Advertisement

A Bayesian Network Model for the Parkinson’s Disease: A Study of Gene Expression Levels

  • Sonia Lilia Mestizo-GutiérrezEmail author
  • Joan Arturo Jácome-Delgado
  • Viviana Yarel Rosales-Morales
  • Nicandro Cruz-Ramírez
  • Gonzalo Emiliano Aranda-Abreu
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 815)

Abstract

Parkinson’s disease (PD) is a neurodegenerative disorder characterized by tremor, postural instability, bradykinesia and rigidity. It is the second most prevalent neurodegenerative disease in the world. Currently, no cure has been found. Multiple investigations have proposed environmental and genetic factors, but no one has been determined as the trigger for the development of this disease, so it is a public health challenge in our society characterized by the increase of elderly people. The use of machine learning techniques has increased in the medical field and to help solve biological problems. The unprecedented volume of biomedical data provides a great opportunity for better understanding, prediction and decision making of conditions. In this study, we modeled gene expression profiles of peripheral blood samples from 105 individuals (50 with PD, 33 with control of neurodegenerative diseases, other than PD, and 22 healthy controls) using Bayesian networks with different dimensionality reduction techniques to create several sets of genes. From the obtained sets, classification models were generated and some genes that could be considered as PD candidates were obtained and some genes previously reported with this disease were corroborated.

Keywords

Parkinson’s disease Bayesian network Gene expression levels Microarray 

Notes

Acknowledgements

The authors thankfully acknowledge the computer resources, technical expertise and support provided by the Laboratorio Nacional de Supercómputo del Sureste de México, CONACYT network of national laboratories.

References

  1. 1.
    Rietdijk, C.D., Perez-Pardo, P., Garssen, J., van Wezel, R.J.A., Kraneveld, A.D.: Exploring Braak’s hypothesis of Parkinson’s disease. Front. Neurol. 8, 37 (2017)CrossRefGoogle Scholar
  2. 2.
    Secretaría de Salud: Diagnóstico y tratamiento de la Enfermedad de Parkinson inicial y avanzada en el tercer nivel de atención, p. 95 (2010)Google Scholar
  3. 3.
    Castro Toro, A., Buritic, O.F.: Enfermedad de parkinson: criterios diagnósticos, factores de riesgo y de progresión, y escalas de valoración del estadio clínico. Acta Neurol. Colomb 30, 300–306 (2014)Google Scholar
  4. 4.
    Parkinson, J.: An essay on the shaking palsy. J. Neuropsychiatry Clin. Neurosci. 14, 223–236 (2002)CrossRefGoogle Scholar
  5. 5.
    Allam, M.: Metaanális de los factores de riesgo en la enfermedad de Parkinson (2003)Google Scholar
  6. 6.
    Gómez-Chavarín, M., Torres-Ortiz, M.C., Perez-Soto, G.: Interacción entre factores genéticosambientales y la epigénesis de la enfermedad de Parkinson. Arch. Neurociencias 21, 32–44 (2016)Google Scholar
  7. 7.
    Parkinsons Disease Foundation: Parkinson’s Disease, pp. 1–12 (2014)Google Scholar
  8. 8.
    Gallagher, C., Adam Rindfleisch, J., Podein, R.: Capítulo 17—Enfermedad de Parkinson. Presented at the (2009)Google Scholar
  9. 9.
    Lyons, J., Lieberman, A.: Medicamentos para la enfermedad de Parkinson, AD (2008)Google Scholar
  10. 10.
    Martín Lunar, M., Elvira Peña, L., Gutiérrez Casares, J.R.: Fenómenos on-off de conducta en la enfermedad de Parkin. Psiquiatr. Biológica 10, 36–41 (2003)Google Scholar
  11. 11.
    Martínez-Fernández., R., Gasca-Salas C.C., Sánchez-Ferro, Á., Ángel Obeso, J.: Actualización En La Enfermedad De Parkinson. Rev. Médica Clínica Las Condes 27, 363–379 (2016)Google Scholar
  12. 12.
    García-Crespo, A., Alor-Hernández, G., Battistella, L., Rodríguez-González, A.: Editorial: methods and models for diagnosis and prognosis in medical systems. Comput. Math. Methods Med. (2013)Google Scholar
  13. 13.
    Rodríguez-González, A., Torres-Niño, J., Valencia-Garcia, R., Mayer, M.A., Alor-Hernandez, G.: Using experts feedback in clinical case resolution and arbitration as accuracy diagnosis methodology. Comput. Biol. Med. 43, 975–986 (2013)CrossRefGoogle Scholar
  14. 14.
