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Associative learning plays a fundamental role in the life of honey bees, especially in the context of foraging for food sources. This learning capacity can be investigated through controlled experiments conducted under laboratory, semi-natural, and near-natural conditions, to understand the general principles of learning and motivation. Honey bees can be trained to solve different elemental and non-elemental learning tasks by pairing a conditioned stimulus such as an odor with sucrose as an unconditioned stimulus and reward. Laboratory studies with restrained bees demonstrated that sucrose responsiveness is positively correlated with both elemental olfactory learning performance and responsiveness to stimuli of different sensory modalities, such as odors and visual stimuli. Here, we tested for the first time how responsiveness to sucrose and light is related to performance in elemental and non-elemental visual learning under free-flying conditions. Sensory responsiveness and learning proficiency did not correlate, nor did sucrose responsiveness correlate with responsiveness to light. These results indicate that relationships among responsiveness to sucrose and light and learning proficiency, as established under restrained laboratory conditions, may not translate to the natural behavior of bees in the field. This finding points toward the context-dependent importance of responsiveness to light and sucrose during associative learning under restrained or free-flying conditions.

Associative learning allows animals to establish links between stimuli based on their concomitance. In the case of Pavlovian conditioning, a single stimulus A (the conditional stimulus, CS) is reinforced unambiguously with an unconditional stimulus (US) eliciting an innate response. This conditioning constitutes an ‘elemental’ association to elicit a learnt response from A + without US presentation after learning. However, associative learning may involve a ‘complex’ CS composed of several components. In that case, the compound may predict a different outcome than the components taken separately, leading to ambiguity and requiring the animal to perform so-called non-elemental discrimination. Here, we focus on such a non-elemental task, the negative patterning (NP) problem, and provide the first evidence of NP solving in Drosophila . We show that Drosophila learn to discriminate a simple component (A or B) associated with electric shocks (+) from an odour mixture composed either partly (called ‘feature-negative discrimination’ A + versus AB − ) or entirely (called ‘NP’ A + B + versus AB − ) of the shock-associated components. Furthermore, we show that conditioning repetition results in a transition from an elemental to a configural representation of the mixture required to solve the NP task, highlighting the cognitive flexibility of Drosophila .

Previous experience with discrimination problems that can only be solved by learning about stimulus configurations enhances performance on new configural discriminations. Some of these effects can be explained by a shift toward increased configural processing (learning about combinations of cues rather than about individual elements), or by a tendency to generalize a learned rule to a new training set. We investigated whether fluid abilities influence the extent that previous experience with configural discriminations improves performance on subsequent discriminations. In Experiments 1 and 2 we used patterning discriminations that could be solved by applying a simple rule, whereas in Experiment 3 we used biconditional discriminations that could not be solved using a rule. Fluid abilities predicted the improvement on the second training set in all experiments, including Experiment 3 in which rule-based generalization could not explain the improvement on the second discrimination. This supports the idea that fluid abilities contribute to performance by inducing a shift toward configural processing rather than rule-based generalization. However, fluid abilities also predicted performance on a rule-based transfer test in Experiment 2. Taken together, these results suggest that fluid abilities contribute to both a flexible shift toward configural processing and to rule-based generalization.

In the present study, we investigated response decisions made under conditions of incomplete information in rats. In Experiment 1, rats were trained on either a positive patterning (PP; A-, B-, AB+) or a negative patterning (NP; A+, B+, AB-) instrumental lever-press discrimination. Subjects that had learned an NP discrimination responded less to Cue A when Cue B was covered at test. The cover did not, however, affect test responses to Cue A in the PP condition. In Experiment 2, rats received concurrent training on both PP and NP discriminations. After concurrent training, responses to Cue A were different with B covered versus uncovered for both NP and PP discriminations. We discuss possible accounts for why exposure to a nonlinearly soluble discrimination (NP) may have affected sensitivity to cue ambiguity produced by the cover. These results have interesting implications for representational processes engaged in problem solving.

