The last decade has seen a marked rise in the use of cancer tissues obtained from research autopsies. Such resources have been invaluable for studying cancer evolution or the mechanisms of therapeutic resistance to targeted therapies. Degradation of biomolecules is a potential challenge to usage of cancer tissues obtained in the post-mortem setting and remains incompletely studied. We analysed the nucleic acid quality in 371 different frozen tissue samples collected from 80 patients who underwent a research autopsy, including eight normal tissue types, primary and metastatic tumors. Our results indicate that RNA integrity number (RIN) of normal tissues decline with the elongation of post-mortem interval (PMI) in a tissue-type specific manner. Unlike normal tissues, the RNA quality of cancer tissues is highly variable with respect to post-mortem interval. The kinetics of DNA damage also has tissue type-specific features. Moreover, while DNA degradation is an indicator of low RNA quality, the converse is not true. Finally, we show that despite RIN values as low as 5.0, robust data can be obtained by RNA sequencing that reliably discriminates expression signatures. In turn patient . While p patient .Sequencing of the human genome has led to a revolution in understanding of cancer etiology by revealing the genetic alterations characteristic of human tumors –7, the gDespite the emergence of and implementation of research autopsy programs at a variety of institutions for obtaining cancer tissues, to date there are few formal evaluations of the quality of biomolecules in postmortem materials. A challenge to performing such evaluations is limited tissue resources. In some instances carefully screened cases and selected tissue types have been used to establish the relationship between potential quality-controlling factors and tissue sample quality –20. AlteNucleic acid quality was analyzed in 371 different frozen tissue samples collected from 80 autopsied patients, 81% of which had been diagnosed with pancreatic cancer. The remaining patients had been diagnosed with breast cancer, lung cancer, colorectal cancer, germ cell tumor or melanoma  samples attempted. The overall average RIN for all 269 samples was 5.94 ± 2.5, with a median RIN of 6.4. By contrast, for 10 of 287 samples (6%) the RNA yield was exceedingly low leading to unreported or unreliable RIN, and these samples were assigned an RIN value of 0 , or when limiting the comparison to 17 matched pairs of normal pancreas and primary PDA . This finding thus does not support the long-held “myth” that PDA tissues have worse quality than other tumor types. This may be partially explained by observations that PDA is characterized by a prominent desmoplastic/stromal reaction that is hypovascular compared to adjacent normal pancreas [Forty-three of the 52 primary tumors (83%) analyzed were pancreatic ductal adenocarcinomas (PDA) Figure , providipancreas . NonetheWhile generally more stable than RNA, DNA is also subject to degradation in the postmortem period . Most men = 7), normal kidney (n = 9), primary tumors (n = 10), liver metastases (n = 5) and lung (n = 5) metastases.With these factors in mind we developed a semi-quantitative method to evaluate DNA quality to facilitate comparison among samples from independent experiments. In addition, unlike most studies detecting one locus, we simultaneously examined five chromosome loci of varying potential stability and susceptibility to DNA damage-inducing factors thereby achieving high sensitivity in detecting DNA damage in well-preserved samples Table . DNA wasWe successfully extracted genomic DNA from all 36 samples including one that failed in RNA extraction. No DNA damage was detected by our assay in 32/36 samples analyzed (89%) even though 17 of these 32 (53%) had RIN values less than 5. In the four samples with DNA damage three were from PMI category IV, two of which showed degradation at all four sensor loci Figure . InteresGenomic DNA from postmortem tissues has been used successfully for next generation sequencing despite potential DNA degradation , 15, 27.AXGP1-GJC3 and SIDT2-TAGLN, in six of nine postmortem RNA samples . These two fusions were recently reported in normal pancreatic tissues within the context of a larger pan-tissue analysis [Sequencing libraries were successfully generated from all samples using the poly-A enrichment method and used to generate 80 million reads per sample. Moreover, quality metrics of each library showed a sound distribution of coverage along transcripts and fragment lengths irrespective of RIN values Figure . One noranalysis .Collectively, these data are encouraging and suggest that despite being collected under postmortem conditions RNA samples can provide biologically meaningful information in downstream analyses. These findings are particularly exciting considering recent reports of improved methods to directly assess mRNA integrity and control for it in analyses of RNAseq data , 36, 37.Previous evaluations of the quality of biomolecules in postmortem tissues have been in the context of brain banking, forensic analyses or minimally invasive autopsies , 38–40. Despite an increasing demand for research autopsy samples in the cancer research community, collecting high quality tissue samples is challenging due to numerous factors that can be legal, ethnic, emotional or social in nature. Exclusion of samples with a degree of poor quality is not always realistic, particularly when not all downstream applications are equally sensitive to sample quality. Thus, analyses that can incorporate these variables would be expected to improve comparisons across patients, tissue types and sample sets. Table Autopsy samples were collected in association with the Johns Hopkins Gastrointestinal Cancer Rapid Medical Donation Program (GICRMDP) or the Memorial Sloan Kettering Cancer Center Medical Donation Program (MDP). Both programs were approved by the IRB at their respective institutions and in accordance with an assurance filed with and approved by the U.S. Department of Health and Human Services. Details of the program have been described previously . BrieflyFor each sample, approximately 30 mg tissue was carefully harvested on ice and homogenized using the Fastprep-24 system with ceramic beads . Total RNA was then extracted using RNeasy mini kits or Fibrous tissue mini kits (Qiagen) following the manufacturer's instruction. The RNA extraction step was carefully monitored by simultaneously extracting RNA from freshly sacrificed snap frozen mouse tissues of the same tissue type as a positive control. Total RNA was eluted in DNase/RNase free water. RNA was quantified using a Nanodrop 2000 spectrophotometer (Thermo Fisher Scientific) and subsequently stored at −80°C. No more than 10 samples were extracted at one time. To confirm reproducibility of our extraction procedure, 10 samples were randomly selected and RNA re-extracted from the same tissue. In all cases similar yields were obtained from the first and second extraction. Any sample for which RNA could not be extracted was independently extracted at least one more time to rule out technical errors during the extraction procedure.The RNA integrity number (RIN) was determined using an Agilent 2100 Bioanalyzer (Agilent Technologies) with an RNA nano-kit system as described in the manufacturer's instructions. For each chip analysis, a commercially available tissue-matched control human RNA with high RNA quality (ZYAGEN) was run together with autopsy RNA samples to monitor the whole analysis procedure. Data reproducibility was confirmed by repeating the RNA chip analysis in 10 randomly selected samples at least two days apart from the first analysis. In all cases the results were highly reproducible with an overall difference in RIN value between the two chip assays of < 1.Taq PCR Core Kit (Qiagen). PCR conditions were 94°C for 2 min; 35 cycles of 94°C for 1 min, 60°C for 1 min and 72°C for 1 min 30 sec followed by 1 cycle of 72°C for 7 min. 3 ul of PCR products amplified from each locus were pooled in a new PCR tube and run on DNA screening gel cartridges (Cartridge ID C15D4D3A11) on a QIAxcel advanced system (Qiagen) according to manufacturer instructions.Genomic DNA was extracted using DNeasy Mini Kits (Qiagen) according to the manufacturer's instructions. Genomic DNA was quantified by Qubit fluorometer (Invitrogen) and diluted to 20 ng/ul. 20 ng diluted DNA was subjected to PCR amplification in a total volume of 20 ul. PCR was carried out using a http://www.sigmaaldrich.com/technical-documents/articles/life-science-innovations/qualitative-multiplex.html) for analysis of snap frozen postmortem tissues. The original assay consists of five primer sets derived from the NCBI UniSTS database that amplify products ranging from 132 bp to 295 bp; some or all of these products will fail to amplify when DNA damage is present. PCR primers were modified to increase the amplicon sizes from 474 to 980 bp  tissues . The raw count matrix generated by HTSeq was then processed using the R/Bioconductor package DESeq (www-huber.embl.de/users/anders/DESeq) [p-value was less than 0.05. Normalized counts were log2 transformed after addition of 1 to all values. Hierarchical clustering was performed using the R hclust function with the Euclidean distance measure on normalized log2 transformed counts after addition of 1 to all values. A heatmap was generated using the heatmap. 2 function from the gplots R package. The data plot shows the mean centered normalized log2 expression of the top 250 genes differentially expressed between tumor and normal tissues. To to detect fusion chimeras from RNA-seq data, meta-analysis that runs four fusion detection algorithms  was applied. The pipeline computes a meta-score for each detected fusion thus alleviating a problem of high numbers of false positives in each method taken independently.Selected RNA samples from postmortem tissues with RIN value above 5 were used for RNA sequencing. RNA sequencing was performed in the MSK Genomics Core using the Illumina Truseq RNA sample prep protocol. Briefly, RNA sequencing libraries were generated with poly-A-enrichment method and sequencing was performed on an Illumina HiSeq2000 following standard protocols. Reads were paired-end 50 bp in length with a total of 80 millions of reads per sample. All sequenced libraries were mapped to the human genome (hg19) using rnaSTAR aligner . After ms/DESeq) , which iR2 value and p value. Curve fits were added to scatterplots by performing linear regressions. Patient-matched tumor-metastasis comparisons were compared by a two-tailed Student t test. A p value of 0.05 or less was considered statistically significant.Statistical analysis was performed using GraphPad Prism Version 6.0 . To determine the relationship between RIN and PMI, correlations were performed to determine the


