FBP1 loss disrupts liver metabolism and promotes tumorigenesis through a hepatic stellate cell senescence secretome

Abstract

The crosstalk between deregulated hepatocyte metabolism and cells within the tumour microenvironment, as well as the consequent effects on liver tumorigenesis, are not completely understood. We show here that hepatocyte-specific loss of the gluconeogenic enzyme fructose 1,6-bisphosphatase 1 (FBP1) disrupts liver metabolic homeostasis and promotes tumour progression. FBP1 is universally silenced in both human and murine liver tumours. Hepatocyte-specific Fbp1 deletion results in steatosis, concomitant with activation and senescence of hepatic stellate cells (HSCs), exhibiting a senescence-associated secretory phenotype. Depleting senescent HSCs by ‘senolytic’ treatment with dasatinib/quercetin or ABT-263 inhibits tumour progression. We further demonstrate that FBP1-deficient hepatocytes promote HSC activation by releasing HMGB1; blocking its release with the small molecule inflachromene limits FBP1-dependent HSC activation, the subsequent development of the senescence-associated secretory phenotype and tumour progression. Collectively, these findings provide genetic evidence for FBP1 as a metabolic tumour suppressor in liver cancer and establish a critical crosstalk between hepatocyte metabolism and HSC senescence that promotes tumour growth.

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Fig. 1: Universal FBP1 loss in human and murine liver tumours.
Fig. 2: Hepatic FBP1 loss disrupts liver metabolism.
Fig. 3: Hepatic FBP1 loss promotes DEN-induced liver tumour progression in mice.
Fig. 4: Hepatic FBP1 loss elicits senescence and SASP in HSCs.
Fig. 5: SEN HSCs promote HCC growth in vitro and in vivo.
Fig. 6: Senolytic treatment limits HSC SASP and tumour progression driven by FBP1 loss.
Fig. 7: HMGB1 mediates crosstalk between FBP1-deficient hepatocytes and HSCs.

Data availability

The human liver cancer data were derived from the TCGA Research Network (https://cancergenome.nih.gov/cancersselected/LiverHepatocellularCarcinoma). Two public mouse NAFLD-HCC datasets (GSE67680, ref. 19, and GSE99010, ref. 20) are available at https://www.ncbi.nlm.nih.gov/geo/. The RNA-seq data generated in this study have been deposited in the Gene Expression Omnibus with the accession number GSE135616. The mass spectrometry data have been deposited in ProteomeXchange with the primary accession code PXD017831. The authors declare that the data supporting the findings of this study are available within the paper and its Supplementary information files. All other data supporting the findings of this study are available from the corresponding author on reasonable request.

Code availability

The software and algorithms for data analyses used in this study are all well established from previous work and are referenced throughout the manuscript. No custom code was used in this study.

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Acknowledgements

We thank the members of the Simon laboratory for their helpful discussions and insights on the manuscript. We also thank S. Berger for critical reading of the manuscript and K. E. Wellen for providing the D37 cells. H.-Y. Tang assisted in hepatocyte secretome profiling and data analysis. C. Mesaros and L. Weng assisted in lipid profiling and data analysis. We are grateful to J. Tobias for help with processing the human TCGA and mouse RNA-seq data, and A. Durham for histopathologic examination. We also thank W. Quinn III, A. J. Merrell, H. Xie and H. Weinstein for their technical assistance. This work was supported by the National Key Research and Development Program (grant no. 2016YFA0502600) of China (to B.L.) and National Cancer Institute (NCI) grant nos P01CA104838, R35CA197602 and P30CA016520 (to M.C.S.).

Author information

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Authors

Contributions

F.L., B.L. and M.C.S. conceived the project and designed the research studies. F.L. performed most of the experiments described. M.B. and K.G. provided help with animal husbandry and technical assistance in the mouse experiments. B.L. performed the Fbp1 gene targeting and generated the chimaeric mice. P.H., R.R., J.G. and I.A.B. assisted with the in vitro assays. K.E.L. and N.L. provided technical assistance for the flow cytometry and data analysis. P.H., P.L. and B.K. provided conceptual advice and helpful discussion. F.L. analysed data. F.L., B.K., B.L. and M.C.S. wrote the manuscript.