This study examined hippocampal theta power during configural and non-configural tasks in rats. Experiment 1 compared hippocampal theta power during a negative patterning task (A+, B+, AB−) to a configural task and a simple discrimination task (A+, B−) as a non-configural task. The results showed that hippocampal theta power during the non-reinforcement trial (non-RFT) of the negative patterning task was higher than that during the simple discrimination task. However, this hippocampal power may reflect sensory processing for compound stimuli that have cross-modality features (the non-RFT of the negative patterning task was presented together with visual and auditory stimuli, but the non-RFT of the simple discrimination task was presented with visual or auditory stimulus alone). Thus, in experiment 2, we examined whether the experiment 1 results were attributable to sensory processing of a compound stimulus by comparing hippocampal theta power during negative patterning (A+, B+, AB−), simultaneous feature-negative (A+, AB−), and simple discrimination tasks (A+, B−). Experiment 2 showed that hippocampal theta activity during the non-RFT in the negative patterning task was higher than that in the simultaneous feature-negative and simple discrimination tasks. Thus, we showed that hippocampal theta activity increased during configural tasks but not during non-configural tasks.

Pigeons were trained in two experiments with negative patterning discriminations that were accompanied by an irrelevant cue. For Experiment 1, the discriminations were of the form AX+ BX+ ABX–, where A and B were relevant, X was irrelevant, and + or – indicate whether or not reinforcement was delivered. The discriminations for Experiment 2 were of the form A+ B+ AX+ BX+ ABX–. A subsequent test phase in both experiments revealed that the associability of A and B, and hence the attention paid to these stimuli, was less than the associability of X. The results are explained with a modified version of a configural theory of associative learning.

To translate insights in developmental biology into medical applications, techniques are needed to ensure correct cell localization. Morphogen gradients allow precise and highly reproducible pattern formation during development. Through in vitro experiments and modeling, Li et al. tested the effects of unusual properties of Hedgehog (HH) signaling. The HH morphogen's receptor, Patched (PTCH), sends an inhibitory signal when no ligand is bound, which is relieved by ligand binding. PTCH also regulates spatial distribution of the signal by sequestering the HH ligand. Furthermore, signaling through the receptor promotes synthesis of more inhibitory receptor. These characteristics help speed gradient formation and explain the robustness of the system to changes in the rate of morphogen production. Science , this issue p. 543 Insights from building a morphogen gradient in cell culture are discussed. In developing tissues, cells estimate their spatial position by sensing graded concentrations of diffusible signaling proteins called morphogens. Morphogen-sensing pathways exhibit diverse molecular architectures, whose roles in controlling patterning dynamics and precision have been unclear. In this work, combining cell-based in vitro gradient reconstitution, genetic rewiring, and mathematical modeling, we systematically analyzed the distinctive architectural features of the Sonic Hedgehog pathway. We found that the combination of double-negative regulatory logic and negative feedback through the PTCH receptor accelerates gradient formation and improves robustness to variation in the morphogen production rate compared with alternative designs. The ability to isolate morphogen patterning from concurrent developmental processes and to compare the patterning behaviors of alternative, rewired pathway architectures offers a powerful way to understand and engineer multicellular patterning.

The Rho GTPases — RHOA, RAC1 and CDC42 — are small GTP binding proteins that regulate basic biological processes such as cell locomotion, cell division and morphogenesis by promoting cytoskeleton-based changes in the cell cortex. This regulation results from active (GTP-bound) Rho GTPases stimulating target proteins that, in turn, promote actin assembly and myosin 2-based contraction to organize the cortex. This basic regulatory scheme, well supported by in vitro studies, led to the natural assumption that Rho GTPases function in vivo in an essentially linear matter, with a given process being initiated by GTPase activation and terminated by GTPase inactivation. However, a growing body of evidence based on live cell imaging, modelling and experimental manipulation indicates that Rho GTPase activation and inactivation are often tightly coupled in space and time via signalling circuits and networks based on positive and negative feedback. In this Review, we present and discuss this evidence, and we address one of the fundamental consequences of coupled activation and inactivation: the ability of the Rho GTPases to self-organize, that is, direct their own transition from states of low order to states of high order. We discuss how Rho GTPase self-organization results in the formation of diverse spatiotemporal cortical patterns such as static clusters, oscillatory pulses, travelling wave trains and ring-like waves. Finally, we discuss the advantages of Rho GTPase self-organization and pattern formation for cell function.The Rho GTPases are small GTP binding proteins that regulate basic biological processes such as cell locomotion, cell division and morphogenesis by promoting cytoskeleton-based changes in the cell cortex. These different functions are driven by the ability of Rho GTPases to self-organize, forming diverse spatiotemporal cortical patterns.

