Common Transcriptional Program of Liver Fibrosis in Mouse Genetic Models and Humans

Transcriptomics-driven metabolic pathway analysis reveals similar alterations in lipid metabolism in mouse MASH model and human

BACKGROUND

Metabolic dysfunction-associated steatotic liver disease (MASLD) is a prevalent chronic liver disease worldwide, and can rapidly progress to metabolic dysfunction-associated steatohepatitis (MASH). Accurate preclinical models and methodologies are needed to understand underlying metabolic mechanisms and develop treatment strategies. Through meta-analysis of currently proposed mouse models, we hypothesized that a diet- and chemical-induced MASH model closely resembles the observed lipid metabolism alterations in humans.

METHODS

We developed transcriptomics-driven metabolic pathway analysis (TDMPA), a method to aid in the evaluation of metabolic resemblance. TDMPA uses genome-scale metabolic models to calculate enzymatic reaction perturbations from gene expression data. We performed TDMPA to score and compare metabolic pathway alterations in MASH mouse models to human MASH signatures. We used an already-established WD+CCl4-induced MASH model and performed functional assays and lipidomics to confirm TDMPA findings.

RESULTS

Both human MASH and mouse models exhibit numerous altered metabolic pathways, including triglyceride biosynthesis, fatty acid beta-oxidation, bile acid biosynthesis, cholesterol metabolism, and oxidative phosphorylation. We confirm a significant reduction in mitochondrial functions and bioenergetics, as well as in acylcarnitines for the mouse model. We identify a wide range of lipid species within the most perturbed pathways predicted by TDMPA. Triglycerides, phospholipids, and bile acids are increased significantly in mouse MASH liver, confirming our initial observations.

CONCLUSIONS

We introduce TDMPA, a methodology for evaluating metabolic pathway alterations in metabolic disorders. By comparing metabolic signatures that typify human MASH, we show a good metabolic resemblance of the WD+CCl4 mouse model. Our presented approach provides a valuable tool for defining metabolic space to aid experimental design for assessing metabolism.

N4-acetylcytidine-dependent GLMP mRNA stabilization by NAT10 promotes head and neck squamous cell carcinoma metastasis and remodels tumor microenvironment through MAPK/ERK signaling pathway

N4-acetylcytidine (ac4C) is a post-transcriptional RNA modification that regulates in various important biological processes. However, its role in human cancer, especially lymph node metastasis, remains largely unknown. Here, we demonstrated N-Acetyltransferase 10 (NAT10), as the only known "writer" of ac4C mRNA modification, was highly expressed in head and neck squamous cell carcinoma (HNSCC) patients with lymph node metastasis. High NAT10 levels in the lymph nodes of patients with HNSCC patients are a predictor of poor overall survival. Moreover, we found that high expression of NAT10 was positively upregulated by Nuclear Respiratory Factor 1 (NRF1) transcription factor. Gain- and loss-of-function experiments displayed that NAT10 promoted cell metastasis in mice. Mechanistically, NAT10 induced ac4C modification of Glycosylated Lysosomal Membrane Protein (GLMP) and stabilized its mRNA, which triggered the activation of the MAPK/ERK signaling pathway. Finally, the NAT10-specific inhibitor, remodelin, could inhibit HNSCC tumorigenesis in a 4-Nitroquinoline 1-oxide (4NQO)-induced murine tumor model and remodel the tumor microenvironment, including angiogenesis, CD8+ T cells and Treg recruitment. These results demonstrate that NAT10 promotes lymph node metastasis in HNSCC via ac4C-dependent stabilization of the GLMP transcript, providing a potential epitranscriptomic-targeted therapeutic strategy for HNSCC.

Guided extraction of genome-scale metabolic models for the integration and analysis of omics data

Omics data can be integrated into a reference model using various model extraction methods (MEMs) to yield context-specific genome-scale metabolic models (GEMs). How to chose the appropriate MEM, thresholding rule and threshold remains a challenge. We integrated mouse transcriptomic data from a Cyp51 knockout mice diet experiment (GSE58271) using five MEMs (GIMME, iMAT, FASTCORE, INIT an tINIT) in a combination with a recently published mouse GEM iMM1865. Except for INIT and tINIT, the size of extracted models varied with the MEM used (t-test: p-value < 0.001). The Jaccard index of iMAT models ranged from 0.27 to 1.0. Out of the three factors under study in the experiment (diet, gender and genotype), gender explained most of the variability ( > 90%) in PC1 for FASTCORE. In iMAT, each of the three factors explained less than 40% of the variability within PC1, PC2 and PC3. Among all the MEMs, FASTCORE captured the most of the true variability in the data by clustering samples by gender. Our results show that for the efficient use of MEMs in the context of omics data integration and analysis, one should apply various MEMs, thresholding rules, and thresholding values to select the MEM and its configuration that best captures the true variability in the data. This selection can be guided by the methodology as proposed and used in this paper. Moreover, we describe certain approaches that can be used to analyse the results obtained with the selected MEM and to put these results in a biological context.