Novel cell types, neurosecretory cells, and body plan of the early-diverging metazoan Trichoplax adhaerens.

BACKGROUND: Trichoplax adhaerens is the best-known member of the phylum Placozoa, one of the earliest-diverging metazoan phyla. It is a small disk-shaped animal that glides on surfaces in warm oceans to feed on algae. Prior anatomical studies of Trichoplax revealed that it has a simple three-layered organization with four somatic cell types. RESULTS: We reinvestigate the cellular organization of Trichoplax using advanced freezing and microscopy techniques to identify localize and count cells. Six somatic cell types are deployed in stereotyped positions. A thick ventral plate, comprising the majority of the cells, includes ciliated epithelial cells, newly identified lipophil cells packed with large lipid granules, and gland cells. Lipophils project deep into the interior, where they alternate with regularly spaced fiber cells whose branches contact all other cell types, including cells of the dorsal and ventral epithelium. Crystal cells, each containing a birefringent crystal, are arrayed around the rim. Gland cells express several proteins typical of neurosecretory cells, and a subset of them, around the rim, also expresses an FMRFamide-like neuropeptide. CONCLUSIONS: Structural analysis of Trichoplax with significantly improved techniques provides an advance in understanding its cell types and their distributions. We find two previously undetected cell types, lipohil and crystal cells, and an organized body plan in which different cell types are arranged in distinct patterns. The composition of gland cells suggests that they are neurosecretory cells and could control locomotor and feeding behavior.

The handaxe and the microscope: individual and social learning in a multidimensional model of adaptation

When individuals learn by trial-and-error, they perform randomly chosen actions and then reinforce those actions that led to a high payoff. However, individuals do not always have to physically perform an action in order to evaluate its consequences. Rather, they may be able to mentally simulate actions and their consequences without actually performing them. Such fictitious learners can select actions with high payoffs without making long chains of trial-and-error learning. Here, we analyze the evolution of an n-dimensional cultural trait (or artifact) by learning, in a payoff landscape with a single optimum. We derive the stochastic learning dynamics of the distance to the optimum in trait space when choice between alternative artifacts follows the standard logit choice rule. We show that for both trial-and-error and fictitious learners, the learning dynamics stabilize at an approximate distance of root n/(2 lambda(e)) away from the optimum, where lambda(e) is an effective learning performance parameter depending on the learning rule under scrutiny. Individual learners are thus unlikely to reach the optimum when traits are complex (n large), and so face a barrier to further improvement of the artifact. We show, however, that this barrier can be significantly reduced in a large population of learners performing payoff-biased social learning, in which case lambda(e) becomes proportional to population size. Overall, our results illustrate the effects of errors in learning, levels of cognition, and population size for the evolution of complex cultural traits. (C) 2013 Elsevier Inc. All rights reserved.

Genetic factors of obesity and eating disorders: Copy number variations (CNV) involving SH2B1

