Brief UltraRapid Communication

Stoichiometry of Gata4, Mef2c, and Tbx5 Influences the Efficiency and Quality of Induced Cardiac Myocyte ReprogrammingNovelty and Significance

Li Wang, Ziqing Liu, Chaoying Yin, Huda Asfour, Olivia Chen, Yanzhen Li, Nenad Bursac, Jiandong Liu, Li Qian
https://doi.org/10.1161/CIRCRESAHA.116.305547
Circulation Research. 2015;116:237-244
Originally published November 21, 2014

Jump to

Abstract

Rationale: Generation of induced cardiac myocytes (iCMs) directly from fibroblasts offers great opportunities for cardiac disease modeling and cardiac regeneration. A major challenge of iCM generation is the low conversion rate of fibroblasts to fully reprogrammed iCMs, which could in part be attributed to unbalanced expression of reprogramming factors Gata4 (G), Mef2c (M), and Tbx5 (T) using the current gene delivery approach.

Objective: We aimed to establish a system to express distinct ratios of G, M, T proteins in fibroblasts and determine the effect of G, M, T stoichiometry on iCM reprogramming.

Methods and Results: We took advantage of the inherent feature of the polycistronic system and generated all possible combinations of G, M, T with identical 2A sequences in a single transgene. We demonstrated that each splicing order of G, M, T gave rise to distinct G, M, T protein expression levels. Combinations that resulted in higher protein level of Mef2c with lower levels of Gata4 and Tbx5 significantly enhanced reprogramming efficiency compared with separate G, M, T transduction. Importantly, after further optimization, the MGT vector resulted in more than 10-fold increase in the number of mature beating iCM loci. Molecular characterization revealed that more optimal G, M, T stoichiometry correlated with higher expression of mature cardiac myocyte markers.

Conclusions: Our results demonstrate that stoichiometry of G, M, T protein expression influences the efficiency and quality of iCM reprogramming. The established optimal G, M, T expression condition will provide a valuable platform for future iCM studies.

Introduction

The generation of induced cardiac myocyte (iCM) from fibroblast holds great promise for cardiac disease modeling and regenerative medicine.1–15 However, the inefficient iCM generation has become a major hurdle for deciphering the mechanism of cardiac reprogramming and in vitro cardiac disease modeling.7,16 Although the low conversion rate of fibroblasts to reprogrammed iCMs suggests the existence of major rate-limiting barrier(s), it might also reflect a requirement for a balanced expression of Gata4 (G), Mef2c (M), and Tbx5 (T) to promote successful and complete reprogramming. Current iCM generation involves transducing fibroblasts with pooled viruses encoding the 3 individual reprogramming factors. This approach suffers from heterogeneous and uncontrollable ratios of G, M, and T expression among the transduced fibroblasts. Inagawa et al9 attempted to address this issue through the use of a polycistronic vector that encoded G, M, and T in a single transgene; however, their study showed only a marginal positive effect on reprogramming efficiency when compared with use of individual viruses.

Editorial, see p 216

In This Issue, see p 215

G, M, and T are the master regulators residing at the top of the transcriptional hierarchy of the cardiac gene regulatory networks.17–33 During heart development, faithful execution of cardiac developmental processes requires a precise dosage and temporal expression of these 3 factors.17–33 Disruption of this delicate balance is likely to compromise cardiac specification and differentiation and cause severe cardiac anomalies.24–26,34–36 We therefore postulated that when G, M, and T were delivered in separate viruses, only a subpopulation of cardiac fibroblasts (CFs) would express the optimal amounts of G, M, and T required to trigger cardiac reprogramming. Thus, we surmised that it might be possible to establish a more appropriate balance of G, M, and T expression in nonmyocytes through the use of splice-ordered polycistronic vectors.

In this study, we generated a complete set of polycistronic constructs containing G, M, and T in all possible splicing orders with identical 2A sequences in a single mRNA. We found that each splicing order of G, M, and T gave rise to distinct ratios of G, M, and T protein expression and significantly different reprogramming efficiencies. On further optimization, the most desirable combination resulted in a >10-fold increase in generation of beating iCMs. Importantly, at the molecular level, the more optimal G, M, and T stoichiometry, defined by higher protein expression level of Mef2c with lower levels of Gata4 and Tbx5, correlated with higher expression of mature cardiac myocyte markers. Thus, our study demonstrates that stoichiometry of G, M, and T influences both efficiency and quality of iCM induction.

Methods

An expanded Methods section is available in the Online Data Supplement.

