Gene Networks Underlying Convergent and Pleiotropic Phenotypes in a Large and Systematically-Phenotyped Cohort with Heterogeneous Developmental Disorders
Developmental disorders occur in ∼3% of live births, and exhibit a broad range of abnormalities including: intellectual disability, autism, heart defects, and other neurological and morphological problems. Often, patients are grouped into genetic syndromes which are defined by a specific set of mutations and a common set of abnormalities. However, many mutations are unique to a single patient and many patients present a range of abnormalities which do not fit one of the recognized genetic syndromes, making diagnosis difficult. Using a dataset of 197 patients with systematically described abnormalities, we identified molecular pathways whose disruption was associated with specific abnormalities among many patients. Importantly, patients with mutations in the same pathway often exhibited similar co-morbid symptoms and thus the commonly disrupted pathway appeared responsible for the broad range of shared abnormalities amongst these patients. These findings support the general concept that patients with mutations in distinct genes could be etiologically grouped together through the common pathway that these mutated genes participate in, with a view to improving diagnoses, prognoses and therapeutic outcomes.
Vyšlo v časopise:
Gene Networks Underlying Convergent and Pleiotropic Phenotypes in a Large and Systematically-Phenotyped Cohort with Heterogeneous Developmental Disorders. PLoS Genet 11(3): e32767. doi:10.1371/journal.pgen.1005012
Kategorie:
Research Article
prolekare.web.journal.doi_sk:
https://doi.org/10.1371/journal.pgen.1005012
Souhrn
Developmental disorders occur in ∼3% of live births, and exhibit a broad range of abnormalities including: intellectual disability, autism, heart defects, and other neurological and morphological problems. Often, patients are grouped into genetic syndromes which are defined by a specific set of mutations and a common set of abnormalities. However, many mutations are unique to a single patient and many patients present a range of abnormalities which do not fit one of the recognized genetic syndromes, making diagnosis difficult. Using a dataset of 197 patients with systematically described abnormalities, we identified molecular pathways whose disruption was associated with specific abnormalities among many patients. Importantly, patients with mutations in the same pathway often exhibited similar co-morbid symptoms and thus the commonly disrupted pathway appeared responsible for the broad range of shared abnormalities amongst these patients. These findings support the general concept that patients with mutations in distinct genes could be etiologically grouped together through the common pathway that these mutated genes participate in, with a view to improving diagnoses, prognoses and therapeutic outcomes.
Zdroje
1. Dolk H, Loane M, Garne E (2010) The prevalence of congenital anomalies in Europe. Adv Exp Med Biol 686: 349–364. doi: 10.1007/978-90-481-9485-8_20 20824455
2. Schaaf CP, Wiszniewska J, Beaudet AL (2011) Copy number and SNP arrays in clinical diagnostics. Annu Rev Genomics Hum Genet 12: 25–51. doi: 10.1146/annurev-genom-092010-110715 21801020
3. van Zelst-Stams WA, Scheffer H, Veltman JA (2014) Clinical exome sequencing in daily practice: 1,000 patients and beyond. Genome Med 6: 2. doi: 10.1186/gm521 24456652
4. de Ligt J, Willemsen MH, van Bon BW, Kleefstra T, Yntema HG, et al. (2012) Diagnostic exome sequencing in persons with severe intellectual disability. N Engl J Med 367: 1921–1929. doi: 10.1056/NEJMoa1206524 23033978
5. Robinson PN (2012) Deep phenotyping for precision medicine. Hum Mutat 33: 777–780. doi: 10.1002/humu.22080 22504886
6. Houle D, Govindaraju DR, Omholt S (2010) Phenomics: the next challenge. Nat Rev Genet 11: 855–866. doi: 10.1038/nrg2897 21085204
7. Ramanan VK, Shen L, Moore JH, Saykin AJ (2012) Pathway analysis of genomic data: concepts, methods, and prospects for future development. Trends Genet 28: 323–332. doi: 10.1016/j.tig.2012.03.004 22480918
8. Vidal M, Cusick ME, Barabasi AL (2011) Interactome networks and human disease. Cell 144: 986–998. doi: 10.1016/j.cell.2011.02.016 21414488
9. Oti M, Brunner HG (2007) The modular nature of genetic diseases. Clin Genet 71: 1–11. 17204041
10. Stankiewicz P, Lupski JR (2010) Structural variation in the human genome and its role in disease. Annu Rev Med 61: 437–455. doi: 10.1146/annurev-med-100708-204735 20059347
11. Kohler S, Doelken SC, Mungall CJ, Bauer S, Firth HV, et al. (2014) The Human Phenotype Ontology project: linking molecular biology and disease through phenotype data. Nucleic Acids Res 42: D966–974. doi: 10.1093/nar/gkt1026 24217912
12. Robinson PN, Kohler S, Bauer S, Seelow D, Horn D, et al. (2008) The Human Phenotype Ontology: a tool for annotating and analyzing human hereditary disease. Am J Hum Genet 83: 610–615. doi: 10.1016/j.ajhg.2008.09.017 18950739
13. Webber C (2011) Functional Enrichment Analysis with Structural Variants: Pitfalls and Strategies. Cytogenet Genome Res.
