Impacts des polluants métalliques sur l'abeille : de la colonie au cerveau

Les abeilles sont des pollinisateurs essentiels. Une pléthore de facteurs de stress environnementaux, tels que les produits agrochimiques, a été identifiée comme contribuant à leur déclin mondial. En particulier, ces facteurs de stress altèrent les processus cognitifs impliqués dans les comportements fondamentaux. Jusqu'à présent, cependant, on ne sait pratiquement rien de l'impact de l'exposition à des métaux lourds, dont la toxicité est avérée chez de nombreux organismes. Pourtant, leurs émissions mondiales résultant des activités humaines ont élevé leurs concentrations bien au-dessus des niveaux naturels dans l'air, le sol, l'eau et la flore, exposant ainsi les abeilles à tous les stades de leur vie. Le but de ma thèse était d'examiner les effets de la pollution métallique sur l'abeille domestique en utilisant une approche multi-échelle, du cerveau à la colonie, en laboratoire et sur le terrain. J'ai d'abord observé que les abeilles exposées à une gamme de concentrations de trois métaux communs (arsenic, plomb et zinc) en laboratoire étaient incapables de percevoir et éviter des concentrations usuelles, néanmoins nocives, de ces métaux dans leur nourriture. J'ai ensuite exposé de façon chronique des colonies à des concentrations réalistes de plomb dans la nourriture et démontré que la consommation de ce métal altérait la cognition et le développement morphologique des abeilles. Comme les polluants métalliques se trouvent souvent dans des mélanges complexes dans l'environnement, j'ai exploré l'effet des cocktails de métaux, montrant que l'exposition au plomb, à l'arsenic ou au cuivre seul était suffisante pour ralentir l'apprentissage et perturber le rappel de la mémoire, et que les combinaisons de ces métaux induisaient des effets négatifs additifs sur ces deux processus cognitifs. J'ai finalement étudié l'impact de l'exposition naturelle aux polluants métalliques dans un environnement contaminé, en collectant des abeilles à proximité d'une ancienne mine d'or, et montré que les individus des populations les plus exposées aux métaux présentaient des capacités d'apprentissage et de mémoire plus faibles, et des altérations de leur développement conduisant à une réduction de la taille de leur cerveau. Une analyse plus systématique des abeilles non exposées a révélé une relation entre la taille de la tête, la morphométrie du cerveau et les performances d'apprentissage dans différentes tâches comportementales, suggérant que l'exposition aux polluants métalliques amplifie ces variations naturelles. Ainsi, mes résultats suggèrent que les abeilles domestiques sont incapables d'éviter l'exposition à des concentrations réalistes de métaux qui sont préjudiciables au développement et aux fonctions cognitives, et appellent à une révision des niveaux environnementaux considérés comme "sûrs". Ma thèse est la première analyse intégrée de l'impact de plusieurs polluants métalliques sur la cognition, la morphologie et l'organisation cérébrale chez l'abeille, et vise à encourager de nouvelles études sur la contribution de la pollution métallique dans le déclin signalé des abeilles, et plus généralement, des insectes.Honey bees are crucial pollinators. A plethora of environmental stressors, such as agrochemicals, have been identified as contributors to their global decline. Especially, these stressors impair cognitive processes involved in fundamental behaviours. So far however, virtually nothing is known about the impact of metal pollutants, despite their known toxicity to many organisms. Their worldwide emissions resulting from human activities have elevated their concentrations far above natural baselines in the air, soil, water and flora, exposing bees at all life stages. The aim of my thesis was to examine the effects of metallic pollution on honey bees using a multiscale approach, from brain to colonies, in laboratory and field conditions. I first observed that bees exposed to a range of concentrations of three common metals (arsenic, lead and zinc) in the laboratory were unable to perceive and avoid, low, yet harmful, field-realistic concentrations of those metals in their food. I then chronically exposed colonies to field-realistic concentrations of lead in food and demonstrated that consumption of this metal impaired bee cognition and morphological development, leading to smaller adult bees. As metal pollutants are often found in complex mixtures in the environment, I explored the effect of cocktails of metals, showing that exposure to lead, arsenic or copper alone was sufficient to slow down learning and disrupt memory retrieval, and that combinations of these metals induced additive negative effects on both cognitive processes. I finally investigated the impact of natural exposure to metal pollutants in a contaminated environment, by collecting bees in the vicinity of a former gold mine, and showed that individuals from populations most exposed to metals exhibited lower learning and memory abilities, and development impairments conducing to reduced brain size. A more systematic analysis of unexposed bees revealed a relationship between head size, brain morphometrics and learning performances in different behavioural tasks, suggesting that exposure to metal pollutants magnifies these natural variations. Hence, altogether, my results suggest that honey bees are unable to avoid exposure to field-realistic concentrations of metals that are detrimental to development and cognitive functions; and call for a revision of the environmental levels considered as 'safe'. My thesis is the first integrated analysis of the impact of several metal pollutants on bee cognition, morphology and brain structure, and should encourage further studies on the contribution of metal pollution in the reported decline of honey bees, and more generally, of insects