Results

G, M, and T Protein Levels Differ in Fibroblasts Expressing Each of the 6 Polycistronic Constructs

To manipulate the relative levels of G, M, and T protein expression, we generated 6 polycistronic constructs with identical 2A sequences to include all possible splicing orders of G, M, and T in a single mRNA (Figure 1A). In a first set of experiments, we transduced CFs with G, M, or T retroviruses separately. Western blot analysis showed that G, M, or T proteins were detected at the appropriate molecular weight (Online Figure IA). Next, we performed Western blot on CFs transduced with each of the 6 constructs and observed higher protein expression level when the reprogramming factor was placed at the 5′ end versus 3′ end of the construct (Figure 1B and 1C). After normalization to the loading control, quantification of band intensities revealed that each construct produced distinct ratios of G, M, and T protein expression (Figure 1B and 1C; Online Figure IB). We also determined G, M, and T transcript levels in transduced cells (Online Figure II). As expected, after virus infection, G, M, and T transcript levels were significantly elevated; however, the CFs expressing each of these 6 constructs did not exhibit significant differences in G, M, and T total and endogenous transcripts levels (Online Figure II). These data suggest that our complete set of polycistronic vectors can effectively produce distinct ratios of G, M, and T protein expression in transduced CFs.

Figure 1.
Figure 1.

A complete set of polycistronic vectors that result in different Gata4, Mef2c, and Tbx5 (G, M, T) protein levels. A, Diagram of the 6 polycistronic vectors with G, M, T in different splicing orders. Reprogramming factors were cloned in all possible orders (separated by identical P2A and T2A sequences) into the retroviral vector pMX. B, Western blot analysis of cardiac fibroblasts expressing each of the 6 different polycistronic vectors. Cell lysate was collected at 3 days after infection. C, Quantification of G, M, T protein expression levels. Error bars represent SEM. *P<0.05; **P<0.01.

Different iCM Reprogramming Efficiency Using Each of the 6 Polycistronic Constructs

We next sought to determine iCM reprogramming efficiency using these 6 polycistronic constructs. We isolated CFs from α-muscle heavy chain (αMHC)-green fluorescent protein (GFP) reporter mice,1,2 which express GFP in differentiated cardiac myocytes (CMs) but not in CFs. Thus, activation of GFP could allow us to follow the emergence of newly induced iCMs. Furthermore, we used cardiac Troponin T as an additional differentiated CM marker to monitor CM fate induction. We transduced CFs with retroviruses encoding the 6 polycistronic mRNAs. Interestingly, flow analysis and quantification showed that these 6 vectors resulted in significantly different reprogramming efficiencies as indicated by the differences in the percentage of αMHC-GFP+ and cardiac Troponin T+ cells (Figure 2A and 2B). Noticeably, only 2 vectors (MGT and MTG) enhanced reprogramming efficiency compared with separate G, M, and T vectors, whereas the other 4 resulted in a decrease in reprogramming efficiency (Figure 2A and 2B). Similar results were obtained by performing immunocytochemistry with antibody against αMHC-GFP (Figure 2C; Online Figure III). Likewise, Western blot analysis of CFs reprogrammed with the 6 different vectors indicated that reprogramming with MGT resulted in the highest upregulation of αMHC-GFP and α-actinin protein expression (Figure 2D). Based on the analyzed markers, these data suggest that the 6 polycistronic constructs conferred differential effects on iCM induction. Although the experiments were performed with neonatal CFs, these polycistronic vectors exhibited similar relative reprogramming efficiencies (based on the percentage of αMHC-GFP+ iCMs) in neonatal tail tip fibroblasts, adult CFs, and adult tail tip fibroblasts (Online Figure IV). We then focused on the vector with the highest efficiency (MGT) and performed additional experiments to determine and quantify its reprogramming efficiency relative to that of separate G, M, T vectors (Online Figures V and VI). Collectively, our results demonstrate that the 6 polycistronic vectors exhibit significantly different iCM reprogramming capacities, and a more optimal G, M, and T stoichiometry, defined by higher protein level of Mef2c with lower levels of Gata4 and Tbx5, significantly increases iCM reprogramming efficiency.

Figure 2.
Figure 2.

Induced cardiac myocyte (iCM) reprogramming efficiencies using the 6 polycistronic vectors. A, Representative flow cytometry plots of cardiac Troponin T (cTnT)+, α-muscle heavy chain (αMHC)-green fluorescent protein (GFP)+, and double positive iCMs. B, Quantification of the flow cytometry data. C, Representative immunocytochemistry images of iCMs generated using each of the 6 constructs. Note that only 2 vectors resulted in higher reprogramming efficiencies relative to others. Scale bar, 200 μm. *P<0.05; **P<0.01. D, Western blot analysis of GFP and α-actinin expression in the CFs expressing each of the 6 constructs. Quantification of GFP and α-actinin expression from 3 independent experiments is shown in the bar graph. Error bars represent SEM. DAPI indicates 4′,6-diamidino-2-phenylindole.

