Class Ia ribonucleotide reductases (RNRs) are subject to allosteric regulation to maintain the appropriate deoxyribonucleotide levels for accurate DNA biosynthesis and repair. RNR activity requires a precise alignment of its α2 and β2 subunits such that a catalytically-essential radical species is transferred from β2 to α2. In E. coli, when too many deoxyribonucleotides are produced, dATP binding to RNR generates an inactive α4β4 state in which β2 and α2 are separated, preventing radical transfer. ATP binding breaks the α−β interface, freeing β2 and restoring activity. Here we investigate the molecular basis for allosteric activity regulation in the prototypic E. coli class Ia RNR. Through the determination of six crystal structures we are able to establish how dATP binding creates a binding pocket for β on α that traps β2 in the inactive α4β4 state. These structural snapshots also reveal the numerous ATP-induced conformational rearrangements that are responsible for freeing β2. We further discover, and validate through binding and mutagenesis studies, a previously unknown nucleotide binding site on the α subunit that is crucial for the ability of ATP to dismantle the inactive α4β4 state. These findings have implications for the design of allosteric inhibitors for bacterial RNRs.

The role of dopamine as a reward prediction error signal in reinforcement learning tasks has been well-established over the past decades. Recent work has shown that the reward prediction error interpretation can also account for the effects of dopamine on interval timing by controlling the speed of subjective time. According to this theory, the timing of the dopamine signal relative to reward delivery dictates whether subjective time speeds up or slows down: Early DA signals speed up subjective time and late signals slow it down. To test this bidirectional prediction, we reanalyzed measurements of dopaminergic neurons in the substantia nigra pars compacta of mice performing a self-timed movement task. Using the slope of ramping dopamine activity as a read-out of subjective time speed, we found that trial-by-trial changes in the slope could be predicted from the timing of dopamine activity on the previous trial. This result provides a key piece of evidence supporting a unified computational theory of reinforcement learning and interval timing.

Clues from human movement disorders have long suggested that the neurotransmitter dopamine plays a key role in motor control, but how the endogenous dopaminergic system regulates movement is unknown. Here we show dynamic dopaminergic signaling over seconds-long timescales controls movement timing in mice. Animals were trained to initiate licking after a self-timed interval following a start-timing cue. The movement time was variable from trial-to-trial, as expected from previous studies. Surprisingly, dopaminergic signals ramped-up over seconds between the start-timing cue and the self-timed movement, with variable dynamics that predicted the movement time on single trials. Steeply rising signals preceded early lick-initiation, whereas slowly rising signals preceded later initiation. Higher baseline signals also predicted earlier self-timed movements. Optogenetic activation of dopamine neurons during self-timing did not trigger immediate movements, but rather caused systematic early-shifting of movement initiation, whereas inhibition caused late-shifting, as if modulating the probability of movement. Consistent with this view, the dynamics of the endogenous dopaminergic signals quantitatively predicted the moment-by-moment probability of movement initiation on single trials. These results reveal a causal role for dynamic dopaminergic signaling unfolding over seconds in modulating the decision of when to move.

Significance Type 1 diabetes (T1D) is characterized by the autoimmune destruction of pancreatic β cells and it burdens millions worldwide. T1D patients typically require the life-long administration of insulin or immunosuppressive agents following transplantation. A macroencapsulation device (MED) acts as a bioartificial pancreas and can immunoprotect encapsulated β cells. However, conventional MEDs suffer from limited, cell-loading capacity and slow, glucose-stimulated insulin secretion (GSIS) because of the sole reliance on diffusion. Here, we developed a convection-enhanced MED (ceMED) to afford 3D capsule geometry for maximized cell loading and faster GSIS driven by convection. Overall, we demonstrated that the ceMED significantly improves nutrient exchange that enhances cell viability and GSIS, ultimately leading to a rapid reduction of hyperglycemia.

