Identification of Conserved Epitopes in SARS-CoV-2 Spike and Nucleocapsid Protein

Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a novel virus that first occurred in Wuhan in December 2019. The spike glycoproteins and nucleocapsid proteins are the most common targets for the development of vaccines and antiviral drugs. Objective: We herein analyze the rate of evolution along with the sequences of spike and nucleocapsid proteins in relation to the spatial locations of their epitopes, previously suggested to contribute to the immune response caused by SARS-CoV-2 infections. Methods: We compare homologous proteins of seven human coronaviruses: HCoV-229E, HCoV-OC43, SARS-CoV, HCoV-NL63, HCoV-HKU1, MERS-CoV, and SARS-CoV-2. We then focus on the local, structural order-disorder propensity of the protein regions where the SARS-CoV-2 epitopes are located. Results : We show that most of nucleocapsid protein epitopes overlap the RNA-binding and dimerization domains, and some of them are characterized by a low rate of evolutions. Similarly, spike protein epitopes are preferentially located in regions that are predicted to be ordered and well-conserved, in correspondence of the heptad repeats 1 and 2. Interestingly, both the receptor-binding motif to ACE2 and the fusion peptide of spike protein are characterized by a high rate of evolution. Conclusion: Our results provide evidence for conserved epitopes that might help develop broad-spectrum SARS-CoV-2 vaccines.

Evolution of plasmid mobility: origin and fate of non-conjugative plasmids

Conjugation drives horizontal gene transfer of many adaptive traits across prokaryotes. Yet, only a fourth of the plasmids encode the functions necessary to conjugate autonomously, others being non-mobile or mobilizable by other elements. How these different plasmids evolve is poorly understood. Here, we studied plasmid evolution in terms of their gene repertoires and relaxases. We observed that gene content in plasmid varies rapidly in relation to the rate of evolution of relaxases, such that plasmids with 95% identical relaxases have on average fewer than 50% of homologs. The identification of 249 recent transitions in terms of mobility types revealed that they are associated with even greater changes in gene repertoires, possibly mediated by transposable elements that are more abundant in such plasmids. These changes include pseudogenization of the conjugation locus, exchange of replication initiators, and extensive gene loss. In some instances, the transition between mobility types also leads to the genesis of novel plasmid taxonomic units. Most of these transitions are short-lived, suggesting a source-sink dynamic, where conjugative plasmids constantly generate mobilizable and putatively non-mobilizable plasmids by gene deletion. Yet, in few cases such transitions resulted in the emergence of large clades of relaxases present only in mobilizable plasmids, suggesting successful specialization of these families in the hijacking of diverse conjugative systems. Our results shed further light on the huge plasticity of plasmids, suggest that many non-conjugative plasmids emerged recently from conjugative elements and allowed to quantify how changes in plasmid mobility shape the variation of their gene repertoires.

Evolution of the threshold temperature definition of a heat wave vs. evolution of the minimum mortality temperature: a case study in Spain during the 1983–2018 period

Background An area of current study concerns analysis of the possible adaptation of the population to heat, based on the temporal evolution of the minimum mortality temperature (MMT). It is important to know how is the evolution of the threshold temperatures (Tthreshold) due to these temperatures provide the basis for the activation of public health prevention plans against high temperatures. The objective of this study was to analyze the temporal evolution of threshold temperatures (Tthreshold) produced in different Spanish regions during the 1983–2018 period and to compare this evolution with the evolution of MMT. The dependent variable used was the raw rate of daily mortality due to natural causes ICD X: (A00-R99) for the considered period. The independent variable was maximum daily temperature (Tmax) during the summer months registered in the reference observatory of each region. Threshold values were determined using dispersion diagrams (annual) of the prewhitened series of mortality temperatures and Tmax. Later, linear fit models were carried out between the different values of Tthreshold throughout the study period, which permitted detecting the annual rate of change in Tthreshold. Results The results obtained show that, on average, Tthreshold has increased at a rate of 0.57 ºC/decade in Spain, while Tmax temperatures in the summer have increased at a rate of 0.41 ºC/decade, suggesting adaptation to heat. This rate of evolution presents important geographic heterogeneity. Also, the rate of evolution of Tthreshold was similar to what was detected for MMT. Conclusions The temporal evolution of the series of both temperature measures can be used as indicators of population adaptation to heat. The temporal evolution of Tthreshold has important geographic variation, probably related to sociodemographic and economic factors, that should be studied at the local level.

