SOX17 Enhancer Variants Disrupt Transcription Factor Binding And Enhancer Inactivity Drives Pulmonary Hypertension.

BACKGROUND: Pulmonary arterial hypertension (PAH) is a rare disease characterized by remodeling of the pulmonary arteries, increased vascular resistance, and right-sided heart failure. Genome-wide association studies of idiopathic/heritable PAH established novel genetic risk variants, including conserved enhancers upstream of transcription factor (TF) SOX17 containing 2 independent signals. SOX17 is an important TF in embryonic development and in the homeostasis of pulmonary artery endothelial cells (hPAEC) in the adult. Rare pathogenic mutations in SOX17 cause heritable PAH. We hypothesized that PAH risk alleles in an enhancer region impair TF-binding upstream of SOX17, which in turn reduces SOX17 expression and contributes to disturbed endothelial cell function and PAH development. METHODS: CRISPR manipulation and siRNA were used to modulate SOX17 expression. Electromobility shift assays were used to confirm in silico-predicted TF differential binding to the SOX17 variants. Functional assays in hPAECs were used to establish the biological consequences of SOX17 loss. In silico analysis with the connectivity map was used to predict compounds that rescue disturbed SOX17 signaling. Mice with deletion of the SOX17-signal 1 enhancer region (SOX17-4593/enhKO) were phenotyped in response to chronic hypoxia and SU5416/hypoxia. RESULTS: CRISPR inhibition of SOX17-signal 2 and deletion of SOX17-signal 1 specifically decreased SOX17 expression. Electromobility shift assays demonstrated differential binding of hPAEC nuclear proteins to the risk and nonrisk alleles from both SOX17 signals. Candidate TFs HOXA5 and ROR-α were identified through in silico analysis and antibody electromobility shift assays. Analysis of the hPAEC transcriptomes revealed alteration of PAH-relevant pathways on SOX17 silencing, including extracellular matrix regulation. SOX17 silencing in hPAECs resulted in increased apoptosis, proliferation, and disturbance of barrier function. With the use of the connectivity map, compounds were identified that reversed the SOX17-dysfunction transcriptomic signatures in hPAECs. SOX17 enhancer knockout in mice reduced lung SOX17 expression, resulting in more severe pulmonary vascular leak and hypoxia or SU5416/hypoxia-induced pulmonary hypertension. CONCLUSIONS: Common PAH risk variants upstream of the SOX17 promoter reduce endothelial SOX17 expression, at least in part, through differential binding of HOXA5 and ROR-α. Reduced SOX17 expression results in disturbed hPAEC function and PAH. Existing drug compounds can reverse the disturbed SOX17 pulmonary endothelial transcriptomic signature.

Modelling novelty detection in the thalamocortical loop.

In complex natural environments, sensory systems are constantly exposed to a large stream of inputs. Novel or rare stimuli, which are often associated with behaviorally important events, are typically processed differently than the steady sensory background, which has less relevance. Neural signatures of such differential processing, commonly referred to as novelty detection, have been identified on the level of EEG recordings as mismatch negativity (MMN) and on the level of single neurons as stimulus-specific adaptation (SSA). Here, we propose a multi-scale recurrent network with synaptic depression to explain how novelty detection can arise in the whisker-related part of the somatosensory thalamocortical loop. The "minimalistic" architecture and dynamics of the model presume that neurons in cortical layer 6 adapt, via synaptic depression, specifically to a frequently presented stimulus, resulting in reduced population activity in the corresponding cortical column when compared with the population activity evoked by a rare stimulus. This difference in population activity is then projected from the cortex to the thalamus and amplified through the interaction between neurons of the primary and reticular nuclei of the thalamus, resulting in rhythmic oscillations. These differentially activated thalamic oscillations are forwarded to cortical layer 4 as a late secondary response that is specific to rare stimuli that violate a particular stimulus pattern. Model results show a strong analogy between this late single neuron activity and EEG-based mismatch negativity in terms of their common sensitivity to presentation context and timescales of response latency, as observed experimentally. Our results indicate that adaptation in L6 can establish the thalamocortical dynamics that produce signatures of SSA and MMN and suggest a mechanistic model of novelty detection that could generalize to other sensory modalities.

