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Keywords:

  • Reprogramming;
  • Induced pluripotent stem cells;
  • Metabolism;
  • Hypoxia-inducible factor 1α;
  • Pyruvate dehydrogenase kinase 1;
  • Pyruvate kinase isoform M2

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Conclusion
  8. Acknowledgments
  9. Author Contributions
  10. Disclosure of Potential Conflicts of Interest
  11. References
  12. Supporting Information

Reprogramming somatic cells to a pluripotent state drastically reconfigures the cellular anabolic requirements, thus potentially inducing cancer-like metabolic transformation. Accordingly, we and others previously showed that somatic mitochondria and bioenergetics are extensively remodeled upon derivation of induced pluripotent stem cells (iPSCs), as the cells transit from oxidative to glycolytic metabolism. In the attempt to identify possible regulatory mechanisms underlying this metabolic restructuring, we investigated the contributing role of hypoxia-inducible factor one alpha (HIF1α), a master regulator of energy metabolism, in the induction and maintenance of pluripotency. We discovered that the ablation of HIF1α function in dermal fibroblasts dramatically hampers reprogramming efficiency, while small molecule-based activation of HIF1α significantly improves cell fate conversion. Transcriptional and bioenergetic analysis during reprogramming initiation indicated that the transduction of the four factors is sufficient to upregulate the HIF1α target pyruvate dehydrogenase kinase (PDK) one and set in motion the glycolytic shift. However, additional HIF1α activation appears critical in the early upregulation of other HIF1α-associated metabolic regulators, including PDK3 and pyruvate kinase (PK) isoform M2 (PKM2), resulting in increased glycolysis and enhanced reprogramming. Accordingly, elevated levels of PDK1, PDK3, and PKM2 and reduced PK activity could be observed in iPSCs and human embryonic stem cells in the undifferentiated state. Overall, the findings suggest that the early induction of HIF1α targets may be instrumental in iPSC derivation via the activation of a glycolytic program. These findings implicate the HIF1α pathway as an enabling regulator of cellular reprogramming. Stem Cells 2014;32:364–376


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Conclusion
  8. Acknowledgments
  9. Author Contributions
  10. Disclosure of Potential Conflicts of Interest
  11. References
  12. Supporting Information

Oxygen concentration plays a critical role in mediating modifications of the cellular metabolic profile [1]. Under physiological normoxic conditions, human somatic cells are characterized by active mitochondria and oxidative phosphorylation (OXPHOS)-based metabolism, while oxygen-deprived cells exhibit increased conversion of glucose to lactate (the “Pasteur effect”). However, tumor cells fine-tune their cellular bioenergetics to respond to higher cellular demands and can shift to glycolysis-based metabolism even in the presence of high level of oxygen, a phenomenon known as aerobic glycolysis or “Warburg effect” [2, 3]. This metabolic shift seems apparently counter-intuitive given the low efficiency of glycolytic metabolism in terms of generation of ATP molecules. Nevertheless, several lines of evidence demonstrate that this metabolic adaptation endows proliferative cells with critical advantages.

First, anabolic pathways branching out from the glycolytic path supply the intermediates for cell growth, including amino acids and lipid precursors [4, 5]. Thus, rapidly dividing cells, such as cancer cells, increase the flux through glycolysis to satisfy their need of macromolecules, by enhancing glucose uptake and by slowing the entry of pyruvate into mitochondria [6]. Second, the energy reconfiguration can provide protection against oxidative stress [5], by avoiding high levels of reactive oxygen species (ROS), common by-products of mitochondrial respiration, and by re-routing glycolytic intermediates into the pentose phosphate pathway (PPP), which generates not only essential nucleotide precursors but also the reducing factor NADPH, required for the activity of antioxidant enzymes [7]. Recent data on pyruvate kinase (PK), which catalyzes the conversion of phosphoenolpyruvate into pyruvate in the last step of the glycolytic cascade, support the idea that the Warburg effect may promote cellular redox homeostasis. Pyruvate kinase isoform M2 (PKM2) is highly expressed in cancer cells [8, 9] and upon oxidation it looses activity, thereby reducing pyruvate formation and diverting the glycolytic flux into the PPP, which eventually supports antioxidant activities [10-12].

A key mediator of the metabolic reconfiguration occurring under low oxygen conditions is the transcription factor hypoxia-inducible factor one (HIF1) [13, 14]. HIF1 is a heterodimer consisting of a constitutively expressed HIF1 β subunit and an oxygen-regulated HIF1α, which is physiologically degraded under normoxic conditions by oxygen-dependent prolyl-hydroxylases (PHD1–3). When oxygen level decreases, or when PHD enzymes are pharmacologically inhibited, HIF1α protein escapes degradation and translocates into the nucleus, where it initiates a gene expression program which leads to a switch from OXPHOS to glycolysis. HIF1α target genes include glucose transporters, to increase glucose uptake, and pyruvate dehydrogenase kinases (PDK1–3) [15-17], to shunt pyruvate away from the mitochondria through the inhibition of pyruvate dehydrogenase. In addition, HIF1α interacts with PKM2 and promotes its gene transcription [18], further implying an instructive role for HIF1α downstream signaling in the Warburg-like restructuring of glucose metabolism.

The derivation of induced pluripotent stem cells (iPSCs), allowing somatic cells to acquire embryonic stem cells (ESCs)-like features [19], is also associated with a profound reconfiguration of anabolic demands. Indeed, iPSCs show high proliferation rate and distinct cell cycle features compared to parental somatic cells [20]. This suggests that a corresponding reprogramming of energy metabolism may also be in place. Accordingly, we previously discovered that somatic mitochondria and cellular bioenergetics are extensively remodeled upon cellular reprogramming as the cells adopt a glycolytic metabolism [21]. Following these initial observations, several groups further confirmed that a Warburg-like metabolic reconfiguration takes place in both mouse and human iPSCs [22-26]. Indeed, the process of “metabolic reprogramming” is now being recognized as an emerging important step of the induction of pluripotency [27-30].

The metabolic switch of cell fate reprogramming does not appear to be due to dysfunctional mitochondria, which are in fact capable of respiring and consume oxygen, as demonstrated by bioenergetic profiling studies [25, 26, 31]. However, like cancer cells, proliferating pluripotent stem cells (PSCs) may opt for glycolysis as they necessitate building biomass and at the same time maintaining redox homeostasis. In accordance, PSCs exhibit upregulation of genes involved in glucose uptake and the initial steps of glycolysis, increased expression of PDK1 [25, 31], suggesting the rerouting of metabolism outside of the mitochondria, and elevated levels of glucose-6-phosphate [31], indicative of enhanced flux through the pentose phosphate pathway. Moreover, ROS levels are also reduced in PSCs and so is the amount of oxidative damage [21, 32]. Finally, exposure to hypoxic environment favorably supports self-renewal and pluripotency [33-35], enhances iPSC generation [36], and maintain hESCs in a more developmentally immature state [37].