    Rodríguez-González, A., Alor-Hernández, G.: An approach for solving multi-level diagnosis in high sensitivity medical diagnosis systems through the application of semantic technologies. Comput. Biol. Med. 43, 51–62 (2013)CrossRefGoogle Scholar
  15. 15.
    Rodríguez-González, A., Torres-Niño, J., Alor-Hernandez, G.: IKS index: a knowledge-model driven index to estimate the capability of medical diagnosis systems to produce results. Expert Syst. Appl. 40, 6798–6804 (2013)CrossRefGoogle Scholar
  16. 16.
    Chicco, D.: Ten quick tips for machine learning in computational biology. BioData Min. 10, 35 (2017)CrossRefGoogle Scholar
  17. 17.
    Berg, D., Lang, A.E., Postuma, R.B., Maetzler, W., Deuschl, G., Gasser, T., Siderowf, A., Schapira, A.H., Oertel, W., Obeso, J.A., Olanow, C.W., Poewe, W., Stern, M.: Changing the research criteria for the diagnosis of Parkinson’s disease: obstacles and opportunities. Lancet Neurol. 12, 514–524 (2013)CrossRefGoogle Scholar
  18. 18.
    Romo-Gutiérrez, D., Petra-Yescas, López-López, M., Boll, M.C.: Factores genéticos de la demencia en la enfermedad de parkinson (EP). Gac. Med. Mex. 151, 110–118 (2015)Google Scholar
  19. 19.
    Schulte, C., Gasser, T.: Genetic basis of Parkinson’s disease: inheritance, penetrance, and expression. Appl. Clin. Genet. 4, 67–80 (2011)Google Scholar
  20. 20.
    Miranda, J., Bringas, R.: Análisis de datos de microarreglos de ADN Parte I: Antecedentes de la tecnología y diseño experimental. Biotecnol. Apl. 25, 82–96 (2008)Google Scholar
  21. 21.
    Pashaei, E., Ozen, M., Aydin, N.: Biomarker discovery based on BBHA and AdaboostM1 on microarray data for cancer classification. In: 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3080–3083 (2016)Google Scholar
  22. 22.
    Wu, M., Dai, D., Shi, Y., Yan, H., Zhang, X.: Biomarker identification and cancer classification based on microarray data using Laplace Naive Bayes model with mean shrinkage. IEEE/ACM Trans. Comput. Biol. Bioinforma. 9, 1649–1662 (2012)CrossRefGoogle Scholar
  23. 23.
    Rivas-Lopez, M.J., Sánchez-Santos, J.M., De Las Rivas, J.: Estructura y análisis de microarrays. BEIO 21(1998), 10–15 (2005)Google Scholar
  24. 24.
    Cano Gutiérrez, C.: Extracción de conocimiento de microarrays y literatura biomédica para el estudio de la regulación genética. http://hdl.handle.net/10481/4864 (2010)
  25. 25.
    Elo, L.L., Filen, S., Lahesmaa, R., Aittokallio, T.: Reproducibility-optimized test statistic for ranking genes in microarray studies. IEEE/ACM Trans. Comput. Biol. Bioinf. 5, 423–431 (2008)CrossRefGoogle Scholar
  26. 26.
    Nagarajan, R., Upreti, M.: Correlation statistics for cDNA microarray image analysis. IEEE/ACM Trans. Comput. Biol. Bioinf. 3, 232–238 (2006)CrossRefGoogle Scholar
  27. 27.
    Hu, P., Greenwood, C.M., Beyene, J.: Integrating affymetrix microarray data sets using probe-level test statistic for predicting prostate cancer. In: 2006 IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology, pp. 1–8 (2006)Google Scholar
  28. 28.
    Kumar, G., Lahiri, T., Kumar, R.: Statistical discrimination of breast cancer microarray data. In: 2016 International Conference on Bioinformatics and Systems Biology (BSB), pp. 1–4 (2016)Google Scholar
  29. 29.
    Shashirekha, H.L., Wani, A.H.: A comparative study of statistical and clustering techniques based meta-analysis to identify differentially expressed genes. In: 2016 IEEE International Conference on Advances in Computer Applications (ICACA), pp. 87–93 (2016)Google Scholar
  30. 30.
    Sheela, T., Rangarajan, L.: Statistical class prediction method for efficient microarray gene expression data sample classification. In: 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 73–78 (2017)Google Scholar
  31. 31.
