In silico molecular cytogenetics: a bioinformatic approach to prioritization of candidate genes and copy number variations for basic and clinical genome research

Molecular Cytogenetics

Yuri B Yurov²  Svetlana G Vorsanova²  Ivan Y Iourov¹ 

[1] Department of Medical Genetics, Russian Medical Academy of Postgraduate Education, Moscow 123995, Russia;Russian National Research Medical University named after N.I. Pirogov, Separated Structural Unit “Clinical Research Institute of Pediatrics”, Ministry of Health of Russian Federation, Moscow, 125412, Russia

关键词: Somatic mosacism;    Molecular cytogenetics;    Gene expression;    Copy number variation;    Chromosome imbalances;    Candidate genes;    Bioinformatics;   

Others  :  1162970
DOI  :  10.1186/s13039-014-0098-z

 received in 2014-11-27, accepted in 2014-12-02,  发布年份 2014

【 摘 要 】

Background

The availability of multiple in silico tools for prioritizing genetic variants widens the possibilities for converting genomic data into biological knowledge. However, in molecular cytogenetics, bioinformatic analyses are generally limited to result visualization or database mining for finding similar cytogenetic data. Obviously, the potential of bioinformatics might go beyond these applications. On the other hand, the requirements for performing successful in silico analyses (i.e. deep knowledge of computer science, statistics etc.) can hinder the implementation of bioinformatics in clinical and basic molecular cytogenetic research. Here, we propose a bioinformatic approach to prioritization of genomic variations that is able to solve these problems.

Results

Selecting gene expression as an initial criterion, we have proposed a bioinformatic approach combining filtering and ranking prioritization strategies, which includes analyzing metabolome and interactome data on proteins encoded by candidate genes. To finalize the prioritization of genetic variants, genomic, epigenomic, interactomic and metabolomic data fusion has been made. Structural abnormalities and aneuploidy revealed by array CGH and FISH have been evaluated to test the approach through determining genotype-phenotype correlations, which have been found similar to those of previous studies. Additionally, we have been able to prioritize copy number variations (CNV) (i.e. differentiate between benign CNV and CNV with phenotypic outcome). Finally, the approach has been applied to prioritize genetic variants in cases of somatic mosaicism (including tissue-specific mosaicism).

Conclusions

In order to provide for an in silico evaluation of molecular cytogenetic data, we have proposed a bioinformatic approach to prioritization of candidate genes and CNV. While having the disadvantage of possible unavailability of gene expression data or lack of expression variability between genes of interest, the approach provides several advantages. These are (i) the versatility due to independence from specific databases/tools or software, (ii) relative algorithm simplicity (possibility to avoid sophisticated computational/statistical methodology) and (iii) applicability to molecular cytogenetic data because of the chromosome-centric nature. In conclusion, the approach is able to become useful for increasing the yield of molecular cytogenetic techniques.