BackgroundThe evolution of central nervous systems (CNSs) is a fascinating and complex topic; further work is needed to understand the genetic and developmental homology between organisms with a CNS. Research into a limited number of species suggests that CNSs may be homologous across Bilateria. This hypothesis is based in part on similar functions of BMP signaling in establishing fates along the dorsal-ventral (D-V) axis, including limiting neural specification to one ectodermal region. From an evolutionary-developmental perspective, the best way to understand a system is to explore it in a wide range of organisms to create a full picture.MethodsHere, we expand our understanding of BMP signaling in Spiralia, the third major clade of bilaterians, by examining phenotypes after expression of a dominant-negative BMP Receptor 1 and after knock-down of the putative BMP antagonist Chordin-like using CRISPR/Cas9 gene editing in the annelid Capitella teleta (Pleistoannelida).ResultsEctopic expression of the dominant-negative Ct-BMPR1 did not increase CNS tissue or alter overall D-V axis formation in the trunk. Instead, we observed a unique asymmetrical phenotype: a distinct loss of left tissues, including the left eye, brain, foregut, and trunk mesoderm. Adding ectopic BMP4 early during cleavage stages reversed the dominant-negative Ct-BMPR1 phenotype, leading to a similar loss or reduction of right tissues instead. Surprisingly, a similar asymmetrical loss of left tissues was evident from CRISPR knock-down of Ct-Chordin-like but concentrated in the trunk rather than the episphere.ConclusionsOur data highlight a novel asymmetrical phenotype, giving us further insight into the complicated story of BMP’s developmental role. We further solidify the hypothesis that the function of BMP signaling during the establishment of the D-V axis and CNS is fundamentally different in at least Pleistoannelida, possibly in Spiralia, and is not required for nervous system delimitation in this group.

