RNA-related applications of the next-generation sequencing (NGS) technologies require context-specific interpretations: e.g., sequence mismatches may indicate sites of RNA editing, or uneven read coverage often points to mature form of microRNA. Existing visualization tools traditionally show RNA molecules in two dimensions, with their base pairing and the resulting secondary structure. However, it is not straightforward to combine a linear NGS data display with the 2-D RNA depictions.Drosophila melanogaster.We present a novel approach for interactive representation of nucleotide substitutions and modifications in the transcribed genome. With the focus on RNA secondary structure in the context of NGS data, it provides intuitive visualization of genomic environment, sequence reads, nucleotide polymorphisms and editing events integrated with the structural and functional elements of both coding and non-coding RNA molecules. Using our approach we present and discuss examples and general trends of polymorphisms and editing in the context of the secondary structure of microRNAs. As expected, most of the substitutions comprised A to G and C to T events, consistent with typical RNA editing patterns. However, we did not observe prevalence of editing in double-stranded regions of the microRNA stem-loop. We describe novel prominent editing event candidates, observed across several small RNA libraries of In contrast to the existing general tools for NGS data visualization, the power of our approach is not only in the display of read alignments and their counts, but the integration of RNA secondary structure, sequencing depth, and rates/patterns of editing or other modifications. It provides a comprehensive picture, important for large-scale studies and detailed analyses, helping to gain insight into the intricate relationships between different events in RNA biogenesis. RNA molecules are traditionally shown in either one dimension in FASTA format or two dimensions, with the purpose of showing their base pairing and the resulting secondary structures, important for their stability and function. A number of tools for displaying RNA molecules in 2-D has been created, such as RNAViz, VARNA, RnallViewer, jViz.Rna and 4SALE -5, to nade novo into contigs or aligning them to a reference genome sequence and finding meaningful deviation of the sequence itself or the read coverage from expected models. In RNA-related applications, these interpretations are context-specific: for instance, sequence mismatches are often interpreted as potential sites of RNA editing, or uneven read coverage is taken as an indicator of mature form of microRNA. However, it is not straightforward to combine a linear graphical display of NGS data with the 2-D RNA depictions.The advent of the next-generation sequencing (NGS) technologies has changed the research approaches in molecular biology and NGS is quickly becoming a standard. The technologies are based on determining sequences of short fragments of DNA or RNA ("reads"), assembling them Some of the tools mentioned above provide a linear view of the RNA, but none allow for clear connection with genomic features. We have developed a novel representation of RNA secondary structure that is integrated with the display of reads generated by NGS. It is similar to a linear Feynman diagram but implemented in an interactive Java applet and provides intuitive visualization of genomic environment, sequence reads, nucleotide polymorphisms and editing sites together with the structural and functional elements of the encoded RNA molecules. It is not intended to replace the 2-D depictions but, rather, to usefully supplement them with providing visual links to genomic features and experimental data, which can be overwhelming in NGS projects.MicroRNAs are endogenous short non-coding RNAs that are involved with regulation of messenger RNAs through either RNA degradation or translational repression. The widespread functionality of these molecules has been implicated in many different areas including development and various disease states and conditions . As a reIt is important to note that the microRNA secondary structure plays a crucial role for proper processing of these molecules. For example, Drosha, an exonuclease, recognizes a transcript hairpin structure, which it then cleaves and generates a precursor microRNA. Additionally, Dicer, another protein involved with cleavage, must recognize the loop region and a specific nucleotide duplex in the microRNA stem-loop after nuclear transportation. The stem-loop length varies from microRNA to microRNA, but it includes a duplex that contains a mature microRNA and a star strand, as well as a loop region, which is removed and then subsequently degraded ,9. FurthEditing is a phenomenon that is often observed in RNA and has been shown to play important roles in development, tissue specificity and RNA structure . RNA ediadenosine deaminase acting on RNA), responsible for editing by site-specific deamination of adenosines. It specifically converts A to I. This type of editing is mostly active in the brain, but also has been implicated elsewhere, including various tissues and developmental stages [Drosophila, although methylated C is known to spontaneously deaminate to U. In this study, we use a novel visualization approach to overlap RNA secondary structure of known Drosophila melanogaster microRNAs with potential editing events or other single nucleotide changes.The RNA editing is performed by the enzyme called Adar , as shown in Figure mir-1012. Line 1 shows the mismatch frequencies or the percentage of that particular mismatch event in all reads for the specified libraries. Line 2 displays the sequencing depth of the reads mapped to the microRNA stem-loop, while line 3 shows the genomic environment (exon-intron structure for a mirtron in this case) and line 4 - the reference sequence.Figure Library reads are shown in line 5 (some are removed from view for compactness as described below). The list of available libraries is shown here in the menu in the center of the display and the selected library is highlighted in pink in the main display. Libraries can be de-selected to remove from view for less cluttered display. In different libraries, identical reads are summarized in a single bar with the number of counts and, when applicable, the colors on top of the bar reflect the mismatches, as compared to the reference genome.One can also inspect actual read sequences and the secondary structure, shown in line 6 and 7, respectively. This makes the RNA secondary structure visualization intuitive and as a result we can clearly see the pre-microRNA hairpin. In this case, the secondary structure consists of single stranded and double stranded regions referring to the 5P, loop region, and 3P fragments of the stem-loop. Finally, the bottom line shows the single-stranded sections, while the double-stranded sections are above. The double stranded portion of the structure is clickable and also contains pertinent base-pairing information. In other words, if a user clicks on a position in this line, its corresponding base-pair in a stem will be connected by a magenta line in the display (as shown). Finally, the green boxes show bulge lengths in the RNA strand opposite to the bulge.mir-1012 the sequencing depth (in red) is significant in the 5P and 3P arms, indicating that both the mature and star strands are detected. We can easily extrapolate that mir-1012 is a mirtron and is Drosha independent, since it is located in an intronic region in the genome. More so, this visualization allows us to infer that single nucleotide changes mostly occur on the 3P arm in a bulge region. In sum, the display allows us to connect editing levels, events and locality, RNA structure, genomic regions, and NGS read counts and depth in a cohesive and meaningful manner.In this particular case Figure , we obseThe structural complexities of microRNAs, including 5'/3' trimming, editing, and base pairing and folding, can be conveniently visualized together using our approach. These features have been shown to be very important for proper modulation and for accurate processing ,8,19.bantam . On a more global level, we were also able to identify microRNA editing candidates by analyzing several publicly available libraries  and read numbers, while other events were mostly supported by only a few reads and likely represented experimental noise. These results indicate that the canonical type and method of editing is amongst the most prevalent type and supports previous studies [For this purpose, we selected all cases where editing level  exceeded a threshold of 10% with >20 reads per library. This filtering was done to avoid mismatches that result from sequencing errors. We found some 30 microRNAs that pass the filter  as would be expected from the properties of Adar, if it discriminated against bulges.ss and Ods, respectively) and expected  rates of editing from publicly available libraries (Table 1) as described in Materials and Methods. The ratios of observed to expected events of Oss/Ess = 1.04 and Ods/Eds = 1.00 for single- and double-stranded editing sites, respectively, indicated that editing is essentially non-discriminatory when it comes to microRNA stem regions. We observed no editing events in the loop regions, even in those microRNAs that contained significant number of reads (>100) partially  or entirely in loop regions, such as mir-34 (data not shown). A reason for this observation may be due to the fact that the loop regions are contiguously single stranded, which is not a canonical structure of an editing target. In contrast, single-stranded bulge regions in microRNA stems show a very slight prevalence for editing events, suggesting that Adar may tolerate bulges, if it is responsible for the editing events in microRNA.For single- and double-stranded regions we obtained the observed . This can be viewed for any set of microRNAs by inspecting start/end positions of the read and comparing it to the grey shades on the bottom of our display (annotated 5P and 3P forms).In bantam, we can observe a high number of read counts with the end positions on the 5P arm that differ from known mirBase annotations ,22. We sThe overall pattern of the NGS analysis of bantam suggests that Dicer cleavage can occur at multiple sites resulting in different isoforms. Notably, both of the most abundant isoforms on the 5P arm display the same substitution (editing event) in base 34. We also observe differential end positions on the 3P arm, which may hint at a 3' trimming event.In addition to our observations and conclusions above, what makes this example more interesting is that we see in different libraries reads that correspond to only mature or both the mature and star strand. We can also see how the same microRNA is modified differently in specific libraries, which may be an effect of the cellular environment. This is a further indication that the star strand may be functional and that functionality as well as editing may be context-dependent.In this paper, we described an approach to display microRNA features and visualize them in the context of the genome environment, RNA secondary structure, nucleotide substitution and NGS. A number of visualization tools has been developed for NGS  and genome/sequence features relevant for them, which makes our visualization approach both versatile and robust. The Java applet is available from the authors upon request.Drosophila genome (dm3) using Bowtie. In order to filter out potential noise and artifacts, all mismatched reads that also successfully aligned elsewhere in the genome with at most one mismatch were removed. These steps were performed for each of the ten libraries utilized in this study. We then quantified observed mismatches for every position in all known microRNAs in Drosophila.Publicly available small RNA-Seq libraries were downloaded from the NCBI SRA database. Before clipping and mapping, we removed (i) the 3 adaptors from the reads, (ii) all reads shorter than 16 nucleotides and (iii) those with one or more "N" nucleotides present. The remaining reads were then mapped against the stem-loop sequences obtained from mirBase  using BoTo limit the experimental artifacts, we used specific thresholds for read count (>20) and editing rate (>10%), multiple library and sequence mappings (i.e. different sequences from the same library having the same position edited and different sequences from different libraries having the same position edited), and other similar measures. While we cannot guarantee that we removed all sequencing artifacts, they are significantly reduced with such filtering.ss ) and double-stranded regions (Eds ) as follows:MicroRNA secondary structure annotation was taken from mirBase . For ourds and Nss are the total numbers of nucleotides in double- and single-stranded regions, respectively. We then obtained ratios of observed vs expected events for these region types and did not detect any significant difference, with a slight prevalence of the observed single-stranded edits.where S is the total number of edits observed and NThe authors declare that they have no competing interests.TC wrote the visualization program and scripts for data preparation, AN performed the data analysis and prepared the data for visualization, AG conceived and directed the study, wrote the visualization program. AN and AG wrote the manuscript.


