Lumbricus terrestris to (i) microbial cell lysate (to simulate gizzard-disrupted cells) and (ii) dominant biopolymers of such biomass, protein, and RNA. The microbial cell lysate augmented the production of H2, CO2, and diverse fatty acids  in anoxic gut content microcosms, indicating that the cell lysate triggered diverse fermentations. Protein and RNA also augmented diverse fermentations in anoxic microcosms of gut contents, each yielding a distinct product profile . The combined product profile of protein and RNA treatments was similar to that of cell lysate treatments, and 16S rRNA-based analyses indicated that many taxa that responded to cell lysate were similar to taxa that responded to protein or RNA. In particular, protein stimulated Peptostreptococcaceae, Clostridiaceae, and Fusobacteriaceae, whereas RNA stimulated Aeromonadaceae. These findings demonstrate the capacity of gut-associated obligate anaerobes and facultative aerobes to catalyze biopolymer-driven fermentations and highlight the potential importance of protein and RNA as substrates linked to the overall turnover dynamics of organic carbon in the alimentary canal of the earthworm.Earthworms are a dominant macrofauna in soil ecosystems and have determinative effects on soil fertility and plant growth. These invertebrates feed on ingested material, and gizzard-linked disruption of ingested fungal and bacterial cells is conceived to provide diverse biopolymers in the anoxic alimentary canals of earthworms. Fermentation in the gut is likely important to the utilization of ingested biopolymer-derived compounds by the earthworm. This study therefore examined the fermentative responses of gut content-associated microbes of the model earthworm IMPORTANCE The subsurface lifestyle of earthworms makes them an unnoticed component of the terrestrial biosphere. However, the propensity of these invertebrates to consume their home, i.e., soil and litter, has long-term impacts on soil fertility, plant growth, and the cycling of elements. The alimentary canals of earthworms can contain up to 500 ml anoxic gut content per square meter of soil, and ingested soil may contain 109 or more microbial cells per gram dry weight, considerations that illustrate that enormous numbers of soil microbes are subject to anoxia during gut passage. Feeding introduces diverse sources of biopolymers to the gut, and the gut fermentation of biopolymers could be important to the transformation of matter by the earthworm and its capacity to utilize fermentation-derived fatty acids. Thus, this study examined the capacity of microbes in earthworm gut contents to ferment protein and RNA, dominant biopolymers of cells that become disrupted during gut passage. The a–rthworms –11. The  the gut , 12, 13.O and N2 , 9. In tO and N2 , illustrSaccharomyces cerevisiae and Escherichia coli n) .1.57N0.27O0.30S0.013]n for protein  and [C9.5H11.75N3.75O7P]n for RNA (based on 50% GC content and deprotonated phosphate). The 10-fold concentrated stock solutions of protein  were prepared by adding the respective amounts to 10 ml autoclaved anoxic sodium phosphate buffer. RNA  was less soluble than protein, and less concentrated stock solutions (8.8 mg/ml) were prepared, and the pH was adjusted to pH 7 with 1 M NaOH. Stock solutions of protein and RNA were filter sterilized  and transferred to sterile anoxic 100-ml serum vials that were crimp sealed with sterile rubber stoppers; the vials were then flushed with sterile argon (100%). According to the manufacturer's specifications, the RNA contained ≤10% water, which was neglected for all calculations.The following theoretical chemical formulas for protein and RNA were used to calculate the amount of carbon provided in a given treatment: ). RNA and DNA were coextracted from 0.2 to 0.8 g fresh weight of pelleted material  by bead-beating lysis , organic solvent extraction, and precipitation  or DNase I  yielded DNA or RNA, respectively. No 16S RNA gene-specific PCR products were obtained from RNA samples, indicating that DNA digestion was successful.The solid phase of microcosms was obtained by centrifugation  and SuperScript III reverse transcriptase  according to the manufacturer's protocol. Isopropanol precipitation was performed to prepare DNA and cDNA samples for storage and shipment .Saccharomyces cerevisiae  or Cyberlindnera jadinii , an undesired amplification of substrate-derived 16S rRNA sequences was not possible and was also not observed.DNA and cDNA were sent to Microsynth for PCR amplification, amplicon sequencing, and initial sequence analyses. Amplifications of the V3 and V4 regions of 16S rRNA genes were with primers Bakt_341F (5′-CCTACGGGNGGCWGCAG-3′) and Bakt_805R (5′-GACTACHVGGGTATCTAATCC-3′), yielding PCR products of approximately 460 bp that covered most bacterial linages . The ampThe following protocol was used for PCR amplification: initial denaturation, 3 min at 95°C; 20 cycles of denaturation , annealing , and elongation ; final elongation, 5 min at 72°C. A Kapa HiFi HotStart PCR kit  was used per the manufacturer's protocol.Amplicon sequencing was by Illumina MiSeq. Raw sequences were demultiplexed, stitched, quality filtered, and checked for chimeras using the QIIME software package . For expRepresentative sequences of phylotypes with ≥0.1% relative abundance were reanalyzed with the ARB software package . Highly The relative abundances of all sequences, including less abundant sequences not highlighted in Results, are provided in Table S10 (lysate treatment) and Table S11 (protein and RNA treatments).PRJEB15377 and PRJEB15410 for cell lysate and protein/RNA treatments, respectively. Representative sequences of phylotypes with ≥0.1% relative abundance were deposited under accession numbers LT626667 to LT626823 (cell lysate treatment) and LT626824 to LT626940 (protein/RNA treatments).The sequences were deposited at the European Nucleotide Archive (ENA) under study numbers


Obtaining high quality RNA from complex biological tissues, such as the brain, is needed for establishing high-fidelity cell-type specific transcriptomes. Although combining genetic labeling techniques with laser capture microdissection (LCM) is generally sufficient, concerns over RNA degradation and limited yields call into question results of many sequencing studies. Here we set out to address both of these issues by: (1) developing a fluorescence-assisted LCM protocol that yields high quality RNA from fresh-frozen tissues; and (2) determining a suitable RNA-Seq library generation method for limited amounts of RNA . The latter focused on comparing commercially available kits able to produce libraries of sufficient concentration and complexity while limiting PCR amplification biases. We find that high quality RNA  of sufficient concentration can be isolated from laser-captured material from thinly-sectioned tissues when digestion time and temperature are minimized. Furthermore, we found that library generation approaches that retain ribosomal RNA (rRNA) through cDNA library generation required fewer cycles of PCR, minimizing bias in the resulting libraries. Lastly, end stage depletion of rRNA prior to sequencing enriches for target RNAs, thereby increasing read depth and level of gene detection while decreasing sequencing costs. Here we describe our protocol for generating robust RNA-Seq libraries from laser-captured tissue and demonstrate that with this method, we obtain samples with RNA quality superior to the current standard in the LCM field, and show that low-input RNA-Seq kits that minimize PCR bias produce high fidelity sequencing metrics with less variability compared to current practices. Transcriptome profiling of minute samples is critical for understanding complex tissues. Given the unstable nature of RNA, current methods have been unable to ensure that high quality RNA is obtained for subsequent sequencing applications. Here we compared commercially available low-input RNA isolation kits to evaluate RNA quality and low-input RNA-Seq library kits to evaluate the robustness of low-input library generation approaches. We find that high quality RNA can be extracted in sufficient quantity from thinly-sectioned laser-captured tissues when minimal digestion time and temperature are used. Furthermore, we found that library generation approaches that retain rRNA through cDNA library generation required fewer cycles of PCR, which minimized bias in the libraries and produced more uniform gene coverage with less variability than current practices. Combined, our methods enable high quality and robust transcriptome profiling from minute tissue samples.Laser capture microdissection (LCM) is a microscopy-based technique developed to isolate select cell populations from complex, heterogeneous tissue. Using a thermoplastic polymer film that can be focally activated by infrared (IR) or ultraviolet (UV) lasers, discrete regions of tissue can be rapidly procured while retaining cellular morphology and location useful for identifying certain cell populations  housed under a 12:12 light/dark cycle with access to food and water RRID:MMRRC_033018-UCD) and bred for at least 10 generations onto C57bl6/n background. Mice were deeply anesthetized with Fatal Plus (50 mg/kg) before decapitation and swift removal of the brain (<2 min). Brains were bisected in the sagittal plane and individually flash frozen in 22 × 22 mm disposable cryomold  filled with Tissue-Tek Optimal Cutting Temperature  compound and then submerged in isopentane cooled to −20°C in a dry ice and ethanol slurry. For midbrain sections: adult C57bI6 mice were treated with a single systemic injection of LPS (5 