Compensatory variability in network parameters enhances memory performance in the Drosophila mushroom body.

Neural circuits use homeostatic compensation to achieve consistent behavior despite variability in underlying intrinsic and network parameters. However, it remains unclear how compensation regulates variability across a population of the same type of neurons within an individual and what computational benefits might result from such compensation. We address these questions in the Drosophila mushroom body, the fly's olfactory memory center. In a computational model, we show that under sparse coding conditions, memory performance is degraded when the mushroom body's principal neurons, Kenyon cells (KCs), vary realistically in key parameters governing their excitability. However, memory performance is rescued while maintaining realistic variability if parameters compensate for each other to equalize KC average activity. Such compensation can be achieved through both activity-dependent and activity-independent mechanisms. Finally, we show that correlations predicted by our model's compensatory mechanisms appear in the Drosophila hemibrain connectome. These findings reveal compensatory variability in the mushroom body and describe its computational benefits for associative memory.

Signal neutrality, scalar property, and collapsing boundaries as consequences of a learned multi-timescale strategy.

We postulate that three fundamental elements underlie a decision making process: perception of time passing, information processing in multiple timescales and reward maximisation. We build a simple reinforcement learning agent upon these principles that we train on a random dot-like task. Our results, similar to the experimental data, demonstrate three emerging signatures. (1) signal neutrality: insensitivity to the signal coherence in the interval preceding the decision. (2) Scalar property: the mean of the response times varies widely for different signal coherences, yet the shape of the distributions stays almost unchanged. (3) Collapsing boundaries: the "effective" decision-making boundary changes over time in a manner reminiscent of the theoretical optimal. Removing the perception of time or the multiple timescales from the model does not preserve the distinguishing signatures. Our results suggest an alternative explanation for signal neutrality. We propose that it is not part of motor planning. It is part of the decision-making process and emerges from information processing on multiple timescales.

The pathophysiological role of novel pulmonary arterial hypertension gene SOX17.

Pulmonary arterial hypertension (PAH) is a progressive disease predominantly targeting pre-capillary blood vessels. Adverse structural remodelling and increased pulmonary vascular resistance result in cardiac hypertrophy and ultimately failure of the right ventricle. Recent whole-genome and whole-exome sequencing studies have identified SOX17 as a novel risk gene in PAH, with a dominant mode of inheritance and incomplete penetrance. Rare deleterious variants in the gene and more common variants in upstream enhancer sites have both been associated with the disease, and a deficiency of SOX17 expression may predispose to PAH. This review aims to consolidate the evidence linking genetic variants in SOX17 to PAH, and explores the numerous targets and effects of the transcription factor, focusing on the pulmonary vasculature and the pathobiology of PAH.

Non-numerical strategies used by bees to solve numerical cognition tasks.

We examined how bees solve a visual discrimination task with stimuli commonly used in numerical cognition studies. Bees performed well on the task, but additional tests showed that they had learned continuous (non-numerical) cues. A network model using biologically plausible visual feature filtering and a simple associative rule was capable of learning the task using only continuous cues inherent in the training stimuli, with no numerical processing. This model was also able to reproduce behaviours that have been considered in other studies indicative of numerical cognition. Our results support the idea that a sense of magnitude may be more primitive and basic than a sense of number. Our findings highlight how problematic inadvertent continuous cues can be for studies of numerical cognition. This remains a deep issue within the field that requires increased vigilance and cleverness from the experimenter. We suggest ways of better assessing numerical cognition in non-speaking animals, including assessing the use of all alternative cues in one test, using cross-modal cues, analysing behavioural responses to detect underlying strategies, and finding the neural substrate.

Myospryn deficiency leads to impaired cardiac structure and function and schizophrenia-associated symptoms.