Enhancement of the Quality of iCM Reprogramming Using MGT With Antibiotic Selection

To determine whether a more optimal G, M, and T stoichiometry of the MGT vector could be used to improve functional maturation of iCMs, we added a puromycin resistance gene in the vector (puro-MGT) to enable selection of transduced CFs (Figure 3). Using puro-MGT to reprogram freshly isolated CFs, we observed a 3-fold increase in the percentage of αMHC-GFP+ cells and 5-fold increase in the percentage of cardiac Troponin T+ cells (Figure 3A and 3B). To obtain a global view of iCMs, we took 228 snapshots of 10× immunocytochemistry pictures to cover a full well of a 24-well plate. After stitching all pictures together, we observed a population of cells that were highly enriched with iCMs, which frequently aggregated to form clusters (Figure 3C). These iCMs formed sarcomere structures (stained for cardiac Troponin T and α-actinin) resembling the ones of fetal cardiac myocytes and expressed the gap junction protein Connexin43 at the cell–cell contacts (Figure 3D). We next analyzed intracellular Ca2+ flux by Rhod3 dye labeling after 4 weeks of culture. With puromycin selection, ≈35% of transduced cells showed spontaneous Ca2+ oscillations and their frequency was variable, as previously reported in reprogrammed iCMs.11 Figure 3E shows a representative iCM-rich area containing 8 iCMs. Intracellular Ca2+ oscillation in this area was recorded in 4 loci, and 3 of them showed periodic Ca2+ transients (Nos. 1–3 representative traces) suggestive of advanced reprogramming stages, whereas the fourth trace showed aperiodic oscillations. The number of Ca2+ transient-expressing loci was increased by application of puromycin selection (Figure 3E). In addition to periodic Ca2+ transients, MGT-transduced iCMs showed spontaneous contractile activity after 3 weeks in culture (Online Movies I and II). With increase in culture time to 6 weeks, the number of beating iCM loci generated using the puro-MGT vector increased to 30 to 40 loci per well of a 24-well plate (Figure 3F), which was >10-fold higher than previously reported numbers of beating loci generated using the same or similar reprogramming factors.1,8 Collectively, our data suggest that a more optimal stoichiometry of the reprogramming factors affords a more efficient and complete conversion of a CF into a CM fate.

Figure 3.
Figure 3.

Further enhancement of induced cardiac myocyte (iCM) reprogramming efficiency using puro-MGT. A, Representative flow cytometry plots of cardiac Troponin T (cTnT)+, α-muscle heavy chain (αMHC)-green fluorescent protein (GFP)+, and double positive iCMs. B, Quantification of flow cytometry data. **P<0.01. C, Stitched (from 228 individual image) immunocytochemistry (ICC) picture showing iCMs (GFP positive) in 1 well of 24-well plate under optimized condition. Inset showing a representative high-resolution image of an iCM cluster. D, ICC of cardiac markers cTnT, α-actinin, and Connexin43 (Cx43) double labeling with αMHC-GFP. The middle panels are enlarged areas from the left-hand panels as indicated by the white rectangles. Note the striated pattern of sarcomeres in the top 2 middle panels and Cx43 positive spots at the cell–cell contacts in the bottom-middle panel. Scale bar, 200 μm. E, iCM calcium transients measured by Rhod3 dye labeling. Each trace corresponds to the spot numbered in the upper-left panel. Scale bar, 200 μm. Quantification is shown in the lower left histogram. F, Quantification of the number of beating iCM loci over time. Error bars represent SEM. DAPI indicates 4′,6-diamidino-2-phenylindole

Molecular Characterization of Reprogramming Cells Resulted From Varied G, M, and T Stoichiometry

Next, we sought to determine how G, M, and T protein stoichiometry influenced iCM gene expression profile. We performed real-time quantitative polymerase chain reaction using a set of CM and CF markers that were previously used to characterize iCMs3 (Figure 4). Interestingly, the 6 constructs led to significant differences in CM marker gene expression. The most efficient MGT and MTG vectors resulted in a higher upregulation of sarcomere structure genes Myh6, Myl7, Tnnt2, Actc1, muscle contractility genes Pln, Slc8a1, Scn5a, gap junction protein gene Gja1, and ion channel genes Kcnj2, Cacba1c (Figure 4). Noticeably, we observed a reverse correlation of upregulation of cardiac stress genes Nppa and Nppb expression with reprogramming efficiency (Figure 4D and 4E). The 4 constructs that resulted in a decreased reprogramming efficiency upregulated Nppa and Nppb genes expression (Figure 4D and 4E), possibly because of overactivation by high levels of Tbx5 and Gata4. In contrast, fibroblast markers, such as Col1a1, Col3a1, and Eln, were all significantly downregulated with no significant difference among the 6 constructs, suggesting that they exhibited a similar capacity to repress gene expression of fibroblast markers (Figure 4L–4N). Taken together, these data suggest that the stoichiometry of G, M, and T influences not only the efficiency of iCM induction but also the activation of cardiac myocyte markers in iCMs.

Figure 4.
Figure 4.

Gene expression analyses of cardiac fibroblasts (CFs) expressing each individual polycistronic vector. Relative expression of a panel of cardiac myocyte or fibroblast marker genes in CFs infected with each of the 6 polycistronic vectors compared with CFs infected with mock (dsRed) or pooled G+M+T at 10 days after infections. Error bars represent SEM.

