Autoimmunity-Associated LYP-W620 Does Not Impair Thymic Negative Selection of Autoreactive T Cells.

A C1858T (R620W) variation in the PTPN22 gene encoding the tyrosine phosphatase LYP is a major risk factor for human autoimmunity. LYP is a known negative regulator of signaling through the T cell receptor (TCR), and murine Ptpn22 plays a role in thymic selection. However, the mechanism of action of the R620W variant in autoimmunity remains unclear. One model holds that LYP-W620 is a gain-of-function phosphatase that causes alterations in thymic negative selection and/or thymic output of regulatory T cells (Treg) through inhibition of thymic TCR signaling. To test this model, we generated mice in which the human LYP-W620 variant or its phosphatase-inactive mutant are expressed in developing thymocytes under control of the proximal Lck promoter. We found that LYP-W620 expression results in diminished thymocyte TCR signaling, thus modeling a "gain-of-function" of LYP at the signaling level. However, LYP-W620 transgenic mice display no alterations of thymic negative selection and no anomalies in thymic output of CD4(+)Foxp3(+) Treg were detected in these mice. Lck promoter-directed expression of the human transgene also causes no alteration in thymic repertoire or increase in disease severity in a model of rheumatoid arthritis, which depends on skewed thymic selection of CD4(+) T cells. Our data suggest that a gain-of-function of LYP is unlikely to increase risk of autoimmunity through alterations of thymic selection and that LYP likely acts in the periphery perhaps selectively in regulatory T cells or in another cell type to increase risk of autoimmunity.

Temperature-dependent turnovers in sex-determination mechanisms: a quantitative model.

Sex determination is often seen as a dichotomous process: individual sex is assumed to be determined either by genetic (genotypic sex determination, GSD) or by environmental factors (environmental sex determination, ESD), most often temperature (temperature sex determination, TSD). We endorse an alternative view, which sees GSD and TSD as the ends of a continuum. Both effects interact a priori, because temperature can affect gene expression at any step along the sex-determination cascade. We propose to define sex-determination systems at the population- (rather than individual) level, via the proportion of variance in phenotypic sex stemming from genetic versus environmental factors, and we formalize this concept in a quantitative-genetics framework. Sex is seen as a threshold trait underlain by a liability factor, and reaction norms allow modeling interactions between genotypic and temperature effects (seen as the necessary consequences of thermodynamic constraints on the underlying physiological processes). As this formalization shows, temperature changes (due to e.g., climatic changes or range expansions) are expected to provoke turnovers in sex-determination mechanisms, by inducing large-scale sex reversal and thereby sex-ratio selection for alternative sex-determining genes. The frequency of turnovers and prevalence of homomorphic sex chromosomes in cold-blooded vertebrates might thus directly relate to the temperature dependence in sex-determination mechanisms.

Selectome update: quality control and computational improvements to a database of positive selection.

Selectome (http://selectome.unil.ch/) is a database of positive selection, based on a branch-site likelihood test. This model estimates the number of nonsynonymous substitutions (dN) and synonymous substitutions (dS) to evaluate the variation in selective pressure (dN/dS ratio) over branches and over sites. Since the original release of Selectome, we have benchmarked and implemented a thorough quality control procedure on multiple sequence alignments, aiming to provide minimum false-positive results. We have also improved the computational efficiency of the branch-site test implementation, allowing larger data sets and more frequent updates. Release 6 of Selectome includes all gene trees from Ensembl for Primates and Glires, as well as a large set of vertebrate gene trees. A total of 6810 gene trees have some evidence of positive selection. Finally, the web interface has been improved to be more responsive and to facilitate searches and browsing.

Evolution of self-organized division of labor in a response threshold model.

