Biomarker Prediction in Autism Spectrum Disorder Using a Network-based Approach

Overview

Journal BMC Med Genomics

Publisher Biomed Central

Specialty Genetics

Date 2023 Jan 23

PMID 36691005

Authors

Maryam Rastegari

Najmeh Salehi

Fatemeh Zare-Mirakabad

Affiliations

Soon will be listed here.

Abstract

Background: Autism is a neurodevelopmental disorder that is usually diagnosed in early childhood. Timely diagnosis and early initiation of treatments such as behavioral therapy are important in autistic people. Discovering critical genes and regulators in this disorder can lead to early diagnosis. Since the contribution of miRNAs along their targets can lead us to a better understanding of autism, we propose a framework containing two steps for gene and miRNA discovery.

Methods: The first step, called the FA_gene algorithm, finds a small set of genes involved in autism. This algorithm uses the WGCNA package to construct a co-expression network for control samples and seek modules of genes that are not reproducible in the corresponding co-expression network for autistic samples. Then, the protein-protein interaction network is constructed for genes in the non-reproducible modules and a small set of genes that may have potential roles in autism is selected based on this network. The second step, named the DMN_miRNA algorithm, detects the minimum number of miRNAs related to autism. To do this, DMN_miRNA defines an extended Set Cover algorithm over the mRNA-miRNA network, consisting of the selected genes and corresponding miRNA regulators.

Results: In the first step of the framework, the FA_gene algorithm finds a set of important genes; TP53, TNF, MAPK3, ACTB, TLR7, LCK, RAC2, EEF2, CAT, ZAP70, CD19, RPLP0, CDKN1A, CCL2, CDK4, CCL5, CTSD, CD4, RACK1, CD74; using co-expression and protein-protein interaction networks. In the second step, the DMN_miRNA algorithm extracts critical miRNAs, hsa-mir-155-5p, hsa-mir-17-5p, hsa-mir-181a-5p, hsa-mir-18a-5p, and hsa-mir-92a-1-5p, as signature regulators for autism using important genes and mRNA-miRNA network. The importance of these key genes and miRNAs is confirmed by previous studies and enrichment analysis.

Conclusion: This study suggests FA_gene and DMN_miRNA algorithms for biomarker discovery, which lead us to a list of important players in ASD with potential roles in the nervous system or neurological disorders that can be experimentally investigated as candidates for ASD diagnostic tests.

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References

Upadhyaya Y, Xie L, Salama P, Cao S, Nho K, Saykin A . Differential co-expression analysis reveals early stage transcriptomic decoupling in alzheimer's disease. BMC Med Genomics. 2020; 13(Suppl 5):53. PMC: 7118822. DOI: 10.1186/s12920-020-0689-y. View

Carter C . Autism genes and the leukocyte transcriptome in autistic toddlers relate to pathogen interactomes, infection and the immune system. A role for excess neurotrophic sAPPα and reduced antimicrobial Aβ. Neurochem Int. 2019; 126:36-58. DOI: 10.1016/j.neuint.2019.03.007. View

Edgar R, Domrachev M, Lash A . Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 2001; 30(1):207-10. PMC: 99122. DOI: 10.1093/nar/30.1.207. View

He X, Zhang J . Why do hubs tend to be essential in protein networks?. PLoS Genet. 2006; 2(6):e88. PMC: 1473040. DOI: 10.1371/journal.pgen.0020088. View

Vaccaro T, Sorrentino J, Salvador S, Veit T, Souza D, de Almeida R . Alterations in the MicroRNA of the Blood of Autism Spectrum Disorder Patients: Effects on Epigenetic Regulation and Potential Biomarkers. Behav Sci (Basel). 2018; 8(8). PMC: 6115946. DOI: 10.3390/bs8080075. View

Almehmadi K, Tsilioni I, Theoharides T . Increased Expression of miR-155p5 in Amygdala of Children With Autism Spectrum Disorder. Autism Res. 2019; 13(1):18-23. DOI: 10.1002/aur.2205. View

