Phenotypic robustness can increase phenotypic variability after non-genetic perturbations in gene regulatory circuits

Non-genetic perturbations, such as environmental change or developmental noise, can induce novel phenotypes. If an induced phenotype confers a fitness advantage, selection may promote its genetic stabilization. Non-genetic perturbations can thus initiate evolutionary innovation. Genetic variation that is not usually phenotypically visible may play an important role in this process. Populations under stabilizing selection on a phenotype that is robust to mutations can accumulate such variation. After non-genetic perturbations, this variation can become a source of new phenotypes. We here study the relationship between a phenotype's robustness to mutations and a population's potential to generate novel phenotypic variation. To this end, we use a well-studied model of transcriptional regulation circuits. Such circuits are important in many evolutionary innovations. We find that phenotypic robustness promotes phenotypic variability in response to non-genetic perturbations, but not in response to mutation. Our work suggests that non-genetic perturbations may initiate innovation more frequently in mutationally robust gene expression traits.Comment: 11 pages, 5 figure

Exploring protein fitness landscapes by directed evolution

Directed evolution circumvents our profound ignorance of how a protein's sequence encodes its function by using iterative rounds of random mutation and artificial selection to discover new and useful proteins. Proteins can be tuned to adapt to new functions or environments by simple adaptive walks involving small numbers of mutations. Directed evolution studies have shown how rapidly some proteins can evolve under strong selection pressures and, because the entire 'fossil record' of evolutionary intermediates is available for detailed study, they have provided new insight into the relationship between sequence and function. Directed evolution has also shown how mutations that are functionally neutral can set the stage for further adaptation

Machine learning-guided directed evolution for protein engineering

Machine learning (ML)-guided directed evolution is a new paradigm for biological design that enables optimization of complex functions. ML methods use data to predict how sequence maps to function without requiring a detailed model of the underlying physics or biological pathways. To demonstrate ML-guided directed evolution, we introduce the steps required to build ML sequence-function models and use them to guide engineering, making recommendations at each stage. This review covers basic concepts relevant to using ML for protein engineering as well as the current literature and applications of this new engineering paradigm. ML methods accelerate directed evolution by learning from information contained in all measured variants and using that information to select sequences that are likely to be improved. We then provide two case studies that demonstrate the ML-guided directed evolution process. We also look to future opportunities where ML will enable discovery of new protein functions and uncover the relationship between protein sequence and function.Comment: Made significant revisions to focus on aspects most relevant to applying machine learning to speed up directed evolutio

Rapid evolution of chemosensory receptor genes in a pair of sibling species of orchid bees (Apidae: Euglossini).

BackgroundInsects rely more on chemical signals (semiochemicals) than on any other sensory modality to find, identify, and choose mates. In most insects, pheromone production is typically regulated through biosynthetic pathways, whereas pheromone sensory detection is controlled by the olfactory system. Orchid bees are exceptional in that their semiochemicals are not produced metabolically, but instead male bees collect odoriferous compounds (perfumes) from the environment and store them in specialized hind-leg pockets to subsequently expose during courtship display. Thus, the olfactory sensory system of orchid bees simultaneously controls male perfume traits (sender components) and female preferences (receiver components). This functional linkage increases the opportunities for parallel evolution of male traits and female preferences, particularly in response to genetic changes of chemosensory detection (e.g. Odorant Receptor genes). To identify whether shifts in pheromone composition among related lineages of orchid bees are associated with divergence in chemosensory genes of the olfactory periphery, we searched for patterns of divergent selection across the antennal transcriptomes of two recently diverged sibling species Euglossa dilemma and E. viridissima.ResultsWe identified 3185 orthologous genes including 94 chemosensory loci from five different gene families (Odorant Receptors, Ionotropic Receptors, Gustatory Receptors, Odorant Binding Proteins, and Chemosensory Proteins). Our results revealed that orthologs with signatures of divergent selection between E. dilemma and E. viridissima were significantly enriched for chemosensory genes. Notably, elevated signals of divergent selection were almost exclusively observed among chemosensory receptors (i.e. Odorant Receptors).ConclusionsOur results suggest that rapid changes in the chemosensory gene family occurred among closely related species of orchid bees. These findings are consistent with the hypothesis that strong divergent selection acting on chemosensory receptor genes plays an important role in the evolution and diversification of insect pheromone systems