Deconstructing Spinal Interneurons, one cell type at a time

Documenting the extent of cellular diversity is a critical step in defining the functional organization of the nervous system. In this context, we sought to develop statistical methods capable of revealing underlying cellular diversity given incomplete data sampling - a common problem in biological systems, where complete descriptions of cellular characteristics are rarely available. We devised a sparse Bayesian framework that infers cell type diversity from partial or incomplete transcription factor expression data. This framework appropriately handles estimation uncertainty, can incorporate multiple cellular characteristics, and can be used to optimize experimental design. We applied this framework to characterize a cardinal inhibitory population in the spinal cord. Animals generate movement by engaging spinal circuits that direct precise sequences of muscle contraction, but the identity and organizational logic of local interneurons that lie at the core of these circuits remain unresolved. By using our Sparse Bayesian approach, we showed that V1 interneurons, a major inhibitory population that controls motor output, fractionate into diverse subsets on the basis of the expression of nineteen transcription factors. Transcriptionally defined subsets exhibit highly structured spatial distributions with mediolateral and dorsoventral positional biases. These distinctions in settling position are largely predictive of patterns of input from sensory and motor neurons, arguing that settling position is a determinant of inhibitory microcircuit organization. Finally, we extensively validated inferred cell types by direct experimental measurement and then, extend our Bayesian framework to full transcriptome technologies. Together, these findings provide insight into the diversity and organizational logic through which inhibitory microcircuits shape motor output

Activation of the pro-resolving receptor Fpr2 attenuates inflammatory microglial activation

Poster number: P-T099 Theme: Neurodegenerative disorders & ageing Activation of the pro-resolving receptor Fpr2 reverses inflammatory microglial activation Authors: Edward S Wickstead - Life Science & Technology University of Westminster/Queen Mary University of London Inflammation is a major contributor to many neurodegenerative disease (Heneka et al. 2015). Microglia, as the resident immune cells of the brain and spinal cord, provide the first line of immunological defence, but can become deleterious when chronically activated, triggering extensive neuronal damage (Cunningham, 2013). Dampening or even reversing this activation may provide neuronal protection against chronic inflammatory damage. The aim of this study was to determine whether lipopolysaccharide (LPS)-induced inflammation could be abrogated through activation of the receptor Fpr2, known to play an important role in peripheral inflammatory resolution. Immortalised murine microglia (BV2 cell line) were stimulated with LPS (50ng/ml) for 1 hour prior to the treatment with one of two Fpr2 ligands, either Cpd43 or Quin-C1 (both 100nM), and production of nitric oxide (NO), tumour necrosis factor alpha (TNFα) and interleukin-10 (IL-10) were monitored after 24h and 48h. Treatment with either Fpr2 ligand significantly suppressed LPS-induced production of NO or TNFα after both 24h and 48h exposure, moreover Fpr2 ligand treatment significantly enhanced production of IL-10 48h post-LPS treatment. As we have previously shown Fpr2 to be coupled to a number of intracellular signaling pathways (Cooray et al. 2013), we investigated potential signaling responses. Western blot analysis revealed no activation of ERK1/2, but identified a rapid and potent activation of p38 MAP kinase in BV2 microglia following stimulation with Fpr2 ligands. Together, these data indicate the possibility of exploiting immunomodulatory strategies for the treatment of neurological diseases, and highlight in particular the important potential of resolution mechanisms as novel therapeutic targets in neuroinflammation. References Cooray SN et al. (2013). Proc Natl Acad Sci U S A 110: 18232-7. Cunningham C (2013). Glia 61: 71-90. Heneka MT et al. (2015). Lancet Neurol 14: 388-40

