A large fraction of microRNAs (miRNAs) are derived from intergenic non-coding loci and the identification of their promoters remains 'elusive'. Here, we present microTSS, a machine-learning algorithm that provides highly accurate, single-nucleotide resolution predictions for intergenic miRNA transcription start sites (TSSs). MicroTSS integrates high-resolution RNA-sequencing data with active transcription marks derived from chromatin immunoprecipitation and DNase-sequencing to enable the characterization of tissue-specific promoters. MicroTSS is validated with a specifically designed Drosha-null/conditional-null mouse model, generated using the conditional by inversion (COIN) methodology. Analyses of global run-on sequencing data revealed numerous pri-miRNAs in human and mouse either originating from divergent transcription at promoters of active genes or partially overlapping with annotated long non-coding RNAs. MicroTSS is readily applicable to any cell or tissue samples and constitutes the missing part towards integrating the regulation of miRNA transcription into the modelling of tissue-specific regulatory networks.

Conditional mutagenesis is becoming a method of choice for studying gene function, but constructing conditional alleles is often laborious, limited by target gene structure, and at times, prone to incomplete conditional ablation. To address these issues, we developed a technology termed conditionals by inversion (COIN). Before activation, COINs contain an inverted module (COIN module) that lies inertly within the antisense strand of a resident gene. When inverted into the sense strand by a site-specific recombinase, the COIN module causes termination of the target gene's transcription and simultaneously provides a reporter for tracking this event. COIN modules can be inserted into natural introns (intronic COINs) or directly into coding exons as part of an artificial intron (exonic COINs), greatly simplifying allele design and increasing flexibility over previous conditional KO approaches. Detailed analysis of over 20 COIN alleles establishes the reliability of the method and its broad applicability to any gene, regardless of exon-intron structure. Our extensive testing provides rules that help ensure success of this approach and also explains why other currently available conditional approaches often fail to function optimally. Finally, the ability to split exons using the COIN's artificial intron opens up engineering modalities for the generation of multifunctional alleles.

BACKGROUND:The Conditional by Inversion (COIN) method for engineering conditional alleles relies on an invertible optimized gene trap-like element, the COIN module, for imparting conditionality. The COIN module contains an optimized 3' splice site-polyadenylation signal pair, but is inserted antisense to the target gene and therefore does not alter transcription, until it is inverted by Cre recombinase. In order to make COIN applicable to all protein-coding genes, the COIN module has been engineered within an artificial intron, enabling insertion into an exon.METHODOLOGY/PRINCIPAL FINDINGS:Therefore, theoretically, the COIN method should be applicable to single exon genes, and to test this idea we engineered a COIN allele of Sox2. This single exon gene presents additional design challenges, in that its proximal promoter and coding region are entirely contained within a CpG island, and are also spanned by an overlapping transcript, Sox2Ot, which contains mmu-miR1897. Here, we show that despite disruption of the CpG island by the COIN module intron, the COIN allele of Sox2 (Sox2(COIN)) is phenotypically wild type, and also does not interfere with expression of Sox2Ot and miR1897. Furthermore, the inverted COIN allele of Sox2, Sox2(INV) is functionally null, as homozygotes recapitulate the phenotype of Sox2(βgeo/βgeo) mice, a well-characterized Sox2 null. Lastly, the benefit of the eGFP marker embedded in the COIN allele is demonstrated as it mirrors the expression pattern of Sox2.CONCLUSIONS/SIGNIFICANCE:Our results demonstrate the applicability of the COIN technology as a method of choice for targeting single exon genes.