Discussion

In this study, we took advantage of the inherent feature of polycistronic vectors to manipulate the protein expression levels of cardiac reprogramming factors G, M, and T and reveal a critical role of G, M, and T protein stoichiometry in iCM reprogramming. We established that the optimal G, M, and T expression for reprogramming is a relative high level of Mef2c expression and low levels of Gata4 and Tbx5 expression. In addition, our studies suggest that an optimal balance among these 3 reprogramming factors could allow more efficient and complete conversion of CFs into cardiac myocyte–like cells.

To the best of our knowledge, this is the first work that has built a complete set of polycistronic vectors for a reprogramming cocktail. Carey et al37 has previously compared the induced pluripotent stem cell reprogramming between 2 existing polycistronic transgenes (OSKM versus OKSM). They demonstrated that these 2 transgenes conferred differential effects on induced pluripotent stem cell induction, possibly because of the differences in Oct4, Sox2, Klf4, and cMyc protein expression levels as a result of the splicing order of Oct4, Sox2, Klf4, and cMyc.37 However, the 2 transgenes were generated by different research groups in a way that the backbone of the vectors and the polycistronic cleavage sites were not identical. In this study, we generated a whole series of polycistronic constructs to include all possible splicing orders of cardiac reprogramming factors in the same construct with identical 2A peptides. Our studies demonstrated that difference in protein stoichiometry of G, M, and T alone is sufficient to confer a significantly different effect on cardiac reprogramming outcomes. It will be important to determine whether the inter-regulatory relationships among the 3 factors play a role in controlling and maintaining the optimal balance of G, M, and T expression needed for reprogramming. In addition to the polycistronic system, independent approaches such as those using promoters with different strengths to manipulate the relative levels of G, M, and T protein expression could be undertaken to further determine the effect of stoichiometry on iCM reprogramming. It remains to be determined whether optimized stoichiometry of human G, M, and T could be sufficient to induce cardiac fate in human fibroblasts without the use of additional factors and microRNAs.13–15 Moreover, we found that the unbalanced protein expression of G, M, and T (when using TMG, TGM, GMT, and GTM vectors) led to an inefficient iCM reprogramming. Although these unbalanced G, M, and T combinations were equally capable of suppressing gene expression of fibroblast markers, they failed to robustly activate cardiac program, and instead induced cardiac stress genes Nppa and Nppb. Our results thus could explain some discrepancies among different groups that attempted G, M, and T–mediated iCM induction.7,9,16 For example, similar to G, M, and T expression when using our TGM construct, the inefficient cardiac reprogramming reported by Chen et al7 was likely caused by relatively high expression of Tbx5 and Gata4 as well as the enrichment for potential iCMs based on the expression of Tbx5. The marginal effects of using a polycistronic construct TMG reported by Inagawa et al9 could be similarly attributed to the nonoptimal stoichiometry of reprogramming factors.

In spite of excitement and significant potential of iCM reprogramming in regenerative medicine, the low conversion rate of fibroblasts into iCMs has been a major challenge for future translational efforts. Although this low efficiency is expected, as it has been reported for other reprogramming technologies during their fledgling phase,38 there are ongoing efforts to overcome the hurdles and to identify small molecules to replace transcription factors for therapeutic purposes. All of these attempts would not be fruitful without a consistent and reproducible platform. Our most efficient polycistronic construct that has been further modified by adding antibiotic selection (pMX-puro-MGT) not only eliminates the need for multiple constructs, providing a homogeneous gene expression stoichiometry, but also allows for further enrichment of transduced cells with antibiotic selection. This platform offers a reasonable starting point for future screening and mechanistic studies, especially those that require large amounts of materials to explore genome-wide molecular changes that occur during reprogramming. In addition, our complete set of polycistronic constructs can be further used to study the optimal strategies to convert other nonmyocytes in addition to fibroblasts into cardiac myocytes, offering insights into how stoichiometry of G, M, and T expression can control cardiac fate acquisition under a variety of conditions.

Acknowledgments

We are grateful for the expert technical assistance from the UNC Flow Cytometry Core and UNC Microscopy Core. We thank Dr Taylor and members of the Qian laboratory and the J. Liu laboratory for helpful discussions and critical reviews of the article.

Sources of Funding

This study was supported by National Institutes of Health/National Heart, Lung, and Blood Institute (NIH/NHLBI) 1R01HL104326 grant to Dr Bursac, NIH/NHLBI R00 HL109079 grant to Dr J. Liu, NIH T35-DK007386 to Dr O. Chen, and American Heart Association Scientist Development Grant 13SDG17060010 and the Ellison Medical Foundation New Scholar Grant AG-NS-1064-13 to Dr Qian.

Disclosures

None.

Footnotes

  • In October, 2014, the average time from submission to first decision for all original research papers submitted to Circulation Research was 16 days.

  • Brief UltraRapid Communications are designed to be a format for manuscripts that are of outstanding interest to the readership, report definitive observations, but have a relatively narrow scope. Less comprehensive than Regular Articles but still scientifically rigorous, BURCs present seminal findings that have the potential to open up new avenues of research. A decision on BURCs is rendered within 7 days of submission.

