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Fatty acid binding protein 5 suppression attenuates obesity-induced hepatocellular carcinoma by promoting ferroptosis and intratumoral immune rewiring

Nature Metabolism volume 6, pages 741–763 (2024)Cite this article

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Abstract

Due to the rise in overnutrition, the incidence of obesity-induced hepatocellular carcinoma (HCC) will continue to escalate; however, our understanding of the obesity to HCC developmental axis is limited. We constructed a single-cell atlas to interrogate the dynamic transcriptomic changes during hepatocarcinogenesis in mice. Here we identify fatty acid binding protein 5 (FABP5) as a driver of obesity-induced HCC. Analysis of transformed cells reveals that FABP5 inhibition and silencing predispose cancer cells to lipid peroxidation and ferroptosis-induced cell death. Pharmacological inhibition and genetic ablation of FABP5 ameliorates the HCC burden in male mice, corresponding to enhanced ferroptosis in the tumour. Moreover, FABP5 inhibition induces a pro-inflammatory tumour microenvironment characterized by tumour-associated macrophages with increased expression of the co-stimulatory molecules CD80 and CD86 and increased CD8+ T cell activation. Our work unravels the dual functional role of FABP5 in diet-induced HCC, inducing the transformation of hepatocytes and an immunosuppressive phenotype of tumour-associated macrophages and illustrates FABP5 inhibition as a potential therapeutic approach.

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Fig. 1: scRNA-seq uncovers dynamic changes in TCA cycle metabolic flux in obesity-induced HCC.
Fig. 2: FABP5 is specifically upregulated and associated with worse survival in HCC.
Fig. 3: FABP5 inhibition sensitizes cancer cells to lipid peroxidation, ER stress and ferroptosis.
Fig. 4: FABP5 inhibition ameliorates HCC progression.
Fig. 5: FABP5 inhibition restores HCC FAO and promotes lipid peroxidation-induced ferroptosis.
Fig. 6: Genetic ablation of FABP5 reduces HCC burden by inducing lipid peroxidation and ferroptosis.
Fig. 7: SBFI-103 promotes the accumulation of pro-inflammatory macrophages and cytotoxic T cells.
Fig. 8: FABP5 inhibition in macrophages enhances CD8+ T cell co-stimulation to promote CD8 proliferation and cytotoxicity.

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Data availability

All data that support the findings of this study are available within source data and extended data and supplementary figure files. RNA-seq for FABP5 siRNA and FABP5 inhibitor-treated Huh7 cells are deposited in the NCBI Gene Expression Omnibus under accession code GSE228430. scRNA-seq raw data are also deposited in the NCBI database under accession code GSE232182 for the progression from healthy liver to HCC, GSE231818 for FABP5 inhibitor-treated HCC and GSE248010 for hepatocyte-specific KO of FABP5 during HCC progression. The scRNA-seq data of the carcinogenic DEN+CCL4 model were obtained from GSE181515, originally published by Zhou et al.38. Source data are provided with this paper.

Code availability

No custom code was used for this study.

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Acknowledgements

This work was supported by grants from the National Institutes of Health (R35HL135820 to C.F.-H.), the American Heart Association (20TPA35490416 to C.F.-H. and 874771 to P.F.-T.) and Programa Postdoctoral de Perfecionamiento de Personal del Gobierno Vasco (Spain) (to P.F.-T.). This project was partly supported by the Yale Liver Center award P30 DK034989 core (Morphology, Cellular and Molecular Physiology Core Facility). Illustrations were created with BioRender.com. We thank C. Rothlin for her scientific input in this study, W. Zhu for performing venous catheterizations in mice and enabling the in vivo metabolic flux analysis, N. L. Price for helping collect samples from in vivo experiments and T. Badri for editing the paper. Finally, we thank P. Chambon and D. Metzger (University of Strasbourg, IGBMC, France) for sharing the AlbCREERT2 mice.