Canine distemper (CD) is a widespread infectious disease that can severely impact a variety of species in the order Carnivora, as well as non-carnivore species such as non-human primates. Despite large-scale vaccination campaigns, several fatal outbreaks have been reported in wild and domestic carnivore populations. This, in association with expansion of the disease host range and the development of vaccine-escape strains, has contributed to an increased demand for therapeutic strategies synergizing with vaccine programs for effectively controlling canine distemper. 6-methylmercaptopurine riboside (6MMPr) is a modified thiopurine nucleoside with known antiviral properties against certain RNA viruses.We tested the inhibitory effects of 6MMPr against a wild-type CDV strain infection in cell culture. We measured infectious particle production and viral RNA levels in treated and untreated CDV-infected cells. Ribavirin (RIB) was used as a positive control.Here, we report for the first time the antiviral effects of 6MMPr against canine distemper virus (CDV) in vitro. 6MMPr was able to reduce viral RNA levels and to inhibit the production of infectious CDV particles. The therapeutic selectivity of 6MMPr was approximately six times higher than that of ribavirin.Our results indicate that 6MMPr has high anti-CDV potential and warrants further testing against other paramyxoviruses, as well as clinical testing of the compound against CDV. Morbillivirus within the Paramyxoviridae family of the Mononegavirales order. The CDV genome encodes six structural proteins: hemagglutinin (H), fusion (F), envelope-associated matrix (M), phospho- (P), large polymerase (L) and nucleoprotein (N) ) × 6.022 × 1023 = Y ssRNA molecules/μL. Standard curves were constructed with five points in triplicate from serial 10-fold dilutions of transcripts. RNA samples were tested in duplicate and the inhibition of CDV replication was expressed as RNA copy number.Total RNA was extracted from cell lysate plus SUP (500 μL) using Trizol reagent (Invitrogen) according the manufacturer’s instructions. The concentration and quality of RNA was checked using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific). For each real-time assay, 100 ng of RNA template was analyzed by qRT-PCR using GoTaq 1-Step RT-qPCR System kit (Promega). CDV-specific primers for nucleoprotein gene (N) CDV-F (AGTTAGTTTCATCTTAACTATCAAATT) and CDV-R (TTAACTCTCCAGAAAACTCATGC) had been previously designed . Primer 50 and EC50 were calculated from a linear regression equation. All the graphs show the mean ± standard error from three independent experiments. A p-value <0.05 was considered statistically significant.Statistical analysis was performed to assess the differences in viral yield of infected cells in contact with 6MMPr for different doses and time intervals. Data were submitted to two-way ANOVA  using GraphPad Prism Software version 5.01 for Windows . The Tukey test was carried out to perform pairwise comparisons among means. Values of CC50 values of 6MMPr and RIB were 1409 μM and 424 μM, respectively . While RIB activity decreased approximately 0.5 log10 of viral growth and RNA synthesis, 6MMPr reduced up to 1.3 log10 of virus RNA and over 2.2 log10 of infectious titers . PRA analysis showed strong agreement with antiviral activity assays. 6MMPr and RIB exhibited EC50 values of 28.7 and 52 μM, respectively. As shown in Table The plaque-reduction assay (PRA) was used to confirm compound activity and to determine the 50% inhibitory concentration . As shown in Fig. Canine distemper virus causes a devastating multisystemic disease in domestic and wild carnivores, with high morbidity and mortality rates. Furthermore, there are growing concerns about the impact of CDV infection because of its recurrent outbreaks, conservation threat to wildlife, economic impact, the emergence of vaccine-escaping strains, host-switching events, and potential risk for humans , 8, 25. In this study, 6MMPr showed inhibition effects against a wild-type virulent CDV strain. At the highest concentrations (169 and 338 μM), 6MMPr was able to reduce the production of infectious particles to undetectable levels. The antiviral properties of 6MMPr are dependent upon virus specificity and presumably acts by blocking viral RNA replication . Taking Although the details of CDV replication are not thoroughly understood, the eclipse phase was found to be approximately 16 h and the complete replication cycle takes place approximately 24 h after the virus penetrates the cellular membrane . In ordeInitially, negative-stranded RNA viruses direct the RNA synthesis machinery to mRNA production. Replication processes are then initiated . Both tr10, previous studies reported that RIB was able to decrease between 1 and 5 log10 of CDV infectious titer in Vero cells. However, we found an EC50 of 12.7 μg/mL (52 μM) for RIB, which is close to the average EC50 values of RIB previously exhibited for CDV inhibition [50 mean values ranged between 27.2 μg/mL [While we found that antiviral activity of RIB achieved a maximum reduction in CDV infectivity of 0.6 loghibition –16. Ther.2 μg/mL  to over .2 μg/mL . Hence, .2 μg/mL  to 42.4 Together, our results show that 6MMPr is highly effective at inhibiting CDV. The compound exhibited strong inhibitory potential even when added subsequent to viral challenge. More studies are needed to gain further insight into the precise mechanisms of 6MMPr anti-CDV activity. Thus, 6MMPr represents a promising candidate for clinical applications against CDV infection.


The innate immune system has evolved a number of sensors that recognize viral RNA (vRNA) to restrict infection, yet the full spectrum of host-encoded RNA binding proteins that target these foreign RNAs is still unknown. The RNA decay machinery, which uses exonucleases to degrade aberrant RNAs largely from the 5′ or 3′ end, is increasingly recognized as playing an important role in antiviral defense. The 5′ degradation pathway can directly target viral messenger RNA (mRNA) for degradation, as well as indirectly attenuate replication by limiting specific pools of endogenous RNAs. The 3′ degradation machinery (RNA exosome) is emerging as a downstream effector of a diverse array of vRNA sensors. This review discusses our current understanding of the roles of the RNA decay machinery in controlling viral infection. RNA viruses produce RNAs which differ substantially from normal cellular RNAs, leading to their recognition by host-encoded RNA-binding proteins. Unlike cellular RNAs, the genomes of RNA viruses are replicated by RNA-dependent RNA polymerases (RdRp) through antigenome intermediates, creating both transient double-stranded RNA (dsRNA) structures and 5′ triphosphate ends not normally present in cellular messenger RNAs (mRNAs) . For 5′ The RNAs produced during viral replication serve as an important sign of infection, and a series of sensors have evolved to detect these RNAs. In mammals, many cytosolic vRNAs are recognized by the RIG-I-like receptors (RLRs), RIG-I and MDA-5, which are homologous DEAD-box RNA helicases . Each reDrosophila homolog, Dicer-2, recognizes viral dsRNA intermediates generated during infection [Drosophila is essential for immune defense; flies with mutations in this pathway rapidly succumb to viral infection. To evade this immune defense, natural insect pathogens such as Drosophila C virus encode suppressors of RNAi [Just as mammalian cells utilize the RLR DEAD-box RNA helicases to recognize cytosolic vRNA, the closest nfection ,29,30. Hnfection . The ant of RNAi . Dicer-2 of RNAi .New roles are continually emerging for the larger family of DEAD-box helicases in recognizing vRNA. Many of these genes have roles both in normal cellular metabolism as well as in the control of viral infection. For example, DDX17 normally binds stem-loop structures of primary microRNAs (pri-miRNAs) in the nucleus and recruits the Drosha-anchored microprocessor complex to produce pre-miRNAs ,35,36,37Endosomal RNAs are sensed by toll-like receptors (TLRs) including TLR3 and TLR7, which detect dsRNA and ssRNA, respectively. These sensors signal through the adaptors TRIF and MyD88 to activate antiviral transcription programs . EndosomMammalian cells possess additional cytosolic sensors of vRNA including the dsRNA-activated protein kinase R (PKR) ,44. ActiAnother cytoplasmic sensor of viral dsRNA, 2′-5′ oligoadenylate synthetase (OAS), is an IFN-inducible enzyme and, thus, is up-regulated in response to viral detection by sensors, such as the RLRs and TLRs ,60. UponDrosha, a nuclear RNase III enzyme, has a canonical role in processing pri-miRNAs to pre-miRNAs before they are exported to the cytoplasm for Dicer processing. Recent studies have shown that Drosha has antiviral activity . Drosha New research continues to uncover RNase activity among previously identified antiviral proteins. SAMHD1 was identified as a restriction factor for human immunodeficiency virus (HIV) that is antagonized by the viral protein Vpx . An initEmerging data suggest that the canonical RNA decay machinery, which is largely dependent on exonucleases, also plays an important role in antiviral immunity. In general, RNA decay proceeds from either the 5′ or 3′ end of an RNA transcript, and has roles in RNA biogenesis, RNA quality control, and normal RNA turnover. Ribosomal RNAs (rRNAs), small nuclear RNAs (snRNAs), and small nucleolar RNAs (snoRNAs) all require RNA processing in the nucleus to reach their mature forms . QualityNonsense-mediated decay (NMD) is the process by which mRNAs with stop codons far from the 3′ end of an mRNA, either due to a premature stop codon or a long 3′ UTR, are detected and degraded at either the 5′ or 3′ ends ,80,81. SEndogenous mRNAs targeted for 5′ decay are typically first deadenylated by the CCR4-NOT complex, often assisted by other deadenylating enzymes, before they can be targeted for decay ,86. AlthDeadenylation, decapping, and 5′ degradation activities coalesce in ribonucleoprotein (RNP) structures known as processing bodies (P bodies). These RNPs consist of mRNAs targeted for decay as well as components of the decapping and 5′ degradation machinery  ,92. RNAsThe 5′ to 3′ RNA decay machinery can inhibit viral replication in a number of different ways . StudiesP body structure is also altered during many viral infections. The up-regulation of 5′ decay during RVFV infection prevents the formation of P bodies due to depletion of the RNA targets around which they nucleate . Poliovi3′ to 5′ degradation is largely mediated by the RNA exosome. This complex consists of a hexameric barrel (six proteins with RNase PH homology) and a cap structure (three proteins with S1 RNA-binding domains) ,108,109.The exosome is both structurally and functionally conserved from yeast to humans . Indeed,Though the exosome degrades RNAs indiscriminately in vitro, it degrades RNAs in vivo in a regulated fashion by relying on RNA-binding cofactor complexes that bind specific targets and recruit the exosome for degradation . All knoThe Superkiller (Ski) complex is the major cytoplasmic exosome cofactor complex in yeast, named for the “superkilling” phenotype of dsRNA viruses, which are lethal to yeast deficient in these genes . The SkiEscherichia coli, which, unlike eukaryotic polyadenylation, targets RNAs for decay [The yeast TRAMP (Trf4/5-Air1/2-Mtr4-Polyadenylation) complex, located in the nucleus, has known roles in degrading hypomodified tRNAs, hypoadenylated mRNAs, cryptic unstable transcripts (CUTs), and in the biogenesis of rRNA, snRNA, and snoRNA ,137,138.or decay ,143. In or decay .The TRAMP complex is conserved in humans, but nuclear RNA degradation machinery has additional complexity. As in yeast, human TRAMP is composed of a helicase, hMTR4, a zinc-finger Air-like protein, hZCCHC7, and a poly(A) polymerase, hTRF4-1 or hTRF4-2 ,146. HowStudies have implicated the exosome in antiviral defense. A number of antiviral RNA-binding proteins co-immunoprecipitate with the exosome, suggesting that their mechanism of action may involve exosomal degradation. DDX17 restricts RVFV by binding a miRNA-like stem loop structure encoded in the vRNA . Though Drosophila and human cells [Recent work has implicated the exosome and components of a canonical cofactor complex in the direct recognition and degradation of specific vRNAs. RNAi screening revealed an antiviral role for exosome core components as well as the TRAMP components Mtr4 and Zcchc7 against three RNA viruses from distinct families, VSV, SINV, and RVFV, in both an cells . Though Taken together, these studies suggest that the exosome is a broad antiviral effector downstream of diverse sensors which bind distinct vRNAs to recruit the exosome for degradation . Though Increasing evidence suggests that the RNA decay machinery plays important roles in antiviral defense. This can involve either direct effects on vRNA stability or indirect regulation of the intracellular milieu. Furthermore, an emerging theme suggests that many RNA binding proteins can be repurposed from their endogenous roles in the nucleus to antiviral roles in the cytoplasm. Future studies are necessary to further elucidate how these RNA binding proteins recognize foreign RNAs and how they interface with the RNA decay machinery to restrict vRNA replication.


