Iakov I. Davydov

Evolution of the protein stoichiometry in the L12 stalk of bacterial and organellar ribosomes

The emergence of ribosomes and translation factors is central for understanding the origin of life. Recruitment of translation factors to bacterial ribosomes is mediated by the L12 stalk composed of protein L10 and several copies of protein L12, the only multi-copy protein of the ribosome. Here we predict stoichiometries of L12 stalk for >1,200 bacteria, mitochondria and chloroplasts by a computational analysis, and validate the predictions by quantitative mass spectrometry. The majority of bacteria have L12 stalks allowing for binding of four or six copies of L12, largely independent of the taxonomic group or living conditions of the bacteria, whereas some cyanobacteria have eight copies. Mitochondrial and chloroplast ribosomes can accommodate six copies of L12. The last universal common ancestor probably had six molecules of L12 molecules bound to L10. Changes of the stalk composition provide a unique possibility to trace the evolution of protein components of the ribosome.

Detection of pathways affected by positive selection in primate lineages ancestral to humans.

Abstract Gene set enrichment approaches have been increasingly successful in finding signals of recent polygenic selection in the human genome. In this study, we aim at detecting biological pathways affected by positive selection in more ancient human evolutionary history. Focusing on four branches of the primate tree that lead to modern humans, we tested all available protein coding gene trees of the Primates clade for signals of adaptation in these branches, using the likelihood-based branch site test of positive selection. The results of these locus-specific tests were then used as input for a gene set enrichment test, where whole pathways are globally scored for a signal of positive selection, instead of focusing only on outlier “significant” genes. We identified signals of positive selection in several pathways that are mainly involved in immune response, sensory perception, metabolism, and energy production. These pathway-level results are highly significant, even though there is no functional enrichment when only focusing on top scoring genes. Interestingly, several gene sets are found significant at multiple levels in the phylogeny, but different genes are responsible for the selection signal in the different branches. This suggests that the same function has been optimized in different ways at different times in primate evolution.

Prediction of Epitopes in Closely Related Proteins Using a New Algorithm

Latrophilin 1 (presynaptic receptor) binds α-latrotoxin from black widow spider venom and regulates neurotransmitter release from nerve endings. The study of the mechanism of action of this receptor is impeded by the existence of closely related latrophilins 2 and 3. A profile of differences detecting the most differing and identical sites in several proteins was developed in order to obtain highly specific antibodies for differentiation between isoforms of related proteins. In addition, we used an algorithm for prediction of immunogenic sites of the protein, based on the basic vector method. The peptides selected using this algorithm were used for immunization of animals. The resultant sera exhibited the estimated specificity and high affinity for the corresponding receptor forms.

Application of mid-infrared molecular spectroscopy for assessment of biochemical parameters of blood serum.

The method of mid-infrared molecular spectroscopy allows precise measuring of the concentrations of a large number of biological molecules in a minimal sample volume. Method of projections on latent structures was used for plotting the calibration models. On the basis on mid-infrared spectral data we obtained calibration models for calculation of serum content of various substances: albumin, cholesterol, glucose, total protein, urea, 70 Da heat shock protein, and malonic dialdehyde.

