Many polyphenisms are examples of adaptive phenotypic plasticity where a single genotype produces distinct phenotypes in response to environmental cues. Such alternative phenotypes occur as winged and wingless parthenogenetic females in the pea aphid (Acyrthosiphon pisum). However, the proportion of winged females produced in response to a given environmental cue varies between clonal genotypes. Winged and wingless phenotypes also occur in males of the sexual generation. In contrast to parthenogenetic females, wing production in males is environmentally insensitive and controlled by the sex-linked, biallelic locus, aphicarus (api). Hence, environmental or genetic cues induce development of winged and wingless phenotypes at different stages of the pea aphid life cycle. We have tested whether allelic variation at the api locus explains genetic variation in the propensity to produce winged females. We assayed clones from an F2 cross that were heterozygous or homozygous for alternative api alleles for their propensity to produce winged offspring. We found that clones with different api genotypes differed in their propensity to produce winged offspring. The results indicate genetic linkage of factors controlling the female wing polyphenism and male wing polymorphism. This finding is consistent with the hypothesis that genotype by environment interaction at the api locus explains genetic variation in the environmentally cued wing polyphenism.

Can neuronal morphology predict functional synaptic circuits? In the rat barrel cortex, ’barrels’ and ’septa’ delineate an orderly matrix of cortical columns. Using quantitative laser scanning photostimulation we measured the strength of excitatory projections from layer 4 (L4) and L5A to L2/3 pyramidal cells in barrel- and septum-related columns. From morphological reconstructions of excitatory neurons we computed the geometric circuit predicted by axodendritic overlap. Within most individual projections, functional inputs were predicted by geometry and a single scale factor, the synaptic strength per potential synapse. This factor, however, varied between projections and, in one case, even within a projection, up to 20-fold. Relationships between geometric overlap and synaptic strength thus depend on the laminar and columnar locations of both the pre- and postsynaptic neurons, even for neurons of the same type. A large plasticity potential appears to be incorporated into these circuits, allowing for functional ’tuning’ with fixed axonal and dendritic arbor geometry.

The blood-brain barrier of Drosophila is established by surface glia, which ensheath the nerve cord and insulate it against the potassium-rich hemolymph by forming intercellular septate junctions. The mechanisms underlying the formation of this barrier remain obscure. Here, we show that the G protein-coupled receptor (GPCR) Moody, the G protein subunits G alpha i and G alpha o, and the regulator of G protein signaling Loco are required in the surface glia to achieve effective insulation. Our data suggest that the four proteins act in a complex common pathway. At the cellular level, the components function by regulating the cortical actin and thereby stabilizing the extended morphology of the surface glia, which in turn is necessary for the formation of septate junctions of sufficient length to achieve proper sealing of the nerve cord. Our study demonstrates the importance of morphogenetic regulation in blood-brain barrier development and places GPCR signaling at its core.

How different is local cortical circuitry from a random network? To answer this question, we probed synaptic connections with several hundred simultaneous quadruple whole-cell recordings from layer 5 pyramidal neurons in the rat visual cortex. Analysis of this dataset revealed several nonrandom features in synaptic connectivity. We confirmed previous reports that bidirectional connections are more common than expected in a random network. We found that several highly clustered three-neuron connectivity patterns are overrepresented, suggesting that connections tend to cluster together. We also analyzed synaptic connection strength as defined by the peak excitatory postsynaptic potential amplitude. We found that the distribution of synaptic connection strength differs significantly from the Poisson distribution and can be fitted by a lognormal distribution. Such a distribution has a heavier tail and implies that synaptic weight is concentrated among few synaptic connections. In addition, the strengths of synaptic connections sharing pre- or postsynaptic neurons are correlated, implying that strong connections are even more clustered than the weak ones. Therefore, the local cortical network structure can be viewed as a skeleton of stronger connections in a sea of weaker ones. Such a skeleton is likely to play an important role in network dynamics and should be investigated further.

Nervous systems often face the problem of classifying stimuli and making decisions based on these classifications. The neurons involved in these tasks can be characterized as sensory or motor, according to their correlation with sensory stimulus or motor response. In this study we define a third class of neurons responsible for making perceptual decisions. Our mathematical formalism enables the weighting of neuronal units according to their contribution to decision making, thus narrowing the field for more detailed studies of underlying mechanisms. We develop two definitions of a contribution to decision making. The first definition states that decision making activity can be found at the points of emergence for behavioral correlations in the system. The second definition involves the study of propagation of noise in the network. The latter definition is shown to be equivalent to the first one in the cases when they can be compared. Our results suggest a new approach to analyzing decision making networks.