    Poreva, A., Karplyuk, Y., Vaityshyn, V.: Machine learning techniques application for lung diseases diagnosis. In: 2017 5th IEEE Workshop on Advances in Information, Electronic and Electrical Engineering (AIEEE), pp. 1–5 (2017)Google Scholar
  32. 32.
    Anakal, S., Sandhya, P.: Clinical decision support system for chronic obstructive pulmonary disease using machine learning techniques. In: 2017 International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques (ICEECCOT), pp. 1–5 (2017)Google Scholar
  33. 33.
    Raut, A., Dalal, V.: A machine learning based approach for detection of Alzheimer’s disease using analysis of hippocampus region from MRI scan. In: 2017 International Conference on Computing Methodologies and Communication (ICCMC), pp. 236–242 (2017)Google Scholar
  34. 34.
    Noticias CIEMAT: El proyecto IMED seleccionado como caso de éxito de I + D + i (2016)Google Scholar
  35. 35.
    Romano, M., Nissen, M.D., Del Huerto, N., Parquet, C.: Enfermedad de Alzheimer. Rev. posgrado la Vía Cátedra Med. 75, 9–12 (2007)Google Scholar
  36. 36.
    Bermejo-Pareja, F., Llamas-Velasco, S., Villarejo-Galende, A.: Prevención de la enfermedad de Alzheimer: un camino a seguir. Rev. Clínica Española 216, 495–503 (2016)CrossRefGoogle Scholar
  37. 37.
    Graham, J.G., Oppenheimer, D.R.: Orthostatic hypotension and nicotine sensitivity in a case of multiple system atrophy. J. Neurol. Neurosurg. Psychiatry 32, 28–34 (1969)CrossRefGoogle Scholar
  38. 38.
    Pereiro, I., Arias, M., Requena, I.: Signo de santiaguiño en la atrofia multisistémica. Neurología 25, 336–337 (2010)CrossRefGoogle Scholar
  39. 39.
    Pérez Rodríguez, M., Álvarez Gómez, T., Lezcano Pérez, Y., Tahir Shabbir, M., Valdivia Cañizares, S.: Atrofia multisistémica. Presentación de un caso (2014)Google Scholar
  40. 40.
    Litvan, I., Mangone, C.A., McKee, A., Verny, M., Parsa, A., Jellinger, K., D’Olhaberriague, L., Chaudhuri, K.R., Pearce, R.K.: Natural history of progressive supranuclear palsy (Steele-Richardson-Olszewski syndrome) and clinical predictors of survival: a clinicopathological study. J. Neurol. Neurosurg. Psychiatry 60, 615–620 (1996)CrossRefGoogle Scholar
  41. 41.
    Ling, H.: Clinical approach to progressive supranuclear palsy. J. Mov. Disord. 9, 3–13 (2016)CrossRefGoogle Scholar
  42. 42.
    Abramson, J.H.: Métodos de estudio en medicina comunitaria: una introducción a los estudios epidemiológicos y de evaluación. Ediciones Díaz de Santos (1990)Google Scholar
  43. 43.
    Lewin, B.: genes IX. 2008. Jones Barlett Publ. (2008)Google Scholar
  44. 44.
    Moreno, V., Solé, X.: Uso de chips de ADN (microarrays) en medicina: fundamentos técnicos y procedimientos básicos para el análisis estadístico de resultados. Med. Clin. (Barc) 122, 73–79 (2004)CrossRefGoogle Scholar
  45. 45.
    Pontes, B., Rodríguez-Baena, D., Díaz-Díaz, N.: Análisis de Datos de Expresión Genética. Jornadas de. (2006)Google Scholar
  46. 46.
    Arango, S.: Biomarcadores para la evaluación de riesgo en la salud humana. Rev. Fac. Nac. Salud Pública 30, 75–82 (2012)Google Scholar
  47. 47.
    Bazazeh, D., Shubair, R.M., Malik, W.Q.: Biomarker discovery and validation for Parkinson’s disease: a machine learning approach. In: 2016 International Conference on Bio-engineering for Smart Technologies (BioSMART), pp. 1–6 (2016)Google Scholar
  48. 48.
    Sharma, S., Moon, C.S., Khogali, A., Haidous, A., Chabenne, A., Ojo, C., Jelebinkov, M., Kurdi, Y., Ebadi, M.: Biomarkers in Parkinson’s disease (recent update). Neurochem. Int. 63, 201–229 (2013)CrossRefGoogle Scholar
  49. 49.