mg/kg) or saline at 2 months old and a body weight of roughly 22–24 g for an unrelated study. After 10 months, brains were removed and immediately embedded in 4557 standard cryomold  with OCT on dry ice. For liver sections: liver tissues were harvested from male C57bI6 mice  and embedded as above for midbrain sections. Frozen tissue blocks were stored at −80°C until processing.For hippocampal sections: brains were harvested from adult male Amigo2-EGFP transgenic mice  born from separate litters. The Amigo2-EGFP line was acquired from GENSAT  that was pre-set to –18°C and allowed to equilibrate for at least 10 min prior to cutting. To avoid contamination across samples, a clean paint brush was used instead of the roll plate. In order to accommodate five sections per slide, the frozen OCT blocks were trimmed on all sides using a razor blade, leaving 3–4 mm of OCT surrounding the tissue for handling with the paint brush. Eight micron thick sections were cut and mounted on either RNase-free Superfrost Plus glass slides (FISHER) for use with the PixCell instrument, or MicroDissect polyethylene terephthalate (PET) membrane single frame slides  for use with the CellCut instrument. Prior to LCM, a few sections were stained with cresyl violet to verify coordinates  using the 1st edition Watson and Paxinos . EGFP flFive serial 8-micron thick sections from Amigo2-EGFP mouse brains were lined up in the cryostat and simultaneously mounted onto the slide . Approximately, 10 sections (2 slides) were harvested on one CapSure HS LCM cap per region for a maximum microdissection time of 30 min. Immediately after LCM, the cap was placed on the ExtracSure device in a CapSure HS Alignment Tray. Lysis was performed using either the PicoPure or QIAGEN kits as described below. Subsequent slides were processed until the entire target region was harvested.Slides were removed one at a time from xylene and air-dried in a fume hood for 5 min before capturing the target region using a PixCell IIe LCM instrument (Arcturus/Molecular Devices) equipped with an IR laser and an inverted epifluorescence microscope. The CapSure HS LCM caps  were used for collecting each region separately. For the microdissection of dorsal hippocampal CA2, approximately 20 slides (five sections per slide) were generated and captured over 2 days from the left hemisphere of each mouse  equipped with an UV laser and an inverted epifluorescence microscope. PET slides were inverted and placed onto a glass slide so that the tissue section was sandwiched between the membrane and glass slide. Each target region was collected using 0.5 ml MicroDissect caps  as described for the PixCell instrument. Immediately after LCM, lysis was performed using either the PicoPure or QIAGEN kits as described below. Subsequent slides were processed until the entire target region was captured.® Micro kit  was added directly on to the cap. A new RNase-free 0.5 ml microcentrifuge tube was placed onto the CapSure ExtracSure assembly and the set-up incubated at room temperature for 5 min. The microcentrifuge tube was spun with the CapSureExtracSure assembly at 800× g for 2 min to collect the cell extract into the microcentrifuge tube. Cell extracts were immediately frozen on dry ice. Lysate samples were stored at −80°C until RNA isolation. For RNA isolation, samples were thawed at room temperature and samples from one region were pooled into one lysate in an RNase-free 1.5 ml microcentrifuge tube. One volume of freshly prepared RNase-free 70% ethanol was added to the lysate and total RNA purification was performed by following RNeasy® Micro user guide . RNase-free DNase set  was utilized to remove genomic DNA that can interfere with downstream applications. One sample was processed at a time to limit RNA degradation.Ten microliter RLT lysis buffer with ß-ME from the RNeasyg for 2 min to collect cell extract into the microcentrifuge tube. Cell extracts were immediately frozen on dry ice. Lysate samples were stored at −80°C until RNA isolation. For RNA isolation, conditioning Buffer  was added to the PicoPure purification spin column and incubated for 5 min at room temperature. The purification spin column was then spun in the provided collection tube at 16,000× g for 1 min. Microdissected samples from one region were thawed and pooled into one lysate in an RNase-free 1.5 ml microcentrifuge tube and mixed with one volume 70% ethanol (supplied). The lysates were loaded onto the spin column and total RNA purification was performed by following PicoPure® RNA isolation kit user guide , including on-column DNase-treatment . One sample region was processed at a time to limit RNA degradation.Ten microliter XB extraction buffer from the Picopure RNA Isolation kit  was added into the buffer well. An RNase-free 0.5 ml microcentrifuge tube was placed onto the CapSureExtracSure assembly and incubated for 30 min at 42°C. After incubation, the CapSureExtracSure assembly with the microcentrifuge tube was spun at 800× RRID:SCR_002798). Normality  and variances were tested prior to two-way analysis of variance (ANOVA) analyses .Total RNA samples were analyzed for RIN and concentration using the 2100 Agilent Bioanalyzer instrument and RNA 6000 Pico assays . Statistical tests comparing RNA quality and yield were performed using Graphpad Prism (v7) software  were depleted of rRNA sequences using the Clontech RiboGone mammalian kit according to manufacturer’s instructions. Note that we also tested the Illumina/Epicenter Ribo-Zero rRNA Removal Kit for mouse using the Clontech product note for truly low-input RNA samples but were unsuccessful in obtaining sufficient yield after the protocol. Thus, we prepared stranded RNA-Seq libraries from the RiboGone depleted RNA samples using the Clontech SMARTer Stranded RNA-Seq Kit according to manufacturer’s instructions using 20 cycles of PCR to amplify the final library. Twenty cycles of PCR were required achieve sufficient library concentration for quantitation and sequencing.N = 3 mice. The RNA was extracted using the QIAGEN kit. We found no differences in any parameters tested . To directly compare across kits, however, we used 20 cycles of PCR. Further, the cDNA samples used for the kit comparison were not fragmented using the Covaris S series ultrasonicator, whereas the three CA2 LCM samples were fragmented by ultrasonication. This step is recommended to reduce the amount of rRNA mapped reads and to ensure equal 5′ to 3′ read distribution. Lastly, Covaris fragmentation is recommended if sequencing on the Illumina NextSeq500 system because it is optimal for libraries less than 400 nucleotides (nt). Because smaller sized libraries are recommended for the NextSeq500 instrument, fewer total reads were acquired for the NuGEN samples (average library ~500 nt) than for the Clontech samples (average library ~200 nt) during multiplexed sequencing (see below).Stranded RNA-Seq libraries were prepared using 3.0 ng total RNA per sample (max input 5 μl) with the NuGEN Ovation RNA-Seq Systems 1–16 for Model Organisms (mouse) according to manufacturer’s instructions. In this kit, rRNA is depleted prior to library amplification instead of prior to cDNA synthesis. This yield saving step allows for only 16 cycles of PCR to be sufficient when amplifying the final library. We compared 16, 18 and 20 cycles of PCR using LCM CA2 RNA samples from a separate cohort of Libraries were analyzed for size and concentration using the 2100 Agilent Bioanalyzer instrument and the High Sensitivity DNA assay . Libraries prepared from the Clontech SMARTer and NuGEN Ovation kits were four-plexed  or three-plexed (3 CA2 LCM samples) and run on a mid-level NextSeq500 lane acquiring 100 bp paired-end reads. The data are available on Gene Expression Omnibus (GEO) #GSE95257. All code used for generating the data is available upon request.Reads were trimmed using Sickle . FASTQ files were run through identical pipelines as described above with the following changes; the GTF file was derived from vM12 GENCODE gene models, the Hipposeq dataset was aligned with STAR using single-end and unstranded parameters, and no genes were excluded from analyses. Approximately 22 million reads were mapped from the Farris dataset  and 32 million reads from the Hipposeq dataset . The correlation plot consists of the mean per dataset from 14,045 genes with at least one count in all replicates of both datasets. The MA plots were centered by the mean of each dataset and one representative sample from each dataset is shown. The coverage profiles and heatmaps were generated as described above.Comparisons with Hipposeq  captured from various mouse brain and liver samples Table  using twp < 0.0001, CellCut vs. PixCell F = 114.6, two-way ANOVA). This was true when using either extraction kit, however, we note that our PixCell PicoPure samples consisted solely of samples captured from brain tissue and our CellCut QIAGEN samples were entirely from liver tissue  = 732.5, two-way ANOVA). This was true across numerous experimental and biological replicates . The difference in RNA quality can clearly be seen in the representative bioanalyzer gels and electropherograms in Figures p = 0.0925, F = 4.845, two-way ANOVA). Note that the anatomical area of the CA2 region is smaller than the anatomical area of the CA1 region , use random hexamer primers to generate cDNA  and maintain strandedness. The kits differ in their methods to remove rRNA, with the Clontech kit removing rRNA prior to cDNA synthesis and the NuGEN kit depleting rRNA prior to library amplification. The kits also differ in their chemistry and length of protocol, with the Clontech kit taking less