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Spatially resolved characterization of tissue metabolic compartments in fasted and high-fat diet livers

ABSTRACT: Cells adapt their metabolism to physiological stimuli, and metabolic heterogeneity exists between cell types, within tissues, and subcellular compartments. The liver plays an essential role in maintaining whole-body metabolic homeostasis and is structurally defined by metabolic zones. These zones are well-understood on the transcriptomic level, but have not been comprehensively characterized on the metabolomic level. Mass spectrometry imaging (MSI) can be used to map hundreds of metabolites directly from a tissue section, offering an important advance to investigate metabolic heterogeneity in tissues compared to extraction-based metabolomics methods that analyze tissue metabolite profiles in bulk. We established a workflow for the preparation of tissue specimens for matrix-assisted laser desorption/ionization (MALDI) MSI that can be implemented to achieve broad coverage of central carbon, nucleotide, and lipid metabolism pathways. Herein, we used this approach to visualize the effect of nutrient stress and excess on liver metabolism. Our data revealed a highly organized metabolic tissue compartmentalization in livers, which becomes disrupted under high fat diet. Fasting caused changes in the abundance of several metabolites, including increased levels of fatty acids and TCA intermediates while fatty livers had higher levels of purine and pentose phosphate-related metabolites, which generate reducing equivalents to counteract oxidative stress. This spatially conserved approach allowed the visualization of liver metabolic compartmentalization at 30 µm pixel resolution and can be applied more broadly to yield new insights into metabolic heterogeneity in vivo.

ORGANISM(S): Mouse Mus Musculus

TISSUE(S): Brain, Liver, Kidney

SUBMITTER: Sylwia Stopka

PROVIDER: ST002219 | MetabolomicsWorkbench | Thu May 12 00:00:00 BST 2022

REPOSITORIES: MetabolomicsWorkbench

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Project description:Purpose: To study the effects of high-fat diet feeding in mouse liver tissues with and without the hepatocellular carcinoma-inducing carcinogen DEN. Here, we used RNA sequencing to identify gene expression patterns associated with high-fat diet feeding. Methods: C57BL/6N mice were injected at 2-weeks of age with vehicle control (PBS) or DEN. At 6 weeks, mice were randomized to control diet, or 60% high-fat diet. After 8-weeks of diet exposure, mice underwent a 13C6-glucose labelling protocol, and liver tissues were extracted for anlaysis (metabolomic, transcriptomic). RNA was isolated from mouse liver tissue using Triazol extraction, purified, and libraries generated using KAPA Stranded mRNA Sequencing kit. After cDNA synthesis, adapter ligation, and final cDNA library generation, samples were sequenced on a flow cell (1x50bp single-end reads) and HiSeq4000 (Illumina). Data processing was conducted in an NGS pipeline (Snakemake) and quality control was performed with FastQC. Trimmed data was analyzed for differential expression (DEseq2) and gene set enrichment analysis (GSEA) to look for KEGG pathways and gene ontologies (GO) of interest. Results: Using 13C6-glucose labelling, we determined there was a glycolytic phenotype caused by high-fat diet exposure. Therefore, we focused on a diet effect in our transcriptomcis dataset. By comparing control diet to high-fat diet liver tissue, we found gene sets invloved in peroxisomes and lipid metabolism to be enriched in high-fat diet exposed livers. These findings were in line with in vitro testing of a liver cell line, showing peroxisomal metabolism of palmitate drives ROS production and a glycolytic phenotype. Conclusions: We found high-fat diet exposed liver exhibits a strong metabolic phenotype towards increased glucose metabolism. At the transcriptome level, we found a lipid-reporgramming signature, and not a glucose metabolism signature. The lipid reprogramming signature was in line with in vitro work using liver cell lines, in which exposure to palmitate stimulated a glycolyitic phenotype that was inhibited by targeting peroxisomal-derived ROS species. In combination with metabolic and lipidomic data after long-term exposure to DEN and subsequent tumor formtion, we discovered that high-fat diet can prime liver tissue for carcinogenesis and tumor development by stimulating a Warburg-like metabolism. These findings suggest that fat can induce similar changes in non-transformed liver cells than found in HCC. In conclusion, we show that normal, non-transformed livers respond to fat by inducing glucose metabolism.

Project description:Following parenchymal loss, the liver regenerates restoring normal mass and metabolic function. Prevailing theories on triggering events leading to regeneration include humoral, metabolic and flow-mediated mechanisms, the latter emphasizing the importance of shear stress mediated nitric oxide (NO) regulation. We aimed to investigate whether the grade of resection and hence the portal venous pressure and sinusoidal shear stress increase, would be reflected in the gene expression profiles in the liver remnant by employing a global porcine cDNA microarray chip with approximately 23 000 genes represented. Six pig livers were resected with 62% (Low Portal Pressure Resection, LPPR) and 75% (High Portal Pressure Resection) resulting in a portal venous pressure increase from a baseline of 6.1 mmHg to 8.2 and 12 mmHg respectively. By sampling consecutive biopsies from the liver remnants we found differentially expressed genes in the HPPR group to have functions related primarily to apoptosis, nitric oxide metabolism and oxidative stress, whereas differentially expressed genes in the LPPR group potentially regulate the cell cycle. Common to both groups was the upregulation of genes regulating inflammation, transport, cell proliferation and development and protein metabolism. Also common to both groups was both up- and downregulation of genes regulating cell-cell signaling, signal transduction, cell adhesion and translation. Genes regulating the metabolism of lipids, hormones, amines, and alcohol were downregulated in both groups. Conclusions: The genetic regenerative response in the liver remnant to varies according to the level of resection. Keywords: time course, treatment comparison Three pigs underwent a 62% liver resection (low-pressure resection, LPR) and three underwent a 75% resection (high-pressure resection, HPR). Biopsies from the liver remnant were taken from all animals at time points 1, 30, 90, minutes and 3, 4 and 6 hours after resection. Expression profiling was conducted by hybridising each sample against a common reference, consisting of liver RNA from an unrelated animal.

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Spatially resolved characterization of tissue metabolic compartments in fasted and high-fat diet livers

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