Additionally, we validated several A-to-G edited sites by Sanger sequencing. Altogether, our study suggests a role for RNA editing in miRNA-mediated gene regulation and splicing in human lungs. In this study, we have generated a RNA editome of human lung tissue that can be compared with other RNA editomes across different lung tissues to delineate a role for RNA editing in normal and diseased states.RNA editing is a post-transcriptional modification of RNA. The majority of these changes result from adenosine deaminase acting on RNA (ADARs) catalyzing the conversion of adenosine residues to inosine in double-stranded RNAs (dsRNAs). Massively parallel sequencing has enabled the identification of RNA editing sites in human transcriptomes. In this study, we sequenced DNA and RNA from human lungs and identified RNA editing sites with high confidence via a computational pipeline utilizing stringent analysis thresholds. We identified a total of 3,447 editing sites that overlapped in three human lung samples, and with 50% of these sites having canonical A-to-G base changes. Approximately 27% of the edited sites overlapped with Alu repeats, and showed A-to-G clustering (>3 clusters in 100 bp). The majority of edited sites mapped to either 3′ untranslated regions (UTRs) or introns close to splice sites; whereas, only few sites were in exons resulting in non-synonymous amino acid changes. Interestingly, we identified 652 A-to-G editing events in the 3′ UTR of 205 target genes that mapped to 932 potential miRNA target binding sites. Several of these miRNA edited sites were validated  In recent years, the focus of molecular biology has been radically shifted from the “central dogma” (DNA to RNA to protein) to encompass the role of modifications of RNA nucleotides via co- or post-transcription mechanisms termed “RNA editing”. RNA editing involves alteration in the sequence of RNA that is derived from DNA. The diverse type of RNA editing events leads to different functional consequences . RNA ediRNA editing can occur in either coding or non-coding regions of dsRNA. The site-specific deamination of adenosine residues in pre-mRNAs can alter the individual codons resulting in protein diversity. Moreover, ADARs can edit multiple sites on the same mRNA species thereby further diversifying the functional outcomes . RNA ediDysregulation of RNA editing has been linked to several neurodegenerative diseases such as epilepsy, amytrophic lateral sclerosis (ALS), depression, brain ischemia and tumor , 14, andWith the advent of high-throughput sequencing (HTS) technology and developmental of computational tools, it has been possible to identify RNA editing sites in human and mouse transcriptomes , 21-23. http://150.145.82.212/ernesto/reditools/doc/) using the criteria: minimum depth of coverage 10X in DNA and RNA, no variant alleles in the DNA sequencing, at least 2 alternate alleles and at least 10% alternate alleles in RNA sequencing. These criteria were based on information available from previous publication [In the present study, we investigated RNA editing in three normal human lungs using high-throughput (exome) DNA- and RNA-sequencing data from the same sample and computational pipeline. We mapped reads to reference genome and that included all splice junctions and introns for unique mapping of reads. We used a stringent computational pipeline to identify RNA editing sites in three normal lung samples with REDITools software package   of target genes. Many of the hyper-edited sites were found in A-to-G clusters. Several of these editing sites were validated by Sanger sequencing. Interestingly, we identified editing within the miRNA binding sites in 3′UTR of candidate genes for normal lung samples and several miRNA edited sites were validated by We sequenced DNA (exome plus UTRs) and RNA from three normal human lung samples using the Hiseq2500 platform (Illumina). For DNA sequencing, we generated an average of 110 million reads per sample of which ∼98% aligned uniquely to the human reference genome (hg19). 79% of reads aligned to the targeted exome giving an exome coverage of 69X. 92% of targeted bases were covered at 10X coverage and 81% at 20X (data not shown). For RNA sequencing, we generated an average of 130 million reads per sample of which ∼88% mapped to the human reference genome (hg19) with only 5% of the reads mapping to multiple loci (data not shown). For each lung sample, we identified both the canonical and non-canonical editing sites by comparing their own DNA and RNA sequences using REDItools as previously described , 29. Furet al., 2012, to reduce false positives. First, we have sequenced RNA and DNA from the same tissues removing the possibility that rare DNA variants are interpreted as editing events. Secondly, we have adopted a strict two-pass alignment strategy adopted as the GATK best practice for calling sequencing variants from RNA (https://www.broadinstitute.org/gatk/guide/article?id=3891). Finally, we have utilized a strict heuristic filter to reduce false positive including coverage of at least 20X, 20% alternate allele frequency, and absence of the site in the DNA sequencing. Identification of a vast majority of canonical A>G editing sites suggests that overall false positive rates are low as reported in a recent paper [At 10X coverage, we identified 5,538 edited sites that were present in all the three lung samples, and of these 2,805 edited sites were A-to-G, 344 were C-to-T and 2,389 were all other types of base substitutions. For more confident filtering of edited sites, we used 20X coverage and 20% alternate RNA allele frequency and identified 3,447 sites that were present in the three human lung samples. These included both canonical editing sites  and all other types  of non-canonical editing sites Figure . We repoin vitro instead of relying completely on computational analysis alone. Similar to human studies, RNA-editing analysis in different tissues of mice identified 242 A-to-G editing events. All other non-canonical editing sites (683) were shown to be an artifact of the sequencing reaction [The profiling of a RNA editome of a lymphoblastoid cell line, which was derived from a single human individual using RNA-seq data, led to the identification of 22,688 editing events. Majority  were A-to-G editing events, and the rest were all other types of non-canonical editing events , RNA-DNAreaction . Taken tDcp2, Pms2, Senp3, and Znf551 resulted in non-synonomous amino acid change.Further analysis of all overlapping canonical  and non-canonical editing  events in lung tissue at 20X coverage and 20% alternate alleles revealed enrichment in the 3′ UTR and introns of genes consistent with previous findings. Conversely, the coding sequences represented only a small fraction of edited sites in the target genes that is consistent with previous reports . In our http://darned.ucc.ie/download) [In the ENCODE project, the A-to-G editing sites in the lymphoblastoid cell line similarly mapped to introns (51%) and 3′UTRs (39%) as observed in our dataset; whereas in the non A-to-G editing sites, there was a 82% enrichment near splicing boundaries . Similarownload) . We founownload) , 34. TakFor the functional characterization of target sites, we focused on A-to-G canonical editing events in lung tissue that are likely to be mediated by ADARs. We mapped the edited sites that overlapped in all three normal lung samples to identify 513 unique genes with at least one edited sites at 20X coverage. We have listed all the genes, the chromosomal region showing the edited sites, type of editing and genomic location in Ctsc, RhoA and Adam19 were visualized by IGV . The edited sites in V Figure . IGV imaV Figure .in vitro, we randomly selected (A-to-G) canonical editing sites comprising both hyper- and single-edited sites in the 3′ UTR regions of genes for Sanger sequencing. For the hyper- or single-edited sites, we PCR amplified 400bp region flanking the edited sites using specific primers tagged with M13 sequence to amplify both genomic DNA and cDNA. Sanger sequencing was performed for each PCR-amplified product (Genomic and cDNA) using both forward and reverse M13 primers for all three lung samples resulting in a total of twelve sequences per transcript. These sequence files (AB-1) were aligned and further analyzed using Sequencher 5.1 software (Gene Code Corporation). We validated several canonical editing sites in all three lung samples in vitro by Sanger sequencing. We show representative images for Ctsc, RhoA and Adam19, respectively . For RhoA, we validated A-to-G editing at a single site . Similarly, for Adam19, we confirmed A-to-G editing at five different sites . For sites that were validated, an “A” (un-edited reference peak) in the genomic trace and two peaks in cDNA trace, namely an “A” (un-edited reference peak) and a “G” (edited peak) were observed in the positive strand  by an in silico approach using miRanda. As an example, we show the A-to-G editing in the miRNA binding sites for two genes Rbbp4 and Spcs3   and 327 (2nd) within the seed sequence  Figure . SimilarIn summary, we have generated an editome of normal lung tissue that defines the RNA variants overlapping in three samples using (exome) DNA- and RNA-sequencing data and REDItools RNA editing analysis. In future studies, we will adapt this methodology to interrogate large datasets of lung tissues to generate a RNA editome of normal and diseased samples. Profiling the RNA editome will assist in understanding the role of RNA editing in both normal and diseased states, and will be a novel approach to understanding the molecular basis of lung pathogenesis.This study was reviewed and approved by the Institutional Review Board (IRB) of the University of South Florida.De-identified normal human lung tissues (N=3) collected from peripheral regions of lungs from deceased individuals, 40-60 yrs of age  were obtained from National Disease Research Interchange (NDRI), Philadelphia, PA. These “normal human lung” tissues refer to the samples obtained from non-diseased individuals.Genomic DNA was extracted from three normal human lung samples using DNeasy kit as per manufacturer's instructions (Qiagen). Total RNA was extracted from three normal human lung samples using Trizol reagent (Life Technologies) combined with RNeasy kit as per manufacturer's instructions (Qiagen). To prevent genomic DNA contamination of RNA, we treated RNA samples to on column DNase digestion according to manufacturer's instructions (Qiagen). Quality control for both genomic DNA and RNA was carried out using Agilent 2100 Bio-analyzer at the Center for Genomic Technology (CGT), John P. Hussman Institute for Human Genomics at the University of Miami Miller School of Medicine.Massively parallel sequencing (whole exome sequencing and RNA sequencing) was performed at the CGT. DNA-seq libraries for three normal human lung samples were prepared with Agilent Xt reagents and captured with the Agilent SureSelect 50MB + UTR exome V5 sample kit. RNA-seq libraries were prepared for three normal human lung samples using the Epicenter Ribozero ScriptSeq-v2 RNA prep kit (Illumina). All samples were sequenced 3 per lane with paired end 100bp reads on a HiSeq2500 (Illumina).DNA reads were processed according to Genome Analysis Toolkit (GATK) best practices. DNA reads were aligned to the human hg19 (GRCh37) genome with bwa and exome enrichment statistics calculated with PICARD. DNA variants were called with the GATK Unified Genotyper for each individual exome sample. Variants were filtered for sites at a minimum depth of 8X and PL score > 100 for alternate alleles. We used ANNOVAR for annotation and included annotation from RefGene, dbSNP, frequency in the Exome Variant Server (EVS), frequency in 1000 Genome project, and frequency in the Exome Aggregation Consortium (ExAC).RNA reads were processed for variant calling according to the GATK best practices including quality and adapter trimming with TrimGalore, alignment in a 2-pass alignment with the STAR aligner to the human genome hg9/GRCh37, and base quality recalibration.http://150.145.82.212/ernesto/reditools/doc/) [We used the REDItools software package to create tables of every potential RNA editing position (ls/doc/) . We requls/doc/) . These cls/doc/) .http://lsresearch.thomsonreuters.com/pages/solutions/1/metacore) and CytoScape BinGo (http://www.psb.ugent.be/cbd/papers/BiNGO/Home.html).For the pathway/network analysis, we first identified all the genes that overlapped in the three normal human lung samples that contained at least one editing site in the 3′ UTR. This resulted in a total of 336 genes. We performed gene enrichment analysis using MetaCore  and random primers as per the manufacturer's instructions (Biorad). PCR was carried out using primers that were designed flanking the editing sites to amplify at least a 400 bp product. Primers were designed such that they could be used on both genomic DNA (gDNA), and spliced cDNA. We also tagged primers with M13 primers at the end so that it could be amenable to Sanger sequencing using M13 primers alone. Amplified PCR products were purified using PCR purification kit (Qiagen) and subjected to Sanger sequencing using M13 primers at both ends using standard protocol at Moffitt Genomics facility. The potential edited sites were considered validated if the cDNA sequence at the edited site contained two peaks, (both a reference peak and an edited peak) but the corresponding gDNA contained only one reference peak. Sites were considered to be un-validated if it contained only a single reference peak in both cDNA and gDNA traces.A list of potential human miRNA target sites were derived from Ensembl (GRCh37) transcripts using miRanda . miRNA tThe RNA-seq data and DNA-seq data are deposited within Short Read Archive (SRA) under accession number SRP061159. This is the site where the FASTQ files will be made publically available.


