IL-10-Dependent Crosstalk between Murine Marginal Zone B Cells, Macrophages, and CD8α+ Dendritic Cells Promotes Listeria monocytogenes Infection.

Type 1 CD8α+ conventional dendritic cells (cDC1s) are required for CD8+ T cell priming but, paradoxically, promote splenic Listeria monocytogenes infection. Using mice with impaired cDC2 function, we ruled out a role for cDC2s in this process and instead discovered an interleukin-10 (IL-10)-dependent cellular crosstalk in the marginal zone (MZ) that promoted bacterial infection. Mice lacking the guanine nucleotide exchange factor DOCK8 or CD19 lost IL-10-producing MZ B cells and were resistant to Listeria. IL-10 increased intracellular Listeria in cDC1s indirectly by reducing inducible nitric oxide synthase expression early after infection and increasing intracellular Listeria in MZ metallophilic macrophages (MMMs). These MMMs trans-infected cDC1s, which, in turn, transported Listeria into the white pulp to prime CD8+ T cells. However, this also facilitated bacterial expansion. Therefore, IL-10-mediated crosstalk between B cells, macrophages, and cDC1s in the MZ promotes both Listeria infection and CD8+ T cell activation.

Gate-tunable subband degeneracy in semiconductor nanowires.

Degeneracy and symmetry have a profound relation in quantum systems. Here, we report gate-tunable subband degeneracy in PbTe nanowires with a nearly symmetric cross-sectional shape. The degeneracy is revealed in electron transport by the absence of a quantized plateau. Utilizing a dual gate design, we can apply an electric field to lift the degeneracy, reflected as emergence of the plateau. This degeneracy and its tunable lifting were challenging to observe in previous nanowire experiments, possibly due to disorder. Numerical simulations can qualitatively capture our observation, shedding light on device parameters for future applications.

SPDesign: protein sequence designer based on structural sequence profile using ultrafast shape recognition.

Protein sequence design can provide valuable insights into biopharmaceuticals and disease treatments. Currently, most protein sequence design methods based on deep learning focus on network architecture optimization, while ignoring protein-specific physicochemical features. Inspired by the successful application of structure templates and pre-trained models in the protein structure prediction, we explored whether the representation of structural sequence profile can be used for protein sequence design. In this work, we propose SPDesign, a method for protein sequence design based on structural sequence profile using ultrafast shape recognition. Given an input backbone structure, SPDesign utilizes ultrafast shape recognition vectors to accelerate the search for similar protein structures in our in-house PAcluster80 structure database and then extracts the sequence profile through structure alignment. Combined with structural pre-trained knowledge and geometric features, they are further fed into an enhanced graph neural network for sequence prediction. The results show that SPDesign significantly outperforms the state-of-the-art methods, such as ProteinMPNN, Pifold and LM-Design, leading to 21.89%, 15.54% and 11.4% accuracy gains in sequence recovery rate on CATH 4.2 benchmark, respectively. Encouraging results also have been achieved on orphan and de novo (designed) benchmarks with few homologous sequences. Furthermore, analysis conducted by the PDBench tool suggests that SPDesign performs well in subdivided structures. More interestingly, we found that SPDesign can well reconstruct the sequences of some proteins that have similar structures but different sequences. Finally, the structural modeling verification experiment indicates that the sequences designed by SPDesign can fold into the native structures more accurately.

The chromatin accessibility dynamics during cell fate specifications in zebrafish early embryogenesis.

Chromatin accessibility plays a critical role in the regulation of cell fate decisions. Although gene expression changes have been extensively profiled at the single-cell level during early embryogenesis, the dynamics of chromatin accessibility at cis-regulatory elements remain poorly studied. Here, we used a plate-based single-cell ATAC-seq method to profile the chromatin accessibility dynamics of over 10 000 nuclei from zebrafish embryos. We investigated several important time points immediately after zygotic genome activation (ZGA), covering key developmental stages up to dome. The results revealed key chromatin signatures in the first cell fate specifications when cells start to differentiate into enveloping layer (EVL) and yolk syncytial layer (YSL) cells. Finally, we uncovered many potential cell-type specific enhancers and transcription factor motifs that are important for the cell fate specifications.

GraphGPSM: a global scoring model for protein structure using graph neural networks.

