American Journal of Pathology最新文献

Retinal detachment (RD) is an ocular emergency that can lead to irreversible vision loss. However, because of the cellular heterogeneity within the retina, the pathologic alterations following RD remain insufficiently elucidated. In this study, single-cell RNA sequencing was performed on retinal tissues from patients with RD, and the data were analyzed using the single-cell graphical Gaussian model, a gene co-expression network analysis algorithm developed by our team. Single-cell graphical Gaussian model analysis revealed several cell-type-specific gene modules (GMs) following RD, which were further validated. We observed a GM associated with the glycolytic process that was up-regulated across most cell clusters, and further confirmed that anaerobic glycolysis in the retina was markedly increased following RD. In addition, a GM associated with apoptosis regulation was significantly enriched in rod cells. In Müller cells, the GM related to extracellular matrix organization was down-regulated. In microglia, GMs related to leukocyte migration were up-regulated, potentially involving the fibronectin 1 pathway, and limited evidence suggested T-cell infiltration into the retina following RD, with both findings remaining preliminary and requiring further validation. Overall, this study reveals cell-type-specific pathologic changes following RD, providing deeper insight into the pathologic mechanisms underlying visual dysfunction following RD.

Acute pancreatitis (AP) is a common, potentially severe inflammatory disease of the pancreas. Although environmental triggers such as alcohol and gallstones are well known, only a subset of exposed individuals develop AP, suggesting that genetic or intrinsic factors contribute to disease onset and severity. Endoplasmic reticulum (ER) stress has emerged as a key pathogenic mechanism in AP due to the essential role of the ER in protein synthesis, folding, and degradation (proteostasis). Mesencephalic astrocyte-derived neurotrophic factor (MANF) is an ER stress-inducible protein highly expressed in the pancreas and critical for proteostasis, but its role in AP remains unclear. To investigate this, pancreas-specific Manf knockout mice were generated using the Cre/loxP system and subjected them to caerulein- or alcohol-induced AP. In both models, MANF deficiency worsened pancreatic injury, as evidenced by elevated ER stress markers [phosphorylated eukaryotic initiation factor 2 alpha (p-eIF2α) and glucose-regulated protein 78 (GRP78)], apoptosis (cleaved caspase-3), inflammation (IL-6 and tumor necrosis factor α), regeneration (Ki67), and elevated pancreatic lipase levels. In the caerulein model, male Manf knockout mice exhibited higher oxidative stress and macrophage infiltration than female mice. In the alcohol model, both sexes showed increased inflammation and macrophage infiltration, but oxidative stress and high mobility group box 1 expression were again more prominent in male mice. These findings suggest that MANF contributes to pancreatic resilience under stress conditions and may influence sex-dependent responses during AP.

Geleophysic dysplasia (GD) is caused by recessive mutations in ADAMTSL2 (GD1, ∼50% of cases), or dominant mutations in FBN1 (GD2, ∼50% of cases) or LTBP3 (GD3, <1% of cases). GD is characterized by severe short stature and other skeletal abnormalities, characteristic facial features, thick skin, and hypermuscular build. Life-threatening complications can arise from progressive heart valve disease and narrowing of the large airways, resulting in ∼33% mortality before the age of five. Despite high childhood mortality and significant morbidity, no disease-modifying treatments exist for GD. To model disease progression and enable efficacy testing of mechanism-based therapeutic approaches, a mouse model for severe GD1 was generated by introducing the patient-specific ADAMTSL2 c.499G>A (p.D167N) mutation into the mouse Adamtsl2 locus. Homozygous Adamtsl2^D167N/D167N (D167N) mice had reduced postnatal survival and developed short stature. Like GD1 patients, radiographs demonstrated significantly shortened hind- and forelimb bones with delayed mineralization and abnormally shaped (ovoid) vertebrae. Histological investigation revealed a shortened growth plate, suggesting abnormalities in chondrogenesis. Cardiac histomorphometry revealed dysplastic aortic heart valves, consistent with progressive heart valve disease observed in GD1 patients. In the lungs, bronchial obstruction by vesicular structures was observed, as previously reported for global Adamtsl2 knockout mice, likely resulting in occlusion of the affected airways. Thus, the ADAMTSL2 D167N mouse model recapitulates key clinical manifestations of GD1 patients.

