Secreted Proteins Defy the Expression Level-Evolutionary Rate Anticorrelation

Chloroplast gene expression level is negatively correlated with evolutionary rates and selective pressure while positively with codon usage bias in Ophioglossum vulgatum L

BACKGROUND

Characterization of the key factors determining gene expression level has been of significant interest. Previous studies on the relationship among evolutionary rates, codon usage bias, and expression level mostly focused on either nuclear genes or unicellular/multicellular organisms but few in chloroplast (cp) genes. Ophioglossum vulgatum is a unique fern and has important scientific and medicinal values. In this study, we sequenced its cp genome and transcriptome to estimate the evolutionary rates (dN and dS), selective pressure (dN/dS), gene expression level, codon usage bias, and their correlations.

RESULTS

The correlation coefficients between dN, dS, and dN/dS, and Transcripts Per Million (TPM) average values were -0.278 (P = 0.027 < 0.05), -0.331 (P = 0.008 < 0.05), and -0.311 (P = 0.013 < 0.05), respectively. The codon adaptation index (CAI) and tRNA adaptation index (tAI) were significantly positively correlated with TPM average values (P < 0.05).

CONCLUSIONS

Our results indicated that when the gene expression level was higher, the evolutionary rates and selective pressure were lower, but the codon usage bias was stronger. We provided evidence from cp gene data which supported the E-R (E stands for gene expression level and R stands for evolutionary rate) anti-correlation.

The Missing Expression Level-Evolutionary Rate Anticorrelation in Viruses Does Not Support Protein Function as a Main Constraint on Sequence Evolution

One of the central goals in molecular evolutionary biology is to determine the sources of variation in the rate of sequence evolution among proteins. Gene expression level is widely accepted as the primary determinant of protein evolutionary rate, because it scales with the extent of selective constraints imposed on a protein, leading to the well-known negative correlation between expression level and protein evolutionary rate (the E-R anticorrelation). Selective constraints have been hypothesized to entail the maintenance of protein function, the avoidance of cytotoxicity caused by protein misfolding or nonspecific protein-protein interactions, or both. However, empirical tests evaluating the relative importance of these hypotheses remain scarce, likely due to the nontrivial difficulties in distinguishing the effect of a deleterious mutation on a protein's function versus its cytotoxicity. We realized that examining the sequence evolution of viral proteins could overcome this hurdle. It is because purifying selection against mutations in a viral protein that result in cytotoxicity per se is likely relaxed, whereas purifying selection against mutations that impair viral protein function persists. Multiple analyses of SARS-CoV-2 and nine other virus species revealed a complete absence of any E-R anticorrelation. As a control, the E-R anticorrelation does exist in human endogenous retroviruses where purifying selection against cytotoxicity is present. Taken together, these observations do not support the maintenance of protein function as the main constraint on protein sequence evolution in cellular organisms.

Gene expression of functionally-related genes coevolves across fungal species: detecting coevolution of gene expression using phylogenetic comparative methods

BACKGROUND

Researchers often measure changes in gene expression across conditions to better understand the shared functional roles and regulatory mechanisms of different genes. Analogous to this is comparing gene expression across species, which can improve our understanding of the evolutionary processes shaping the evolution of both individual genes and functional pathways. One area of interest is determining genes showing signals of coevolution, which can also indicate potential functional similarity, analogous to co-expression analysis often performed across conditions for a single species. However, as with any trait, comparing gene expression across species can be confounded by the non-independence of species due to shared ancestry, making standard hypothesis testing inappropriate.

RESULTS

We compared RNA-Seq data across 18 fungal species using a multivariate Brownian Motion phylogenetic comparative method (PCM), which allowed us to quantify coevolution between protein pairs while directly accounting for the shared ancestry of the species. Our work indicates proteins which physically-interact show stronger signals of coevolution than randomly-generated pairs. Interactions with stronger empirical and computational evidence also showing stronger signals of coevolution. We examined the effects of number of protein interactions and gene expression levels on coevolution, finding both factors are overall poor predictors of the strength of coevolution between a protein pair. Simulations further demonstrate the potential issues of analyzing gene expression coevolution without accounting for shared ancestry in a standard hypothesis testing framework. Furthermore, our simulations indicate the use of a randomly-generated null distribution as a means of determining statistical significance for detecting coevolving genes with phylogenetically-uncorrected correlations, as has previously been done, is less accurate than PCMs, although is a significant improvement over standard hypothesis testing. These methods are further improved by using a phylogenetically-corrected correlation metric.

CONCLUSIONS

Our work highlights potential benefits of using PCMs to detect gene expression coevolution from high-throughput omics scale data. This framework can be built upon to investigate other evolutionary hypotheses, such as changes in transcription regulatory mechanisms across species.

