Quantitative proteomics of HFD-induced fatty liver uncovers novel transcription factors of lipid metabolism - PubMed (original) (raw)
. 2022 May 1;18(8):3298-3312.
doi: 10.7150/ijbs.71431. eCollection 2022.
Affiliations
- PMID: 35637971
- PMCID: PMC9134917
- DOI: 10.7150/ijbs.71431
Quantitative proteomics of HFD-induced fatty liver uncovers novel transcription factors of lipid metabolism
Shang Zhi et al. Int J Biol Sci. 2022.
Abstract
Nonalcoholic fatty liver disease (NAFLD) has become the most common chronic liver disease, which progression is tightly regulated by transcription factors (TFs), nuclear receptors, and cellular enzymes. In this study, a label-free quantitative proteomic approach was used to determine the effect of the high-fat diet on the proteomics profile of liver tissue and to identify novel NAFLD related TFs. Mice were fed with HFD for 16 weeks to establish a NAFLD mouse model. Mice fed with normal chow diet were taken as controls. Liver samples were collected from each group for proteomics analysis. A total of 2298 proteins were quantified, among which 106 proteins were downregulated, while 256 proteins were upregulated in HFD-fed mice compared with the controls with fold change more than 1.5 and p value less than 0.05. Bioinformatic analysis revealed that metabolic-related functions and pathways were most significantly enriched. A subgroup of 11 TFs were observed to share interactions with metabolic-related enzymes and kinases by protein-protein interaction analysis. Among them, 7 TFs were selected for verification, and 3 TFs were finally validated, including Rbbp4, Tcea1, and ILF2. Downregulating each of the 3 TFs could significantly promote lipid accumulation in AML12 hepatocytes, by regulating the expression of fatty acid synthesis- or β-oxidation-related genes. In contrast, overexpression of Tcea1, Rbbp4, and ILF2, respectively, could ameliorate hepatocyte steatosis. These findings propose novel lipid metabolism related TFs, which might have potential roles in preventing NAFLD.
Keywords: NAFLD; lipid metabolism; quantitative proteomics; transcription factor.
© The author(s).
Conflict of interest statement
Competing Interests: The authors have declared that no competing interest exists.
Figures
Figure 1
Mice model of high-fat diet induced nonalcoholic fatty liver. (A) Body weight gain of HFD- and NCD-fed mice (n=7 mice per group). (B) Gross morphology of livers from HFD- and NCD-fed mice. (C-K) Liver weight, liver/body weight ratio, liver TG, serum ALT, serum AST, serum TC, serum TG, serum LDL-C, and serum HDL-C of the HFD and NCD groups (n=7 mice per group). (L) H&E staining of livers from HFD- and NCD-fed mice. Magnification: 100x and 400x, scale bar: 50 μm. (M) Oil red O staining of livers from HFD- and NCD-fed mice. Scale bar: 50 μm. All data represent the mean ± SEM. Student's t test,* p < 0.05, ** p < 0.01, *** p < 0.001.
Figure 2
Label-free quantification proteomics of livers from HFD- and NCD-fed mice. (A) Principle component analysis (PCA) for proteome of all liver samples. (B) Volcano plot of DEPs. Red and blue dots represent upregulated and downregulated proteins, respectively. (C) Heatmap of DEPs. (D) Biological process enrichment of DEPs. (E) Cell component enrichment of DEPs. (F) KEGG pathway enrichment of DEPs. (G) DEPs-upstream regulators-biological processes interaction network constructed by IPA. Blue represents inhibition of protein/biological process, Orange represents activation of protein/biological process.
Figure 3
Bioinformatics analysis of DEPs revealed metabolic dysfunction related TFs. (A)Molecular function enrichment of DEPs. (B) Heatmap of differential abundance TFs. (C) Protein-protein interaction network contructed by IPA. Green represents downregulated DEPs, red represents upregulated DEPs, gray represents upstream reguators of DEPs.
