Bacillus licheniformis.The production of enzymes by an industrial strain requires a complex adaption of the bacterial metabolism to the conditions within the fermenter. Regulatory events within the process result in a dynamic change of the transcriptional activity of the genome. This complex network of genes is orchestrated by proteins as well as regulatory RNA elements. Here we present an RNA-Seq based study considering selected phases of an industry-oriented fermentation of Bacillus subtilis as well as a multitude of completely new candidates for regulatory RNAs. An unexpectedly high number of 855 RNA features are encoded antisense to annotated protein and RNA genes, in addition to 461 independently transcribed small RNAs. These antisense transcripts contain molecules with a remarkable size range variation from 38 to 6348 base pairs in length. The genome of the type strain B. licheniformis DSM13 was completely reannotated using data obtained from RNA-Seq analyses and from public databases.A detailed analysis of 20 strand-specific RNA-Seq datasets revealed a multitude of transcriptionally active genomic regions. 3314 RNA features encoded by such active loci have been identified and sorted into ten functional classes. The identified sequences include the expected RNA features like housekeeping sRNAs, metabolic riboswitches and RNA switches well known from studies on Bacillus licheniformis strains.The hereby generated data-sets represent a solid amount of knowledge on the dynamic transcriptional activities during the investigated fermentation stages. The identified regulatory elements enable research on the understanding and the optimization of crucial metabolic activities during a productive fermentation of  Bacillus licheniformis is a spore-forming soil bacterium closely related to the Gram-positive model organism Bacillus subtilis. The species’ saprophytic life style, based on the secretion of biopolymer-degrading enzymes, predestinates strains of B. licheniformis as ideal candidates for the large-scale industrial production of exoenzymes, such as amylases and peptide antibiotics [B. licheniformis one of the most important bacterial workhorses in industrial enzyme production [B. licheniformis DSM13 was published in 2004 [ibiotics . Especiaoduction . Due to oduction ,3. Attemoduction ,5, proteoduction ,7, and coduction ,8. Since in 2004 , several in 2004 ,11. Howe in 2004  RNA-Seq Trans-encoded ncRNAs, often referred to as small RNAs (sRNAs), are encoded independently from protein genes and are able to modulate the mRNA of genes located at distant chromosomal loci or to interact with target proteins [trans-encoded ncRNAs interact with their target RNAs by imperfect base pairing, which is triggered by the binding of a seed region of at least six contiguous nucleotides [cis, which means that these ncRNAs are transcribed from the antisense strand of protein-coding genes [cis-encoded antisense RNAs (asRNA) range from 100 to 300 nt in size, but some asRNAs are also shown to be substantially longer [cis-encoded asRNA might work as a trans-encoded sRNA for another target [cis-regulatory elements, mainly present at the 5′ untranslated region (5′UTR) of mRNA transcripts, e.g. riboswitches, T-boxes or thermosensors [cis-regulatory elements. They can be prone to RNA-binding proteins or antisense RNAs, carry attenuation systems [RNA-based regulatory elements are involved in the regulation of metabolism, growth processes, the adaptation to stress and varying culture conditions  and can proteins . Upon foleotides ,16. Thisleotides ,17. It hleotides ,16. A seng genes . Hence, ng genes . Most dey longer ,20. Antir target . Antisenr target  and, witr target . A seconosensors . Whereasosensors ,25, resp systems ,23 and p systems . In cont systems . It is kMycobacterium tuberculosis[Streptomyces coelicolor[Sinorhizobium meliloti[The development of next-generation sequencing techniques including RNA sequencing (RNA-Seq) enabled the genome-wide identification of RNA-based regulatory elements in an unprecedented depth. The high dynamic range of RNA-Seq allows the identification of transcripts which are expressed at vastly different levels. Also, this method does not exhibit background noise and is therefore appropriate for the identification of lowly abundant transcripts . RNA-Seqerculosis, Streptooelicolor and Sino meliloti.B. licheniformis subtilisin fermentation process have been examined by strand-specific RNA-Seq and differential RNA-Seq (dRNA-Seq) [The major goal of the project in which this study is embedded is the improvement of production strains and thus ultimately the enhancement of enzyme production. This study is targeted on the identification of active regulatory RNA elements within the different phases of a productive fermentation process. Therefore samples from crucial stages of an industrial-oriented RNA-Seq) . A comprB. licheniformis MW3Δspo, a germination deficient mutant of B. licheniformis DSM13, transformed with an expression plasmid encoding an alkaline serine protease, was grown in fed-batch mode in 6 L cultures. The fermentations were carried out in complex amino acid broth under conditions resembling the parameters used in industrial fermentation processes , as described by Sharma et al. [a et al. .B. licheniformis samples yielded more than 500 million reads with a specific length of 50 nucleotides. The number of reads for each library ranged from 2.4 × 107 to 4.3 × 107.Strand-specific deep sequencing of the whole transcriptome of 15 B. licheniformis genome is not precisely mappable when considering the applied read length of 50 nucleotides. Thus, all reads mapping completely to such repeat regions have been excluded from further analysis. This pertains mainly to those 68.5 to 88.8% of reads which map to tRNA and rRNA genes. The majority of these rRNA matching reads can be assigned to 5S rRNA genes, which is in accordance with the fact that the applied depletion targets especially 16S and 23S rRNAs. Also, all reads mapping to the plasmid were removed from the dataset, as this analysis is focused on the transcriptional activity of the chromosome. Finally, 4.4 to 12.0% of the initial reads were taken for further analyses. These reads enabled the identification of transcriptional units and the determination of their boundaries to assign the transcriptional activity of coding as well as non-coding regions of the chromosome (see Methods).After the application of a strict quality processing (see Methods), 77.3 to 93.9% of these reads have been found to map to the chromosome and the expression plasmid used in this study  value as single nucleotide-resolution measure of transcriptional activity (see Methods). NPKMs for each RNA feature and for every gene were calculated and are available at Additional file To facilitate the comparison of different transcription levels between samples, we introduce the Differential RNA-Seq (dRNA-Seq) has been designed by Sharma et al.  to allowThe differential sequencing of samples L-I to L-V resulted in 22,047,373 reads  reveals a 5’UTR of 113 nt Figure A, whereaescribed ,41. The  subtilis,43, whiccis-regulatory elements [cis-regulatory elements have been predicted for B. licheniformis DSM13 by covariance models [cysE and the tRNA ligase subunit genes glyQ and pheS, could be identified by comparison to the Rfam database. In B. subtilis, 92 cis-regulatory elements have been described [B. licheniformis  downstream of the cell envelope stress response operon liaIHGFSR  and a hypothetical protein (BLi03491). A second terminator structure can be found 370 nt upstream of the end of the transcript.1365 3’UTRs . The boundaries of the identified transcripts were determined by upshifts or downshifts of transcriptional activity. All identified RNA features were checked for similarities to complete protein genes as well as protein domains to ensure that they indeed represent non-coding RNAs.k-means algorithm [To extract the basic types of ncRNA expression profiles during the examined fermentation process, cluster analysis based on the lgorithm  was applA5, A3, AI, Amisc and indep. Selected ncRNAs are listed in Table All assigned non-coding RNAs were categorized according to the scheme displayed in Figure indep transcripts are defined as non-coding RNAs not localized antisense to any mRNA. Instead they can be found in intergenic regions or any other position of the chromosome. In total, 53 indep RNAs with sizes between 51 and 602 nt have been identified, of which 40 have TSS verified by dRNA-Seq. Within this group five housekeeping sRNAs could be annotated: the tmRNA SsrA (BLi_r2758), the 6S RNAs BsrA (BLi_r2163) and BsrB (BLi_r1454), the RNA component of RNase P RnpB (BLi_r1808) and the signal recognition particle Scr (BLi_r0016) [indep transcripts exhibited NPKM values ≥100 in at least three samples, reflecting a strong transcriptional activity of the encoding genomics regions. For example the sRNA Scr, an essential part of the protein secretion system [indep ncRNAs seem to be transcribed constitutively under the examined conditions  [As depicted in Figure i_r0016) . 87% of n system , reachese ≥0,99) ), as illAI, A3, A5, and Amisc comprise non-coding transcripts localized antisense to annotated protein-coding genes. They either target the protein-coding region of a gene (AI) or the 5’ and 3’ untranslated regions (A5 and A3). Furthermore, ncRNAs that target more than one gene or that are only partially antisense are classified (Amisc). In this study, 242 Amisc RNAs  are located opposite to ribosome binding sites and could therefore function as inhibitors of translation, a very common mechanism of cis-encoded asRNAs [Amisc RNAs are less than 400 nt in length. Twenty-seven of these short Amisc RNAs reach maximal NPKM values ≥100, suggesting putative sRNA mechanisms. However, some Amisc RNAs are much longer, i.e. BLi_r2246 is 6348 nt in length and spans six genes. The occurrence of antisense transcripts of such length is not unexpected, as asRNAs with very diverse sizes, reaching more than 7000 nt, have been described for several species [AI transcripts  s Figure C/D couldd asRNAs . The len species . FurtherThe general aim of our group is the identification of productivity related features. Thus, a special focus has been set on the identification of antisense transcripts with a putative impact on protease production as targets for strain improvement.apr, BLi01109) represents the major secreted feeding protease of B. licheniformis. Thus, it competes for energetic and secretory resources with the production protease. We identified a cis-encoded 144 nt AI asRNA (BLi_r0872) which is located at the 3’end of the apr mRNA  At last, it has to be experimentally excluded, especially for low abundant instances, that the found ncRNAs originate from spurious transcriptional events, for instance driven by alternative sigma factors [It is exciting to think about a regulatory impact of the mentioned ncRNAs, but there are also some noteworthy limitations to putative effects. (i) The completed RNA-Seq experiments cannot discern if the sense and the antisense transcript are transcribed in the same type of differentiated cells, which especially challenges stoichiometric estimations of asRNAs and their mRNA targets. Whether they can influence each other or fulfill different purposes in different cell types has to be a topic of single cell targeted investigations. (ii) It has been reported that functional sRNAs are produced in excess amounts over the targeted mRNA ,51. Ther factors .indep ncRNAs (see Non-coding RNA features). For Synechocystis sp. PCC6803, Sinorhizobium meliloti and the archaea Sulfolobus solfataricus P2 and Methanosarcina mazei Gö1 between 50 and 107 non-coding RNAs per Mb were identified [B. subtilis, the close relative of B. licheniformis, Nicolas et al. [B. licheniformis are antisense RNAs (89%), transcribed independently from protein-coding genes. The identification of more antisense transcripts in B. licheniformis might be accounted to the reduced background noise in RNA-Seq in comparison to tiling arrays, which allows a better detection of low abundant transcripts [B. licheniformis ncRNAs are located in regions with high sequence similarity to B. subtilis[B. subtilis transcriptome [B. subtilis transcriptome studies show similar low levels of accordance [In total, we determined 461 candidate non-coding RNA transcripts, including antisense, as well as entified ,52-54, ms et al.  have founscripts . 