Universally high transcript error rates in bacteria

deTELpy: Python package for high-throughput detection of amino acid substitutions in mass spectrometry datasets

MOTIVATION

Errors in the processing of genetic information during protein synthesis can lead to phenotypic mutations, such as amino acid substitutions, e.g. by transcription or translation errors. While genetic mutations can be readily identified using DNA sequencing, and mutations due to transcription errors by RNA sequencing, translation errors can only be identified proteome-wide using mass spectrometry.

RESULTS

Here, we provide a Python package implementation of a high-throughput pipeline to detect amino acid substitutions in mass spectrometry datasets. Our tools enable users to process hundreds of mass spectrometry datasets in batch mode to detect amino acid substitutions and calculate codon-specific and site-specific translation error rates. deTELpy will facilitate the systematic understanding of amino acid misincorporation rates (translation error rates), and the inference of error models across organisms and under stress conditions, such as drug treatment or disease conditions.

AVAILABILITY AND IMPLEMENTATION

deTELpy is implemented in Python 3 and is freely available with detailed documentation and practical examples at https://git.mpi-cbg.de/tothpetroczylab/detelpy and https://pypi.org/project/deTELpy/ and can be easily installed via pip install deTELpy.

Environment modulates protein heterogeneity through transcriptional and translational stop codon readthrough

Stop codon readthrough events give rise to longer proteins, which may alter the protein's function, thereby generating short-lasting phenotypic variability from a single gene. In order to systematically assess the frequency and origin of stop codon readthrough events, we designed a library of reporters. We introduced premature stop codons into mScarlet, which enabled high-throughput quantification of protein synthesis termination errors in E. coli using fluorescent microscopy. We found that under stress conditions, stop codon readthrough may occur at rates as high as 80%, depending on the nucleotide context, suggesting that evolution frequently samples stop codon readthrough events. The analysis of selected reporters by mass spectrometry and RNA-seq showed that not only translation but also transcription errors contribute to stop codon readthrough. The RNA polymerase was more likely to misincorporate a nucleotide at premature stop codons. Proteome-wide detection of stop codon readthrough by mass spectrometry revealed that temperature regulated the expression of cryptic sequences generated by stop codon readthrough in E. coli. Overall, our findings suggest that the environment affects the accuracy of protein production, which increases protein heterogeneity when the organisms need to adapt to new conditions.

Engineering tRNAs for the Ribosomal Translation of Non-proteinogenic Monomers

Ribosome-dependent protein biosynthesis is an essential cellular process mediated by transfer RNAs (tRNAs). Generally, ribosomally synthesized proteins are limited to the 22 proteinogenic amino acids (pAAs: 20 l-α-amino acids present in the standard genetic code, selenocysteine, and pyrrolysine). However, engineering tRNAs for the ribosomal incorporation of non-proteinogenic monomers (npMs) as building blocks has led to the creation of unique polypeptides with broad applications in cellular biology, material science, spectroscopy, and pharmaceuticals. Ribosomal polymerization of these engineered polypeptides presents a variety of challenges for biochemists, as translation efficiency and fidelity is often insufficient when employing npMs. In this Review, we will focus on the methodologies for engineering tRNAs to overcome these issues and explore recent advances both in vitro and in vivo. These efforts include increasing orthogonality, recruiting essential translation factors, and creation of expanded genetic codes. After our review on the biochemical optimizations of tRNAs, we provide examples of their use in genetic code manipulation, with a focus on the in vitro discovery of bioactive macrocyclic peptides containing npMs. Finally, an analysis of the current state of tRNA engineering is presented, along with existing challenges and future perspectives for the field.

Targeted accurate RNA consensus sequencing (tARC-seq) reveals mechanisms of replication error affecting SARS-CoV-2 divergence

RNA viruses, like SARS-CoV-2, depend on their RNA-dependent RNA polymerases (RdRp) for replication, which is error prone. Monitoring replication errors is crucial for understanding the virus's evolution. Current methods lack the precision to detect rare de novo RNA mutations, particularly in low-input samples such as those from patients. Here we introduce a targeted accurate RNA consensus sequencing method (tARC-seq) to accurately determine the mutation frequency and types in SARS-CoV-2, both in cell culture and clinical samples. Our findings show an average of 2.68 × 10-5 de novo errors per cycle with a C > T bias that cannot be solely attributed to APOBEC editing. We identified hotspots and cold spots throughout the genome, correlating with high or low GC content, and pinpointed transcription regulatory sites as regions more susceptible to errors. tARC-seq captured template switching events including insertions, deletions and complex mutations. These insights shed light on the genetic diversity generation and evolutionary dynamics of SARS-CoV-2.

