Adaptive evolution of molecular phenotypes

Molecular phenotypes link genomic information with organismic functions, fitness, and evolution. Quantitative traits are complex phenotypes that depend on multiple genomic loci. In this paper, we study the adaptive evolution of a quantitative trait under time-dependent selection, which arises from environmental changes or through fitness interactions with other co-evolving phenotypes. We analyze a model of trait evolution under mutations and genetic drift in a single-peak fitness seascape. The fitness peak performs a constrained random walk in the trait amplitude, which determines the time-dependent trait optimum in a given population. We derive analytical expressions for the distribution of the time-dependent trait divergence between populations and of the trait diversity within populations. Based on this solution, we develop a method to infer adaptive evolution of quantitative traits. Specifically, we show that the ratio of the average trait divergence and the diversity is a universal function of evolutionary time, which predicts the stabilizing strength and the driving rate of the fitness seascape. From an information-theoretic point of view, this function measures the macro-evolutionary entropy in a population ensemble, which determines the predictability of the evolutionary process. Our solution also quantifies two key characteristics of adapting populations: the cumulative fitness flux, which measures the total amount of adaptation, and the adaptive load, which is the fitness cost due to a population's lag behind the fitness peak.Comment: Figures are not optimally displayed in Firefo

Observational and Theoretical Studies of 27 δ Scuti Stars with Investigation of the Period–luminosity Relation

The multi-color CCD photometric study of 27 δ Scuti stars is presented. By using approximately three years of photometric observations, we obtained the times of maxima and magnitude changes during the observation time interval for each star. The ephemerides of our δ Scuti stars were calculated based on the Markov Chain Monte Carlo (MCMC) method using the observed times of maxima and the period of the stars’ oscillations. We used the Gaia EDR3 parallaxes to calculate the luminosities and also the absolute magnitudes of these δ Scuti stars. The fundamental physical parameters of all the stars in our sample such as masses and radii were estimated. We determined the pulsation modes of the stars based on the pulsation constants. Moreover, the period–luminosity (P–L) relation of δ Scuti stars was investigated and discussed. Then, by using a machine learning classification, new P–L relations for fundamental and overtone modes are presented. © 2021. The Astronomical Society of the Pacific. All rights reserved.This work was supported by the Ministry of Science and Education, FEUZ-2020-0030. Popov A.A. acknowledges support by the Ministry of Science and Higher Education of the Russian Federation under the grant 075-15-2020-780. The machine learning section of this study has been performed according to the scientific agreement with Raderon Lab Inc. (https:// raderonlab.ca) with contract number R\AST\2021\1001. The authors would like to appreciate Dr. Fahri Alicavus and Dr. Somayeh Khakpash for their contributions to the research. Also, great thanks to Paul D. Maley for editing the text. The authors would like to thank the reviewer for comments and suggestions that helped to improve the paper

Formation of regulatory modules by local sequence duplication

Turnover of regulatory sequence and function is an important part of molecular evolution. But what are the modes of sequence evolution leading to rapid formation and loss of regulatory sites? Here, we show that a large fraction of neighboring transcription factor binding sites in the fly genome have formed from a common sequence origin by local duplications. This mode of evolution is found to produce regulatory information: duplications can seed new sites in the neighborhood of existing sites. Duplicate seeds evolve subsequently by point mutations, often towards binding a different factor than their ancestral neighbor sites. These results are based on a statistical analysis of 346 cis-regulatory modules in the Drosophila melanogaster genome, and a comparison set of intergenic regulatory sequence in Saccharomyces cerevisiae. In fly regulatory modules, pairs of binding sites show significantly enhanced sequence similarity up to distances of about 50 bp. We analyze these data in terms of an evolutionary model with two distinct modes of site formation: (i) evolution from independent sequence origin and (ii) divergent evolution following duplication of a common ancestor sequence. Our results suggest that pervasive formation of binding sites by local sequence duplications distinguishes the complex regulatory architecture of higher eukaryotes from the simpler architecture of unicellular organisms

Gene expression variability across cells and species shapes innate immunity.