Corresponding authors

Correspondence to Bo Li or M. Celeste Simon.

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The authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Decreased FBP1 expression in human and murine liver tumours.

a, Metabolic gene set analysis of TCGA HCC RNA-sequencing data. A total of 374 HCC tumours and 50 adjacent normal tissues were included, and 2752 genes encoding all known human metabolic enzymes and transporters were classified according to the Kyoto Encyclopedia of Genes and Genomes (KEGG). Generated metabolic gene sets were ranked based on their median fold expression changes in HCC tumours vs normal tissue, and plotted as median ± median absolute deviation. b, Representative FBP1 IHC staining on human liver tissue array with adjacent normal, grade 2 and 3 HCC tissues. Scale bar: 100 µm. c, Statistical analysis of FBP1 IHC staining in (b). n=20 for normal, n=30 for grade 2, n=30 for grade 3 samples. In each box plot, the top-most line is the maximum, the top of the box is the third quartile, the centre line is the median, the bottom of the box is the first quartile and the bottom-most line is the minimum. d, Representative H&E staining in liver sections from 24-week control (Ctrl) and DEN-treated (DEN) C57BL/6 mice (n = 3 independent experiments with similar results). T, tumour. Scale bar: 100 µm. e, Serum alanine transaminase (ALT) activity from 24-week Ctrl and DEN mice. n = 4 for Ctrl, n = 5 for DEN. Graph in e show mean ± SEM, and P value was calculated using a two-tailed t-test. Numerical source data are provided in Source Data Extended Data Fig. 1. Source data

Extended Data Fig. 2 Hepatic FBP1 loss disrupts liver metabolism in mice.

a, Scheme for generating Fbp1fl/fl mice by homologous recombination. b, Scheme for hepatocyte-specific Fbp1 deletion in Fbp1fl/fl mice. c, qRT-PCR analysis of gluconeogenic gene expression in 24-week GFP (n = 4) and Cre (n=4) livers. d, Immunoblotting analysis of 24-week GFP and Cre livers (n = 3 independent experiments). GAPDH was used as loading control. e, H&E staining of 24-week GFP and Cre kidney sections (n = 3 independent experiments). Scale bar: 100 µm. f, Immunoblotting analysis of 24-week GFP (n=2) and Cre (n=2) kidneys. GAPDH was used as loading control. g, qRT-PCR analysis of gluconeogenic gene expression in 24-week GFP (n=4) and Cre (n=4) kidneys. h, Serum free fatty acid (FFA) and β-hydroxybutyrate (BHBA) levels of fasted (16-h) and refed (4-h) GFP (n=7) and Cre (n=8) mice (24-week). i, Liver gross appearance of 16-h fasted animals (24-week) (n = 3 independent experiments). j,k, qRT-PCR analysis of lipid metabolism (j) and unfolded protein response (UPR) (k) gene signatures in 16-h fasted GFP (n = 5) and Cre (n=5) livers (24-week). In each box plot, the top-most line is the maximum, the top of the box is the third quartile, the centre line is the median, the bottom of the box is the first quartile and the bottom-most line is the minimum. l, Growth rates of GFP and Cre mice. GFP: n=5 for female or male mice, Cre: n=5 for female, n = 8 for male mice. m,n, Quantification of triglyceride (TG) (m) and Oil Red O staining (% area) (n) in 24-week GFP (n = 6) and Cre (n = 5) mouse livers. Graphs in c, g, h, l-n show mean ± SEM. All P values were calculated using a two-tailed t-test. Scanned images of unprocessed blots in c and e are shown in Source Data Extended Data Fig. 2. Numerical source data are provided in Source Data Extended Data Fig. 2. Source data

Extended Data Fig. 3 Hepatic FBP1 loss promotes tumour progression in p53fl/fl and NAFLD models.

