Trans-acting siRNA form through a refined RNAi mechanism in plants. miRNA-guided cleavage triggers entry of precursor transcripts into an RNA-DEPENDENT RNA POLYMERASE6 pathway, and sets the register for phased tasiRNA formation by DICER-LIKE4. Here, we show that miR390-ARGONAUTE7 complexes function in distinct cleavage or noncleavage modes at two target sites in TAS3a transcripts. The AGO7 cleavage, but not the noncleavage, function could be provided by AGO1, the dominant miRNA-associated AGO, but only when AGO1 was guided to a modified target site through an alternate miRNA. AGO7 was highly selective for interaction with miR390, and miR390 in turn was excluded from association with AGO1 due entirely to an incompatible 5' adenosine. Analysis of AGO1, AGO2, and AGO7 revealed a potent 5' nucleotide discrimination function for some, although not all, ARGONAUTEs. miR390 and AGO7, therefore, evolved as a highly specific miRNA guide/effector protein pair to function at two distinct tasiRNA biogenesis steps.

MicroRNAs (miRNAs) are small regulatory RNAs found in diverse eukaryotic lineages. In plants, a minority of annotated MIRNA gene families are conserved between plant families, while the majority are family-or species-specific, suggesting that most known MIRNA genes arose relatively recently in evolutionary time. Given the high proportion of young MIRNA genes in plant species, new MIRNA families are likely spawned and then lost frequently. Unlike highly conserved, ancient miRNAs, young miRNAs are often weakly expressed, processed imprecisely, lack targets, and display patterns of neutral variation, suggesting that young MIRNA loci tend to evolve neutrally. Genome-wide analyses from several plant species have revealed that variation in miRNA foldback expression, structure, processing efficiency, and miRNA size have resulted in the unique functionality of MIRNA loci and resulting miRNAs. Additionally, some miRNAs have evolved specific properties and functions that regulate other transcriptional or posttranscriptional silencing pathways. The evolution of miRNA processing and functional diversity underscores the dynamic nature of miRNA-based regulation in complex regulatory networks.

RNA interference pathways may involve amplification of secondary siRNAs by RNA-dependent RNA polymerases. In plants, RDR6-dependent secondary siRNAs arise from transcripts targeted by some microRNA (miRNA). Here, Arabidopsis thaliana secondary siRNA from mRNA, and trans-acting siRNA, are shown to be triggered through initial targeting by 22 nt miRNA that associate with AGO1. In contrast to canonical 21 nt miRNA, 22 nt miRNA primarily arise from foldback precursors containing asymmetric bulges. Using artificial miRNA constructs, conversion of asymmetric foldbacks to symmetric foldbacks resulted in production of 21 nt forms of miR173, miR472 and miR828. Both 21 and 22 nt forms associated with AGO1 and guided accurate slicer activity, but only 22 nt miRNA were competent to trigger RDR6-dependent siRNA from target RNA. These data suggest that AGO1 functions differentially with 21 and 22 nt miRNA to engage the RDR6-associated amplification apparatus.

Single cell RNA sequencing can yield high-resolution cell-type-specific expression signatures that reveal new cell types and the developmental trajectories of cell lineages. Here, we apply this approach to Arabidopsis (Arabidopsis thaliana) root cells to capture gene expression in 3,121 root cells. We analyze these data with Monocle 3, which orders single cell transcriptomes in an unsupervised manner and uses machine learning to reconstruct single cell developmental trajectories along pseudotime. We identify hundreds of genes with cell-type-specific expression, with pseudotime analysis of several cell lineages revealing both known and novel genes that are expressed along a developmental trajectory. We identify transcription factor motifs that are enriched in early and late cells, together with the corresponding candidate transcription factors that likely drive the observed expression patterns. We assess and interpret changes in total RNA expression along developmental trajectories and show that trajectory branch points mark developmental decisions. Finally, by applying heat stress to whole seedlings, we address the longstanding question of possible heterogeneity among cell types in the response to an abiotic stress. Although the response of canonical heat-shock genes dominates expression across cell types, subtle but significant differences in other genes can be detected among cell types. Taken together, our results demonstrate that single cell transcriptomics holds promise for studying plant development and plant physiology with unprecedented resolution. RESULTS Single-Cell RNA-Seq of Whole A. thaliana Roots Reveals Distinct Populations of Cortex, Endodermis, Hair, Nonhair, and Stele CellsWe used whole Arabidopsis roots from 7d-old seedlings to generate protoplasts for transcriptome analysis using the 103

Our ability to predict protein expression from DNA sequence alone remains poor, reflecting our limited understanding of -regulatory grammar and hampering the design of engineered genes for synthetic biology applications. Here, we generate a model that predicts the protein expression of the 5' untranslated region (UTR) of mRNAs in the yeast We constructed a library of half a million 50-nucleotide-long random 5' UTRs and assayed their activity in a massively parallel growth selection experiment. The resulting data allow us to quantify the impact on protein expression of Kozak sequence composition, upstream open reading frames (uORFs), and secondary structure. We trained a convolutional neural network (CNN) on the random library and showed that it performs well at predicting the protein expression of both a held-out set of the random 5' UTRs as well as native 5' UTRs. The model additionally was used to computationally evolve highly active 5' UTRs. We confirmed experimentally that the great majority of the evolved sequences led to higher protein expression rates than the starting sequences, demonstrating the predictive power of this model.