Current genomic studies are limited by the availability of fresh tissue samples. Here, we show that Illumina RNA sequencing of formalin-fixed diagnostic tumor samples produces gene expression that is strongly correlated with matched frozen tumor samples (r > 0.89). In addition, sequence variations identified from FFPE RNA show 99.67% concordance with that from exome sequencing of matched frozen tumor samples. Because FFPE is a routine diagnostic sample preparation, the feasibility results reported here will facilitate the setup of large-scale research and clinical studies in medical genomics that are currently limited by the availability of fresh frozen samples. Cancer is a generic term for a large group of malignant diseases that can affect any part of the body. It is characterized by invasive, abnormal growth which grows beyond the usual boundaries and spreads to adjoining or distant organs (metastasis). It is important to analyze and characterize genomic structural, sequence and expression variations that cause cancer or are associated with cancer in order to advance our knowledge of the disease and to develop new treatments for cancer3To identify somatic mutations associated with various cancer subtypes and clinical behavior, it is necessary to have the clinical information on the disease progression and patient outcome678910Formalin-Fixed Paraffin-Embedding (FFPE) has been the standard diagnostic sample preparation for pathologists for decades. This resource offers a potentially large volume of biospecimens to match disease and normal tissues for large-scale correlative studiesPrevious studies have successfully used DNA from FFPE samples for copy number analysis and mutation detection using targeted sequencing of single genes1416171820In this study, we performed a comprehensive analysis of RNA-sequencing data from six pairs of FF/FFPE tumor samples. We used NanoString technology as an orthogonal verification platform to compare gene expression between FF and FFPE. Finally, we used SNPiR bioinformatics tool and customer filters to analyze sequence variations from RNA-sequencing and used DNA-sequencing data for verification purpose. These analyses indicate the feasibility of performing comprehensive genomic characterization of tumor samples from archived FFPE samples.Six matched pairs of FF and FFPE ovarian tumor samples have been processed for RNA sequencing and DNA whole exome sequencing. RNA from these six pairs were also subjected to the NanoString gene expression analysis. Fresh tumor samples served as references to validate mutations and gene expression in FFPE samples . DNA extractions were done using Qiagen All Prep DNA/RNA FFPE Kit , according to Qiagen’s supplementary protocol for FFPE tissue (QIAamp DNA FFPE Tissue Handbook). We implemented the following protocol changes: All FFPE samples were digested with 20 μL Proteinase K shaking overnight at 56 °C and then supplemented with an additional 15 μL for an additional hour for complete protein digestion. Samples were eluted in 50 μL elution buffer. RNA and DNA from FF tumor samples were extracted using Trizol (Invitrogen) and Qiagen DNAeasy kit, respectively. RNA quality was determined by Agilent Bioanalyzer.Stranded Total RNA library preparations were prepared using the Illumina TruSeq Stranded Total RNA LT kit (with Ribo-Zero Human/Mouse/Rat). 500 ng of FFPE RNA was used to initiate the ribosomal RNA reduction. To avoid over fragmentation of FFPE RNA, the standard 8 second fragmentation time for ribosomal reduced RNA was varied from 0 to 4 seconds to accommodate the average size distribution of each degraded FFPE RNA sample as determined from the Agilent Bioanlayzer quality control run. Fragmentation time was based on Agilent Bioanalyzer size range examples provided in the Alternate Fragmentation Protocols—Appendix A of the Illumina TruSeq Stranded Total RNA Sample Preparation Guide (part#15031048 Rev. D April 2013). The remaining stranded library preparation were performed according to the kit instructions. 500 ng of total RNA from FF samples was used with Illumina TruSeq Stranded mRNA Sample Preparation Kit. Validation of the library preparations was performed on an Agilent Bioanalyzer using the DNA1000 kit. Libraries were quantified using a Roche LightCycler96 with FastStart Essential DNA Green Master mix. Library concentrations were adjusted to 4 nM and pooled for multiplex sequencing. Pooled libraries were denatured and diluted to 15 pM then clonally clustered onto the sequencing flow cell using the Illumina cBOT Cluster Generation Station and TruSeq Paired-End Cluster Kit v3-cBot-HS. The clustered flow cell was sequenced on the Illumina HiSeq2500 Sequencing System using the TruSeq SBS Kit v3-HS.All libraries were prepared from 50 ng of genomic DNA using the Nextera DNA Sample Prep Kit  following the manufacturer’s protocol. Sample specific indexes were added during 10 cycles of PCR amplification. Excess primers and primer dimers were removed using Agencourt AMPureX beads  at a 0.8X bead ratio. Libraries were quantified by qPCR using primers specific to Illumina adaptor sequences and library size was assessed on a high sensitivity chip run on the Agilent 2100 Bioanalyer . All sample libraries underwent targeted enrichment for the whole exome per the Nextera Enrichment Sample Prep Protocol . Illumina’s TruSeq Exome pool consisting of 90-mer biotinlyated oligos was used to enrich for the whole exome, covering known protein-coding genes, 5′ and 3′ UTRs, microRNA, and other non-coding RNA .To check the general quality of reads generated from Illumina sequencing, FastQC and FASTX-Toolkit have been used to detect abnormalities in a number of quality metrics. Next, reads were aligned to NCBI reference genome GRCh37 utilizing Tophat2et al., 2015)We used bwa (v0.6.2) aligned with seed length of 25 and default parameters, piping into samtools (v0.1.8) to sort. Additional sorting and adding read groups were processed with Picard-tools (v1.77). All bam files were then merged with samtools. The resulting merged file was processed with Picard-tools for PCR duplicate removal and with GATK (v2.4-7) for realignment, base recalibration, reducing of reads for faster variant calling. The comparison of SNV calls from DNA sequencing of corresponding FF and FFPE samples are reported elsewhere ; 27619 transcripts met this criterion medium transcripts length (4–8 kbp); 8530 transcripts met this criterion long transcripts length (>8 kbp); 1521 transcripts met this criterion The coverage plot over normalized transcript length for all six FF/FFPE pairs indicates a smaller, but discernible increase in 3′ coverage of all transcripts . To testResults in We then used the NanoString technology as an orthogonal verification platform to determine the extent of correlation in gene expression between FF and FFPE samples . The genAll matching FF and FFPE samples cluster together . These rThe differential gene expression of six pairs of FF and FFPE samples was calculated utilizing DEseq2. RNA-sequencing data from seven normal fallopian tube samples was used as reference. The results show a very strong correlation of fold-change, relative to normal samples, in gene expression between matched FF and FFPE tumor samples . To deteWe then performed differential gene expression analysis between FFPE tumor samples and FF normal samples as well as between FF tumor and FF normal samples. P-values of each transcripts were compared between FF and FFPE sample groups in relationship to the average level of gene expression . We do nFour hundred seventy out of 742 (63.3%) genes, upregulated in FF tumor samples with reference to normal samples, were also upregulated in FFPE samples. One hundred fifty one out of 270 (55.9%) genes downregulated in FF tumor samples were also downregulated in FFPE samples. To perform pathway analysis, two lists of up-regulated genes from FF and FFPE samples were used as inputs for DAVID  analysisFinally, a modified version of the pipeline “SNPiR” was used to analyze base changes in FF and FFPE RNA sequencing data sets. Mutations in RNA transcripts were called from both storage types (FF and FFPE), and the results were compared to mutations found in corresponding FF DNA sample by whole exome sequencing analysis. To visualize the comparison, for each sample the RNA variant allele frequency (variant detection rate) was plotted on the left y-axis and the DNA variant allele frequency (mutation rate) was plotted on the right y-axis . Each liInitially, FFPE samples produced a large number of discordant/false positive mutation calls . The majThese results show that RNA sequencing from FFPE samples can be used to identify high-confident single-nucleotide variations in tumor samples. It is important to note that neither FF nor FFPE RNA sequencing will pick up all mutations that can be found in DNA due to missing coverage in RNA (e.g. not expressed gene) or as a result of limitation in detecting mutations around splice sites from RNA sequencing data. FFPE samples have a false positive rate of approximately 4%. The slightly higher false positive rate is most likely caused by the formalin fixation process.et al.RNA sequencing analysis indicates that gene expression between matched FF and FFPE pairs are strongly correlated, indicating the feasibility of using FFPE samples as a suitable replacement for FF samples. These results are consistent with prior studies by Hedegaard The observed overlap of approximately 50% in differential gene expression and pathway analysis may seem less impressive. However, considering that differential gene expression analysis of two randomly assigned groups within FF samples showed similar percentage of overlap in differentially expressed genes relative to 7 normal samples (data not shown), it is important to recognize the heterogeneity between tumor samples and the limitation of small sample size in current study that may be contributing to lower overlap in differentially expressed genes between FF and FFPE samples. Finally, our analysis demonstrates that FFPE sequence variation artifacts can be effectively removed by applying an allele fraction filter to C>T and G>A substitutions. Information will be useful for investigators planning to include FFPE samples for translational and clinical genomic studies.How to cite this article: Graw, S. et al. Robust gene expression and mutation analyses of RNA-sequencing of formalin-fixed diagnostic tumor samples. Sci. Rep.5, 12335; doi: 10.1038/srep12335 (2015).


