A recent study challenges the oft-held notion that ester bonds in prodrug molecules are cleaved rapidly and completely inside cells by endogenous, nonspecific esterases. Structure-activity relationship studies on acylated sugars reveal that regioisomeric compounds display disparate biological activity, suggesting that ester bonds can persist in a cellular context.

Pairwise sequence covariations are a signal of conserved RNA secondary structure. We describe a method for distinguishing when lack of covariation signal can be taken as evidence against a conserved RNA structure, as opposed to when a sequence alignment merely has insufficient variation to detect covariations. We find that alignments for several long noncoding RNAs previously shown to lack covariation support do have adequate covariation detection power, providing additional evidence against their proposed conserved structures.

One approach to super-resolution fluorescence microscopy, termed stochastic localization microscopy, relies on the nanometer scale spatial localization of individual fluorescent emitters that stochastically label specific features of the specimen. The precision of emitter localization is an important determinant of the resulting image resolution but is insufficient to specify how well the derived images capture the structure of the specimen. We address this deficiency by considering the inference of specimen structure based on the estimated emitter locations. By using estimation theory, we develop a measure of spatial resolution that jointly depends on the density of the emitter labels, the precision of emitter localization, and prior information regarding the spatial frequency content of the labeled object. The Nyquist criterion does not set the scaling of this measure with emitter number. Given prior information and a fixed emitter labeling density, our resolution measure asymptotes to a finite value as the precision of emitter localization improves. By considering the present experimental capabilities, this asymptotic behavior implies that further resolution improvements require increases in labeling density above typical current values. Our treatment also yields algorithms to enhance reliable image features. Overall, our formalism facilitates the rigorous statistical interpretation of the data produced by stochastic localization imaging techniques.

Upon exposure to ethanol, Drosophila display behaviors that are similar to ethanol intoxication in rodents and humans. Using an inebriometer to measure ethanol-induced loss of postural control, we identified cheapdate, a mutant with enhanced sensitivity to ethanol. Genetic and molecular analyses revealed that cheapdate is an allele of the memory mutant amnesiac. amnesiac has been postulated to encode a neuropeptide that activates the cAMP pathway. Consistent with this, we find that enhanced ethanol sensitivity of cheapdate can be reversed by treatment with agents that increase cAMP levels or PKA activity. Conversely, genetic or pharmacological reduction in PKA activity results in increased sensitivity to ethanol. Taken together, our results provide functional evidence for the involvement of the cAMP signal transduction pathway in the behavioral response to intoxicating levels of ethanol.

BACKGROUND: It has become increasingly clear that molecular and neural mechanisms underlying learning and memory and drug addiction are largely shared. To confirm and extend these findings, we analyzed ethanol-responsive behaviors of a collection of Drosophila long-term memory mutants.

METHODS: For each mutant, sensitivity to the acute uncoordinating effects of ethanol was quantified using the inebriometer. Additionally, 2 distinct forms of ethanol tolerance were measured: rapid tolerance, which develops in response to a single brief exposure to a high concentration of ethanol vapor; and chronic tolerance, which develops following a sustained low-level exposure.

RESULTS: Several mutants were identified with altered sensitivity, rapid or chronic tolerance, while a number of mutants exhibited multiple defects.

CONCLUSIONS: The corresponding genes in these mutants represent areas of potential overlap between learning and memory and behavioral responses to alcohol. These genes also define components shared between different ethanol behavioral responses.

BACKGROUND: Increased ethanol intake, a major predictor for the development of alcohol use disorders, is facilitated by the development of tolerance to both the aversive and pleasurable effects of the drug. The molecular mechanisms underlying ethanol tolerance development are complex and are not yet well understood.

METHODS: To identify genetic mechanisms that contribute to ethanol tolerance, we examined the time course of gene expression changes elicited by a single sedating dose of ethanol in Drosophila, and completed a behavioral survey of strains harboring mutations in ethanol-regulated genes.

RESULTS: Enrichment for genes in metabolism, nucleic acid binding, olfaction, regulation of signal transduction, and stress suggests that these biological processes are coordinately affected by ethanol exposure. We also detected a coordinate up-regulation of genes in the Toll and Imd innate immunity signal transduction pathways. A multi-study comparison revealed a small set of genes showing similar regulation, including increased expression of 3 genes for serine biosynthesis. A survey of Drosophila strains harboring mutations in ethanol-regulated genes for ethanol sensitivity and tolerance phenotypes revealed roles for serine biosynthesis, olfaction, transcriptional regulation, immunity, and metabolism. Flies harboring deletions of the genes encoding the olfactory co-receptor Or83b or the sirtuin Sir2 showed marked changes in the development of ethanol tolerance.