Division of labor in social insects is determinant to their ecological success. Recent models emphasize that division of labor is an emergent property of the interactions among nestmates obeying to simple behavioral rules. However, the role of evolution in shaping these rules has been largely neglected. Here, we investigate a model that integrates the perspectives of self-organization and evolution. Our point of departure is the response threshold model, where we allow thresholds to evolve. We ask whether the thresholds will evolve to a state where division of labor emerges in a form that fits the needs of the colony. We find that division of labor can indeed evolve through the evolutionary branching of thresholds, leading to workers that differ in their tendency to take on a given task. However, the conditions under which division of labor evolves depend on the strength of selection on the two fitness components considered: amount of work performed and on worker distribution over tasks. When selection is strongest on the amount of work performed, division of labor evolves if switching tasks is costly. When selection is strongest on worker distribution, division of labor is less likely to evolve. Furthermore, we show that a biased distribution (like 3:1) of workers over tasks is not easily achievable by a threshold mechanism, even under strong selection. Contrary to expectation, multiple matings of colony foundresses impede the evolution of specialization. Overall, our model sheds light on the importance of considering the interaction between specific mechanisms and ecological requirements to better understand the evolutionary scenarios that lead to division of labor in complex systems. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s00265-012-1343-2) contains supplementary material, which is available to authorized users.

Active Contour-Based Segmentation of Head and Neck with Adaptive Atlas Selection

This paper presents automated segmentation of structuresin the Head and Neck (H\&N) region, using an activecontour-based joint registration and segmentation model.A new atlas selection strategy is also used. Segmentationis performed based on the dense deformation fieldcomputed from the registration of selected structures inthe atlas image that have distinct boundaries, onto thepatient's image. This approach results in robustsegmentation of the structures of interest, even in thepresence of tumors, or anatomical differences between theatlas and the patient image. For each patient, an atlasimage is selected from the available atlas-database,based on the similarity metric value, computed afterperforming an affine registration between each image inthe atlas-database and the patient's image. Unlike manyof the previous approaches in the literature, thesimilarity metric is not computed over the entire imageregion; rather, it is computed only in the regions ofsoft tissue structures to be segmented. Qualitative andquantitative evaluation of the results is presented.

Group selection and kin selection: two concepts but one process.

In a recent paper, Traulsen and Nowak use a multilevel selection model to show that cooperation can be favored by group selection in finite populations [Traulsen A, Nowak M (2006) Proc Natl Acad Sci USA 103:10952-10955]. The authors challenge the view that kin selection may be an appropriate interpretation of their results and state that group selection is a distinctive process "that permeates evolutionary processes from the emergence of the first cells to eusociality and the economics of nations." In this paper, we start by addressing Traulsen and Nowak's challenge and demonstrate that all their results can be obtained by an application of kin selection theory. We then extend Traulsen and Nowak's model to life history conditions that have been previously studied. This allows us to highlight the differences and similarities between Traulsen and Nowak's model and typical kin selection models and also to broaden the scope of their results. Our retrospective analyses of Traulsen and Nowak's model illustrate that it is possible to convert group selection models to kin selection models without disturbing the mathematics describing the net effect of selection on cooperation.

An immune-competent model of spontaneous breast cancer metastasis to the brain

In breast cancer, brain metastases are often seen as late complications of recurrent disease and represent a particularly serious condition, since there are limited therapeutic options and patients have an unfavorable prognosis. The frequency of brain metastases in breast cancer is currently on the rise. This might be due to the fact that adjuvant chemotherapeutic and targeted anticancer drugs, while they effectively control disease progression in the periphery, they only poorly cross the blood-brain barrier and do not reach effectively cancer cells disseminated in the brain. It is therefore of fundamental clinical relevance to investigate mechanisms involved in breast cancer metastasis to the brain. To date experimental models of breast cancer metastasis to the brain described in literature are based on the direct intracarotid or intracardiac injection of breast cancer cells. We recently established a brain metastasis breast cancer model in immunocompetent mice based on the orthotopic injection of 4T1 murine breast carcinoma cells in the mammary gland of syngeneic BALB/c mice. 4T1-derived tumors recapitulate the main steps of human breast cancer progression, including epithelial-to-mesenchymal transition, local invasion and metastatic spreading to lung and lymph nodes. 4T1 cells were engineered to stably express firefly Luciferase allowing noninvasive in vivo and ex vivo monitoring of tumor progression and metastatic spreading to target organs. Bioluminescence imaging revealed the appearance of spontaneous lesions to the lung and lymph nodes and, at a much lower frequency, to the brain. Brain metastases were confirmed by macroscopic and microscopic evaluation of the brains at necropsy. We then isolated brain metastatic cells, re-injected them orthotopically in new mice and isolated again lines from brain metastases. After two rounds of selection we obtained lines metastasizing to the brain with 100% penetrance (named 4T1-BM2 for Brain Metastasis, 2nd generation) compared to lines derived after two rounds of in vivo growth from primary tumors (4T1-T2) or from lung metastases (4T1-LM2). We are currently performing experiments to unravel differences in cell proliferation, adhesion, migration, invasion and survival of the 4T1-BM2 line relative to the 4T1-T2 and 4T1-LM2 lines. Initial results indicate that 4T1-BM2 cells are not more invasive or more proliferative in vitro and do not show a more mesenchymal phenotype. Our syngeneic (BALB/c) model of spontaneous breast carcinoma metastasis to the brain is a unique and clinically relevant model to unravel the mechanisms of metastatic breast cancer colonization of the brain. Genes identified in this model represent potentially clinically relevant therapeutic targets for the prevention and the treatment of brain metastases in breast cancer patients.