14. Kariminejad R, Lind-Thomsen A, Tumer Z, Erdogan F, Ropers HH, et al. (2011) High frequency of rare copy number variants affecting functionally related genes in patients with structural brain malformations. Hum Mutat 32: 1427–1435. doi: 10.1002/humu.21585 21882292
15. Gai X, Xie HM, Perin JC, Takahashi N, Murphy K, et al. (2012) Rare structural variation of synapse and neurotransmission genes in autism. Mol Psychiatry 17: 402–411. doi: 10.1038/mp.2011.10 21358714
16. Steinberg J, Webber C (2013) The roles of FMRP-regulated genes in autism spectrum disorder: single- and multiple-hit genetic etiologies. Am J Hum Genet 93: 825–839. doi: 10.1016/j.ajhg.2013.09.013 24207117
17. Noh HJ, Ponting CP, Boulding HC, Meader S, Betancur C, et al. (2013) Network topologies and convergent aetiologies arising from deletions and duplications observed in individuals with autism. PLoS Genet 9: e1003523. doi: 10.1371/journal.pgen.1003523 23754953
18. Webber C, Hehir-Kwa JY, Nguyen DQ, de Vries BB, Veltman JA, et al. (2009) Forging links between human mental retardation-associated CNVs and mouse gene knockout models. PLoS Genet 5: e1000531. doi: 10.1371/journal.pgen.1000531 19557186
19. Doelken SC, Kohler S, Mungall CJ, Gkoutos GV, Ruef BJ, et al. (2013) Phenotypic overlap in the contribution of individual genes to CNV pathogenicity revealed by cross-species computational analysis of single-gene mutations in humans, mice and zebrafish. Dis Model Mech 6: 358–372. doi: 10.1242/dmm.010322 23104991
20. Andorf C, Dobbs D, Honavar V (2007) Exploring inconsistencies in genome-wide protein function annotations: a machine learning approach. BMC Bioinformatics 8: 284. 17683567
21. Gillis J, Pavlidis P (2013) Assessing identity, redundancy and confounds in Gene Ontology annotations over time. Bioinformatics 29: 476–482. doi: 10.1093/bioinformatics/bts727 23297035
22. Schnoes AM, Brown SD, Dodevski I, Babbitt PC (2009) Annotation error in public databases: misannotation of molecular function in enzyme superfamilies. PLoS Comput Biol 5: e1000605. doi: 10.1371/journal.pcbi.1000605 20011109
23. Lee I, Marcotte EM (2008) Integrating functional genomics data. Methods Mol Biol 453: 267–278. doi: 10.1007/978-1-60327-429-6_14 18712309
24. Wabnik K, Hvidsten TR, Kedzierska A, Van Leene J, De Jaeger G, et al. (2009) Gene expression trends and protein features effectively complement each other in gene function prediction. Bioinformatics 25: 322–330. doi: 10.1093/bioinformatics/btn625 19050035
25. The Gene Ontology Consortium, Ashburner M, Ball CA, Blake JA, Botstein D, et al. (2000) Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 25: 25–29. 10802651
26. Kanehisa M, Goto S (2000) KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res 28: 27–30. 10592173
27. Shaikh TH, Haldeman-Englert C, Geiger EA, Ponting CP, Webber C (2011) Genes and biological processes commonly disrupted in rare and heterogeneous developmental delay syndromes. Hum Mol Genet 20: 880–893. doi: 10.1093/hmg/ddq527 21147756
28. Rossin EJ, Lage K, Raychaudhuri S, Xavier RJ, Tatar D, et al. (2011) Proteins encoded in genomic regions associated with immune-mediated disease physically interact and suggest underlying biology. PLoS Genet 7: e1001273. doi: 10.1371/journal.pgen.1001273 21249183
29. Bragin E, Chatzimichali EA, Wright CF, Hurles ME, Firth HV, et al. (2014) DECIPHER: database for the interpretation of phenotype-linked plausibly pathogenic sequence and copy-number variation. Nucleic Acids Res 42: D993–D1000. doi: 10.1093/nar/gkt937 24150940
