Biology subjects

Cope, A. L.

Publications and source records attributed to Cope, A. L..

13 recordsLinked to original sources

bioRxiv2025

Biophysical constraints on mRNA decay rates shape macroevolutionary divergence in steady-state abundances

Interspecific comparisons of cell-type-specific gene expression levels can provide information about the evolutionary processes that drove divergence between species. From these comparisons, it is now evident that the predominant mode of gene expression evolution has been stabilizing selection, both on the steady-state (mean) protein levels as well as on the mRNA levels with additional lineage-specific shifts resulting from directional selection. However, as all previous work has used bulk RNA measurements, it has been impossible to determine which of the many cellular processes that contribute to mean abundances are highly constrained and which are more evolutionary labile. Assessing this is further complicated by the expectation that components of complex systems will evolve over time independent of changes in the selective regime so long as the net output of a system (i.e., mean expression) remains near the evolutionary optima. This process, known as evolutionary systems drift (ESD), has been frequently invoked as a non-adaptive explanation for changes in cellular phenotypes but has never been quantitatively tested or accounted for in any statistical test for selective constraints. Here, we develop a new paradigm that addresses both of these open problems simultaneously. Using single-cell expression data and biophysical models, we estimate mRNA transcriptional bursting rates, splicing rates, and decay rates across multiple vertebrate species. We then derive new mathematical results that describe how these various biophysical parameters are expected to co-evolve under ESD and then test whether we need additional evolutionary constraints to explain the divergences in these parameters. We find evidence that the biophysical parameters are indeed evolving in a coordinated manner as predicted by ESD and that there are additional strong constraints on transcriptional bursting, likely as a consequence of selection to reduce noise in expression. More broadly, this work opens up a whole new approach for studying the evolutionary dynamics of complex cellular systems.

evolutionary biology↗

bioRxiv2024

Evolution of tRNA pool shapes variation in selection on codon usage across the Saccharomycotina subphylu

Across taxonomical domains, synonymous codons of an amino acid are found to be used at unequal frequencies within genes. This codon usage bias (CUB) is highly variable across species. Genome-wide CUB reflects a balance between adaptive and non-adaptive microevolutionary processes within a species. Variation in microevolutionary processes results in across-species variation in CUB. As CUB is tightly linked to important molecular and biophysical processes, it is critical to understand how changes to these processes are linked to changes in microevolutionary processes. We employed a population genetics model to quantify natural selection and mutation biases on a per-codon basis across the Saccharomycotina budding yeast subphylum. We found that the strength of natural selection and mutation biases varied significantly between closely related yeasts. Across-species variation in natural selection reflected the evolution of tRNA gene copy number. Additionally, we found evidence that changes to tRNA modification expression can contribute to changes in natural selection across species independent of changes to tGCN. Both lines of evidence support the link between the evolution of the tRNA pool and natural selection in codon usage through changes in the translation efficiency of a codon. Furthermore, we found that changes to tGCN often reflected changes to genome-wide GC%, suggesting changes to the tRNA pool reflect changes to mutation bias. Our work establishes how changes in microevolutionary processes impact changes in molecular mechanisms, ultimately shaping the macroevolutionary variation of a trait. Significance statementCodon usage bias (CUB) - the non-uniform usage of synonymous codons - is a feature of all genomes and varies across closely related species. Differences in CUB imply differences in the underlying microevolutionary processes (natural selection, mutation bias) driving CUB. CUB is hypothesized to be tightly linked to key molecular processes, particularly mRNA translation. We used a population genetics model to quantify natural selection and mutation bias on a per-codon basis across 327 budding yeasts. We found high variability in the microevolution of CUB and showed that changes in natural selection were correlated with the evolution of the tRNA pool. Our work establishes how variation in molecular mechanisms relates to variation in microevolution, shaping variation in a trait across species.

evolutionary biology↗

bioRxiv2024

The Importance of Nonsense Errors: Estimating the Rate and Implications of Drop-Off Errors during Protein Synthesis

