Evolution and function of the extended miR-2 microRNA family

MicroRNAs are essential post-transcriptional regulators. Many animal microRNAs are clustered in the genome, and it has been shown that clustered microRNAs may be transcribed as a single transcript. Polycistronic microRNAs are often members of the same family, suggesting a role of tandem duplication in the emergence of clusters. The mir-2 microRNA family is the largest in Drosophila melanogaster, with 8 members that are mostly clustered in the genome. Previous studies suggest that the copy number and genomic distribution of mir-2 family members has been subject to significant change during evolution. The effects of such changes on their function are still unknown. Here we study the evolution of function in the mir-2 family. Our analyses show that, in spite of the change in number and organization among invertebrates, most mir-2 loci produce very similar mature microRNA products. Multiple mature miR-2 sequences are predicted to target genes involved in neural development in Drosophila. These targeting properties are conserved in the distant species Caenorhabditis elegans. Duplication followed by functional diversification is frequent during protein-coding gene evolution. However, our results suggest that the production of microRNA clusters by gene duplication rarely involves functional changes. This pattern of functional redundancy among clustered paralogous microRNAs reflects birth-and-death evolutionary dynamics. However, we identified a small number of mir-2 sequences in Drosophila that may have undergone functional shifts associated with genomic rearrangements. Therefore, redundancy in microRNA families may facilitate the acquisition of novel functional features

Annotation of mammalian primary microRNAs.

BACKGROUND: MicroRNAs (miRNAs) are important regulators of gene expression and have been implicated in development, differentiation and pathogenesis. Hundreds of miRNAs have been discovered in mammalian genomes. Approximately 50% of mammalian miRNAs are expressed from introns of protein-coding genes; the primary transcript (pri-miRNA) is therefore assumed to be the host transcript. However, very little is known about the structure of pri-miRNAs expressed from intergenic regions. Here we annotate transcript boundaries of miRNAs in human, mouse and rat genomes using various transcription features. The 5' end of the pri-miRNA is predicted from transcription start sites, CpG islands and 5' CAGE tags mapped in the upstream flanking region surrounding the precursor miRNA (pre-miRNA). The 3' end of the pri-miRNA is predicted based on the mapping of polyA signals, and supported by cDNA/EST and ditags data. The predicted pri-miRNAs are also analyzed for promoter and insulator-associated regulatory regions. RESULTS: We define sets of conserved and non-conserved human, mouse and rat pre-miRNAs using bidirectional BLAST and synteny analysis. Transcription features in their flanking regions are used to demarcate the 5' and 3' boundaries of the pri-miRNAs. The lengths and boundaries of primary transcripts are highly conserved between orthologous miRNAs. A significant fraction of pri-miRNAs have lengths between 1 and 10 kb, with very few introns. We annotate a total of 59 pri-miRNA structures, which include 82 pre-miRNAs. 36 pri-miRNAs are conserved in all 3 species. In total, 18 of the confidently annotated transcripts express more than one pre-miRNA. The upstream regions of 54% of the predicted pri-miRNAs are found to be associated with promoter and insulator regulatory sequences. CONCLUSION: Little is known about the primary transcripts of intergenic miRNAs. Using comparative data, we are able to identify the boundaries of a significant proportion of human, mouse and rat pri-miRNAs. We confidently predict the transcripts including a total of 77, 58 and 47 human, mouse and rat pre-miRNAs respectively. Our computational annotations provide a basis for subsequent experimental validation of predicted pri-miRNAs

miRBase: annotating high confidence microRNAs using deep sequencing data

ABSTRACT We describe an update of the miRBase databas

MicroRNAs from the same precursor have different targeting properties

UnlabelledBackgroundThe processing of a microRNA results in an intermediate duplex of two potential mature products that derive from the two arms (5' and 3') of the precursor hairpin. It is often suggested that one of the sequences is degraded and the other is incorporated into the RNA-induced silencing complex. However, both precursor arms may give rise to functional levels of mature microRNA and the dominant product may change from species to species, from tissue to tissue, or between developmental stages. Therefore, both arms of the precursor have the potential to produce functional mature microRNAs.ResultsWe have investigated the relationship between predicted mRNA targets of mature sequences derived from the 5' and 3' arms of the same pre-microRNAs. Using six state-of-the-art target prediction algorithms, we find that 5'/3' microRNA pairs target different sites in 3' untranslated regions of mRNAs. We also find that these pairs do not generally target overlapping sets of genes, or functionally related genes.ConclusionsWe show that alternative mature products produced from the same precursor microRNAs have different targeting properties and therefore different biological functions. These data strongly suggest that developmental or evolutionary changes in arm choice will have significant functional consequences

MicroRNA evolution by arm switching

MicroRNAs (miRNAs) modulate transcript stability and translation. Functional mature miRNAs are processed from one or both arms of the hairpin precursor. The miR-100/10 family has undergone three independent evolutionary events that have switched the arm from which the functional miRNA is processed. The dominant miR-10 sequences in the insects Drosophila melanogaster and Tribolium castaneum are processed from opposite arms. However, the duplex produced by Dicer cleavage has an identical sequence in fly and beetle. Expression of the Tribolium miR-10 sequence in Drosophila S2 cells recapitulates the native beetle pattern. Thus, arm usage is encoded in the primary miRNA sequence, but outside the mature miRNA duplex. We show that the predicted messenger RNA targets and inferred function of sequences from opposite arms differ significantly. Arm switching is likely to be general, and provides a fundamental mechanism to evolve the function of a miRNA locus and target gene network

