Ubiquitination is an important post-translational modification, which controls protein turnover by labeling malfunctional and redundant proteins for proteasomal degradation, and also serves intriguing non-proteolytic regulatory functions. E3 ubiquitin ligases, whose substrate specificity determines the recognition of target proteins of ubiquitination, play crucial roles in ubiquitin–proteasome system. UbiNet 2.0 is an updated version of the database UbiNet. It contains 3332 experimentally verified E3–substrate interactions (ESIs) in 54 organisms and rich annotations useful for investigating the regulation of ubiquitination and the substrate specificity of E3 ligases. Based on the accumulated ESIs data, the recognition motifs in substrates for each E3 were also identified and a functional enrichment analysis was conducted on the collected substrates. To facilitate the research on ESIs with different categories of E3 ligases, UbiNet 2.0 performed strictly evidence-based classification of the E3 ligases in the database based on their mechanisms of ubiquitin transfer and substrate specificity. The platform also provides users with an interactive tool that can visualize the ubiquitination network of a group of self-defined proteins, displaying ESIs and protein–protein interactions in a graphical manner. The tool can facilitate the exploration of inner regulatory relationships mediated by ubiquitination among proteins of interest. In summary, UbiNet 2.0 is a user-friendly web-based platform that provides comprehensive as well as updated information about experimentally validated ESIs and a visualized tool for the construction of ubiquitination regulatory networks available at http://awi.cuhk.edu.cn/~ubinet/index.php.

Recent studies have demonstrated that the matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) could be used to detect superbugs, such as methicillin-resistant Staphylococcus aureus (MRSA). Due to an increasingly clinical need to classify between MRSA and methicillin-sensitive Staphylococcus aureus (MSSA) efficiently and effectively, we were motivated to develop a systematic pipeline based on a large-scale dataset of MS spectra. However, the shifting problem of peaks in MS spectra induced a low effectiveness in the classification between MRSA and MSSA isolates. Unlike previous works emphasizing on specific peaks, this study employs a binning method to cluster MS shifting ions into several representative peaks. A variety of bin sizes were evaluated to coalesce drifted or shifted MS peaks to a well-defined structured data. Then, various machine learning methods were performed to carry out the classification between MRSA and MSSA samples. Totally 4858 MS spectra of unique S. aureus isolates, including 2500 MRSA and 2358 MSSA instances, were collected by Chang Gung Memorial Hospitals, at Linkou and Kaohsiung branches, Taiwan. Based on the evaluation of Pearson correlation coefficients and the strategy of forward feature selection, a total of 200 peaks (with the bin size of 10 Da) were identified as the marker attributes for the construction of predictive models. These selected peaks, such as bins 2410–2419, 2450–2459 and 6590–6599 Da, have indicated remarkable differences between MRSA and MSSA, which were effective in the prediction of MRSA. The independent testing has revealed that the random forest model can provide a promising prediction with the area under the receiver operating characteristic curve (AUC) at 0.8450. When comparing to previous works conducted with hundreds of MS spectra, the proposed scheme demonstrates that incorporating machine learning method with a large-scale dataset of clinical MS spectra may be a feasible means for clinical physicians on the administration of correct antibiotics in shorter turn-around-time, which could reduce mortality, avoid drug resistance and shorten length of stay in hospital in the future.