30. Winter RM, Baraitser M (1987) The London Dysmorphology Database. J Med Genet 24: 509–510. 3656376
31. Lalani SR, Safiullah AM, Fernbach SD, Harutyunyan KG, Thaller C, et al. (2006) Spectrum of CHD7 mutations in 110 individuals with CHARGE syndrome and genotype-phenotype correlation. Am J Hum Genet 78: 303–314. 16400610
32. Perrault I, Hamdan FF, Rio M, Capo-Chichi JM, Boddaert N, et al. (2014) Mutations in DOCK7 in Individuals with Epileptic Encephalopathy and Cortical Blindness. Am J Hum Genet 94: 891–897. doi: 10.1016/j.ajhg.2014.04.012 24814191
33. Germanaud D, Rossi M, Bussy G, Gerard D, Hertz-Pannier L, et al. (2011) The Renpenning syndrome spectrum: new clinical insights supported by 13 new PQBP1-mutated males. Clin Genet 79: 225–235. doi: 10.1111/j.1399-0004.2010.01551.x 20950397
34. des Portes V, Boddaert N, Sacco S, Briault S, Maincent K, et al. (2004) Specific clinical and brain MRI features in mentally retarded patients with mutations in the Oligophrenin-1 gene. Am J Med Genet A 124A: 364–371. 14735583
35. Ng SB, Bigham AW, Buckingham KJ, Hannibal MC, McMillin MJ, et al. (2010) Exome sequencing identifies MLL2 mutations as a cause of Kabuki syndrome. Nat Genet 42: 790–793. doi: 10.1038/ng.646 20711175
36. Gjuvsland AB, Vik JO, Beard DA, Hunter PJ, Omholt SW (2013) Bridging the genotype-phenotype gap: what does it take? J Physiol 591: 2055–2066. doi: 10.1113/jphysiol.2012.248864 23401613
37. Robinson PN, Webber C (2014) Phenotype ontologies and cross-species analysis for translational research. PLoS Genet 10: e1004268. doi: 10.1371/journal.pgen.1004268 24699242
38. Consortium G (2013) The Genotype-Tissue Expression (GTEx) project. Nat Genet 45: 580–585. doi: 10.1038/ng.2653 23715323
39. Ballif BC, Rosenfeld JA, Traylor R, Theisen A, Bader PI, et al. (2012) High-resolution array CGH defines critical regions and candidate genes for microcephaly, abnormalities of the corpus callosum, and seizure phenotypes in patients with microdeletions of 1q43q44. Hum Genet 131: 145–156. doi: 10.1007/s00439-011-1073-y 21800092
40. Samuels IS, Karlo JC, Faruzzi AN, Pickering K, Herrup K, et al. (2008) Deletion of ERK2 mitogen-activated protein kinase identifies its key roles in cortical neurogenesis and cognitive function. J Neurosci 28: 6983–6995. doi: 10.1523/JNEUROSCI.0679-08.2008 18596172
41. Fagerberg CR, Graakjaer J, Heinl UD, Ousager LB, Dreyer I, et al. (2013) Heart defects and other features of the 22q11 distal deletion syndrome. Eur J Med Genet 56: 98–107. doi: 10.1016/j.ejmg.2012.09.009 23063575
42. Riviere JB, van Bon BW, Hoischen A, Kholmanskikh SS, O'Roak BJ, et al. (2012) De novo mutations in the actin genes ACTB and ACTG1 cause Baraitser-Winter syndrome. Nat Genet 44: 440–444, S441–442. doi: 10.1038/ng.1091 22366783
43. Lindhurst MJ, Sapp JC, Teer JK, Johnston JJ, Finn EM, et al. (2011) A mosaic activating mutation in AKT1 associated with the Proteus syndrome. N Engl J Med 365: 611–619. doi: 10.1056/NEJMoa1104017 21793738
44. Najm J, Horn D, Wimplinger I, Golden JA, Chizhikov VV, et al. (2008) Mutations of CASK cause an X-linked brain malformation phenotype with microcephaly and hypoplasia of the brainstem and cerebellum. Nat Genet 40: 1065–1067. doi: 10.1038/ng.194 19165920
45. Feng Y, Walsh CA (2004) Mitotic spindle regulation by Nde1 controls cerebral cortical size. Neuron 44: 279–293. 15473967
