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Regulation of mitochondrial biogenesis in erythropoiesis by mTORC1-mediated protein translation

Abstract

Advances in genomic profiling present new challenges of explaining how changes in DNA and RNA are translated into proteins linking genotype to phenotype. Here we compare the genome-scale proteomic and transcriptomic changes in human primary haematopoietic stem/progenitor cells and erythroid progenitors, and uncover pathways related to mitochondrial biogenesis enhanced through post-transcriptional regulation. Mitochondrial factors including TFAM and PHB2 are selectively regulated through protein translation during erythroid specification. Depletion of TFAM in erythroid cells alters intracellular metabolism, leading to elevated histone acetylation, deregulated gene expression, and defective mitochondria and erythropoiesis. Mechanistically, mTORC1 signalling is enhanced to promote translation of mitochondria-associated transcripts through TOP-like motifs. Genetic and pharmacological perturbation of mitochondria or mTORC1 specifically impairs erythropoiesis in vitro and in vivo. Our studies support a mechanism for post-transcriptional control of erythroid mitochondria and may have direct relevance to haematologic defects associated with mitochondrial diseases and ageing.

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Figure 1: Comparative proteomic and transcriptomic analyses of human erythropoiesis.
Figure 2: Comparative transcriptomic and proteomic analyses revealed post-transcriptional control of mitochondrial pathways.
Figure 3: Dynamic regulation of mitochondria during erythroid differentiation.
Figure 4: TFAM and PHB2 are post-transcriptionally regulated and indispensable for mitochondria and erythropoiesis.
Figure 5: TFAM and PHB2 are required for erythroid development in vivo.
Figure 6: TFAM depletion leads to altered metabolism and histone hyperacetylation in erythroid cells.
Figure 7: mTORC1 regulates mitochondrial biogenesis in erythroid cells.
Figure 8: mTORC1 selectively regulates protein translation of mitochondria-associated mRNAs in erythroid cells.

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Acknowledgements

We thank S. H. Orkin and L. I. Zon for assistance and discussion, Z. Zhou and Y. Liu for assistance with polysome profiling, U. Eskiocak, S. Yuan, S. Hasan and J. Sudderth at the Children’s Research Institute at UT Southwestern, USA for reagents and discussion, and P. Mishra for critical reading of the manuscript. We thank B. van Handel and H. Mikkola at UCLA, USA for providing the fetal CD34+ cells. This work was supported by NIH grants R35CA197532 (to N.S.C.) and T32HL076139 (to S.E.W.), by the Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institute of Higher Learning (TP2015003) (to F.Z.), by the 100-Talent Program of the Chinese Academy of Sciences (Y516C11851 to Z.S.) and the Science and Technology Commission of Shanghai Municipality (14PJ1410000 to Z.S. and 16ZR1448600 to Y.Z.), by NIH grants K01DK093543, R03DK101665 and R01DK111430, by a Cancer Prevention and Research Institute of Texas (CPRIT) New Investigator award (RR140025), by the American Cancer Society award (IRG-02-196) and the Harold C. Simmons Comprehensive Cancer Center at UT Southwestern, and by an American Society of Hematology Scholar Award (to J.X.).

Author information

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Authors

Contributions

J.X. conceived the project. X.L., M.N., H.C., D.L., Z.G. and J.X. performed experiments and analysed the data. F.Z. performed the proteomic profiling and analysed the data. Y.Z., Z.S., M.N., M.L., F.Z. and J.X. performed bioinformatic analyses. R.A.J.S. performed HPG incorporation experiments and analysed the data. Z.H., M.N. and R.J.D. performed the metabolomic profiling and analysed the data. S.E.W. and N.S.C. contributed the Tfam flox mouse strain. J.X. wrote the manuscript and X.L., Y.Z., M.N., K.E.D., F.Z. and Z.S. edited it.

Corresponding authors

Correspondence to Feng Zhou or Zhen Shao or Jian Xu.

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Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Differentially expressed transcripts and proteins in human primary fetal and adult HSPCs and ProEs.

