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MicroRNA mir-34 provides robustness to environmental stress response via the DAF-16 network in C. elegans

Isik, Meltem, Blackwell, T. Keith, Berezikov, Eugene·2016·Scientific Reports
DOI 10.1038/srep36766PMC PMC5131338
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Abstract

Diverse stresses and aging alter expression levels of microRNAs, suggesting a role for these posttranscriptional regulators of gene expression in stress modulation and longevity. Earlier studies demonstrated a central role for the miR-34 family in promoting cell cycle arrest and cell death following stress in human cells. However, the biological significance of this response was unclear. Here we show that in C. elegans mir-34 upregulation is necessary for developmental arrest, correct morphogenesis, and adaptation to a lower metabolic state to protect animals against stress-related damage. Either deletion or overexpression of mir-34 lead to an impaired stress response, which can largely be explained by perturbations in DAF-16/FOXO target gene expression. We demonstrate that mir-34 expression is regulated by the insulin signaling pathway via a negative feedback loop between miR-34 and DAF-16/FOXO. We propose that mir-34 provides robustness to stress response programs by controlling noise in the DAF-16/FOXO-regulated gene network.

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Figure 1 Pmir-342.2kb::gfp is expressed in various tissues during development of C. elegans and its expression is upregulated in dauers. (A) Expression of Pmir-342.2kb::gfp reporter at different stages of animal development. (B) Changes in the expression pattern of Pmir-342.2kb::gfp from predauer to dauer stage and detailed expression pattern of Pmir-342.2kb::gfp at dauer stage. (C) Elevated Pmir-342.2kb::gfp expression is associated with the dauer larva gene expression program. i, WT, ii and iii, insulin-like signaling pathway mutants: daf-2(e1370), pdk-1(sa680), respectively. iv, v and vi, TGF-β signaling pathway mutants: daf-1(e1287), daf-3(mgDf90), daf-7(e1372). vii, DAF-12/NHR signaling: daf-9(e1406). viii, daf-16(mu86);daf-7(e1372). (D) Pmir-342.2kb::gfp expression levels are increase by various stress factors. (i) Three days old animal grown at 20 °C. (ii) One day old animal grown at 25 °C, (iii) Three days old animal starved for two days. (iv) Ten days old animal grown at 20 °C.
Figure 1 Pmir-342.2kb::gfp is expressed in various tissues during development of C. elegans and its expression is upregulated in dauers. (A) Expression of Pmir-342.2kb::gfp reporter at different stages of animal development. (B) Changes in the expression pattern of Pmir-342.2kb::gfp from predauer to dauer stage and detailed expression pattern of Pmir-342.2kb::gfp at dauer stage. (C) Elevated Pmir-342.2kb::gfp expression is associated with the dauer larva gene expression program. i, WT, ii and iii, insulin-like signaling pathway mutants: daf-2(e1370), pdk-1(sa680), respectively. iv, v and vi, TGF-β signaling pathway mutants: daf-1(e1287), daf-3(mgDf90), daf-7(e1372). vii, DAF-12/NHR signaling: daf-9(e1406). viii, daf-16(mu86);daf-7(e1372). (D) Pmir-342.2kb::gfp expression levels are increase by various stress factors. (i) Three days old animal grown at 20 °C. (ii) One day old animal grown at 25 °C, (iii) Three days old animal starved for two days. (iv) Ten days old animal grown at 20 °C.CC BY · PubMed Central
Figure 2 Native levels of mir-34 expression are required for correct morphogenesis of dauers and dauer survival. (A) qPCR confirmation of miR-34 expression levels in the rescue and the overexpression strains. Error bars represent SD. N = 300, **P < 0.01, ***P < 0.0001, unpaired two-tailed t test. (B) Differences in body sizes of WT, mir-34(gk437), mir-34OE and rescue strains. Error bars represent SD. *P < 0.05, **P < 0.01, ****P < 0.0001, unpaired two-tailed t test. (C) Differences in dauer survival of mir-34(gk437) and mir-34OE dauers at 20 °C and 25 °C. Error bars represent SD. 250 animals are scored per experiment, 3 replicate experiments per condition. Statistical significance of differences between N2 and other genetic backgrounds at a given temperature is calculated using unpaired two-tailed t test. **P < 0.01, ***P < 0.001, ****P < 0.0001. (D) Differences in dauer formation rates of mir-34(gk437) dauers in daf-2(e1370) and daf-7(e1372) backgrounds. Error bars represent SD. 250 animals were assayed per condition, experiments were performed in triplicate. Statistical significance is calculated by two-tailed unpaired t test. **P < 0.01.