RNA modifications are indispensable for the translation machinery to provide accurate and efficient protein synthesis. Whereas the importance of transfer RNA (tRNA) and ribosomal RNA (rRNA) modifications has been well described and is unquestioned for decades, the significance of internal messenger RNA (mRNA) modifications has only recently been revealed. Novel experimental methods have enabled the identification of thousands of modified sites within the untranslated and translated regions of mRNAs. Thus far, N6‐methyladenosine (m6A), pseudouridine (Ψ), 5‐methylcytosine (m5C) and N1‐methyladenosine (m1A) were identified in eukaryal, and to some extent in prokaryal mRNAs. Several of the functions of these mRNA modifications have previously been reported, but many aspects remain elusive. Modifications can be important factors for the direct regulation of protein synthesis. The potential diversification of genomic information and regulation of RNA expression through editing and modifying mRNAs is versatile and many questions need to be addressed to completely elucidate the role of mRNA modifications. Herein, we summarize and highlight some recent findings on various co‐ and post‐transcriptional modifications, describing the impact of these processes on gene expression, with emphasis on protein synthesis. WIREs RNA 2017, 8:e1375. doi: 10.1002/wrna.1375WIREs website.For further resources related to this article, please visit the  Messenger RNA (mRNA) translation is a central process in every living organism. The assembly and operation of the translation machinery are very costly and can consume up to 40% of the cellular energy.Most of the modifications described thus far have been identified in tRNAs.Thermus thermophilusEscherichia coli)in vitro, suggesting that these RNA modifications are not fundamental for all basic steps of protein biosynthesis.The second class of RNA that requires modification for functionality is ribosomal RNA. The number of rRNA modifications identified in prokaryotic organisms is rather small  of mRNAs.N6‐methyladenosine (6Am) and low levels of 5‐methylcytosin (5Cm) were revealed in mRNAs of eukaryotic cells.N1‐methyladenosine (1Am),mRNAs. Whereas most of the nucleotide derivatives were found in eukaryotic organisms, some derivatives were also abundant in prokaryotic mRNAs.Post‐transcriptional modifications of RNA can be historically classified into two groups: edited RNA and modified RNA. RNA editing is usually understood as posttranscriptional RNA processing  that changes the RNA nucleotide sequence compared with the genetically encoded sequence. This processing can be achieved through the insertion/deletion of nucleotides or deamination of nucleobases, generating either standard nucleotides or the rare nucleotide inosine (I).RNAs are essential processes that influence and regulate gene expression at the post‐transcriptional level. In this review, we summarized and highlighted important findings in this field. mRNA modifications are involved in many aspects of mRNA processing, stability, folding and translation. We also specifically focus on the involvement of mRNA modifications in protein synthesis, and discuss the impact of these processes on gene expression.The modification and editing of mIn 1986 Benne and co‐workers first described striking discrepancies between the DNA sequence of a gene and the RNA sequence of the corresponding transcript.mRNAs of the Paramyxoviruses and the Ebola viruses, respectively.mRNAs of Physarum polycephalum harbor co‐transcriptionally inserted cytosines (Cs) and even various dinucleotides .In addition, guanosines (Gs) and adenosines (As) are also inserted into In addition to the insertions/deletions of nucleotides, the message can also be revised by changing the identity of RNA nucleotides. These changes are achieved through the enzymatic alteration of the chemical composition of nucleobases, resulting in a new nucleotide identity considered as a nucleotide substitution.The first example described was a C to U substitution within the ORF of apolipoprotein B (apoB).Thus far, C‐to‐U editing has only been observed in eukaryotes, but not in bacteria and archaea. C‐to‐U editing is highly prominent in plants. With only few exceptions, the mitochondrial and plastid mRNAs of all land plants show editing.C‐to‐U editing is certainly not restricted to the coding sequences of mRNAs. Several editing sites have been detected, particularly in the 3′ UTRs of mRNAs, which therefore do not alter the amino acid sequence of the resulting product.In addition to the C‐to‐U editing, substitutions of U with C were observed in land plants and mammals.Recently, also G‐to‐A editing has been described for the WT1 mRNA6 position, resulting in the conversion of A to the rare nucleotide I. The substitution of this amino group as a hydrogen donor with a carbonyl‐oxygen as a hydrogen acceptor generates a similar Watson–Crick edge as G. Therefore, this type of editing is also occasionally referred to as A‐to‐G editing.The conversion of adenosine to inosine (I) is the most prevalent form of RNA editing. More than 100,000,000 editing sites were computationally predicted within the human transcriptome.The enzymes responsible for the deamination reaction are adenosine deaminases acting on RNA (ADARs). These enzymes are highly conserved across metazoans,In the human transcriptome, more than 99% of the editing sites are reported to be positioned in Alu sequences, which are short interspersed nuclear elements (SINEs).2+ permeability of the AMPA receptor.2C. A total of 5 positions are edited, and these alterations affect the activity of the receptor as a result of altered receptor:G‐protein coupling.Drosophila melanogaster and viruses.Although infrequent, the editing of protein‐coding sequences dramatically affects the protein product. The interpretation of I as G by the translation machinery can lead to non‐synonymous substitutions that significantly alter the function or activity of the protein products. The AMPA  glutamate receptor GluR‐B is the first RNA substrate identified.Not only can A‐to‐I conversions change the genetic code and thereby influence gene expression, A‐to‐I editing even regulates regulatory small RNAs, such as miRNA. Numerous effects of A‐to‐I editing on the functions of miRNAs function have been reported. The editing alters pri‐miRNA biogenesis, miRNA expression and miRNA selectivity  was among the first post‐transcriptional modifications reported as abundant at high levels within mRNAs.6A modification has been identified in the mRNAs of eukaryal organisms ranging from yeast and plants to mammals.6A has also been described as a naturally occurring mRNA modification in bacteria.6A is the most abundant internal mRNA modification, accounting for 0.1–0.5% of all As (m6A/A),6A residues per mRNA.In the 1970s, 6A within transcripts is debated, primarily because initial techniques could not map m6A at single‐base resolution.6A‐specific antibodies, as m6A does not affect base pairing and is not prone to chemical modifications that would facilitate detection, enabling refined mapping and detailed quantifications.6A is enriched in regions in direct proximity to stop codons, in long exons and transcription start sites.N6,2′‐O‐dimethyladenosine (m6Am), it is feasible that especially hits in the vicinity of transcription start sites also derive from m6Am, which is part of the 5′ cap.6A  has been proposed, but only a fraction of the consensus sequences actually harbors m6A.The precise location of m6A is induced during meiosis,6A might not only be cell type‐dependent but also dynamic during the cell cycle and development.In yeast m6A unique between other thus far described modifications.6A ‘writers’, which deposit m6A modifications, (2) m6A ‘erasers’ that catalyze the removal of m6A from the transcripts, and (3) m6A ‘readers’, which mediate the downstream effects of this distinct mRNA modification , which itself does not exhibit methyltransferase activity, but might be crucial for the localization of the complex.The first ‘writer’ described is METTL3 (methyltransferase‐like 3), a 70 kDa protein, functioning as a methyltransferase within a multi‐enzyme complex.6A to adenosine  and N6‐formyladenosine (f6A), whose biological functions remain elusive.6A demethylase was identified in mammals, namely ALKBH5. This enzyme does not form intermediates and directly converts m6A to A.The first ‘eraser’ identified is the demethylase FTO (fat mass and obesity‐associated protein), which catalyzes the reversion of me Figure .83 The r6A writers and erasers have attracted interest in the past, because of the compelling dynamic nature of the m6A landscape and the unexpected link to human obesity,6A readers is of equal importance. These factors represent the direct link between m6A and its functional repertoire  processing, and vice versa miRNAs themselves can regulate m6A formation.Overall, the impact of m6A research is still facing a knowledge gap on how modified mRNAs are translated into proteins. Is the ribosome directly affected by m6A modifications? If so, which step of translation is targeted? Zhou and colleagues have shown that m6A promotes the initiation of translation via the m6A reader protein YTHDF2.6A methylations within the 5′ UTR of mRNAs are shielded from FTO‐mediated demethylation by the binding of YTHDF2 and facilitate cap‐independent translational initiation. In addition, a single m6A residue within the 5′ UTR enabled the translation of an uncapped mRNA,However, m6A sites located near the 3′ end of mRNAs and promotes the translation of the respective mRNA via an interaction with the ribosomal initiation complex.6A reader proteins YTHDF1 and YTHDF2 both promote translation by facilitating the rate‐limiting step of translational initiation, YTHDF2 also determines the lifetime of an mRNA by chaperoning it to mRNA decay sites.Translation initiation is also regulated through YTHDF1.6A and its interaction with diverse proteins has been extensively studied, but equally interesting are the interactions of modified bases with other nucleotides. m6A exclusively base pairs with uridine, indicating that the N6‐methyl group does not alter canonical base pairing.6A to thymine. However, how does the ribosome process an m6A‐modified codon?The role of min vitro translation system.6A content in mRNAs beyond 5% strongly inhibits translation.6A on translational elongation in bacterial systems.6A was site‐specifically incorporated into the first, second, or third codon position of mRNAs employed for in vitro translation systems. Analyzing the protein products revealed codon position‐dependent effects of m6A.N6‐methyl group present in the first codon position (m6AAA). The second (Am6AA) and the third codon position (AAm6A) were less sensitive to this modification.6A on single steps of translational elongation led to the same conclusions and showed that m6A delays tRNA accommodation.6A sites might slow ribosomal decoding. Consequently, methylations could reduce protein yield or they might bring protein synthesis into accordance with protein folding or recognition by chaperones.Initial studies employing methylated mRNAs have reported the stimulation of translation in a rabbit reticulocyte 6A, pseudouridine (Ψ) was isolated from calf liver and initially described as the ‘fifth nucleotide’.5‐glycoside isomer of the nucleoside uridine, is formed after the breakage of the N1‐glycosidic bond and a 180° rotation of the base through the attachment of the C5 atom to the sugar ring. The isomerization does not affect base pairing at the Watson–Crick edge, however, a second hydrogen bond donor is liberated at the Hoogsteen edge that equips Ψ with distinct chemical properties.In the early 1950s, prior to the characterization of mGenerally, Ψ formation is catalyzed by two independent enzymatic reactions  proteins can modify tRNAs, rRNAs and snRNAs independently of guide RNAs.N‐cyclohexyl‐N′‐(2‐morpholinoethyl)carbodiimide metho‐p‐toluenesulfonate) and subsequent reverse transcription. CMC specifically labels Ψs thereby blocking the reverse transcriptase one nucleotide downstream of the Ψ site. These sequencing techniques were designated as Pseudo‐seq, Ψ‐seq and PSI‐seq.6As within mRNAs.