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

Most eukaryotic genes undergo alternative splicing (AS), but the overall functional significance of this process remains a controversial issue. It has been noticed that the complexity of organisms (assayed by the number of distinct cell types) correlates positively with their genome-wide AS rate. This has been interpreted as evidence that AS plays an important role in adaptive evolution by increasing the functional repertoires of genomes. However, this observation also fits with a totally opposite interpretation: given that 'complex' organisms tend to have small effective population sizes (Ne), they are expected to be more affected by genetic drift, and hence more prone to accumulate deleterious mutations that decrease splicing accuracy. Thus, according to this 'drift barrier' theory, the elevated AS rate in complex organisms might simply result from a higher splicing error rate. To test this hypothesis, we analyzed 3496 transcriptome sequencing samples to quantify AS in 53 metazoan species spanning a wide range of Ne values. Our results show a negative correlation between Ne proxies and the genome-wide AS rates among species, consistent with the drift barrier hypothesis. This pattern is dominated by low abundance isoforms, which represent the vast majority of the splice variant repertoire. We show that these low abundance isoforms are depleted in functional AS events, and most likely correspond to errors. Conversely, the AS rate of abundant isoforms, which are relatively enriched in functional AS events, tends to be lower in more complex species. All these observations are consistent with the hypothesis that variation in AS rates across metazoans reflects the limits set by drift on the capacity of selection to prevent gene expression errors.

Fitness Effects of Phenotypic Mutations at Proteome-Scale Reveal Optimality of Translation Machinery

Errors in protein translation can lead to non-genetic, phenotypic mutations, including amino acid misincorporations. While phenotypic mutations can increase protein diversity, the systematic characterization of their proteome-wide frequencies and their evolutionary impact has been lacking. Here, we developed a mechanistic model of translation errors to investigate how selection acts on protein populations produced by amino acid misincorporations. We fitted the model to empirical observations of misincorporations obtained from over a hundred mass spectrometry datasets of E. coli and S. cerevisiae. We found that on average 20% to 23% of proteins synthesized in the cell are expected to harbor at least one amino acid misincorporation, and that deleterious misincorporations are less likely to occur. Combining misincorporation probabilities and the estimated fitness effects of amino acid substitutions in a population genetics framework, we found 74% of mistranslation events in E. coli and 94% in S. cerevisiae to be neutral. We further show that the set of available synonymous tRNAs is subject to evolutionary pressure, as the presence of missing tRNAs would increase codon-anticodon cross-reactivity and misincorporation error rates. Overall, we find that the translation machinery is likely optimal in E. coli and S. cerevisiae and that both local solutions at the level of codons and a global solution such as the tRNA pool can mitigate the impact of translation errors. We provide a framework to study the evolutionary impact of codon-specific translation errors and a method for their proteome-wide detection across organisms and conditions.

Mutation bias and adaptation in bacteria

Genetic mutation, which provides the raw material for evolutionary adaptation, is largely a stochastic force. However, there is ample evidence showing that mutations can also exhibit strong biases, with some mutation types and certain genomic positions mutating more often than others. It is becoming increasingly clear that mutational bias can play a role in determining adaptive outcomes in bacteria in both the laboratory and the clinic. As such, understanding the causes and consequences of mutation bias can help microbiologists to anticipate and predict adaptive outcomes. In this review, we provide an overview of the mechanisms and features of the bacterial genome that cause mutational biases to occur. We then describe the environmental triggers that drive these mechanisms to be more potent and outline the adaptive scenarios where mutation bias can synergize with natural selection to define evolutionary outcomes. We conclude by describing how understanding mutagenic genomic features can help microbiologists predict areas sensitive to mutational bias, and finish by outlining future work that will help us achieve more accurate evolutionary forecasts.

Inosine triphosphate pyrophosphatase: A guardian of the cellular nucleotide pool and potential mediator of RNA function

Inosine triphosphate pyrophosphatase (ITPase), encoded by the ITPA gene in humans, is an important enzyme that preserves the integrity of cellular nucleotide pools by hydrolyzing the noncanonical purine nucleotides (deoxy)inosine and (deoxy)xanthosine triphosphate into monophosphates and pyrophosphate. Variants in the ITPA gene can cause partial or complete ITPase deficiency. Partial ITPase deficiency is benign but clinically relevant as it is linked to altered drug responses. Complete ITPase deficiency causes a severe multisystem disorder characterized by seizures and encephalopathy that is frequently associated with fatal infantile dilated cardiomyopathy. In the absence of ITPase activity, its substrate noncanonical nucleotides have the potential to accumulate and become aberrantly incorporated into DNA and RNA. Hence, the pathophysiology of ITPase deficiency could arise from metabolic imbalance, altered DNA or RNA regulation, or from a combination of these factors. Here, we review the known functions of ITPase and highlight recent work aimed at determining the molecular basis for ITPA-associated pathogenesis which provides evidence for RNA dysfunction. This article is categorized under: RNA in Disease and Development > RNA in Disease RNA in Disease and Development > RNA in Development.

