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Ubiquitination, a post-translational modification, refers to the covalent attachment of ubiquitin molecules to substrates. This modification plays a critical role in diverse cellular processes such as protein degradation. The specificity of ubiquitination for substrates is regulated by E3 ubiquitin ligases. Dysregulation of ubiquitination has been associated with numerous diseases, including cancers. In our study, we first investigated the protein expression patterns of E3 ligases across 12 cancer types. Our findings indicated that E3 ligases tend to be up-regulated and exhibit reduced tissue specificity in tumors. Moreover, the correlation of protein expression between E3 ligases and substrates demonstrated significant changes in cancers, suggesting that E3-substrate specificity alters in tumors compared to normal tissues. By integrating transcriptome, proteome, and ubiquitylome data, we further characterized the E3-substrate regulatory patterns in lung squamous cell carcinoma. Our analysis revealed that the upregulation of the SKP2 E3 ligase leads to excessive degradation of BRCA2, potentially promoting tumor cell proliferation and metastasis. Furthermore, the upregulation of E3 ubiquitin–protein ligase TRIM33 was identified as a biomarker associated with a favorable prognosis by inhibiting the cell cycle. This work exemplifies how leveraging multi-omics data to analyze E3 ligases across various cancers can unveil prognosis biomarkers and facilitate the identification of potential drug targets for cancer therapy. [ABSTRACT FROM AUTHOR]

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The emergence of drug resistance among pathogens has become a major challenge to human health on a global scale. Among them, antibiotic resistance is already a critical issue, and finding new therapeutic agents to address this problem is therefore urgent. One of the most promising alternatives to antibiotics are antibacterial peptides (ABPs), i.e., short peptides with antibacterial activity. In this study, we propose a novel ABP recognition method, called ABPCaps. It integrates a convolutional neural network (CNN), a long short-term memory (LSTM), and a new type of neural network named the capsule network. The capsule network can extract critical features automatically from both positive and negative samples, leading to superior performance of ABPCaps over all baseline models built on hand-crafted peptide descriptors. Evaluated on independent test sets, ABPCaps achieves an accuracy of 93.33% and an F1-score of 91.34%, and consistently outperforms the baseline models in other extensive experiments as well. Our study demonstrates that the proposed ABPCaps, built on the capsule network method, is a valuable addition to the current state-of-the-art in the field of ABP recognition and has significant potential for further development. [ABSTRACT FROM AUTHOR]

Importance: Yarrowia lipolytica , also known as Candida lipolytica , is an emerging opportunistic "rare pathogenic yeast". Due to the limited data on its antifungal susceptibility, the clinical treatments become challenging. Based on the China Hospital Invasive Fungal Surveillance Network (2009-2022), we conducted a comprehensive multi-method study on clinical isolates from various central hospitals. This study is currently the largest study carried out to assess the antifungal susceptibility of Y. lipolytica . It is also the first to establish local epidemiological cut-off values (L-ECOFFs), identify its ERG11 mutations, and assess the consistency between the three prevalent commercial antifungal susceptibility testing methods and the broth microdilution method. We recommend the Sensititre YeastOne as the best option for antifungal susceptibility testing for Y. lipolytica , followed by the ATB FUNGUS 3. Nevertheless, practitioners should use the MIC test strip with discretion., Competing Interests: The authors declare no conflict of interest.