The scoring models used for protein structure modeling and ranking are mainly divided into unified field and protein-specific scoring functions. Although protein structure prediction has made tremendous progress since CASP14, the modeling accuracy still cannot meet the requirements to a certain extent. Especially, accurate modeling of multi-domain and orphan proteins remains a challenge. Therefore, an accurate and efficient protein scoring model should be developed urgently to guide the protein structure folding or ranking through deep learning. In this work, we propose a protein structure global scoring model based on equivariant graph neural network (EGNN), named GraphGPSM, to guide protein structure modeling and ranking. We construct an EGNN architecture, and a message passing mechanism is designed to update and transmit information between nodes and edges of the graph. Finally, the global score of the protein model is output through a multilayer perceptron. Residue-level ultrafast shape recognition is used to describe the relationship between residues and the overall structure topology, and distance and direction encoded by Gaussian radial basis functions are designed to represent the overall topology of the protein backbone. These two features are combined with Rosetta energy terms, backbone dihedral angles and inter-residue distance and orientations to represent the protein model and embedded into the nodes and edges of the graph neural network. The experimental results on the CASP13, CASP14 and CAMEO test sets show that the scores of our developed GraphGPSM have a strong correlation with the TM-score of the models, which are significantly better than those of the unified field score function REF2015 and the state-of-the-art local lDDT-based scoring models ModFOLD8, ProQ3D and DeepAccNet, etc. The modeling experimental results on 484 test proteins demonstrate that GraphGPSM can greatly improve the modeling accuracy. GraphGPSM is further used to model 35 orphan proteins and 57 multi-domain proteins. The results show that the average TM-score of the models predicted by GraphGPSM is 13.2 and 7.1% higher than that of the models predicted by AlphaFold2. GraphGPSM also participates in CASP15 and achieves competitive performance in global accuracy estimation.

Assessing protein model quality based on deep graph coupled networks using protein language model.

Model quality evaluation is a crucial part of protein structural biology. How to distinguish high-quality models from low-quality models, and to assess which high-quality models have relatively incorrect regions for improvement, are remain a challenge. More importantly, the quality assessment of multimer models is a hot topic for structure prediction. In this study, we propose GraphCPLMQA, a novel approach for evaluating residue-level model quality that combines graph coupled networks and embeddings from protein language models. The GraphCPLMQA consists of a graph encoding module and a transform-based convolutional decoding module. In encoding module, the underlying relational representations of sequence and high-dimensional geometry structure are extracted by protein language models with Evolutionary Scale Modeling. In decoding module, the mapping connection between structure and quality is inferred by the representations and low-dimensional features. Specifically, the triangular location and residue level contact order features are designed to enhance the association between the local structure and the overall topology. Experimental results demonstrate that GraphCPLMQA using single-sequence embedding achieves the best performance compared with the CASP15 residue-level interface evaluation methods among 9108 models in the local residue interface test set of CASP15 multimers. In CAMEO blind test (20 May 2022 to 13 August 2022), GraphCPLMQA ranked first compared with other servers (https://www.cameo3d.org/quality-estimation). GraphCPLMQA also outperforms state-of-the-art methods on 19, 035 models in CASP13 and CASP14 monomer test set.

A granularity-level information fusion strategy on hypergraph transformer for predicting synergistic effects of anticancer drugs.

Combination therapy has exhibited substantial potential compared to monotherapy. However, due to the explosive growth in the number of cancer drugs, the screening of synergistic drug combinations has become both expensive and time-consuming. Synergistic drug combinations refer to the concurrent use of two or more drugs to enhance treatment efficacy. Currently, numerous computational methods have been developed to predict the synergistic effects of anticancer drugs. However, there has been insufficient exploration of how to mine drug and cell line data at different granularity levels for predicting synergistic anticancer drug combinations. Therefore, this study proposes a granularity-level information fusion strategy based on the hypergraph transformer, named HypertranSynergy, to predict synergistic effects of anticancer drugs. HypertranSynergy introduces synergistic connections between cancer cell lines and drug combinations using hypergraph. Then, the Coarse-grained Information Extraction (CIE) module merges the hypergraph with a transformer for node embeddings. In the CIE module, Contranorm is a normalization layer that mitigates over-smoothing, while Gaussian noise addresses local information gaps. Additionally, the Fine-grained Information Extraction (FIE) module assesses fine-grained information's impact on predictions by employing similarity-aware matrices from drug/cell line features. Both CIE and FIE modules are integrated into HypertranSynergy. In addition, HypertranSynergy achieved the AUC of 0.93${\pm }$0.01 and the AUPR of 0.69${\pm }$0.02 in 5-fold cross-validation of classification task, and the RMSE of 13.77${\pm }$0.07 and the PCC of 0.81${\pm }$0.02 in 5-fold cross-validation of regression task. These results are better than most of the state-of-the-art models.