Context : It is now clearly shown that genetic factors in association with environment play a key role in obesity and eating disorders. This project studies the clinical symptoms and molecular abnormalities in patients carrying a strong hereditary predisposition to obesity and eating behavior disorders. We have previously published the association between the 16:29.5-30.1 deletion and a very penetrant form of morbid obesity and macrocephaly. We have also demonstrated the association between the reciprocal 16:29.5-30.1 duplication and underweight and small head circumference. These 2 studies demonstrate that gene dosage of one or several genes in this region regulates BMI as well as brain growth. At present, there are no data pointing towards particular candidate genes. We are currently investigating a second non-overlapping recurrent CNV encompassing SH2B1, upstream of the aforementioned rearrangement. SNPs in this gene have been associated with BMI in GWAS studies and mice models confirmed this association. Bokuchova et al have reported an association between deletions encompassing this gene and severe early onset obesity, as well as insulin resistance. We are currently collecting and analyzing data to fully characterize the phenotype and the transcriptional patterns associated with this rearrangement. Aims : 1. Identify carriers of any CNVs in the greater 16p11.2 region (between 16:28MB and 32MB) in the EGG consortium. 2. Perform association studies between SNPs in the greater 16p11.2 region (16:28-32MB) and anthropometric measures with adjusted "locus-wide significance", to identify or prioritize candidate genes potentially driving the association observed in patients with the CNVs (and thus worthy of further validation and sequencing). 3. Explore associations between GSV genome-wide and brain volume. 4. Explore relationship between brain volumes (whole brain and regional for those who underwent brain MRI), head circumference and BMI. 5. Extrapolate this procedure to other regions covered by the Metabochip. Methods : - Examine and collect clinical informations, as well as molecular informations in these patients. - Analysis of MRI data in children and adults with BMI > 2SD. Compare changes to MRI data obtained in patients with monogenic forms of obesity (data from Lausanne study) and to underweight (BMI<-2SD) individuals from EGG. - Test whether opposite extremes of the phenotypic distribution may be highly informative Expected results : This is a highly focused study, pertaining to approximately 1 0/00 of the human genome. Yet it is clear that if successful, the lessons learned from this study could be extrapolated to other segments of the genome and would need validation and replication by additional studies. Altogether they will contribute to further explore the missing heritability and point to etiologic genes and pathways underlying these important health burdens.

Robust Nonlinear Mapping of Soil Contamination Using Support Regression

Spatial data analysis mapping and visualization is of great importance in various fields: environment, pollution, natural hazards and risks, epidemiology, spatial econometrics, etc. A basic task of spatial mapping is to make predictions based on some empirical data (measurements). A number of state-of-the-art methods can be used for the task: deterministic interpolations, methods of geostatistics: the family of kriging estimators (Deutsch and Journel, 1997), machine learning algorithms such as artificial neural networks (ANN) of different architectures, hybrid ANN-geostatistics models (Kanevski and Maignan, 2004; Kanevski et al., 1996), etc. All the methods mentioned above can be used for solving the problem of spatial data mapping. Environmental empirical data are always contaminated/corrupted by noise, and often with noise of unknown nature. That's one of the reasons why deterministic models can be inconsistent, since they treat the measurements as values of some unknown function that should be interpolated. Kriging estimators treat the measurements as the realization of some spatial randomn process. To obtain the estimation with kriging one has to model the spatial structure of the data: spatial correlation function or (semi-)variogram. This task can be complicated if there is not sufficient number of measurements and variogram is sensitive to outliers and extremes. ANN is a powerful tool, but it also suffers from the number of reasons. of a special type ? multiplayer perceptrons ? are often used as a detrending tool in hybrid (ANN+geostatistics) models (Kanevski and Maignank, 2004). Therefore, development and adaptation of the method that would be nonlinear and robust to noise in measurements, would deal with the small empirical datasets and which has solid mathematical background is of great importance. The present paper deals with such model, based on Statistical Learning Theory (SLT) - Support Vector Regression. SLT is a general mathematical framework devoted to the problem of estimation of the dependencies from empirical data (Hastie et al, 2004; Vapnik, 1998). SLT models for classification - Support Vector Machines - have shown good results on different machine learning tasks. The results of SVM classification of spatial data are also promising (Kanevski et al, 2002). The properties of SVM for regression - Support Vector Regression (SVR) are less studied. First results of the application of SVR for spatial mapping of physical quantities were obtained by the authorsin for mapping of medium porosity (Kanevski et al, 1999), and for mapping of radioactively contaminated territories (Kanevski and Canu, 2000). The present paper is devoted to further understanding of the properties of SVR model for spatial data analysis and mapping. Detailed description of the SVR theory can be found in (Cristianini and Shawe-Taylor, 2000; Smola, 1996) and basic equations for the nonlinear modeling are given in section 2. Section 3 discusses the application of SVR for spatial data mapping on the real case study - soil pollution by Cs137 radionuclide. Section 4 discusses the properties of the modelapplied to noised data or data with outliers.

Gliotransmitters Travel in Time and Space.

