High trophic level feedbacks on global ocean carbon uptake and marine ecosystem dynamics under climate change

Despite recurrent emphasis on their ecological and economic roles, the importance of high trophic levels (HTLs) on ocean carbon dynamics, through passive (fecal pellet production, carcasses) and active (vertical migration) processes, is still largely unexplored, notably under climate change scenarios. In addition, HTLs impact the ecosystem dynamics through top-down effects on lower trophic levels, which might change under anthropogenic influence. Here we compare two simulations of a global biogeochemical–ecosystem model with and without feedbacks from large marine animals. We show that these large marine animals affect the evolution of low trophic level biomasses, hence net primary production and most certainly ecosystem equilibrium, but seem to have little influence on the 21st-century anthropogenic carbon uptake under the RCP8.5 scenario. These results provide new insights regarding the expectations for trophic amplification of climate change through the marine trophic chain and regarding the necessity to explicitly represent marine animals in Earth System Models.     

Examination of the success rate of secondary symbiont manipulation by microinjection methods in the pea aphid system

Insects harbor a wide range of microbial symbionts, but their influence on host phenotypes is described in a limited number of biological models. One experimental approach to gain knowledge on the effects of symbionts to their hosts is to create insect lines with and without symbionts and examine their phenotypes. However, the success rate of symbiont elimination and introduction methods is dependent on several parameters that are scarcely tested or described. The pea aphid, Acyrthosiphon pisum Harris (Hemiptera: Aphididae), is a model insect of symbiosis studies. It harbors a primary symbiont that supplies the host with essential amino acids, and an array of secondary symbionts whose effects have been assessed by manipulating their presence/absence in the insect. Here, we describe the influence of key parameters on the success rate of symbiont manipulation using the pea aphid–secondary symbiont system. We compared two elimination methods differing in antibiotic treatment using several aphid–symbiont combinations. We also created new aphid host–symbiont combinations by secondary symbiont introduction and examined the effects of larval stage of recipient aphids on introduction success. Our study revealed that the aphid–symbiont combination has strong influence on both symbiont introduction and elimination success rates, and that the type of antibiotics and the larval stage of recipient aphids influence the elimination and introduction success rate, respectively.     

Creating artificial human genomes using generative neural networks

Generative models have shown breakthroughs in a wide spectrum of domains due to recent advancements in machine learning algorithms and increased computational power. Despite these impressive achievements, the ability of generative models to create realistic synthetic data is still under-exploited in genetics and absent from population genetics. Yet a known limitation in the field is the reduced access to many genetic databases due to concerns about violations of individual privacy, although they would provide a rich resource for data mining and integration towards advancing genetic studies. In this study, we demonstrated that deep generative adversarial networks (GANs) and restricted Boltzmann machines (RBMs) can be trained to learn the complex distributions of real genomic datasets and generate novel high-quality artificial genomes (AGs) with none to little privacy loss. We show that our generated AGs replicate characteristics of the source dataset such as allele frequencies, linkage disequilibrium, pairwise haplotype distances and population structure. Moreover, they can also inherit complex features such as signals of selection. To illustrate the promising outcomes of our method, we showed that imputation quality for low frequency alleles can be improved by data augmentation to reference panels with AGs and that the RBM latent space provides a relevant encoding of the data, hence allowing further exploration of the reference dataset and features for solving supervised tasks. Generative models and AGs have the potential to become valuable assets in genetic studies by providing a rich yet compact representation of existing genomes and high-quality, easy-access and anonymous alternatives for private databases.     

Gene therapy vectors: the prospects and potentials of the cut-and-paste transposons

Gene therapy applications require efficient tools for the stable delivery of genetic information into eukaryotic genomes. Most current gene delivery strategies are based on viral vectors. However, a number of drawbacks, such as the limited cargo capacity, host immune response and mutational risks, highlight the need for alternative gene delivery tools. A comprehensive gene therapy tool kit should contain a range of vectors and techniques that can be adapted to different targets and purposes. Transposons provide a potentially powerful approach. However, transposons encompass a large number of different molecular mechanisms, some of which are better suited to gene delivery applications than others. Here, we consider the range and potentials of the various mechanisms, focusing on the cut-and-paste transposons as one of the more promising avenues towards gene therapy applications. Several cut-and-paste transposition systems are currently under development. We will first consider the mechanisms of piggyBac and the hAT family elements Tol1 and Tol2, before focusing on the mariner family elements including Mos1, Himar1 and Hsmar1.