RNA degradation eliminates developmental transcripts during murine embryonic stem cell differentiation via CAPRIN1-XRN2

Insights into ADAR gene complement, expression patterns, and RNA editing landscape in Chlamys farreri

Adenosine Deaminases Acting on RNA (ADARs) are evolutionarily conserved enzymes known to convert adenosine to inosine in double-stranded RNAs and participate in host-virus interactions. Conducting a meta-analysis of available transcriptome data, we identified and characterised eight ADAR transcripts in Chlamys farreri, a farmed marine scallop susceptible to Acute viral necrosis virus (AVNV) infections and mortality outbreaks. Accordingly, we identified six ADAR genes in the Zhikong scallop genome, revised previous gene annotations, and traced alternative splicing variants. In detail, each ADAR gene encodes a unique combination of functional domains, always including the Adenosine deaminase domain, RNA binding domains and, in one case, two copies of a Z-DNA binding domain. After phylogenetic analysis, five C. farreri ADARs clustered in the ADAR1 clade along with sequences from diverse animal phyla. Gene expression analysis indicated CF051320 as the most expressed ADAR, especially in the eye and male gonad. The other four ADAR1 genes and one ADAR2 gene exhibited variable expression levels, with CF105370 and CF051320 significantly increasing during early scallop development. ADAR-mediated single-base editing, evaluated across adult C. farreri tissues and developmental stages, was mainly detectable in intergenic regions (83 % and 85 %, respectively). Overall, the expression patterns of the six ADAR genes together with the editing and hyper-editing values computed on scallops RNA-seq samples support the adaptive value of ADAR1-mediated editing, particularly in the pre-settling larval stages.

Prediction of protein-RNA interactions from single-cell transcriptomic data

Proteins are crucial in regulating every aspect of RNA life, yet understanding their interactions with coding and noncoding RNAs remains limited. Experimental studies are typically restricted to a small number of cell lines and a limited set of RNA-binding proteins (RBPs). Although computational methods based on physico-chemical principles can predict protein-RNA interactions accurately, they often lack the ability to consider cell-type-specific gene expression and the broader context of gene regulatory networks (GRNs). Here, we assess the performance of several GRN inference algorithms in predicting protein-RNA interactions from single-cell transcriptomic data, and propose a pipeline, called scRAPID (single-cell transcriptomic-based RnA Protein Interaction Detection), that integrates these methods with the catRAPID algorithm, which can identify direct physical interactions between RBPs and RNA molecules. Our approach demonstrates that RBP-RNA interactions can be predicted from single-cell transcriptomic data, with performances comparable or superior to those achieved for the well-established task of inferring transcription factor-target interactions. The incorporation of catRAPID significantly enhances the accuracy of identifying interactions, particularly with long noncoding RNAs, and enables the identification of hub RBPs and RNAs. Additionally, we show that interactions between RBPs can be detected based on their inferred RNA targets. The software is freely available at https://github.com/tartaglialabIIT/scRAPID.

Characterization of the long noncoding RNA transcriptome in human preimplantation embryo development

PURPOSE

Infertility remains a human health burden globally. Only a fraction of embryos produced via assisted reproductive technologies (ARTs) develop to the blastocyst stage in vitro. lncRNA abundance changes significantly during human early embryonic development, indicating vital regulatory roles of lncRNAs in this process. The aim of this study is to obtain insights into the transcriptional basis of developmental events.

METHODS

scRNA-seq data and SUPeR-seq data were used to investigate the lncRNA profiles of human preimplantation embryos. The top 50 highly expressed unique and shared lncRNAs in each stage of preimplantation development were identified. Comparative analysis of the two datasets was used to verify the consistent expression patterns of the lncRNAs. Differentially expressed lncRNAs were identified and subjected to functional enrichment analysis.

RESULTS

The lncRNA profiles of human preimplantation embryos in the E-MTAB-3929 dataset were similar to those in the GSE71318 dataset. The ratios of overlap among the top 50 highly expressed lncRNAs between two pairs of stages (2-cell stage vs. 4-cell stage and 8-cell stage vs. morula) were aberrantly low compared with those between other stages. Each stage of preimplantation development exhibited unique and shared lncRNAs among the top 50 highly expressed lncRNAs. Among the between-group comparisons, the 2-cell stage vs. 4-cell stage showed the highest number of differentially expressed lncRNAs. Functional enrichment analysis revealed that differentially expressed lncRNAs and their associated super enhancers and RNA binding proteins (RBPs) are closely involved in regulating embryonic development. These lncRNAs could function as important cell markers for distinguishing fetal germ cells.

CONCLUSIONS

Our study paves the way for understanding the regulation of developmental events, which might be beneficial for improved reproductive outcomes.

Clearing the slate: RNA turnover to enable cell state switching?

The distribution of mRNA in tissue is determined by the balance between transcription and decay. Understanding the control of RNA decay during development has been somewhat neglected compared with transcriptional control. Here, we explore the potential for mRNA decay to trigger rapid cell state transitions during development, comparing a bistable switch model of cell state conversion with experimental evidence from different developmental systems. We also consider another potential role for large-scale RNA decay that has emerged from studies of stress-induced cell state transitions, in which removal of mRNA unblocks the translation machinery to prioritise the synthesis of proteins that establish the new cell state.

Calculating RNA degradation rates using large-scale normalization in mouse embryonic stem cells

Data normalization is critical to the process of estimating RNA degradation by analyzing RNA levels when transcription is blocked. Here, we present a protocol for measuring mRNA degradation rates, optimized for mouse embryonic stem cells, using α-amanitin inhibitor. We describe steps for a time course α-amanitin treatment, RNA-seq, and alignment; we then detail procedures for analyzing data and sequence enrichment. Our method relies on large-scale normalization of stable transcripts in genomic RNA-seq measurements, providing reliable readouts. For complete details on the use and execution of this protocol, please refer to Viegas et al.1.