant height [40]. This overabundance of possible explanatory variables suggests that massive genome-scale datasets and careful statistical analysis are required to tease out the influence of treatment-specific expression on evolutionary rates.…”

Contrasting gene‐level signatures of selection with reproductive fitness

“…A complementary set of lineage-specific dN/dS estimates (n = 7086) were obtained from our previous study (Bush et al, 2015), calculated using the method of Toll-Riera et al (2011). This used the genome of the extremophile crucifer Thellungiella parvula (Dassanayake et al, 2011) as an outgroup and assumed an unrooted tree topology of ([A. thaliana, A. lyrata], T. parvula).…”

A benchmark of gene expression tissue-specificity metrics

“…Tau varies from 0 to 1, where 0 means broadly expressed, and 1 is very specific (used in e.g. (Smeds et al 2015;Piasecka et al 2012;Assis & Bachtrog 2013;Assis & Kondrashov 2014;Bush et al 2015;Kryuchkova-Mostacci & Robinson-Rechavi 2015;Weber & Hurst 2011;Zhao et al 2015)).…”