Department of Mathematics and Computer Science, Amirkabir University of Technology (Tehran, Polytechnic), 424, Hafez Ave, P.O. Box: 15875-4413, Tehran, Iran.
School of Biological Science, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran.
BMC Med Genomics. 2023 Jan 23;16(1):12. doi: 10.1186/s12920-023-01439-5.
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.
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.
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.
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.
自闭症是一种神经发育障碍，通常在幼儿期诊断。在自闭症患者中，及时诊断和早期开始行为疗法等治疗非常重要。发现这种疾病的关键基因和调节剂可以实现早期诊断。由于 miRNA 及其靶标之间的相互作用可以帮助我们更好地理解自闭症，因此我们提出了一个包含两个步骤的基因和 miRNA 发现框架。
第一步称为 FA_gene 算法，用于发现与自闭症相关的一小部分基因。该算法使用 WGCNA 包为对照组样本构建一个共表达网络，并寻找在相应自闭症样本的共表达网络中不可重复的基因模块。然后，为不可重复模块中的基因构建蛋白质-蛋白质相互作用网络，并根据该网络选择一小部分可能在自闭症中发挥作用的基因。第二步称为 DMN_miRNA 算法，用于检测与自闭症相关的最小数量的 miRNA。为此，DMN_miRNA 算法在 mRNA-miRNA 网络上定义了一个扩展的集合覆盖算法，该算法由选定的基因和相应的 miRNA 调节剂组成。
在框架的第一步中，FA_gene 算法发现了一组重要基因，包括 TP53、TNF、MAPK3、ACTB、TLR7、LCK、RAC2、EEF2、CAT、ZAP70、CD19、RPLP0、CDKN1A、CCL2、CDK4、CCL5、CTSD、CD4、RACK1、CD74，使用共表达和蛋白质-蛋白质相互作用网络。在第二步中，DMN_miRNA 算法使用重要基因和 mRNA-miRNA 网络提取关键 miRNA，包括 hsa-mir-155-5p、hsa-mir-17-5p、hsa-mir-181a-5p、hsa-mir-18a-5p 和 hsa-mir-92a-1-5p，作为自闭症的特征调节剂。这些关键基因和 miRNA 的重要性得到了先前研究和富集分析的证实。
本研究提出了 FA_gene 和 DMN_miRNA 算法用于生物标志物发现，这些算法为我们提供了自闭症中具有潜在作用的重要参与者列表，这些参与者可能在神经系统或神经障碍中发挥作用，可以作为自闭症诊断测试的候选物进行实验研究。