Figure 2 Native levels of mir-34 expression are required for correct morphogenesis of dauers and dauer survival. (A) qPCR confirmation of miR-34 expression levels in the rescue and the overexpression strains. Error bars represent SD. N = 300, **P < 0.01, ***P < 0.0001, unpaired two-tailed t test. (B) Differences in body sizes of WT, mir-34(gk437), mir-34OE and rescue strains. Error bars represent SD. *P < 0.05, **P < 0.01, ****P < 0.0001, unpaired two-tailed t test. (C) Differences in dauer survival of mir-34(gk437) and mir-34OE dauers at 20 °C and 25 °C. Error bars represent SD. 250 animals are scored per experiment, 3 replicate experiments per condition. Statistical significance of differences between N2 and other genetic backgrounds at a given temperature is calculated using unpaired two-tailed t test. **P < 0.01, ***P < 0.001, ****P < 0.0001. (D) Differences in dauer formation rates of mir-34(gk437) dauers in daf-2(e1370) and daf-7(e1372) backgrounds. Error bars represent SD. 250 animals were assayed per condition, experiments were performed in triplicate. Statistical significance is calculated by two-tailed unpaired t test. **P < 0.01.CC BY · PubMed Central
Figure 3 mir-34 is regulated by DAF-16 and targets daf-16. (A) Upstream region of mir-34 pre-miRNA (in red) showing sequences used as promoters in Pmir34::gfp strains in blue and location of DAF-12, DAF-16 and PQM-1 modENCODE ChIP-seq peaks in black. (B) Expression patters of Pmir34::gfp strains with different promoter length. (C) Last exon and 3′UTR of daf-16 transcript (in blue), location of AGO-CLIP regions (in green) from Grosswendt et al.39 and highly-scoring MIRZA miR-34 target prediction (in red). (D). Detailed MIRZA alignment of the miR-34 target in daf-16 mRNA shown in panel C. (E) Expression of DAF-16::GFP is elevated with temperature in amphid neurons (indicated by arrows) in mir-34(gk437) mutants but not in wild-type animals. (F) Quantification of fluorescence density of DAF-16::GFP in wild-type and mir-34(gk437) background at 27.5 °C. Data are expressed in arbitrary fluorescence units. Error bars represent SD. Statistical significance is calculated by two-tailed unpaired t test. *P < 0.05.
Figure 3 mir-34 is regulated by DAF-16 and targets daf-16. (A) Upstream region of mir-34 pre-miRNA (in red) showing sequences used as promoters in Pmir34::gfp strains in blue and location of DAF-12, DAF-16 and PQM-1 modENCODE ChIP-seq peaks in black. (B) Expression patters of Pmir34::gfp strains with different promoter length. (C) Last exon and 3′UTR of daf-16 transcript (in blue), location of AGO-CLIP regions (in green) from Grosswendt et al.39 and highly-scoring MIRZA miR-34 target prediction (in red). (D). Detailed MIRZA alignment of the miR-34 target in daf-16 mRNA shown in panel C. (E) Expression of DAF-16::GFP is elevated with temperature in amphid neurons (indicated by arrows) in mir-34(gk437) mutants but not in wild-type animals. (F) Quantification of fluorescence density of DAF-16::GFP in wild-type and mir-34(gk437) background at 27.5 °C. Data are expressed in arbitrary fluorescence units. Error bars represent SD. Statistical significance is calculated by two-tailed unpaired t test. *P < 0.05.CC BY · PubMed Central
Figure 4 Effects of mir-34 deletion or overexpression on gene expression under various conditions. (A–E) Number of differentially expressed genes (EXPR) and enrichment statistics for several regulatory signals. DAF-16, PQM-1 and MDL-1, occupancy in promoter regions based on modENCODE ChIP-seq data for respective transcription factors. DBE, DAE, presence of the respective sequence motives in the promoter regions. MIRZA, miR-34 target predictions calculated by MIRZA. ALG – presence of AGO-CLIP regions from Grosswendt et al.39, overlapping miR-34 MIRZA predictions. The first number in each column relates to genes expressed higher in the first conditions, the second – to the genes expressed higher in the second condition. Up- and down- arrows represent significant over- and under-representation respectively. Statistical significance is calculated by Pearson’s Chi-squared test with Yates’ continuity correction and adjusted for multiple comparisons by Bonferroni correction. Adjusted P < 0.01 was considered as significant. ns – not significant. (F,G) Overexpression of miR-34 at 20 °C shifts gene expression profile in the same direction as temperature stress. (H) Genes that behave differently in WT and mir-34 mutants upon temperature stress.