Ψ is particularly enriched in rRNAs and tRNAs, but is also detected in snRNAs.6A formation, the introduction might not be reversible, as Ψ forms an inert C‐C bond.Ψ formation is dynamically induced in response to environmental cues. However, unlike min vivo, and these findings therefore might not be relevant for regulating endogenous translation.Karijolich and colleagues investigated the impact of Ψs on translation termination  did not stimulate translational mis‐/recoding based on mass spectrometry of the synthesized peptides.It is a longstanding enigma whether Ψ might also interfere with codon recognition during translation elongation. Thus, Ψs could potentially expand the genetic code through recoding translation, i.e., changes in the amino acid composition of the translated peptide, without adjustments in the primary nucleotide sequence of the mRNA.Whereas the decoding process is not affected by Ψs, the translational rates and protein expression levels increased.in vitro translation system, i.e., rabbit reticulocyte lysate.E. coli based in vitro translation system, whereas single Ψs did not dramatically change E. coli translational rates and kinetics.Although translation is a highly conserved process, the stimulating effect of Ψ on translation is not universal  is not only a well‐characterized DNA modification e.g., reported to be an epigenetic marker in gene regulation and crucial for X‐inactivation, but m5C also decorates RNA.6A and Ψ, little is known about the functions of m5C within RNAs. Thus far, m5C has been identified in bacterial, archaeal and eukaryal rRNAs, whereas in the latter two domains, tRNAs are also m5C‐modified.5C has also been reported in ncRNAs and described to regulate their processing.5‐Methylcytosine .5Cs are translated in vitro at reduced levels, whereas hm5C did not affect protein yields. In vivo, however, hm5C containing mRNAs were associated with polysomes, indicating higher levels of translation.5C on translation in vitro and in vivo.5C within coding sequences of mRNAs on eukaryal translation is not yet fully clarified.With the rise of bisulfite deep sequencing and its adaptation for RNA research, m5C modification has not yet been identified within bacterial mRNA. However, employing a bacterial in vitro translation system, it was recently demonstrated that single m5C modifications do not strongly inhibit protein synthesis independent of their localization within a codon.5C induces mis‐/recoding when positioned in the second codon position of a proline codon (Cm5CC).5CC codons was induced 50‐ to 500‐fold, assuming an endogenous translational error rate of 10−3 to 10−4.The mIn the last decade of RNA research, significant technical advances have been made. With the refinement of next‐generation sequencing2C, thereby potentially regulating gene expression.In vitro studies have shown that 2′‐O‐methylations, particularly at the second nucleotide of the codon strongly repress protein synthesis, independent of the sequence context.Methylations of the ribose 2′‐OH of mRNA nucleotides within the coding sequence have not unambiguously been identified thus far. However, there are indications that mRNAs are potentially methylated in a snoRNA‐dependent manner. The class of C/D box snoRNAs typically guides a protein complex to the rRNA target, consequently leading to a 2′‐O‐methylation.N1‐methyladenosine (m1A) within thousands of the mRNAs of several human and murine cell lines and in yeast.1A pattern is conserved in these cell types.1A is dynamically deposited in response to environmental cues within 5′ UTRs around canonical and alternative translation initiation sites and in highly structured RNA regions in the vicinity of start codons.1A also affects the structure of RNAs.1A‐modified mRNAs are translated at higher rates compared with non‐methylated mRNAs, the authors hypothesized that m1A might affect mRNA folding around the translational initiation sites thereby facilitating translation.1A generates a binding site for proteins, thereby promoting initiation. Overall, the stress‐induced deposition of m1A, respectively its reversibility and the proposed implication in translation are reminiscent of m6A.Recently, two independent groups reported 1A, but also m6A and Ψ) has been shown to be dynamically regulated and introduced within transcripts in response to stress.O6‐methylguanosine (m6G), known as DNA lesion have been recently investigated for their impact on protein synthesis.6G also affects translation only when present in the second codon position.A subset of mRNA modifications (m6A‐ and Ψ‐modified RNAs, respectively, but might also be applicable to other RNA modifications.The emerging roles of mRNA modifications are extremely diverse, ranging from inducing mRNA decay,It will also be challenging to refine the reported modification patterns to single nucleotide resolution. Sequencing approaches based on immuno‐precipitation narrow down the modification site, but do not precisely map the modified nucleotides. However, improvements of these high‐throughput approaches enable the identifications of some mRNA modifications at single nucleotide resolution.mRNA modifications also modulate protein synthesis Table . InitialRNA modifications were initially described decades ago, whereas the knowledge concerning the presence of these modifications within the coding sequence of mRNAs is rather novel. Thus, investigating the influence of these modifications on pivotal cellular processes, such as mRNA translation, will generate new research opportunities and will change our understanding of gene regulation.


Based on the review of the literature, perinatal inflammation often induced by infection is the only consistent independent risk factor of neonatal arterial ischemic stroke (NAIS). Preclinical studies show that acute inflammatory processes take place in placenta, cerebral arterial wall of NAIS-susceptible arteries and neonatal brain. A top research priority in NAIS is to further characterize the nature and spatiotemporal features of the inflammatory processes involved in multiple levels of the pathophysiology of NAIS, to adequately design randomized control trials using targeted anti-inflammatory vasculo- and neuroprotective agents. Neonatal arterial ischemic stroke (NAIS) is defined by a symptomatic arterial ischemic stroke occurring in the neonatal period, i.e., the first 28 days of life . NAIS haNeonatal arterial ischemic stroke is one of the commonest forms of pediatric stroke, causing a heavy burden of life-long motor, cognitive, and/or behavioral disabilities . The patAfter having reviewed the literature to assess epidemiological, clinical and fundamental data, we discuss the role of perinatal inflammation in the causal pathways leading to NAIS.According to the five population-based studies currently available, the range of prevalence of NAIS varies from 6 to 17/100,000 . This reBased on the review of the literature, the risk factors of NAIS as defined from the seven case–control studies dedicated to NAIS – or other studies in which specific data about NAIS could be extracted—have been summarized in the Table peripartum asphyxia (Peripartum asphyxia can either be secondary to infection and subsequent inflammation triggered by systemic exposure to pathogen-associated molecular patterns (PAMPs), or be a powerful inducer of sterile inflammation via the systemic or intracerebral release of damage-associated molecular patterns (DAMP) ..peripartMaterno-fetal and postnatal inflammation is mostly caused by infection. Neonatal bacterial meningitis is classically complicated by arterial ischemic stroke due to focal arteritis , 18–22. Few non-infectious/inflammatory features are associated with NAIS. Male sex was found as an independent risk factor in only one of the seven case–control studies  Table 11. GenetiIn sum, perinatal infection/inflammation is the only independent risk factor of NAIS consistently reported up to now. Genetic prothrombotic risk factors do not appear to be associated with NAIS occurrence.Given the tight reciprocal activation between inflammatory and coagulation cascades, it is quite possible that inflammation promotes thrombus formation within placental, umbilical cord or other vessels feeding the cerebral blood flow. According to a classic pathophysiological hypothesis of NAIS, such thrombus would then migrate and occlude cerebral arteries leading to embolic stroke . This emversus posterior intracranial arterial territories even when the asymmetry of anterior versus posterior blood flows is taken into account  the imbalanced distribution of NAIS between the anterior  account , 28, 29; account , 28, 29;ial wall , 28, 29.Escherichia coli], we showed that end-gestational inflammation combined with a classic prothrombotic stress (transcutaneous laser exposure of the artery of interest), but not sole prothrombotic stress, targeting the MCA triggers NAIS -1/IL-1 receptor antagonist (IL-1Ra) ratio] within NAIS-susceptible, but not non-susceptible, arteries  from ers NAIS . On one arteries .These preclinical results support, beside the embolic hypothesis, the contribution of a focal arteritis and thrombosis in the pathophysiology of NAIS.Inflammation has been well characterized as a potent procoagulant phenomenon.in vivo (via TF and vWF binding sites (via TF inducible expression (The C-reactive protein (CRP) and proinflammatory cytokines, including IL-1β, IL-6, and tumor necrosis factor (TNF)-α, are well-established mediators which are implicated in the materno-fetal inflammation, combining maternal immune activation and/or fetal inflammatory response syndrome (FIRS) –33  and the increased dioxygen (O2) tension. This combination triggers a vasoconstriction  4 and cluster of differentiation (CD) 62  62 ]  62 , 37, 40. (CD) 62 , 40.Hence, immunothrombotic processes occurring within the lumen and the resulting inflammation of the adjacent wall of NAIS-susceptible arteries might be part of a vicious circle driving the pathophysiology of NAIS, even in the frame of a primary embolic etiopathogenic process triggering arterial wall inflammation .Based on the above clinical and preclinical findings, NAIS results from a multiple hit mechanism combining perinatal inflammation and hypoxia–ischemia (HI). In sharp contrast to pre-natal HI or postnatal inflammation happening at least 24 h before HI, which have been shown in preclinical models to be neuroprotective  –60, pren2+, and glutamate release, as well as ionic imbalance and oxidative stress  such as TLR recognize pathogens as well as DAMP , 90–94. via Fas-ligand (Fas-L) interaction through Fas-associated death domain (FADD) (Necroptosis is an early cell death pathway which is triggered by inflammatory mediators led by TNF-α and TNF family death receptor (TNFR): TNFR-1, FAS, and TLR, namely TLR-3 and TLR-4 , 84, 99.n (FADD) , 96, 99.n (FADD) , 71. Hown (FADD) .The secondary phase of ischemic injury occurs between 24 and 72 h after NAIS and implicates apoptosis, anoikis and autophagy cell deaths. Intrinsic and extrinsic forms of apoptosis are known to be involved in NAIS due to pure HI or HI combined to pathogen exposure , 68, 72 It is well known that apoptotic and autophagic cell death pathways crosstalk, and that autophagy can block apoptosis by sequestration of mitochondria  Figure B. The inGiven that the diagnosis of NAIS is often delayed due, in most cases, to the prenatal onset and/or to the absence, or paucity and diagnostic delays, of neonatal symptoms, future therapies should focus on the control of preinsult determinants most often acting prenatally—e.g., through anti-inflammatory intervention, such as IL-1Ra, or on postinsult neonatal mechanisms—e.g., through hypothermia therapy (HT)—rather than on less feasible per-insult acute interventions.Our team and others uncovered that the upregulation of IL-1 plays a key role in chorioamnionitis, and in associated neonatal ischemic brain injuries , 94–97. Hypothermia therapy is now a mandatory standard of care for term newborns suffering for diffuse HI encephalopathy –111. HowErythropoietin presents anti-inflammatory and neuroprotective properties mainly through dampening free radical release and neural cells apoptosis that have been well-established on preclinical models of neonatal brain infarcts , 123. Erversus initial MRI injuries were quantitatively compared at 3 months of age between the