The desmin-associated protein myospryn, encoded by the cardiomyopathy-associated gene 5 (CMYA5), is a TRIM-like protein associated to the BLOC-1 (Biogenesis of Lysosomes Related Organelles Complex 1) protein dysbindin. Human myospryn mutations are linked to both cardiomyopathy and schizophrenia; however, there is no evidence of a direct causative link of myospryn to these diseases. Therefore, we sought to unveil the role of myospryn in heart and brain. We have genetically inactivated the myospryn gene by homologous recombination and demonstrated that myospryn null hearts have dilated phenotype and compromised cardiac function. Ultrastructural analyses revealed that the sarcomere organization is not obviously affected; however, intercalated disk (ID) integrity is impaired, along with mislocalization of ID and sarcoplasmic reticulum (SR) protein components. Importantly, cardiac and skeletal muscles of myospryn null mice have severe mitochondrial defects with abnormal internal vacuoles and extensive cristolysis. In addition, swollen SR and T-tubules often accompany the mitochondrial defects, strongly implying a potential link of myospryn together with desmin to SR- mitochondrial physical and functional cross-talk. Furthermore, given the reported link of human myospryn mutations to schizophrenia, we performed behavioral studies, which demonstrated that myospryn-deficient male mice display disrupted startle reactivity and prepulse inhibition, asocial behavior, decreased exploratory behavior, and anhedonia. Brain neurochemical and ultrastructural analyses revealed prefrontal-striatal monoaminergic neurotransmitter defects and ultrastructural degenerative aberrations in cerebellar cytoarchitecture, respectively, in myospryn-deficient mice. In conclusion, myospryn is essential for both cardiac and brain structure and function and its deficiency leads to cardiomyopathy and schizophrenia-associated symptoms.

Constitutive differences in glucocorticoid responsiveness are related to divergent spatial information processing abilities.

The stress response facilitates survival through adaptation and is intimately related to cognitive processes. The Morris water maze task probes spatial learning and memory in rodents and glucocorticoids (i.e. corticosterone (CORT) in rats) have been suggested to elicit a facilitating action on memory formation. Moreover, the early aging period (around 16-18 months of age) is susceptible to stress- and glucocorticoid-mediated acceleration of cognitive decline. In this study, we tested three lines of rats selectively bred according to their individual differences in CORT responsiveness to repeated stress exposure during juvenility. We investigated whether endogenous differences in glucocorticoid responses influenced spatial learning, long-term memory, and reversal learning abilities in a Morris water maze task at early aging. Additionally, we assessed the quality of the different swimming strategies of the rats. Our results indicate that rats with differential CORT responsiveness exhibit similar spatial learning abilities but different long-term memory retention and reversal learning. Specifically, the high CORT responding line had a better long-term spatial memory, while the low CORT responding line was impaired for both long-term retention and reversal learning. Our modeling analysis of performance strategies revealed further important line-related differences. Therefore, our findings support the view that individuals with high CORT responsiveness would form stronger long-term memories to navigate in stressful environments. Conversely, individuals with low CORT responsiveness would be impaired at different phases of spatial learning and memory.

Abstract concept learning in a simple neural network inspired by the insect brain.

The capacity to learn abstract concepts such as 'sameness' and 'difference' is considered a higher-order cognitive function, typically thought to be dependent on top-down neocortical processing. It is therefore surprising that honey bees apparantly have this capacity. Here we report a model of the structures of the honey bee brain that can learn sameness and difference, as well as a range of complex and simple associative learning tasks. Our model is constrained by the known connections and properties of the mushroom body, including the protocerebral tract, and provides a good fit to the learning rates and performances of real bees in all tasks, including learning sameness and difference. The model proposes a novel mechanism for learning the abstract concepts of 'sameness' and 'difference' that is compatible with the insect brain, and is not dependent on top-down or executive control processing.

A Model for an Angular Velocity-Tuned Motion Detector Accounting for Deviations in the Corridor-Centering Response of the Bee.