A variable-rate quantitative trait evolution model using penalized-likelihood

In recent years it has become increasingly popular to use phylogenetic comparative methods to investigate heterogeneity in the rate or process of quantitative trait evolution across the branches or clades of a phylogenetic tree. Here, I present a new method for modeling variability in the rate of evolution of a continuously-valued character trait on a reconstructed phylogeny. The underlying model of evolution is stochastic diffusion (Brownian motion), but in which the instantaneous diffusion rate (σ2) also evolves by Brownian motion on a logarithmic scale. Unfortunately, it’s not possible to simultaneously estimate the rates of evolution along each edge of the tree and the rate of evolution of σ2 itself using Maximum Likelihood. As such, I propose a penalized-likelihood method in which the penalty term is equal to the log-transformed probability density of the rates under a Brownian model, multiplied by a ‘smoothing’ coefficient, λ, selected by the user. λ determines the magnitude of penalty that’s applied to rate variation between edges. Lower values of λ penalize rate variation relatively little; whereas larger λ values result in minimal rate variation among edges of the tree in the fitted model, eventually converging on a single value of σ2 for all of the branches of the tree. In addition to presenting this model here, I have also implemented it as part of my phytools R package in the function multirateBM. Using different values of the penalty coefficient, λ, I fit the model to simulated data with: Brownian rate variation among edges (the model assumption); uncorrelated rate variation; rate changes that occur in discrete places on the tree; and no rate variation at all among the branches of the phylogeny. I then compare the estimated values of σ2 to their known true values. In addition, I use the method to analyze a simple empirical dataset of body mass evolution in mammals. Finally, I discuss the relationship between the method of this article and other models from the phylogenetic comparative methods and finance literature, as well as some applications and limitations of the approach.

A variable-rate quantitative trait evolution model using penalized-likelihood

In recent years it's become increasingly popular to use phylogenetic comparative methods to investigate heterogeneity in the rate or process of quantitative trait evolution across the branches or clades of a phylogenetic tree. Here, I present a new method for modeling variability in the rate of evolution of a continuously-valued character trait on a reconstructed phylogeny. The underlying model of evolution is stochastic diffusion (Brownian motion), but in which the instantaneous diffusion rate (σ2) also evolves by Brownian motion on a log-scale. Unfortunately, it's not possible to simultaneously estimate the rates of evolution along each edge of the tree and the rate of evolution of σ2 itself using Maximum Likelihood. As such, I propose a penalized-likelihood method in which the penalty term is equal to the log-transformed probability density of the rates under a Brownian model, multiplied by a 'smoothing' coefficient, λ, selected by the user. λ determines the magnitude of penalty that's applied to rate variation between edges. Lower values of λ penalize rate variation relatively little; whereas larger λ values result in minimal rate variation among edges of the tree in our fitted model, eventually converging on a single value of σ2 for all of the branches of the tree. In addition to presenting this model here, I've also implemented it as part of my phytools R package in the function multirateBM. Using different values of the penalty coefficient, λ, I fit the model to simulated data with: Brownian rate variation among edges (the model assumption); uncorrelated rate variation; rate changes that occur in discrete places on the tree; and no rate variation at all among the branches of the phylogeny. I then compare the estimated values of σ2 to their known true values. In addition, I use the method to analyze a simple empirical dataset of body mass evolution in mammals. Finally, I discuss some applications and limitations of the approach.

Филогенетическая реконструкция начальных этапов распространения вируса SARS-CoV-2 на Евроазиатском и Американском континентах посредством анализа геномных данных (краткое сообщение)

Проанализированы 252 полных генома вируса SARS-CoV-2 первой волны (декабря 2019 - июль 2020 г.) пандемии COVID-19 из 21 страны мира, включая Россию, посредством Байесовского филогенетического метода с молекулярными часами. Используемая нами методика показала, что первые заболевшие COVID-19 в человеческой популяции появились в период с июля по ноябрь 2019 г. в Китае. Распространение SARS-CoV-2 из Китая по всем регионам мира произошло с декабря 2019 по начало февраля 2020 года. Появление вируса в России датируется второй половиной января 2020 года. Скорость эволюции кодирующей части генома SARS-CoV-2 равная в среднем 7.3×10-4 (5.95×10-4 – 8.68×10-4) нуклеотидных замен на сайт в год сопоставима со скоростями накопления замен в геномах других человеческих РНК-содержащих вирусах (Measles morbillivirus, Rubella virus, Enterovirus C). 252 complete genomes of the SARS-CoV-2 isolated during the first wave (December 2019 - July 2020) of the global COVID-19 pandemic from 21 countries of the world, including Russia, were analyzed using the Bayesian phylogenetic method with a molecular clock. Results showed that the first cases of COVID-19 in the human population appeared in the period between July and November 2019 in China. The spread of SARS-CoV-2 from China toward all regions of the world occurred from December 2019 to early February 2020. The appearance of the virus in Russia dates back to the second half of January 2020. The rate of evolution of the coding part of the SARS-CoV-2 genome equal to 7.3×10-4 (5.95×10-4 - 8.68×10-4) nucleotide substitutions per site per year is comparable to the rates of accumulation of substitutions in genomes of other human RNA viruses (Measles morbillivirus, Rubella virus, Enterovirus C).