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Author notes

  1. These authors jointly supervised this work: Yajaira Suárez, Carlos Fernández-Hernando.

Authors and Affiliations

  1. Vascular Biology and Therapeutics Program, Yale University School of Medicine, New Haven, CT, USA

    Jonathan Sun, Enric Esplugues, Alicia Bort, Magdalena P. Cardelo, Inmaculada Ruz-Maldonado, Pablo Fernández-Tussy, Clara Wong, Yajaira Suárez & Carlos Fernández-Hernando

  2. Yale Center for Molecular and System Metabolism, Yale University School of Medicine, New Haven, CT, USA

    Jonathan Sun, Enric Esplugues, Alicia Bort, Magdalena P. Cardelo, Inmaculada Ruz-Maldonado, Pablo Fernández-Tussy, Clara Wong, Rachel Perry, Yajaira Suárez & Carlos Fernández-Hernando

  3. Department of Comparative Medicine, Yale University School of Medicine, New Haven, CT, USA

    Jonathan Sun, Enric Esplugues, Alicia Bort, Magdalena P. Cardelo, Inmaculada Ruz-Maldonado, Pablo Fernández-Tussy, Clara Wong, Yajaira Suárez & Carlos Fernández-Hernando

  4. Department of Pathology, Yale University School of Medicine, New Haven, CT, USA

    Jonathan Sun, Yajaira Suárez & Carlos Fernández-Hernando

  5. Department of Chemistry, Stony Brook University, New York, NY, USA

    Hehe Wang & Iwao Ojima

  6. Institute of Chemical Biology and Drug Discovery, Stony Brook University, New York, NY, USA

    Iwao Ojima & Martin Kaczocha

  7. Department of Anesthesiology, Renaissance School of Medicine. Stony Brook University, New York, NY, USA

    Martin Kaczocha

  8. Department of Cellular and Molecular Physiology, Yale University School of Medicine, New Haven, CT, USA

    Rachel Perry

  9. Department of Medicine (Endocrinology), Yale University School of Medicine, New Haven, CT, USA

    Rachel Perry

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  1. Jonathan Sun
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J.S., Y.S. and C.F.-H. designed the research. J.S., A.B., M.P.C., I.R.-M., P.F.-T., C.W., E.E. and R.P. performed research and analysed data. H.W. and I.O. provided the FABP5 chemical inhibitor (SBFI-103) and together with M.K. analysed data and edited the paper. J.S., Y.S. and C.F.-H. wrote the paper. R.P. and E.E. edited the paper.

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Correspondence to Yajaira Suárez or Carlos Fernández-Hernando.

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Competing interests

I.O. and M.K. declare financial support from Artelo Biosciences. I.O. and M.K. have patents issued to the Research Foundation of the State University of New York. The other authors declare no competing interests.

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Nature Metabolism thanks Maurizio Parola and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Yanina-Yasmin Pesch, in collaboration with the Nature Metabolism team.

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Extended data

Extended Data Fig. 1 Long-term CD-HFD feeding induces metabolic abnormalities, hepatic inflammation, fibrosis, and HCC.