Pervasive sequence-level variation in the transcriptome of Plasmodium falciparum

Single-nucleotide variations (SNVs) in RNA, arising from co- and post-transcriptional phenomena including transcription errors and RNA-editing, are well studied in a range of organisms. In the malaria parasite Plasmodium falciparum, stage-specific and non-specific gene-expression variations accompany the parasite's array of developmental and morphological phenotypes over the course of its complex life cycle. However, the extent, rate and effect of sequence-level variation in the parasite's transcriptome are unknown. Here, we report the presence of pervasive, non-specific SNVs in the P. falciparum transcriptome. SNV rates for a gene were correlated to gene length (r[Formula: see text]0.65-0.7) but not to the AT-content of that gene. Global SNV rates for the P. falciparum lines we used, and for publicly available P. vivax and P. falciparum clinical isolate datasets, were of the order of 10-3 per base, ∼10× higher than rates we calculated for bacterial datasets. These variations may reflect an intrinsic transcriptional error rate in the parasite, and RNA editing may be responsible for a subset of them. This seemingly characteristic property of the parasite may have implications for clinical outcomes and the basic biology and evolution of P. falciparum and parasite biology more broadly. We anticipate that our study will prompt further investigations into the exact sources, consequences and possible adaptive roles of these SNVs.

Rates of Mutations and Transcript Errors in the Foodborne Pathogen Salmonella enterica subsp. enterica

Because errors at the DNA level power pathogen evolution, a systematic understanding of the rate and molecular spectra of mutations could guide the avoidance and treatment of infectious diseases. We thus accumulated tens of thousands of spontaneous mutations in 768 repeatedly bottlenecked lineages of 18 strains from various geographical sites, temporal spread, and genetic backgrounds. Entailing over ∼1.36 million generations, the resultant data yield an average mutation rate of ∼0.0005 per genome per generation, with a significant within-species variation. This is one of the lowest bacterial mutation rates reported, giving direct support for a high genome stability in this pathogen resulting from high DNA-mismatch-repair efficiency and replication-machinery fidelity. Pathogenicity genes do not exhibit an accelerated mutation rate, and thus, elevated mutation rates may not be the major determinant for the diversification of toxin and secretion systems. Intriguingly, a low error rate at the transcript level is not observed, suggesting distinct fidelity of the replication and transcription machinery. This study urges more attention on the most basic evolutionary processes of even the best-known human pathogens and deepens the understanding of their genome evolution.

Density- and elongation speed-dependent error correction in RNA polymerization

Backtracking of RNA polymerase (RNAP) is an important pausing mechanism during DNA transcription that is part of the error correction process that enhances transcription fidelity. We model the backtracking mechanism of RNA polymerase, which usually happens when the polymerase tries to incorporate a noncognate or "mismatched" nucleotide triphosphate. Previous models have made simplifying assumptions such as neglecting the trailing polymerase behind the backtracking polymerase or assuming that the trailing polymerase is stationary. We derive exact analytic solutions of a stochastic model that includes locally interacting RNAPs by explicitly showing how a trailing RNAP influences the probability that an error is corrected or incorporated by the leading backtracking RNAP. We also provide two related methods for computing the mean times for error correction and incorporation given an initial local RNAP configuration. Using these results, we propose an effective interacting-RNAP lattice that can be readily simulated.

Universal Constraints on Protein Evolution in the Long-Term Evolution Experiment with Escherichia coli

Although it is well known that abundant proteins evolve slowly across the tree of life, there is little consensus for why this is true. Here, I report that abundant proteins evolve slowly in the hypermutator populations of Lenski's long-term evolution experiment with Escherichia coli (LTEE). Specifically, the density of all observed mutations per gene, as measured in metagenomic time series covering 60,000 generations of the LTEE, significantly anticorrelates with mRNA abundance, protein abundance, and degree of protein-protein interaction. The same pattern holds for nonsynonymous mutation density. However, synonymous mutation density, measured across the LTEE hypermutator populations, positively correlates with protein abundance. These results show that universal constraints on protein evolution are visible in data spanning three decades of experimental evolution. Therefore, it should be possible to design experiments to answer why abundant proteins evolve slowly.