Potyviridae encompasses ∼30% of plant viruses and is responsible for significant economic losses worldwide. Recently, a small overlapping coding sequence, termed pipo, was found to be conserved in the genomes of all potyvirids. PIPO is expressed as part of a frameshift protein, P3N-PIPO, which is essential for virus cell-to-cell movement. However, the frameshift expression mechanism has hitherto remained unknown. Here, we demonstrate that transcriptional slippage, specific to the viral RNA polymerase, results in a population of transcripts with an additional “A” inserted within a highly conserved GAAAAAA sequence, thus enabling expression of P3N-PIPO. The slippage efficiency is ∼2% in Turnip mosaic virus and slippage is inhibited by mutations in the GAAAAAA sequence. While utilization of transcriptional slippage is well known in negative-sense RNA viruses such as Ebola, mumps and measles, to our knowledge this is the first report of its widespread utilization for gene expression in positive-sense RNA viruses.The family  KA White (August 2015)See also: Potyviridae encompasses around 30% of known plant virus species and causes more than half of viral crop damage worldwide Potyvirus, Rymovirus, Bymovirus, Ipomovirus, Tritimovirus, Macluravirus, Poacevirus and Brambyvirus, with genus Potyvirus containing the most species. Family members have single-stranded monopartite positive-sense RNA genomes except in the genus Bymovirus where the genome is bipartite. The genomic RNA has a covalently linked 5′-terminal protein (VPg) and a 3′ poly(A) tail. Subgenomic transcripts are not produced pipo, that overlaps the P3-encoding region of the polyprotein ORF in the −1/+2 reading frame  to direct a proportion of ribosomes into an alternative reading frame. In eukaryotic systems, efficient −1 PRF normally requires a “slippery” heptanucleotide sequence where the shift in reading frame takes place, and a 3′-adjacent stimulatory element which normally comprises an RNA stem-loop or pseudoknot structure separated from the slippery heptanucleotide by a “spacer” region of 5–9 nt trans-acting factors Potyvirus species have a “G” preceding the conserved GAA_AAA_A sequence , while one might propose that other species use −1 PRF but with little re-pairing in the P site.In general, RNA structures typical of −1 PRF stimulatory elements are not predicted to form at an appropriate spacing downstream of the potyvirus GAA_AAA_A sequence. Nonetheless, this could still be consistent with −1 PRF if only a very low level of frameshifting were required, or if there were atypical stimulatory elements  motif in the negative-sense template and results in the insertion of one or more additional “G”s in the positive-sense (5′-AnGm-3′) mRNA for the phosphoprotein gene. In paramyxoviruses, directionality is provided by the ability of RNA to form G:U pairs, but not A:C pairs. In ebolaviruses, stuttering occurs on a run of 7 “U”s in the negative-sense template to insert one or more additional “A”s in the positive-sense mRNA for the glyco-protein gene. However, transcriptional slippage was not considered an obvious explanation for pipo expression because of the short length of the conserved homopolymeric run (just 6 “A”s), and because the conserved 5′ “G”  appeared to favour nucleotide deletions over insertions, and two deletions (or one insertion) would be required to provide access to the pipo ORF.An alternative explanation is that frameshifting occurs at the transcriptional level. In several single-stranded negative-sense RNA viruses, such as members of the genus pipo expression, we engineered an infectious Turnip mosaic virus  clone to express epitope-tagged P3/P3N-PIPO and used it to assess frameshift-ing efficiency in the natural context of virus infection. We performed mutational analyses of the GAA_AAA_A sequence and investigated the mutant phenotypes. Finally, we performed high-throughput sequencing of RNA derived from virus infections. We found that an extra “A” is inserted into the GA6 sequence in approximately 2% of TuMV transcripts, thus enabling expression of P3N-PIPO. Comparable editing frequencies (0.8–1.3%) were observed for two other potyviruses.To resolve the conundrum of To investigate the potyvirus frameshifting mechanism, we used a GFP-expressing infectious clone of TuMV  Fig. Coat prVirus movement was monitored in inoculated leaves by confocal microscopy Fig. For WT To ensure that the movement-deficient phenotype of P and FSko was not simply an artefact of defective virus replication (with GFP fluorescence observed in single cells resulting from translation of RNA transcribed directly from the plasmid), these mutants were tested for their ability to replicate within cells. Mutants were introduced into plants by agroinfiltration Fig. A repli6 sequence were used to reverse transcribe and amplify a region surrounding the GA6 site, and the resulting cDNAs were subjected to high-throughput sequencing , thus allowing expression of P3N-PIPO at a level of ∼2%, consistent with Western blots of V5-tagged virus , as well as from virions and polysomes. Primers designed to anneal just upstream or downstream of the GAncing Fig. Around N. benthamiana RNA polymerase II from T-DNA, although it is conceivable that some transcripts produced in infiltrated A. tumefaciens cells may also be present. Prior to reverse transcription, samples were excessively treated with DNase and complete elimination of contaminating DNA verified by PCR. Insertion of a single “A” at the GA6 site occurred at a level of 0.05–0.07% in these samples—around 33-fold lower than was seen with WT TuMV. Two host genes containing GA6G sequences (similar to TuMV) were also tested: ubiquitin-conjugating enzyme E2 (Ubi E2) and eukaryotic translation initiation factor 5B (eIF5B). For eIF5B, two distinct GA6 sites were tested. In two of the three cases, insertion of an additional “A” at the GA6 site occurred at a level of 0.05–0.07% (around 33-fold below WT TuMV), while the third site was more slip-prone, with “A” insertions occurring at a level of 0.20–0.25% (around 9-fold below WT TuMV). In addition to testing specificity of higher levels of slippage to the viral polymerase, these experiments also put upper bounds on slippage introduced during reverse transcription. In contrast, the TuMV WT deletion rate (0.13–0.15%) was similar to that of the ΔGDD control (0.12–0.14%), indicating that pipo-site deletions are not specific to the viral polymerase.To test whether transcriptional slippage was specific to the viral polymerase, we also tested RNAs transcribed by host cell RNA polymerase. Samples from leaf patches agroinfiltrated with the ΔGDD TuMV mutant were analysed using the same primers as above. In these samples, TuMV RNA is transcribed by pipo slip site, total RNA from systemically infected leaves and virion-derived RNA were subjected to high-throughput sequencing (ENA databank accession PRJEB9490). Similar to before, single-nucleotide insertions at the GA6 sequence were observed at a level of 1.9 and 2.1% for total and virion RNA, respectively. Elsewhere in the TuMV genome, an average insertion rate of 0.001% per nucleotide was seen with both samples and no other insertion “hotspots” were detected of similar magnitude to the pipo slip site  and Bean common mosaic necrosis virus . In these species, insertion of a single “A” at the GA6 sequence occurred at a level of 1.3% and 0.8%, respectively, and in both an elevated level of deletions was observed in comparison with the TuMV samples. We also analysed Plum pox virus data available in the National Center for Biotechnology Information (NCBI) Short Read Archive (SRA) database . The mean frequency of an “A” insertion at the GA6 site was 0.8%, with frequencies for individual samples ranging from 0.3 to 1.0% (6 site) has been reported for Zucchini yellow mosaic virusTo confirm that transcriptional slippage was not specific to TuMV, we analysed RNA from plants infected with two other potyviruses,  to 1.0% . Similar6 site. Although previous mass spectrometric analysis of cDNAs derived from RNA from TuMV-infected plants failed to detect transcripts with insertions or deletions Together, these results indicate that around 0.8–2% of viral transcripts produced by the potyvirus polymerase allow expression of P3N-PIPO as a consequence of an “A” insertion at the GA6 sequences, selection might act against such sequences spontaneously arising at other locations (in any reading frame). In view of this, we re-ana-lysed all NCBI Potyviridae RefSeqs. Of 123 RefSeqs, only seven lack a GA6 sequence at the 5′ end of the pipo ORF  non-pipo GA6 sequences in the polyprotein ORF . In HCV-1, slippage on an A10 sequence allows expression of the F ORF, which overlaps the polyprotein ORF in the +1 reading frame 10 sequence Paramyxoviridae, Rhabdoviridae and Orthomyxoviridae) for polyadenylation of the mRNA transcripts Transcriptional slippage for gene expression has been reported in several bacterial genes Picornavirus genus constrain a duplex of 7–8 bp Thermus thermophilus dnaX, slippage occurs at an A9 sequence (T9 template) and is ∼50% efficient 8 and A8, only modestly at U7, and perhaps very slightly at U66 and a comparable level (0.8–2.9%) for potyviral RdRps. The role of the conserved 5′ “G” is, however, unclear. Mutation of “G” to “C” or “A” in Soybean mosaic virus did not prevent cell-to-cell movement The ability of RNA polymerases to slip on homopolymeric sequences above a certain length is believed to be linked to the length and stability of the template:nascent RNA duplex within the polymerase 7 or A8 at the slippage site, with A8 dominating in cell culture and A7 dominating in vivoTobacco etch virus, translation through a region near the 3′ end of the polyprotein ORF is required for the genome to be used efficiently as a replication template cis-active replication proteins. Transcripts with one or two insertions or deletions will lead to a frameshift and early termination of translating ribosomes, and therefore, these RNAs will not be replicated.Transcriptional slippage poses a problem for RNA viruses because, unlike cellular organisms, the RNA is also the replicative form. Propagation of edited transcripts might lead to a homopolymeric tract of ever-increasing and variable length. The first line of defence against runaway editing may be simple selection during founder events such as transmission between hosts: infection will only be established if a functional polyprotein can be translated from the input genome(s). Ebolaviruses exist as a mixed population of genomes containing either Ain cisThe observation of transcriptional slippage in potyviruses opens the possibility of transcriptional slippage for gene expression in other positive-sense RNA viruses, particularly those with polyadenylated genomes [whose polymerases may therefore have evolved stuttering mechanisms to maintain poly(A) tail length] and where replication is linked to translation through certain genomic regions that become inaccessible in edited transcripts. Relevant to this, many positive-sense RNA virus genomes are preferentially replicated While this manuscript was under review, related studies were published by García and colleagues Further details are given in TuMV-GFP  and reads less abundant than 0.01% of the most abundant read were excluded. Insertions and deletions were quantified using custom scripts and manually verified. For whole-genome sequencing, libraries were prepared using the TruSeq Stranded mRNA Library Prep Kit (Illumina), and reads were processed using the FASTX Toolkit and mapped to the TuMV genome with BWA.Whole-genome sequencing data are available in the ENA databank under study Accession Number PRJEB9490.