Figure 4
Verification of candidate TFs in vivo and in vitro. (A) Verification of candidate genes mRNA expression in liver tissues of HFD- and NCD-fed mice (n=5). (B) Verification of candidate genes mRNA expression in AML12 cells after PAOA treatment for 24 h (n=3). (C) Verification of candidate genes mRNA expression in primary hepatocytes after PAOA treatment for 24 h (n=3). (D) Representative images of immunohistochemistry analysis of Tcea1, Rbbp4, ILF2 expression and localization in liver sections from HFD- and NCD-fed mice. Magnification: 200x and 400x, scale bar: 50 μm. (E) Western blot analysis of ILF2, Rbbp4, Tcea1, and Npm1 expression in liver tissues from HFD- and NCD-fed mice. (F) Quantification of western blot analysis of ILF2, Rbbp4, Tcea1, and Npm1 protein expression (n=4). (G) Quantification of immunohistochemistry analysis of Tcea1, Rbbp4, and ILF2 expression (n=3). (H) Western blot analysis of ILF2, Rbbp4, and Tcea1 expression in liver tissues from NAFLD patients and control. (I) Quantification of western blot analysis of ILF2, Rbbp4, and Tcea1protein expression (n=5). All data represent the mean ± SEM. Student's t test, * P < 0.05, ** P < 0.01, *** P < 0.001.
Figure 5
Tcea1, Rbbp4, and ILF2 regulated fatty acid synthesis or β-oxidation related gene expression. (A-B) RT-qPCR analysis of fatty acid synthesis related genes Acc1, Fasn, and Srebp1, and β-oxidation related genes Cpt1α, Acox1 expression in AML12 and HepG2 cells after Tcea1, Rbbp4, and ILF2 knockdown, respectively, compared with the siNC group. (C) Western blot analysis of Acc1, Fasn, Srebp1, Cpt1α, and Acox1 expression in AML12 cells after Tcea1, Rbbp4, and ILF2 knockdown, respectively. (D-H) Quantification of relative protein expression level. n=3 biological replicates. All data represent the mean ± SEM. Student's t test, * P < 0.05, ** P < 0.01, *** P < 0.001.
Figure 6
Tcea1, Rbbp4, and ILF2 deficiency promoted lipid accumulation of hepatocytes, respectively. (A) Representative images of Oil red O (ORO) staining of AML12 cells treated with or without PAOA for 24 h after Tcea1, Rbbp4, and ILF2 knockdown, respectively. Scale bar: 100 μm. (B) Western blot analysis of Tcea1, Rbbp4, and ILF2 expression in AML12 cells used for ORO staining. (C) Quantification of relative ORO positive area. (D) Measurement of cellular TG concentration in gene knockdown AML12 cells after PAOA treatment for 24 h. (E-I) RT-qPCR analysis of Acc1, Fasn, Srebp1, Cpt1α, and Acox1 gene expression in AML12 cells treated with or without PAOA after Tcea1, Rbbp4, and ILF2 knockdown, respectively. siNC represents cells transfected with negative control siRNA. n=3 biological replicates. All data represent the mean ± SEM. Student's t test, ** P < 0.01, *** P < 0.001 compared with the siNC group; ## P < 0.01, ### P < 0.001 compared with the siNC+PAOA group.
Figure 7
Overexpression of Tcea1, Rbbp4, and ILF2, respectively, in AML12 cells ameliorated hepatocyte steatosis. (A) Representative images of ORO staining of AML12 cells treated with PAOA after 48 h of transfection with Flag-NC, Flag-Tcea1, Flag-Rbbp4, and Flag- ILF2 plasmids, respectively. Scale bar: 100 μm. (B) Protein expression analysis of Flag-tagged Tcea1, Rbbp4, and ILF2 expression in AML12 cells after 48 h of transfection with corresponding plasmids. (C) Quantification of ORO positive area per field. n=3 biological replicates. (D-F) RT-qPCR analysis of Acc1, Fasn, Srebp1, Cpt1α, and Acox1 expression in AML12 cells after Tcea1, Rbbp4, and ILF2 overexpression, respectively, compared with the Flag-NC group. All data represent the mean ± SEM. Student's t test, * P < 0.05, ** P < 0.01.
References
- Lonardo A, Byrne CD, Caldwell SH, Cortez-Pinto H, Targher G. Global epidemiology of nonalcoholic fatty liver disease: Meta-analytic assessment of prevalence, incidence, and outcomes. Hepatology. 2016;64:1388–9. - PubMed
- Yip TC, Lee HW, Chan WK, Wong GL, Wong VW. Asian perspective on NAFLD-associated HCC. J Hepatol. 2021;76:726–34. - PubMed
- Ioannou GN. Epidemiology and risk-stratification of NAFLD-associated HCC. J Hepatol. 2021;75:1476–84. - PubMed
Publication types
MeSH terms
Substances
LinkOut - more resources
Full Text Sources
Medical
Miscellaneous