Dopaminergic neurons (DANs) exhibit complex dynamics across a variety of behavioral contexts, often in ways that seem task-specific and even incompatible with results across different paradigms. Dopaminergic signaling during timing tasks has been a prime example. In behavioral timing, dopaminergic dynamics predict the initiation of self-timed movement via a seconds-long ramp up of activity prior to movement onset, similar to ramping seen in visuospatial reward approach and multi-step, goal-directed behaviors. By contrast, in perceptual timing, DANs exhibit more complex dynamics whose direction of modulation seems to be the opposite of that observed in behavioral timing. [Mikhael et al. (2022)][1] recently proposed a formal model in which dopaminergic dynamics encode reward expectation in the form of an “ongoing” reward-prediction error (RPE) that arises from resolving uncertainty of one’s position in the value landscape (i.e., one’s spatial-temporal distance to reward delivery/omission). Here, we show that application of this framework recapitulates and reconciles the seemingly contradictory dopaminergic dynamics observed in behavioral vs perceptual timing. These results suggest a common neural mechanism that broadly underlies timing behavior: trial-by-trial variation in the rate of the internal “pacemaker,” manifested in DAN signals that reflect stretching or compression of the derivative of the subjective value function relative to veridical time. In this view, faster pacemaking is associated with relatively high amplitude dopaminergic signaling, whereas slower pacemaking is associated with relatively low levels of dopaminergic signaling, consistent with findings from pharmacological and lesion studies.### Competing Interest StatementThe authors have declared no competing interest. [1]: #ref-21

OBJECTIVE:An emerging paradigm for understanding how anesthetics induce altered arousal is relating receptor targeting in specific neural circuits to electroencephalogram (EEG) activity. Enhanced gamma amino-butyric acid A (GABAA) inhibitory post-synaptic currents (IPSCs) manifest with large-amplitude slow (0.1-1Hz) and frontally coherent alpha (8-12Hz) EEG oscillations during general anesthesia. Therefore, we investigated the EEG signatures of modern day derivatives of ether (MDDE) anesthesia to assess the extent to which we could obtain insights into MDDE anesthetic mechanisms.METHODS:We retrospectively studied cases from our database in which patients received isoflurane anesthesia vs. isoflurane/ketamine anesthesia (n=10 each) or desflurane anesthesia vs. desflurane/ketamine anesthesia (n=9 each). We analyzed the EEG recordings with spectral power and coherence methods.RESULTS:Similar to known GABAA circuit level mechanisms, we found that MDDE anesthesia induced large amplitude slow and frontally coherent alpha oscillations. Additionally, MDDE anesthesia also induced frontally coherent theta (4-8Hz) oscillations. Reduction of GABAergic IPSCs with ketamine resulted in beta/gamma (13-40Hz) oscillations, and significantly reduced MDDE anesthesia-induced slow, theta and alpha oscillation power.CONCLUSIONS:Large amplitude slow oscillations and coherent alpha and theta oscillations are moderated by ketamine during MDDE anesthesia.SIGNIFICANCE:These observations are consistent with the notion that GABAA circuit-level mechanisms are associated with MDDE anesthesia-induced unconsciousness.

OBJECTIVES:Ketamine is an N-methyl-d-aspartate (NMDA) receptor antagonist commonly administered as a general anesthetic. However, neural circuit mechanisms to explain ketamine anesthesia-induced unconsciousness in humans are yet to be clearly defined. Disruption of frontal-parietal network connectivity has been proposed as a mechanism to explain this brain state. However, this mechanism was recently demonstrated at subanesthetic doses of ketamine in awake-patients. Therefore, we investigated whether there is an electroencephalogram (EEG) signature specific for ketamine anesthesia-induced unconsciousness. METHODS:We retrospectively studied the EEG in 12 patients who received ketamine for the induction of general anesthesia. We analyzed the EEG dynamics using power spectral and coherence methods. RESULTS:Following the administration of a bolus dose of ketamine to induce unconsciousness, we observed a "gamma burst" EEG pattern that consisted of alternating slow-delta (0.1-4Hz) and gamma (∼27-40Hz) oscillations. This pattern was also associated with increased theta oscillations (∼4-8Hz) and decreased alpha/beta oscillations (∼10-24Hz). CONCLUSIONS:Ketamine anesthesia-induced unconsciousness is associated with a gamma burst EEG pattern. SIGNIFICANCE:The EEG signature of ketamine anesthesia-induced unconsciousness may offer new insights into NMDA circuit mechanisms for unconsciousness.

