A linear discriminant analysis model of imbalanced associative learning in the mushroom body compartment.

To adapt to their environments, animals learn associations between sensory stimuli and unconditioned stimuli. In invertebrates, olfactory associative learning primarily occurs in the mushroom body, which is segregated into separate compartments. Within each compartment, Kenyon cells (KCs) encoding sparse odor representations project onto mushroom body output neurons (MBONs) whose outputs guide behavior. Associated with each compartment is a dopamine neuron (DAN) that modulates plasticity of the KC-MBON synapses within the compartment. Interestingly, DAN-induced plasticity of the KC-MBON synapse is imbalanced in the sense that it only weakens the synapse and is temporally sparse. We propose a normative mechanistic model of the MBON as a linear discriminant analysis (LDA) classifier that predicts the presence of an unconditioned stimulus (class identity) given a KC odor representation (feature vector). Starting from a principled LDA objective function and under the assumption of temporally sparse DAN activity, we derive an online algorithm which maps onto the mushroom body compartment. Our model accounts for the imbalanced learning at the KC-MBON synapse and makes testable predictions that provide clear contrasts with existing models.

Coordinated drift of receptive fields in Hebbian/anti-Hebbian network models during noisy representation learning.

Recent experiments have revealed that neural population codes in many brain areas continuously change even when animals have fully learned and stably perform their tasks. This representational 'drift' naturally leads to questions about its causes, dynamics and functions. Here we explore the hypothesis that neural representations optimize a representational objective with a degenerate solution space, and noisy synaptic updates drive the network to explore this (near-)optimal space causing representational drift. We illustrate this idea and explore its consequences in simple, biologically plausible Hebbian/anti-Hebbian network models of representation learning. We find that the drifting receptive fields of individual neurons can be characterized by a coordinated random walk, with effective diffusion constants depending on various parameters such as learning rate, noise amplitude and input statistics. Despite such drift, the representational similarity of population codes is stable over time. Our model recapitulates experimental observations in the hippocampus and posterior parietal cortex and makes testable predictions that can be probed in future experiments.

Biologically plausible single-layer networks for nonnegative independent component analysis.

An important problem in neuroscience is to understand how brains extract relevant signals from mixtures of unknown sources, i.e., perform blind source separation. To model how the brain performs this task, we seek a biologically plausible single-layer neural network implementation of a blind source separation algorithm. For biological plausibility, we require the network to satisfy the following three basic properties of neuronal circuits: (i) the network operates in the online setting; (ii) synaptic learning rules are local; and (iii) neuronal outputs are nonnegative. Closest is the work by Pehlevan et al. (Neural Comput 29:2925-2954, 2017), which considers nonnegative independent component analysis (NICA), a special case of blind source separation that assumes the mixture is a linear combination of uncorrelated, nonnegative sources. They derive an algorithm with a biologically plausible 2-layer network implementation. In this work, we improve upon their result by deriving 2 algorithms for NICA, each with a biologically plausible single-layer network implementation. The first algorithm maps onto a network with indirect lateral connections mediated by interneurons. The second algorithm maps onto a network with direct lateral connections and multi-compartmental output neurons.

A Biologically Plausible Neural Network for Multichannel Canonical Correlation Analysis.

Cortical pyramidal neurons receive inputs from multiple distinct neural populations and integrate these inputs in separate dendritic compartments. We explore the possibility that cortical microcircuits implement canonical correlation analysis (CCA), an unsupervised learning method that projects the inputs onto a common subspace so as to maximize the correlations between the projections. To this end, we seek a multichannel CCA algorithm that can be implemented in a biologically plausible neural network. For biological plausibility, we require that the network operates in the online setting and its synaptic update rules are local. Starting from a novel CCA objective function, we derive an online optimization algorithm whose optimization steps can be implemented in a single-layer neural network with multicompartmental neurons and local non-Hebbian learning rules. We also derive an extension of our online CCA algorithm with adaptive output rank and output whitening. Interestingly, the extension maps onto a neural network whose neural architecture and synaptic updates resemble neural circuitry and non-Hebbian plasticity observed in the cortex.

Correction to Identification of Quinoline-Based RIP2 Kinase Inhibitors with an Improved Therapeutic Index to the hERG Ion Channel.

[This corrects the article DOI: 10.1021/acsmedchemlett.8b00344.].

Discovery of Pyrazolocarboxamides as Potent and Selective Receptor Interacting Protein 2 (RIP2) Kinase Inhibitors.

Herein we report the discovery of pyrazolocarboxamides as novel, potent, and kinase selective inhibitors of receptor interacting protein 2 kinase (RIP2). Fragment based screening and design principles led to the identification of the inhibitor series, and X-ray crystallography was used to inform key structural changes. Through key substitutions about the N1 and C5 N positions on the pyrazole ring significant kinase selectivity and potency were achieved. Bridged bicyclic pyrazolocarboxamide 11 represents a selective and potent inhibitor of RIP2 and will allow for a more detailed investigation of RIP2 inhibition as a therapeutic target for autoinflammatory disorders.

