Mitoribosomal defects aggravate liver cancer via aberrant glycolytic flux and T cell exhaustion

Background Mitochondria are involved in cancer energy metabolism, although the mechanisms underlying the involvement of mitoribosomal dysfunction in hepatocellular carcinoma (HCC) remain poorly understood. Here, we investigated the effects of mitoribosomal impairment-mediated alterations on the immunometabolic characteristics of liver cancer. Methods We used a mouse model of HCC, liver tissues from patients with HCC, and datasets from The Cancer Genome Atlas (TCGA) to elucidate the relationship between mitoribosomal proteins (MRPs) and HCC. In a mouse model, we selectively disrupted expression of the mitochondrial ribosomal protein CR6-interacting factor 1 (CRIF1) in hepatocytes to determine the impact of hepatocyte-specific impairment of mitoribosomal function on liver cancer progression. The metabolism and immunophenotype of liver cancer was assessed by glucose flux assays and flow cytometry, respectively. Results Single-cell RNA-seq analysis of tumor tissue and TCGA HCC transcriptome analysis identified mitochondrial defects associated with high-MRP expression and poor survival outcomes. In the mouse model, hepatocyte-specific disruption of the mitochondrial ribosomal protein CRIF1 revealed the impact of mitoribosomal dysfunction on liver cancer progression. Crif1 deficiency promoted programmed cell death protein 1 expression by immune cells in the hepatic tumor microenvironment. A [U-13C6]-glucose tracer demonstrated enhanced glucose entry into the tricarboxylic acid cycle and lactate production in mice with mitoribosomal defects during cancer progression. Mice with hepatic mitoribosomal defects also exhibited enhanced progression of liver cancer accompanied by highly exhausted tumor-infiltrating T cells. Crif1 deficiency induced an environment unfavorable to T cells, leading to exhaustion of T cells via elevation of reactive oxygen species and lactate production. Conclusions Hepatic mitoribosomal defects promote glucose partitioning toward glycolytic flux and lactate synthesis, leading to T cell exhaustion and cancer progression. Overall, the results suggest a distinct role for mitoribosomes in regulating the immunometabolic microenvironment during HCC progression.


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The membranes were blocked for 1 h with 5% skimmed milk (#T145.2, Roth) in TBS/T buffer (20 mM Tris, 150 mM NaCl, 0.1% Tween 20, pH 7.6) and then incubated overnight at 4°C with appropriate primary antibodies. After washing three times with TBS/T, the membranes were incubated for 1 h at room temperature with secondary antibodies and visualized using ECL solution (#34580, Thermo Fisher Scientific). Target protein levels were normalized to those of glyceraldehyde 3-phosphate. The antibodies used are listed in Supplemental Table 4.

Blue-native PAGE
For mitochondrial isolation, 30 mg of liver tissue was homogenized in isolation buffer (210 nM mannitol, 70 mM sucrose, 1 mM EGTA, 5 mM HEPES, pH 7.2) using WiseStir (HS-30E; Daihan, Wonju, South Korea). The samples were then centrifuged at 600 × g for 10 min at 4°C and the supernatants were re-centrifuged at 17,000 × g for 10 min at 4°C. The pelleted mitochondria were resuspended and the constituent proteins were separated using a Native PAGE Novex Bis-Tris Gel system (Invitrogen, Carlsbad, CA, USA). To quantify the mitochondrial OxPhos complexes, 30 μg of each mitochondrial fraction in Native PAGE sample buffer (Invitrogen) and 10% n-dodecyl-β-D-maltoside were loaded onto Native PAGE 3-12% Bis-Tris gels. After electrophoresis, the separated proteins were transferred to PVDF membranes using iBlot gel transfer stacks (Invitrogen) and fixed in 8% (v/v) acetic acid. After overnight drying, membranes were incubated for 90 min with anti-OxPhos complex cocktail (#457999, Invitrogen; sc-58347, Santa Cruz) and visualized using a Western Breeze Chromogenic Western Blot Immuno-detection kit (Invitrogen). The antibodies used are listed in Supplemental Table 4.

Data selection and gene expression analysis
RNA-seq gene expression data from 368 subjects with hepatocellular carcinoma were acquired from the NIH's Genome Data Commons as raw count-mapped reads. Samples belonging to control or normal types were excluded. RNA-seq data were also generated for control and LivKO mice, and their gene counts were analyzed with DESeq R package (data are available under accession number GSE149553). 3

Gene set enrichment analyses
To explore potential molecular signatures underlying the constructed prognostic gene signatures, GSEA (Gene Set Enrichment Analyses) was performed to identify enriched terms predicted to correlate with the following terms: Hallmark in C1; the Kyoto Encyclopedia of Genes and Genomes pathway in C2; and a gene set that contain genes annotated by the same gene ontology term in C5. A P value of < 0.01 and a FDR (false discovery rate) of q < 0.05 were considered statistically significant.

Reporter metabolite analysis
Dysregulated metabolites were identified by reporter metabolite analysis based on changes in expression of genes encoding metabolic enzymes catalyzing production of the given metabolites. 4 Reporter metabolite analysis was performed using a high-quality generic genome-scale metabolic model of humans, 5 and differential gene expression statistics (i.e., pvalues and fold changes) from the LivKO and control groups of mice, and the tumor and nontumor groups, of patients with HCC.

