Structurally Resolved SARS-CoV-2 Antibody Shows High Efficacy in Severely Infected Hamsters and Provides a Potent Cocktail Pairing Strategy.

Understanding how potent neutralizing antibodies (NAbs) inhibit SARS-CoV-2 is critical for effective therapeutic development. We previously described BD-368-2, a SARS-CoV-2 NAb with high potency; however, its neutralization mechanism is largely unknown. Here, we report the 3.5-Å cryo-EM structure of BD-368-2/trimeric-spike complex, revealing that BD-368-2 fully blocks ACE2 recognition by occupying all three receptor-binding domains (RBDs) simultaneously, regardless of their "up" or "down" conformations. Also, BD-368-2 treats infected adult hamsters at low dosages and at various administering windows, in contrast to placebo hamsters that manifested severe interstitial pneumonia. Moreover, BD-368-2's epitope completely avoids the common binding site of VH3-53/VH3-66 recurrent NAbs, evidenced by tripartite co-crystal structures with RBDs. Pairing BD-368-2 with a potent recurrent NAb neutralizes SARS-CoV-2 pseudovirus at pM level and rescues mutation-induced neutralization escapes. Together, our results rationalized a new RBD epitope that leads to high neutralization potency and demonstrated BD-368-2's therapeutic potential in treating COVID-19.

Potent Neutralizing Antibodies against SARS-CoV-2 Identified by High-Throughput Single-Cell Sequencing of Convalescent Patients' B Cells.

The COVID-19 pandemic urgently needs therapeutic and prophylactic interventions. Here, we report the rapid identification of SARS-CoV-2-neutralizing antibodies by high-throughput single-cell RNA and VDJ sequencing of antigen-enriched B cells from 60 convalescent patients. From 8,558 antigen-binding IgG1+ clonotypes, 14 potent neutralizing antibodies were identified, with the most potent one, BD-368-2, exhibiting an IC50 of 1.2 and 15 ng/mL against pseudotyped and authentic SARS-CoV-2, respectively. BD-368-2 also displayed strong therapeutic and prophylactic efficacy in SARS-CoV-2-infected hACE2-transgenic mice. Additionally, the 3.8 Å cryo-EM structure of a neutralizing antibody in complex with the spike-ectodomain trimer revealed the antibody's epitope overlaps with the ACE2 binding site. Moreover, we demonstrated that SARS-CoV-2-neutralizing antibodies could be directly selected based on similarities of their predicted CDR3H structures to those of SARS-CoV-neutralizing antibodies. Altogether, we showed that human neutralizing antibodies could be efficiently discovered by high-throughput single B cell sequencing in response to pandemic infectious diseases.

Insulin signaling establishes a developmental trajectory of adipose regulatory T cells.

Systematic characterizations of adipose regulatory T (Treg) cell subsets and their phenotypes remain uncommon. Using single-cell ATAC-sequencing and paired single-cell RNA and T cell receptor (TCR) sequencing to map mouse adipose Treg cells, we identified CD73hiST2lo and CD73loST2hi subsets with distinct clonal expansion patterns. Analysis of TCR-sharing data implied a state transition between CD73hiST2lo and CD73loST2hi subsets. Mechanistically, we revealed that insulin signaling occurs through a HIF-1α-Med23-PPAR-γ axis to drive the transition of CD73hiST2lo into a CD73loST2hi adipose Treg cell subset. Treg cells deficient in insulin receptor, HIF-1α or Med23 have decreased PPAR-γ expression that in turn promotes accumulation of CD73hiST2lo adipose Treg cells and physiological adenosine production to activate beige fat biogenesis. We therefore unveiled a developmental trajectory of adipose Treg cells and its dependence on insulin signaling. Our findings have implications for understanding the dynamics of adipose Treg cell subsets in aged and obese contexts.

Hypothalamic Menin regulates systemic aging and cognitive decline.