than 1 day and the NuGEN kit requiring 2 days to generate libraries for sequencing.We were able to successfully generate cDNA libraries from both kits using 3 ng total RNA input Figure . AdditioThe fact that about half as many reads were acquired with the NuGEN samples can likely be attributed to the fact that we omitted the cDNA shearing step that would have resulted in NuGEN cDNA libraries within the preferred range (<400 bp) of the NEXTseq instrument (Clontech cDNA libraries are ~200 bp). The lack of shearing also resulted in a slight 5′ bias in read coverage for NuGEN samples Figure . HoweverLastly, likely due to the differences in the rRNA depletion methods, the NuGEN kit generated libraries of suitable concentration for sequencing with fewer PCR cycles than the Clontech kit (16 cycles vs. 20 cycles). This is important because fewer PCR cycles minimizes the amount of amplification bias in the resulting libraries providing a more faithful representation of the starting material. Using the RiboGone rRNA depletion kit prior to cDNA synthesis in the Clontech kit likely results in a loss of yield that requires an increased number of PCR cycles to achieve sufficient cDNA concentration for sequencing. Note that Clontech has since introduced a library generation kit  that depletes rRNA prior to library amplification similar to the NuGEN kit but with SMARTer chemistry. As of yet we have not compared performance of this new kit with those tested here.N = 3 mice) to a commendable recent RNA-Seq study that used manual sorting of fluorescently-labeled mouse hippocampal neurons, including area CA2 (N = 3 mice), from microdissected slices for RNA-Seq  vs. CellCut Hippocampus/Midbrain PicoPure RIN mean = 6.65 (N = 11), unpaired, one-tailed Mann Whitney t-test), indicating that, at least for brain with the methods tested here, the IR-instrument produced a modest but statistically significant increase in RNA quality. Additional experiments varying only the LCM instrument are needed to test whether this is also the case for liver. Furthermore, we found the effect of LCM instrument on RNA integrity was minimal  compared to the impact of RNA isolation kit ; the interaction effect was essentially additive (IR QIAGEN-UV PicoPure RIN mean difference = 2.76). The major difference between the QIAGEN and PicoPure RNA isolation kits is the tissue lysis step. QIAGEN incubates the LCM tissue for 5 min at room temp whereas PicoPure incubates the tissue for 30 min at 42°C. In our system, the shorter incubation time in the QIAGEN kit did not significantly affect the RNA yield/area, suggesting that longer lysis incubation times negatively affect RNA quality without much gain in RNA yield. Interestingly, when comparing previous LCM articles that used either PicoPure -tagging , suggesting that both methods reliably detect the CA2 transcriptome. However, our replicates had less variability, which when compared to other samples should produce a greater number of differentially expressed genes. The lower variability seen in our dataset could be attributable to differences in the RNA extraction methods (QIAGEN vs. PicoPure), library generation methods  and/or sequence format (paired-end vs. single-end). Furthermore, our dataset had more uniform 5′-3′ coverage than the Hipposeq dataset. When adding that our libraries were strand-specific and sequenced with paired-end reads, it suggests our methods would be particularly suitable for quantification of isoform-specific expression.The cross-correlation analysis between our CA2 RNA-Seq and the Hipposeq CA2 neuron RNA-Seq data demonstrated a high degree of correlation (Our improvements yield high-quality RNA from minute tissues that can generate highly reproducible transcriptome data. Thus, we believe our optimized methods for producing RNA-Seq data from minute tissue samples will greatly enhance the robustness of RNA-Seq datasets in the field.SF, YW, JMW and SMD designed research. SF and YW performed research. SF and JMW analyzed data. SF and SMD wrote the article.The 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.


SRH), haemagglutinin inhibition (HAI) and microneutralization (MN). However, here are limited data on SRH as well as HAI and MN as correlates of protection against influenza in children and adolescents. There are also limited data that compare SRH to HAI and MN.Serum antibodies are often used as correlates of protection for influenza. Three commonly used serological assays for detecting influenza‐specific serum antibodies are single radial haemolysis ; between MN and SRH 0.82 (P<.01); and between HAI and MN 0.50 (P<.01). Sixteen per cent of participants had SRH titres below the detection limit, compared to 7% and 34% for the MN and HAI assays.The estimates of protective efficacy fluctuated when the cut‐off SRH had the worst correlation with protection against seasonal H3N2 in children and adolescents compared to MN and HAI. SRH, HAI and MN titres were significantly correlated with each other. SRH was less sensitive than MN but more sensitive than HAI. In fact, we found only two such studies and they were limited to equine influenza viruses. In the first study, SRH, HAI and MN tests were performed on sera from horses immunized against two equine influenza viruses including Prague (H7N7) and Miami (H3N8) strains.We previously reported that for influenza A H3N2, MN is superior to HAI in terms of showing correlates of protection in children and adolescents.22.1In this study, we enrolled healthy Hutterite children and adolescents aged 3‐15 years from Hutterite colonies in Saskatchewan and Alberta in the 2008‐2009 influenza season who participated in a cluster randomized trial .2.23 was added to the mixture and incubated for 10 minutes at room temperature. The suspension was then centrifuged at 550 × g for 15 minutes. The supernatant was removed and the pellet was resuspended with phosphate‐buffered saline. Prior to the incubation of the antigen‐erythrocyte mixture, a solution of 1.5% agarose was prepared and kept in a water bath at 47.5°C. The resuspended erythrocyte‐antigen mixture followed by guinea pig complement (Sigma) was added to the agarose gel. The mixture was then spread onto plates (7.2 cm × 2.3 cm) and incubated for 30 minutes at room temperature and then for 30 minutes at 4°C. Twenty holes were then made with a 2.25‐mm calibrated punch into each plate. Serum samples and controls (6 μL each) were seeded into the holes and returned to 4°C immediately. The plates were then incubated for 18 hours at 4°C in a humid chamber. The plates were then incubated for 90 minutes in the humid chamber at 37°C and the diameter of each haemolysis halo was read in millimetres using the TG Calibrating Viewer.SRH assays were prepared as described in Trombetta et al.The HAI assay was performed as previously described using turkey erythrocytes and reference antigens for A/Brisbane/10/2007 (H3N2)‐like viruses.2. We then transferred the serum and virus to plates containing a monolayer of MDCK cells in MegaVir medium with 1 × TPCK ‐treated trypsin. After 3 hours at 37°C with 5% CO2, we refreshed the medium in each well in the plates with MegaVir medium with 0.75 × TPCK‐treated trypsin. After 4‐5 days of incubation, we observed cells for the presence of cytopathic effect. We defined the MN titre value as the highest dilution to retain a confluent cell monolayer. We assigned a value of 1:5 for MN titres below the limit of detection (<1:10) for statistical analysis.MN titres were determined as previously described.For all three assays, we used internally sourced and validated positive and negative controls. We also included back titration of virus for HAI and MN assays.2.3We evaluated the correlation of protection against influenza of SRH titre by calculating protective effectiveness at each titre threshold using Cox's proportional hazards model,3We detected seven (2.0%) PCR‐confirmed seasonal H3N2 cases in participants who had received TIV influenza vaccine and 19 (4.9%) in participants who had received hepatitis A vaccine.The distribution of pre‐ and post‐vaccination antibody responses tested by SRH, HAI and MN assays is shown in Table 2. The estimates then increased with the increase in SRH cut‐off values and reached a plateau of 60% at a cut‐off value of 55 mm2. The estimates increased again after a cut‐off value of 75 mm2 and reached a maximum at 85 mm2. In contrast, for HAI titre ; MN, 320 (40–1280). Sixteen per cent of participants had SRH titres below the limit of detection (<3.99 mm2); 34% had HAI titres below the limit of detection (<1:10); and 7% had MN titres below the limit of detection (<1:10). The Spearman rank correlation between HAI and SRH titres was 0.53 (P<.01); between MN and SRH 0.82 (P<.01); and between HAI and MN titres 0.50 (P<.01). Based on linear regression, an HAI titre of 1:40 corresponds to a MN titre of approximately 1:160, and to a SRH titre of 38 mm2, respectively  for these three methods were as follows: SRH, 42 (19–64) mm42 vs >25 mm2; HAI, >1:640 vs >1:40).The major finding of this study is that the threshold values for SRH titres as well as HAI titres corresponding to 50% protection against influenza in children ≥3 years and adolescents ≤15 years are much higher than the criteria recommended by CHMP  was much larger than that between SRH and HAI  and between HAI and MN . Morley et al.We also found that SRH, HAI and MN titres were all significantly correlated, and the correlation coefficient between SRH and MN  was available for only 15 of the RT‐PCR‐confirmed cases. Another limitation is that our surveillance did not detect natural exposures (symptomatic or asymptomatic).


