Making sense of zebrafish neural development in the Minervois

The meeting 'From sensory perception to motor output: genetic bases of behavior in the zebrafish embryo' was held at Minerve (South of France) on March 16–18, 2007. The meeting site was beautifully situated in the heart of the Minervois wine country, and its remoteness promoted conversations and interaction over the course of the program. The meeting covered neurogenesis and eye development on day 1, ear and lateral line development on day 2, and brain connectivity and behavior on day 3. Underlying all sessions, however, ran the growing importance of live imaging, an approach that takes full advantage of the transparency of fish embryos and early larvae, as illustrated by several movies and links in this report

Control of cell migration in the development of the posterior lateral line: antagonistic interactions between the chemokine receptors CXCR4 and CXCR7/RDC1

BACKGROUND: The formation of the posterior lateral line of teleosts depends on the migration of a primordium that originates near the otic vesicle and moves to the tip of the tail. Groups of cells at the trailing edge of the primordium slow down at regular intervals and eventually settle to differentiate as sense organs. The migration of the primordium is driven by the chemokine SDF1 and by its receptor CXCR4, encoded respectively by the genes sdf1a and cxcr4b. cxcr4b is expressed in the migrating cells and is down-regulated in the trailing cells of the primordium. sdf1a is expressed along the path of migration. There is no evidence for a gradient of sdf1a expression, however, and the origin of the directionality of migration is not known. RESULTS: Here we document the expression of a second chemokine receptor gene, cxcr7, in the migrating primordium. We show that cxcr7 is highly expressed in the trailing cells of the primordium but not at all in the leading cells, a pattern that is complementary to that of cxcr4b. Even though cxcr7 is not expressed in the cells that lead primordium migration, its inactivation results in impaired migration. The phenotypes of cxcr4b, cxcr7 double morphant embryos suggest, however, that CXCR7 does not contribute to the migratory capabilities of primordium cells. We also show that, in the absence of cxcr4b, expression of cxcr7 becomes ubiquitous in the stalled primordium. CONCLUSION: Our observations suggest that CXCR7 is required to provide directionality to the migration. We propose that directionality is imposed on the primordium as soon as it comes in contact with the stripe of SDF1, and is maintained throughout migration by a negative interaction between the two receptors

Integrated species distribution models fitted in INLA are sensitive to mesh parameterisation

The ever-growing popularity of citizen science, as well as recent technological and digital developments, have allowed the collection of data on species' distributions at an extraordinary rate. In order to take advantage of these data, information of varying quantity and quality needs to be integrated. Point process models have been proposed as an elegant way to achieve this for estimates of species distributions. These models can be fitted efficiently using Bayesian methods based on integrated nested Laplace approximations (INLA) with stochastic partial differential equations (SPDEs). This approach uses an efficient way to model spatial autocorrelation using a Gaussian random field and a triangular mesh over the spatial domain. The mesh is constructed by user-defined variables, so effectively represents a free parameter in the model. However, there is a lack of understanding about how to set these mesh parameters, and their effect on model performance. Here, we assess how mesh parameters affect predictions and model fit to estimate the distribution of the serotine bat, Eptesicus serotinus, in Great Britain. A Bayesian INLA model was fitted using five meshes of varying densities to a dataset comprising both structured observations from a national monitoring programme and opportunistic records. We demonstrate that mesh density impacted spatial predictions with a general loss of accuracy with increasing mesh coarseness. However, we also show that the finest mesh was unable to overcome spatial biases in the data. In addition, the magnitude of the covariate effects differed markedly between meshes. This confirms that mesh parameterisation is an important and delicate process with implications for model inference. We discuss how species distribution modellers might adapt their use of INLA in the light of these findings

Integrated species distribution models fitted in INLA are sensitive to mesh parameterisation

The ever-growing popularity of citizen science, as well as recent technological and digital developments, have allowed the collection of data on species' distributions at an extraordinary rate. In order to take advantage of these data, information of varying quantity and quality needs to be integrated. Point process models have been proposed as an elegant way to achieve this for estimates of species distributions. These models can be fitted efficiently using Bayesian methods based on integrated nested Laplace approximations (INLA) with stochastic partial differential equations (SPDEs). This approach uses an efficient way to model spatial autocorrelation using a Gaussian random field and a triangular mesh over the spatial domain. The mesh is constructed by user-defined variables, so effectively represents a free parameter in the model. However, there is a lack of understanding about how to set these mesh parameters, and their effect on model performance. Here, we assess how mesh parameters affect predictions and model fit to estimate the distribution of the serotine bat, Eptesicus serotinus, in Great Britain. A Bayesian INLA model was fitted using five meshes of varying densities to a dataset comprising both structured observations from a national monitoring programme and opportunistic records. We demonstrate that mesh density impacted spatial predictions with a general loss of accuracy with increasing mesh coarseness. However, we also show that the finest mesh was unable to overcome spatial biases in the data. In addition, the magnitude of the covariate effects differed markedly between meshes. This confirms that mesh parameterisation is an important and delicate process with implications for model inference. We discuss how species distribution modellers might adapt their use of INLA in the light of these findings

The transmembrane inner ear (tmie) gene contributes to vestibular and lateral line development and function in the zebrafish ( Danio rerio )

The inner ear is a complex organ containing sensory tissue, including hair cells, the development of which is not well understood. Our long-term goal is to discover genes critical for the correct formation and function of the inner ear and its sensory tissue. A novel gene, transmembrane inner ear ( Tmie ), was found to cause hearing-related disorders when defective in mice and humans. A homologous tmie gene in zebrafish was cloned and its expression characterized between 24 and 51 hours post-fertilization. Embryos injected with morpholinos (MO) directed against tmie exhibited circling swimming behavior (∼37%), phenocopying mice with Tmie mutations; semicircular canal formation was disrupted, hair cell numbers were reduced, and maturation of electrically active lateral line neuromasts was delayed. As in the mouse, tmie appears to be required for inner ear development and function in the zebrafish and for hair cell maturation in the vestibular and lateral line systems as well. Developmental Dynamics 237:941–952, 2008. © 2008 Wiley-Liss, Inc.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/58082/1/21486_ftp.pd

Data integration for large-scale models of species distributions

With the expansion in the quantity and types of biodiversity data being collected, there is a need to find ways to combine these different sources to provide cohesive summaries of species’ potential and realized distributions in space and time. Recently, model-based data integration has emerged as a means to achieve this by combining datasets in ways that retain the strengths of each. We describe a flexible approach to data integration using point process models, which provide a convenient way to translate across ecological currencies. We highlight recent examples of large-scale ecological models based on data integration and outline the conceptual and technical challenges and opportunities that arise