(a,c,e,g) Scatter plot of transcriptomic profiling between the indicated adult CD34+ HSPCs (A0), fetal CD34+ HSPCs (F0), adult ProEs (A5), and fetal ProEs (F5) is shown. The numbers of significantly upregulated or downregulated transcripts (fold change ≥1.5, P-value ≤ 0.01) are indicated. (b,d,f,h) Gene ontology (GO) analysis of transcripts upregulated in the indicated adult HSPCs (A0), fetal HSPCs (F0), adult ProEs (A5) or fetal ProEs (F5) was performed using DAVID tools. (i,k,m,o) Scatter plot of proteomic profiling between the indicated adult CD34+ HSPCs (A0), fetal CD34+ HSPCs (F0), adult ProEs (A5), and fetal ProEs (F5) is shown. The numbers of significantly upregulated or downregulated proteins (fold change ≥1.3, P-value ≤ 0.01) are indicated. (j,l,n,p) GO analysis of proteins upregulated in the indicated adult HSPCs (A0), fetal HSPCs (F0), adult ProEs (A5) or fetal ProEs (F5) was performed using DAVID tools. (r) The correlation between RNA and protein expression changes in adult HSPCs (A0) and ProEs (A5) is shown. Differentially expressed RNAs and proteins (red, upregulated; blue, downregulated) were accessed for overlap. After filtering genes detected exclusively by RNA-seq or proteomics, 2,160 and 785 significant downregulated RNAs (RNA-all) and proteins (Protein-all) were identified, respectively. The differentially expressed genes were further divided into three groups based on significant changes at RNA (RNA-only), protein (Protein-only), and both levels, respectively. (s) Similar to r, the correlation between RNA and protein expression changes in fetal HSPCs (F0) and ProEs (F5) is shown. Differentially expressed RNAs and proteins (dark green, upregulated; light green, downregulated) were accessed for overlap. After filtering genes detected exclusively by RNA-seq or proteomics, 987 and 976 significant upregulated RNAs (RNA-all) and proteins (Protein-all) were identified, respectively. The differentially expressed genes were further divided into three groups based on significant changes at RNA (RNA-only), protein (Protein-only), and both levels, respectively. (t) The correlation between downregulated RNA transcripts and proteins in fetal HSPCs (F0) and ProEs (F5) is shown.

Supplementary Figure 2 Mitochondria-related GO terms and signature genes are enriched in upregulated ‘protein-only’ genes.

Expression heatmaps are shown for genes associated with ‘respiratory electron transport chain’ (a), ‘oxidative phosphorylation’ (b), ‘branched-chain amino acid catabolic process’ (c), ‘aerobic respiration’ (d), and ‘ATP biosynthetic process’ (e). The Protein-only genes are shown on the bottom. n. d. not detected. (f) The annotated mitochondria-related genes from GO database1 were compared with the upregulated ‘RNA-all’, ‘RNA-only’, ‘Both’, ‘Protein-only’, and ‘Protein-all’ genes between A0 HSPCs and A5 ProEs. The percentage and significance of the overlap are shown. P values are calculated by Fisher’s exact test to quantify the significance of the overlap using all expressed mRNAs or proteins as background. n. s. not significant. (g) The annotated mitochondria-related genes from Mootha et al. 2 were compared with the upregulated ‘RNA-all’, ‘RNA-only’, ‘Both’, ‘Protein-only’, and ‘Protein-all’ genes between A0 HSPCs and A5 ProEs.

Supplementary Figure 3 Post-transcriptional regulation of TFAM and PHB2 expression in mouse erythropoiesis.

(a) Cells at various stages of erythroid differentiation based on expression of CD71 and Ter119 were FACS-sorted from E14.5 mouse fetal livers. (b) mRNA expression of Tfam, Phb2 and Atp5b was assessed by qRT-PCR analysis in the FACS-sorted cells (R1 to R5). (c) Western blot of TFAM, PHB2 and ATP5B protein expression in the FACS-sorted cells (R1 to R5). GAPDH was analyzed as a loading control. (d) Quantification of Western blot analysis. (e) Mitochondrial DNA (mtDNA) content was determined by qPCR analysis. (f) Cells at various stages of erythroid differentiation based on expression of CD71 and Ter119 were FACS-sorted from bone marrows of 8-week old mice. (g) The relative RNA expression of Tfam, Phb2, and Atp5b was assessed by qRT-PCR analysis in the FACS-sorted cells (R1 to R5). (h) Western blot of TFAM, PHB2 and ATP5B protein expression in the FACS-sorted cells (R1 to R5). GAPDH was analyzed as a loading control. (i) Quantification of Western blot analysis. (j) mtDNA content was analyzed by qPCR as in f. Results are shown as mean ± s.e.m. of n = 3 independent experiments. Differences relative to R1 cells were assessed using a repeated-measures one-way ANOVA followed by Dunnett’s test for multiple comparisons (b,d,e,g,i,j). P < 0.05, P < 0.01, P < 0.001. See Statistics Source Data in Supplementary Table 8. Unprocessed original scans of blots are shown in Supplementary Fig. 9.

Supplementary Figure 4 TFAM and PHB2 are required for human erythropoiesis.