(A) Developmental timeline of obesity-induced HCC in the CD-HFD model outlining key experiments to examine metabolic, transcriptomic, and pathogenic phenotypes. (B) Body weight in C57BL/6 mice fed with CD-HFD or control diet. N = 20 for ND-fed mice and N = 40 for CD-HFD-fed mice. (C) Body composition analysis by MRI of fat mass from 3-, 6-, and 12-months CD-HFD fed C57BL/6 mice. N = 6 for each ND-fed and CD-HFD-fed mice at 3 months, CD-HFD-fed mice at 6 months. N = 8 for ND-fed mice at 6 months, ND-fed and CD-HFD-fed mice at 12 months. (D) Representative H&E staining of 3- and 6-month CD-HFD fed mice demonstrating steatosis and inflammation. (E) Representative Oil Red O staining illustrates lipid accumulation in 3- and 6-month CD-HFD-fed mice. (F) Quantification of liver triglyceride content in 3- and 6-month CD-HFD fed mice. N = 6 for each experimental condition. (G) Quantification of liver cholesterol esters (CE) content in 3- and 6-month CD-HFD fed mice. N = 6 for each experimental condition. (H) Quantification of circulating total cholesterol in 3-, 6-, 12-, and 15-months CD-HFD fed mice. N = 8 for each experimental condition. (I) Cholesterol content of FPLC-fractionated lipoproteins from pooled plasma of 6 months of CD-HFD or control diet. Plasma samples were pooled from 4 mice for each condition. (J) Fasting glucose measured from 3-, 6-, 12-, and 15-months CD-HFD fed mice. N = 6 for experimental conditions at 3 months, N = 10 for experimental conditions at 6 months, N = 8 for experimental conditions at 12 and 15 months. (K) Glucose tolerance test performed with 6-month CD-HFD-fed mice with Area Under Curve (AUC) shown (top right). N = 8 for each experimental condition. (L) Insulin tolerance test (left) performed with 6-month CD-HFD-fed mice with Area Under Curve (AUC) shown (top right). N = 8 for each experimental condition. (M) Flow cytometry quantification of hepatic CD45+ population in C57BL/6 mice after 6 months of CD-HFD feeding. N = 4 for ND-fed mice and N = 5 for CD-HFD-fed mice. (N) Flow cytometry quantification of hepatic CD11blow F480high Kupffer Cells (KCs), CD11bhigh F480low Monocyte-derived Macrophages (MoMPs) and CD44+, CD62L+ activated CD8+ T cell after 6 months of CD-HFD feeding. N = 4 for ND-fed mice and N = 5 for CD-HFD-fed mice. (O) Picosirius red staining analysis (left) and quantification (right) in the liver after 6 months of CD-HFD feeding. N = 4 for each experimental condition. (P) Representative gross images of High and Low AFP liver from 15 months CD-HFD and ND-fed mice. Total of 16 ND livers, 18 CD-HFD Low AFP livers and 14 CD-HFD High AFP livers. (Q) Representative H&E image from steatotic and non-steatotic HCC sections of 15 months CD-HFD fed mice. Scale bars: 200 µm in (d-e); 20 µm at highest magnification in (q). Statistical Analysis: (b) one-way ANOVA followed by Dunnett’s post hoc test; (c, f-h, j-o) non-parametric two-sided t-tests. P-value of < 0.05 considered statistically significant. For (c, f-h, j-o) each dot represents an individual animal and bar height indicates mean and SEM. Data (c, f-q) representation of 2 or more independent experiments.

Source data

Extended Data Fig. 2 Single-Cell RNA sequencing identifies distinctive inflammatory signatures in Mononuclear Phagocytes and T cells.

(A) UMAP representation of 29 discrete clusters from 15 months CD-HFD and ND-fed mice. (B) UMAP representation of G1, G2M and S cell cycle phase cells from 15 months CD-HFD and ND-fed mice. (C) Feature plot visualization of representative cell marker genes across all sequenced cells. (D) Heatmap showing top 3 differentially expressed markers across all identified cell types. (E) Composition of immune cell subtypes in total PTPRC+ immune cells for experimental conditions after 15 months CD-HFD and ND feeding. (F) UMAP representation of 6 distinctive cell clusters corresponding to Monocyte-derived Macrophages (MoMP) or Kupffer Cells (KC) after subsetting on Mononuclear Phagocytes (MPs). (G) Composition of MoMP and KC sub-clusters for experimental conditions after 15 months of CD-HFD and ND feeding. (H) Expression of pro-inflammatory and anti-inflammatory gene signatures in MPs across experimental conditions after 15 months of CD-HFD and ND feeding. (I) Flow cytometry analysis of CD11b+ Ly6C+ inflammatory MPs across experimental conditions after 15 months of CD-HFD and ND feeding. N = 3 for ND, Low AFP and HCC conditions. N = 4 for High AFP condition. (J) UMAP representation of 6 distinctive cell clusters corresponding to CD8+ T cells (CD8), CD4+ T cells (CD4), Natural Killer cells (NK) or Natural Killer T cells (NKT) after on CD3E+ lymphocytes. (K) Violet plot analysis of NR4A2, TOX and PDCD1 expression across experimental conditions after 15 months CD-HFD and ND feeding. N = 4 for all experimental conditions. (L) Flow cytometry quantification of CD8+ T cells across experimental conditions after 15 months of CD-HFD and ND feeding. N = 4 for all experimental conditions. (M) Flow cytometry analysis of TOX and PD1 in CD8+ T cells across experimental conditions after 15 months of CD-HFD and ND feeding. N = 4 for all experimental conditions. Statistical Analysis: (i, l-m) non-parametric two-sided t-tests. P-value of < 0.05 considered statistically significant. For (i, l-m) each dot represents an individual animal and the bar height indicates the mean and SEM. Data (i, l-m) representation of 2 or more independent experiments.