We present a novel neurally based model for estimating angular velocity (AV) in the bee brain, capable of quantitatively reproducing experimental observations of visual odometry and corridor-centering in free-flying honeybees, including previously unaccounted for manipulations of behaviour. The model is fitted using electrophysiological data, and tested using behavioural data. Based on our model we suggest that the AV response can be considered as an evolutionary extension to the optomotor response. The detector is tested behaviourally in silico with the corridor-centering paradigm, where bees navigate down a corridor with gratings (square wave or sinusoidal) on the walls. When combined with an existing flight control algorithm the detector reproduces the invariance of the average flight path to the spatial frequency and contrast of the gratings, including deviations from perfect centering behaviour as found in the real bee's behaviour. In addition, the summed response of the detector to a unit distance movement along the corridor is constant for a large range of grating spatial frequencies, demonstrating that the detector can be used as a visual odometer.

Blood DNA methylation profiling identifies cathepsin Z dysregulation in pulmonary arterial hypertension.

Pulmonary arterial hypertension (PAH) is characterised by pulmonary vascular remodelling causing premature death from right heart failure. Established DNA variants influence PAH risk, but susceptibility from epigenetic changes is unknown. We addressed this through epigenome-wide association study (EWAS), testing 865,848 CpG sites for association with PAH in 429 individuals with PAH and 1226 controls. Three loci, at Cathepsin Z (CTSZ, cg04917472), Conserved oligomeric Golgi complex 6 (COG6, cg27396197), and Zinc Finger Protein 678 (ZNF678, cg03144189), reached epigenome-wide significance (p < 10-7) and are hypermethylated in PAH, including in individuals with PAH at 1-year follow-up. Of 16 established PAH genes, only cg10976975 in BMP10 shows hypermethylation in PAH. Hypermethylation at CTSZ is associated with decreased blood cathepsin Z mRNA levels. Knockdown of CTSZ expression in human pulmonary artery endothelial cells increases caspase-3/7 activity (p < 10-4). DNA methylation profiles are altered in PAH, exemplified by the pulmonary endothelial function modifier CTSZ, encoding protease cathepsin Z.

SpaRCe: Improved Learning of Reservoir Computing Systems Through Sparse Representations.

"Sparse" neural networks, in which relatively few neurons or connections are active, are common in both machine learning and neuroscience. While, in machine learning, "sparsity" is related to a penalty term that leads to some connecting weights becoming small or zero, in biological brains, sparsity is often created when high spiking thresholds prevent neuronal activity. Here, we introduce sparsity into a reservoir computing network via neuron-specific learnable thresholds of activity, allowing neurons with low thresholds to contribute to decision-making but suppressing information from neurons with high thresholds. This approach, which we term "SpaRCe," optimizes the sparsity level of the reservoir without affecting the reservoir dynamics. The read-out weights and the thresholds are learned by an online gradient rule that minimizes an error function on the outputs of the network. Threshold learning occurs by the balance of two opposing forces: reducing interneuronal correlations in the reservoir by deactivating redundant neurons, while increasing the activity of neurons participating in correct decisions. We test SpaRCe on classification problems and find that threshold learning improves performance compared to standard reservoir computing. SpaRCe alleviates the problem of catastrophic forgetting, a problem most evident in standard echo state networks (ESNs) and recurrent neural networks in general, due to increasing the number of task-specialized neurons that are included in the network decisions.

A robotic model of hippocampal reverse replay for reinforcement learning.

Hippocampal reverse replay, a phenomenon in which recently active hippocampal cells reactivate in the reverse order, is thought to contribute to learning, particularly reinforcement learning (RL), in animals. Here, we present a novel computational model which exploits reverse replay to improve stability and performance on a homing task. The model takes inspiration from the hippocampal-striatal network, and learning occurs via a three-factor RL rule. To augment this model with hippocampal reverse replay, we derived a policy gradient learning rule that associates place-cell activity with responses in cells representing actions and a supervised learning rule of the same form, interpreting the replay activity as a 'target' frequency. We evaluated the model using a simulated robot spatial navigation task inspired by the Morris water maze. Results suggest that reverse replay can improve performance stability over multiple trials. Our model exploits reverse reply as an additional source for propagating information about desirable synaptic changes, reducing the requirements for long-time scales in eligibility traces combined with low learning rates. We conclude that reverse replay can positively contribute to RL, although less stable learning is possible in its absence. Analogously, we postulate that reverse replay may enhance RL in the mammalian hippocampal-striatal system rather than provide its core mechanism.