167 of  subtilis and 126 criptome , emphasicordance ,57. Howecordance , and henB. subtilis, 22 sRNAs have been validated experimentally [B. licheniformis  resemble the SR4 antitoxin, especially in stem loops SL3, SL4 and TSL [For mentally ,58. Compand RnaA ,60. The and RsaE -64. The n et al.  describe and TSL  directlyB. licheniformis within five relevant growth stages of an industrial-oriented fermentation process. A detailed analysis of the transcriptome data enabled us to accomplish a high quality functional genome reannotation of B. licheniformis DSM13  transcripts have been identified. A striking observation was the identification of 855 RNA features, which mapped antisense to annotated genomic features  was used for the fermentation experiments. B. licheniformis MW3Δspo is a derivate of the B. licheniformis wild type strain DSM13, bearing three deletions: ΔhsdR and ΔhsdR2 coding for restriction endonucleases [yqfD[ucleases  and Δyqfses [yqfD to preveses [yqfD.2PO4, 21 mM (NH4)2SO4, 0.53 mM Mn(II)SO4, 0.17 mM Fe(II)SO4, 2.0 mM CaCl2 * 2 H2O, 5.7 mM MgSO4, 0.4% v/v PPG200, 0.03 mM tetracycline and 3% w/v glucose. The pH value was regulated to a set point of 7.9 with sodium hydroxide solution. Glucose-feed was started after exceeding the point of biphasic growth.Fermentation was carried out for 46 h in aerated 16 L fermenters with a culture volume of 6 L at 39°C. Medium contained 12% w/v of a complex nitrogen source, 57 mM KHg, the supernatant was removed, the sample was snap-frozen in liquid nitrogen and finally stored at -80°C. The cells were separated from the remainders of the fermentation broth by washing repeatedly with Buffer RLT (Qiagen). Subsequent RNA isolation was carried out with a modified protocol of the RNeasy Midi Kit  to retain short RNAs. The cells were disintegrated with the ball mill Mikro-Dismembrator U (B. Braun Biotech) in 400 μL Buffer RLT and afterwards resuspended in 1.4 mL Buffer RLT and 2.7 mL pure ethanol. The initial washing step of the column was done using 4 mL Buffer RWT (Qiagen). The DNA was digested successively with two different DNases , with a purification step after the first treatment. Purification was performed with a protocol adapted for small RNA purification of the RNeasy MinElute Cleanup Kit (Qiagen). Instead of 250 μL, 675 μL pure ethanol were added to the RNA before binding to the column to shift the binding capacity of the column. A control PCR with 35 cycles was conducted to confirm complete DNA removal. Depletion of rRNA was obtained using the MICROBExpress™ Bacterial mRNA Enrichment Kit (Ambion) according to manufacturer’s instructions. The following purification step was also carried out with the described adaption to the RNeasy MinElute Cleanup Kit.5 mL of the harvested cells were mixed with 5 mL of RNAprotect Bacteria Reagent (Qiagen) directly upon sampling. After 10 min incubation at room temperature the samples were centrifuged at 4500× http://www.vertis-biotech.com). For whole transcriptome libraries [cDNA libraries were prepared by vertis Biotechnologie AG, Germany  cannot be mapped on the genome. A detailed sequence analysis of these unmappable reads revealed that they mainly contain poly(A) tails or concatenated adapters and therefore represent methodic artifacts. All datasets were depleted for plasmid-mapping reads and have been deposited in NCBI Sequence Read Archive database under accession number SRP018744 . The generation of five datasets describing each sampling point was processed accordingly.To obtain the maximal number of features, a dataset containing all reads of the 15 samples was prepared and is referred to as nucleotide activity per kilobase of exon model per million mapped reads (NPKM) value. An NPKM is defined as:The analytical methods used to process the 15 generated RNA-Seq datasets require the use of single nucleotide activities instead of read mappings. This makes RPKMs  inapplicn and m are the start and stop of the region of interest, f(i) is the base activity of base i on a specific strand and g(i) is the sum of the activities of base i of positive and negative strands.Where NPKM values are a derivate of RPKMs , adapted5’ and 3’ UTRs were considered as regions of continuous, non-interrupted transcriptional activity upstream or downstream of annotated genomic features, respectively. The boundary of an identified 5’UTRs was set at the point of the rising of the continuous transcript from zero transcriptional activity. The boundary of a 3’ UTRs was accordingly set at the point of the downshift of the continuous transcript to zero transcriptional activity.pooled RNA-Seq data. Few 5’ and 3’UTRs were manually extended on account of adjacent transcripts which are only separated from the UTR by a very short downshift and potentially are part of the UTR. To exclude that the resulting UTRs correspond to previously not annotated protein genes, searches versus the InterPro and the UniProtKB/Swiss-Prot databases were performed [The analysis of 5’ and 3’untranslated regions was aimed to find the longest UTR, as the longest transcript should cover all possible alternative UTRs and contain all transcribed regulatory elements. Therefore, the computational analysis was based on the erformed ,71.A5’UTR and A3’UTR. The respective UTRs were computationally examined and assigned to be antisense when their overlap to an opposite gene exceeded 100 nt in length.5’ and 3’UTRs which are antisense to an adjacent gene on the opposite strand were classified as Art.Intergenic read-through transcripts localized antisense to an opposite gene were determined manually and classified as A3, A5, AI, Amisc, and indep).The RNA-Seq data were scanned for transcriptionally active regions that were clearly separated from the transcripts corresponding to any annotated gene or its untranslated regions. This primary computational search identified transcripts which were either located on the opposite strand of a protein-coding gene, in intergenic regions or any other region of the chromosome. The boundaries of the identified transcripts were set to those nucleotides with the first and last occurrence of transcriptional activity higher than zero of the corresponding transcriptional unit. NPKM values for the resulting loci were generated from each of the 15 datasets. Subsequently, all results from the computational search were evaluated as depicted in Additional file indep comprises all identified ncRNAs that are not located antisense to any protein-coding gene or its respective untranslated regions. Several transcripts of this category were added manually as this class comprises RNA transcripts which could not clearly be distinguished from surrounding mRNAs by complete down-shifts of transcriptional activity, but were detected by their remarkably higher abundance.The class A3, A5, AI and Amisc comprise ncRNAs which are localized antisense to protein-coding genes or their respective untranslated regions. The class AI contains all ncRNAs with an antisense localization solely towards a protein-coding gene. The class A5 contains all ncRNAs with an antisense localization solely towards the 5’UTR of an opposite mRNA. The class A3 contains all ncRNAs with an antisense localization solely towards the 3’UTR of an opposite mRNA. The class Amisc contains all ncRNAs with an antisense localization towards more than one protein-coding gene and all ncRNAs which are only partially antisense to an mRNA transcript.The categories Transcriptional start sites were determined by the identification of significant increases of the log-scaled expression strength of the dRNA-Seq data from succeeding bases greater than ln 4. The reference value of ln 4 was empirically determined based on the observation that ln 4 represents the smallest expression strength increase for TSS present across all samples of one sampling point. In a second step, all TSS in promoter regions of rRNA or tRNA genes and all TSS being apart less than 20 bp were excluded. TSS matching the boundaries of RNA-Seq predicted 5’UTRs or ncRNAs were determined accordingly to the flow chart depicted in Additional file Additionally, the gained RNA-Seq data were used to generate logarithmic scaled, color coded graphs representing strand-specific transcription.Operon predictions based on whole transcriptome sequencing, dRNA-Seq transcription start sites, and operon and transcription terminator site determination with DOOR , OperonDB. subtilis, B. pumilus and related, manually annotated organisms, the comparisons to UniProtKB/Swiss-Prot and UniProtKB/TrEMBL databases [B. licheniformis DSM13 genome data initially submitted by Veith et al. [Functional reannotation was carried out using the ERGO software tool   and the atabases  and the atabases . The annatabases  and compatabases ,71. The atabases  and compatabases ,71. The atabases  followedatabases . The reaFigure of merit (FOM) analysis, which basically is an estimate of the predictive power of a clustering algorithm [k-means clustering [Cluster analysis to elucidate the fundamental types of ncRNA expression profiles was performed based on the respective NPKM values . Templates for in vitro transcription were generated by PCR using primer pairs (Additional file l medium . Cells w°C: Degrees Celsius; μL: Microliter; A: Adenine; asRNA: Antisense RNA; bp: Base pairs; C: Cytosine; cDNA: Complementary DNA; dRNA-Seq: Differential RNA sequencing; g: Gram; g: Gravitational constant; G: Guanine; h: Hours; L: Liter; ln: Natural logarithm; Mb: Megabase pairs; min: Minute; mL: Milliliter; mM: Millimolar; mRNA: Messenger RNA; ncRNA: