Neurological Manifestations of COVID-19 Feature T Cell Exhaustion and Dedifferentiated Monocytes in Cerebrospinal Fluid.

Patients suffering from Coronavirus disease 2019 (COVID-19) can develop neurological sequelae, such as headache and neuroinflammatory or cerebrovascular disease. These conditions-termed here as Neuro-COVID-are more frequent in patients with severe COVID-19. To understand the etiology of these neurological sequelae, we utilized single-cell sequencing and examined the immune cell profiles from the cerebrospinal fluid (CSF) of Neuro-COVID patients compared with patients with non-inflammatory and autoimmune neurological diseases or with viral encephalitis. The CSF of Neuro-COVID patients exhibited an expansion of dedifferentiated monocytes and of exhausted CD4+ T cells. Neuro-COVID CSF leukocytes featured an enriched interferon signature; however, this was less pronounced than in viral encephalitis. Repertoire analysis revealed broad clonal T cell expansion and curtailed interferon response in severe compared with mild Neuro-COVID patients. Collectively, our findings document the CSF immune compartment in Neuro-COVID patients and suggest compromised antiviral responses in this setting.

DrugMetric: quantitative drug-likeness scoring based on chemical space distance.

The process of drug discovery is widely known to be lengthy and resource-intensive. Artificial Intelligence approaches bring hope for accelerating the identification of molecules with the necessary properties for drug development. Drug-likeness assessment is crucial for the virtual screening of candidate drugs. However, traditional methods like Quantitative Estimation of Drug-likeness (QED) struggle to distinguish between drug and non-drug molecules accurately. Additionally, some deep learning-based binary classification models heavily rely on selecting training negative sets. To address these challenges, we introduce a novel unsupervised learning framework called DrugMetric, an innovative framework for quantitatively assessing drug-likeness based on the chemical space distance. DrugMetric blends the powerful learning ability of variational autoencoders with the discriminative ability of the Gaussian Mixture Model. This synergy enables DrugMetric to identify significant differences in drug-likeness across different datasets effectively. Moreover, DrugMetric incorporates principles of ensemble learning to enhance its predictive capabilities. Upon testing over a variety of tasks and datasets, DrugMetric consistently showcases superior scoring and classification performance. It excels in quantifying drug-likeness and accurately distinguishing candidate drugs from non-drugs, surpassing traditional methods including QED. This work highlights DrugMetric as a practical tool for drug-likeness scoring, facilitating the acceleration of virtual drug screening, and has potential applications in other biochemical fields.

RPG interacts with E3-ligase CERBERUS to mediate rhizobial infection in Lotus japonicus.

Symbiotic interactions between rhizobia and legumes result in the formation of root nodules, which fix nitrogen that can be used for plant growth. Rhizobia usually invade legume roots through a plant-made tunnel-like structure called an infection thread (IT). RPG (Rhizobium-directed polar growth) encodes a coiled-coil protein that has been identified in Medicago truncatula as required for root nodule infection, but the function of RPG remains poorly understood. In this study, we identified and characterized RPG in Lotus japonicus and determined that it is required for IT formation. RPG was induced by Mesorhizobium loti or purified Nodulation factor and displayed an infection-specific expression pattern. Nodule inception (NIN) bound to the RPG promoter and induced its expression. We showed that RPG displayed punctate subcellular localization in L. japonicus root protoplasts and in root hairs infected by M. loti. The N-terminal predicted C2 lipid-binding domain of RPG was not required for this subcellular localization or for function. CERBERUS, a U-box E3 ligase which is also required for rhizobial infection, was found to be localized similarly in puncta. RPG co-localized and directly interacted with CERBERUS in the early endosome (TGN/EE) compartment and near the nuclei in root hairs after rhizobial inoculation. Our study sheds light on an RPG-CERBERUS protein complex that is involved in an exocytotic pathway mediating IT elongation.

GmNLP1 and GmNLP4 activate nitrate-induced CLE peptides NIC1a/b to mediate nitrate-regulated root nodulation.