It is found that RNApps has comparable performance with the former one and outdoes the latter in terms of structure predictions. The approach holds great promise for accurate and efficient RNA tertiary structure prediction.The knowledge of the tertiary structure of RNA loops is important for understanding their functions. In this work we develop an efficient approach named RNApps, specifically designed for predicting the tertiary structure of RNA loops, including hairpin loops, internal loops, and multi-way junction loops. It includes a probabilistic coarse-grained RNA model, an all-atom statistical energy function, a sequential Monte Carlo growth algorithm, and a simulated annealing procedure. The approach is tested with a dataset including nine RNA loops, a 23S ribosomal RNA, and a large dataset containing 876 RNAs. The performance is evaluated and compared with a homology modeling based predictor and an  RNA is an important and versatile macromolecule participating in a variety of biological processes. In addition to experimental approaches, computational prediction of 3D structure of RNAs and loops is an alternative and important source of gaining structure information and insights into their functions. The prediction of RNA loop structures is of particular interest since RNA functions often reside in the loop regions and about 46% of nucleotides in an RNA chain remain unpaired. For this purpose, we develop an approach RNApps based on a probabilistic coarse-grained RNA model. The probabilistic nature of the model, together with a sequential Monte Carlo (SMC) growth algorithm, allows a natural and continuous sampling of structures in 3D space, making the approach unique. The coarse-graining nature of the model further increases the efficiency. Here we test this new approach with various types of loops, including hairpin loops, internal loops, and multi-way junction loops, and make comparisons with other structure predictors. RNAs are a type of macromolecule of crucial and versatile biological importance. In addition to their long-discovered functions of carrying genetic information and acting as a part of translation machinery, recently they were found to be able to participate in the regulation of gene expressions and protein synthesis, and to act as scaffolds for higher-order complexes and transmit signals between cells, etc –4. To fuThe prediction of the tertiary structures of RNA loops, as part of the effort of structure prediction, merits specific attention. First, this is because RNA functions often reside in loop regions and about 46% of nucleotides in an RNA chain remain unpaired, according to Dima and her colleagues’ research . Some peIn this work we develop an approach that is specifically designed for predicting the tertiary structures of RNA loops or missing fragments which can be part in helical regions and part in loop regions. The loops can be hairpin loops, internal loops, or multi-way junction loops. The approach includes a probabilistic CG RNA model, a sequential Monte Carlo growth method, a simulated annealing strategy, and a statistical potential used for scoring the generated structural candidates. The probabilistic model is inspired by BARNACLE. However, the original BARNACLE model considers all the seven torsional angles in the RNA chain, as is not always necessary since some dihedral angles are relatively rigid. The advantage of the new model developed here is multifold. First, the probabilistic nature of the model allows a continuous sampling in the conformational space and therefore is able to cover all the relevant conformations, as is difficult for fragment assembly methods or our previous one based on a discrete-state model , 29. SecThe atomistic structure of an RNA is defined by seven torsional angles, as illustrated in θ, η, μ, ϕ, and ω, as illustrated in θ+ is calculated as the torsional angle formed by Pi-C4’i-Pi+1-C4’i+1, η+ by C4’i-Pi+1-C4’i+1-Pi+2, μ+ by C4’i-Pi+1-C4’i+1-Bci+1, ϕ+ by Pi+1-C4’i+1-Bci+1-N1i+1, and ω+ by C4’i+1-Bci+1-N1i+1-C2i+1; from the 3’-end to the 5’-end, θ- is calculated as the torsional angle formed by C4’i+1-Pi+1-C4’i-Pi, η- by Pi+2-C4’i+1-Pi+1-C4’i, μ- by C4’i+1-Pi+1-C4’i-Bci, ϕ- by Pi+1-C4’i-Bci-N1i, and ω- by C4’i-Bci-N1i-C2i.In the current model all the bond lengths and bond angles are fixed. Five torsional angles are defined based on the CG model, including Once all the torsional angles along the RNA chain are known, the tertiary structure of the RNA molecule can be built in a “growing” way by calculating the coordinates of the to-be-grown nucleotide based on the values of the torsional angles and the previously grown nucleotides. The growing process is realized in both the “forward” way and the “reverse” way, to take advantage of the position constraints as much as possible.θ, η, and ϕ. This is because μ is linearly dependent on η (if grown in a forward direction) or θ (if grown reversely), and ω is approximately linearly dependent on ϕ, as supported by the statistics given in θ, η, and ϕ, and it is done in an iterative and probabilistic way, as illustrated in In the five torsional angles defined above, only three are independent, i.e., p(η+|θ+) as an example. We first calculate all the  pairs in the loops in the database and plot the data points in a two-dimensional surface, as shown in K bins and calculate the conditional probability numerically as followsθ+(i) and η+(j) are the center of the i-th and j-th bins, respectively, Ni is the number of data points with their θ+ values falling into the i-th bin, and nij is the number of data points with their θ+ values falling into the i-th bin and η+ falling into the j-th bin simultaneously. K is set to 72, and thus the bin width is Δ = 360°/K = 5°. The other conditional probabilities are similarly calculated. The conditional probabilities between η+ and θ+ and between ϕ+ and η+ are shown in ϕ+ belongs, while the former are not. This is because one  pair involves two nucleotides and thus there are 16 types of dinucleotides; to classify the data into 16 types will lower the statistical quality since the number of data is limited.The conditional probabilities are calculated from the loop structures in the RNA09 database. Before the calculation, we first strip the helices in the structures. If two consecutive base pairs are formed, they are determined to be a helix. Three types of base pairs are considered, including A-U, G-C, and G-U. Take Based on the probabilistically generated torsional angles, we build the CG structure of RNA loops/fragments by adding nucleotides one by one on the previously grown nucleotides with a sequential Monte Carlo (SMC) method , which iX1X2⋯Xn whose structure is to be predicted, we grow it alternatively in two directions in 3D space from two anchor nucleotides to which this loop is attached. The algorithm also works if there is only one anchor, which happens when the loop is at one end of the RNA. To be more specific, for the i-th nucleotide to be grown, we randomly determine D = 10 possible positions of attaching it to the (i − 1)-th nucleotide (if grown in a forward way) by probabilistically generating the corresponding θ, η, and ϕ values based on the algorithm described in D random configurations, we obtain a total of N = L × D partial chains, where L is the number of chains before the growth of the i-th nucleotide. If N is greater than a threshold M, we do a resampling procedure by randomly choosing M partial chains out of N with the probability of choosing the m-th one proportional to exp(−Em/RT1), otherwise we keep all the N chains. Here Em is the statistical energy of the m-th partial chain that will be described later. The temperature T1 is used to control the relative probabilities between the candidates and RT1 is set to 300 RASP energy unit.Specifically, for a loop sequence M conformations for the first chain using the SMC method described above. Next for each in M conformations, we grow D conformations for the first nucleotide in the second chain and perform resampling to select M conformations out of N = M × D ones. We repeat these steps for the rest nucleotides in the second chain and then for the third chain. Finally we get M chains after the whole three-way junction loop is built.The above procedure works for a single chain. For internal loops or multi-way junction loops with more than one chain, the procedure is similar. Take three-way junction loop as an example. We first build M and D are chosen as a compromise between the accuracy and efficiency of the SMC algorithm. A large value increases the accuracy while deteriorates the efficiency, whereas a small value does the opposite. We use M = 80 and D = 20 for reasonable run time. These values are the default in this work unless otherwise stated.The parameters M structural candidates with low energies. These structures are further optimized with simulated annealing (SA) }, where Eold and Enew are the energies of the deleted and regrown fragments, respectively; T2 = l * T3 is the controlling temperature proportional to the regrown loop length l, since the strength of interactions needed to break to escape from local minima increases with loop length; if the newly grown fragment is accepted, the new conformation of the whole loop is recorded for following cluster analysis;a Metropolis criterion is then used to determine whether the new fragment is accepted or not; specifically, the probability is calculated as min{1, exp[ to (iv) are repeated 50 times at each temperature g_cluster command in software Gromacs with gromos cluster method [the recorded structures during SA process are clustered using r method  and cutoIn both the SMC and SA procedures, we adopt the Ribonucleic Acids Statistical Potential (RASP) developed by Capriotti et al. . In brieSince RASP is an all-atom statistical potential, the corresponding all-atom structures of the CG conformations generated by our approach need to be reconstructed. The procedure is as follows. Based on the atoms of the nucleobases, namely N1, C2, and Bc, the coordinates of the heavy atoms in the nucleobases are calculated with the pre-built templates taken from the RNA09 database. There are four such templates, built for A, U, G, and C, respectively. The coordinates of atoms in the backbone are calculated based on the coordinates of the atoms P, C4’, and N1 (in the bases of U and C) or N9 (in the bases of A and G) with a pre-built template. After the reconstruction we check if there are steric clashes between atoms. If any clash is found, the structure is discarded.ab initio way of prediction based on physical interactions and discrete molecular dynamics (MD) simulation. These two are selected to represent two very different strategies of structure prediction in the field. The testing set that appears in the original RLooM paper [Here we test our approach and compare it with two other RNA tertiary structure predictors, i.e., RLooM  and iFoloM paper  is used oM paper . For iFooM paper . For RNAIn the first part of the table, RNApps is compared with RLooM and iFoldRNA. It can be seen that for this specific testing set, RNApps performs comparably with RLooM or slightly worse in a few cases. This is normal since the homology modeling based predictors usually perform better than those based on energy rules. However, such predictors are sensitive to the testing set. For example, we tested RLooM with 28 fragments of length of 8-nt taken from the RNA pseudoknot 1E95 and found that RLooM failed for all of them. The reason is that there are no homological partners of this RNA in the RLooM database. In contrast, our method guarantees to give a reasonable prediction   consistiWe further test our approach with a much larger dataset—RNA 3D Hub . It contIn In Two loops are less well predicted. The loop shown in The loop of length 17-nt shown in In this work we develop an approach named RNApps specifically designed for loop structure prediction. The approach includes a probabilistic coarse-grained RNA model, a sequential Monte Carlo growth algorithm, a simulated annealing procedure and an all-atom statistical energy function.ab initio predictor iFoldRNA. It was found that RNApps performs comparably with RLooM while considerably better than iFoldRNA. However, we also noted that RLooM cannot guarantee a return of valid structures for some targets, due to the lack of their homology information in the database. In contrast, RNApps and iFoldRNA guarantee a result, and iFoldRNA can also be used for studying the folding dynamics. The tests with a ribosomal RNA showed that the average RMSD is 2.55 Å, 3.23 Å, and 3.97 Å for the rebuilt fragments of length 5-, 8-, and 10-nt, respectively. The tests with RNA 3D Hub showed that the average RMSDs for hairpin loops are around 4 Å and increase slightly with the loop length. The performance for internal loops, three-way and four-way junction loops is even better than that for hairpin loops, mostly due to the increased number of anchor nucleotides. Further analysis showed that although most RNA loops are predicted with good accuracy, some ones with non-canonical base pairs, base triples, or rare torsional angles are reproduced with a lower quality.We tested the approach with a set of nine RNA loops, a 23S ribosomal RNA, and a large RNA dataset containing 876 RNAs . For the testing set including nine RNA loops, six loops can be predicted with good accuracy (RMSD < 2.5 Å), one loop has an RMSD of 3.01 Å and two have RMSDs around 6-7 Å. We compared the results with a homology modeling based predictor RLooM and an We note that our approach in it current form does not perform considerably better than the other predictors and even slightly worse than the homology modeling based one. However, our approach has many unique and promising features. First, it is not only designed for predicting structures of hairpin loops, but also for internal loops, three-way and four-way junction loops and even complex cases. One motivation is that many predictors have been designed to predict the relative position and orientation of the helices of an RNA molecule, however, less attention was paid to the construction of the loops connecting the helices. This makes our work necessary. Second, the efficiency of our approach is high. With the parameters used in this work, a prediction of loop with medium length takes several minutes without SA optimization or several tens of minutes with SA optimization. For example, the computational time for the longest loop (26-nt) in three- and four-way junctions is approximately one hour. This efficiency is much higher than that of most MD based algorithms and the approach is free from the problems associated with homology modeling based methods. The efficiency will be further improved by optimizing the SMC and SA algorithms. Third, the probabilistic and continuous nature of the approach guarantees the sampling of all the relevant phase space in principle, and allows a dynamic adjustment between accuracy and efficiency, which can be determined by users based on their own computational capacity. Fourth, the SMC framework of the approach makes the incorporation of constraints very easy. The constraints may be the experimental information of atomic distance, base pairs or base stacking, or information from users’ experience. With the introducing of such information, the sampling space can be greatly reduced and both accuracy and efficiency will be significantly improved. The way of incorporation of constraints into the SMC framework can be found in previous work . We beliWe also noticed that there are two recent works in protein loop predictions that are similar to ours. In the first one, Tang et al. developed an approach named DiSGro based on sequential Monte Carlo method , which iS1 Fig(A) Secondary structure of pseudoknot 1E95. (B) shows the RMSDs of predicted structures for all fragments of length 5, 8, and 10 in 1E95. The x-axis stands for the index of the first nucleotide of the tested fragment.(EPS)Click here for additional data file.S2 Figp) in the nine-loop testing set, the quantity termed similarity probability is defined as the number of  pairs in the loops of the same sequence as p in the templates over the total number of  pairs in all the loops in the templates. As shown in the figure, the similarity probabilities are all less than 0.5%. Therefore, the presence of homologs has minimal effect on the conditional probability and hence the final prediction results.For each loop (denotes as (EPS)Click here for additional data file.S1 Table(PDF)Click here for additional data file.