Nanoconfined polymerization limits crack propagation in hysteresis-free gels.

Consecutive mechanical loading cycles cause irreversible fatigue damage and residual strain in gels, affecting their service life and application scope. Hysteresis-free hydrogels within a limited deformation range have been created by various strategies. However, large deformation and high elasticity are inherently contradictory attributes. Here we present a nanoconfined polymerization strategy for producing tough and near-zero-hysteresis gels under a large range of deformations. Gels are prepared through in situ polymerization within nanochannels of covalent organic frameworks or molecular sieves. The nanochannel confinement and strong hydrogen bonding interactions with polymer segments are crucial for achieving rapid self-reinforcement. The rigid nanostructures relieve the stress concentration at the crack tips and prevent crack propagation, enhancing the ultimate fracture strain (17,580 ± 308%), toughness (87.7 ± 2.3 MJ m-3) and crack propagation strain (5,800%) of the gels. This approach provides a general strategy for synthesizing gels that overcome the traditional trade-offs of large deformation and high elasticity.

GSK3ß: A ray of hope for the treatment of diabetic kidney disease.

Diabetic kidney disease (DKD), a major microvascular complication of diabetes, is characterized by its complex pathogenesis, high risk of chronic renal failure, and lack of effective diagnosis and treatment methods. GSK3ß (glycogen synthase kinase 3ß), a highly conserved threonine/serine kinase, was found to activate glycogen synthase. As a key molecule of the glucose metabolism pathway, GSK3ß participates in a variety of cellular activities and plays a pivotal role in multiple diseases. However, these effects are not only mediated by affecting glucose metabolism. This review elaborates on the role of GSK3ß in DKD and its damage mechanism in different intrinsic renal cells. GSK3ß is also a biomarker indicating the progression of DKD. Finally, the protective effects of GSK3ß inhibitors on DKD are also discussed.

Berberine increases the killing effect of pirarubicin on HCC cells by inhibiting ATG4B-autophagy pathway.

Pirarubicin (THP) is a new generation of cell cycle non-specific anthracycline-based anticancer drug. In the clinic, THP and THP combination therapies have been shown to be effective in hepatocellular carcinoma (HCC) patients with transcatheter arterial chemoembolization (TACE) without serious side effects. However, drug resistance limits its therapeutic efficacy. Berberine (BBR), an isoquinoline alkaloid, has been shown to possess antitumour properties against various malignancies. However, the synergistic effect of BBR and THP in the treatment of HCC is unknown. In the present study, we demonstrated for the first time that BBR sensitized HCC cells to THP, including enhancing THP-induced growth inhibition and apoptosis of HCC cells. Moreover, we found that BBR sensitized THP by reducing the expression of autophagy-related 4B (ATG4B). Mechanistically, the inhibition of HIF1α-mediated ATG4B transcription by BBR ultimately led to attenuation of THP-induced cytoprotective autophagy, accompanied by enhanced growth inhibition and apoptosis in THP-treated HCC cells. Tumor-bearing experiments in nude mice showed that the combination treatment with BBR and THP significantly suppressed the growth of HCC xenografts. These results reveal that BBR is able to strengthen the killing effect of THP on HCC cells by repressing the ATG4B-autophagy pathway, which may provide novel insights into the improvement of chemotherapeutic efficacy of THP, and may be conducive to the further clinical application of THP in HCC treatment.

Building a novel TRUCK by harnessing the endogenous IFN-gamma promoter for cytokine expression.

