Deiver Suarez-Gómez¹

Karlianie Rivera-Rodríguez²

Isis Y. Narvaez-Bandera¹

Clara Isaza ⁴,⁵

Mauricio Cabrera-Ríos¹,³

1 Graduate Program in Bioengineering

2 Department of Industrial Biotechnology

3 Department of Industrial Engineering

4 Public Health Program

5 Department of Basic Sciences

University of Puerto Rico, Mayagüez Campus

Ponce Health Sciences University

Autism (ASD) and schizophrenia (SCZ) are neurodevelopmental conditions that share numerous interpersonal and cognitive deficits. These two conditions have overlapping traits, symptoms, and gene-expression patterns even though they are considered clinically separate. This study aims to help understand the genetic differences and similarities between ASD and SCZ from their joint analysis via BioOptimatics, the study of biological data using mathematical optimization. To achieve this aim, this work proposes first to identify differentially expressed genes across multiple performance measures through multiple criteria optimization (MCO). The genes identified this way will be proposed as potential biomarkers. Secondly, these genes will be analyzed to obtain the maximum statistical correlation structure among them as a proxy to a signaling pathway. To this end, network optimization will be used to obtain the minimum spanning tree (MST).

Our preliminary results in SCZ involve the analysis of the GSE35974 microarray dataset from the human cerebellum and the GSE35977 microarray dataset from the human parietal lobe. When MCO was applied to GSE35974 the following genes were found as differentially expressed: DCLK1, GSTM1, MIR29B2, VCAM1, ZBTB16. For GSE35977 the differentially expressed genes were: AQP4, FOS, METTL7A, MGST1, RFX4, RNVU1-18, SLC1A2, SLC39A12. Guided by the subsequent MST results, we searched for biological information using KEGG and GeneCards. This information will be further processed through optimal group formation to pair genes and cellular processes to help explain SCZ first. In time, the joint analysis SCZ-ASD will follow a similar analysis path to arrive to conclusions that are strongly supported both by data and by a solid biological explanation.

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