Many plant viruses have positive-strand RNA [(+)RNA] as their genome. Therefore, it is not surprising that RNA-binding proteins (RBPs) play important roles during (+)RNA virus infection in host plants. Increasing evidence demonstrates that viral and host RBPs play critical roles in multiple steps of the viral life cycle, including translation and replication of viral genomic RNAs, and their intra- and intercellular movement. Although studies focusing on the RNA-binding activities of viral and host proteins, and their associations with membrane targeting, and intercellular movement of viral genomes have been limited to a few viruses, these studies have provided important insights into the molecular mechanisms underlying the replication and movement of viral genomic RNAs. In this review, we briefly overview the currently defined roles of viral and host RBPs whose RNA-binding activity have been confirmed experimentally in association with their membrane targeting, and intercellular movement of plant RNA virus genomes. Positive-strand RNA [(+)RNA] plant viruses are the most abundant in plant viruses, and cause diverse diseases in host plants. Because of the RNA nature of their genomes, it is not surprising that RNA-binding proteins (RBPs) of viral and host origin affect multiple steps of virus infection. It is known that viral RNA (vRNA) has critical non-template functions in addition to the essential function as a replication template . After eFollowing the translation and replication processes, (+)RNA plant viruses spread their infection into neighboring uninfected cells through plasmodesmata (PD) by using their encoded movement protein (MP). Some plant viruses move through PD in the form of virus particles and others are thought to move in ribonucleoprotein (RNP) complexes that contain MP . MPs froIn this review, we overview the currently defined roles of viral- and host-derived RBPs in virus infection by focusing on the proteins whose RNA-binding activity has been confirmed experimentally in association with their membrane targeting, and intercellular movement of plant RNA virus genomes. We recommend readers consult several current reviews that discuss other topics relating to the roles of host RBPs during RNA virus infection .Red clover necrotic mosaic virus (RCNMV) has a bipartite genome, RNA1 and RNA2  in its 3′-untranslated region  affinity and immunoprecipitation assay showed that the YRE is necessary and sufficient to interact with p27 and with the 480 kDa replication complex . Interestingly, the p27–YRE interaction is modulated by Hsp90  and related Cucumber necrosis virus (CNV) takes place at the peroxisomal membrane  takes place in ER membrane-associated invaginations or spherules induced by replication protein 1a (in vivo stabilization of RNA2 and RNA3 (RNA replication of otein 1a . In thesotein 1a . Althougotein 1a . The intand RNA3 .Tobacco mosaic virus (TMV) binds the 5′-UTR of the genomic RNA cotranslationally, and that this binding plays an important role in regulation of vRNA translation and replication.A recent publication by cis-acting RNA element essential for TBSV replicase complex assembly and (–)RNA synthesis (in vitro (Eukaryotic translation elongation factor 1A (eEF1A) is an abundant cellular protein that functions in protein degradation, apoptosis, nucleocytoplasmic trafficking, heat shock, and organization of the actin cytoskeleton in addition to its canonical role in delivering aminoacyl-tRNA to the elongating ribosome . Severalynthesis . Pharmacin vitro . These fin vitro .Bamboo mosaic virus (BaMV) RNA has been detected in chloroplasts, a putative replication site of BaMV (Nicotiana benthamiana can bind to the 3′-UTR of BaMV (+) RNA  is inhibited, suggesting that PGK is required for the replication of BaMV. Interestingly, overexpression of cytosolic- or nuclear-localized PGK mutants has detrimental effects on BaMV infection. This is consistent with the author’s prediction that these PGK mutants mislead BaMV RNA away from chloroplasts to the cytoplasm or the nucleus that are unsuitable sites for viral replication. BaMV seems to use the trafficking system of chl-PGK to target vRNA to the chloroplasts for replication. In support of this, artificial chloroplast-targeting EF1a, which binds to BaMV (+)RNA, can partially complement BaMV accumulation in chl-PGK-knockdown N. benthamiana ( of BaMV . Chlorop (+) RNA . PGK is  (+) RNA . The rol (+) RNA . In protthamiana . BaMV RNthamiana . It willCucumber mosaic virus (CMV), and that BRP1 plays an important role in the nuclear importation and multiplication of the satellite RNA. BRP1 also binds Potato spindle tuber viroid RNA both in vitro and in vivo, and is involved in the nuclear transportation and multiplication of the viroid  forms a high concentration salt-resistant vRNP (Turnip crinkle virus (TCV) MP is a restricting factor for systemic infection by the virus . RCNMV MP is recruited to the VRC through the interaction with a host protein contained in the VRC  of PVX genomic RNA. The green fluorescent protein gene containing the 107 nt terminal sequence of the PVX 5′-UTR was transported effectively to neighbor cells by cobombarded PVX -coated beads, and release of bound proteins by nuclease digestion has successfully identified a number of RBPs that bind specifically to cellular mRNAs RNA viruses, translation, replication, and intra- and intercellular movements are often closely linked . To invear mRNAs . ApplicaThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


We report the draft genome sequences of ciliovirus and brinovirus, two members of a likely new family of RNA viruses assembled from San Francisco wastewater. Based on sequence alignments and a nonuniversal genetic code, we believe these to be the first described RNA viruses of ciliates; however, more work is necessary to confirm their host. Hyalophysa chattoni, and a large DNA virus, chlorella virus, has been isolated from Paramecium bursaria  of >500 nucleotides. However, translation of two contigs utilizing the ciliate genetic code revealed ORFs of 9,954 and >9,260 nucleotides that preserved the BLASTx alignments. HHPred analysis of the contigs translated in the ciliate genetic code revealed a superfamily II helicase similar to eIF4A followed by a picornavirus/flavivirus-like RNA-dependent RNA polymerase followed by a birnavirus/sobemovirus-like viral capsid protein, suggesting a genome organization most similar to that of the Potyviridae (While performing weekly metagenomic sequencing of San Francisco wastewater, we recovered two contigs of 10,381 and 9,565 nucleotides that distantly aligned by BLASTx to a small portion of an RNA-dependent RNA polymerase of various members of the Ciliophora organisms with half of these reads belonging to the Tetrahymenidae family, making it one of the most abundant organisms in the sample and strengthening the case for these viruses having ciliate hosts (Notably, both contigs were recovered in only one wastewater sample from 25 January 2010 that was taken after a large rainstorm that left >5 inches of rain over the preceding week. Over one-quarter of the alignable nonchordate eukaryotic reads from the DNAsed sample aligned to Sample processing was performed on 1 liter of wastewater that was concentrated to <5 mL with particles between the sizes of 0.22 μm and 300 kDa using Millipore Pellicon XL 300-kDa filters and 0.22-μm spin columns. Viral particle-enriched sample was treated with micrococcal nuclease and nucleic acid was extracted using a Zymo Viral DNA/RNA kit and half of the recovered nucleic acid was treated with DNase. Both contigs were assembled using PRICE v1.0 and Geneious v8.0 Assembler with metagenomic analysis using SURPI v1.0 from a total of 15,719,690 paired-end 65-bp reads sequenced on an Illumina GAIIx split between these DNAsed and untreated nucleic acid preparations (JN661159 and KF412899, respectively.The GenBank accession numbers for ciliovirus and brinovirus are


Mitochondrial transcript maturation in African trypanosomes requires a U-nucleotide specific RNA editing reaction. In its most extreme form hundreds of U’s are inserted into and deleted from primary transcripts to generate functional mRNAs. Unfortunately, both origin and biological role of the process have remained enigmatic. Here we report a so far unrecognized structural feature of pre-edited mRNAs. We demonstrate that the cryptic pre-mRNAs contain numerous clustered G-nt, which fold into G-quadruplex (GQ) structures. We identified 27 GQ’s in the different pre-mRNAs and demonstrate a positive correlation between the steady state abundance of guide (g)RNAs and the sequence position of GQ-elements. We postulate that the driving force for selecting G-rich sequences lies in the formation of DNA/RNA hybrid G-quadruplex (HQ) structures between the pre-edited transcripts and the non-template strands of mitochondrial DNA. HQ’s are transcription termination/replication initiation sites and thus guarantee an unperturbed replication of the mt-genome. This is of special importance in the insect-stage of the parasite. In the transcription-on state, the identified GQ’s require editing as a GQ-resolving activity indicating a link between replication, transcription and RNA editing. We propose that the different processes have coevolved and suggest the parasite life-cycle and the single mitochondrion as evolutionary driving forces. African trypanosomes are single cell blood-parasites and as such they are of medical importance45Trypanosoma brucei we identified an extreme purine nucleotide bias in all pan-edited pre-mRNAs and we demonstrate that specifically G-nucleotides are arranged in tracts of G’s. G-rich DNA and RNA sequences have the ability to fold into thermodynamically very stable, non-canonical, higher-order structures known as G-quadruplex (GQ)-folds8T. brucei we analyzed whether GQ-elements can be identified in these RNAs using a combination of bioinformatic search algorithms and a reverse transcriptase stop assay. We demonstrate that all but one of the pan-edited pre-mRNAs contain multiple, up to five GQ-folds. We further demonstrate that RNA editing progressively resolves the different GQ-elements and that the positions of the various GQ’s correlate with the steady state abundance of gRNAs. In analogy to other biological systems we hypothesize that the GQ-elements function to separate mitochondrial transcription from mitochondrial DNA-synthesis, which provides a new conceptual framework for the evolutionary driving force(s) of RNA editing in African trypanosomes and other kinetoplastid organisms.Here we advocate for a G-nt focussed viewpoint . By analPan-edited pre-mRNAs in their unedited state lack substantial nucleotide information (on average 45%). Furthermore, they are characterized by an unusual high G-content. On average 34% of the nucleotides are G’s . As a coT. brucei maxicircle for putative GQ-forming sequences using predictive algorithms. >2Ny1G>2Ny2G>2Ny3G>2. Strikingly, we identified a very high number of putative GQ-forming G-nt almost exclusively in the nine pan-edited transcripts . The same result was obtained using an energy function-based RNA-folding algorithm -elements . They coViennaRNA and was rentolae . Althoug+- and Li+ -ions since G-quartets fold in a cation-dependent manner (K+ ≫ Na+>Li+). In toto we mapped 27 GQ-folds in the 9 pan-edited transcripts. With the exception of the RPS12 transcripti.e. 3 G-tetrades/GQ-fold  stop assay/GQ-fold . Almost /GQ-fold .T. brucei, L. tarentolae and T. cruzi. Thus, the gRNA-directed insertion and deletion of U-residues resolves the individual GQ-elements in a stepwise fashion to generate less structured ORF’s. Eleven of the 17 gRNAs (65%) are required to resolve the 3 GQ’s of pre-edited