【 授权许可】

   
2014 Iourov et al.; licensee BioMed Central Ltd.

【 图 表 】

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【 参考文献 】

• [1]Moreau Y, Tranchevent LC: Computational tools for prioritizing candidate genes: boosting disease gene discovery. Nat Rev Genet 2012, 13:523-536.
• [2]Gan M, Dou X, Jiang R: From ontology to semantic similarity: calculation of ontology-based semantic similarity. Sci World J 2013, 2013:793091.
• [3]Aerts S, Lambrechts D, Maity S, Van Loo P, Coessens B, De Smet F, Tranchevent LC, De Moor B, Marynen P, Hassan B, Carmeliet P, Moreau Y: Gene prioritization through genomic data fusion. Nat Biotechnol 2006, 24:537-544.
• [4]Köhler S, Bauer S, Horn D, Robinson PN: Walking the interactome for prioritization of candidate disease genes. Am J Hum Genet 2008, 82:949-958.
• [5]Chen J, Aronow BJ, Jegga AG: Disease candidate gene identification and prioritization using protein interaction networks. BMC Bioinforma 2009, 10:73. BioMed Central Full Text
• [6]Kimmel C, Visweswaran S: An algorithm for network-based gene prioritization that encodes knowledge both in nodes and in links. PLoS One 2013, 8:e79564.
• [7]Iourov IY, Vorsanova SG, Yurov YB: Molecular cytogenetics and cytogenomics of brain diseases. Curr Genomics 2008, 9:452-465.
• [8]Hehir-Kwa JY, Wieskamp N, Webber C, Pfundt R, Brunner HG, Gilissen C, de Vries BB, Ponting CP, Veltman JA: Accurate distinction of pathogenic from benign CNVs in mental retardation. PLoS Comput Biol 2010, 6:e1000752.
• [9]Zeng H, Weier JF, Wang M, Kassabian HJ, Polyzos AA, Baumgartner A, O'Brien B, Weier HU: Bioinformatic tools identify chromosome-specific DNA probes and facilitate risk assessment by detecting aneusomies in extra-embryonic tissues. Curr Genomics 2012, 13:438-445.
• [10]Iourov IY, Vorsanova SG, Yurov YB: Single cell genomics of the brain: focus on neuronal diversity and neuropsychiatric diseases. Curr Genomics 2012, 13:477-488.
• [11]O'Brien B, Zeng H, Polyzos AA, Lemke KH, Weier JF, Wang M, Zitzelsberger HF, Weier HU: Bioinformatics tools allow targeted selection of chromosome enumeration probes and aneuploidy detection. J Histochem Cytochem 2013, 61:134-147.
• [12]Iourov IY, Vorsanova SG, Yurov YB: Somatic cell genomics of brain disorders: a new opportunity to clarify genetic-environmental interactions. Cytogenet Genome Res 2013, 139:181-188.
• [13]Xu F, Li L, Schulz VP, Gallagher PG, Xiang B, Zhao H, Li P: Cytogenomic mapping and bioinformatic mining reveal interacting brain expressed genes for intellectual disability. Mol Cytogenet 2014, 7:4. BioMed Central Full Text
• [14]Iourov IY, Vorsanova SG, Liehr T, Kolotii AD, Yurov YB: Increased chromosome instability dramatically disrupts neural genome integrity and mediates cerebellar degeneration in the ataxia-telangiectasia brain. Hum Mol Genet 2009, 18:2656-2669.
• [15]Iourov IY, Vorsanova SG, Saprina EA, Yurov YB: Identification of candidate genes of autism on the basis of molecular cytogenetic and in silico studies of the genome organization of chromosomal regions involved in unbalanced rearrangements. Russ J Genet 2010, 46:1190-1193.
• [16]Iourov IY, Vorsanova SG, Kurinnaia OS, Zelenova MA, Silvanovich AP, Yurov YB: Molecular karyotyping by array CGH in a Russian cohort of children with intellectual disability, autism, epilepsy and congenital anomalies. Mol Cytogenet 2012, 5:46. BioMed Central Full Text
• [17]Wu C, Orozco C, Boyer J, Leglise M, Goodale J, Batalov S, Hodge CL, Haase J, Janes J, Huss JW 3rd, Su AI: BioGPS: an extensible and customizable portal for querying and organizing gene annotation resources. Genome Biol 2009, 10:R130. BioMed Central Full Text
• [18]Iourov IY, Vorsanova SG, Yurov YB: Chromosomal variation in mammalian neuronal cells: known facts and attractive hypotheses. Int Rev Cytol 2006, 249:143-191.
• [19]Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, Coller H, Loh ML, Downing JR, Caligiuri MA, Bloomfield CD, Lander ES: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 1999, 286:531-537.
