Multi-omics blood atlas reveals unique features of immune and platelet responses to SARS-CoV-2 Omicron breakthrough infection.

Although host responses to the ancestral SARS-CoV-2 strain are well described, those to the new Omicron variants are less resolved. We profiled the clinical phenomes, transcriptomes, proteomes, metabolomes, and immune repertoires of >1,000 blood cell or plasma specimens from SARS-CoV-2 Omicron patients. Using in-depth integrated multi-omics, we dissected the host response dynamics during multiple disease phases to reveal the molecular and cellular landscapes in the blood. Specifically, we detected enhanced interferon-mediated antiviral signatures of platelets in Omicron-infected patients, and platelets preferentially formed widespread aggregates with leukocytes to modulate immune cell functions. In addition, patients who were re-tested positive for viral RNA showed marked reductions in B cell receptor clones, antibody generation, and neutralizing capacity against Omicron. Finally, we developed a machine learning model that accurately predicted the probability of re-positivity in Omicron patients. Our study may inspire a paradigm shift in studying systemic diseases and emerging public health concerns.

Van der Waals polarity-engineered 3D integration of 2D complementary logic.

Vertical three-dimensional integration of two-dimensional (2D) semiconductors holds great promise, as it offers the possibility to scale up logic layers in the z axis1-3. Indeed, vertical complementary field-effect transistors (CFETs) built with such mixed-dimensional heterostructures4,5, as well as hetero-2D layers with different carrier types6-8, have been demonstrated recently. However, so far, the lack of a controllable doping scheme (especially p-doped WSe2 (refs. 9-17) and MoS2 (refs. 11,18-28)) in 2D semiconductors, preferably in a stable and non-destructive manner, has greatly impeded the bottom-up scaling of complementary logic circuitries. Here we show that, by bringing transition metal dichalcogenides, such as MoS2, atop a van der Waals (vdW) antiferromagnetic insulator chromium oxychloride (CrOCl), the carrier polarity in MoS2 can be readily reconfigured from n- to p-type via strong vdW interfacial coupling. The consequential band alignment yields transistors with room-temperature hole mobilities up to approximately 425 cm2 V-1 s-1, on/off ratios reaching 106 and air-stable performance for over one year. Based on this approach, vertically constructed complementary logic, including inverters with 6 vdW layers, NANDs with 14 vdW layers and SRAMs with 14 vdW layers, are further demonstrated. Our findings of polarity-engineered p- and n-type 2D semiconductor channels with and without vdW intercalation are robust and universal to various materials and thus may throw light on future three-dimensional vertically integrated circuits based on 2D logic gates.

DEMO-EM2: assembling protein complex structures from cryo-EM maps through intertwined chain and domain fitting.

The breakthrough in cryo-electron microscopy (cryo-EM) technology has led to an increasing number of density maps of biological macromolecules. However, constructing accurate protein complex atomic structures from cryo-EM maps remains a challenge. In this study, we extend our previously developed DEMO-EM to present DEMO-EM2, an automated method for constructing protein complex models from cryo-EM maps through an iterative assembly procedure intertwining chain- and domain-level matching and fitting for predicted chain models. The method was carefully evaluated on 27 cryo-electron tomography (cryo-ET) maps and 16 single-particle EM maps, where DEMO-EM2 models achieved an average TM-score of 0.92, outperforming those of state-of-the-art methods. The results demonstrate an efficient method that enables the rapid and reliable solution of challenging cryo-EM structure modeling problems.

CR-I-TASSER: assemble protein structures from cryo-EM density maps using deep convolutional neural networks.

Cryo-electron microscopy (cryo-EM) has become a leading approach for protein structure determination, but it remains challenging to accurately model atomic structures with cryo-EM density maps. We propose a hybrid method, CR-I-TASSER (cryo-EM iterative threading assembly refinement), which integrates deep neural-network learning with I-TASSER assembly simulations for automated cryo-EM structure determination. The method is benchmarked on 778 proteins with simulated and experimental density maps, where CR-I-TASSER constructs models with a correct fold (template modeling (TM) score >0.5) for 643 targets that is 64% higher than the best of some other de novo and refinement-based approaches on high-resolution data samples. Detailed data analyses showed that the main advantage of CR-I-TASSER lies in the deep learning-based Cα position prediction, which significantly improves the threading template quality and therefore boosts the accuracy of final models through optimized fragment assembly simulations. These results demonstrate a new avenue to determine cryo-EM protein structures with high accuracy and robustness covering various target types and density map resolutions.