Figure 4 Effects of mir-34 deletion or overexpression on gene expression under various conditions. (A–E) Number of differentially expressed genes (EXPR) and enrichment statistics for several regulatory signals. DAF-16, PQM-1 and MDL-1, occupancy in promoter regions based on modENCODE ChIP-seq data for respective transcription factors. DBE, DAE, presence of the respective sequence motives in the promoter regions. MIRZA, miR-34 target predictions calculated by MIRZA. ALG – presence of AGO-CLIP regions from Grosswendt et al.39, overlapping miR-34 MIRZA predictions. The first number in each column relates to genes expressed higher in the first conditions, the second – to the genes expressed higher in the second condition. Up- and down- arrows represent significant over- and under-representation respectively. Statistical significance is calculated by Pearson’s Chi-squared test with Yates’ continuity correction and adjusted for multiple comparisons by Bonferroni correction. Adjusted P < 0.01 was considered as significant. ns – not significant. (F,G) Overexpression of miR-34 at 20 °C shifts gene expression profile in the same direction as temperature stress. (H) Genes that behave differently in WT and mir-34 mutants upon temperature stress.CC BY · PubMed Central

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Research works printed but not linked: 73

  1. Leung, A. K. L. & Sharp, P. A. MicroRNA functions in stress responses. Mol. Cell 40, 205–215 (2010). · DOI · Paper page
  2. Bushati, N. & Cohen, S. M. MicroRNA Functions. Annual Review of Cell and Developmental Biology 23, 175–205 (2007). · DOI · Paper page
  3. Leaman, D. et al. Antisense-mediated depletion reveals essential and specific functions of microRNAs in Drosophila development. Cell 121, 1097–1108 (2005). · DOI
  4. Li, X., Cassidy, J. J., Reinke, C. A., Fischboeck, S. & Carthew, R. W. A microRNA imparts robustness against environmental fluctuation during development. Cell 137, 273–282 (2009). · DOI · Paper page
  5. van Rooij, E. et al. Control of stress-dependent cardiac growth and gene expression by a microRNA. Science 316, 575–579 (2007). · DOI
  6. Flynt, A. S. et al. miR-8 microRNAs regulate the response to osmotic stress in zebrafish embryos. J Cell Biol 185, 115–127 (2009). · DOI
  7. Garcia-Segura, L. et al. High-Throughput Profiling of Caenorhabditis elegans Starvation-Responsive microRNAs. Plos One 10, e0142262 (2015). · DOI
  8. Ibáñez-Ventoso, C. et al. Modulated microRNA expression during adult lifespan in Caenorhabditis elegans. Aging Cell 5, 235–246 (2006). · DOI
  9. Karp, X., Hammell, M., Ow, M. C. & Ambros, V. Effect of life history on microRNA expression during C. elegans development. RNA 17, 639–651 (2011). · DOI
