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The EV-A71 poses a serious threat to the health and lives of children. The EV-A71 can be transmitted by direct and indirect skin contact. Therefore, there is an urgent need to create novel skin models using human-derived cells to study the biology and pathogenesis of the virus and facilitate drug screening. Here, we use human induced pluripotent stem cells-derived skin organoids (hiPSC-SOs) as a model for EV-A71 infection and find that multiple cell types within the skin organoids, including epidermal cells, hair follicle cells, fibroblasts, and nerve cells, express EV-A71 receptors and are susceptible to EV-A71 infection. We elucidate the specific response of different cell types to EV-A71 and reveal that EV-A71 infection can degrade extracellular collagen and affect fibroblasts. We find that EV-A71 can mediate epidermal cell damage through autophagy and Integrin/Hippo-YAP/TAZ signaling pathways, thereby promoting hyperproliferation of progenitor cells. Based on this finding, we identify an autophagy-associated protein as a drug target of EV-A71 and discover an EV-A71 replication inhibitor. Altogether, these data suggest that hiPSC-SOs can be used as an infectious disease model to study skin infectious diseases, providing a valuable resource for drug screening to identify candidate virus therapeutics. Here, the authors establish an EV-A71 infected skin organoid model to study the virus infection pathogenesis and drug screening. The authors identify an autophagy-associated protein as drug target of EV-A71 and discover an EV-A71 replication inhibitor.

Idiopathic pulmonary fibrosis (IPF) is a chronic interstitial lung disease with a high mortality rate and unclarified aetiology. Immune response is elaborately regulated during the progression of IPF, but immune cells subsets are complicated which has not been detailed described during IPF progression. Therefore, in the current study, we sought to investigate the role of immune regulation by elaborately characterize the heterogeneous of immune cells during the progression of IPF. To this end, we performed single-cell profiling of lung immune cells isolated from four stages of bleomycin-induced pulmonary fibrosis—a classical mouse model that mimics human IPF. The results revealed distinct components of immune cells in different phases of pulmonary fibrosis and close communication between macrophages and other immune cells along with pulmonary fibrosis progression. Enriched signals of SPP1, CCL5 and CXCL2 were found between macrophages and other immune cells. The more detailed definition of the subpopulations of macrophages defined alveolar macrophages (AMs) and monocyte-derived macrophages (mo-Macs)—the two major types of primary lung macrophages—exhibited the highest heterogeneity and dynamic changes in expression of profibrotic genes during disease progression. Our analysis suggested that Gpnmb and Trem2 were both upregulated in macrophages and may play important roles in pulmonary fibrosis progression. Additionally, the metabolic status of AMs and mo-Macs varied with disease progression. In line with the published data on human IPF, macrophages in the mouse model shared some features regarding gene expression and metabolic status with that of macrophages in IPF patients. Our study provides new insights into the pathological features of profibrotic macrophages in the lung that will facilitate the identification of new targets for disease intervention and treatment of IPF.

Background With the rapid increase in the amount of Protein-Protein Interaction (PPI) data, the establishment of an event-centered PPI ontology that contains temporal and spatial vocabularies is urgently needed to clarify PPI biological annotations. In this paper, we propose a precisely designed schema - PPIO (PPI Ontology) for representing the biological context of PPIs. Results Inspired by the event model and the distinct characteristics of PPI events, PPIO consists of six core aspects of the information required for reporting a PPI event, including the interactor (who), the biological process (when), the subcellular location (where), the interaction type (how), the biological function (what) and the detection method (which). PPIO is implemented through the integration of appropriate terms from the corresponding vocabularies/ontologies, e.g., Gene Ontology, Protein Ontology, PSI-MI/MOD, etc. To assess PPIO, an approach based on PPIO in developed to extract PPI biological annotations from an open standard corpus “BioCreAtIvE-PPI”. The experiment results demonstrate PPIO’s high performance, a precision of 0.69, a recall of 0.72 and an F-score of 0.70. Conclusions PPIO is a well-constructed essential ontology in the interpretation of PPI biological context. The results of the experiments conducted on the BioCreAtIvE corpus demonstrate that PPIO is able to facilitate PPI annotation extraction from biomedical literature effectively and enrich essential annotation for PPIs.