The process of mRNA translation is both energetically costly and relatively error-prone compared to transcription and replication. Nonsense errors during mRNA translation occur when a ribosome drops off a transcript before reaching a stop codon, resulting in energetic investment in an incomplete and likely non-functional protein. Nonsense errors impose a potentially significant energy burden on the cell, making it critical to quantify their frequency and energetic cost. Here, we present a model of ribosome movement for estimating protein production, elongation, and nonsense error rates from high-throughput ribosome profiling data. Applying this model to an exemplary ribosome profiling dataset in S. cerevisiae, we find that nonsense error rates vary between codons, in conflict with the general assumption of uniform rates across sense codons. Using our parameter estimates, we find multiple lines of evidence that selection against nonsense errors is a prominent force shaping coding-sequence evolution, including that nonsense errors place an energetic burden on cells comparable to ribosome pausing. Our results indicate greater consideration should be given to the impact of nonsense errors in shaping coding-sequence evolution. Author SummaryThe process of mRNA translation is both energetically expensive and relatively error-prone. As such, natural selection is thought to shape the evolution of CDSs to reduce the cost of these errors when they occur. Nonsense errors (NSEs) occur when a ribosome stops translation prior to completing a functional protein, resulting in wasted energy on non-functional product. Despite their functional consequences, NSEs and their effects on sequence evolution are generally understudied compared to other types of translation errors. This is in part due to the challenge of quantifying these errors from omics-scale data. We present a model for quantifying codon-specific estimates of elongation and NSE rates from ribosome profiling data, which gives a snapshot of the actively translating ribosomes in a cell. Although it is well-established that sense codons vary in their elongation rates, we find evidence that codons also vary in their NSE rates. Using our parameter estimates, we find multiple lines of evidence for selection against NSEs shaping patterns of codon usage bias. Our results suggest the cost of NSEs are comparable to the cost of ribosome pausing, and thus may play a greater role in coding-sequence evolution than previously appreciated.

evolutionary biology↗

bioRxiv2024

Phylogenetic analysis reveals how selection and mutation shape the coevolution of mRNA and protein abundances

The regulatory mechanisms that shape mRNA and protein abundances are intensely studied. Much less is known about the evolutionary processes that shape the relationship between these two levels of gene expression. To disentangle the contributions of mutational and selective processes, we derive a novel phylogenetic model and fit it to multi-species data from mammalian skin tissue. We find that over macroevolutionary time: 1) there has been strong stabilizing selection on protein abundances; 2) mutations impacting mRNA abundances have minimal influence on protein abundances; 3) mRNA abundances are under selection to track protein abundances, and 4) mRNA abundances adapt more quickly than protein abundances due to increased mutational opportunity. We find additional support for these findings by comparing gene-specific parameter estimates from our model to human functional genomic data. More broadly, our new phylogenetic approach provides a foundation for testing hypotheses about the processes that led to divergence in gene expression.

evolutionary biology↗

bioRxiv2023

Determining the effects of temperature on the evolution of bacterial tRNA pools

The genetic code consists of 61 codon coding for 20 amino acids. These codons are recognized by transfer RNAs (tRNA) that bind to specific codons during protein synthesis. Most organisms utilize less than all 61 possible anticodons due to base pair wobble: the ability to have a mismatch with a codon at its third nucleotide. Previous studies observed a correlation between the tRNA pool of bacteria and the temperature of their respective environments. However, it is unclear if these patterns represent biological adaptations to maintain the efficiency and accuracy of protein synthesis in different environments. A mechanistic mathematical model of mRNA translation is used to quantify the expected elongation rates and error rate for each codon based on an organisms tRNA pool. A comparative analysis across a range of bacteria that accounts for covariance due to shared ancestry is performed to quantify the impact of environmental temperature on the evolution of the tRNA pool. We find that thermophiles generally have more anticodons represented in their tRNA pool than mesophiles or psychrophiles. Based on our model, this increased diversity is expected to lead to increased missense errors. The implications of this for protein evolution in thermophiles are discussed. SignificanceProtein synthesis is a vital biological process; however, our understanding of the impact of environmental factors, such as temperature, on the evolution of the molecular mechanisms involved in protein synthesis is limited. In this study, we investigated the impact of environmental temperature on the evolution of the tRNA pool. Our analyses revealed that heat-loving bacteria (thermophiles) generally have more anticodons represented in their tRNA pool. Based on a simple model of ribosome elongation, this observed increase in tRNA diversity in thermophiles is expected to also increase the frequency of translation errors. We speculate that the increased diversity of the tRNA pool could be due to the decreased efficiency of wobble base pairing at higher temperatures, necessitating more tRNA with exact codon-anticodon pairings. Our findings provide key insights into the role of the environment in shaping the tRNA pool.