Non-coding RNA; NPKM: Nucleotide activity per kilobase of exon model per million mapped reads; nt: Nucleotides; OD: Optical density; ORF: Open reading frame; PCR: Polymerase chain reaction; RNA-Seq: RNA sequencing; rpm: Revolutions per minute; rRNA: Ribosomal RNA; T: Thymine; TAP: Tobacco Acid Pyrophosphatase; TEX: Terminator™ 5′-Phosphate-Dependent Exonuclease; tmRNA: Transfer-messenger RNA; tRNA: Transfer RNA; TSS: Transcription start sites; UTR: Untranslated region; V: Volt; v/v: Volume per volume; w/v: Weight per volume.The authors declare that they have no competing interests.SW performed the experiments, analyzed data and wrote paper, SD developed the analysis tools, RH performed northern blots, JB and SE provided industrial fermentation facilities and performed the fermentation, SV prepared submission of genome and transcriptome data, RD wrote paper and provided research facilities, HL wrote paper, designed research and analyzed data. All authors read and approved the final version of the manuscript.Figure S2: Comparative operon prediction. Figure S3: Northern blot confirmation of non-coding RNAs. Figure S4: Antisense RNAs with putative impact on productivity. Figure S5: BsrG/SR4-like loci in B. licheniformis. Figure S6: Work flow charts.Distribution of whole transcriptome sequencing reads. Click here for fileTable S2: NPKM values of RNA features. Table S3: NPKM values of all genes. Table S4: Differential RNA-Seq reads. Table S5: Transcription start sites. Table S6: Identified RNA features. Table S7: Predicted operons. Table S8: Corrected genes. Table S9: New genes. Table S10: Removed genes. Table S11: Predicted cis-regulatory elements. Table S12: Comparison of cis-regulatory elements known from B. subtilis. Table S13: Cluster analysis of ncRNA expression profiles. Table S14: Comparison of ncRNAs to B. subtilis. Table S15: Comparison of small RNAs from B. subtilis to identified ncRNAs. Table S16: Sequence Read Archive accession. Table S17: Primer pairs for Northern blots.Whole transcriptome sequencing reads. Click here for file


The 3′ RR is a candidate for an “end” of B cell-specific regulation of the Igh locus. The 3′ RR contains several B cell-specific enhancers associated with DNase I hypersensitive sites (hs1–4), which are essential for CSR and for high levels of IgH expression in plasma cells. Downstream of this enhancer-containing region is a region of high-density CTCF binding sites, which extends through hs5, 6, and 7 and further downstream. CTCF, with its enhancer-blocking activities, has been associated with all mammalian insulators and implicated in multiple chromosomal interactions. Here we address the 3′ RR CTCF-binding region as a potential insulator of the Igh locus, an independent regulatory element and a predicted modulator of the activity of 3′ RR enhancers. Using chromosome conformation capture technology, chromatin immunoprecipitation, and genetic approaches, we have found that the 3′ RR with its CTCF-binding region interacts with target sequences in the VH, Eμ, and CH regions through DNA looping as regulated by protein binding. This region impacts on B cell-specific Igh processes at different stages of B cell development.The immunoglobulin heavy chain locus undergoes a series of DNA rearrangements and modifications to achieve the construction and expression of individual antibody heavy chain genes in B cells. These events affect variable regions, through VDJ joining and subsequent somatic hypermutation, and constant regions through class switch recombination (CSR). Levels of IgH expression are also regulated during B cell development, resulting in high levels of secreted antibodies from fully differentiated plasma cells. Regulation of these events has been attributed primarily to two  Igh) undergoes an amazing array of DNA rearrangements and mutagenic events during B cell differentiation , DH (diversity), and JH (joining) segments, as well as for constant region (CH) genes that, when translated, confer different functional capabilities on antibody molecules. During bone marrow B cell development, the locus undergoes sequential DNA rearrangement and mutational events that generate an enormous range of antibody heavy chain genes, each specifying individual antigen binding sites associated with specific constant regions. The initial event, i.e., recombinase-activator genes (RAG)-mediated V(D)J joining, involves first, a DJ join, and then V to DJ joining, both accompanied by deletions of intervening sequences; these lead to expression of a IgM heavy chain bearing a single variable region. Successful expression of one allele halts rearrangements on the other allele  and prompts VJ joining on the light chain allele. Upon leaving the bone marrow, the B cell with its H2L2 surface IgM is poised to receive signals through antigen and other receptors for T cell surface proteins and secreted cytokines that trigger further DNA targeted events, such as class switch recombination (CSR) and somatic hypermutation. CSR is initiated by germline transcription (GT) of the non-IgM CH gene to which subsequent DNA rearrangement will occur. The DNA rearrangement event results in a shift of the VDJ gene segment from its position upstream of μ to upstream of γ, ε or α genes; as in VDJ joining, intervening DNA is deleted as a circle. VH-hypermutation results, upon antigen selection, in B cells with higher affinity antigen-binding sites. Both CSR and somatic hypermutation depend on the activity of activation-dependent cytidine deaminase (AID). In fully differentiated plasma cells, heavy chain gene expression occurs at high levels. These multiple processes of VDJ joining, GT and CSR, and increased Igh expression levels require tight regulation to contain these potentially mutagenic events within the confines of the Igh locus.The Igh control elements have been identified. Our focus here is on a large (∼50 kb) 3′ regulatory region (3′ RR), located downstream of the CH genes , suggestive of a natural boundary. At the 3′ CH-end, beyond the terminus of the 3′ RR, hole (Tmem121), Crip1/2, and mta1 are the nearest non-Igh downstream genes  followed by the rest of the chromosome  method in a plasma cell line: here we showed physical interactions involving 3′ RR enhancers and its CTCF-binding region with the JH sequence that was part of the expressed VH gene. The adjacent Eμ sequences were not essential for this interaction . Potentially, even a fraction of CTCF sites in this region or other CTCF-interacting sites are sufficient for appropriate biological activity. Similarly, we had anticipated that a reduction in insulator activity resulting from deletion of a large group of CTCF sites from the 3′ RR would enable the upstream unaffected 3′ RR enhancers to promote expression of downstream, non-Igh genes. However, our studies revealed only a modest increase in expression of the nearest downstream gene, Tmem121, while further downstream genes were unaffected  can be distinguished by their stage during VDJ joining, their association with a polymorphic DNA segment that is subject to DNA demethylation  the distinctive cluster of CTCF sites in hs5–7 that is located downstream of the CH part of the locus and (2) ∼20 kb further downstream, the nearest non-Igh downstream gene, Tmem121, i.e., hole. In collaborative studies  CTCF sites upstream of DH that are essential for normal VDJ joining and allelic Igh expression in pre-B cells; (2) I/switch sequences required for GT and CSR in B cells, and c) JH and Eμ, which support Igh expression in plasma cells. While 3′ RR enhancers are essential for GT and CSR, as demonstrated by targeted deletions, independent deletion of at least seven of an estimated nine CTCF sites in the 3′ RR resulted in only a mild phenotype (H-locus contraction, a twofold increase in usage of proximal VH7183 genes and an apparent increase in DQ52-JH3 usage. Steps in GT and CSR appeared generally indistinguishable from wild-type, as was the chromosomal architecture of the 3′ RR assessed by 3C. In all, we conclude that the CTCF-binding region is a nidus for physical interactions with Igh targets of important biological consequence. However, there must be many back-ups that provide functional compensation to CTCF. These back-ups may include local regulators, such as CTCF sites associated with other neighboring genes, or proteins other than CTCF and/or epigenetic regulators that terminate B cell-specific regulation of the Igh locus.Here we have discussed two major modules of the 3′ RR, which extends ∼40 kb beginning downstream of Cα. The 5′ 28 kb segment contains four enhancers, which, collectively, support GT, CSR, and high levels of IgH expression in plasma cells. The ∼10 kb 3′ segment contains a region of high-density CTCF- and Pax5-binding sites with insulator activity. During B cell development, the 3′ RR-its enhancers and CTCF-binding region – is involved, via loop formation, with various target henotype . We founIgh locus in particular subnuclear domains for DNA rearrangements, mutation, etc. during different stages of B cell development? How does the 3′ RR function? What mediates architectural interactions among the 3′ RR enhancers themselves and between the enhancer and CTCF-binding modules? How does loop formation in the Igh locus relate to loops in other loci? What specifically does CTCF contribute to the structure and function of the Igh locus?Which proteins/nucleic acids/other molecules are essential for loop formation? Are there different kinds of structural and functional loops? What do loops do? Do they engage the The author declares 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 RNA exosome is an important protein complex that functions in the 3′ processing and degradation of RNA in archaeal and eukaryotic organisms. The archaeal exosome is functionally similar to bacterial polynucleotide phosphorylase (PNPase) and RNase PH enzymes as it uses inorganic phosphate (Pi) to processively cleave RNA substrates releasing nucleoside diphosphates. To shed light on the mechanism of catalysis, we have determined the crystal structures of mutant archaeal exosome in complex with either Pi or with both RNA and Pi at resolutions of 1.8 Å and 2.5 Å, respectively. These structures represent views of precatalytic states of the enzyme and allow the accurate determination of the substrate binding geometries. In the structure with both Pi and RNA bound, the Pi closely approaches the phosphate of the 3′-end nucleotide of the RNA and is in a perfect position to perform a nucleophilic attack. The presence of negative charge resulting from the close contacts between the phosphates appears to be neutralized by conserved positively charged residues in the active site of the archaeal exosome. The high degree of structural conservation between the archaeal exosome and the PNPase including the requirement for divalent metal ions for catalysis is discussed. Thein vivo , 21 thanin vivo , 22.Sulfolobus solfataricus [Pyrococcus abyssi [Archaeoglobus fulgidus [Methanothermobacter