Phenotypes can be robust and evolvable if mutations have non-local effects on sequence constraints.

The mapping between biological genotypes and phenotypes plays an important role in evolution, and understanding the properties of this mapping is crucial to determine the outcome of evolutionary processes. One of the most striking properties observed in several genotype-phenotype (GP) maps is the positive correlation between the robustness and evolvability of phenotypes. This implies that a phenotype can be strongly robust against mutations and at the same time evolvable to a diverse range of alternative phenotypes. Here, we examine the causes for this positive correlation by introducing two analytically tractable GP map models that follow the principles of real biological GP maps. The first model is based on gene-like GP maps, reflecting the way in which genetic sequences are organized into protein-coding genes, and the second one is based on the GP map of RNA secondary structure. For both models, we find that a positive correlation between phenotype robustness and evolvability only emerges if mutations at one sequence position can have non-local effects on the sequence constraints at another position. This highlights that non-local effects of mutations are closely related to the coexistence of robustness and evolvability in phenotypes, and are likely to be an important feature of many biological GP maps

Using small samples to estimate neutral component size and robustness in the genotype-phenotype map of RNA secondary structure.

In genotype-phenotype (GP) maps, the genotypes that map to the same phenotype are usually not randomly distributed across the space of genotypes, but instead are predominantly connected through one-point mutations, forming network components that are commonly referred to as neutral components (NCs). Because of their impact on evolutionary processes, the characteristics of these NCs, like their size or robustness, have been studied extensively. Here, we introduce a framework that allows the estimation of NC size and robustness in the GP map of RNA secondary structure. The advantage of this framework is that it only requires small samples of genotypes and their local environment, which also allows experimental realizations. We verify our framework by applying it to the exhaustively analysable GP map of RNA sequence length L = 15, and benchmark it against an existing method by applying it to longer, naturally occurring functional non-coding RNA sequences. Although it is specific to the RNA secondary structure GP map in the first place, our framework can probably be transferred and adapted to other sequence-to-structure GP maps.MW was supported by the EPSRC and the Gatsby Charitable Foundation. SEA was supported by the Gatsby Charitable Foundation

Neutral components show a hierarchical community structure in the genotype-phenotype map of RNA secondary structure.

Genotype-phenotype (GP) maps describe the relationship between biological sequences and structural or functional outcomes. They can be represented as networks in which genotypes are the nodes, and one-point mutations between them are the edges. The genotypes that map to the same phenotype form subnetworks consisting of one or multiple disjoint connected components-so-called neutral components (NCs). For the GP map of RNA secondary structure, the NCs have been found to exhibit distinctive network features that can affect the dynamical processes taking place on them. Here, we focus on the community structure of RNA secondary structure NCs. Building on previous findings, we introduce a method to reveal the hierarchical community structure solely from the sequence constraints and composition of the genotypes that form a given NC. Thereby, we obtain modularity values similar to common community detection algorithms, which are much more complex. From this knowledge, we endorse a sampling method that allows a fast exploration of the different communities of a given NC. Furthermore, we introduce a way to estimate the community structure from genotype samples, which is useful when an exhaustive analysis of the NC is not feasible, as is the case for longer sequence lengths.MW was supported by the EPSRC and the Gatsby Charitable Foundation. SEA was supported by the Gatsby Charitable Foundation and the Alan Turing Institute

DeepSurveyCam — A Deep Ocean Optical Mapping System

Underwater photogrammetry and in particular systematic visual surveys of the deep sea are by far less developed than similar techniques on land or in space. The main challenges are the rough conditions with extremely high pressure, the accessibility of target areas (container and ship deployment of robust sensors, then diving for hours to the ocean floor), and the limitations of localization technologies (no GPS). The absence of natural light complicates energy budget considerations for deep diving flash-equipped drones. Refraction effects influence geometric image formation considerations with respect to field of view and focus, while attenuation and scattering degrade the radiometric image quality and limit the effective visibility. As an improvement on the stated issues, we present an AUV-based optical system intended for autonomous visual mapping of large areas of the seafloor (square kilometers) in up to 6000 m water depth. We compare it to existing systems and discuss tradeoffs such as resolution vs. mapped area and show results from a recent deployment with 90,000 mapped square meters of deep ocean floor

Morphology engineering for novel antibiotics: Effect of glass microparticles and soy lecithin on rebeccamycin production and cellular morphology of filamentous actinomycete Lentzea aerocolonigenes