Tobacco bushy top virus (TBTV) evolution on the degeneration of tobacco bushy top disease, three TBTV isolates with mild virulence were compared with wild-type TBTV to assess the translation of p35, which relies on a BYDV-like translation element (BTE) in a cap-independent manner. The in vitro expression of p35 in the mild isolates was only 20% to 40% of the expression observed in wt TBTV. Based on translation data from chimeric TBTV RNA, low-level p35 expression in the three mild isolates was associated with two regions: the 5′ terminal 500 nt region (RI) and the 3′ internal region (RV), which included the BTE. For the RV region, low level p35 expression was mainly associated with structural alterations of the BTE instead of specific sequence mutations within the BTE based on SHAPE structural probing and mutation analysis. Additionally, structural alteration of the TBTV BTE resulted from mutations outside of the BTE, implying structural complexity of the local region surrounding the BTE. This study is the first report on the structural alteration of the 3′ cap-independent translation element among different isolates of a given RNA virus, which is associated with variations in viral virulence.To identify the molecular effects of  The effective initiation and cyclic utilization of ribosomes ensures a high translation efficiency.The rapid life cycles of viruses require effective expression of viral proteins using the host translation machinery. The 5′ m7GpppN cap of most eukaryotic mRNAs recruits the ribosome by binding to numerous eukaryotic initiation factors (eIFs), such as eIF4E and eIF4G6. These non-canonical translation mechanisms mainly involve two types of cis-elements: 5′ internal ribosome entry sites(IRESs) and 3′ cap-independent translation enhancers(CITEs), which both possess diverse secondary structures to recruit ribosomes directly or via the assistance of a subset of eIFs8. The 3′ CITEs have been predominantly characterized in plant viruses, including members of the family Tombusviridae and the genus Luteovirus of the family Luteoviridae9. Furthermore, similar translational enhancers have also been identified in human transcripts10. The 3′ CITEs in plant RNA viruses have been classified into several typical types based on their origins, secondary structures and ability to bind to eIFs or ribosomes as follows: the translation enhancer domain (TED) derived from satellite tobacco necrosis virus (sTNV)11; the BYDV-like translation element (BTE) derived from Barley yellow dwarf virus (BYDV)12; the PMV-like translation element (PTE) derived from Panicum mosaic virus (PMV)13; the tRNA-like T-shaped structure (TSS) derived from Turnip crinkle virus (TCV)14; the large Y-shaped structure derived from Tomato bushy stunt virus (TBSV)15; the I-shaped structure derived from Melon necrotic spot virus (MNSV)16; and the kissing-loop T-shaped structure (kl-TSS) derived from Pea enation mosaic virus 2 (PEMV-2)17. To the best of our knowledge, RNA viruses undergo rapid evolution under natural selection with a higher mutation rate (10−3–10−5) due to error-prone replication in infected cells and organisms19. Although different 3′ CITEs have been identified from various RNA viruses, few studies have focused on the structural variations of these 3′ CITEs among different isolates of given RNA virus.Many RNA viruses lacking a 5′ cap and/or 3′ poly(A) tail have evolved non-canonical mechanisms to recruit ribosome for the translation of viral proteinsTobacco bushy top virus (TBTV), which is a member of the genus Umbravirus in the family Tombusviridae, and Tobacco vein distorting virus (TVDV), which is a member of the genus Polerovirus in the family Luteoviridae, were causal agents of tobacco bushy top disease in sub-Saharan Africa in the 1960s20 and in Asia, including China, in the 1990s21. Although aphid transmission of TBTV requires the presence of TVDV and its coat protein (CP), TBTV is independently pathogenic21. TBTV has a positive single-strand RNA genome of 4152 nt lacking a 5′ cap structure and a 3′ poly (A) tail22. TBTV encodes four ORFs that encode the replicase components including p35 and its −1 frameshift product p98 (the RdRp)24 and the movement proteins p26 and p2726. TBTV contains a functional BTE in its 3′ UTR that can regulate the cap-independent translation of a reporter gene27. Tobacco bushy top disease has suddenly appeared at 1990s in Yunnan province of China, and during the past decade the previously severe symptoms are currently more attenuated in the field24. During TBTV infection, the symptom could be associated with translation of the viral proteins, replication of the viral genome, virus movement and so on. In this study, the emphasis was focused on the relationship and mechanism between the differential expression of p35 and TBTV virulence in different isolates. cDNA clones of three TBTV isolates showing mild virulence in the field were constructed, and the cap-independent translation of p35 was analyzed in vitro. The p35 expression level of these mild isolates was only 20% to 40% of wild-type TBTV-Ch (TBTV China isolate in this study). This low level expression was partially associated with structural alterations of BTE instead of specific mutations within BTE.27. In this study, we firstly confirmed the translational enhancement properties of the TBTV BTE using a Fluc reporter construct  and tested for p35 expression. Mutations in the SL-I loop (BTm) or RNA-RNA interactions between the 5′ UTR and the BTE (5Um or 3Um) produced ~10% to 20% of the wt p35 expression level Fig. , indicatin vivo accumulation of TBTV was also identified by analyzing the relative RNA accumulation of mutants associated with BTE region in protoplasts. Mutation (5 Um) disrupting the long-distance RNA-RNA interaction between BTE and 5′UTR decreased RNA accumulation to 35% at 20 hpi and 21% at 40 hpi of wt  in protoplasts from BY-2. The relative RNA accumulation of these mild isolates was only 40 to 47% of the wt at 20 hpi and 30 to 40% of the wt at 40 hpi , four additional phosphorylation sites were identified at T46, T94, S97 and S133 in p35 of TB-JC compared with TBTV-Ch  were cloned from samples exhibiting mild virulencehpi Fig. , and theely Fig. . These fely Fig. , whereasFor identifying the regions associated with the attenuated expression of p35 in mild TBTV isolates, TBTV genome was divided into six regions (RI to RVI) from 5′ to 3′ based on available single-cut enzyme sites in pTBTV-Ch. Local RNA domains such as BTE based on RNA structure prediction of TBTV were also taken into account. To map the core regions causing lower p35 expression, 18 chimeric TBTV clones were constructed through replacement of regions RI to RVI in TBTV-Ch with the corresponding regions from TB-JC, TB-MD-I and TB-MD-II Fig. . ChimeriTo determine why the RV region in TB-JC, TB-MD-I and TB-MD-II caused lower p35 expression, the RV region sequences were aligned to TBTV-Ch. Compared with TBTV-Ch, nine mutual variable positions  in RV regions of TB-JC, TB-MD-I and TB-MD-II were identified. The G3794U and G3836A mutations located within the BTE were separately introduced into TBTV-Ch Fig. . The G37Next, U3580C, G3390A and G3639A locating upstream of the BTE within the RV region,, which have potential characteristic changing local structure, were individually introduced into TBTV-Ch. U3580C reduced p35 synthesis by ~50%, whereas G3390A and G3639A did not significantly decrease the p35 levels Fig. . Moreove28, and the percentages of full-length genome structures containing the BTE structure were determined  of the BTE region in different TBTV isolates, full-length TBTV gRNAs were subjected to RNA structure probing using selective 2-hydroxyl acylation and analyzed by primer extension (SHAPE). In TBTV-Ch (wt), the BTE region contained 5 stem-loops  have been reported in different RNA viruses possessing different structural characteristics31. Although sequences within the BTE region are conserved, TBTV may not monitor structural alterations of the BTE resulting from other nucleotide mutations outside the BTE region. This possibility also suggested the complexity of the local region surrounding the BTE. The complexity of the local region was also identified in the 3′ UTR region of Turnip crinkle virus (TCV), which contained an RNA-RNA interaction network mediated by Watson-Crick or non-Watson-Crick base pairing32.In this study, structural evolution of the BTE was identified in three mild TBTV isolates Fig. , which wDengue virus33. For these mild TBTV isolates, the ratio of the linear form of the viral genome may be the majority, which is adverse to the cap-independent translation of p35.In addition to the negative effects of structural alterations of the BTE on the p35 expression level, the RI region of the three mild TBTV isolates also contained mutations that attenuated p35 expression Fig. . As show34. In 2014, only 52 samples were detected to contain TBTV and TVDV from 269 diseased samples on Yunnan province35. Natural selection must play an essential role in pathogen evolution, although successful artificial management to prevent the transmission vector aphid in the field is important. TBTV and TVDV are the natural major pathogens of tobacco bushy top disease. In this study, three new mild TBTV isolates were isolated from Yunnan province. Their mild virulence should be partially associated with lower expression of both p35  of TBTV-Ch (NC004366) downstream of the Fluc gene in the pT7-F-3′ UTRssp vector using Pml I and Ssp I; the in vitro transcript from this plasmid was designated F-TB-3U. pFluc-TB-5U3U contained the 5′ UTR and 3′ UTR of TBTV-Ch (NC004366) flanking the Fluc gene; the in vitro transcript of this plasmid was termed F-TB-5U3U. Mutant plasmids derived from pFluc-TB-5U3U were constructed by primer-mediated site-directed mutagenesis along with overlap