全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用：99美元
投递稿件

查看量下载量

相关文章

更多...
PLOS ONE  2014 

The Inferred Cardiogenic Gene Regulatory Network in the Mammalian Heart

DOI: 10.1371/journal.pone.0100842

Full-Text   Cite this paper   Add to My Lib

Abstract:

Cardiac development is a complex, multiscale process encompassing cell fate adoption, differentiation and morphogenesis. To elucidate pathways underlying this process, a recently developed algorithm to reverse engineer gene regulatory networks was applied to time-course microarray data obtained from the developing mouse heart. Approximately 200 genes of interest were input into the algorithm to generate putative network topologies that are capable of explaining the experimental data via model simulation. To cull specious network interactions, thousands of putative networks are merged and filtered to generate scale-free, hierarchical networks that are statistically significant and biologically relevant. The networks are validated with known gene interactions and used to predict regulatory pathways important for the developing mammalian heart. Area under the precision-recall curve and receiver operator characteristic curve are 9% and 58%, respectively. Of the top 10 ranked predicted interactions, 4 have already been validated. The algorithm is further tested using a network enriched with known interactions and another depleted of them. The inferred networks contained more interactions for the enriched network versus the depleted network. In all test cases, maximum performance of the algorithm was achieved when the purely data-driven method of network inference was combined with a data-independent, functional-based association method. Lastly, the network generated from the list of approximately 200 genes of interest was expanded using gene-profile uniqueness metrics to include approximately 900 additional known mouse genes and to form the most likely cardiogenic gene regulatory network. The resultant network supports known regulatory interactions and contains several novel cardiogenic regulatory interactions. The method outlined herein provides an informative approach to network inference and leads to clear testable hypotheses related to gene regulation.