Here, we sought to investigate the mechanisms underlying the metabolic reprogramming occurring upon cell fate transition and specifically dissect the contribution of the HIF1α pathway. We found that a small molecule mimicking HIF1α activation enhances reprogramming, while the ablation of HIF1α results in a dramatic loss of colony formation. By performing transcriptional and bioenergetic profiling during early reprogramming, we discovered that the transduction of the four Yamanaka factors (4F: OCT4, SOX2, KLF4, and c-MYC) is sufficient to upregulate PDK1 and thereby initiating a glycolytic shift. The exposure to hypoxia or to HIF1α activation further stimulates the expression PDK3 and PKM2, resulting in increased early switch to glycolysis and more efficient iPSC generation. The additional upregulation of PDK3 and PKM2 might be critical in enhancing reprogramming, since we observed that their expression is elevated in undifferentiated PSCs and is coupled to reduced PK activity, all traits associated with a glycolytic state. Taken together, early induction of HIF1α-associated glycolytic modulators may be instrumental in the establishment of pluripotency and may possibly represent an enabling regulatory step during cell fate conversion.

Materials and Methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Conclusion
  8. Acknowledgments
  9. Author Contributions
  10. Disclosure of Potential Conflicts of Interest
  11. References
  12. Supporting Information

Cell Lines and Culture Conditions

Neonatal foreskin fibroblasts (FFs) HFF1 and BJ were purchased from ATCC (#SCRC-1041 and #CRL-2522, respectively), and dermal fibroblasts (DFs) NFH2 were previously derived from an 84-year-old woman [38]. All fibroblasts were cultured using Dulbecco's modified Eagle's medium (DMEM) supplemented with 10% bovine serum, nonessential amino acids, l-glutamine, penicillin/streptomycin, and sodium pyruvate (all from Invitrogen, Carlsbad, CA, http://www.invitrogen.com). All iPSC lines were previously generated using the four Yamanaka factors (4F: OCT4, SOX2, KLF4, and c-MYC) retroviral cocktail: HFF1-derived iPSCs (lines iPS2 and iPS4) [21], BJ-derived iPSCs (lines iB4 and iB5) [31], NFH2-derived iPSCs (lines OiPS3, OiPS6, OiPS8, and OiPS16) [38]. Human embryonic stem cell (hESC) lines H1 and H9 (WiCell Research Institute, Madison, WI, http://www.wicell.org) and iPSCs were cultured in hESCs media containing KO-DMEM supplemented with 20% knockout serum replacement, nonessential amino acids, l-glutamine, penicillin/streptomycin, sodium pyruvate, 0.1 mM β-mercaptoethanol (all from Invitrogen), and 8 ng/mL basic fibroblast growth factor (Peprotech, Rocky Hill, NJ, http://www.peprotech.com). PSCs were harvested in feeder-free conditions using DMEM-F12 media supplemented with N2/B27. All cultures were normally kept in a humidified atmosphere of 5% CO2 at 37°C under atmospheric oxygen condition (20%).

HIF1α Activation

To generate hypoxic conditions, the oxygen concentration was set to 1% and the cells were maintained under hypoxia for 24 hours. A small molecule activator of HIF1α was used, ethyl 3,4-dihydroxybenzoate (EDHB) (Sigma, #E24859), at a concentration of 100 µM, as previously shown [39]. To test the effect of HIF1α activation on the early reprogramming-initiating events, FFs were transduced twice with the four factor (4F) retroviral cocktail, as previously described [40], alone or in combination with 100 µM EDHB treatment. The cells were then harvested after 24 hours from the first transduction (4F 24h and 4F EDHB 24h), after 48 hours from the first transduction (that means 24 hours after the second transduction) (4F 48h and 4F EDHB 48h), and after 72 hours from the first transduction (4F 72h and 4F EDHB 72h). In addition, FFs were treated only with 100 µM EDHB for 24 hours (EDHB 24h), 48 hours (EDHB 48h), and 72 hours (EDHB 72h).

HIF1α Knockdown

To stably knockdown HIF1α, BJ fibroblasts were transduced with lentiviruses containing short hairpin RNA (shRNA) sequences against human HIF1α (BJ-HIF1-KD) and scrambled control oligonucleotides (BJ-SCR-KD) (TIB MOLBIOL, Berlin, Germany, http://www.tib-molbiol.de) [41]. Oligonucleotides were inserted into the lentiviral bicistronic vector pPR1, which allows for coexpression of green fluorescent protein (GFP) [42]. Recombinant lentiviruses were produced in 293T cells using the calcium-phosphate method. Human BJ fibroblasts stably expressing shRNAs were generated by double transduction with lentiviruses at a multiplicity of infection of 10 for 24 hours. Transduction efficiency of target cells was determined by flow cytometry analysis of GFP using a FACSCalibur (Becton Dickinson, Heidelberg, Germany, http://www.bd.com).

Cellular Reprogramming

To test the consequences of HIF1α manipulation on the overall efficiency of iPSC generation, BJ fibroblasts, SCR-KD BJ fibroblasts, HIF1-KD BJ fibroblasts, and BJ fibroblasts treated with EDHB were reprogrammed to pluripotency using retroviral vectors expressing the four Yamanaka factors, following our previously published protocol [21]. To test the effect of HIF1α knockdown on viral-free iPSC derivation, BJ fibroblasts, SCR-KD BJ fibroblasts, and HIF1-KD BJ fibroblasts were transfected using three nonintegrative episomal plasmids containing a total of seven factors (4F plus NANOG, LIN28, and SVLT), as previously described [43]. Briefly, 8 × 105 fibroblasts (BJ, SCR-KD, and HIF1-KD) were nucleofected using the Amaxa Cell Line Nucleofector Kit (Lonza, Basel, Switzerland, http://www.lonza.com). After nucleofection, fibroblasts were immediately mixed with 500 µL DMEM medium before seeding into six-well plates. The following day, the cells were cultured using hESCs medium supplemented with a small molecule cocktail composed of CHIR99021, A-83-01, PD0325901, and Y-27632 (all from Stemgent, https://www.stemgent.com) [43]. Four weeks after either retroviral or plasmid reprogramming, the cells were fixed and stained for NANOG expression using the ABC method (see below). The reprogramming efficiency was defined as the number of NANOG positive colonies relative the total starting number of fibroblasts.

Global Gene Expression Analysis

Biotin-labeled cRNA samples were produced as previously described [21] and hybridized onto Illumina human-8 BeadChips version 3 (Illumina, San Diego, CA). The following samples were used: BJ, BJ-HIF1-KD, BJ-SCR-KD, 4F 24h, 4F 48h, 4F 72h (hybridized in duplicate), EDHB 24h, EDHB 48h, EDHB 72h, 4F EDHB 24h, 4F EDHB 48h, 4F EDHB 72h (hybridized in single). In addition, previously generated array data were incorporated in the analysis, including: amniotic fluid cells (AFCs), FFs (HFF1 and BJ), DFs (NFH2), hESC lines (H1 and H9), FFiPSC lines (iPS2, iPS4, iB4, and iB5), DFiPSC lines (OiPS3, OiPS6, OiPS8, and OiPS16) (all hybridized in duplicate), and AFiPSC (lines 4, 5, 6, 10, hybridized in single, and line 41, hybridized in duplicate) [21, 31, 38, 44]. Microarray analysis, principal component analysis (PCA) plot, and the general heatmap were performed using the R/Bioconductor package. Genes were considered significantly expressed with detection p values ≤.01. Differential expression analysis was performed using the Illumina custom method, using differential p values ≤.01, fold change ratio >1.5. The heatmap for energy metabolism was generated using Microarray Software Suite TM4 (TMEV.bat) with an input list adapted from SA Biosciences PCR arrays (Human Glucose Metabolism PCR Array, www.sabiosciences.com). Pathway analysis was determined by mapping onto the kyoto encyclopedia of genes and genomes (KEGG) pathways using Database for Annotation, Visualization and Integrated Discovery (DAVID) (http://david.abcc.ncifcrf.gov). Microarray results have been deposited in the gene expression omnibus (GEO) database (accession number GSE37709).

