American Journal of Pathology最新文献

The etiology of adenomyosis remains unclear. The association between epithelial-mesenchymal transition (EMT) and mitochondrial dysfunction has been proposed to be involved in fibrotic diseases. Adenomyosis is defined as the existence of endometrial glands and stroma in the myometrium with EMT and ultimate fibrosis. Thus, we aimed to investigate the involvement of mitochondrial dysfunction in fibrotic adenomyosis. Mitochondrial integrity was examined in mouse and human adenomyotic tissues. Control and tamoxifen-treated mice were treated with 3-nitropropionic acid (a mitochondrial dysfunction inducer) and NG-nitro-L-arginine methyl ester (a mitochondrial dysfunction inhibitor), respectively, at postnatal day 21, followed by an evaluation of adenomyosis, EMT and fibrosis as well as the expression of mitophagy, oxidative stress, and transforming growth factor-β1 (TGF-β1). The gene profiles of adenomyotic uteri were examined at postnatal day 42. Adenomyotic mice exhibited increased development of EMT and fibrosis. Adenomyotic tissues showed consistent mitochondrial destruction with increased fission, mitophagosomes, and lysosomes. Besides, mitophagy, oxidative stress, and TGF-β1 levels were consistently increased. The mitochondrial dysfunction, the development of mitophagy and fibrosis, and TGF-β1 expression were induced by 3-nitropropionic acid in control uteri. In contrast, NG-nitro-L-arginine methyl ester attenuated mitochondrial dysfunction, mitophagy, fibrosis, and TGF-β1 in adenomyotic uteri. Gene profiling demonstrated increased expression of mitochondrial dysfunction-related genes in adenomyotic uteri. We showed that mitochondrial dysfunction-induced TGF-β1 dysregulation and fibrosis were associated with the development of adenomyosis.

During progression, luminal muscle-invasive bladder cancer (MIBC) can transition to the aggressive basal-squamous (Ba/Sq) subtype. Reduced expression of forkhead box A1 (FOXA1) in the urothelium is a hallmark and driver of the Ba/Sq transcriptional state and squamous differentiation. Ba/Sq tumors are highly inflamed; however, the specific inflammatory pathways contributing to the Ba/Sq state are unknown. In this study, transcriptomic analyses of The Cancer Genome Atlas MIBC cohort were performed to determine whether immune response gene signatures were associated with MIBC molecular states. Results showed that Ba/Sq MIBCs were enriched for the interferon-γ (IFN-γ)-dominant signature. Ba/Sq MIBCs exhibited increased IFN-γ/Janus kinase (JAK)/STAT pathway activity, corresponding to reduced FOXA1 regulon activity. Immunohistochemistry of MIBC specimens demonstrated that JAK1 expression was significantly increased in tumor areas with squamous differentiation. IFN-γ treatment of luminal MIBC cell lines significantly decreased the expression of luminal transcriptional drivers, including FOXA1, and increased the expression of Ba/Sq markers in a STAT1-dependent manner. RNA-sequencing analyses identified IFN-γ as a driver of the Ba/Sq state. The ability of IFN-γ to repress FOXA1 in luminal cells was abrogated by ruxolitinib inhibition of JAK1/2 activity. Additionally, pharmacologic inhibition or genetic ablation of JAK1 restored FOXA1 expression in Ba/Sq MIBC cells. These findings are the first to identify IFN-γ as an epithelial cell-extrinsic mechanism to repress FOXA1 and drive the Ba/Sq state in MIBC.

Computational neurodegenerative neuropathology represents a transformative approach in the analysis and understanding of neurodegenerative diseases through utilization of whole slide images (WSIs) and advanced machine learning/artificial intelligence (ML/AI) techniques. This review explores the emerging field of computational neurodegenerative neuropathology, emphasizing its potential to enhance neuropathologic assessment, diagnosis, and research. Recent advancements in ML/AI technologies have significantly affected image-based medical fields, including anatomic pathology, by automating disease staging, identifying novel morphologic biomarkers, and uncovering new clinical insights via multi-modal AI approaches. Despite its promise, the field faces several challenges, including limited expert annotations, slide scanning inaccessibility, inter-institutional variability, and the complexities of sharing large WSI data sets. This review discusses the importance of improving deep learning model accuracy and efficiency for better interpretation of neuropathologic data. It highlights the potential of unsupervised learning to identify patterns in unannotated data. Furthermore, the development of explainable AI models is crucial for experimental neuropathology. By addressing these challenges and leveraging cutting-edge AI techniques, computational neurodegenerative neuropathology has the potential to revolutionize the field and significantly advance our understanding of disease.

Recent studies have demonstrated that the transcription factor hepatocyte nuclear factor 4α (HNF4A) drives epithelial differentiation in the renal proximal tubules (PTs) and is critical for maintaining a mature PT phenotype. Furthermore, HNF4A down-regulation has been observed following kidney injury in mouse models. The aim of the present work was to investigate the role of HNF4A during acute and chronic human kidney disease and the loss of the mature PT phenotype in cultured PT cells. By immunohistochemistry, we discovered that loss of HNF4A expression and gain of vimentin expression are reciprocal and gradual during both acute and chronic kidney disease. Healthy human kidneys demonstrated partial or total loss of HNF4A expression in vimentin-positive scattered tubular cells. Primary cell isolation and subculture of PT cells recapitulated HNF4A-associated loss of the PT phenotype. Re-expression of HNF4A in cultured PT cells by adenoviral transduction increased transcripts related to brush border formation as well as absorptive and transport processes, as shown by RNA sequencing and gene set enrichment analyses. Thus, the reduction of HNF4A and increase of vimentin expression are connected to both acute and chronic kidney disease and represent a stereotypic injury response of the PT, resulting in dedifferentiation. HNF4A re-expression in cultured primary PT cells could provide a more reliable and predictive in vitro model to study PT function and injury.

Chronic diseases of the liver are major public health concerns worldwide. Steatosis and steatohepatitis associated with alcoholic liver disease, metabolic dysfunction-associated fatty liver disease/nonalcoholic fatty liver disease, and hepatitis B and C contribute to chronic diseases of the liver. Liver fibrosis occurs in all forms of advanced chronic diseases of the liver, the confirmation of which is typically performed by needle biopsy. Imaging approaches for liver diagnosis exist but do not provide sufficient diagnostic accuracy for defining the various stages of fibrosis or steatosis. Therefore, there is a need for improved imaging capabilities to enhance disease diagnosis. Ultrasonography-based photoacoustic imaging has recently emerged as a noninvasive, nonionizing modality, capable of capturing structural details and oxygen saturation changes during disease progression. However, its potential for detecting surrogate metabolic dysfunction-associated fatty liver disease markers, such as collagen and lipids, which are often poorly resolved by other conventional imaging techniques, has yet to be investigated in detail. The novelty of this study lies in the innovative use of spectral photoacoustic imaging for the direct detection and quantification of key biomarkers of liver disease, such as fibrosis, collagen, lipids, and oxygenated and deoxygenated hemoglobin, in a mouse model of steatotic fatty liver disease. We established that ultrasonography-based photoacoustic imaging, validated with magnetic resonance imaging, effectively identified increases in liver adiposity and fibrosis, enabling the noninvasive detection of changes in liver pathology associated with metabolic dysfunction.