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.

ADAR1 links R-loop homeostasis to ATR activation in replication stress response.

Unscheduled R-loops are a major source of replication stress and DNA damage. R-loop-induced replication defects are sensed and suppressed by ATR kinase, whereas it is not known whether R-loop itself is actively involved in ATR activation and, if so, how this is achieved. Here, we report that the nuclear form of RNA-editing enzyme ADAR1 promotes ATR activation and resolves genome-wide R-loops, a process that requires its double-stranded RNA-binding domains. Mechanistically, ADAR1 interacts with TOPBP1 and facilitates its loading on perturbed replication forks by enhancing the association of TOPBP1 with RAD9 of the 9-1-1 complex. When replication is inhibited, DNA-RNA hybrid competes with TOPBP1 for ADAR1 binding to promote the translocation of ADAR1 from damaged fork to accumulate at R-loop region. There, ADAR1 recruits RNA helicases DHX9 and DDX21 to unwind R-loops, simultaneously allowing TOPBP1 to stimulate ATR more efficiently. Collectively, we propose that the tempo-spatially regulated assembly of ADAR1-nucleated protein complexes link R-loop clearance and ATR activation, while R-loops crosstalk with blocked replication forks by transposing ADAR1 to finetune ATR activity and safeguard the genome.

Simultaneous SERS Sensing of Cysteine and Homocysteine in Blood Based on the CBT-Cys Click Reaction: Toward Precisive Diagnosis of Schizophrenia.

At present, there is a lack of sufficiently specific laboratory diagnostic indicators for schizophrenia. Serum homocysteine (Hcy) levels have been found to be related to schizophrenia. Cysteine (Cys) is a demethylation product in the metabolism of Hcy, and they always coexist with highly similar structures in vivo. There are few reports on the use of Cys as a diagnostic biomarker for schizophrenia in collaboration with Hcy, mainly because the rapid, economical, accurate, and high-throughput simultaneous detection of Cys and Hcy in serum is highly challenging. Herein, a click reaction-based surface-enhanced Raman spectroscopy (SERS) sensor was developed for simultaneous and selective detection of Cys and Hcy. Through the efficient and specific CBT-Cys click reaction between the probe containing cyan benzothiazole and Cys/Hcy, the tiny methylene difference between the molecular structures of Cys and Hcy was converted into the difference between the ring skeletons of the corresponding products that could be identified by plasmonic silver nanoparticle enhanced molecular fingerprint spectroscopy to realize discriminative detection. Furthermore, the SERS sensor was successfully applied to the detection in related patient serum samples, and it was found that the combined analysis of Cys and Hcy can improve the diagnostic accuracy of schizophrenia compared to a single indicator.

Bi-Functional Electrolyte Additive Leading to a Highly Reversible and Stable Zinc Anode.

A stable stripping/plating process of the zinc anode is extremely critical for the practical application of aqueous zinc metal batteries. However, obstacles, including parasitic reactions and dendrite growth, notoriously deteriorate the stability and reversibility of zinc anode. Herein, Methyl l-α-aspartyl-l-phenylalaninate (Aspartame) is proposed as an effective additive in the ZnSO4 system to realize high stability and reversibility. Aspartame molecule with rich polar functional groups successfully participates in the solvation sheath of Zn2+ to suppress water-induced side reactions. The self-driven adsorption of Aspartame on zinc anode improves uniform deposition with a dose of 10 mm. These synergetic functions endow the zinc anode with a significantly long cycling lifespan of 4500 h. The cell coupled with a vanadium-based cathode also exhibited a high-capacity retention of 71.8% after 1000 cycles, outperforming the additive-free counterparts.

Accurate flexible refinement for atomic-level protein structure using cryo-EM density maps and deep learning.