• [20]Oliver KL, Lukic V, Thorne NP, Berkovic SF, Scheffer IE, Bahlo M: Harnessing gene expression networks to prioritize candidate epileptic encephalopathy genes. PLoS One 2014, 9:e102079.
• [21]Paratore S, Pezzino S, Cavallaro S: Identification of pharmacological targets in amyotrophic lateral sclerosis through genomic analysis of deregulated genes and pathways. Curr Genomics 2012, 13:321-333.
• [22]Chen Y, Wang W, Zhou Y, Shields R, Chanda SK, Elston RC, Li J: In silico gene prioritization by integrating multiple data sources. PLoS One 2011, 6:e21137.
• [23]Dierssen M, Herault Y, Estivill X: Aneuploidy: from a physiological mechanism of variance to Down syndrome. Physiol Rev 2009, 89:887-920.
• [24]Vorsanova SG, Iourov IY, Voinova-Ulas VY, Weise A, Monakhov VV, Kolotii AD, Soloviev IV, Novikov PV, Yurov YB, Liehr T: Partial monosomy 7q34-qter and 21pter-q22.13 due to cryptic unbalanced translocation t(7;21) but not monosomy of the whole chromosome 21: a case report plus review of the literature. Mol Cytogenet 2008, 1:13. BioMed Central Full Text
• [25]Van Vooren S, Coessens B, De Moor B, Moreau Y, Vermeesch JR: Array comparative genomic hybridization and computational genome annotation in constitutional cytogenetics: suggesting candidate genes for novel submicroscopic chromosomal imbalance syndromes. Genet Med 2007, 9:642-649.
• [26]Giglio S, Broman KW, Matsumoto N, Calvari V, Gimelli G, Neumann T, Ohashi H, Voullaire L, Larizza D, Giorda R, Weber JL, Ledbetter DH, Zuffardi O: Olfactory receptor-gene clusters, genomic-inversion polymorphisms, and common chromosome rearrangements. Am J Hum Genet 2001, 68:874-883.
• [27]Zhou W, Zhang F, Chen X, Shen Y, Lupski JR, Jin L: Increased genome instability in human DNA segments with self-chains: homology-induced structural variations via replicative mechanisms. Hum Mol Genet 2013, 22:2642-2651.
• [28]Vorsanova SG, Yurov YB, Soloviev IV, Iourov IY: Molecular cytogenetic diagnosis and somatic genome variations. Curr Genomics 2010, 11:440-446.
• [29]Hoischen A, Krumm N, Eichler EE: Prioritization of neurodevelopmental disease genes by discovery of new mutations. Nat Neurosci 2014, 17:764-772.
• [30]Yurov YB, Vorsanova SG, Iourov IY: GIN'n'CIN hypothesis of brain aging: deciphering the role of somatic genetic instabilities and neural aneuploidy during ontogeny. Mol Cytogenet 2009, 2:23. BioMed Central Full Text
• [31]Yurov YB, Vorsanova SG, Iourov IY: The DNA replication stress hypothesis of Alzheimer's disease. Sci World J 2011, 11:2602-2612.
• [32]Li W, Mills AA: Architects of the genome: CHD dysfunction in cancer, developmental disorders and neurological syndromes. Epigenomics 2014, 6:381-395.
• [33]Iourov IY, Vorsanova SG, Yurov YB: Somatic genome variations in health and disease. Curr Genomics 2010, 11:387-396.
• [34]Oromendia AB, Amon A: Aneuploidy: implications for protein homeostasis and disease. Dis Model Mech 2014, 7:15-20.
• [35]Varetti G, Pellman D, Gordon DJ: Aurea mediocritas : the importance of a balanced genome.Cold Spring Harb Perspect Biol 2014, 6(11).
• [36]Valind A, Jin Y, Gisselsson D: Elevated tolerance to aneuploidy in cancer cells: estimating the fitness effects of chromosome number alterations by in silico modelling of somatic genome evolution. PLoS One 2013, 8:e70445.
• [37]Iourov IY, Vorsanova SG, Liehr T, Yurov YB: Aneuploidy in the normal, Alzheimer's disease and ataxia-telangiectasia brain: differential expression and pathological meaning. Neurobiol Dis 2009, 34:212-220.
• [38]Heng HH, Liu G, Stevens JB, Bremer SW, Ye KJ, Abdallah BY, Horne SD, Ye CJ: Decoding the genome beyond sequencing: the new phase of genomic research. Genomics 2011, 98:242-252.
• [39]Iourov IY, Vorsanova SG, Yurov YB: Chromosomal mosaicism goes global. Mol Cytogenet 2008, 1:26. BioMed Central Full Text
• [40]Dumanski JP, Piotrowski A: Structural genetic variation in the context of somatic mosaicism. Methods Mol Biol 2012, 838:249-272.
