The transcriptome serves as a bridge that links genomic variation and phenotype diversity. A vast number of studies using next-generation RNA sequencing (RNA-seq) in the last two decades emphasize the essential roles of plant transcriptome in response to developmental and environmental conditions, leading to numerous insights into the dynamic change, evolutionary trace and elaborate regulation of plant transcriptome. With substantial improvement in accuracy and throughput, direct RNA sequencing (DRS) has emerged as a new and powerful sequencing platform for the precise detection of native and full-length transcripts, which overcomes many limitations such as read length and PCR bias that are inherent to short-read RNA-seq. Here, we reviewed recent advances in dissecting the complexity and diversity of plant transcriptome utilizing DRS as a main technological mean from many aspects of RNA metabolism, including novel isoforms, poly(A) tail and RNA modification, and proposed a comprehensive workflow for the data process of plants DRS. Many challenges concerning the application of DRS in plants, such as machine learning tools tailored to plant transcriptome, remain to be solved, and together we prospect the future biological questions that can be potentially answered by DRS such as allele-specific RNA modification. This technology provides convenient support on which the connection of distinct RNA features is tightly built, sustainably refining our understanding of the biological functions of plant transcriptome.

La-related proteins (LARPs) are a family of RNA-binding proteins that share a conserved La motif (LaM) domain. LARP1 plays a role in regulating ribosomal protein synthesis and stabilizing mRNAs and has a unique structure without an RNA binding RRM domain adjoining the LaM domain. In this study, we investigated the physical basis for LARP1 specificity for poly(A) sequences and observed an unexpected bias for sequences with single guanines. Multiple guanine substitutions did not increase the affinity, demonstrating preferential recognition of singly guanylated sequences. We also observed that the cyclic di-nucleotides in the cCAS/STING pathway, cyclic-di-GMP and 3\',3\'-cGAMP, bound with sub-micromolar affinity. Isothermal titration measurements were complemented by high-resolution crystal structures of the LARP1 LaM with six different RNA ligands, including two stereoisomers of a phosphorothioate linkage. The selectivity for singly substituted poly(A) sequences suggests LARP1 may play a role in the stabilizing effect of poly(A) tail guanylation. [Figure: see text].

The poly(A) tail is an essential structural component of mRNA required for the latter\'s stability and translation. Recent technologies have enabled transcriptome-wide profiling of the length and composition of poly(A) tails, shedding light on their overlooked regulatory capacities. Notably, poly(A) tails contain not only adenine but also uracil, cytosine, and guanine residues. These findings strongly suggest that poly(A) tails could encode a wealth of regulatory information, similar to known reversible RNA chemical modifications. This review aims to succinctly summarize our current knowledge on the composition, dynamics, and regulatory functions of RNA poly(A) tails. Given their capacity to carry rich regulatory information beyond the genetic code, we propose the concept of \'poly(A) tail epigenetic information\' as a new layer of RNA epigenetic regulation.

The presence of a poly(A) tail is indispensable for the post-transcriptional regulation of gene expression in cancer. This dynamic and modifiable feature of transcripts is under the control of various nuclear and cytoplasmic proteins. This study aimed to develop a novel cytoplasmic poly(A)-related signature for predicting prognosis, clinical attributes, tumor immune microenvironment (TIME), and treatment response in hepatocellular carcinoma (HCC). Utilizing RNA sequencing (RNA-seq) data from The Cancer Genome Atlas (TCGA), non-negative matrix factorization (NMF), and principal-component analysis (PCA) were employed to categorize HCC patients into three clusters, thus demonstrating the pivotal prognostic role of cytoplasmic poly(A) tail regulators. Furthermore, machine learning algorithms such as least absolute shrinkage and selection operator (LASSO), survival analysis, and Cox proportional hazards modeling were able to distinguish distinct cytoplasmic poly(A) subtypes. As a result, a 5-gene signature derived from TCGA was developed and validated using International Cancer Genome Consortium (ICGC) HCC datasets. This novel classification based on cytoplasmic poly(A) regulators has the potential to improve prognostic predictions and provide guidance for chemotherapy, immunotherapy, and transarterial chemoembolization (TACE) in HCC.

Most eukaryotic mRNAs and different non-coding RNAs undergo a form of 3\' end processing known as polyadenylation. Polyadenylation machinery is present in almost all organisms except few species. In bacteria, the machinery has evolved from PNPase, which adds heteropolymeric tails, to a poly(A)-specific polymerase. Differently, a complex machinery for accurate polyadenylation and several non-canonical poly(A) polymerases are developed in eukaryotes. The role of poly(A) tail has also evolved from serving as a degradative signal to a stabilizing modification that also regulates translation. In this review, we discuss poly(A) tail emergence in prokaryotes and its development into a stable, yet dynamic feature at the 3\' end of mRNAs in eukaryotes. We also describe how appearance of novel poly(A) polymerases gives cells flexibility to shape poly(A) tail. We explain how poly(A) tail dynamics help regulate cognate RNA metabolism in a context-dependent manner, such as during oocyte maturation. Finally, we describe specific mRNAs in metazoans that bear stem-loops instead of poly(A) tails. We conclude with how recent discoveries about poly(A) tail can be applied to mRNA technology. This article is categorized under: RNA Evolution and Genomics > RNA and Ribonucleoprotein Evolution RNA Processing > 3\' End Processing RNA Turnover and Surveillance > Regulation of RNA Stability.

