Computational protein sequence design has the ambitious goal of modifying existing or creating new proteins; however, designing stable and functional proteins is challenging without predictability of protein dynamics and allostery. Informing protein design methods with evolutionary information limits the mutational space to more native-like sequences and results in increased stability while maintaining functions. Recently, language models, trained on millions of protein sequences, have shown impressive performance in predicting the effects of mutations. Assessing Rosetta-designed sequences with a language model showed scores that were worse than those of their original sequence. To inform Rosetta design protocols with language model predictions, we added a new metric to restrain the energy function during design using the Evolutionary Scale Modeling (ESM) model. The resulting sequences have better language model scores and similar sequence recovery, with only a minor decrease in the fitness as assessed by Rosetta energy. In conclusion, our work combines the strength of recent machine learning approaches with the Rosetta protein design toolbox.

Hybridization is an important evolutionary force with a principal role in the origin of new species, known as hybrid speciation. However, ongoing hybridization can create hybrid swamping, in which parental genomes are completely lost. This can become a biodiversity threat if it involves species that have adapted to certain environmental conditions and occur nowhere else. Because conservation scientists commonly have a negative attitude toward hybrids, it is important to improve understanding of the influence of interspecific gene flow on the persistence of species. We reviewed the literature on species hybridization to build a list of all known cases in the order Carnivora. To examine the relative impact, we also noted level of introgression, whether fertile offspring were produced, and whether there was mention of negative or positive evolutionary effects (hybrid speciation and swamping). To evaluate the conservation implications of hybrids, we developed a decision-making tree with which to determine which actions should be taken to manage hybrid species. We found 53 hybrids involving 68 unique taxa, which is roughly 23% of all carnivore species. They mainly involved monophyletic (83%) and sympatric species (75%). For 2 species, the outcome of the assessment was to eliminate or restrict the hybrids: Ethiopian wolf (Canis simensis) and Scottish wildcat (Felis silvestris silvestris). Both species hybridize with their domestic conspecifics. For all other cases, we suggest hybrids be protected in the same manner as native species. We found no evidence of genomic extinction in Carnivora. To the contrary, some species appear to be of hybrid origin, such as the Asiatic black bear (Ursus thibetanus) and African golden wolf (Canis lupaster). Other positive outcomes of hybridization are novel genetic diversity, adaptation to extreme environments, and increased reproductive fitness. These outcomes are particularly valuable for counterbalancing genetic drift and enabling adaptive introgression in a human-dominated world.
La especiación por hibridación es una fuerza evolutiva importante con un papel principal en el origen de una nueva especie. Sin embargo, la hibridación continua puede generar un estancamiento híbrido en el que se pierden por completo los genomas parentales. Esto puede convertirse en una amenaza para la biodiversidad si involucra a una especie que se ha adaptado a ciertas condiciones ambientales y sólo se encuentra en un lugar. Ya que los científicos de la conservación suelen tener una actitud negativa hacia los híbridos, es importante incrementar el entendimiento de la influencia que tiene el flujo interespecífico sobre la persistencia de las especies. Revisamos la literatura sobre la hibridación de especies para generar una lista de todos los casos conocidos en el orden Carnívora. También observamos el nivel de introgresión, si se produjo descendencia fértil y si hubo mención de los efectos evolutivos positivos o negativos (especiación híbrida y estancamiento) para analizar el impacto relativo. Desarrollamos un árbol de decisión con el cual determinar cuáles acciones deberían tomarse en el manejo de las especies híbridas para evaluar las implicaciones que tienen los híbridos para la conservación. Encontramos 53 híbridos de 68 taxones únicos, lo que representa aproximadamente el 23% de todos los carnívoros. Estos híbridos incluyen principalmente a especies monofiléticas (83%) y simpátricas (75%). Para dos especies, los resultados del análisis fueron la eliminación o restricción de los híbridos: el lobo etíope (Canis simensis) y el lince escocés (Felis silvestris silvestris). Ambas especies hibridan con sus coespecíficos domésticos. Para todos los demás casos sugerimos que se proteja a los híbridos de la misma manera que a las especies nativas. No encontramos evidencias de una extinción genómica en el orden Carnívora. Al contrario, algunas especies parecen tener un origen híbrido, como el oso negro asiático (Ursus thibetanus) y el lobo dorado africano (Canis lupaster). Otros resultados positivos de la hibridación son la diversidad genética novedosa, la adaptación a ambientes extremos y el incremento en la adaptabilidad reproductiva. Estos resultados son de valor particular para contrarrestar la deriva génica y permitir la introgresión adaptativa en un mundo dominado por humanos. Evaluación de la especiación y estancamiento en carnívoros silvestres con una estrategia de árbol de decisión.