Gene expression variation in the brains of harvester ant foragers is associated with collective behavior

Natural selection on collective behavior acts on variation among colonies in behavior that is associated with reproductive success. In the red harvester ant (Pogonomyrmex barbatus), variation among colonies in the collective regulation of foraging in response to humidity is associated with colony reproductive success. We used RNA-seq to examine gene expression in the brains of foragers in a natural setting. We find that colonies differ in the expression of neurophysiologically-relevant genes in forager brains, and a fraction of these gene expression differences are associated with two colony traits: sensitivity of foraging activity to humidity, and forager brain dopamine to serotonin ratio. Loci that were correlated with colony behavioral differences were enriched in neurotransmitter receptor signaling & metabolic functions, tended to be more central to coexpression networks, and are evolving under higher protein-coding sequence constraint. Natural selection may shape colony foraging behavior through variation in gene expression.

The Old and the New: Discovery Proteomics Identifies Putative Novel Seminal Fluid Proteins in Drosophila

Seminal fluid proteins (SFPs), the nonsperm component of male ejaculates produced by male accessory glands, are viewed as central mediators of reproductive fitness. SFPs effect both male and female post-mating functions and show molecular signatures of rapid adaptive evolution. Although Drosophila melanogaster, is the dominant insect model for understanding SFP evolution, understanding of SFP evolutionary causes and consequences require additional comparative analyses of close and distantly related taxa. Although SFP identification was historically challenging, advances in label-free quantitative proteomics expands the scope of studying other systems to further advance the field. Focused studies of SFPs has so far overlooked the proteomes of male reproductive glands and their inherent complex protein networks for which there is little information on the overall signals of molecular evolution. Here we applied label-free quantitative proteomics to identify the accessory gland proteome and secretome in Drosophila pseudoobscura,, a close relative of D. melanogaster,, and use the dataset to identify both known and putative novel SFPs. Using this approach, we identified 163 putative SFPs, 32% of which overlapped with previously identified D. melanogaster, SFPs and show that SFPs with known extracellular annotation evolve more rapidly than other proteins produced by or contained within the accessory gland. Our results will further the understanding of the evolution of SFPs and the underlying male accessory gland proteins that mediate reproductive fitness of the sexes.

Position Matters: Network Centrality Considerably Impacts Rates of Protein Evolution in the Human Protein-Protein Interaction Network

The proteins of any organism evolve at disparate rates. A long list of factors affecting rates of protein evolution have been identified. However, the relative importance of each factor in determining rates of protein evolution remains unresolved. The prevailing view is that evolutionary rates are dominantly determined by gene expression, and that other factors such as network centrality have only a marginal effect, if any. However, this view is largely based on analyses in yeasts, and accurately measuring the importance of the determinants of rates of protein evolution is complicated by the fact that the different factors are often correlated with each other, and by the relatively poor quality of available functional genomics data sets. Here, we use correlation, partial correlation and principal component regression analyses to measure the contributions of several factors to the variability of the rates of evolution of human proteins. For this purpose, we analyzed the entire human protein–protein interaction data set and the human signal transduction network—a network data set of exceptionally high quality, obtained by manual curation, which is expected to be virtually free from false positives. In contrast with the prevailing view, we observe that network centrality (measured as the number of physical and nonphysical interactions, betweenness, and closeness) has a considerable impact on rates of protein evolution. Surprisingly, the impact of centrality on rates of protein evolution seems to be comparable, or even superior according to some analyses, to that of gene expression. Our observations seem to be independent of potentially confounding factors and from the limitations (biases and errors) of interactomic data sets.

Correlates of evolutionary rates in the murine sperm proteome

Background

Protein-coding genes expressed in sperm evolve at different rates. To gain deeper insight into the factors underlying this heterogeneity we examined the relative importance of a diverse set of previously described rate correlates in determining the evolution of murine sperm proteins.

Results

Using partial rank correlations we detected several major rate indicators: Phyletic gene age, numbers of protein-protein interactions, and survival essentiality emerged as particularly important rate correlates in murine sperm proteins. Tissue specificity, numbers of paralogs, and untranslated region lengths also correlate significantly with sperm genes’ evolutionary rates, albeit to a lesser extent. Multifunctionality, coding sequence or average intron lengths, and mean expression level have insignificant or virtually no independent effects on evolutionary rates in murine sperm genes. Gene ontology enrichment analyses of three equally sized murine sperm protein groups classified based on their evolutionary rates indicate strongest sperm-specific functional specialization in the most quickly evolving gene class.

Conclusions

We propose a model according to which slowly evolving murine sperm proteins tend to be constrained by factors such as survival essentiality, network connectivity, and/or broad expression. In contrast, evolutionary change may arise especially in less constrained sperm proteins, which might, moreover, be prone to specialize to reproduction-related functions. Our results should be taken into account in future studies on rate variations of reproductive genes.

Electronic supplementary material

The online version of this article (10.1186/s12862-018-1157-6) contains supplementary material, which is available to authorized users.