Neuronal ELAV-like (nELAVL) RNA binding proteins have been linked to numerous neurological disorders. We performed crosslinking-immunoprecipitation and RNAseq on human brain, and identified nELAVL binding sites on 8681 transcripts. Using knockout mice and RNAi in human neuroblastoma cells, we showed that nELAVL intronic and 3' UTR binding regulates human RNA splicing and abundance. We validated hundreds of nELAVL targets among which were important neuronal and disease-associated transcripts, including Alzheimer's disease (AD) transcripts. We therefore investigated RNA regulation in AD brain, and observed differential splicing of 150 transcripts, which in some cases correlated with differential nELAVL binding. Unexpectedly, the most significant change of nELAVL binding was evident on non-coding Y RNAs. nELAVL/Y RNA complexes were specifically remodeled in AD and after acute UV stress in neuroblastoma cells. We propose that the increased nELAVL/Y RNA association during stress may lead to nELAVL sequestration, redistribution of nELAVL target binding, and altered neuronal RNA splicing.DOI:http://dx.doi.org/10.7554/eLife.10421.001 When a gene is active, its DNA is copied into a molecule of RNA. This molecule then undergoes a process called splicing which removes certain segments, and the resulting ‘messenger RNA’ molecule is then translated into protein. Many messenger RNAs go through alternative splicing, whereby different segments can be included or excluded from the final molecule. This allows more than one type of protein to be produced from a single gene.Specialized RNA binding proteins associate with messenger RNAs and regulate not only their splicing, but also their abundance and location within the cell. These activities are crucially important in the brain where forming memories and learning new skills requires thousands of proteins to be made rapidly. Many members of a family of RNA binding proteins called ELAV-like proteins are unique to neurons. These proteins have also been associated with conditions such as Alzheimer’s disease, but it was not known which messenger RNAs were the targets of these proteins in the human brain.Scheckel, Drapeau et al. have now addressed this question and used a method termed 'CLIP' to identify thousands of messenger RNAs that directly bind to neuronal ELAV-like proteins in the human brain. Many of these messenger RNAs coded for proteins that are important for the health of neurons, and neuronal ELAV-like proteins were shown to regulate both the alternative splicing and the abundance of these messenger RNAs.The regulation of RNA molecules in post-mortem brain samples of people with or without Alzheimer’s disease was then compared. Scheckel, Drapeau et al. unexpectedly observed that, in the Alzheimer’s disease patients, the neuronal ELAV-like proteins were very often associated with a class of RNA molecules known as Y RNAs. These RNA molecules do not code for proteins, and are therefore classified as non-coding RNA. Moreover, massive shifts in the binding of ELAV-like proteins onto Y RNAs were observed in neurons grown in the laboratory that had been briefly stressed by exposure to ultraviolet radiation.Scheckel, Drapeau et al. suggest that the strong tendency of neuronal ELAV-like proteins to bind to Y RNAs in conditions of short- or long-term stress, including Alzheimer’s disease, might prevent these proteins from associating with their normal messenger RNA targets. This was supported by finding that some messenger RNAs targeted by neuronal ELAV-like proteins showed altered regulation after stress. Such changes to the normal regulation of these messenger RNAs could have a large impact on the proteins that are produced from them.Together, these findings link Y RNAs to both neuronal stress and Alzheimer’s disease, and suggest a new way that a cell can alter which messenger RNAs are expressed in response to changes in its environment. The next step is to explore what causes the shift in neuronal ELAV-like protein binding from messenger RNAs to Y RNAs and how it might contribute to disease.DOI:http://dx.doi.org/10.7554/eLife.10421.002 RNA binding proteins (RBPs) associate with RNAs throughout their life cycle, regulating all aspects of RNA metabolism and function. More than 800 RBPs have been described in human cells . The uniFUS, TDP-43 and ATXN2 mutations have been found in familial amyotrophic lateral sclerosis patients  and Parkinson’s Disease (PD)  , STEX haerosis 4 , and spis 4  and NOVA RBPs are targeted by the immune system in paraneoplastic neurodegenerative disorders . Mammaliectively .Elavl3/4 KO mice, demonstrated that nELAVL directly regulates neuronal mRNA abundance and alternative splicing by binding to U-rich elements with interspersed purine residues in 3’UTRs and introns in mouse brain  that were significant (p<0.01) and that were present in at least five out of 8 individuals . nELAVL binding at these sites correlated between individuals , and theTo relate nELAVL binding to mRNA abundance, we performed RNAseq on the same brain samples used for CLIP analysis . 74,423 To identify genes most likely to be impacted by nELAVL, we defined the top 1000 nELAVL targets  between wildtype and Elavl3/4 KO mice , while the ubiquitously expressed paralog ELAVL1 was not affected by nELAVL RNAi depletion.To further validate potential nELAVL targets, we analyzed the effect of nELAVL depletion on mRNA abundance and splicing in human neuroblastoma IMR-32 cells. We subjected IMR-32 cells to mock or proteins . These cproteins . The steed cells , with ~4nELAVL RNAi, specifically examining 3’UTR targets that did not display any intronic binding. These transcripts were less abundant in nELAVL RNAi conditions , and were used for subsequent analyses. Since nELAVL binding to 3’UTRs can mediate mRNA stabilization, we investigated the change in mRNA abundance of 3’UTR targets upon AVL RNAi .nELAVL RNAi, 743 genes were expressed in human brain, 327 of which decreased while 416 increased. We investigated which of these transcripts were direct targets of nELAVL based on nELAVL 3’UTR binding , and that 16% of downregulated transcripts were even among top 3’UTR targets . In contrast, only 7% of upregulated transcripts were among top 3’UTR targets , further supporting a role of nELAVL 3’UTR binding in positively regulating mRNA abundance. Several 3’UTR targets showed an mRNA abundance change in both mouse and IMR-32 datasets (n = 8), and in all but one case this change correlated positively between the datasets, providing support for the accuracy of target validation applied here. Because the abundance of multiple disease associated genes, including APPBP2, ATXN3, and SHANK2  . We foun-14; hypergeometric test; BIN1 and PTPRD, have previously been linked to AD  occurred on a specific class of non-coding RNAs, Y RNAs . Y RNAs Surprisingly, we observed nELAVL binding to a total of 320 Y RNAs, although Y RNA copies other than the canonical four Y1/3/4/5 genes had previously been considered to be non-functional and were labeled 'pseudogenes' . We founWe next explored nELAVL/Y RNA binding in AD brain. We observed a drastic increase in nELAVL/Y RNA association in AD subjects , while YnELAVL by RNAi three days prior to the UV exposure, and analyzed Y RNA levels by RNAseq. Y RNA abundance was not affected by nELAVL depletion in UV stressed IMR-32 cells  discarded numerous reads that were assigned to multiple Y RNAs. We therefore re-mapped CLIP tags, allowing multiple alignments, but reporting only the best match, permitting a more accurate estimate of overall Y RNA binding. The fraction of the short CLIP reads mapping to Y RNAs was considerably higher using this strategy, revealing that up to 6% of nELAVL CLIP tags map to Y RNAs in AD and UV stressed cells, compared to less than 0.5% in control brain and ~1% in non-stressed cells . The signELAVL depletion by RNAi. Of note is our observation that nELAVL binding decreased at only a subset of intronic binding sites.To investigate if the increased nELAVL/Y RNA association was linked to decreased intronic and 3’UTR nELAVL binding, we grouped AD subjects based on their Y RNA association and compared the two different AD groups to control subjects. We found that the majority of changing nELAVL binding sites decreased in AD subjects with high nELAVL/Y RNA, while nELAVL binding in AD subjects with low nELAVL/Y RNA association was mostly increasing . BecausenELAVL RNAi treated IMR-32 cells to UV stress and analyzing these cells by RNAseq. nELAVL mRNA and proteins levels decreased upon nELAVL RNAi in non-stressed and UV stressed cells but were not affected by UV stress , and found a significant overlap between the two lists  to intronic nELAVL binding sites in human brain, and observed that 7 alternatively spliced exons were indeed next to intronic nELAVL binding sites . RemarkaTo directly test the model of Y RNA mediated nELAVL sequestration, we overexpressed canonical Y3wt (wild type) and Y3mut (with a mutated nELAVL binding site) using lentiviral infections of IMR-32 cells. We initially confirmed the overexpression of the infected Y RNAs using qPCR, which was more pronounced in the Y3mut infected cells . The dis-175; hypergeometric test). To investigate virus-independent changes, we focused on the changes between Y3wt and Y3mut infection. nELAVL 3’UTR targets were not enriched among mRNAs that changed between Y3mut compared to Y3wt infected cells . Most of the observed splicing changes are therefore likely to be specific to Y RNA overexpression. Importantly, we observed an enrichment of nELAVL bound exons and of pression , 5. The nELAVL proteins are abundant neuron-specific RNA binding proteins which have been suggested to regulate various neurological processes and have been linked to neurodegenerative disorders including AD and PD. Yet the RNA targets of nELAVL in human brain were completely unknown. Here, we generated a comprehensive genome-wide RNA binding map of nELAVL in human brain, identifying 75,592 significant binding events within 8681 transcripts. We observed a significant overlap between these binding sites and disease-associated 3’UTR SNPs, and the potential disruption of nELAVL-mediated RNA regulation at these sites might contribute to disease manifestation. Most deleterious variants to date have been identified by exome sequencing while as many as 50% of disease-causing mutations are thought to affect splicing . With whElavl3/4 KO mice and nELAVL RNAi depletion in neuroblastoma cells. Due to the incomplete RNAi depletion of nELAVL in neuroblastoma cells, and potential differences in mRNA abundance and therefore nELAVL binding between the different samples, it is likely that we validated only a fraction of nELAVL-regulated transcripts. Despite these technical limitations we demonstrated that nELAVL impacts mRNA abundance and/or splicing of hundreds of targets. Among the nELAVL regulated transcripts were many transcripts implicated in human disease, including AD, which led us to investigate RNA regulation in AD subjects. Due to the relatively small sample size and the heterogeneity between these samples, likely due to both differences between individuals and sample preservation during postmortem collection, we did not detect many reproducible changes in mRNA