The diagnosis of Behçet’s disease (BD) predominantly relies on clinical symptoms, indicating an urgent for identifying potential biomarkers for early diagnosis and disease stratification. We employed an in‐depth proteomics platform based on data‐independent acquisition mass spectrometry (DIA‐MS) and customizable antibody microarray technology, combined with machine learning methods. By analyzing the proteomic data in the training cohort, we trained an XGBoost machine learning model, and validated the model in an independent cohort. The model displayed a favorable performance in BD diagnosis and stratification. In the training set, the area under the curve (AUC) of the diagnostic model was 0.984 with an accuracy of 0.935. In the validation set, the AUC was 0.967 with an accuracy of 0.871. The AUCs for differentiating different severity BD groups ranged from 0.897 to 0.986 in the training set, and from 0.718 to 0.960 in the validation set. Functional analysis indicated that processes such as defense response, protein activation cascade, and complement activation were related to disease severity. Complement C4B was crucial in the protein‐protein interaction network. This study is the first to construct an artificial intelligence‐based BD diagnosis and stratification model, providing potential biomarkers and new strategies for precise diagnosis and treatment of BD. The diagnosis of BD predominantly relies on clinical symptoms, indicating an urgent for identifying potential biomarkers for early diagnosis and disease stratification. We employed an in‐depth proteomics platform based on data‐independent acquisition mass spectrometry (DIA‐MS) and customizable antibody microarray technology, combined with machine learning methods. By analyzing the proteomic data in the training cohort, we trained an XGBoost machine learning model, and validated the model in an independent cohort. The model displayed a favorable performance in BD diagnosis and stratification. In the training set, the area under the curve (AUC) of the diagnostic model was 0.984 with an accuracy of 0.935. In the validation set, the AUC was 0.967 with an accuracy of 0.871. The AUCs for differentiating different severity BD groups ranged from 0.897 to 0.986 in the training set, and from 0.718 to 0.960 in the validation set. Functional analysis indicated that processes such as defense response, protein activation cascade, and complement activation were related to disease severity. Complement C4B was crucial in the protein‐protein interaction network. This study is the first to construct an artificial intelligence‐based BD diagnosis and stratification model, providing potential biomarkers and new strategies for precise diagnosis and treatment of BD. Molecular and functional landscape of BD progression and severity based on in‐depth proteomics. Tree boosting machine learning models with favorable performance in BD diagnosis and stratification. Efficient biomarker panels for BD diagnosis and stratification.

Localized scleroderma (LoS) is a rare chronic disease with extensive tissue fibrosis, inflammatory infiltration, microvascular alterations, and epidermal appendage lesions. However, a deeper understanding of the pathogenesis and treatment strategies of LoS is currently limited. In the present work, a proteome map of LoS skin is established, and the pathological features of LoS skin are characterized. Most importantly, a human‐induced pluripotent stem cell‐derived epithelial and mesenchymal (EM) organoids model in a 3D culture system for LoS therapy is established. According to the findings, the application of EM organoids on scleroderma skin can significantly reduce the degree of skin fibrosis. In particular, EM organoids enhance the activity of epidermal stem cells in the LoS skin and promotes the regeneration of sweat glands and blood vessels. These results highlight the potential application of organoids for promoting the recovery of scleroderma associated phenotypes and skin‐associated functions. Furthermore, it can provide a new therapeutic alternative for patients suffering from disfigurement and skin function defects caused by LoS. A proteome map of localized scleroderma (LoS) skin is established. A human‐induced pluripotent stem cell‐derived epithelial and mesenchymal organoids model is constructed and applied for LoS therapy. The study results highlight the potential application of organoids for promoting the recovery of scleroderma associated phenotypes and skin‐associated functions.

Neurosyphilis (NS) is a clinical condition caused by infection of the central nervous system (CNS) by Treponema pallidum (Tp) that can lead to asymptomatic meningitis and more serious neurological diseases, such as dementia and blindness. However, current studies on the pathogenesis of NS are limited. Here, through the integration analysis of proteomics and single‐cell transcriptomics, Toll‐like/NF‐κB signaling is identified as the key pathway involved in CNS damage caused by Tp. Moreover, monocyte‐derived macrophages are key cells involved in the inflammatory response to Tp in the CNS of NS patients. In addition, it is found that inflammatory cells in peripheral blood may cause neurological damage through disruption of the blood‒brain barrier (BBB) in individuals with NS. Notably, activation of the Toll‐like/NF‐κB signaling pathway, as well as dysregulation of neural function, is likewise validated in an in vitro NS brain organoid model. In conclusion, the results revealed the mechanisms of inflammation‐mediated brain injury in Tp‐induced NS and provided new ideas for the clinical treatment of Tp infection. This study reveals the mechanisms of inflammation and immune‐mediated brain injury in patients with Neurosyphilis (NS). By proteomic analysis of brain tissues and single‐cell sequencing of cerebrospinal fluid from NS patients, researchers identified Toll‐like/NF‐κB signaling as the key pathway involved in the damage of central nervous system in NS. Meanwhile, researchers verified the damaging effects of peripheral inflammatory cells on the brain using organoids.