 References

[1]  Gardner TA, Cote IM, Gill JA, Grant A, Watkinson AR (2003) Long-term region-wide declines in Caribbean corals. Science 301: 958–960. doi: 10.1126/science.1086050
[2]  Gardner TS, Faith JJ (2005) Reverse-engineering transcription control networks. Phys Life Rev 2: 65–88. doi: 10.1016/j.plrev.2005.01.001
[3]  Bansal M, Belcastro V, Ambesi-Impiombato A, di Bernardo D (2007) How to infer gene networks from expression profiles. Mol Syst Biol 3: 78. doi: 10.1038/msb4100158
[4]  Lee WP, Tzou WS (2009) Computational methods for discovering gene networks from expression data. Brief Bioinform 10: 408–423. doi: 10.1093/bib/bbp028
[5]  D'Haeseleer P, Liang S, Somogyi R (2000) Genetic network inference: from co-expression clustering to reverse engineering. Bioinformatics 16: 707–726. doi: 10.1093/bioinformatics/16.8.707
[6]  Schmittgen TD, Lee EJ, Jiang J (2008) High-throughput real-time PCR. Methods Mol Biol 429: 89–98. doi: 10.1007/978-1-60327-040-3_7
[7]  Edgar R, Domrachev M, Lash AE (2002) Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res 30: 207–210. doi: 10.1093/nar/30.1.207
[8]  Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, et al. (2013) NCBI GEO: archive for functional genomics data sets–update. Nucleic Acids Res 41: D991–995. doi: 10.1093/nar/gks1193
[9]  Morin R, Bainbridge M, Fejes A, Hirst M, Krzywinski M, et al. (2008) Profiling the HeLa S3 transcriptome using randomly primed cDNA and massively parallel short-read sequencing. Biotechniques 45: 81–94. doi: 10.2144/000112900
[10]  Stolovitzky G, Monroe D, Califano A (2007) Dialogue on reverse-engineering assessment and methods: the DREAM of high-throughput pathway inference. Ann N Y Acad Sci 1115: 1–22. doi: 10.1196/annals.1407.021
[11]  Marbach D, Costello JC, Kuffner R, Vega NM, Prill RJ, et al. (2012) Wisdom of crowds for robust gene network inference. Nat Methods 9: 796–804. doi: 10.1038/nmeth.2016
[12]  Karlebach G, Shamir R (2008) Modelling and analysis of gene regulatory networks. Nat Rev Mol Cell Biol 9: 770–780. doi: 10.1038/nrm2503
[13]  Bozdag S, Li A, Wuchty S, Fine HA (2010) FastMEDUSA: a parallelized tool to infer gene regulatory networks. Bioinformatics 26: 1792–1793. doi: 10.1093/bioinformatics/btq275
[14]  Eisen MB, Spellman PT, Brown PO, Botstein D (1998) Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci U S A 95: 14863–14868. doi: 10.1073/pnas.95.25.14863
[15]  Schmitt WA Jr, Raab RM, Stephanopoulos G (2004) Elucidation of gene interaction networks through time-lagged correlation analysis of transcriptional data. Genome Res 14: 1654–1663. doi: 10.1101/gr.2439804
[16]  Shaw OJ, Harwood C, Steggles LJ, Wipat A (2004) SARGE: a tool for creation of putative genetic networks. Bioinformatics 20: 3638–3640. doi: 10.1093/bioinformatics/bth395
[17]  Wahde M, Hertz J (2000) Coarse-grained reverse engineering of genetic regulatory networks. Biosystems 55: 129–136. doi: 10.1016/s0303-2647(99)00090-8
[18]  Vohradsky J, Ramsden JJ (2001) Genome resource utilization during prokaryotic development. FASEB J 15: 2054–2056. doi: 10.1096/fj.00-0889fje
[19]  Hartemink AJ, Gifford DK, Jaakkola TS, Young RA (2002) Combining location and expression data for principled discovery of genetic regulatory network models. Pac Symp Biocomput: 437–449.
[20]  Nazri A, Lio P (2012) Investigating meta-approaches for reconstructing gene networks in a mammalian cellular context. PLoS One 7: e28713. doi: 10.1371/journal.pone.0028713
[21]  Marbach D, Prill RJ, Schaffter T, Mattiussi C, Floreano D, et al. (2010) Revealing strengths and weaknesses of methods for gene network inference. Proc Natl Acad Sci U S A 107: 6286–6291. doi: 10.1073/pnas.0913357107