abundance or nELAVL binding between AD and non-AD subjects. However, we found that 150 transcripts were differentially spliced in AD subjects, which in some cases coincided with differential nELAVL binding. Unexpectedly, the most significant binding change in AD was a dramatic increase in nELAVL binding to a class of non-coding RNAs, termed Y RNAs. This change was evident on a specific subset of Y RNAs harboring the nELAVL binding site. nELAVL/Y RNA binding also increased during UV stress in human neuroblastoma cells, while the abundance of Y RNAs remained constant in AD subjects and upon UV exposure. The increased nELAVL/Y RNA association correlated with decreased nELAVL binding at a subset of intronic binding sites, and was associated with similar splicing changes as induced by nELAVL depletion, suggesting that nELAVL/Y RNP remodeling during acute and chronic stress sequesters nELAVL from its mRNA targets. We provided further evidence for a Y RNA dependent nELAVL sequestration by overexpressing Y3 RNAs harboring either a wild type or mutated nELAVL binding site. Exons that were differentially spliced upon Y RNA overexpression were enriched for nELAVL bound exons, indicating nELAVL sequestration, which was dependent on an intact nELAVL binding site in the Y RNA.To analyze the functional consequences of nELAVL binding, we used two different loss-of-function models: nELAVL 3’UTR binding has been implicated in increasing mRNA abundance in vivo . We descnELAVL has also been shown to regulate splicing in mouse brain by binding to intronic sequences . We obseSplicing defects have been associated with many neurological diseases , and amoWhile Y RNAs have not been linked to AD before, they have been implicated in various types of stress responses. The RNA binding protein RO60 usually associates with Y RNAs and is required for their stabilization . BesidesIn addition to the four canonical human Y RNAs, hY1/3/4/5, hundreds of additional Y RNA genes are distributed throughout the human genome . The appTaken together, our data indicate that nELAVL becomes strongly associated with Y RNAs in some AD subjects as well as in cells subjected to UV stress, and this is linked to a sequestration of nELAVL from some of its intronic targets, partially recapitulating splicing changes induced by nELAVL depletion. Our results are consistent with a hypothesis that a relatively subtle and perhaps long-term effect of Y RNA binding on normal nELAVL stoichiometry may underlie subtle and long-term changes in nELAVL biology. Perhaps analogously, the sequestration of the RBP, TDP-43, has previously been linked to neurodegenerative disorders . While tIn conclusion, we have determined a robust and reproducible map of nELAVL binding sites on both mRNA and Y RNA targets in human brain. This database has both common and human-specific features that confirm and enhance previous work in mice and underscore its value as a resource for scientific inquiry. We have linked the data to genome-wide measurements of both mRNA levels and splicing suggesting specific functions for these important interactions. Moreover, we have uncovered a stress-modulated interaction of nELAVL proteins with non-coding Y RNAs. Y RNPs are remodeled during both UV-induced and chronic AD-related stress, which may be causally related to the pathophysiology of stress by causing a redistribution of nELAVL RNA target binding. Data implicating nELAVL proteins in several neurologic diseases, as well as the overlap of nELAVL binding sites we describe here with SNPs linked to the same and additional human diseases underscore the importance of this resource for the ongoing study of the molecular basis of human neurologic disease.Frozen brain tissue was obtained from the Mount Sinai Brain Bank. Subjects were classified according to CERAD criteria. Summaries of cognitive performance , neurofibrillary tangles  and plaque pathology (assessed by plaque counts) are shown in 2) with 15 cm distance from the UV source using a Stratalinker. Cells were allowed to recover for 24 hr in fresh media before being harvested. For RNAseq, cells were washed in 5 ml PBS, and frozen in 1 ml Trizol (Invitrogen) at -80°C. For CLIP, cells were washed in 5 ml PBS and irradiated at 400 mJ/cm2 (see above). Cell pellets were collected by centrifugation at 2500 rpm for 3 min at 4°C and frozen at -80°C.Human HEK293T and neuroblastoma IMR-32 cells were obtained from ATCC  and maintained in DMEM , containing 10% (v/v) fetal bovine serum , and 100 unit/ml penicillin/streptomycin . 0.25% trypsin and 1% EDTA (Invitrogen) were used to passage cells every three days. Prior to UV treatment, cells were transferred to poly-D-lysine-coated 10-cm culture dishes  and allowed to adhere for at least 24 hr. Cells were washed in PBS (Invitrogen), layered with 5 ml PBS, and exposed to UVC  and allowed to adhere for at least 24 hr. Accell Non-targeting pool (Cat#D-001910-10-20), and a mixture of Accell siRNA pools against Elavl2/3 and 4  from Dharmacon  were added to Accell siRNA Delivery Media (Cat#B-001910-10), supplemented with 2% FBS for a final concentration of 1 µM per pool. Growth medium was removed and 1 ml Accell siRNA and media mixture was added. The Accell siRNA and media mixture was replaced with growth medium after 48h, cells were UV treated at 72 hr , and cells were harvested at 96 hr for RNAseq analysis. Cells were washed in 1 ml PBS and frozen in 1 ml Trizol (75%) or lysed (25%) in 50 µl CLIP Wash Buffer 1 (see below).For AgeI and EcoRI into the transfer plasmid plKO.1  were cloned with GCTGGTCCGAGTGCAGTGGTGTTTACAACTAATTGATCACAACCAGTTACAGATTTCTTTGTTCCTTCTCCACTCCCACTGCTTCACTTGACTAGCCTTTTCTACAACTAATTGATCACAACCAGTTACAGATCTCTCCGTTCCTTCTCCACTCCCACTGCTTCACTTGACTAGCCTTTTGCTGGTCCGAGTGCAGTGGTGTg for 5 min at 4°C. Cells were lysed in 500 µl buffer A , and incubated on ice for 10 min. Cytoplasm and nuclei were separated with a 10 min spin at 3000 g at 4°C, and both fractions were washed in 500 µl buffer A before an additional 10 min spin at 3000 g at 4°C. Nuclei were resuspended in 500 µl buffer A and sonicated 3x for 5 s. RNA was Trizol (Life Technologies) extracted from 100 µl lysate and reverse transcribed using iScript . qPCR was performed with FastStart SYBR Green Master , requiring at least one primer in each mRNA primer pair to be specific for an exon junction. qPCR results were normalized as indicated. Western blot analysis was performed using 15 µl cell lysate per lane, and the following antibodies: α-GAPDH , Hu subject antiserum (1:1000 dilution), α-HSP90 , α-H3 , α-RNA PolII , and α-RO60 . Immunoreactive bands were analyzed with the Odyssey Infrared Imaging System  and normalized as indicated.Cells grown in 10-cm culture dishes were scraped in 1 ml PBS after media removal and spun at 500 nELAVL HITS CLIP was performed as previously described  with theWash buffer 1: 1xPBS; 0.1%SDS; 0.5%Na-DOC; 0.5% NP-40Wash buffer 2: 5xPBS; 0.1%SDS; 0.5%NA-DOC; 0.5% NP-40Wash buffer 3: 15 mM Tris, pH7.4; 5 mM EDTA; 2.5 mM EGTA; 1% Triton X-100; 1% Na-DOC; 0.1% SDS; 120 mM NaCl; 25 mM KClWash buffer 4: 15 mM Tris, pH7.4; 5 mM EDTA; 2.5 mM EGTA; 1% Triton X-100; 1% Na-DOC; 0.1% SDS; 1 mM NaClWash buffer 5: 15 mM Tris, pH7.4; 5 mM EDTA2; 0.5% NP-40Wash buffer 6: 50 mM Tris, pH7.4; 10 mM MgCl3’ RNA linker: 5’P – GUG UCA GUC ACU UCC AGC GG 3’ – puromycin5’ RNA linker: 5’OH – AGG GAG GAC GAU GCG GNN NNG 3’ – OH5’ RNA linker indexed: 5’OH – AGG GAG GAC GAU GCG GXX NNN NG 3’ – OHDP5: 5’ – AGG GAG GAC GAT GCG G – 3’DP3: 5’ – CCG CTG GAA GTG ACT GAC AC – 3’DSFP5: AATGATACGGCGACCACCGACTATGGATACTTAGTCAGGGAGGACGATGCGGDSFP3: CAAGCAGAAGACGGCATACGACCGCTGGAAGTGACTGACACSP5-PE: AATGATACGGCGACCACCGAGATCTACACCTATGGATACTTAGTCAGGGAGGACGATGCGGSP3-PE:CAAGCAGAAGACGGCATACGAGATCTCGGCATTCCTGCCGCTGGAAGTGACTGACACMSFP3: CAAGCAGAAGACGGCATACGAGATCCGCTGGAAGTGACTGACACMSFP5: AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCTXXXXAGGGAGGACGATGCGGSSP1: CTATGGATACTTAGTCAGGGAGGACGATGCGGhttp://main.g2.bx.psu.edu/), in addition to publicly available in-house tools. Data was visualized with UCSC genome browser (http://genome.ucsc.edu/). To reduce mis-alignments due to sequencing errors, reads were initially filtered based on quality score . Exact sequences were collapsed to remove PCR duplicates. The degenerate barcode (and index if applicable) were removed and the 3’ linker was trimmed. Using FASTA files as input, reads were subsequently mapped to the hg18 build of the human genome by novoalign (www.novocraft.com), requiring unambiguous mapping and a maximum of two mismatches. To identify unique CLIP tags, we applied stringent filtering, and collapsed reads with the same starting genomic positions -extracted, Ribo-Zero-selected , DNase-treated (Roche), and prepared for sequencing, following the Illumina High-throughput TruSeq RNA Sample Preparation Guidelines. The libraries from subjects 6–8 and 15–17, as well Y RNA overexpression samples, were sequenced on an Illumina HiSeq 2500 at the New York Genome Center, generating 125-bp paired end reads. The remaining libraries were sequenced on an Illumina HiSeq 2000 at the Rockefeller University Genomics Resource Center, yielding 100-bp paired end reads. Reads were aligned to the hg18 build of the human genome using TopHat, allowing a maximum of two mismatches. Only unambiguously mapped reads were kept for analysis. Additional exon junctions not observed due to the gap between paired end mates were inferred using a Bayesian method, and a set of non-redundant constitutive exons with relative high inclusion rate (according to ESTs) was used to estimate gene expression . Exon anwww.r-project.org; [CLIP and RNAseq data were deposited in the GEO database with accession number GSE53699. Analyses of unique CLIP tags and RNAseq data were carried out in R  using Bect.org; ) to corrY RNAs were derived from GENCODE (v19 release). nELAVL binding on Y RNAs was defined as CLIP tags on Y RNAs. To discriminate between individual Y RNA copies we used CLIP reads, aligned with novoalign. While this mapping strategy allowed us to discrimate between individual Y RNA copies with high confidence, CLIP reads that mapped to multiple Y RNAs were discarded. Exclusively for The MEME-ChIP Suite  and HOMEhttp://www.yworks.com/en/products_yed_about.html). In a second network, six AD genes  were concatenated with the input seed gene lists to investigate their relationship with nELAVL target genes. The composition and the clustering of the PPI networks with and without those six genes were identical. Only the network including those additional seeds is shown.The 1000 nELAVL top target gene products were seeded in a large protein-protein interaction (PPI) network containing 14,191 genes and more than 197,000 interactions  compiledThe clusters of nELAVL target genes in the PPI subnetwork represent protein clusters based on direct physical interactions. Gene products in clusters with at least 10 nodes were examined for an enrichment of functional annotations with Enrichr  using th In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.eLife. Your submission has been favorably evaluated by a Senior Editor, and two reviewers, one of whom, Douglas Black, is a member of our Board of Reviewing Editors.Thank you for submitting your work entitled "Regulatory consequences of nELAVL binding