Exposure to adversity during early life is linked to lasting detrimental effects on evolutionary fitness across many taxa. However, due to the challenges of collecting longitudinal data, especially in species where one sex disperses, direct evidence from long-lived species remains relatively scarce. Here we test the effects of early life adversity on male and female longevity in a free-ranging population of rhesus macaques (Macaca mulatta) at Cayo Santiago, Puerto Rico. We leveraged six decades of data to quantify the relative importance of ten forms of early life adversity for 6,599 macaques (3,230 male, 3,369 female), with a smaller sample size (N=299) for one form of adversity (maternal social isolation) which required high-resolution behavioral data. We found that individuals who experienced more early life adversity died earlier than those who experienced less adversity. Mortality risk was highest during early life, defined as birth to four years old, suggesting acute survival effects of adversity, but heightened mortality risk was also present in macaques who survived to adulthood. Females and males were affected differently by some forms of adversity, and these differences might be driven by varying energetic demands, female philopatry, and male dispersal. By leveraging data on thousands of macaques collected over decades, our results show that the fitness consequences of early life adversity are not uniform across individuals but vary as a function of the type of adversity, timing, and social context, and thus contribute to our limited but growing understanding of the evolution of early life sensitivities in long-lived species.

Since the discovery of cells by Robert Hooke and Antoni van Leeuwenhoek in the 17th century, thousands of different cell types have been identified, most recently by sequencing-based single-cell profiling techniques. Yet, for many organisms we still do not know, how many different cell types they are precisely composed of. A recent survey of experimental data, using mostly morphology as a proxy for cell type, revealed allometric scaling of cell type diversity with organism size. Here, I argue from an evolutionary fitness perspective and suggest that three simple assumptions can explain the observed scaling: Evolving a new cell type has, 1. a fitness cost that increases with organism size, 2. a fitness benefit that also increases with organism size but 3. diminishes exponentially with the number of existing cell types. I will show that these assumptions result in a quantitative model that fits the observed cell type numbers across organisms of all size and explains why we should not expect isometric scaling.

The genome sequencing of Mycobacterium tuberculosis, the causative organism of tuberculosis, has significantly improved our understanding of the mechanisms that drive the establishment of infection and disease progression. Several clinical strains of M. tuberculosis exhibit single-nucleotide polymorphisms (SNPs), the implications of which are only beginning to be understood. Here, we examined the impact of a specific polymorphism in PhoR, the sensor kinase of the PhoPR two-component system. Biochemical analysis revealed reduced autophosphatase/ATPase activity, which led to enhanced downstream gene expression. We complemented M. tuberculosis H37Ra with the wild-type and mutant phoPR genes and characterized the strains in a cell line infection model. We provide an explanation for the low prevalence of the SNP in clinical strains (∼1%), as the mutation causes a survival disadvantage in the host cells. The study provides a rare example of selection of a signaling node under competing evolutionary forces, wherein a biochemically superior mutation aids bacterial adaptation within-host but has low fitness for infection and hence is not selected. Our study highlights the importance of accounting for such SNPs to test therapeutic and co-therapeutic methods to combat TB.