Aging is a systemic process, which is a risk factor for impaired physiological functions, and finally death. The molecular mechanisms driving aging process and the associated cognitive decline are not fully understood. The hypothalamus acts as the arbiter that orchestrates systemic aging through neuroinflammatory signaling. Our recent findings revealed that Menin plays important roles in neuroinflammation and brain development. Here, we found that the hypothalamic Menin signaling diminished in aged mice, which correlates with systemic aging and cognitive deficits. Restoring Menin expression in ventromedial nucleus of hypothalamus (VMH) of aged mice extended lifespan, improved learning and memory, and ameliorated aging biomarkers, while inhibiting Menin in VMH of middle-aged mice induced premature aging and accelerated cognitive decline. We further found that Menin epigenetically regulates neuroinflammatory and metabolic pathways, including D-serine metabolism. Aging-associated Menin reduction led to impaired D-serine release by VMH-hippocampus neural circuit, while D-serine supplement rescued cognitive decline in aged mice. Collectively, VMH Menin serves as a key regulator of systemic aging and aging-related cognitive decline.

Co-evolution-based prediction of metal-binding sites in proteomes by machine learning.

Metal ions have various important biological roles in proteins, including structural maintenance, molecular recognition and catalysis. Previous methods of predicting metal-binding sites in proteomes were based on either sequence or structural motifs. Here we developed a co-evolution-based pipeline named 'MetalNet' to systematically predict metal-binding sites in proteomes. We applied MetalNet to proteomes of four representative prokaryotic species and predicted 4,849 potential metalloproteins, which substantially expands the currently annotated metalloproteomes. We biochemically and structurally validated previously unannotated metal-binding sites in several proteins, including apo-citrate lyase phosphoribosyl-dephospho-CoA transferase citX, an Escherichia coli enzyme lacking structural or sequence homology to any known metalloprotein (Protein Data Bank (PDB) codes: 7DCM and 7DCN ). MetalNet also successfully recapitulated all known zinc-binding sites from the human spliceosome complex. The pipeline of MetalNet provides a unique and enabling tool for interrogating the hidden metalloproteome and studying metal biology.

Activation of nicotinic acetylcholine receptor α7 subunit limits Zika viral infection via promoting autophagy and ferroptosis.

Vagus nerve regulates viral infection and inflammation via the alpha 7 nicotinic acetylcholine receptor (α7 nAChR); however, the role of α7 nAChR in ZIKA virus (ZIKV) infection, which can cause severe neurological diseases such as microcephaly and Guillain-Barré syndrome, remains unknown. Here, we first examined the role of α7 nAChR in ZIKV infection in vitro. A broad effect of α7 nAChR activation was identified in limiting ZIKV infection in multiple cell lines. Combined with transcriptomics analysis, we further demonstrated that α7 nAChR activation promoted autophagy and ferroptosis pathways to limit cellular ZIKV viral loads. Additionally, activation of α7 nAChR prevented ZIKV-induced p62 nucleus accumulation, which mediated an enhanced autophagy pathway. By regulating proteasome complex and an E3 ligase NEDD4, activation of α7 nAChR resulted in increased amount of cellular p62, which further enhanced the ferroptosis pathway to reduce ZIKV infection. Moreover, utilizing in vivo neonatal mouse models, we showed that α7 nAChR is essential in controlling the disease severity of ZIKV infection. Taken together, our findings identify an α7 nAChR-mediated effect that critically contributes to limiting ZIKV infection, and α7 nAChR activation offers a novel strategy for combating ZIKV infection and its complications.

DNA-TCP complex structures reveal a unique recognition mechanism for TCP transcription factor families.

