bioRxivPlant Biology
Trans-species microRNA loci in the parasitic plant Cuscuta campestris have a U6-like snRNA promoter
Abstract
Small regulatory RNAs can move between organisms during pathogenic interactions and regulate gene expression in the recipient. If and how such "trans-species" small RNAs are distinguished from normal small RNAs is not known. The parasitic plant Cuscuta campestris produces a number of microRNAs that specifically accumulate at the interface between parasite and host, several of which have been demonstrated to have trans-species activity. We find that induction of C. campestris interface-induced microRNAs was similar regardless of host species, and can be replicated in haustoria stimulated to develop in the complete absence of a host. We also find that the loci encoding C. campestris interface-induced microRNAs are distinguished by a common 10 base-pair cis-regulatory element. This element is identical to a previously described upstream sequence element used by all plant small nuclear RNA loci. The sequence context of this element strongly suggests U6-like transcription by RNA polymerase III. The element promotes accumulation of interface-induced miRNAs in a heterologous system. This common promoter element distinguishes C. campestris interface-induced microRNA loci from other plant small RNAs; other plant small RNA loci are transcribed by polymerases II or IV, and lack any common promoter motifs. Our data suggest that C. campestris interface-induced miRNAs are produced in a manner distinct from canonical miRNAs. All confirmed C. campestris microRNAs with confirmed trans-species activity are interface-induced and possess these features. We speculate that this distinct production may allow these miRNAs to be exported to hosts.
- organism: C. elegans
- organism: plant (other)
- assay: bulk RNA-seq
- assay: DNA methylation
- research focus: cell type annotation
- research focus: gene regulatory network inference
- research focus: transcription & RNA processing
- approach: experimental assay
- approach: statistical model
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