    Kaddurah-Daouk, R., Soares, J.C., Quinones, M.P.: Metabolomics: a global biochemical approach to the discovery of biomarkers for psychiatric disorders BT—biomarkers for psychiatric disorders. Presented at the (2009)Google Scholar
  50. 50.
    Hazan, H., Hilu, D., Manevitz, L., Ramig, L.O., Sapir, S.: Early diagnosis of Parkinson’s disease via machine learning on speech data. In: 2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel, pp. 1–4 (2012)Google Scholar
  51. 51.
    Sapir, S., Ramig, L.O., Spielman, J.L., Fox, C.: Formant centralization ratio: a proposal for a new acoustic measure of dysarthric speech. J. Speech Lang. Hear. Res. 53, 114 (2010)CrossRefGoogle Scholar
  52. 52.
    Sapir, S., Spielman, J.L., Ramig, L.O., Story, B.H., Fox, C.: Shimon Sapir. Hear. Res. 50, 899–913 (2007)Google Scholar
  53. 53.
    Skodda, S., Visser, W., Schlegel, U.: Vowel articulation in Parkinson’s disease. J. Voice 25, 467–472 (2011)CrossRefGoogle Scholar
  54. 54.
    Vadovský, M., Paralič, J.: Parkinson’s disease patients classification based on the speech signals. In: 2017 IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI), pp. 321–326 (2017)Google Scholar
  55. 55.
    Yadav, G., Kumar, Y., Sahoo, G.: Predication of Parkinson’s disease using data mining methods: a comparative analysis of tree, statistical and support vector machine classifiers. In: 2012 National Conference on Computing and Communication Systems, pp. 1–8 (2012)Google Scholar
  56. 56.
    Xiao, H.: Diagnosis of Parkinson’s disease using genetic algorithm and support vector machine with acoustic characteristics. In: 2012 5th International Conference on BioMedical Engineering and Informatics, pp. 1072–1076 (2012)Google Scholar
  57. 57.
    Prashanth, R., Dutta Roy, S., Mandal, P.K., Ghosh, S.: High-accuracy detection of early parkinson’s disease through multimodal features and machine learning. Int. J. Med. Inform. 90, 13–21 (2016)CrossRefGoogle Scholar
  58. 58.
    Challa, K.N.R., Pagolu, V.S., Panda, G., Majhi, B.: An improved approach for prediction of Parkinson’s disease using machine learning techniques. In: 2016 International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES), pp. 1446–1451 (2016)Google Scholar
  59. 59.
    Joshi, S., Shenoy, D., Vibhudendra Simha, G.G., Rrashmi, P.L., Venugopal, K.R., Patnaik, L.M.: Classification of Alzheimer’s disease and Parkinson’s disease by using machine learning and neural network methods. In: 2010 Second International Conference on Machine Learning and Computing, pp. 218–222 (2010)Google Scholar
  60. 60.
    Manap, H.H., Tahir, N.M., Abdullah, R.: Parkinsonian gait motor impairment detection using decision tree. In: 2013 European Modelling Symposium, pp. 209–214 (2013)Google Scholar
  61. 61.
    Morales, D.A., Vives-Gilabert, Y., Gómez-Ansón, B., Bengoetxea, E., Larrañaga, P., Bielza, C., Pagonabarraga, J., Kulisevsky, J., Corcuera-Solano, I., Delfino, M.: Predicting dementia development in Parkinson’s disease using Bayesian network classifiers. Psychiatry Res. Neuroimaging 213, 92–98 (2013)CrossRefGoogle Scholar
  62. 62.
    Exarchos, T.P., Tzallas, A.T., Baga, D., Chaloglou, D., Fotiadis, D.I., Tsouli, S., Diakou, M., Konitsiotis, S.: Using partial decision trees to predict Parkinson’s symptoms: a new approach for diagnosis and therapy in patients suffering from Parkinson’s disease. Comput. Biol. Med. 42, 195–204 (2012)CrossRefGoogle Scholar
  63. 63.
    Sateesh Babu, G., Suresh, S.: Parkinson’s disease prediction using gene expression—a projection based learning meta-cognitive neural classifier approach. Expert Syst. Appl. 40, 1519–1529 (2013)CrossRefGoogle Scholar
  64. 64.
    Sachnev, V., Kim, H.J.: Parkinson disease classification based on binary coded genetic algorithm and extreme learning machine. http://www.scopus.com/inward/record.url?scp=84903721396&partnerID=8YFLogxK (2014)
  65. 65.
