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PIM1 promotes hepatic conversion by suppressing reprogramming-induced ferroptosis and cell cycle arrest - PubMed

  • ️Sat Jan 01 2022

PIM1 promotes hepatic conversion by suppressing reprogramming-induced ferroptosis and cell cycle arrest

Yangyang Yuan et al. Nat Commun. 2022.

Abstract

Protein kinase-mediated phosphorylation plays a critical role in many biological processes. However, the identification of key regulatory kinases is still a great challenge. Here, we develop a trans-omics-based method, central kinase inference, to predict potentially key kinases by integrating quantitative transcriptomic and phosphoproteomic data. Using known kinases associated with anti-cancer drug resistance, the accuracy of our method denoted by the area under the curve is 5.2% to 29.5% higher than Kinase-Substrate Enrichment Analysis. We further use this method to analyze trans-omic data in hepatocyte maturation and hepatic reprogramming of human dermal fibroblasts, uncovering 5 kinases as regulators in the two processes. Further experiments reveal that a serine/threonine kinase, PIM1, promotes hepatic conversion and protects human dermal fibroblasts from reprogramming-induced ferroptosis and cell cycle arrest. This study not only reveals new regulatory kinases, but also provides a helpful method that might be extended to predict central kinases involved in other biological processes.

© 2022. The Author(s).

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Conflict of interest statement

The Authors declare the following competing interests: Huazhong University of Science and Technology and ShanghaiTech University on behalf of the authors Y.X., P.H., S.L. and Y.Y. have filed a Chinese patent application (201811561138.3) related to the CKI methodology. This patent has been granted and published by China National Intellectual Property Administration. All other authors (C.W., X.Z., M.L., W.D., J.Z., L.L., L.M., W.P., J.C., Q.W. and Y.S.) report no competing interests.

Figures

Fig. 1
Fig. 1. Schematic diagram of the CKI algorithm.

First, the transcriptomes and phosphoproteomes of control and treated samples in a defined biological process were quantified by RNA-seq and TMT-based LC-MS/MS technology. Bowtie, TopHat, and Cufflinks were used to process the transcriptomic data, then Cuffdiff in Cufflinks or edgeR was used to identify differentially expressed mRNAs (DEMs) and map differentially expressed protein kinases (DEPKs). We developed an intensity-based method and a network-based method to identify differentially altered PKs using the phosphoproteomic data. These three types of data were then combined to synergistically predict potentially central PKs in regulating a defined biological process.

Fig. 2
Fig. 2. CKI-based analysis of mouse hepatocyte maturation.

a Experimental design of the trans-omics-based analysis of immature hepatocytes generated from liver progenitor cells (CLiP-Hep) and mature hepatocytes (MH) isolated from mouse liver. Representative image of bile duct structure (b) and CK19 immunofluorescence staining (c) of biliary epithelial cells induced from liver progenitor cells (CLiP-BEC). Scale bars = 100 μm. n = 4 biological replicates. d Immunofluorescence staining of ALBUMIN in hepatocytes generated from CLiPs (CLiP-Hep). Scale bars = 100 μm. n = 3 biological replicates. e Gene expression analysis by RT-qPCR demonstrated significant differences of hepatic and biliary marker genes between CLiP-BEC and CLiP-Hep cells for Aat (p = 0.0067), Alb (p = 0.0007), Ae2 (p = 0.0065), Aqp1 (p = 0.0109), Cftr (p = 0.0002), Ck19 (p = 0.0065), n = 3 biological samples. Data are shown as the mean + SD. *p < 0.05, **p < 0.01, ***p < 0.001 (unpaired two-sided Student’s t-test). f Number of raw and clean reads sequenced from MH and CLiP-Hep samples (n = 3 biological samples). Box and whisker plots present the means (lines inside the boxes), the 1st and 3rd quartiles (bottom and top bounds of the boxes), and the extents of the data (whiskers). g Number of mapped mRNAs in mouse cell samples (n = 3). h Number of up- and down-regulated DEMs in CLiP-Hep compared to MH. i Number of potentially central PKs predicted from different data types for CLiP-Hep vs. MH. j Comparison of central PKs predicted with CKI, KSEA, , and the individual datasets comprising CKI. k Expression levels of liver metabolic genes in CLiP-Hep cells overexpressing individual candidate central PK as quantified by qRT-PCR (n = 3). Source data are provided as a Source Data file.