Symbiotic nitrogen fixation is an energy-intensive process, to maintain the balance between growth and nitrogen fixation, high concentrations of nitrate inhibit root nodulation. However, the precise mechanism underlying the nitrate inhibition of nodulation in soybean remains elusive. In this study, CRISPR-Cas9-mediated knockout of GmNLP1 and GmNLP4 unveiled a notable nitrate-tolerant nodulation phenotype. GmNLP1b and GmNLP4a play a significant role in the nitrate-triggered inhibition of nodulation, as the expression of nitrate-responsive genes was largely suppressed in Gmnlp1b and Gmnlp4a mutants. Furthermore, we demonstrated that GmNLP1b and GmNLP4a can bind to the promoters of GmNIC1a and GmNIC1b and activate their expression. Manipulations targeting GmNIC1a and GmNIC1b through knockdown or overexpression strategies resulted in either increased or decreased nodule number in response to nitrate. Additionally, transgenic roots that constitutively express GmNIC1a or GmNIC1b rely on both NARK and hydroxyproline O-arabinosyltransferase RDN1 to prevent the inhibitory effects imposed by nitrate on nodulation. In conclusion, this study highlights the crucial role of the GmNLP1/4-GmNIC1a/b module in mediating high nitrate-induced inhibition of nodulation.

BatmanNet: bi-branch masked graph transformer autoencoder for molecular representation.

Although substantial efforts have been made using graph neural networks (GNNs) for artificial intelligence (AI)-driven drug discovery, effective molecular representation learning remains an open challenge, especially in the case of insufficient labeled molecules. Recent studies suggest that big GNN models pre-trained by self-supervised learning on unlabeled datasets enable better transfer performance in downstream molecular property prediction tasks. However, the approaches in these studies require multiple complex self-supervised tasks and large-scale datasets , which are time-consuming, computationally expensive and difficult to pre-train end-to-end. Here, we design a simple yet effective self-supervised strategy to simultaneously learn local and global information about molecules, and further propose a novel bi-branch masked graph transformer autoencoder (BatmanNet) to learn molecular representations. BatmanNet features two tailored complementary and asymmetric graph autoencoders to reconstruct the missing nodes and edges, respectively, from a masked molecular graph. With this design, BatmanNet can effectively capture the underlying structure and semantic information of molecules, thus improving the performance of molecular representation. BatmanNet achieves state-of-the-art results for multiple drug discovery tasks, including molecular properties prediction, drug-drug interaction and drug-target interaction, on 13 benchmark datasets, demonstrating its great potential and superiority in molecular representation learning.

Deterministic programming of human pluripotent stem cells into microglia facilitates studying their role in health and disease.

Microglia, the resident immune cells of the central nervous system (CNS), are derived from yolk-sac macrophages that populate the developing CNS during early embryonic development. Once established, the microglia population is self-maintained throughout life by local proliferation. As a scalable source of microglia-like cells (MGLs), we here present a forward programming protocol for their generation from human pluripotent stem cells (hPSCs). The transient overexpression of PU.1 and C/EBPß in hPSCs led to a homogenous population of mature microglia within 16 d. MGLs met microglia characteristics on a morphological, transcriptional, and functional level. MGLs facilitated the investigation of a human tauopathy model in cortical neuron-microglia cocultures, revealing a secondary dystrophic microglia phenotype. Single-cell RNA sequencing of microglia integrated into hPSC-derived cortical brain organoids demonstrated a shift of microglia signatures toward a more-developmental in vivo-like phenotype, inducing intercellular interactions promoting neurogenesis and arborization. Taken together, our microglia forward programming platform represents a tool for both reductionist studies in monocultures and complex coculture systems, including 3D brain organoids for the study of cellular interactions in healthy or diseased environments.

De novo genome assembly and functional insights of the first commercial pink Auricularia cornea.

A pioneering pink cultivar of Auricularia cornea, first commercially cultivated in 2022, lacks genomic data, hindering research in genetic breeding, gene discovery, and product development. Here, we report the de novo assembly of the pink A. cornea Fen-A1 genome and provide a detailed functional annotation. The genome is 73.17 Mb in size, contains 86 scaffolds (N50 âˆ¼ 5.49 Mb), 59.09% GC content and encodes 19,120 predicted genes with a BUSCO completeness of 92.60%. Comparative genomic analysis reveals the phylogenetic relatedness of Fen-A1 and remarkable gene family dynamics. Putative genes were found mapped to 3 antibiotic-related, 36 light-dependent and 25 terpene metabolites. In addition, 789 CAZymes genes were classified, revealing the dynamics of quality loss due to postharvest refrigeration. Overall, our work is the first report on a pink A. cornea genome and provides a comprehensive insight into its complex functions.