extension PCR  of TBTV-Ch (NC004366) between the T7 promoter and firefly luciferase (Fluc)gene in the pT7-F-3′ UTRssp vector (a firefly luciferase reporter construct) using 0 nt of Tin vitro transcript from this plasmid was termed wt-TBTV in this study. Similar to pTBTV-Ch, pTBTV-JC, pTBTV-MD-I and pTBTV-MD-II contained the whole genome sequence of the JC (KM016224), MD-I (KM016225) and MD-II (KM067277) isolates of TBTV, respectively, and the corresponding in vitro transcripts were termed TB-JC, TB-MD-I and TB-MD-II.pTBTV-Ch was constructed by inserting the whole genome sequence of TBTV (NC004366) fused with the T7 promoter sequence into pMD18-T (Takara); the All plasmids containing chimeric TBTV genomes were generated through digestion of PCR fragments amplified from TBTV-JC, TBTV-MD-I or TBTV-MD-II and replacement of the corresponding fragments in pTBTV-Ch using the corresponding enzyme sites. Mutant plasmids derived from pTBTV-Ch or other TBTV plasmids containing full-length genomes of TBTV isolates were constructed by primer-mediated site-directed mutagenesis along with overlap extension PCR. The primers used to generate the DNA constructs are listed in Supplement Table EcoR V, and all luciferase reporter constructs containing the TBTV 5U and/or 3U fragments were linearized with Ssp I to make templates for RNA preparation. RNA was transcribed in vitro with the bacteriophage T7 RNA polymerase according to the manufacturer’s instructions . The RNA integrity was verified by 1.0% agarose gel electrophoresis. The RNA concentration was determined by spectrophotometry.All plasmids containing full-length or mutant TBTV were linearized with 6 cells/ml) prepared by cellulose and pectinase digestion from BY-237 were inoculated with 20 µg of different TBTV or genomic RNA transcripts as previously described28. Total RNA was extracted from protoplasts at 20 hpi and 40 hpi and subjected to qRT-PCR to evaluate the relative RNA accumulation of different TBTV constructs. The internal control for the qRT-PCR was elongation factor 1 alpha (EF1α); the fragments detected from TBTV were located at positions 700–970 and 1927–2170 using the primers shown in Supplement Table Protoplasts (5 × 10In vitro translation was assessed in wheat germ extract (WGE). RNA transcripts(1.6 pmol for TBTV and 0.4 pmol for the luciferase reporter vectors) were translated in a total reaction volume of 25 µl  at 25 °C for 1.5 h as described by the manufacturer (Promega). To detect p35 from TBTV, the translated products were separated on an SDS-PAGE gel, probed with a p35 antibody31, detected with the ChampChemiTM Top 420 Imager (Sage Creation Co. Ltd.) and quantified using the Quantity One one-dimensional analysis software (Bio-Rad). For the luciferase reporter RNA, the luciferase activity of the translated products was measured using the luciferase assay reporter system (Promega) in a GloMaxTM 20/20 luminometer (Promega). At least three independent assays were performed for each constructs, and the relative expression levels and error bars was calculated by Microsoft Office Excel 2007.http://unafold.rna.albany.edu/?q=mfold/RNA-Folding-Form2.3)38. The folding temperature was 25 °C and other default settings were used.The RNA secondary structures of TBTV or its derivatives were predicted with the RNA Folding Form (version 2.3 energies) from the Mfold web server 2, and 160 mM NH4Cl) at 37 °C for 20 min, followed by a subsequent 2 min on ice. Then, the folded RNA was treated with either 15 mM N-methylisatoic anhydride (NMIA) or the same volume of dimethyl sulfoxide (DMSO) as a negative control at 37 °C for 40 min. The RNA was recovered by ethanol precipitation and re-suspended in 0.5xTE buffer. Primer extension reactions were performed using [γ-32P] ATP-labeled oligonucleotides and the SuperScript III reverse transcriptase (Invitrogen) as previously described39. A primer(5-GAGAGGGAACCTCCAGAGT-3)complementary to positions 3866–3884 of TBTV was used for structural probing of the BTE region. The reaction products and ladders generated by Sanger sequencing were resolved on polyacrylamide gels containing 8 M urea. Then, the gels were dried and exposed to a phosphorimager screen, followed by detection with the Typhoon FLA-7000. The NMIA reactivity of each nucleotide was assigned as none, low to moderate and moderately high to high by visually inspecting the intensities of the individual bands compared with the DMSO reaction control. The RNA secondary structures were generated from the structure probing results combined with the Mfold predictions38. At least two independent SHAPE assays were performed for each constructs, and only reproducible effects are described.SHAPE structural probing of the TBTV BTE region was evaluated as previously described with some modificationsSupplement figure and tables


RNAs have been found to carry diverse functionalities in nature. Inferring the similarity between two given RNAs is a fundamental step to understand and interpret their functional relationship. The majority of functional RNAs show conserved secondary structures, rather than sequence conservation. Those algorithms relying on sequence-based features usually have limitations in their prediction performance. Hence, integrating RNA structure features is very critical for RNA analysis. Existing algorithms mainly fall into two categories: alignment-based and alignment-free. The alignment-free algorithms of RNA comparison usually have lower time complexity than alignment-based algorithms.An alignment-free RNA comparison algorithm was proposed, in which novel numerical representations RNA-TVcurve (triple vector curve representation) of RNA sequence and corresponding secondary structure features are provided. Then a multi-scale similarity score of two given RNAs was designed based on wavelet decomposition of their numerical representation. In support of RNA mutation and phylogenetic analysis, a web server (RNA-TVcurve) was designed based on this alignment-free RNA comparison algorithm. It provides three functional modules: 1) visualization of numerical representation of RNA secondary structure; 2) detection of single-point mutation based on secondary structure; and 3) comparison of pairwise and multiple RNA secondary structures. The inputs of the web server require RNA primary sequences, while corresponding secondary structures are optional. For the primary sequences alone, the web server can compute the secondary structures using free energy minimization algorithm in terms of RNAfold tool from Vienna RNA package.http://ml.jlu.edu.cn/tvcurve/.RNA-TVcurve is the first integrated web server, based on an alignment-free method, to deliver a suite of RNA analysis functions, including visualization, mutation analysis and multiple RNAs structure comparison. The comparison results with two popular RNA comparison tools, RNApdist and RNAdistance, showcased that RNA-TVcurve can efficiently capture subtle relationships among RNAs for mutation detection and non-coding RNA classification. All the relevant results were shown in an intuitive graphical manner, and can be freely downloaded from this server. RNA-TVcurve, along with test examples and detailed documents, are available at: The online version of this article (doi:10.1186/s12859-017-1481-7) contains supplementary material, which is available to authorized users. RNAs are known to carry important functionalities among diverse species, while those with similar functions are usually less conserved at sequence level in structured regions. Hence, it is critical to consider the structural information in RNA comparison, aiming to elucidate their functional and evolutional relationship. RNA three dimensional structure mainly determine the Current methods for RNA secondary structure comparison can be generally classified into two categories, i.e., alignment-based and alignment-free.3M) and O (n2M), respectively, given M RNA sequences with lengths of n. Although this algorithm has a very good performance in prediction, its heavy computational complexity greatly limits its application.The alignment-based methods are mostly conducted in a dynamic programming framework, relying on a string or tree representation of RNA secondary structure –6. The RTherefore, in order to be more practical, there are many simplifications of Sankoff algorithm  includinAn alignment-free algorithm is usually based on a numerical representation of sequences, which requires a novel idea and alternative way to visualize, analyze and compare DNAs, RNAs and proteins. Hence, compared to the alignment-based methods for RNA structure comparison, the studies on alignment-free algorithms are limited. In , variousIn addition, with steadily increasing numbers of known 3D RNA structures, some 3D structure alignment algorithms and their web serves including SETTER , 49, Mulhttp://ml.jlu.edu.cn/tvcurve/.We developed a web server, RNA-TVcurve, for inferring RNAs’ relationship based on comparing their secondary-structures. The underlying alignment-free algorithm is summarized in the next section and more details can be found in . This meThe details of the underlying algorithm of RNA-TVcurve can be found in . Here weStep 1: The minimum free energy method in terms of Vienna’s RNAfold [ RNAfold  is used Step 2: The characteristic representation of RNA secondary structure is designed based on eight symbols, in which A, C, G and U were used for the unpaired nucleotide bases, and A′, C′, G′, and U′ were used for the same bases if paired by hydrogen bonds.Step 3: A new numerical representation of RNA is constructed, named as Triple vector curves (TV-curve), by integrating RNA sequence and secondary structure features. The triple vectors are designed to represent the eight A, T, C, G, A′, U′, G′ and C′ symbols respectively as follows:The schematic diagram of triple vectors representing eight letters