Advertisement

Cellular and Molecular Life Sciences

, Volume 76, Issue 7, pp 1397–1417 | Cite as

Mitochondrial stress triggers a pro-survival response through epigenetic modifications of nuclear DNA

  • Lía MayorgaEmail author
  • Betiana N. Salassa
  • Diego M. Marzese
  • Mariana A. Loos
  • Hernán D. Eiroa
  • Fabiana Lubieniecki
  • Clara García Samartino
  • Patricia S. Romano
  • María Roqué
Original Article
  • 191 Downloads

Abstract

Mitochondrial dysfunction represents an important cellular stressor and when intense and persistent cells must unleash an adaptive response to prevent their extinction. Furthermore, mitochondria can induce nuclear transcriptional changes and DNA methylation can modulate cellular responses to stress. We hypothesized that mitochondrial dysfunction could trigger an epigenetically mediated adaptive response through a distinct DNA methylation patterning. We studied cellular stress responses (i.e., apoptosis and autophagy) in mitochondrial dysfunction models. In addition, we explored nuclear DNA methylation in response to this stressor and its relevance in cell survival. Experiments in cultured human myoblasts revealed that intense mitochondrial dysfunction triggered a methylation-dependent pro-survival response. Assays done on mitochondrial disease patient tissues showed increased autophagy and enhanced DNA methylation of tumor suppressor genes and pathways involved in cell survival regulation. In conclusion, mitochondrial dysfunction leads to a “pro-survival” adaptive state that seems to be triggered by the differential methylation of nuclear genes.

Keywords

Mitochondrial dysfunction DNA methylation Stress response Autophagy Apoptosis Mitochondrial diseases Survival 

Notes

Acknowledgements

Firstly, we thank the patients and their families for contributing to research in the field. We also acknowledge: Dr. G.A. Truskey (HSkM cells—Duke University, NC, USA), Dr. E. Pusiol and Dr. E Cassone (control samples—Mendoza, Argentina), C. Tagliavini (blood sample shipping—Garrahan), R.Grosso, Dr. I. Cebrian, Dr. E. Muñoz, Dr. D.Croci, Dr. C. Fader, Dr. C. Amaya, Dr. R. Militello and Dr M.I. Colombo (reagents—IHEM, Mendoza, Argentina), Dr. M Galigniana, N. Zgajnar (IIF supplies—IBYME, Buenos Aires, Argentina), Dr. ML. Kotler (reagents - IQUIBICEN, FCEN-UBA, Buenos Aires, Argentina)  and Dr. LS. Mayorga (manuscript revision—IHEM, Mendoza, Argentina).

Author contributions

MR conducted the research project. LM performed the experiments with the help of BS and CG. DM processed big data and did its statistical analysis. LM, ML and HE provided clinical information from the patients. FL did the muscle histological studies from the MT patients. PR designed the autophagy assays. LM and MR wrote the paper. All authors read and approved the final manuscript.

Funding

Servicio Tecnológico de Alto Nivel “Análisis genético molecular”-IHEM, CONICET-Mendoza, Argentina. Secretaría de Ciencia Técnica y Posgrado—UNCuyo.

Compliance with ethical standards

Ethical approval

This project was approved by the Garrahan Children's Hospital (Buenos Aires–Argentina) and Central Hospital (Mendoza–Argentina) investigation and ethics committees. Written informed consent was obtained from patients and controls for research participation and publication.

Conflict of interest

The authors declare no competing interests.

