Biology subjects

Morozov, A. V.

Publications and source records attributed to Morozov, A. V..

4 recordsLinked to original sources

bioRxiv2020

Widespread selection against deleterious mutations in the Drosophila genome

We have developed a computational approach to simultaneous genome-wide inference of key population genetics parameters: selection strengths, mutation rates rescaled by the effective population size and the fraction of viable genotypes, solely from an alignment of genomic sequences sampled from the same population. Our approach is based on a generalization of the Ewens sampling formula, used to compute steady-state probabilities of allelic counts in a neutrally evolving population, to populations subjected to selective constraints. Patterns of polymorphisms observed in alignments of genomic sequences are used as input to Approximate Bayesian Computation, which employs the generalized Ewens sampling formula to infer the distributions of population genetics parameters. After carrying out extensive validation of our approach on synthetic data, we have applied it to the evolution of the Drosophila melanogaster genome, where an alignment of 197 genomic sequences is available for a single ancestral-range population from Zambia, Africa. We have divided the Drosophila genome into 100-bp windows and assumed that sequences in each window can exist in either low- or high-fitness state. Thus, the steady-state population in our model is subject to a constant influx of deleterious mutations, which shape the observed frequencies of allelic counts in each window. Our approach, which focuses on deleterious mutations and accounts for intra-window linkage and epistasis, provides an alternative description of background selection. We find that most of the Drosophila genome evolves under selective constraints imposed by deleterious mutations. These constraints are not confined to known functional regions of the genome such as coding sequences and may reflect global biological processes such as the necessity to maintain chromatin structure. Furthermore, we find that inference of mutation rates in the presence of selection leads to mutation rate estimates that are several-fold higher than neutral estimates widely used in the literature. Our computational pipeline can be used in any organism for which a sample of genomic sequences from the same population is available.

evolutionary biology↗

bioRxiv2019

Sparse epistatic patterns in the evolution of terpene synthases

We explore sequence determinants of enzyme activity and specificity in a major enzyme family of terpene synthases. Most enzymes in this family catalyze reactions that produce cyclic terpenes - complex hydrocarbons widely used by plants and insects in diverse biological processes such as defense, communication, and symbiosis. To analyze the molecular mechanisms of emergence of terpene cyclization, we have carried out in-depth examination of mutational space around (E)-{beta}-farnesene synthase, an Artemisia annua enzyme which catalyzes production of a linear hydrocarbon chain. Each mutant enzyme in our synthetic libraries was characterized biochemically, and the resulting reaction rate data was used as input to the Michaelis-Menten model of enzyme kinetics, in which free energies were represented as sums of one-amino-acid contributions and two-amino-acid couplings. Our model predicts measured reaction rates with high accuracy and yields free energy landscapes characterized by relatively few coupling terms. As a result, the Michaelis-Menten free energy landscapes have simple, interpretable structure and exhibit little epistasis. We have also developed biophysical fitness models based on the assumption that highly fit enzymes have evolved to maximize the output of correct products, such as cyclic products or a specific product of interest, while minimizing the output of byproducts. This approach results in a non-linear fitness landscape which is considerably more epistatic. Overall, our experimental and computational framework provides focused characterization of evolutionary emergence of novel enzymatic functions in the context of micro-evolutionary exploration of sequence space around naturally occurring enzymes.

evolutionary biology↗

bioRxiv2019

Inferring biophysical models of evolution from genome-wide patterns of codon usage

Frequencies of synonymous codons are typically non-uniform, despite the fact that such codons correspond to the same amino acid in the genetic code. This phenomenon, known as codon bias, is broadly believed to be due to a combination of factors including genetic drift, mutational biases, and selection for speed and accuracy of codon translation; however, quantitative modeling of codon bias has been elusive. We have developed a biophysical population genetics model which explains genome-wide codon frequencies observed across 20 organisms. We assume that codons evolve independently of each other under the influence of mutation and selection forces, and that the codon population has reached evolutionary steady state. Our model implements codon-level treatment of mutations with transition/transversion biases, and includes two contributions to codon fitness which describe codon translation speed and accuracy. Furthermore, our model includes wobble pairing - the possibility of codon-anticodon base pairing mismatches at the 3 nucleotide position of the codon. We find that the observed patterns of genome-wide codon usage are consistent with a strong selective penalty for mistranslated amino acids. Thus codons undergo purifying selection and their relative frequencies are affected in part by mutational robustness. We find that the dependence of codon fitness on translation speed is weaker on average compared to the strength of selection against mistranslation. Although no constraints on codon-anticodon pairing are imposed a priori, a reasonable hierarchy of pairing rates, which conforms to the wobble hypothesis and is consistent with available structural evidence, emerges spontaneously as a model prediction. Finally, treating the translation process explicitly in the context of a finite ribosomal pool has allowed us to estimate mutation rates per nucleotide directly from the coding sequences. Reminiscent of Drakes observation that mutation rates are inversely correlated with the genome size, we predict that mutation rates are inversely proportional to the number of genes. Overall, our approach offers a unified biophysical and population genetics framework for studying codon bias across all domains of life.

biophysics↗

bioRxiv2018

RefCell: Multi-dimensional analysis of image-based high-throughput screens based on ‘typical cells’

BackgroundImage-based high-throughput screening (HTS) reveals a high level of heterogeneity in single cells and multiple cellular states may be observed within a single population. Cutting-edge high-dimensional analysis methods are successful in characterizing cellular heterogeneity, but they suffer from the \"curse of dimensionality\" and non-standardized outputs.\n\nResultsHere we introduce RefCell, a multi-dimensional analysis pipeline for image-based HTS that reproducibly captures cells with typical combinations of features in reference states, and uses these \"typical cells\" as a reference for classification and weighting of metrics. RefCell quantitatively assesses the heterogeneous deviations from typical behavior for each analyzed perturbation or sample.\n\nConclusionsWe apply RefCell to the analysis of data from a high-throughput imaging screen of a library of 320 ubiquitin protein targeted siRNAs selected to gain insights into the mechanisms of premature aging (progeria). RefCell yields results comparable to a more complex clustering based single cell analysis method, which both reveal more potential hits than conventional average based analysis.

bioinformatics↗