Heterogeneity, selection and labor market disparities

We study the incentives to acquire skill in a model where heterogeneous firmsand workers interact in a labor market characterized by matching frictions and costlyscreening. When effort in acquiring skill raises both the mean and the variance of theresulting ability distribution, multiple equilibria may arise. In the high-effort equilibrium, heterogeneity in ability is sufficiently large to induce firms to select the bestworkers, thereby confirming the belief that effort is important for finding good jobs.In the low-effort equilibrium, ability is not sufficiently dispersed to justify screening,thereby confirming the belief that effort is not so important. The model has implications for wage inequality, the distribution of firm characteristics, sorting patternsbetween firms and workers, and unemployment rates that can help explaining observedcross-country variation in socio-economic and labor market outcomes.

Growth, selection and appropriate contracts

We study a dynamic model where growth requires both long-term investmentand the selection of talented managers. When ability is not ex-ante observable and contracts are incomplete, managerial selection imposes a cost, as managers facing the risk ofbeing replaced choose a sub-optimally low level of long-term investment. This generates atrade-off between selection and investment that has implications for the choice of contractualrelationships and institutions. Our analysis shows that rigid long-term contracts sacrificingmanagerial selection may prevail at early stages of economic development and when heterogeneity in ability is low. As the economy grows, however, knowledge accumulation increasesthe return to talent and makes it optimal to adopt flexible contractual relationships, wheremanagerial selection is implemented even at the cost of lower investment. Measures of investor protection aimed at limiting the bargaining power of managers improve selection undershort-term contract. Given that knowledge accumulation raises the value of selection, theoptimal level of investor protection increases with development.

Spatial statistical analysis and selection of genotypes in plant breeding

The objective of this study was to evaluate the efficiency of spatial statistical analysis in the selection of genotypes in a plant breeding program and, particularly, to demonstrate the benefits of the approach when experimental observations are not spatially independent. The basic material of this study was a yield trial of soybean lines, with five check varieties (of fixed effect) and 110 test lines (of random effects), in an augmented block design. The spatial analysis used a random field linear model (RFML), with a covariance function estimated from the residuals of the analysis considering independent errors. Results showed a residual autocorrelation of significant magnitude and extension (range), which allowed a better discrimination among genotypes (increase of the power of statistical tests, reduction in the standard errors of estimates and predictors, and a greater amplitude of predictor values) when the spatial analysis was applied. Furthermore, the spatial analysis led to a different ranking of the genetic materials, in comparison with the non-spatial analysis, and a selection less influenced by local variation effects was obtained.

Comprehensive approach for the detection of antifungal compounds using a susceptible strain of Candida albicans and confirmation of in vivo activity with the Galleria mellonella model.