Strategies discovery in the active allothetic place avoidance task.

The Active Allothetic Place Avoidance task is an alternative setup to Morris Water Maze that allows studying spatial memory in a dynamic world in the presence of conflicting information. In this task, a rat, freely moving on a rotating circular arena, has to avoid a sector defined within the room frame where shocks are presented. While for Morris Water Maze several studies have identified animal strategies which specifically affect performance, there were no such studies for the Active Allothetic Place Avoidance task. Using standard machine learning methods, we were able to reveal for the first time, to the best of our knowledge, explainable strategies that the animals employ in this task and demonstrate that they can provide a high-level interpretation for performance differences between an animal group treated with silver nanoparticles (AgNPs) and the control group.

An organ-on-chip model of pulmonary arterial hypertension identifies a BMPR2-SOX17-prostacyclin signalling axis.

Pulmonary arterial hypertension (PAH) is an unmet clinical need. The lack of models of human disease is a key obstacle to drug development. We present a biomimetic model of pulmonary arterial endothelial-smooth muscle cell interactions in PAH, combining natural and induced bone morphogenetic protein receptor 2 (BMPR2) dysfunction with hypoxia to induce smooth muscle activation and proliferation, which is responsive to drug treatment. BMPR2- and oxygenation-specific changes in endothelial and smooth muscle gene expression, consistent with observations made in genomic and biochemical studies of PAH, enable insights into underlying disease pathways and mechanisms of drug response. The model captures key changes in the pulmonary endothelial phenotype that are essential for the induction of SMC remodelling, including a BMPR2-SOX17-prostacyclin signalling axis and offers an easily accessible approach for researchers to study pulmonary vascular remodelling and advance drug development in PAH.

A robust model of Stimulus-Specific Adaptation validated on neuromorphic hardware.

Stimulus-Specific Adaptation (SSA) to repetitive stimulation is a phenomenon that has been observed across many different species and in several brain sensory areas. It has been proposed as a computational mechanism, responsible for separating behaviorally relevant information from the continuous stream of sensory information. Although SSA can be induced and measured reliably in a wide variety of conditions, the network details and intracellular mechanisms giving rise to SSA still remain unclear. Recent computational studies proposed that SSA could be associated with a fast and synchronous neuronal firing phenomenon called Population Spikes (PS). Here, we test this hypothesis using a mean-field rate model and corroborate it using a neuromorphic hardware. As the neuromorphic circuits used in this study operate in real-time with biologically realistic time constants, they can reproduce the same dynamics observed in biological systems, together with the exploration of different connectivity schemes, with complete control of the system parameter settings. Besides, the hardware permits the iteration of multiple experiments over many trials, for extended amounts of time and without losing the networks and individual neural processes being studied. Following this "neuromorphic engineering" approach, we therefore study the PS hypothesis in a biophysically inspired recurrent networks of spiking neurons and evaluate the role of different linear and non-linear dynamic computational primitives such as spike-frequency adaptation or short-term depression (STD). We compare both the theoretical mean-field model of SSA and PS to previously obtained experimental results in the area of novelty detection and observe its behavior on its neuromorphic physical equivalent model. We show how the approach proposed can be extended to other computational neuroscience modelling efforts for understanding high-level phenomena in mechanistic models.

Behavioral characteristics as potential biomarkers of the development and phenotype of epilepsy in a rat model of temporal lobe epilepsy.