46. Alkuraya FS, Cai X, Emery C, Mochida GH, Al-Dosari MS, et al. (2011) Human mutations in NDE1 cause extreme microcephaly with lissencephaly [corrected]. Am J Hum Genet 88: 536–547. doi: 10.1016/j.ajhg.2011.04.003 21529751
47. Barkovich AJ, Guerrini R, Kuzniecky RI, Jackson GD, Dobyns WB (2012) A developmental and genetic classification for malformations of cortical development: update 2012. Brain 135: 1348–1369. doi: 10.1093/brain/aws019 22427329
48. Vadodaria KC, Brakebusch C, Suter U, Jessberger S (2013) Stage-specific functions of the small Rho GTPases Cdc42 and Rac1 for adult hippocampal neurogenesis. J Neurosci 33: 1179–1189. doi: 10.1523/JNEUROSCI.2103-12.2013 23325254
49. Leone DP, Srinivasan K, Brakebusch C, McConnell SK (2010) The rho GTPase Rac1 is required for proliferation and survival of progenitors in the developing forebrain. Dev Neurobiol 70: 659–678. doi: 10.1002/dneu.20804 20506362
50. Okae H, Iwakura Y (2010) Neural tube defects and impaired neural progenitor cell proliferation in Gbeta1-deficient mice. Dev Dyn 239: 1089–1101. doi: 10.1002/dvdy.22256 20186915
51. Menard C, Hein P, Paquin A, Savelson A, Yang XM, et al. (2002) An essential role for a MEK-C/EBP pathway during growth factor-regulated cortical neurogenesis. Neuron 36: 597–610. 12441050
52. Kenwrick S, Watkins A, De Angelis E (2000) Neural cell recognition molecule L1: relating biological complexity to human disease mutations. Hum Mol Genet 9: 879–886. 10767310
53. Sennvik K, Boekhoorn K, Lasrado R, Terwel D, Verhaeghe S, et al. (2007) Tau-4R suppresses proliferation and promotes neuronal differentiation in the hippocampus of tau knockin/knockout mice. FASEB J 21: 2149–2161. 17341679
54. Shin EY, Shin KS, Lee CS, Woo KN, Quan SH, et al. (2002) Phosphorylation of p85 beta PIX, a Rac/Cdc42-specific guanine nucleotide exchange factor, via the Ras/ERK/PAK2 pathway is required for basic fibroblast growth factor-induced neurite outgrowth. J Biol Chem 277: 44417–44430. 12226077
55. Lee HJ, Lee K, Im H (2012) alpha-Synuclein modulates neurite outgrowth by interacting with SPTBN1. Biochem Biophys Res Commun 424: 497–502. doi: 10.1016/j.bbrc.2012.06.143 22771809
56. Marzinke MA, Mavencamp T, Duratinsky J, Clagett-Dame M (2013) 14-3-3epsilon and NAV2 interact to regulate neurite outgrowth and axon elongation. Arch Biochem Biophys 540: 94–100. doi: 10.1016/j.abb.2013.10.012 24161943
57. Curry CJ, Rosenfeld JA, Grant E, Gripp KW, Anderson C, et al. (2013) The duplication 17p13.3 phenotype: analysis of 21 families delineates developmental, behavioral and brain abnormalities, and rare variant phenotypes. Am J Med Genet A 161A: 1833–1852. doi: 10.1002/ajmg.a.35996 23813913
58. Bruno DL, Anderlid BM, Lindstrand A, van Ravenswaaij-Arts C, Ganesamoorthy D, et al. (2010) Further molecular and clinical delineation of co-locating 17p13.3 microdeletions and microduplications that show distinctive phenotypes. J Med Genet 47: 299–311. doi: 10.1136/jmg.2009.069906 20452996
59. Tartaglia M, Pennacchio LA, Zhao C, Yadav KK, Fodale V, et al. (2007) Gain-of-function SOS1 mutations cause a distinctive form of Noonan syndrome. Nat Genet 39: 75–79. 17143282
60. Rodriguez-Viciana P, Tetsu O, Tidyman WE, Estep AL, Conger BA, et al. (2006) Germline mutations in genes within the MAPK pathway cause cardio-facio-cutaneous syndrome. Science 311: 1287–1290. 16439621