Fig. 3
Fig. 3. CKI-based analysis of hepatic reprogramming.

a Experimental design of the trans-omics-based analysis of HDFs undergoing hepatic reprogramming by overexpression of FHH. b Heatmap of 15 potentially central PKs predicted by CKI using a threshold of ≥5 of 9 pairwise comparisons; the minus-log transformed p values were calculated for the indicated comparisons. c Expression levels of hepatic functional genes in HDFs overexpressing FHH and individual candidate central PKs for 5 days as quantified by qRT-PCR (n = 3). Overexpression of PIM1 (ALB p < 0.0001, AAT p < 0.0001, ARG p = 0.0020), PIM2 (ALB p = 0.0043, AAT p = 0.0071, ARG p = 0.0133), PIM3 (AAT p = 0.0022, ARG p < 0.0001), TRIB2 (ALB p = 0.0017), TSSK2 (ALB p = 0.0163, AAT p = 0.0027), TSSK3 (ALB p = 0.0077, AAT p = 0.0048), PSKH1 (AAT p = 0.0199), or CAMKV (AAT p = 0.0081) showed significantly changed transcript levels of hepatic functional genes. d Representative image of ALBUMIN immunofluorescence staining in HDFs overexpressing the indicated genes after infection with FHH for 12 days. Scale bars = 100 μm. n = 2 biological replicates. e Expression levels of hepatic functional genes in HDFs overexpressing FHH and shRNAs of candidate central PKs for 5 days as quantified by qRT-PCR (n = 3). Knockdown of PLK1 (ALB p = 0.0011, AAT p = 0.0104, ARG p = 0.0227), PLK2 (ALB p = 0.0038, AAT p = 0.0084, ARG p = 0.0010), PLK4 (ALB p = 0.0001, AAT p = 0.0070), PIM1 (ALB p = 0.0006, AAT p = 0.0113, ARG p = 0.0002), PIM2 (ALB p = 0.0398, AAT p = 0.0010, ARG p = 0.0222), TRIB2 (ALB p = 0.0392), ROCK2 (ALB p = 0.0125, ARG p = 0.0010), or TSSK3 (AAT p = 0.0459, ARG p = 0.0101) showed significantly changed transcript levels of hepatic functional genes. Data are shown as the mean + SD. *p < 0.05, **p < 0.01, ***p < 0.001 (unpaired two-sided Student’s t-test). Source data are provided as a Source Data file.

Fig. 4
Fig. 4. Experimental analysis of PIM1 roles in hepatic reprogramming.

a Transcript levels of PIM1 significantly increased during hepatic reprogramming as quantified by RT-qPCR (2.25d p = 0.0251, 5d p < 0.0001, n = 3). b Immunoblotting of PIM1 in HDFs infected with FHH for the indicated number of days. GAPDH was used as the reference protein. n = 3 biological replicates. c Expression of hepatic genes in HDFs infected with FHH and PIM1 shRNA as quantified by qRT-PCR (n = 3). A non-targeted shRNA (shNT) was used as the control. Knockdown of PIM1 showed significantly decreased transcript levels of ALB (3d p = 0.0026, 4d p = 0.0002, 5d p < 0.0001), TTR (4d p = 0.0009, 5d p = 0.0055), APOA2 (4d p = 0.0012, 5d p = 0.0028). d PAS staining and DiI-ac-LDL uptake assay of HDFs infected with FHH and either PIM1 shRNA or non-targeted shRNA for 12 days. Scale bars = 100 μm. n = 3 biological replicates. Expression of hepatic functional genes (e) and endogenous FHH TFs (f) in HDFs infected with FHH and either PIM1 or GFP for 5 days as quantified by qRT-PCR (n = 3). Overexpression of PIM1 increased the transcript levels of ALB (p < 0.0001), AAT (p = 0.0002), ARG2 (p = 0.0002), CYP3A4 (p = 0.0292), TTR (p = 0.0109), APOA2 (p = 0.0003), GJA5 (p = 0.0321), endogenous HNF4A (p = 0.0007). g Quantification of ALBUMIN+ cells by immunofluorescence staining in GFP- or PIM1-overexpressing HDFs after infection with FHH for 12 days (p < 0.0001, n = 5). h PAS staining and DiI-ac-LDL uptake assay of GFP- or PIM1-overexpressing HDFs after infection with FHH for 12 days. Scale bars = 100 μm. n = 3 biological replicates. Data are shown as the mean + SD. *p < 0.05, **p < 0.01, ***p < 0.001 (unpaired two-sided Student’s t-test). Source data are provided as a Source Data file.