CR4, which demonstrates that the U-insertion/deletion reaction not only serves to create a translatable ORF, it also functions to eliminate GQ-folds. This is further substantiated by the observation that the steady state abundance of gRNAs correlates with the position(s) of G-tracts in the different transcripts. The top 35% of gRNAsAs a next step we analyzed the structural fate of the different GQ’s as a consequence of editing. For that we calculated the G-score- and 2D-structure-changes of the different transcripts over the course of all gRNA-directed editing steps. Trypanosoma brucei are substrates of a U-insertion/U-deletion-type RNA editing reaction in order to be converted into translatable mRNAs. The reaction arguably represents one of the most bizarre RNA processing reactions since in several cases more than 50% of the final mRNA nucleotide content is a result of editing. Although the mechanism and the catalytic machinery of the biochemical process have been studied in detailT. brucei transcripts. We discovered that specifically all pan-edited mitochondrial transcripts contain numerous clustered G-nt, which fold into multiple G-quadruplex structures. Altogether we experimentally identified 27 GQ-elements in eight of the nine pan-edited pre-mRNAs, with an average of 3 GQ’s per transcript. In addition, we uncovered that the different GQ-folds become resolved as the editing reaction proceeds from the 3′- to the 5′-ends of the transcripts, indicating a GQ-resolving quality of the process. This is further supported by the fact that the steady state abundance of guide (g)RNAs is positively correlated with the sequence positions of the different GQ-elements. Highly abundant gRNAs direct the editing reaction in predominantly GQ-fold containing RNA domains.The majority of mitochondrial pre-mRNAs in the protozoan parasite T. brucei transcripts permits a new outlook onto the enigmatic evolutionary origin of the process16172021T. brucei subspecies T. evansi and T. equiperdumImportantly, this so far unrecognized structural feature of all pan-edited T. brucei are only marginally understood3L. tarentolaeT. brucei editing accessory factor MRB1590 represents a mitochondrial GQ-interacting proteinTrypanosoma cruzi30The formation of HQ-folds is likely restricted to the kinetoplast S-phase, which precedes DNA-duplication of the nuclear genome2Our hypothesis is consistent with the observation that U-insertion/deletion-type RNA editing is restricted to the kinetoplastid protist lineage, an early diverging branch of the eukaryotes. Kinetoplastids are characterized by a single mitochondrion and strong alterations in the amount of mitochondrial involvement during their life cycle stages. Within this context, it is important to note that our hypothesis shares some aspects with the “RNA editing countering gene loss model” of Speijer16Lastly, we would like to stress that alternative interpretations including multiple functions of the different GQ-elements as well as inter-transcript quadruplexes can be envisaged. However, our structural data and the derived hypothesis clearly provide a novel framework for the evolutionary basis of RNA editing, which we hope will function as a stimulus to falsify the hypothesis.T. brucei genomic DNA (strain Lister 427) followed by cloning into the SacI and KpnI restriction endonuclease sites of phagemid pBS SK− II (Invitrogen). Sequences were verified by DNA sequencing and RNA transcripts were generated by run-off transcription using T7 RNA polymerase and linearized plasmids following standard protocols. Samples were DNaseI treated  followed by phenol extraction. Non-incorporated rNTP’s were removed by size exclusion chromatography and RNA transcripts were recovered by EtOH precipitation. RNAs were dissolved in 10 mM Tris/HCl pH 7.5, 1 mM EDTA (TE) and RNA concentrations were calculated from UV-light absorbancy measurements at 260 nm. The homogeneity and molecular sizes of the different RNAs was analyzed electrophoretically in 6% (w/v) urea-containing (8 M) polyacrylamide gels , NADH dehyrogenase subunits 3, 5, 7, 8 and 9 , cytochrome C oxidase subunits 1 and 3 , C-rich regions 3 and 4  and ribosomal protein S12 (RPS12) were PCR-amplified from ide gels .In silico predictions of GQ-structures were performed for all T. brucei and L. tarentolae mitochondrial transcripts in their edited and unedited states. RNA sequences were retrieved from the U-insertion/deletion Edited-Sequence-Database (http://dna.kdna.ucla.edu/trypanosome/files/tbma-xi.html) and analyzed using QGRS Mapper+) and GQ-disfavouring  cation conditions. RT-stop reactions were performed using 1 pmol pre-edited transcripts and equimolar amounts of 5′-fluorescenctly labeled DNA-oligonucleotide primer molecules. After heat denaturation  DNA-primers were annealed at 50–65 °C (7.5 min) followed by snap-cooling. Reverse transcription was performed by adding a concentrated reaction mix to yield final concentrations of 50 nM transcript, 50 nM DNA primers, 50 mM Tris/HCl pH8.3, 3 mM MgCl2, 5 mM dithiothreitol, 0.75 U/μL RiboLockTM and 0.5 mM dNTP (each). Monovalent cation concentrations were varied between 1 mM and 75 mM until a sufficient readthrough was achieved  concentration of 10 U/μL. RT-reactions were terminated by snap cooling followed by RNA hydrolysis in 400 mM NaOH . Resulting cDNAs were EtOH-precipitated, resuspended in Hi-Di™ formamide and analyzed by capillary electrophoresis (CE). Raw CE-traces were baseline adjusted and signal decay corrected using ShapeFinder+) versus GQ-disfavouring  conditions. Per transcript minimally 5 independent experiments were performed (mean r = 0.83). For a detailed analysis see The ability of G-clusters to adopt GQ-folds was experimentally verified at single nucleotide resolution using a differential reverse transcriptase (RT) stop assay at GQ-favouring .


C. elegans. To increase the throughput of the study, we trapped multiple worms in parallel in a microfluidic device and illuminated for photoactivation of channelrhodopsin-2 to induce contractions in body wall muscles. Using image processing, the change in body size was quantified over time. A total of five parameters including rate constants for contraction and relaxation were extracted from the optogenetic assay as descriptors of sarcomere functions. To potentially relate the genes encoding the sarcomeric proteins functionally, a hierarchical clustering analysis was conducted on the basis of those parameters. Because it assesses physiological output different from conventional assays, this method provides a complement to the phenotypic analysis of C. elegans muscle mutants currently performed in many labs; the clusters may provide new insights and drive new hypotheses for functional relationships among the many sarcomere components.The sarcomere, the fundamental unit of muscle contraction, is a highly-ordered complex of hundreds of proteins. Despite decades of genetics work, the functional relationships and the roles of those sarcomeric proteins in animal behaviors remain unclear. In this paper, we demonstrate that optogenetic activation of the motor neurons that induce muscle contraction can facilitate quantitative studies of muscle kinetics in  Caenorhabditis elegans (C. elegans) is a valuable model system to study the assembly, maintenance, and function of the sarcomere in striated muscle. Many genes that are required for organization of sarcomeres and the regulation of muscle contraction have been identified in C. elegans. The proteins encoded by these genes have been localized in the sarcomere, and their molecular and/or genetic interactions have been partly identified. For instance, unc-54 encodes myosin heavy chain B which is a component of the thick filaments12unc-22 encodes a large muscle filament protein, twitchin, that is localized to the A-bands, where the thick filaments and the thin filaments are overlapped4unc-89 encodes a homologue of the vertebrate protein, obscurin, a structural component of the M-line, where the thick filaments are anchored78unc-27 and lev-11 encode troponin I and tropomyosin, respectively, which are parts of the thin-filament associated regulatory system for actin-myosin interactions10unc-60B and unc-78 encode a muscle-specific actin depolymerizing factor/cofilin isoform and a homologue of actin-interacting protein 1, respectively, which promote actin dynamics and are required for proper actin filament assembly12C. elegans body wall striated muscleContractile activity of muscle is required for animal movement. The sarcomere, the fundamental unit of muscle contraction, is composed of hundreds of proteins and highly conserved in most animals. The body wall muscle of the nematode C. elegans model system has been, and continues to be, the genetic analysis of sarcomere components – “classical” forward genetics, reverse genetics, transgenesis, and more recently the prospect of gene editing. In fact, classical genetics led to the first identification of many of these conserved sarcomeric proteins in any system; examples include twitchin, UNC-89 (obscurin), UNC-112 (kindlin), UNC-45 (myosin chaperone), and UNC-78 (AIP1). However, phenotypic analysis of these mutants, especially at the physiological level has been limited. This is partially because the phenotyping so far has mainly relied on subjective characterization, and the descriptors have been fairly broad. This is evident in the naming of many muscle-affecting mutants and genes: there are many “unc”s (uncoordinated) and this broad descriptor includes mutants that show a variety of abnormal body movements. An often-used technique is to simply count the number of times a worm bends in C-shaped fashion in liquid over a specified time, usually one minuteC. elegans was investigated through computer vision-based image analysis. For instance, Nahabedian et al. quantitatively measured the maximum bending amplitude of crawling worms which have defects in genes encoding muscle focal adhesion componentset al. quantified various biomechanical properties such as bending frequency and amplitude of swimming in synaptic mutantsA very important contribution of the Drosophila melanogasterC. elegans and its mono-nucleated muscle cells, these single muscle fiber experiments cannot be done. Therefore, we have taken an alternative approach to measuring the kinetics of muscle contraction/relaxation. To control the contractile activity of C. elegans muscle, we utilized optogenetics, a technique that permits control of the electrical activities of neurons with visible light. C. elegans moves by the alternating contraction and relaxation of dorsal and ventral body wall striated muscle cells. We employed a worm strain carrying unc-17p::ChR2(H134R)::YFP, which expresses the light-induced channelrhodopsin-2 (ChR2) in its cholinergic motor neurons, to control the contractile activity of the body wall muscle cells and hence the locomotion of the whole nematode, as first described by Liewald et al.C. elegans muscle mutants.The ability to mechanically analyze individual muscle cells (aka fibers) from vertebrate skeletal muscle, has a long history and has yielded many insights into muscle physiologyC. elegans was enabled by optogenetically-induced activation of motor neurons that induce muscle contraction. To investigate roles of sarcomeric proteins in the behavior of nematodes, mutants in 15 sarcomeric proteins were examined , or alleles were chosen that exhibit mild or the mildest defects among existing alleles (unc-22(e105), unc-54(s74), unc-60B (r398), lev-11(x12)). Many of our mutants were chosen that have minimal known defects on nematode locomotion (unc-22(e105), mak-1, atn-1, scpl-1, dim-1, uig-1, lim-9). This criteria was used so that we might determine if optogenetic