GSK3β Is Involved in JNK2-Mediated β-Catenin Inhibition

We have recently reported that mitogen-activated protein kinase (MAPK) JNK1 downregulates beta-catenin signaling and plays a critical role in regulating intestinal homeostasis and in suppressing tumor formation. This study was designed to determine whether JNK2, another MAPK, has similar and/or different functions in the regulation of beta-catenin signaling.We used an in vitro system with manipulation of JNK2 and beta-catenin expression and found that activated JNK2 increased GSK3beta activity and inhibited beta-catenin expression and transcriptional activity. However, JNK2-mediated downregulation of beta-catenin was blocked by the proteasome inhibitor MG132 and GSK3beta inhibitor lithium chloride. Moreover, targeted mutations at GSK3beta phosphorylation sites (Ser33 and Ser37) of beta-catenin abrogated JNK2-mediated suppression of beta-catenin. In vivo studies further revealed that JNK2 deficiency led to upregulation of beta-catenin and increase of GSK3-beta phosphorylation in JNK2-/- mouse intestinal epithelial cells. Additionally, physical interaction and co-localization among JNK2, beta-catenin and GSK3beta were observed by immunoprecipitation, mammalian two-hybridization assay and confocal microscopy, respectively.In general, our data suggested that JNK2, like JNK1, interacts with and suppresses beta-catenin signaling in vitro and in vivo, in which GSK3beta plays a key role, although previous studies have shown distinct functions of JNK1 and JNK2. Our study also provides a novel insight into the crosstalk between Wnt/beta-catenin and MAPK JNKs signaling

Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer with Deep Learning

Objective. To develop an artificial intelligence method predicting lymph node metastasis (LNM) for patients with colorectal cancer (CRC). Impact Statement. A novel interpretable multimodal AI-based method to predict LNM for CRC patients by integrating information of pathological images and serum tumor-specific biomarkers. Introduction. Preoperative diagnosis of LNM is essential in treatment planning for CRC patients. Existing radiology imaging and genomic tests approaches are either unreliable or too costly. Methods. A total of 1338 patients were recruited, where 1128 patients from one centre were included as the discovery cohort and 210 patients from other two centres were involved as the external validation cohort. We developed a Multimodal Multiple Instance Learning (MMIL) model to learn latent features from pathological images and then jointly integrated the clinical biomarker features for predicting LNM status. The heatmaps of the obtained MMIL model were generated for model interpretation. Results. The MMIL model outperformed preoperative radiology-imaging diagnosis and yielded high area under the curve (AUCs) of 0.926, 0.878, 0.809, and 0.857 for patients with stage T1, T2, T3, and T4 CRC, on the discovery cohort. On the external cohort, it obtained AUCs of 0.855, 0.832, 0.691, and 0.792, respectively (T1-T4), which indicates its prediction accuracy and potential adaptability among multiple centres. Conclusion. The MMIL model showed the potential in the early diagnosis of LNM by referring to pathological images and tumor-specific biomarkers, which is easily accessed in different institutes. We revealed the histomorphologic features determining the LNM prediction indicating the model ability to learn informative latent features

Using Sparse Patch Annotation for Tumor Segmentation in Histopathological Images

Tumor segmentation is a fundamental task in histopathological image analysis. Creating accurate pixel-wise annotations for such segmentation tasks in a fully-supervised training framework requires significant effort. To reduce the burden of manual annotation, we propose a novel weakly supervised segmentation framework based on sparse patch annotation, i.e., only small portions of patches in an image are labeled as ‘tumor’ or ‘normal’. The framework consists of a patch-wise segmentation model called PSeger, and an innovative semi-supervised algorithm. PSeger has two branches for patch classification and image classification, respectively. This two-branch structure enables the model to learn more general features and thus reduce the risk of overfitting when learning sparsely annotated data. We incorporate the idea of consistency learning and self-training into the semi-supervised training strategy to take advantage of the unlabeled images. Trained on the BCSS dataset with only 25% of the images labeled (five patches for each labeled image), our proposed method achieved competitive performance compared to the fully supervised pixel-wise segmentation models. Experiments demonstrate that the proposed solution has the potential to reduce the burden of labeling histopathological images

c-Jun N-terminal kinase 1 interacts with and negatively regulates Wnt/beta-catenin signaling through GSK3beta pathway

