Erratum: The Non-Coding RNA Journal Club: Highlights on Recent Papers-4. Non-Coding RNA 2016, 2, 9.

Please note that in the published editorial [1], affiliations 1, and 8 contained errors.[...]

Structural characterization of naturally occurring RNA single mismatches

RNA is known to be involved in several cellular processes; however, it is only active when it is folded into its correct 3D conformation. The folding, bending and twisting of an RNA molecule is dependent upon the multitude of canonical and non-canonical secondary structure motifs. These motifs contribute to the structural complexity of RNA but also serve important integral biological functions, such as serving as recognition and binding sites for other biomolecules or small ligands. One of the most prevalent types of RNA secondary structure motifs are single mismatches, which occur when two canonical pairs are separated by a single non-canonical pair. To determine sequence–structure relationships and to identify structural patterns, we have systematically located, annotated and compared all available occurrences of the 30 most frequently occurring single mismatch-nearest neighbor sequence combinations found in experimentally determined 3D structures of RNA-containing molecules deposited into the Protein Data Bank. Hydrogen bonding, stacking and interaction of nucleotide edges for the mismatched and nearest neighbor base pairs are described and compared, allowing for the identification of several structural patterns. Such a database and comparison will allow researchers to gain insight into the structural features of unstudied sequences and to quickly look-up studied sequences

Complete chloroplast genome sequence of Holoparasite Cistanche Deserticola (Orobanchaceae) reveals gene loss and horizontal gene transfer from Its host Haloxylon Ammodendron (Chenopodiaceae)

The central function of chloroplasts is to carry out photosynthesis, and its gene content and structure are highly conserved across land plants. Parasitic plants, which have reduced photosynthetic ability, suffer gene losses from the chloroplast (cp) genome accompanied by the relaxation of selective constraints. Compared with the rapid rise in the number of cp genome sequences of photosynthetic organisms, there are limited data sets from parasitic plants. The authors report the complete sequence of the cp genome of Cistanche deserticola, a holoparasitic desert species belonging to the family Orobanchaceae

Can Clustal-style progressive pairwise alignment of multiple sequences be used in RNA secondary structure prediction?

<p>Abstract</p> <p>Background</p> <p>In ribonucleic acid (RNA) molecules whose function depends on their final, folded three-dimensional shape (such as those in ribosomes or spliceosome complexes), the secondary structure, defined by the set of internal basepair interactions, is more consistently conserved than the primary structure, defined by the sequence of nucleotides.</p> <p>Results</p> <p>The research presented here investigates the possibility of applying a progressive, pairwise approach to the alignment of multiple RNA sequences by simultaneously predicting an energy-optimized consensus secondary structure. We take an existing algorithm for finding the secondary structure common to two RNA sequences, Dynalign, and alter it to align profiles of multiple sequences. We then explore the relative successes of different approaches to designing the tree that will guide progressive alignments of sequence profiles to create a multiple alignment and prediction of conserved structure.</p> <p>Conclusion</p> <p>We have found that applying a progressive, pairwise approach to the alignment of multiple ribonucleic acid sequences produces highly reliable predictions of conserved basepairs, and we have shown how these predictions can be used as constraints to improve the results of a single-sequence structure prediction algorithm. However, we have also discovered that the amount of detail included in a consensus structure prediction is highly dependent on the order in which sequences are added to the alignment (the guide tree), and that if a consensus structure does not have sufficient detail, it is less likely to provide useful constraints for the single-sequence method.</p

The Mode of Action of Maleic Hydrazide: Inhibition of Growth

Maleic hydrazide (MH) inhibits corn root elongation through an effect on cell division apparently without inhibiting cell enlargement. The decrease in the rate of elongation was apparent only after a considerable lag, over 14 hours, even with a concentration as high as 5 mM. MH (1 mM) did not inhibit His growth of roots from corn seeds given very large doses of Γ-irradiation or excised corn root segments including the elongation Zone or the cell enlargement induced by IAA in corn coleoptile sections. Many compounds including purines, pyrimidines, nucleosides. cysteine, pyridoxal, pyruvate. kinetin and CoCl 2 , many of which had previously been reported to alleviate MH inhibition in other tissues, were tested for their ability to prevent the inhibition of corn root elongation by MH, but none were effective. These data do not support the theory that MH acts by inhibiting the synthesis of or competing with some simple metabolite or hormone. Whatever its mechanism of action the failure of MH to inhibit cell enlargement in most systems indicates that it is fairly selective.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/74891/1/j.1399-3054.1969.tb07375.x.pd

Predicting RNA secondary structure by the comparative approach: how to select the homologous sequences

<p>Abstract</p> <p>Background</p> <p>The secondary structure of an RNA must be known before the relationship between its structure and function can be determined. One way to predict the secondary structure of an RNA is to identify covarying residues that maintain the pairings (Watson-Crick, Wobble and non-canonical pairings). This "comparative approach" consists of identifying mutations from homologous sequence alignments. The sequences must covary enough for compensatory mutations to be revealed, but comparison is difficult if they are too different. Thus the choice of homologous sequences is critical. While many possible combinations of homologous sequences may be used for prediction, only a few will give good structure predictions. This can be due to poor quality alignment in stems or to the variability of certain sequences. This problem of sequence selection is currently unsolved.</p> <p>Results</p> <p>This paper describes an algorithm, <it>SSCA</it>, which measures the suitability of sequences for the comparative approach. It is based on evolutionary models with structure constraints, particularly those on sequence variations and stem alignment. We propose three models, based on different constraints on sequence alignments. We show the results of the <it>SSCA </it>algorithm for predicting the secondary structure of several RNAs. <it>SSCA </it>enabled us to choose sets of homologous sequences that gave better predictions than arbitrarily chosen sets of homologous sequences.</p> <p>Conclusion</p> <p><it>SSCA </it>is an algorithm for selecting combinations of RNA homologous sequences suitable for secondary structure predictions with the comparative approach.</p

