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The prevalence of non-obese nonalcoholic fatty liver disease (NAFLD) is increasing worldwide with unclear etiology and pathogenesis. Here, we show GP73, a Golgi protein upregulated in livers from patients with a variety of liver diseases, exhibits Rab GTPase-activating protein (GAP) activity regulating ApoB export. Upon regular-diet feeding, liver-GP73-high mice display non-obese NAFLD phenotype, characterized by reduced body weight, intrahepatic lipid accumulation, and gradual insulin resistance development, none of which can be recapitulated in liver-GAP inactive GP73-high mice. Common and specific gene expression signatures associated with GP73-induced non-obese NAFLD and high-fat diet (HFD)-induced obese NAFLD are revealed. Notably, metformin inactivates the GAP activity of GP73 and alleviates GP73-induced non-obese NAFLD. GP73 is pathologically elevated in NAFLD individuals without obesity, and GP73 blockade improves whole-body metabolism in non-obese NAFLD mouse model. These findings reveal a pathophysiological role of GP73 in triggering non-obese NAFLD and may offer an opportunity for clinical intervention.

Recent reports have shown the critical role of the mitochondrial antiviral signaling (MAVS) protein in virus-induced apoptosis, but the involvement of MAVS in tumorigenesis is still poorly understood. Herein, we report that MAVS is a key regulator of p53 activation and is critical for protecting against tumorigenesis. We find that MAVS promotes p53-dependent cell death in response to DNA damage. MAVS interacts with p53 and mediates p53 mitochondrial recruitment under genotoxic stress. Mechanistically, MAVS inhibits p53 ubiquitination by blocking the formation of the p53-murine double-minute 2 (MDM2) complex, leading to the stabilization of p53. Notably, compared with their wild-type littermates, MAVS knockout mice display decreased resistance to azoxymethane (AOM) or AOM/dextran sulfate sodium salt (DSS)-induced colon cancer. MAVS expression is significantly downregulated in human colon cancer tissues. These results unveil roles for MAVS in DNA damage response and tumor suppression.

Modeling and predicting mutations are critical for COVID-19 and similar pandemic preparedness. However, existing predictive models have yet to integrate the regularity and randomness of viral mutations with minimal data requirements. Here, we develop a non-demanding language model utilizing both regularity and randomness to predict candidate SARS-CoV-2 variants and mutations that might prevail. We constructed the “grammatical frameworks” of the available S1 sequences for dimension reduction and semantic representation to grasp the model’s latent regularity. The mutational profile, defined as the frequency of mutations, was introduced into the model to incorporate randomness. With this model, we successfully identified and validated several variants with significantly enhanced viral infectivity and immune evasion by wet-lab experiments. By inputting the sequence data from three different time points, we detected circulating strains or vital mutations for XBB.1.16, EG.5, JN.1, and BA.2.86 strains before their emergence. In addition, our results also predicted the previously unknown variants that may cause future epidemics. With both the data validation and experiment evidence, our study represents a fast-responding, concise, and promising language model, potentially generalizable to other viral pathogens, to forecast viral evolution and detect crucial hot mutation spots, thus warning the emerging variants that might raise public health concern.

In this study, we specifically addressed the connection between the SARS-CoV-2 virus with host cholesterol metabolism. Plasma lipid profile was measured in 861 COVID-19 patients classified as mild (n=215), moderate (n=364), severe (n=217) or critical (n=65) and 1108 age- and sex-matched healthy individuals. We showed that the levels of both TG and HDL-C were significantly lower in patients with severe disease than in patients with moderate or mild disease. After successful treatment, cholesterol metabolism was reestablished in patients with SARS-CoV-2 infection. The serum concentrations of TC and HDL-C can be used as indicators of disease severity and prognosis in COVID-19 patients.