Quantitative Real-Time Polymerase Chain Reaction

Real-time polymerase chain reaction (PCR) was performed in 384 or 96 Well Optical Reaction Plates (Applied Biosystems, Foster City, CA, http://www.appliedbiosystems.com) using SYBRGreen PCR Master Mix (Applied Biosystems). Reactions were carried out on the ABI PRISM 7900HT Sequence Detection System (Applied Biosystems). Duplicate or triplicate amplifications were carried out for each target gene with at least three wells serving as negative controls. Quantification was performed using the comparative Ct method (ABI instruction manual), normalized over ACTB, and presented as a log2 values with respect to the biological controls. The list of all primers used in this study is presented in Supporting Information Table 4.

Immunostaining and Western Blotting

Avidin-biotin complex (ABC) method was used to stain NANOG-positive hESC-like colonies, as described elsewhere [45]. Briefly, after primary incubation with NANOG antibody (1:100, #ab62734; Abcam, Cambridge, U.K., http://www.abcam.com), biotinylated universal secondary antibody was applied following the manufacturer's instruction (ABC universal kit #PK-6200; Vector Laboratories, Burlingame, CA, http://www.vectorlabs.com). The ABC reagent was then added to bind horseradish peroxidase (HRP) to the primary/secondary complex. HRP enzyme activity was then visualized using the chromogen substrate diaminobenzidine tetrachloride (Sigma # D5637). Senescence-associated β-galactosidase staining was performed according to the manufacturer's protocol (Cell Signaling, Danvers, MA, www.cellsignal.com). Cells stained for NANOG or β-galactosidase were photographed using a digital camera (Canon).

For Western blot analysis, nuclear protein extracts were prepared as described before [46], then resolved by electrophoresis on an 8% sodium dodecyl sulfate-polyacrylamide gel, and transferred to a nitrocellulose membrane (Amersham Biosciences, Freiburg, Germany). Blots were probed with antibodies against HIF1α (AB1536; R&D Systems, Minneapolis, MN, http://www.rndsystems.com), HIF2α (ab199; Abcam), and YY1 (sc-281; Santa Cruz Biotechnology, Santa Cruz, CA, http://www.scbt.com). Secondary antibodies were conjugated to HRP (Dianova, Hamburg, Germany) and peroxidase activity was visualized using the Western Lightning Chemiluminescence Reagent Plus (Perkin Elmer Life Sciences, Boston, MA, http://www.perkinelmer.com).

Absolute Quantification of PKM1 and PKM2

Mass spectrometry-based absolute quantification was used as described previously [8] to quantify PKM1 and PKM2 isoforms, employing an AQUA method. Briefly, protein samples from yeasts carrying p414TEF-PKM1 or p413TEF-PKM2, somatic cells, and pluripotent stem cells were separated on a 10% SDS-PAGE gels, and the mass region between 50 and 70 kDa was excised. The gel pieces were then subjected to an “in-gel” tryptic digest. For quantitation of the tryptic peptides of interest, their corresponding AQUA analog were spiked in the samples after the tryptic digest. Analysis was performed on a nanoLC (Eksigent, Ultra 2D) coupled online to a hybrid triple quadrupole/ion trap mass spectrometer (AB/SCIEX, QTRAP 5500). The identity of the quantified peptides was confirmed by collecting of tandem mass spectrometry (MS/MS) spectra on the QTRAP instrument operating in iontrap mode. In order to confirm specificity of the selected tryptic peptides, yeasts expressing either only PKM1 or PKM2 were used. The quantitative values obtained for PKM1 and PKM2 were set in ratio with PKM1+2, and all samples were corrected accordingly.

PK Activity

PK was assessed using the pyruvate kinase activity assay kit (MAK072; Sigma), according to the manufacturer's instruction. Briefly, cells were rapidly homogenized and the rate of PK activity was measured by assessing the fluorescence intensity every 5 minutes until the value of the most active sample was higher than the one of the highest standard. The results were then reported to the total number of cells calculated according to the BCA protein assay kit (23225, Pierce, Thermo Scientific, Rockford, IL, http://www.piercenet.com). Both PK activity and protein measurements were obtained with a Tecan reader (InfiniteM200, http://www.tecan.com).

Bioenergetic Profiling

Assessment of cellular energy metabolism was performed using Seahorse XF24 extracellular flux analyzer (Seahorse Bioscience, www.seahorsebio.com), as previously described [31]. The instrument allows the simultaneous quantification of mitochondrial respiration (oxygen consumption rate, OCR) and glycolysis to lactic acid (extracellular acidification rate, ECAR). Four mitochondrial inhibitors (all from Sigma) were used in succession. After three basal measurements, 1 µM oligomycin, a complex V blocker, was added to inhibit OXPHOS. After time point 6, the uncoupling agent carbonyl cyanide 4-(trifluoromethoxy)phenylhydrazone (FCCP) was injected into the wells leading to the collapse of the mitochondrial membrane potential and to the consumption of oxygen in the absence of ATP production. The same FCCP concentration (1 µM) was added again after time point 9 to monitor the continuous mitochondrial uncoupling. Finally, 1 µM rotenone (complex I blocker) and 1 µM antimycin A (complex III blocker) were simultaneously injected to completely inhibit mitochondrial respiration, thus enabling the calculations of both mitochondrial and non-mitochondrial respiratory fractions. 40,000 fibroblasts were plated into each well of the XF-24 well plates approximately 18 hours before the analysis. Assays were initiated by removing the growth medium and replacing it with unbuffered media, prepared as previously described [31].

Statistical Analysis

Data are expressed as mean and SEM, unless stated otherwise. Data were analyzed using GraphPad-Prism software (Prism 4.0, GraphPad Software, Inc.) and Windows XP Excel (Microsoft).

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Conclusion
  8. Acknowledgments
  9. Author Contributions
  10. Disclosure of Potential Conflicts of Interest
  11. References
  12. Supporting Information

HIF1α Activation Stimulates Glycolysis and Enhances the Efficiency of Reprogramming

We previously discovered that the protein expression level of PDK1 is elevated in human PSCs compared to somatic cells or to PSC-differentiated cells [31]. These findings have been independently demonstrated [25] and further supported by the observation that small molecule-based PDK1 induction can significantly improve cellular reprogramming [22]. Since PDK1 is a known downstream target of HIF1α [15, 16], we first sought to determine the level of HIFα protein expression in undifferentiated PSCs. In agreement with previous data obtained on hESCs [34], we verified that HIF1α and HIF2α are not constitutively activated in human PSCs (Fig. 1A). The pattern of nuclear accumulation following hypoxic stimulation was a bit different for HIF1α and HIF2α, as it was comparable in both fibroblasts and PSCs for HIF1α and instead only present in PSCs for HIF2α, suggesting that HIF2α could play a more important role in undifferentiated stem cells, as previously described [47].