Supplementary material

18_2019_3008_MOESM1_ESM.jpg (1.4 mb)
Supplementary Figure 1: Assessment of mitochondrial dysfunction in Rotenone treatments. S1a:Flow cytometry mitochondrial membrane potential measurements in Rotenone treatments using TMRE: Mitochondrial membrane potential was measured at day 1 and 4 into treatment. Cell population was divided into TMRE (+) and (-) using a threshold based on autofluorescence. Then, the percentage of TMRE (+) cells was relativized to the control’s (DMSO) value. The dose dependent mitochondrial dysfunction was evident at day 1. At day 4, low and high dose treated cells showed the same level of mitochondrial dysfunction (*p<0.05, **p<0.01, ***p<0.001, One-way ANOVA + Dunnett’s Multiple Comparison Test). S1b: Fluorescent microscopy to measure mitochondrial membrane potential changes in Rotenone treatments using TMRE: We show fluorescent images of control cells (DMSO), Rot-10µM (1-day treatment) and the mitochondrial uncoupler CCCP, confirming that Rot decreased mitochondrial membrane potential and that TMRE is functioning in a non-quenching manner. S1c:Evaluation of ROS generation using DCF-DA through flow cytometry: Median fluorescence values relative to the control condition (DMSO) were used for quantification (*p<0.05, One-way ANOVA + Dunnett’s Multiple Comparison Test). Control (DMSO), Rot-0.1µM (1-day), Rot 10 µM (1 and 4-days) treatments were analyzed. Low dose long-term experiments were not tested due to their high mortality rate. (JPEG 1483 kb)
18_2019_3008_MOESM2_ESM.jpg (1.2 mb)
Supplementary Figure 2: Cell death and autophagy assays in Rot 1-day treated cells. S2a. Flow cytometry cell death assays using Annexin V (apoptosis) and TO-PRO-3 (necrosis). These assays were performed with the cells that were attached to the well at day 1 (no supernatants were used due to diverging results). There was no significant difference in the level of cell death between conditions. S2b.LC3 Western Blot Rot-1-day. LC3 forms from low and high dose treated cells are shown next to their corresponding control condition (DMSO). LC3 was increased in the high dose situation, and the contrary happened in the low dose group (*p<0.05, Student’s T-Tests) (JPEG 1234 kb)
18_2019_3008_MOESM3_ESM.jpg (1.1 mb)
Supplementary figure 3. Further characterization of autophagy in Rot treated cells. S3a.LC3 Western Blot with short (4h) Bafilomycin treatment to determine autophagy flow at day 4 of Rotenone treatment. Bafilomycin (Baf) was added for the last 4 hours of the 4-day Rot treatment in control (DMSO) and Rot-10µM treated cells. LC3-II was increased in the Rot-10 µM+Baf condition compared to the same condition without Baf, indicating an enhanced autophagy flow at the expense of autophagosome degradation. (*p<0.05, One-tailed Paired Student’s T-test). S3b.Mitophagy evaluation with fluorescence microscopy. EGFP-LC3 transfected HSkM cells were treated with Rot 10µM for 1 and 4 days. Then, Indirect immunofluorescence for the mitochondrial TOM20 protein was performed. Images were taken under confocal fluorescent microscopy. LC3 recruitment to mitochondria was not evidenced as shown in the merged images. White bar: 10µm. (JPEG 1127 kb)
18_2019_3008_MOESM4_ESM.jpg (1.3 mb)
Supplementary figure 4: ATP and S-adenosylmethionine (SAM) cell content in Rot treatment. S4a.Intracellular ATP content measured with Quinacrine through flow cytometry: A threshold based on autofluorescence was set to divide cell populations as ATP (+) or (-). Quantification was performed in comparison to the control value. ATP content is shown significantly decreased in the high-dose long-term treatment. (**p<0.01, One-way ANOVA+ Dunnett’s Multiple Comparison Test). A control for ATP reduction was performed using CCCP+ Oligomycin. S4b.S-Adenosyl Methionine (SAM) concentration measured through fluorometry. Comparing to a SAM standard curve, SAM concentration from the different conditions was calculated. Quantification is represented as relative to the control condition. (*p<0.05, One-way ANOVA+ Dunnett’s Multiple Comparison Test). (JPEG 1329 kb)