There has been extensive interest in metabolic dysfunction-associated steatotic liver disease (MASLD) and its more advanced stage, metabolic dysfunction-associated steatohepatitis (MASH). Although several studies have highlighted the importance of hepatocyte Notch signaling in MASH-related liver fibrosis, the role of macrophage Notch signaling in MASH-related fibrosis has been less explored. This study found that Notch1 signaling in hepatic macrophages was significantly activated in a high-fat diet (HFD)-induced mouse model of MASH. Depletion of myeloid Notch1 led to a significant reduction in disease progression in this HFD-induced MASH model, as evidenced by reduced hepatic inflammation, steatosis, and fibrosis in HFD-fed, myeloid-specific Notch1 knockout mice. Intriguingly, disruption of Notch1 suppressed the expression of Gli1, a key transcription factor in the Hedgehog signaling pathway, and promoted M2-like macrophage polarization in the injured liver. Furthermore, the Notch1 deletion-mediated effects-namely, M2 macrophage polarization and the reduction in hepatic steatosis and fibrosis-were reversed by treatment with the Hedgehog agonist SAG in HFD-fed mice. In vitro experiments further demonstrated that Notch1 deletion promoted a shift in macrophage polarization toward the M2 phenotype by inhibiting Gli1 expression. Collectively, these findings identify the Notch1-Gli1 axis as a novel regulator of macrophage polarization, hepatic inflammation, steatosis, and fibrosis in MASH.

Propranolol is currently the first-line therapy for infantile hemangioma (IH); however, its use is limited by notable adverse effects. The non-beta blocker enantiomer of propranolol, R-propranolol, represents a promising alternative for IH treatment. In this study, the efficacy of R-propranolol and its underlying mechanism in IH involution were investigated. The protein kinase RNA-like endoplasmic reticulum kinase (PERK) signaling pathway was required for the adipogenic potential of hemangioma stem cells (HemSCs), whereas prolonged PERK activation led to apoptosis. R-propranolol and racemic propranolol induces comparable HemSCs adipogenesis. R-propranolol showed a stronger ability to disrupt protein homeostasis in HemSCs than racemic propranolol, as indicated by increased protein misfolding and enhanced activation of the PERK signaling pathway. Notably, R-propranolol induced HemSCs apoptosis more effectively than racemic propranolol. This effect was attenuated by the PERK inhibitor GSK2606414. Immunostaining of clinical specimens further showed that PERK expression was higher in involuting IH samples than in proliferating IH samples. Together, these findings demonstrate that R-propranolol promotes adipogenesis and subsequent apoptosis of HemSCs through activation of the PERK signaling pathway.

Lung cancer is projected to become the leading cause of cancer-related mortality in both smoking and non-smoking populations. Rapid onsite evaluation (ROSE) of fine-needle aspiration specimens is essential for timely diagnosis and procedural decision-making during lung cancer assessment. We developed a machine-learning pipeline for cell-based adequacy assessment and lesion detection that integrates automated cell detection, convolutional neural network-based cell classification, and slide-level aggregation using a random forest model. On held-out test data, binary classifiers for lymphocytes and tumor cells achieved accuracies of 91.5% and 92.7% with recalls of 92.6% and 93.1%, respectively. The end-to-end ROSE system demonstrated class accuracies of 82-85%, comparable to human cytologist performance, and a lesion-focused classifier reached a recall of 92.0%. These findings indicate that machine-learning-based cell analysis can support ROSE by expediting adequacy assessment and improving diagnostic yield during TBNA procedures.

Psoriasis is a chronic, immune-mediated dermatological disorder characterized by hyperproliferation of keratinocytes and dysregulated immune signaling. Although genome-wide association studies (GWAS) have identified susceptibility loci, the disease's multifactorial nature underscores the importance of non-genetic regulatory mechanisms. Among these epigenetic modifications are those that critically link genetic predisposition with environmental stimuli. This review offers an in-depth overview of current insights into the role of epigenetic regulation in psoriasis' pathophysiology. Key mechanisms, including aberrant DNA methylation, histone post-translational modifications (e.g., H3K27ac, H3K4me3), and dysregulated non-coding RNAs (ncRNA), are discussed in the context of inflammatory signaling and immune cell function. This study explored how environmental factors such as ultraviolet radiation and air pollution induce epigenetic reprogramming that perpetuates pro-inflammatory states. Furthermore, it highlights the translational potential of targeting epigenetic regulators and epigenome editing technologies, including Clustered Regularly Interspaced Short Palindromic Repeat (CRISPR) fusion systems, as precision therapeutic strategies. In parallel, advances in single-cell epigenomics, spatial transcriptomics, and the profiling of circulating biomarkers offer novel diagnostic tools. Despite advances, challenges persist, including the limited predictive value of preclinical models and variable epigenetic profiles. Positioning epigenetics as the bridge between genetic risk, environmental triggers, and therapeutic advances, this review presents a precision medicine framework for psoriasis.

