Sleep restores an optimal computational regime in cortical networks.

Sleep is assumed to subserve homeostatic processes in the brain; however, the set point around which sleep tunes circuit computations is unknown. Slow-wave activity (SWA) is commonly used to reflect the homeostatic aspect of sleep; although it can indicate sleep pressure, it does not explain why animals need sleep. This study aimed to assess whether criticality may be the computational set point of sleep. By recording cortical neuron activity continuously for 10-14 d in freely behaving rats, we show that normal waking experience progressively disrupts criticality and that sleep functions to restore critical dynamics. Criticality is perturbed in a context-dependent manner, and waking experience is causal in driving these effects. The degree of deviation from criticality predicts future sleep/wake behavior more accurately than SWA, behavioral history or other neural measures. Our results demonstrate that perturbation and recovery of criticality is a network homeostatic mechanism consistent with the core, restorative function of sleep.

Detecting change points in neural population activity with contrastive metric learning.

Finding points in time where the distribution of neural responses changes (change points) is an important step in many neural data analysis pipelines. However, in complex and free behaviors, where we see different types of shifts occurring at different rates, it can be difficult to use existing methods for change point (CP) detection because they can't necessarily handle different types of changes that may occur in the underlying neural distribution. Additionally, response changes are often sparse in high dimensional neural recordings, which can make existing methods detect spurious changes. In this work, we introduce a new approach for finding changes in neural population states across diverse activities and arousal states occurring in free behavior. Our model follows a contrastive learning approach: we learn a metric for CP detection based on maximizing the Sinkhorn divergences of neuron firing rates across two sides of a labeled CP. We apply this method to a 12-hour neural recording of a freely behaving mouse to detect changes in sleep stages and behavior. We show that when we learn a metric, we can better detect change points and also yield insights into which neurons and sub-groups are important for detecting certain types of switches that occur in the brain.

Tauopathy severely disrupts homeostatic set-points in emergent neural dynamics but not in the activity of individual neurons.

The homeostatic regulation of neuronal activity is essential for robust computation; key set-points, such as firing rate, are actively stabilized to compensate for perturbations. From this perspective, the disruption of brain function central to neurodegenerative disease should reflect impairments of computationally essential set-points. Despite connecting neurodegeneration to functional outcomes, the impact of disease on set-points in neuronal activity is unknown. Here we present a comprehensive, theory-driven investigation of the effects of tau-mediated neurodegeneration on homeostatic set-points in neuronal activity. In a mouse model of tauopathy, we examine 27,000 hours of hippocampal recordings during free behavior throughout disease progression. Contrary to our initial hypothesis that tauopathy would impact set-points in spike rate and variance, we found that cell-level set-points are resilient to even the latest stages of disease. Instead, we find that tauopathy disrupts neuronal activity at the network-level, which we quantify using both pairwise measures of neuron interactions as well as measurement of the network's nearness to criticality, an ideal computational regime that is known to be a homeostatic set-point. We find that shifts in network criticality 1) track with symptoms, 2) predict underlying anatomical and molecular pathology, 3) occur in a sleep/wake dependent manner, and 4) can be used to reliably classify an animal's genotype. Our data suggest that the critical set-point is intact, but that homeostatic machinery is progressively incapable of stabilizing hippocampal networks, particularly during waking. This work illustrates how neurodegenerative processes can impact the computational capacity of neurobiological systems, and suggest an important connection between molecular pathology, circuit function, and animal behavior.

A non-oscillatory, millisecond-scale embedding of brain state provides insight into behavior.

Sleep and wake are understood to be slow, long-lasting processes that span the entire brain. Brain states correlate with many neurophysiological changes, yet the most robust and reliable signature of state is enriched in rhythms between 0.1 and 20 Hz. The possibility that the fundamental unit of brain state could be a reliable structure at the scale of milliseconds and microns has not been addressed due to the physical limits associated with oscillation-based definitions. Here, by analyzing high resolution neural activity recorded in 10 anatomically and functionally diverse regions of the murine brain over 24 h, we reveal a mechanistically distinct embedding of state in the brain. Sleep and wake states can be accurately classified from on the order of 100 to 101 ms of neuronal activity sampled from 100 µm of brain tissue. In contrast to canonical rhythms, this embedding persists above 1,000 Hz. This high frequency embedding is robust to substates and rapid events such as sharp wave ripples and cortical ON/OFF states. To ascertain whether such fast and local structure is meaningful, we leveraged our observation that individual circuits intermittently switch states independently of the rest of the brain. Brief state discontinuities in subsets of circuits correspond with brief behavioral discontinuities during both sleep and wake. Our results suggest that the fundamental unit of state in the brain is consistent with the spatial and temporal scale of neuronal computation, and that this resolution can contribute to an understanding of cognition and behavior.