The identification of the presence of active signaling between astrocytes and neurons in a process termed gliotransmission has caused a paradigm shift in our thinking about brain function. However, we are still in the early days of the conceptualization of how astrocytes influence synapses, neurons, networks, and ultimately behavior. In this Perspective, our goal is to identify emerging principles governing gliotransmission and consider the specific properties of this process that endow the astrocyte with unique functions in brain signal integration. We develop and present hypotheses aimed at reconciling confounding reports and define open questions to provide a conceptual framework for future studies. We propose that astrocytes mainly signal through high-affinity slowly desensitizing receptors to modulate neurons and perform integration in spatiotemporal domains complementary to those of neurons.

Developmental critical period in genetic redox dysregulation: animal and human studies in schizophrenia

Converging evidence favors an abnormal susceptibility to oxidative stress in schizophrenia. Decreased levels of glutathione (GSH), the major cellular antioxidant and redox regulator, was observed in cerebrospinal-fluid and prefrontal cortex of patients. Importantly, abnormal GSH synthesis of genetic origin was observed: Two case-control studies showed an association with a GAG trinucleotide repeat (TNR) polymorphism in the GSH key synthesizing enzyme glutamate-cysteine-ligase (GCL) catalytic subunit (GCLC) gene. The most common TNR genotype 7/7 was more frequent in controls, whereas the rarest TNR genotype 8/8 was three times more frequent in patients. The disease associated genotypes (35% of patients) correlated with decreased GCLC protein, GCL activity and GSH content. Similar GSH system anomalies were observed in early psychosis patients. Such redox dysregulation combined with environmental stressors at specific developmental stages could underlie structural and functional connectivity anomalies. In pharmacological and knock-out (KO) models, GSH deficit induces anomalies analogous to those reported in patients. (a) morphology: spine density and GABA-parvalbumine immunoreactivity (PV-I) were decreased in anterior cingulate cortex. KO mice showed delayed cortical PV-I at PD10. This effect is exacerbated in mice with increased DA from PD5-10. KO mice exhibit cortical impairment in myelin and perineuronal net known to modulate PV connectivity. (b) physiology: In cultured neurons, NMDA response are depressed by D2 activation. In hippocampus, NMDA-dependent synaptic plasticity is impaired and kainate induced g-oscillations are reduced in parallel to PV-I. (c) cognition: low GSH models show increased sensitivity to stress, hyperactivity, abnormal object recognition, olfactory integration and social behavior. In a clinical study, GSH precursor N-acetyl cysteine (NAC) as add on therapy, improves the negative symptoms and decreases the side effects of antipsychotics. In an auditory oddball paradigm, NAC improves the mismatched negativity, an evoked potential related to pre-attention and to NMDA receptors function. In summary, clinical and experimental evidence converge to demonstrate that a genetically induced dysregulation of GSH synthesis combined with environmental insults in early development represent a major risk factor contributing to the development of schizophrenia

Division of labor in insect societies : genetic components and physiological regulation

