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1. Plant Extracellular Vesicles Contain Diverse Small RNA Species and Are Enriched in 10 to 17 Nucleotide "Tiny" RNAs

   https://doi.org/10.1105/tpc.18.00872  

   Baldrich, Patricia; Rutter, Brian D.; Zand Karimi, Hana; Podicheti, Ram; Meyers, Blake C.; Innes, Roger W. (January 2019, The Plant Cell)

   Small RNAs (sRNAs) that are 21 to 24 nucleotides (nt) in length are found in most eukaryotic organisms and regulate numerous biological functions, including transposon silencing, development, reproduction, and stress responses, typically via control of the stability and/or translation of target mRNAs. Major classes of sRNAs in plants include microRNAs (miRNAs) and small interfering RNAs (siRNAs); sRNAs are known to travel as a silencing signal from cell to cell, root to shoot, and even between host and pathogen. In mammals, sRNAs are transported inside extracellular vesicles (EVs), which are mobile lipid compartments that participate in intercellular communication. In addition to sRNAs, EVs carry proteins, lipids, metabolites, and potentially other types of nucleic acids. Here we report that plant EVs also contain diverse species of sRNA. We found that specific miRNAs and siRNAs are preferentially loaded into plant EVs. We also report a previously overlooked class of "tiny RNAs" (10 to 17 nt) that are highly enriched in EVs. This new RNA category of unknown function has a broad and very diverse genome origin and might correspond to degradation products. 

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2. Small RNA biogenesis and degradation

   https://doi.org/10.1007/978-3-319-55520-1_6  

   Yu, Q.; Liu, Y.; Li, M.; Yu, B. (January 2017, RNA technologies)

   Small RNAs (sRNAs), ~20–25 nucleotide (nt) in size, regulate various biological processes in plants through directing sequence-specific gene silencing. sRNAs are derived from either single- or double-stranded precursor RNAs. Proper levels of sRNAs are crucial for plant growth, development, genomic stability, and adaptation to abiotic and biotic stresses. Studies have identified the machineries controlling sRNA levels through biogenesis and degradation. This chapter covers recent progresses related to mechanisms governing small RNA biogenesis and degradation. 

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3. Extracellular RNA: mechanisms of secretion and potential functions

   https://doi.org/10.1093/jxb/erac512  

   Borniego, M Lucía; Innes, Roger W (January 2023, Journal of Experimental Botany)

   Manavella, Pablo (Ed.)

   Abstract Extracellular RNA (exRNA) has long been considered as cellular waste that plants can degrade and utilize to recycle nutrients. However, recent findings highlight the need to reconsider the biological significance of RNAs found outside of plant cells. A handful of studies suggest that the exRNA repertoire, which turns out to be an extremely heterogenous group of non-coding RNAs, comprises species as small as a dozen nucleotides to hundreds of nucleotides long. They are found mostly in free form or associated with RNA-binding proteins, while very few are found inside extracellular vesicles (EVs). Despite their low abundance, small RNAs associated with EVs have been a focus of exRNA research due to their putative role in mediating trans-kingdom RNAi. Therefore, non-vesicular exRNAs have remained completely under the radar until very recently. Here we summarize our current knowledge of the RNA species that constitute the extracellular RNAome and discuss mechanisms that could explain the diversity of exRNAs, focusing not only on the potential mechanisms involved in RNA secretion but also on post-release processing of exRNAs. We will also share our thoughts on the putative roles of vesicular and extravesicular exRNAs in plant–pathogen interactions, intercellular communication, and other physiological processes in plants. 

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4. Prediction of Bacterial sRNAs Using Sequence-Derived Features and Machine Learning

   https://doi.org/10.1177/11779322221118335  

   Jha, Tony; Mendel, Jovinna; Cho, Hyuk; Choudhary, Madhusudan (January 2022, Bioinformatics and Biology Insights)

   Small ribonucleic acid (sRNA) sequences are 50–500 nucleotide long, noncoding RNA (ncRNA) sequences that play an important role in regulating transcription and translation within a bacterial cell. As such, identifying sRNA sequences within an organism’s genome is essential to understand the impact of the RNA molecules on cellular processes. Recently, numerous machine learning models have been applied to predict sRNAs within bacterial genomes. In this study, we considered the sRNA prediction as an imbalanced binary classification problem to distinguish minor positive sRNAs from major negative ones within imbalanced data and then performed a comparative study with six learning algorithms and seven assessment metrics. First, we collected numerical feature groups extracted from known sRNAs previously identified in Salmonella typhimurium LT2 (SLT2) and Escherichia coli K12 ( E. coli K12) genomes. Second, as a preliminary study, we characterized the sRNA-size distribution with the conformity test for Benford’s law. Third, we applied six traditional classification algorithms to sRNA features and assessed classification performance with seven metrics, varying positive-to-negative instance ratios, and utilizing stratified 10-fold cross-validation. We revisited important individual features and feature groups and found that classification with combined features perform better than with either an individual feature or a single feature group in terms of Area Under Precision-Recall curve (AUPR). We reconfirmed that AUPR properly measures classification performance on imbalanced data with varying imbalance ratios, which is consistent with previous studies on classification metrics for imbalanced data. Overall, eXtreme Gradient Boosting (XGBoost), even without exploiting optimal hyperparameter values, performed better than the other five algorithms with specific optimal parameter settings. As a future work, we plan to extend XGBoost further to a large amount of published sRNAs in bacterial genomes and compare its classification performance with recent machine learning models’ performance. 

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5. Small RNAs >26 nt in length associate with AGO1 and are upregulated by nutrient deprivation in the alga Chlamydomonas

   https://doi.org/10.1093/plcell/koad093  

   Li, Yingshan; Kim, Eun-Jeong; Voshall, Adam; Moriyama, Etsuko N; Cerutti, Heriberto (March 2023, The Plant Cell)

   Abstract Small RNAs (sRNAs) associate with ARGONAUTE (AGO) proteins forming effector complexes with key roles in gene regulation and defense responses against molecular parasites. In multicellular eukaryotes, extensive duplication and diversification of RNA interference (RNAi) components have resulted in intricate pathways for epigenetic control of gene expression. The unicellular alga Chlamydomonas reinhardtii also has a complex RNAi machinery, including 3 AGOs and 3 DICER-like proteins. However, little is known about the biogenesis and function of most endogenous sRNAs. We demonstrate here that Chlamydomonas contains uncommonly long (>26 nt) sRNAs that associate preferentially with AGO1. Somewhat reminiscent of animal PIWI-interacting RNAs, these >26 nt sRNAs are derived from moderately repetitive genomic clusters and their biogenesis is DICER-independent. Interestingly, the sequences generating these >26-nt sRNAs have been conserved and amplified in several Chlamydomonas species. Moreover, expression of these longer sRNAs increases substantially under nitrogen or sulfur deprivation, concurrently with the downregulation of predicted target transcripts. We hypothesize that the transposon-like sequences from which >26-nt sRNAs are produced might have been ancestrally targeted for silencing by the RNAi machinery but, during evolution, certain sRNAs might have fortuitously acquired endogenous target genes and become integrated into gene regulatory networks. 

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