  • The online-only Data Supplement is available with this article at http://circres.ahajournals.org/lookup/suppl/doi:10.1161/CIRCRESAHA.116.305547/-/DC1.

  • Nonstandard Abbreviations and Acronyms
    αMHC/Myh6
    α-muscle heavy chain, also known as myosin heavy chain 6
    CF
    cardiac fibroblast
    CM
    cardiac myocyte
    G, M, T
    Gata4, Mef2c, and Tbx5
    GFP
    green fluorescent protein
    iCM
    induced cardiac myocyte

  • Received November 2, 2014.
  • Revision received November 20, 2014.
  • Accepted November 21, 2014.

References

  1. 1.↵
    1. Ieda M,
    2. Fu JD,
    3. Delgado-Olguin P,
    4. Vedantham V,
    5. Hayashi Y,
    6. Bruneau BG,
    7. Srivastava D
    . Direct reprogramming of fibroblasts into functional cardiomyocytes by defined factors. Cell. 2010;142:375–386. doi:10.1016/j.cell.2010.07.002.
    OpenUrlCrossRefPubMed
  2. 2.↵
    1. Qian L,
    2. Berry EC,
    3. Fu JD,
    4. Ieda M,
    5. Srivastava D
    . Reprogramming of mouse fibroblasts into cardiomyocyte-like cells in vitro. Nat Protoc. 2013;8:1204–1215. doi:10.1038/nprot.2013.067.
    OpenUrlCrossRefPubMed
  3. 3.↵
    1. Qian L,
    2. Huang Y,
    3. Spencer CI,
    4. Foley A,
    5. Vedantham V,
    6. Liu L,
    7. Conway SJ,
    8. Fu JD,
    9. Srivastava D
    . In vivo reprogramming of murine cardiac fibroblasts into induced cardiomyocytes. Nature. 2012;485:593–598. doi:10.1038/nature11044.
    OpenUrlCrossRefPubMed
  4. 4.↵
    1. Jayawardena TM,
    2. Egemnazarov B,
    3. Finch EA,
    4. Zhang L,
    5. Payne JA,
    6. Pandya K,
    7. Zhang Z,
    8. Rosenberg P,
    9. Mirotsou M,
    10. Dzau VJ
    . MicroRNA-mediated in vitro and in vivo direct reprogramming of cardiac fibroblasts to cardiomyocytes. Circ Res. 2012;110:1465–1473. doi:10.1161/CIRCRESAHA.112.269035.
    OpenUrlAbstract/FREE Full Text
  5. 5.↵
    1. Protze S,
    2. Khattak S,
    3. Poulet C,
    4. Lindemann D,
    5. Tanaka EM,
    6. Ravens U
    . A new approach to transcription factor screening for reprogramming of fibroblasts to cardiomyocyte-like cells. J Mol Cell Cardiol. 2012;53:323–332. doi:10.1016/j.yjmcc.2012.04.010.
    OpenUrlCrossRefPubMed
  6. 6.↵
    1. Song K,
    2. Nam YJ,
    3. Luo X,
    4. Qi X,
    5. Tan W,
    6. Huang GN,
    7. Acharya A,
    8. Smith CL,
    9. Tallquist MD,
    10. Neilson EG,
    11. Hill JA,
    12. Bassel-Duby R,
    13. Olson EN
    . Heart repair by reprogramming non-myocytes with cardiac transcription factors. Nature. 2012;485:599–604. doi:10.1038/nature11139.
    OpenUrlCrossRefPubMed
  7. 7.↵
    1. Chen JX,
    2. Krane M,
    3. Deutsch MA,
    4. Wang L,
    5. Rav-Acha M,
    6. Gregoire S,
    7. Engels MC,
    8. Rajarajan K,
    9. Karra R,
    10. Abel ED,
    11. Wu JC,
    12. Milan D,
    13. Wu SM
    . Inefficient reprogramming of fibroblasts into cardiomyocytes using Gata4, Mef2c, and Tbx5. Circ Res. 2012;111:50–55. doi:10.1161/CIRCRESAHA.112.270264.
    OpenUrlAbstract/FREE Full Text
  8. 8.↵
    1. Muraoka N,
    2. Yamakawa H,
    3. Miyamoto K,
    4. et al
    . MiR-133 promotes cardiac reprogramming by directly repressing Snai1 and silencing fibroblast signatures. EMBO J. 2014;33:1565–1581. doi:10.15252/embj.201387605.
    OpenUrlAbstract/FREE Full Text
  9. 9.↵
    1. Inagawa K,
    2. Miyamoto K,
    3. Yamakawa H,
    4. Muraoka N,
    5. Sadahiro T,
    6. Umei T,
    7. Wada R,
    8. Katsumata Y,
    9. Kaneda R,
    10. Nakade K,
    11. Kurihara C,
    12. Obata Y,
    13. Miyake K,
    14. Fukuda K,
    15. Ieda M
    . Induction of cardiomyocyte-like cells in infarct hearts by gene transfer of Gata4, Mef2c, and Tbx5. Circ Res. 2012;111:1147–1156. doi:10.1161/CIRCRESAHA.112.271148.
    OpenUrlAbstract/FREE Full Text
  10. 10.↵
    1. Addis RC,
    2. Ifkovits JL,
    3. Pinto F,
    4. Kellam LD,
    5. Esteso P,
    6. Rentschler S,
    7. Christoforou N,
    8. Epstein JA,
    9. Gearhart JD
    . Optimization of direct fibroblast reprogramming to cardiomyocytes using calcium activity as a functional measure of success. J Mol Cell Cardiol. 2013;60:97–106. doi:10.1016/j.yjmcc.2013.04.004.
    OpenUrlCrossRefPubMed
  11. 11.↵
    1. Christoforou N,
    2. Chellappan M,
    3. Adler AF,
    4. Kirkton RD,
    5. Wu T,
    6. Addis RC,
    7. Bursac N,
    8. Leong KW
