Molecular basis of β-arrestin coupling to the metabotropic glutamate receptor mGlu3
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Efficient computation using spatial-photonic Ising machines with low-rank and circulant matrix constraints
Spatial-photonic Ising machines (SPIMs) have shown promise as an energy-efficient Ising machine, but currently can only solve a limited set of Ising problems. There is currently limited understanding on what experimental constraints may impact the performance of SPIM, and what computationally intensive problems can be efficiently solved by SPIM. Our results indicate that the performance of SPIMs is critically affected by the rank and precision of the coupling matrices. By developing and assessing advanced decomposition techniques, we expand the range of problems SPIMs can solve, overcoming the limitations of traditional Mattis-type matrices. Our approach accommodates a diverse array of coupling matrices, including those with inherently low ranks, applicable to complex NP-complete problems. We explore the practical benefits of the low-rank approximation in optimisation tasks, particularly in financial optimisation, to demonstrate the real-world applications of SPIMs. Finally, we evaluate the computational limitations imposed by SPIM hardware precision and suggest strategies to optimise the performance of these systems within these constraints.
Host-microbe computational proteomic landscape in oral cancer revealed key functional and metabolic pathways between Fusobacterium nucleatum and cancer progression
Oral squamous cell carcinoma (OSCC) is the most common manifestation of oral cancer. It has been proposed that periodontal pathogens contribute to OSCC progression, mainly by their virulence factors. However, the main periodontal pathogen and its mechanism to modulate OSCC cells remains not fully understood. In this study we investigate the main host-pathogen pathways in OSCC by computational proteomics and the mechanism behind cancer progression by the oral microbiome. The main host-pathogen pathways were analyzed in the secretome of biopsies from patients with OSCC and healthy controls by mass spectrometry. Then, functional assays were performed to evaluate the host-pathogen pathways highlighted in oral cancer. Host proteins associated with LPS response, cell migration/adhesion, and metabolism of amino acids were significantly upregulated in the human cancer proteome, whereas the complement cascade was downregulated in malignant samples. Then, the microbiome analysis revealed large number and variety of peptides from Fusobacterium nucleatum (F. nucleatum) in OSCC samples, from which several enzymes from the L-glutamate degradation pathway were found, indicating that L-glutamate from cancer cells is used as an energy source, and catabolized into butyrate by the bacteria. In fact, we observed that F. nucleatum modulates the cystine/glutamate antiporter in an OSCC cell line by increasing SLC7A11 expression, promoting L-glutamate efflux and favoring bacterial infection. Finally, our results showed that F. nucleatum and its metabolic derivates promote tumor spheroids growth, spheroids-derived cell detachment, epithelial-mesenchymal transition and Galectin-9 upregulation. Altogether, F. nucleatum promotes pro-tumoral mechanism in oral cancer.
The design space of E(3)-equivariant atom-centred interatomic potentials
Molecular dynamics simulation is an important tool in computational materials science and chemistry, and in the past decade it has been revolutionized by machine learning. This rapid progress in machine learning interatomic potentials has produced a number of new architectures in just the past few years. Particularly notable among these are the atomic cluster expansion, which unified many of the earlier ideas around atom-density-based descriptors, and Neural Equivariant Interatomic Potentials (NequIP), a message-passing neural network with equivariant features that exhibited state-of-the-art accuracy at the time. Here we construct a mathematical framework that unifies these models: atomic cluster expansion is extended and recast as one layer of a multi-layer architecture, while the linearized version of NequIP is understood as a particular sparsification of a much larger polynomial model. Our framework also provides a practical tool for systematically probing different choices in this unified design space. An ablation study of NequIP, via a set of experiments looking at in- and out-of-domain accuracy and smooth extrapolation very far from the training data, sheds some light on which design choices are critical to achieving high accuracy. A much-simplified version of NequIP, which we call BOTnet (for body-ordered tensor network), has an interpretable architecture and maintains its accuracy on benchmark datasets.
A force-sensitive adhesion GPCR is required for equilibrioception
Equilibrioception (sensing of balance) is essential for mammals to perceive and navigate the three-dimensional world. A rapid mechanoelectrical transduction (MET) response in vestibular hair cells is crucial for detecting position and motion. Here, we identify the G protein-coupled receptor (GPCR) LPHN2/ADGRL2, expressed on the apical membrane of utricular hair cells, as essential for maintaining normal balance. Loss of LPHN2 specifically in hair cells impaired both balance behavior and the MET response in mice. Functional analyses using hair-cell-specific Lphn2-knockout mice and an LPHN2-specific inhibitor suggest that LPHN2 regulates tip-link-independent MET currents at the apical surface of utricular hair cells. Mechanistic studies in a heterologous system show that LPHN2 converts force stimuli into increased open probability of transmembrane channel-like protein 1 (TMC1). LPHN2-mediated force sensation triggers glutamate release and calcium signaling in utricular hair cells. Importantly, reintroducing LPHN2 into the hair cells of Lphn2-deficient mice restores vestibular function and MET response. Our data reveal that a mechanosensitive GPCR is required for equilibrioception.
The dopaminergic effects of esketamine are mediated by a dual mechanism involving glutamate and opioid receptors
Esketamine represents a new class of drugs for treating mood disorders. Unlike traditional monoaminergic-based therapies, esketamine primarily targets N-methyl-D-aspartate receptors (NMDAR). However, esketamine is a complex drug with low affinity for NMDAR and can also bind to other targets, such as opioid receptors. Its precise mechanism of action for its antidepressant properties remains debated, as does its potential for misuse. A key component at the intersection of mood and reward processing is the dopaminergic system. In this study, we evaluated the effects of esketamine in locomotion, anxiety tests and operant responding and we used in vivo fiber photometry to explore the neurochemical effects of esketamine in the nucleus accumbens of mice. Our findings demonstrated multifaceted effects of esketamine on neurotransmitter dynamics. In freely behaving mice, esketamine increased locomotion and increased extracellular dopamine tone -by impairing dopamine clearance rather than promoting dopamine release- while decreasing glutamatergic activity. However, it decreased dopamine spontaneous release event frequency and impaired reward-evoked dopamine release, leading to a reduction in operant responding rates. These dopaminergic effects were partially, and conditionally, blocked by the opioid antagonist naloxone and required glutamatergic input. In summary, our study reveals a complex interaction between neurotransmitter systems, suggesting that the neurochemical effects of esketamine are both circuit- and state-dependent.
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