Papillary thyroid cancer (PTC) is the most frequent subtype of thyroid cancer, but 20% of cases are indeterminate (i.e., cannot be accurately diagnosed) based on preoperative cytology, which might lead to surgical removal of a normal thyroid gland. To address this concern, we performed an in-depth analysis of the serum proteomes of 26 PTC patients and 23 healthy controls using antibody microarrays and data-independent acquisition mass spectrometry (DIA-MS). We identified a total of 1091 serum proteins spanning 10–12 orders of magnitude. 166 differentially expressed proteins were identified that participate in complement activation, coagulation cascades, and platelet degranulation pathways. Furthermore, the analysis of serum proteomes before and after surgery indicated that the expression of proteins such as lactate dehydrogenase A and olfactory receptor family 52 subfamily B member 4, which participate in fibrin clot formation and extracellular matrix-receptor interaction pathways, were changed. Further analysis of the proteomes of PTC and neighboring tissues revealed integrin-mediated pathways with possible crosstalk between the tissue and circulating compartments. Among these cross-talk proteins, circulating fibronectin 1 (FN1), gelsolin (GSN) and UDP-glucose 4-epimerase (GALE) were indicated as promising biomarkers for PTC identification and validated in an independent cohort. In differentiating between patients with benign nodules or PTC, FN1 produced the best ELISA result (sensitivity = 96.89%, specificity = 91.67%). Overall, our results present proteomic landscapes of PTC before and after surgery as well as the crosstalk between tissue and the circulatory system, which is valuable to understand PTC pathology and improve PTC diagnostics in the future.

Neurosyphilis (NS) is a central nervous system (CNS) infection caused by Treponema pallidum (T. pallidum). NS can occur at any stage of syphilis and manifests as a broad spectrum of clinical symptoms. Often referred to as “the great imitator,” NS can be easily overlooked or misdiagnosed due to the absence of standard diagnostic tests, potentially leading to severe and irreversible organ dysfunction. In this study, proteomic and machine learning model techniques are used to characterize 223 cerebrospinal fluid (CSF) samples to identify diagnostic markers of NS and provide insights into the underlying mechanisms of the associated inflammatory responses. Three biomarkers (SEMA7A, SERPINA3, and ITIH4) are validated as contributors to NS diagnosis through multicenter verification of an additional 115 CSF samples. We anticipate that the identified biomarkers will become effective tools for assisting in diagnosis of NS. Our insights into NS pathogenesis in brain tissue may inform therapeutic strategies and drug discoveries for NS patients. This study presents an innovative approach to diagnosing Neurosyphilis (NS), a complex central nervous system (CNS) infection. By analyzing 223 cerebrospinal fluid samples using proteomic techniques and machine learning models, researchers identifies three key biomarkers (SEMA7A, SERPINA3, and ITIH4) for NS. These findings, validated through multicenter research with additional 115 samples, offer new diagnostic tools and deepen understanding of NS pathogenesis.

Host immunity is central to the virus's spread dynamics, which is significantly influenced by vaccination and prior infection experiences. In this work, we analyzed the co‐evolution of SARS‐CoV‐2 mutation, angiotensin‐converting enzyme 2 (ACE2) receptor binding, and neutralizing antibody (NAb) responses across various variants in 822 human and mice vaccinated with different non‐Omicron and Omicron vaccines is analyzed. The link between vaccine efficacy and vaccine type, dosing, and post‐vaccination duration is revealed. The classification of immune protection against non‐Omicron and Omicron variants is co‐evolved with genetic mutations and vaccination. Additionally, a model, the Prevalence Score (P‐Score) is introduced, which surpasses previous algorithm‐based models in predicting the potential prevalence of new variants in vaccinated populations. The hybrid vaccination combining the wild‐type (WT) inactivated vaccine with the Omicron BA.4/5 mRNA vaccine may provide broad protection against both non‐Omicron variants and Omicron variants, albeit with EG.5.1 still posing a risk. In conclusion, these findings enhance understanding of population immunity variations and provide valuable insights for future vaccine development and public health strategies. This study analyzes the co‐evolution of SARS‐CoV‐2 mutation, ACE2 receptor binding, and NAb responses across various variants using SARS‐CoV‐2 broad neutralizing antibody (bNAb) assay. The results reveal the link between vaccine efficacy and vaccine type, dosing, and the hybrid vaccination combining the WT inactivated vaccine with the Omicron BA.4/5 mRNA vaccine may provide broad protection against both non‐Omicron variants and Omicron variants.