image

Figure 1. Mimicking HIF1α activation facilitates iPSC reprogramming. (A): HIF1α and HIF2α nuclear accumulation in FFs, hESCs, and FFiPSCs under normoxic conditions (N) and following 24 hours hypoxic incubation with 1% oxygen (H). The transcription factor YY1 was used for normalization of nuclear extracts. (B): BJ fibroblasts were transduced with the four factor cocktail (4F) alone or in combination with daily treatment with 100 µM EDHB (4F EDHB). All cells were plated under reprogramming conditions, fixed 4 weeks later, and immunostained against the pluripotency-associated protein NANOG, according to the Avidin-Biotin Complex (ABC) protocol. The experiments were repeated three times. Bar graphs represent the mean and SD of the average number of NANOG-positive hESC-like colonies detected. **, p = .0063, two-tailed unpaired Student's t test, 4F EDHB versus 4F. (C): OCR, indicative of OXPHOS activity, was assessed using the Seahorse cellular flux analysis. Wild-type FFs (BJ cells) (black line) were compared to FFs treated with EDHB for 24 hours (yellow line), 48 hours (orange line), and 72 hours (red line). (D): ECAR, indicative of glycolytic activity, was measured at the same time as OCR in the same samples. (E): OCR/ECAR ratio was calculated in order to generate a clear estimate of the overall metabolic state of the cells. ***, p < .005, two-tailed unpaired Student's t test: EDHB 24h versus BJ (p = .0043), EDHB 48h versus BJ (p = .0025), and EDHB 72h versus BJ (p = .0032). Abbreviations: EDHB, ethyl 3,4-dihydroxybenzoate; ECAR, extracellular acidification rate; FFs, foreskin fibroblasts; FFiPSCs, FF-derived iPSCs; H, hypoxic condition; hESCs, human embryonic stem cells; HIF1α, hypoxia inducible factor 1α; iPSC, induced pluripotent stem cells; N, normoxic condition; OCR, oxygen consumption rate.

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We then investigated whether HIF1α activation may influence the induction of pluripotency. We observed that the daily addition of 100 µM of the PHD inhibitor Ethyl 3,4-dihydroxybenzoate (EDHB)) to BJ foreskin fibroblasts could lead to significant increase in the efficiency of reprogramming (from 0.001% in BJ cells to 0.005% in BJ cells treated with EDHB). This was assayed based on the number of hESC-like colonies expressing the pluripotency-regulating protein NANOG 4 weeks after retroviral transduction of the four Yamanaka factors (4F) (Fig. 1B, Student's t test, 4F vs. 4F EDHB, p = .0063). These findings are in agreement with a previous report showing that a different PHD inhibitor, N-oxaloylglycine, and another HIF1α inducer, Quercetin, could lead to increased efficiency of cellular reprogramming in human fibroblast cells [22]. Moreover, HIF1α overexpression has been found to improve the induction of iPSC-like colonies in the A549 cancer cell line [48].

Since we previously demonstrated that reprogramming to pluripotency is associated with a shift to glycolysis [21], we tested whether the HIF1α activator that facilitated reprogramming was capable of enhancing glycolysis. Indeed, short-term treatment with EDHB was sufficient to decrease the rate of cellular OXPHOS (Fig. 1C) and increase glycolytic metabolism (Fig. 1D). Several parameters related to mitochondrial respiration were strongly lowered by the treatment, including the basal respiration, the ATP turnover, the maximal respiration rate, and the spare respiratory capacity (Supporting Information Fig. 1). Overall EDHB significantly reduced the ratio between OCR and ECAR (Fig. 1E, Student's t test, EDHB treated vs. untreated, p < .005), indicative of a switch to glycolysis. Hence, mimicking HIF1α stimulation in somatic cells can amplify the Warburg-like metabolic shift thereby improving iPSC generation.

HIF1α Depletion Downregulates the Glycolytic Pathway and Hampers iPSC Generation

In order to establish whether the activation of the HIF1α pathway during reprogramming is critical for the generation of iPSCs rather than simply supportive, we stably knocked-down HIF1α in BJ fibroblasts (HIF1-KD) using a lentivirus-based RNA interference approach. A scrambled knockdown (SCR-KD) was included as control. Immunoblot analysis confirmed that HIF1-KD fibroblasts were incapable of accumulating HIF1α protein within the nucleus under hypoxic exposure (Fig. 2A). Both HIF1-KD BJ and SCR-KD BJ exhibited normal fibroblast-like growth features and did not show signs of early senescence, as shown by β-galactosidase staining (Supporting Information Fig. 2A).

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Figure 2. HIF1α knockdown inhibits reprogramming. (A): Immunoblot analysis confirmed that HIF1-KD BJ fibroblasts were incapable of accumulating HIF1α protein in the nucleus upon hypoxic stimulation while SCR-KD BJ fibroblasts retained this ability. (B): Wild-type BJ fibroblasts, SCR-KD BJ fibroblasts, and HIF1-KD BJ fibroblasts were reprogrammed to pluripotency using either a classical retroviral approach with the four Yamanaka factors (4F: OCT4, SOX2, KLF4, and c-MYC) or with episomal plasmids (expressing the four factors plus NANOG, LIN28, and SV40L). After 4 weeks of culturing under human embryonic stem cell (hESC) conditions, wild-type BJ and SCR-KD BJ cells developed hESC-like colonies in a comparable fashion, as shown by the similar number of colonies that resulted positive for the pluripotency-associated marker NANOG (monitored with the Avidin-Biotin Complex method). However, NANOG-positive hESC-like colonies were not generated in HIF1-KD BJ cells, regardless of the reprogramming method used. (C): List of the most significantly downregulated pathways (fold change >1.5) in HIF1-KD BJ compared to SCR-KD BJ. (D): The ratio of OCR/ECAR, indicating the metabolic cell state, was calculated in fibroblasts maintained under basal conditions using the seahorse bioanalyzer. Abbreviations: ECM, extracellular matrix; ECAR, extracellular acidification rate; HIF1α, hypoxia inducible factor 1α; HIF1-KD, HIF-1α knockdown; H, hypoxic condition; N, normoxic condition; mTOR, mammalian target of rapamycin; OCR, oxygen consumption rate; SCR-KD, scrambled knockdown.

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Strikingly, NANOG-positive colonies failed to appear in HIF1-KD cells reprogrammed with the 4F retroviral transduction (Fig. 2B), while SCR-KD cells retained the ability to give rise to NANOG-positive hESC-like colonies with an efficiency approximately similar to that of wild-type BJ fibroblasts (around 0.001%) (Fig. 2B). The addition of EDHB was not sufficient to overcome the reprogramming block in HIF1-KD cells (data not shown). In addition, we assessed the effect of HIF1α depletion using an alternative nonintegrative episomal plasmid reprogramming approach [43]. Consistent with the retroviral data, wild-type BJ and SCR-KD BJ fibroblasts transfected with the episomal plasmids could generate NANOG-positive colonies with a similar efficiency (around 0.0008%) after 4 weeks of reprogramming (Fig. 2B). However, HIF1-KD BJ fibroblasts failed to derive hESC-like colonies (Fig. 2B). These results imply that cells depleted of HIF1α may be incapable of achieving a pluripotent state regardless of the reprogramming method, pointing toward an instrumental role of HIF1α in the induction of pluripotency.

