Stem–loop formation drives RNA folding in mechanical unzipping experiments

Accurate knowledge of RNA hybridization is essential for understanding RNA structure and function. Here we mechanically unzip and rezip a 2-kbp RNA hairpin and derive the 10 nearest-neighbor base pair (NNBP) RNA free energies in sodium and magnesium with 0.1 kcal/mol precision using optical tweezers. Notably, force–distance curves (FDCs) exhibit strong irreversible effects with hysteresis and several intermediates, precluding the extraction of the NNBP energies with currently available methods. The combination of a suitable RNA synthesis with a tailored pulling protocol allowed us to obtain the fully reversible FDCs necessary to derive the NNBP energies. We demonstrate the equivalence of sodium and magnesium free-energy salt corrections at the level of individual NNBP. To characterize the irreversibility of the unzipping–rezipping process, we introduce a barrier energy landscape of the stem–loop structures forming along the complementary strands, which compete against the formation of the native hairpin. This landscape correlates with the hysteresis observed along the FDCs. RNA sequence analysis shows that base stacking and base pairing stabilize the stem–loops that kinetically trap the long-lived intermediates observed in the FDC. Stem–loops formation appears as a general mechanism to explain a wide range of behaviors observed in RNA folding.

Single chain models illustrate the 3D RNA folding shape during translation

The three-dimensional conformation of RNA is important in the function and fate of the molecule. The common conformation of mRNA is formed based on the closed-loop structure and internal base pairings with the activity of the ribosome movements. However, recent reports suggest that the closed-loop structure might not be formed in many mRNAs. This implies that mRNA can be considered as a single polymer in the cell. We developed TRIP; Three-dimensional RNA Illustration Program, to model the three-dimensional RNA folding shape based on single-chain models. We identified the angle restriction of each bead component from previously reported single-molecule FISH experimental data. This simulation method was able to recapitulate the mRNA conformation change of the translation activity and three-dimensional positional interaction between organelle and its localized mRNAs. Within the analyzed cases base-pairing interactions only have minor effects on the three-dimensional mRNA conformation, and instead single-chain polymer characteristics have a more significant impact on the conformation. This method will be used to predict the potential aggregation force for mRNA and long noncoding RNA conformation in specific cellular conditions such as nucleolus and phase-separated granules.

Genetic Variation Among Certain Fish Species in Terms of Evolution and Lineages

Three tests of phylogenetic including likelihood-joining tree, neighbour-joining tree, and minimum evolution tree have been used based on sox3 gene. Phylogenetic analysis was used to detect the genetic affinity and common ancestors for selected species that belong to the same or different families. This study showed the most appropriate methods for testing the genetic affinity among species and the methodology of each test according to the requirement of molecular applications. Secondary RNA predicted structure and minimum free energy were also included in this study because of their contribution to the detection of the orthologous gene and variance in RNA folding among species related to the different families. The genetic distance in the studied populations was calculated to know the most appropriate way to find out the genetic similarity among the studied species. The low distance-variance value of each group indicated significant genetic affinity among the species of the same family, this result is more consistent with the test of maximum-likelihood tree indicating the validity of this test to measure the genetic affinity among species that have common ancestors.

Coupling Between Production of Ribosomal RNA and Maturation: Just at the Beginning

Ribosomal RNA (rRNA) production represents the most active transcription in the cell. Synthesis of the large rRNA precursors (35S/47S in yeast/human) is achieved by up to hundreds of RNA polymerase I (Pol I) enzymes simultaneously transcribing a single rRNA gene. In this review, we present recent advances in understanding the coupling between rRNA production and nascent rRNA folding. Mapping of the distribution of Pol I along ribosomal DNA at nucleotide resolution, using either native elongating transcript sequencing (NET-Seq) or crosslinking and analysis of cDNAs (CRAC), revealed frequent Pol I pausing, and CRAC results revealed a direct coupling between pausing and nascent RNA folding. High density of Pol I per gene imposes topological constraints that establish a defined pattern of polymerase distribution along the gene, with a persistent spacing between transcribing enzymes. RNA folding during transcription directly acts as an anti-pausing mechanism, implying that proper folding of the nascent rRNA favors elongation in vivo. Defects in co-transcriptional folding of rRNA are likely to induce Pol I pausing. We propose that premature termination of transcription, at defined positions, can control rRNA production in vivo.

AGO CLIP-based imputation of potent siRNA sequences targeting SARS-CoV-2 with antifibrotic miRNA-like activity

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is associated with fatal pulmonary fibrosis. Small interfering RNAs (siRNAs) can be developed to induce RNA interference against SARS-CoV-2, and their susceptible target sites can be inferred by Argonaute crosslinking immunoprecipitation sequencing (AGO CLIP). Here, by reanalysing AGO CLIP data in RNA viruses, we delineated putative AGO binding in the conserved non-structural protein 12 (nsp12) region encoding RNA-dependent RNA polymerase (RdRP) in SARS-CoV-2. We utilised the inferred AGO binding to optimise the local RNA folding parameter to calculate target accessibility and predict all potent siRNA target sites in the SARS-CoV-2 genome, avoiding sequence variants. siRNAs loaded onto AGO also repressed seed (positions 2–8)-matched transcripts by acting as microRNAs (miRNAs). To utilise this, we further screened 13 potential siRNAs whose seed sequences were matched to known antifibrotic miRNAs and confirmed their miRNA-like activity. A miR-27-mimicking siRNA designed to target the nsp12 region (27/RdRP) was validated to silence a synthesised nsp12 RNA mimic in lung cell lines and function as an antifibrotic miR-27 in regulating target transcriptomes related to TGF-β signalling. siRNA sequences with an antifibrotic miRNA-like activity that could synergistically treat COVID-19 are available online (http://clip.korea.ac.kr/covid19).

Easy-Prime: a machine learning–based prime editor design tool

Prime editing is a revolutionary genome-editing technology that can make a wide range of precise edits in DNA. However, designing highly efficient prime editors (PEs) remains challenging. We develop Easy-Prime, a machine learning–based program trained with multiple published data sources. Easy-Prime captures both known and novel features, such as RNA folding structure, and optimizes feature combinations to improve editing efficiency. We provide optimized PE design for installation of 89.5% of 152,351 GWAS variants. Easy-Prime is available both as a command line tool and an interactive PE design server at: http://easy-prime.cc/.

Learning the Fastest RNA Folding Path Based on Reinforcement Learning and Monte Carlo Tree Search

RNA molecules participate in many important biological processes, and they need to fold into well-defined secondary and tertiary structures to realize their functions. Like the well-known protein folding problem, there is also an RNA folding problem. The folding problem includes two aspects: structure prediction and folding mechanism. Although the former has been widely studied, the latter is still not well understood. Here we present a deep reinforcement learning algorithms 2dRNA-Fold to study the fastest folding paths of RNA secondary structure. 2dRNA-Fold uses a neural network combined with Monte Carlo tree search to select residue pairing step by step according to a given RNA sequence until the final secondary structure is formed. We apply 2dRNA-Fold to several short RNA molecules and one longer RNA 1Y26 and find that their fastest folding paths show some interesting features. 2dRNA-Fold is further trained using a set of RNA molecules from the dataset bpRNA and is used to predict RNA secondary structure. Since in 2dRNA-Fold the scoring to determine next step is based on possible base pairings, the learned or predicted fastest folding path may not agree with the actual folding paths determined by free energy according to physical laws.