  10. de Lencastre, A. et al. MicroRNAs both promote and antagonize longevity in C. elegans. Curr. Biol. 20, 2159–2168 (2010). · DOI
  11. Khanna, A., Muthusamy, S., Liang, R., Sarojini, H. & Wang, E. Gain of survival signaling by down-regulation of three key miRNAs in brain of calorie-restricted mice. Aging (Albany NY) 3, 223–236 (2011). · DOI
  12. Li, X., Khanna, A., Li, N. & Wang, E. Circulatory miR34a as an RNAbased, noninvasive biomarker for brain aging. Aging (Albany NY) 3, 985–1002 (2011). · DOI
  13. Liu, N. et al. The microRNA miR-34 modulates ageing and neurodegeneration in Drosophila. Nature 482, 519–523 (2012). · DOI · Paper page
  14. Achari, C., Winslow, S., Ceder, Y. & Larsson, C. Expression of miR-34c induces G2/M cell cycle arrest in breast cancer cells. BMC Cancer 14, (2014). · DOI
  15. Chakraborty, C., George Priya Doss, C. & Bandyopadhyay, S. miRNAs in insulin resistance and diabetes-associated pancreatic cancer: the ‘minute and miracle’ molecule moving as a monitor in the ‘genomic galaxy’. Curr Drug Targets 14, 1110–1117 (2013). · DOI
  16. Chen, F. & Hu, S.-J. Effect of microRNA-34a in cell cycle, differentiation, and apoptosis: a review. J. Biochem. Mol. Toxicol. 26, 79–86 (2012). · DOI
  17. Cole, K. A. et al. A Functional Screen Identifies miR-34a as a Candidate Neuroblastoma Tumor Suppressor Gene. Mol Cancer Res 6, 735–742 (2008). · DOI
  18. Esguerra, J. L. S., Mollet, I. G., Salunkhe, V. A., Wendt, A. & Eliasson, L. Regulation of Pancreatic Beta Cell Stimulus-Secretion Coupling by microRNAs. Genes (Basel) 5, 1018–1031 (2014). · DOI
  19. Misso, G. et al. Mir-34: A New Weapon Against Cancer? Mol Ther Nucleic Acids 3, e194 (2014). · DOI
  20. Rokavec, M., Li, H., Jiang, L. & Hermeking, H. The p53/miR-34 axis in development and disease. J Mol Cell Biol 6, 214–230 (2014). · DOI
  21. Rottiers, V. & Näär, A. M. MicroRNAs in metabolism and metabolic disorders. Nat Rev Mol Cell Biol 13, 239–250 (2012). · DOI · Paper page
  22. Xu, Y. et al. A metabolic stress-inducible miR-34a-HNF4α pathway regulates lipid and lipoprotein metabolism. Nat Commun 6, 7466 (2015). · DOI
  23. Soni, K. et al. miR-34 is maternally inherited in Drosophila melanogaster and Danio rerio. Nucleic Acids Res. 41, 4470–4480 (2013). · DOI
  24. Kato, M. et al. The mir-34 microRNA is required for the DNA damage response in vivo in C. elegans and in vitro in human breast cancer cells. Oncogene 28, 2419–2424 (2009). · DOI
  25. Murakami, S. & Johnson, T. E. A Genetic Pathway Conferring Life Extension and Resistance to UV Stress in Caenorhabditis elegans. Genetics 143, 1207–1218 (1996). · DOI
  26. Riddle, D. L. & Albert, P. S. In C. elegans II (eds. Riddle, D. L., Blumenthal, T., Meyer, B. J. & Priess, J. R. ) (Cold Spring Harbor Laboratory Press, 1997).