We then sought to gain insights into the possible mechanisms responsible for the inability of HIF1-KD cells to undergo efficient reprogramming and performed global transcriptomics. Reassuringly, the results confirmed HIF1A as the most downregulated gene in HIF1-KD BJ compared to SCR-KD BJ (Supporting Information Table 1). Interestingly, pathway analysis revealed that among the most significantly downregulated pathways in HIF1-KD compared to SCR-KD (fold change >1.5) there were the mammalian target of rapamycin (mTOR) signaling pathway, which is known to be associated with HIF1α and the regulation of energy metabolism [4], and the pathways related to glycolysis and gluconeogenesis (Fig. 2C). We then assessed the bioenergetic profiling of fibroblasts and found that the lack of HIF1α did not alter their basal glucose metabolism, as indicated by the maintenance of OCR/ECAR ratio (Fig. 2D). Indeed, the rate of OXPHOS (Supporting Information Fig. 2B) and glycolysis (Supporting Information Fig. 2B) appeared similar in HIF1-KD fibroblasts, SCR-KD, and wild-type BJ fibroblasts. Overall, the data suggest that HIF1α ablation may not be sufficient to alter the basal metabolism of fibroblasts but it might hinder reprogramming through the downregulation of target genes that have to be activated in order to enable the establishment of pluripotency. Since genes associated with glycolysis and gluconeogenesis have been previously found to be upregulated in undifferentiated PSCs compared to fibroblasts [25, 31], it may be conceivable that cells that are not capable of correctly activating a glycolytic program may also be refractory to efficient iPSC conversion.

Upregulation of HIF1α-Associated Metabolic Regulators During Initiation of Reprogramming

To follow-up the hypothesis that knockdown of HIF1α may alter the transcriptional reconfiguration of energy metabolism and that this may be crucial for reprogramming, we sought to focus on regulated gene expression occurring within the initiation phase of iPSC derivation. Indeed, we previously observed that the gene ontology biological process of “response to hypoxia” was significantly regulated during early reprogramming [40]. We thus analyzed the transcriptome of FFs at 24 hours, 48 hours, and 72 hours after (a) treatment with 100 µM EDHB (EDHB 24h, 28h, 72h), (b) transduction with the four reprogramming factors (4F 24h, 48h, 72h), and (c) both (4F EDHB 24h, 48h, 72h). These data were compared to the transcriptome of fully reprogrammed iPSCs derived from FFs [21, 31], from adult DFs [38], and from AFCs [44], and of hESCs.

Principal component analysis revealed that at these early time points neither HIF1α manipulation nor 4F retroviral transduction was sufficient to extensively alter the global transcriptional signature of somatic cells, which clustered together and apart from all PSCs (Fig. 3A). The expression level of the most significantly highly upregulated or downregulated genes (fold change >20) in PSCs compared to somatic cells remained unaffected in somatic fibroblasts exposed to 4F transduction and/or HIF1α activation (Fig. 3B). In particular, among these top-regulated genes, none appeared regulated in EDHB-treated fibroblasts (Supporting Information Table 2) and only a few genes related to the viral-mediated introduction of transcription factors (such as OCT4, the OCT4 pseudogene-1, and the cancer-associated H19) were upregulated in early 4F-transduced fibroblasts (Supporting Information Table 2).

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Figure 3. Transcriptional modulation during reprogramming initiation. (A): Principal component analysis (PCA) showing the clustering of the transcriptomes of the following somatic cells: FFs, DFs, AFCs, FFs knocked-down for HIF1α (HIF-KD) and knocked-down for a scrambled transcript (SCR-KD), FFs transduced with the four factors only for 24, 48, and 72 hours (4F 24h, 4F 48h, 4F 72h) or in combination with daily 100 µM EDHB (4F EDHB 24h, 4F EDHB 48h, 4F EDHB 72h), and FFs only treated with EDHB (EDHB 24h, EDHB 48h, EDHB 72h), and the following pluripotent stem cells: hESCs, FFiPSCs, DFiPSCs, and AFiPSCs. (B): Heatmap depicting the genes most highly downregulated and upregulated (fold change >20) in somatic-derived and embryonic-derived pluripotent stem cells compared to wild-type untreated somatic cells. Different iPSCs were compared to their respective somatic cells, while hESCs were compared to the average of all wild-type somatic cells. The samples include wild-type untreated somatic cells (gray bar), FFs harvested 24 hours, 48 hours, and 72 hours after 4F transduction (green bar), FFs treated with EDHB for the same time points (yellow bar), FFs exposed to both 4F and EDHB treatment (purple bar), iPSCs (black bar), and hESCs (red bar). Values indicate row-normalized log2 average expression values; downregulated genes are indicated in green, upregulated genes in red. Abbreviations: AFCs, amniotic fluid cells; AFiPSCs, AFC-derived iPSCs; DFs, dermal fibroblasts; DFiPSCs, DF-derived iPSCs; EDHB, ethyl 3,4-dihydroxybenzoate; FFs, foreskin fibroblasts; FFiPSCs, FF-derived fibroblasts; hESCs, human embryonic stem cells; HIF1α, hypoxia inducible factor 1α; iPSCs, induced pluripotent stem cells; PC, principal component; SCR-KD, scrambled knockdown.

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We next focused on genes associated with HIF1α, energy metabolism, and mTOR signaling pathways (Supporting Information Table 3). The mRNA expression level of HIF1A and HIF2A genes was not altered upon early reprogramming or EDHB treatment (Supporting Information Table 3), as also confirmed by quantitative real-time PCR (qPCR) analyses (Supporting Information Fig. 3A). Accordingly, no changes in HIF1A or HIF2A gene expression could be observed in PSCs compared to somatic cells, both under normoxic and hypoxic exposure (Supporting Information Fig. 3B). This is in agreement with previous findings showing the lack of HIF1A transcriptional activation under hypoxia in hESCs [34]. Interestingly, however, the expression of some of the genes associated with energy metabolism underwent modifications during early reprogramming (Supporting Information Table 3, Supporting Information Fig. 4). Among the metabolism-related genes exhibiting early upregulation in 4F-transduced cells or EDHB-treated cells, we identified three HIF1α target factors known to regulate a reconfiguration of energy flux: PKM2, PDK1, and PDK3 (Supporting Information Fig. 4).

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Figure 4. Upregulation of HIF1α-related metabolic regulators during early reprogramming. (A): Quantitative real-time polymerase chain reaction analysis of HIF1α targets known to play a role in regulating glycolytic metabolism (PKM2, PDK1, and PDK3) during the first 3 days of reprogramming initiation. Relative mRNA level to ACTB is presented in comparison to wild-type untreated FFs. Green line: FFs transduced with the four factors; yellow line: FFs treated with EDHB; purple line: FFs transduced with the 4F and treated with EDHB at the same time. (B): Expression of PKM2, PDK1, and PDK3 after 24 hours of EDHB treatment in wild-type FFs (BJ and HFF1), SCR-KD fibroblasts, and HIF1-KD fibroblasts. ***, p < .005, one-way ANOVA single factor: PKM2 (p = 5E − 07), PDK1 (p = 9E − 06), and PDK3 (p = 1 E − 07). (C): Transcriptional level of the three glycolytic regulators after 24 hours of exposure to 1% hypoxia. ***, p < .005, one-way ANOVA single factor: PKM2 (p = 4E − 05), PDK1 (p = 9E − 08), and PDK3 (p = 1E − 07). Abbreviations: EDHB, ethyl 3,4-dihydroxybenzoate; FFs, foreskin fibroblasts; HIF1α, hypoxia inducible growth factor; HIF1-KD, HIF-1α knockdown; PKM2, pyruvate kinase isoform M2; PDK1, pyruvate dehydrogenase kinase 1; PDK3, pyruvate dehydrogenase kinase 3; SCR-KD, scrambled knockdown.

