Bioinformatics and machine learning-driven key genes screening for vortioxetine

Year 2024, , 17 - 27, 29.10.2024

Meliha Merve Hız , Sabire Kılıçarslan

https://doi.org/10.54559/jauist.1515129

Abstract

Vortioxetine is a pharmacological agent that acts as a serotonin modulator and stimulant, with safety and tolerability being important health issues. This study aimed to use bioinformatic and machine learning methods to find differentially expressed genes (DEG) between rats exposed to vortioxetine and matched controls. The GSE236207 dataset (Rattus norvegicus) was obtained from the National Center for Biotechnology Information (NCBI) and analyzed with R, followed by genetic ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) enrichment analyses, and String's protein-protein interaction network was established to identify important genes. The original datasets were preprocessed in the second step by detecting and correcting missing and noisy data and then merged. After feature selection for the cleaned dataset, machine learning algorithms such as the K-nearest neighbors' algorithm, Naive Bayes, and Support Vector Machine (SVM) were used. In addition, an accuracy of 0.90 was observed with SVM. Leveraging these techniques, the study linked IGFBP7, KLRA22, PROB1, SHQ1, NTNG1, and LOC102546359 to vortioxetine exposure. The bioinformatic analysis revealed 18 upregulated genes and 27 downregulated genes, with all approaches identifying only one common locus, LOC102546359, responsible for noncoding ribonucleic acid (ncRNA) synthesis. The crucial point is that this locus bears no connection to any disease or trigger mechanism, thereby bolstering the safety of vortioxetine.

