Local and dynamic regulation of neuronal glycolysis in vivo.

Energy metabolism supports neuronal function. While it is well established that changes in energy metabolism underpin brain plasticity and function, less is known about how individual neurons modulate their metabolic states to meet varying energy demands. This is because most approaches used to examine metabolism in living organisms lack the resolution to visualize energy metabolism within individual circuits, cells, or subcellular regions. Here, we adapted a biosensor for glycolysis, HYlight, for use in Caenorhabditis elegans to image dynamic changes in glycolysis within individual neurons and in vivo. We determined that neurons cell-autonomously perform glycolysis and modulate glycolytic states upon energy stress. By examining glycolysis in specific neurons, we documented a neuronal energy landscape comprising three general observations: 1) glycolytic states in neurons are diverse across individual cell types; 2) for a given condition, glycolytic states within individual neurons are reproducible across animals; and 3) for varying conditions of energy stress, glycolytic states are plastic and adapt to energy demands. Through genetic analyses, we uncovered roles for regulatory enzymes and mitochondrial localization in the cellular and subcellular dynamic regulation of glycolysis. Our study demonstrates the use of a single-cell glycolytic biosensor to examine how energy metabolism is distributed across cells and coupled to dynamic states of neuronal function and uncovers unique relationships between neuronal identities and metabolic landscapes in vivo.

Monitoring glycolytic dynamics in single cells using a fluorescent biosensor for fructose 1,6-bisphosphate.

Cellular metabolism is regulated over space and time to ensure that energy production is efficiently matched with consumption. Fluorescent biosensors are useful tools for studying metabolism as they enable real-time detection of metabolite abundance with single-cell resolution. For monitoring glycolysis, the intermediate fructose 1,6-bisphosphate (FBP) is a particularly informative signal as its concentration is strongly correlated with flux through the whole pathway. Using GFP insertion into the ligand-binding domain of the Bacillus subtilis transcriptional regulator CggR, we developed a fluorescent biosensor for FBP termed HYlight. We demonstrate that HYlight can reliably report the real-time dynamics of glycolysis in living cells and tissues, driven by various metabolic or pharmacological perturbations, alone or in combination with other physiologically relevant signals. Using this sensor, we uncovered previously unknown aspects of ß-cell glycolytic heterogeneity and dynamics.

Performance on the mouse vibration actuating search task is compromised by sleep deprivation.

As we go about our daily routines we are continuously bombarded with environmental feedback that requires appraisal and response. Sleep loss can compromise the efficiency by which these cognitive processes function. Operationally, poor performance caused by insufficient sleep translates to increased health and safety risks in settings where attention and timely and/or accurate decisions to respond are critical (e.g., at work, on the road, etc.). Current rodent tasks that assess altered cognition after sleep deprivation (SD) do not accurately model the continuous multisensory feedback that informs goal-oriented behavior in humans. Herein, we describe the vibration actuating search task (VAST), which consists of a vibrating open field with pseudo-randomly selected entrance and target destination points. To successfully complete a trial, mice use feedback from rotary motor-induced floor vibrations to navigate from the entrance point to the target destination. Sets of 20 trials were conducted on 3 consecutive days, and before testing on the third day control mice were undisturbed while other mice were sleep deprived for 10 h. On the first 2 days mice learned the task with high success rates. Alternatively, VAST performance was compromised following SD as measured by increased failures in task completion, time to target, time spent immobile, and decreased speed as compared with undisturbed mice. The VAST enables the analysis of continuous feedback via multiple sensory modalities in mice and is applicable to a variety of operational settings.NEW & NOTEWORTHY The vibration actuating search task (VAST) is a novel performance assay that uses continuous auditory and haptic feedback to motivate and direct search behaviors in mice. The VAST is rapidly acquired by mice and performance is disrupted by sleep deprivation. The VAST has practical application in occupational settings. The cognitive aspects of the sensorimotor integration in the VAST may prove useful for rodent models of neurodegenerative disease.

Removal of unwanted variation reveals novel patterns of gene expression linked to sleep homeostasis in murine cortex.

BACKGROUND: Why we sleep is still one of the most perplexing mysteries in biology. Strong evidence indicates that sleep is necessary for normal brain function and that sleep need is a tightly regulated process. Surprisingly, molecular mechanisms that determine sleep need are incompletely described. Moreover, very little is known about transcriptional changes that specifically accompany the accumulation and discharge of sleep need. Several studies have characterized differential gene expression changes following sleep deprivation. Much less is known, however, about changes in gene expression during the compensatory response to sleep deprivation (i.e. recovery sleep). RESULTS: In this study we present a comprehensive analysis of the effects of sleep deprivation and subsequent recovery sleep on gene expression in the mouse cortex. We used a non-traditional analytical method for normalization of genome-wide gene expression data, Removal of Unwanted Variation (RUV). RUV improves detection of differential gene expression following sleep deprivation. We also show that RUV normalization is crucial to the discovery of differentially expressed genes associated with recovery sleep. Our analysis indicates that the majority of transcripts upregulated by sleep deprivation require 6 h of recovery sleep to return to baseline levels, while the majority of downregulated transcripts return to baseline levels within 1-3 h. We also find that transcripts that change rapidly during recovery (i.e. within 3 h) do so on average with a time constant that is similar to the time constant for the discharge of sleep need. CONCLUSIONS: We demonstrate that proper data normalization is essential to identify changes in gene expression that are specifically linked to sleep deprivation and recovery sleep. Our results provide the first evidence that recovery sleep is comprised of two waves of transcriptional regulation that occur at different times and affect functionally distinct classes of genes.