to coding and non-coding RNAs in human brain" for peer review at The reviewers have discussed the reviews with one another and the Reviewing editor has drafted this decision to help you prepare a revised submission.This paper from the Darnell group examines the RNA binding sites of the nELAVL family in human brain. The authors perform CLIP analysis and RNAseq in the BA9 region of the prefrontal cortex. They identify a large set of RNA binding sites and presumed target transcripts. They define the nELAVL binding motif as similar to the previously defined motif for the mouse and show that there is strong overlap between the targets seen in mouse and human. They show that nELAVL knockdown in human neuroblastoma cells causes reduced expression of mRNAs that exhibit 3' UTR binding. Similarly, exons with adjacent nELAVL binding show altered splicing with knockdown. Interestingly, both positively and negatively regulated exons exhibit enriched binding upstream, and thus do not present the same correlation of binding position with positive or negative regulation as seen with some other splicing regulators. Earlier results had made a connection of nELAVL proteins and Alzheimer's disease, and the authors next analyzed splicing and nELAVL binding in AD brains, finding some exons and binding events that differ between AD and control brains. The most significant change was a greatly increased level of nELAVL binding to a subset of Y RNAs in the AD brains. Y RNAs are a class of small noncoding RNAs in eukaryotic cells that affect RNA scavenging and quality control pathways. A subset of these RNAs contain an nELAVL binding site. Cellular stress is known to relocalize ELAV proteins to the cytoplasm, and they find that stressing tissue culture cells with low UV doses also increases nELAV/Y RNA binding as measured by CLIP. This is accompanied by changes in nELAV dependent splicing, presumably due to reduced nuclear protein.This is a novel study of a topic with broad interest. The nELAV proteins have been widely studied. The extension of these analyses to the human system and to AD is significant, with the connection of nELAVL proteins to the Y RNAs being potentially very important. The authors present a large amount of work and the datasets generated will be a valuable resource for further studies. However, the analysis does not extend past identifying broad correlations between datasets, leaving the biological or mechanistic conclusions unclear. A number of unaddressed issues regarding the identified nELAV/Y RNA interaction make its significance hard to judge. The paper is also difficult to digest. It is hard to follow what was actually done in the various statistical comparisons, what the results mean, and the authors conclusions often seem vague. They often point to rather weak correlations as being "consistent" with a particular model. Sometimes they use abbreviations and acronyms that don't seem to have been defined . There are a number of places where the analytical methods used could be improved or at least better explained.Major issue:1) The most significant finding in the study is the binding of nELAVL to the Y RNAs, but this is not taken far enough to draw many conclusions. Is it the increased Y RNA binding that is causing the change in splicing or is it a consequence of increased cytoplasmic protein that is the result of cellular stress? How much Y RNA is there relative to nELAVL? What percent of the nELAVL is actually bound by the Y RNAs? Are the amounts sufficient to sequester most of the ELAVL and thus have an effect? Why do so many Y RNAs without ELAVL binding motifs also appear to be binding? Can the authors connect the splicing or expression changes observed in the UV treated IMR32 cells to those seen in AD? Without this, it is difficult to make the case that the cytoplasmic relocalization of the nELAVL proteins is relevant to AD. The reviewers all felt that the authors should work to make some of these mechanistic connections between the Y RNAs and AD and thus strengthen this most interesting finding.Issues regarding the analytical methods:1) The peak finding in the CLIP analysis needs to be better described. The authors refer to Licatalosi (2012), but that paper describes a relatively outmoded method of defining peaks above a gene specific background, as well as the use of the much better CIMS analysis from the Darnell and Zhang groups. It doesn't appear that CIMS analysis was used here, so what exactly was done?2) Similarly, MEME-ChIP has been shown to not work well for RBP motif finding. Stating that the motif identified in human is in excellent agreement with that found in mouse is not very meaningful since both studies used the same MEME-ChIP analysis. The authors should investigate the use of newer tools such as Graphprot  or Zagros  for motif discovery.3) In 4) There are also normalization questions regarding 5) The authors use the human genome release hg18 for their alignments, although the conclusions do not likely depend on the genome release. Most people in the field are using the now 6-year-old hg19 and it would make it much easier for others to use these results if the authors were to update their pipelines to hg19. This paper from the Darnell group examines the RNA binding sites of the nELAVL family in human brain. The authors perform CLIP analysis and RNAseq in the BA9 region of the prefrontal cortex. They identify a large set of RNA binding sites and presumed target transcripts. They define the nELAVL binding motif as similar to the previously defined motif for the mouse and show that there is strong overlap between the targets seen in mouse and human. They show that nELAVL knockdown in human neuroblastoma cells causes reduced expression of mRNAs that exhibit 3' UTR binding. Similarly, exons with adjacent nELAVL binding show altered splicing with knockdown. Interestingly, both positively and negatively regulated exons exhibit enriched binding upstream, and thus do not present the same correlation of binding position with positive or negative regulation as seen with some other splicing regulators. Earlier results had made a connection of nELAVL proteins and Alzheimer's disease, and the authors next analyzed splicing and nELAVL binding in AD brains, finding some exons and binding events that differ between AD and control brains. The most significant change was a greatly increased level of nELAVL binding to a subset of Y RNAs in the AD brains. Y RNAs are a class of small noncoding RNAs in eukaryotic cells that affect RNA scavenging and quality control pathways. A subset of these RNAs contain an nELAVL binding site. Cellular stress is known to relocalize ELAV proteins to the cytoplasm, and they find that stressing tissue culture cells with low UV doses also increases nELAV/Y RNA binding as measured by CLIP. This is accompanied by changes in nELAV dependent splicing, presumably due to reduced nuclear protein. This is a novel study of a topic with broad interest. The nELAV proteins have been widely studied. The extension of these analyses to the human system and to AD is significant, with the connection of nELAVL proteins to the Y RNAs being potentially very important. The authors present a large amount of work and the datasets generated will be a valuable resource for further studies. However, the analysis does not extend past identifying broad correlations between datasets, leaving the biological or mechanistic conclusions unclear. A number of unaddressed issues regarding the identified nELAV/Y RNA interaction make its significance hard to judge. The paper is also difficult to digest. It is hard to follow what was actually done in the various statistical comparisons, what the results mean, and the authors conclusions often seem vague. They often point to rather weak correlations as being "consistent" with a particular model. Sometimes they use abbreviations and acronyms that don't seem to have been defined . There are a number of places where the analytical methods used could be improved or at least better explained.We have streamlined our analysis, added more details and explanation on comparisons and abbreviations in the manuscript, and included more information on the analysis in the Methods. We hope these changes will make the manuscript easier to follow.Major issue:1) The most significant finding in the study is the binding of nELAVL to the Y RNAs, but this is not taken far enough to draw many conclusions. Is it the increased Y RNA binding that is causing the change in splicing or is it a consequence of increased cytoplasmic protein that is the result of cellular stress?The reviewers suggest two possibilities to account for the change in splicing upon stress. We addressed the possibility that a decrease in nuclear nELAVL might lead to the splicing changes in AD and upon UV stress. To address this point, we have added cell fractionation experiments. To address the reviewers’ second possibility, we have included new data to examine the role of Y RNA induced splicing changes (see below).How much Y RNA is there relative to nELAVL? What percent of the nELAVL is actually bound by the Y RNAs? Are the amounts sufficient to sequester most of the ELAVL and thus have an effect?To address this point, we have estimated how much of the total amount of nELAVL is bound by Y RNAs under control versus stress conditions. Each Y RNA family (Y1/Y3/Y4/Y5) consists of multiple variants expressed from different genomic loci. These copies are relatively short and very similar, yet too different to be detected by one Taqman probe or one qPCR primer set. This makes absolute quantification of Y RNA levels difficult, and thus prevented us from quantifying the exact ratio of Y RNAs to nELAVL. Nonetheless, we were able to estimate that up to 6% of nELAVL was bound by Y RNAs, which is shown in Why do so many Y RNAs without ELAVL binding motifs also appear to be binding?We used a stringent definition of the nELAVL binding motif when searching Y RNA sequences . Y RNAs that are bound by nELAVL and don’t have a motif, have a more degenerate version of the motif.A total of 320 Y RNAs are bound by nELAVL, and we found the stringent definition of the nELAVL binding motif in 202 of these Y RNAs. We examined the 118 Y RNAs that did not fit this consensus in more detail. 