Background: XueBiJing injection (XBJ) is renowned for its multi-target pharmacological effects, including immunomodulatory, antithrombotic, and antioxidant activities, offering potential therapeutic benefits for patients with severe infections such as sepsis and Coronavirus disease 2019 (COVID-19). Despite its clinical effectiveness, the molecular targets and mechanisms of XBJ remain unclear, warranting further investigation. Purpose: This study aimed to identify the key bioactive compounds in XBJ and elucidate their molecular targets and mechanisms. Methods: The zebrafish model was first used to evaluate the anti-inflammatory and antioxidant effects of XBJ, and the differentially expressed genes (DEGs) were identified by RNA sequencing and network analysis. Network pharmacology was used to analyze the relationship between bioactive compounds and molecular targets, and molecular docking and kinetic simulation were used to explore the target binding ability of key compounds. Cellular Thermal Shift Assay-Western Blot (CETSA-WB) and Surface Plasmon Resonance (SPR) further verified the interaction between compounds and targets; finally, the key pathways were confirmed by gene silencing experiments. Results: The zebrafish model results reveal that XBJ significantly reduced neutrophil and macrophage counts in a dose-dependent manner, emphasizing its potent anti-inflammatory effects. A transcriptomic analysis highlighted the differential expression of key genes in the KEAP1/NRF2 pathway, including HMOX1, SLC7A11, NQO1, and TXNRD1. A network analysis further pinpointed KEAP1 as a central molecular target, with tanshinone IIA, baicalein, and luteolin identified as key active compounds modulating this pathway. Among these, tanshinone IIA and baicalein exhibited strong binding interactions with KEAP1, which were confirmed through molecular docking and kinetic simulations. Further validation showed that baicalein directly targets KEAP1, as demonstrated by CETSA-WB and SPR analysis. Additionally, the gene silencing experiments of KEAP1 and NRF2 reinforced their crucial roles in activating the KEAP1/NRF2 pathway. Conclusion: These findings collectively establish baicalein as a critical bioactive compound in XBJ, driving its antioxidant and anti-inflammatory effects via KEAP1/NRF2 pathway activation through direct binding to KEAP1, providing new insights into the mechanism of action of XBJ.

As an agri-food by-product, the rice bran of pigmented rice, encompassing varieties such as red, black, and purple rice, has garnered increasing attention due to its richness in terms of bioactive compounds. Being mainly composed of the pericarp, aleuron, seed coat, and germ, the brown outer layer of the rice kernel offers potential health benefits and has applications in skincare. Human skin serves as the primary barrier against external threats, including pathogens, pollutants, and ultraviolet (UV) radiation. Notably, UV radiation accelerates the aging process and contributes to various skin issues. Recent trends suggest a heightened interest in incorporating pigmented rice into skincare regimens, motivated by its potential to mitigate oxidative stress, inflammation, and pigmentation, which are pivotal factors in skin aging and photodamage. With increasing consumer demand for natural and sustainable ingredients, pigmented rice has emerged as a promising candidate within the skincare and personal care sectors, effectively bridging the gap between nutrition and dermatological health. This review examines the applications of pigmented rice in skincare, with a particular focus on its bioactive components and potential mechanisms of action that contribute to skin health. The unique chemical composition of pigmented rice, which includes compounds such as anthocyanins, flavonoids, phenolic acids, and vitamin E, underlies its antioxidant, anti-inflammatory, and skin-protective properties. Despite the increasing recognition of its benefits, a comprehensive understanding of the underlying mechanisms remains limited, underscoring the necessity for further research to exploit the potential of pigmented rice in skincare applications fully.

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.

Triple-negative breast cancer (TNBC) poses a major clinical challenge due to its aggressive progression and limited treatment options, making early diagnosis and prognosis critical. MicroRNAs (miRNAs) are crucial post-transcriptional regulators that influence gene expression. In this study, we unveil novel miRNA–mRNA interactions and introduce a prognostic model based on miRNA–target interaction (MTI), integrating miRNA–mRNA regulatory correlation inference and the machine learning method to effectively predict the survival outcomes in TNBC cohorts. Using this method, we identified four key miRNAs (miR-181b-5p, miR-21-5p, miR-210-3p, miR-183-5p) targeting eight downstream target genes, forming a novel regulatory network of 19 validated miRNA–mRNA pairs. A prognostic model constructed based on the top 10 significant MTI pairs using random forest combination effectively classified patient survival outcomes in both TCGA and independent dataset GSE19783 cohorts, demonstrating good predictive accuracy and valuable prognostic insights for TNBC patients. Further analysis uncovered a complex network of 71 coherent feed-forward loops involving transcription factors, miRNAs, and target genes, shedding light on the mechanisms driving TNBC progression. This study underscores the importance of considering regulatory networks in cancer prognosis and provides a foundation for new therapeutic strategies aimed at improving TNBC treatment outcomes.