is shown in subfigure of Step 3 in Fig. Step 4: Firstly, the wavelet decomposition [L-level of RNA TV-Curves are computed. After the L-level transformation, the detailed coefficients and approximation coefficients at the different resolution are obtained. L is usually taken as four by default. Next, a novel similarity metric, named multi-scale similarity, was designed to capture both the global and local properties of the constructed TV-curve based on wavelet decomposition. The Pearson correlation coefficients at different decomposition levels are calculated and the weighted sum is taken in the multi-scale similarity calculation.position  with L-lN, there are three possible types of mutations for each position. For the original RNA sequence, if the original structure is not provided, the secondary structure of the original RNA sequence will be predicted with the minimum free energy algorithm. All the 3*N single point mutated RNA sequences are constructed. For each single point mutated RNA sequence, the minimum free energy algorithm is used to predict the secondary structure. In order to identify the maximal structural mutation, the similarity between each mutated RNA sequence and original RNA sequence based RNA-TVcurve is computed. The similarity score is changed into a distance score by one minus it. The mutation with the maximal structural distance is identified. Furthermore, the histogram of the structure distance scores of all the single point mutation is provided to help users to comprehensively understand the landscape of all the single point mutation. In addition, to describe the structural stability of each position, the deleterious profile is defined as the maximum distance in structures between the wild-type and the possible single point mutants at each position. The structurally important sites can be easily identified by the peaks with a larger structural distance on the profile. The distributions of the distances between the wild-type and all the possible single mutants are shown as histograms. The histograms of the distances between the wild-type and all the possible single mutants provide the visual understanding on RNA mutational robustness.For an RNA sequence of length The main steps of construction the RNA phylogenetic tree are listed as follows:Step 1: Compute TV-curve for each sequence in a given RNA set.Step 2: Calculate similarity matrix of N (N-1)/2 similarity between all pairs of N sequences by pairwise RNA secondary structure comparison based on RNA-TVcurve as mentioned above.Step 3: Converting raw similarity scores to distances scores by 1-minus-similarity.Step 4: Construct a phylogenetic tree from the distance matrix by UPGMA (Unweighted Pair Group Method with Arithmetic Mean). Firstly, cluster a pair of RNAs (taxa) with the smallest distance. Then re-calculate an average distance between the new cluster and other taxa, obtaining a new distance matrix. Finally, repeat previous step until clusters converge.TVCurve: visualization and generation of TV-curve for a provided RNA sequence; (2) RNA Mutation: RNA single point mutation detection for a given RNA sequence; (3) RNA Multiple:construction of RNA phylogenetic tree for multiple RNAs. (4) RNA Pairwise: perform pairwise comparison based on RNA-TVcurve. All these usages are freely available and no login information is required.RNA-TVcurve offers four different functional modules . Each functional module of the web serve of RNA-TVcurve has been tested through four examples. The test samples for each module and the examples shown in this paper are listed in the server and also included in Additional file The server is accessible at: All functional modules require the input in FASTA format or a modified FASTA including the secondary structure in dot-bracket. Upon a job is submitted to the server, a job ID will be generated. A user can just save the job ID and use it to extract the results. For a submitted job, its results are saved up to 1 month so that the user can query and retrieve. The probable long-running jobs include the RNA structural mutation detection for a longer RNA sequence with a length larger than 1000 nt, and the RNA phylogenetic tree construction for more than 300 RNA sequences. Some important results including the TV-curve, the mutated structure with the maximal structural difference and the phylogenetic tree are shown in a more intuitive graphical manner. The output page also offers the download of the results as a single packed file in “.zip” format for offline analysis. The README file in the packed file provides information on of the downloaded results. The detailed steps and functionality of each module are introduced in the following.This module provides a visualization for the TV curve constructed from provided RNA sequences and secondary structures. The input should be RNA sequences in the FASTA format that are provided with or without secondary structure information. If the structures of some RNAs are not provided, the secondary structures are predicted with the minimum free energy algorithm. The output provides the TV-curve representation and the multi-scale decomposition of the TV-curve, and the secondary structure for each RNA. All the results including TV-curve representation, the multi-scale decomposition of the TV-curve, and the secondary structure can be freely downloaded. Detailed executive process and results of the example for this module are presented in Additional file This module is to accomplish the RNA single point structural mutation detection. The required input for this module is a single RNA sequence and structure or a sequence alone. If multiple RNA sequences are submitted, only the first sequence is calculated. For this functional module, there is an option “Compare with RNA distance and RNApdist” to benchmark the results with both tools. This option is not selected by default. The maximum distance in structures between the wild-type and those with mutations at each position is extracted into a structural deleteriousness profile. This module provides the deleteriousness profiles and their histograms for RNA-TVcurve. The structure with the maximum structure difference calculated by RNA-TVcurve is given the corresponding mutation types and mutation sites are also provided. In addition, in order to help users to visualize the structural difference between the wild-type and all the mutants, the secondary structures of the wild-type RNA and the mutated RNA with the maximum structural distance are also displayed on the web page. All the structural distance scores of the single point mutations detected by RNA-TVcurve are ranked, which are included in the downloaded file. If users select the option “Compare with RNA distance and RNApdist”, this module provides the deleteriousness profiles and their histograms for RNA-TVcurve, RNAdistance and RNApdist, respectively. The structure with the maximum structure difference calculated by RNA-TVcurve, RNAdistance and RNApdist are given and the corresponding mutation types and mutation sites are also provided. We will rank all the structural differences of the single point mutations detected by the three methods respectively. The detailed executive process and results of the example for this module are presented in Additional file For Module 3, the input is a set of RNA sequences and their structures or RNA sequences and part of structures or primary sequences alone (at least three RNA sequences are required). The phylogenetic tree computed by RNA-TVcurve is displayed on the web page. All the related results include the phylogenetic tree, TV curves and distance matrix using RNA-TVcurve can be freely downloaded. Similar to the module of “RNA Mutation”, users have an option “Compare with RNA distance and RNApdist”. If this option is selected, the three phylogenetic trees computed by RNA-TVcurve, RNApdist and RNAdistance are all shown in an intuitive graphical manner on the web page, which can help users make comparison and further analysis. In this case, the downloaded file includes three phylogenetic trees and distance matrix computed by RNA-TVcurve, RNApdist and RNAdistance. When the number of the sequences is larger, the alignment-based RNApdist needs more time to compute. In addition, the labels of the leaves on the tree are hard to read when the number of multiple RNAs is large. We construct the tree at horizontal direction in order to more clearly display the labels of the leaves on the tree when the number of multiple RNAs is larger than 20.The example of construction the phylogenetic trees for multiple RNAs is 100 sequences of four non-coding RNA families including miRNA, RNaseP_arch, 5S_rRNA and tRNA. In order to make the performance for each method clearly, four types of colors are marked for each family. The phylogenetic tree by RNA-TVcurve has four noticeable branches and only four RNAs are assigned at the wrong branch, where each branch represents one RNA non-coding family. The detailed executive process and results of the example for this module are presented in Additional file This module is to accomplish RNA pairwise comparison based on RNA-TVcurve. The input of this module includes two sets of RNA sequences. One set is named as query set and the other one as target sets. Both of the two sequence sets include RNA sequences and their structures or RNA sequences and part of structures or primary sequences alone. They should be in the FASTA format and contains the same number of RNAs. In this module, the RNAs in the query set and the corresponding RNAs in the target set are compared by RNA-TVcurve. The distance score for each pair of RNAs are shown on the web server. In addition, the RNA TV curve and secondary structure of each sequence are also displayed on the web server to help users further understand the structure difference between a pair of RNAs. All the