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PKM2, PDK1, and PDK3 gene expression in fibroblasts showed an increase following EDHB treatment, as confirmed by qPCR analysis (Fig. 4A). This is in agreement with previous data linking HIF1α activation with PDK1–3 induction [15-17] and with PKM2 gene transcription [18]. Importantly, the sole introduction of the four factors into fibroblasts resulted in the early upregulation of PDK1 (Fig. 4A). On the other hand, PKM2 and PDK3 were not upregulated following 4F transduction (Fig. 4A), indicating that the conventional Yamanaka protocol may not be sufficient to induce the early expression of these two factors which instead require the additional activation of the HIF1α pathway.

Finally, we confirmed that elevated expression of these three glycolytic regulators could occur in human fibroblasts following both EDHB treatment (Fig. 4B) and hypoxic exposure (Fig. 4C). Importantly, PKM2, PDK1, and PDK3 were not upregulated in HIF1-KD fibroblasts treated with EDHB (Fig. 4B, one-way ANOVA, p < .005) or cultured under hypoxia (Fig. 4C, one-way ANOVA, p < .005). No alteration of the basal expression of genes associated with the pathways of HIF1α, energy metabolism, and mTOR could be detected in HIF1-KD compared to SCR-KD (with the exception of the downregulation of HIF1a) (Supporting Information Table 3). Hence, although fibroblasts bearing HIF1α knockdown showed a normal fibroblast-like basal metabolism (Fig. 2D) and normal fibroblast-like metabolic-related transcriptional signature (Supporting Information Table 3), they appeared incapable of upregulating the expression of key glycolytic inducers (PDK1, PDK3, and PKM2). This implicates the Warburg-like transcriptional modulation of energy metabolism as a potential enabling step for initiating cellular reprogramming.

Elevated Expression of the HIF1α-Related Glycolytic Regulators PKM2, PDK1, and PDK3 in PSCs

We next investigated the association of the three HIF1α-related metabolic regulators with pluripotency. The expression level of PDK1, PDK3, and PKM2 was measured in FFiPSCs, DFiPSCs and hESCs, under both normoxia and 24 hours 1% hypoxia, and compared it to that in FFs (for FFiPSCs), DFs (for DFiPSCs), and all somatic fibroblasts (for hESCs) grown under normoxic conditions. In agreement with previous protein expression data [25, 31], the transcriptional level of PDK1 appeared upregulated in all PSCs (Fig. 5A). However, PDK1 induction was statistically significant in iPSCs only under hypoxic conditions (Fig. 5A, Student's t test, hypoxic iPSCs vs. normoxic fibroblasts, p < .05), while both normoxic and hypoxic hESCs exhibited significant PDK1 upregulation (Fig. 5A, Student's t test, normoxic/hypoxic hESCs vs. normoxic fibroblasts p < .005). PDK3 was significantly upregulated in both iPSCs (Fig. 5B, Student's t test, normoxic/hypoxic iPSCs vs. normoxic fibroblasts p < .05) and hESCs (Fig. 5B, Student's t test, normoxic/hypoxic hESCs vs. normoxic fibroblasts p < .005) under normoxia and hypoxia, although it was more elevated in iPSCs under hypoxic growth (Fig. 5B, Student's t test, hypoxic iPSCs vs. normoxic fibroblasts p < .005). Hence, the exposure to hypoxia led to increased upregulation of PDK1 and PDK3 in all iPSCs but not in hESCs, which showed elevated expression of the two genes already under normoxic growth (Fig. 5A, 5B). This differential response to hypoxic stimuli between somatic-derived and embryonic-derived PSCs may be explained by recent evidence demonstrating that they may not be equivalent in terms of their pluripotency [49-51]. Alternatively, it may be simply due to the known heterogeneity displayed by different PSC lines [52]. In any case, further studies are warranted to dissect this differential response to hypoxic stimuli between iPSCs and hESCs.

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Figure 5. PKM2, PDK1, and PDK3 are highly expressed in pluripotent stem cells (PSCs). (A): Relative PDK1 mRNA expression in: FFiPSCs (iPS2, iPS4, iB4, and iB5) under normoxia and hypoxia in relation to normoxic FFs (HFF1 and BJ); DFiPSCs (OiPS6, OiPS8, and OiPS16) under normoxia and hypoxia in relation to normoxic DFs (NFH2); and hESCs (H1 and H9) under normoxia and hypoxia in relation to all fibroblasts (HFF1, BJ, and NFH2) grown under normoxic conditions. **, p = .0098, two-tailed unpaired Student's t test, hypoxic FFiPSCs versus normoxic FFs. *, p = .043, two-tailed unpaired Student's t test, hypoxic DFiPSCs versus normoxic DFs. ***, p < .005, two-tailed unpaired Student's t test: normoxic hESCs versus all normoxic fibroblasts (p = .0005), hypoxic hESCs versus all normoxic fibroblasts (p = .0011). (B): Relative expression of PDK3 in PSCs under normoxia and hypoxia compared to normoxic somatic fibroblasts. *, p = .023, two-tailed unpaired Student's t test, normoxic FFiPSCs versus normoxic FFs. **, p = .0061, two-tailed unpaired Student's t test, normoxic DFiPSCs versus normoxic DFs. ***, p < .005, two-tailed unpaired Student's t test: normoxic hESCs versus all normoxic fibroblasts (p = .0007), hypoxic FFiPSCs versus normoxic FFs (p = .0009), hypoxic DFiPSCs versus normoxic DFs (p = .0021), hypoxic hESCs versus all normoxic fibroblasts (p = .0002) (C): Absolute protein quantification of PKM1 in FFs, FFiPSCs, and hESCs. *, p < .05, two-tailed unpaired Student's t test, FFiPSCs versus FFs and hESCs versus FFs. (D): Absolute protein quantification of PKM2 in FFs (HFF1 and BJ), FFiPSCs (iPS2, iPS4, iB4, and iB5), and hESCs (H1 and H9). ***, p < .005, two-tailed unpaired Student's t test, FFiPSCs versus FFs and hESCs versus FFs. (E): PKM2/PKM1 ratio in FFs, FFiPSCs, and hESCs grown under normoxic and hypoxic conditions. **p < .01, two-tailed unpaired Student's t test, normoxic hESCs versus normoxic FFs. ***, p < .005, two-tailed unpaired Student's t test, normoxic FFiPSCs versus normoxic FFs, hypoxic FFiPSCs versus hypoxic FFs, and hypoxic hESCs versus hypoxic FFs. (F): Rate of PK activity, normalized over the total protein amount, was measured in wild-type FFs (BJ and HFF1), SCR-KD, and HIF1-KD fibroblasts, FFiPSCs (iB4, and iB5), and hESCs (H1 and H9). ***, p < .005, two-tailed unpaired Student's t test, iB4 versus BJ, iB5 versus BJ, H1 versus BJ, and H9 versus BJ. Abbreviations: DFs, dermal fibroblasts; DFiPSCs, DF-derived iPSCs; FFs, foreskin fibroblasts; FFiPSCs, FF-derived iPSCs; hESCs, human embryonic stem cells; HIF-1α, hypoxia inducible factor 1α; HIF1-KD, HIF-1α knockdown; PDK, pyruvate dehydrogenase kinase; PKM2, pyruvate kinase isoform M2; SCR-KD, scrambled knockdown.

