Biology subjects

Rozhonova, H.

Publications and source records attributed to Rozhonova, H..

4 recordsLinked to original sources

bioRxiv2023

Protein evolvability under rewired genetic codes

The standard genetic code defines the rules of translation for nearly every life form on Earth. It also determines the amino acid changes accessible via single-nucleotide mutations, thus influencing protein evolvability -- the ability of mutation to bring forth adaptive variation in protein function. One of the most striking features of the standard genetic code is its robustness to mutation, yet it remains an open question whether this robustness facilitates or frustrates protein evolvability. To answer this question, we use data from massively-parallel sequence-to-function assays to construct and analyze empirical adaptive landscapes under hundreds of thousands of rewired genetic codes, including those of codon compression schemes relevant to protein engineering and synthetic biology. We find that robust genetic codes tend to enhance protein evolvability by rendering smooth adaptive landscapes with few peaks, which are readily accessible from throughout sequence space. By constructing low-dimensional visualizations of these landscapes, which each comprise more than 16 million mRNA sequences, we demonstrate that alternative genetic codes can radically alter the topological features of the network of high-fitness genotypes. Whereas the genetic codes that optimize evolvability depend to some extent on the detailed relationship between amino acid sequence and protein function, we also uncover general design principles for engineering non-standard genetic codes for enhanced and diminished evolvability, which may facilitate directed protein evolution experiments and the biocontainment of synthetic organisms, respectively. Our findings demonstrate that the standard genetic code, a critical and near-universal cellular information processing system, not only mitigates replication and translation errors as compared to most alternative genetic codes, but also facilitates predictable and directional adaptive evolution by enabling evolving populations to readily find mutational paths to adaptation.

evolutionary biology↗

bioRxiv2021

SECEDO: SNV-based subclone detection using ultra-low coverage single-cell DNA sequencing

MotivationSeveral recently developed single-cell DNA sequencing technologies enable whole-genome sequencing of thousands of cells. However, the ultra-low coverage of the sequenced data (< 0.05x per cell) mostly limits their usage to the identification of copy number alterations in multi-megabase segments. Many tumors are not copy number-driven, and thus single-nucleotide variant (SNV)-based subclone detection may contribute to a more comprehensive view on intra-tumor heterogeneity. Due to the low coverage of the data, the identification of SNVs is only possible when superimposing the sequenced genomes of hundreds of genetically similar cells. Thus, we have developed a new approach to efficiently cluster tumor cells based on a Bayesian filtering approach of relevant loci and exploiting read overlap and phasing. ResultsWe developed Single Cell Data Tumor Clusterer (SECEDO, lat. to separate), a new method to cluster tumor cells based solely on SNVs, inferred on ultra-low coverage single-cell DNA sequencing data. We applied SECEDO to a synthetic dataset simulating 7,250 cells and eight tumor subclones from a single patient, and were able to accurately reconstruct the clonal composition, detecting 92.11% of the somatic SNVs, with the smallest clusters representing only 6.9% of the total population. When applied to four real single-cell sequencing datasets from a breast cancer patient, each consisting of {approx}2,000 cells, SECEDO was able to recover the major clonal composition in each dataset at the original coverage of 0.03x, achieving an ARI score of {approx}0.6. The current state-of-the-art SNV-based clustering method achieved an ARI score of {approx}0, even after increasing the coverage in silico by a factor of 10, and was only able to match SECEDOs performance when pooling data from all four datasets, in addition to artificially increasing the sequencing coverage by a factor of 7. Variant calling on the resulting clusters recovered more than twice as many SNVs as would have been detected if calling on all cells together. Further, the allelic ratio of the called SNVs on each subcluster was more than double relative to the allelic ratio of the SNVs called without clustering, thus demonstrating that calling variants on subclones, in addition to both increasing sensitivity of SNV detection and attaching SNVs to subclones, significantly increases the confidence of the called variants. AvailabilitySECEDO is implemented in C++ and is publicly available at https://github.com/ratschlab/secedo.

bioinformatics↗

bioRxiv2021

Mutation bias shapes the spectrum of adaptive substitutions

Evolutionary adaptation often occurs via the fixation of beneficial point mutations, but different types of mutation may differ in their relative frequencies within the collection of substitutions contributing to adaptation in any given species. Recent studies have established that this spectrum of adaptive substitutions is enriched for classes of mutations that occur at higher rates. Yet, little is known at a quantitative level about the precise extent of this enrichment, or its dependence on other factors such as the beneficial mutation supply or demographic conditions. Here we address the extent to which the mutation spectrum shapes the spectrum of adaptive amino acid substitutions by applying a codon-based negative binomial regression model to three large data sets that include thousands of amino acid changes identified in natural and experimental adaptation in S. cerevisiae, E. coli, and M. tuberculosis. We find that the mutation spectrum has a strong and roughly proportional influence on the spectrum of adaptive substitutions in all three species. In fact, we find that by inferring the mutation rates that best explain the spectrum of adaptive substitutions, we can accurately recover species-specific mutational spectra obtained via mutation accumulation experiments. We complement this empirical analysis with simulations to determine the factors that influence how closely the spectrum of adaptive substitutions mirrors the spectrum of amino acid variants introduced by mutation, and find that the predictive power of mutation depends on multiple factors including population size and the breadth of the mutational target for adaptation. SIGNIFICANCE STATEMENTHow do mutational biases influence the process of adaptation? Classical neo-Darwinian thinking assumes that selection alone determines the course of adaptation from abundant pre-existing variation. Yet, theoretical work shows that under some circumstances the mutation rate to a given variant may have a strong impact on the probability of that variant contributing to adaptation. Here we introduce a statistical approach to analyzing how mutation shapes protein sequence adaptation, and show that the mutation spectrum has a proportional influence on the changes fixed in adaptation observed in three large data sets. We also show via computer simulations that a variety of factors can influence how closely the spectrum of adaptive substitutions mirrors the spectrum of variants introduced by mutation.

evolutionary biology↗

bioRxiv2021

Little evidence the standard genetic code is optimized for resource conservation

Selection for resource conservation can shape the coding sequences of organisms living in nutrient-limited environments. Recently, it was proposed that selection for resource conservation, specifically for nitrogen and carbon content, has also shaped the structure of the standard genetic code, such that the missense mutations it allows tend to cause small increases in the number of nitrogen and carbon atoms in amino acids. Moreover, it was proposed that this optimization is not confounded by known optimizations of the standard genetic code, such as for polar requirement or hydropathy. We challenge these claims. We show the proposed optimization for nitrogen conservation is highly sensitive to choice of null model and the proposed optimization for carbon conservation is confounded by the known conservative nature of the standard genetic code with respect to the molecular volume of amino acids. There is therefore little evidence the standard genetic code is optimized for resource conservation.

evolutionary biology↗