Digital control networks for virtual creatures

Robot control systems evolved with genetic algorithms traditionally take the form of floating-point neural network models. This thesis proposes that digital control systems, such as quantised neural networks and logical networks, may also be used for the task of robot control. The inspiration for this is the observation that the dynamics of discrete networks may contain cyclic attractors which generate rhythmic behaviour, and that rhythmic behaviour underlies the central pattern generators which drive lowlevel motor activity in the biological world. To investigate this a series of experiments were carried out in a simulated physically realistic 3D world. The performance of evolved controllers was evaluated on two well known control tasks—pole balancing, and locomotion of evolved morphologies. The performance of evolved digital controllers was compared to evolved floating-point neural networks. The results show that the digital implementations are competitive with floating-point designs on both of the benchmark problems. In addition, the first reported evolution from scratch of a biped walker is presented, demonstrating that when all parameters are left open to evolutionary optimisation complex behaviour can result from simple components

Using reconstructed visual reality in ant navigation research

Insects have low resolution eyes and a tiny brain, yet they continuously solve very complex navigational problems; an ability that underpins fundamental biological processes such as pollination and parental care. Understanding the methods they employ would have profound impact on the fields of machine vision and robotics. As our knowledge on insect navigation grows, our physical, physiological and neural models get more complex and detailed. To test these models we need to perform increasingly sophisticated experiments. Evolution has optimised the animals to operate in their natural environment. To probe the fine details of the methods they utilise we need to use natural visual scenery which, for experimental purposes, we must be able to manipulate arbitrarily. Performing physiological experiments on insects outside the laboratory is not practical and our ability to modify the natural scenery for outdoor behavioural experiments is very limited. The solution is reconstructed visual reality, a projector that can present the visual aspect of the natural environment to the animal with high fidelity, taking the peculiarities of insect vision into account. While projectors have been used in insect research before, during my candidature I designed and built a projector specifically tuned to insect vision. To allow the ant to experience a full panoramic view, the projector completely surrounds her. The device (Antarium) is a polyhedral approximation of a sphere. It contains 20 thousand pixels made out of light emitting diodes (LEDs) that match the spectral sensitivity of Myrmecia. Insects have a much higher fusion frequency limit than humans, therefore the device has a very high flicker frequency (9kHz) and also a high frame rate (190fps). In the Antarium the animal is placed in the centre of the projector on a trackball. To test the trackball and to collect reference data, outdoor experiments were performed where ants were captured, tethered and placed on the trackball. The apparatus with the ant on it was then placed at certain locations relative to the nest and the foraging tree and the movements of the animal on the ball were recorded and analysed. The outdoor experiments proved that the trackball was well suited for our ants, and also provided the baseline behaviour reference for the subsequent Antarium experiments. To assess the Antarium, the natural habitat of the experimental animals was recreated as a 3-dimensional model. That model was then projected for the ants and their movements on the trackball was recorded, just like in the outdoor experiments Initial feasibility tests were performed by projecting a static image, which matches what the animals experienced during the outdoor experiments. To assess whether the ant was orienting herself relative to the scene we rotated the projected scene around her and her response monitored. Statistical methods were used to compare the outdoor and in-Antarium behaviour. The results proved that the concept was solid, but they also uncovered several shortcomings of the Antarium. Nevertheless, even with its limitations the Antarium was used to perform experiments that would be very hard to do in a real environment. In one experiment the foraging tree was repositioned in or deleted from the scene to see whether the animals go to where the tree is or where by their knowledge it should be. The results suggest the latter but the absence or altered location of the foraging tree certainly had a significant effect on the animals. In another experiment the scene, including the sky, were re-coloured to see whether colour plays a significant role in navigation. Results indicate that even very small amount of UV information statistically significantly improves the navigation of the animals. To rectify the device limitations discovered during the experiments a new, improved projector was designed and is currently being built

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp