RNA secondary structure profiling in zebrafish reveals unique regulatory features

In Giardia lamblia, enhanced translation of luciferase mRNA, flanked between the 5'-untranslated region (UTR) and 3 '-end of giardiavirus transcript, requires the presence of the initial 264-nucleotide (nt) viral capsid-coding region. By introducing the transcripts of dicistronic viral constructs into Giardia, we demonstrated that the 264-nt downstream region alone is insufficient to function as an internal ribosome entry site (IRES) without including a portion of the 5 '-UTR as well. Deletion analysis showed that efficient internal initiation requires the last 253 nts (nts 114-367) of the 5 '-UTR in combination with the downstream 264 nts. Specific mutations that disrupted the predicted secondary structural elements in either the 5 '-UTR or the 264-nt capsid-coding region completely abolished the IRES-mediated translation of downstream cistron, suggesting that the IRES activity requires the presence of these structures in both regions. Mutations that abolished translation of the first cistron did not, however, affect the IRES-mediated translation of the second cistron, indicating that this IRES-mediated translation is independent of the translation of the upstream cistron. This is, to our knowledge, the first reported identification of a viral IRES with an estimated size of 517 nts that extends to both sides of the initiation site.

To better elucidate RNA structure-function relationships and to improve the design of pharmaceutical agents that target specific RNA motifs, an understanding of RNA primary, secondary, and tertiary structure is necessary. The prediction of RNA secondary structure from sequence is an intermediate step in predicting RNA three-dimensional structure. RNA secondary structure is typically predicted using a nearest neighbor model based on free energy parameters. The current free energy parameters for 2 × 3 nucleotide loops are based on a 23-member data set of 2 × 3 loops and internal loops of other sizes. A database of representative RNA secondary structures was searched to identify 2 × 3 nucleotide loops that occur in nature. Seventeen of the most frequent 2 × 3 nucleotide loops in this database were studied by optical melting experiments. Fifteen of these loops melted in a two-state manner, and the associated experimental ΔG°(37,2×3) values are, on average, 0.6 and 0.7 kcal/mol different from the values predicted for these internal loops using the predictive models proposed by Lu, Turner, and Mathews [Lu, Z. J., Turner, D. H., and Mathews, D. H. (2006) Nucleic Acids Res. 34, 4912-4924] and Chen and Turner [Chen, G., and Turner, D. H. (2006) Biochemistry 45, 4025-4043], respectively. These new ΔG°(37,2×3) values can be used to update the current algorithms that predict secondary structure from sequence. To improve free energy calculations for duplexes containing 2 × 3 nucleotide loops that still do not have experimentally determined free energy contributions, an updated predictive model was derived. This new model resulted from a linear regression analysis of the data reported here combined with 31 previously studied 2 × 3 nucleotide internal loops. Most of the values for the parameters in this new predictive model are within experimental error of those of the previous models, suggesting that approximations and assumptions associated with the derivation of the previous nearest neighbor parameters were valid. The updated predictive model predicts free energies of 2 × 3 nucleotide internal loops within 0.4 kcal/mol, on average, of the experimental free energy values. Both the experimental values and the updated predictive model can be used to improve secondary structure prediction from sequence.

An Interactive Framework for RNA Secondary Structure Prediction with a Dynamical Treatment of Constraints

19] Computer modeling and display of RNA secondary and tertiary structures

The second most common form of Charcot-Marie-Tooth neuropathy (CMT), X-linked CMT type X1 (CMTX1), is caused by coding and non-coding mutations in the gap junction beta 1 (GJB1) gene. The non-coding GJB1 c.-103C > T mutation (NM_000166.5) has been reported to cause CMTX1 in multiple families. This study assessed the internal ribosomal entry site (IRES) activity previously reported for the rat Gjb1 P2 5’ untranslated region (UTR). Using a bicistronic assay and transfecting RT4 Schwann cells, IRES activity of the human GJB1 P2 5’ UTR was compared to the GJB1 P2 5’ UTR containing either the c.-103C > T mutation or the non-pathogenic c.-102G > A variant. No differences in GJB1 P2 5’ UTR IRES activity were observed between the negative control, the wild-type P2 5’ UTR, the c.-103C > T 5’ UTR or the c.-102G > A 5’ UTR, irrespective of the GJB1 intron being present (p = .429 with intron, and p = .865 without). A theoretical c.-131A > G variant was predicted to result in the same RNA secondary structure as the GJB1 c.-103C > T P2 5’ UTR. However, no significant difference was observed between expression from the wild-type GJB1 P2 5’ UTR and the GJB1 c.-131A > G variant (p = .688). Deletion of the conserved region surrounding the c.-103C > T mutation (c.-108_-103del) resulted in significantly higher expression than the c.-103C > T mutation alone (p = .019), suggesting that the conserved c.-108_-103 region was not essential for translation. The reporter assays in this study do not recapitulate the previously reported GJB1 IRES activity and suggest an alternate pathogenic mechanism for the c.-103C > T CMTX1 non-coding mutation.