    Karlsson, M.K., Lönneborg, A., Sæbø, S.: Microarray-based prediction of Parkinson’s disease using clinical data as additional response variables. Stat. Med. 31, 4369–4381 (2012)MathSciNetCrossRefGoogle Scholar
  66. 66.
    Scherzer, C.R., Eklund, A.C., Morse, L.J., Liao, Z., Locascio, J.J., Fefer, D., Schwarzschild, M.A., Schlossmacher, M.G., Hauser, M.A., Vance, J.M., Sudarsky, L.R., Standaert, D.G., Growdon, J.H., Jensen, R.V, Gullans, S.R.: Molecular markers of early Parkinson’s disease based on gene expression in blood. Proc. Natl. Acad. Sci. 104, 955 LP-960 (2007)CrossRefGoogle Scholar
  67. 67.
    Brazma, A., Hingamp, P., Quackenbush, J., Sherlock, G., Spellman, P., Stoeckert, C., Aach, J., Ansorge, W., Ball, C.A., Causton, H.C., Gaasterland, T., Glenisson, P., Holstege, F.C.P., Kim, I.F., Markowitz, V., Matese, J.C., Parkinson, H., Robinson, A., Sarkans, U., Schulze-Kremer, S., Stewart, J., Taylor, R., Vilo, J., Vingron, M.: Minimum information about a microarray experiment (MIAME)—toward standards for microarray data. Nat. Genet 29, 365 (2001)CrossRefGoogle Scholar
  68. 68.
    Stafford, P.: Methods in microarray normalization. CRC Press (2008)Google Scholar
  69. 69.
    Allen, T.: Detecting differential gene expression using affymetrix microarrays. Math. J. 15 (2013)Google Scholar
  70. 70.
    Miranda, J., Bringas, R.: Análisis de datos de microarreglos de ADN. Parte II: Cuantificación y análisis de la expresión génica. Biotecnol. Apl. 25, 290–311 (2008)Google Scholar
  71. 71.
    Irizarry, R.A., Hobbs, B., Collin, F., Beazer-Barclay, Y.D., Antonellis, K.J., Scherf, U., Speed, T.P.: Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 4, 249–264 (2003)CrossRefGoogle Scholar
  72. 72.
    Wu, Z., Irizarry, R.: Description of gcrma package. R Packag. Vignette 1–6 (2014)Google Scholar
  73. 73.
    Affymetrix, Inc., Statistical Algorithms Description Document © 2002 (2002)Google Scholar
  74. 74.
    Gautier, L., Irizarry, R., Cope, L., Bolstad, B.: Description of affy. Changes 1–29 (2009)Google Scholar
  75. 75.
    Bolstad, B.: Affy: built-in processing methods, pp. 1–7 (2017)Google Scholar
  76. 76.
    Pearl, J.: Probabilistic reasoning in intelligent systems: networks of plausible inference, Elsevier (2014)Google Scholar
  77. 77.
    Heckerman, D.: A tutorial on learning with bayesian networks. Microsoft Res. 1995, 1996Google Scholar
  78. 78.
    Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian network classifiers. Mach. Learn. 29, 131–163 (1997)CrossRefGoogle Scholar
  79. 79.
    Friedman, N., Linial, M., Nachman, I., Pe’er, D.: Using Bayesian networks to analyze expression data. J. Comp. Biol. 7, 601–620 (2000)CrossRefGoogle Scholar
  80. 80.
    Le, P.P., Bahl, A., Ungar, L.H.: Using prior knowledge to improve genetic network reconstruction from microarray data. Silico Biol. 4, 335–353 (2004)Google Scholar
  81. 81.
    Ching, J.Y., Wong, A.K.C., Chan, K.C.C.: Class-dependent discretization for inductive learning from continuous and mixed-mode data. IEEE Trans. Pattern Anal. Mach. Intell. 17, 641–651 (1995)CrossRefGoogle Scholar
  82. 82.
    Wong, A.K.C., Chiu, D.K.Y.: Synthesizing statistical knowledge from incomplete mixed-mode data. IEEE Trans. Pattern Anal. Mach. Intell. PAMI-9 796–805 (1987)CrossRefGoogle Scholar
  83. 83.
    Kerber, R.: ChiMerge: discretization of numeric attributes. In: Proceedings of the Tenth National Conference on Artificial Intelligence, pp. 123–128. AAAI Press (1992)Google Scholar
  84. 84.