In my comment on the target article of Ocklenburg et al. [Laterality 2020: Entering the next decade. Laterality, 1-33. doi:10.1080/1357650X.2020.1804396], I point out the relevance of studies in non-human species within natural settings for understanding the ecological pressures, which shape the direction and degree of brain asymmetries. I therefore outline some major research projects, which are not included in the paper of Ocklenburg et al. and which require comparative animal research.

Here, we propose a computational approach to explore evolutionary fitness in complex biological systems based on empirical data using artificial neural networks. The essence of our approach is the following. We first introduce a ranking order of inherited elements (behavioral strategies or/and life history traits) in considered self-reproducing systems: we use available empirical information on selective advantages of such elements. Next, we introduce evolutionary fitness, which is formally described as a certain function reflecting the introduced ranking order. Then, we approximate fitness in the space of key parameters using a Taylor expansion. To estimate the coefficients in the Taylor expansion, we utilize artificial neural networks: we construct a surface to separate the domains of superior and interior ranking of pair inherited elements in the space of parameters. Finally, we use the obtained approximation of the fitness surface to find the evolutionarily stable (optimal) strategy which maximizes fitness. As an ecologically important study case, we apply our approach to explore the evolutionarily stable diel vertical migration of zooplankton in marine and freshwater ecosystems. Using machine learning we reconstruct the fitness function of herbivorous zooplankton from empirical data and predict the daily trajectory of a dominant species in the northeastern Black Sea.

All living matter is subject to continuous adaptation and functional optimization via natural selection. Consequentially, structures with close morphological resemblance repeatedly appear across the phylogenetic tree. How these designs emerge at the cellular level is not fully understood. Here, we explore core concepts of functional morphology and discuss its cause and consequences, with a specific focus on emerging properties of self-organizing systems as the potential driving force. We conclude with open questions and limitations that are present when studying shape-function interdependence in single cells and cellular ensembles.

Within the field of evolutionary biology, natural selection is often thought to favor traits that lead to individuals behaving as if they were maximizing their evolutionary fitness. The concept of the individual as a maximizer is also popular in behavioral psychology, especially when it comes to theories of operant learning. Here, the individual is taken to adapt its behavior to the local environment, such that the expected amount of reinforcer value is maximized. Whereas there is a considerable consensus concerning the formal properties of an evolutionary maximand (\'fitness\'), there is no generally accepted conceptualization of a corresponding behavioral maximand (\'reinforcer value\'). However, such theoretical clarification is crucial to the development and empirical testing of learning theories, since it is impossible to decide whether the concept of reinforcer maximization is adequate, as long as the maximand is not well defined. This paper presents a formal model of reinforcer value that is consistent with existing work on the nature of reinforcement and provides an explicit link between behavioral psychology and evolutionary biology. The main result is that the reinforcer value of an additional time unit spent at a behavior equals its expected marginal effects on evolutionary fitness. Applying the model to matching behavior, it is further demonstrated how the established link between reinforcer value and evolutionary fitness can be used to derive new hypotheses.

Modelling of natural selection in self-replicating systems has been heavily influenced by the concept of fitness which was inspired by Darwin\'s original idea of the survival of the fittest. However, so far the concept of fitness in evolutionary modelling is still somewhat vague, intuitive and often subjective. Unfortunately, as a result of this, using different definitions of fitness can lead to conflicting evolutionary outcomes. Here we formalise the definition of evolutionary fitness to describe the selection of strategies in deterministic self-replicating systems for generic modelling settings which involve an arbitrary function space of inherited strategies. Our mathematically rigorous definition of fitness is closely related to the underlying population dynamic equations which govern the selection processes. More precisely, fitness is defined based on the concept of the ranking of competing strategies which compares the long-term dynamics of measures of sets of inherited units in the space of strategies. We also formulate the variational principle of modelling selection which states that in a self-replicating system with inheritance, selection will eventually maximise evolutionary fitness. We demonstrate how expressions for evolutionary fitness can be derived for a class of models with age structuring including systems with delay, which has previously been considered as a challenge.