CONCLUSIONS: Our findings implicate novel roles for these genes in regulating ethanol behavioral responses.

Transcriptional regulation is achieved through combinatorial interactions between regulatory elements in the human genome and a vast range of factors that modulate the recruitment and activity of RNA polymerase. Experimental approaches for studying transcription in vivo now extend from single-molecule techniques to genome-wide measurements. Parallel to these developments is the need for testable quantitative and predictive models for understanding gene regulation. These conceptual models must also provide insight into the dynamics of transcription and the variability that is observed at the single-cell level. In this Review, we discuss recent results on transcriptional regulation and also the models those results engender. We show how a non-equilibrium description informs our view of transcription by explicitly considering time- and energy-dependence at the molecular level.

Genetically encoded optical indicators hold the promise of enabling non-invasive monitoring of activity in identified neurons in behaving organisms. However, the interpretation of images of brain activity produced using such sensors is not straightforward. Several recent studies of sensory coding used G-CaMP 1.3-a calcium sensor-as an indicator of neural activity; some of these studies characterized the imaged neurons as having narrow tuning curves, a conclusion not always supported by parallel electrophysiological studies. To better understand the possible cause of these conflicting results, we performed simultaneous in vivo 2-photon imaging and electrophysiological recording of G-CaMP 1.3 expressing neurons in the antennal lobe (AL) of intact fruitflies. We find that G-CaMP has a relatively high threshold, that its signal often fails to capture spiking response kinetics, and that it can miss even high instantaneous rates of activity if those are not sustained. While G-CaMP can be misleading, it is clearly useful for the identification of promising neural targets: when electrical activity is well above the sensor’s detection threshold, its signal is fairly well correlated with mean firing rate and G-CaMP does not appear to alter significantly the responses of neurons that express it. The methods we present should enable any genetically encoded sensor, activator, or silencer to be evaluated in an intact neural circuit in vivo in Drosophila.

Neuron models, in particular conductance-based compartmental models, often have numerous parameters that cannot be directly determined experimentally and must be constrained by an optimization procedure. A common practice in evaluating the utility of such procedures is using a previously developed model to generate surrogate data (e.g., traces of spikes following step current pulses) and then challenging the algorithm to recover the original parameters (e.g., the value of maximal ion channel conductances) that were used to generate the data. In this fashion, the success or failure of the model fitting procedure to find the original parameters can be easily determined. Here we show that some model fitting procedures that provide an excellent fit in the case of such model-to-model comparisons provide ill-balanced results when applied to experimental data. The main reason is that surrogate and experimental data test different aspects of the algorithm’s function. When considering model-generated surrogate data, the algorithm is required to locate a perfect solution that is known to exist. In contrast, when considering experimental target data, there is no guarantee that a perfect solution is part of the search space. In this case, the optimization procedure must rank all imperfect approximations and ultimately select the best approximation. This aspect is not tested at all when considering surrogate data since at least one perfect solution is known to exist (the original parameters) making all approximations unnecessary. Furthermore, we demonstrate that distance functions based on extracting a set of features from the target data (such as time-to-first-spike, spike width, spike frequency, etc.)–rather than using the original data (e.g., the whole spike trace) as the target for fitting-are capable of finding imperfect solutions that are good approximations of the experimental data.

Success in the projects aimed at providing an advanced understanding of the brain is directly predicated on making critical advances in nanotechnology. This Perspective addresses the unique interface of neuroscience and nanomaterials by considering the foundational problem of sensing neuron membrane voltage and offers a potential solution that may be facilitated by a prototypical nanomaterial. Despite substantial improvements, the visualization of instantaneous voltage changes within individual neurons, whether in cell culture or in vivo, at both the single-cell and network level at high speed remains complex and problematic. The unique properties of semiconductor quantum dots (QDs) have made them powerful fluorophores for bioimaging. What is not widely appreciated, however, is that QD photoluminescence is exquisitely sensitive to proximal electric fields. This property should be suitable for sensing voltage changes that occur in the active neuronal membrane. Here, we examine the potential role of QDs in addressing the important challenge of real-time optical voltage imaging.