    Liu, H., Setiono, R.: Feature selection via discretization. IEEE Trans. Knowl. Data Eng. 9, 642–645 (1997)CrossRefGoogle Scholar
  85. 85.
    Fayyad, U., Irani, K.: Multi-interval discretization of continuous-valued attributes for classification learning (1993)Google Scholar
  86. 86.
    Kurgan, L.A., Cios, K.J.: CAIM discretization algorithm. IEEE Trans. Knowl. Data Eng. 16, 145–153 (2004)CrossRefGoogle Scholar
  87. 87.
    Bie, C.Y.C., Shen, H.C., Chiu, D.K.Y.: Hierarchical maximum entropy partitioning in texture image analysis. Pattern Recognit. Letter 14, 421–429 (1993)Google Scholar
  88. 88.
    McDonald, J.H.: Handbook of biological statistics. sparky house publishing Baltimore, MD (2009)Google Scholar
  89. 89.
    Hall, M.A., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. SIGKDD Explor. 11, 10–18 (2009)CrossRefGoogle Scholar
  90. 90.
    Gautier, L., Cope, L., Bolstad, B.M., Irizarry, R.A.: Affy—analysis of Affymetrix GeneChip data at the probe level. Bioinformatics 20, 307–315 (2004)CrossRefGoogle Scholar
  91. 91.
    Zhang, L., Guo, X.Q., Chu, J.F., Zhang, X., Yan, Z.R., Li, Y.Z.: Potential hippocampal genes and pathways involved in Alzheimer’s disease: a bioinformatic analysis. Genet. Mol. Res. 14, 7218–7232 (2015)CrossRefGoogle Scholar
  92. 92.
    Sun, A.-G., Wang, J., Shan, Y.-Z., Yu, W.-J., Li, X., Cong, C.-H., Wang, X.: Identifying distinct candidate genes for early Parkinson’s disease by analysis of gene expression in whole blood. Neuro Endocrinol. Lett. 35, 398–404 (2014)Google Scholar
  93. 93.
    Edlich, F., Fischer, G.: Pharmacological targeting of catalyzed protein folding: the example of peptide bond cis/trans isomerases BT—molecular chaperones in health and disease. Presented at the (2006)Google Scholar
  94. 94.
    Gerard, M., Deleersnijder, A., Daniëls, V., Schreurs, S., Munck, S., Reumers, V., Pottel, H., Engelborghs, Y., Van den Haute, C., Taymans, J.-M., Debyser, Z., Baekelandt, V.: Inhibition of FK506 binding proteins reduces α-Synuclein aggregation and Parkinsons disease-like pathology. J. Neurosci. 30, 2454 LP-2463 (2010)CrossRefGoogle Scholar
  95. 95.
    Safran, M., Dalah, I., Alexander, J., Rosen, N., Iny Stein, T., Shmoish, M., Nativ, N., Bahir, I., Doniger, T., Krug, H.: GeneCards Version 3: the human gene integrator. Database (2010)Google Scholar
  96. 96.
    Mescheriakova, J.Y., Verkerk, A.J.M.H., Amin, N., Uitterlinden, A.G., van Duijn, C.M., Hintzen, R.Q.: Linkage analysis and whole exome sequencing identify a novel candidate gene in a Dutch multiple sclerosis family. Mult. Scler. J. 1352458518777202 (2018)Google Scholar
  97. 97.
    McCoy, M.K., Kaganovich, A., Rudenko, I.N., Ding, J., Cookson, M.R.: Hexokinase activity is required for recruitment of parkin to depolarized mitochondria. Hum. Mol. Genet. 23, 145–156 (2014)CrossRefGoogle Scholar
  98. 98.
    Yang, Y., Zhang, Y., Qu, X., Xia, J., Li, D., Li, X., Wang, Y., He, Z., Li, S., Zhou, Y., Xie, L., Yang, Z., Yang, Y., Zhang, Y., Qu, X., Xia, J., Li, D., Li, X., Wang, Y., He, Z., Li, S., Zhou, Y., Xie, L., Yang, Z.: Identification of differentially expressed genes in the development of osteosarcoma using RNA-seq. Oncotarget 7, 87194–87205 (2016)Google Scholar
  99. 99.
    Shen, Z.-J., Hu, J., Esnault, S., Dozmorov, I., Malter, J.S.: RNA Seq profiling reveals a novel expression pattern of TGF-β target genes in human blood eosinophils. Immunol. Letter 167, 1–10 (2015)Google Scholar
  100. 100.