(a) Erythroid maturation was assessed by the expression of CD71 and CD235a in cells transduced with shRNAs targeting TFAM, PHB2, or control (shNT). (b) Example cytospin preparations from uninfected, control (shNT), TFAM, or PHB2-depleted cells stained with May-Grunwald-Giemsa. Scale bars, 20 μm. The images represent one out of three independent experiments. (c) Expression of hemoglobin genes (HBA1 and HBB), GATA1 and GATA2 was assessed using qRT-PCR. Results are mean ± s.e.m. of n = 3 independent experiments. Differences relative to shNT were assessed using a repeated-measures one-way ANOVA followed by Dunnett’s test for multiple comparisons. P < 0.01, P < 0.001. See Statistics Source Data in Supplementary Table 8. (d,e) Gene set enrichment analysis (GSEA) of gene signatures associated with apoptotic signaling pathways using the RNA-seq data of shTFAM or shPHB2 relative to shNT, respectively. The normalized enrichment score (NES) and nominal P-value are shown.

Supplementary Figure 5 Increased apoptosis in Tfam-deficient erythroid cells.

(a) Erythroid differentiation in mouse E13.5 fetal livers (FL) was assessed by the expression of CD71 and Ter119 in five differentiation stages: R1 (CD71Ter119), R2 (CD71hiTer119), R3 (CD71hiTer119+), R4 (CD71medTer119+), and R5 (CD71low/−Ter119+). A representative FACS cytogram for Tfam wild-type (WT) E13.5 fetal livers is shown. (b) Cell apoptosis was assessed by staining with FITC-labeled Annexin V together with 7-AAD. Histograms for two representative Tfam WT E13.5 fetal livers are shown. (c) A representative FACS cytogram for Tfam heterozygous knockout (HET) E13.5 FL is shown. (d) Annexin V staining histograms for two representative Tfam HET E13.5 fetal livers are shown. (e) A representative FACS cytogram for Tfam homozygous knockout (KO) E13.5 FL is shown. (f) Annexin V staining histograms for two representative Tfam KO E13.5 fetal livers are shown. (g,h) Gene set enrichment analysis (GSEA) of gene signatures associated with two apoptotic signaling pathways using the RNA-seq data of Tfam WT and KO erythroid cells, respectively. The normalized enrichment score (NES) and nominal P-value are shown.

Supplementary Figure 6 Depletion of TFAM leads to altered intracellular metabolism and histone acetylation in human erythroid cells.

(a) Quantification of Western blot analysis in Fig. 6a. Results are mean ± s.e.m. of n = 3 independent experiments. Differences relative to shNT were assessed using a repeated-measures one-way ANOVA followed by Dunnett’s test for multiple comparisons. P < 0.05. (b) Western blot of H3K9ac, H3K27ac and unmodified H3 in Tfam WT (n = 3) or KO (n = 3) mouse E13.5 fetal liver cells. (c) Quantification of Western blot analysis of H3K9ac. Results are mean ± s.e.m. of Tfam WT (n = 3) or KO (n = 3) mouse E13.5 fetal liver cells. P-value is calculated by a two-tailed t-test. (d) Quantification of Western blot analysis of H3K27ac. Results are mean ± s.e.m. of Tfam WT (n = 7) or KO (n = 6) mouse E13.5 fetal liver cells. P-value is calculated by a two-tailed t-test. See Statistics Source Data in Supplementary Table 8. (e) The distribution of H3K27ac intensity around all RefSeq-annotated genes. The average ChIP-seq intensity (RPKM) is shown between 5kb upstream of TSS and 5kb downstream of TES in cells transduced with TFAM or control (shNT) shRNAs. (f) Heatmap is shown for all metabolites with significant changes (see Methods) in human erythroid (K562) cells transduced with TFAM or control (shNT) shRNAs. The metabolites are ranked based on the fold changes in levels between control (shNT) and TFAM knockdown cells. n = 3 biological replicates per group. (g) Heatmap is shown for intermediate metabolites from TCA cycle with significant changes in human erythroid (K562) cells transduced with TFAM or control (shNT) shRNAs.

Supplementary Figure 7 Effects of mTORC1 hyperactivation or TFAM overexpression on mitochondria and erythropoiesis.