related results are packed in a zip file for download.The detailed executive process and results of the example for this module is presented in Additional file We assessed the performance of RNA-TVcurve compared with the two popular alignment-based programs, RNAdistance and RNApdist. In order to systematically evaluate the performance of RNA-TVcurve, two types of experiments are designed, one of which is to test the capability to infer the evolution for different species and the other is to validate the performance to distinguish the different types of RNA families.Archaea and Eukaryotes, only Dicyema misakiense is wrongly placed on the Archaea branch, however the other two phylogenetic trees constructed by RNAdistance and RNApdist do not show such good property. In addition, on the phylogenetic trees constructed by RNA-TVcurve and RNApdist, the three groups of Fungi, Metazoa and Crenarchaeot are all placed closely. The phylogenetic trees for set II of RNase P and RNase MRP constructed by the three programs are listed in Fig. M.jannaschii is wrongly placed on the branch of RNase MRP. The groups of Alpha subdivision and Cyanobacterial are all grouped closely. The other two phylogenetic trees constructed by RNAdistance and RNApdist have limited power in distinguishing the RNase MRP and RNase P.The sets of different types of RNAs from different species in  are desin3). RNA-TVcurve dramatically reduce the computational time complexity to O (n) [n is the length of an RNA sequence. The comparison of Pearson correlation coefficients of RNA-TVcurve, RNAdistance and RNApdist for the 5,000 experiments is shown in Fig. Next, different RNA families from Rfam database are used to evaluate the ability of these three programs in distinguishing the different types of RNA families. The sequences of rRNA, tRNA, miRNA and RNase_P families are downloaded from the Rfam database. In order to directly compare the performance for distinguishing the different types of RNAs, Pearson correlation is used to measure the consistency between the similarity measure and the background co-membership matrix. It is actually the correlation between the similarity matrix computed and the background co-membership matrix . Intuitito O (n) , where nThe RNA-TVcurve server provides an access to our alignment-free RNA secondary structure comparison algorithms. It is the first integrated web server for RNA analysis based on alignment-free method, which provides a novel visualization of RNA secondary structure, mutation analysis, pairwise and multiple RNAs secondary structure comparison. This method can provide a novel view to visualize and analyze RNAs. Our evaluations suggest that the web server is a good visualization tool, and more importantly, it can be used to solve a wide range of RNA related analysis and mining problems. Particularly, it is very useful for making deleterious mutation prediction, RNA structural feature extraction and multiple RNA structural comparison. We believe the server will be a useful tool for RNA comparison and fill a void in this field.A future improvement could be RNA-TVcurve capacity extension to handle multiple point mutations. In addition, the native secondary structure of an RNA is often a suboptimal structure not the predicted structure with minimum free energy due to limitations of thermodynamic models. Integrating multiple predicted suboptimal structures in the RNA structural similarity measurement is still very challenging and there is still a big room for improvement in its performance. In further study, we will focus on developing an RNA comparison tool based on integration of multiple suboptimal structural features. Since real structures of RNAs are very close to the structures with the minimum free energy, it is very import to consider a reasonable filter process. In addition, we will develop a new tool and a web server to search the public RNA datasets such as Human 3′ UTR, Mouse 3′ UTR, Human lncRNAs, Mouse lncRNAs, and Structured Rfam based on RNA-TVcurve. These computational techniques will be very useful and crucial for in-depth inference of RNA functions.


The negative implications of the thermal sensitivity of reef corals became clear with coral bleaching throughout the Caribbean in the 1980’s, and later globally, with the severe El Niño of 1998 and extensive seawater warming in 2005. These events have substantially contributed to declines in coral cover, and therefore the El Niño of 2016 raised concerns over the implications for coral reefs; on the Great Barrier Reef these concerns have been realized. A different outcome developed in Mo’orea, French Polynesia, where in situ seawater temperature from 15 March 2016 to 15 April 2016 was an average of 0.4°C above the upper 95% CI of the decadal mean temperature, and the NOAA Degree Heating Weeks (DHW) metric supported a Level 1 bleaching alert (DHW ≥ 4.0). Starting 1 September 2016 and for the rest of the year (122 d), in situ seawater temperature was an average of 0.4°C above the 95% CI of long-term values, although DHW remained at zero. Minor coral bleaching  occurred on the outer reef (10-m and 17-m depth) in April 2016, by May 2016 it had intensified to affect 1.3–16.8% of the coral, but by August 2016, only 1.4–3.0% of the coral was bleached. Relative to the previous decade, recruitment of scleractinians to settlement tiles on the outer- (10 m) and back- (2 m) reef over 2016/17 was high, both from January 2016 to August 2016, and from August 2016 to January 2017, with increased relative abundances of pocilloporids on the outer reef, and acroporids in the back reef. The 2016 El Niño created a distinctive signature in seawater temperature for Mo’orea, but it did not cause widespread coral bleaching or mortality, rather, it was associated with high coral recruitment. While the 2016 El Niño has negatively affected other coral reefs in the Indo-Pacific, the coral communities of Mo’orea continue to show signs of resilience, thus cautioning against general statements regarding the effects of the 2015/16 El Niño on coral reefs in the region. El Niño events arise from the coupling of global atmospheric and oceanographic conditions to perturb atmospheric pressure, winds, rainfall, ocean currents, and sea level throughout the tropical Pacific Ocean ,2. TheseThe effects of El Niño often are striking on coral reefs, where they typically are revealed through coral bleaching ,7,8. SevThe primary mechanism by which El Niño impacts reef corals is through warming ,19, althSymbiodinium algal endosymbionts ) was 1.78-fold greater than recorded over the previous decade (4.42 ± 0.53 recruits tile-1 [n = 18 records]) . Likewise, mean recruitment between August 2016 and January 2017 (1.59 ± 0.31 recruits tile-1 [n = 8 sites]) was 2.2-fold greater than over the previous decade (0.79 ± 0.16 recruits tile-1 [n = 16 records]) .Recruitment in 2016 differed from recruitment in the preceding decade on the outer (10 m) and back  reef. On-1, Site 7) and August and January  exceeded recruitment in the back reef over the previous decade (-1 [n = 9]) was 9.0-fold higher than over the previous decade in the same period (0.73 ± 0.15 recruits tile-1 [n = 30]) . Likewise, mean recruitment (± SE) between August 2016 and January 2017 (1.43 ± 0.30 recruits tile-1 [n = 8]) was 4.0-fold higher than over the previous decade in the same period (0.36 ± 0.08 recruits tile-1 [n = 30]) .Similar trends were recorded in the back reef during 2016 , where ts decade . Between2 = 55.112, df = 3, P < 0.001), and August and January , and for the back reef between January and August  and August and January . In 2016 on the outer reef, the relative abundance of pocilloporids increased 19% between January and August, while other taxa became less common, and from August to January, acroporids increased 2.5-fold while other taxa became less common. In 2016 on the back reef, between January and August, pocilloporids accounted for 92% of the recruits (versus 34% in the decade before), and from August to January, the abundance of pocilloporids increased 1.7-fold, and the abundance of acroporids 4.7-fold , with the effect attributed to small variation in all taxa, but generally, fewer-than-expected acroporids and poritids, and more- than-expected pocilloporids and “others” in the outer versus the back reef , but much of the difference was caused by fewer-than-expected poritids in the back- versus the outer- reef, and also more-than-expected pocilloporids in the back- versus the outer- reef  for scl-Pacific ,59, and -Pacific ) to promhttps://coralreefwatch.noaa.gov/)  metric for the Society Archipelago exceeded 4.0, leading to a Level 1 bleaching alert , and coiaa.gov/) . Comparef Tahiti .-2 y-1 in the back reef, and 17–84 recruits m-2 y-1 at 8–10-m depth on the north shore outer reef  for the back reef) relative to previous years . This trend suggests that growth rates of recruits in 2016 may have been more rapid than in the previous decade. Seawater temperature again is a likely cause of this effect, as temperature accentuates coral growth depending on the range over which the increase occurs relative to the thermal threshold for maximum growth [Finally, it is possible that the environmental conditions of 2016 contributed to rapid growth of coral recruits, which can serve as a mechanism to avoid the high risks of remaining small , thus acm growth .Pocillopora spp. The back reef on the north shore provides more opportunities for recruitment to modify coral community structure, because large areas of hard substrata were available in 2016 , and the availability of these substratum categories have been accentuated by coral mortality that has reduced coral cover on the north shore from 29.1 ± 11.4% in 2005 to 4.8 ± 2.4% in 2016 , and on a longer time scale, has depleted the community of Acropora spp. [Acropora spp. cover on the north shore back reef in 2015 was ≈ 0.05%. Macroalgae, particularly Turbinaria and Sargassum, also are becoming more abundant in the back reef of Moorea [It is too soon to determine whether coral recruitment in 2016 in Mo’orea will promote a change in coral community structure, but if survivorship of the recruits is sufficiently high, such an outcome is feasible given the high density, and the taxonomic composition of the recruits. The taxonomic effect is striking in the back reef, where 2016 brought strong representation of pocilloporids from January to August , and many acroporids and pocilloporids from August 2016 to January 2017 . Realizing the possibility of change in coral community structure for the outer reef at 10-m depth on the north shore seems unlikely, however, because most of the benthos (56%) was covered by coral in 2016, and the majority was ora spp. –80; Acrof Moorea , particuf Moorea . Thus, wThe results of this study, together with recently-published results , emphasi