assays might reveal more subtle defects in muscle function. Finally, our collection includes mutants in several known (UNC-27 (troponin I), LEV-11 (tropomyosin)) or suspected proteins (UNC-22 (twitchin)) that regulate muscle contraction.Quantitative study of muscle contraction and relaxation kinetics in examined . This sezxIs6 [unc-17p::ChR2(H134R)::YFP + lin-15(+)] transgenic worms, which express ChR2 molecules in cholinergic motor neurons by the unc-17 promotor20C. elegans. Here the exposure time was set as 15 s based on preliminary experiments with various exposure times from 5 to 30 s; the contracted body area did not significantly change after 15 s of illumination. When the light was turned off, the relative body area began to increase due to relaxation of the body muscles , mak-1(ok2987), scpl-1(ok1080), unc-22(e66), unc-96(sf18), and unc-98(sf19) contracted significantly faster than wild type animals, while unc-54(s74) showed significantly slower contraction compared to the wild type animals , lim-9(gf210), mak-1(ok2987), unc-22(e105), unc-22(e66), unc-27(e155), unc-89(su75), unc-96(sf18), and unc-98(sf19) relaxed significantly faster than the wild type animals after turning off the illumination  showed a unique behavior in that the body size gradually increased due to a slight relaxation of the body muscles, just after the maximum contraction point where the relative body size had a minimum value, even though they were still illuminated (unc-22(e105) at the maximum contraction was similar to that at the steady state, similar to wild type (lim-9(gf210), mak-1(ok2987), uig-1(ok884), unc-22(e66), unc-89(su75), and unc-98(sf19) showed significantly smaller predicted plateau after contraction than the wild type animals, while unc-27(e155), unc-60B(r398), and unc-78(ok27) showed significantly higher values , mak-1(ok2987), scpl-1(ok1080), uig-1(ok884), unc-89(su75), and unc-98(sf19) was significantly smaller than that of the wild type animals at the steady state, while that of lev-11(x12), unc-22(e66), unc-27(e155), unc-60B(r398), and unc-78(gk27) was significantly larger than the wild type  and unc-22(e66) show smaller or similar plateau after contraction than the wild type animals, while their relative body sizes at the steady state are significantly larger than that of wild type. In these mutants, the muscles relax after the contraction process despite the sustained light illumination. Interestingly, both lev-11(x12) and unc-22(e66) also show twitching locomotion while freely moving. This suggests that the inability to maintain the contraction of muscles might be related to the twitching motility observed in free locomotion.The difference between the plateau after contraction and the relative body area at steady state indicates the ability to maintain the contraction of the muscles. No significant difference between those two values means the muscle contraction is maintained. Amongst all mutants examined, scpl-1(ok1080) and lim-9(gf120), showed significantly larger minimum radii of curvature , dim-1(ra102), lim-9(gf210), mak-1(ok2987), unc-22(e105), unc-22(e66), unc-27(e155), unc-54(s74), unc-60B(r398), unc-89(su75), unc-96(sf18), and unc-98(sf19) beat significantly slower than the wild type , lev-11(x12), unc-27(e155), unc-54(s74), unc-96(sf18), and unc-98(sf19) bent their bodies less than in dorsal direction than the wild type , unc-27(e155), unc-96(sf18), and unc-98(sf19) showed smaller amplitudes in ventral bending compared to wild type  showed significantly larger bending amplitudes in both dorsal and ventral directions than the wild type , lev-11(x12), mak-1(ok2987), uig-1(ok884), and unc-22(e105) was significantly higher than that of the wild type animal, while that of unc-27(e155), unc-54(s74), unc-60B(r398), unc-78(gk27), and unc-96(sf18) was significantly lower (P < 0.01) (unc-27(e155), unc-96(sf18), and unc-98(sf19) was significantly larger than that of the wild type, while that of unc-54(s74) was significantly smaller than that of the wild type (P < 0.01) (lev-11(x12), mak-1(ok2987), unc-27(e155), unc-54(s74), unc-60B(r398), unc-78(gk27), and unc-89(su75) was significantly smaller as compared to that of the wild type (P < 0.01)  plate and their head was touched with a platinum wire in a gentle manner, leading to their backward crawling motion 16. From  < 0.01) . The wav < 0.01) . The amp < 0.01) .unc-27(e155), unc-60B(r398), unc-78(gk27), and lev-11(x12), were clustered in one group (indicated by blue lines to the right of dim-1(ra102), lim-9(gk210), mak-1(ok2987), scpl-1(ok1080), uig-1(ok884), unc-89(su75), unc-96(sf18), and unc-98(sf19), are in one cluster (indicated by red lines to the right of unc-22(e105) and unc-54(s74), which have a normal or nearly normal sarcomere structure2dim-1(ra102), lim-9(gk210), uig-1(ok884), unc-89(su75), unc-96(sf18), and unc-98(sf19), were included in one cluster. While the clustering analysis based on the parameters from the optogenetic assays showed a result that is consistent with current knowledge, we did not find any reliable clusters from the analysis based on the features extracted from the conventional locomotion assays  and the reproducibility (by enhancing the ease of image processing).Our results demonstrate the feasibility of a new quantitative assay to study the functions of genes encoding sarcomere proteins in striated body wall muscles of unc-54(s74) was the only mutant that showed a decrease in the rate of contraction. This result is perhaps expected. unc-54(s74) is a missense mutation (Arg to Cys) in the myosin head domain of the major myosin heavy chain of body wall muscle (MHC B), and results in worms with a slow and stiff locomotion, but normal muscle structure225lim-9, mak-1, unc-22(e66), unc-96, and unc-98) showed both increases in contraction and relaxation rates. This suggests that multiple sarcomeric proteins normally inhibit the rates of contraction and relaxation.We have measured, for the first time, several key parameters of the kinetics of the muscle contraction relaxation cycle, including rate constants for contraction and for relaxation. These data were obtained by studying the contraction/relaxation of the whole animal, the only now practical way such data could be obtained since isolation and manipulation of nematode muscle cells is not currently achievable. We have surveyed a set of 16 muscle-affecting mutant strains (in 15 genes), and found that many are defective in these and related parameters. unc-27 encodes one of four troponin I isoforms in C. elegansunc-22, e105 and e66, showed increased relaxation rates. In fact, unc-22(e66), yielded the greatest increase in relaxation rate among all 9 mutants that increased relaxation rates. unc-22 encodes the giant polypeptide twitchinC. elegans twitchin inhibits the rate of relaxation: (i) physiological studies in Aplysia suggest that twitchin inhibits the rate of muscle relaxationii) in molluscs, some smooth muscles display the “catch” state – a state in which high tension is maintained over long periods with little expenditure of ATP; the release of catch is correlated with protein kinase A phosphorylation of twitchiniii) for molluscan muscle, twitchin can be shown to act as a physical linker between thick and thin filaments in vitrounc-22, e66 which has disorganized sarcomeres, and e105 which has normally organized sarcomeresTwo mutants that showed increased relaxation rates might be explained from what is known or suspected about their roles in muscle activity. We also conducted a clustering analysis to find functional relationships among the genes encoding the sarcomere proteins. Standard hierarchical clustering based on parameters extracted from the optogenetic assays  yielded clusters of gene/proteins that corroborate with what is known about the functions of these genes/proteins in the sarcomere. One cluster included all the thin filament proteins in our set of 15 proteins (UNC-27(troponin I), LEV-11(tropomyosin), UNC-60B(ADF/cofilin), UNC-78(AIP1)). Another cluster consisted of 8 of 9 of the proteins that are localized to M-lines and/or dense bodies . Based on this experience, we suggest that when a new sarcomere component is identified, it would be useful to conduct a similar optogenetic and clustering analysis to provide evidence for how such a new component works together with other known components of the sarcomere. A further indication that the optogenetic approach is powerful is that cluster analysis, using parameters extracted from the conventional locomotion assays, did not reveal any statistically significant clusters. In conventional assays, movement relies on the simultaneously-coordinated contraction of some muscles and relaxation of others. With optogenetic regulation of muscle contractility, we were able to isolate the different phases of contraction and relaxation. Optogenetics enabled us to induce the contraction of body wall muscles to maximum capacity directly, thus revealing aspects of the functions of individual sarcomeric proteins that would be too subtle to be detected by conventional locomotion assays.E.coli OP50 bacteria as a food source. For optogenetic experiments, all-trans retinal , as an essential cofactor for ChR2 activation, was added to E. coli OP50 cultures to a final concentration of 100 μM and spread onto 5.5-cm NGM plates. The animals were kept in the dark until the assay to avoid unwanted ChR2 photoactivation.Nematodes were grown at 20 °C on standard nematode growth medium (NGM) plates seeded with zxIs6V, GB254: unc-22(e105);zxIs6, GB255: unc-22(e66);zxIs6, GB256: mak-1(ok2987);zxIs6, GB257: unc-89(su75);zxIs6, GB258: unc-98(sf19);zxIs6, GB259: unc-54(s74);zxIs6, GB262: unc-96(sf18);zxIs6, GB263: dim-1(ra102);zxIs6, GB261: atn-1(ok84);zxIs6, GB264: scpl-1(ok1080);zxIs6, GB265: uig-1(ok884);zxIs6, GB260: lim-9(ok210);zxIs6, ON295: unc-27(e155);zxIs6, ON302: lev-11(x12);zxIs6, ON303: unc-60B(r398);zxIs6, and ON304: unc-78(gk27);zxIs6.Wild-type nematodes were the N2 strain. Strains used in this study are as follows: N2: wild type (Bristol isolate), ZX460: N2;For on-chip optogenetic assays, two-layer microfluidic devices made of polydimethylsolxane  were fabricated using standard multi-layer soft lithography. The device has 16 parallel microchannels for trapping worms, and their opening and closing are controlled by two valves in the upper layer . Each ch2) for 15 s to induce ChR2 photoactivation. For measurements of projected body area, movies were recorded using a CCD camera . After completing the light illumination and image acquisition, the exit valve was open and the loading pressure was turned on to flush out the worms, followed by another cycle. All the processes including valve actuation, worm injection, and image acquisition were controlled by LabVIEW software.To trap worms in the microchannels, the valve at the exit of the channels were initially closed. After young adult animals were delivered into the channels by a loading pressure, the valve at the entrance of the channels was closed and the loading pressure was turned off. The animals were illuminated with blue light . Wilcoxon Rank sum test was conducted for comparison of the data when appropriate. A standard hierarchical clustering analysis was conducted using MATLAB. After normalizing the mean values of the parameters obtained from the experiments into Z-score, the phenotypic distances between the strains were calculated. Based on the distance information, the strains were linked, and the hierarchical cluster tree was created.How to cite this article: Hwang, H. et al. Muscle contraction phenotypic analysis enabled by optogenetics reveals functional relationships of sarcomere components in Caenorhabditis elegans. Sci. Rep.6, 19900; doi: 10.1038/srep19900 (2016).