Genome evolution in Burkholderia spp

Abstract Background The genus Burkholderia consists of species that occupy remarkably diverse ecological niches. Its best known members are important pathogens, B. mallei and B. pseudomallei, which cause glanders and melioidosis, respectively. Burkholderia genomes are unusual due to their multichromosomal organization. Results We performed integrated genomic analysis of 127 Burkholderia strains. The pan-genome is open with the saturation to be reached between 86,000 and 88,000 genes. The reconstructed rearrangements indicate a strong avoidance of intra-replichore inversions that is likely caused by selection against the transfer of large groups of genes between the leading and the lagging strands. Translocated genes also tend to retain their position in the leading or the lagging strand, and this selection is stronger for large syntenies. Integrated reconstruction of chromosome rearrangements in the context of strains phylogeny reveals parallel rearrangements that may indicate inversion-based phase variation and integration of new genomic islands. In particular, we detected parallel inversions in the second chromosomes of B. pseudomallei with breakpoints formed by genes encoding membrane components of multidrug resistance complex, that may be linked to a phase variation mechanism. Two genomic islands, spreading horizontally between chromosomes, were detected in the B. cepacia group. Conclusions This study demonstrates the power of integrated analysis of pan-genomes, chromosome rearrangements, and selection regimes. Non-random inversion patterns indicate selective pressure, inversions are particularly frequent in a recent pathogen B. mallei, and, together with periods of positive selection at other branches, may indicate adaptation to new niches. One such adaptation could be a possible phase variation mechanism in B. pseudomallei.Background The genus Burkholderia consists of species that occupy remarkably diverse ecological niches. Its best known members are important pathogens, B. mallei and B. pseudomallei, which cause glanders and melioidosis, respectively. Burkholderia genomes are unusual due to their multichromosomal organization. Results We performed pan-genome analysis of 127 Burkholde-ria strains. The pan-genome is open with the saturation to be reached between 86,000 and 88,000 genes. The reconstructed rearrangements indicate a strong avoidance of intra-replichore inversions that is likely caused by selection against the transfer of large groups of genes between the leading and the lagging strands. Translocated genes also tend to retain their position in the leading or the lagging strand, and this selection is stronger for large syntenies. We detected parallel inversions in the second chromosomes of seven B. pseudomallei. Breakpoints of these inversions are formed by genes encoding components of multidrug resistance complex. The membrane components of this system are exposed to the host’s immune system, and hence these inversions may be linked to a phase variation mechanism. We identified 197 genes evolving under positive selection. We found seventeen genes evolving under positive selection on individual branches; most of the positive selection periods map to the branches that are ancestral to species clades. This might indicate rapid adaptation to new ecological niches during species formation. Conclusions This study demonstrates the power of integrated analysis of pan-genomes, chromosome rearrangements, and selection regimes. Non-random inversion patterns indicate selective pressure, inversions are particularly frequent in a recent pathogen B. mallei, and, together with periods of positive selection at other branches, may indicate adaptation to new niches. One such adaptation could be possible phase variation mechanism in B. pseudomallei.

Modeling codon rate variation improves protein positive selection inference and detects nucleotide selection

There are numerous sources of variation in the rate of synonymous substitutions inside genes, such as direct selection on the nucleotide sequence, or mutation rate variation. However, the majority of the codon models which are developed and widely used today still incorporate an assumption of effectively neutral synonymous substitution rate, constant between sites of each gene. Here we propose a simple yet effective extension to codon models, which incorporates codon substitution rate variation along the gene sequence. We find strong effects of substitution rate variation on positive selection inference. The computational load of our approach remains tractable, and therefore we are able to apply it to genome scale positive selection scans in vertebrates and Drosophila. Our new model is strongly favored by the data. More than 70% of the genes detected by the classical branch-site model are presumably false positives caused by the incorrect assumption of uniform synonymous substitution rate. The new model is able to capture signatures of nucleotide level selection acting on translation initiation and on splicing sites within the coding region. Finally, we show that rate variation is highest in the highly recombining regions, and we hypothesize that recombination and mutation rate variation, such as high CpG mutation rate, are the two main sources of nucleotide rate variation. While we detect fewer genes under positive selection in Drosophila than without rate variation, the genes which we detect contain a stronger signal of adaptation to Wolbachia. We provide software to perform positive selection analysis using the new model.There are numerous sources of variation in the rate of synonymous substitutions inside genes, such as direct selection on the nucleotide sequence, or mutation rate variation. Yet scans for positive selection rely on codon models which incorporate an assumption of effectively neutral synonymous substitution rate, constant between sites of each gene. Here we perform a large-scale comparison of approaches which incorporate codon substitution rate variation and propose our own simple yet effective modification of existing models. We find strong effects of substitution rate variation on positive selection inference. More than 70% of the genes detected by the classical branch-site model are presumably false positives caused by the incorrect assumption of uniform synonymous substitution rate. We propose a new model which is strongly favored by the data while remaining computationally tractable. With the new model we can capture signatures of nucleotide level selection acting on translation initiation and on splicing sites within the coding region. Finally, we show that rate variation is highest in the highly recombining regions, and we propose that recombination and mutation rate variation, such as high CpG mutation rate, are the two main sources of nucleotide rate variation. While we detect fewer genes under positive selection in Drosophila than without rate variation, the genes which we detect contain a stronger signal of adaptation of dynein, which could be associated with Wolbachia infection. We provide software to perform positive selection analysis using the new model.There are numerous sources of variation in the rate of synonymous substitutions inside genes, such as direct selection on the nucleotide sequence, or mutation rate variation. However the majority of the codon models which are developed and widely used today still incorporate an assumption of effectively neutral synonymous substitution rate, constant between sites of each gene. Here we propose a simple yet effective extension to codon models, which incorporates codon substation rate variation along the gene sequence. We assess the performance of our approach in simulations and on real data. We find strong effects of nucleotide rate variation on positive selection inference, both under models with variation of protein selection and with branch-site variation of protein selection. We also demonstrate that the computational load of our approach remains tractable, and therefore we are able to apply it to genome scale positive selection scans. We apply our new method to two datasets: 767 vertebrate orthologs and 8,606 orthologs from twelve Drosophila species. We demonstrate that our new model is strongly favored by the data, and the support of the model increases with the amount of information. Moreover, it is able to capture signatures of nucleotide level selection acting on translation initiation and on splicing sites within the coding region. Finally, we show that rate variation is highest in the highly recombining regions,and we hypothesize that recombination and mutation rate variation, such as high CpG mutation rate, are the two main sources of nucleotide rate variation. Overall, nucleotide rate variation in substitutions is an important feature to capture, both to detect positive selection and to understand gene evolution, and the approach that we propose allows to do this in genome-wide scans.