Despite the remarkable success of chimeric antigen receptor (CAR) T therapy in hematological malignancies, its efficacy in solid tumors remains limited. Cytokine-engineered CAR T cells offer a promising avenue, yet their clinical translation is hindered by the risks associated with constitutive cytokine expression. In this proof-of-concept study, we leverage the endogenous interferon (IFN)-γ promoter for transgenic interleukin (IL)-15 expression. We demonstrate that IFN-γ expression is tightly regulated by T cell receptor signaling. By introducing an internal ribosome entry site IL15 into the 3' UTR of the IFN-γ gene via homology directed repair-mediated knock-in, we confirm that IL-15 expression can co-express with IFN-γ in an antigen stimulation-dependent manner. Importantly, the insertion of transgenes does not compromise endogenous IFN-γ expression. In vitro and in vivo data demonstrate that IL-15 driven by the IFN-γ promoter dramatically improves CAR T cells' antitumor activity, suggesting the effectiveness of IL-15 expression. Last, as a part of our efforts toward clinical translation, we have developed an innovative two-gene knock-in approach. This approach enables the simultaneous integration of CAR and IL-15 genes into TRAC and IFN-γ gene loci using a single AAV vector. CAR T cells engineered to express IL-15 using this approach demonstrate enhanced antitumor efficacy. Overall, our study underscores the feasibility of utilizing endogenous promoters for transgenic cytokines expression in CAR T cells.

Dual-field-of-view high-spectral-resolution lidar: Simultaneous profiling of aerosol and water cloud to study aerosol-cloud interaction.

SignificanceAerosol-cloud interaction affects the cooling of Earth's climate, mostly by activation of aerosols as cloud condensation nuclei that can increase the amount of sunlight reflected back to space. But the controlling physical processes remain uncertain in current climate models. We present a lidar-based technique as a unique remote-sensing tool without thermodynamic assumptions for simultaneously profiling diurnal aerosol and water cloud properties with high resolution. Direct lateral observations of cloud properties show that the vertical structure of low-level water clouds can be far from being perfectly adiabatic. Furthermore, our analysis reveals that, instead of an increase of liquid water path (LWP) as proposed by most general circulation models, elevated aerosol loading can cause a net decrease in LWP.

CircPHKB decreases the sensitivity of liver cancer cells to sorafenib via miR-1234-3p/CYP2W1 axis.

Sorafenib is currently the first-line treatment for patients with advanced liver cancer, but its therapeutic efficacy declines significantly after a few months of treatment. Therefore, it is of great importance to investigate the regulatory mechanisms of sorafenib sensitivity in liver cancer cells. In this study, we provided initial evidence demonstrating that circPHKB, a novel circRNA markedly overexpressed in sorafenib-treated liver cancer cells, attenuated the sensitivity of liver cancer cells to sorafenib. Mechanically, circPHKB sequestered miR-1234-3p, resulting in the up-regulation of cytochrome P450 family 2 subfamily W member 1 (CYP2W1), thereby reducing the killing effect of sorafenib on liver cancer cells. Moreover, knockdown of circPHKB sensitized liver cancer cells to sorafenib in vivo. The findings reveal a novel circPHKB/miR-1234-3p/CYP2W1 pathway that decreases the sensitivity of liver cancer cells to sorafenib, suggesting that circPHKB and the axis may serve as promising targets to improve the therapeutic efficacy of sorafenib against liver cancer.

PtPd Atomic Layer Shelled PdCu Hollow Nanoparticles on Partially Unzipped Carbon Nanotubes for Breaking the Activity-Stability Trade-Off toward the Hydrogen Oxidation Reaction in Alkaline Media.

Developing highly active yet stable catalysts for the hydrogen oxidation reaction (HOR) in alkaline media remains a significant challenge. Herein, we designed a novel catalyst of atomic PtPd-layer shelled ultrasmall PdCu hollow nanoparticles (HPdCu NPs) on partially unzipped carbon nanotubes (PtPd@HPdCu/W-CNTs), which can achieve a high mass activity, 5 times that of the benchmark Pt/C, and show exceptional stability with negligible decay after 20,000 cycles of accelerated degradation test. The atomically thin PtPd shell serves as the primary active site for the HOR and a protective layer that prevents Cu leaching. Additionally, the HPdCu substrate not only tunes the adsorption properties of the PtPd layer but also prevents corrosive Pt from reaching the interface between NPs and the carbon support, thereby mitigating carbon corrosion. This work introduces a new strategy that leverages the distinct advantages of multiple components to address the challenges associated with slow kinetics and poor durability toward the HOR.