The plant hormone auxin is thought to provide positional information for patterning during development. It is still unclear, however, precisely how auxin is distributed across tissues and how the hormone is sensed in space and time. The control of gene expression in response to auxin involves a complex network of over 50 potentially interacting transcriptional activators and repressors, the auxin response factors (ARFs) and Aux/IAAs. Here, we perform a large‐scale analysis of the Aux/IAA‐ARF pathway in the shoot apex of Arabidopsis , where dynamic auxin‐based patterning controls organogenesis. A comprehensive expression map and full interactome uncovered an unexpectedly simple distribution and structure of this pathway in the shoot apex. A mathematical model of the Aux/IAA‐ARF network predicted a strong buffering capacity along with spatial differences in auxin sensitivity. We then tested and confirmed these predictions using a novel auxin signalling sensor that reports input into the signalling pathway, in conjunction with the published DR5 transcriptional output reporter. Our results provide evidence that the auxin signalling network is essential to create robust patterns at the shoot apex. Synopsis The plant hormone auxin is a key morphogenetic signal involved in the control of cell identity throughout development. A striking example of auxin action is at the shoot apical meristem (SAM), a population of stem cells generating the aerial parts of the plant. Organ positioning and patterning depends on local accumulations of auxin in the SAM, generated by polar transport of auxin (Vernoux et al , 2010 ). However, it is still unclear how auxin is distributed at cell resolution in tissues and how the hormone is sensed in space and time during development. A complex ensemble of 29 Aux/IAAs and 23 ARFs is central to the regulation of gene transcription in response to auxin (for review, see Leyser, 2006 ; Guilfoyle and Hagen, 2007 ; Chapman and Estelle, 2009 ). Protein–protein interactions govern the properties of this transduction pathway (Del Bianco and Kepinski, 2011 ). Limited interaction studies suggest that, in the absence of auxin, the Aux/IAA repressors form heterodimers with the ARF transcription factors, preventing them from regulating target genes. In the presence of auxin, the Aux/IAA proteins are targeted to the proteasome by an SCF E3 ubiquitin ligase complex (Chapman and Estelle, 2009 ; Leyser, 2006 ). In this process, auxin promotes the interaction between Aux/IAA proteins and the TIR1 F‐box of the SCF complex (or its AFB homologues) that acts as an auxin co‐receptor (Dharmasiri et al , 2005a , 2005b ; Kepinski and Leyser, 2005 ; Tan et al , 2007 ). The auxin‐induced degradation of Aux/IAAs would then release ARFs to regulate transcription of their target genes. This includes activation of most of the Aux/IAA genes themselves, thus establishing a negative feedback loop (Guilfoyle and Hagen, 2007 ). Although this general scenario provides a framework for understanding gene regulation by auxin, the underlying protein–protein network remains to be fully characterized. In this paper, we combined experimental and theoretical analyses to understand how this pathway contributes to sensing auxin in space and time (Figure 1 ). We first analysed the expression patterns of the ARFs , Aux/IAAs and TIR1 / AFBs genes in the SAM. Our results demonstrate a general tendency for most of the 25 ARFs and Aux/IAAs detected in the SAM: a differential expression with low levels at the centre of the meristem (where the stem cells are located) and high levels at the periphery of the meristem (where organ initiation takes place). We also observed a similar differential expression for TIR1/AFB co‐receptors. To understand the functional significance of the distribution of ARFs and Aux/IAAs in the SAM, we next investigated the global structure of the Aux/IAA‐ARF network using a high‐throughput yeast two‐hybrid approach and uncover a rather simple topology that relies on three basic generic features: (i) Aux/IAA proteins interact with themselves, (ii) Aux/IAA proteins interact with ARF activators and (iii) ARF repressors have no or very limited interactions with other proteins in the network. The results of our interaction analysis suggest a model for the Aux/IAA‐ARF signalling pathway in the SAM, where transcriptional activation by ARF activators would be negatively regulated by two independent systems, one involving the ARF repressors, the other the Aux/IAAs. The presence of auxin would remove the inhibitory action of Aux/IAAs, but leave the ARF repressors to compete with ARF activators for promoter‐binding sites. To explore the regulatory properties of this signalling network, we developed a mathematical model to describe the transcriptional output as a function of the signalling input that is the combinatorial effect of auxin concentration and of its perception. We then used the model and a simplified view of the meristem (where the same population of Aux/IAAs and ARFs exhibit a low expression at the centre and a high expression in the peripheral zone) for investigating the role of auxin signalling in SAM function. We show that in the model, for a given ARF activator‐to‐repressor ratio, the gene induction capacity increases with the absolute levels of ARF proteins. We thus predict that the differential expression of the ARF s generates differences in auxin sensitivities between the centre (low sensitivity) and the periphery (high sensitivity), and that the expression of TIR1/AFB participates to this regulation (prediction 1). We also use the model to analyse the transcriptional response to rapidly changing auxin concentrations. By simulating situations equivalent either to the centre or the periphery of our simplified representation of the SAM, we predict that the signalling pathway buffers its response to the auxin input via the balance between ARF activators and repressors, in turn generated by their differential spatial distributions (prediction 2). To test the predictions from the model experimentally, we needed to assess both the input (auxin level and/or perception) and the output (target gene induction) of the signalling cascade. For measuring the transcriptional output, the widely used DR5 reporter is perfectly adapted (Figure 5 ) (Ulmasov et al , 1997 ; Sabatini et al , 1999 ; Benkova et al , 2003 ; Heisler et al , 2005 ). For assaying pathway input, we designed DII‐VENUS, a novel auxin signalling sensor that comprises a constitutively expressed fusion of the auxin‐binding domain (termed domain II or DII) (Dreher et al , 2006 ; Tan et al , 2007 ) of an IAA to a fast‐maturating variant of YFP, VENUS (Figure 5 ). The degradation patterns from DII‐VENUS indicate a high auxin signalling input both in flower primordia and at the centre of the SAM. This is in contrast to the organ‐specific expression pattern of DR5::VENUS (Figure 5 ). These results indicate that the signalling pathway limits gene activation in response to auxin at the meristem centre and confirm the differential sensitivity to auxin between the centre and the periphery (prediction 1). We further confirmed the buffering capacities of the signalling pathway (prediction 2) by carrying out live imaging experiments to monitor DII‐VENUS and DR5::VENUS expression in real time (Figure 5 ). This analysis reveals the presence of important temporal variations of DII‐VENUS fluorescence, while DR5::VENUS does not show such global variations. Our approach thus provides evidence that the Aux/IAA‐ARF pathway has a key role in patterning in the SAM, alongside the auxin transport system. Our results illustrate how the tight spatio‐temporal regulation of both the distribution of a morphogenetic signal and the activity of the downstream signalling pathway provides robustness to a dynamic developmental process. We provide a comprehensive expression map of the different genes (TIR1/AFBs, ARFs and Aux/IAAs) involved in the signalling pathway regulating gene transcription in response to auxin in the shoot apical meristem (SAM). We demonstrate a relatively simple structure of this pathway using a high‐throughput yeast two‐hybrid approach to obtain the Aux/IAA‐ARF full interactome. The topology of the signalling network was used to construct a model for auxin signalling and to predict a role for the spatial regulation of auxin signalling in patterning of the SAM. We used a new sensor to monitor the input in the auxin signalling pathway and to confirm the model prediction, thus demonstrating that auxin signalling is essential to create robust patterns at the SAM.