    Xu, T., Jin, Z., Yuan, Y., Zheng, H., Li, C., Hou, W., Guo, Q., Hua, B.: Tat-Interacting Protein 30 (TIP30) expression serves as a new biomarker for tumor prognosis: a systematic review and meta-analysis. PLoS ONE 11, e0168408 (2016)CrossRefGoogle Scholar
  101. 101.
    Nanok, C., Jearanaikoon, P., Proungvitaya, S., Limpaiboon, T.: Aberrant methylation of HTATIP2 and UCHL1 as a predictive biomarker for cholangiocarcinoma. Mol. Med. Rep. 17, 4145–4153 (2018)Google Scholar
  102. 102.
    Valente, A.X.C.N., Sousa, J.A.B., Outeiro, T.F., Ferreira, L.: A stem-cell ageing hypothesis on the origin of Parkinson’s disease. arXiv:1003.1993 (2010)
  103. 103.
    Scherzer, C.R., Eklund, A.C., Morse, L.J., Liao, Z., Locascio, J.J., Fefer, D., Schwarzschild, M.A., Schlossmacher, M.G., Hauser, M.A., Vance, J.M., Sudarsky, L.R., Standaert, D.G., Growdon, J.H., Jensen, R.V, Gullans, S.R.: Molecular markers of early Parkinsons disease based on gene expression in blood. Proc. Natl. Acad. Sci. 104, 955 LP-960 (2007)CrossRefGoogle Scholar
  104. 104.
    Liang, L., Gao, C., Li, Y., Sun, M., Xu, J., Li, H., Jia, L., Zhao, Y.: miR-125a-3p/FUT5-FUT6 axis mediates colorectal cancer cell proliferation, migration, invasion and pathological angiogenesis via PI3K-Akt pathway. Cell Death Dis. 8, e2968 (2017)CrossRefGoogle Scholar
  105. 105.
    Li, N., Liu, Y., Miao, Y., Zhao, L., Zhou, H., Jia, L.: MicroRNA-106b targets FUT6 to promote cell migration, invasion, and proliferation in human breast cancer. IUBMB Life 68, 764–775 (2016)CrossRefGoogle Scholar
  106. 106.
    Shen, X., Klarić, L., Sharapov, S., Mangino, M., Ning, Z., Wu, D., Trbojević-Akmačić, I., Pučić-Baković, M., Rudan, I., Polašek, O.: Multivariate discovery and replication of five novel loci associated with immunoglobulin GN-glycosylation. Nat. Commun. 8, 447 (2017)CrossRefGoogle Scholar
  107. 107.
    Russell, A.C., Šimurina, M., Garcia, M.T., Novokmet, M., Wang, Y., Rudan, I., Campbell, H., Lauc, G., Thomas, M.G., Wang, W.: The N-glycosylation of immunoglobulin G as a novel biomarker of Parkinson’s disease. Glycobiology 27, 501–510 (2017)CrossRefGoogle Scholar
  108. 108.
    Khan, M.A., Windpassinger, C., Ali, M.Z., Zubair, M., Gul, H., Abbas, S., Khan, S., Badar, M., Mohammad, R.M., Nawaz, Z.: Molecular genetic analysis of consanguineous families with primary microcephaly identified pathogenic variants in the ASPM gene. J. Genet. 96, 383–387 (2017)CrossRefGoogle Scholar
  109. 109.
    Yigit, G., Brown, K.E., Kayserili, H., Pohl, E., Caliebe, A., Zahnleiter, D., Rosser, E., Bögershausen, N., Uyguner, Z.O., Altunoglu, U.: Mutations in CDK 5 RAP 2 cause Seckel syndrome. Mol. Genet. genomic Med. 3, 467–480 (2015)CrossRefGoogle Scholar
  110. 110.
    Zhang, H., Zhu, Q., Cui, J., Wang, Y., Chen, M.J., Guo, X., Tagliabracci, V.S., Dixon, J.E., Xiao, J.: Structure and evolution of the Fam20 kinases. Nat. Commun. 9, 1218 (2018)CrossRefGoogle Scholar
  111. 111.
    Chang, D., Nalls, M.A., Hallgrímsdóttir, I.B., Hunkapiller, J., van der Brug, M., Cai, F., Kerchner, G.A., Ayalon, G., Bingol, B., Sheng, M.: A meta-analysis of genome-wide association studies identifies 17 new Parkinson’s disease risk loci. Nat. Genet. 49, 1511 (2017)CrossRefGoogle Scholar
  112. 112.