Objective— An emerging paradigm for understanding how anesthetics induce altered arousal is relating receptor targeting in specific neural circuits to electroencephalogram (EEG) activity. We have previously found that enhanced gamma amino-butyric acid A (GABA A ) inhibitory postsynaptic currents (IPSCs) manifest with large-amplitude slow (0.1–1 Hz) and frontally coherent alpha (8–12 Hz) oscillations. Therefore, we investigated the EEG signatures of modern day derivatives of ether (MDDE) to assess the extent to which we could obtain insights into MDDE anesthetic mechanisms Methods— We retrospectively studied cases from our database in which patients received isoflurane vs. isoflurane/ketamine (n=10 each) or desflurane vs. desflurane/ketamine (n = 9 each). We analyzed the EEG recordings with spectral power and coherence methods. Results— Similar to known GABA A circuit level mechanisms, we found that MDDE induced large amplitude slow and frontally coherent alpha oscillations. Additionally, MDDE also induced frontally coherent theta (4–8 Hz) oscillations. Reduction of GABAergic IPSCs with ketamine resulted in beta/gamma (13–40 Hz) oscillations, and significantly reduced MDDE-induced slow, theta and alpha oscillation power. Conclusions— MDDE are associated with large amplitude slow oscillations and coherent alpha and theta oscillations. Significance— These observations are consistent with the notion that GABA A circuit-level mechanisms are associated with MDDE-induced unconsciousness. the NMDA circuit level mechanisms both MDDE- and ketamine-induced coherent theta oscillations. A prediction of a similar mechanism for MDDE-induced theta oscillations and ketamine-induced theta oscillations is that the MDDE induced theta oscillations should be potentiated by ketamine. However, we found that ketamine did not potentiate MDDE-induced theta oscillations. Rather, ketamine decreased the power and coherence of these oscillations. Therefore, our results suggest that different mechanisms are responsible for MDDE-induced theta oscillations and ketamine-induced theta oscillations. This distinct EEG dynamic may be studied further to provide additional insights into the molecular and neural circuit mechanisms of the human theta rhythm that is observed during the anesthesia-induced brain states.

Derived from any somatic cell type and possessing unlimited self-renewal and differentiation potential, induced pluripotent stem cells (iPSCs) are poised to revolutionize stem cell biology and regenerative medicine research, bringing unprecedented opportunities for treating debilitating human diseases. To overcome the limitations associated with safety, efficiency, and scalability of traditional iPSC derivation, expansion, and differentiation protocols, biomaterials have recently been considered. Beyond addressing these limitations, the integration of biomaterials with existing iPSC culture platforms could offer additional opportunities to better probe the biology and control the behavior of iPSCs or their progeny in vitro and in vivo. Herein, we discuss the impact of biomaterials on the iPSC field, from derivation to tissue regeneration and modeling. Although still exploratory, we envision the emerging combination of biomaterials and iPSCs will be critical in the successful application of iPSCs and their progeny for research and clinical translation.

Deciding when to move is a universal aspect of behavior. Pharmacological studies implicate the neurotransmitter dopamine as a regulator of self-timed movements, with increased dopamine availability generally leading to earlier movements, as if speeding an internal clock. How dopamine affects self-timed movements is unclear; a recent study even suggested that increased activity in nigrostriatal dopamine neurons (DANs) is associated with slower internal timing. Here we show the dynamics of DAN activity control the timing of self-timed movements in mice. Animals were trained to make a self-timed lick several seconds after a start-timing cue. Movement times were highly variable from trial-to-trial, typical for self-timed actions. Higher pre-trial DAN signals predicted earlier movements, consistent with pharmacological studies. However, surprisingly, DAN signals ramped-up over seconds following the start-timing cue, with the steepness of ramping predicting the trial-by-trial movement time. Steeply ramping signals preceded early lick-times whereas shallow ramping preceded later lick-times, reminiscent of a ramp-to-threshold process. Optogenetic DAN activation during the timed interval caused systematic early-shifting of self-timed movements, whereas inhibition caused systematic late-shifting. These results reveal a novel, causal role for dynamic DAN activity unfolding over seconds-long timescales in controlling the moment-by-moment decision of when to move.