61. Okamoto K, Bosch M, Hayashi Y (2009) The roles of CaMKII and F-actin in the structural plasticity of dendritic spines: a potential molecular identity of a synaptic tag? Physiology (Bethesda) 24: 357–366. doi: 10.1152/physiol.00029.2009 19996366
62. de Quervain DJ, Papassotiropoulos A (2006) Identification of a genetic cluster influencing memory performance and hippocampal activity in humans. Proc Natl Acad Sci U S A 103: 4270–4274. 16537520
63. Lam BY, Chawla S (2007) MEF2D expression increases during neuronal differentiation of neural progenitor cells and correlates with neurite length. Neurosci Lett 427: 153–158. 17945419
64. Le Meur N, Holder-Espinasse M, Jaillard S, Goldenberg A, Joriot S, et al. (2010) MEF2C haploinsufficiency caused by either microdeletion of the 5q14.3 region or mutation is responsible for severe mental retardation with stereotypic movements, epilepsy and/or cerebral malformations. J Med Genet 47: 22–29. doi: 10.1136/jmg.2009.069732 19592390
65. Chandler CH, Chari S, Dworkin I (2013) Does your gene need a background check? How genetic background impacts the analysis of mutations, genes, and evolution. Trends Genet 29: 358–366. doi: 10.1016/j.tig.2013.01.009 23453263
66. Vulto-van Silfhout AT, Hehir-Kwa JY, van Bon BW, Schuurs-Hoeijmakers JH, Meader S, et al. (2013) Clinical significance of de novo and inherited copy-number variation. Hum Mutat 34: 1679–1687. doi: 10.1002/humu.22442 24038936
67. Shaikh TH, Gai X, Perin JC, Glessner JT, Xie H, et al. (2009) High-resolution mapping and analysis of copy number variations in the human genome: a data resource for clinical and research applications. Genome Res 19: 1682–1690. doi: 10.1101/gr.083501.108 19592680
68. Smith CL, Eppig JT (2009) The Mammalian Phenotype Ontology: enabling robust annotation and comparative analysis. Wiley Interdiscip Rev Syst Biol Med 1: 390–399. doi: 10.1002/wsbm.44 20052305
69. Eppig JT, Blake JA, Bult CJ, Richardson JE, Kadin JA, et al. (2007) Mouse genome informatics (MGI) resources for pathology and toxicology. Toxicol Pathol 35: 456–457. 17474068
70. Benjamini Y, Hochbert Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Roy Statist Soc Ser B 57: 289–300.
71. Obayashi T, Hayashi S, Shibaoka M, Saeki M, Ohta H, et al. (2008) COXPRESdb: a database of coexpressed gene networks in mammals. Nucleic Acids Res 36: D77–82. 17932064
72. Boriah SC, Varun; Kuman, Vipin (2008) Similarity measures for categorical data: A commparative evaluation. Proceedings of the eighth SIAM International Conference on Data Mining 30: 234–254.
73. Elliott B, Joyce E, Shorvon S (2009) Delusions, illusions and hallucinations in epilepsy: 1. Elementary phenomena. Epilepsy Res 85: 162–171. doi: 10.1016/j.eplepsyres.2009.03.018 19423297
74. Nguyen DQ, Webber C, Ponting CP (2006) Bias of selection on human copy-number variants. PLoS Genet 2: e20. 16482228
Štítky
Genetika Reprodukčná medicínaČlánok vyšiel v časopise
PLOS Genetics
2015 Číslo 3
- Je „freeze-all“ pro všechny? Odborníci na fertilitu diskutovali na virtuálním summitu
- Gynekologové a odborníci na reprodukční medicínu se sejdou na prvním virtuálním summitu
Najčítanejšie v tomto čísle
- Clonality and Evolutionary History of Rhabdomyosarcoma
- Morphological Mutations: Lessons from the Cockscomb
- Maternal Filaggrin Mutations Increase the Risk of Atopic Dermatitis in Children: An Effect Independent of Mutation Inheritance
- Transcriptomic Profiling of Reveals Reprogramming of the Crp Regulon by Temperature and Uncovers Crp as a Master Regulator of Small RNAs