erythropoietin-treated patients versus 10 untreated matched historical controls. The percentage of tissue loss within the ischemic area was not different between the treated versus untreated group. Hence, the effectiveness of erythropoietin in terms of neuroprotection in the NAIS context remains to be established.There was no adverse effect on blood cells counts, or coagulation. The residual versus non NAIS patients. In addition, there is an urgent need to design efficient diagnostic tools adapted to the rapid and non-invasive diagnosis of chorioamnionitis and other maternofetal infectious/inflammatory diseases. In this line, MRI of the placenta, which has been shown to rapidly detect abnormal placental signals in a preclinical model of chorioamnionitis might be a promising diagnostic option  of newborns at risk of NAIS , by compc option . Based oA low threshold for early head MRI should be applied in newborns presenting acute neurological symptoms compatible with neonatal stroke. Adapted and thorough protocol of parenchymal and angiographic imaging should be continuously updated and more systematically applied , as receSC and GS contributed to the design of the manuscript. AG, CG, MC, SC, and GS drafted the manuscript. All the authors contributed to the editing of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The diversity of processed transcripts in eukaryotic genomes poses a challenge for the classification of their biological functions. Sparse sequence conservation in non-coding sequences and the unreliable nature of RNA structure predictions further exacerbate this conundrum. Here, we describe a computational method, DotAligner, for the unsupervised discovery and classification of homologous RNA structure motifs from a set of sequences of interest. Our approach outperforms comparable algorithms at clustering known RNA structure families, both in speed and accuracy. It identifies clusters of known and novel structure motifs from ENCODE immunoprecipitation data for 44 RNA-binding proteins.The online version of this article (doi:10.1186/s13059-017-1371-3) contains supplementary material, which is available to authorized users. As genomic technologies progress, an ever-increasing number of non-protein-coding RNAs (ncRNAs) are being discovered. Long non-coding RNAs (lncRNAs) are of particular interest for functional genome annotation given their abundance throughout the genome. So far, few lncRNAs have been functionally characterised, and those that have seem to be involved in regulation of gene expression and epigenetic states , 2. UndeMany ncRNAs lack sequence conservation, in contrast to protein-coding genes. Most small ncRNAs have well characterised secondary and tertiary structures, as evidenced in Rfam, the largest collection of curated RNA families (2588 families as of version 12.2 ). In conWe, and others, hypothesise that lncRNAs act as scaffolds for the recruitment of proteins and assembly of ribonucleoproteins (RNPs), mediated by the presence of modular RNA structures, akin to the domain organisation of proteins , 9–14. Pdot plots) was first introduced by CARNA [The identification of RNAs with similar functions involves comparing both their primary sequence and higher-order structures simultaneously. However, sequence-based methods to identify common structural features perform poorly when sequence identity falls below 60 % . Hence, by CARNA , 30, whiThese pairwise RNA structure alignment algorithms can be used to identify clusters of homologous RNA structure motifs from a set of sequences of interest. Will et al. first showed that a (dis)similarity matrix can be constructed from all-vs-all pairwise RNA structure alignments with the pairwise alignment tool LocaRNA, identifying known and novel groups of homologous RNAs using hierarchical clustering . HoweverHere, we describe a computational pipeline for the identification and clustering of homologous RNA structures from a large set of query sequences. At its core lies DotAligner, a heuristic pairwise sequence alignment algorithm that considers the ensemble of base pair probabilities for each queried sequence. We benchmark the performance of DotAligner with other pairwise RNA structure alignment algorithms through several iterations of a stochastic sampling strategy across all Rfam seed alignments, highlighting the speed and accuracy of our method. We combine DotAligner with density-based clustering for the unsupervised identification of RNA structure motifs, which can identify both known Rfam families and novel RNA structure motifs from ENCODE enhanced cross-linked immunoprecipitation (eCLIP) data. Finally, we exemplify how clusters of homologous RNA structures identified by our method can be used to search for homologous structures across reference genomes and transcriptomes to generate a map of functionally related RNA structure motifs.We developed an algorithm that leverages the diversity of suboptimal solutions from a partition function of RNA alignments to identify an optimal sequence-structure alignment of two RNAs. The algorithm, termed DotAligner, overcomes the limitations of comparing unique RNA secondary structures (such as minimum free energy predictions) to yield a pairwise alignment that considers mutual base pair probabilities. A schematic of how DotAligner functions is illustrated in Fig. DotAligner was developed with an emphasis on runtime performance to facilitate all-vs-all pairwise comparisons of RNA structures on large data sets. Consequently, it uses pre-calculated RNA dot plots to perform alignments. It also uses the observation that a significant subset of stochastic sequence alignments between two RNAs will overlap the correct structure-based alignment, even though the optimal sequence alignment deviates significantly from the structural alignment . The algWe first tested DotAligner on BRAliBase 2.1 pairwise RNA structure alignments, a reference data set specifically designed for algorithm benchmarking , 35  scores above those from the BRAliBase 2.1 reference alignments . As justified below, the benefits of sampling are outweighed by other properties of the algorithm.DotAligner’s capacity to produce competitive pairwise alignments is demonstrated via a 5S-adenosyl methionine riboswitch  to control for and ascertain any sequence-dependent biases (see ‘Methods’). DotAligner alignment scores were then compared to a binary classification matrix representing the distinct Rfam families . The OPTICS (Ordering Points to Identify the Clustering Structure) algorithm  was chosIn comparison to GraphClust, the combination of DotAligner and OPTICS performs comparably well  that overlap evolutionarily conserved secondary structure predictions, 25 significant clusters of homologous RNA were detected, including all 11 positive controls . This pipeline was applied to eCLIP sequencing data from 44 RNA binding proteins from the ENCODE consortium , with 10spike-in Rfam sequences facilitate the identification of similar RNA structures, such as the homologues to SNORNA72 depicted in Fig. Indeed, the Our method also identifies RNA structure families impartially, as exemplified by several clusters of DKC1-associated sequences, which present consensus secondary structures indicative of snoRNAs Fig. e. CloseThe increasing accessibility of next-generation sequencing and immunoprecipitation protocols provides large resources for in-depth transcriptome and interactome profiling. Elucidating the structural features of RNAs associated with RNA-binding proteins and RNP complexes, combined with the systematic classification of their genome- or transcriptome-wide occurrence, can identify recurrent functional motifs that may form components of regulatory networks. Pragmatically, the method we describe facilitates this process by enabling rapid and unsupervised clustering of RNA structure motifs with reasonable accuracy. We also show that clustering eCLIP sequences can identify new RNA structures and their homologues throughout the genome similarity matrices for cluster analysis than other pairwise structure alignment algorithms, or at least produce them with reasonable computational power. In addition to its speed, the strength of DotAligner lies in its capacity to accurately score structurally homologous RNA sequences and the suboptimal structural landscape of RNAs, reducing several dimensions of information into a single discriminative numeric value. Our results show that this can be sufficient to extract structurally and functionally related sequences from a large amount of noisy input. It is an ideal application for screening high-throughput sequencing data – such as RNA immunoprecipitation data – for common structural motifs.The algorithm generates pairwise alignments that differ qualitatively to reference structural alignments at lower ranges of sequence identity, but it performs better than more complex algorithms within ranges of sequence similarity that substantially overlap those of functionally related RNAs, as presented in Rfam. This could be a consequence of refining the runtime parameters through testing on independently and stochastically sampled Rfam sequences. It is not impossible that other algorithms could undergo comparable parameter optimisation. However, the significantly higher computational complexity of other related tools compared to our method makes it fairly difficult (and resource intensive) to perform such brute-force parameter optimisation.High-throughput CLIPseq data pose a challenge for the detection of consensus motifs since several molecules that are in close physical proximity to the target molecule can co-precipitate together. Consequently, other RNA sequences that do not directly bind to the target protein may be present. We have shown that our method is, nonetheless, suitable for such noisy biological data. For example, the UPF1 cluster we describe may be an example of an indirect binding event, as UPF1 directly interacts with STAU1, a double-stranded RNA-binding protein that has been reported to target ALU sequences . Other cDotAligner has several variables that can influence the clustering results and speed depending on the type of input data. The most influential variables are the weight between sequence and structure similarity, and the exploration depth of suboptimal alignments in the stochastic backtracking. We have shown that stochastic sampling of suboptimal string alignments improves the alignment of RNA dot plots. However, the performance increase does not outweigh the increase in runtime associated with sampling suboptimal sequence alignments. Our Rfam clustering benchmark using a binary classification strategy has shown that the best trade-off between alignment accuracy and speed comes with the abandonment of sampling, as supported by the de novo structures identified from the ENCODE eCLIP data. Future optimisation of DotAligner parameters will likely increase its usability. For example, dynamic parameters could be implemented that adjust the degree of sampling diversity and number of samples based on the sequence identity obtained from step 1 of DotAligner. This could tune the algorithm’s performance based on the nature of the input, potentially improving DotAligner’s performance across all ranges of sequence identity. Another potential enhancement could be achieved in the stochastic sampling by considering only elements of the ensemble with probabilities larger than a threshold. By doing so, we could (1) reduce the number of useless samples, (2) guarantee that cells of high probability are passed  and (3) leave time/samples to explore the ensemble space  around these suboptimals.Another great challenge lies in the accurate depiction of RNA structure motif boundaries. Whereas global structures may stabilise the RNA molecule, local structural domains are often sufficient for recognition by RNA binding proteins. A strategy to find optimal local alignments would be desirable for this. DotAligner can search for semi-local alignments by introducing penalty-free gaps at the sequence extremities . In this study, we did not investigate the optimisation of these local pairwise similarity scores, because they may miss parts of the functional units (RNA structure) and, hence, hinder the search for optimal clusters. Instead, we circumvented this issue by