Source data

Extended Data Fig. 3 Single-cell RNA sequencing analysis of hepatocyte gene signatures by slingshot pseudotime analysis and comparison analysis in DEN + CCL4 model.

(A) UMAP representation of 4 experimental conditions in hepatocytes after 15 months CD-HFD or ND-fed mice. (B)Heatmap showing 5 periportal and central vein gene marker expression across hepatocyte subtypes (left). Feature plot representation of select periportal and central vein gene markers in hepatocytes (right). (C) Expression of upregulated genes ENO1, FBP1, GAPDH as a function of pseudotime value. (D) Expression of downregulated genes mt-CO1, mt-CYTB, mt-NO3 as a function of pseudotime value. (E) UMAP representation of 4 distinctive experimental conditions after re-clustering by slingshot from 15 months CD-HFD or ND-fed mice. (F) UMAP representation of 5 distinctive clusters after re-clustering by slingshot from 15 months CD-HFD or ND-fed mice. Identified trajectory shown by connecting central nodules between each cluster. (G) Pseudotime value visualization by slingshot across hepatocytes and cancer cells from 15-month CD-HFD and ND-fed mice. (H) GO ontology analysis of upregulated differentially expressed genes identified as a function of slingshot pseudotime progression. (I) GO ontology analysis of downregulated differentially expressed genes identified as a function of slingshot pseudotime progression. (J) UMAP representation of combined analysis including 15 months CD-HFD or ND-fed mice and WT mice treated with DEN + CCL4 sequenced at 3 days, 10 days and 30 days post-tumour initiation. (K) Expression of AFP in hepatocytes and cancer cells in diet-induced and carcinogenic HCC models by Feature plot. (L) Expression of FABP5 in hepatocytes and cancer cells in diet-induced and carcinogenic HCC models by Feature plot. (M) Dotplot expression of FABP5 and AFP from single-cell analysis from diet-induced and carcinogenic HCC models. (N) Dotplot expression of mt-Cytb, mt-Nd4 and mt-Co1 from single-cell analysis from diet-induced and carcinogenic HCC models.

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Extended Data Fig. 4 Single-cell RNA sequencing identifies FABP5 upregulation during HCC progression.

(a) Feature plot representation of AFP and FABP5 expression in hepatocytes and transformed cancer cells. (b) Feature plot representation of CSF1R and FABP5 expression in Mononuclear Phagocytes. (c) Immunofluorescence (IF) imaging of FABP5 and AFP staining in the CD-HFD model. (d) IF imaging of FABP5 and CD68 staining in the CD-HFD model. (e) IF imaging of FABP5 and Phalloidin staining in the CD-HFD model. (f) Whole tumour IF imaging of FABP5 (green) in HCC and adjacent healthy liver. (g) Whole tumour H&E staining in HCC and adjacent healthy liver. (h) Whole tumour CD68 immunohistochemistry staining in HCC and adjacent healthy liver. Scale bars: 100 µm in (c); 50 µm in (d); 37 µm in (e) and 1 mm in (f), (g) and (h). Data (c-e, f-h) representation of 2 or more independent experiments.

Source data

Extended Data Fig. 5 FABP5 inhibition and silencing leads to lipid peroxidation, ER stress and ferroptosis.