Plant-specific TCP transcription factors are key regulators of diverse plant functions. TCP transcription factors have long been annotated as basic helix-loop-helix (bHLH) transcription factors according to remote sequence homology without experimental validation, and their consensus DNA-binding sequences and protein-DNA recognition mechanisms have remained elusive. Here, we report the crystal structures of the class I TCP domain from AtTCP15 and the class II TCP domain from AtTCP10 in complex with different double-stranded DNA (dsDNA). The complex structures reveal that the TCP domain is a distinct DNA-binding motif and the homodimeric TCP domains adopt a unique three-site recognition mode, binding to dsDNA mainly through a central pair of ß-strands formed by the dimer interface and two basic flexible loops from each monomer. The consensus DNA-binding sequence for class I TCPs is a perfectly palindromic 11 bp (GTGGGNCCCAC), whereas that for class II TCPs is a near-palindromic 11 bp (GTGGTCCCCAC). The unique DNA binding mode allows the TCP domains to display broad specificity for a range of DNA sequences even shorter than 11 bp, adding further complexity to the regulatory network of plant TCP transcription factors.

Enriched Single-Nucleus RNA-Sequencing Reveals Unique Attributes of Distal Convoluted Tubule Cells.

SIGNIFICANCE STATEMENT: High-resolution single-nucleus RNA-sequencing data indicate a clear separation between primary sites of calcium and magnesium handling within distal convoluted tubule (DCT). Both DCT1 and DCT2 express Slc12a3, but these subsegments serve distinctive functions, with more abundant magnesium-handling genes along DCT1 and more calcium-handling genes along DCT2. The data also provide insight into the plasticity of the distal nephron-collecting duct junction, formed from cells of separate embryonic origins. By focusing/changing gradients of gene expression, the DCT can morph into different physiological cell states on demand. BACKGROUND: The distal convoluted tubule (DCT) comprises two subsegments, DCT1 and DCT2, with different functional and molecular characteristics. The functional and molecular distinction between these segments, however, has been controversial. METHODS: To understand the heterogeneity within the DCT population with better clarity, we enriched for DCT nuclei by using a mouse line combining "Isolation of Nuclei Tagged in specific Cell Types" and sodium chloride cotransporter-driven inducible Cre recombinase. We sorted the fluorescently labeled DCT nuclei using Fluorescence-Activated Nucleus Sorting and performed single-nucleus transcriptomics. RESULTS: Among 25,183 DCT cells, 75% were from DCT1 and 25% were from DCT2. In addition, there was a small population (<1%) enriched in proliferation-related genes, such as Top2a , Cenpp , and Mki67 . Although both DCT1 and DCT2 expressed sodium chloride cotransporter, magnesium transport genes were predominantly expressed along DCT1, whereas calcium, electrogenic sodium, and potassium transport genes were more abundant along DCT2. The transition between these two segments was gradual, with a transitional zone in which DCT1 and DCT2 cells were interspersed. The expression of the homeobox genes by DCT cells suggests that they develop along different trajectories. CONCLUSIONS: Transcriptomic analysis of an enriched rare cell population using a genetically targeted approach clarifies the function and classification of distal cells. The DCT segment is short, can be separated into two subsegments that serve distinct functions, and is speculated to derive from different origins during development.

Electrosynthesis of Unusual Nonfcc Palladium Hydride Nanoparticles.

Intercalation of hydrogen into the palladium atomic layers during the growth of Pd nanoparticles can lead to the synthesis of unique palladium hydride phases. Here, we discover an unusual nonfcc palladium hydride nanoparticle, a structure that is not face-centered cubic (fcc), formed through coreduction of water molecules and Pd ions in solution. Crystal structure determination based on atomic electron tomography points to potential triclinic unit cells, indicating the presence of more than one nonfcc phase, with some of those being a stack of loosened and distorted close-packed layer of atoms. The probability of finding the nonfcc phase in single-crystalline particles varies depending on the number and distribution of contact area with other particles. Roughly half of the isolated and one side-coalesced single-crystal particles exhibit a nonfcc structure, while fcc dominates multiple side-coalesced single crystals as well as polycrystal particles. These observations suggest a coalescence-induced phase transition from a nonfcc to a stable fcc structure, due to the metastable nature of the nonfcc phases. While hydrogen is proven to be a key component for the synthesis of the nonfcc structure, there was limited formation of the unusual phase in a H2 gas bubbling system. Thus, electrochemical pathways can be promising for the in situ creation and study of unique metastable nanomaterials.

The Mass-Balancing between Positive and Negative Electrodes for Optimizing Energy Density of Supercapacitors.