overlapping eCLIP peaks to evolutionarily conserved RNA secondary structure predictions with well-characterised flanking helices supported by base pair covariation . While pAn efficient pairwise RNA sequence alignment heuristic, which intrinsically considers suboptimal base pairings, accurately discriminates between distinct structured RNA families. When combined with a noise-tolerant density-based clustering algorithm, this approach identifies known and novel RNA structure motifs from a set of input sequences without a priori knowledge. The resulting RNA structure motifs are subsequently used to identify homologues in the human genome, improving the annotation of lncRNAs and increasing the repertoire of functional genetic elements.k and t from 0 to 1 in increments of 0.1; o and e from 0.2 to 1 in increments of 0.2. For each set of parameter combinations, the number of alignments producing identical structural topologies to the reference alignment was determined using RNAdistance, SCI, a robust measure of RNA structural alignment integrity [predicted and reference RNA secondary structure were also calculated for all resulting alignments: The DotAligner algorithm implements several parameters that first need to be tuned before it can be applied to biological sequence analysis. All combinations of core parameters were tested on the 8976 pairwise RNA structure alignments curated in the BRAliBase 2.1 reference data set . We firsntegrity  based onThe baseline parameters were then selected via a product rank of these two metrics and subjected to refinement using a binary classification approach, described in the next section.Further refinement of the optimal parameters was performed using a binary classifier for two sets of 200 stochastically sampled Rfam entries with published structures: (i) a low pairwise sequence identity (PSI) set and (ii) a high PSI set, where any two sequences from the same family share between 0–55 % and 56–95 % PSI, respectively Fig. a, b. Thx and y have a score of 1 if they belong to the same Rfam family, or a score of 0 if they do not. The similarity matrix resulting from all-vs-all pairwise comparisons with DotAligner was tested for accuracy using the AUC of the receiving operator characteristic, as calculated by the R package pROC [θ=0.5, κ=0.3, go=1 and gext=0.05  is the weight of sequence similarity compared to the similarity of unpaired probabilities, κ (or -k) is the weight between sequence-based similarity and dot plot similarity, go (or -go) is the gap opening penalty and gext (-go) is the gap extension penalty. Sampling-specific parameters s (number of samples) and T (measure of sampling diversity) had minimal impact in the refined parameter optimisation from sampled Rfam clans, although the parameters can increase alignment scores in low and medium pairwise sequence identity ranges  variant, we ran the program with default parameters, whereas for the NoFold (filtered) variant, we reduced the feature space to 1902 CMs by removing Rfam families from our benchmark set.The following clustering performance metrics were used: https://www.encodeproject.org/search). The resulting 5,040,096 peaks were filtered to keep only those with ≥eightfold enrichment over the total input background and an associated P value ≤10−4. Furthermore, peaks were merged if they overlapped by more than 50 nt to avoid over-representing the same sequence  using the command opticsXi, xi = 0.006, minimum=T). The sequences for each cluster were then extracted and submitted to multiple structure alignment with mLocaRNA version 1.9.1 using parameters --probabilistic --iterations=10 --consistency-transformation --noLP.The normalised similarity matrix resulting from all-vs-all pairwise comparisons with DotAligner was then subjected to clustering with the dbscan 1.1-1 R package from Michael Hahsler Additional file 2Supplementary tables and figures with descriptions. (PDF 3890 kb)


ADAR (adenosine deaminase acting on RNA) enzymes that converts adenosine to inosine in RNA sequence can generate mutations and alter gene regulation in metazoans. Previous studies have shown that A-to-I RNA-editing plays vital roles in mouse embryogenesis. However, the RNA-editing activities in early human embryonic development have not been investigated.A-to-I RNA-editing mediated by ADAR expression level changes, the genome-wide A-to-I RNA-editing levels in cells remained relatively stable until 4-cell stage, but dramatically decreased at 8-cell stage, continually decreased at morula stage. We detected 37 non-synonymously RNA-edited genes, of which 5 were frequently found in cells of multiple embryonic stages. Moreover, we found that A-to-I editings in miRNA-targeted regions of a substantial number of genes preferably occurred in one or two sequential stages.Here, we characterized genome-wide A-to-I RNA-editing activities during human early embryogenesis by profiling 68 single cells from 29 human embryos spanning from oocyte to morula stages. We demonstrate dynamic changes in genome-wide RNA-editing during early human embryogenesis in a stage-specific fashion. In parallel with Our single-cell analysis reveals dynamic changes in genome-wide RNA-editing during early human embryogenesis in a stage-specific fashion, and provides important insights into early human embryogenesis.The online version of this article (doi:10.1186/s12864-016-3115-2) contains supplementary material, which is available to authorized users. ADAR (adenosine deaminase acting on RNA) enzymes is the major RNA-editing that post-transcriptionally modifies nucleotide sequences on RNA molecules in metazoans , reference strand frequency , and reference strand preference [absolute (reference strand frequency minus 0.5)]. (c) We filtered out variant sites displaying significant strand bias, defined as either FET P-value < 0.05 plus read end frequency > read middle frequency, or read end frequency > 0.9.Because the mismatches at ends of sequencing reads are less credible than the ones in the middle of reads, therefore, we filter out variants with position bias, i.e. the majority of variant supporting bases are located at read ends. Read end is defined as 10 bp at 3’-end or 5 bp at 5’-end. Position bias are defined as either FET To reduce false positives introduced by misalignment of sequencing reads to high similarity regions of the reference sequences, we perform a realignment filtering. Specifically, we extract variant-supporting reads on candidate variant sites and realign them against a combination reference (hg19 genome + ensemble transcript reference v75) by bwa-0.6.2. We retain a candidate variant site if at least 90 % of its variant-supporting reads are realigned to this site.http://hgdownload.cse.ucsc.edu/goldenpath/hg19/database/), or in homopolymer regions (runs of ≥ 5 bp). For sites located in non-Alu regions, we additionally remove the sites within 5 bp from a splicing site.We remove variant sites that are either in simple repeat regions , 1000 Genome SNP (Phase 3), and human populations of Dutch , MongoliFor rare SNPs filtering, we filter out RNA variants that are only detected in one embryo, because true editing sites are often present in different individuals, whereas rare SNPs are most likely not, as Ramaswami suggested .We also filter out candidate RNA variants if their mismatch frequencies > 0.95.We only selected the data of 68 single cells from 29 embryos each with over 0.5 Gb uniquely mapped bases for further analyses. We summarized the base calls of pre-processed aligned RNA-reads to the human reference in pileup format. To identify candidate RNA-editing sites, we only used sequencing bases with base-quality ≥ 20. We determined RNA-editing sites as follows:Finally, we tried to filter out private DNA-induced variants using the exon DNA sequencing data from the father of 12 embryos in Xue’s study. We found no additional paternal exonic DNA variants in the RNA mismatches we identified  to store the editing frequency (0 for no edit and −1 for depth < 4) on site i in sample k. We calculated the euclidean distances among cells based on this matrix using the dist function and performed hierarchical clustering using the hclust function with default parameters. Both functions could be found in R package stats. The heatmap was drawn using R package pheatmap.P-values of genes used in this study were reported by edgeR.We calculate the number of reads aligned to each gene using the featureCounts function in R package Rsubread . We perfhttp://www.microrna.org/microrna/getDownloads.do .We used ANNOVAR , 32 to fhttp://software.broadinstitute.org/gsea/msigdb/annotate.jsp [The gene set enrichment analysis is performed on website tate.jsp , 35. The


Tdrd6-/- diplotene spermatocytes PRMT5 association with SmB and arginine dimethylation of SmB are much reduced. TDRD6 deficiency impairs the assembly of spliceosomes, which feature 3.5-fold increased levels of U5 snRNPs. In the nucleus, these deficiencies in spliceosome maturation correlate with decreased numbers of SMN-positive bodies and Cajal bodies involved in nuclear snRNP maturation. Transcriptome analysis of TDRD6-deficient diplotene spermatocytes revealed high numbers of splicing defects such as aberrant usage of intron and exons as well as aberrant representation of splice junctions. Together, this study demonstrates a novel function of TDRD6 in spliceosome maturation and mRNA splicing in prophase I spermatocytes.Tudor containing protein 6 (TDRD6) is a male germ line-specific protein essential for chromatoid body (ChB) structure, elongated spermatid development and male fertility. Here we show that in meiotic prophase I spermatocytes TDRD6 interacts with the key protein arginine methyl transferase PRMT5, which supports splicing. TDRD6 also associates with spliceosomal core protein SmB in the absence of RNA and in an arginine methylation dependent manner. In  Tdrd6-/- diplotene spermatocytes PRMT5’s association with SmB and arginine dimethylation of SmB are much reduced. Abrogation of arginine methylation impairs the assembly of spliceosomes and the presence of the spliceosomal RNA U5 is aberrantly increased. These deficiencies in spliceosome maturation correlate with decreased numbers of Cajal bodies and gems involved in later stages, i.e. nuclear snRNP maturation. To reveal functional consequences of these deficiencies, transcriptome analysis of primary spermatocytes showed high numbers of splicing defects such as aberrant usage of intron and exons as well as aberrant representation of splice junctions upon TDRD6 loss. This study reveals a novel function of TDRD6 in spliceosome maturation and mRNA splicing in spermatocytesVery little is known about splicing and its regulation in germ cells, particularly during meiosis. This paper describes the role of a male germ cell-specific protein, Tudor containing protein 6 (TDRD6), in assembly of the spliceosome in spermatocytes. We show that in prophase I TDRD6 interacts with the key protein methyltransferase of the splicing pathway PRMT5. PRMT5 methylates arginines in substrate proteins. In a methylation dependent manner, TDRD6 also associates with spliceosomal core protein SmB in the absence of RNA, thus before an RNP-type spliceosome has been assembled. In  Spermatogenesis is essential for the generation of haploid male gametes required for sexual reproduction in higher eukaryotes. Spermatogenesis in mice starts at app. day 6 postpartum (dpp) as spermatogonia undergo mitotic expansion, generate tetraploid cells (4N) during premeiotic S-phase and enter meiosis. Meiosis is composed of two successive nuclear divisions. In the first meiotic division (meiosis I), pairs of homologous chromosomes are segregated and primary spermatocytes are reduced in chromosome content to diploid secondary spermatocytes (2N). These 2N cells then undergo reduction to haploid spermatids (N) in the second meiotic division (meiosis II) through a mitosis-like division segregation of sister chromatids. Meiotic prophase I is by far the longest phase of meiosis, lasting approximately three weeks in most mammals. It is described by four sequential substages, i.e. leptotene, zygotene, pachytene and diplotene. Prophase I features unique chromosome properties and behavior such as the pairing of homologous chromosomes and formation of the synaptonemal