TRIM44 Promotes Rabies Virus Replication by Autophagy-Dependent Mechanism.

Tripartite motif (TRIM) proteins are a multifunctional E3 ubiquitin ligase family that participates in various cellular processes. Recent studies have shown that TRIM proteins play important roles in regulating host-virus interactions through specific pathways, but their involvement in response to rabies virus (RABV) infection remains poorly understood. Here, we identified that several TRIM proteins are upregulated in mouse neuroblastoma cells (NA) after infection with the rabies virus using RNA-seq sequencing. Among them, TRIM44 was found to regulate RABV replication. This is supported by the observations that downregulation of TRIM44 inhibits RABV replication, while overexpression of TRIM44 promotes RABV replication. Mechanistically, TRIM44-induced RABV replication is brought about by activating autophagy, as inhibition of autophagy with 3-MA attenuates TRIM44-induced RABV replication. Additionally, we found that inhibition of autophagy with rapamycin reverses the TRIM44-knockdown-induced decrease in LC3B expression and autophagosome formation as well as RABV replication. The results suggest that TRIM44 promotes RABV replication by an autophagy-dependent mechanism. Our work identifies TRIM44 as a key host factor for RABV replication, and targeting TRIM44 expression may represent an effective therapeutic strategy.

Inducing mitochondriopathy-like damages by transformable nucleopeptide nanoparticles for targeted therapy of bladder cancer.

Mitochondriopathy inspired adenosine triphosphate (ATP) depletions have been recognized as a powerful way for controlling tumor growth. Nevertheless, selective sequestration or exhaustion of ATP under complex biological environments remains a prodigious challenge. Harnessing the advantages of in vivo self-assembled nanomaterials, we designed an Intracellular ATP Sequestration (IAS) system to specifically construct nanofibrous nanostructures on the surface of tumor nuclei with exposed ATP binding sites, leading to highly efficient suppression of bladder cancer by induction of mitochondriopathy-like damages. Briefly, the reported transformable nucleopeptide (NLS-FF-T) self-assembled into nuclear-targeted nanoparticles with ATP binding sites encapsulated inside under aqueous conditions. By interaction with KPNA2, the NLS-FF-T transformed into a nanofibrous-based ATP trapper on the surface of tumor nuclei, which prevented the production of intracellular energy. As a result, multiple bladder tumor cell lines (T24, EJ and RT-112) revealed that the half-maximal inhibitory concentration (IC50) of NLS-FF-T was reduced by approximately 4-fold when compared to NLS-T. Following intravenous administration, NLS-FF-T was found to be dose-dependently accumulated at the tumor site of T24 xenograft mice. More significantly, this IAS system exhibited an extremely antitumor efficacy according to the deterioration of T24 tumors and simultaneously prolonged the overall survival of T24 orthotopic xenograft mice. Together, our findings clearly demonstrated the therapeutic advantages of intracellular ATP sequestration-induced mitochondriopathy-like damages, which provides a potential treatment strategy for malignancies.

Photostable Cascade-Activatable Peptide Self-Assembly on a Cancer Cell Membrane for High-Performance Identification of Human Bladder Cancer.

Missed or residual tumor burden results in high risk for bladder cancer relapse. However, existing fluorescent probes cannot meet the clinical needs because of inevitable photobleaching properties. Performance can be improved by maintaining intensive and sustained fluorescence signals via resistance to intraoperative saline flushing and intrinsic fluorescent decay, providing surgeons with sufficiently clear and high-contrast surgical fields, avoiding residual tumors or missed diagnosis. This study designs and synthesizes a photostable cascade-activatable peptide, a target reaction-induced aggregation peptide (TRAP) system, which can construct polypeptide-based nanofibers in situ on the cell membrane to achieve long-term and stable imaging of bladder cancer. The probe has two parts: a target peptide (TP) targets CD44v6 to recognize bladder cancer cells, and a reaction-induced aggregation peptide (RAP) is introduced, which effectively reacts with the TP via a click reaction to enhance the hydrophobicity of the whole molecule, assembling into nanofibers and further nanonetworks. Accordingly, probe retention on the cell membrane is prolonged, and photostability is significantly improved. Finally, the TRAP system is successfully employed in the high-performance identification of human bladder cancer in ex vivo bladder tumor tissues. This cascade-activatable peptide molecular probe based on the TRAP system enables efficient and stable imaging of bladder cancer.

DSMENet: Detail and Structure Mutually Enhancing Network for under-sampled MRI reconstruction.

