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Background: A number of studies have indicated that Ubiquitin-conjugating enzyme E2T (UBE2T), as an oncogene, promotes progression and metastasis of lung cancer, including lung adenocarcinoma (LUAD), but it is completely unknown whether and how UBE2T is ubiquitylated and degraded, and by which E3 ligase. NEDD4L plays a critical role in the regulation of cellular processes of various cancers, most of which is attributed to its E3 ubiquitin ligase function. However, the relationship between NEDD4L and UBE2T in LUAD has not been elucidated. Methods: The relationship between NEDD4L and UBE2T in LUAD tissues and cells was found by bioinformatic analyses and immunoblotting. Cell counting kit-8, colony formation assay, half-life analysis and the in vivo ubiquitylation assay, generation of xenograft model were performed to determine how NEDD4L regulates UBE2T and its downstream signaling pathway in vitro and in vivo. Results: Bioinformatic analyses found that NEDD4L, as a potential correlation E3 ligase of UBE2T, was negatively correlated with UBE2T in LUAD. Consistently, UBE2T protein half-life was shortened or extended by NEDD4L overexpression or depletion, respectively. NEDD4L inhibited LUAD cell progression in vitro and in vivo via inducing the ubiquitination-mediated UBE2T degradation, which repressed PI3K-AKT signaling. Similarly, NEDD4L predicted a better patient survival, whereas UBE2T predicted a worse survival. Conclusions: Collectively, our results reveal that NEDD4L is a novel E3 ligase of UBE2T, which can inhibit PI3K-AKT signaling by targeting for UBE2T ubiquitination and degradation, resulting in repression of LUAD cell progression. [ABSTRACT FROM AUTHOR]

Background: COVID-19 continuously threated public health heavily. Present study aimed to investigate the lymphocyte subset alterations with disease severity, imaging manifestation, and delayed hospitalization in COVID-19 patients.Methods: Lymphocyte subsets was classified using flow cytometry with peripheral blood collected from 106 patients.Results: Multivariate logistic regression showed that chest tightness, lymphocyte count, and γ-glutamyl transpeptidase were the independent predictors for severe COVID-19. The T cell, CD4+ T cell and B cell counts in severe patients were significantly lower than that in mild patients (p = 0.004, 0.003 and 0.046, respectively). Only the T cell count was gradually decreased with the increase of infiltrated quadrants of lesions in computed tomography (CT) (p = 0.043). The T cell, CD4+ T cell, and CD8+ T cell counts were gradually decreased with the increase of infiltrated area of the maximum lesion in CT (p = 0.002, 0.003, 0.028; respectively). For severe patients, the counts of T cell, CD4+ T cell, CD8+ T cell gradually decreased with the increased delayed hospitalization (p = 0.001, 0.03, and <  0.001, respectively). The proportions of T cell, CD8+ T cell gradually decreased with the increased delayed hospitalization (both p <  0.001), but the proportions of NK cell, B cell gradually increased with the increased delayed hospitalization (p = 0.007, and 0.002, respectively). For mild patients, only the NK cell count was gradually decreased with the increased delayed hospitalization (p = 0.012).Conclusion: T lymphocyte and its subset negatively correlated with disease severity, CT manifestation and delayed hospitalization. The counts of lymphocyte subset were changed more profound than their proportions. [ABSTRACT FROM AUTHOR]

Background: As a result of the growing body of protein phosphorylation sites data, the number of phosphoprotein databases is constantly increasing, and dozens of tools are available for predicting protein phosphorylation sites to achieve fast automatic results. However, none of the existing tools has been developed to predict protein phosphorylation sites in rice., Results: In this paper, the phosphorylation site predictors, NetPhos 2.0, NetPhosK, Kinasephos, Scansite, Disphos and Predphosphos, were integrated to construct meta-predictors of rice-specific phosphorylation sites using several methods, including unweighted voting, unreduced weighted voting, reduced unweighted voting and weighted voting strategies. PhosphoRice, the meta-predictor produced by using weighted voting strategy with parameters selected by restricted grid search and conditional random search, performed the best at predicting phosphorylation sites in rice. Its Matthew's Correlation Coefficient (MCC) and Accuracy (ACC) reached to 0.474 and 73.8%, respectively. Compared to the best individual element predictor (Disphos&#95;default), PhosphoRice archieved a significant increase in MCC of 0.071 (P < 0.01), and an increase in ACC of 4.6%., Conclusions: PhosphoRice is a powerful tool for predicting unidentified phosphorylation sites in rice. Compared to the existing methods, we found that our tool showed greater robustness in ACC and MCC. PhosphoRice is available to the public at http://bioinformatics.fafu.edu.cn/PhosphoRice.

Background: High temperature is a critical abiotic stress that reduces crop yield and quality. Rice (Oryza sativa L.) plants remodel their proteomes in response to high temperature stress. Moreover, phosphorylation is the most common form of protein post-translational modification (PTM). However, the differential expression of phosphoproteins induced by heat in rice remains unexplored., Methods: Phosphoprotein in the leaves of rice under heat stress were displayed using two-dimensional electrophoresis (2-DE) and Pro-Q Diamond dye. Differentially expressed phosphoproteins were identified by MALDI-TOF-TOF-MS/MS and confirmed by Western blotting., Results: Ten heat-phosphoproteins were identified from twelve protein spots, including ribulose bisphos-phate carboxylase large chain, 2-Cys peroxiredoxin BAS1, putative mRNA binding protein, Os01g0791600 protein, OSJNBa0076N16.12 protein, putative H(+)-transporting ATP synthase, ATP synthase subunit beta and three putative uncharacterized proteins. The identification of ATP synthase subunit beta was further validated by Western-blotting. Four phosphorylation site predictors were also used to predict the phosphorylation sites and the specific kinases for these 10 phosphoproteins., Conclusion: Heat stress induced the dephosphorylation of RuBisCo and the phosphorylation of ATP-β, which decreased the activities of RuBisCo and ATP synthase. The observed dephosphorylation of the mRNA binding protein and 2-Cys peroxiredoxin may be involved in the transduction of heat-stress signaling, but the functional importance of other phosphoproteins, such as H+-ATPase, remains unknown.