An efficient screening strategy for the identification of potentially interesting low-abundance antifungal natural products in crude extracts that combines both a sensitive bioautography assay and high performance liquid chromatography (HPLC) microfractionation was developed. This method relies on high performance thin layer chromatography (HPTLC) bioautography with a hypersusceptible engineered strain of Candida albicans (DSY2621) for bioactivity detection, followed by the evaluation of wild type strains in standard microdilution antifungal assays. Active extracts were microfractionated by HPLC in 96-well plates, and the fractions were subsequently submitted to the bioassay. This procedure enabled precise localisation of the antifungal compounds directly in the HPLC chromatograms of the crude extracts. HPLC-PDA-mass spectrometry (MS) data obtained in parallel to the HPLC antifungal profiles provided a first chemical screening about the bioactive constituents. Transposition of the HPLC analytical conditions to medium-pressure liquid chromatography (MPLC) allowed the efficient isolation of the active constituents in mg amounts for structure confirmation and more extensive characterisation of their biological activities. The antifungal properties of the isolated natural products were evaluated by their minimum inhibitory concentration (MIC) in a dilution assay against both wild type and engineered strains of C. albicans. The biological activity of the most promising agents was further evaluated in vitro by electron microscopy and in vivo in a Galleria mellonella model of C. albicans infection. The overall procedure represents a rational and comprehensive means of evaluating antifungal activity from various perspectives for the selection of initial hits that can be explored in more in-depth mode-of-action studies. This strategy is illustrated by the identification and bioactivity evaluation of a series of antifungal compounds from the methanolic extract of a Rubiaceae plant, Morinda tomentosa, which was used as a model in these studies.

Designs and model effects definitions in the initial stage of a plant breeding program

The objective of this work was to compare the relative efficiency of initial selection and genetic parameter estimation, using augmented blocks design (ABD), augmented blocks twice replicated design (DABD) and group of randomised block design experiments with common treatments (ERBCT), by simulations, considering fixed effect model and mixed model with regular treatment effects as random. For the simulations, eight different conditions (scenarios) were considered. From the 600 simulations in each scenario, the mean percentage selection coincidence, the Pearsons´s correlation estimates between adjusted means for the fixed effects model, and the heritability estimates for the mixed model were evaluated. DABD and ERBCT were very similar in their comparisons and slightly superior to ABD. Considering the initial stages of selection in a plant breeding program, ABD is a good alternative for selecting superior genotypes, although none of the designs had been effective to estimate heritability in all the different scenarios evaluated.

Strategy selection: An introduction to the modeling challenge

Modeling the mechanisms that determine how humans and other agents choose among different behavioral and cognitive processes-be they strategies, routines, actions, or operators-represents a paramount theoretical stumbling block across disciplines, ranging from the cognitive and decision sciences to economics, biology, and machine learning. By using the cognitive and decision sciences as a case study, we provide an introduction to what is also known as the strategy selection problem. First, we explain why many researchers assume humans and other animals to come equipped with a repertoire of behavioral and cognitive processes. Second, we expose three descriptive, predictive, and prescriptive challenges that are common to all disciplines which aim to model the choice among these processes. Third, we give an overview of different approaches to strategy selection. These include cost‐benefit, ecological, learning, memory, unified, connectionist, sequential sampling, and maximization approaches. We conclude by pointing to opportunities for future research and by stressing that the selection problem is far from being resolved.

The branch-site test of positive selection is surprisingly robust but lacks power under synonymous substitution saturation and variation in GC

Positive selection is widely estimated from protein coding sequence alignments by the nonsynonymous-to-synonymous ratio omega. Increasingly elaborate codon models are used in a likelihood framework for this estimation. Although there is widespread concern about the robustness of the estimation of the omega ratio, more efforts are needed to estimate this robustness, especially in the context of complex models. Here, we focused on the branch-site codon model. We investigated its robustness on a large set of simulated data. First, we investigated the impact of sequence divergence. We found evidence of underestimation of the synonymous substitution rate for values as small as 0.5, with a slight increase in false positives for the branch-site test. When dS increases further, underestimation of dS is worse, but false positives decrease. Interestingly, the detection of true positives follows a similar distribution, with a maximum for intermediary values of dS. Thus, high dS is more of a concern for a loss of power (false negatives) than for false positives of the test. Second, we investigated the impact of GC content. We showed that there is no significant difference of false positives between high GC (up to similar to 80%) and low GC (similar to 30%) genes. Moreover, neither shifts of GC content on a specific branch nor major shifts in GC along the gene sequence generate many false positives. Our results confirm that the branch-site is a very conservative test.