Increasing evidence shows that there is an interaction between mitogen-activated protein kinase and Wnt signaling and that their interaction plays important roles in a variety of cellular processes. However, how the two signaling interacts is not clear. In this study, we found that beta-catenin expression was strikingly increased in the intestinal normal mucosa and tumors of c-Jun N-terminal kinase (JNK) 1-deficient mice by immunohistochemical staining and that both beta-catenin expression and transcriptional activity were significantly upregulated in JNK1-deficient mouse embryonic fibroblasts. However, active JNK1 significantly inhibited beta-catenin expression and suppressed beta-catenin-mediated transcriptional activity by enhancing glycogen synthase kinase 3beta (GSK3beta) activity. But beta-catenin inhibition was significantly reduced by GSK3beta RNA interference or GSK3beta inhibitor lithium chloride and proteasome inhibitor MG132. Further, mutant beta-catenin at the phosphorylation sites of Ser33 and Ser37 by GSK3beta was resistant to activated JNK1-induced beta-catenin degradation. Moreover, the physical interaction between JNK1 and beta-catenin was detected by immunoprecipitation, and their colocalization was seen in cellular nuclei and cytoplasm. Taken together, our data provide direct evidence that JNK1 interacts with and negatively regulates beta-catenin signaling through GSK3beta pathway and that the beta-catenin alteration is probably responsible for the intestinal tumor formation in JNK1-deficient mice

Activated JNK2 interacts with β-catenin and GSK3β.

<p>(A) Active JNK2 binding to β-catenin and GSK3β was analyzed by immunoprecipitation. β-catenin (HA tagged) was co-transfected with empty vector or active JNK2 (Flag tagged) into HEK293T cells. Immunoprecipitation was performed with a Flag antibody. (B) Mammalian two-hybridization assays showed a strong binding of β-catenin and JNK2 protein. The experiments were triplicated independently. (C) Active JNK2 and β-catenin co-localized in the cell nucleus and cytoplasm. Active JNK2 (Flag tagged) and pEGFP-β-catenin were co-transfected into HEK293T cells. The cells were immunostained with a Flag antibody. Co-localization (yellow fluorescence) of active JNK2 (red fluorescence) and β-catenin (green fluorescence) was detected in the nucleus and cytoplasm.</p

Invasive carcinoma segmentation in whole slide images using MS-ResMTUNet

Identifying the invasive cancer area is a crucial step in the automated diagnosis of digital pathology slices of the breast. When examining the pathological sections of patients with invasive ductal carcinoma, several evaluations are required specifically for the invasive cancer area. However, currently there is little work that can effectively distinguish the invasive cancer area from the ductal carcinoma in situ in whole slide images. To address this issue, we propose a novel architecture named ResMTUnet that combines the strengths of vision transformer and CNN, and uses multi-task learning to achieve accurate invasive carcinoma recognition and segmentation in breast cancer. Furthermore, we introduce a multi-scale input model based on ResMTUnet with conditional random field, named MS-ResMTUNet, to perform segmentation on WSIs. Our systematic experimentation has shown that the proposed network outperforms other competitive methods and effectively segments invasive carcinoma regions in WSIs. This lays a solid foundation for subsequent analysis of breast pathological slides in the future. The code is available at: https://github.com/liuyiqing2018/MS-ResMTUNe

Active JNK2-mediated β-catenin degradation occurred through the proteasome system and GSK3β.

<p>(A) HEK293T cells were co-transfected with pcDNA3-HA-β-catenin and pcDNA3-Flag-MKK7-JNK2 (lane 3 and 4) or empty vector (lane 1 and 2). Forty-four hours after transfection, 25 µM MG132 was added to the indicated samples (lane 2 and 4). Four hours later cells were harvested for immunoblotting analysis to detect the expression of HA-β-catenin and p-JNK. (B) Blocking GSK3β activity by LiCl reduced β-catenin expression inhibition by activated JNK2. pcDNA3-HA-β-catenin was transfected into HEK293T cells along with pcDNA3-Flag-MKK7-JNK2 (lane 3 and 4) or empty vector (lane 1 and 2). Thirty-six hours after transfection, half of the cultures were treated overnight with 30 mM LiCl (lane 2 and 4) and then harvested for immunoblotting analysis to detect the expression of HA-β-catenin, phospho-Ser-9 GSK3β, and p-JNK. (C) Mutant β-catenin was resistant to activated JNK2 induced degradation. Wild-type β-catenin (HA- β-catenin) (lanes 1 and 2) or various β-catenin mutants (HA-S33F β-catenin, lanes 3 and 4; HA-S33Y β-catenin, lanes 5 and 6; HA-S37A β-catenin, lanes 7 and 8) were transfected into HEK293T cells along with pcDNA3-Flag-MKK7-JNK2 (lane 2,4,6,8) or empty vector (lanes 1,3,5,7). 48 hours after transfection, cells were harvested for immunoblotting analysis to determine the protein levels of HA-β-catenin. β-actin served as loading control.</p