| Autor: |
Zohud O; Department of Clinical Microbiology and Immunology, Faculty of Medicine and Health Sciences, Tel-Aviv University, Tel-Aviv 69978, Israel., Midlej K; Department of Clinical Microbiology and Immunology, Faculty of Medicine and Health Sciences, Tel-Aviv University, Tel-Aviv 69978, Israel., Lone IM; Department of Clinical Microbiology and Immunology, Faculty of Medicine and Health Sciences, Tel-Aviv University, Tel-Aviv 69978, Israel., Nashef A; Department of Oral and Maxillofacial Surgery, Baruch Padeh Medical Center, Poriya Tebaria 42310, Israel.; Department of Oral and Maxillofacial Surgery, Meir Medical Center, Faculty of Medicine and Health Sciences, Tel-Aviv University, Kfar-Saba 69978, Israel., Abu-Elnaaj I; Department of Oral and Maxillofacial Surgery, Baruch Padeh Medical Center, Poriya Tebaria 42310, Israel.; Azrieli Faculty of Medicine, Bar-Ilan University, Tsaft 33241, Israel., Iraqi FA; Department of Clinical Microbiology and Immunology, Faculty of Medicine and Health Sciences, Tel-Aviv University, Tel-Aviv 69978, Israel. |
| Abstrakt: |
Juvenile polyposis syndrome (JPS) is a rare autosomal dominant disorder characterized by multiple juvenile polyps in the gastrointestinal tract, often associated with mutations in genes such as Smad4 and BMPR 1 A . This study explores the impact of Smad4 knock-out on the development of intestinal polyps using collaborative cross (CC) mice, a genetically diverse model. Our results reveal a significant increase in intestinal polyps in Smad4 knock-out mice across the entire population, emphasizing the broad influence of Smad4 on polyposis. Sex-specific analyses demonstrate higher polyp counts in knock-out males and females compared to their WT counterparts, with distinct correlation patterns. Line-specific effects highlight the nuanced response to Smad4 knock-out, underscoring the importance of genetic variability. Multimorbidity heat maps offer insights into complex relationships between polyp counts, locations, and sizes. Heritability analysis reveals a significant genetic basis for polyp counts and sizes, while machine learning models, including k-nearest neighbors and linear regression, identify key predictors, enhancing our understanding of juvenile polyposis genetics. Overall, this study provides new information on understanding the intricate genetic interplay in the context of Smad4 knock-out, offering valuable insights that could inform the identification of potential therapeutic targets for juvenile polyposis and related diseases. |