    . Transcription factors MYOCD, SRF, Mesp1 and SMARCD3 enhance the cardio-inducing effect of GATA4, TBX5, and MEF2C during direct cellular reprogramming. PLoS One. 2013;8:e63577. doi:10.1371/journal.pone.0063577.
    OpenUrlCrossRefPubMed
  12. 12.↵
    1. Mathison M PGR,
    2. Nasser A,
    3. Lilo S,
    4. Korman M,
    5. Fourman M,
    6. Hackett N,
    7. Shroyer K,
    8. Yang J,
    9. Ma Y,
    10. Crystal RG,
    11. Rosengart TK
    . In vivo cardiac cellular reprogramming efficacy is enhanced by angiogenic preconditioning of the infarcted myocardium with vascular endothelial growth factor. Journal of the American Heart Association. 2013;1
  13. 13.↵
    1. Wada R,
    2. Muraoka N,
    3. Inagawa K,
    4. et al
    . Induction of human cardiomyocyte-like cells from fibroblasts by defined factors. Proc Natl Acad Sci U S A. 2013;110:12667–12672. doi:10.1073/pnas.1304053110.
    OpenUrlAbstract/FREE Full Text
  14. 14.↵
    1. Nam YJ,
    2. Song K,
    3. Luo X,
    4. Daniel E,
    5. Lambeth K,
    6. West K,
    7. Hill JA,
    8. DiMaio JM,
    9. Baker LA,
    10. Bassel-Duby R,
    11. Olson EN
    . Reprogramming of human fibroblasts toward a cardiac fate. Proc Natl Acad Sci USA. 2013;110:5588–5593. doi:10.1073/pnas.1301019110.
    OpenUrlAbstract/FREE Full Text
  15. 15.↵
    1. Fu JD,
    2. Stone NR,
    3. Liu L,
    4. Spencer CI,
    5. Qian L,
    6. Hayashi Y,
    7. Delgado-Olguin P,
    8. Ding S,
    9. Bruneau BG,
    10. Srivastava D
    . Direct reprogramming of human fibroblasts toward a cardiomyocyte-like state. Stem Cell Rep. 2013;1:235–247. doi:10.1016/j.stemcr.2013.07.005.
    OpenUrlCrossRef
  16. 16.↵
    1. Qian L,
    2. Srivastava D
    . Direct cardiac reprogramming: from developmental biology to cardiac regeneration. Circ Res. 2013;113:915–921. doi:10.1161/CIRCRESAHA.112.300625.
    OpenUrlAbstract/FREE Full Text
  17. 17.↵
    1. Martin JF,
    2. Schwarz JJ,
    3. Olson EN
    . Myocyte enhancer factor (MEF) 2C: a tissue-restricted member of the MEF-2 family of transcription factors. Proc Natl Acad Sci U S A. 1993;90:5282–5286.
    OpenUrlAbstract/FREE Full Text
  18. 18.↵
    1. Lin Q,
    2. Schwarz J,
    3. Bucana C,
    4. Olson EN
    . Control of mouse cardiac morphogenesis and myogenesis by transcription factor MEF2C. Science. 1997;276:1404–1407.
    OpenUrlAbstract/FREE Full Text
  19. 19.↵
    1. Jiang Y,
    2. Evans T
    . The Xenopus GATA-4/5/6 genes are associated with cardiac specification and can regulate cardiac-specific transcription during embryogenesis. Dev Biol. 1996;174:258–270. doi:10.1006/dbio.1996.0071.
    OpenUrlCrossRefPubMed
  20. 20.↵
    1. Grépin C,
    2. Robitaille L,
    3. Antakly T,
    4. Nemer M
    . Inhibition of transcription factor GATA-4 expression blocks in vitro cardiac muscle differentiation. Mol Cell Biol. 1995;15:4095–4102.
    OpenUrlAbstract/FREE Full Text
  21. 21.↵
    1. Kuo CT,
    2. Morrisey EE,
    3. Anandappa R,
    4. Sigrist K,
    5. Lu MM,
    6. Parmacek MS,
    7. Soudais C,
    8. Leiden JM
    . GATA4 transcription factor is required for ventral morphogenesis and heart tube formation. Genes Dev. 1997;11:1048–1060.
    OpenUrlAbstract/FREE Full Text
  22. 22.↵
    1. Jiang Y,
    2. Tarzami S,
    3. Burch JB,
    4. Evans T
    . Common role for each of the cGATA-4/5/6 genes in the regulation of cardiac morphogenesis. Dev Genet. 1998;22:263–277. doi:10.1002/(SICI)1520-6408(1998)22:3<263::AID-DVG8>3.0.CO;2-4.
    OpenUrlCrossRefPubMed
  23. 23.↵
    1. Molkentin JD,
    2. Lin Q,
    3. Duncan SA,
    4. Olson EN
    . Requirement of the transcription factor GATA4 for heart tube formation and ventral morphogenesis. Genes Dev. 1997;11:1061–1072.
    OpenUrlAbstract/FREE Full Text
  24. 24.↵
    1. Li QY,
    2. Newbury-Ecob RA,
    3. Terrett JA,
    4. et al
    . Holt-Oram syndrome is caused by mutations in TBX5, a member of the Brachyury (T) gene family. Nat Genet. 1997;15:21–29. doi: 10.1038/ng0197-21.
    OpenUrlCrossRefPubMed
  25. 25.↵
    1. Basson CT,
    2. Bachinsky DR,
    3. Lin RC,
    4. Levi T,
    5. Elkins JA,
    6. Soults J,
    7. Grayzel D,
    8. Kroumpouzou E,
    9. Traill TA,
    10. Leblanc-Straceski J,
    11. Renault B,
    12. Kucherlapati R,
    13. Seidman JG,
    14. Seidman CE