P2X7 receptors in body temperature, locomotor activity, and brain mRNA and lncRNA responses to sleep deprivation.

The ionotropic purine type 2X7 receptor (P2X7R) is a nonspecific cation channel implicated in sleep regulation and brain cytokine release. Many endogenous rhythms covary with sleep, including locomotor activity and core body temperature. Furthermore, brain-hypothalamic cytokines and purines play a role in the regulation of these physiological parameters as well as sleep. We hypothesized that these parameters are also affected by the absence of the P2X7 receptor. Herein, we determine spontaneous expression of body temperature and locomotor activity in wild-type (WT) and P2X7R knockout (KO) mice and how they are affected by sleep deprivation (SD). We also compare hypothalamic, hippocampal, and cortical cytokine- and purine-related receptor and enzyme mRNA expressions before and after SD in WT and P2X7RKO mice. Next, in a hypothesis-generating survey of hypothalamic long noncoding (lnc) RNAs, we compare lncRNA expression levels between strains and after SD. During baseline conditions, P2X7RKO mice had attenuated temperature rhythms compared with WT mice, although locomotor activity patterns were similar in both strains. After 6 h of SD, body temperature and locomotion were enhanced to a greater extent in P2X7RKO mice than in WT mice during the initial 2-3 h after SD. Baseline mRNA levels of cortical TNF-α and P2X4R were higher in the KO mice than WT mice. In response to SD, the KO mice failed to increase hypothalamic adenosine deaminase and P2X4R mRNAs. Further, hypothalamic lncRNA expressions varied by strain, and with SD. Current data are consistent with a role for the P2X7R in thermoregulation and lncRNA involvement in purinergic signaling.

Contextual fear conditioning induces differential alternative splicing.

The process of memory consolidation requires transcription and translation to form long-term memories. Significant effort has been dedicated to understanding changes in hippocampal gene expression after contextual fear conditioning. However, alternative splicing by differential transcript regulation during this time period has received less attention. Here, we use RNA-seq to determine exon-level changes in expression after contextual fear conditioning and retrieval. Our work reveals that a short variant of Homer1, Ania-3, is regulated by contextual fear conditioning. The ribosome biogenesis regulator Las1l, small nucleolar RNA Snord14e, and the RNA-binding protein Rbm3 also change specific transcript usage after fear conditioning. The changes in Ania-3 and Las1l are specific to either the new context or the context-shock association, while the changes in Rbm3 occur after context or shock only. Our analysis revealed novel transcript regulation of previously undetected changes after learning, revealing the importance of high throughput sequencing approaches in the study of gene expression changes after learning.

Neuronal activity-driven O-GlcNAcylation promotes mitochondrial plasticity.

Neuronal activity is an energy-intensive process that is largely sustained by instantaneous fuel utilization and ATP synthesis. However, how neurons couple ATP synthesis rate to fuel availability is largely unknown. Here, we demonstrate that the metabolic sensor enzyme O-linked N-acetyl glucosamine (O-GlcNAc) transferase regulates neuronal activity-driven mitochondrial bioenergetics in hippocampal and cortical neurons. We show that neuronal activity upregulates O-GlcNAcylation in mitochondria. Mitochondrial O-GlcNAcylation is promoted by activity-driven glucose consumption, which allows neurons to compensate for high energy expenditure based on fuel availability. To determine the proteins that are responsible for these adjustments, we mapped the mitochondrial O-GlcNAcome of neurons. Finally, we determine that neurons fail to meet activity-driven metabolic demand when O-GlcNAcylation dynamics are prevented. Our findings suggest that O-GlcNAcylation provides a fuel-dependent feedforward control mechanism in neurons to optimize mitochondrial performance based on neuronal activity. This mechanism thereby couples neuronal metabolism to mitochondrial bioenergetics and plays a key role in sustaining energy homeostasis.

Neuronal activity-driven O-GlcNAcylation promotes mitochondrial plasticity.

