GPR34 senses demyelination to promote neuroinflammation and pathologies.

Sterile neuroinflammation is a major driver of multiple neurological diseases. Myelin debris can act as an inflammatory stimulus to promote inflammation and pathologies, but the mechanism is poorly understood. Here, we showed that lysophosphatidylserine (LysoPS)-GPR34 axis played a critical role in microglia-mediated myelin debris sensing and the subsequent neuroinflammation. Myelin debris-induced microglia activation and proinflammatory cytokine expression relied on its lipid component LysoPS. Both myelin debris and LysoPS promoted microglia activation and the production of proinflammatory cytokines via GPR34 and its downstream PI3K-AKT and ERK signaling. In vivo, reducing the content of LysoPS in myelin or inhibition of GPR34 with genetic or pharmacological approaches reduced neuroinflammation and pathologies in the mouse models of multiple sclerosis and stroke. Thus, our results identify GPR34 as a key receptor to sense demyelination and CNS damage and promote neuroinflammation, and suggest it as a potential therapeutic target for demyelination-associated diseases.

Multi-task bioassay pre-training for protein-ligand binding affinity prediction.

Protein-ligand binding affinity (PLBA) prediction is the fundamental task in drug discovery. Recently, various deep learning-based models predict binding affinity by incorporating the three-dimensional (3D) structure of protein-ligand complexes as input and achieving astounding progress. However, due to the scarcity of high-quality training data, the generalization ability of current models is still limited. Although there is a vast amount of affinity data available in large-scale databases such as ChEMBL, issues such as inconsistent affinity measurement labels (i.e. IC50, Ki, Kd), different experimental conditions, and the lack of available 3D binding structures complicate the development of high-precision affinity prediction models using these data. To address these issues, we (i) propose Multi-task Bioassay Pre-training (MBP), a pre-training framework for structure-based PLBA prediction; (ii) construct a pre-training dataset called ChEMBL-Dock with more than 300k experimentally measured affinity labels and about 2.8M docked 3D structures. By introducing multi-task pre-training to treat the prediction of different affinity labels as different tasks and classifying relative rankings between samples from the same bioassay, MBP learns robust and transferrable structural knowledge from our new ChEMBL-Dock dataset with varied and noisy labels. Experiments substantiate the capability of MBP on the structure-based PLBA prediction task. To the best of our knowledge, MBP is the first affinity pre-training model and shows great potential for future development. MBP web-server is now available for free at: https://huggingface.co/spaces/jiaxianustc/mbp.

Pituitary hormone α-MSH promotes tumor-induced myelopoiesis and immunosuppression.

The hypothalamic-pituitary (HP) unit can produce various hormones to regulate immune responses, and some of its downstream hormones or effectors are elevated in cancer patients. We show that the HP unit can promote myelopoiesis and immunosuppression to accelerate tumor growth. Subcutaneous implantation of tumors induced hypothalamus activation and pituitary α-melanocyte-stimulating hormone (α-MSH) production in mice. α-MSH acted on bone marrow progenitors to promote myelopoiesis, myeloid cell accumulation, immunosuppression, and tumor growth through its melanocortin receptor MC5R. MC5R peptide antagonist boosted antitumor immunity and anti-programmed cell death protein 1 (anti-PD-1) immunotherapy. Serum α-MSH concentration was elevated and correlated with circulating myeloid-derived suppressor cells in cancer patients. Our results reveal a neuroendocrine pathway that suppresses tumor immunity and suggest MC5R as a potential target for cancer immunotherapy.

Activity Origin and Design Principles for Oxygen Reduction on Dual-Metal-Site Catalysts: A Combined Density Functional Theory and Machine Learning Study.

Dual-metal-site catalysts (DMSCs) are emerging as a new frontier in the field of oxygen reduction reaction (ORR). However, there is a lack of design principles to provide a universal description of the relationship between intrinsic properties of DMSCs and the catalytic activity. Here, we identify the origin of ORR activity and unveil design principles for graphene-based DMSCs by means of density functional theory computations and machine learning (ML). Our results indicate that several experimentally unexplored DMSCs can show outstanding ORR activity surpassing that of platinum. Remarkably, our ML study reveals that the ORR activity of DMSCs is intrinsically governed by some fundamental factors, such as electron affinity, electronegativity, and radii of the embedded metal atoms. More importantly, we propose predictor equations with acceptable accuracy to quantitatively describe the ORR activity of DMSCs. Our work will accelerate the search for highly active DMSCs for ORR and other electrochemical reactions.