Large analysis of genetic manipulations reveals an inverse correlation between initial alcohol resistance and rapid tolerance phenotypes.

Tolerance occurs when, following an initial experience with a substance, more of the substance is required subsequently to induce identical behavioral effects. Tolerance is not well-understood, and numerous researchers have turned to model organisms, particularly Drosophila melanogaster, to unravel its mechanisms. Flies have high translational relevance for human alcohol responses, and there is substantial overlap in disease-causing genes between flies and humans, including those associated with Alcohol Use Disorder. Numerous Drosophila tolerance mutants have been described; however, approaches used to identify and characterize these mutants have varied across time and labs and have mostly disregarded any impact of initial resistance/sensitivity to ethanol on subsequent tolerance development. Here, we analyzed our own, as well as data published by other labs to uncover an inverse correlation between initial ethanol resistance and tolerance phenotypes. This inverse correlation suggests that initial resistance phenotypes can explain many 'perceived' tolerance phenotypes, thus classifying such mutants as 'secondary' tolerance mutants. Additionally, we show that tolerance should be measured as a relative increase in time to sedation between an initial and second exposure rather than an absolute change in time to sedation. Finally, based on our analysis, we provide a method for using a linear regression equation to assess the residuals of potential tolerance mutants. These residuals provide predictive insight into the likelihood of a mutant being a 'primary' tolerance mutant, where a tolerance phenotype is not solely a consequence of initial resistance, and we offer a framework for understanding the relationship between initial resistance and tolerance.

Large genetic analysis of alcohol resistance and tolerance reveals an inverse correlation and suggests 'true' tolerance mutants.

Tolerance occurs when, following an initial experience with a substance, more of the substance is required subsequently to induce the same behavioral effects. Tolerance is historically not well-understood, and numerous researchers have turned to model organisms, particularly Drosophila melanogaster, to unravel its mechanisms. Flies have high translational relevance for human alcohol responses, and there is substantial overlap in disease-causing genes between flies and humans, including those associated with Alcohol Use Disorder. Numerous Drosophila tolerance mutants have been described; however, approaches used to identify and characterize these mutants have varied across time and between labs and have mostly disregarded any impact of initial resistance/sensitivity to ethanol on subsequent tolerance development. Here, we have analyzed a large amount of data - our own published and unpublished data and data published by other labs - to uncover an inverse correlation between initial ethanol resistance and tolerance phenotypes. This inverse correlation suggests that initial resistance phenotypes can explain many 'perceived' tolerance phenotypes. Additionally, we show that tolerance should be measured as a relative increase in time to sedation between an initial and second exposure rather than an absolute change in time to sedation. Finally, based on our analysis, we provide a method for using a linear regression equation to assess the residuals of potential tolerance mutants. We show that these residuals provide predictive insight into the likelihood of a mutant being a 'true' tolerance mutant, and we offer a framework for understanding the relationship between initial resistance and tolerance.

The Stage-Based Model of Addiction-Using Drosophila to Investigate Alcohol and Psychostimulant Responses.

Addiction is a progressive and complex disease that encompasses a wide range of disorders and symptoms, including substance use disorder (SUD), for which there are few therapeutic treatments. SUD is the uncontrolled and chronic use of substances despite the negative consequences resulting from this use. The progressive nature of addiction is organized into a testable framework, the neurobiological stage-based model, that includes three behavioral stages: (1) binge/intoxication, (2) withdrawal/negative affect, and (3) preoccupation/anticipation. Human studies offer limited opportunities for mechanistic insights into these; therefore, model organisms, like Drosophila melanogaster, are necessary for understanding SUD. Drosophila is a powerful model organism that displays a variety of SUD-like behaviors consistent with human and mammalian substance use, making flies a great candidate to study mechanisms of behavior. Additionally, there are an abundance of genetic tools like the GAL4/UAS and CRISPR/Cas9 systems that can be used to gain insight into the molecular mechanisms underlying the endophenotypes of the three-stage model. This review uses the three-stage framework and discusses how easily testable endophenotypes have been examined with experiments using Drosophila, and it outlines their potential for investigating other endophenotypes.

Differential encoding of temporal context and expectation under representational drift across hierarchically connected areas.