18_2019_3008_MOESM5_ESM.jpg (1.2 mb)
Supplementary figure 5: Corroboration of Methylation ofCAMK2A’s promoter region.CAMK2A methylation detected by methyl-sensitive digestion (HpaII) + absolute quantification with ddPCR. To evaluate CAMK2A’s methylation, each sample was digested with HpaII (that only cuts the non-methylated CpG in CAMK2A’s promoter) and as control, the same sample was digested in parallel with MboI (not able to cut the amplicon). In the upper panel, we show the ddPCR dot plots, where blue dots are considered “positive droplets” and black dots are “negative droplets”. Concentration is calculated based on Poisson’s binomial distribution and are shown in the bottom panel. To quantify the methylation of this CpG site we use the [HpaII amplicon]/ [MboI amplicon]. CM8: control muscle 8, MTM7: MT patient muscle 7; NTC: no-template control (blank). *p<0.05, One-tailed Unpaired Student’s T-test. (JPEG 1237 kb)
18_2019_3008_MOESM6_ESM.jpg (1.9 mb)
Supplementary figure 6: Enrichment plots of the hypermethylated GO gene sets in MT patient muscle determined by GREAT. Significant differentially hypermethylated GO gene sets identified by GREAT analysis of RRBS study in muscle from MT disease patients. Arrows point to gene sets that could be more important in mitochondrial disease skeletal muscle. (JPEG 1970 kb)
18_2019_3008_MOESM7_ESM.jpg (2.3 mb)
Supplementary figure 7: Enrichment plots of the hypomethylated GO gene sets in MT patient muscle determined by GREAT. Significant differentially hypomethylated GO gene sets identified by GREAT analysis of RRBS study in muscle from MT disease patients. Arrows point to gene sets that could be more important in mitochondrial disease skeletal muscle. (JPEG 2358 kb)
18_2019_3008_MOESM8_ESM.xlsx (9.3 mb)
Supplementary file 1: Differentially methylated CpGs (DMCs) and regions (DMRs) in MT patient muscles determined by RRBS. We provide the list of the differentially methylated sites and regions, using the hg19 refGene and CpG island annotation from UCSC (Univeristy of California Santa Cruz) genome browser. The annotations are not mutually exclusive since there is a large overlapping between the different categories. (+) differential methylations read as hypermethylated in the MTM state, (-) differential methylations read as hypomethylated in the MTM state. (XLSX 9479 kb)
18_2019_3008_MOESM9_ESM.xlsx (33 kb)
Supplementary file 2: Metascape analysis of the differentially methylated promoters that have “membership” in apoptosis, autophagy or DNA methylation. We provide the list of genes that were differentially methylated in the promoter category and have “membership” in apoptosis, autophagy or DNA methylation when analyzed with Metascape. Sheets are separated as DM (+) prom: promoters differentially hypermethylated in MT patient muscle, DM (-) prom: promoters differentially hypomethylated in MT patient muscle, associated to each term. The GO annotations by which the genes are members of the different terms are provided in the last column. (XLSX 32 kb)
18_2019_3008_MOESM10_ESM.xlsx (10 kb)
Supplementary file 3: Differentially methylated and expressed genes in MT muscle. Comparison of our RRBS analysis + public expression data sets: GSE42986 - GSE1462. From the 615 differentially expressed genes, 116 were associated with the differentially methylated genomic regions (Hypergeometric test; RF: 0.7, p < 1.6e-04) (XLSX 10 kb)

