o8G Site-Specifically Modified tRF-1-AspGTC: A Novel Therapeutic Target and Biomarker for Pulmonary Hypertension.

RATIONALE: Hypoxia and oxidative stress contribute to the development of pulmonary hypertension (PH). tRNA-derived fragments play important roles in RNA interference and cell proliferation, but their epitranscriptional roles in PH development have not been investigated. OBJECTIVE: We aimed to gain insight into the mechanistic contribution of oxidative stress-induced 8-oxoguanine in pulmonary vascular remodeling. METHODS AND RESULTS: Through small RNA modification array analysis and quantitative polymerase chain reaction, a significant upregulation of the 8-oxoguanine-modified tRF-1-AspGTC was found in the lung tissues and the serum of patients with PH. This modification occurs at the fifth 8-oxoguanine (5o8G) tRF in the seed region of the tRNA-derived fragments. Inhibition of the 5o8G tRF reversed hypoxia-induced proliferation and apoptosis resistance in pulmonary artery smooth muscle cells. Further investigation unveiled that the 5o8G tRF retargeted mRNA of WNT5A and CASP3 and inhibited their expression. Ultimately, BMPR2 (bone morphogenetic protein receptor 2)-reactive oxygen species/5o8G tRF/WNT5A signaling pathway exacerbated the progression of PH. CONCLUSIONS: Our study highlights the role of site-specific 8-oxoguanine-modified tRF in promoting the development of PH. Our findings present a promising therapeutic avenue for managing PH and propose 5o8G tRF as a potential innovative marker for diagnosing this disease.

ADAR1 regulates vascular remodeling in hypoxic pulmonary hypertension through N1-methyladenosine modification of circCDK17.

Pulmonary hypertension (PH) is an extremely malignant pulmonary vascular disease of unknown etiology. ADAR1 is an RNA editing enzyme that converts adenosine in RNA to inosine, thereby affecting RNA expression. However, the role of ADAR1 in PH development remains unclear. In the present study, we investigated the biological role and molecular mechanism of ADAR1 in PH pulmonary vascular remodeling. Overexpression of ADAR1 aggravated PH progression and promoted the proliferation of pulmonary artery smooth muscle cells (PASMCs). Conversely, inhibition of ADAR1 produced opposite effects. High-throughput whole transcriptome sequencing showed that ADAR1 was an important regulator of circRNAs in PH. CircCDK17 level was significantly lowered in the serum of PH patients. The effects of ADAR1 on cell cycle progression and proliferation were mediated by circCDK17. ADAR1 affects the stability of circCDK17 by mediating A-to-I modification at the A5 and A293 sites of circCDK17 to prevent it from m1A modification. We demonstrate for the first time that ADAR1 contributes to the PH development, at least partially, through m1A modification of circCDK17 and the subsequent PASMCs proliferation. Our study provides a novel therapeutic strategy for treatment of PH and the evidence for circCDK17 as a potential novel marker for the diagnosis of this disease.

Deep-Masking Generative Network: A Unified Framework for Background Restoration From Superimposed Images.

Restoring the clean background from the superimposed images containing a noisy layer is the common crux of a classical category of tasks on image restoration such as image reflection removal, image deraining and image dehazing. These tasks are typically formulated and tackled individually due to diverse and complicated appearance patterns of noise layers within the image. In this work we present the Deep-Masking Generative Network (DMGN), which is a unified framework for background restoration from the superimposed images and is able to cope with different types of noise. Our proposed DMGN follows a coarse-to-fine generative process: a coarse background image and a noise image are first generated in parallel, then the noise image is further leveraged to refine the background image to achieve a higher-quality background image. In particular, we design the novel Residual Deep-Masking Cell as the core operating unit for our DMGN to enhance the effective information and suppress the negative information during image generation via learning a gating mask to control the information flow. By iteratively employing this Residual Deep-Masking Cell, our proposed DMGN is able to generate both high-quality background image and noisy image progressively. Furthermore, we propose a two-pronged strategy to effectively leverage the generated noise image as contrasting cues to facilitate the refinement of the background image. Extensive experiments across three typical tasks for image background restoration, including image reflection removal, image rain steak removal and image dehazing, show that our DMGN consistently outperforms state-of-the-art methods specifically designed for each single task.