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OSCA (reduced hyperosmolality-induced [Ca2+]i increase) is a family of mechanosensitive calcium-permeable channels that play a role in osmosensing and stomatal immunity in plants. Oryza sativa has 11 OsOSCA genes; some of these were shown to complement hyperosmolality-induced [Ca2+]cyt increases (OICIcyt), salt stress-induced [Ca2+]cyt increases (SICIcyt), and the associated growth phenotype in the Arabidopsis thaliana mutant osca1. However, their biological functions in rice remain unclear. In this paper, we found that OsOSCA1.1 mediates OICIcyt and SICIcyt in rice roots, which are critical for stomatal closure, plant survival, and gene expression in shoots, in response to hyperosmolality and the salt stress treatment of roots. Compared with wild-type (Zhonghua11, ZH11) plants, OICIcyt and SICIcyt were abolished in the roots of 10-day-old ososca1.1 seedlings, in response to treatment with 250 mM of sorbitol and 100 mM of NaCl, respectively. Moreover, hyperosmolality- and salt stress-induced stomatal closure were also disrupted in a 30-day-old ososca1.1 mutant, resulting in lower stomatal resistance and survival rates than that in ZH11. However, overexpression of OsOSCA1.1 in ososca1.1 complemented stomatal movement and survival, in response to hyperosmolality and salt stress. The transcriptomic analysis further revealed the following three types of OsOSCA1.1-regulated genes in the shoots: 2416 sorbitol-responsive, 2349 NaCl-responsive and 1844 common osmotic stress-responsive genes after treated with 250 mM of sorbitol and 125 mM NaCl of in 30-day-old rice roots for 24 h. The Gene Ontology enrichment analysis showed that these OsOSCA1.1-regulated genes were relatively enriched in transcription regulation, hormone response, and phosphorylation terms of the biological processes category, which is consistent with the Cis-regulatory elements ABRE, ARE, MYB and MYC binding motifs that were overrepresented in 2000-bp promoter regions of these OsOSCA1.1-regulated genes. These results indicate that OsOSCA-mediated calcium signaling specifically regulates gene expression, in response to drought and salt stress in rice.

Retention time (RT) alignment is one of the crucial steps in liquid chromatography mass spectrometry (LC-MS)-based proteomic and metabolomic experiments, especially for large cohort studies. And it can be achieved using computational methods; the most popular methods are the warping function method and the direct matching method. However, the existing tools can hardly handle monotonic and non-monotonic RT shifts simultaneously. To overcome this, we developed a deep learning-based RT alignment tool, named DeepRTAlign, for large cohort LC-MS data analysis. It firstly performs a coarse alignment by calculating the average time shift between any two samples and then uses RT and intensity as the main features to train its deep learning-based model. We demonstrate that DeepRTAlign has improved performances on several proteomic and metabolomic datasets especially when handling complex samples by benchmarking it against current state-of-the-art approaches. Ultimately, we show that DeepRTAlign can improve the identification sensitivity of MS data without compromising the quantitative precision compared to MaxQuant, FragPipe and DIA-NN with match between runs. In a single-cell data-independent acquisition MS dataset, DeepRTAlign can align 298 (42.7%) more peptides on average than the existing popular tool DIA-NN in each cell.

Hepatocellular carcinoma (HCC) takes the predominant malignancy of hepatocytes with bleak outcomes owing to high heterogeneity among patients. Personalized treatments based on molecular profiles will better improve patients’ prognosis. Lysozyme (LYZ), a secretory protein with antibacterial function generally expressed in monocytes/macrophages, has been observed for the prognostic implications in different types of tumors. However, studies about the explicit applicative scenarios and mechanisms for tumor progression are still quite limited, especially for HCC. Here, based on the proteomic molecular classification data of early-stage HCC, we revealed that the LYZ level was elevated significantly in the most malignant HCC subtype and could serve as an independent prognostic predictor for HCC patients. Molecular profiles of LYZ-high HCCs were typical of those for the most malignant HCC subtype, with impaired metabolism, along with promoted proliferation and metastasis characteristics. Further studies demonstrated that LYZ tended to be aberrantly expressed in poorly differentiated HCC cells, which was regulated by STAT3 activation. LYZ promoted HCC proliferation and migration in both autocrine and paracrine manners independent of the muramidase activity through the activation of downstream protumoral signaling pathways via cell surface GRP78. Subcutaneous and orthotopic xenograft tumor models indicated that targeting LYZ inhibited HCC growth markedly in NOD/SCID mice. These results propose LYZ as a prognostic biomarker and therapeutic target for the subclass of HCC with an aggressive phenotype.