Episodic evolution of coadapted sets of amino acid sites in mitochondrial proteins

The rate of evolution differs between protein sites and changes with time. However, the link between these two phenomena remains poorly understood. Here, we design a phylogenetic approach for distinguishing pairs of amino acid sites that evolve concordantly, i.e., such that substitutions at one site trigger subsequent substitutions at the other; and also pairs of sites that evolve discordantly, so that substitutions at one site impede subsequent substitutions at the other. We distinguish groups of amino acid sites that undergo coordinated evolution and evolve discordantly from other such groups. In mitochondrion-encoded proteins of metazoans and fungi, we show that concordantly evolving sites are clustered in protein structures. By analysing the phylogenetic patterns of substitutions at concordantly and discordantly evolving site pairs, we find that concordant evolution has two distinct causes: epistatic interactions between amino acid substitutions and episodes of selection independently affecting substitutions at different sites. The rate of substitutions at concordantly evolving groups of protein sites changes in the course of evolution, indicating episodes of selection limited to some of the lineages. The phylogenetic positions of these changes are consistent between proteins, suggesting common selective forces underlying them.

Hybridization Slows Rate of Evolution in Crop-Wild Compared to Wild Populations of Weedy Raphanus Across a Moisture Gradient

Hybrid offspring of crops and their wild relatives commonly possess non-adaptive phenotypes and diminished fitness. Regularly, diminished success in early-generation hybrid populations is interpreted to suggest reduced biosafety risk regarding the unintended escape of novel traits from crop populations. Yet hybrid populations have been known to evolve to recover fitness relative to wild progenitors and can do so more rapidly than wild populations, although rates of evolution (for both hybrid populations and their wild progenitors) are sensitive to environmental context. In this research, we asked whether hybrid populations evolved more rapidly than wild populations in the context of soil moisture. We estimated evolutionary rates for 40 Raphanus populations that varied in their history of hybridization and environmental context (imposed by an experimental moisture cline) in two common gardens. After five generations of growing wild and crop-wild hybrid populations across a soil-moisture gradient, hybrid populations exhibited increased seedling emergence frequencies (~6% more), earlier emergence (~1 day), later flowering (~3 days), and larger body size (15–35%)—traits correlated with fitness—relative to wild populations. Hybrid populations, however, exhibited slower evolutionary rates than wild populations. Moreover, the rate of evolution in hybrid populations was consistent across evolutionary watering environments, but varied across watering environments in wild populations. These consistent evolutionary rates exhibited in hybrid populations suggests the evolution of robust traits that perform equally across soil moisture environments—a survival strategy characterized as “jack of all trades.” Although, diverse integrated weed management practices must be applied to wild and hybrid genotypes to diversify selection on these populations, evaluating the evolutionary rates of weeds in diverse environments will support the development of multi-faceted weed control strategies and effective integrated weed management policies.

Impact of population size at a rate of morphological and molecular evolution – the use of an object-oriented model

A generalization of Moran model of evolution is created using object-oriented method of modelling. A population consists of individuals which have a genotype and a phenotype. The genotype is inherited by descendants and it can mutate. The phenotype is dependent on the genotype. Moreover, the phenotype causes changes in the fitness of the individuals (natural selection which four kinds are defined and analysed). Evolution of the population appears spontaneously. This model is used to analyse how population size influence the rate of evolution. Evolution is manifested by two processes: the increase of the phenotype size (morphological evolution) and number of mutations accumulated on genes (molecular evolution). The rate of evolution increases if population size increases. An adaptive natural selection causes nonlinear changes in the phenotype size and number of mutations accumulated on genes. A competitive natural selection causes linear evolution. A surviving natural selection causes the faster evolution than a reproductive natural selection.

Decay of Hamiltonian Breathers Under Dissipation

We study metastable behavior in a discrete nonlinear Schrödinger equation from the viewpoint of Hamiltonian systems theory. When there are $$n<\infty $$ n < ∞ sites in this equation, we consider initial conditions in which almost all the energy is concentrated in one end of the system. We are interested in understanding how energy flows through the system, so we add a dissipation of size $$\gamma $$ γ at the opposite end of the chain, and we show that the energy decreases extremely slowly. Furthermore, the motion is localized in the phase space near a family of breather solutions for the undamped system. We give rigorous, asymptotic estimates for the rate of evolution along the family of breathers and the width of the neighborhood within which the trajectory is confined.