The recent approval of messenger ribonucleic acid (mRNA) as vaccine to combat the COVID-19 pandemic has been a scientific turning point. Today, the applicability of mRNA is being demonstrated beyond infectious diseases, for example in cancer immunotherapy, protein replacement therapy and gene editing. mRNA is produced by in vitro transcription (IVT) from a linear DNA template and modified at the 3\' and 5\' ends to improve translational efficiency and stability. Co-existing impurities such as RNA fragments and double-stranded RNA (dsRNA), amongst others, can drastically impact mRNA quality and efficacy. In this study, size-exclusion chromatography (SEC) is evaluated for the characterization of IVT-mRNA. The effect of mobile phase composition (ionic strength and organic modifier), pH, column temperature and pore size (300 Å, 1000 Å, and 2000 Å) on the separation performance and structural integrity of IVT-mRNA varying in size is described. Non-replicating, self-amplifying (saRNA), temperature degraded, and ribonuclease (RNase) digested mRNA, the latter to characterize the 3\' poly(A) tail, were included in the study. Beyond ultraviolet (UV) detection, refractive index (RI) and multi-angle light scattering (MALS) detection were implemented to accurately determine molecular weight (MW) of mRNA. Finally, mass photometry is introduced as a complementary methodology to study mRNA under native conditions.

Transcription termination by RNA polymerase II (RNA Pol II) is linked to RNA 3\' end processing by the cleavage and polyadenylation factor (CPF or CPSF). CPF contains endonuclease, poly(A) polymerase, and protein phosphatase activities, which cleave and polyadenylate pre-mRNAs and dephosphorylate RNA Pol II to control transcription. Exactly how the RNA 3\' end processing machinery is coupled to transcription remains unclear. Here, we combine in vitro reconstitution, structural studies, and genome-wide analyses to show that yeast CPF physically and functionally interacts with RNA Pol II. Surprisingly, CPF-mediated dephosphorylation promotes the formation of an RNA Pol II stalk-to-stalk homodimer in vitro. This dimer is compatible with transcription but not with the binding of transcription elongation factors. Disruption of the dimerization interface in cells causes transcription defects, including altered RNA Pol II abundance on protein-coding genes, tRNA genes, and intergenic regions. We hypothesize that RNA Pol II dimerization may provide a mechanistic basis for the allosteric model of transcription termination.

Dynamic tuning of the poly(A) tail is a crucial mechanism for controlling translation and stability of eukaryotic mRNA. Achieving a comprehensive understanding of how this regulation occurs requires unbiased abundance quantification of poly(A)-tail transcripts and simple poly(A)-length measurement using high-throughput sequencing platforms. Current methods have limitations due to complicated setups and elaborate library preparation plans. To address this, we introduce central limit theorem (CLT)-managed RNA-seq (CLT-seq), a simple and straightforward homopolymer-sequencing method. In CLT-seq, an anchor-free oligo(dT) primer rapidly binds to and unbinds from anywhere along the poly(A) tail string, leading to position-directed reverse transcription with equal probability. The CLT mechanism enables the synthesized poly(T) lengths, which correspond to the templated segment of the poly(A) tail, to distribute normally. Based on a well-fitted pseudogaussian-derived poly(A)-poly(T) conversion model, the actual poly(A)-tail profile is reconstructed from the acquired poly(T)-length profile through matrix operations. CLT-seq follows a simple procedure without requiring RNA-related pre-treatment, enrichment or selection, and the CLT-shortened poly(T) stretches are more compatible with existing sequencing platforms. This proof-of-concept approach facilitates direct homopolymer base-calling and features unbiased RNA-seq. Therefore, CLT-seq provides unbiased, robust and cost-efficient transcriptome-wide poly(A)-tail profiling. We demonstrate that CLT-seq on the most common Illumina platform delivers reliable poly(A)-tail profiling at a transcriptome-wide scale in human cellular contexts. We find that the poly(A)-tail-tuned ncRNA regulation undergoes a dynamic, complex process similar to mRNA regulation. Overall, CLT-seq offers a simplified, effective and economical approach to investigate poly(A)-tail regulation, with potential implications for understanding gene expression and identifying therapeutic targets.

A sequence of deadenylation events, or the shortening of the poly(A) tail, is a highly regulated process during the life cycle of mRNAs. Advances in biochemistry have enabled the study of deadenylation events at single-nucleotide resolution. Here we describe mathematical models and their applications to estimate the kinetics of a single deadenylation event in vitro. We demonstrate how this quantitative approach is used for assessing reactions with synthetic RNA with poly(A) tails and the CCR4-NOT complex. This method is also applicable to investigating the catalytic activities of other exonucleases and RNA substrates. All example data and custom software are available on GitHub: https://github.com/2yngsklab/deadenylation-kinetics .

The polyadenylation of the 3\' ends of messenger RNAs is an important regulator of stability and translation. We developed the single-molecule poly(A) tail sequencing method, SM-PATseq, to assay tail lengths of the whole transcriptome at nucleotide resolution using long-read sequencing. This method generates cDNA using an oligo-dT 3\' splint adaptor ligation to prime first-strand cDNA synthesis, followed by random hexamer priming for second-strand synthesis. By directly sequencing the cDNA on long-read platforms, we can resolve tail lengths at nucleotide resolution, identify non-A bases within the tail, and quantify transcript abundance analogous to traditional RNAseq methods. Here, we discuss the method for generating, sequencing, and primary analysis of poly(A) tail data from total RNA using the Pacific Biosciences Sequel platform.