Cytokine release syndrome (CRS) is a major cause of the multi-organ injury and fatal outcome induced by SARS-CoV-2 infection in severe COVID-19 patients. Metabolism can modulate the immune responses against infectious diseases, yet our understanding remains limited on how host metabolism correlates with inflammatory responses and affects cytokine release in COVID-19 patients. Here we perform both metabolomics and cytokine/chemokine profiling on serum samples from healthy controls, mild and severe COVID-19 patients, and delineate their global metabolic and immune response landscape. Correlation analyses show tight associations between metabolites and proinflammatory cytokines/chemokines, such as IL-6, M-CSF, IL-1α, IL-1β, and imply a potential regulatory crosstalk between arginine, tryptophan, purine metabolism and hyperinflammation. Importantly, we also demonstrate that targeting metabolism markedly modulates the proinflammatory cytokines release by peripheral blood mononuclear cells isolated from SARS-CoV-2-infected rhesus macaques ex vivo, hinting that exploiting metabolic alterations may be a potential strategy for treating fatal CRS in COVID-19.

In this work we are inspired to explore gold nanoclusters supported on mesoporous CeO2 nanospheres as nanocatalysts for the reduction of nitrobenzene. Ultrasmall Au nanoclusters (NCs) and mesoporous CeO2 nanospheres were readily synthesized and well characterized. Due to their ultrasmall size, the as-prepared Au clusters can be easily absorbed into the mesopores of the mesoporous CeO2 nanospheres. Owing to the unique mesoporous structure of the CeO2 support, Au nanoclusters in the Au@CeO2 may effectively prevent the aggregation which usually results in a rapid decay of the catalytic activity. It is notable that the ultrasmall gold nanoclusters possess uniform size distribution and good dispersibility on the mesoporous CeO2 supports. Compared to other catalyst systems with different oxide supports, the as-prepared Au nanocluster-CeO2 nanocomposite nanocatalysts showed efficient catalytic performance in transforming nitrobenzene into azoxybenzene. In addition, a plausible mechanism was deeply investigated to explain the transforming process. Au@CeO2 exhibited efficient catalytic activity for reduction of nitrobenzene. This strategy may be easily extended to fabricate many other heterogeneous catalysts including ultrasmall metal nanoclusters and mesoporous oxides.

Lipidomics, which reveals comprehensive characterization of molecular lipids, is a rapidly growing technology used in biomedical research. Lipid extraction is a critical step in lipidomic analysis. However, the effectiveness of different lipid extract solvent systems from cellular samples still remains unclear. In the current study, the protocol of reverse-phase liquid chromatography mass spectrometry (LC/MS)-based lipidomics was optimized for extraction and detection of lipids from human pancreatic cancer cell line PANC-1. Four different extraction methods were compared, including methanol/methyl-tert-butyl ether (MTBE)/HO, methanol/chloroform, methanol/MTBE/chloroform, and hexane/isopropanol. Data were acquired using high-resolution mass spectrometry in positive and negative ion modes respectively. The number of total detected and identified lipids was assessed with the aid of automated lipid identification software LipidSearch. Results demonstrated that methanol/MTBE/HO provided a better extraction efficiency for different lipid classes, which was chosen as the optimized extraction solvent system. This validated method enables highly sensitive and reproducible analysis for a variety of cellular lipids, which was further applied to an untargeted lipidomic study on human pancreatic cancer PANC-1 cell lines. Moreover, this optimized extraction solvent system can be further applied to other cancer cell lines with similar chemical and physical properties. Graphical abstract Optimized UHPLC-ESI-HRMS-based lipidomic analysis of cancer cells.

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