Mechanisms of feedback inhibition and sequential firing of active sites in plant aspartate transcarbamoylase

Aspartate transcarbamoylase (ATC), an essential enzyme for de novo pyrimidine biosynthesis, is uniquely regulated in plants by feedback inhibition of uridine 5-monophosphate (UMP). Despite its importance in plant growth, the structure of this UMP-controlled ATC and the regulatory mechanism remain unknown. Here, we report the crystal structures of Arabidopsis ATC trimer free and bound to UMP, complexed to a transition-state analog or bearing a mutation that turns the enzyme insensitive to UMP. We found that UMP binds and blocks the ATC active site, directly competing with the binding of the substrates. We also prove that UMP recognition relies on a loop exclusively conserved in plants that is also responsible for the sequential firing of the active sites. In this work, we describe unique regulatory and catalytic properties of plant ATCs that could be exploited to modulate de novo pyrimidine synthesis and plant growth.

UMP inhibition and sequential firing in aspartate transcarbamoylase open ways to regulate plant growth

ABSTRACTPyrimidine nucleotides are essential to plant development. We proved that Arabidopsis growth can be inhibited or enhanced by down- or upregulating aspartate transcarbamoylase (ATC), the first committed enzyme for de novo biosynthesis of pyrimidines in plants. To understand the unique mechanism of feedback inhibition of this enzyme by uridine 5-monophosphate (UMP), we determined the crystal structure of the Arabidopsis ATC trimer free and bound to UMP, demonstrating that the nucleotide binds and blocks the active site. The regulatory mechanism relies on a loop exclusively conserved in plants, and a single-point mutation (F161A) turns ATC insensitive to UMP. Moreover, the structures in complex with a transition-state analog or with carbamoyl phosphate proved a mechanism in plant ATCs for sequential firing of the active sites. The disclosure of the unique regulatory and catalytic properties suggests new strategies to modulate ATC activity and to control de novo pyrimidine synthesis and plant growth.

Hippocampal ensembles represent sequential relationships among discrete nonspatial events

ABSTRACTThe hippocampus is critical to the temporal organization of our experiences, including the ability to remember past event sequences and predict future ones. Although this fundamental capacity is conserved across modalities and species, its underlying neuronal mechanisms remain poorly understood. Here we recorded hippocampal ensemble activity as rats remembered a sequence of nonspatial events (5 odor presentations unfolding over several seconds), using a task with established parallels in humans. Using novel statistical methods and deep learning techniques, we then identified new forms of sequential organization in hippocampal activity linked with task performance. We discovered that sequential firing fields (“time cells”) provided temporal information within and across events in the sequence, and that distinct types of task-critical information (stimulus identity, temporal order, and trial outcome) were also sequentially differentiated within event presentations. Finally, as previously only observed with spatial information, we report that the representations of past, present and future events were sequentially activated within individual event presentations, and that these sequential representations could be compressed within an individual theta cycle. These findings strongly suggest that a fundamental function of the hippocampal network is to encode and preserve the sequential order of experiences, and use these representations to generate predictions to inform decision-making.

Emergence of stable striatal D1R and D2R neuronal ensembles with distinct firing sequence during motor learning

The dorsolateral striatum (DLS) is essential for motor and procedure learning, but the role of DLS spiny projection neurons (SPNs) of direct and indirect pathways, as marked, respectively, by D1 and D2 receptor (D1R and D2R) expression, remains to be clarified. Long-term two-photon calcium imaging of the same neuronal population during mouse learning of a cued lever-pushing task revealed a gradual emergence of distinct D1R and D2R neuronal ensembles that reproducibly fired in a sequential manner, with more D1R and D2R neurons fired during the lever-pushing period and intertrial intervals (ITIs), respectively. This sequential firing pattern was specifically associated with the learned motor behavior, because it changed markedly when the trained mice performed other cued motor tasks. Selective chemogenetic silencing of D1R and D2R neurons impaired the initiation of learned motor action and suppression of erroneous lever pushing during ITIs, respectively. Thus, motor learning involves reorganization of DLS neuronal activity, forming stable D1R and D2R neuronal ensembles that fired sequentially to regulate different aspects of the learned behavior.

Artificial Neural Network Model Adopting Combinatorial Inhibition Process in Multiple Solution Problems

Exploration of Artificial Neural Network (ANN) research continually opens rooms for improvement and implementation of mathematical models to solve various problems. This research work was not only to direct on the objective of problem-solving, instead the goal is to mimic basic biological functions of the brain in problem-solving situations. The basic biological theories of “Selection”, “Combination” and “Inhibition” were successfully implemented in the earlier works. This work conceived another biological theory named “Sequential Firing” of neuron in solving complex problems like sum-of-subset problems. The non-modular combinatorial inhibition neural model has been proposed and implemented successfully using the time delayed sequential firing between neurons. As per the biological theories knowledge representation is a preliminary phase of learning. This work not only illustrates the sequential process of firing between neurons, it paves the way to utilize this neural model for the learning process.

Model of the songbird nucleus HVC as a network of central pattern generators

We propose a functional architecture of the adult songbird nucleus HVC in which the core element is a “functional syllable unit” (FSU). In this model, HVC is organized into FSUs, each of which provides the basis for the production of one syllable in vocalization. Within each FSU, the inhibitory neuron population takes one of two operational states: 1) simultaneous firing wherein all inhibitory neurons fire simultaneously, and 2) competitive firing of the inhibitory neurons. Switching between these basic modes of activity is accomplished via changes in the synaptic strengths among the inhibitory neurons. The inhibitory neurons connect to excitatory projection neurons such that during state 1 the activity of projection neurons is suppressed, while during state 2 patterns of sequential firing of projection neurons can occur. The latter state is stabilized by feedback from the projection to the inhibitory neurons. Song composition for specific species is distinguished by the manner in which different FSUs are functionally connected to each other. Ours is a computational model built with biophysically based neurons. We illustrate that many observations of HVC activity are explained by the dynamics of the proposed population of FSUs, and we identify aspects of the model that are currently testable experimentally. In addition, and standing apart from the core features of an FSU, we propose that the transition between modes may be governed by the biophysical mechanism of neuromodulation.

Synchronized excitability in a network enables generation of internal neuronal sequences

Hippocampal place field sequences are supported by sensory cues and network internal mechanisms. In contrast, sharp-wave (SPW) sequences, theta sequences, and episode field sequences are internally generated. The relationship of these sequences to memory is unclear. SPW sequences have been shown to support learning and have been assumed to also support episodic memory. Conversely, we demonstrate these SPW sequences were present in trained rats even after episodic memory was impaired and after other internal sequences – episode field and theta sequences – were eliminated. SPW sequences did not support memory despite continuing to ‘replay’ all task-related sequences – place- field and episode field sequences. Sequence replay occurred selectively during synchronous increases of population excitability -- SPWs. Similarly, theta sequences depended on the presence of repeated synchronized waves of excitability – theta oscillations. Thus, we suggest that either intermittent or rhythmic synchronized changes of excitability trigger sequential firing of neurons, which in turn supports learning and/or memory.