UHRF1 regulates alternative splicing by interacting with splicing factors and U snRNAs in a H3R2me involved manner.

The well-established functions of UHRF1 converge to DNA biological processes, as exemplified by DNA methylation maintenance and DNA damage repair during cell cycles. However, the potential effect of UHRF1 on RNA metabolism is largely unexplored. Here, we revealed that UHRF1 serves as a novel alternative RNA splicing regulator. The protein interactome of UHRF1 identified various splicing factors. Among them, SF3B3 could interact with UHRF1 directly and participate in UHRF1-regulated alternative splicing events. Furthermore, we interrogated the RNA interactome of UHRF1, and surprisingly, we identified U snRNAs, the canonical spliceosome components, in the purified UHRF1 complex. Unexpectedly, we found H3R2 methylation status determines the binding preference of U snRNAs, especially U2 snRNAs. The involvement of U snRNAs in UHRF1-containing complex and their binding preference to specific chromatin configuration imply a finely orchestrated mechanism at play. Our results provided the resources and pinpointed the molecular basis of UHRF1-mediated alternative RNA splicing, which will help us better our understanding of the physiological and pathological roles of UHRF1 in disease development.

BMI1 and MEL18 Promote Colitis-Associated Cancer in Mice via REG3B and STAT3.

BACKGROUND & AIMS: Polycomb group proteins are epigenetic factors that silence gene expression; they are dysregulated in cancer cells and contribute to carcinogenesis by unclear mechanisms. We investigated whether BMI1 proto-oncogene, polycomb ring finger (BMI1), and polycomb group ring finger 2 (PCGF2, also called MEL18) are involved in the initiation and progression of colitis-associated cancer (CAC) in mice. METHODS: We generated mice containing floxed alleles of Bmi1 and/or Mel18 and/or Reg3b using the villin-Cre promoter (called Bmi1ΔIEC, Mel18ΔIEC, DKO, and TKO mice). We also disrupted Bmi1 and/or Mel18 specifically in intestinal epithelial cells (IECs) using the villin-CreERT2-inducible promoter. CAC was induced in cre-negative littermate mice (control) and mice with conditional disruption of Bmi1 and/or Mel18 by intraperitoneal injection of azoxymethane (AOM) followed by addition of dextran sulfate sodium (DSS) to drinking water. Colon tissues were collected from mice and analyzed by histology and immunoblots; IECs were isolated and used in cDNA microarray analyses. RESULTS: Following administration of AOM and DSS, DKO mice developed significantly fewer polyps than control, Bmi1ΔIEC, Mel18ΔIEC, Reg3bΔIEC, or TKO mice. Adenomas in the colons of DKO mice were low-grade dysplasias, whereas adenomas in control, Bmi1ΔIEC, Mel18ΔIEC, Reg3bΔIEC, or TKO mice were high-grade dysplasias with aggressive invasion of the muscularis mucosa. Disruption of Bmi1 and Mel18 (DKO mice) during late stages of carcinogenesis significantly reduced the numbers of large adenomas and the load of total adenomas, reduced proliferation, and increased apoptosis in colon tissues. IECs isolated from DKO mice after AOM and DSS administration had increased expression of Reg3b compared with control, Bmi1ΔIEC, or Mel18ΔIEC mice. Expression of REG3B was sufficient to inhibit cytokine-induced activation of STAT3 in IECs. The human REG3ß protein, the functional counterpart of mouse REG3B, inhibited STAT3 activity in human 293T cells, and its expression level in colorectal tumors correlated inversely with pSTAT3 level and survival times of patients. CONCLUSIONS: BMI1 and MEL18 contribute to the development of CAC in mice by promoting proliferation and reducing apoptosis via suppressing expression of Reg3b. REG3B negatively regulates cytokine-induced activation of STAT3 in colon epithelial cells. This pathway might be targeted in patients with colitis to reduce carcinogenesis.

Constructing a Clinical Patient Similarity Network of Gastric Cancer.

OBJECTIVES: Clinical molecular genetic testing and molecular imaging dramatically increase the quantity of clinical data. Combined with the extensive application of electronic health records, a medical data ecosystem is forming, which calls for big-data-based medicine models. We tried to use big data analytics to search for similar patients in a cancer cohort, showing how to apply artificial intelligence (AI) algorithms to clinical data processing to obtain clinically significant results, with the ultimate goal of improving healthcare management. METHODS: In order to overcome the weaknesses of most data processing algorithms that rely on expert labeling and annotation, we uniformly adopted one-hot encoding for all types of clinical data, calculating the Euclidean distance to measure patient similarity and subgrouping via an unsupervised learning model. Overall survival (OS) was investigated to assess the clinical validity and clinical relevance of the model. RESULTS: We took gastric cancers (GCs) as an example to build a high-dimensional clinical patient similarity network (cPSN). When performing the survival analysis, we found that Cluster_2 had the longest survival rates, while Cluster_5 had the worst prognosis among all the subgroups. As patients in the same subgroup share some clinical characteristics, the clinical feature analysis found that Cluster_2 harbored more lower distal GCs than upper proximal GCs, shedding light on the debates. CONCLUSION: Overall, we constructed a cancer-specific cPSN with excellent interpretability and clinical significance, which would recapitulate patient similarity in the real-world. The constructed cPSN model is scalable, generalizable, and performs well for various data types.