Deletion of BTB and CNC homology 1 protects against Staphylococcus aureus-induced acute lung injury.

BTB and CNC homology 1 (BACH1) plays a crucial role in the pathogenesis of acute lung injury (ALI) caused by gram-negative bacteria. However, its exact mechanisms and roles in Staphylococcus aureus (SA)-induced ALI, a gram-positive bacterial infection, remain incompletely understood. In this study, we generated a BACH1-knockout mouse model (BACH1-/-) to investigate the role of BACH1 and its underlying mechanisms in regulating the development of sepsis-induced acute lung injury (ALI). Elevated levels of BACH1 were observed in both serum samples from septic patients and mouse models. Deletion of BACH1 alleviated ALI symptoms induced by sepsis. In bone marrow-derived macrophages, BACH1 deletion or knockdown suppressed NF-κB p65 phosphorylation and the induction of pro-inflammatory cytokines. Mechanistic studies demonstrated that BACH1 downregulated tumor necrosis factor-alpha-induced protein 3 (TNFAIP3) mRNA expression by binding to its promoter region. These findings uncover inhibiting BACH1 may be a promising therapeutic strategy for treating gram-positive bacteria-induced ALI.

Systematic Constructing FeOCl/BiVO4 Hetero-Interfacial Hybrid Photoanodes for Efficient Photoelectrochemical Water Splitting.

Bismuth vanadate (BiVO4), as a promising photoanode for photoelectrochemical (PEC) water splitting, suffers from poor charge separation efficiency and light absorption efficiency. Herein, iron oxychloride (FeOCl) is introduced as a novel cocatalyst simply grafted on BiVO4 to construct an integrated photoanode, enhancing PEC performance. The optimized FeOCl/BiVO4 photoanode exhibits a superior photocurrent density value of 5.23 mA cm-2 at 1.23 V versus reversible hydrogen electrode (RHE) under AM 1.5G illuminations. From experimental analysis, such high PEC performance is ascribed to the unique properties of FeOCl, facilitating charge transport, increasing light absorption efficiency, and promoting water oxidation kinetics. Density functional theory calculations further confirm that FeOCl optimizes the Gibbs free energy of H and O-containing intermediates (OOH*) during PEC processes, boosting the catalytic kinetics of PEC water splitting. This work presents FeOCl as a promising catalyst for constructing high efficient PEC water-splitting photoanodes.

Synergetic Dual-Additive Electrolyte Enables Highly Stable Performance in Sodium Metal Batteries.

Sodium (Na)-metal batteries (SMBs) are considered one of the most promising candidates for the large-scale energy storage market owing to their high theoretical capacity (1,166 mAh g-1) and the abundance of Na raw material. However, the limited stability of electrolytes still hindered the application of SMBs. Herein, sulfolane (Sul) and vinylene carbonate (VC) are identified as effective dual additives that can largely stabilize propylene carbonate (PC)-based electrolytes, prevent dendrite growth, and extend the cycle life of SMBs. The cycling stability of the Na/NaNi0.68Mn0.22Co0.1O2 (NaNMC) cell with this dual-additive electrolyte is remarkably enhanced, with a capacity retention of 94% and a Coulombic efficiency (CE) of 99.9% over 600 cycles at a 5 C (750 mA g-1) rate. The superior cycling performance of the cells can be attributed to the homogenous, dense, and thin hybrid solid electrolyte interphase consisting of F- and S-containing species on the surface of both the Na metal anode and the NaNMC cathode by adding dual additives. Such unique interphases can effectively facilitate Na-ion transport kinetics and avoid electrolyte depletion during repeated cycling at a very high rate of 5 C. This electrolyte design is believed to result in further improvements in the performance of SMBs.