Background Brother of regulator of imprinted sites (BORIS) is expressed in most cancers and often associated with short survival and poor prognosis in patients. BORIS inhibits apoptosis and promotes proliferation of cancer cells. However, its mechanism of action has not been elucidated, and there is no known inhibitor of BORIS. Methods A phage display library was used to find the BORIS inhibitory peptides and BTApep-TAT was identified. The RNA sequencing profile of BTApep-TAT-treated H1299 cells was compared with that of BORIS-knockdown cells. Antitumor activity of BTApep-TAT was evaluated in a non-small cell lung cancer (NSCLC) xenograft mouse model. BTApep-TAT was also used to investigate the post-translational modification (PTM) of BORIS and the role of BORIS in DNA damage repair. Site-directed mutants of BORIS were constructed and used for investigating PTM and the function of BORIS. Results BTApep-TAT induced DNA damage in cancer cells and suppressed NSCLC xenograft tumor progression. Investigation of the mechanism of action of BTApep-TAT demonstrated that BORIS underwent ADP ribosylation upon double- or single-strand DNA damage. Substitution of five conserved glutamic acid (E) residues with alanine residues (A) between amino acids (AAs) 198 and 228 of BORIS reduced its ADP ribosylation. Inhibition of ADP ribosylation of BORIS by a site-specific mutation or by BTApep-TAT treatment blocked its interaction with Ku70 and impaired the function of BORIS in DNA damage repair. Conclusions The present study identified an inhibitor of BORIS, highlighted the importance of ADP ribosylation of BORIS, and revealed a novel function of BORIS in DNA damage repair. The present work provides a practical method for the future screening or optimization of drugs targeting BORIS.

The Brother of Regulator of Imprinted Sites (BORIS, gene symbol CTCFL) has previously been shown to promote colorectal cancer cell proliferation, inhibit cancer cell apoptosis, and resist chemotherapy. However, it is unknown whether Boris plays a role in the progression of in situ colorectal cancer. Here Boris knockout (KO) mice were constructed. The function loss of the cloned Boris mutation that was retained in KO mice was verified by testing its activities in colorectal cell lines compared with the Boris wild‐type gene. Boris knockout reduced the incidence and severity of azoxymethane/dextran sulfate‐sodium (AOM/DSS)‐induced colon cancer. The importance of Boris is emphasized in the progression of in situ colorectal cancer. Boris knockout significantly promoted the phosphorylation of γH2AX and the DNA damage in colorectal cancer tissues and suppressed Wnt and MAPK pathways that are responsible for the callback of DNA damage repair. This indicates the strong inhibition of colorectal cancer in Boris KO mice. By considering that the DSS‐promoted inflammation contributes to tumorigenesis, Boris KO mice were also studied in DSS‐induced colitis. Our data showed that Boris knockout alleviated DSS‐induced colitis and that Boris knockdown inhibited the NF‐κB signaling pathway in RAW264.7 cells. Therefore Boris knockout eliminates colorectal cancer generation by inhibiting DNA damage repair in cancer cells and relieving inflammation in macrophages. Our findings demonstrate the importance of Boris in the development of in situ colorectal cancer and provide evidence for the feasibility of colorectal cancer therapy on Boris.

Sea fog is a common weather phenomenon at sea, which reduces visibility and causes tremendous hazards to marine transportation, marine fishing and other maritime operations. Traditional sea fog monitoring methods have enormous difficulties in characterizing the diversity of sea fog and distinguishing sea fog from low-level clouds. Thus, we propose a cloud image retrieval method for sea fog recognition (CIR-SFR) in a deep learning framework by combining the advantages of metric learning. CIR-SFR includes the feature extraction module and the retrieval-based sea fog recognition module. The feature extraction module adopts the Double Branch Residual Neural Network (DBRNN) to comprehensively extract the global and local features of cloud images. By introducing local branches and using activation masks, DBRNN can focus on regions of interest in cloud images. Moreover, cloud image features are projected into the semantic space by introducing Multi-Similarity Loss, which effectively improves the discrimination ability of sea fog and lowlevel clouds. For the retrieval-based sea fog recognition module, similar cloud images are retrieved from the cloud image dataset according to the distance in the feature space, and accurate sea fog recognition results are obtained by counting the percentage of various cloud image types in the retrieval results. To evaluate the sea fog recognition system, we establish a dataset of 2544 cloud images including clear sky, low-level cloud, medium high cloud and sea fog. Experimental results show that the proposed method outperforms the traditional methods in sea fog recognition, which provides a new way for sea fog recognition.

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