[22]  Markowetz F, Spang R (2007) Inferring cellular networks—a review. BMC Bioinformatics 8 Suppl 6S5. doi: 10.1186/1471-2105-8-s6-s5
[23]  di Bernardo D, Thompson MJ, Gardner TS, Chobot SE, Eastwood EL, et al. (2005) Chemogenomic profiling on a genome-wide scale using reverse-engineered gene networks. Nat Biotechnol 23: 377–383. doi: 10.1038/nbt1075
[24]  Haury AC, Mordelet F, Vera-Licona P, Vert JP (2012) TIGRESS: Trustful Inference of Gene REgulation using Stability Selection. BMC Syst Biol 6: 145. doi: 10.1186/1752-0509-6-145
[25]  Hasty J, McMillen D, Isaacs F, Collins JJ (2001) Computational studies of gene regulatory networks: in numero molecular biology. Nat Rev Genet 2: 268–279. doi: 10.1038/35066056
[26]  Butte AJ, Tamayo P, Slonim D, Golub TR, Kohane IS (2000) Discovering functional relationships between RNA expression and chemotherapeutic susceptibility using relevance networks. Proc Natl Acad Sci U S A 97: 12182–12186. doi: 10.1073/pnas.220392197
[27]  Margolin AA, Nemenman I, Basso K, Wiggins C, Stolovitzky G, et al. (2006) ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinformatics 7 Suppl 1S7. doi: 10.1186/1471-2105-7-s1-s7
[28]  Faith JJ, Hayete B, Thaden JT, Mogno I, Wierzbowski J, et al. (2007) Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles. PLoS Biol 5: e8. doi: 10.1371/journal.pbio.0050008
[29]  Meyer PE, Kontos K, Lafitte F, Bontempi G (2007) Information-theoretic inference of large transcriptional regulatory networks. EURASIP J Bioinform Syst Biol: 79879.
[30]  Altay G, Emmert-Streib F (2010) Inferring the conservative causal core of gene regulatory networks. BMC Syst Biol 4: 132. doi: 10.1186/1752-0509-4-132
[31]  Bazil JN, Qi F, Beard DA (2011) A parallel algorithm for reverse engineering of biological networks. Integr Biol (Camb) 3: 1215–1223. doi: 10.1039/c1ib00117e
[32]  Franco D, Lamers WH, Moorman AF (1998) Patterns of expression in the developing myocardium: towards a morphologically integrated transcriptional model. Cardiovasc Res 38: 25–53. doi: 10.1016/s0008-6363(97)00321-0
[33]  Srivastava D (2006) Making or breaking the heart: from lineage determination to morphogenesis. Cell 126: 1037–1048. doi: 10.1016/j.cell.2006.09.003
[34]  Behfar A, Perez-Terzic C, Faustino RS, Arrell DK, Hodgson DM, et al. (2007) Cardiopoietic programming of embryonic stem cells for tumor-free heart repair. J Exp Med 204: 405–420. doi: 10.1084/jem.20061916
[35]  Chiriac A, Nelson TJ, Faustino RS, Behfar A, Terzic A (2010) Cardiogenic induction of pluripotent stem cells streamlined through a conserved SDF-1/VEGF/BMP2 integrated network. PLoS One 5: e9943. doi: 10.1371/journal.pone.0009943
[36]  Tomita-Mitchell A, Mahnke DK, Struble CA, Tuffnell ME, Stamm KD, et al. (2012) Human gene copy number spectra analysis in congenital heart malformations. Physiol Genomics 44: 518–541. doi: 10.1152/physiolgenomics.00013.2012
[37]  Li X, Martinez-Fernandez A, Hartjes KA, Kocher JP, Olson TM, et al.. (2014) Transcriptional Atlas of Cardiogenesis Maps Congenital Heart Disease Interactome. Physiol Genomics.
[38]  Mistry M, Pavlidis P (2008) Gene Ontology term overlap as a measure of gene functional similarity. BMC Bioinformatics 9: 327. doi: 10.1186/1471-2105-9-327
[39]  Bolstad BM, Irizarry RA, Astrand M, Speed TP (2003) A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19: 185–193. doi: 10.1093/bioinformatics/19.2.185
[40]  Liao J, Aggarwal VS, Nowotschin S, Bondarev A, Lipner S, et al. (2008) Identification of downstream genetic pathways of Tbx1 in the second heart field. Dev Biol 316: 524–537. doi: 10.1016/j.ydbio.2008.01.037