thermautotrophicus [M. thermautotrophicus exosome core revealed the presence of one inorganic phosphate (Pi) ion bound at the active site of the complex [S. solfataricus exosome mutant in complex with both RNA and Pi. This structure represents a precatalytic complex prior to the nucleophilic attack by the phosphate leading to 3′-end cleavage of the RNA substrate. Based on this structure, we present a model for the archaeal exosome highlighting the importance of divalent cations in catalysis.A number of crystal structures have been determined of archaeal exosomes in both apo and RNA bound forms from ataricus , 22, 23,s abyssi , Archaeorophicus . These s complex . The genS. solfataricus exosome was recombinantly expressed in E. coli, purified and crystallized as previously described [http://www.pymol.org/). D182A/42 mutant exosome to obtain the conformation of the D182 side chain. The position of the divalent cation was obtained by superimposing the coordinating aspartates (D486 and D492) of E. coli PNPase structure (pdb code 3GME) onto the equivalent residues (D182 and D188) of the S. solfataricus exosome.The escribed , 27. Theescribed . The strescribed  or PHENIescribed  and modeescribed . All figS. solfataricus with the D182A point mutation in the Rrp41 protein (Rrp41D182A) was used. This point mutation was previously shown to completely abolish RNase activity but is not directly involved in RNA or phosphate binding and presumably allows for both substrates to bind without being turned over [D182A/Rrp42 mutant exosome crystallized in the cubic space group P213 with one copy of each subunit in the asymmetric unit and the full 9-subunit complex is generated by a crystallographic 3-fold axis  complex from ned over , 22. Rrpold axis . Pi and S. solfataricus exosomes crystallized in the absence of Pi displayed spherical density at the putative phosphate binding site compatible with a chloride ion [− is bound) and facilitates accurate refinement of the four oxygen atom positions of the phosphate as well as determination of binding geometry (S. solfataricus sequence). A total of eight contacts with binding distances of 2.6–3.2 Å are observed creating a strong phosphate anion binding site  in place of the S. solfataricus Rrp41 serine (S138). However, in both cases the side chain hydroxyl interacts with the Pi ion. The position of the Pi-binding site is highly conserved between archaeal exosomes and RNase PH [Previously determined crystal structures of ride ion . The dengeometry . The smageometry . This aning site . The onl PH ring . Comparihosphate . The onlRNase PH  and PNPaRNase PH  indicatiS. solfataricus exosome crystals soaked with RNA for 48 h followed by a quick Pi soak displayed clear density for the 4 most 3′-end nt of RNA as well as one Pi ion revealing a pre-catalytic state with both substrates bound (S. solfataricus Rrp41/42 RNase PH ring (not shown) and P. abyssi Rrp41/42 in the absence of Pi (S. solfataricus Rrp41) that neutralize the charge of the phosphates .The 2.5 Å difference map obtained from es bound . The RNAce of Pi  confirmiosphates . Only smosphates  suggestiS. solfataricus exosome bound to RNA, it was suggested that D182 could serve the role as a general base by donating a proton required to form the hydroxyl-group of the 3′-end of the RNA substrate after nt cleavage [S. solfataricus exosome demonstrates that magnesium is indeed required for both the degradation and the polyadenylation activities of the archaeal exosome [E. coli PNPase in complex with manganese ions demonstrate the existence of a divalent cation binding site coordinated by residues equivalent to D182 and D188 of S. solfataricus Rrp41 [S. solfataricus exosome in complex with manganese but only for the Rrp41D182A mutant in which the divalent cation binding site of the active site is disrupted [The structural data presented here suggest that D182 of Rrp41 is required for the initial step of the phosphorolytic mechanism, namely, the nucleophilic attack by the Pi ion on the 3′-end nt of the RNA substrate. Based on the 3 Å resolution structures of the hexameric RNase PH core of the cleavage . This pr exosome . Additious Rrp41 . A crystD182A/Rrp42 bound to Pi and RNA presented here with wild-type archaeal exosome and E. coli PNPase bound to manganese ions, a structural model for the presumably fully functional phosphorolytic active site of archaeal-like exosome complexes can be obtained (E. coli PNPase [++ binding site to lower the energy of the transition state to facilitate phosphorolytic degradation of RNA. Given the loss of phosphorolytic activity of most eukaryotic exosomes, it appears that the archaeal exosome from a functional perspective is much more similar to bacterial PNPases than to eukaryotic exosomes.By superimposing the structures of Rrp4/Rrp41obtained . In thisobtained . This moi PNPase . As no a


In this study, we have characterised the RNA determinants involved in the stability of MicA and analysed how they influence the expression of its targets. Site-directed mutagenesis was used to construct MicA mutated forms. The 5′linear domain, the structured region with two stem-loops, the A/U-rich sequence or the 3′ poly(U) tail were altered without affecting the overall secondary structure of MicA. The stability and the target regulation abilities of the wild-type and the different mutated forms of MicA were then compared. The 5′ domain impacted MicA stability through an RNase III-mediated pathway. The two stem-loops showed different roles and disruption of stem-loop 2 was the one that mostly affected MicA stability and abundance. Moreover, STEM2 was found to be more important for the in vivo repression of both ompA and ecnB mRNAs while STEM1 was critical for regulation of tsx mRNA levels. The A/U-rich linear sequence is not the only Hfq-binding site present in MicA and the 3′ poly(U) sequence was critical for sRNA stability. PNPase was shown to be an important exoribonuclease involved in sRNA degradation. In addition to the 5′ domain of MicA, the stem-loops and the 3′ poly(U) tail are also shown to affect target-binding. Disruption of the 3′U-rich sequence greatly affects all targets analysed. In conclusion, our results have shown that it is important to understand the “sRNA anatomy” in order to modulate its stability. Furthermore, we have demonstrated that MicA RNA can use different modules to regulate its targets. This knowledge can allow for the engineering of non-coding RNAs that interact differently with multiple targets.Bacterial Hfq-associated small regulatory RNAs (sRNAs) parallel animal microRNAs in their ability to control multiple target mRNAs. The small non-coding MicA RNA represses the expression of several genes, including major outer membrane proteins such as  Escherichia coli and many more have been predicted Small RNA-mediated networks control a wide variety of cellular processes. The development of new experimental strategies has contributed enormously to the increasing number of small RNAs identified in bacteria trans-encoded small RNAs bind to their target mRNAs through the establishment of short and imperfect antisense base pairing interactions in a close parallel to the action of eukaryotic miRNAs The interactions between sRNA and mRNAs contribute to the differential modulation of gene expression. Bacterial The well characterised small RNA MicA was used as model for our study. MicA was initially identified to repress the synthesis of several major outer membrane proteins (OMPs) SalmonellaThe 5′ end of MicA was suggested to be the principal target recognition domain, like it happens for many other small RNAs E. coli trans-encoded sRNA such as MicA bind to RNA chaperone Hfq, a protein homologous to Sm and Sm-like proteins involved in RNA processing in eukaryotes All the rho-independent transcriptional terminator followed by a short U-rich sequence is ubiquitous amongst small RNAs fhlA mRNA iscRSUA policistronic transcript Although sRNAs are usually structured molecules, the degree of RNA folding varies according to the GC-content of the sRNA analysed. A stem-loop corresponding to the micA gene in order to disrupt independently each predefined domain without affecting the overall conformation of the molecule. We demonstrate that several elements present in the 3′ end of MicA act as stabilizers of this small RNA and this region is also suggested to play important roles in the MicA-dependent riboregulation of different target mRNAs. In addition to the well characterised role of the 5′ end of MicA in the interaction with targets we suggest that elements present in the 3′ end of MicA can also contribute to the differential regulation of the mRNA targets.Through use of mutational studies we analysed the modular domain organization of MicA. Several nucleotide changes were introduced in the Base pairing with target mRNAs is determinant for small RNA function and stability. The 5′ region of MicA was predicted to be the major region involved in the interaction with the target mRNAs. However, additional elements present in the small RNA molecule can potentially influence MicA activity and/or stability. From the analysis of MicA sequence and its secondary structure  to a C-rich stretch (5′-ACCAC-3′) as these mutations are predicted to almost disrupt Hfq-binding to the RNA  tail of the in vitro and in vivo the RNA secondary structures of these two mutants, we have used a broad range of enzymatic and chemical probes. Namely, the techniques performed were dimethyl sulphate (DMS) modification of RNA nucleotides, the use of RNase A (cuts C and U unpaired residues) or RNase T1 (identifies unpaired G residues) and detection of single-stranded residues by lead acetate (PbAc) and in line probing.Although mfold prediction was previously shown to agree with the structure of the wild-type MicA in vitro and in vivo probing with DMS, which methylates unpaired adenosines and cytidines. After DMS treatment, a specific antisense primer to the 3′ end of MicA was used to perform primer extension reactions. However, this amplification revealed to be highly problematic probably due to the strong STEM2 present in MicA secondary structure that prevented annealing of a primer in this region. In fact, we only succeeded in obtaining cDNA from cells expressing the MicA-STEM1_2 variant; this result seems to support the relaxation of the STEM2 secondary structure. Only the use of a modified LNA primer allowed amplification with good resolution from nucleotides 1 to 50. Complementary studies using additional methods allowed then a better resolution of the most 3′ end nucleotides.First we carried out The mfold prediction of the MicA-WT RNA structure was very accurate compared to our experimental data  and to wIn order to study the impact of the different MicA modules on the stability of this sRNA, we measured the decay rate for the MicA-WT and compared it with the altered forms of MicA   also does not cleave wild-type MicA in vitro but is able to cleave it when it is bound to its target ompA mRNA RNase III is a double-stranded RNA endonuclease  cleaved . This isin vivo impact of E. coli RNase III in this regulation, we constructed the double mutant ΔmicA Δrnc and measured the stability of the trans-encoded MicAs. As expected, MicA-WT was highly stabilized in the absence of RNase III (MicA barely decayed even after 90 min after blocking of transcription)  and STEM2 (closer to 3′ end) . ComputamicA cells. The expression and stability of these MicA variants were tested by Northern blotting  or the altered STEM1 (pMicA-STEM1) or STEM2 (pMicA-STEM2) versions were used to transform Δblotting . Resultsblotting . In contblotting . In Salmncreased . The othmicA Δrnc when compared to the ΔmicA strain  than the MicA-WT . This cle of Hfq .