Multi-proxy analysis of waterlogged preserved Late Neolithic canine excrements

Vegetation History and Archaeobotany - Multi-proxy analysis of the coprolites which were found during excavations at two Late Neolithic (fourth millennium bc) pile-dwelling sites (Črnelnik and...     

Elevated rates of positive selection drive the evolution of pestiferousness in the Colorado potato beetle (Leptinotarsa decemlineata,Say)

Contextualizing evolutionary history and identifying genomic features of an insect that might contribute to its pest status is important in developing early detection and control tactics. In order to understand the evolution of pestiferousness, which we define as the accumulation of traits that contribute to an insect population's success in an agroecosystem, we tested the importance of known genomic properties associated with rapid adaptation in the Colorado potato beetle (CPB), Leptinotarsa decemlineata Say. Within the leaf beetle genus Leptinotarsa, only CPB, and a few populations therein, has risen to pest status on cultivated nightshades, Solanum. Using whole genomes from ten closely related Leptinotarsa species native to the United States, we reconstructed a high‐quality species tree and used this phylogenetic framework to assess evolutionary patterns in four genomic features of rapid adaptation: standing genetic variation, gene family expansion and contraction, transposable element abundance and location, and positive selection at protein‐coding genes. Throughout approximately 20 million years of history, Leptinotarsa species show little evidence of gene family turnover and transposable element variation. However, there is a clear pattern of CPB experiencing higher rates of positive selection on protein‐coding genes. We determine that these rates are associated with greater standing genetic variation due to larger effective population size, which supports the theory that the demographic history contributes to rates of protein evolution. Furthermore, we identify a suite of coding genes under positive selection that are putatively associated with pestiferousness in the Colorado potato beetle lineage. They are involved in the biological processes of xenobiotic detoxification, chemosensation and hormone function.     

The PLB measurement for the connector in Phi29 bacteriophage reveals the function of its channel loop

The connector protein, also known as the portal protein, located at the portal vertex in the Phi29 bacteriophage has been found to play a key role in the genome DNA packaging motor. There is a disordered region, composed of 12 sets of 18-residue loops N229–N246, that has been assumed to serve as a “clamp” to retain the DNA within the pressurized capsid when DNA is fully packaged. However, the process remains undefined about how the clamping of DNA occurs and what signal is used to engage the channel loops to clamp the DNA near the end of DNA packaging. In this study, we use the planar lipid bilayer (PLB) membrane technique to study the connector with its loops cleaved. The channel properties are compared with those of the connector with corresponding wild-type loops at different membrane potentials. On the basis of the hypothesis of the Donnan effects in the flashing Brownian ratchet model, we associate the PLB experimental results with the outcomes from the relevant biochemical experiments on the proheads containing the connectors without the loops, which enables us to provide a clear picture about how the DNA clamping occurs. A mathematical relationship between the Donnan potential and the DNA packaging density is established, demonstrating that they are both in essence the same signal that is received and transmitted by the connector to dictate DNA clamping and the termination of DNA packaging. At the end of the study, the PLB technique is proposed as a viral research tool, and its potential use to study the functions of specific domains in a portal protein of the tailed bacteriophages is highlighted.     

Stochastic combinations of actin regulatory proteins are sufficient to drive filopodia formation

Assemblies of actin and its regulators underlie the dynamic morphology of all eukaryotic cells. To understand how actin regulatory proteins work together to generate actin-rich structures such as filopodia, we analyzed the localization of diverse actin regulators within filopodia in Drosophila embryos and in a complementary in vitro system of filopodia-like structures (FLSs). We found that the composition of the regulatory protein complex where actin is incorporated (the filopodial tip complex) is remarkably heterogeneous both in vivo and in vitro. Our data reveal that different pairs of proteins correlate with each other and with actin bundle length, suggesting the presence of functional subcomplexes. This is consistent with a theoretical framework where three or more redundant subcomplexes join the tip complex stochastically, with any two being sufficient to drive filopodia formation. We provide an explanation for the observed heterogeneity and suggest that a mechanism based on multiple components allows stereotypical filopodial dynamics to arise from diverse upstream signaling pathways.     

Convolutional neural networks improve species distribution modelling by capturing the spatial structure of the environment

Convolutional Neural Networks (CNNs) are statistical models suited for learning complex visual patterns. In the context of Species Distribution Models (SDM) and in line with predictions of landscape ecology and island biogeography, CNN could grasp how local landscape structure affects prediction of species occurrence in SDMs. The prediction can thus reflect the signatures of entangled ecological processes. Although previous machine-learning based SDMs can learn complex influences of environmental predictors, they cannot acknowledge the influence of environmental structure in local landscapes (hence denoted “punctual models”). In this study, we applied CNNs to a large dataset of plant occurrences in France (GBIF), on a large taxonomical scale, to predict ranked relative probability of species (by joint learning) to any geographical position. We examined the way local environmental landscapes improve prediction by performing alternative CNN models deprived of information on landscape heterogeneity and structure (“ablation experiments”). We found that the landscape structure around location crucially contributed to improve predictive performance of CNN-SDMs. CNN models can classify the predicted distributions of many species, as other joint modelling approaches, but they further prove efficient in identifying the influence of local environmental landscapes. CNN can then represent signatures of spatially structured environmental drivers. The prediction gain is noticeable for rare species, which open promising perspectives for biodiversity monitoring and conservation strategies. Therefore, the approach is of both theoretical and practical interest. We discuss the way to test hypotheses on the patterns learnt by CNN, which should be essential for further interpretation of the ecological processes at play.