91 (77%) of these Y RNAs contained the motif with an A or C instead of a G, or a 5mer version of the motif. The remaining 27 Y RNAs harbored UG rich stretches. We have added these points to the current version of the manuscript (subsection “Non-coding Y RNAs are bound by nELAVL in AD”).Can the authors connect the splicing or expression changes observed in the UV treated IMR32 cells to those seen in AD? Without this, it is difficult to make the case that the cytoplasmic relocalization of the nELAVL proteins is relevant to AD.The splicing changes in UV treated IMR-32 cells do not significantly overlap with the splicing changes observed in AD (subsection “nELAVL/Y RNA association correlates with loss of nELAVL-mediated splicing”). This is certainly in part due to the wide difference between the two systems that were analyzed. As noted above, the splicing changes do not seem to relate to changes in the cytoplasmic fraction of nELAVL proteins. We recognize that increased nELAVL/Y RNA association may lead to nELAVL sequestration within cellular compartments in human brain or IMR-32 cells, but we were not able to generate high quality data to address this issue. We recognize the limitations and potential interest of these observations for future studies and have added this to our manuscript (subsection “Y RNPs are remodeled during UV stress”).The reviewers all felt that the authors should work to make some of these mechanistic connections between the Y RNAs and AD and thus strengthen this most interesting finding.To prove that the increased nELAVL/Y RNA association in AD indeed sequesters nELAVL, we would have to manipulate Y RNA levels in AD patients. As this is impossible, we sought to manipulate Y RNA levels in IMR-32 cells. Since the expressed Y RNA copies are too diverse to be collectively targeted by, for example, shRNAs, we instead overexpressed Y RNAs to strengthen the mechanistic connection between Y RNAs and nELAVL sequestration. We infected IMR-32 cells with lentivirus containing Y3wt RNA (contains an nELAVL binding site) and Y3mut RNA (with a mutated nELAVL binding site), and analyzed changes in RNA regulation upon Y RNA infection. Because of similarities between different Y RNAs, we quantified the overexpression in several different ways, shown in Importantly, the differentially spliced exons upon Y3wt expression were enriched for exons with adjacent intronic nELAVL binding sites, and this enrichment was dependent on the presence of the nELAVL-binding site in the Y RNA . These rWe believe that the clarifications added to the manuscript and the addition of cell fractionation experiments, as well as an entire dataset describing the consequences of Y RNA overexpression, address the reviewers’ concerns and strengthen our conclusions.Issues regarding the analytical methods:1) The peak finding in the CLIP analysis needs to be better described. The authors refer to Licatalosi (2012), but that paper describes a relatively outmoded method of defining peaks above a gene specific background, as well as the use of the much better CIMS analysis from the Darnell and Zhang groups. It doesn't appear that CIMS analysis was used here, so what exactly was done?While the analysis of CIMS (crosslinked-induced mutations) is indeed a further development of the initial peak analysis, CIMS analysis does not replace peak analysis. While CIMS analysis provides nucleotide resolution of RNA protein interactions, CIMS are only found in a small minority of peaks. To provide a more complete picture of nELAVL binding sites in the brain, we investigated nELAVL binding via peak analysis. We have added more information on the peak analysis in the Methods section.2) Similarly, MEME-ChIP has been shown to not work well for RBP motif finding. Stating that the motif identified in human is in excellent agreement with that found in mouse is not very meaningful since both studies used the same MEME-ChIP analysis. The authors should investigate the use of newer tools such as Graphprot  or Zagros  for motif discovery.http://homer.salk.edu/homer/motif/), and used this to validate the motif identified with MEME-ChIP. The newly identified motif was very similar to the previously identified motif and is shown in We agree with the reviewers that a motif agreement solely based on MEME-ChIP would not be very meaningful. However, the motif identified in mice was verified via an independent method, in vitro binding assays . We additionally followed the reviewers’ suggestion and compared a number of different tools, including the ones the reviewers suggested. We found the best independent tool to be HOMER (3) In No, the peak numbers in 4) There are also normalization questions regarding We thank the reviewers for suggesting this way of normalization. We have now normalized each binding site for sequence density (rpkm), before summarizing binding sites per transcript (or per 3’UTR or introns). We have therefore redefined top targets (about two third of the top targets overlapped with previously defined top targets), as well as top 3’UTR in the manuscript.5) The authors use the human genome release hg18 for their alignments, although the conclusions do not likely depend on the genome release. Most people in the field are using the now 6-year-old hg19 and it would make it much easier for others to use these results if the authors were to update their pipelines to hg19.We agree with the reviewers that the use of the hg19 build would be more appropriate at this point. To address this point and make our datasets more useful to a wider audience, we added hg19 coordinates of all supplied genomic coordinates using the UCSC liftover tool. We hope the reviewers will agree with us that the amount of time it would take us to remap the large number of datasets, and re-do the entire analysis as well as all of the tables and figures, is not justified by the relatively minor improvement in data usage compared with the liftover we included in this revised version. We would also like to emphasize that all raw sequences have been deposited at GEO for future studies.


Here we present an optimized pipeline for extraction of high quality mRNA from stool samples.The complex microbiome of the gut has an enormous impact on human health. Analysis of the transcriptional activity of microorganisms through mRNA sequencing (metatranscriptomics) opens a completely new window into their activity E.coli cells expressing fluorescing proteins were 8.7-9.7 % for SuperfolderGFP and 14.7-17.8 % for mCherry. The mRNA of stabilized stool samples as well as of snap-frozen controls was sequenced with Illumina Hiseq, yielding on average 74 million reads per sample. PCoA analysis, taxonomic classification using Kraken and functional classification using bwa showed that the transcriptomes of samples conserved in RNA Later were unchanged for up to 6 days even at room temperature, while RNA Protect was inefficient for storage durations exceeding 24 h. However, our data indicate that RNA Later introduces a bias which is then maintained throughout storage, while RNA Protect conserved samples are initially more similar to the snap frozen controls. RNA Later conserved samples had a reduced abundance of e.g. Prevotellaceae transcripts and were depleted for e.g. COG category “Carbohydrate transport and metabolism”.Comparison of three commercially available RNA extraction kits with the method of Zoetendal revealed that the Powermicrobiome Kit (MoBio) performed best with respect to RNA yield and purity. Next, the influence of the stabilization reagent during sample storage for up to 15 days was studied. RIN analysis and qRT-PCR of spiked-in and indigenous genes revealed that RNA Later preserved mRNA integrity most efficiently, while samples conserved in RNA Protect showed substantial mRNA decay. Using the optimized pipeline developed here, recovery rates for spiked-in Since the overall similarity between all stool transcriptional profiles studied here was >0.92, these differences are unlikely to affect global comparisons, but should be taken into account when rare but critically important members of the stool microbiome are being studied.The online version of this article (doi:10.1186/s12864-015-1694-y) contains supplementary material, which is available to authorized users. The influence of the microbiota inhabiting the human body on an individual’s health has become a major research topic and enormous efforts are made to address this question . Howeveret al. evaluated the transcriptional diversity of the gut microbiomes of a monozygotic twin pair [at al. [et al. [et al. analysed changes of the total and active metagenome (16S rDNA and rRNA), metabolome, metatranscriptome and metaproteome during treatment of one patient with a common ß-lactam antibiotic. Franzosa et al. [In a first metatranscriptome study Turnbaugh win pair . Maurice [at al.  and Pere [et al.  provideda et al.  used stoa et al. , ineffeca et al. , high ama et al. , 10 can a et al. , 11.et al. [E. coli spike-in cells overexpressing mCherry and sFGFP we demonstrate a high absolute recovery rate of mRNA when applying our optimized protocol.Thus, in this study we focused on the technical optimization of RNA stabilization and extraction to provide high quality RNA for high throughput RNA sequencing for stool metatranscriptome analysis. We compared 3 different commercially available RNA extraction kits with the method of Zoetendal et al.  with resSamples were then subject to full strand specific mRNA sequencing on an Illumina HiSeq2000 platform to detect shifts in the community transcriptome caused by the stabilizing reagent, storage time or storage temperature. Sequencing reads were functionally and taxonomically assigned using the Burrows-Wheeler alignment tool (bwa)  against Our data provide high resolution information on the stability of the metatranscriptome in stool samples under various preservation and storage conditions. The analyses were conducted on sub-samples from one stool sample from a healthy donor to exclude the variability introduced by differences in stool composition from different donors. Twelve deeply sequenced metatranscriptomes were obtained, reflecting four different storage conditions and 3 time-points. The data can be used to plan proper sampling, conservation and processing of stool samples for metatranscriptomics. This might be particularly relevant for clinical trials and cohort studies where experimental demands need to be compatible with high sample throughput and routine clinical practice.et al. [For optimizing RNA extraction from stool samples, we tested different commercially available kits , Powermicrobiome RNA Isolation Kit (MoBio) and RNeasy Mini Kit (Qiagen)) and compared them with the method established by Zoetendal et al. . The latThe same sample pretreatment was used in all cases  for the different extraction protocols is shown in Fig. et al. [Highly effective extraction methods are necessary to retrieve the entire metatranscriptome of a sample. The efficiency of cell lysis may vary considerably among different species of bacteria. For example, the cell wall of Firmicutes, one of the two dominant phyla colonizing the human gut, consists of multiple layers of peptidoglycan and is therefore hard to lyse. Lakay et al.  comparedStool samples contain high amounts of inhibitory substances like humic acids, bile salts, billirubins and complex carbohydrates , 15 whicE. coli cells expressing mCherry and GFP under the control of the Lac promoter. The amount of the two spikes was calculated to represent approximately 0.9 % each of the total cell number in the sample, assuming that 1 g stool contains approximately 1011 cells [The stability of RNA is a critical factor for metatranscriptome analyses since both in the clinic and in private settings  stool samples can often not be transferred to −70 °C or −20 °C freezers immediately. Thus the transfer time between conservation of the stool sample in a stabilizing reagent and its arrival at −70 °C is a crucial and variable factor. An ideal stabilizing reagent should prevent RNA degradation even at room temperature for at least several hours. We therefore compared RNA stability in 3 different RNA stabilizing reagents , Powermicrobiome RNA Isolation Kit (MoBio) and