a, Scheme for Fbp1 deletion in DEN-induced liver cancer model. b, Gross liver appeara = nce and tumour multiplicity in 80-week p53 and p53/Fbp1 mice treated with AAV8-TBG-Cre. Yellow arrows indicate liver tumours. Scale bar: 1 cm. c, Quantification of surface tumour numbers in p53 and p53/Fbp1 animals in (b). n = 7 mice for p53 or p53/Fbp1 cohorts. 4 of 7 p53 mice and 7 of 7 p53/Fbp1 mice had surface tumours. d, Representative H&E staining and α-SMA IHC staining of liver sections from 80-wk p53 and p53/Fbp1 mice (n = 3 independent experiments). Scale bar: 100 µm. e, Gross liver appearances of 32-wk GFP and Cre mice with diet- and CCl4-induced NAFLD (see Materials and Methods for details). Scale bar: 1 cm. (f-i) Quantification of surface tumour number (f), liver-to-body weight ratio (g), liver weight (h) and body weight (i) in 32-wk GFP (n = 5) and Cre (n = 5) mice with NAFLD. j,k, qRT-PCR analysis of lipogenic (j) and fibrotic (k) gene expression from 32-wk GFP (n = 5) and Cre (n = 5) mouse livers with NAFLD. l, Representative H&E staining, Sirius Red staining of 32-wk GFP and Cre NAFLD mouse liver sections (n = 3 independent experiments). Scale bar: 100 µm. m, Quantification of Sirius Red staining in (l). n = 5 mice for each group. All graphs represent the mean ± SEM. In each box plot of j, k and m, the top-most line is the maximum, the top of the box is the third quartile, the centre line is the median, the bottom of the box is the first quartile and the bottom-most line is the minimum. Graphs in c, f-I show mean ± SEM. All P values were calculated using a two-tailed t-test. Numerical source data are provided in Source Data Extended Data Fig. 3. Source data

Extended Data Fig. 4 Impact of hepatic FBP1 loss on tumour microenvironment in DEN mice.

a, Representative CYP2E1 IHC staining in liver sections from 24-week DEN/GFP and DEN/Cre mice (n = 3 independent experiments). Scale bar: 100 µm. b, Flow cytometry quantification of T cell subpopulations in 24-week DEN/GFP (n = 4) and DEN/Cre (n = 5) livers. c, Flow cytometry quantification of B cells and B cell subpopulations in 24-week DEN/GFP (n = 4) and DEN/Cre (n = 5) livers. d, Flow cytometry quantification of total macrophages and CD11b+ or CD206+ subsets in 24-week DEN/GFP (n = 4) and DEN/Cre (n = 5) livers. e,f, Representative flow cytometry plots (e) and quantification (f) (% CD45+ cells) of NK cells (CD3NKp46+) in 24-week DEN/GFP (n = 4) and DEN/Cre (n = 5) livers. g,h, Representative NKp46 IHC staining (g) and quantification (h) in 24-wk DEN/GFP (n = 4) and DEN/Cre (n = 5) liver sections. Scale bar: 100 µm. i,j, Representative flow cytometry plots (i) and quantification (j) (% CD45+ cells) of MDSC cells (CD11b+Ly6C+) in 24-week DEN/GFP (n = 4) and DEN/Cre (n = 5) livers. k, A heatmap showing relative abundance of individual ceramide species in 24-wk DEN/GFP (N = 7) and DEN/Cre (n = 9) mouse livers by lipidomic profiling. Graphs in b-d, f, h and j show mean ± SEM, and P values were calculated using a two-tailed t-test. Numerical source data are provided in Source Data Extended Data Fig. 4. Source data

Extended Data Fig. 5 Hepatic FBP1 loss leads to HSC activation and senescence.

a, Representative SA-β-Gal staining, α-SMA and IL6 IHC staining of serial cryosections from 36-week mouse livers (n=3 independent experiments). Scale bar: 100 µm. b, Representative Sirius Red staining and quantification, Ki67/α-SMA IF staining and quantification of 24-week non-DEN liver sections. For Sirius Red staining quantification, n = 20 fields of view (FOV, 200x) from 6 mice for GFP, n = 18 fields of view (FOV, 200x) from 6 mice for Cre. For Ki67/α-SMA IF staining quantification, n = 6 mice for each group. Scale bar: 100 µm. c, SA-β-Gal staining and quantification (% of cells) in 24-week liver sections from non-DEN mice. Black arrows indicate SA-β-Gal staining. n = 6 mice for each group. Scale bar: 100 µm. d, α-SMA and γ-H2AX IHC staining of 24-week non-DEN Cre liver sections. Scale bar: 100 µm. e, Quantification of α-SMA/γ-H2AX IHC staining and SASP component IF staining of 24-week non-DEN Cre (n = 6) liver sections. Graphs in b, c and e show mean ± SEM, and P values were calculated using a two-tailed t-test. Numerical source data are provided in Source Data Extended Data Fig. 5. Source data