Pest recognition in microstates state: an improvement of YOLOv7 based on Spatial and Channel Reconstruction Convolution for feature redundancy and vision transformer with Bi-Level Routing Attention.

Introduction: In order to solve the problem of precise identification and counting of tea pests, this study has proposed a novel tea pest identification method based on improved YOLOv7 network. Methods: This method used MPDIoU to optimize the original loss function, which improved the convergence speed of the model and simplifies the calculation process. Replace part of the network structure of the original model using Spatial and Channel reconstruction Convolution to reduce redundant features, lower the complexity of the model, and reduce computational costs. The Vision Transformer with Bi-Level Routing Attention has been incorporated to enhance the flexibility of model calculation allocation and content perception. Results: The experimental results revealed that the enhanced YOLOv7 model significantly boosted Precision, Recall, F1, and mAP by 5.68%, 5.14%, 5.41%, and 2.58% respectively, compared to the original YOLOv7. Furthermore, when compared to deep learning networks such as SSD, Faster Region-based Convolutional Neural Network (RCNN), and the original YOLOv7, this method proves to be superior while being externally validated. It exhibited a noticeable improvement in the FPS rates, with increments of 5.75 HZ, 34.42 HZ, and 25.44 HZ respectively. Moreover, the mAP for actual detection experiences significant enhancements, with respective increases of 2.49%, 12.26%, and 7.26%. Additionally, the parameter size is reduced by 1.39 G relative to the original model. Discussion: The improved model can not only identify and count tea pests efficiently and accurately, but also has the characteristics of high recognition rate, low parameters and high detection speed. It is of great significance to achieve realize the intelligent and precise prevention and control of tea pests.

Genome-wide expression of non-coding RNA and global chromatin modification.

Traditionally, we know that genomic DNA will produce transcripts named messenger RNA and then translate into protein following the instruction of genetic central dogma, and RNA works here as a pass-by messenger. Now increasing evidence shows that RNA is a key regulator as well as a message transmitter. It is discovered by next-generation sequencing techniques that most genomic DNA are generally transcribed to non-coding RNA, highly beyond the percentage of coding mRNA. These non-coding RNAs (ncRNAs), belonging to several groups, have critical roles in many cellular processes, expanding our understanding of the RNA world. We review here the different categories of ncRNA according to genome location and how ncRNAs guide and recruit chromatin modification complex to specific loci of genome to modulate gene expression by affecting chromatin state.

Clinical significance of chromosomal integrity in gastric cancers.

BACKGROUND: A whole-exome or targeted cancer genes panel by next-generation sequencing has been used widely in assisting individualized treatment decisions. Currently, multiple algorithms are developed to estimate DNA copy numbers based on sequencing data, which makes a comprehensive global glance at chromosomal integrity possible. We aim to classify gastric cancers based on chromosomal integrity to guide personalized therapy. METHODS: We investigated copy number variations (CNV) across the entire genome of 124 gastric carcinomas via exome or targeted sequencing. Chromosomal integrity was classified as chromosomal stability (CS), chromosomal instability (CIN) and intermediate state (CIN/CS) based on CNV results. Chromosomal integrity was correlated to molecular features and clinical characteristics. RESULTS: According the states of chromosomal integrity, gastric carcinomas can be stratified into two cohorts: CS and CIN. Our results showed a significant relationship between CIN status and TP53 mutation, but not RB1, phosphatase and tensin homolog (PTEN), or other reported DNA damage repair genes. The mutation frequency of the TP53 gene had great relevance. Our study initially revealed clinical significance of chromosomal integrity that CIN patients were prone to HER2-positive and mucinous adenocarcinoma, while CS patients were a diffuse subtype and poorly differentiated but had longer overall survival. CONCLUSIONS: We classified gastric carcinomas into two states of chromosomal integrity with clinical implications. The dichotomy is applicable to clinical transformation. We proposed that classifying gastric cancers based on chromosomal integrity would enable us to achieve personalized therapy for patients and may be beneficial to patient stratification in future clinical trials.