    Pamphlett, R., Morahan, J.M., Yu, B.: Using case-parent trios to look for rare de novo genetic variants in adult-onset neurodegenerative diseases. J. Neurosci. Methods 197, 297–301 (2011)CrossRefGoogle Scholar
  113. 113.
    Toma, C., Hervás, A., Balmaña, N., Vilella, E., Aguilera, F., Cuscó, I., del Campo, M., Caballero, R., De Diego-Otero, Y., Ribasés, M.: Association study of six candidate genes asymmetrically expressed in the two cerebral hemispheres suggests the involvement of BAIAP2 in autism. J. Psychiatr. Res. 45, 280–282 (2011)CrossRefGoogle Scholar
  114. 114.
    McKinney, B., Ding, Y., Lewis, D.A., Sweet, R.A.: DNA methylation as a putative mechanism for reduced dendritic spine density in the superior temporal gyrus of subjects with schizophrenia. Transl. Psychiatry 7, e1032 (2017)CrossRefGoogle Scholar
  115. 115.
    Luksys, G., Ackermann, S., Coynel, D., Fastenrath, M., Gschwind, L., Heck, A., Rasch, B., Spalek, K., Vogler, C.: Papassotiropoulos, A., BAIAP2 is related to emotional modulation of human memory strength. PLoS ONE 9, e83707 (2014)CrossRefGoogle Scholar
  116. 116.
    Mohammadi, A., Mehdizadeh, A.R.: Deep brain stimulation and gene expression alterations in Parkinson’s disease. J. Biomed. Phys. Eng. 6 (2016)Google Scholar
  117. 117.
    Chen, Z., Simmons, M.S., Perry, R.T., Wiener, H.W., Harrell, L.E., Go, R.C.P.: Genetic association of neurotrophic tyrosine kinase receptor type 2 (NTRK2) with Alzheimer’s disease. Am. J. Med. Genet. Part B Neuropsychiatr. Genet. 147, 363–369 (2008)CrossRefGoogle Scholar
  118. 118.
    Torres, C.M., Siebert, M., Bock, H., Mota, S.M., Krammer, B.R., Duarte, J.Á., Bragatti, J.A., Castan, J.U., de Castro, L.A., Saraiva-Pereira, M.L.: NTRK2 (TrkB gene) variants and temporal lobe epilepsy: a genetic association study. Epilepsy Res. 137, 1–8 (2017)CrossRefGoogle Scholar
  119. 119.
    Yanai, A., Huang, K., Kang, R., Singaraja, R.R., Arstikaitis, P., Gan, L., Orban, P.C., Mullard, A., Cowan, C.M., Raymond, L.A.: Palmitoylation of huntingtin by HIP14is essential for its trafficking and function. Nat. Neurosci. 9, 824 (2006)CrossRefGoogle Scholar
  120. 120.
    Alvarado, C.V., Rubio, M.F., Larrosa, P.N.F., Panelo, L.C., Azurmendi, P.J., Grecco, M.R., Martínez-Nöel, G.A., Costas, M.A.: The levels of RAC3 expression are up regulated by TNF in the inflammatory response. FEBS Open Bio. 4, 450–457 (2014)CrossRefGoogle Scholar
  121. 121.
    Nagle, M.W., Latourelle, J.C., Labadorf, A., Dumitriu, A., Hadzi, T.C., Beach, T.G., Myers, R.H.: The 4p16. 3 Parkinson disease risk locus is associated with GAK expression and genes involved with the synaptic vesicle membrane. PLoS ONE 11, e0160925 (2016)CrossRefGoogle Scholar
  122. 122.
    Greene, L.A., Levy, O., Malagelada, C.: Akt as a victim, villain and potential hero in Parkinson’s disease pathophysiology and treatment. Cell. Mol. Neurobiol. 31, 969–978 (2011)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Sonia Lilia Mestizo-Gutiérrez
    • 1
    Email author
  • Joan Arturo Jácome-Delgado
    • 2
  • Viviana Yarel Rosales-Morales
    • 3
  • Nicandro Cruz-Ramírez
    • 3
  • Gonzalo Emiliano Aranda-Abreu
    • 4
  1. 1.Facultad de Ciencias QuímicasUniversidad VeracruzanaXalapaMexico
  2. 2.Laboratorio Nacional de Informática AvanzadaXalapaMéxico
  3. 3.Centro de Investigación en Inteligencia ArtificialUniversidad VeracruzanaXalapaMéxico
  4. 4.Centro de Investigaciones CerebralesUniversidad VeracruzanaXalapaMéxico

Personalised recommendations