Loss of sphingosine kinase 2 promotes the expansion of hematopoietic stem cells by improving their metabolic fitness.

Hematopoietic stem cells (HSCs) have reduced capacities to properly maintain and replenish the hematopoietic system during myelosuppressive injury or aging. Expanding and rejuvenating HSCs for therapeutic purposes has been a long-sought goal with limited progress. Here, we show that the enzyme Sphk2 (sphingosine kinase 2), which generates the lipid metabolite sphingosine-1-phosphate, is highly expressed in HSCs. The deletion of Sphk2 markedly promotes self-renewal and increases the regenerative potential of HSCs. More importantly, Sphk2 deletion globally preserves the young HSC gene expression pattern, improves the function, and sustains the multilineage potential of HSCs during aging. Mechanistically, Sphk2 interacts with prolyl hydroxylase 2 and the Von Hippel-Lindau protein to facilitate HIF1α ubiquitination in the nucleus independent of the Sphk2 catalytic activity. Deletion of Sphk2 increases hypoxic responses by stabilizing the HIF1α protein to upregulate PDK3, a glycolysis checkpoint protein for HSC quiescence, which subsequently enhances the function of HSCs by improving their metabolic fitness; specifically, it enhances anaerobic glycolysis but suppresses mitochondrial oxidative phosphorylation and generation of reactive oxygen species. Overall, targeting Sphk2 to enhance the metabolic fitness of HSCs is a promising strategy to expand and rejuvenate functional HSCs.

Comprehensive analysis of PSMG3 in pan-cancer and validation of its role in hepatocellular carcinoma.

BACKGROUND: Proteasome assembly chaperone 3 (PSMG3), a subunit of proteasome, has been found to be associated with lung cancer. However, the role of PSMG3 in other cancers has not been elucidated. The objective of this study was to explore the immune role of PSMG3 in pan-cancer and confirm the oncogenic significance in liver hepatocellular carcinoma (LIHC). METHODS: We examined the differential expression of PSMG3 across various cancer types using data from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) databases. We investigated the prognostic value of PSMG3 and examined its relationship with tumor mutation burden (TMB), microsatellite instability (MSI), and immune infiltration. The functional enrichment analysis was performed to explore the potential molecular mechanism of PSMG3. To elucidate the biological function of PSMG3, we conducted in vitro experiments using liver cancer cell lines. RESULTS: PSMG3 was highly expressed in most cancers. The high PSMG3 expression value of PSMG3 was closely related to poor prognosis. We observed correlations between PSMG3 and TMB, and MSI immune infiltration. PSMG3 may be involved in metabolic reprogramming, cell cycle, and PPAR pathways. The over-expression of PSMG3 promoted the proliferation, migration, and invasion capabilities of liver cancer cells. CONCLUSION: Our study demonstrated that PSMG3 was a pivotal oncogene in multiple cancers. PSMG3 contributed to the progression and immune infiltration in pan-cancer, especially in LIHC.

A three-stage deep learning-based training frame for spectra baseline correction.

For spectrometers, baseline drift seriously affects the measurement and quantitative analysis of spectral data. Deep learning has recently emerged as a powerful method for baseline correction. However, the dependence on vast amounts of paired data and the difficulty in obtaining spectral data limit the performance and development of deep learning-based methods. Therefore, we solve these problems from the network architecture and training framework. For the network architecture, a Learned Feature Fusion (LFF) module is designed to improve the performance of U-net, and a three-stage training frame is proposed to train this network. Specifically, the LFF module is designed to adaptively integrate features from different scales, greatly improving the performance of U-net. For the training frame, stage 1 uses airPLS to ameliorate the problem of vast amounts of paired data, stage 2 uses synthetic spectra to further ease reliance on real spectra, and stage 3 uses contrastive learning to reduce the gap between synthesized and real spectra. The experiments show that the proposed method is a powerful tool for baseline correction and possesses potential for application in spectral quantitative analysis.