∗)  (micA strain. In order to attenuate inefficient transcription termination that could arise from modification of the 3′ poly(U) sequence, an additional stretch of 8 T’s was included immediately after the 4U residues in both cloning strategies. The MicA-3′mut1 variant showed decreased stability when compared to the wild-type  (We constructed two MicA mutants in which we changed the nucleotides in the 3′ poly(U) tail. In the MicA-3′mut1 variant, the 5′-UUUU-3′ linear sequence immediately after the terminator was changed to a 5′-Uo 6 min) . ExtendemicA DNA (CTTTT-3′5′-TTTT) which can lead to slightly longer MicA species, as we observe in The 3′ altered MicA’s are slightly longer than the wild-type MicA . HoweverpcnB gene) In contrast, inactivation of the poly(A) polymerase  and Tsx (nucleotide transporter) were recently shown to be specifically regulated by MicA in SalmonellaE. coli as for the first time tsx and ecnB transcripts are also shown to be regulated by MicA.We wanted to investigate whether our different MicA mutants were functional in riboregulation. To test this, we analysed the RNA levels of three different targets: micA was transformed either with the empty plasmid (pWSK29), the plasmid expressing the wild-type copy of MicA or with the plasmids harbouring the different MicA variants and the levels of the target mRNAs were then evaluated. This provided a simple approach to test how the different mutations were affecting MicA repressor activity. Overexpression of the wild-type MicA is very efficient in the downregulation of its target mRNAs as compared to ΔmicA transformed with the empty vector  while it did not function as well to downregulate ompA or tsx mRNA levels  present in tsx mRNA . From these, tsx mRNA was the less affected by the mutations in the 5′ end of MicA, probably because the nucleotide changes still resulted in an extensive complementation with this target, as predicted by computational analysis  analysis . Howeveranalysis .in vivo repression of both ompA and ecnB mRNAs than STEM1, unlike we could expect from the RNAhybrid prediction  ompA mRNA levels than on the other targets. RNAhybrid predicted that mutations affecting the 3′ poly(U) sequence of MicA would affect more the base pairing with target mRNAs but failed to predict the impact of disrupt the internal A/U-rich sequence of MicA in the regulation of ompA mRNA levels  were shown to accumulate at levels inferior to the MicA-WT . HoweverHfq is known to protect sRNA from RNase E and PNPase-mediated degradation ompA expression) while the 3′ end of MicA harbours elements that may be more relevant for regulation of other targets (such as tsx mRNA). These findings can most probably be extended to other regulatory RNAs, judging by the results of a computational search which predicted that several other small RNAs can potentially use different regions to establish base pairing interactions with their targets The modular structure of MicA helps explaining the dynamics of interaction with its multiple targets. The 5′ end of MicA is critical for repression of some targets . SOC medium was used to recover cells after heat-shock in plasmid transformation steps. When required, antibiotics were present at the following concentrations: chloramphenicol, 50 µg ml−1, kanamycin, 50 µg ml−1; ampicillin, 100 µg ml−1.micA variants  were amplified by PCR overlapping using the oligonucleotides indicated in Table III. Partial fragments were amplified with MicA-HindIII and the respective mutagenic forward primer or MicA-PstI and the respective mutagenic reverse primer (Table III). PCR bands were gel eluted using the gel extraction NucleoSpin Extract II kit (Macherey-Nagel). For each mutation, the partial PCRs carry an overlapping region of 20 nucleotides. Approximately equal amounts of each partial PCR (for a given construct) were added to Pfu reaction mix containing dNTPs but lacking primers. The extension step  proceeded for 15 cycles. The external primers MicA-HindIII and MicA-PstI were then added and the PCR reactions run for 20 cycles. A DNA fragment (274 bp) encompassing the entire wild-type MicA (MicA-WT) was directly amplified using primers MicA-HindIII and MicA-PstI. All DNA inserts include the MicA natural promoter .All plasmids used in this work are based on the very low copy number pWSK29 −1. Culture samples were withdrawn at defined timepoints thereafter and mixed with an equal volume of RNA stop buffer . Total RNA was extracted by the phenol:chloroform method from stationary phase cultures as previously described ecnB mRNA) or 10% polyacrylamide/7 M urea gels in TBE (for detection of the sRNAs) or by 1.2% agarose formaldehyde-denaturing gel in MOPS buffer (for detection of ompA and tsx mRNAs). RNAs were transferred onto Hybond-N+ membrane  and U.V. crosslinked by UV irradiation using a UVC 500 apparatus (Amersham Biosciences). Membranes were hybridized with radiolabelled specific probes overnight in PerfectHyb Plus Hybridization Buffer (Sigma Aldrich) at 42–68°C. Specific probes were obtained either by 5′ end-labelling of antisense oligonucleotides using helloγ-32P]-ATP and T4 polynucleotide Kinase (Fermentas) or by in vitro transcription reactions with PCR DNA templates carrying a T7 promoter sequence through the use of helloα-32P]-UTP and T7 RNA polymerase (Promega). Radiolabelled probes were purified on G25 Microspin columns . The probe used to detect MicA in the Northern blot experiments correspond to an antisense riboprobe complementary to the entire wild-type MicA sequence (about 74 nucleotides in length). RNA was analysed by Phosphorimaging (Storm860) using the ImageQuant software (Molecular Dynamics). The half-lives of RNA were determined by linear regression using the logarithmic of the percentage of RNA remaining versus time, considering the amount of RNA at 0 min as 100%. Primers used in this work were obtained from Stab Vida  and are described in For decay experiments, blocking of transcription was obtained by adding rifampicin to a final concentration of 500 mg ml6-tagged Hfq purified protein (kindly provided by Eliane Hajnsdorf) and a constant amount of radiolabeled [α-32P]-UTP MicA as substrate. Reactions were incubated at 37°C for 30 min. EMSA samples were then electrophoresed on native 5% polyacrylamide gels in 1× TBE buffer in a cold room. Dried gels were then exposed on Phosphorimager screens and the corresponding signals were analysed using the ImageQuant software (Molecular Dynamics). Binding data were fit to a Polynomial Quadratic curve and Kd values were calculated from the fit of the curve using SigmaPlot software (Systat Software).Binding assays were performed in 10 mM Tris–HCl (pH 8), 1 mM EDTA, 80 mM NaCl and 1% glycerol (v/v) 32P]ATP and T4 polynucleotide kinase (Fermentas). Labelled RNAs were then purified by 10% polyacrylamide/7M urea/1x TBE gel electrophoresis, eluted and precipitated with ethanol.MicA-WT and MicA-mutant RNA variants were transcribed with T7 RNA polymerase from PCR products obtained with primers described on Table III and relevant plasmid eluted from agarose gel as DNA templates. After deposphorylation with Calf intestine alkaline phosphatase (Fermentas), RNA was 5′-end labelled with -5′-end labelled MicA-DMS LNA™ primer (Exiqon) and the Transcriptor reverse transcriptase (Roche). After RNA alkaline hydrolysis, cDNA was resuspended in 6 µl formamide loading buffer. Samples were analysed on 6% or 8% polyacrylamide/7M urea gels run in TBE 1x buffer. The lead acetate cleavages were carried out as decribed The dimethyl sulphate (DMS) modification of unpaired adenosine and cytidine nucleotides was carried out essentially as described Ribonucleases T1 (0.01 U) and RNase A (0.01 U) (Ambion) were incubated with the RNA for 15 minutes at 37°C following manufacturer’s instructions. Before use, 5′-end labelled RNAs were renatured in structure buffer 1x (Ambion). Unfolded RNAs were prepared in sequencing buffer 1x and a ladder of G-specific cleavages was obtained upon RNase T1 incubation. Alkaline ladders correspond to incubation of the RNA in the alkaline hydrolysis buffer for 15 minutes at 90°C. Reactions were stopped by adding 10 µl of loading buffer II (Ambion). Samples were then fractionated on 10% polyacrylamide/7 M urea gels run in TBE 1x buffer. Gels were dried and exposed on the phosphor screen.32P]-UTP MicA-WT RNA as substrate. RNase III reaction buffer consisted of 160 mM NaCl, 30 mM Tris–HCl (pH 8), 0.1 mM EDTA, 0.1 mM DTT and 10 mM MgCl2Reactions were performed using 1000 µM RNase III purified protein (kindly provided by Allen Nicholson) and radiolabeled [α-Figure S1Identification of the nucleotide changes introduced in the synthetic MicA variants.E. coli MicA wild-type sequence is indicated on top and the mutated MicA variants are shown below. Designation of each MicA variant is indicated on the left of each sequence. A multiple alignment of MicA in several eubacteria (see eria see  identifi(TIF)Click here for additional data file.Figure S2In line probing analysis of MicA RNAs. 5′-end labeled MicA-RNA was prepared in 50 mM Tris pH8, 20 mM MgCl2 and 100 mM KCl. In line probing reactions (TIF)Click here for additional data file.Figure S3In vitro RNase III cleavage assay.In vitro activity assay (TIF)Click here for additional data file.Figure S4Predicted Interactions between MicA-WT and the synthetic MicA variants with ompA, tsx and ecnB mRNAs. The RNAhybrid software ompA mRNA, tsx mRNA and ecnB mRNA), using the default parameters. A segment of the 5′ end of each target mRNA was chosen as previously described (TIF)Click here for additional data file.Figure S5Mutagenesis of the 3′ end U-rich tail of RybB. Decay measurement of the RybB. Deleted rybB cells (ΔrybB) were transformed with a plasmid expressing either the wild-type copy (pRybB-WT) or a RybB variant in which the 3′ U-rich tail was modified to a CG-rich sequence (pRybB-3′mut). Total RNA was extracted from stationary phase cultures.(TIF)Click here for additional data file.Figure S6Northern blot analysis of MicA RNA. A band denoted nonspecific is detected on Northern blot analysis from RNA extracted from the wild-type (wt) and ΔmicA strains (transformed or not with plasmid pMicA-WT) when using the MicA riboprobe described in Materials and Methods.(TIF)Click here for additional data file.


