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.

Genetically encoded Ca indicators (GECIs) enable long-term monitoring of cellular and subcellular dynamics of this second messenger in response to environmental and developmental cues without relying on exogenous dyes. Continued development and optimization in GECIs, combined with advances in gene manipulation, offer new opportunities for investigating the mechanism of Ca signaling in fungi, ranging from documenting Ca signatures under diverse conditions and genetic backgrounds to evaluating how changes in Ca signature impact calcium-binding proteins and subsequent cellular changes. Here, we attempted to express multi-color (green, yellow, blue, cyan, and red) circularly permuted fluorescent protein (FP)-based Ca indicators driven by multiple fungal promoters in Fusarium oxysporum, F. graminearum, and Neurospora crassa. Several variants were successfully expressed, with GCaMP5G driven by the Magnaporthe oryzae ribosomal protein 27 (P) and F. verticillioides elongation factor-1α (P) gene promoters being optimal for F. graminearum and F. oxysporum, respectively. Transformants expressing GCaMP5G were compared with those expressing YC3.60, a ratiometric Cameleon Ca indicator. Wild-type and three Ca signaling mutants of F. graminearum expressing GCaMP5G exhibited improved signal-to-noise and increased temporal and spatial resolution and are also more amenable to studies involving multiple FPs compared to strains expressing YC3.60.

The K2 Summit camera was initially the only commercially available direct electron detection camera that was optimized for high-speed counting of primary electrons and was also the only one that implemented centroiding so that the resolution of the camera can be extended beyond the Nyquist limit set by the physical pixel size. In this study, we used well-characterized two-dimensional crystals of the membrane protein aquaporin-0 to characterize the performance of the camera below and beyond the physical Nyquist limit and to measure the influence of electron dose rate on image amplitudes and phases.

The efficient cytosolic delivery of proteins is critical for advancing novel therapeutic strategies. Current delivery methods are severely limited by endosomal entrapment, and detection methods lack sophistication in tracking the fate of delivered protein cargo. HaloTag, a commonly used protein in chemical biology and a challenging delivery target, is an exceptional model system for understanding and exploiting cellular delivery. Here, we employed a combinatorial strategy to direct HaloTag to the cytosol. We established the use of Virginia Orange, a pH-sensitive fluorophore, and Janelia Fluor 585, a similar but pH-agnostic fluorophore, in a fluorogenic assay to ascertain protein localization within human cells. Using this assay, we investigated HaloTag delivery upon modification with cell-penetrating peptides, carboxyl group esterification, and cotreatment with an endosomolytic agent. We found efficacious cytosolic entry with two distinct delivery methods. This study expands the toolkit for detecting the cytosolic access of proteins and highlights that multiple intracellular delivery strategies can be used synergistically to effect cytosolic access. Moreover, HaloTag is poised to serve as a platform for the delivery of varied cargo into human cells.

Efficient hydrolysis of amide bonds has long been a reaction of interest for organic chemists. The rate constants of proteases are unmatched by those of any synthetic catalyst. It has been proposed that a dipeptide containing serine and histidine is an effective catalyst of amide hydrolysis, based on an apparent ability to degrade a protein. The capacity of the Ser-His dipeptide to catalyze the hydrolysis of several discrete ester and amide substrates is investigated using previously described conditions. This dipeptide does not catalyze the hydrolysis of amide or unactivated ester groups in any of the substrates under the conditions evaluated.