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In order to dissect the role of PKM2 in PSCs, we employed mass spectrometry-based targeted proteomic analysis. This approach allows the quantification of the absolute amount of the two alternatively spliced isoforms encoded by the PKM2 gene, PKM1 and PKM2, and it was previously carried out in healthy and tumor-associated tissues [8]. Remarkably, in comparison to fibroblasts, both iPSCs and hESCs exhibited reduced PKM1 (Fig. 5C, Student's t test, PSCs vs. FFs p < .05) coupled with significantly elevated amounts of PKM2 (Fig. 5D, Student's t test, PSCs vs. FFs p < .005). Overall, the PKM2/PKM1 ratio appeared significantly increased in PSCs under both normoxic and hypoxic conditions (Fig. 5E, Student's t test, normoxic/hypoxic PSCs vs. normoxic/hypoxic FFs p < .01). The PKM2 isoform is known to display reduced enzymatic activity [12]. Thus, we next measured the PK functionality in fibroblasts and PSCs and identified a diminished PK activity in all PSCs (Fig. 5F, Student's t test, PSCs vs. wild-type BJ, p < .005). This implies that the elevated expression of PKM2 in PSCs may be functionally relevant and could contribute to the maintenance of the glycolytic phenotype of PSCs.

Taken together, undifferentiated iPSCs and hESCs express high levels of three HIF1α downstream targets, whose function mediates a Warburg-like effect by increasing the energy flux in the upstream glycolytic branches and PPP, eventually leading to biomass stimulation and redox maintenance [6, 7]. Furthermore, since reprogramming efficiency is increased upon EDHB treatment (Fig. 1B) or hypoxic stimulation [36], it is tempting to speculate that the elevated expression of PKM2 and PDK3 detected in fibroblasts exposed to EDHB or hypoxia (Fig. 4B, 4C) may possibly play a role in improving the conversion to iPSCs by additional metabolic modulation toward a glycolytic state.

Metabolic Remodeling During Initiation of Reprogramming

Finally, we asked whether the transcriptional activation of metabolic regulators during reprogramming initiation was sufficient to induce a metabolic reconfiguration. Bioenergetic profiling of fibroblasts following 4F transduction indicated that mitochondrial respiration increased over the first 3 days of reprogramming (Fig. 6A). However, the elevation of glycolysis appeared more pronounced (Fig. 6B). The additional treatment with EDHB resulted in enhanced glycolytic conversion, with drastic OCR reduction (Fig. 6C) and higher ECAR values (Fig. 6D). Overall, 4F transduction led to progressive decrease in the OCR/ECAR ratio, indicative of initial conversion to glycolytic metabolism (Fig. 6E, Student's t test, 4F-transduced BJ vs. wild-type BJ, p < .05). In accordance, the amount of extracellular lactate in 4F-transduced fibroblasts showed a similar gradual increase during the first days of reprogramming (Fig. 6F, Student's t test, 4F-transduced BJ vs. wild-type BJ, p < .05). Nonetheless, this metabolic remodeling does not appear to be completed at these early stages, as shown by the much higher lactate secretion occurring in fully reprogrammed iPSCs and hESCs (Fig. 6F, Student's t test, PSCs vs. BJ, p < .005). The additional exposure to a drug mimicking HIF1α activation may thus improve the efficiency of iPSC generation through the early enhancement of glycolytic activation, as shown by a dramatic OCR/ECAR decrease (Fig. 6E, Student's t test, 4F-EDHB treated BJ vs. BJ, p < .005).

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Figure 6. Metabolic shift during early reprogramming. (A): OCR profile of wild-type FFs (BJ) and FFs after 24 hours, 48 hours, and 72 hours from 4F transduction. (B): ECAR profile of FFs at the basal level and after 4F introduction. (C): OCR profile in BJ fibroblasts treated with the HIF1α mimicker EDHB in addition to the 4F transduction. (D): ECAR profile in FFs and 4F-EDHB FFs. (E): OCR/ECAR ratio gradually decreases during reprogramming initiation while it is drastically reduced upon the additional introduction of EDHB to the reprogramming cocktail. *, p = .0370, two-tailed unpaired Student's t test, 4F 48h versus BJ. **, p = .0062, two-tailed unpaired Student's t test, 4F 72h versus BJ. ***, p < .005, two-tailed unpaired Student's t test: 4F EDHB 24h versus BJ (p = .0045), 4F EDHB 48h versus BJ (p = .0001), and EDHB 72h versus BJ (p = .0002). (F): Production of extracellular lactate in FFs (BJ, SCR-KD, HIF1-KD), FFs transduced with the 4F, hESCs (H1 and H9), and BJ-FFiPSCs (iB4 and iB5). The values are reported to the amount of lactate generated in control wild-type BJ fibroblasts. *, p < .05, two, two-tailed unpaired Student's t test: 4F 24h versus BJ (p = .035), 4F 48h versus BJ (p = .021). **, p < .01, two-tailed unpaired Student's t test, 4F 72h versus BJ (p = .008). ***, p < .005, two-tailed unpaired Student's t test: hESCs versus BJ (p = .0035), BJ FFiPSCs versus BJ (p = .0041). Abbreviations: ECAR, extracellular acidification rate; EDBH, ethyl 3,4-dihydroxybenzoate; FFiPSCs, foreskin fibroblast-derived induced pluripotent stem cells; hESCs, human embryonic stem cells; HIF-1α, hypoxia inducible factor 1α; HIF1-KD, HIF-1α knockdown; OCR, oxygen consumption rate; SCR-KD, scrambled knockdown.

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On the basis of data presented here, we suggest that the introduction of the four Yamanaka factors in human fibroblasts may be sufficient to upregulate the HIF1α target PDK1, which can in turn initiate a glycolytic program. On the other hand, the inclusion of hypoxia or EDHB treatment may stimulate the expression of additional HIF1α-related metabolic regulators PDK3 and PKM2, enhancing the glycolytic shift at the very early stages of reprogramming and eventually leading to improved iPSC derivation. Finally, reprogramming may be inhibited in cells that are incapable of activating HIF1α and upregulating these three metabolic regulators, further underlying the importance of HIF1α-associated metabolic restructuring in the induction of pluripotency in somatic cells (Fig. 7).

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Figure 7. HIF1α-associated metabolic reconfiguration during reprogramming initiation. Cartoon depicting the potential mechanisms through which the introduction of the 4F or the modulation of HIF1α pathway might regulate glycolytic metabolism during the early stages of somatic cell reprogramming. The introduction of the 4F in somatic fibroblasts upregulates the HIF1α target PDK1, which re-routes the energy flux outside the mitochondria, thereby enhancing the glycolytic metabolism. HIF1α activation upregulates additional HIF1α targets PKM2 and PDK3, further increasing the glycolytic shift and eventually resulting in improved conversion to pluripotency. Abbreviations: EDHB, ethyl 3,4-dihydroxybenzoate; HIF1α, hypoxia inducible factor 1α; OXPHOS, oxidative phosphorylation; PDK, pyruvate dehydrogenase kinase; PDH, pyruvate dehydrogenase; PEP, phosphoenolpyruvate; PKM2, pyruvate kinase M2.

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Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Conclusion
  8. Acknowledgments
  9. Author Contributions
  10. Disclosure of Potential Conflicts of Interest
  11. References
  12. Supporting Information

We and others previously demonstrated that cell fate reprogramming is associated with a transition from respiratory to glycolytic metabolism [21-26]. Tumor cells are believed to undergo similar metabolic transformation events in order to sustain the cost of anabolic reactions caused by their high proliferative rate and resistance to stress signals [4]. Thus, metabolic adaptation appears as a crucial mechanism for proliferating cells, which require to build biomass and the same time preserve the redox balance [6]. Here, we sought to uncover possible mechanistic pathways underlying the metabolic reprogramming of iPSCs and demonstrated that HIF1α, a master regulator of glucose metabolism [13, 14], plays a critical role during the induction of pluripotency by modulating the early establishment of a glycolytic program.