The present study performed a detailed analysis of behavior in a rat model of epilepsy using both established and novel methodologies to identify behavioral impairments that may differentiate between animals with a short versus long latency to spontaneous seizures and animals with a low versus high number of seizures. Temporal lobe epilepsy was induced by electrical stimulation of the amygdala. Rats were stimulated for 25 min with 100-ms trains of 1-ms biphasic square-wave pluses that were delivered every 0.5 s. Electroencephalographic recordings were performed to classify rats into groups with a short latency (< 20 days, n = 7) and long latency (> 20 days, n = 8) to the first spontaneous seizure and into groups with a low number of seizures (62 ± 64.5, n = 8) and high number of seizures (456 ± 185, n = 7). To examine behavioral impairments, we applied the following behavioral tests during early and late stages of epilepsy: behavioral hyperexcitability, open field, novel object exploration, elevated plus maze, and Morris water maze. No differences in stress levels (e.g., touch response in the behavioral hyperexcitability test), activity (e.g., number of entries into the open arms of the elevated plus maze), or learning (e.g., latency to find the platform in the Morris water maze test during training days) were observed between animals with a short versus long latency to develop spontaneous seizures or between animals with a low versus high number of seizures. However, we found a higher motor activity measured by higher number of entries into the closed arms of the elevated plus maze at week 26 post-stimulation in animals with a high number of seizures compared with animals with a low number of seizures. The analysis of the Morris water maze data categorized the strategies that the animals used to locate the platform showing that the intensity of epilepsy and duration of epileptogenesis influenced swimming strategies. These findings indicate that behavioral impairments were relatively mild in the present model, but some learning strategies may be useful biomarkers in preclinical studies.

Neuromorphic computation with a single magnetic domain wall.

Machine learning techniques are commonly used to model complex relationships but implementations on digital hardware are relatively inefficient due to poor matching between conventional computer architectures and the structures of the algorithms they are required to simulate. Neuromorphic devices, and in particular reservoir computing architectures, utilize the inherent properties of physical systems to implement machine learning algorithms and so have the potential to be much more efficient. In this work, we demonstrate that the dynamics of individual domain walls in magnetic nanowires are suitable for implementing the reservoir computing paradigm in hardware. We modelled the dynamics of a domain wall placed between two anti-notches in a nickel nanowire using both a 1D collective coordinates model and micromagnetic simulations. When driven by an oscillating magnetic field, the domain exhibits non-linear dynamics within the potential well created by the anti-notches that are analogous to those of the Duffing oscillator. We exploit the domain wall dynamics for reservoir computing by modulating the amplitude of the applied magnetic field to inject time-multiplexed input signals into the reservoir, and show how this allows us to perform machine learning tasks including: the classification of (1) sine and square waves; (2) spoken digits; and (3) non-temporal 2D toy data and hand written digits. Our work lays the foundation for the creation of nanoscale neuromorphic devices in which individual magnetic domain walls are used to perform complex data analysis tasks.

Adaptive gain modulation in V1 explains contextual modifications during bisection learning.

The neuronal processing of visual stimuli in primary visual cortex (V1) can be modified by perceptual training. Training in bisection discrimination, for instance, changes the contextual interactions in V1 elicited by parallel lines. Before training, two parallel lines inhibit their individual V1-responses. After bisection training, inhibition turns into non-symmetric excitation while performing the bisection task. Yet, the receptive field of the V1 neurons evaluated by a single line does not change during task performance. We present a model of recurrent processing in V1 where the neuronal gain can be modulated by a global attentional signal. Perceptual learning mainly consists in strengthening this attentional signal, leading to a more effective gain modulation. The model reproduces both the psychophysical results on bisection learning and the modified contextual interactions observed in V1 during task performance. It makes several predictions, for instance that imagery training should improve the performance, or that a slight stimulus wiggling can strongly affect the representation in V1 while performing the task. We conclude that strengthening a top-down induced gain increase can explain perceptual learning, and that this top-down signal can modify lateral interactions within V1, without significantly changing the classical receptive field of V1 neurons.