(A) RT-PCR analysis of FABP5 mRNA expression in Huh7 cell lines treated with SBFI-103 for 48 hours. N = 3 for both experimental conditions. (B) Principle Complement Analysis (PCA) of SBFI-103 treated Huh7 cells for RNA sequencing analysis. N = 3 for both experimental conditions. (C) Volcano plot of differentially expressed genes in SBFI-103 treated Huh7 cell lines. Select upregulated and downregulated genes are indicated. (D) Top up- and downregulated GSEA pathways from differentially expressed genes in SBFI-103 treated Huh7 cells. (E) RT-PCR analysis of FABP5 mRNA expression in Huh7 cell lines treated with FABP5 siRNA for 48 hours. N = 3 for both experimental conditions. (F) PCA of FABP5 siRNA-treated Huh7 cells for RNA sequencing analysis. N = 3 for both experimental conditions. (G) Volcano plot of differentially expressed genes in FABP5 siRNA-treated Huh7 cell lines. Select upregulated and downregulated genes are indicated. (H) Top up- and downregulated GSEA pathways from differentially expressed genes in FABP5 siRNA-treated Huh7 cells. (I) Flow cytometry analysis of BODIPY C16 MFI in 5uM SBFI-103 and FABP siRNA-treated Huh7 cells. N = 3 for each experimental condition. (J) Flow cytometry analysis of 2-NBDG MFI in 5uM SBFI-103 and FABP5 siRNA-treated Huh7 cells. N = 3 for each experimental condition. (K) MDA concentration in FABP5 siRNA-treated Huh7 cells as measured through colorimetric assay. N = 3 for both experimental conditions. (L) Flow cytometry analysis of BODIPY 581/591 C11 MFI in FABP5 siRNA-treated Huh7 cells. N = 3 for both experimental conditions. (M) Flow cytometry analysis of CellRox MFI in FABP5 siRNA-treated Huh7 cells. N = 3 for both experimental conditions. (N) Western blot of PERK, ATF4, IRE1A and BIP in 5uM FABP5 and control siRNA-treated Huh7 cells. N = 3 for both experimental conditions. Statistical Analysis: (a, e, i-m) non-parametric two-sided t-tests. P-value of < 0.05 considered statistically significant. For (a, e, i-m) each dot represents an individual animal and bar height indicates mean and SEM. Data (i-m) representation of 2 or more independent experiments.

Source data

Extended Data Fig. 6 FABP5 inhibition and silencing promotes mitochondrial respiration and oxidation.

(A) Violin plot of FABP5, FABP1, and FABP2 expression in SBFI-103 and vehicle-treated cancer cells. (B) Downregulated GSEA pathways from differentially expressed genes in SBFI-103 treated cancer cells. (C) EM quantification of mitochondrial size from vehicle-treated liver, vehicle-treated HCC, and SBFI-103-treated HCC. N = 3 for each experimental condition. (D) Vcs flux and Vpc/Vcs ratio analysed by PINTA and ex vivo NMR in the vehicle-treated liver, vehicle-treated HCC and SBFI-103-treated HCC. N = 3 for each experimental condition. (E) Western blot analysis of electron transport complexes in the vehicle-treated liver, vehicle-treated HCC and SBFI-103-treated HCC. N = 3 for each experimental condition. (F) Schematic outlining FABP5 inhibition and silencing treatment conditions on Huh7 cell lines before metabolic analysis. (G) ATP production in Huh7 cells treated with SBFI-103 for 5 µM 24 hours. N = 3 for each experimental condition. (H) ATP production in Huh7 cells treated with FABP5 siRNA for 24 hours. N = 3 for each experimental condition. (a) Oxygen consumption rate (OCR) was measured after 24 hours of treatment with FABP5 siRNA in Huh7 cells in and without the presence of 20 µm Etoximir (Eto) by Seahorse extracellular flux analyser. Basal and spare respiratory capacity were quantified after adding 1 µm Oligomycin, 2 µm FCCP and 2.5 µm Rotenone + Antimycin. N = 6 for each experimental condition. Statistical Analysis: (c-d, g-i) non-parametric two-sided t-tests. P-value of < 0.05 considered statistically significant. For (d, g-i) each dot represents an individual animal and bar height indicates mean and SEM. Data (e-i) representation of 2 or more independent experiments.

Source data

Extended Data Fig. 7 Genetic ablation of FABP5 does not affect body weight, circulating glucose and cholesterol, and hepatic inflammation after 15 months of CD-HFD feeding.