With the rapid progress of deep learning in cryo-electron microscopy and protein structure prediction, improving the accuracy of the protein structure model by using a density map and predicted contact/distance map through deep learning has become an urgent need for robust methods. Thus, designing an effective protein structure optimization strategy based on the density map and predicted contact/distance map is critical to improving the accuracy of structure refinement. In this article, a protein structure optimization method based on the density map and predicted contact/distance map by deep-learning technology was proposed in accordance with the result of matching between the density map and the initial model. Physics- and knowledge-based energy functions, integrated with Cryo-EM density map data and deep-learning data, were used to optimize the protein structure in the simulation. The dynamic confidence score was introduced to the iterative process for choosing whether it is a density map or a contact/distance map to dominate the movement in the simulation to improve the accuracy of refinement. The protocol was tested on a large set of 224 non-homologous membrane proteins and generated 214 structural models with correct folds, where 4.5% of structural models were generated from structural models with incorrect folds. Compared with other state-of-the-art methods, the major advantage of the proposed methods lies in the skills for using density map and contact/distance map in the simulation, as well as the new energy function in the re-assembly simulations. Overall, the results demonstrated that this strategy is a valuable approach and ready to use for atomic-level structure refinement using cryo-EM density map and predicted contact/distance map.

Multi contact-based folding method for de novo protein structure prediction.

Meta contact, which combines different contact maps into one to improve contact prediction accuracy and effectively reduce the noise from a single contact map, is a widely used method. However, protein structure prediction using meta contact cannot fully exploit the information carried by original contact maps. In this work, a multi contact-based folding method under the evolutionary algorithm framework, MultiCFold, is proposed. In MultiCFold, the thorough information of different contact maps is directly used by populations to guide protein structure folding. In addition, noncontact is considered as an effective supplement to contact information and can further assist protein folding. MultiCFold is tested on a set of 120 nonredundant proteins, and the average TM-score and average RMSD reach 0.617 and 5.815 Å, respectively. Compared with the meta contact-based method, MetaCFold, average TM-score and average RMSD have a 6.62 and 8.82% improvement. In particular, the import of noncontact information increases the average TM-score by 6.30%. Furthermore, MultiCFold is compared with four state-of-the-art methods of CASP13 on the 24 FM targets, and results show that MultiCFold is significantly better than other methods after the full-atom relax procedure.

Palladium-Catalyzed Allylation of Endocyclic 1-Azaallyl Anions.

Endocyclic 1-azaallyl anions engage allyl acetates in a palladium-catalyzed allylation followed by reduction to give unprotected 2-(hetero)aryl-3-allylpiperidines and 2-allyl-3-arylmorpholines, products not easily accessible by other means. The allyl group is then readily transformed into a variety of functional groups. Preliminary studies on the asymmetric variant of the reaction using an enantiomerically pure BI-DIME-type ligand provide the product with moderate enantioselectivity. Computational studies suggest that energy barriers of inner-sphere reductive elimination and outer-sphere nucleophilic substitution are almost the same, which makes both of them possible reaction pathways. In addition, the inner-sphere mechanism displays an enantiodiscriminating C-C bond forming step, while the outer-sphere mechanism is much less selective, which combined to give the asymmetric variant of the reaction moderate enantioselectivity.

Oxidative Free-Radical C(sp2)-H Bond Chlorination of Enaminones with LiCl: Access to Highly Functionalized α-Chlorinated Enaminones.

An oxidative free-radical C(sp2)-H bond chlorination strategy of enaminones has been developed by using LiCl as a chlorinating reagent and K2S2O8 as an oxidant. This transformation provides a new and straightforward synthetic methodology to afford highly functionalized α-chlorinated enaminones with a Z-configuration in good to excellent yields.

Exploring the relationship between infectious agents and autoimmune diseases: a review.

The relationship between infectious agents and autoimmune diseases is a complex issue. In recent years, increasing clinical cases have indicated that infectious agents play an important role in the development of autoimmune diseases. Molecular mimicry is currently widely regarded as the primary pathogenic mechanism of various autoimmune diseases in humans. Components of infectious agents can undergo molecular mimicry with components in patients' bodies, leading to the development of various autoimmune diseases. In this article, we provide a brief overview of current research of the current research status on the relationship between infectious agents and autoimmune diseases, and describe our current understanding of their mechanisms of action in order to better understand the pathogenesis, diagnosis, and treatment of autoimmune diseases.

The Protective Role of IL-36/IL-36R Signal in Con A-Induced Acute Hepatitis.