[41]  Bounova G, de Weck O (2012) Overview of metrics and their correlation patterns for multiple-metric topology analysis on heterogeneous graph ensembles. Phys Rev E Stat Nonlin Soft Matter Phys 85: 016117. doi: 10.1103/physreve.85.016117
[42]  Saito R, Smoot ME, Ono K, Ruscheinski J, Wang PL, et al. (2012) A travel guide to Cytoscape plugins. Nat Methods 9: 1069–1076. doi: 10.1038/nmeth.2212
[43]  Bindea G, Mlecnik B, Hackl H, Charoentong P, Tosolini M, et al. (2009) ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics 25: 1091–1093. doi: 10.1093/bioinformatics/btp101
[44]  Zoubarev A, Hamer KM, Keshav KD, McCarthy EL, Santos JR, et al. (2012) Gemma: a resource for the reuse, sharing and meta-analysis of expression profiling data. Bioinformatics 28: 2272–2273. doi: 10.1093/bioinformatics/bts430
[45]  Kohonen T (1990) Cortical maps. Nature 346: 24. doi: 10.1038/346024b0
[46]  Kangas JA, Kohonen TK, Laaksonen JT (1990) Variants of self-organizing maps. IEEE Trans Neural Netw 1: 93–99. doi: 10.1109/72.80208
[47]  Popescu M, Keller JM, Mitchell JA (2006) Fuzzy measures on the Gene Ontology for gene product similarity. IEEE/ACM Trans Comput Biol Bioinform 3: 263–274. doi: 10.1109/tcbb.2006.37
[48]  Langfelder P, Horvath S (2007) Eigengene networks for studying the relationships between co-expression modules. BMC Syst Biol 1: 54. doi: 10.1186/1752-0509-1-54
[49]  Bazil JN, Buzzard GT, Rundell AE (2012) A global parallel model based design of experiments method to minimize model output uncertainty. Bull Math Biol 74: 688–716. doi: 10.1007/s11538-011-9686-9
[50]  Donahue MM, Buzzard GT, Rundell AE (2010) Experiment design through dynamical characterisation of non-linear systems biology models utilising sparse grids. IET Syst Biol 4: 249–262. doi: 10.1049/iet-syb.2009.0031
[51]  Davis J, Goadrich M (2006) The relationship between Precision-Recall and ROC curves. Proceedings of the 23rd international conference on Machine learning. Pittsburgh, Pennsylvania: ACM. pp. 233–240.
[52]  Barabasi AL, Oltvai ZN (2004) Network biology: understanding the cell's functional organization. Nat Rev Genet 5: 101–113. doi: 10.1038/nrg1272
[53]  Holtzinger A, Evans T (2005) Gata4 regulates the formation of multiple organs. Development 132: 4005–4014. doi: 10.1242/dev.01978
[54]  Zeisberg EM, Ma Q, Juraszek AL, Moses K, Schwartz RJ, et al. (2005) Morphogenesis of the right ventricle requires myocardial expression of Gata4. J Clin Invest 115: 1522–1531. doi: 10.1172/jci23769
[55]  Oka T, Maillet M, Watt AJ, Schwartz RJ, Aronow BJ, et al. (2006) Cardiac-specific deletion of Gata4 reveals its requirement for hypertrophy, compensation, and myocyte viability. Circ Res 98: 837–845. doi: 10.1161/01.res.0000215985.18538.c4
[56]  Prall OW, Elliott DA, Harvey RP (2002) Developmental paradigms in heart disease: insights from tinman. Ann Med 34: 148–156. doi: 10.1080/713782134
[57]  Greulich F, Rudat C, Kispert A (2011) Mechanisms of T-box gene function in the developing heart. Cardiovasc Res 91: 212–222. doi: 10.1093/cvr/cvr112
[58]  Okuda Y, Ogura E, Kondoh H, Kamachi Y (2010) B1 SOX coordinate cell specification with patterning and morphogenesis in the early zebrafish embryo. PLoS Genet 6: e1000936. doi: 10.1371/journal.pgen.1000936
[59]  Johannessen M, Moller S, Hansen T, Moens U, Van Ghelue M (2006) The multifunctional roles of the four-and-a-half-LIM only protein FHL2. Cell Mol Life Sci 63: 268–284. doi: 10.1007/s00018-005-5438-z
[60]  Booth HA, Holland PW (2004) Eleven daughters of NANOG. Genomics 84: 229–238. doi: 10.1016/j.ygeno.2004.02.014