Fig. 5
Fig. 5. Analysis of PIM in reprogramming-induced cell cycle arrest.

a Diagram of the experimental design. b Up-regulated genes (fold change > 4) and down-regulated genes (fold change > 2) in response to PIM1 overexpression in HDFs transfected with FHH for 5 days (n = 3). The names of liver- or fetal liver-enriched genes (based on BioGPS data) are shown at right. c KEGG-based enrichment analysis of DEMs derived from GFP- or PIM1-overexpressing HDFs infected with FHH for 5 days. d Cell numbers after introduction of the indicated genes into HDFs for 5 days (GFP-5d vs. FHH-5d p = 0.0007 n = 3, FHH+GFP(5d) vs. FHH+PIM1(5d) p < 0.0001 n = 4). Representative image (e) of EdU positive cells on day 3 of hepatic reprogramming using the EdU staining assay (n = 3 biological replicates). The EdU+ cells were quantified by a flow cytometer (p = 0.0224, n = 3 biological replicates) (f). Representative image (g) of EdU positive cells on day 3 of hepatic transdifferentiation with GFP or PIM1 overexpression using the EdU staining assay (n = 3 biological replicates). The EdU+ cells were quantified by a flow cytometer (p < 0.0001, n = 3 biological replicates) (h). i Immunoblotting of PIM1 downstream substrate proteins in HDFs undergoing hepatic reprogramming. n = 2 biological replicates. j Immunoblotting of PIM1 downstream substrate proteins in HDFs undergoing hepatic reprogramming with GFP or PIM1 overexpression. GAPDH was used as the loading control. n = 2 biological replicates. Data are shown as the mean + SD. *p < 0.05, **p < 0.01, ***p < 0.001 (unpaired two-sided Student’s t-test). Source data are provided as a Source Data file.

Fig. 6
Fig. 6. PIM1 suppresses hepatic reprogramming-induced ferroptosis.

a Representative image of FHH-induced cell death of HDFs on day 5. Scale bars = 100 μm. n = 4 biological replicates. b FHH-induced cell death was analyzed by Annexin V/7-AAD staining on day 5. Proportions of 7-AAD/Annexin V positive cells were quantified (p < 0.0001, n = 5). c Expression levels of ferroptosis-related genes were increased on day 5 of hepatic transdifferentiation (SLC7A11 p = 0.0009, PTGS2 p = 0.0007, ACSL4 p = 0.0013, HMOX1 p < 0.0001, n = 3). d Knockdown of PIM1 increased the transcript levels of ferroptosis genes on day 5 of hepatic transdifferentiation (SLC7A11 p = 0.0015, PTGS2 p = 0.0002, ACSL4 p = 0.0121, HMOX1 p = 0.0002, n = 3). e Overexpression of PIM1 decreased the transcript levels of ferroptosis genes on day 5 of hepatic transdifferentiation (SLC7A11 p = 0.0062, PTGS2 p = 0.0011, ACSL4 p = 0.0025, HMOX1 p = 0.0080, n = 3). The samples were the same with those used in Fig. 4e, f. f Propidium iodide (PI) staining assay showing that treatment with the ferroptosis inhibitors ferrostatin-1 and liproxstatin-1 inhibited FHH-induced cell death in HDFs. Scale bars = 100 μm. n = 2 biological replicates. g Treatment of NAC, Fer-1, or Lip-1 increased the transcript levels of liver-specific genes on day 5 of hepatic transdifferentiation (NAC treatment: ALB p = 0.0041, CYP3A4 p = 0.0004; Fer-1 treatment: ALB p = 0.0008, CYP3A4 p = 0.0001; Lip-1 treatment: ALB p = 0.0198, CYP3A4 p = 0.0404; n = 3). h Cellular NADP/NADPH levels on day 3 of hepatic transdifferentiation with GFP or PIM1 overexpression (NADPH p = 0.0015, NADPH/NADP+ p = 0.0041, n = 3). i Cellular GSH levels on day 5 of hepatic transdifferentiation with GFP or PIM1 overexpression (p = 0.0002, n = 3). Data are shown as the mean + SD. *p < 0.05, **p < 0.01, ***p < 0.001 (unpaired two-sided Student’s t-test). Source data are provided as a Source Data file.

Fig. 7
Fig. 7. Network analysis of the major molecular landscape in hepatic reprogramming.

a An integrative TPCW based on the findings of this study and existing knowledge illustrates the regulatory relationships among FHH, 24 FHH-regulated TFs, two screened central PKs, and 60 curated genes related to hepatic lineage, ferroptosis, and cell cycle progression. b PIM1-centered subnetwork during hepatic lineage reprogramming. c Working model showing potential roles of PIM1 in hepatic lineage reprogramming.

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