Hypoxia and HIF1α have been previously implicated in the maintenance of pluripotency. Indeed, hypoxic conditions have been found to lead to improved stemness and reprogramming [33, 36]. HIF1α activation could induce hESC-like signature in cancer cell lines [48] and drive mouse ESCs to acquire glycolytic features upon transition toward an epiblast stem cell-like state [53]. Moreover, stabilization of hypoxia and HIF1α inhibit the metabolic shift to OXPHOS that is required for efficient differentiation of human mesenchymal stem cells [54]. Hypoxic exposure may also increase the protein expression uncoupling protein two (UCP2) [55], a regulator of energy metabolism in undifferentiated PSCs [26]. Finally, hypoxia might lead to OCT4 reactivation to promote the dedifferentiation of PSC-derived progenies [56]. Our results support all these findings and demonstrate that cells incapable of activating a HIF1α response might also be refractory to reprogramming, thus underlying the critical importance of this pathway for the induction and maintenance of pluripotency.

Our data suggest that a glycolytic shift may be instrumental for reprogramming as it may be set into motion during the reprogramming initiation stage through the early upregulation of the HIF1α target PDK1. Indeed, PSCs exhibit elevated PDK1 protein expression [25, 31]. Moreover, small molecule-based activation of PDK1 can improve iPSC derivation [22], while PDK1 inhibitors lead to reduced hESC-like colony formation [23]. Hence, PDK1 may possibly represent an early marker of reprogramming involved in the Warburg-like metabolic restructuring associated with the conversion to pluripotency.

The findings also provide additional support to previous data showing that during reprogramming, glycolysis-associated genes may be upregulated prior to genes involved in self-renewal and pluripotency [23]. Furthermore, transcriptional changes in the processes related to cellular metabolism have been detected during the first initial wave of reprogramming, which has been suggested to be under the control of KLF4 and c-MYC [57]. It is thus tempting to speculate that KLF4 and c-MYC may be driving the transcriptional and bioenergetic modulation observed during the initiation of cellular reprogramming. Indeed, the oncogene c-MYC can co-operate with HIF1α to induce a transcriptional program leading to stimulated glycolytic activity [58, 59], although a glycolytic shift has been observed even in iPSCs derived in the absence of c-MYC [23]. KLF4 may be capable of activating glycolytic metabolism in cancer cells [60], and KLF5, which can substitute KLF4 in the reprogramming cocktail [61], can regulate energy metabolism and upregulate UCP2 [62], a protein recently linked to the glycolytic state of PSCs [26]. Nonetheless, it may as well be that the key stemness factor OCT4 could regulate downstream targets implicated in OXPHOS and glycolysis [28, 63]. In fact, OCT4 expression intermingles with HIF signaling pathways [47] and may positively interact with PKM2 [64]. Further studies are warranted to clearly dissect the specific roles of the Yamanaka factors in the remodeling of energy metabolism of reprogrammed cells.

Finally, since reconfiguration of glucose metabolism could have significant advantages in preventing redox imbalance [5, 65], it is conceivable that iPSC generation may require such metabolic reprogramming in order to safeguard the genome integrity. Nuclear and mitochondrial genetic defects have been reported in human iPSCs [31, 66, 67]. Perhaps, the suppression of metabolic resetting may function as a reprogramming roadblock by inducing an uncontrolled rise of genomic damage which may be incompatible with continuous cell growth. In accordance, pro-oxidant reactions can promote PSC differentiation [68].

It is important to note that our analyses were performed on whole cultures and may thus not necessarily mirror the situation occurring in the small percentage of cells actually undergoing reprogramming. Reassuringly, a recent work using single-cell analysis showed that reprogramming is initiated in the majority of virally transduced fibroblasts, although only completed in a smaller cellular subpopulation [69]. Since we mainly focused on early reprogramming initiation, it is then possible that our data may reflect real reprogramming-related cellular conditions. Nevertheless, given the known heterogeneity of single PSC lines [49, 52], it would be of interest to repeat our relatively small-scale investigation (which included 2 hESC lines and 13 human iPSC lines derived from 3 different cell sources, i.e., FFs, DFs, and AFCs) using larger datasets of several human and murine PSCs to clearly demonstrate the role of HIF1α and glycolytic regulation in cell fate conversion.

Conclusion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Conclusion
  8. Acknowledgments
  9. Author Contributions
  10. Disclosure of Potential Conflicts of Interest
  11. References
  12. Supporting Information

Overall, our results indicate that HIF1α-mediated reconfiguration of glucose metabolism may represent an early enabling step of cellular reprogramming, a barrier that has to be overcome in order to make somatic cells capable of sustaining their newly acquired proliferative and biosynthetic needs. We anticipate that the study of metabolism in stem cells may unveil critical mechanisms governing the induction of pluripotency, eventually elucidating the pathways responsible for allowing this remarkable example of cellular plasticity.

Acknowledgments

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Conclusion
  8. Acknowledgments
  9. Author Contributions
  10. Disclosure of Potential Conflicts of Interest
  11. References
  12. Supporting Information

The authors would like to thank Beata Lukaszewska for help in the PKM quantification and Claudia Vogelgesang and Aydah Sabah at the microarray facility. We acknowledge support from the Max Planck Society. A.P. acknowledges support from the Fritz Thyssen Foundation (grant AZ. 10.11.2.160). M.R. is a Wellcome Trust Research Career Development and Wellcome-Beit fellow. J.A. acknowledges support from the German Federal Ministry of Education and Research (BMBF) grants (01GN1005, 01GN0807), and 0315717A, which is a partner of the ERASysBio+ initiative supported under the EU ERA-NET Plus scheme in FP7.

Author Contributions

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Conclusion
  8. Acknowledgments
  9. Author Contributions
  10. Disclosure of Potential Conflicts of Interest
  11. References
  12. Supporting Information

A.P.: conception and design, collection and assembly of data, data analysis and interpretation, financial support, and manuscript writing; N.R. and K.D.: collection and assembly of data; S.H., B.M., R.B., and K.B.: collection of data; E.E.W.: financial support; M.R. and T.C.: data analysis and interpretation and financial support; J.A.: conception and interpretation, financial support, and editing and final approval of the manuscript.

References

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Conclusion
  8. Acknowledgments
  9. Author Contributions
  10. Disclosure of Potential Conflicts of Interest
  11. References
  12. Supporting Information

Supporting Information

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Conclusion
  8. Acknowledgments
  9. Author Contributions
  10. Disclosure of Potential Conflicts of Interest
  11. References
  12. Supporting Information

Additional Supporting information may be found in the online version of this article.

FilenameFormatSizeDescription
stem1552-sup-0001-suppfig1.tif1246KSupporting Information Figure 1.
stem1552-sup-0002-suppfig2.tif12470KSupporting Information Figure 2.
stem1552-sup-0003-suppfig3.tif930KSupporting Information Figure 3.
stem1552-sup-0004-suppfig4.tif5523KSupporting Information Figure 4.
stem1552-sup-0005-supptab1.xls27KSupporting Information Table 1.
stem1552-sup-0006-supptab2.xls93KSupporting Information Table 2.
stem1552-sup-0007-supptab3.xls44KSupporting Information Table 3.
stem1552-sup-0008-supptab4.doc31KSupporting Information Table 4.

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