Metabolite enrichment measurement
Tracer labeled metabolites of glycolysis, serine biosynthesis, and the TCA cycle were analyzed using GC-MS (Models 7890A/5975C; Agilent Technologies, Santa Clara, CA). To extract metabolites from liver samples, ~30 mg of frozen liver tissue were ground using a mortar, mixed with 70% ethanol, heated at 70°C, and vortexed for 30 seconds. After this, samples were incubated at 95°C for 5 min, as previously described. 6  at -80°C was added to plasma to extract the metabolites. Following incubation at 4°C for 10 min, samples were centrifuged at 16,000 × g for 10 min. To extract metabolites from liver samples, ~20 mg of frozen tissue was weighed and ground using a mortar; mixed with a solution of methanol, acetonitrile, and water (40:40:20) at -20°C; vortexed for 10 sec; and incubated for 10 min at 4°C. The samples were then centrifuged at 16,000 × g for 10 min at 4°C. Supernatants from liver and plasma samples were transferred to autosampler vials for LC-MS analysis. Labeling of metabolites was analyzed in a quadrupole mass spectrometer using positive ion mode coupled with electrospray ionization. An XBridge BEH Amide column (2.1 mm × 150 mm, 2.5 μm particle size, 130 Å pore size; Waters, Milford, MA) was utilized for LC separation via a gradient of solvent A (20 mM ammonium acetate + 20 mM ammonium hydroxide in 95:5 water:acetonitrile, pH 9.45) and solvent B (acetonitrile). Multiple reaction monitoring mode was utilized to detect the transition from precursor to product ions. The transitions m/z 149.8 → 104.1 and 153.9 → 108.1, for creatine and methionine, respectively, were monitored. Data were analyzed using Analyst software (AB Sciex, Foster City, CA).
Glucose and lactate enrichment was analyzed using GC-MS. To extract metabolites from plasma samples, 70% acetonitrile was added to plasma, the mixture was centrifuged at 10,000 × g for 10 min at 4°C, and the supernatant was desiccated under a SpeedVac. The dried sample was derivatized as described above. Ions with an m/z of 319 (M+0) and 323 (M+4) for glucose and 219 (M+0) and 222 (M+3) for lactate were monitored.

Labeling of metabolites from [U-13 C6]-glucose
To calculate the relative metabolic flux originating from glycolysis, labeling (%) of where TTR is tracer to tracee ratio, expressed as M+i / M+0 (i = mass number of mass isotopomer above the most abundant lowest mass, M+0).

Calculation of metabolic kinetics
Glucose flux, i.e., the appearance rate (Ra same as Rd in fasting state, nmol/g/min) and the metabolic clearance rate (MCR, ml/g/min), was calculated at steady state: 8 where F represents the isotope tracer infusion rate (nmol/g BW/min); Ep represents plateau isotopic enrichment (%); and C1 and C2 are the concentrations of glucose (mmol/L) at sampling times t1 and t2, respectively.

Hematoxylin and eosin (H&E) and Ki-67 staining
Liver samples were obtained from DEN-injured control and LivKO mice that had been

Measurement of the oxygen consumption rate and extracellular acidification rate
The mitochondrial oxygen consumption rate (OCR) and extracellular acidification rate

Exposure of HepG2 cell lines to hypoxia
HepG2 cell lines were purchased from the American Type Culture Collection (Manassas, VA, USA) and cultured in low-glucose DMEM (Welgene) containing 10% fetal bovine serum (Thermo Fisher Scientific) and 1% PS (penicillin and streptomycin; Welgene). Cell cultures were authenticated and checked routinely to ensure that they were mycoplasma-free. HepG2 cells were seeded onto each well of 12-well plates and exposed to 20% and 1% O2 for 24 h.

Measurement of global translation
Primary hepatocytes from WT and LivKO mice were treated, or not, for 12 h with 1 or 2 μM gamitrinib (LegoChem Biosciences Inc., Daejeon, Republic of Korea). Puromycin (1 μg/mL) was added during the last 30 min of incubation. Cells were washed with 1× PBS and collected for protein analysis.

ROS measurements
Primary hepatocytes (2 × 10 5 cells) were washed with PBS, and the cells were then incubated at 37°C for 15 min with the ROS detection reagent 6-chloromethyl-2′,7′- Then, IFN-γ levels from the culture supernatants were measured. Surface marker expression by CD8+ T cells was determined by flow cytometry analysis.

Real-time PCR analysis
Real-time PCR analysis was performed using specific primers. Total RNA was extracted from liver and adipose tissue using TRIzol Reagent (Invitrogen, Eugene, OR, USA).
Complementary DNA (cDNA) was synthesized from the same quantity of RNA using M-MLV reverse transcriptase and oligo-dT primers (Invitrogen). Quantitative real-time PCR was performed using cDNA, 2×SYBR Green PCR Mix (Applied Biosystems) and analyzed on an ABI Prism 7000 Sequence Detection System (Applied Biosystems). The comparative Ct method was used to quantitate transcript levels, the expression of which was normalized to that of 18s RNA. The results were analyzed using the ΔΔCt method and the values expressed as fold differences relative to the control.

Single-cell analysis
Gene-cell matrix of single-cell transcriptomics was download from the GEO (GSE151530) 9  and Lonp1 mRNA in HepG2 cells exposed to 20% and 1% O2 for 24 h. Data are expressed as the mean ± SEM. *P < 0.05, **P < 0.01 (two-tailed t-test).