(a) RT-PCR analysis of FABP5 mRNA expression in non-parenchymal cells (NPCs) and hepatocytes after tamoxifen administration in Fabp5HKO mice. N = 3 for both experimental conditions. (b) Body weight before and after 15 months of CD-HFD feeding in Fabp5HKO mice. N = 8 for WT mice before diet, N = 5 for Fabp5HKO mice before diet, N = 12 for WT mice after diet and N = 13 for Fabp5HKO mice after diet. (c) Fasting glucose in Fabp5HKO mice after 15 months of CD-HFD feeding. N = 10 for WT mice and N = 13 for Fabp5HKO mice. (d) Total cholesterol in Fabp5HKO mice after 15 months of CD-HFD feeding. N = 10 for WT mice and N = 13 for Fabp5HKO mice. (e) ALT activity in Fabp5HKO mice after 15 months of CD-HFD feeding. N = 9 for WT mice and N = 13 for Fabp5HKO mice. (f) AST activity in Fabp5HKO mice after 15 months of CD-HFD feeding. N = 9 for WT mice and N = 13 for Fabp5HKO mice. (g) UMAP representation of 7 distinctive immune cell types from Fabp5HKO or WT HCC. (h) UMAP representation showing immune cells belonging to Fabp5HKO or WT HCC (left). Quantification of percentage composition from each cell type is shown (right). (i) UMAP representation of 4 distinctive mononuclear phagocyte clusters from Fabp5HKO or WT HCC (left). Top 5 signature genes from each cluster are shown via heatmap (right). (j) Expression of infiltrating (CHIL3, CCR2) and resident markers (CLEC4F, CD5L) in Fabp5HKO and WT mononuclear phagocytes. (k) UMAP representation showing mononuclear phagocytes belonging to Fabp5HKO or WT HCC (left). Quantification of percentage composition from each mononuclear phagocyte cluster is shown (right). (l) Flow cytometry quantification of CD11b+ Ly6G− Ly6C+ Infil MPs in Fabp5HKO HCCs. N = 3 for WT mice and N = 2 for Fabp5HKO mice. (m) Flow cytometry analysis of CD86 expression in CD11b+F4/80+ TAMs in Fabp5HKO HCCs. N = 3 for WT mice and N = 2 for Fabp5HKO mice. (n) Flow cytometry analysis of CD80 expression in CD11b+F4/80+ TAMs in Fabp5HKO HCCs. N = 3 for WT mice and N = 2 for Fabp5HKO mice. (o) Flow cytometry analysis of CD206 expression in CD11b+F4/80+ TAMs in Fabp5HKO HCCs. N = 3 for WT mice and N = 2 for Fabp5HKO mice. Statistical Analysis: (a-d) non-parametric two-sided t-tests. P-value of < 0.05 considered statistically significant. For (a-d) each dot represents an individual animal and the bar height indicates the mean and SEM.

Source data

Extended Data Fig. 8 FABP5 inhibition promotes macrophage co-stimulatory receptor expression and T cell proliferation in HCC and subcutaneous tumours.

(A) Expression of CD9 and TREM2 by Feature plot in SBFI-103 and vehicle-treated HCC. (B) Expression of CD9 and TREM2 by Vioin plot in SBFI-103 and vehicle-treated HCC. (C) Zoomed out RNAscope representation of CX3CR1 (green) and CLEC4F (red) mRNA expression in SBFI-103 or vehicle-treated HCC. (D) Flow cytometry quantification of CD11b+ F4/80+ TAMs in SBFI-103 treated HCC. N = 4 for vehicle-treated and N = 5 for SBFI-103-treated HCC. (E) Flow cytometry quantification of CD11b+ Ly6G− Ly6C+ Infiltrating MPs in SBFI-103 treated HCC. N = 4 for vehicle-treated and N = 5 for SBFI-103-treated HCC. (F) Flow cytometry analysis of Ly6C expression in CD11b+F4/80+ TAMs upon SBFI-103 treatment. N = 4 for vehicle-treated and N = 5 for SBFI-103-treated HCC. (G) Flow cytometry analysis of CD206 expression in CD11b+F4/80+ TAMs upon SBFI-103 treatment. N = 4 for vehicle-treated and N = 5 for SBFI-103-treated HCC. (H) Flow cytometry quantification of CD44+, CD62L− intratumoral CD8+ T cells upon SBFI-103 treatment. N = 4 for vehicle-treated and N = 5 for SBFI-103-treated HCC. (I) CD8 immunohistochemistry analysis (left) and quantification (right) in HCC after SBFI-103 treatment. N = 4 for each experimental condition. (J) Flow cytometry quantification of PD1+ intratumoral CD8+ (left) and CD4+ (right) T cells upon SBFI-103 treatment. N = 4 for vehicle-treated and N = 5 for SBFI-103-treated HCC. (K) Circos plot of upregulated secreted factors and downstream ligands from immune cells in SBFI-103 treated conditions identified by NicheNet analysis. Secreted factors are labelled in purple, blue or yellow while downstream signalling ligands are labelled in red. Scale bars: 60 µm in (c). Statistical Analysis: (d-i) non-parametric two-sided t-tests. P-value of < 0.05 considered statistically significant. For (d-i) each dot represents an individual animal and bar height indicates mean and SEM. Data (c-i) representation of 2 or more independent experiments.