Discovery of a First-in-Class Receptor Interacting Protein 2 (RIP2) Kinase Specific Clinical Candidate, 2-((4-(Benzo[d]thiazol-5-ylamino)-6-(tert-butylsulfonyl)quinazolin-7-yl)oxy)ethyl Dihydrogen Phosphate, for the Treatment of Inflammatory Diseases.

RIP2 kinase has been identified as a key signal transduction partner in the NOD2 pathway contributing to a variety of human pathologies, including immune-mediated inflammatory diseases. Small-molecule inhibitors of RIP2 kinase or its signaling partners on the NOD2 pathway that are suitable for advancement into the clinic have yet to be described. Herein, we report our discovery and profile of the prodrug clinical compound, inhibitor 3, currently in phase 1 clinical studies. Compound 3 potently binds to RIP2 kinase with good kinase specificity and has excellent activity in blocking many proinflammatory cytokine responses in vivo and in human IBD explant samples. The highly favorable physicochemical and ADMET properties of 3 combined with high potency led to a predicted low oral dose in humans.

Identification of Quinoline-Based RIP2 Kinase Inhibitors with an Improved Therapeutic Index to the hERG Ion Channel.

RIP2 kinase was recently identified as a therapeutic target for a variety of autoimmune diseases. We have reported previously a selective 4-aminoquinoline-based RIP2 inhibitor GSK583 and demonstrated its effectiveness in blocking downstream NOD2 signaling in cellular models, rodent in vivo models, and human ex vivo disease models. While this tool compound was valuable in validating the biological pathway, it suffered from activity at the hERG ion channel and a poor PK/PD profile thereby limiting progression of this analog. Herein, we detail our efforts to improve both this off-target liability as well as the PK/PD profile of this series of inhibitors through modulation of lipophilicity and strengthening hinge binding ability. These efforts have led to inhibitor 7, which possesses high binding affinity for the ATP pocket of RIP2 (IC50 = 1 nM) and inhibition of downstream cytokine production in human whole blood (IC50 = 10 nM) with reduced hERG activity (14 µM).

The Identification and Pharmacological Characterization of 6-(tert-Butylsulfonyl)-N-(5-fluoro-1H-indazol-3-yl)quinolin-4-amine (GSK583), a Highly Potent and Selective Inhibitor of RIP2 Kinase.

RIP2 kinase is a central component of the innate immune system and enables downstream signaling following activation of the pattern recognition receptors NOD1 and NOD2, leading to the production of inflammatory cytokines. Recently, several inhibitors of RIP2 kinase have been disclosed that have contributed to the fundamental understanding of the role of RIP2 in this pathway. However, because they lack either broad kinase selectivity or strong affinity for RIP2, these tools have only limited utility to assess the role of RIP2 in complex environments. We present, herein, the discovery and pharmacological characterization of GSK583, a next-generation RIP2 inhibitor possessing exquisite selectivity and potency. Having demonstrated the pharmacological precision of this tool compound, we report its use in elucidating the role of RIP2 kinase in a variety of in vitro, in vivo, and ex vivo experiments, further clarifying our understanding of the role of RIP2 in NOD1 and NOD2 mediated disease pathogenesis.

Functional characterization of TRPV4 as an osmotically sensitive ion channel in porcine articular chondrocytes.

OBJECTIVE: Transient receptor potential vanilloid 4 (TRPV4) is a Ca(2+)-permeable channel that can be gated by tonicity (osmolarity) and mechanical stimuli. Chondrocytes, the cells in cartilage, respond to their osmotic and mechanical environments; however, the molecular basis of this signal transduction is not fully understood. This study was undertaken to demonstrate the presence and functionality of TRPV4 in chondrocytes. METHODS: TRPV4 protein expression was measured by immunolabeling and Western blotting. In response to TRPV4 agonist/antagonists, osmotic stress, and interleukin-1 (IL-1), changes in Ca(2+) signaling, cell volume, and prostaglandin E(2) (PGE(2)) production were measured in porcine chondrocytes using fluorescence microscopy, light microscopy, or immunoassay, respectively. RESULTS: TRPV4 was expressed abundantly at the RNA and protein levels. Exposure to 4alpha-phorbol 12,13-didecanoate (4alphaPDD), a TRPV4 activator, caused Ca(2+) signaling in chondrocytes, which was blocked by the selective TRPV4 antagonist, GSK205. Blocking TRPV4 diminished the chondrocytes' response to hypo-osmotic stress, reducing the fraction of Ca(2+) responsive cells, the regulatory volume decrease, and PGE(2) production. Ca(2+) signaling was inhibited by removal of extracellular Ca(2+) or depletion of intracellular stores. Specific activation of TRPV4 restored the defective regulatory volume decrease caused by IL-1. Chemical disruption of the primary cilium eliminated Ca(2+) signaling in response to either 4alphaPDD or hypo-osmotic stress. CONCLUSION: Our findings indicate that TRPV4 is present in articular chondrocytes, and chondrocyte response to hypo-osmotic stress is mediated by this channel, which involves both an extracellular Ca(2+) and intracellular Ca(2+) release. TRPV4 may also be involved in modulating the production or influence of proinflammatory molecules in response to osmotic stress.