With the discovery of small non-coding RNA (ncRNA) molecules as regulators for cellular processes, it became intriguing to develop technologies by which these regulators can be applied in molecular biology and molecular medicine. The application of ncRNAs has significantly increased our knowledge about the regulation and functions of a number of proteins in the cell. It is surprising that similar successes in applying these small ncRNAs in biotechnology and molecular medicine have so far been very limited. The reasons for these observations may lie in the high complexity in which these RNA regulators function in the cells and problems with their delivery, stability and specificity. Recently, we have described mirror-image hammerhead ribozymes and DNAzymes (Spiegelzymes®) which can sequence-specifically hydrolyse mirror-image nucleic acids, such as our mirror-image aptamers (Spiegelmers) discovered earlier. In this paper, we show for the first time that Spiegelzymes are capable of recognising complementary enantiomeric substrates (D-nucleic acids), and that they efficiently hydrolyse them at submillimolar magnesium concentrations and at physiologically relevant conditions. The Spiegelzymes are very stable in human sera, and do not require any protein factors for their function. They have the additional advantages of being non-toxic and non-immunogenic. The Spiegelzymes can be used for RNA silencing and also as therapeutic and diagnostic tools in medicine. We performed extensive three-dimensional molecular modelling experiments with mirror-image hammerhead ribozymes and DNAzymes interacting with D-RNA targets. We propose a model in which L/D-double helix structures can be formed by natural Watson-Crick base pairs, but where the nucleosides of one of the two strands will occur in an anticlinal conformation. Interestingly enough, the duplexes (L-RNA/D-RNA and L-DNA/D-RNA) in these models can show either right- or left-handedness. This is a very new observation, suggesting that molecular symmetry of enantiomeric nucleic acids is broken down. Indeed mirror-image aptamers, called Spiegelmers, were developed more than 15 years ago in vivoA fundamental property of the biological proteins and nucleic acid macromolecules is their homochirality, i.e. that they are composed of only one optically active isomer of amino acids or nucleotides, respectively. Native proteins are composed exclusively of L-amino acids, while natural nucleic acids contain D-sugar residues. However, some D-amino acids and short D-oligopeptides are present in the cell walls of some bacteria .In this paper, we show for the first time that mirror-image hammerhead ribozymes and DNAzymes (L-zymes) cleave sequence-specific complementary D-RNA, i.e. a target of different chirality. These mirror-image nucleic acid zymes, Spiegelzymes, have some features which could make them valuable for future therapeutic and diagnostic applications. The precise mechanism of action of the Spiegelzymes is not known and will require further investigation. However, what we are reporting in this communication are extensive three-dimensional molecular modelling studies, from which we currently favour a model in which the D-RNA targets are interacting with the Spiegelzymes by Watson-Crick interaction of the bases, and in which their sugar residues acquire a new type of structural arrangement1UUCAAGUCCGCCA14-3′ are labelled with fluorescein at the 5′ end. In the target sequence, the hammerhead ribozyme hydrolyses the GUC sequence after the C and the DNAzyme hydrolyse the AGU sequence between the G and the U.All RNA and DNA oligonucleotides used in these studies were chemically synthesized at IBA (Germany) or ChemGenes (USA). The 14 nt synthetic D- and L-RNA oligomers, a fragment of GFP mRNA (252–265 nt), with the sequence 5′-C1GGCGCUGAUGAGGCCGAAAGGCCGAAACUUGA33-3′. The 27-nucleotide-long sequences of the synthetic L- and D-DNAzymes were selected to be 5′-G1GCGGAGGCTAGCTACAACGATTGAAG27-3′.The HH ribozyme (D-RNA) and HH Spiegelzyme (L-RNA) were of 33 nt lengths with the sequence 5′-U-RNA targets were measured in 10 µl reaction volumes containing 50 mM Tris-HCl buffer, pH 7.5, at 37°C. The target RNA and HH Spiegelzyme, or HH ribozyme, were denatured for 2 min at 73°C and cooled down in a heating block to 25°C at a rate of 1°C/min. Hydrolysis reactions were carried out at MgCl2 concentrations between 0 and 25 mM. Hydrolysis products were separated by 20% polyacrylamide gel electrophoresis (PAGE) in the presence of 7 M urea in 0.09 M Tris-borate buffer at pH 8.3. The reaction products were visualized with a Fuji Film FLA 5100 phosphoimager using the manufacturer's (Fuji) software.The activities of the HH Spiegelzyme and the HH ribozyme with fluorescein-labelled L- and D2 at 37°C for 1–128 min or 5 h in the presence of 2–5 µM D- and L-DNAzyme. Prior to the reaction, the substrate (D- and L-RNA) and D- and L-DNAzyme were denatured, as described for the ribozyme experiments. The analysis of the reactions by PAGE was carried out as described above.An amount of 0.2 µM of fluorescein-labelled D- and L-RNA was incubated in 10 µl of a reaction mixture containing 50 mM Tris-HCl buffer, pH 7.5, and 0–25 mM MgCl5) were plated in 500 µl of growth medium  to achieve 70–80% confluency 24 h before transfection in a 24-well format. The cells were transfected with plasmid pEGFP (1 µg) and HH Spiegelzyme or HH ribozyme in final concentrations of 25–300 nM, and 25–100 nM in the case of D- or L-DNAzymes using lipofectamine 2000. Changes in GFP fluorescence were monitored with a microscope (Leica) and plate reader.The cells (2×105) were plated in 500 µl of growth medium  to achieve 70–80% confluency. Transfection of Spiegelzyme (1 µg) with lipofectamine 2000 was performed according to the protocol from Ambion. Images were taken with a Leica 4000B instrument at 0, 24, 72, 96, and 120 h after transfection.The cells . The 25 µl reaction mixture was prepared with a DyNAmo HS Sybr Green qPCR Kit (FINNZYMES) and included 1× MasterMix, 0.3× ROX reference dye, 0.3 µM of each primer, 1 µl of template cDNA, and water to a final volume of 25 µl. The PCR reaction conditions for all genes were as follows: initial denaturation , a four step amplification programme repeated 40 times , and a melting curve programme . Standard curves were generated by amplifying serials of five times dilutions of cDNA.Results were reported as the relative expression to that of Actb and Hprt in the same samples. The quality of PCR products was checked by analysis of the melting curve. All experiments were repeated three times.6) were washed with PBS, resuspended in 10 mM Tris, pH 7.5, and centrifuged for 10 min at 4°C and 13.2×103 rpm. Each sample was denatured by heating at 95°C for 10 min. The samples were subjected to electrophoresis in 0.1% SDS polyacrylamide gels, and separated proteins were transferred to a PVDF membrane (PerkinElmer) using Western Unit (BioRad) in Towbin buffer . The membrane was blocked with 10% skimmed milk in 1×PBS/0.05%Tween20 at 4°C overnight, and afterwards, washed three times in 1×PBS/0.05%Tween20 at room temperature. The membrane was incubated with the antibodies for GAPDH  and GFP  for 2 h at room temperature. After washing three times in 1×PBS/0.05%Tween20, the membrane was treated with secondary Anti-Mouse Ig-Biotin antibody  for 2 h at room temperature. The membrane was washed three times and then incubated with Streptavidine Alkaline Phosphatase Conjugate for 15 min at room temperature. After washing, the membrane was developed using BCIP/NBT Liquid Substrate System (Sigma), and bands were quantified using the ImageQuant software from Molecular Dynamics. See also The cells  for the homochiral system  mRNA. As a check of the efficiency of delivery of L-RNAs into cells, we carried out a transfection of COS-7 cells with the 5′-fluorescein-labelled hammerhead Spiegelzyme in the presence of lipofectamine 2000 . Fluoresin vivo validated with qPCR and Western blot analysis shows that the silencing effect observed on the protein level reached 80% and 60% for hammerhead Spiegelzyme and D-hammerhead ribozyme, respectively  or substitutions at the 2′-position of the ribose or even its replacement with deoxyribose G↓UC for a DNAzyme is indicated The D-DNAzyme designed and its mirror image (Spiegelzyme) were 27 nucleotides long and their potential interactions with the D- and L-RNA substrates of 14 nucleotides length are shown in 2  in vivo, we transfected HeLa cells with an L-DNA Spiegelzyme targeted at the G↓UC sequence within the GFP open reading frame. L-DNA Spiegelzyme showed a silencing effect on the GFP expression in HeLa cells, and its activity was slightly higher than that of the corresponding DNAzyme  is identical to that from the D-hammerhead ribozyme , it suggMost of the studies on heterochiral duplexes of nucleic acids in the past were aimed at analysing the L-enantiomer as a possible antisense DNA oligonucleotide. In those studies, there were no attempts to analyse hybridization between all D- and all L-enantiomers of RNA oligonucleotides. The possibility of forming either parallel or anti-parallel heterochiral duplexes has been suggested by NMR studies. Although those results did not provide a definite answer regarding the nature of the interactions, they revealed a strong association between D- and L-RNA penta nucleotides In order to get a possible insight into understanding the experimental results of the catalytic activity of the heterochiral complexes, we turned to three-dimensional model building. The 3-D models of L-HH-ribozyme and L-DNAzyme with their D-RNA substrates are shown in We can conclude from our 3-D modelling experiments (see Supporting information below) that the sequence-specific cleavage of the D-RNA target by the L-hammerhead ribozyme and the L-DNAzyme is feasible at the cleavage sites observed in our wet experiments.. The Spiegelzymes described by us recently With the results presented in this communication we are able to show for the first time that mirror image hammerhead ribozymes and mirror image DNAzymes, which we are calling Spiegelzymes, are capable to cleave in a sequence specific manor natural RNA molecules. We have demonstrated this by specifically cleaving green fluorescent protein mRNAs in HeLa cells. Concerning the specificity, it is of interest that Western Blot experiments show that the mRNA dependent synthesis of several other proteins are not affected by the use of the anti GFP mRNA Spiegelzymes. By extensive molecular modelling experiments we are able to present three-dimensional structural models in which heterochiral interactions between an L-hammerhead ribozyme or an L-DNAzyme with a D-RNA substrate are following Watson-Crick base-pairing rules. The apparent requirement for these interactions is anticlinal conformation of nucleosidesFigure S1Heterochiral D/L-RNA-duplex showing either right- or left-handedness. The duplex shown consists of the short sequences D-(5′-CGCCA-3′) and L-(5′-UGGCG-3′) and the base moieties are paired in a canonical Watson-Crick manner. The D-RNA strands are always shown in red, whereas the L-RNA strands are shown in blue. Interestingly, due to the heterochirality of the base pairs, D/L-duplexes can show either right- (A) or left-handedness (B). A. The D/L-RNA duplex shown in its right-handed, anti-parallel conformation. The D-RNA strand (red) is in a natural A-RNA form configuration, whereas the nucleotides of the mirror-image L-RNA strand (blue) show ribose rings, which are rotated about the N-glycosidic bonds, which connect the bases with their ribose rings. This arrangement of the nucleotides leads to a smaller minor groove and a larger major groove of the duplex. One can say that the D-RNA strand has forced its natural (right-)handedness upon the L-RNA strand. Only for comparison reasons: The transparent red D-RNA strand shown arranged together with the opaque red D-RNA strand would form a natural, right-handed D/D-RNA duplex in A-RNA form. The atom positions of the base moieties of the natural D/D-duplex and that of the D/L-RNA duplex are identical. B. The same D/L-RNA duplex, but now shown in its left-handed, anti-parallel conformation. The L-RNA strand (blue) is in its preferred left-handed A-form configuration, whereas the nucleotides of the natural D-RNA strand (red) show ribose rings which are rotated about the N-glycosidic bonds connecting their bases. In this case, one can say that the L-RNA strand, for which the natural handedness is left-handed, has forced its handedness upon the D-RNA strand. For comparison reasons: The transparent blue L-RNA-strand shown arranged together with the opaque blue L-RNA strand would form a conventional, left-handed L/L-RNA duplex.(TIF)Click here for additional data file.Figure S2The detailed view of ribonucleotide conformations in homochiral and heterochiral complexes. The natural nucleotides are shown in red, whereas the mirror-image nucleotides are shown in blue: A. Comparison between the natural D/D-base pair (above) and the right-handed conformation of the D/L-base pair (below). B. Superimposition of the D/D- with the D/L-base pair. C. Diagonal side-view of the D/D-base pair superimposed with the D/L-base pair. As one can see, the D-ribose ring and the L-ribose ring show a common symmetry plane as soon as the necessary ribose ring rotation has taken place.(TIF)Click here for additional data file.Text S1Supporting Information for Proposed Molecular Models.(DOCX)Click here for additional data file.