evolutionary biology↗

bioRxiv2023

Decoupling of evolutionary changes in mRNA and protein levels

Variation in gene expression across lineages is thought to explain much of the observed phenotypic variation and adaptation. The protein is closer to the target of natural selection but gene expression is typically measured as the amount of mRNA. The broad assumption that mRNA levels are good proxies for protein levels has been undermined by a number of studies reporting moderate or weak correlations between the two measures across species. One biological explanation for this discrepancy is that there has been compensatory evolution between the mRNA level and regulation of translation. However, we do not understand the evolutionary conditions necessary for this to occur nor the expected strength of the correlation between mRNA and protein levels. Here we develop a theoretical model for the coevolution of mRNA and protein levels and investigate the dynamics of the model over time. We find that compensatory evolution is widespread when there is stabilizing selection on the protein level, which is true across a variety of regulatory pathways. When the protein level is under directional selection, the mRNA level of a gene and its translation rate of the same gene were negatively correlated across lineages but positively correlated across genes. These findings help explain results from comparative studies of gene expression and potentially enable researchers to disentangle biological and statistical hypotheses for the mismatch between transcriptomic and proteomic studies.

evolutionary biology↗

bioRxiv2022

Matters Arising: Re-examining the correlations between codon usage and dihedral bond angles using a population genetics model

A recent paper by Rosenberg et al. [2022] found a surprising correlation between synonymous codon usage and the dihedral bond angles of the resulting amino acid. However, their analysis did not account for the strongest known correlate of codon usage: gene expression. We applied the approach of Rosenberg et al. [2022] to simulated protein-coding sequences that (1) maintain the general relationship between codon usage and gene expression and (2) completely random codon usage. The analysis of the simulated data assuming a general relationship between gene expression and codon usage returned results remarkably similar to the real data. More concerning was the large number of significant results detected when sequences with random codon usage were analyzed. We believe that the specific results of Rosenberg et al. [2022] were confounded by the relationship between codon usage and gene expression, but also that their method is generally prone to detecting noise in protein bond angle distributions.

evolutionary biology↗

bioRxiv2021

Intragenomic variation in mutation biases causes underestimation of selection on synonymous codon usage

Patterns of non-uniform usage of synonymous codons (codon bias) varies across genes in an organism and across species from all domains of life. The bias in codon usage is due to a combination of both non-adaptive (e.g. mutation biases) and adaptive (e.g. natural selection for translation efficiency/accuracy) evolutionary forces. Most population genetics models quantify the effects of mutation bias and selection on shaping codon usage patterns assuming a uniform mutation bias across the genome. However, mutation biases can vary both along and across chromosomes due to processes such as biased gene conversion, potentially obfuscating signals of translational selection. Moreover, estimates of variation in genomic mutation biases are often lacking for non-model organisms. Here, we combine an unsupervised learning method with a population genetics model of synonymous codon bias evolution to assess the impact of intragenomic variation in mutation bias on the strength and direction of natural selection on synonymous codon usage across 49 Saccharomycotina budding yeasts. We find that in the absence of a priori information, unsupervised learning approaches can be used to identify regions evolving under different mutation biases. We find that the impact of intragenomic variation in mutation bias varies widely, even among closely-related species. We show that the overall strength and direction of selection on codon usage can be underestimated by failing to account for intragenomic variation in mutation biases. Interestingly, genes falling into clusters identified by machine learning are also often physically clustered across chromosomes, consistent with processes such as biased gene conversion. Our results indicate the need for more nuanced models of sequence evolution that systematically incorporate the effects of variable mutation biases on codon frequencies.

evolutionary biology↗

bioRxiv2021

Tracking extracellular vesicle (EV) cargo as a platform for studying EVomics, signaling, and targeting in vivo

Extracellular vesicle (EV)-based signaling is a challenge to study, due to EV small size, heterogeneity, and limited information on cargo content in vivo. We present Caenorhabditis elegans as a discovery platform that allows single EV tracking from source to target tissue in living animals. We enriched ciliary EVs using GFP-tagged PKD-2 cargo followed by mass spectrometry analysis to identify 2,888 cargo candidates. By integrating our dataset with single-cell transcriptomic data, we identified EV cargo produced by individual neurons and other cell and tissue types. A single cilium produces multiple EVs with distinct protein content. Ciliary EVs carry nucleic acid binding proteins. We observed transfer of EV cargo from the male reproductive tract to the hermaphrodite uterus during mating, a direct demonstration of animal-to-animal EV targeting. One-Sentence SummaryHere we present a discovery platform for studying animal extracellular vesicle composition and biogenesis.

cell biology↗

bioRxiv2021

riboviz 2: A flexible and robust ribosome profiling data analysis and visualization workflow