Supercapacitors (SCs) are some of the most promising energy storage devices, but their low energy density is one main weakness. Over the decades, superior electrode materials and suitable electrolytes have been widely developed to enhance the energy storage ability of SCs. Particularly, constructing asymmetric supercapacitors (ASCs) can extend their electrochemical stable voltage windows (ESVWs) and thus achieve high energy density. However, only full utilization of the electrochemical stable potential windows (ESPWs) of both positive and negative electrodes can endow the ASC devices with a maximum ESVW by using a suitable mass-ratio between two electrodes (the mass-balancing). Nevertheless, insufficient attention is directed to mass-balancing, and even numerous misunderstandings and misuses have appeared. Therefore, in this Perspective, we focus on the mass-balancing: summarize theoretic basis of the mass-balancing, derive relevant relation equations, analyze and discuss the change trends of the specific capacitance and energy density of ASCs with mass-ratios, and finally recommend some guidelines for the normative implementation of the mass-balancing. Especially, the issues related to pseudocapacitive materials, hybrid devices, and different open circuit potentials (OCPs) of the positive and negative electrodes in the mass-balancing are included and emphasized. These analyses and guidelines can be conducive to understanding and performing mass-balancing for developing high-performance SCs.

Dysregulation of the WNK4-SPAK/OSR1 pathway has a minor effect on baseline NKCC2 phosphorylation.

The with-no-lysine kinase 4 (WNK4)-sterile 20/SPS-1-related proline/alanine-rich kinase (SPAK)/oxidative stress-responsive kinase 1 (OSR1) pathway mediates activating phosphorylation of the furosemide-sensitive Na+-K+-2Cl- cotransporter (NKCC2) and the thiazide-sensitive NaCl cotransporter (NCC). The commonly used pT96/pT101-pNKCC2 antibody cross-reacts with pT53-NCC in mice on the C57BL/6 background due to a five amino acid deletion. We generated a new C57BL/6-specific pNKCC2 antibody (anti-pT96-NKCC2) and tested the hypothesis that the WNK4-SPAK/OSR1 pathway strongly regulates the phosphorylation of NCC but not NKCC2. In C57BL/6 mice, anti-pT96-NKCC2 detected pNKCC2 and did not cross-react with NCC. Abundances of pT96-NKCC2 and pT53-NCC were evaluated in Wnk4-/-, Osr1-/-, Spak-/-, and Osr1-/-/Spak-/- mice and in several models of the disease familial hyperkalemic hypertension (FHHt) in which the CUL3-KLHL3 ubiquitin ligase complex that promotes WNK4 degradation is dysregulated (Cul3+/-/Δ9, Klhl3-/-, and Klhl3R528H/R528H). All mice were on the C57BL/6 background. In Wnk4-/- mice, pT53-NCC was almost absent but pT96-NKCC2 was only slightly lower. pT53-NCC was almost absent in Spak-/- and Osr1-/-/Spak-/- mice, but pT96-NKCC2 abundance did not differ from controls. pT96-NKCC2/total NKCC2 was slightly lower in Osr1-/- and Osr1-/-/Spak-/- mice. WNK4 expression colocalized not only with NCC but also with NKCC2 in Klhl3-/- mice, but pT96-NKCC2 abundance was unchanged. Consistent with this, furosemide-induced urinary Na+ excretion following thiazide treatment was similar between Klhl3-/- and controls. pT96-NKCC2 abundance was also unchanged in the other FHHt mouse models. Our data show that disruption of the WNK4-SPAK/OSR1 pathway only mildly affects NKCC2 phosphorylation, suggesting a role for other kinases in NKCC2 activation. In FHHt models NKCC2 phosphorylation is unchanged despite higher WNK4 abundance, explaining the thiazide sensitivity of FHHt.NEW & NOTEWORTHY The renal cation cotransporters NCC and NKCC2 are activated following phosphorylation mediated by the WNK4-SPAK/OSR1 pathway. While disruption of this pathway strongly affects NCC activity, effects on NKCC2 activity are unclear since the commonly used phospho-NKCC2 antibody was recently reported to cross-react with phospho-NCC in mice on the C57BL/6 background. Using a new phospho-NKCC2 antibody specific for C57BL/6, we show that inhibition or activation of the WNK4-SPAK/OSR1 pathway in mice only mildly affects NKCC2 phosphorylation.