As the first line of defence against pathogens, cells mount an innate immune response, which varies widely from cell to cell. The response must be potent but carefully controlled to avoid self-damage. How these constraints have shaped the evolution of innate immunity remains poorly understood. Here we characterize the innate immune response's transcriptional divergence between species and variability in expression among cells. Using bulk and single-cell transcriptomics in fibroblasts and mononuclear phagocytes from different species, challenged with immune stimuli, we map the architecture of the innate immune response. Transcriptionally diverging genes, including those that encode cytokines and chemokines, vary across cells and have distinct promoter structures. Conversely, genes that are involved in the regulation of this response, such as those that encode transcription factors and kinases, are conserved between species and display low cell-to-cell variability in expression. We suggest that this expression pattern, which is observed across species and conditions, has evolved as a mechanism for fine-tuned regulation to achieve an effective but balanced response

T cell immune responses deciphered

A machine-learning approach reveals antigen encoding that predicts T cell responses An immune response involves a co-ordinated orchestra of antigen-recognizing cells (e.g., T cells) and signaling molecules to mount a specific response against a pathogen. Although systems immunology offers a growing list of molecular interactions that are involved in antigen-specific immune responses, an understanding of how a response is mediated by different antigen characteristics is still lacking

Optimal Evolutionary Control for Artificial Selection on Molecular Phenotypes

Controlling an evolving population is an important task in modern molecular genetics, including directed evolution for improving the activity of molecules and enzymes, in breeding experiments in animals and in plants, and in devising public health strategies to suppress evolving pathogens. An optimal intervention to direct evolution should be designed by considering its impact over an entire stochastic evolutionary trajectory that follows. As a result, a seemingly suboptimal intervention at a given time can be globally optimal as it can open opportunities for desirable actions in the future. Here, we propose a feedback control formalism to devise globally optimal artificial selection protocol to direct the evolution of molecular phenotypes. We show that artificial selection should be designed to counter evolutionary tradeoffs among multivariate phenotypes to avoid undesirable outcomes in one phenotype by imposing selection on another. Control by artificial selection is challenged by our ability to predict molecular evolution. We develop an information theoretical framework and show that molecular timescales for evolution under natural selection can inform how to monitor a population in order to acquire sufficient predictive information for an effective intervention with artificial selection. Our formalism opens a new avenue for devising artificial selection methods for directed evolution of molecular functions

Learning and Organization of Memory for Evolving Patterns

Storing memory for molecular recognition is an efficient strategy for responding to external stimuli. Biological processes use different strategies to store memory. In the olfactory cortex, synaptic connections form when stimulated by an odor and establish an associative distributed memory that can be retrieved upon reexposure to the same odors. In contrast, the immune system encodes specialized memory by diverse receptors that can recognize a multitude of evolving pathogens. Despite the mechanistic differences between memory storage in the olfactory system and the immune system, these processes can still be viewed as different information encoding strategies. Here, we develop analytical and numerical techniques for a generalized Hopfield network to probe the utility of distinct memory strategies against both static and dynamic (evolving) patterns. We find that while classical Hopfield networks with distributed memory can efficiently encode a memory of static patterns, they are inadequate against evolving patterns. To follow an evolving pattern, we show that a Hopfield network should use a higher learning rate, which can in turn distort the energy landscape associated with the stored memory attractors. Specifically, we observe the emergence of narrow connecting paths between memory attractors that lead to misclassification of evolving patterns. We demonstrate that compartmentalized networks with specialized subnetworks are the optimal solutions to memory storage for evolving patterns. We postulate that evolution of pathogens may be the reason for the immune system to be encoded in a focused memory, in contrast to the distributed memory used in the olfactory cortex that interacts with mixtures of static odors. Our approach offers a principled framework to study learning and memory retrieval in out-of-equilibrium dynamical systems

Deep generative selection models of T and B cell receptor repertoires with soNNia

Subclasses of lymphocytes carry different functional roles to work together and produce an immune response and lasting immunity. Additionally to these functional roles, T and B cell lymphocytes rely on the diversity of their receptor chains to recognize different pathogens. The lymphocyte subclasses emerge from common ancestors generated with the same diversity of receptors during selection processes. Here, we leverage biophysical models of receptor generation with machine learning models of selection to identify specific sequence features characteristic of functional lymphocyte repertoires and subrepertoires. Specifically, using only repertoire-level sequence information, we classify CD4+ and CD8+ T cells, find correlations between receptor chains arising during selection, and identify T cell subsets that are targets of pathogenic epitopes. We also show examples of when simple linear classifiers do as well as more complex machine learning methods