Langfelder P, Horvath S . WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics. 2008; 9:559. PMC: 2631488. DOI: 10.1186/1471-2105-9-559. View

Cirnigliaro M, Barbagallo C, Gulisano M, Domini C, Barone R, Barbagallo D . Expression and Regulatory Network Analysis of miR-140-3p, a New Potential Serum Biomarker for Autism Spectrum Disorder. Front Mol Neurosci. 2017; 10:250. PMC: 5554380. DOI: 10.3389/fnmol.2017.00250. View

Segura M, Pedreno C, Obiols J, Taurines R, Pamias M, Grunblatt E . Neurotrophin blood-based gene expression and social cognition analysis in patients with autism spectrum disorder. Neurogenetics. 2014; 16(2):123-31. DOI: 10.1007/s10048-014-0434-9. View

10.

Buttini M, Westland C, Masliah E, Yafeh A, Wyss-Coray T, Mucke L . Novel role of human CD4 molecule identified in neurodegeneration. Nat Med. 1998; 4(4):441-6. DOI: 10.1038/nm0498-441. View

11.

Gabrielli A, Manzardo A, Butler M . GeneAnalytics Pathways and Profiling of Shared Autism and Cancer Genes. Int J Mol Sci. 2019; 20(5). PMC: 6429377. DOI: 10.3390/ijms20051166. View

12.

Latkowski T, Osowski S . Computerized system for recognition of autism on the basis of gene expression microarray data. Comput Biol Med. 2014; 56:82-8. DOI: 10.1016/j.compbiomed.2014.11.004. View

13.

Liu W, Dou F, Feng J, Yan Z . RACK1 is involved in β-amyloid impairment of muscarinic regulation of GABAergic transmission. Neurobiol Aging. 2009; 32(10):1818-26. PMC: 2888795. DOI: 10.1016/j.neurobiolaging.2009.10.017. View

14.

Karagkouni D, Paraskevopoulou M, Chatzopoulos S, Vlachos I, Tastsoglou S, Kanellos I . DIANA-TarBase v8: a decade-long collection of experimentally supported miRNA-gene interactions. Nucleic Acids Res. 2017; 46(D1):D239-D245. PMC: 5753203. DOI: 10.1093/nar/gkx1141. View

15.

Xie J, Huang L, Li X, Li H, Zhou Y, Zhu H . Immunological cytokine profiling identifies TNF-α as a key molecule dysregulated in autistic children. Oncotarget. 2017; 8(47):82390-82398. PMC: 5669898. DOI: 10.18632/oncotarget.19326. View

16.

Garcia-Esparcia P, Sideris-Lampretsas G, Hernandez-Ortega K, Grau-Rivera O, Sklaviadis T, Gelpi E . Altered mechanisms of protein synthesis in frontal cortex in Alzheimer disease and a mouse model. Am J Neurodegener Dis. 2017; 6(2):15-25. PMC: 5498849. View

17.

Saffari A, Arno M, Nasser E, Ronald A, Wong C, Schalkwyk L . RNA sequencing of identical twins discordant for autism reveals blood-based signatures implicating immune and transcriptional dysregulation. Mol Autism. 2019; 10:38. PMC: 6839145. DOI: 10.1186/s13229-019-0285-1. View

18.

Grove J, Ripke S, Als T, Mattheisen M, Walters R, Won H . Identification of common genetic risk variants for autism spectrum disorder. Nat Genet. 2019; 51(3):431-444. PMC: 6454898. DOI: 10.1038/s41588-019-0344-8. View

19.

Langfelder P, Luo R, Oldham M, Horvath S . Is my network module preserved and reproducible?. PLoS Comput Biol. 2011; 7(1):e1001057. PMC: 3024255. DOI: 10.1371/journal.pcbi.1001057. View

20.

Huang D, Sherman B, Lempicki R . Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009; 4(1):44-57. DOI: 10.1038/nprot.2008.211. View