Chrysophanol accelerates astrocytic mitochondria transfer to neurons and attenuates the cerebral ischemia-reperfusion injury in rats.

Astrocytes transfer extracellular functional mitochondria into neurons to rescue injured neurons after a stroke. However, there are no reports on drugs that interfere with intercellular mitochondrial transfer. Chrysophanol (CHR) was an effective drug for the treatment of cerebral ischemia-reperfusion injury (CIRI) and was selected as the test drug. The oxygen-glucose deprivation/reoxygenation (OGD/R) cell model and the middle cerebral artery occlusion animal model were established to investigate the effect of CHR on CIRI. The result showed that astrocytes could act as mitochondrial donors to ameliorate neuronal injury. Additionally, the neuroprotective effect of astrocytes was enhanced by CHR, the CHR improved the neuronal mitochondrial function, decreased the neurological deficit score and infarction volume, recovered cell morphology in ischemic penumbra. The mitochondrial fluorescence probe labeling technique has shown that the protective effect of CHR is associated with accelerated astrocytic mitochondrial transfer to neurons. The intercellular mitochondrial transfer may be an important way to ameliorate ischemic brain injury and be used as a key target for drug treatment.

Defective Tungsten Oxides with Stacking Faults for Proton Exchange Membrane Green-Hydrogen Generation.

Defects can introduce atomic structural modulation and tailor performance of materials. Herein, it demonstrates that semiconductor WO3 with inert electrocatalytic behavior can be activated through defect-induced tensile strains. Structural characterizations reveal that when simply treated in Ar/H2 atmosphere, oxygen vacancies will generate in WO3 and cause defective structures. Stacking faults are found in defects, thus modulating electronic structure and transforming electrocatalytic-inert WO3 into highly active electrocatalysts. Density functional theory (DFT) calculations are performed to calculate *H adsorption energies on various WOx surfaces, revealing the oxygen vacancy composition and strain predicted to optimize the catalytic activity of hydrogen evolution reaction (HER). Such defective tungsten oxides can be integrated into commercial proton exchange membrane (PEM) electrolyser with comparable performance toward Pt-based PEM. This work demonstrates defective metal oxides as promising non-noble metal catalysts for commercial PEM green-hydrogen generation.

Operando Spectroscopy Observation of Mo Clusters-Ti3 C2 TX Catalyst/Support Interface's Dynamic Evolution in Hydrogen Evolution Reaction.

The interaction between catalyst and support plays an important role in electrocatalytic hydrogen evolution (HER), which may explain the improvement in performance by phase transition or structural remodeling. However, the intrinsic behavior of these catalysts (dynamic evolution of the interface under bias, structural/morphological transformation, stability) has not been clearly monitored, while the operando technology does well in capturing the dynamic changes in the reaction process in real time to determine the actual active site. In this paper, nitrogen-doped molybdenum atom-clusters on Ti3 C2 TX (MoACs /N-Ti3 C2 TX ) is used as a model catalyst to reveal the dynamic evolution of MoAcs on Ti3 C2 TX during the HER process. Operando X-ray absorption structure (XAS) theoretical calculation and in situ Raman spectroscopy showed that the Mo cluster structure evolves to a 6-coordinated monatomic Mo structure under working conditions, exposing more active sites and thus improving the catalytic performance. It shows excellent HER performance comparable to that of commercial Pt/C, including an overpotential of 60 mV at 10 mA cm-2 , a small Tafel slope (56 mV dec-1 ), and high activity and durability. This study provides a unique perspective for investigating the evolution of species, interfacial migration mechanisms, and sources of activity-enhancing compounds in the process of electroreduction.