References

  1. 1.
    Vojta A, Zoldoš V (2013) Adaptation or malignant transformation: the two faces of epigenetically mediated response to stress. Biomed Res Int.  https://doi.org/10.1155/2013/954060 Google Scholar
  2. 2.
    Mizushima N, Komatsu M (2011) Autophagy: renovation of cells and tissues. Cell 147:728–741CrossRefGoogle Scholar
  3. 3.
    He C, Klionsky DJ (2009) Regulation mechanisms and signaling pathways of autophagy. Annu Rev Genet 43:67–93.  https://doi.org/10.1146/annurev-genet-102808-114910 CrossRefGoogle Scholar
  4. 4.
    Mancini D, Singh S, Ainsworth P, Rodenhiser D (1997) Constitutively methylated CpG dinucleotides as mutation hot spots in the retinoblastoma gene (RB1). Am J Hum Genet 61:80–87.  https://doi.org/10.1086/513898 CrossRefGoogle Scholar
  5. 5.
    Cooper DN, Youssoufian H (1988) The CpG dinucleotide and human genetic disease. Hum Genet 78:151–155CrossRefGoogle Scholar
  6. 6.
    Ma C, Niu R, Huang T et al (2018) N6-methyldeoxyadenine is a transgenerational epigenetic signal for mitochondrial stress adaptation. Nat Cell Biol.  https://doi.org/10.1038/s41556-018-0238-5 Google Scholar
  7. 7.
    Kwak SH, Park KS, Lee K-U, Lee HK (2010) Mitochondrial metabolism and diabetes. J Diabetes Investig 1:161–169.  https://doi.org/10.1111/j.2040-1124.2010.00047.x CrossRefGoogle Scholar
  8. 8.
    Hanahan D, Weinberg RA (2011) Hallmarks of cancer: the next generation. Cell 144:646–674.  https://doi.org/10.1016/j.cell.2011.02.013 CrossRefGoogle Scholar
  9. 9.
    Lin MT, Beal MF (2006) Mitochondrial dysfunction and oxidative stress in neurodegenerative diseases. Nature 443:787–795.  https://doi.org/10.1038/nature05292 CrossRefGoogle Scholar
  10. 10.
    Zhang Z, Falk MJ (2014) Integrated transcriptome analysis across mitochondrial disease etiologies and tissues improves understanding of common cellular adaptations to respiratory chain dysfunction. Int J Biochem Cell Biol 50:16–111.  https://doi.org/10.1016/j.biocel.2014.02.012 Google Scholar
  11. 11.
    Crimi M (2005) Skeletal muscle gene expression profiling in mitochondrial disorders. FASEB J.  https://doi.org/10.1096/fj.04-3045fje Google Scholar
  12. 12.
    Cízková A, Stránecký V, Ivánek R et al (2008) Development of a human mitochondrial oligonucleotide microarray (h-MitoArray) and gene expression analysis of fibroblast cell lines from 13 patients with isolated F1Fo ATP synthase deficiency. BMC Genom 9:38.  https://doi.org/10.1186/1471-2164-9-38 CrossRefGoogle Scholar
  13. 13.
    Colombo M, Kruhoeffer M, Gregersen S et al (2006) Energy restriction prevents the development of type 2 diabetes in Zucker diabetic fatty rats: coordinated patterns of gene expression for energy metabolism in insulin-sensitive tissues and pancreatic islets determined by oligonucleotide microarray analysi. Metabolism 55:43–52.  https://doi.org/10.1016/j.metabol.2005.07.005 CrossRefGoogle Scholar
  14. 14.
    Leister D (2012) Retrograde signaling in plants: from simple to complex scenarios. Front Plant Sci 3:1–9.  https://doi.org/10.3389/fpls.2012.00135 Google Scholar
  15. 15.
    Liu Z, Butow RA (2006) Mitochondrial retrograde signaling. Annu Rev Genet 40:159–185.  https://doi.org/10.1146/annurev.genet.40.110405.090613 CrossRefGoogle Scholar
  16. 16.
    Jazwinski SM, Kriete A (2012) The yeast retrograde response as a model of intracellular signaling of mitochondrial dysfunction. Front Physiol 3:1–12CrossRefGoogle Scholar
  17. 17.
    Ždralević M., Guaragnella N., Giannattasio S (2015) Yeast as a tool to study mitochondrial retrograde pathway en route to cell stress response. In: Weissig V, Edeas M (eds) Mitochondrial Medicine. Methods in Molecular Biology, vol 1265. Humana Press, New York, NYGoogle Scholar
  18. 18.
    Feinberg A, Tycko B (2004) The history of cancer epigenetics. Nat Rev 4:143CrossRefGoogle Scholar
  19. 19.
    Feinberg AP (2007) Phenotypic plasticity and the epigenetics of human disease. Nature 447:433–440.  https://doi.org/10.1038/nature05919 CrossRefGoogle Scholar