The IL-36 family, including IL-36α, IL-36ß, IL-36γ, and IL-36R antagonist, belong to the IL-1 superfamily. It was reported that IL-36 plays a role in immune diseases. However, it remains unclear how IL-36 regulates inflammation. To determine the role of IL-36/IL-36R signaling pathways, we established an acute hepatitis mouse model (C57BL/6) by i.v. injection of the plant lectin Con A. We found that the levels of IL-36 were increased in the liver after Con A injection. Our results demonstrated the infiltrated neutrophils, but not the hepatocytes, were the main source of IL-36 in the liver. Using the IL-36R-/- mouse model (H-2b), we surprisingly found that the absence of IL-36 signals led to aggravated liver injury, as evidenced by increased mortality, elevated serum alanine aminotransferase and aspartate aminotransferase levels, and severe liver pathological changes. Further investigations demonstrated that a lack of IL-36 signaling induced intrahepatic activation of CD4+ and CD8+ T lymphocytes and increased the production of inflammatory cytokines. In addition, IL-36R-/- mice had reduced T regulatory cell numbers and chemokines in the liver. Together, our results from the mouse model suggested a vital role of IL-36 in regulating T cell function and homeostasis during liver inflammation.

Identification of novel biomarkers based on lipid metabolism-related molecular subtypes for moderately severe and severe acute pancreatitis.

BACKGROUND: Acute pancreatitis (AP) is an unpredictable and potentially fatal disorder. A derailed or unbalanced immune response may be the root of the disease's severe course. Disorders of lipid metabolism are highly correlated with the occurrence and severity of AP. We aimed to characterize the contribution and immunological characteristics of lipid metabolism-related genes (LMRGs) in non-mild acute pancreatitis (NMAP) and identify a robust subtype and biomarker for NMAP. METHODS: The expression mode of LMRGs and immune characteristics in NMAP were examined. Then LMRG-derived subtypes were identified using consensus clustering. The weighted gene co-expression network analysis (WGCNA) was utilized to determine hub genes and perform functional enrichment analyses. Multiple machine learning methods were used to build the diagnostic model for NMAP patients. To validate the predictive effectiveness, nomograms, receiver operating characteristic (ROC), calibration, and decision curve analysis (DCA) were used. Using gene set variation analysis (GSVA) and single-cell analysis to study the biological roles of model genes. RESULTS: Dysregulated LMRGs and immunological responses were identified between NMAP and normal individuals. NMAP individuals were divided into two LMRG-related subtypes with significant differences in biological function. The cluster-specific genes are primarily engaged in the regulation of defense response, T cell activation, and positive regulation of cytokine production. Moreover, we constructed a two-gene prediction model with good performance. The expression of CARD16 and MSGT1 was significantly increased in NMAP samples and positively correlated with neutrophil and mast cell infiltration. GSVA results showed that they are mainly upregulated in the T cell receptor complex, immunoglobulin complex circulating, and some immune-related routes. Single-cell analysis indicated that CARD16 was mainly distributed in mixed immune cells and macrophages, and MGST1 was mainly distributed in exocrine glandular cells. CONCLUSIONS: This study presents a novel approach to categorizing NMAP into different clusters based on LMRGs and developing a reliable two-gene biomarker for NMAP.

Magnolol promotes the autophagy of esophageal carcinoma cells by upregulating HACE1 gene expression.

Esophagus cancer (EC) is one of the most aggressive malignant digestive system tumors and has a high clinical incidence worldwide. Magnolol, a natural compound, has anticancer effects on many cancers, including esophageal carcinoma, but the underlying mechanism has not been fully elucidated. Here, we first find that magnolol inhibits the proliferation of esophageal carcinoma cells and enhances their autophagy activity in a dose- and time-dependent manner. This study demonstrates that magnolol increases the protein levels of LC3 II, accompanied by increased HACE1 protein levels in both esophageal carcinoma cells and xenograft tumors. HACE1-knockout (KO) cell lines are generated, and the ablation of HACE1 eliminates the anti-proliferative and autophagy-inducing effects of magnolol on esophageal carcinoma cells. Additionally, our results show that magnolol primarily promotes HACE1 expression at the transcriptional level. Therefore, this study shows that magnolol primarily exerts its antitumor effect by activating HACE1-OPTN axis-mediated autophagy. It can be considered a promising therapeutic drug for esophageal carcinoma.

Stable Electron-Transport-Layer-Free Perovskite Solar Cells with over 22% Power Conversion Efficiency.