Neutral network sizes of biological RNA molecules can be computed and are not atypically small

BACKGROUND: Neutral networks or sets consist of all genotypes with a given phenotype. The size and structure of these sets has a strong influence on a biological system's robustness to mutations, and on its evolvability, the ability to produce phenotypic variation; in the few studied cases of molecular phenotypes, the larger this set, the greater both robustness and evolvability of phenotypes. Unfortunately, any one neutral set contains generally only a tiny fraction of genotype space. Thus, current methods cannot measure neutral set sizes accurately, except in the smallest genotype spaces. Results: Here we introduce a generalized Monte Carlo approach that can measure neutral set sizes in larger spaces. We apply our method to the genotype-to-phenotype mapping of RNA molecules, and show that it can reliably measure neutral set sizes for molecules up to 100 bases. We also study neutral set sizes of RNA structures in a publicly available database of functional, noncoding RNAs up to a length of 50 bases. We find that these neutral sets are larger than the neutral sets in 99.99% of random phenotypes. Software to estimate neutral network sizes is available at http://www.bioc.uzh.ch/wagner/publications-software.html. Conclusions: The biological RNA structures we examined are more abundant than random structures. This indicates that their robustness and their ability to produce new phenotypic variants may also be high

From genotypes to organisms: State-of-the-art and perspectives of a cornerstone in evolutionary dynamics

Understanding how genotypes map onto phenotypes, fitness, and eventually organisms is arguably the next major missing piece in a fully predictive theory of evolution. We refer to this generally as the problem of the genotype-phenotype map. Though we are still far from achieving a complete picture of these relationships, our current understanding of simpler questions, such as the structure induced in the space of genotypes by sequences mapped to molecular structures, has revealed important facts that deeply affect the dynamical description of evolutionary processes. Empirical evidence supporting the fundamental relevance of features such as phenotypic bias is mounting as well, while the synthesis of conceptual and experimental progress leads to questioning current assumptions on the nature of evolutionary dynamics-cancer progression models or synthetic biology approaches being notable examples. This work delves into a critical and constructive attitude in our current knowledge of how genotypes map onto molecular phenotypes and organismal functions, and discusses theoretical and empirical avenues to broaden and improve this comprehension. As a final goal, this community should aim at deriving an updated picture of evolutionary processes soundly relying on the structural properties of genotype spaces, as revealed by modern techniques of molecular and functional analysis.Comment: 111 pages, 11 figures uses elsarticle latex clas

Extensive Copy-Number Variation of Young Genes across Stickleback Populations

MM received funding from the Max Planck innovation funds for this project. PGDF was supported by a Marie Curie European Reintegration Grant (proposal nr 270891). CE was supported by German Science Foundation grants (DFG, EI 841/4-1 and EI 841/6-1). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Facilitated Variation: How Evolution Learns from Past Environments To Generalize to New Environments

One of the striking features of evolution is the appearance of novel structures in organisms. Recently, Kirschner and Gerhart have integrated discoveries in evolution, genetics, and developmental biology to form a theory of facilitated variation (FV). The key observation is that organisms are designed such that random genetic changes are channeled in phenotypic directions that are potentially useful. An open question is how FV spontaneously emerges during evolution. Here, we address this by means of computer simulations of two well-studied model systems, logic circuits and RNA secondary structure. We find that evolution of FV is enhanced in environments that change from time to time in a systematic way: the varying environments are made of the same set of subgoals but in different combinations. We find that organisms that evolve under such varying goals not only remember their history but also generalize to future environments, exhibiting high adaptability to novel goals. Rapid adaptation is seen to goals composed of the same subgoals in novel combinations, and to goals where one of the subgoals was never seen in the history of the organism. The mechanisms for such enhanced generation of novelty (generalization) are analyzed, as is the way that organisms store information in their genomes about their past environments. Elements of facilitated variation theory, such as weak regulatory linkage, modularity, and reduced pleiotropy of mutations, evolve spontaneously under these conditions. Thus, environments that change in a systematic, modular fashion seem to promote facilitated variation and allow evolution to generalize to novel conditions