    . Mutations in human TBX5 [corrected] cause limb and cardiac malformation in Holt-Oram syndrome. Nat Genet. 1997;15:30–35. doi:10.1038/ng0197-30.
    OpenUrlCrossRefPubMed
  26. 26.↵
    1. Bruneau BG,
    2. Nemer G,
    3. Schmitt JP,
    4. Charron F,
    5. Robitaille L,
    6. Caron S,
    7. Conner DA,
    8. Gessler M,
    9. Nemer M,
    10. Seidman CE,
    11. Seidman JG
    . A murine model of Holt-Oram syndrome defines roles of the T-box transcription factor Tbx5 in cardiogenesis and disease. Cell. 2001;106:709–721.
    OpenUrlCrossRefPubMed
  27. 27.↵
    1. Liberatore CM,
    2. Searcy-Schrick RD,
    3. Yutzey KE
    . Ventricular expression of tbx5 inhibits normal heart chamber development. Dev Biol. 2000;223:169–180. doi:10.1006/dbio.2000.9748.
    OpenUrlCrossRefPubMed
  28. 28.↵
    1. McCulley DJ,
    2. Black BL
    . Transcription factor pathways and congenital heart disease. Curr Top Dev Biol. 2012;100:253–277. doi:10.1016/B978-0-12-387786-4.00008-7.
    OpenUrlCrossRefPubMed
  29. 29.↵
    1. Brown DD,
    2. Martz SN,
    3. Binder O,
    4. Goetz SC,
    5. Price BM,
    6. Smith JC,
    7. Conlon FL
    . Tbx5 and Tbx20 act synergistically to control vertebrate heart morphogenesis. Development. 2005;132:553–563. doi:10.1242/dev.01596.
    OpenUrlAbstract/FREE Full Text
  30. 30.↵
    1. Harvey RP,
    2. Rosenthal N
    1. Conlon FL,
    2. Yutzey K
    . T-box factors. Harvey RP, Rosenthal N, eds. In: Heart Development and Regeneration. Burlington, MA: Academic Press; 2010:651–672.
  31. 31.↵
    1. Harvey RP,
    2. Rosenthal N
    1. Nemer G,
    2. Nemer M
    . Gata4 in heart development and disease. Harvey RP, Rosenthal N, eds. In: Heart development and regeneration. Burlington, MA: Academic Press; 2010:599–616.
  32. 32.↵
    1. He A,
    2. Shen X,
    3. Ma Q,
    4. Cao J,
    5. von Gise A,
    6. Zhou P,
    7. Wang G,
    8. Marquez VE,
    9. Orkin SH,
    10. Pu WT
    . PRC2 directly methylates GATA4 and represses its transcriptional activity. Genes Dev. 2012;26:37–42. doi:10.1101/gad.173930.111.
    OpenUrlAbstract/FREE Full Text
  33. 33.↵
    1. Harvey RP,
    2. Rosenthal N
    1. Black BL,
    2. Cripps RM
    . Myocyte enhancer factor 2 transcription factors in heart development and disease. Harvey RP, Rosenthal N, eds. In: Heart development. Burlington, MA: Academic Press; 2010:673–699.
  34. 34.↵
    1. Garg V,
    2. Kathiriya IS,
    3. Barnes R,
    4. Schluterman MK,
    5. King IN,
    6. Butler CA,
    7. Rothrock CR,
    8. Eapen RS,
    9. Hirayama-Yamada K,
    10. Joo K,
    11. Matsuoka R,
    12. Cohen JC,
    13. Srivastava D
    . GATA4 mutations cause human congenital heart defects and reveal an interaction with TBX5. Nature. 2003;424:443–447. doi:10.1038/nature01827.
    OpenUrlCrossRefPubMed
  35. 35.
    1. Moskowitz IP,
    2. Pizard A,
    3. Patel VV,
    4. Bruneau BG,
    5. Kim JB,
    6. Kupershmidt S,
    7. Roden D,
    8. Berul CI,
    9. Seidman CE,
    10. Seidman JG
    . The T-Box transcription factor Tbx5 is required for the patterning and maturation of the murine cardiac conduction system. Development. 2004;131:4107–4116. doi:10.1242/dev.01265.
    OpenUrlAbstract/FREE Full Text
  36. 36.
    1. Moskowitz IP,
    2. Kim JB,
    3. Moore ML,
    4. Wolf CM,
    5. Peterson MA,
    6. Shendure J,
    7. Nobrega MA,
    8. Yokota Y,
    9. Berul C,
    10. Izumo S,
    11. Seidman JG,
    12. Seidman CE
    . A molecular pathway including Id2, Tbx5, and Nkx2-5 required for cardiac conduction system development. Cell. 2007;129:1365–1376. doi:10.1016/j.cell.2007.04.036.
    OpenUrlCrossRefPubMed
  37. 37.↵
    1. Carey BW,
    2. Markoulaki S,
    3. Hanna JH,
    4. Faddah DA,
    5. Buganim Y,
    6. Kim J,
    7. Ganz K,
    8. Steine EJ,
    9. Cassady JP,
    10. Creyghton MP,
    11. Welstead GG,
    12. Gao Q,
    13. Jaenisch R
    . Reprogramming factor stoichiometry influences the epigenetic state and biological properties of induced pluripotent stem cells. Cell Stem Cell. 2011;9:588–598. doi:10.1016/j.stem.2011.11.003.
    OpenUrlCrossRefPubMed
  38. 38.↵
    1. Plath K,
    2. Lowry WE
    . Progress in understanding reprogramming to the induced pluripotent state. Nat Rev Genet. 2011;12:253–265. doi:10.1038/nrg2955.
    OpenUrlCrossRefPubMed