Due to their low cost and simplified production process, electron-transport-layer-free (ETL-free) perovskite solar cells (PSCs) have attracted great attention recently. However, the performance of ETL-free PSCs is still at a disadvantage compared to cells with a conventional n-i-p structure due to the severe recombination of charge carriers at the perovskite/anode interface. Here, we report a strategy to fabricate stable ETL-free FAPbI3 PSCs by in situ formation of a low dimensional perovskite layer between the FTO and the perovskite. This interlayer gives rise to the energy band bending and reduced defect density in the perovskite film and indirect contact and improved energy level alignment between the anode and perovskite, which facilitates charge carrier transport and collection and suppresses charge carrier recombination. As a result, ETL-free PSCs with a power conversion efficiency (PCE) exceeding 22% are achieved under ambient conditions.

Research on mining land subsidence by intelligent hybrid model based on gradient boosting with categorical features support algorithm.

Land subsidence induced by coal mining (MLS) has posed a huge threat to the ecological environment, buildings, roads, and other infrastructure safety in mining areas. However, the prediction and evaluation of MLS is relatively complex, and the reliability of the prediction results is closely related to factors such as the professional knowledge and engineering experience of researchers. This paper aims to combine intelligent optimization algorithms: ant lion optimizer (ALO), bald eagle search (BES), bird swarm algorithm (BSA), harris hawks optimization (HHO), and sparrow search algorithm (SSA), with machine learning model of gradient boosting with categorical features support algorithm (CatBoost) to predict MLS. To achieve this goal, five hybrid models based CatBoost were developed and the prediction accuracy and reliability of the models were compared and analyzed. The prediction performance of the hybrid models has been significantly improved on the basis of a single model, of which the SSA-CatBoost model has the most obvious improvement (from R2 = 0.927 to 0.965, RMSE = 0.541 to 0.377, MAE = 0.386 to 0.297, VAF = 92.720 to 95.837). The importance and predictive contribution of all input features to predictive labels were studied with the Shapley method. The research results indicate that hybrid model technology is a reliable MLS prediction method. This study can help mining technicians use machine learning methods to study the degree of MLS damage to the surface environment and provide scientific advanced prediction and evaluation for the protection and management of the ecological environment in mining areas and the formulation of safety production measures.

Advancements in Characterization Techniques for Microemulsions: From Molecular Insights to Macroscopic Phenomena.

Microemulsions are thermodynamically stable, optically isotropic, transparent, or semi-transparent mixed solutions composed of two immiscible solvents stabilized by amphiphilic solutes. This comprehensive review explores state-of-the-art techniques for characterizing microemulsions, which are versatile solutions essential across various industries, such as pharmaceuticals, food, and petroleum. This article delves into spectroscopic methods, nuclear magnetic resonance, small-angle scattering, dynamic light scattering, conductometry, zeta potential analysis, cryo-electron microscopy, refractive index measurement, and differential scanning calorimetry, examining each technique's strengths, limitations, and potential applications. Emphasizing the necessity of a multi-technique approach for a thorough understanding, it underscores the importance of integrating diverse analytical methods to unravel microemulsion structures from molecular to macroscopic scales. This synthesis provides a roadmap for researchers and practitioners, fostering advancements in microemulsion science and its wide-ranging industrial applications.

An Immunochromatographic Test Strip for Rapid Quantitative Control of Monoclonal Antibodies against Programmed Cell Death Protein 1.

Cancer is one of the major public health challenges in the world, which is characterized by rapid progression and high mortality. Immunotherapy, represented by PD-1 monoclonal antibody, has significantly improved the efficacy of malignant tumors and has become one of the most popular immunotherapy methods at present. Therefore, there is an increasing demand for novel detection methods for PD-1 monoclonal antibodies. The aim of this work was to establish a rapid, simple, and sensitive immunochromatographic test strip (ICTS) based on the AuNPs enlargement for both visual and instrumental detection of the PD-1 monoclonal antibody concentration. The mixed solution of NH2OH·HCl and HAuCl4 was used as an enhancement solution to lower the detection limit and achieve higher sensitivity. A test strip reader was used to construct a visualized quantitative detection standard curve for the PD-1 monoclonal antibody concentration. The LOD was 1.58 ng/mL through a triple signal-to-noise ratio. The detection time was within 10 min. The constructed test strips can rapidly, accurately, and efficiently detect the concentration of PD-1 monoclonal antibody in real samples.