In recent years, the important functional roles of RNAs in biological processes have been repeatedly demonstrated. Computing the similarity between two RNAs contributes to better understanding the functional relationship between them. But due to the long-range correlations of RNA, many efficient methods of detecting protein similarity do not work well. In order to comprehensively understand the RNA’s function, the better similarity measure among RNAs should be designed to consider their structure features (base pairs). Current methods for RNA comparison could be generally classified into alignment-based and alignment-free.In this paper, we propose a novel wavelet-based method based on RNA triple vector curve representation, named multi-scale RNA comparison. Firstly, we designed a novel numerical representation of RNA secondary structure termed as RNA triple vectors curve (TV-Curve). Secondly, we constructed a new similarity metric based on the wavelet decomposition of the TV-Curve of RNA. Finally we also applied our algorithm to the classification of non-coding RNA and RNA mutation analysis. Furthermore, we compared the results to the two well-known RNA comparison tools: RNAdistance and RNApdist. The results in this paper show the potentials of our method in RNA classification and RNA mutation analysis.We provide a better visualization and analysis tool named TV-Curve of RNA, especially for long RNA, which can characterize both sequence and structure features. Additionally, based on TV-Curve representation of RNAs, a multi-scale similarity measure for RNA comparison is proposed, which can capture the local and global difference between the information of sequence and structure of RNAs. Compared with the well-known RNA comparison approaches, the proposed method is validated to be outstanding and effective in terms of non-coding RNA classification and RNA mutation analysis. From the numerical experiments, our proposed method can capture more efficient and subtle relationship of RNAs. RNA once is considered as the fundamental information medium in central dogma of molecular biology. A number of studies have indicated that RNAs play a more active role and carry diverse functionalities in nature, including mediating the synthesis of proteins, regulating cellular activities, and exhibiting enzyme-like catalysis and post-transcriptional activities. Furthermore, many recent discoveries have shown that the number and biological significance of functional RNAs has been underestimated. In living cells, RNAs do not remain in a linear form, which folds its secondary structure through base pairs including canonical bonds of A-U and G-C and wobble pair of G-U. For understanding RNA's functionality, the alignment and similarity of RNA should consider not only the primary structure (sequence) but also the secondary structure (base pairs).Numerous approaches were proposed to measure the similarity between RNA secondary structures, which can be broadly categorized into two classes: alignment based string or tree representation of RNA secondary structure, and comparison based some numerical representation without alignment.Most studies usually adopt dynamic programming algorithms and tree models. Some are usually based on the alignment of a string representation of the secondary structures such as the dot-bracket representation, in which a score function or a distance function to represent insertion, deletion and substitution of letters in the compared structures -4. SequeOthers are almost based on alignment of a tree representation of the RNA secondary structure elements or the base pairing probability matrices -9. ShapiEach tree model offers a more or less detailed views of an RNA structure. Given the tree representations of two RNA secondary structure, one comparison way is based on the computation of the edit distance between the trees while the other focus on the alignment of the trees using the score of the alignment as a measure of the distance between the trees. Popular tools for optimal alignment of RNA secondary structures include RNAdistance  and RNAfO(n4) in memory and O(n6) in time for two RNA sequences of lengthn. So these algorithms are still impractical for long RNA sequences. Recently some comparison algorithms without aligning them are proposed. Kin [Because the above methods rely on dynamic programming algorithms, they are computation-intensive even if the pseudoknots are ignored. For example, the Sankoff's algorithm  simultansed. Kin  gave a kThe graphical representations of biosequences  could be out of the mainstream but a new research view and tool to understand and analyze such biosequences. M.Randic  reviewedIn -29, eighIn this paper, motivated by DV curve representation of DNA sequences ,34, we pWe performed the experiments on 100 RNA sequences to test the ability to distinguish non-coding RNA families. We randomly chose 25 sequences from each of the four RNA classes  in RFAM database.Firstly, the secondary structures of the 100 RNA sequences are predicted by the Vienna RNA folding prediction package. Secondly, their characteristic representations are constructed according to the primary sequence and the predicted secondary structure. Thirdly, the TV-Curves can be obtained based on their characteristic representations. Then we computed the similarity between any two RNA among these 100 RNA sequences by the proposed multi-scale similarity measure algorithm based on TV-Curve. Furthermore, all the similarity values are arranged into a similarity matrix. For validation of our algorithm, we computed the distance matrixes using RNApdist and RNAdistance tools respectively.X and Y be n×n matrices, where X indicates the observed similarity coefficient between the RNA i and j, and Y represents the ground information defined as follows:For the comparison of our multi-scale similarity measure with the popular RNA comparison tools, the validation index used here is Hubert’s statistic . Let X aX indicates the observed distance between RNA i and j, then Y is defined asIf X and Y , which is defined as follows:The Hubert's statistic represents the correlation between the matrices X and Y. The larger absolute value of H indicates the better coherence between the similarity matrix X and the ground matrix Y. The value of H can be used to estimate the quality of the similarity measure.where The Hubert's statistic for different similarity matrixes are shown in Table However, the Hubert's statistic of our proposed multi-scale similarity measure based on our algorithm is 0.7205. Obviously, our similarity measure is more closer to the real data compared with RNApdist and RNAdistance.In addition, to further compare the performance of our method with the RNApdist and RNAdistance, we reconstructed three phylogenetic trees , citrus leaf rugose virus (CiLRV), tobacco streak virus (TSV), citrus variegation virus (CVV), apple mosaic virus (APMV), prune dwarf ilarvirus (PDV), lilac ring mottle virus (LRMV), elm mottle virus (EMV) and asparagus virus II (AVII). The predicted corresponding secondary structures and corresponding TV-Curves are given in Figure To further present our result, we constructed a phylogenetic tree with UPGA algorithm for the nine virus using the multi-scale similarity measure based on TV-Curves shown in Figure Observing Table Mutations in RNA structure may lead to impair functions resulting in diseases, but RNA structure mutations could be beneficial in some situation. Consequently, it is very important to search the most significant point mutation. Our proposed method is very efficient to find the significant point mutation compared with the popular RNA mutation analysis tool: RDMAS . RDMAS iAdditionally, in order to further validate the efficiency of our method, we test the 21 rRNA fragments of the thermus thermophilus from Ribosomal data-set in  comparedIn this paper, we provide a better visualization and analysis tool TV-Curve for RNA to indicate the information of sequence and secondary structure especially for long RNA. Additionally, based on TV-Curves representation of RNA, a multi-scale similarity measure for RNA comparison is proposed, which can capture the local and global difference between the information of sequence and structure of RNA. Compared with the popular RNA comparison approaches, the proposed method is evaluated to be outstanding and effective. But as we know, the native secondary structure of a RNA is often a suboptimal structure not the predicted structure with minimum free energy (MFE) due to limitations of thermodynamic models. The structural similarity measurement using multiple predicted suboptimal structures is still a challenge. In the further research, we will focus on how to measure the structural similarity to integrate multiple structures with different energy levels.In this section, we describe the construction of TV-Curve of the secondary structure of RNA. Firstly, we give the characteristic representation of RNA based on the primary and secondary structure of RNA.A-U, G-C or G-U, respectively. A characteristic sequence of the secondary structure can be obtained. The RNA secondary structure is predicted by the Vienna RNA folding prediction package [In , Liao pr package .(U48228.1/7-166) in the following:>tRNA (U48228.1/7-166)CAAU'C'U'UAA'CG'A'U'G'G'AUG'U'C'U'U'GG'U'U'CC'UAUAG'CG'A'U'GA'A'G'G'C'C'G'C'A'G'C'A'AAGU'G'C'GAUAU'G'C'A'AU'GAAAA'AU'G'C'A'AUUACU'G'U'G'AAU'C'A'U'C'AG'A'A'U'G'C'UGAA'U'G'U'AAA'CUAUAC'C'A'U'A'UUUACCCUU'A'U'G'G'GCAAAU'UAA'C'G'U'GG'U'A'U'U'C'CU'ACA'G'A'AA.Combining the information of the sequence and secondary structure, we give the corresponding characteristic sequence of the secondary structure of tRNA In this subsection, the construction of TV-Curve is given. As shown in Figure S1S2 ⋯ Sn where Si ∈ {A, T, C, G, A ', T ', C ', G '} and n is the length of this characteristic sequence. Define the corresponding TV-Curve asTV-Curve can be obtained by connecting all the vectors one by one. We give two corresponding mathematical models of TV-Curve. Denote a characteristic sequence of RNA as S = which can be obtained by the following formulas:For a given TV-Curve of RNA, we can retrieve its characteristic representation from equation (2).U48228.1/7-166) and 5S_rRNA (U05019.1/544-658) from the equation (1) and (2)  and tRNA (U48228.1/7-166). In order to further prove the difference of the two TV-Curves, the fractal dimensions of the TV-Curves of 5S_rRNA (U05019.1/544-658) and tRNA (U48228.1/7-166) is 0.6404 and 0.5958.For example, we give the secondary structures and the corresponding TV-Curves of tRNA . TV-Curve extends 3 units along X-axis to represent each unpaired nucleotide  and paired nucleotide .yi − 23 − yi − 33 = 1, the i-th nucleotide is paired. If yi − 23 − yi − 33 = − 1, the i-th nucleotide is unpaired.(2). From TV-Curve, one can immediately grasp the information about RNA sequence and structure information. From a given TV-Curve, we can obtain its unique sequence and secondary structure representation. Moreover, for a given RNA sequence and structure, there is a unique TV-Curve representation. The correspondence between TV-Curves and the RNA information of sequences and secondary structures is one to one and no loss of information. If one wants to know whether the i-th nucleotide in RNA sequence is paired, only need to examine the difference between the values at (3i-2) and (3i-3) of TV-Curve. If xend of the TV-Curve indicates the length of RNA sequence n, i.e. n = xend/3.