USP7 regulates HMOX-1 via deubiquitination to suppress ferroptosis and ameliorate spinal cord injury in rats

Heme oxygenase 1 (HMOX-1) is overexpressed in spinal cord injury (SCI) and relevant to ferroptosis. Ubiquitin-specific-processing protease 7 (USP7) has unveiled its role in regulating HMOX-1 stabilization while its function in SCI remains unknown. This study is to explore the potential molecular mechanism of the USP7-HMOX-1 axis in ferroptosis in a SCI rat model. SCI was assessed with Basso, Beattie, Bresnahan locomotion evaluation, hematoxylin-eosin histological staining, and immunofluorescence detection of NeuN. Ferroptosis was assessed by detections of the iron content, malondialdehyde and glutathione levels, mitochondrial damage, and glutathione peroxidase 4, 4-hydroxynonenal, USP7, and HMOX-1 expression in spinal cord. Co-immunoprecipitation was used to detect the binding of USP7 to HMOX-1. The ubiquitination level of HMOX-1 was measured after USP7 overexpression. USP7 expression was downregulated and HMOX-1 expression was upregulated in SCI rat models. HMOX-1 or USP7 overexpression promoted motor function recovery, ameliorated spinal cord damage, increased NeuN expression, and blocked the occurrence of ferroptosis in SCI rat models. In SCI rats, USP7 directly bound to HMOX-1 and its overexpression promoted HMOX-1 expression via deubiquitination. To sum up, USP7 overexpression facilitated the expression of HMOX-1 through deubiquitination, thereby reducing ferroptosis and alleviating SCI.

Multimodal data fusion for supervised learning-based identification of USP7 inhibitors: a systematic comparison

Ubiquitin-specific-processing protease 7 (USP7) is a promising target protein for cancer therapy, and great attention has been given to the identification of USP7 inhibitors. Traditional virtual screening methods have now been successfully applied to discover USP7 inhibitors aiming at reducing costs and speeding up time in several studies. However, due to their unsatisfactory accuracy, it is still a difficult task to develop USP7 inhibitors. In this study, multiple supervised learning classifiers were built to distinguish active USP7 inhibitors from inactive ligands. Physicochemical descriptors, MACCS keys, ECFP4 fingerprints and SMILES were first calculated to represent the compounds in our in-house dataset. Two deep learning (DL) models and nine classical machine learning (ML) models were then constructed based on different combinations of the above molecular representations under three activity cutoff values, and a total of 15 groups of experiments (75 experiments) were implemented. The performance of the models in these experiments was evaluated, compared and discussed using a variety of metrics. The optimal models are ensemble learning models when the dataset is balanced or severely imbalanced, and SMILES-based DL performs the best when the dataset is slightly imbalanced. Meanwhile, multimodal data fusion in some cases can improve the performance of ML and DL models. In addition, SMOTE, unbiased decoy selection and SMILES enumeration can improve the performance of ML and DL models when the dataset is severely imbalanced, and SMOTE works the best. Our study established highly accurate supervised learning classification models, which would accelerate the development of USP7 inhibitors. Some guidance was also provided for drug researchers in selecting supervised models and molecular representations as well as handling imbalanced datasets.

Pleiotropic effects of methionine adenosyltransferases deregulation as determinants of liver cancer progression and prognosis

Downregulation of liver-specific MAT1A gene, encoding S-adenosylmethionine (SAM) synthesizing isozymes MATI/III, and upregulation of widely expressed MAT2A, encoding MATII isozyme, known as MAT1A:MAT2A switch, occurs in hepatocellular carcinoma (HCC). Being inhibited by its reaction product, MATII isoform upregulation cannot compensate for MATI/III decrease. Therefore, MAT1A:MAT2A switch contributes to decrease in SAM level in rodent and human hepatocarcinogenesis. SAM administration to carcinogen-treated rats prevents hepatocarcinogenesis, whereas MAT1A-KO mice, characterized by chronic SAM deficiency, exhibit macrovesicular steatosis, mononuclear cell infiltration in periportal areas, and HCC development. This review focuses upon the pleiotropic changes, induced by MAT1A/MAT2A switch, associated with HCC development. Epigenetic control of MATs expression occurs at transcriptional and post-transcriptional levels. In HCC cells, MAT1A/MAT2A switch is associated with global DNA hypomethylation, decrease in DNA repair, genomic instability, and signaling deregulation including c-MYC overexpression, rise in polyamine synthesis, upregulation of RAS/ERK, IKK/NF-kB, PI3K/AKT, and LKB1/AMPK axis. Furthermore, decrease in MAT1A expression and SAM levels results in increased HCC cell proliferation, cell survival, and microvascularization. All of these changes are reversed by SAM treatment in vivo or forced MAT1A overexpression or MAT2A inhibition in cultured HCC cells. In human HCC, MAT1A:MAT2A and MATI/III:MATII ratios correlate negatively with cell proliferation and genomic instability, and positively with apoptosis and global DNA methylation. This suggests that SAM decrease and MATs deregulation represent potential therapeutic targets for HCC. Finally, MATI/III:MATII ratio strongly predicts patients' survival length suggesting that MAT1A:MAT2A expression ratio is a putative prognostic marker for human HCC.