  27. Karp, X., Hammell, M., Ow, M. C. & Ambros, V. Effect of life history on microRNA expression during C. elegans development. RNA 17, 639–651 (2011). · DOI
  28. Smith-Vikos, T. & Slack, F. J. MicroRNAs and their roles in aging. J Cell Sci 125, 7–17 (2012). · DOI · Paper page
  29. Ogg, S. et al. The Fork head transcription factor DAF-16 transduces insulin-like metabolic and longevity signals in C. elegans. Nature 389, 994–999 (1997). · DOI
  30. Lin, K. daf-16: An HNF-3/forkhead Family Member That Can Function to Double the Life-Span of Caenorhabditis elegans. Science 278, 1319–1322 (1997). · DOI
  31. Tepper, R. G. et al. PQM-1 Complements DAF-16 as a Key Transcriptional Regulator of DAF-2-Mediated Development and Longevity. Cell 154, 676–690 (2013). · DOI
  32. Contrino, S. et al. modMine: flexible access to modENCODE data. Nucleic Acids Res 40, D1082–D1088 (2012). · DOI
  33. Henderson, S. T. & Johnson, T. E. daf-16 integrates developmental and environmental inputs to mediate aging in the nematode Caenorhabditis elegans. Current Biology 11, 1975–1980 (2001). · DOI
  34. Wolf, M., Nunes, F., Henkel, A., Heinick, A. & Paul, R. J. The MAP kinase JNK-1 of Caenorhabditis elegans: Location, activation, and influences over temperature-dependent insulin-like signaling, stress responses, and fitness. J. Cell. Physiol. 214, 721–729 (2008). · DOI
  35. Leiser, S. F., Begun, A. & Kaeberlein, M. HIF-1 modulates longevity and healthspan in a temperature-dependent manner. Aging Cell 10, 318–326 (2011). · DOI
  36. Grosswendt, S. et al. Unambiguous identification of miRNA:target site interactions by different types of ligation reactions. Mol. Cell 54, 1042–1054 (2014). · DOI · Paper page
  37. Khorshid, M., Hausser, J., Zavolan, M. & van Nimwegen, E. A biophysical miRNA-mRNA interaction model infers canonical and noncanonical targets. Nature Methods 10, 253–255 (2013). · DOI
  38. Murphy, C. T. et al. Genes that act downstream of DAF-16 to influence the lifespan of Caenorhabditis elegans. Nature 424, 277–283 (2003). · DOI · Paper page
  39. Jones, S. J. et al. Changes in gene expression associated with developmental arrest and longevity in Caenorhabditis elegans. Genome Res. 11, 1346–1352 (2001). · DOI
  40. Wang, J. & Kim, S. K. Global analysis of dauer gene expression in Caenorhabditis elegans. Development 130, 1621–1634 (2003). · DOI
  41. Malmevik, J. et al. Distinct cognitive effects and underlying transcriptome changes upon inhibition of individual miRNAs in hippocampal neurons. Scientific Reports 6, 19879 (2016). · DOI
  42. Zhou, K. I., Pincus, Z. & Slack, F. J. Longevity and stress in Caenorhabditis elegans. Aging (Albany NY) 3, 733–753 (2011). · DOI
  43. Riesen, M. et al. MDL-1, a growth- and tumor-suppressor, slows aging and prevents germline hyperplasia and hypertrophy in C. elegans. Aging (Albany NY) 6, 98–117 (2014). · DOI
  44. Yuan, J., Tirabassi, R. S., Bush, A. B. & Cole, M. D. The C. elegans MDL-1 and MXL-1 proteins can functionally substitute for vertebrate MAD and MAX. Oncogene 17, 1109–1118 (1998). · DOI
  45. Johnson, D. W. et al. The Caenorhabditis elegans Myc-Mondo/Mad complexes integrate diverse longevity signals. PLoS Genet. 10, e1004278 (2014). · DOI
  46. Hausser, J. & Zavolan, M. Identification and consequences of miRNA-target interactions — beyond repression of gene expression. Nat Rev Genet 15, 599–612 (2014). · DOI
  47. Selbach, M. et al. Widespread changes in protein synthesis induced by microRNAs. Nature 455, 58–63 (2008). · DOI · Paper page
  48. Baek, D. et al. The impact of microRNAs on protein output. Nature 455, 64–71 (2008). · DOI · Paper page
  49. Djuranovic, S., Nahvi, A. & Green, R. miRNA-Mediated Gene Silencing by Translational Repression Followed by mRNA Deadenylation and Decay. Science 336, 237–240 (2012). · DOI · Paper page
  50. He, L. et al. A microRNA component of the p53 tumour suppressor network. Nature 447, 1130–1134 (2007). · DOI · Paper page