Pairs of nucleotides within functional nucleic acid secondary structures often display evidence of coevolution that is consistent with the maintenance of base-pairing. Here, we introduce a sequence evolution model, MESSI (Modeling the Evolution of Secondary Structure Interactions), that infers coevolution associated with base-paired sites in DNA or RNA sequence alignments. MESSI can estimate coevolution while accounting for an unknown secondary structure. MESSI can also use graphics processing unit parallelism to increase computational speed. We used MESSI to infer coevolution associated with GC, AU (AT in DNA), GU (GT in DNA) pairs in noncoding RNA alignments, and in single-stranded RNA and DNA virus alignments. Estimates of GU pair coevolution were found to be higher at base-paired sites in single-stranded RNA viruses and noncoding RNAs than estimates of GT pair coevolution in single-stranded DNA viruses. A potential biophysical explanation is that GT pairs do not stabilize DNA secondary structures to the same extent that GU pairs do in RNA. Additionally, MESSI estimates the degrees of coevolution at individual base-paired sites in an alignment. These estimates were computed for a SHAPE-MaP-determined HIV-1 NL4-3 RNA secondary structure. We found that estimates of coevolution were more strongly correlated with experimentally determined SHAPE-MaP pairing scores than three nonevolutionary measures of base-pairing covariation. To assist researchers in prioritizing substructures with potential functionality, MESSI automatically ranks substructures by degrees of coevolution at base-paired sites within them. Such a ranking was created for an HIV-1 subtype B alignment, revealing an excess of top-ranking substructures that have been previously identified as having structure-related functional importance, among several uncharacterized top-ranking substructures.

Nucleus-encoded proteins interact with cis-acting elements in chloroplast transcripts to promote RNA stability and translation. We have analyzed the structure and function of three such elements within the Chlamydomonas petD 5' untranslated region; petD encodes subunit IV of the cytochrome b(6)/f complex. These elements were delineated by linker-scanning mutagenesis, and RNA secondary structures were investigated by mapping nuclease-sensitive sites in vitro and by in vivo dimethyl sulfate RNA modification. Element I spans a maximum of 8 nucleotides (nt) at the 5' end of the mRNA; it is essential for RNA stability and plays a role in translation. This element appears to form a small stem-loop that may interact with a previously described nucleus-encoded factor to block 5'-->3' exoribonucleolytic degradation. Elements II and III, located in the center and near the 3' end of the 5' untranslated region, respectively, are essential for translation, but mutations in these elements do not affect mRNA stability. Element II is a maximum of 16 nt in length, does not form an obvious secondary structure, and appears to bind proteins that protect it from dimethyl sulfate modification. Element III spans a maximum of 14 nt and appears to form a stem-loop in vivo, based on dimethyl sulfate modification and the sequences of intragenic suppressors of element III mutations. Furthermore, mutations in element II result in changes in the RNA structure near element III, consistent with a long-range interaction that may promote translation.

A number of non-coding RNA are known to contain functionally important or conserved pseudoknots. However, pseudoknotted structures are more complex than orthodox, and most methods for analyzing secondary structures do not handle them. I present here a way to decompose and represent general secondary structures which extends the tree representation of the stem-loop structure, and use this to analyze the frequency of pseudoknots in known and in random secondary structures. This comparison shows that, though a number of pseudoknots exist, they are still relatively rare and mostly of the simpler kinds. In contrast, random secondary structures tend to be heavily knotted, and the number of available structures increases dramatically when allowing pseudoknots. Therefore, methods for structure prediction and non-coding RNA identification that allow pseudoknots are likely to be much less powerful than those that do not, unless they penalize pseudoknots appropriately.