Boosting Electrocatalytic Ammonia Synthesis via Synergistic Effect of Iron-Based Single Atoms and Clusters.

Electrocatalytic reduction of nitrate to ammonia (NO3RR) is gaining attention for low carbon emissions and environmental protection. However, low ammonia production rate and poor selectivity have remained major challenges in this multi-proton coupling process. Herein, we report a facile strategy toward a novel Fe-based hybrid structure composed of Fe single atoms and Fe3C atomic clusters that demonstrates outstanding performance for synergistic electrocatalytic NO3RR. By operando synchrotron Fourier transform infrared spectroscopy and theoretical computation, we clarify that Fe single atoms serve as the active site for NO3RR, while Fe3C clusters facilitate H2O dissociation to provide protons (*H) for continued hydrogenation reactions. As a result, the Fe-based electrocatalyst exhibits ammonia Faradaic efficiency of nearly 100%, with a corresponding production rate of 24768 µg h-1 cm-2 at -0.4 V vs RHE, exceeding most reported metal-based catalysts. This research provides valuable guidance toward multi-step reactions.

Overexpression of vacuolar transporters OsVIT1 and OsVIT2 reduces cadmium accumulation in rice.

Excessive cadmium in rice grain in agricultural production is an important issue to be addressed in some southern regions of China. In this study, we constructed transgenic rice overexpressing OsVIT1 and OsVIT2 driven by 35S promoter in the cultivar ZH11. Compared with ZH11, OsVIT1 expression in leaves was significantly increased by 3-6.6 times and OsVIT2 expression in leaves was significantly increased by 2-2.5 times. Hydroponic experiments showed that overexpression of OsVIT1 and OsVIT2 increased the tolerance to Fe deficiency, significantly reduced Cd content in shoot and xylem sap, and had no effect on Cd tolerance in rice. Two years of field trials showed that the Fe content in the grain of OsVIT1 and OsVIT2 overexpressed materials was significantly reduced by 20-40% and the straw Fe content was significantly increased by 10-45%, and the grain Fe content distribution ratio was significantly decreased and the straw Fe distribution ratio was significantly increased compared with the wild type. The OsVIT1 and OsVIT2 overexpressed materials significantly reduced the Cd content of grain by 40-80% and the Cd content of straws by 37-77%, and the bioconcentration factor of Cd was significantly reduced in both grains and straw of OsVIT1 and OsVIT2 overexpressed materials. Overexpression of OsVIT1 and OsVIT2 did not affect the concentration of other metal ions in rice straw and grain. qRT-PCR analysis showed that the expression of the low affinity cation transporter OsLCT1 was significantly downregulated in the OsVIT1 and OsVIT2 overexpressed materials. In conclusion, overexpression of OsVIT1 and OsVIT2 reduced Cd accumulation in straw and grains, providing a strategy for Cd reduction in rice.

Highly Efficient Iron-Based Catalyst for Light-Driven Selective Hydrogenation of Nitroarenes.

Light-driven hydrogenation of nitro compounds to functionalized amines is of great importance yet a challenge for practical applications, which calls for the development of high-performance, nonprecious photocatalysts and efficient catalytic systems. Herein, we report a high-efficiency Fe3O4@TiO2 photocatalyst via a sol-gel and subsequent pyrolysis strategy, which exhibits desirable photothermal hydrogenation performance of nitro compounds to functionalized amines with the excellent selectivity of >90% exceeding those of the state-of-the-art heterogeneous photocatalysts. Our experimental results and theoretical calculations for the first time reveal that Fe3O4 is the major active phase, and the strong metal-support interaction between Fe3O4 and reducible TiO2 further leads to performance improvement, taking advantage of the enhanced photothermal effect and the improved adsorption for the reactant and hydrazine hydrate. Notably, a variety of halonitrobenzenes and pharmaceutical intermediates can be completely converted to functionalized amines with high selectivities, even in gram-scale reactions. This work provides a new insight into the rational design of nonprecious photo/thermo-catalysts for other catalytic reactions.