  51. Raver-Shapira, N. et al. Transcriptional Activation of miR-34a Contributes to p53-Mediated Apoptosis. Molecular Cell 26, 731–743 (2007). · DOI
  52. Burke, S. L., Hammell, M. & Ambros, V. Robust Distal Tip Cell Pathfinding in the Face of Temperature Stress Is Ensured by Two Conserved microRNAS in Caenorhabditis elegans. Genetics 200, 1201–1218 (2015). · DOI
  53. Yang, J. et al. MiR-34 modulates Caenorhabditis elegans lifespan via repressing the autophagy gene atg9. AGE 35, 11–22 (2013). · DOI
  54. Lee, R. Y. N., Hench, J. & Ruvkun, G. Regulation of C. elegans DAF-16 and its human ortholog FKHRL1 by the daf-2 insulin-like signaling pathway. Current Biology 11, 1950–1957 (2001). · DOI
  55. de Lencastre, A. et al. MicroRNAs Both Promote and Antagonize Longevity in C. elegans. Current Biology 20, 2159–2168 (2010). · DOI
  56. Bansal, A. et al. Transcriptional regulation of Caenorhabditis elegans FOXO/DAF-16 modulates lifespan. Longevity & Healthspan 3, 5 (2014). · DOI
  57. Kress, T. R. et al. The MK5/PRAK kinase and Myc form a negative feedback loop that is disrupted during colorectal tumorigenesis. Mol. Cell 41, 445–457 (2011). · DOI
  58. Bader, A. G. miR-34 – a microRNA replacement therapy is headed to the clinic. Frontiers in Genetics 3 (2012). · DOI
  59. Henrich, K.-O., Schwab, M. & Westermann, F. 1p36 tumor suppression–a matter of dosage? Cancer Res. 72, 6079–6088 (2012). · DOI
  60. Brenner, S. The Genetics of Caenorhabditis Elegans. Genetics 77, 71–94 (1974). · DOI
  61. Isik, M. & Berezikov, E. Biolistic transformation of Caenorhabditis elegans. Methods Mol. Biol. 940, 77–86 (2013). · DOI
  62. Ailion, M. & Thomas, J. H. Dauer formation induced by high temperatures in Caenorhabditis elegans. Genetics 156, 1047–1067 (2000). · DOI
  63. Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Research 43, e47–e47 (2015). · DOI · Paper page
  64. Kim, D., Langmead, B. & Salzberg, S. L. HISAT: a fast spliced aligner with low memory requirements. Nature Methods 12, 357–360 (2015). · DOI · Paper page
  65. Hartley, S. W. & Mullikin, J. C. QoRTs: a comprehensive toolset for quality control and data processing of RNA-Seq experiments. BMC Bioinformatics 16 (2015). · DOI · Paper page
  66. Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010). · DOI · Paper page
  67. Forman, J. J., Legesse-Miller, A. & Coller, H. A. A search for conserved sequences in coding regions reveals that the let-7 microRNA targets Dicer within its coding sequence. Proceedings of the National Academy of Sciences 105, 14879–14884 (2008). · DOI · Paper page
  68. Qin, W. et al. miR-24 Regulates Apoptosis by Targeting the Open Reading Frame (ORF) Region of FAF1 in Cancer Cells. Plos One 5, e9429 (2010). · DOI
  69. Ott, C. E. et al. MicroRNAs differentially expressed in postnatal aortic development downregulate elastin via 3′ UTR and coding-sequence binding sites. PLoS ONE 6, e16250 (2011). · DOI
  70. Huang, F. W. D., Qin, J., Reidys, C. M. & Stadler, P. F. Target prediction and a statistical sampling algorithm for RNA-RNA interaction. Bioinformatics 26, 175–181 (2010). · DOI
  71. Hausser, J., Syed, A. P., Bilen, B. & Zavolan, M. Analysis of CDS-located miRNA target sites suggests that they can effectively inhibit translation. Genome Research 23, 604–615 (2013). · DOI · Paper page
  72. Huang, D. W., Sherman, B. T. & Lempicki, R. A. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Research 37, 1–13 (2009). · DOI · Paper page
  73. Huang, D. W., Sherman, B. T. & Lempicki, R. A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nature Protocols 4, 44–57 (2008). · DOI · Paper page

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Authors
Isik, M., Blackwell, T. K., & Berezikov, E.
Venue
Scientific Reports
Publisher
Springer Science and Business Media LLC
Published
2016-12-01
Volume
6
Issue
1
ISSN
2045-2322
DOI
10.1038/srep36766
PMC
PMC5131338
Cited by
50
References
5
Language
en
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