CircRNAs are a novel type of RNAs. With the newly developed technology of next-generation sequencing (NGS), especially RNA-seq technology, over 30,000 circRNAs have already been found. Owing to their unique structure, they are more stable than linear RNAs. CircRNAs play important roles in the carcinogenesis of cancer. The expression of circRNAs is correlated with patients’ clinical characteristics, and circRNAs play a vital role in many aspects of malignant phenotypes, including cell cycle, apoptosis, vascularization, and invasion; metastasis as a RNA sponge, binding to RBP; or translation. Therefore, it is meaningful to further study the mechanism of interactions between circRNAs and tumors. The role of circRNAs as molecular markers or potential targets will provide promising application perspectives, such as early tumor diagnosis, therapeutic evaluation, prognosis prediction, and even gene therapy for tumors. Circular RNAs (circRNAs) are a large group of RNAs that form covalently closed continuous loops. The first circRNA was found in a RNA virus in 1976 . Not lonCircRNAs have long been regarded as an accidental byproduct, resulting from errors during post-transcriptional processing, and recent studies have found important functions of circRNAs in multiple biological processes . CircRNACircRNAs can be sorted into three types, including exonic circRNAs, intronic circRNAs, and retained-intron circRNAs, which are produced from different cycling mechanisms. During the formation of circRNAs, specific sequences are conserved and sequences from upstream and downstream regions are linked together in a reverse direction, all of which contribute to mature circRNAs by back splicing . AlthougThe mechanism of intronic circRNA formation is different from that of exonic circRNA. The formation of intronic circRNAs relies on GU-rich sequences near the 5′ splice site and C-rich sequences near the branch point. During the back splicing process, the two segments bind into a circle first. Then exonic sequences and intronic sequences in the binding part are cut out by the spliceosome. The remaining introns are eventually pieced together to form mature circRNA , 18. RecHIPK3 pre-mRNA can be spliced into HIPK3 mRNA and its second exon can also form circRNA hsa_circ_0018082 [Similar to linear RNA, the biogenesis of circRNAs is regulated by various types of proteins, such as the RNA spliceosome and RNA binding protein (RBP). The RNA spliceosome and spicing process are vital research frontiers in biomedicine. The RNA spliceosome is a RNA-based enzyme with a U5 core consisting of 5 snRNAs and a number of proteins. Under the effect the U6 and U2 promoters, many proteins cooperatively process pre-RNAs . Similar_0018082 . There i_0018082 , which m_0018082   or linear RNAs. There is also a greater possibility to screen for sensitive and specific molecular biomarkers from thousands of circRNAs with regard to the relatively small number of miRNAs. Testing circRNAs by RT-PCR and in-situ hybridization is also more specific and sensitive than detecting proteins by an antigen-antibody reaction. In conclusion, the use of circRNAs outweighs the use of current tumor biomarkers in many fields, and they could be promising molecular biomarkers and new targets for future drugs.CircRNAs are widely involved in physiological and pathological processes, and studies have found that circRNAs can be involved in biological activities by acting as a miRNA sponge , bindingCDR1 gene. It was first found to be upregulated in malignant tumor cell lines, such as HeLa cells, and further studies found more than seventy miRNA binding sites in ciRS-7 [EGFR and XIAP; and promotes vascularization, metastasis and reproduction of tumor cells [ciRS-7, also called CDT1as, is one of the earliest known circRNAs and regulates miRNA in tumor cells . ciRS-7 n ciRS-7 . Later, n ciRS-7 . miR-671n ciRS-7 , 43. Thior cells , 44.Foxo3, the Foxo3 gene can encode for two noncoding RNAs, the pseudogene FOXO3P and circ-Foxo3. Yang et al. found that the two RNAs can both act as miRNA sponges for miR-22, miR-136*, miR-138, miR-149*, miR-433, miR-762, miR-3614-5p, and miR-3622b-5p, influencing their function by binding to them. Thus, they are involved in the progression of tumors [CDK2 to form a circ-Foxo3-p21-CDK2 tripolymer. Thus, in this way, the function of CDK2 is restricted, leading to a decrease in cell proliferation, and the silencing of circ-Foxp3 will contribute to much faster growth in malignant tumor cells [Despite encoding for f tumors . Studiesor cells .ABCC1 gene. The official name of hsa_circ_001569 is hsa_circ_0000677. Liang et al. found that the hsa_circ_001569 had significantly high expression in colorectal cancer tissue and that it correlated positively with the degree of clinical features, such as the TNM stage [E2F5, BAG4, and FMNL2, and acts as a positive regulator in cell proliferation and invasion of colorectal cancer.Hsa_circ_001569 is a circRNA of 1776 bp that is spliced by linear a RNA precursor from the NM stage . This meHIPK3 gene. Huang found higher circRNA levels, including circHIPK3, by RNA sequencing seven types of tumor samples and six types of normal tissues [iASPP [circHIPK3 is produced by the second exon of the  tissues . Silencis [iASPP .PML/RARa and MLL genes fused and produced f-circM9 and f-circPR and knockouts of f-circM9 and f-circPR lead to apoptosis of a large amount of tumor cells as well as increases their sensitivity to drugs, such as arsenic [The genomes of tumor cells are unstable; thus, many chromosome translocations exist, especially in lymphoma. Gene fusions may lead to incorrectly spliced mRNAs, during which circRNA  comes into being. For example, the  arsenic . TogetheE2F1, NF-κB, Sp1, HIF-1, AP-1, STAT3, and STAT5, thus inhibiting tube formation of tumor cells. However, studies show that cZNF292 does not function as a RNA sponge, and the mechanism requires further study [Silencing cZNF292 in human endothelium cells will inhibit the tube formation process these cells undergo . cZNF292er study .CDK6 by working as a RNA sponge for miR-103a-3p and miR-107. Thus, it promotes the proliferation and metastasis of urinary bladder carcinoma [In 467 samples of carcinomas of the urinary bladder, Zhang et al. detected circRNAs with prominent differential expression through microarray analysis. It was confirmed by qRT-PCR that six circRNAs, including circTCF25 (hsa_circ_0041103), were highly expressed in tumor tissues. Another study demonstrated that circTCF25 can upregulate the levels of arcinoma .CircRNAs are novel molecules that have been discovered in recent years. They are no longer recognized as products of transcription errors. On the contrary, they have become a popular research subject, after miRNAs and lncRNAs, owing to their important functions –56. The A tremendous amount of data indicate that circRNAs regulate other genes at the transcriptional and post-transcriptional levels. Some can even encode proteins directly and are thus involved in physiological and pathological processes, including influencing malignant tumors. Until now, the miRNA sponge has been found to be a possible mechanism for circRNA function. They can specifically bind to miRNAs and regulate gene expression by competing with competing endogenous RNA (ceRNA), including lncRNAs, mRNAs, and pseudogenes. The participation of circRNAs in the ceRNA network renders it more complete and complex. CircRNAs compete with mRNAs from the same preRNAs during the splicing process, and circRNAs were recently found to encode for proteins. This demonstrates their involvement in biological procedures, provides a more complete understanding of the RNA language, and enriches the central dogma –59. KnowCircRNAs are much more stable compared to linear RNAs owing to their unique structure. With regard to biological markers of cancer, circRNAs have demonstrated their wide perspective. Studies have already found differential expression of circRNAs in tissues and blood, and their expression differs from those of miRNA and lncRNA –62. It iPrevious studies have reported that circRNAs are one type of molecular marker in tumors and that they can influence apoptosis and infiltration. CircRNAs are potential targets for new drugs. For example, the fusion-circRNA found in leukemia is associated with chemotherapy resistance, and knocking out expression of f-circM9 can lead to apoptosis of leukemia cells and decrease its drug resistance to Ara-C . On the In conclusion, current achievements in the study of circRNAs and their functions are still very limited. To our excitement, this mystery will eventually be solved through scientists’ endeavors and applications of new methods and techniques. CircRNAs function in an increasingly significant way in the diagnosis and therapy of cancers and will provide precise strategies for affecting human cancers.