Lentzeaaerocolonigenes, as an actinomycete, is a natural producer of the antibiotic and antitumoral drug rebeccamycin. Due to the filamentous cellular morphology handling in cultivations is challenging; therefore, morphology engineering techniques are mandatory to enhance productivity. One promising approach described in the literature is the addition of mineral particles in the micrometer range to precisely adjust cellular morphology and the corresponding product synthesis (microparticle-enhanced cultivation, MPEC). Glass microparticles are introduced in this study as a novel supplementation type for bioprocess intensification in filamentous organisms. Several investigations were conducted to screen for an optimal particle setup, including particle size and concentration regarding their impact and effects on enhanced productivity, microparticle incorporation behavior into the biopellets, the viability of pellets, and morphological changes. Glass microparticles (10 g·L−1) with a median diameter of 7.9 µm, for instance, induced an up to fourfold increase in product synthesis accompanied by overall enhanced viability of biomass. Furthermore, structural elucidations showed that biopellets isolated from MPEC tend to have lower hyphal density than unsupplemented control pellets. In this context, oxygen microprofiling was conducted to better understand how internal structural changes interwind with oxygen supply into the pellets. Here, the resulting oxygen profiles are of a contradictive trend of steeper oxygen consumption with increasing glass microparticle supplementation. Eventually, MPEC was combined with another promising cultivation strategy, the supplementation of soy lecithin (7.5 g·L−1), to further increase the cultivation performance. A combination of both techniques in an optimized setup resulted in a rebeccamycin concentration of 213 mg·L−1 after 10 days of cultivation, the highest value published so far for microparticle-supplemented shake flask cultivations of L. aerocolonigenes

Sebacinales Everywhere: Previously Overlooked Ubiquitous Fungal Endophytes

Inconspicuous basidiomycetes from the order Sebacinales are known to be involved in a puzzling variety of mutualistic plant-fungal symbioses (mycorrhizae), which presumably involve transport of mineral nutrients. Recently a few members of this fungal order not fitting this definition and commonly referred to as ‘endophytes’ have raised considerable interest by their ability to enhance plant growth and to increase resistance of their host plants against abiotic stress factors and fungal pathogens. Using DNA-based detection and electron microscopy, we show that Sebacinales are not only extremely versatile in their mycorrhizal associations, but are also almost universally present as symptomless endophytes. They occurred in field specimens of bryophytes, pteridophytes and all families of herbaceous angiosperms we investigated, including liverworts, wheat, maize, and the non-mycorrhizal model plant Arabidopsis thaliana. They were present in all habitats we studied on four continents. We even detected these fungi in herbarium specimens originating from pioneering field trips to North Africa in the 1830s/40s. No geographical or host patterns were detected. Our data suggest that the multitude of mycorrhizal interactions in Sebacinales may have arisen from an ancestral endophytic habit by specialization. Considering their proven beneficial influence on plant growth and their ubiquity, endophytic Sebacinales may be a previously unrecognized universal hidden force in plant ecosystems

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

Corona Health -- A Study- and Sensor-based Mobile App Platform Exploring Aspects of the COVID-19 Pandemic

Physical and mental well-being during the COVID-19 pandemic is typically assessed via surveys, which might make it difficult to conduct longitudinal studies and might lead to data suffering from recall bias. Ecological momentary assessment (EMA) driven smartphone apps can help alleviate such issues, allowing for in situ recordings. Implementing such an app is not trivial, necessitates strict regulatory and legal requirements, and requires short development cycles to appropriately react to abrupt changes in the pandemic. Based on an existing app framework, we developed Corona Health, an app that serves as a platform for deploying questionnaire-based studies in combination with recordings of mobile sensors. In this paper, we present the technical details of Corona Health and provide first insights into the collected data. Through collaborative efforts from experts from public health, medicine, psychology, and computer science, we released Corona Health publicly on Google Play and the Apple App Store (in July, 2020) in 8 languages and attracted 7,290 installations so far. Currently, five studies related to physical and mental well-being are deployed and 17,241 questionnaires have been filled out. Corona Health proves to be a viable tool for conducting research related to the COVID-19 pandemic and can serve as a blueprint for future EMA-based studies. The data we collected will substantially improve our knowledge on mental and physical health states, traits and trajectories as well as its risk and protective factors over the course of the COVID-19 pandemic and its diverse prevention measures