• [41]Campbell IM, Yuan B, Robberecht C, Pfundt R, Szafranski P, McEntagart ME, Nagamani SC, Erez A, Bartnik M, Wiśniowiecka-Kowalnik B, Plunkett KS, Pursley AN, Kang SH, Bi W, Lalani SR, Bacino CA, Vast M, Marks K, Patton M, Olofsson P, Patel A, Veltman JA, Cheung SW, Shaw CA, Vissers LE, Vermeesch JR, Lupski JR, Stankiewicz P: Parental somatic mosaicism is underrecognized and influences recurrence risk of genomic disorders. Am J Hum Genet 2014, 95:173-182.
• [42]Yurov YB, Vostrikov VM, Vorsanova SG, Monakhov VV, Iourov IY: Multicolor fluorescent in situ hybridization on post-mortem brain in schizophrenia as an approach for identification of low-level chromosomal aneuploidy in neuropsychiatric diseases. Brain Dev 2001, 23(Suppl. 1):S186-S190.
• [43]McConnell MJ, Kaushal D, Yang AH, Kingsbury MA, Rehen SK, Treuner K, Helton R, Annas EG, Chun J, Barlow C: Failed clearance of aneuploid embryonic neural progenitor cells leads to excess aneuploidy in the Atm-deficient but not the Trp53-deficient adult cerebral cortex. J Neurosci 2004, 24:8090-8096.
• [44]Yurov YB, Vorsanova SG, Iourov IY, Demidova IA, Beresheva AK, Kravetz VS, Monakhov VV, Kolotii AD, Voinova-Ulas VY, Gorbachevskaya NL: Unexplained autism is frequently associated with low-level mosaic aneuploidy. J Med Genet 2007, 44:521-525.
• [45]Yurov YB, Iourov IY, Vorsanova SG, Demidova IA, Kravetz VS, Beresheva AK, Kolotii AD, Monakchov VV, Uranova NA, Vostrikov VM, Soloviev IV, Liehr T: The schizophrenia brain exhibits low-level aneuploidy involving chromosome 1. Schizophr Res 2008, 98:139-147.
• [46]Devalle S, Sartore RC, Paulsen BS, Borges HL, Martins RA, Rehen SK: Implications of aneuploidy for stem cell biology and brain therapeutics. Front Cell Neurosci 2012, 6:36.
• [47]Bushman DM, Chun J: The genomically mosaic brain: aneuploidy and more in neural diversity and disease. Semin Cell Dev Biol 2013, 24:357-369.
• [48]Yurov YB, Vorsanova SG, Liehr T, Kolotii AD, Iourov IY: X chromosome aneuploidy in the Alzheimer's disease brain. Mol Cytogenet 2014, 7:20. BioMed Central Full Text
• [49]Ulrich H, Bocsi J, Glaser T, Tárnok A: Cytometry in the brain: studying differentiation to diagnostic applications in brain disease and regeneration therapy. Cell Prolif 2014, 47:12-19.
• [50]Iourov IY, Vorsanova SG, Yurov YB: Recent patents on molecular cytogenetics. Recent Pat DNA Gene Seq 2008, 2:6-15.
• [51]Vorsanova SG, Yurov YB, Iourov IY: Human interphase chromosomes: a review of available molecular cytogenetic technologies. Mol Cytogenet 2010, 3:1. BioMed Central Full Text
• [52]Yurov YB, Vorsanova SG, Iourov IY: Human Interphase Chromosomes: Biomedical Aspects. Springer; 2013.
• [53]Vidal M, Cusick ME, Barabási AL: Interactome networks and human disease. Cell 2011, 144:986-998.
• [54]Haider S, Pal R: Integrated analysis of transcriptomic and proteomic data. Curr Genomics 2013, 14:91-110.
• [55]Carter H, Hofree M, Ideker T: Genotype to phenotype via network analysis. Curr Opin Genet Dev 2013, 23:611-621.
• [56]Iourov IY, Vorsanova SG, Voinova VY, Kurinnaia OS, Zelenova MA, Demidova IA, Yurov YB: Xq28 (MECP2) microdeletions are common in mutation-negative females with Rett syndrome and cause mild subtypes of the disease. Mol Cytogenet 2013, 6:53. BioMed Central Full Text
• [57]Vorsanova SG, Iourov IY, Beresheva AK, Demidova IA, Monakhov VV, Kravets VS, Bartseva OB, Goyko EA, Soloviev IV, Yurov YB: Non-disjunction of chromosome 21, alphoid DNA variation, a nd sociogenetic features of Down syndrome. Tsitol Genet 2005, 39(6):30-36.
• [58]Iourov IY, Vorsanova SG, Pellestor F, Yurov YB: Brain tissue preparations for chromosomal PRINS labeling. Methods Mol Biol 2006, 334:123-132.
• [59]Shlomi T, Cabili MN, Herrgård MJ, Palsson BØ, Ruppin E: Network-based prediction of human tissue-specific metabolism. Nat Biotechnol 2008, 26:1003-1010.
• [60]Smoot ME, Ono K, Ruscheinski J, Wang PL, Ideker T: Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics 2011, 27:431-432.
• [61]Croft D, Mundo AF, Haw R, Milacic M, Weiser J, Wu G, Caudy M, Garapati P, Gillespie M, Kamdar MR, Jassal B, Jupe S, Matthews L, May B, Palatnik S, Rothfels K, Shamovsky V, Song H, Williams M, Birney E, Hermjakob H, Stein L, D'Eustachio P: The Reactome pathway knowledgebase. Nucleic Acids Res 2014, 42(Database issue):D472-D477.