A collaborative backbone resource for comparative studies of subterranean evolution: The World Asellidae database.

Transition to novel environments, such as groundwater colonization by surface organisms, provides an excellent research ground to study phenotypic evolution. However, interspecific comparative studies on evolution to groundwater life are few because of the challenge in assembling large ecological and molecular resources for species-rich taxa comprised of surface and subterranean species. Here, we make available to the scientific community an operational set of working tools and resources for the Asellidae, a family of freshwater isopods containing hundreds of surface and subterranean species. First, we release the World Asellidae database (WAD) and its web application, a sustainable and FAIR solution to producing and sharing data and biological material. WAD provides access to thousands of species occurrences, specimens, DNA extracts and DNA sequences with rich metadata ensuring full scientific traceability. Second, we perform a large-scale dated phylogenetic reconstruction of Asellidae to support phylogenetic comparative analyses. Of 424 terminal branches, we identify 34 pairs of surface and subterranean species representing independent replicates of the transition from surface water to groundwater. Third, we exemplify the usefulness of WAD for documenting phenotypic shifts associated with colonization of subterranean habitats. We provide the first phylogenetically controlled evidence that body size of males decreases relative to that of females upon groundwater colonization, suggesting competition for rare receptive females selects for smaller, more agile males in groundwater. By making these tools and resources widely accessible, we open up new opportunities for exploring how phenotypic traits evolve in response to changes in selective pressures and trade-offs during groundwater colonization.

Evaluation of Methods to Detect Shifts in Directional Selection at the Genome Scale.

Identifying the footprints of selection in coding sequences can inform about the importance and function of individual sites. Analyses of the ratio of nonsynonymous to synonymous substitutions (dN/dS) have been widely used to pinpoint changes in the intensity of selection, but cannot distinguish them from changes in the direction of selection, that is, changes in the fitness of specific amino acids at a given position. A few methods that rely on amino-acid profiles to detect changes in directional selection have been designed, but their performances have not been well characterized. In this paper, we investigate the performance of six of these methods. We evaluate them on simulations along empirical phylogenies in which transition events have been annotated and compare their ability to detect sites that have undergone changes in the direction or intensity of selection to that of a widely used dN/dS approach, codeml's branch-site model A. We show that all methods have reduced performance in the presence of biased gene conversion but not CpG hypermutability. The best profile method, Pelican, a new implementation of Tamuri AU, Hay AJ, Goldstein RA. (2009. Identifying changes in selective constraints: host shifts in influenza. PLoS Comput Biol. 5(11):e1000564), performs as well as codeml in a range of conditions except for detecting relaxations of selection, and performs better when tree length increases, or in the presence of persistent positive selection. It is fast, enabling genome-scale searches for site-wise changes in the direction of selection associated with phenotypic changes.

Totoro: Identifying Active Reactions During the Transient State for Metabolic Perturbations.

Motivation: The increasing availability of metabolomic data and their analysis are improving the understanding of cellular mechanisms and how biological systems respond to different perturbations. Currently, there is a need for novel computational methods that facilitate the analysis and integration of increasing volume of available data. Results: In this paper, we present Totoro a new constraint-based approach that integrates quantitative non-targeted metabolomic data of two different metabolic states into genome-wide metabolic models and predicts reactions that were most likely active during the transient state. We applied Totoro to real data of three different growth experiments (pulses of glucose, pyruvate, succinate) from Escherichia coli and we were able to predict known active pathways and gather new insights on the different metabolisms related to each substrate. We used both the E. coli core and the iJO1366 models to demonstrate that our approach is applicable to both smaller and larger networks. Availability: Totoro is an open source method (available at https://gitlab.inria.fr/erable/totoro) suitable for any organism with an available metabolic model. It is implemented in C++ and depends on IBM CPLEX which is freely available for academic purposes.