DICER-LIKE (DCL) enzymes process double-stranded RNA into small RNAs that act as regulators of gene expression. Arabidopsis DCL4 and DCL2 each allow the post-transcriptional gene silencing (PTGS) of viruses and transgenes, but primary PTGS-prone DCL4 outcompetes transitive PTGS-prone DCL2 in wild-type plants. This hierarchy likely prevents DCL2 having any detrimental effects on endogenous genes. Indeed, dcl4 mutants exhibit developmental defects and increased sensitivity to genotoxic stress. In this study, the mechanism underlying dcl4 defects was investigated using genetic, biochemical and high-throughput sequencing approaches. We show that the purple phenotype of dcl4 leaves correlates with carbohydrate over-accumulation and defective phloem transport, and depends on the activity of SUPPRESSOR OF GENE SILENCING 3, RNA-DEPENDENT RNA POLYMERASE 6 (RDR6) and DCL2. This phenotype correlates with the downregulation of two genes expressed in the apex and the vasculature, SMAX1-LIKE 4 (SMXL4) and SMXL5, and the accumulation of DCL2- and RDR6-dependent small interfering RNAs derived from these two genes. Supporting a causal effect, smxl4 smxl5 double mutants exhibit leaf pigmentation, enhanced starch accumulation and defective phloem transport, similar to dcl4 plants. Overall, this study elucidates the detrimental action of DCL2 when DCL4 is absent, and indicates that DCL4 outcompeting DCL2 in wild-type plants is crucial to prevent the degradation of endogenous transcripts by DCL2- and RDR6-dependent transitive PTGS.
Exon junction complexes (EJCs) are deposited on mRNAs during splicing and displaced by ribosomes during the pioneer round of translation. Nonsense-mediated mRNA decay (NMD) degrades EJC-bound mRNA, but the lack of suitable methodology has prevented the identification of other degradation pathways. Here, we show that the RNA degradomes of Arabidopsis (Arabidopsis thaliana), rice (Oryza sativa), worm (Caenorhabditis elegans), and human (Homo sapiens) cells exhibit an enrichment of 59 monophosphate (59P) ends of degradation intermediates that map to the canonical EJC region. Inhibition of 59 to 39 exoribonuclease activity and overexpression of an EJC disassembly factor in Arabidopsis reduced the accumulation of these 59P ends, supporting the notion that they are in vivo EJC footprints. Hundreds of Arabidopsis NMD targets possess evident EJC footprints, validating their degradation during the pioneer round of translation. In addition to premature termination codons, plant microRNAs can also direct the degradation of EJC-bound mRNAs. However, the production of EJC footprints from NMD but not microRNA targets requires the NMD factor SUPPRESSOR WITH MORPHOLOGICAL EFFECT ON GENITALIA PROTEIN7. Together, our results demonstrating in vivo EJC footprinting in Arabidopsis unravel the composition of the RNA degradome and provide a new avenue for studying NMD and other mechanisms targeting EJC-bound mRNAs for degradation before steady state translation.
Banana ( Musa spp.) is one of the world’s most important staple and cash crops. Despite accumulating genetic and transcriptomic data, low transformation efficiency in agronomically important Musa spp. render translational researches in banana difficult by using conventional knockout approaches. To develop tools for translational research in bananas, we developed a virus induced-gene silencing (VIGS) system based on a banana-infecting cucumber mosaic virus (CMV) isolate, CMV 20. CMV 20 genomic RNA 1, 2, and 3, were separately cloned in Agrobacterium pJL89 binary vectors, and a cloning site was introduced on RNA 2 immediately after the 2a open reading frame to insert the gene targeted for silencing. An efficient Agrobacterium inoculation method was developed for banana, which enabled the CMV 20 VIGS vector infection rate to reach 95% in our experiments. CMV 20-based silencing of Musa acuminata cv. Cavendish (AAA group) glutamate 1-semialdehyde aminotransferase ( MaGSA ) produced a typical chlorotic phenotype and silencing of M. acuminata phytoene desaturase ( MaPDS ) produced a photobleachnig phenotype. We show this approach efficiently reduced GSA and PDS transcripts to 10% and 18% of the control, respectively. The high infection rate and extended silencing of this VIGS system will provide an invaluable tool to accelerate functional genomic studies in banana.
