Biology subjects

Hockenberry, A. J.

Publications and source records attributed to Hockenberry, A. J..

9 recordsLinked to original sources

bioRxiv2023

Growth-dependent gene expression variation influences the strength of codon usage biases

The most highly expressed genes in microbial genomes tend to use a limited set of synonymous codons, often referred to as "preferred codons." The existence of preferred codons is commonly attributed to selection pressures on various aspects of protein translation including accuracy and/or speed. However, gene expression is condition-dependent and even within single-celled organisms transcript and protein abundances can vary depending on a variety of environmental and other factors. Here, we show that growth rate-dependent expression variation is an important constraint that significantly influences the evolution of gene sequences. Using large-scale transcriptomic and proteomic data sets in Escherichia coli and Saccharomyces cerevisiae, we confirm that codon usage biases are strongly associated with gene expression but highlight that this relationship is most pronounced when gene expression measurements are taken during rapid growth conditions. Specifically, genes whose relative expression increases during periods of rapid growth have stronger codon usage biases than comparably expressed genes whose expression decreases during rapid growth conditions. These findings highlight that gene expression measured in any particular condition tells only part of the story regarding the forces shaping the evolution of microbial gene sequences. More generally, our results imply that microbial physiology during rapid growth is critical for explaining long-term translational constraints.

evolutionary biology↗

bioRxiv2021

Virulent but not temperate bacteriophages display hallmarks of rapid translation initiation

Bacteriophages rely almost exclusively on host-cell machinery to produce their proteins, and their mRNAs must therefore compete with host mRNAs for valuable translational resources. In many bacterial species, highly translated mRNAs are characterized by the presence of a Shine-Dalgarno sequence motif upstream of the start codon and weak secondary structure within the start codon region. However, the general constraints and principles underlying the translation of phage mRNAs are largely unknown. Here, we show that phage mRNAs are highly enriched in strong Shine-Dalgarno sequences and have comparatively weaker secondary structures in the start codon region than host-cell mRNAs. Phage mRNAs appear statistically similar to the most highly expressed host genes in E. coli according to both features, strongly suggesting that they initiate translation at particularly high rates. Interestingly, we find that these observations are driven largely by virulent phages and that temperate phages encode mRNAs with similar start codon features to their host genes. These findings apply broadly across a wide-diversity of host-species and phage genomes. Further study of phage translational regulation--with a particular emphasis on virulent phages--may provide new strategies for engineering phage genomes and recombinant expression systems more generally.

genomics↗

bioRxiv2020

Generating complex patterns of gene expression without regulatory circuits

Synthetic biology has successfully advanced our ability to design and implement complex, time-varying genetic circuits to control the expression of recombinant proteins. However, these circuits typically require the production of regulatory genes whose only purpose is to coordinate expression of other genes. When designing very small genetic constructs, such as viral genomes, we may want to avoid introducing such auxiliary gene products while nevertheless encoding complex expression dynamics. To this end, here we demonstrate that varying only the placement and strengths of promoters, terminators, and RNase cleavage sites in a computational model of a bacteriophage genome is sufficient to achieve solutions to a variety of basic gene expression patterns. We discover these genetic solutions by computationally evolving genomes to reproduce desired gene expression time-course data. Our approach shows that non-trivial patterns can be evolved, including complex patterns where the relative ordering of genes by abundance changes over time. We find that some patterns are easier to evolve than others, and comparable expression patterns can be achieved via different genetic architectures. Our work opens up a novel avenue to genome engineering via fine-tuning the balance of gene expression and gene degradation rates. Author summaryViruses that infect bacteria, commonly called bacteriophages, typically have small genomes that encode as few as 10 genes. From the perspective of understanding genome design and regulation, these organisms are important model systems. Similar to cellular species, the genes encoded on a phage genome often must be expressed at different levels and at particular times during the phage lifecycle. Given their unique size constraints, it may be advantageous for phages to accomplish differential gene expression without having to produce a variety of dedicated regulatory molecules--which are frequently encoded on the larger and more complex genomes of free-living species. Here, we use a computational simulation of phage infection coupled with an evolutionary selection algorithm to illustrate that phage genomes can encode complex time-dependent gene expression patterns without the need for dedicated regulatory molecules. We anticipate that this simulation framework may additionally aid future phage genome design and engineering efforts.

synthetic biology↗

bioRxiv2020

BACPHLIP: Predicting bacteriophage lifestyle from conserved protein domains

Bacteriophages are broadly classified into two distinct lifestyles: temperate and virulent. Temperate phages are capable of a latent phase of infection within a host cell (lysogenic cycle), whereas virulent phages directly replicate and lyse host cells upon infection (lytic cycle). Accurate lifestyle identification is critical for determining the role of individual phage species within ecosystems and their effect on host evolution. Here, we present BACPHLIP, a BACterioPHage LIfestyle Predictor. BACPHLIP detects the presence of a set of conserved protein domains within an input genome and uses this data to predict lifestyle via a Random Forest classifier that was trained on a dataset of 634 phage genomes. On an independent test set of 423 phages, BACPHLIP has an accuracy of 98%, greatly exceeding that of the previously existing tools (79%). BACPHLIP is freely available on GitHub (https://github.com/adamhockenberry/bacphlip) and the code used to build and test the classifier is provided in a separate repository (https://github.com/adamhockenberry/bacphlip-model-dev) for users wishing to interrogate and re-train the underlying classification model.

bioinformatics↗

bioRxiv2019

Phylogenetic weighting does little to improve the accuracy of evolutionary coupling analyses