Dynamical gene regulatory networks are tuned by transcriptional autoregulation with microRNA feedback

From Springer Nature via Jisc Publications RouterHistory: received 2020-03-05, accepted 2020-07-06, registration 2020-07-21, pub-electronic 2020-07-31, online 2020-07-31, collection 2020-12Publication status: PublishedFunder: Wellcome Trust; doi: http://dx.doi.org/10.13039/100004440; Grant(s): 110566/Z/15/Z, 090868/Z/09/ZFunder: Biotechnology and Biological Sciences Research Council; doi: http://dx.doi.org/10.13039/501100000268; Grant(s): BB/M011275/1Abstract: Concepts from dynamical systems theory, including multi-stability, oscillations, robustness and stochasticity, are critical for understanding gene regulation during cell fate decisions, inflammation and stem cell heterogeneity. However, the prevalence of the structures within gene networks that drive these dynamical behaviours, such as autoregulation or feedback by microRNAs, is unknown. We integrate transcription factor binding site (TFBS) and microRNA target data to generate a gene interaction network across 28 human tissues. This network was analysed for motifs capable of driving dynamical gene expression, including oscillations. Identified autoregulatory motifs involve 56% of transcription factors (TFs) studied. TFs that autoregulate have more interactions with microRNAs than non-autoregulatory genes and 89% of autoregulatory TFs were found in dual feedback motifs with a microRNA. Both autoregulatory and dual feedback motifs were enriched in the network. TFs that autoregulate were highly conserved between tissues. Dual feedback motifs with microRNAs were also conserved between tissues, but less so, and TFs regulate different combinations of microRNAs in a tissue-dependent manner. The study of these motifs highlights ever more genes that have complex regulatory dynamics. These data provide a resource for the identification of TFs which regulate the dynamical properties of human gene expression

Rfam: annotating non-coding RNAs in complete genomes

Rfam is a comprehensive collection of non-coding RNA (ncRNA) families, represented by multiple sequence alignments and profile stochastic context-free grammars. Rfam aims to facilitate the identification and classification of new members of known sequence families, and distributes annotation of ncRNAs in over 200 complete genome sequences. The data provide the first glimpses of conservation of multiple ncRNA families across a wide taxonomic range. A small number of large families are essential in all three kingdoms of life, with large numbers of smaller families specific to certain taxa. Recent improvements in the database are discussed, together with challenges for the future. Rfam is available on the Web at http://www.sanger.ac.uk/Software/Rfam/ and http://rfam.wustl.edu/

Clusters of microRNAs emerge by new hairpins in existing transcripts

Genetic linkage may result in the expression of multiple products from a polycistronic transcript, under the control of a single promoter. In animals, protein-coding polycistronic transcripts are rare. However, microRNAs are frequently clustered in the genomes of animals, and these clusters are often transcribed as a single unit. The evolution of microRNA clusters has been the subject of much speculation, and a selective advantage of clusters of functionally related microRNAs is often proposed. However, the origin of microRNA clusters has not been so far explored. Here, we study the evolution of microRNA clusters in Drosophila melanogaster. We observed that the majority of microRNA clusters arose by the de novo formation of new microRNA-like hairpins in existing microRNA transcripts. Some clusters also emerged by tandem duplication of a single microRNA. Comparative genomics show that these clusters are unlikely to split or undergo rearrangements. We did not find any instances of clusters appearing by rearrangement of pre-existing microRNA genes. We propose a model for microRNA cluster evolution in which selection over one of the microRNAs in the cluster interferes with the evolution of the other linked microRNAs. Our analysis suggests that the study of microRNAs and small RNAs must consider linkage associations

Detection of microRNAs in color space

MotivationDeep sequencing provides inexpensive opportunities to characterize the transcriptional diversity of known genomes. The AB SOLiD technology generates millions of short sequencing reads in color-space; that is, the raw data is a sequence of colors, where each color represents 2 nt and each nucleotide is represented by two consecutive colors. This strategy is purported to have several advantages, including increased ability to distinguish sequencing errors from polymorphisms. Several programs have been developed to map short reads to genomes in color space. However, a number of previously unexplored technical issues arise when using SOLiD technology to characterize microRNAs.ResultsHere we explore these technical difficulties. First, since the sequenced reads are longer than the biological sequences, every read is expected to contain linker fragments. The color-calling error rate increases toward the 3(') end of the read such that recognizing the linker sequence for removal becomes problematic. Second, mapping in color space may lead to the loss of the first nucleotide of each read. We propose a sequential trimming and mapping approach to map small RNAs. Using our strategy, we reanalyze three published insect small RNA deep sequencing datasets and characterize 22 new microRNAs.Availability and implementationA bash shell script to perform the sequential trimming and mapping procedure, called SeqTrimMap, is available at: http://www.mirbase.org/tools/seqtrimmap/[email protected] informationSupplementary data are available at Bioinformatics online