Novelty and Significance

What Is Known?

  • Fibroblasts can be directly reprogrammed into cardiac myocyte–like cells (called iCMs).

  • A combination of Gata4 (G), Mef2c (M), and Tbx5 (T) is sufficient to convert fibroblasts into iCMs.

  • Generation of iCM in vivo has been shown to improve heart function and reduce scar size in a mouse myocardial infarction model.

What New Information Does This Article Contribute?

  • Stoichiometry of G, M, T protein expression plays important roles in iCM reprogramming.

  • The desirable G, M, T expression for reprogramming is a relative high level of Mef2c protein expression and low levels of Gata4 and Tbx5 expression.

  • An optimal balance of G, M, T could allow more efficient and complete conversion of fibroblasts into iCMs.

Direct conversion of fibroblasts into cardiac myocyte–like cells (iCMs) using defined factors such as Gata4 (G), Mef2c(M), and Tbx5(T) holds great promise for regenerative medicine. However, the low conversion rate and the considerable variability in iCM generation have hindered further mechanistic studies and optimization for clinical applications. We took advantage of the inherent feature of the polycistronic system and generated a complete set of polycistronic constructs to include all possible splicing orders of G, M, T in a single mRNA. Using this unique tool, we found that varying stoichiometry of G, M, T protein expression resulted in significant differences in iCM reprogramming efficiency and quality. Moreover, we found the optimal stoichiometry for iCM reprogramming to be a relative high level of Mef2c protein expression and low levels of Gata4 and Tbx5 expression. By addition of an antibiotic selection cassette to the optimal G, M, T combination (MGT), we further enriched population of transduced fibroblasts yielding an even higher efficiency of generation of functional iCMs. Our approach provides a valuable platform for further mechanistic studies of direct cardiac reprogramming.

View Abstract

This Issue

Jump to

Article Tools

Share this Article

  • Email
  • Stoichiometry of Gata4, Mef2c, and Tbx5 Influences the Efficiency and Quality of Induced Cardiac Myocyte ReprogrammingNovelty and Significance
    Li Wang, Ziqing Liu, Chaoying Yin, Huda Asfour, Olivia Chen, Yanzhen Li, Nenad Bursac, Jiandong Liu and Li Qian
    Circulation Research. 2015;116:237-244, originally published November 21, 2014
    https://doi.org/10.1161/CIRCRESAHA.116.305547
    del.icio.us logo Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo

Related Articles

Cited By...

Subjects