  20. 20.
    Zhou W, Tian D, He J et al (2016) Repeated PM2.5 exposure inhibits BEAS-2B cell P53 expression through ROS-Akt-DNMT3B pathway-mediated promoter hypermethylation. Oncotarget 7:20691–20703.  https://doi.org/10.18632/oncotarget.7842 Google Scholar
  21. 21.
    Afanas’ev I (2014) New nucleophilic mechanisms of ros-dependent epigenetic modifications: comparison of aging and cancer. Aging Dis 5:52–62.  https://doi.org/10.14336/AD.2014.050052 CrossRefGoogle Scholar
  22. 22.
    Peng L, Yuan Z, Ling H et al (2011) SIRT1 deacetylates the DNA methyltransferase 1 (DNMT1) protein and alters its activities. Mol Cell Biol 31:4720–4734.  https://doi.org/10.1128/MCB.06147-11 CrossRefGoogle Scholar
  23. 23.
    Kreuz S, Fischle W (2016) Oxidative stress signaling to chromatin in health and disease. Epigenomics 8:843–862.  https://doi.org/10.2217/epi-2016-0002 CrossRefGoogle Scholar
  24. 24.
    MacLeod AR, Rouleau J, Szyf M (1995) Regulation of DNA methylation by the Ras signaling pathway. J Biol Chem 270:11327–11337CrossRefGoogle Scholar
  25. 25.
    Cheng CS, El-abd Y, Bui K et al (2018) Conditions that promote primary human skeletal myoblast culture and muscle differentiation in vitro. Am J Physiol Cell Physiol.  https://doi.org/10.1152/ajpcell.00179.2013 Google Scholar
  26. 26.
    Bernier FP, Boneh A, Dennett X et al (2002) Diagnostic criteria for respiratory chain disorders in adults and children. Neurology 59:1406–1411.  https://doi.org/10.1212/01.WNL.0000033795.17156.00 CrossRefGoogle Scholar
  27. 27.
    Nygren AOH, Ameziane N, Duarte HMB et al (2005) Methylation-specific MLPA (MS-MLPA): simultaneous detection of CpG methylation and copy number changes of up to 40 sequences. Nucleic Acids Res 33:1–9.  https://doi.org/10.1093/nar/gni127 CrossRefGoogle Scholar
  28. 28.
    Jühling F, Kretzmer H, Bernhart SH et al. (2016) Metilene: fast and sensitive calling of differentially methylated regions from bisulfite sequencing data. Genome Res 26:256–62.  https://doi.org/10.1101/gr.196394.115 CrossRefGoogle Scholar
  29. 29.
    McLean CY, Bristor D, Hiller M et al (2010) GREAT improves functional interpretation of cis-regulatory regions. Nat Biotechnol 28:495–501.  https://doi.org/10.1038/nbt.1630 CrossRefGoogle Scholar
  30. 30.
    Al Emran A, Marzese DM, Menon DR et al (2018) Distinct histone modifications denote early stress-induced drug tolerance in cancer. Oncotarget 9:8206–8222.  https://doi.org/10.18632/oncotarget.23654 CrossRefGoogle Scholar
  31. 31.
    Zhang Z, Tsukikawa M, Peng M et al (2013) Primary respiratory chain disease causes tissue-specific dysregulation of the global transcriptome and nutrient-sensing signaling network. PLoS One.  https://doi.org/10.1371/journal.pone.0069282 Google Scholar
  32. 32.
    Chen Y-R, Zweier JL (2014) Cardiac mitochondria and reactive oxygen species generation. Circ Res 114:524–537.  https://doi.org/10.1161/CIRCRESAHA.114.300559 CrossRefGoogle Scholar
  33. 33.
    Li J, Lin L, Du L et al (2014) Radioprotective effect of a pan-caspase inhibitor in a novel model of radiation injury to the nucleus of the abducens nerve. Mol Med Rep 10:1433–1437.  https://doi.org/10.3892/mmr.2014.2334 CrossRefGoogle Scholar
  34. 34.
    Redza-Dutordoir M, Averill-Bates DA (2016) Activation of apoptosis signalling pathways by reactive oxygen species. Biochim Biophys Acta Mol Cell Res 1863:2977–2992.  https://doi.org/10.1016/J.BBAMCR.2016.09.012 CrossRefGoogle Scholar
  35. 35.
    Kabeya Y, Mizushima N, Ueno T et al (2000) LC3, a mammalian homologue of yeast Apg8p, is localized in autophagosome membranes after processing. EMBO J 19:5720–5728.  https://doi.org/10.1093/emboj/19.21.5720 CrossRefGoogle Scholar
  36. 36.
    Yamamoto A, Tagawa Y, Yoshimori T et al (1998) Bafilomycin A1 prevents maturation of autophagic vacuoles by inhibiting fusion between autophagosomes and lysosomes in rat hepatoma cell line, H-4-II-E cells. Cell Struct Funct 23:33–42CrossRefGoogle Scholar
  37. 37.