This study aimed to identify novel signaling axes governing keloid pathogenesis by investigating the role of the semaphorin 3C (SEMA3C)/plexin D1 (PLXND1) pathway in fibrotic processes via transforming growth factor (TGF)-β1 signaling, using single-cell RNA sequencing (scRNA-seq) and experimental validation. scRNA-seq analysis was performed on eight keloid and eight normal skin samples from four public data sets, using Seurat and CellChat to map intercellular communication networks. Primary keloid fibroblasts were treated with recombinant SEMA3C, PLXND1-specific siRNA, or the TGF-β1 inhibitor SB431542. Transcriptome sequencing, real-time quantitative PCR, Western blot analysis, and immunofluorescence were used to assess changes in collagen I/III, fibronectin, and TGF-β1 expression. scRNA-seq revealed significantly enhanced intercellular communication in keloids, particularly among fibroblasts, with a 1.65-fold increase in interaction numbers and 17.79-fold stronger communication strength compared with normal skin. A critical ligand-receptor pair, SEMA3C (predominantly secreted by Schwann cells) and its receptor PLXND1 (overexpressed in keloid fibroblasts), was identified as the most prevalent in keloid samples. Experimental assays demonstrated that SEMA3C dose dependently up-regulated collagen I/III, fibronectin, and TGF-β1 expression, whereas PLXND1 knockdown or TGF-β1 inhibition (via SB431542) attenuated these effects, confirming that SEMA3C/PLXND1 drives fibrosis through TGF-β1 signaling. This study is the first to demonstrate that the SEMA3C/PLXND1 axis drives keloid fibrosis by activating TGF-β1, promoting collagen and extracellular matrix deposition. Targeting this axis holds promise for keloid therapy.

Noncoding RNAs (ncRNAs) are important regulators of gene expression in development, immunity, and disease. Among them, long noncoding RNAs (lncRNAs) and tRNA-derived fragments (tRFs) represent two major types of ncRNA that differ in size, structure, and function. As regulators, lncRNAs display remarkable structural complexity and extensive isoform diversity. Discrete motifs (such as hairpins, triple helices, G-quadruplexes, and scaffold domains) harbored by a given isoform govern interactions with DNA, RNA, and proteins, yet isoform-specific structures are rarely addressed, leading to contradictory findings across studies. In contrast, tRFs (14 to 35 nucleotides) arise from precise cleavage of precursor or mature tRNAs and regulate translation, stress responses, and epigenetic inheritance. Their limited length constrains large-scale structural isoform diversity, but their functions are strongly shaped by chemical modifications, which affect stability, localization, and association with RNA-binding proteins. Here, two underappreciated principles are highlighted: isoform- and structure-resolved mechanisms are essential for truly understanding lncRNA biology; and modification-driven rules diversify tRF functions. An integrated framework that combines sequence, structure, isoform, and modification to refine mechanisms of action of ncRNAs and accelerate ncRNA-based diagnostics and therapeutics is proposed.

Flow cytometry immunophenotyping is essential for diagnosing B-cell lymphomas, but manual interpretation of high-dimensional data remains subjective, time-consuming, and prone to interoperator variability. Previous computational approaches often overlook clinically relevant principles, such as Ig light chain restriction. To address this gap, a biologically informed, three-stage machine learning pipeline that integrates Ig κ (IGK) and Ig λ (IGL) signatures to improve B-cell lymphoma detection was developed. A total of 200 peripheral blood samples (100 normal, 100 abnormal) were analyzed, comprising >15 million single-cell events characterized by 21 immunophenotypic markers. Three XGBoost models were trained sequentially: the first classified light chain expression (IGK, IGL, or nuisance), the second identified cell phenotypes using marker intensities and IGK/IGL-based neighborhood enrichment, and the third produced sample-level predictions based on aggregated cell features. The IGK/IGL classifier achieved 88.0% test accuracy [area under the receiver operating characteristic curve (AUC), 0.957], whereas the cell-level classification reached 92.9% accuracy (AUC, 0.983), with IGK/IGL enrichment as the most informative feature. Similarly, sample-level classification achieved 94.7% accuracy (AUC, 0.976), with improved performance when IGK/IGL enrichment was included. These findings demonstrate that incorporating biologically grounded features enhances both the accuracy and interpretability of automated flow cytometry analysis. This approach offers a scalable, reproducible, and clinically aligned alternative to the manual review of flow cytometry data for B-cell lymphomas.