piRNA and miRNA Can Suppress the Expression of Multiple Sclerosis Candidate Genes.
Saltanat KamenovaAksholpan SharapkhanovaAigul AkimniyazovaKarlygash KuzhybayevaAida KondybayevaAizhan RakhmetullinaAnna PyrkovaАnatoliy IvashchenkoPublished in: Nanomaterials (Basel, Switzerland) (2022)
Multiple sclerosis (MS) is a common inflammatory demyelinating disease with a high mortality rate. MS is caused by many candidate genes whose specific involvement has yet to be established. The aim of our study was to identify endogenous miRNAs and piRNAs involved in the regulation of MS candidate gene expression using bioinformatic methods. A program was used to quantify the interaction of miRNA and piRNA nucleotides with mRNA of the target genes. We used 7310 miRNAs from three databases and 40,000 piRNAs. The mRNAs of the candidate genes revealed miRNA binding sites (BSs), which were located separately or formed clusters of BSs with overlapping nucleotide sequences. The miRNAs from the studied databases were generally bound to mRNAs in different combinations, but miRNAs from only one database were bound to the mRNAs of some genes. For the first time, a direct interaction between the complete sequence of piRNA nucleotides and the nucleotides of their mRNA BSs of target genes was shown. One to several clusters of BSs of miRNA and piRNA were identified in the mRNA of ADAM17, AHI1, CD226, EOMES, EVI5, IL12B, IL2RA, KIF21B, MGAT5, MLANA, SOX8, TNFRSF1A, and ZBTB46 MS candidate genes. These piRNAs form the expression regulation system of the MS candidate genes to coordinate the synthesis of their proteins. Based on these findings, associations of miRNAs, piRNAs, and candidate genes for MS diagnosis are recommended.
Keyphrases
- multiple sclerosis
- mass spectrometry
- ms ms
- gene expression
- poor prognosis
- white matter
- genome wide
- binding protein
- genome wide analysis
- dna methylation
- rheumatoid arthritis
- oxidative stress
- type diabetes
- bioinformatics analysis
- transcription factor
- big data
- emergency department
- cardiovascular disease
- single cell
- risk factors
- deep learning
- artificial intelligence
- amino acid
- drug induced