Intragenic MicroRNAs Autoregulate Their Host Genes in Both Direct and Indirect Ways—A Cross-Species Analysis

Abstract

MicroRNAs (miRNAs) function as master switches for post-transcriptional gene expression. Their genes are either located in the extragenic space or within host genes, but these intragenic miRNA::host gene interactions are largely enigmatic. The aim of this study was to investigate the location and co-regulation of all to date available miRNA sequences and their host genes in an unbiased computational approach. The majority of miRNAs were located within intronic regions of protein-coding and non-coding genes. These intragenic miRNAs exhibited both increased target probability as well as higher target prediction scores as compared to a model of randomly permutated genes. This was associated with a higher number of miRNA recognition elements for the hosted miRNAs within their host genes. In addition, strong indirect autoregulation of host genes through modulation of functionally connected gene clusters by intragenic miRNAs was demonstrated. In addition to direct miRNA-to-host gene targeting, intragenic miRNAs also appeared to interact with functionally related genes, thus affecting their host gene function through an indirect autoregulatory mechanism. This strongly argues for the biological relevance of autoregulation not only for the host genes themselves but, more importantly, for the entire gene cluster interacting with the host gene.

NOCICEPTRA: Gene and microRNA Signatures and Their Trajectories Characterizing Human iPSC‐Derived Nociceptor Maturation

“…miRNAs appeared to be involved in certain aspects of neurite formation. The most abundantly expressed intragenic miRNAs hsa‐miR‐363‐3p, hsa‐miR‐20b‐5p, hsa‐miR‐106a‐5p, which are all embedded in the lncRNA AC002407.2, [ 77 ] targeted a variety of pathways implicated in axon guidance and showed strong correlation with NEUROG1, supporting their importance for neuron morphology and neurite outgrowth. [ 78 , 79 ] Interestingly, miR‐20b/miR‐106 directly suppress NEUROG2, [ 80 ] thereby indirectly favoring NEUROG1 expression and nociceptor differentiation.…”

miRGTF-net: Integrative miRNA-gene-TF network analysis reveals key drivers of breast cancer recurrence

“…The advantage of using expression data consists in the ability of quantifying interactions which leads to decreased false positives rate since database interactions can be absent in one or another tissue/cell type. For example, results obtained by application of the proposed method to breast cancer sequencing data suggest an insignificant role of interactions between intronic miRNAs and their host genes, while there is a number of reports which automatically consider all possible interactions of this type [ 24 , 45 , 46 ]: only 11 out of 523 edges (2.1%) were associated with host genes and intronic miRNAs, and the majority of them were isolated. It should be noted that an increasing number of reports highlight the fact that the majority of human miRNAs have independent transcription start sites thus being not co-expressed with their host genes [ 25 , 47 ].…”

NOCICEPTRA: Gene and microRNA signatures and their trajectories characterizing human iPSC-derived nociceptor maturation

“…Every module and each supercluster underwent gene pathway enrichment analysis as well as disease enrichment analysis (DOSE, DisGeNet, www.disgenet.org). g:Profiler (https://biit.cs.ut.ee/gprofiler/gost) was used for the 4 annotation spaces Gene ontology (GO) biological process (GO:BP), GO cellular components (GO:CC), GO molecular function (GO:MF) and Kyoto Encyclopedia of Genes and Genomes (KEGG) by reducing the number of significantly enriched pathways to best per parent by a custom written script, as described previously (77,105). To further reduce the complexity of gene pathway enrichments, only the top 5 GO:BP, GO:MF, GO:CC and KEGG pathways were visualized using the negative log10(pvalue).…”

“…where p is the microT-CDS v5 score considered only if score > 0.9, v is a boolean variable where 1 is validated and 0 is not validated yet, r is the Pearson correlation coefficient between the miRNA and the gene and rank is the percentile rank of the miRNA based on its expression (77). miRNA interaction networks miRNA:mRNA interaction network analysis, for miRNAs known to play a prominent role in the regulation of iPSC-derived nociceptor differentiation, was performed for each expressed miRNA (BaseMean > 5 counts in at least 3 samples).…”