Reconstructing zero-filled MR images (ZF) from partial k-space by convolutional neural networks (CNN) is an important way to accelerate MRI. However, due to the lack of attention to different components in ZF, it is challenging to learn the mapping from ZF to targets effectively. To ameliorate this issue, we propose a Detail and Structure Mutually Enhancing Network (DSMENet), which benefits from the complementary of the Structure Reconstruction UNet (SRUN) and the Detail Feature Refinement Module (DFRM). The SRUN learns structure-dominated information at multiple scales. And the DRFM enriches detail-dominated information from coarse to fine. The bidirectional alternate connections then exchange information between them. Moreover, the Detail Representation Construction Module (DRCM) extracts valuable initial detail representation for DFRM. And the Detail Guided Fusion Module (DGFM) facilitates the deep fusion of these complementary information. With the help of them, various components in ZF can be applied with discriminative attentions and mutually enhanced. In addition, the performance can be further improved by the Deep Enhanced Restoration (DER), a strategy based on recursion and constrain. Extensive experiments on fastMRI and CC-359 datasets demonstrate that DSMENet has robustness in terms of various body parts, under-sampling rates, and masks. Furthermore, DSMENet can achieve promising performance on qualitative and quantitative results, especially the competitive NMSE of 0.0268, PSNE of 33.7, and SSIM of 0.7808 on fastMRI 4 × single-coil knee leaderboard.

Single-cell analyses EMP1 as a marker of the ratio of M1/M2 macrophages is associated with EMT, immune infiltration, and prognosis in bladder cancer.

Background: Bladder cancer is among the most lethal urinary system cancers across the globe. Macrophage 1 and Macrophage 2 play an essential role in the pathogenesis of tumors. Nevertheless, prior studies failed to investigate the implication of the two cells, working in combination, in the development, growth, progression and metastasis of bladder cancer. Methods: We computed the M1/M2 ratio of the samples retrieved from The Cancer Genome Atlas (TCGA) by using the Cibersortx algorithm and calculated the ratio in 32 patients in our series by employing flow cytometry. SurvivalRandomForest was utilized to reduce the dimension of the list of the M1/M2-related genes, with an aim to obtain the most survival-predictive gene (EMP1) encoding epithelial membrane protein 1 (EMP1). The EMP1 was biologically characterized by using Gene Set Enrichment Analysis (GSEA), Gene Set Variation Analysis (GSVA), and Gene Ontology (GO). The single-cell transcriptome (sc-RNA) analysis was then applied to further look into the function of EMP1. Finally, Cellchat was employed to examine the interaction between macrophages and epithelium cells. Results: The results showed that higher M1/M2 ratio was found to be associated with a more favorable prognosis of bladder cancer. EMP1 was identified to be the key gene indicative of M1/M2 ratio and higher EMP1 expression was associated with poor prognosis. Further analyses showed that EMP1 might promote tumor invasion and metastasis via epithelial-mesenchymal transition (EMT) and focal adhesion (FA). Moreover, the expression level of EMP1 could serve as an indicator of immunotherapy efficacy. The scRNA-seq data indicated that EMP1 in cancer cells was strongly associated with tumor proliferation. Finally, the Cellchat results exhibited that EMP1 might promote the interaction between macrophages and cancer cells through the fibronectin 1-syndecan 1 (FN1-SDC1) pathway. Conclusion: Our study identified EMP1, an M1/M2-related gene, the expression of which may act as a prognostic indicator for the proliferation, metastasis, and response to immunotherapy. EMP1 might be involved in the regulation on M1/M2 ratio.

HIWDNet: A hybrid image-wavelet domain network for fast magnetic resonance image reconstruction.

The application of Magnetic Resonance Imaging (MRI) is limited due to the long acquisition time of k-space signals. Recently, many deep learning-based MR image reconstruction methods have been proposed to reduce acquisition time and improve MRI image quality by reconstructing images from under-sampled k-space data. However, these methods suffer from two shortcomings. Firstly, the reconstruction network are mainly designed in the image domain or frequency domain, while ignoring the characteristics of time-frequency features in the wavelet domain. In addition, the existing cross-domain methods design the same reconstruction network in different transform domains, so that the network cannot learn targeted information for different domains. To solve the above problems, we propose a Hybrid Image-Wavelet Domain Reconstruction Network (HIWDNet) for fast MRI reconstruction. Specifically, we employ Cross-scale Dense Feature Fusion Module (CDFFM) in the image domain to reconstruct the basic structure of MR images, while introducing Region Adaptive Artifact Removal Module (RAARM) to remove aliasing artifacts in large areas. Then, a Wavelet Sub-band Reconstruction Module (WSRM) is proposed to refine wavelet sub-bands to improve the accuracy of HIWDNet. The proposed method is evaluated in different sampling modes on the fastMRI dataset, the CC359 dataset and the IXI dataset. Extensive experimental results show that HIWDNet achieves better results on both SSIM and PSNR evaluation metrics compared with other methods.