(a,b) Quantification of Western blot shown in Fig. 7d, e. Results are mean ± s.e.m. of n = 3 experiments. (c) Quantification of Western blot shown in Fig. 7l. Results are mean ± s.e.m. of control (Veh, n = 4), PP242 (n = 4) and Torin1 (n = 4) treated mice. Differences relative to day0 (a), DMSO (b), or Veh (c) were assessed using a one-way ANOVA. P < 0.05, P < 0.01, P < 0.001. (d) Western blot of TFAM and PHB2 in human primary erythroid cells treated with rapamycin (10 nM, 100 nM and 1 μM), PP242 (10 μM) or Torin1 (1 μM) for 5d. (e) Quantification of Western blot in d. (f) Rapamycin treatment resulted in dose-dependent decreases in mtDNA. Results are mean ± s.e.m. of n = 3 experiments. Differences relative to DMSO were assessed using a one-way ANOVA; P < 0.05, P < 0.01, P < 0.001 (e,f). (g) Validation of shRNA-mediated depletion of TSC1 and TSC2 in human primary erythroid cells. (h) Quantification of Western blot in g. Depletion of TSC1 or TSC2 increased MMP (i), impaired erythroid maturation (j), and induced apoptosis (k). Results are mean ± s.e.m. of n = 3 experiments. Differences relative to shNT were assessed using a one-way ANOVA; P < 0.05, P < 0.01, P < 0.001 (ik). (l,m) Hematopoietic-selective Pten KO led to a significant increase in mitochondrial mass (l) and decrease in BM CD71+Ter119+ erythroid cells (m). Results are mean ± s.e.m. of control (Mx1-cre+, Ptenfl/+; n = 3) and Pten KO (Mx1-cre, Ptenfl/fl; n = 3). (n) Validation of TFAM overexpression by Western blot. The quantified TFAM protein expression is shown on the bottom as mean ± s.e.m. of three experiments. (o) TFAM overexpression led to increased mtDNA. (pt) TFAM overexpression in human primary erythroid cells increased mitochondria mass (p), MMP (q), partially rescued the differentiation defects (r), apoptosis (s) and expression of erythroid genes (t) upon PP242 (2.5 μM) or Torin1 (0.25 μM) treatment. Results are mean ± s.e.m. of n = 3 experiments and analyzed by a two-tailed t-test (l,m,ot). P < 0.05, P < 0.01, n. s. not significant. See Statistics Source Data in Supplementary Table 8. Unprocessed original scans of blots are shown in Supplementary Fig. 9.

Supplementary Figure 8 Mitochondria-associated mRNAs are selectively regulated by mTORC1 in erythroid cells.

(a) Representative cytospin preparations from CD34 + HSPCs, differentiated ProEs and granulocytes. Scale bars, 20 μm. The images represent one out of three independent experiments. (b) Granulocyte differentiation is characterized by progressively decreased expression of CD34 and increased expression of granulocyte-specific marker CD66b. Representative histograms for HSPCs, day 3 and 6 differentiated granulocytes are shown. (c) Granulocyte differentiation is characterized by loss of GATA2 expression and increased expression of granulocyte-specific genes DEFA1 and LTF, whereas erythroid differentiation is characterized by increased expression of the hemoglobin gene HBB. Results are mean ± s.e.m. of n = 3 independent experiments. (d) Polysome profiling of granulocytes treated with control (DMSO) or PP242 (2.5 μM) for 12 h. (e) qRT-PCR analysis of indicated transcripts in granulocytes treated with control (DMSO) or PP242. Results are mean ± s.d. of n = 3 independent measurements and shown as the percentage (%) of total mRNA of all fractions combined. (f,g) Inhibition of mTORC1 or depletion of TFAM had minimal effect on the expression of granulocyte-specific marker CD66b or signature genes. Results are shown as mean ± s.e.m. of n = 3 independent experiments and analyzed by a two-tailed t-test. (h) Analysis of Mac1 and Gr1 expression in BM of Mrp8-Cre; Tfamfl/fl (WT, n = 4) and Mrp8-Cre+; Tfamfl/fl (KO, n = 4) mice (4 5 weeks old). The representative FACS cytograms out of four independent experiments are shown. (i) Granulocyte-specific KO of Tfam had minimal effect on the frequency of BM Mac1+Gr1+ granulocytes. (j) Expression of Tfam was determined by qRT-PCR in Mac1+Gr1+ granulocytes from Mrp8-Cre; Tfamfl/fl (WT, n = 4) and Mrp8-Cre+; Tfamfl/fl (KO, n = 4) mice. (k) Granulocyte-specific Tfam KO decreased mtDNA in Mac1+Gr1+ granulocytes. (l) Expression of Defa1 and Ltf mRNA remained unchanged in Mac1+Gr1+ granulocytes upon Tfam KO. Results are mean ± s.e.m. of n = 4 WT and n = 4 KO mice by a two-tailed t-test (il). P < 0.01, P < 0.001, n. s. not significant. See Statistics Source Data in Supplementary Table 8. (m) The wild-type (WT) and mutated (MUT) sequences of TOP-like motifs at the 5′UTRs of TFAM, PHB2 or ATP5D transcripts are shown.

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Liu, X., Zhang, Y., Ni, M. et al. Regulation of mitochondrial biogenesis in erythropoiesis by mTORC1-mediated protein translation. Nat Cell Biol 19, 626–638 (2017). https://doi.org/10.1038/ncb3527

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