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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. [ABSTRACT FROM AUTHOR]

Aromatic compounds, which are traditionally derived from petroleum feedstocks, represent a diverse class of molecules with a wide range of industrial and commercial applications. Significant progress has been made to alternatively and sustainably produce many aromatics from renewable substrates using microbial biocatalysts. While the construction of both natural and non‐natural pathways has expanded the number and diversity of aromatic bioproducts, pathway modularization in both single‐ and multi‐strain systems continues to support the enhancement of key production metrics towards economically‐viable levels. Emerging tools for implementing more precise metabolic control (e.g. CRISPRi, sRNA) as well as the engineering of novel high‐throughput screening platforms utilizing in vivo aromatic biosensors, meanwhile, continue to facilitate further optimization of both pathways and hosts. While product toxicity persists as a key challenge limiting the production of many aromatics, various successful strategies have been demonstrated towards improving tolerance, including via membrane and efflux pump engineering as well as by exploiting alternative production hosts. Finally, as a further step towards sustainable and economical aromatic bioproduction, non‐model substrates including lignin‐derived compounds continue to emerge as viable feedstocks. This review highlights recent and notable achievements related to such efforts while offering future outlooks towards engineering microbial cell factories for aromatic production. © 2018 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

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., Competing Interests: DECLARATION OF INTERESTS D.M.H. is on the scientific advisory board of C2N diagnostics and has equity. D.M.H. is on the scientific advisory board of Denali Therapeutics, Genentech, and Cajal Therapeutics and consults for Asteroid Therapeutics.

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 10 0 to 10 1 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., Competing Interests: Competing Interests No competing interests disclosed

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.