The shift from solitary to social organisms constitutes one of the major transitions in evolution. The highest level of sociality is found in social insects (ants, termites and some species of bees and wasps). Division of labor is central to the organization of insect societies and is thought to be at the root of their ecological success. There are two main levels of division of labor in social insect colonies. The first relates to reproduction and involves the coexistence of queen and worker castes: while reproduction is usually monopolized by one or several queens, functionally sterile workers perform all the tasks to maintain the colony, such as nest building, foraging or brood care. The second level of division of labor, relating to such non-reproductive duties, is characterized by the performance of different tasks or roles by different groups of workers. This PhD aims to better understand the mechanisms underlying division of labor in insect societies, by investigating how genes and physiology influence caste determination and worker behavior in ants. In the first axis of this PhD, we studied the nature of genetic effects on division of labor. We used the Argentine ant Linepithema humile to conduct controlled crosses in the laboratory, which revealed the existence of non-additive genetic effects, such as parent-of-origin and genetic compatibility effects, on caste determination and worker behavior. In the second axis, we focused on the physiological regulation of division of labor. Using Pogonomyrmex seed- harvester ants, we performed experimental manipulation of hibernation, hormonal treatments, gene expression analyses and protein quantification to identify the physiological pathways regulating maternal effects on caste determination. Finally, comparing gene expression between nurses and foragers allowed us to reveal the association between vitellogenin and worker behavior in Pogonomyrmex ants. This PhD provides important insights into the role of genes and physiology in the regulation of division of labor in social insect colonies, helping to better understand the organization, evolution and ecological success of insect societies. - L'une des principales transitions évolutives est le passage de la vie solitaire à la vie sociale. La socialité atteint son paroxysme chez les insectes sociaux que sont les fourmis, les termites et certaines espèces d'abeilles et de guêpes. La division du travail est la clé de voûte de l'organisation de ces sociétés d'insectes et la raison principale de leur succès écologique. La division du travail s'effectue à deux niveaux dans les colonies d'insectes sociaux. Le premier niveau concerne la reproduction et implique la coexistence de deux castes : les reines et les ouvrières. Tandis que la reproduction est le plus souvent monopolisée par une ou plusieurs reines, les ouvrières stériles effectuent les tâches nécessaires au bon fonctionnement de la colonie, telles que la construction du nid, la recherche de nourriture ou le soin au couvain. Le second niveau de division du travail, qui concerne les tâches autres que la reproduction, implique la réalisation de différents travaux par différents groupes d'ouvrières. Le but de ce doctorat est de mieux comprendre les mécanismes sous-jacents de la division du travail dans les sociétés d'insectes en étudiant comment les gènes et la physiologie influencent la détermination de la caste et le comportement des ouvrières chez les fourmis. Dans le premier axe de ce doctorat, nous avons étudié la nature des influences génétiques sur la division du travail. Nous avons utilisé la fourmi d'Argentine, Linepithema humile, pour effectuer des croisements contrôlés en laboratoire. Cette méthode nous a permis de révéler l'existence d'influences génétiques non additives, telles que des influences dépendantes de l'origine parentale ou des effets de compatibilité génétique, sur la détermination de la caste et le comportement des ouvrières. Dans le second axe, nous nous sommes intéressés à la régulation physiologique de la division du travail. Nous avons utilisé des fourmis moissonneuses du genre Pogonomyrmex pour effectuer des hibernations artificieHes, des traitements hormonaux, des analyses d'expression de gènes et des mesures de vitellogénine, ce qui nous a permis d'identifier les mécanismes physiologiques régulant les effets maternels sur la détermination de la caste. Enfin, la comparaison d'expression de gènes entre nourrices et fourrageuses suggère un rôle de la vitellogénine dans la régulation du comportement des ouvrières chez les fourmis moissonneuses. En détaillant les influences des gènes et de la physiologie dans la régulation de la division du travail dans les colonies d'insectes sociaux, ce doctorat fournit d'importantes informations permettant de mieux comprendre l'organisation, l'évolution et le succès écologique des sociétés d'insectes.

Reexamination of the relationship of resting metabolic rate to fat-free mass and to the metabolically active components of fat-free mass in humans.

The ratio of resting metabolic rate (RMR) to fat-free mass (FFM) is often used to compare individuals of different body sizes. Because RMR has not been well described over the full range of FFM, a literature review was conducted among groups with a wide range of FFM. It included 31 data sets comprising a total of 1111 subjects: 118 infants and preschoolers, 323 adolescents, and 670 adults; FFM ranged from 2.8 to 106 kg. The relationship of RMR to FFM was found to be nonlinear and average slopes of the regression equations of the three groups differed significantly (P less than 0.0001). For only the youngest group did the intercept approach zero. The lower slopes of RMR on FFM, at higher measures of FFM, corresponded to relatively greater proportions of less metabolically active muscle mass and to lesser proportions of more metabolically active nonmuscle organ mass. Because the contribution of FFM to RMR is not constant, an arithmetic error is introduced when the ratio of RMR to FFM is used. Hence, alternative methods should be used to compare individuals with markedly different FFM.