Deep experimental profiling of microRNA diversity, deployment, and evolution across theDrosophilagenus

Abstract

Comparative genomic analyses of microRNAs (miRNAs) have yielded myriad insights into their biogenesis and regulatory activity. While miRNAs have been deeply annotated in a small cohort of model organisms, evolutionary assessments of miRNA flux are clouded by the functional uncertainty of orthologs in related species, and insufficient data regarding the extent of species-specific miRNAs. We address this by generating a comparative small RNA (sRNA) catalog of unprecedented breadth and depth across the Drosophila genus, extending our extant deep analyses of D. melanogaster with sRNA data from multiple tissues of 11 other fly species. Aggregate analysis of several billion sRNA reads permits curation of accurate and holistic compendia of miRNAs across this genus, providing abundant opportunities to identify species- and clade-specific variation in miRNA identity, abundance, and processing. Amongst well-conserved miRNAs, we observe unexpected cases of clade-specific variation in 5′ end precision, occasional antisense loci, and some putatively non-canonical loci. We also employ strict criteria to identify a massive set (649) of novel, evolutionarily-restricted miRNAs. Amongst the bulk collection of species-restricted miRNAs, two notable subpopulations of rapidly-evolving miRNAs are splicing-derived mirtrons and testis-restricted, clustered (TRC) canonical miRNAs. We quantify rates of miRNA birth and death using our annotation and a phylogenetic model for estimating rates of miRNA turnover in the presence of annotation uncertainty. We show striking differences in birth and death rates across miRNA classes defined by biogenesis pathway, genomic clustering, and tissue restriction, and even identify variation heterogeneity amongst Drosophila clades. In particular, distinct molecular rationales underlie the distinct evolutionary behavior of different miRNA classes. We broaden observations made from D. melanogaster as Drosophilid-wide principles for opposing evolutionary viewpoints for miRNA maintenance. Mirtrons are associated with a high rate of 3′ untemplated addition, a mechanism that impedes their biogenesis, whereas TRC miRNAs appear to evolve under positive selection. Altogether, these data reveal miRNA diversity amongst Drosophila species and permit future discoveries in understanding their emergence and evolution.

Control of seminal fluid protein expression via regulatory hubs in Drosophila melanogaster

“…A conservation analysis was first conducted to identify all miRNAs in the D. melanogaster genome. All mature miRNAs from 12 Drosophila species [ 44 ] were mapped on the D. melanogaster genome and the miRNA loci then determined using criteria based upon the identification of miRNA hairpin-like secondary structures (specifically: adjusted minimal folding free energy (aMFE) < −20 and no branching adjacent to the miRNA/miRNA* duplex) [ 45 ]. We then determined all 7 and 8 nt seed regions for all mature miRNAs.…”

The evolutionary dynamics of microRNAs in domestic mammals

“…Their analyses also suggested a gradual increase in expression levels for selectively retained microRNA families, along with changes in target repertoires, while many novel microRNAs with neutral or deleterious regulatory effects seem to be rapidly lost. Flynt et al (Alex Flynt 2017) provided an overview of the microRNA diversity and evolution in the Drosophila genus. The authors generated a new microRNA annotation across 11 species, supported by deep sequencing from multiple tissues.…”

The chromosomal distribution of sex-biased microRNAs in Drosophila is non-adaptive

“…In order to characterize differentially expressed microRNAs between males and females in D. pseudoobscura I generated and sequenced small RNA libraries from males and young unfertilised females (see Methods). There are available small RNA datasets for males and females of this species (Mohammed et al 2018). However, these datasets have either no biological replicates, or they have been sequencing using different library construction methods.…”

“…Adapters were trimmed with Cutadapt 1.18 (Martin 2011) prior to mapping. Read counts were obtained with featureCounts 2.0.2 (Liao et al 2014) using the annotation coordinates from miRBase release 21 (Kozomara et al 2018) and the additional microRNAs annotated by (Mohammed et al 2018), and microRNAs not supported by this study were also removed from the analysis. Differential gene expression was conducted with DESeq2 version 1.24.0 (Love et al 2014: 2) with local fitting, using the model , where batch is the sequencing run.…”