Accurate RNA secondary structure information is the cornerstone of gene function research and RNA tertiary structure prediction. However, most traditional RNA secondary structure prediction algorithms are based on the dynamic programming (DP) algorithm, according to the minimum free energy theory, with both hard and soft constraints. The accuracy is particularly dependent on the accuracy of soft constraints (from experimental data like chemical and enzyme detection). With the elongation of the RNA sequence, the time complexity of DP-based algorithms will increase geometrically, as a result, they are not good at coping with relatively long sequences. Furthermore, due to the complexity of the pseudoknots structure, the secondary structure prediction method, based on traditional algorithms, has great defects which cannot predict the secondary structure with pseudoknots well. Therefore, few algorithms have been available for pseudoknots prediction in the past. The ATTfold algorithm proposed in this article is a deep learning algorithm based on an attention mechanism. It analyzes the global information of the RNA sequence via the characteristics of the attention mechanism, focuses on the correlation between paired bases, and solves the problem of long sequence prediction. Moreover, this algorithm also extracts the effective multi-dimensional features from a great number of RNA sequences and structure information, by combining the exclusive hard constraints of RNA secondary structure. Hence, it accurately determines the pairing position of each base, and obtains the real and effective RNA secondary structure, including pseudoknots. Finally, after training the ATTfold algorithm model through tens of thousands of RNA sequences and their real secondary structures, this algorithm was compared with four classic RNA secondary structure prediction algorithms. The results show that our algorithm significantly outperforms others and more accurately showed the secondary structure of RNA. As the data in RNA sequence databases increase, our deep learning-based algorithm will have superior performance. In the future, this kind of algorithm will be more indispensable.

Nucleotide sequences of coat protein (CP) gene from 6 isolates of Apple stem pitting virus (ASPV) were obtained. Sequence analysis of CP gene showed nucleotide identities ranged from 68.7 to 99.7%. Phylogenetic analysis showed that all ASPV isolates fell into three groups. To a certain extent, the groups were related to the host origins of virus. The hypervariable and conserved region could be found in the CP gene. The variations of nucleotide sequence and predicted RNA secondary structure were analyzed for the CP gene, and the characteristic of RNA secondary structure of ASPV 5' and 3' untranslated region (UTR) were also studied. The result indicated that predicted RNA secondary structures could play an important role in studying the molecular variation of virus. The 3' UTR of ASPV RNA was predicted to contain four stem-loop structures, and it was likely to imply that the variation of RNA secondary structure was related to groups of phylogenetic analysis.

Computationally identifying non-coding RNA regions on the genome has much scope for investigation and is essentially harder than gene-finding problems for protein-coding regions. Since comparative sequence analysis is effective for non-coding RNA detection, efficient computational methods are expected for structural alignments of RNA sequences. On the other hand, Hidden Markov Models (HMMs) have played important roles for modeling and analysing biological sequences. Especially, the concept of Pair HMMs (PHMMs) have been examined extensively as mathematical models for alignments and gene finding. We propose the pair HMMs on tree structures (PHMMTSs), which is an extension of PHMMs defined on alignments of trees and provides a unifying framework and an automata-theoretic model for alignments of trees, structural alignments and pair stochastic context-free grammars. By structural alignment, we mean a pairwise alignment to align an unfolded RNA sequence into an RNA sequence of known secondary structure. First, we extend the notion of PHMMs defined on alignments of 'linear' sequences to pair stochastic tree automata, called PHMMTSs, defined on alignments of 'trees'. The PHMMTSs provide various types of alignments of trees such as affine-gap alignments of trees and an automata-theoretic model for alignment of trees. Second, based on the observation that a secondary structure of RNA can be represented by a tree, we apply PHMMTSs to the problem of structural alignments of RNAs. We modify PHMMTSs so that it takes as input a pair of a 'linear' sequence and a 'tree' representing a secondary structure of RNA to produce a structural alignment. Further, the PHMMTSs with input of a pair of two linear sequences is mathematically equal to the pair stochastic context-free grammars. We demonstrate some computational experiments to show the effectiveness of our method for structural alignments, and discuss a complexity issue of PHMMTSs.