Transcriptomic cell type structures in vivo neuronal activity across multiple timescales.

Cell type is hypothesized to be a key determinant of a neuron's role within a circuit. Here, we examine whether a neuron's transcriptomic type influences the timing of its activity. We develop a deep-learning architecture that learns features of interevent intervals across timescales (ms to >30 min). We show that transcriptomic cell-class information is embedded in the timing of single neuron activity in the intact brain of behaving animals (calcium imaging and extracellular electrophysiology) as well as in a bio-realistic model of the visual cortex. Further, a subset of excitatory cell types are distinguishable but can be classified with higher accuracy when considering cortical layer and projection class. Finally, we show that computational fingerprints of cell types may be universalizable across structured stimuli and naturalistic movies. Our results indicate that transcriptomic class and type may be imprinted in the timing of single neuron activity across diverse stimuli.

Directed evolution of Zymomonas mobilis sugar facilitator Glf to overcome glucose inhibition.

Cellular import of D-xylose, the second most abundant sugar in typical lignocellulosic biomass, has been evidenced to be an energy-depriving process in bacterial biocatalysts. The sugar facilitator of Zymomonas mobilis, Glf, is capable of importing xylose at high rates without extra energy input, but is inhibited by D-glucose (the primary biomass sugar), potentially limiting the utility of this transporter for fermentation of sugar mixtures derived from lignocellulose. In this work we developed an Escherichia coli platform strain deficient in glucose and xylose transport to facilitate directed evolution of Glf to overcome glucose inhibition. Using this platform, we isolated nine Glf variants created by both random and site-saturation mutagenesis with increased xylose utilization rates ranging from 4.8-fold to 13-fold relative to wild-type Glf when fermenting 100 g l-1 glucose-xylose mixtures. Diverse point mutations such as A165M and L445I were discovered leading to released glucose inhibition. Most of these mutations likely alter sugar coordinating pocket for the 6-hydroxymethyl group of D-glucose. These discovered glucose-resistant Glf variants can be potentially used as energy-conservative alternatives to the native sugar transport systems of bacterial biocatalysts for fermentation of lignocellulose-derived sugars.

Identification of major malate export systems in an engineered malate-producing Escherichia coli aided by substrate similarity search.

Optimization of export mechanisms for valuable extracellular products is important for the development of efficient microbial production processes. Identification of the relevant export mechanism is the prerequisite step for product export optimization. In this work, we identified transporters involved in malate export in an engineered L-malate-producing Escherichia coli strain using cheminformatics-guided genetics tests. Among all short-chain di- or tricarboxylates with known transporters in E. coli, citrate, tartrate, and succinate are most chemically similar to malate as estimated by their molecular signatures. Inactivation of three previously reported transporters for succinate, tartrate, and citrate, DcuA, TtdT, and CitT, respectively, dramatically decreased malate production and fermentative growth, suggesting that these transporters have substrate promiscuity for different short-chain organic acids and constitute the major malate export system in E. coli. Malate export deficiency led to an increase in cell sizes and accumulation of intracellular metabolites related to malate metabolism.

Parallel experimental evolution reveals a novel repressive control of GalP on xylose fermentation in Escherichia coli.

Efficient xylose utilization will facilitate microbial conversion of lignocellulosic sugar mixtures into valuable products. In Escherichia coli, xylose catabolism is controlled by carbon catabolite repression (CCR). However, in E. coli such as the succinate-producing strain KJ122 with disrupted CCR, xylose utilization is still inhibited under fermentative conditions. To probe the underlying genetic mechanisms inhibiting xylose utilization, we evolved KJ122 to enhance its xylose fermentation abilities in parallel and characterized the potential convergent genetic changes shared by multiple independently evolved strains. Whole-genome sequencing revealed that convergent mutations occurred in the galactose regulon during adaptive laboratory evolution potentially decreasing the transcriptional level or the activity of GalP, a galactose permease. We showed that deletion of galP increased xylose utilization in both KJ122 and wild-type E. coli, demonstrating a common repressive role of GalP for xylose fermentation. Concomitantly, induced expression of galP from a plasmid repressed xylose fermentation. Transcriptome analysis using RNA sequencing indicates that galP inactivation increases transcription levels of many catabolic genes for secondary sugars including xylose and arabinose. The repressive role of GalP for fermenting secondary sugars in E. coli suggests that utilization of GalP as a substitute glucose transporter is undesirable for conversion of lignocellulosic sugar mixtures.