Single-cell analyses highlight the proinflammatory contribution of C1q-high monocytes to Behçet's disease.

Behçet's disease (BD) is a chronic vasculitis characterized by systemic immune aberrations. However, a comprehensive understanding of immune disturbances in BD and how they contribute to BD pathogenesis is lacking. Here, we performed single-cell and bulk RNA sequencing to profile peripheral blood mononuclear cells (PBMCs) and isolated monocytes from BD patients and healthy donors. We observed prominent expansion and transcriptional changes in monocytes in PBMCs from BD patients. Deciphering the monocyte heterogeneity revealed the accumulation of C1q-high (C1qhi) monocytes in BD. Pseudotime inference indicated that BD monocytes markedly shifted their differentiation toward inflammation-accompanied and C1qhi monocyte-ended trajectory. Further experiments showed that C1qhi monocytes enhanced phagocytosis and proinflammatory cytokine secretion, and multiplatform analyses revealed the significant clinical relevance of this subtype. Mechanistically, C1qhi monocytes were induced by activated interferon-γ (IFN-γ) signaling in BD patients and were decreased by tofacitinib treatment. Our study illustrates the BD immune landscape and the unrecognized contribution of C1qhi monocytes to BD hyperinflammation, showing their potential as therapeutic targets and clinical assessment indexes.

Comprehensive assessment of cellular senescence in the tumor microenvironment.

Cellular senescence (CS), a state of permanent growth arrest, is intertwined with tumorigenesis. Due to the absence of specific markers, characterizing senescence levels and senescence-related phenotypes across cancer types remain unexplored. Here, we defined computational metrics of senescence levels as CS scores to delineate CS landscape across 33 cancer types and 29 normal tissues and explored CS-associated phenotypes by integrating multiplatform data from ~20 000 patients and ~212 000 single-cell profiles. CS scores showed cancer type-specific associations with genomic and immune characteristics and significantly predicted immunotherapy responses and patient prognosis in multiple cancers. Single-cell CS quantification revealed intra-tumor heterogeneity and activated immune microenvironment in senescent prostate cancer. Using machine learning algorithms, we identified three CS genes as potential prognostic predictors in prostate cancer and verified them by immunohistochemical assays in 72 patients. Our study provides a comprehensive framework for evaluating senescence levels and clinical relevance, gaining insights into CS roles in cancer- and senescence-related biomarker discovery.

MetSigDis: a manually curated resource for the metabolic signatures of diseases.

Complex diseases cannot be understood only on the basis of single gene, single mRNA transcript or single protein but the effect of their collaborations. The combination consequence in molecular level can be captured by the alterations of metabolites. With the rapidly developing of biomedical instruments and analytical platforms, a large number of metabolite signatures of complex diseases were identified and documented in the literature. Biologists' hardship in the face of this large amount of papers recorded metabolic signatures of experiments' results calls for an automated data repository. Therefore, we developed MetSigDis aiming to provide a comprehensive resource of metabolite alterations in various diseases. MetSigDis is freely available at http://www.bio-annotation.cn/MetSigDis/. By reviewing hundreds of publications, we collected 6849 curated relationships between 2420 metabolites and 129 diseases across eight species involving Homo sapiens and model organisms. All of these relationships were used in constructing a metabolite disease network (MDN). This network displayed scale-free characteristics according to the degree distribution (power-law distribution with R2 = 0.909), and the subnetwork of MDN for interesting diseases and their related metabolites can be visualized in the Web. The common alterations of metabolites reflect the metabolic similarity of diseases, which is measured using Jaccard index. We observed that metabolite-based similar diseases are inclined to share semantic associations of Disease Ontology. A human disease network was then built, where a node represents a disease, and an edge indicates similarity of pair-wise diseases. The network validated the observation that linked diseases based on metabolites should have more overlapped genes.