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Extended Data Fig. 9 FABP5 inhibition promotes macrophage co-stimulatory receptor expression and T cell proliferation in the subcutaneous MC38 tumour model.

(a) Schematic outlining induction of subcutaneous tumour by injection of MC38 syngeneic tumour cells in the flank and daily treatment with SBFI-103. (b) Quantification of MC38 tumour volume after one week of SBFI-103 treatment. N = 6 for both experimental conditions. (c) Quantification of MC38 tumour weight after one week of SBFI-103 treatment. N = 6 for both experimental conditions. (d) Flow cytometry quantification of CD11b+ Ly6C+ immune cells in SBFI-103 treated MC38 tumours. N = 6 for both experimental conditions. (e) Flow cytometry quantification of CD86 expression in TAMs from SBFI-103 treated MC38 tumours. N = 6 for both experimental conditions. (f) Flow cytometry quantification of CD8+ T cells in SBFI-103 treated MC38 tumours. N = 6 for both experimental conditions. (g) Flow cytometry quantification of Ki67 MFI in CD8+ T cells from SBFI-103 treated MC38 tumours. N = 6 for both experimental conditions. Statistical Analysis: (b) one-way ANOVA followed by Dunnett’s post hoc test; (c-g) non-parametric two-sided t-tests. P-value of < 0.05 considered statistically significant. For (c-g) each dot represents an individual animal and bar height indicates mean and SEM. Data (b-g) representation of 2 or more independent experiments.

Source data

Extended Data Fig. 10 FABP5 silencing in macrophages enhances CD8+ T cell co-stimulation to promote CD8 proliferation and cytotoxicity.

(A) RT-PCR analysis of ARG1, MRC1, RETNLA, and YM1 mRNA expression in FABP5 siRNA-treated IL-4 polarized BMDMs. N = 3 for both experimental conditions. (B) Arginase activity in FABP5 siRNA-treated IL-4 polarized BMDMs. N = 3 for both experimental conditions. (C) Flow cytometry gating strategy for the identification of monocultured BMDMs before subsequent analysis. (D) Flow cytometry gating strategy for identifying co-cultured OT-I+ CD8+ T cells before subsequent analysis. (E) Flow cytometry analysis of CD86 MFI in FABP5 siRNA-treated BMDMs in nonpolarized and IL-4-treated. N = 3 for each experimental condition. (F) Flow cytometry analysis of CD80 MFI in FABP5 siRNA-treated BMDMs in nonpolarized and IL-4-treated conditions. N = 3 for each experimental condition. (G) Flow cytometry analysis of CD69+, CD25+ OT-I+ CD8+ T cells after 48 hours of co-culture with FABP5 siRNA-treated BMDMs in nonpolarized and IL-4-treated conditions. N = 3 for each experimental condition. (H) Flow cytometry analysis of CellTrace Violet (CTV) in OT-I+ CD8+ T cells after 48 hours of co-culture with FABP5 siRNA-treated BMDMs in nonpolarized and IL-4-treated conditions. N = 3 for each experimental condition. (I) Flow cytometry analysis of interferon-gamma (IFNγ) OT-I+ CD8+ T cells after 48 hours of co-culture with FABP5 siRNA-treated BMDMs in nonpolarized and IL-4-treated conditions. N = 3 for each experimental condition. (J) Flow cytometry analysis of Caspase 3high, Live/Dead Aquahigh B16-OVA Cancer Cells after 24 hours of co-culture with OT-I+ CD8+ T cells previously activated in the presence of FABP5 or control siRNA-treated BMDMs. N = 3 for each experimental condition. Statistical Analysis: (a-b, e-j) non-parametric two-sided t-tests. P-value of < 0.05 considered statistically significant. For (a-b, e-j) each dot represents an individual animal and bar height indicates mean and SEM. Data (a-b, e-j) representation of 2 or more independent experiments.

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Sun, J., Esplugues, E., Bort, A. et al. Fatty acid binding protein 5 suppression attenuates obesity-induced hepatocellular carcinoma by promoting ferroptosis and intratumoral immune rewiring. Nat Metab 6, 741–763 (2024). https://doi.org/10.1038/s42255-024-01019-6

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