Keywords

DEG, depression, LOC102546359, machine learning, systems biology

References

• G. Chen, A.-M. Højer, J. Areberg, G. Nomikos, Vortioxetine: Clinical pharmacokinetics and drug interactions, Clinical Pharmacokinetics 57 (6) (2018) 673–686.
• A. J. Krupa, K. Wojtasik-Bakalarz, M. Siwek, Vortioxetine - pharmacological properties and use in mood disorders. The current state of knowledge, Psychiatria Polska 57 (6) (2023) 1109–1126.
• S. Kiliçarslan, E. Dönmez, Improved multi-layer hybrid adaptive particle swarm optimization based artificial bee colony for optimizing feature selection and classification of microarray data, Multimedia Tools and Applications 83 (26) (2024) 67259-67281.
• S. Kiliçarslan, A novel nonlinear hybrid HardSReLUE activation function in transfer learning architectures for hemorrhage classification, Multimedia Tools and Applications 82 (4) (2023) 6345-6365.
• I. Pacal, Deep learning approaches for classification of breast cancer in ultrasound (US) images, Journal of the Institute of Science and Technology 12 (4) (2023) 1917–1927.
• I. Pacal, MaxCerVixT: A novel lightweight vision transformer-based approach for precise cervical cancer detection, Knowledge-Based Systems 289 (2024) 111482.
• Version 4.2.2, https://www.r-project.org/ (Accessed 30 May 2024).
• G. K. Smyth, Linear models and empirical Bayes methods for assessing differential expression in microarray experiments, Statistical Applications in Genetics and Molecular Biology 3 (3) (2004).
• G. Yu, L.-G. Wang, Y. Han, Q.-Y. He, ClusterProfiler: An R package for comparing biological themes among gene clusters, Omics: A Journal of Integrative Biology 16 (5) (2012) 284–287.
• Web-based gene set analysis toolkit, https://www.webgestalt.org/option.php (Accessed 30 May 2024).
• Kyoto encyclopedia of genes and genomes, https://www.genome.jp/kegg/ (Accessed 30 May 2024).
• J. C. Oliveros, (2007-2015) Venny. An interactive tool for comparing lists with Venn's diagrams. https://bioinfogp.cnb.csic.es/tools/venny/ (Accessed 30 May 2024).
• Protein-protein interaction networks functional enrichment analysis, https://string-db.org/ (Accessed 30 May 2024).
• V. N. Vapnik, The vicinal risk minimization principle and the SVMs, in The Nature of Statistical Learning Theory, V. N. Vapnik (Ed.), New York, NY: Springer, 2000, pp. 267–290.
• Ö. Akay, M. Tunçeli, Use of the support vector regression in medical data analysis, Experimental and Applied Medical Science 2 (4) (2021) 242–256.
• N. Cristianini, E. Ricci, Support vector machines, in Encyclopedia of Algorithms, M.-Y. Kao (Ed.), Boston, MA: Springer, 2008, pp. 928–932.
• J. L. Januzzi et al., IGFBP7 (Insulin-like growth factor–binding protein-7) and neprilysin inhibition in patients with heart failure, Circulation: Heart Failure 11 (10) (2018) e005133.
• F. Gays, S. Taha, C. G. Brooks, The distal upstream promoter in Ly49 genes, Pro1, is active in mature NK cells and T cells, does not require TATA boxes, and displays enhancer activity, The Journal of Immunology 194 (12) (2015) 6068–6081.
• Ø. Nylenna et al., The genes and gene organization of the Ly49 region of the rat natural killer cell gene complex, European Journal of Immunology 35 (1) (2005) 261–272.
• J. A. Karolak et al., Variants in SKP1, PROB1, and IL17B genes at keratoconus 5q31.1-q35.3 susceptibility locus identified by whole-exome sequencing, European Journal of Human Genetics 25 (1) (2027) 73–78.
• Archer, Hayley L., et al. NTNG1 mutations are a rare cause of Rett syndrome, American Journal of Medical Genetics Part A 140 (7) (2006) 691-694.
• Z. S. J. Liu et al., Effects of psychotropic drugs on ribosomal genes and protein synthesis, International Journal of Molecular Sciences 23 (13) (2022) 7180.
• C. M. Fusco et al., Neuronal ribosomes exhibit dynamic and context-dependent exchange of ribosomal proteins, Nature Communications, 12 (1) (2021) 6127.
• T. R. Powell et al., The genome-wide expression effects of escitalopram and its relationship to neurogenesis, hippocampal volume, and antidepressant response, American Journal of Medical Genetics Part B: Neuropsychiatric Genetics 174 (4) (2017) 427–434.
• L. Akotkar et al., Antidepressant effect of alpha lipoic acid in rats exposed to chronic unpredictable mild stress: Putative role of neurotransmitters and 5ht3 receptor, Future Pharmacology 3 (2) (2023) 407–425.
• B. P. Brennan et al., A placebo-controlled trial of acetyl-l-carnitine and α-lipoic acid in the treatment of bipolar depression, Journal of Clinical Psychopharmacology 33 (5) (2013) 627–635.
• J. Kleinkauf-Rocha, L. D. Bobermin, P. de M. Machado, C.-A. Gonçalves, C. Gottfried, A. Quincozes-Santos, Lipoic acid increases glutamate uptake, glutamine synthetase activity and glutathione content in C6 astrocyte cell line, International Journal of Developmental Neuroscience 31 (3) (2013) 165–170.
• M. R. Salazar, Alpha lipoic acid: a novel treatment for depression, Medical Hypotheses 55 (6) (2000) 510–512.
• M. C. C. Silva et al., Evidence for protective effect of lipoic acid and desvenlafaxine on oxidative stress in a model depression in mice, Progress in Neuro-Psychopharmacology and Biological Psychiatry 64 (4) (2016) 142–148.
• I. Croy, T. Hummel, Olfaction as a marker for depression, Journal of Neurology 264 (4) (2017) 631–638.
• Q. Li et al., Reduced amount of olfactory receptor neurons in the rat model of depression, Neuroscience Letters, 603 (2015) 48–54.
• A. Eskelund et al., Drugs with antidepressant properties affect tryptophan metabolites differently in rodent models with depression-like behavior, Journal of Neurochemistry 142(1) (2017) 118–131.