Extended Data Fig. 6 In vitro Characterization of senolytic effects of D+Q and ABT-263.

a, Growth curves of GRO and SEN human HSCs under 3% O2 (to prevent senescence due to oxidative damage) in regular medium (n = 3 independent experiments). b, Growth curves of mouse D37 cells under 3% O2 in conditioned medium from Vehicle (Veh) or etoposide (Etp)-treated mouse HSCs (n = 3 independent experiments). c, Viability or apoptosis (% Annexin V+) quantification of GRO or SEN human HSCs after D+Q treatment at indicated concentrations/combinations (n = 3 or independent experiments). d, Viability or apoptosis (% Annexin V+) quantification of GRO or SEN human HSCs after treatment with ABT-263 at indicated concentrations (n = 3 independent experiments). e, Apoptosis (% Annexin V+) quantification of GRO or SEN human HSCs after treatment with indicated AZD5991 (50 nM) and ABT-263 (10 μm) combinations (n = 3 independent experiments). f, Viability or apoptosis (% Annexin V+) quantification of mouse D37 cells after D+Q treatment at indicated concentrations/combinations (n = 3 independent experiments). g, Mouse D37 cell viability or apoptosis (% Annexin V+) quantification after treatment with indicated ABT-263 concentrations (n = 3 independent experiments). All graphs show mean ± SEM, and P values were calculated using a two-tailed t-test. Numerical source data are provided in Source Data Extended Data Fig. 6. Source data

Extended Data Fig. 7 Characterizing the senolytic effects of D+Q and ABT-263 in vivo.

a, Scheme of early stage Veh and D+Q treatment of DEN/GFP or DEN/Cre mice. b,c, Liver-to-Body Weight (LW/BW) ratio (b) and body weight (c) quantifications of 24-week Veh (n = 5) and D+Q (n = 6) DEN/Cre mice. d, Quantification of TG levels from 24-week Veh (n = 5) and D+Q (n = 6) DEN/Cre mouse livers. e,f, Representative Sirius Red staining (e) and quantification (f) of 24-week Veh (n = 25) and D+Q (n = 20) DEN/Cre mouse liver sections. FOV: 200x fields of view. Scale bar: 100 µm. g, TUNEL staining of 24-week Veh and D+Q DEN/Cre mouse liver sections (n = 3 independent experiments). Scale bar: 100 µm. h, Scheme of late stage Veh and D+Q treatment of DEN/GFP or DEN/Cre mice. i, Representative SA-β-Gal staining (n = 3 independent experiments), and BrdU/α-SMA IF staining of 36-week Veh and D+Q DEN/Cre mouse liver sections. Scale bar: 100 µm. j, Quantification of BrdU and α-SMA IF staining of 36-week Veh (n = 5) and D+Q (n = 6) DEN/Cre mouse liver sections. k, Surface tumour number and size distributions of 24 wk or 36 wk DEN/GFP mice treated with Veh or D+Q. n = 5 mice for each cohort at each time point. l, Scheme of Veh and ABT-263 treatment of DEN/GFP or DEN/Cre mice. m, Representative TUNEL staining of 36-week Veh and ABT-263-treated DEN/Cre mouse liver sections (n = 3 independent experiments). Scale bar: 100 µm. n, SA-β-Gal staining of 36-week Veh and ABT-263-treated DEN/Cre mouse liver sections (n = 3 independent experiments). Scale bar: 100 µm. o, Surface tumour number and size distributions of DEN/GFP mice treated with Veh (n=5) or ABT-263 (n = 5). Graphs in bd, f, j, k and o show mean ± SEM, and P values were calculated using a two-tailed t-test. Numerical source data are provided in Scanned images of unprocessed blots in Source Data Extended Data Fig. 7. Source data