Selectoscope: A Modern Web-App for Positive Selection Analysis of Genomic Data

Selectoscope is a web application which combines a number of popular tools used to infer positive selection in an easy to use pipeline. A set of homologous DNA sequences to be analyzed and evaluated are submitted to the server by uploading protein-coding gene sequences in the FASTA format. The sequences are aligned and a phylogenetic tree is constructed. The codeml procedure from the PAML package is used first to adjust branch lengths and to find a starting point for the likelihood maximization, then FastCodeML is executed. Upon completion, branches and positions under positive selection are visualized simultaneously on the tree and alignment viewers. Run logs are accessible through the web interface. Selectoscope is based on the Docker virtualization technology. This makes the application easy to install with a negligible performance overhead. The application is highly scalable and can be used on a single PC or on a large high performance clusters. The source code is freely available at https://github.com/anzaika/selectoscope.

State aggregation for fast likelihood computations in molecular evolution

Motivation: Codon models are widely used to identify the signature of selection at the molecular level and to test for changes in selective pressure during the evolution of genes encoding proteins. The large size of the state space of the Markov processes used to model codon evolution makes it difficult to use these models with large biological datasets. We propose here to use state aggregation to reduce the state space of codon models and, thus, improve the computational performance of likelihood estimation on these models. Results: We show that this heuristic speeds up the computations of the M0 and branch‐site models up to 6.8 times. We also show through simulations that state aggregation does not introduce a detectable bias. We analyzed a real dataset and show that aggregation provides highly correlated predictions compared to the full likelihood computations. Finally, state aggregation is a very general approach and can be applied to any continuous‐time Markov process‐based model with large state space, such as amino acid and coevolution models. We therefore discuss different ways to apply state aggregation to Markov models used in phylogenetics. Availability and Implementation: The heuristic is implemented in the godon package (https://bitbucket.org/Davydov/godon) and in a version of FastCodeML (https://gitlab.isb‐sib.ch/phylo/fastcodeml). Contact: nicolas.salamin@unil.ch Supplementary information: Supplementary data are available at Bioinformatics online.

State aggregation for fast likelihood computations in phylogenetics

Motivation Codon models are widely used to identify the signature of selection at the molecular level and to test for changes in selective pressure during the evolution of genes encoding proteins. The large size of the state space of the Markov processes used to model codon evolution makes it difficult to use these models with large biological datasets. We propose here to use state aggregation to reduce the state space of codon models and, thus, improve the computational performance of likelihood estimation on these models. Results We show that this heuristic speeds up the computations of the M0 and branch-site models up to 6.8 times. We also show through simulations that state aggregation does not introduce a detectable bias. We analysed a real dataset and show that aggregation provides highly correlated predictions compared to the full likelihood computations. Finally, state aggregation is a very general approach and can be applied to any continuous-time Markov process-based model with large state space, such as amino acid and coevolution models. We therefore discuss different ways to apply state aggregation to Markov models used in phylogenetics. Availability The heuristic is implemented in the godon package (https://bitbucket.org/Davydov/godon) and in a version of FastCodeML (https://gitlab.isb-sib.ch/phylo/fastcodeml).