Long intergenic non-coding RNAs (lincRNAs) represent one of the most mysterious RNA species encoded by the human genome. Thanks to next generation sequencing (NGS) technology and its applications, we have recently witnessed a surge in non-coding RNA research, including lincRNA research. Here, we summarize the recent advancement in genomics studies of lincRNAs. We review the emerging characteristics of lincRNAs, the experimental and computational approaches to identify lincRNAs, their known mechanisms of regulation, the computational methods and resources for lincRNA functional predictions, and discuss the challenges to understanding lincRNA comprehensively. The mainstream focus of biomedical research has been in elucidating the functions and interactions among proteins within the cell. In line with the central dogma of molecular biology, RNAs were once perceived as the intermediary for protein production and the archaic precursor molecule of DNA. However, RNAs are transcribed from more than 85 % of genomic regions , whereasIt is now becoming evident that ncRNAs are responsible for many aspects of gene regulation. Some small non-coding RNAs, such as microRNAs, siRNAs, snRNAs, snoRNAs, exRNAs and piRNAs, have been well categorized over the past decade. However, long noncoding RNAs (lncRNAs) remained relatively unexplored due to the challenges of computational prediction under poor sequence conservation and low homology within the set of lncRNAs. Some of these challenges have been addressed by the revolutionary inventions of next generation sequencing (NGS) and its applications, such as RNA-Seq, which captures whole transcriptome data, including lncRNAs. Among the human lncRNAs, tens of thousands of long intergenetic noncoding RNAs (lincRNAs) have been discovered in the genomic regions outside of the well-studied coding genomic regions, and they show many intriguing properties, such as associations with various human diseases, tissue-specific expression, and expression changes during development. Consequently, attributing organism complexity to the hidden regulation of lincRNAs is a fascinating new area of research. Here, we review the emerging characteristics of lincRNAs; the experimental and computational approaches to identifying lincRNAs and their mechanisms of regulation; the challenges in computational predictions; and the resources still required to advance our understanding of lincRNA-related genomic regulation.LincRNAs are a putatively heterogeneous group, conventionally defined as ncRNA transcripts of more than 200 bp located in regions with no overlap to any known protein-coding genes. According to Lncipedia, a comprehensive lncRNA database, high-throughput studies of transcriptome data have catalogued over 111,000 lncRNA transcripts, with roughly 50 % coming from intergenic regions . SurprisLincRNA expression is generally much lower than protein coding genes, with a few exceptions such as the XIST lincRNA . For somAnother unique feature of lincRNAs is the low sequence conservation. LincRNAs exhibit 22–25 % of conserved bases under purifying selection, compared to 77 % in protein coding sequences. However, they are considerably more conserved than introns, which have 7 % conservation . Under tChromatin immunoprecipitation sequencing (ChIP-Seq) is an NGS method that has allowed the discovery of global genomic binding sites of DNA-interacting proteins, such as transcription factors and histones. Using ChIP-Seq signatures of histone 3 lysine 4 tri-methylation (H3K4me3) and histone 3 lysine 36 tri-methylation (HK36me3), or so called “K4-K36” clusters, Guttman et al. detected approximately 1700 transcriptional units >5 kb among four mouse cell lines, which were confirmed by tiling microarrays, PCR and northern blots . This tyClearly, decisions made during the library preparation phase of an RNA-seq experiment will affect lincRNA measurements. Since many but not all lincRNA transcripts are poly-adenylated , the decNot all NGS methods are ideal for identifying the precise boundaries of lincRNAs. ChIP-Seq using antibodies against RNA polymerases can only provide a rough estimation of transcription location but not the precise boundaries of transcripts . RNA-SeqMost computational studies of lincRNAs rely on RNA-Seq results initially, with quality-control filtering steps to remove reads arising from spurious background noise . AdditioIn recent years, machine learning based classification approaches have been used to detect lincRNAs , 32–34. LincRNAs identified from exploratory studies are a valuable resource for accumulating information about these relatively unknown transcripts. Information such as location, splice junction, and tissue specificity are important features. There are quite a few specialized databases that provide comprehensive annotations for lincRNAs or lncRNAs. These include The Broad Institute’s Human Body Map project , NONCODEab initio alignment methods, such as those in the Human Body Map. Neither is it as comprehensive as specialized databases.The UCSC ENCODE project provides a feature-rich resource to describe the transcriptional landscape in a variety of tissues from the GENCODE database . The EnsMore specialized lncRNA databases, such as NONCODE and Lncipedia, enumerate a much larger number of lncRNAs Table . These dMethods to elucidate the functions of individual lincRNAs have made much slower progress compared to large-scale genomic assays. In this section we survey the increasing number of genome-scale molecular interaction studies to investigate the cellular functions of lincRNAs.Several genomic approaches have been reported to identify specific functions of lincRNAs. One popular technique is the protein-centric RNA immunoprecipitation (RIP), which selects a particular protein or a group of proteins to co-precipitate RNAs and determines functional relationships based on physical interactions . This alConversely, another approach is to purify certain RNA molecules and then capture the associated proteins (RNA-centric methods); the associated proteins can then be identified via mass-spectroscopy . This apAdditionally, there have also been a handful of “DNA-centric” methods for studying lincRNAs. Methods that investigate DNA modification or the 3D structure of chromosomes have greatly advanced our understanding of gene regulation . For exaHistorically, lincRNAs have been shown to have a greater likelihood to be functionally associated with their nearest neighboring protein-coding genes. However, more recent analyses show that the expression correlation between a lincRNA and its closest coding gene is not statistically significant when compared to the correlation between two neighboring protein-coding genes , 46. WhiEpigenetics is a vital means of DNA patterning to regulate gene expression . PRCs exMany lincRNAs are known to act as transcription co-factors. In some cases, the act of transcription of a lincRNA may positively or negatively affect expression of nearby genes . DimitroThe competing endogenous RNA (ceRNA) hypothesis is a theory that lncRNAs (including lincRNAs) regulate gene expression by acting as microRNA sponges . The inhWhile lincRNAs have less sequence conservation than protein-coding genes, they have a greater degree of secondary motif conservation compared to mRNAs . These eThere have been many attempts to computationally identify the function of lincRNAs. Given the length of lincRNA sequences and the complexity of their potential 3D structures along with the RNA and protein partners, this is a very challenging task. We review the different computational approaches in the following.cis [trans as well, and that the correlation between a lincRNA and its nearby protein coding genes may not necessarily be a causative relationship, but rather a result of sharing a region of active transcription.One of the simplest approaches to determine the function of lincRNAs is to examine their correlations with protein coding genes . Howevercis , 52. Howcis . This suZea Mays [Other more sophisticated tools have been developed to identify more succinct functions. Boerner and McGinnis constructed a pipeline to seek functions of lncRNAs in Zea Mays . Using BZea Mays . Based oZea Mays . Using pMachine learning methods have been used successfully to classify whether transcripts are coding or non-coding. However, machine learning methods to identify the targets of lincRNAs have not seen much success. Comparatively, there has been much more success in using supervised learning approaches to identify microRNA targets, such as TargetScan , SvMicrOPerhaps the least explored lincRNA prediction approach is functional prediction through tertiary and quaternary structure. As the structure of RNA molecules are related to their functions, predicting the structure of complexes between RNA-RNA, and RNA-protein interactions could elucidate functional properties. Several RNA-RNA interaction prediction tools are available, usually based on free-energy, such as RNAhybrid  and RNADReverse engineering of gene regulatory networks has been an area of research before the explosion of next generation sequencing and lincRNA research . ApproacStatistical evaluation studies for lincRNAs are urgently needed, as datasets produced by these various methods have thus far shown only modest overlaps in their identified lincRNAs . Besides