Extended Data Fig. 8 Identification of HMGB1 as a potential mediator between FBP1-deficient hepatocytes and HSCs.

a, Unsupervised hierarchical clustering of normalized protein abundance in CM of 24-week Non-DEN GFP (n = 5) and Cre (n = 5) hepatocytes. b, An Egyptian Pie Chart of 459 proteins with > = 1.5-fold change (adjusted p<0.05) of abundance in CM between Non-DEN GFP (n = 5) and Cre (n = 5) groups. c, ELISA-based quantification of HMGB1 levels in CM of 24-week Non-DEN GFP (n = 4) or Cre (n = 4) hepatocytes. d, Immunoblotting analysis of HMGB1 in the nuclear (Nuc) and cytosolic (Cyto) fractions or total lysates from 24-week non-DEN GFP (n = 2) and Cre (n = 2) mouse livers. H3 and HSP90 were used as loading control for nuclear and cytosolic fractions, respectively. GAPDH was used as loading control for whole tissue lysates. e, qRT-PCR analysis of UPR gene expression in mouse primary hepatocytes after tunicamycin (TUN) treatment (n = 3 independent experiments). f, ELISA-based quantification of HMGB1 levels in primary hepatocyte culture medium of Ctrl and TUN groups (n = 3 independent experiments). g,h, qRT-PCR analysis of SASP (g) or fibrotic (h) gene expression in human HSCs after 1 nM HMGB1 treatment for 15 h (n = 3 independent experiments). Graphs in c, e, f-h show represent the mean ± SEM, and P values were calculated using a two-tailed t-test. Scanned images of unprocessed blots in d are shown in Source Data Extended Data Fig. 8. Numerical source data are provided in Source Data Extended Data Fig. 8. Source data

Extended Data Fig. 9 Characterization of in vivo and in vitro ICM treatment.

a, Scheme for Veh and ICM treatment of DEN/GFP and DEN/Cre mice. b, Gross liver appearances and tumour multiplicity (indicated by yellow arrows) in Veh and ICM DEN/Cre mice. Scale bar: 1 cm. c, H&E staining of Veh (n = 6) and ICM (n = 7) DEN/Cre mouse liver sections. Scale bar: 100 µm. d, Quantification of TG levels in Veh (n = 6) and ICM (n = 7) DEN/Cre mouse livers. e, Immunoblotting analysis of HMGB1 in nuclear (Nuc) and Cytosolic (Cyto) fractions of Veh (n = 2) and ICM (n = 2) DEN/Cre livers. H3 and HSP90 were used as loading control for nuclear and cytosolic fractions, respectively. f, SA-β-Gal staining of Veh (n = 6) and ICM (n = 7) DEN/Cre mouse liver sections. Scale bar: 100 µm. g, TUNEL staining and quantification of Veh (n = 6) and ICM (n = 6) DEN/Cre mouse liver sections. Scale bar: 100 µm. h, Cell viability assays of GRO human HSCs after ICM treatment (n = 3 independent experiments). I,j, Cell viability assays in SEN human HSCs (i) or mouse D37 cells (j) after ICM (10 μm) treatment (n = 3 independent experiments). k, qRT-PCR analysis of SASP gene expression in human HSCs after 10 µm ICM treatment for 24 h (n = 3 independent experiments). l, Representative Sirius Red staining and quantification (% area) of Veh (n = 25 FOV) and ICM (n = 21) DEN/Cre mouse liver sections. FOV: fields of view. Scale bar: 100 µm. m, Quantification of surface tumour number and size distributions from DEN/GFP mice treated with Veh (n = 5) or ICM (n = 5). Graphs in d, gm show mean ± SEM, and P values were calculated using a two-tailed t-test. Scanned images of unprocessed blots in e are shown in Source Data Extended Data Fig. 9. Numerical source data are provided in Source Data Extended Data Fig. 9. Source data

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Li, F., Huangyang, P., Burrows, M. et al. FBP1 loss disrupts liver metabolism and promotes tumorigenesis through a hepatic stellate cell senescence secretome. Nat Cell Biol 22, 728–739 (2020). https://doi.org/10.1038/s41556-020-0511-2

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