Endogenous sterol intermediates of the mevalonate pathway regulate HMGCR degradation and SREBP-2 processing.

Sterol-regulated HMG-CoA reductase (HMGCR) degradation and SREBP-2 cleavage are two major feedback regulatory mechanisms governing cholesterol biosynthesis. Reportedly, lanosterol selectively stimulates HMGCR degradation, and cholesterol is a specific regulator of SREBP-2 cleavage. However, it is unclear whether other endogenously generated sterols regulate these events. Here, we investigated the sterol intermediates from the mevalonate pathway of cholesterol biosynthesis using a CRISPR/Cas9-mediated genetic engineering approach. With a constructed HeLa cell line expressing the mevalonate transporter, we individually deleted genes encoding major enzymes in the mevalonate pathway, used lipidomics to measure sterol intermediates, and examined HMGCR and SREBP-2 statuses. We found that the C4-dimethylated sterol intermediates, including lanosterol, 24,25-dihydrolanosterol, follicular fluid meiosis activating sterol, testis meiosis activating sterol, and dihydro-testis meiosis activating sterol, were significantly upregulated upon mevalonate loading. These intermediates augmented both degradation of HMGCR and inhibition of SREBP-2 cleavage. The accumulated lanosterol induced rapid degradation of HMGCR, but did not inhibit SREBP-2 cleavage. The newly synthesized cholesterol from the mevalonate pathway is dispensable for inhibiting SREBP-2 cleavage. Together, these results suggest that lanosterol is a bona fide endogenous regulator that specifically promotes HMGCR degradation, and that other C4-dimethylated sterol intermediates may regulate both HMGCR degradation and SREBP-2 cleavage.

PIP4K2A regulates intracellular cholesterol transport through modulating PI(4,5)P2 homeostasis.

The transport of LDL-derived cholesterol from lysosomes to peroxisomes is facilitated by membrane contacts formed between the lysosomal protein synaptotagmin VII and the peroxisomal lipid phosphatidylinositol 4, 5-bisphosphate [PI(4,5)P2]. Here, we used RNA interference to search for regulators of PI(4,5)P2 and to study the effects of altered PI(4,5)P2 homeostasis on cholesterol transport. We found that knockdown of phosphatidylinositol 5-phosphate 4-kinase type-2 α (PIP4K2A) reduced peroxisomal PI(4,5)P2 levels, decreased lysosome-peroxisome membrane contacts, and increased accumulation of lysosomal cholesterol in human SV-589 fibroblasts. Forced expression of peroxisome-localized, kinase-active PIP4K2A in the knockdown cells reduced cholesterol accumulation, and in vitro addition of recombinant PIP4K2A restored membrane contacts. These results suggest that PIP4K2A plays a critical role in intracellular cholesterol transport by upregulating PI(4,5)P2 levels in the peroxisomal membrane. Further research into PIP4K2A activity may inform future therapeutic interventions for managing lysosomal storage disorders.

[Applications of machine learning in clinical decision support in the omic era].

With the development of the omic technologies, the acquisition approaches of various biological data on different levels and types are becoming more mature. As a large amount of data will be produced in the process of diagnosis and treatment of diseases, it is necessary to utilize the artificial intelligence such as machine learning to analyze complex, multi-dimensional and multi-scale data and to construct clinical decision support tools. It will provide a method to figure out rapid and effective programs in diagnosis and treatment. In this process, the choice of artificial intelligence seems to be particularly important, such as machine learning. The article reviews the type and algorithm of machine learning used in clinical decision support, such as support vector machines, logistic regression, clustering algorithms, Bagging, random forests and deep learning. The application of machine learning and other methods in clinical decision support has been summarized and classified. The advantages and disadvantages of machine learning are elaborated. It will provide a reference for the selection between machine learning and other artificial intelligence methods in clinical decision support.