The classic view that neural populations in sensory cortices preferentially encode responses to incoming stimuli has been strongly challenged by recent experimental studies. Despite the fact that a large fraction of variance of visual responses in rodents can be attributed to behavioral state and movements, trial-history, and salience, the effects of contextual modulations and expectations on sensory-evoked responses in visual and association areas remain elusive. Here, we present a comprehensive experimental and theoretical study showing that hierarchically connected visual and association areas differentially encode the temporal context and expectation of naturalistic visual stimuli, consistent with the theory of hierarchical predictive coding. We measured neural responses to expected and unexpected sequences of natural scenes in the primary visual cortex (V1), the posterior medial higher order visual area (PM), and retrosplenial cortex (RSP) using 2-photon imaging in behaving mice collected through the Allen Institute Mindscope's OpenScope program. We found that information about image identity in neural population activity depended on the temporal context of transitions preceding each scene, and decreased along the hierarchy. Furthermore, our analyses revealed that the conjunctive encoding of temporal context and image identity was modulated by expectations of sequential events. In V1 and PM, we found enhanced and specific responses to unexpected oddball images, signaling stimulus-specific expectation violation. In contrast, in RSP the population response to oddball presentation recapitulated the missing expected image rather than the oddball image. These differential responses along the hierarchy are consistent with classic theories of hierarchical predictive coding whereby higher areas encode predictions and lower areas encode deviations from expectation. We further found evidence for drift in visual responses on the timescale of minutes. Although activity drift was present in all areas, population responses in V1 and PM, but not in RSP, maintained stable encoding of visual information and representational geometry. Instead we found that RSP drift was independent of stimulus information, suggesting a role in generating an internal model of the environment in the temporal domain. Overall, our results establish temporal context and expectation as substantial encoding dimensions in the visual cortex subject to fast representational drift and suggest that hierarchically connected areas instantiate a predictive coding mechanism.

Connecting single-cell transcriptomes to projectomes in mouse visual cortex.

The mammalian brain is composed of diverse neuron types that play different functional roles. Recent single-cell RNA sequencing approaches have led to a whole brain taxonomy of transcriptomically-defined cell types, yet cell type definitions that include multiple cellular properties can offer additional insights into a neuron's role in brain circuits. While the Patch-seq method can investigate how transcriptomic properties relate to the local morphological and electrophysiological properties of cell types, linking transcriptomic identities to long-range projections is a major unresolved challenge. To address this, we collected coordinated Patch-seq and whole brain morphology data sets of excitatory neurons in mouse visual cortex. From the Patch-seq data, we defined 16 integrated morpho-electric-transcriptomic (MET)-types; in parallel, we reconstructed the complete morphologies of 300 neurons. We unified the two data sets with a multi-step classifier, to integrate cell type assignments and interrogate cross-modality relationships. We find that transcriptomic variations within and across MET-types correspond with morphological and electrophysiological phenotypes. In addition, this variation, along with the anatomical location of the cell, can be used to predict the projection targets of individual neurons. We also shed new light on infragranular cell types and circuits, including cell-type-specific, interhemispheric projections. With this approach, we establish a comprehensive, integrated taxonomy of excitatory neuron types in mouse visual cortex and create a system for integrated, high-dimensional cell type classification that can be extended to the whole brain and potentially across species.

Survey of spiking in the mouse visual system reveals functional hierarchy.

The anatomy of the mammalian visual system, from the retina to the neocortex, is organized hierarchically1. However, direct observation of cellular-level functional interactions across this hierarchy is lacking due to the challenge of simultaneously recording activity across numerous regions. Here we describe a large, open dataset-part of the Allen Brain Observatory2-that surveys spiking from tens of thousands of units in six cortical and two thalamic regions in the brains of mice responding to a battery of visual stimuli. Using cross-correlation analysis, we reveal that the organization of inter-area functional connectivity during visual stimulation mirrors the anatomical hierarchy from the Allen Mouse Brain Connectivity Atlas3. We find that four classical hierarchical measures-response latency, receptive-field size, phase-locking to drifting gratings and response decay timescale-are all correlated with the hierarchy. Moreover, recordings obtained during a visual task reveal that the correlation between neural activity and behavioural choice also increases along the hierarchy. Our study provides a foundation for understanding coding and signal propagation across hierarchically organized cortical and thalamic visual areas.

The Neurotransmitters Involved in Drosophila Alcohol-Induced Behaviors.

Alcohol is a widely used and abused substance with numerous negative consequences for human health and safety. Historically, alcohol's widespread, non-specific neurobiological effects have made it a challenge to study in humans. Therefore, model organisms are a critical tool for unraveling the mechanisms of alcohol action and subsequent effects on behavior. Drosophila melanogaster is genetically tractable and displays a vast behavioral repertoire, making it a particularly good candidate for examining the neurobiology of alcohol responses. In addition to being experimentally amenable, Drosophila have high face and mechanistic validity: their alcohol-related behaviors are remarkably consistent with humans and other mammalian species, and they share numerous conserved neurotransmitters and signaling pathways. Flies have a long history in alcohol research, which has been enhanced in recent years by the development of tools that allow for manipulating individual Drosophila neurotransmitters. Through advancements such as the GAL4/UAS system and CRISPR/Cas9 mutagenesis, investigation of specific neurotransmitters in small subsets of neurons has become ever more achievable. In this review, we describe recent progress in understanding the contribution of seven neurotransmitters to fly behavior, focusing on their roles in alcohol response: dopamine, octopamine, tyramine, serotonin, glutamate, GABA, and acetylcholine. We chose these small-molecule neurotransmitters due to their conservation in mammals and their importance for behavior. While neurotransmitters like dopamine and octopamine have received significant research emphasis regarding their contributions to behavior, others, like glutamate, GABA, and acetylcholine, remain relatively unexplored. Here, we summarize recent genetic and behavioral findings concerning these seven neurotransmitters and their roles in the behavioral response to alcohol, highlighting the fitness of the fly as a model for human alcohol use.