Homologous sequence alignments contain important information about the constraints that shape protein family evolution. Correlated changes between different residues, for instance, can be highly predictive of physical contacts within three-dimensional structures. Detecting such co-evolutionary signals via direct coupling analysis is particularly challenging given xsthe shared phylogenetic history and uneven sampling of different lineages from which protein sequences are derived. Current best practices for mitigating such effects include sequence-identity-based weighting of input sequences and post-hoc re-scaling of evolutionary coupling scores. However, numerous weighting schemes have been previously developed for other applications, and it is unknown whether any of these schemes may better account for phylogenetic artifacts in evolutionary coupling analyses. Here, we show across a dataset of 150 diverse protein families that the current best practices out-perform several alternative sequence- and tree-based weighting methods. Nevertheless, we find that sequence weighting in general provides only a minor benefit relative to post-hoc transformations that re-scale the derived evolutionary couplings. While our findings do not rule out the possibility that an as-yet-untested weighting method may show improved results, the similar predictive accuracies that we observe across distinct weighting methods suggests that there may be little room for further improvement on top of existing strategies.

evolutionary biology↗

bioRxiv2018

Evolutionary couplings detect side-chain interactions

Patterns of amino acid covariation in large protein sequence alignments can inform the prediction of de novo protein structures, binding interfaces, and mutational effects. While algorithms that detect these so-called evolutionary couplings between residues have proven useful for practical applications, less is known about how and why these methods perform so well, and what insights into biological processes can be gained from their application. Evolutionary coupling algorithms are commonly benchmarked by comparison to true structural contacts derived from solved protein structures. However, the methods used to determine true structural contacts are not standardized and different definitions of structural contacts may have important consequences for interpreting the results from evolutionary coupling analyses and understanding their overall utility. Here, we show that evolutionary coupling analyses are significantly more likely to identify structural contacts between side-chain atoms than between backbone atoms. We use both simulations and empirical analyses to highlight that purely backbone-based definitions of true residue-residue contacts (i.e., based on the distance between C atoms) may underestimate the accuracy of evolutionary coupling algorithms by as much as 40% and that a commonly used reference point (C{beta} atoms) underestimates the accuracy by 10-15%. These findings show that co-evolutionary outcomes differ according to which atoms participate in residue-residue interactions and suggest that accounting for different interaction types may lead to further improvements to contact-prediction methods.\n\nSignificance StatementEvolutionary couplings between residues within a protein can provide valuable information about protein structures, protein-protein interactions, and the mutability of individual residues. However, the mechanistic factors that determine whether two residues will co-evolve remains unknown. We show that structural proximity by itself is not sufficient for co-evolution to occur between residues. Rather, evolutionary couplings between residues are specifically governed by interactions between side-chain atoms. By contrast, intramolecular contacts between atoms in the protein backbone display only a weak signature of evolutionary coupling. These findings highlight that different types of stabilizing contacts exist within protein structures and that these types have a differential impact on the evolution of protein structures that should be considered in co-evolutionary applications.

biophysics↗

bioRxiv2018

Predicting bacterial growth conditions from mRNA and protein abundances

Cells respond to changing nutrient availability and external stresses by altering the expression of individual genes. Condition-specific gene expression patterns may provide a promising and low-cost route to quantifying the presence of various small molecules, toxins, or species-interactions in natural environments. However, whether gene expression signatures alone can predict individual environmental growth conditions remains an open question. Here, we used machine learning to predict 16 closely-related growth conditions using 155 datasets of E. coli transcript and protein abundances. We show that models are able to discriminate between different environmental features with a relatively high degree of accuracy. We observed a small but significant increase in model accuracy by combining transcriptome and proteome-level data, and we show that stationary phase conditions are typically more difficult to distinguish from one another than conditions under exponential growth. Nevertheless, with sufficient training data, gene expression measurements from a single species are capable of distinguishing between environmental conditions that are separated by a single environmental variable.

systems biology↗

bioRxiv2018

Selection removes Shine-Dalgarno-like sequences from within protein coding genes

The Shine-Dalgarno (SD) sequence motif facilitates translation initiation and is frequently found upstream of bacterial start codons. However, thousands of instances of this motif occur throughout the middle of protein coding genes in a typical bacterial genome. Here, we use comparative evolutionary analysis to test whether SD sequences located within genes are functionally constrained. We measure the conservation of SD sequences across Gammaproteobacteria, and find that they are significantly less conserved than expected. Further, the strongest SD sequences are the least conserved whereas we find evidence of conservation for the weakest possible SD sequences given amino acid constraints. Our findings indicate that most SD sequences within genes are likely to be deleterious and removed via selection. To illustrate the origin of these deleterious costs, we show that ATG start codons are significantly depleted downstream of SD sequences within genes, highlighting the potential for these sequences to promote erroneous translation initiation.

evolutionary biology↗

bioRxiv2017

Diversity of translation initiation mechanisms across bacterial species is driven by environmental conditions and growth demands

The Shine-Dalgarno (SD) sequence is often found upstream of protein coding genes across the bacterial kingdom, where it enhances start codon recognition via hybridization to the anti-SD (aSD) sequence on the small ribosomal subunit. Despite widespread conservation of the aSD sequence, the proportion of SD-led genes within a genome varies widely across species, and the evolutionary pressures shaping this variation remain largely unknown. Here, we conduct a phylogenetically-informed analysis and show that species capable of rapid growth have a significantly higher proportion of SD-led genes in their genome, suggesting a role for SD sequences in meeting the protein production demands of rapidly growing species. Further, we show that utilization of the SD sequence mechanism co-varies with: i) genomic traits that are indicative of efficient translation, and ii) optimal growth temperatures. In contrast to prior surveys, our results demonstrate that variation in translation initiation mechanisms across genomes is largely predictable, and that SD sequence utilization is part of a larger suite of translation-associated traits whose diversity is driven by the differential growth strategies of individual species.

systems biology↗