Stable Inorganic Colloidal Tin and Tin-Lead Perovskite Nanocrystals with Ultralong Carrier Lifetime via Sn(IV) Control.

Inorganic tin (Sn) perovskite nanocrystals offer a promising solution to the potential toxicity concerns associated with their established lead (Pb)-based counterparts. Yet, achieving their superior stability and optoelectronic properties remains an ongoing challenge. Here, we report a synthesis of high-symmetry α-phase CsSnI3 nanocrystals with an ultralong 278 ns carrier lifetime, exceeding previous benchmarks by 2 orders of magnitude through meticulous Sn(IV) control. The nanocrystals demonstrate excellent colloidal stability, uniform monodispersity, and a distinct exciton peak. Central to these outcomes is our designed solid-liquid antioxidation suspension of tri-n-octylphosphine (TOP) and zerovalent tin (Sn(0)) that fully addresses the unique coexisting oxygen-driven and solvent-driven Sn oxidation mechanisms in Sn perovskite nanocrystal synthesis. We uncover the largely undervalued function of TOP in mitigating oxygen-driven Sn oxidation and introduce Sn(0) powder to generate a synergistic antioxidation function with TOP, significantly reducing Sn(IV)-induced defects and distortions and contributing to enhanced optoelectronic properties. Strikingly, this approach also profoundly impacts inorganic Sn-Pb perovskite nanocrystals, boosting lifetimes by 2 orders of magnitude and increasing photoluminescence quantum yield over 100-fold to 35%. Our findings illuminate the potential of Sn-based nanocrystals for optoelectronic applications.

Continuous Flow System for Highly Efficient and Durable Photocatalytic Oxidative Coupling of Methane.

Photocatalytic oxidative coupling of methane (OCM) into value-added industrial chemicals offers an appealing green technique for achieving sustainable development, whereas it encounters double bottlenecks in relatively low methane conversion rate and severe overoxidation. Herein, we engineer a continuous gas flow system to achieve efficient photocatalytic OCM while suppressing overoxidation by synergizing the moderate active oxygen species, surface plasmon-mediated polarization, and multipoint gas intake flow reactor. Particularly, a remarkable CH4 conversion rate of 218.2 µmol h-1 with an excellent selectivity of ∼90% toward C2+ hydrocarbons and a remarkable stability over 240 h is achieved over a designed Au/TiO2 photocatalyst in our continuous gas flow system with a homemade three-dimensional (3D) printed flow reactor. This work provides an informative concept to engineer a high-performance flow system for photocatalytic OCM by synergizing the design of the reactor and photocatalyst to synchronously regulate the mass transfer, activation of reactants, and inhibition of overoxidation.

Impaired distal renal potassium handling in streptozotocin-induced diabetic mice.

Diabetes is closely associated with K+ disturbances during disease progression and treatment. However, it remains unclear whether K+ imbalance occurs in diabetes with normal kidney function. In this study, we examined the effects of dietary K+ intake on systemic K+ balance and renal K+ handling in streptozotocin (STZ)-induced diabetic mice. The control and STZ mice were fed low or high K+ diet for 7 days to investigate the role of dietary K+ intake in renal K+ excretion and K+ homeostasis, and to explore the underlying mechanism by evaluating K+ secretion-related transport proteins in distal nephrons. K+-deficient diet caused excessive urinary K+ loss, decreased daily K+ balance, and led to severe hypokalemia in STZ mice compared to control mice. In contrast, STZ mice showed an increased daily K+ balance and elevated plasma K+ level under K+-loading conditions. Dysregulation of the NaCl cotransporter (NCC), epithelia Na+ channel (ENaC), and renal outer medullary K+ channel (ROMK) was observed in diabetic mice fed either low or high K+ diet. Moreover, amiloride treatment reduced urinary K+ excretion and corrected hypokalemia in K+-restricted STZ mice. On the other hand, inhibition of SGLT2 by dapagliflozin promoted urinary K+ excretion and normalized plasma K+ level in K+-supplemented STZ mice, at least partly by increasing ENaC activity. We conclude that STZ mice exhibited abnormal K+ balance and impaired renal K+ handling under either low or high K+ diet, which could be primarily attributed to the dysfunction of ENaC-dependent renal K+ excretion pathway, despite the possible role of NCC.