HINGRL: predicting drug-disease associations with graph representation learning on heterogeneous information networks.

Identifying new indications for drugs plays an essential role at many phases of drug research and development. Computational methods are regarded as an effective way to associate drugs with new indications. However, most of them complete their tasks by constructing a variety of heterogeneous networks without considering the biological knowledge of drugs and diseases, which are believed to be useful for improving the accuracy of drug repositioning. To this end, a novel heterogeneous information network (HIN) based model, namely HINGRL, is proposed to precisely identify new indications for drugs based on graph representation learning techniques. More specifically, HINGRL first constructs a HIN by integrating drug-disease, drug-protein and protein-disease biological networks with the biological knowledge of drugs and diseases. Then, different representation strategies are applied to learn the features of nodes in the HIN from the topological and biological perspectives. Finally, HINGRL adopts a Random Forest classifier to predict unknown drug-disease associations based on the integrated features of drugs and diseases obtained in the previous step. Experimental results demonstrate that HINGRL achieves the best performance on two real datasets when compared with state-of-the-art models. Besides, our case studies indicate that the simultaneous consideration of network topology and biological knowledge of drugs and diseases allows HINGRL to precisely predict drug-disease associations from a more comprehensive perspective. The promising performance of HINGRL also reveals that the utilization of rich heterogeneous information provides an alternative view for HINGRL to identify novel drug-disease associations especially for new diseases.

A geometric deep learning framework for drug repositioning over heterogeneous information networks.

Drug repositioning (DR) is a promising strategy to discover new indicators of approved drugs with artificial intelligence techniques, thus improving traditional drug discovery and development. However, most of DR computational methods fall short of taking into account the non-Euclidean nature of biomedical network data. To overcome this problem, a deep learning framework, namely DDAGDL, is proposed to predict drug-drug associations (DDAs) by using geometric deep learning (GDL) over heterogeneous information network (HIN). Incorporating complex biological information into the topological structure of HIN, DDAGDL effectively learns the smoothed representations of drugs and diseases with an attention mechanism. Experiment results demonstrate the superior performance of DDAGDL on three real-world datasets under 10-fold cross-validation when compared with state-of-the-art DR methods in terms of several evaluation metrics. Our case studies and molecular docking experiments indicate that DDAGDL is a promising DR tool that gains new insights into exploiting the geometric prior knowledge for improved efficacy.

Coronavirus GenBrowser for monitoring the transmission and evolution of SARS-CoV-2.

Genomic epidemiology is important to study the COVID-19 pandemic, and more than two million severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomic sequences were deposited into public databases. However, the exponential increase of sequences invokes unprecedented bioinformatic challenges. Here, we present the Coronavirus GenBrowser (CGB) based on a highly efficient analysis framework and a node-picking rendering strategy. In total, 1,002,739 high-quality genomic sequences with the transmission-related metadata were analyzed and visualized. The size of the core data file is only 12.20 MB, highly efficient for clean data sharing. Quick visualization modules and rich interactive operations are provided to explore the annotated SARS-CoV-2 evolutionary tree. CGB binary nomenclature is proposed to name each internal lineage. The pre-analyzed data can be filtered out according to the user-defined criteria to explore the transmission of SARS-CoV-2. Different evolutionary analyses can also be easily performed, such as the detection of accelerated evolution and ongoing positive selection. Moreover, the 75 genomic spots conserved in SARS-CoV-2 but non-conserved in other coronaviruses were identified, which may indicate the functional elements specifically important for SARS-CoV-2. The CGB was written in Java and JavaScript. It not only enables users who have no programming skills to analyze millions of genomic sequences, but also offers a panoramic vision of the transmission and evolution of SARS-CoV-2.

iGRLDTI: an improved graph representation learning method for predicting drug-target interactions over heterogeneous biological information network.