High incidence of Epstein-Barr virus, cytomegalovirus and human herpesvirus 6 infections in children with cancer

BACKGROUND: A prospective single-center study was performed to study infection with lymphotropic herpesviruses (LH) Epstein-Barr virus (EBV), cytomegalovirus (CMV) and human herpesvirus 6 (HHV-6) in children with cancer. METHODS: The group of 186 children was examined for the presence of LH before, during and 2 months after the end of anticancer treatment. Serology of EBV and CMV was monitored in all children, serology of HHV-6 and DNA analysis of all three LH was monitored in 70 children. RESULTS: At the time of cancer diagnosis (pre-treatment), there was no difference between cancer patients and age-matched healthy controls in overall IgG seropositivity for EBV (68.8% vs. 72.0%; p = 0.47) and CMV (37.6% vs. 41.7%; p = 0.36). During anticancer therapy, primary or reactivated EBV and CMV infection was present in 65 (34.9%) and 66 (35.4%) of 186 patients, respectively, leading to increased overall post-treatment IgG seropositivity that was significantly different from controls for EBV (86.6% vs. 72.0%; p = 0.0004) and CMV (67.7% vs. 41.7%; p < 0.0001). Overall pre-treatment IgG seropositivity for HHV-6 was significantly lower in patients than in controls (80.6% vs. 91.3%; p = 0.0231) which may be in agreement with Greaves hypothesis of protective effect of common infections in infancy to cancer development. Primary or reactivated HHV-6 infection was present in 23 (32.9%) of 70 patients during anticancer therapy leading to post-treatment IgG seropositivity that was not significantly different from controls (94.3% vs. 91.3%; p = 0.58). The LH infection occurred independently from leukodepleted blood transfusions given. Combination of serology and DNA analysis in detection of symptomatic EBV or CMV infection was superior to serology alone. CONCLUSION: EBV, CMV and HHV-6 infections are frequently present during therapy of pediatric malignancy

Evaluation of Glycine max mRNA clusters

BACKGROUND: Clustering the ESTs from a large dataset representing a single species is a convenient starting point for a number of investigations into gene discovery, genome evolution, expression patterns, and alternatively spliced transcripts. Several methods have been developed to accomplish this, the most widely available being UniGene, a public domain collection of gene-oriented clusters for over 45 different species created and maintained by NCBI. The goal is for each cluster to represent a unique gene, but currently it is not known how closely the overall results represent that reality. UniGene's build procedure begins with initial mRNA clusters before joining ESTs. UniGene's results for soybean indicate a significant amount of redundancy among some sequences reported to be unique mRNAs. To establish a valid non-redundant known gene set for Glycine max we applied our algorithm to the clustering of only mRNA sequences. The mRNA dataset was run through the algorithm using two different matching stringencies. The resulting cluster compositions were compared to each other and to UniGene. Clusters exhibiting differences among the three methods were analyzed by 1) nucleotide and amino acid alignment and 2) submitting authors conclusions to determine whether members of a single cluster represented the same gene or not. RESULTS: Of the 12 clusters that were examined closely most contained examples of sequences that did not belong in the same cluster. However, neither the two stringencies of PECT nor UniGene had a significantly greater record of accuracy in placing paralogs into separate clusters. CONCLUSION: Our results reveal that, although each method produces some errors, using multiple stringencies for matching or a sequential hierarchical method of increasing stringencies can provide more reliable results and therefore allow greater confidence in the vast majority of clusters that contain only ESTs and no mRNA sequences

Structural Constraints Identified with Covariation Analysis in Ribosomal RNA

Covariation analysis is used to identify those positions with similar patterns of sequence variation in an alignment of RNA sequences. These constraints on the evolution of two positions are usually associated with a base pair in a helix. While mutual information (MI) has been used to accurately predict an RNA secondary structure and a few of its tertiary interactions, early studies revealed that phylogenetic event counting methods are more sensitive and provide extra confidence in the prediction of base pairs. We developed a novel and powerful phylogenetic events counting method (PEC) for quantifying positional covariation with the Gutell lab’s new RNA Comparative Analysis Database (rCAD). The PEC and MI-based methods each identify unique base pairs, and jointly identify many other base pairs. In total, both methods in combination with an N-best and helix-extension strategy identify the maximal number of base pairs. While covariation methods have effectively and accurately predicted RNAs secondary structure, only a few tertiary structure base pairs have been identified. Analysis presented herein and at the Gutell lab’s Comparative RNA Web (CRW) Site reveal that the majority of these latter base pairs do not covary with one another. However, covariation analysis does reveal a weaker although significant covariation between sets of nucleotides that are in proximity in the three-dimensional RNA structure. This reveals that covariation analysis identifies other types of structural constraints beyond the two nucleotides that form a base pair