Transgenic approaches employing RNA interference (RNAi) strategies have been successfully applied to generate desired traits in plants; however, variations between RNAi transgenic siblings and the ability to quickly apply RNAi resistance to diverse cultivars remain challenging. In this study, we assessed the promoter activity of a cauliflower mosaic virus 35S promoter (35S) and a phloem-specific promoter derived from rice tungro bacilliform virus (RTBV) and their efficacy to drive RNAi against the endogenous glutamate-1-semialdehyde aminotransferase gene (GSA) that acts as a RNAi marker, through chlorophyll synthesis inhibition, and against tomato yellow leaf curl Thailand virus (TYLCTHV), a begomovirus (family Geminiviridae) reported to be the prevalent cause of tomato yellow leaf curl disease (TYLCD) in Taiwan. Transgenic Nicotiana benthamiana expressing hairpin RNA of GSA driven by either the 35S or RTBV promoter revealed that RTBV::hpGSA induced stronger silencing along the vein and more uniformed silencing phenotype among its siblings than 35S::hpGSA. Analysis of transgenic N. benthamiana, 35S::hpTYLCTHV, and RTBV::hpTYLCTHV revealed that, although 35S::hpTYLCTHV generated a higher abundance of small RNA than RTBV::hpTYLCTHV, RTBV::hpTYLCTHV transgenic plants conferred better TYLCTHV resistance than 35S::hpTYLCTHV. Grafting of wild-type (WT) scions to TYLCTHV RNAi rootstocks allowed transferable TYLCTHV resistance to the scion. A TYLCTHV-inoculation assay showed that noninfected WT scions were only observed when grafted to RTBV::hpTYLCTHV rootstocks but not 35S::hpTYLCTHV nor WT rootstocks. Together, our findings demonstrate an approach that may be widely applied to efficiently confer TYLCD resistance.
Objective Compared with other regions in the world, the transmission characteristics of the COVID-19 epidemic in Africa are more obvious, has a unique transmission mode in this region; At the same time, the data related to the COVID-19 epidemic in Africa is characterized by low data quality and incomplete data coverage, which makes the prediction method of COVID-19 epidemic suitable for other regions unable to achieve good results in Africa. In order to solve the above problems, this paper proposes a prediction method that nests the in-depth learning method in the mechanism model. From the experimental results, it can better solve the above problems and better adapt to the transmission characteristics of the COVID-19 epidemic in African countries. Methods Based on the SIRV model, the COVID-19 transmission rate and trend from September 2021 to January 2022 of the top 15 African countries (South Africa, Morocco, Tunisia, Libya, Egypt, Ethiopia, Kenya, Zambia, Algeria, Botswana, Nigeria, Zimbabwe, Mozambique, Uganda, and Ghana) in the accumulative number of COVID-19 confirmed cases was fitted by using the data from Worldometer. Non-autoregressive (NAR), Long-short term memory (LSTM), Autoregressive integrated moving average (ARIMA) models, Gaussian and polynomial functions were used to predict the transmission rate β in the next 7, 14, and 21 days. Then, the predicted transmission rate βs were substituted into the SIRV model to predict the number of the COVID-19 active cases. The error analysis was conducted using root-mean-square error (RMSE) and mean absolute percentage error (MAPE). Results The fitting curves of the 7, 14, and 21 days were consistent with and higher than the original curves of daily active cases (DAC). The MAPE between the fitted and original 7-day DAC was only 1.15% and increased with the longer of predict days. Both the predicted β and DAC of the next 7, 14, and 21 days by NAR and LSTM nested models were closer to the real ones than other three ones. The minimum RMSEs for the predicted number of COVID-19 active cases in the next 7, 14, and 21 days were 12,974, 14,152, and 12,211 people, respectively when the order of magnitude for was 106, with the minimum MAPE being 1.79%, 1.97%, and 1.64%, respectively. Conclusion Nesting the SIRV model with NAR, LSTM, ARIMA methods etc. through functionalizing β respectively could obtain more accurate fitting and predicting results than these models/methods alone for the number of confirmed COVID-19 cases in Africa in which nesting with NAR had the highest accuracy for the 14-day and 21-day predictions. The nested model was of high significance for early understanding of the COVID-19 disease burden and preparedness for the response.
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