MOTIVATION: The task of predicting drug-target interactions (DTIs) plays a significant role in facilitating the development of novel drug discovery. Compared with laboratory-based approaches, computational methods proposed for DTI prediction are preferred due to their high-efficiency and low-cost advantages. Recently, much attention has been attracted to apply different graph neural network (GNN) models to discover underlying DTIs from heterogeneous biological information network (HBIN). Although GNN-based prediction methods achieve better performance, they are prone to encounter the over-smoothing simulation when learning the latent representations of drugs and targets with their rich neighborhood information in HBIN, and thereby reduce the discriminative ability in DTI prediction. RESULTS: In this work, an improved graph representation learning method, namely iGRLDTI, is proposed to address the above issue by better capturing more discriminative representations of drugs and targets in a latent feature space. Specifically, iGRLDTI first constructs an HBIN by integrating the biological knowledge of drugs and targets with their interactions. After that, it adopts a node-dependent local smoothing strategy to adaptively decide the propagation depth of each biomolecule in HBIN, thus significantly alleviating over-smoothing by enhancing the discriminative ability of feature representations of drugs and targets. Finally, a Gradient Boosting Decision Tree classifier is used by iGRLDTI to predict novel DTIs. Experimental results demonstrate that iGRLDTI yields better performance that several state-of-the-art computational methods on the benchmark dataset. Besides, our case study indicates that iGRLDTI can successfully identify novel DTIs with more distinguishable features of drugs and targets. AVAILABILITY AND IMPLEMENTATION: Python codes and dataset are available at https://github.com/stevejobws/iGRLDTI/.

Impacts of spatial symmetries on stimulated Brillouin scatterings in nanoscale silicon waveguides: a theoretical and numerical study considering material anisotropies.

Fully considering the mechanical and photoelastic anisotropies of monocrystalline silicon, the impacts of spatial symmetries on the stimulated Brillouin scatterings (SBSs) in nanoscale suspended silicon waveguides are studied theoretically and numerically based on group theory. First, starting from an assumption that the principal material coordinate system can be arbitrarily orientated in a waveguide with fixed geometry, the silicon waveguides are systematically classified into a number of point groups according to their spatial symmetry features. Thereafter, the symmetry characteristics of physical fields and SBS opto-mechanical coupling characteristics in the silicon waveguides belonging to different point groups are further examined, and the major new findings can be summarized as follows: The SBS opto-mechanical couplings in several kinds of silicon waveguides with certain nontrivial symmetry features exhibit relatively predictable behaviors in that the opto-mechanical coupling coefficients can be deterministically vanishing or nonvanishing under very few constraints, which can thus serve as general symmetry selection rules for SBSs in suspended silicon waveguides. The results obtained in the present study could be a useful theoretical reference for the design of novel SBS-active silicon photonic devices.

ASCs Activate cGAS-Type I IFNs-IL-7 Axis Via Pseudomonas aeruginosa-Derived Outer Membrane Vesicles to Resolve Pneumonia.

Mesenchymal stem cells (MSCs) therapy could efficiently attenuate LPS-induced acute lung injury and Pseudomonas aeruginosa (PA)-induced acute pneumonia. However, the underlying molecular mechanisms are still elusive. Here, we report that PA-derived outer membrane vesicles (OMVs) trigger mouse primary adipose tissue-derived mesenchymal stem cells (ASCs) to upregulate cyclic GMP-AMP synthase (cGAS) for sensing of double-stranded DNA (dsDNA) and the expression of interleukin (IL)-7. Loss of cGAS-interferon (IFN)-ß axis abolished the protective function of ASCs to PA-induced acute pneumonia in mice. Mechanistically, OMVs-delivered PA dsDNA primes cGAS-stimulator of interferon genes (STING) signaling pathway and increases the IL-7 production in ASCs via IFN-ß signaling. Meanwhile, dsDNA-primed ASCs furthermore amplifies IL-7 expression in primary lung epithelial cells and mouse lung epithelial (MLE)-12 cell line via increased IFN-ß. Our findings thus implicate a molecular mechanism that ASCs recognize PA-OMVs-derived dsDNA to secrete IL-7 via activating cGAS, suggesting a potential therapeutic strategy of ASCs transfer for PA-induced lung infection and inflammation.