MotivationRibosome profiling, or Ribo-seq, is the state of the art method for quantifying protein synthesis in living cells. Computational analysis of Ribo-seq data remains challenging due to the complexity of the procedure, as well as variations introduced for specific organisms or specialized analyses. Many bioinformatic pipelines have been developed, but these pipelines have key limitations in terms of functionality or usability. ResultsWe present riboviz 2, an updated riboviz package, for the comprehensive transcript-centric analysis and visualization of Ribo-seq data. riboviz 2 includes an analysis workflow built on the Nextflow workflow management system, combining freely available software with custom code. The package is extensively documented and provides example configuration files for organisms spanning the domains of life. riboviz 2 is distinguished by clear separation of concerns between annotation and analysis: prior to a run, the user chooses a transcriptome in FASTA format, paired with annotation for the CDS locations in GFF3 format. The user is empowered to choose the relevant transcriptome for their biological question, or to run alternative analyses that address distinct questions. riboviz 2 has been extensively tested on various library preparation strategies, including multiplexed samples. riboviz 2 is flexible and uses open, documented file formats, allowing users to integrate new analyses with the pipeline. Availabilityriboviz 2 is freely available at github.com/riboviz/riboviz. Supplementary information

bioinformatics↗

bioRxiv2020

Quantifying shifts in natural selection on codon usagebetween protein regions: A population genetics approach

BackgroundCodon usage bias (CUB), the non-uniform usage of synonymous codons, occurs across all domains of life. Adaptive CUB is hypothesized to result from selection for efficient ribosome elongation, accurate translation, mRNA secondary structure, and/or protein folding. Given the critical link between protein folding and protein function, numerous studies have analyzed the relationship between codon usage and protein structure. The results from these studies have often been contradictory, likely reflecting the differing methods used for measuring codon usage and the failure to appropriately control for confounding factors, such as differences in amino acid usage between protein structures and changes in the frequency of different structures with gene expression. ResultsHere we take an explicit population genetics approach to quantify codon-specific shifts in natural selection related to protein structure. As a result, our approach avoids the problem of needing to choose a specific metric of codon usage, naturally takes amino acid usage of a region into account, and explicitly takes variation in gene expression into account. Our results reveal a weak relationship between codon usage and protein structure, indicating that differences in selection between structures are subtle and/or intermittent. While the magnitude of differences in selection are slight, our results indicate the relationship between codon usage and protein structure is more complex than previously believed. ConclusionsWhile our results clearly indicate that selection on codon usage shifts between secondary structure, the overall impact of these shifts on codon usage are small. Nevertheless, our work demonstrates the statistical power and benefits of studying selective shifts on codon usage or other genomic features from an explicitly evolutionary approach. Limitations of this approach are discussed.

evolutionary biology↗

bioRxiv2019

Gene Expression of Functionally-Related Genes Coevolves Across Fungal Species: Detecting Coevolution of Gene Expression Using Phylogenetic Comparative Methods

BackgroundResearchers 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. ResultsWe 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. ConclusionsOur 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.

evolutionary biology↗

bioRxiv2018

Quantifying Selection on Codon Usage in Signal Peptides: Gene Expression and Amino Acid Usage Explain Apparent Selection for Inefficient Codons

The Sec secretion pathway is found across all domains of life. A critical feature of Sec secreted proteins is the signal peptide, a short peptide with distinct physicochemical properties located at the N-terminus of the protein. Previous work indicates signal peptides are biased towards translationally inefficient codons, which is hypothesized to be an adaptation driven by selection to improve the efficacy and efficiency of the protein secretion mechanisms. We investigate codon usage in the signal peptides of E. coli using the Codon Adaptation Index (CAI), the tRNA Adaptation Index (tAI), and the ribosomal overhead cost formulation of the stochastic evolutionary model of protein production rates (ROC-SEMPPR). Comparisons between signal peptides and 5-end of cytoplasmic proteins using CAI and tAI are consistent with a preference for inefficient codons in signal peptides. Simulations reveal these differences are due to amino acid usage and gene expression - we find these differences disappear when accounting for both factors. In contrast, ROC-SEMPPR, a mechanistic population genetics model capable of separating the effects of selection and mutation bias, shows codon usage bias (CUB) of the signal peptides is indistinguishable from the 5-ends of cytoplasmic proteins. Additionally, we find CUB at the 5-ends is weaker than later segments of the gene. Results illustrate the value in using models grounded in population genetics to interpret genetic data. We show failure to account for mutation bias and the effects of gene expression on the efficacy of selection against translation inefficiency can lead to a misinterpretation of codon usage patterns.

evolutionary biology↗