Transcriptional dysregulation has emerged as a potentially important pathogenic mechanism in Huntington’s disease, a neurodegenerative disorder associated with polyglutamine expansion in the huntingtin (htt) protein. Here, we report the development of a biochemically defined in vitro transcription assay that is responsive to mutant htt. We demonstrate that both gene-specific activator protein Sp1 and selective components of the core transcription apparatus, including TFIID and TFIIF, are direct targets inhibited by mutant htt in a polyglutamine-dependent manner. The RAP30 subunit of TFIIF specifically interacts with mutant htt both in vitro and in vivo to interfere with formation of the RAP30-RAP74 native complex. Importantly, overexpression of RAP30 in cultured primary striatal cells protects neurons from mutant htt-induced cellular toxicity and alleviates the transcriptional inhibition of the dopamine D2 receptor gene by mutant htt. Our results suggest a mutant htt-directed repression mechanism involving multiple specific components of the basal transcription apparatus.

The insulin signaling pathway regulates multiple physiological processes, including energy metabolism, organismal growth, aging and reproduction. Here we show that genetic manipulations in Drosophila melanogaster that impair the function of insulin-producing cells or of the insulin-receptor signaling pathway in the nervous system lead to increased sensitivity to the intoxicating effects of ethanol. These findings suggest a previously unknown role for this highly conserved pathway in regulating the behavioral responses to an addictive drug.

Microcircuits composed of principal neuron and interneuron dendrites have an important role in shaping the representation of sensory information in the olfactory bulb. Here we establish the physiological features governing synaptic signaling in dendrodendritic microcircuits of olfactory bulb glomeruli. We show that dendritic gamma-aminobutyric acid (GABA) release from periglomerular neurons mediates inhibition of principal tufted cells, retrograde inhibition of sensory input and lateral signaling onto neighboring periglomerular cells. We find that L-type dendritic Ca(2+) spikes in periglomerular cells underlie dendrodendritic transmission by depolarizing periglomerular dendrites and activating P/Q type channels that trigger GABA release. Ca(2+) spikes in periglomerular cells are evoked by powerful excitatory inputs from a single principal cell, and glutamate release from the dendrites of single principal neurons activates a large ensemble of periglomerular cells.

To compare spatial patterns of gene expression, one must analyze a large number of images as current methods are only able to measure a small number of genes at a time. Bringing images of corresponding tissues into alignment is a critical first step in making a meaningful comparative analysis of these spatial patterns. Significant image noise and variability in the shapes make it hard to pick a canonical shape model. In this paper, we address these problems by combining segmentation and unsupervised shape learning algorithms. We first segment images to acquire structures of interest, then jointly align the shapes of these acquired structures using an unsupervised nonparametric maximum likelihood algorithm along the lines of congealing, while simultaneously learning the underlying shape model and associated transformations. The learned transformations are applied to corresponding images to bring them into alignment in one step. We demonstrate the results for images of various classes of Drosophila imaginal discs and discuss the methodology used for a quantitative analysis of spatial gene expression patterns.

We have used simulations to study the learning dynamics of an autonomous, biologically realistic recurrent network of spiking neurons connected via plastic synapses, subjected to a stream of stimulus-delay trials, in which one of a set of stimuli is presented followed by a delay. Long-term plasticity, produced by the neural activity experienced during training, structures the network and endows it with active (working) memory, i.e. enhanced, selective delay activity for every stimulus in the training set. Short-term plasticity produces transient synaptic depression. Each stimulus used in training excites a selective subset of neurons in the network, and stimuli can share neurons (overlapping stimuli). Long-term plasticity dynamics are driven by presynaptic spikes and coincident postsynaptic depolarization; stability is ensured by a refresh mechanism. In the absence of stimulation, the acquired synaptic structure persists for a very long time. The dependence of long-term plasticity dynamics on the characteristics of the stimulus response (average emission rates, time course and synchronization), and on the single-cell emission statistics (coefficient of variation) is studied. The study clarifies the specific roles of short-term synaptic depression, NMDA receptors, stimulus representation overlaps, selective stimulation of inhibition, and spike asynchrony during stimulation. Patterns of network spiking activity before, during and after training reproduce most of the in vivo physiological observations in the literature.