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Erythropoietic protoporphyria (EPP1) results in painful photosensitivity and severe liver damage in humans due to the accumulation of fluorescent protoporphyrin IX (PPIX). While zebrafish (Danio rerio) models for porphyria exist, the utility of ferrochelatase (fech) knockout zebrafish, which exhibit EPP, for therapeutic screening and biological studies remains unexplored. This study investigated the use of clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9-mediated fech-knockout zebrafish larvae as a model of EPP1 for drug screening. CRISPR/Cas9 was employed to generate fech-knockout zebrafish larvae exhibiting morphological defects without lethality prior to 9 days post-fertilization (dpf). To assess the suitability of this model for drug screening, ursodeoxycholic acid (UDCA), a common treatment for cholestatic liver disease, was employed. This treatment significantly reduced PPIX fluorescence and enhanced bile-secretion-related gene expression (abcb11a and abcc2), indicating the release of PPIX. Acridine orange staining and quantitative reverse transcription polymerase chain reaction analysis of the bax/bcl2 ratio revealed apoptosis in fech−/− larvae, and this was reduced by UDCA treatment, indicating suppression of the intrinsic apoptosis pathway. Neutral red and Sudan black staining revealed increased macrophage and neutrophil production, potentially in response to PPIX-induced cell damage. UDCA treatment effectively reduced macrophage and neutrophil production, suggesting its potential to alleviate cell damage and liver injury in EPP1. In conclusion, CRISPR/Cas9-mediated fech−/− zebrafish larvae represent a promising model for screening drugs against EPP1. [ABSTRACT FROM AUTHOR]

Simple Summary: We developed an attention-based whole slide image (WSI)-level classification deep learning model employing surgically and endoscopically resected specimens to predict LNM in T1 CRC. Our AI model with H&E-stained WSIs and without annotations showed good performance power with the validation of an independent cohort in a single center. The area under the curve of our model was 0.781–0.824, higher than that of previous artificial intelligence (AI) studies with only WSIs. Our AI model, which showed the highest sensitivity (92.9%), reduced unnecessary additional surgery by 14.2% more than using the current JSCCR guidelines (68.3% vs. 82.5%). This revealed the feasibility of using an AI model with only H&E-stained WSIs to predict LNM in T1 CRC. According to the current guidelines, additional surgery is performed for endoscopically resected specimens of early colorectal cancer (CRC) with a high risk of lymph node metastasis (LNM). However, the rate of LNM is 2.1–25.0% in cases treated endoscopically followed by surgery, indicating a high rate of unnecessary surgeries. Therefore, this study aimed to develop an artificial intelligence (AI) model using H&E-stained whole slide images (WSIs) without handcrafted features employing surgically and endoscopically resected specimens to predict LNM in T1 CRC. To validate with an independent cohort, we developed a model with four versions comprising various combinations of training and test sets using H&E-stained WSIs from endoscopically (400 patients) and surgically resected specimens (881 patients): Version 1, Train and Test: surgical specimens; Version 2, Train and Test: endoscopic and surgically resected specimens; Version 3, Train: endoscopic and surgical specimens and Test: surgical specimens; Version 4, Train: endoscopic and surgical specimens and Test: endoscopic specimens. The area under the curve (AUC) of the receiver operating characteristic curve was used to determine the accuracy of the AI model for predicting LNM with a 5-fold cross-validation in the training set. Our AI model with H&E-stained WSIs and without annotations showed good performance power with the validation of an independent cohort in a single center. The AUC of our model was 0.758–0.830 in the training set and 0.781–0.824 in the test set, higher than that of previous AI studies with only WSI. Moreover, the AI model with Version 4, which showed the highest sensitivity (92.9%), reduced unnecessary additional surgery by 14.2% more than using the current guidelines (68.3% vs. 82.5%). This revealed the feasibility of using an AI model with only H&E-stained WSIs to predict LNM in T1 CRC. [ABSTRACT FROM AUTHOR]