Neuronal activity is an energy-intensive process that is largely sustained by instantaneous fuel utilization and ATP synthesis. However, how neurons couple ATP synthesis rate to fuel availability is largely unknown. Here, we demonstrate that the metabolic sensor enzyme O-GlcNAc transferase regulates neuronal activity-driven mitochondrial bioenergetics. We show that neuronal activity upregulates O-GlcNAcylation mainly in mitochondria. Mitochondrial O-GlcNAcylation is promoted by activity-driven fuel consumption, which allows neurons to compensate for high energy expenditure based on fuel availability. To determine the proteins that are responsible for these adjustments, we mapped the mitochondrial O-GlcNAcome of neurons. Finally, we determine that neurons fail to meet activity-driven metabolic demand when O-GlcNAcylation dynamics are prevented. Our findings suggest that O-GlcNAcylation provides a fuel-dependent feedforward control mechanism in neurons to optimize mitochondrial performance based on neuronal activity. This mechanism thereby couples neuronal metabolism to mitochondrial bioenergetics and plays a key role in sustaining energy homeostasis.

Local and dynamic regulation of neuronal glycolysis in vivo.

Energy metabolism supports neuronal function. While it is well established that changes in energy metabolism underpin brain plasticity and function, less is known about how individual neurons modulate their metabolic states to meet varying energy demands. This is because most approaches used to examine metabolism in living organisms lack the resolution to visualize energy metabolism within individual circuits, cells, or subcellular regions. Here we adapted a biosensor for glycolysis, HYlight, for use in C. elegans to image dynamic changes in glycolysis within individual neurons and in vivo. We determined that neurons perform glycolysis cell-autonomously, and modulate glycolytic states upon energy stress. By examining glycolysis in specific neurons, we documented a neuronal energy landscape comprising three general observations: 1) glycolytic states in neurons are diverse across individual cell types; 2) for a given condition, glycolytic states within individual neurons are reproducible across animals; and 3) for varying conditions of energy stress, glycolytic states are plastic and adapt to energy demands. Through genetic analyses, we uncovered roles for regulatory enzymes and mitochondrial localization in the cellular and subcellular dynamic regulation of glycolysis. Our study demonstrates the use of a single-cell glycolytic biosensor to examine how energy metabolism is distributed across cells and coupled to dynamic states of neuronal function, and uncovers new relationships between neuronal identities and metabolic landscapes in vivo.

A Sort-Seq Approach to the Development of Single Fluorescent Protein Biosensors.

Motivated by the growing importance of single fluorescent protein biosensors (SFPBs) in biological research and the difficulty in rationally engineering these tools, we sought to increase the rate at which SFPB designs can be optimized. SFPBs generally consist of three components: a circularly permuted fluorescent protein, a ligand-binding domain, and linkers connecting the two domains. In the absence of predictive methods for biosensor engineering, most designs combining these three components will fail to produce allosteric coupling between ligand binding and fluorescence emission. While methods to construct diverse libraries with variation in the site of GFP insertion and linker sequences have been developed, the remaining bottleneck is the ability to test these libraries for functional biosensors. We address this challenge by applying a massively parallel assay termed "sort-seq," which combines binned fluorescence-activated cell sorting, next-generation sequencing, and maximum likelihood estimation to quantify the brightness and dynamic range for many biosensor variants in parallel. We applied this method to two common biosensor optimization tasks: the choice of insertion site and optimization of linker sequences. The sort-seq assay applied to a maltose-binding protein domain-insertion library not only identified previously described high-dynamic-range variants but also discovered new functional insertion sites with diverse properties. A sort-seq assay performed on a pyruvate biosensor linker library expressed in mammalian cell culture identified linker variants with substantially improved dynamic range. Machine learning models trained on the resulting data can predict dynamic range from linker sequences. This high-throughput approach will accelerate the design and optimization of SFPBs, expanding the biosensor toolbox.

Learning-dependent chromatin remodeling highlights noncoding regulatory regions linked to autism.

Autism spectrum disorder (ASD) is a prevalent neurodevelopmental disorder that is associated with genetic risk factors. Most human disease-associated single-nucleotide polymorphisms (SNPs) are not located in genes but rather are in regulatory regions that control gene expression. The function of regulatory regions is determined through epigenetic mechanisms. Parallels between the cellular basis of development and the formation of long-term memory have long been recognized, particularly the role of epigenetic mechanisms in both processes. We analyzed how learning alters chromatin accessibility in the mouse hippocampus using a new high-throughput sequencing bioinformatics strategy we call DEScan (differential enrichment scan). DEScan, which enabled the analysis of data from epigenomic experiments containing multiple replicates, revealed changes in chromatin accessibility at 2365 regulatory regions-most of which were promoters. Learning-regulated promoters were active during forebrain development in mice and were enriched in epigenetic modifications indicative of bivalent promoters. These promoters were disproportionally intronic, showed a complex relationship with gene expression and alternative splicing during memory consolidation and retrieval, and were enriched in the data set relative to known ASD risk genes. Genotyping in a clinical cohort within one of these promoters (SHANK3 promoter 6) revealed that the SNP rs6010065 was associated with ASD. Our data support the idea that learning recapitulates development at the epigenetic level and demonstrate that behaviorally induced epigenetic changes in mice can highlight regulatory regions relevant to brain disorders in patients.