complex (SC) in pachytene. The SC consists of two axial elements (AE) that form earlier during leptotene. Each AE–often visualized by staining for protein SYCP3 –supports the two sister chromatids of one homologue. In the diplotene stage, homologues desynapse and remain linked only at the sites of crossing-over/chiasmata which are resolved at metaphase/anaphase (reviewed in ). Two sude novo synthesized RNA during spermatogenesis demonstrates a peak in global transcription in late prophase I, which meets the later demand and provides the mRNA needed at this stage [Chromatin compaction during the late steps of spermatogenesis results in silencing of transcription at this stage despite ongoing translation of mRNA. This is enabled through the temporal uncoupling of transcription and translation of mRNA during spermatogenesis . Most mRis stage –7. This is stage , 9). Geris stage , 11).Drosophila Tudor protein. It appears first in late pachytene spermatocytes (day 17.5 post partum) in multiple fine filamentous cytoplasmic granules surrounding the nuclei. In round spermatids, the TDRD6 signal appears as a single bright distinct perinuclear dot representing the ChBs. TDRD6 interacts in vitro with MVH, MILI and MIWI and loss of TDRD6 results in mis-localization of ChB components MVH and MIWI. In the absence of TDRD6 the ChB architecture is severely impaired and spermiogenesis is arrested at the round-to-elongated spermatid stage. Pre-miRNA and miRNA transcripts are mis-regulated in Tdrd6-/- testis [The ChBs contain mRNA, miRNA and piRNA as well as proteins functioning in RNA metabolism such as the DEAD-box RNA helicase MVH –14. Tudo- testis . Lately - testis . OverallMass spectrometric analysis of potential TDRD6 associated proteins  identifiin vivo although this assembly can also take place spontaneously in vitro [The spliceosome is a large and flexible macromolecular machine with more than a hundred RNA and protein components . Small nin vitro –26. The in vitro . SMN-posin vitro . CBs arein vitro , 30. AftA previous mass spectrometric analysis performed in our lab  revealedTdrd6-/- males from all founders. All LAP-TDRD6-positive Tdrd6-/- males were fertile. This proves that the LAP-TDRD6 is fully functional as it can rescue the developmental arrest during spermiogenesis and the sterility of Tdrd6-/- males.To validate the interaction between TDRD6 and PRMT5, we generated a transgenic mouse carrying a Localization and Affinity Purification ‘‘LAP”- tagged TDRD6 BAC construct . The LAPLAP-TDRD6 was immunoprecipitated with an α-EGFP antibody from RIPA extracts of total adult testes and probing the IB for PRMT5 detected the PRMT5 signal . IB probTDRD6 has 6 Tudor domain , which mThe assembly of spliceosomal proteins with U-RNAs is mediated partly through direct interactions between the Tudor domain of SMN and the sDMAs of the spliceosomal proteins SmB, SmD1, SmD3 . We hypoSm proteins bind to the U-RNAs in spliceosomes and RNA could bridge its interaction with TDRD6. Therefore, we precipitated Sm proteins with α-Y12 IgG both in the absence and presence of RNase from total testes of adult males. We used SmB detected as a 25 kDa band to represent spliceosomal proteins. The interaction was completely lost in the absence of RNase . SuccessWe then tested whether the sDMA modification mediates the TDRD6-Sm interaction. We inhibited methylation with 5′-deoxy-5′-(methylthio)adenosine (MTA) in total adult testis cultures for 16 h prior to IP. In the presence of MTA the interaction between LAP-TDRD6 and SmB is undetectable as shown by both α-EGFP  and the Previous studies in our lab revealed that about 20 kDa off the C-terminus of Tdrd6 are removed during the transition from meiosis I to meiosis II . We can Tdrd6-/- cells. Given that the TDRD6 and SmB signals overlap only in diplotene cells  increase in U5 RNA levels in the splicing snRNPs in the absence of TDRD6. There was no significant change in the levels of U2, U12 and U4atac bound to Sm proteins (P > 0.05) . We obs > 0.05) . SmB IB  samples .This finding together with the requirement for RNA exclusion in SmB-TDRD6 interaction suggests that TDRD6 acts as an assembly factor for at least SmB and either competes with certain snRNAs like U5 for SmB binding or can only bind in an early step of spliceosome maturation prior to RNA binding.Exclusion of a certain U-RNA by TDRD6 resembles the mode of interaction of pICln with Sm proteins. pICln acts as a chaperone by binding to the small, highly conserved Sm domain that is N-terminal to the methylated RG motifs and responsible for oligomerization and RNA binding of Sm proteins . As elabOnce the U-snRNP assembly takes place in the cytoplasm, the SMN complex translocates into the nucleus to carry the snRNPs to Cajal Bodies (CBs), where the final steps of spliceosome maturation take place. The rate of spliceosomal snRNP assembly correlates with CB number  and SMN Tdrd6-/- diplotene cells, we hypothesized that formation of both nuclear SMN-positive bodies and CBs would be impaired. Via immunofluorescence staining, we detected a significant decrease by almost 70% in the number of SMN-positive bodies per nucleus (p<0.0001) with a median number of 3 per Tdrd6+/- nucleus versus 1 in Tdrd6-/-  with a median number of 6 bodies in Tdrd6+/-versus 3 in Tdrd6-/- (p = 0.7245). Higher numbers of both SMN-positive bodies and CBs in meiotic cells correlates with the previous data on the increased mRNA transcription and hence splicing in late prophase I [In Tdrd6-/- . In roun p = 0.725. HigherSeveral studies on somatic cells previously revealed improper pre-mRNA splicing upon disruptions at the upstream steps of the spliceosome maturation pathway –52. Our Tdrd6+/- and Tdrd6-/- mice at a depth of 47–51 million paired-end fragments per sample. After aligning the fragments to mouse reference genome (mm10) with GSNAP and annotating them with Ensembl v75, we acquired over 360 million RNA sequence reads in total with a very high unique mappability of more than 90% for each sample  of 0.1. This analysis revealed 1270 genes  with 1850 mis-regulated introns, i.e. 1.5 mis-regulated introns per gene on average . More spTdrd6-/- condition while 646 of them were down-regulated. The remaining 471 events represented significantly mis-regulated splice sites. 381 of them were up-regulated while 90 of them showed down-regulation in Tdrd6-/- primary spermatocytes. These two approaches yielded 240 common genes with mis-spliced mRNA.Another approach to assess the fidelity of splicing was to test the differential exon and splice site usage. The reads at the exon-exon junctions as well as within the exonic sequences were compared using JunctionSeq. 889 genes demonstrated significant differential usage in 1,989 events indicating more than 2 perturbations at exon or junction level per gene  lying within that intron . OverallOur data demonstrates slightly less intron retention (824 introns) than intron removal (1026 introns) among the mis-regulated cases. This suggests that upon TDRD6 loss the specificity of the intronic splicing site selection is affected more than the overall efficiency of splicing. This is plausible considering that loss of TDRD6 leads to an altered snRNP repertoire. U5 snRNP is a common component of both types of splicing machineries and therefore centrally important . IncreasFunctional cluster analysis of the mis-spliced transcripts did not reveal any significantly enriched clustering of these transcripts at a particular cellular function. Also, the mis-regulated miRNAs reported earlier in TDRD6 deficient spermatids  are not The finding that a wide diversity of genes was affected indicates a global role of TDRD6 in the regulation of pre-mRNA splicing.in silico analysis of the amino acid sequence did not reveal prominent motifs.The data reported here suggest a novel function for TDRD6 in the maturation of spliceosomal snRNPs during the transcriptionally highly active prophase I of spermatogenesis. This function adds to the roles of TDRD6 at later stages of spermatogenesis/spermiogenesis in formation of the ChB type 2 and in nonsense-mediated decay. One notable difference between TDRD6 protein in meiosis I and in later stages is the conversion of the early 250 kDa form into the later 230 kDa form, which lacks about 20 kDa off its C-terminus We speculate that the 250 kDa variant is the one acting in spliceosome assembly while the 230 kDa variant–the only form present after meiosis I–acts in ChB type 2 processes. It will be fascinating in the future to assign specific molecular properties to the C-terminal end of TDRD6, which does not bear Tudor domains, and For the role of TDRD6 in spliceosome assembly, we propose the model shown in Why is TDRD6 expressed only in the male germ line and even here only from mid-pachytene onwards? Obviously, TDRD6 is not needed for the universal splicing apparatus found in almost all animal cells, and it is not even needed for oocytes at any stage. There is no definitive answer to this question, but there are no ChB in non-male germ cells or somatic cells. Other bodies or speckles harbor splicing factors in those cells, and some like Cajal bodies are shared between spermatocytes and other cell types. Cajal bodies are reduced in absence of TDRD6 because their assembly depends on prior successful assembly of SMN complexes. Late spermatocytes and spermatids are unique in that they stepwise change their chromatin organisation until the genome is packaged into a protamine-dense body that is largely transcriptionally silenced. Thus the need to store some RNA species in ChBs, i.e. ChB type 2. There may be testis-specific splice events to produce the appropriate RNA molecules and these are not produced in any other cell type, perhaps explaining the need for TDRD6. However, we find a general splicing deficiency in absence of TDRD6, not only impaired splicing of testis-specific RNAs. Still, TDRD6 may have acquired the role of a spliceosome assembly factor to make sure that all RNA splicing events, including those specific for spermatogenesis, are properly carried out. An additional challenge to spermatocytes stems from the great burst of transcription that happens when chromosome pairing is complete, i.e. when the SCs are formed in pachytene. To handle this burst, which includes testis-specific transcripts, a highly efficient splicing machinery may be required and TDRD6 helps providing that efficiency. Since little is known about splicing processes during mammalian spermatogenesis, this holds intriguing questions for future research.Tdrd6-/- mice was described in detail previously [Tdrd6-LAP mice were generated as described in the Supplemental Experimental Procedures. The use of mice was approved by the State of Saxony animal welfare officials, Az DD24-5131/ 339/6 and was performed according to the national and EU guidelines.Generation of eviously . Tdrd6-LAntibodies used in several different applications in this study are listed in Tables 1, 2 and 3 in “Supplementary Experimental Procedures”.2O) for 10min at room temperature (RT) and dried at RT. Cells were incubated on coverslips for 1h at RT, fixed in 2% formaldehyde/PBS. From permeabilization/blocking onwards, the same staining protocol was followed as for the sections .Coverslips were boiled in 1M HCl, rinsed with water, dipped into poly-L-lysine solution adenosine (MTA) for 16h. Control cultures were incubated with 70% (v/v) methanol/water for leptomycin B or with DMSO for MTA.2VO3, 1mM PMSF, 1x protease inhibitor cocktail EDTA-free mini), supplemented with the primary antibody and incubated at 4°C overnight with gentle shaking. 