CD-NuSS: A Web Server for the Automated Secondary Structural Characterization of the Nucleic Acids from Circular Dichroism Spectra Using Extreme Gradient Boosting Decision-Tree, Neural Network and Kohonen Algorithms

Chikungunya virus (CHIKV) is a mosquito-borne RNA virus that poses an emerging threat to humans. In a manner similar to other RNA viruses, CHIKV encodes an error-prone RNA polymerase which, in addition to producing full-length genomes, gives rise to truncated, non-functional genomes, which have been coined defective viral genomes (DVGs). DVGs have been intensively studied in the context of therapy, as they can inhibit viral replication and dissemination in their hosts. In this work, we interrogate the influence of viral RNA secondary structures on the production of CHIKV DVGs. We experimentally map RNA secondary structures of the CHIKV genome using selective 2′-hydroxyl acylation analyzed by primer extension and mutational profiling (SHAPE-MaP), which couples chemical labelling with next-generation sequencing. We correlate the inferred secondary structure with preferred deletion sites of CHIKV DVGs. We document an increased probability of DVG generation with truncations at unpaired nucleotides within the secondary structure. We then generated a CHIKV mutant bearing synonymous changes at the nucleotide level to disrupt the existing RNA secondary structure (CHIKV-D2S). We show that CHIKV-D2S presents altered DVG generation compared to wild-type virus, correlating with the change in RNA secondary structure obtained by SHAPE-MaP. Our work thus demonstrates that RNA secondary structure impacts CHIKV DVG production during replication.

It is a classical result of Stein and Waterman that the asymptotic number S(n) of RNA secondary structures is 1.104366 * pow(n,-3/2) * pow(2.61803,n),where the combinatorial model of RNA concerns a length n homopolymer, such that any base can pair with any other base, subject to the usual convention that hairpin loops must contain at least = 1 unpaired bases. The result of Stein and Waterman is proved by developing recursions,using generating functions and applying Bender's theorem. These recursions form the basis to compute the minimum free energy secondary structure for a given RNA sequence, with respect to the Nussinov energy model, later extended by Zuker to substantially more complicated resursions for the Turner nearest neighbor energy model.In this paper, we study combinatorial asymptotic for two special subclasses of RNA secondary structures - canonical and saturated structures.Canonical secondary structures are defined to have no lonely (isolated) base pairs. This class of secondary structures was introduced b y Bompfuenewerer et al., who noted that the runtime of Vienna RNA Package is substantially decreased when restricting computations to canonical structures. Here we provide an explanation for the speed-up, by proving that the asymptotic number of canonical RNA secondary structures is2.1614 * pow(n,-3/2) * pow(1.96798,n), a result obtained using different methods by Hofacker et al. Saturated secondary structures have the property that no base pairs can be added without violating the definition of secondary structure (i.e. introducing a pseudoknotor base triple). In the Nussinov energy model,where the energy for a base pair is -1, saturated structures correspond to kinetic traps.n prior work, we showed that the asymptotic number of saturated structures of a length n homopolymer is 1.07427 * pow(n,-3/2) * pow(2.35467,n). Here we determine the asymptotic expected number of base pairs in (quasi-) random saturated structures.

Loss-of-function mutations in the CYP24A1 protein-coding region causing reduced 25 hydroxyvitamin D (25OHD) and 1,25 dihydroxyvitamin D (1,25(OH)2 D) catabolism have been observed in some cases of infantile hypercalcemia type 1 (HCINF1), which can manifest as nephrocalcinosis, hypercalcemia and adult-onset hypercalciuria, and renal stone formation. Some cases present with apparent CYP24A1 phenotypes but do not exhibit pathogenic mutations. Here, we assessed the molecular mechanisms driving apparent HCINF1 where there was a lack of CYP24A1 mutation. We obtained blood samples from 47 patients with either a single abnormality of no obvious cause or a combination of hypercalcemia, hypercalciuria, and nephrolithiasis as part of our metabolic and stone clinics. We used liquid chromatography tandem mass spectrometry (LC-MS/MS) to determine serum vitamin D metabolites and direct sequencing to confirm CYP24A1 genotype. Six patients presented with profiles characteristic of altered CYP24A1 function but lacked protein-coding mutations in CYP24A1. Analysis upstream and downstream of the coding sequence showed single nucleotide variants (SNVs) in the CYP24A1 3' untranslated region (UTR). Bioinformatics approaches revealed that these 3' UTR abnormalities did not result in microRNA silencing but altered the CYP24A1 messenger RNA (mRNA) secondary structure, which negatively impacted translation. Our experiments showed that mRNA misfolding driven by these 3' UTR sequence-dependent structural elements was associated with normal 25OHD but abnormal 1,25(OH)2 D catabolism. Using CRISPR-Cas9 gene editing, we developed an in vitro mutant model for future CYP24A1 studies. Our results form a basis for future studies investigating structure-function relationships and novel CYP24A1 mutations producing a semifunctional protein. © 2023 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).