Suárez Gómez, Deiver

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    Gene expression commonalities between autism and schizophrenia via biooptimatics
    (2023-09-19) Suárez Gómez, Deiver; Isaza Brando , Clara E.; College of Engineering; Cabrera Ríos, Mauricio; González Méndez, Ricardo; Latorre Esteves, Magda; Pérez Morales, Jaileene; Other; Zapata Medina, Rocío
    In the past, schizophrenia and autism spectrum disorders were diagnosed as a single disorder, but they are now recognized as separate and distinct conditions due to their different symptoms and age of onset. Nevertheless, similarities between schizophrenia and autism spectrum disorders have been found, such as the sharing of genetic information and the absence of typical behaviors reflecting the deterioration of social cognition. Despite these findings, the etiology of both disorders remains uncertain and there is still no objective diagnosis or effective cure. This study aims to help explain the common genetics between both conditions through gene expression data analysis using optimization methods. For this, public case-control studies of blood and brain tissue samples from autism and schizophrenia were examined. An R software package called Optimization-Based Analysis of Micro Arrays (OBAMA) was developed, which includes multiple criteria optimization (MCO), minimum spanning tree (MST), and -for the first time- optimal group formation (OGF) methodologies. With OBAMA, the following analyses were performed: 1) individual analysis of individual datasets, taking into account the characterization of sex to minimize sex bias; 2) meta-analysis of datasets, with the same consideration of sex characterization; 3) maximum correlation structures of individual and meta-analyses; and 4) formation of groups of genes and biological processes in optimal global conditions. The OBAMA package has the advantage of being portable and able to be run on modest computer hardware, such as personal computers. With its use, this study identified the following genes for autism spectrum disorder and schizophrenia: two genes (VNN2, PLGLB1) in blood samples, four genes in brain tissue (HSPA6, RGS1, RNU4_2, CCL4) and a common gene in blood and brain tissue (S100A8). In addition, signaling pathways involved in inflammation, ribosome, metabolism, and cancer were proposed, among others. Finally, we propose groups of genes involved in different biological processes, including metabolic processes, signals, cell communication, responses to stimuli, among others. The results set, which includes genes, pathways, and biological processes, could be crucial in helping to understand the underlying etiology of these two conditions.