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13. CRISPR/Cas9-Mediated fech Knockout Zebrafish: Unraveling the Pathogenesis of Erythropoietic Protoporphyria and Facilitating Drug Screening.

Author

Wijerathna, Hitihami M. S. M., Shanaka, Kateepe A. S. N., Raguvaran, Sarithaa S., Jayamali, Bulumulle P. M. V., Kim, Seok-Hyung, Kim, Myoung-Jin, Jung, Sumi, and Lee, Jehee

Subjects
CRISPRS, REVERSE transcriptase polymerase chain reaction, ERYTHROPOIETIC protoporphyria, ACRIDINE orange, URSODEOXYCHOLIC acid, BILE
Abstract

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]

Published
2024
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18. Lineage-dependent gene expression programs influence the immune landscape of colorectal cancer

Author

Lee, Hae-Ock, Hong, Yourae, Etlioglu, Hakki Emre, Cho, Yong Beom, Pomella, Valentina, Van den Bosch, Ben, Vanhecke, Jasper, Verbandt, Sara, Hong, Hyekyung, Min, Jae-Woong, Kim, Nayoung, Eum, Hye Hyeon, Qian, Junbin, Boeckx, Bram, Lambrechts, Diether, Tsantoulis, Petros, De Hertogh, Gert, Chung, Woosung, Lee, Taeseob, An, Minae, Shin, Hyun-Tae, Joung, Je-Gun, Jung, Min-Hyeok, Ko, Gunhwan, Wirapati, Pratyaksha, Kim, Seok Hyung, Kim, Hee Cheol, Yun, Seong Hyeon, Tan, Iain Bee Huat, Ranjan, Bobby, Lee, Woo Yong, Kim, Tae-You, Choi, Jung Kyoon, Kim, Young-Joon, Prabhakar, Shyam, Tejpar, Sabine, and Park, Woong-Yang

Published
2020
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32. Prediction of Lymph Node Metastasis in T1 Colorectal Cancer Using Artificial Intelligence with Hematoxylin and Eosin-Stained Whole-Slide-Images of Endoscopic and Surgical Resection Specimens.

Author

Song, Joo Hye, Kim, Eun Ran, Hong, Yiyu, Sohn, Insuk, Ahn, Soomin, Kim, Seok-Hyung, and Jang, Kee-Taek

Subjects
LYMPH nodes, RISK assessment, PREDICTION models, T-test (Statistics), RECEIVER operating characteristic curves, DATA analysis, STATISTICAL significance, RESEARCH funding, ARTIFICIAL intelligence, COLORECTAL cancer, ENDOSCOPIC surgery, MINIMALLY invasive procedures, MANN Whitney U Test, DESCRIPTIVE statistics, METASTASIS, DEEP learning, DATA analysis software, ENDOSCOPY, DISEASE complications
Abstract

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]

Published
2024
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