RNeasy Mini Kit (Qiagen)) at two different storage temperatures (4 °C and RT) for up to 360 h. In addition, the DNA stabilizer solution of the PSP Spin Stool DNA Plus Kit  was also included since this kit would allow to simultaneously isolate RNA and DNA. Allprotect allows the simultaneous analysis of the metabolom, transcriptome and proteome and is therefore of particular interest for multi-omics approaches. RNA Later is also used in the Zoetendal procedure . The exp11 cells . ControlThe integrity of the isolated RNA was determined using the Bionalyzer (Agilent). RNA integrity numbers (RINs) for the different RNA extraction methods are shown in Fig. Faecalibacterium prausnitzii was determined. To quantify the degradation of ribosomal RNA a primer pair targeting the 23S rRNA was used . Primers targeting the two spike-in controls, mCherry and sFGFP, were designed. In addition the copy number of the glyceraldehyde 3-phosphate dehydrogenase (GAPDH) mRNA of Even though the RNAs derived from samples stored in RNA Protect for 144 h were highly degraded RIN 3.3), we initially found no significant decrease in RNA copy number for any of our chosen targets (data not shown). The primers used for qRT-PCR amplified approximately 100 bp of the target gene. The electropherograms from the Bioanalyzer showed a smear of degraded RNA in a size range of 100–200 bp (data not shown). Thus this degraded RNA may still be reverse transcribed and consequently function as a PCR target. We therefore tested how the length of the amplified mRNA affected its recovery in the qRT-PCR analysis. In Additional file .3, we inThe overall trend of RNA decay among the different storage conditions was the same for all targets and confirmed the RIN analysis. Storage at 4 °C in RNA Later conserved the RNA almost completely. The copy number of all RNA targets analyzed was stable even after 15 days of storage. Even at RT, the RNA of stool samples stored in RNA Later was stable for 24 h to 144 h. In contrast, RNA stored in RNA Protect degraded rapidly. The copy numbers for the 4 targets decreased strongly after 144 h. At RT, the copy numbers of the indigenous targets GAPH and the 23S rRNA were significantly reduced already after 24 h, while both spike-ins were unchanged, possibly because of their higher total abundance. The data are in accordance with the RNA integrity analysis using the Bioanalyzer and confirm a good correlation between the stability of rRNA and mRNA. Among the tested stabilizing reagents RNA Later thus is the reagent of choice to conserve stool sample RNA at ambient temperature.E. coli cells with the absolute number of copies determined by qRT-PCR. Assuming that 1 OD600 corresponds to 8*108E. coli cells/ml culture in rich media [9E. coli cells were spiked per gram stool into each sample. This corresponds to approx. 0.9 % of the total cell number (1011) present in 1 g of stool [To estimate the recovery rate of the spike-in controls we compared the theoretical number of mRNA copies spiked into the stool samples within ch media , approxiof stool .9 (RNA Protect) and 8.31*109 (RNA Later) copies of mCherry and between 4.07*109 (RNA Protect) and 4.52*109 (RNA Later) copies of sFGFP per 1 g of stool. Thus per spiked-in E.coli cell 7.66 to 9.23 copies of mCherry mRNA and 4.52 to 5.02 copies of sFGFP transcripts were found. So et al. [The qRT-PCR revealed that we were able to detect between 6.90*10o et al.  showed tin vitro transcribed RNA as spike-in control for metatranscriptomics was so far only reported twice [et al. [in vitro transcribed RNAs spiked into marine bacterioplankton samples prior to RNA isolation and found extremely low recovery rates of 0.00001 % for their spikes. Based on these results they assumed that the sample sequencing depth was in that low range. Satinsky et al. [et al. combined two methods to enrich mRNA and subsequently linearly amplified the mRNA. Thus multiple enzymatic and experimental steps were conducted before sequencing, each accounting for potential loss of the spike.Application of ed twice , 21. Gif [et al.  used in E. coli mRNA was lost during processing. This fraction may be higher for hard-to-lyse stool bacteria.By contrast, the recovery rates of spikes in our experiments, ranging from 9-18 %, appear to be reasonable given the complexity of the stool sample and the instability of mRNA. However, they also indicate that 80 to 90 % of According to the RIN analysis RNA Later and RNA Protect represented the best and the least well suited reagents, respectively, for RNA stabilization in stool samples. Thus we compared the expression profiles of samples stored in these two reagents at room temperature over a period of 144 h using Illumina sequencing of mRNA. Obviously handling demands and shipping cost dramatically increase when samples have to be stored and shipped on ice. Thus we additionally sequenced samples conserved in RNA Later and stored at 4 °C to evaluate whether the stability of the samples would be improved further or if storage at ambient temperature is sufficient to conserve the RNA in this reagent.Reads mapping to rRNA were removed using the SortMeRNA database. Table Previous studies suffered from a low mRNA enrichment efficiency , 11, whiThere was no significant difference between samples conserved in RNA Later that were stored at room temperature with those that were stored at 4 °C with respect to the total number of reads per sample and the efficiency of mRNA enrichment. The fraction of remaining rRNA was between 8 and 16 % and thus significantly higher than for the snap frozen controls (1 – 2 %), but stable for 144 h both at RT and at 4 °C. This finding is important from a practical point of view, since it indicates that samples conserved in RNA Later may be stored and transported at room temperature for up to 6 days.Additional file The relative abundance of the two spikes mCherry and sFGFP determined by qRT-PCR analysis was compared with their normalized sequencing read counts. Figure The Kraken program was used to assign phylogenetic labels to the mRNA sequencing reads. Kraken is based on exact alignment of k-mers (a nucleotide sequence of length = k) against a user specified database of genomes and combines high classification accuracy with very fast computational processing . RecordsHere we utilized the k-mer database of viral and bacterial genomes: A total of 98707 genomes representing 5059 different taxa have been downloaded from NCBI Refseq and were used to build the standard Kraken database with k = 31. Non rRNA reads derived from the SortMeRNA filtering were utilized as input for Kraken. They represent both coding and non-coding RNAs. For the sake of brevity the term mRNA will be used subsequently instead of non rRNA. Fig. The normalized taxonomic composition of the metatranscriptomes is shown Fig. For example, the relative abundance of Prevotellaceae was enhanced in the controls (approx. 15 vs. 5 %), while the Methanobacteriaceae were more abundant in the RNA Later conserved samples (approx. 7 vs. 3 %). The bias introduced by RNA Protect was smaller, showing the same abundance of Prevotellaceae as the controls. Microviridae, a group of bacteriophages ubiquitously found in fecal samples and waste water , comprisSince bwa has been used in most previous metatranscriptome studies, we compared phylogenetic profiles obtained by Kraken (discussed above) with those obtained by bwa  performed best for RNA isolation from stool samples. The established protocol yielded high quality RNA (RIN > 9) suitable for metatranscriptomics. It could easily be adapted to other environmental samples and may be routinely used in large cohort studies.Near-complete RNA extraction and efficient RNA preservation are the basis for successful metatranscriptome analyses that reflect the E. coli spike-in cells expressing fluorescent proteins to mimick mRNA degradation in stool samples during the RNA extraction process. The recovery rates of the spikes were in the range of 9 – 18 % showing the high efficiency of the optimized RNA extraction protocol. With this method, RNA extraction efficiency should be routinely controlled. Quantifying RNA decay either globally (RNA Seq) or for specific targets  clearly revealed that RNA Protect is inefficient in stabilizing the transcriptional profile for periods exceeding 24 h. In contrast, the transcriptional profiles of samples preserved in RNA Later did not change significantly even after 6 days storage at ambient temperature. To our knowledge this is the first study providing quantitative data on mRNA and rRNA decay in stool samples. Our data indicate that storage of a stool sample in a preservation reagent (RNA Later) introduces a bias in the mRNA profile. This study represents the first report showing the applicability of Kraken for a complex metatranscriptomic dataset and demonstrates that the Kraken software tool is more reliable than bwa in assigning taxonomic labels to metatranscriptomic data.To evaluate different RNA stabilization methods for stool samples we used The bias introduced by RNA Later on the stool metatranscriptome may vary from person to person depending, among other factors, on the relative abundance of hard-to-lyse Firmicutes versus the relative abundance of Gram negative species that are easier to lyse, e.g. Prevotellaceae or Fusobacteria. The higher stability of the metatranscriptome in RNA Later is obtained at the cost of losing some of the transcripts of labile species. Thus it depends on the focus of the study if this bias is acceptable or not. The take-home message for cohort studies or clinical trials it that RNA Later should be used when stool samples need to be shipped at ambient temperature to the laboratory, since the RNA will be stable for up to 6 days. When shipping can be realized within 24 h or samples can be shipped on ice, RNA Protect may represent a better alternative since the introduced bias is smaller.The study was exempt from full ethics reviews since stool samples were provided by one healthy adult on his/her own free will, no additional information was collected, and there was no risk of revealing person related or medical information. The Ethics Committee of the State Board of Physicians of the German Federal State of Lower Saxony has exempted studies of this nature from full ethics review. Our institution, the Helmholtz Centre for Infection Research, in Braunschweig, Germany, does not require administrative approval of studies exempt from ethics review.The workflow is shown in Additional file The ice-cold resuspension of the pelleted cells in the MoBio lysis buffer was added to the supplied bead tubes filled with 500 μl ice-cold phenol-choloroform-isoamylalcohol (PCI) solution . Tubes were briefly vortexed to homogenise the mixture. Subsequently the tubes were transferred to a Fast Prep Bead Beater  and beaten at a speed of 5.5 m/s for 45 s followed by 2 min incubation on ice. The beating procedure was repeated twice. Finally samples were centrifuged for 1 min at 11.000 g at RT and the entire upper aqueous phase was transferred to a new reaction tube (approximately 600–700 μl). Further treatment of samples was according to the manufacturer’s instructions, including a DNAse I treatment on the column. RNA was eluted in 100 μl nuclease free water