[61]  Briegel KJ, Joyner AL (2001) Identification and characterization of Lbh, a novel conserved nuclear protein expressed during early limb and heart development. Dev Biol 233: 291–304. doi: 10.1006/dbio.2001.0225
[62]  Li R, Pei H, Watson DK (2000) Regulation of Ets function by protein - protein interactions. Oncogene 19: 6514–6523. doi: 10.1038/sj.onc.1204035
[63]  Verger A, Duterque-Coquillaud M (2002) When Ets transcription factors meet their partners. Bioessays 24: 362–370. doi: 10.1002/bies.10068
[64]  Shimada K, Nakamura M, Anai S, De Velasco M, Tanaka M, et al. (2009) A novel human AlkB homologue, ALKBH8, contributes to human bladder cancer progression. Cancer Res 69: 3157–3164. doi: 10.1158/0008-5472.can-08-3530
[65]  Durocher D, Charron F, Warren R, Schwartz RJ, Nemer M (1997) The cardiac transcription factors Nkx2-5 and GATA-4 are mutual cofactors. EMBO J 16: 5687–5696. doi: 10.1093/emboj/16.18.5687
[66]  Cermenati S, Moleri S, Cimbro S, Corti P, Del Giacco L, et al. (2008) Sox18 and Sox7 play redundant roles in vascular development. Blood 111: 2657–2666. doi: 10.1182/blood-2007-07-100412
[67]  Zhang C, Basta T, Klymkowsky MW (2005) SOX7 and SOX18 are essential for cardiogenesis in Xenopus. Developmental Dynamics 234: 878–891. doi: 10.1002/dvdy.20565
[68]  Perdomo J, Jiang XM, Carter DR, Khachigian LM, Chong BH (2012) SUMOylation Regulates the Transcriptional Repression Activity of FOG-2 and Its Association with GATA-4. PLoS One 7. doi: 10.1371/journal.pone.0050637
[69]  Lu JR, McKinsey TA, Xu HT, Wang DZ, Richardson JA, et al. (1999) FOG-2, a heart- and brain-enriched cofactor for GATA transcription factors. Molecular and Cellular Biology 19: 4495–4502.
[70]  Riazi AM, Takeuchi JK, Hornberger LK, Zaidi SH, Amini F, et al.. (2009) NKX2-5 Regulates the Expression of beta-Catenin and GATA4 in Ventricular Myocytes. PLoS One 4..
[71]  Stennard FA, Costa MW, Elliott DA, Rankin S, Haast SJP, et al. (2003) Cardiac T-box factor Tbx20 directly interacts with Nkx2-5, GATA4, and GATA5 in regulation of gene expression in the developing heart. Developmental Biology 262: 206–224. doi: 10.1016/s0012-1606(03)00385-3
[72]  Schachterle W, Rojas A, Xu SM, Black BL (2012) ETS-dependent regulation of a distal Gata4 cardiac enhancer. Dev Biol 361: 439–449. doi: 10.1016/j.ydbio.2011.10.023
[73]  Haller ST, Kennedy DJ, Shidyak A, Budny GV, Malhotra D, et al. (2012) Monoclonal antibody against marinobufagenin reverses cardiac fibrosis in rats with chronic renal failure. Am J Hypertens 25: 690–696. doi: 10.1038/ajh.2012.17
[74]  Kapoor N, Liang W, Marban E, Cho HC (2013) Direct conversion of quiescent cardiomyocytes to pacemaker cells by expression of Tbx18. Nat Biotechnol 31: 54–62. doi: 10.1038/nbt.2465
[75]  Groh WJ (2012) Arrhythmias in the muscular dystrophies. Heart Rhythm 9: 1890–1895. doi: 10.1016/j.hrthm.2012.06.038
[76]  Chang B, Kustra R, Tian W (2013) Functional-network-based gene set analysis using gene-ontology. PLoS One 8: e55635. doi: 10.1371/journal.pone.0055635
[77]  Gaborit N, Le Bouter S, Szuts V, Varro A, Escande D, et al. (2007) Regional and tissue specific transcript signatures of ion channel genes in the non-diseased human heart. J Physiol 582: 675–693. doi: 10.1113/jphysiol.2006.126714
[78]  Civelek M, Grant GR, Irolla CR, Shi C, Riley RJ, et al. (2010) Prelesional arterial endothelial phenotypes in hypercholesterolemia: universal ABCA1 upregulation contrasts with region-specific gene expression in vivo. Am J Physiol Heart Circ Physiol 298: H163–170. doi: 10.1152/ajpheart.00652.2009

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133