8μl Dynabeads-proteinA/G per 1μg of primary antibody was added and incubated at 4°C with gentle shaking for 2h. Beads were washed 3 times for 10 min with the same IP buffer, resuspended in 2x Laemmli buffer and boiled at 95°C for 5 min to elute the proteins. SDS-PAGE and immunoblotting procedures were followed as described in Supplemental Experimental Procedures. Wherever indicated, 100μg/ml RNase or 1U/μl RNaseOUT was added into the RIPA and IP buffers.100–300μg RIPA extract (see Suppl Exp procedures) was diluted at a 1:2 ratio in IP buffer , the extracts were diluted at a 1:9 ratio in the IP buffer  to quench the excess SDS in the extraction buffer. For elution, beads were boiled in 2x Laemmli buffer first at 70°C for 1h and then at 95°C for 5 min. Densitometric quantification of the blots was performed using Fiji.Immunoprecipitation for the interactions SmB/PRTM5 and LAP-TDRD6/PRMT5 were performed with the following changes: Single cells were fixed in 1% formaldehyde solution for 5 min and then quenched for 5 min with 200mM Glycine at RT. After the cells were pelleted and lysed in the extraction buffer  according to manufacturer’s instructions. Concentration and purity of the RNA samples were determined by UV absorbance measurements using the NanoDrop 2000c Spectrophotometer (Thermo Scientific).Depending on experiment, total RNA was extracted from either whole testes or MACS-purified hCD4100ng to 1μg of total RNA was treated with RQ1 RNase-Free DNase (Promega) according to manufacturer’s instructions and reverse transcribed into first strand cDNA via SuperScript II reverse transcriptase (Invitrogen) and random primer mix (NEB) according to manufacturer’s instructions.(-ΔΔCt) method and normalized to the expression of housekeeping gene Tbp1 for the variability in RNA levels in each sample.2μl of the RT reaction was used directly as a template in a total volume of 20μl of real time PCR reaction with Rotor-Gene SYBR Green PCR Kit (Qiagen) according to manufacturer’s instructions. All the primers were used at a concentration of 1μM and are listed in Table 4 in We performed the same protocol for RIP as in  with theAll statistical analyses for the immunofluorescence and RIP data were performed with two-tailed, unpaired t-test using the statistics tool of GraphPad Prism .Differential intron usage was assessed using DEXSeq (v1.16.10) R package  and diffS1 Fig(A) Schematic representation of a genomic region that contains TDRD6 (orange) and the cassette for tagging at the N-terminus adapted from [(B) Total protein lysates from the testes of an EGFP-positive founder and from an EGFP-negative mouse separated by SDS-PAGE in adjacent lanes and Western blot was performed using α-EGFP and α-TDRD6 IgGs. (C) Fluorescent microscope analysis of EGFP-positive adult testis sections. (i) Sections from stage X-XI and (ii) stage I-II- III were stained with DAPI (blue) only. Cells at different developmental stages are indicated with arrows. Spg: Spermatogonium, P: Pachytene, D: diplotene, RS: Round Spermatid, ES: Elongated Spermatid Scale bar: 50μm.ted from . The neo(TIFF)Click here for additional data file.S2 Figα-EGFP IgG was used to immunoprecipitate LAP-TDRD6 from total cell extracts of adult testis cells cultured in the presence of 5′-deoxy-5′-(methylthio)adenosine (MTA) or DMSO (control) for 16h. Precipitates were divided into two and separated in parallel by SDS-PAGE and Western blotting was performed using α-SYM IgG for sDMA and α-EGFP IgG for LAP-TDRD6.(TIFF)Click here for additional data file.S3 FigRNA was extracted from the flow-through of immunoprecipitations in (TIFF)Click here for additional data file.S4 FigTdrd6-/- mice were used as a control for Annexin V and PI stainings as they show an extensive apoptotic round spermatid cell population .FACS analysis of propidium iodide (PI) and Annexin V stained EGFP-positive LAP-TDRD6 adult mice after 16 h of MTA versus control DMSO treatment. (TIFF)Click here for additional data file.S5 Fig(A) hCD4-positive single cells isolated from the testes of 20dpp Tdrd6+/- and Tdrd6-/- mice were stained with α-SYCP3 to determine the prophase I stage of the cells. All primary spermatocytes were counted and the percentages of diplotene cells, secondary spermatocytes and round spermatids for each genotype were plotted.  (B) Testis sections from 20dpp Tdrd6+/- and Tdrd6-/- littermates were stained with anti-cleaved α-PARP1 (red), α-SYCP3 (green) and DAPI (blue). Scale bar: 100μm. (C) The number of apoptotic cells per tubule at 20dpp based on the cleaved PARP1 staining in (C) is presented in a scatter plot. .(TIFF)Click here for additional data file.S6 FigImmunoblotting was performed using α-Y12 IgG for SmB. One representative blot of 3 biological repeats is displayed.(TIFF)Click here for additional data file.S7 Fig(A) Mapping statistics for transcriptome analysis. Bar plots show the total number of reads (red bars), mapped reads (green bars) which aligned to the reference for each sample and unique reads (blue bars) which aligned uniquely to the reference for each sample. (B) Schematic representation of the primer design for the validation of the differential usage of I1 of Efhb. The 5’ end lies on the left hand-side. Intron and exons with differential usage are shown in magenta. I, intron; E, exon; Fwd, forward primer; Rev, reverse primer.(TIFF)Click here for additional data file.S1 Supplemental Experimental Procedures(PDF)Click here for additional data file.


Escherichia coli and Clostridium thermocellum, substantial work is needed to make the program generally applicable to all bacteria, knowing that the program requires organism specific information. A web server, named SeqTU, was developed to automatically identify TUs with given RNA-seq data of any bacterium using a machine-learning approach. The server consists of a number of utility tools, in addition to TU identification, such as data preparation, data quality check and RNA-read mapping. SeqTU provides a user-friendly interface and automated prediction of TUs from given RNA-seq data. The predicted TUs are displayed intuitively using HTML format along with a graphic visualization of the prediction.A transcription unit (TU) consists of K ≥ 1consecutive genes on the same strand of a bacterial genome that are transcribed into a single mRNA molecule under certain conditions. Their identification is an essential step in elucidation of transcriptional regulatory networks. We have recently developed a machine-learning method to accurately identify TUs from RNA-seq data, based on two features of the assembled RNA reads: the continuity and stability of RNA-seq coverage across a genomic region. While good performance was achieved by the method on  Transcription units (TUs) are basic functional units that each consist of genes consecutively arranged on the same strand of a bacterial genome and transcriptionally co-regulated under specific conditions145SeqTU, for identifying TUs in bacteria based on an organism’s RNA-seq data, and the pipeline is shown in E. coli str. K-12 substr. MG1655 with annotated TUs against predicted ones, and the comparison result suggests that the program is highly reliable.Some computational studies have been published for identification of TUs based on Tiling array and RNA-seq data910http://csbl.bmb.uga.edu/SeqTU_dev/index.php.The predicted TUs are displayed using an intuitive HTML format along with a graphic visualization of the prediction. A link to the HTML page will be sent to the user’s email address once the calculation is done. All users’ computational results will be saved on the server for at least six months. The back-end programs, implemented using PERL and R, along with test examples and documents are freely available online at http://csbl.bmb.uga.edu/DOOR/), covering 2,072 bacteria genomes. For each genome, the identified operons and TUs are shown in a genome browser. By integrating the prediction results of SeqTU and DOOR2, a user can see the dynamic changes of expressions under different conditions.It is noteworthy that we have previously developed an integrative operon database, DOOR2  the email address for forwarding the prediction results  quality check of the RNA-seq data using FastQC. The example SRR400619 has short reads with a length of 36 bps and the sequencing quality is high based on the quality report  the final TU prediction  y report ; (iii) mediction . It is ntalB and mog genes ranging from 8,238 bp to 9,893 bp on the reverse strand. For the strand-specific example (SRR578142), users can follow the same procedure provided above; and the result table and the TU plot are shown in When a job is done, the user will be notified by an email with a link to the final TU prediction. In total, 2,329 predicted TUs are listed in the result table of SRR400619. In addition, the start and end positions of each TU on the reference genome, their strand information, gene functional annotations and a download link to read abundance plots are also provided . AccuracSeqTU server provides a useful and free service for TU prediction from two provided RNA-seq datasets along with the host genomes. A unique characteristic is that the system has a built-in training program that can train an organism-specific classifier for TU prediction. This property makes the prediction highly reliable and desirable. We anticipate that the usefulness of this server will increase over the next few years as more genome-scale transcriptomic data become available and the needs for TU prediction continue to increase.The SeqTU are implemented using Web 2.0 technologies  and PHP server-side scripting language. It runs on a Red Hat Enterprise Linux 6 box (32 Intel Xeon CPUs with 2.4 GHz and 64GB memory). The input RNA-seq data and the computing results will be stored on a local 10TB hard drive. Considering that a job may take hours up to days to complete, it will not be realistic for a user to sit in front of the computer, waiting for the results to return; hence an email address is collected via the input page, to which the final results will be sent.The representation and the logic layers of SeqTU consists of four main functions: (i) data preparation, (ii) RNA-read mapping, (iii) TU prediction, and (iv) result visualization  select a dataset from the NCBI SRA database using the ID of the dataset, e.g. SRR400619; or (ii) upload a RNA-seq dataset from the local computer. For now, we restrict the maximum data size for upload to 500MB. In cases where a user’s data size is larger, special arrangements can be made with the developer to allow uploading the dataset in multiple fragments, which will be integrated into one and stored in a temporary location at the server side. For an organism needed for TU prediction, the user can upload the reference genome to the server or select it from the genome list provided by the server.http://www.bioinformatics.bbsrc.ac.uk/projects/fastqc/), with the check results shown on the result page. The Bowtie2 programBefore mapping the RNA reads to the target genome, quality check on the uploaded RNA-seq reads will be performed using FastQC (TU prediction has two steps: (i) training of an organism-specific classifier for TU prediction; and (ii) TU prediction by the trained classifier based on the given RNA-seq dataset. A highly effective method was developed to prepare for positive and negative datasets for training a TU predictorA TU classifier is trained based on the given RNA-seq data as follows: an expressed TU should have all its genes expressed at a relatively stable level continuously across the whole TU. The detailed characteristics in stability and continuity of gene-expression patterns of true TUs are learned by the classifier through training on both positive and negative data. A support vector machine-based classifier is trained to distinguish the positive training data from the negative ones. A five-fold cross-validation is used to ensure the classifier is reliably trained. All these are done in an automated manner after the input data file is uploaded. After the training is done, the classifier is applied on the input RNA-seq dataset to generate the final result.SeqTU, so we have evaluated its performance using multiple RNA-seq datasets from different species. The test results of this strategy for calling TUs in E. coli and Clostridium thermocellum suggest that this strategy works very wellE. coli str. K-12 substr. MG1655, SRX315217, SRX315218, and SRX315219, the trained TU predictors have the prediction accuracy at 0.95, 0.94, and 0.93, respectively. In all the Clostridium thermocellum RNA-seq datasets, the trained predictors achieved prediction sensitivities over 0.9. The detailed results can be found in the original algorithm paperCurrently, there are no other programs or web servers can do the same thing as How to cite this article: Chen, X. et al. SeqTU: A Web Server for Identification of Bacterial Transcription Units. Sci. Rep.7, 43925; doi: 10.1038/srep43925 (2017).Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.