Maintenance of neural progenitors requires Notch signaling in vertebrate development. Previous study has shown that Jagged2-mediated Notch signaling maintains proliferating neural progenitors in the ventral spinal cord. However, components for Jagged-mediated signaling remain poorly defined during late neurogenesis. Here we performed yeast-two hybrid screening by using the intracellular domain (ICD) of zebrafish Jagged2, and investigated a possible role of PEX1 as a component of Notch signaling for the cell-fate decision and the differentiation of neural precursors in p3 domain. Western blotting showed that zebrafish PEX1 might interacts with the ICD of zebrafish Jagged2 physically. PEX1 morpholino-injected embryos showed the increased number of GABAergic KA" neurons as well as the ectopic expression of secondary motor neurons in the p3 domain. The increased number of KA" neurons was also observed in the zebrafish embryos with PEX1 mutation induced by CRISPR/Cas9. These phenotypes resemble with that of Jagged2 morphant. Our observations imply that a critical role of PEX1 in the cell-fate decision of proliferating neural precursors in the p3 domain during the continuing growth and development of the vertebrate nervous system., Competing Interests: The authors declare no potential conflict of interest., (© Copyright 2024 The Korean Society of Developmental Biology.)

Desmoid-type fibromatosis (DTF) is a benign but locally aggressive neoplasm characterized by persistent fibroblast activation, unlike reactive fibrosis (RF), where fibroblast activation is transient. Although the Wnt/β-catenin signaling pathway is known to play a role in DTF pathogenesis, the specific genetic drivers contributing to this abnormal fibroblast activation are not fully understood. To identify additional driver genes that underlie the persistent activation of fibroblasts in DTF, we conducted a comparative transcriptome analysis between 29 DTF and 14 RF tissue samples, identifying 4267 differentially expressed genes (DEGs) specific to DTF. These DTF-specific DEGs were significantly associated with pathways involved in embryonic limb morphogenesis and muscle contraction, whereas RF-specific DEGs were linked to immune response and apoptosis. Using weighted gene coexpression network analysis to further elucidate the key regulatory circuits associated with persistent activation of DTF fibroblasts, we identified a highly DTF-specific gene module comprising 120 genes. This module was also significantly enriched in other fibroproliferative conditions showing persistent fibroblast activation, such as keloid disease and idiopathic pulmonary fibrosis. Subsequent analyses identified 7 driver transcription factors (ZNF536, IRX5, TWIST2, NKD2, PAX9, SHOX2, and SALL4) within this DTF-specific module that may contribute to the sustained activation of DTF fibroblasts. We further assessed the utility of 5 key genes from this module (TWIST2, LRRC15, CTHRC1, SHOX2, and SALL4) as potential biomarkers to distinguish DTF from RF using immunohistochemistry. All markers demonstrated excellent diagnostic performance, with TWIST2 showing exceptionally high sensitivity and specificity, surpassing β-catenin, the current standard biomarker for DTF. In conclusion, our study identifies gene modules and driver transcription factors that are highly specific to DTF, offering new insights into the genetic underpinnings of abnormal fibroblast activation in DTF. We also propose novel biomarkers that could improve the diagnostic accuracy and clinical management of DTF., (Copyright © 2024 United States & Canadian Academy of Pathology. Published by Elsevier Inc. All rights reserved.)

Although oxaliplatin-based chemotherapy is the current standard adjuvant therapy for colorectal cancer (CRC), the molecular mechanisms underlying oxaliplatin resistance remain unclear. Here, we examined the molecular mechanisms underlying SLC22A18-associated oxaliplatin resistance and strategies for overcoming oxaliplatin resistance. We evaluated the association between SLC22A18 and prognosis in 337 patients with CRC and its functional significance and studied the mechanisms through which SLC22A18 affects oxaliplatin resistance development in CRC cells, using CRC cell lines and patient-derived cells (PDCs). SLC22A18 downregulation was positively correlated with worse survival in patients with CRC. Low SLC22A18-expressing cells showed relatively lower sensitivity to oxaliplatin than high SLC22A18-expressing cells. In addition, ERK activation was found to be involved in the mechanisms underlying SLC22A18-related oxaliplatin resistance. To confirm ERK pathway dependence, we used an ERK inhibitor and found that combined treatment with oxaliplatin and the ERK inhibitor overcame oxaliplatin resistance in the low SLC22A18-expressing cells. Ex vivo approaches using PDC confirmed the correlation between SLC22A18 expression and oxaliplatin resistance. Results of the in vivo study showed that SLC22A18 expression regulated oxaliplatin efficacy, and that combined treatment with an ERK inhibitor could be a useful therapeutic strategy when SLC22A18 is downregulated. Together, our findings indicate that SLC22A18 could serve as a biomarker for the prediction of oxaliplatin resistance. In cases of oxaliplatin resistance due to low SLC22A18 expression, resistance can be overcome by combined treatment with an ERK inhibitor., Competing Interests: None., (AJCR Copyright © 2022.)