It is becoming increasingly clear that the process of dimerization is closely linked with gRNA packaging, and in some retroviruses, the latter depends on the former. Earlier mutational analysis of the 5’ end of the MMTV genome indicated that MMTV gRNA packaging determinants comprise sequences both within the 5’ untranslated region (5’ UTR) and the beginning of In vitro RNA dimerization, SHAPE analysis, and structure prediction approaches on a series of pal mutants revealed that MMTV RNA utilizes a palindromic point of contact to initiate intermolecular interactions between two gRNAs, leading to dimerization. This contact point resides within pal II (5’ CGGCCG 3’) at the 5’ UTR and contains a canonical “GC” dyad and therefore likely constitutes the MMTV RNA dimerization initiation site (DIS). Further analyses of these pal mutants employing in vivo genetic approaches indicate that pal II, as well as pal sequences located in the primer binding site (PBS) are both required for efficient MMTV gRNA packaging.The RNA secondary structure of MMTV gRNA packaging sequences was elucidated employing selective 2’hydroxyl acylation analyzed by primer extension (SHAPE). SHAPE analyses revealed the presence of a U5/Gag long-range interaction (U5/Gag LRI), not predicted by minimum free-energy structure predictions that potentially stabilizes the global structure of this region. Structure conservation along with base-pair covariations between different strains of MMTV further supported the SHAPE-validated model. The 5’ region of the MMTV gRNA contains multiple palindromic  sequences that could initiate intermolecular interaction during RNA dimerization. Employing structural prediction, biochemical, and genetic approaches, we show that pal II functions as a primary point of contact between two MMTV RNAs, leading to gRNA dimerization and its subsequent encapsidation into the assembling virus particles. The results presented here enhance our understanding of the MMTV gRNA dimerization and packaging processes and the role of structural motifs with respect to RNA-RNA and possibly RNA-protein interactions that might be taking place during MMTV life cycle.The online version of this article (doi:10.1186/s12977-014-0096-6) contains supplementary material, which is available to authorized users. Retriewed in -4,7). Oviewed in -3,8-10).The retroviral RNA genome is packaged as a non-covalent dimer and the processes of dimerization and packaging are closely interlinked . TRecent retroviral RNA cross-packaging studies have shown that the specificity of retroviral gRNA packaging can be exchanged by substituting the packaging signals of genetically diverse retroviruses ,13. ThisRetroviral RNA dimerization is usually mediated by a palindromic  sequence known as the dimerization initiation site (DIS) present in the 5’ region of the gRNA. In a number of retroviruses, the DIS has been shown to assume a hairpin structure containing a canonical “GC” dyad which interacts with the DIS loop on the second gRNA copy, resulting in a kissing loop interaction -26;  life cycle ,43. An efor MMTV , we havepagation . Foldingin vitro dimerization, in vivo packaging and transduction assays revealed that RNA dimerization, packaging and propagation processes of pal II mutants was greatly compromised. Results obtained from these complementary approaches indicate that pal II serves as the primary palindromic point of contact during intermolecular interactions, leading to the dimerization and RNA packaging processes during MMTV life cycle.In this study, we employed the technique of selective 2’hydroxyl acylation analyzed by primer extension (SHAPE) -48 to vagag [gag (432 nt of the 5’ end from R) was predicted using Mfold. Specifically, structural analysis of this region predicted six stem loops 1-6  revealed a high degree of conservation relative to the wild type mtv-1 (AF228550.1) strain, especially of sequences of the major structural motifs such as U5/Gag LRI, ssPurines, pal II, and PBS pal  sites needed for integration of the provirus were not affected in pal I mutants, thus allowing us to test the same mutations in both the in vitro dimerization as well as in vivo packaging and propagation assays [2+ ions minimizes dissociation of RNA dimers during electrophoresis, while weak dimers might dissociate during electrophoresis in TB buffer [The mutations introduced in pal I are shown in Figure n assays . As RNA B buffer ,50. Nonein vivo packaging and propagation assay  was observed for all mutants . Deletion of pal II with simultaneous substitution with a stable tetraloop had the same effect on RNA dimerization .To test if pal II modulates MMTV gRNA dimerization and packaging, we introduced a series of similar mutations, including a complete deletion of pal II as well as substitution with a stable tetraloop sequence, in order to maintain the overall RNA secondary structure of this region Figure A. Data ovia the two overlapping pals in the PBS  and to a 3.5-fold decrease in TB condition  sequence was folded as dimer using the RNAstructure software, which predicted that in the absence of pal II, the MMTV RNA packaging signal could potentially dimerize 2 sequenctrans-complementary sequences  in the infected cultures correlated well with the significantly-reduced RNA packaging data Figure E. Lack oAltogether, the dimerization and packaging results suggest that the pal II and PBS pal play important roles in both MMTV gRNA dimerization and packaging. However, the case of mutant SA047P containing substitution of pal II with HIV-1 pal along with flanking purines was intriguing as its RNA packaging and propagation were abolished Figure E, despittrans-complementary mutants SA044 and SA045 were unable to form heterodimers  was largely dimeric when electrophoresis was conducted in TBM buffer, as can be observed in Figures 5 was lars Figure B explainNext we analysed the structure of the SL2 domain, which contains the PBS pal, in the wild type and mutant RNAs. Although the PBS is predicted to be unpaired in monomeric RNA, the overlapping PBS pals were weakly reactive Figure A, especiin vitro dimerization results, we predicted minimal free energy models of the wild type and mutant MMTV RNA dimers using RNAstructure. This software predicted that wild type RNA dimerizes via the pal II sequence  were predicted to homodimerize via the PBS pal  and SA045 (5’GUGCGU 3’) containing flanking purines (highlighted in bold letters) not present in SA044 or SA045, resulting in SA052 (5’ AAACGCACA 3’) and SA053 (5’AAGUGCGUA 3’)  Figure A. As expy Figure C. On theFinally, structural analysis of the double deletion mutant  predicted that this mutant RNA could homodimerize via pal III Figure C, it is in silico RNA structure predictions . Ttrans-complementary mutants [trans-complementary pal II mutants were not successful  displayed wild type RNA dimerization levels; however, its RNA packaging and propagation were abolished , ssPurines, and four palindromic sequences  or 400 nt of gag (containing 712 nt of the 5’ end from R) were predicted using the Mfold server [http://rna.ucsc.edu/rnacenter/xrna/xrna.html). MMTV nucleotide positions refer to HYBMTV a molecular clone created by Shackleford and Varmus [The secondary structure of the 5’ region of the MMTV gRNA containing either 120 nt of d server ,68. The d server  as constd Varmus .mtv-1) [mtv-6) [Nucleotides 1-432 of the gRNA of different strains of MMTV were aligned using Clustal Omega ,72 and amtv-1) , L37517. [mtv-6) , AF03380 [mtv-6) , M15122. [mtv-6) , D16249. [mtv-6) , AF22855 [mtv-6)  and AF22AAGCTTAATACGACTCACTATAGGGCAACAGTCCTAATATTCACG 3’) and the antisense (AS) OTR 985 (5’ AAACCCGGGTTCCCCTGGTCCCATAAG 3’) primers. These primers include the T7 promoter sequence (in bold) at the 5’ end along with flanking restriction sites . The HindIII and XmaI sites were utilized to introduce the MMTV sequence into a pUC-based cloning vector (pIC19R) [gag) was also used as the wild type clone for in vitro gRNA dimerization assays.The 5’ end of MMTV gRNA corresponding to nt 1-712 was amplified by polymerase chain reaction (PCR) using the MMTV sub-genomic transfer vector DA024  as the t(pIC19R) . Introdutrans-complementary sequences, were introduced in pal I (5’ GUCGGCCGAC 3’), pal II (5’ CUGCAG 3’), PBS pal (5’ CAGCUGGCGCC 3’), and pal III (5’ CCUCUCAGAGAGG 3’) through splice overlap extension (SOE) PCR as described previously [via their overlapping complementary sequences, were amplified using outer primers OTR 984 (S) and OTR 985 (AS), generating a final product containing the desired mutation(s). The sequences of S and AS primers used to introduce mutations are provided in Additional file A number of mutations, including deletions, substitutions and substitution containing heterologous eviously ,21,33,81SmaI restriction enzyme, the wild type (SA035) or mutant  clones were transcribed in vitro to generate the RNAs to be used in dimerization assays, as previously described [After linearization with the escribed . Followiescribed . Finallyin vitro transcribed RNAs using benzoyl cyanide (BzCN) following the protocol described recently [2) in the presence of 2 μg total yeast tRNA (Sigma Aldrich). The dimer buffer was used for the SHAPE analysis to help identify points of contact involved in the dimerization process. The modified RNAs were reverse transcribed using two sets of AS primers: OTRs 9, 10, 11 and 12  and OTRs 13, 14, 15 and 16 , labelled with VIC, FAM, NED, or PET, respectively. These two sets of primers enabled the revere transcription of the entire RNA sequence except for a few nucleotides at the 3’ end due to primer binding. The primer extension products were loaded onto an Applied Biosystems 3130xl genetic analyzer and the electropherograms were analysed with the SHAPEfinder [SHAPE -48 was precently . BrieflyPEfinder ,83. The PEfinder ,49. The PEfinder  and alsoIn vitro dimerization assays of wild type and mutant MMTV RNAs were performed as described previously [2) or monomer  buffer. The samples were divided equally and analysed by electrophoresis in TBM  and TB  ethidium bromide prestained agarose gels at 4°C or 20°C, respectively. Density of the dimeric and monomeric species was quantitated using Quantity One software, and percentage of dimerization was calculated and expressed as dimerization relative to the wild type for each mutant, as described previously [in vitro dimerization assay of trans complementary mutants, equal amounts (150 nM) of in vitro transcribed RNAs from both the mutants  were incubated together before electrophoresis, as described above.eviously ,82. Brieeviously . For thein vivo genetic trans-complementation assay used two expression plasmids, JA10 and MD.G, to provide the structural and enzymatic genes from MMTV Gag/Pol and vesicular stomatitis virus (VSV-G) envelope regions, respectively [cis-acting sequences required for RNA packaging, reverse transcription, and integration, served as a source of the packageable vector RNA [The ectively ,84. A thctor RNA . Design ctor RNA . BrieflyHygr gene, resulting in colonies which are measured as colony forming units (CFU/ml). The CFU/ml values were then normalized to the transfection efficiency. The normalized propagation efficiencies were then divided by the wild type values to represent the propagation of the mutants relative to the wild type (Relative CFU/ml).Human embryonic kidney (HEK) 293T producer cells were transfected using the calcium phosphate transfection protocol to produce virus particles containing the wild type or mutated RNAs, as described previously ,45. The Cells from the transfected cultures were fractionated into nuclear and cytoplasmic fractions as described previously ,85. CellTo determine the packaging efficiency of the transfer vector RNA, quantitative qPCR was performed on the cDNA prepared from the cytoplasmic and viral RNAs as described earlier . The endt-test was performed between the wild type and mutant clones to determine statistically significant differences. For in vitro dimerization assays, and in vivo packaging and propagation studies, a p value of ≤0.05 was considered significant.The standard paired, two-tailed Student’s