and the concentration was determined using a Nanodrop ND 1000 . Integrity of RNA was evaluated using a Bioanalyzer 2100 .The suspension of bacterial cells in the lysis solution  was either transferred to the supplied bead tubes (unmodified protocol) or to bead tubes containing sterile 0.1 mm zirconia beads  (modified protocol); each tube contained 500 μl of ice-cold PCI-solution (see above). Bead beating was performed using the same parameters as described above. After centrifugation  the resulting upper aqueous phase was used for RNA extraction according to the Norgen protocol.Pelleted bacterial cells were resuspended in 200 μl of lysozyme/mutanolysin (LM) solution (1xTE buffer (pH 8.0) containing 15 mg/ml lysozyme  and 500 U/ml mutanolysin ) . The ceet al. [The protocol of Zoetendal et al.  was usedF. prausnitzii and the 23S rRNA gene of S. mutans (for primer sequences see table S1) were cloned blunt-end via the EcoRV restriction site into the pGEM 5Zf(+) vector  and the resulting plasmids were transformed in E. coli DH5α. Positive clones were selected via blue/white screening on LB agar plates containing 100 μg/ml ampicillin . The clones were cultivated overnight in LB containing 100 μg/ml ampicillin and the plasmids were isolated using the Plasmid Mini Kit . Cloned plasmids were verified by sequencing.PCR amplified regions of genes encoding mCherry, Superfolder GFP, glyceraldehyde 3-phosphate dehydrogenase (GAPDH) from http://frodo.wi.mit.edu). All primers were purchased from MWG Eurofins Operon . Table S1 shows the targets and the amplified regions of the primers used in this study. The QuantiTect Sybr Green Kit  was used for quantitative PCR of the cDNA. 15 μl reactions with primer concentrations of 0.25 μM were run in the Light Cycler 480 . Threshold (Ct) values were obtained using the Roche software. To determine the primer efficiencies serial dilutions of pooled cDNAs were measured in triplicate for each primer pair. For the determination of the absolute copy numbers of the targets, serial dilutions of cloned plasmid standards with known concentration  were analysed on the same plate as the test samples.For the synthesis of cDNA 1 μg of DNase I treated total RNA was reverse transcribed using the Quantitect Reverse Transcription Kit  in duplicates. Mock reactions replacing the reverse transcriptase with water were used as negative controls to prove the absence of DNA contaminations in the RNA samples and in the components of the kit. The resulting cDNAs were diluted 1:20 and used as templates for PCR. Primers for quantitative PCR were designed using the Primer 3 software . 0.5 ml of an IPTG- induced see Fig. . AliquotPaired-end mRNA Seq Illumina libraries were constructed with the Script Seq Illumina Kit. Strand specific paired end sequencing of samples (100 base pairs) was performed on the HiSeq 2000 Sequencer .Escherichia coli was used as spike-in for the stabilized samples and Alcaligenaceae were found to be mainly represented by reads incorrectly mapping to an Achromobacter genome. The corresponding Achromobacter reference was found to contain an artifact potentially caused by an inappropriate assembly of the reference NC_023061.1. The positions 2434634–2434704 and 6057301–6057337 in this reference were found to be massively covered, whereas more than 99 % of the remaining genome were not covered at all. The origin of these regions is unclear at the moment, but we suspect the reads being an artifact of the Illumina sequencing machines.For taxonomic classification of the reads we employed Kraken   was performed with Primer 6 software based on the Bray-Curtis dissimilarities between samples. Hierarchical Clustering of the samples and OTUs was performed with kraken and was also based on the Bray-Curtis-dissimilarity. The heatmap based on this clustering was generated using the hclust script from the MetaPhlAn package.To obtain functional information about the mRNA reads, transcripts were mapped to the gut specific HMP database using bwa alignment as described above. For genes with known functional annotation clusters of orthologous groups (COG) terms are deposited in the HMP database. Custom user scripts were applied to extract the COG annotation for the mapped reads. COG categorization was then used to bin the mapped mRNA reads into functional categories.The data sets supporting the results of this article are included within the article and its additional files.


Both 7,8,4'-trihydroxyisoflavone and 6,7,4'-trihydroxyisoflavone attenuated the binding affinity in a concentrated manner as evidenced by QDs conjugated RNA oligonucleotide. At a concentration of 0.01 μg·mL−1, 7,8,4'-trihydroxyisoflavone and 6,7,4'-trihydroxyisoflavone showed more than a 30% inhibition activity of a nanoparticle-based RNA oligonucleotide biochip system.Globally, over hundreds of million people are infected with the hepatitis C virus: the global rate of death as a direct result of the hepatitis C virus has increased remarkably. For this reason, the development of efficient drug treatments for the biological effects of the hepatitis C virus is highly necessary. We have previously shown that quantum dots (QDs)-conjugated RNA oligonucleotide can recognize the hepatitis C virus NS3 protein specifically and sensitively. In this study, we elucidated that this biochip can analyze inhibitors to the hepatitis C virus NS3 protein using a nanoparticle-based RNA oligonucleotide. Among the polyphenolic compounds examined, 7,8,4 Hepatitis C virus is a small (50 nm in size), enveloped, single-stranded, positive sense RNA virus about 9,600 nucleotides in length encoding approximately 3,010 amino acids . Hepatit'-trihydroxyisoflavone and 6,7,4'-trihdyroxyisoflavone) are of growing scientific interest because of their ability to enhance health-related qualities in humans. The hydroxylated compounds at a specific ortho position of isoflavones have potent antioxidant properties that contribute to their cholesterol lowering effect, cardiovascular protection, anti-tumor effect, and anti-carcinogenic properties ['-trihydroxyisoflavone and 6,7,4'-trihydroxyisoflavone on hepatitis C virus NS3 protein using a nanoparticle-based RNA oligonucleotide platform.Polyphenolic compounds are phytochemicals found in numerous plants and fruits ,5. They operties ,10. Quanoperties ,12. In t'-ethylcarbodiimide hydrochloride) and kanamycin were purchased from Sigma-Aldrich Chemical Co. . Quantum dots (QDs605) were purchased from Invitrogen Corporation . ProlinkerTM-terminated glass slide were purchased from Proteogen . All other chemicals were of the highest grade. EDC (N-(3-dimethylaminopropyl)-N'-GCAGUAGUGUAUAGGC-3') was first covalently conjugated onto the surface of the carboxyl-terminated QDs . That is, 10 pM of QDs were conjugated with 400 pM of oligonucleotide with 1 μL of 40 nM EDC, to activate amide bond formation to produce QDs-conjugated oligonucleotide (QDs-NS5B oligonucleotide) at a QDs:RNA oligonucleotide molar ratio of 1:40 for 1 h at room temperature. Thereafter, the QDs-oligonucleotide conjugate was collected using centrifugal filtration at 15,000 rpm for 30 min, followed by several washing steps with a Tris buffer . After centrifugal filtration and washing, a pellet of the QDs-conjugated RNA oligonucleotide was dispersed through a brief sonication  using a sonic dismembrator .An amine group with a terminal modification of the NS3 RNA oligonucleotide was synthesized by BIONEER Co. Ltd.  and carboxyl-terminated QDs605 was purchased from Invitrogen . The amino group of the NS3 RNA oligonucleotide  for 1 min. The glass chip was analyzed using a confocal laser-scanning microscope, an LSM 510 META . The signal intensity was determined using software for the LSM510 (LSM Image Browser). A histogram of the intensity was obtained from the region of the spotted chip. The value of the signal intensity was obtained by calculating and expressing it as the mean intensity.For the inhibitor screening of the hepatitis C virus NS3 protein, we designed the QDs-conjugated specific RNA oligonucleotide for specific hepatitis C virus NS3 protein targeting: first, the hepatitis C virus NS3 protein (1 μL) was immobilized on a glass chip; second, QDs-conjugated RNA oligonucleotide conjugates (1 μL) were bound on an immobilized chip; third, inhibitor treatment was performed on the conjugated RNA oligonucleotide and hepatitis C virus NS3 protein; fourth, washing and unspecific binding removal was done; fifth, detection was achieved to show directly the specific recognition of the inhibition effect of hepatitis C virus NS3 protein on the biochip. The schematic design of the inhibitor screening for effective monitoring of hepatitis C virus NS3 protein is illustrated in E. coli expression system and purified it by Ni-NTA affinity chromatography. In the purification step, the protein was eluted in 1 mL fraction with a 500 mM imidazole buffer. To verify the purity and homogeneity of the eluted NS3, aliquots of eluted fractions were analyzed by Coomassie blue staining of the SDS-PAGE prior to pooling the fractions. Eluates with the highest purity of 95% were pooled, dialyzed and stored with 50% glycerol in aliquots at −80 °C. The HCV NS3 protein was purified by a single chromatography step on a Ni2+ affinity column. The C-terminally his-tagged HCV NS3 was visualized with a molecular mass of approximately 68 kDa on a SDS-PAGE (data not shown).We expressed the recombinant HCV NS3 in the '-trihydroxyisoflavone and 6,7,4'-trihydroxyisoflavone showed high anti-viral activity. The chemical structures of 7,8,4'-trihydroxyisoflavone and 6,7,4'-trihydroxyisoflavone are shown in '-trihydroxyisoflavone and 6,7,4'-trihydroxyisoflavone had high inhibition activity in a concentrated manner against the hepatitis C virus NS3 protein. At a concentration of 0.01 μg·mL–1, 7,8,4'-trihydroxyisoflavone and 6,7,4'-trihydroxyisoflavone showed more than 30% inhibition activity of a QDs-RNA oligonucleotide biochip platform. As shown in '-trihydroxyisoflavone and 6,7,4'-trihydroxyisoflavone showed a similar pattern when comparing the concentration-dependent anti-viral activity. The half-maximal inhibitory concentration (IC50) values of 7,8,4'-trihydroxyisoflavone and 6,7,4'-trihydroxyisoflavone were found to be approximately 0.1 μg·mL–1 and 0.5 μg·mL–1, respectively ('-trihydroxyisoflavone and 6,7,4'-trihydroxyisoflavone as an antiviral agent. The discovery of antiviral drugs has been of considerable interest in developing efficient and sensitive methods for high-throughput screening in medicine. Furthermore, this proposed method can be considered a real-time monitoring method for inhibitor screening of HCV viral protein and is expected to be applicable to other types of diseases.In '-trihydroxyisoflavone and 6,7,4'-trihydroxyisoflavone as an antiviral agent. The discovery of antiviral drugs has been of considerable interest in developing efficient and effective methods for HTS screening in medicine. Our main goal in this study was to demonstrate a proof-of-concept that viral protein can be inhibited and detected with remarkable sensitivity and simplicity. In this study, we demonstrated inhibitor screening on a designed biochip platform using a nanoparticle based RNA oligonucleotide. We discovered a novel function of 7,8,4