    Rubinsztein DC, Cuervo AM, Ravikumar B et al (2009) In search of an “autophagomometer”. Autophagy 5:585–589.  https://doi.org/10.4161/auto.5.5.8823 CrossRefGoogle Scholar
  38. 38.
    Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition). PubMed. NCBI. https://www.ncbi.nlm.nih.gov/pubmed/26799652. Accessed 15 Dec 2018
  39. 39.
    Auré K, Fayet G, Leroy JP et al (2006) Apoptosis in mitochondrial myopathies is linked to mitochondrial proliferation. Brain 129:1249–1259.  https://doi.org/10.1093/brain/awl061 CrossRefGoogle Scholar
  40. 40.
    Tripathi S, Pohl MO, Zhou Y et al (2015) Meta- and orthogonal integration of influenza “OMICs” data defines a role for UBR4 in virus budding. Cell Host Microbe 18:723–735.  https://doi.org/10.1016/j.chom.2015.11.002 CrossRefGoogle Scholar
  41. 41.
    Yuan HX, Xiong Y, Guan KL (2013) Nutrient sensing, metabolism, and cell growth control. Mol Cell 49:379–387.  https://doi.org/10.1016/j.molcel.2013.01.019 CrossRefGoogle Scholar
  42. 42.
    Turner BM (2000) Histone acetylation and an epigenetic code. BioEssays 22:836–845.  https://doi.org/10.1002/1521-1878(200009)22:9%3c836:AID-BIES9%3e3.0.CO;2-X CrossRefGoogle Scholar
  43. 43.
    Rose NR, Klose RJ (2014) Understanding the relationship between DNA methylation and histone lysine methylation. Biochim Biophys Acta Gene Regul Mech 1839:1362–1372.  https://doi.org/10.1016/j.bbagrm.2014.02.007 CrossRefGoogle Scholar
  44. 44.
    Wang L, Cano M, Handa JT (2014) p62 provides dual cytoprotection against oxidative stress in the retinal pigment epithelium. Biochim Biophys Acta 1843:1248–1258.  https://doi.org/10.1016/j.bbamcr.2014.03.016 CrossRefGoogle Scholar
  45. 45.
    Jamilloux Y, Lagrange B, Di Micco A et al (2018) p62/SQSTM1 is a caspase-1 substrate. J Biol Chem.  https://doi.org/10.1074/jbc.ra117.000435 Google Scholar
  46. 46.
    Hardie DG (2003) Minireview: the AMP-activated protein kinase cascade: the key sensor of cellular energy status. Endocrinology 144:5179–5183.  https://doi.org/10.1210/en.2003-0982 CrossRefGoogle Scholar
  47. 47.
    Circu ML, Aw TY (2010) Reactive oxygen species, cellular redox systems, and apoptosis. Free Radic Biol Med 48:749–762.  https://doi.org/10.1016/j.freeradbiomed.2009.12.022 CrossRefGoogle Scholar
  48. 48.
    Kaminskyy VO, Zhivotovsky B (2014) Free radicals in cross talk between autophagy and apoptosis. Antioxid Redox Signal 21:86–102.  https://doi.org/10.1089/ars.2013.5746 CrossRefGoogle Scholar
  49. 49.
    Wang Q, Guo W, Hao B et al (2016) Mechanistic study of TRPM2-Ca2+-CAMK2-BECN1 signaling in oxidative stress induced autophagy inhibition. Autophagy.  https://doi.org/10.1080/15548627.2016.1187365 Google Scholar
  50. 50.
    Eblen ST (2018) Extracellular-regulated kinases: signaling from Ras to ERK substrates to control biological outcomes. Adv Cancer Res 138:99–142.  https://doi.org/10.1016/BS.ACR.2018.02.004 CrossRefGoogle Scholar
  51. 51.
    Eijkelenboom A, Burgering BMT (2013) FOXOs: signalling integrators for homeostasis maintenance. Nat Rev Mol Cell Biol 14:83–97.  https://doi.org/10.1038/nrm3507 CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Lía Mayorga
    • 1
    Email author
  • Betiana N. Salassa
    • 1
    • 6
  • Diego M. Marzese
    • 2
  • Mariana A. Loos
    • 3
  • Hernán D. Eiroa
    • 3
  • Fabiana Lubieniecki
    • 3
  • Clara García Samartino
    • 4
  • Patricia S. Romano
    • 1
    • 4
  • María Roqué
    • 1
    • 5
  1. 1.Instituto de Histología y Embriología de Mendoza (IHEM, Universidad Nacional de Cuyo, CONICET)-Centro Universitario UNCuyoMendozaArgentina
  2. 2.John Wayne Cancer InstituteSanta MonicaUSA
  3. 3.Hospital de Pediatría J.P. GarrahanBuenos AiresArgentina
  4. 4.Facultad de Ciencias MédicasUniversidad Nacional de Cuyo-Centro Universitario UNCuyoMendozaArgentina
  5. 5.Facultad de Ciencias Exactas y NaturalesUniversidad Nacional de Cuyo-Centro Universitario UNCuyoMendozaArgentina
  6. 6.Facultad de OdontologíaUniveridad Nacional de Cuyo- Centro Universitario UNCuyoMendozaArgentina

Personalised recommendations