(3). The X-axis end point In this section, based on TV-Curves of RNA, we propose a multi-scale similarity measure for RNA comparison in terms of the multi-scale property of wavelet transform.We estimate RNA similarity using the weighted correlations in the wavelet domains at the different scales. The main characteristics of wavelet transforms are time-frequency localization and multi-resolution property. Wavelet can capture the global and local property of a signal synchronously and can focus on the any detail of a signal. In this sense wavelets are referred to as a mathematical microscope. In the following, we briefly introduce the discrete wavelet transform ,40.ψ(x) and the scaling function ∅ϕ(x), whichThe wavelet transform relies on the wavelet function satisfies the following two-scale relation:hn} is a low-pass filter .Where {The associated wavelet function constructed using scaling function satisfies the following equation:gn} is a high-pass filter (wavelet filter)Where {log2N levels at most. The wavelet decomposition of the signal s analyzed at level one is provided with two sets of coefficients: approximation coefficients cA1, and detail coefficients cD1. cA1 is obtained by convolving s with the low-pass filter and then is downsampled (keep the even index elements) for approximation, and cD1 is also obtained by the high-pass filter and then is downsampled for detail.Given a signal s with length N, the wavelet transform consists of cA1 in two sets using the same scheme, replacing s by cA1, and producing the approximation coefficients cA2 and detail coefficients cD2. The wavelet decomposition of the signal s analyzed at level j has the approximation coefficients cAj and detailed coefficients cDj, …, and cD1 at different level. In Figure The wavelet decomposition at level two analyzed the approximation coefficients cA0 = {ck0} = s. At level j, the corresponding approximation coefficient cAj = {ckj} and detail coefficient cDj = {dkj} can be fast computed by Mallat algorithm [For any signal s denote lgorithm  as follohl}l and {gl}l are orthogonal, there is gl = (−1)lhl1 − . While in the biorthogonal condition there are four filters (two group filters): decomposition filters {hl}l, {gl}l, reconstruction filters And if {U05019.1/544-658) and 5S_rRNA (U05019.1/ 544–658)  using Unweighted Pair Group Method with Arithmetic Mean (UPGMA) for the four RNA classes . Figure S3: Largest structure mutation for 21 RNA Ribosomal sequences using RNAmscTV-Curve, RNAdistance and RNApdist.The Phylogenetic tree by RNApdist using Unweighted Pair Group Method with Arithmetic Mean (UPGMA) for the four RNA classes . Click here for fileTable S2: The mutations with the largest difference from the wild types of 21 ribosomal RNA fragments using RNAmscTV-Curve, RNAdistance and RNApdist.21 ribosomal RNA fragments of thermus thermophilus HB8. Click here for file


Neurospora crassa have been shown to mediate gene silencing in the DNA damage response (DDR) pathway. However, the mechanism for RNA silencing in the DDR pathway is largely unknown in plants. Here, we report that a class of small RNAs (qiRNAs) derived from rDNA was markedly induced after treatment by DNA-damaging agents [ethyl methanesulphonate (EMS and UV-C)], and that aberrant RNAs (aRNAs) as precursors were also highly induced after the DNA damage treatment in rice. However, these RNAs were completely abolished in OsRecQ1 (RecQ DNA helicase homologue) and OsRDR1 (RNA-dependent RNA polymerase homologue) mutant lines where either gene was disrupted by the insertion of rice retrotransposon Tos17 after the same treatment. DNA damage resulted in a more significant increase in cell death and a more severe inhibition of root growth in both mutant lines than in the WT. Together, these results strongly suggest that both OsRecQ1 and OsRDR1 play a pivotal role in the aRNA and qiRNA biogenesis required for the DDR and repair pathway in rice, and it may be a novel mechanism of regulation to the DDR through the production of qiRNA in plants.Small RNA-mediated gene silencing pathways play important roles in the regulation of development, genome stability and various stress responses in many eukaryotes. Recently, a new type of small interfering RNAs (qiRNAs) approximately 20–21 nucleotides long in  Chromosomal DNA damage in most organisms is caused by two major sources from exogenous factors such as ultraviolet light (UV), ionizing radiation and chemical exposure Neurospora crassa have been shown to be involved in the generation of double-stranded RNA (dsRNA) induced RNA silencing Arabidopsis, some homologues of RDRs  have been shown to be responsible for RNA silencing or the antiviral pathway OsRecQ1, a QDE-3 homologue, is thought to participated in the process of allowing inverted repeat (IR) DNA to be transcribed into dsRNA that can trigger RNA silencing OsRDR1 has been reported to be involved in virus mediated RNA silencing chromomethyltransferase 3 (OsCMT3) has been anticipated to be involved in the epigenetic process in affecting genome activity during abiotic stress Suppressor of gene silencing 3 (SGS3) in Arabidopsis has been shown to be required for the biogenesis of trans-acting small interfering RNAs (ta-siRNAs) SGS3 [OsSGS3a (AK064995) and OsSGS3b (AK100699)] in rice, and a recent finding showed that rice (OsSGS3a) interacted with a silencing suppressor, Rice stripe virus (RSV) p2 protein, which has been demonstrated to be be targets of TAS3-derived ta-siRNAs, was up-regulated in RSV-infected rice RNA silencing is a manner of gene regulation by degrading sequence-specific RNA, which is conserved among eukaryotes including fungi, animals and plants Neurospora and animals OsRecQ1 and OsRDR1 in the small RNA regulating DDR in rice and propose a novel mechanism of gene regulation to the DDR through small RNA biogenesis in plants.Small RNA (sRNA), including small interfering RNA (siRNA) and microRNA (miRNA), plays an important role in the RNA silencing pathway during diverse biological processes in plants and animals Neurospora crassa is involved in regulation of gene silencing in the DDR pathway OsRecQ1 mutant line (ND8004)  in (ND8004) . These rOsRecQ1 mutant line  as precursors ant line  and a sint lines . These rOsCMT3OsSGS3OsRDR1 as well as other mutant lines including OsCMT1, OsCMT3 and OsSGS3bOsCMT1, OsCMT3 and OsSGS3b (OsRDR1 mutant lines (ND2001 and ND2059) OsCMT1, OsCMT3 and OsSGS3b   OsSGS3b . Notably OsSGS3b , but notOsRecQ1 and OsRDR1 mutant lines, two-week-old seedlings were treated by UV-C irradiation for 0, 8, 16, 24 h. In Arabidopsis, Rad51A gene family is found to be involved in the DDR OsRad51A1 and OsRad51A2 were checked by RT-PCR analysis, and the results show that the transcription of OsRad51A2 was strongly induced by UV-C irradiation in the root tip (RT) of WT, but not in the OsRecQ1 and OsRDR1 mutant lines  after UV-C treatment, but not in the mature leaf (ML) possibly because of no proliferating stage  are directly/indirectly involved in the transcription of OsRAD51A2, and that OsRecQ1 and OsRDR1 may play an important role in the processing of cell proliferation in the DDR pathway. It would be interesting to investigate whether this is general for other genes related to the DDR such as BRCA1 (Breast Cancer 1), MRE11 (Meiotic Recombination 11) or RECQ4 (Recombination Q4) in rice.The dynamic state of DNA metabolism acts as replication, recombination and repair for tolerating and repairing numerous types of damage in all living organisms nt lines . These r1 is not , suggestNeurospora crassaNeurospora crassa. In OsRecQ1 and OsRDR1 are required for aRNA and qiRNA biogenesis after DNA-damaging agent (EMS or UV) treatments, and aRNAs are required for qiRNA biogenesis as precursors. In our experiments not only qiRNA but also aRNA were found to be double-stranded because either sense or antisense RNA probe could detect their RNA bands in the northern blots. aRNA is thought to be single-stranded Arabidopsis showed polymerase activity on ssRNA as well as ssDNA in in vitro polymerase activity assay Although the mechanisms of DNA damage and repair have been clearly established in bacteria, yeast, and mammals, it is worthwhile determining whether these mechanisms exist in higher plants Arabidopsis and in human cells More recently, a kind of small RNAs  has been reported to be involved in the DSB repair pathway. diRNAs are generated from the sequences in the vicinity of DSB sites in OsRecQ1 and OsRDR1 are required for aRNA and qiRNA biogenesis in the DDR pathway, qiRNAs derived from rDNAs repeats are important for efficient DDR. It will be very exciting to have further studies on dissecting the mechanisms by which the production of small RNAs participate in the DDR pathways in plants.In summary, we demonstrated that both Oryza sativa L. cv. Nipponbare) and its knockout mutant lines were used in this study. OsRecQ1 (ND8004 and ND0059) and OsRDR1 (ND2001 and ND2059) mutant lines were reported previously OsSGS3b (NE5050), OsCMT1 (NE7010) and OsCMT3 (NC4949) mutant lines were used in this study. RNA silencing induction by particle bombardment was defective in these three mutants (unpublished data). The seeds of homozygous mutant lines were used to produce calli, and the plants and its calli were grown in proper conditions as previously described The WT (OsRAD51A1 (AB080262) and OsRAD51A2 (AB080264) genes were amplified by the following pairs of primers: 5′-GCTCATGCTTCCACAACAAG-3′ (OsRad51-F), 5′-GGCAGAAAACTTACTTCG-3′ (OsRad51A1-R) and 5′-AATTCTGGCTCGTCTAAC-3′ (OsRad51A2-R), respectively.Two-week-old seedlings were irradiated under ultraviolet light  using a germicidal lamp (Matsuda) for 0, 8, 16, 24 h as previously described For the root tissues staining by the Evan’s blue and suspension cell assay, aliquots of RT were removed from treatments and performed by Evan’s blue assay for monitoring cell death estimation by spectrophotometry or by microscopic observation as previously described For the root growth assay, 20 seeds of rice WT and each mutant line were grown in a Petri dish for liquid cultures containing 0, 10, 20, 30 µM EMS (Sigma-Aldrich) for two weeks with a modified version as previously described For the detection of qiRNAs and aRNAs, four leaf segments (about 6 cm) of seedlings at the two-week-old stage or one-month-old calli were used for the treatments by irradiation under UV-C light for 24 h or by liquid cultures containing 0.4% EMS (Sigma-Aldrich) for 48 h. After the treatments, northern blot and RT-qPCR analyses were performed as described below.Total RNA was extracted from rice leaves and calli after DNA damage treatments as previously described + mRNA was purified by an Oligotex-dT30 mRNA purification kit (TaKaRa). Reverse transcription using random hexamers was carried out with equal amounts of mRNA (100 ng) by an AMV reverse transcriptase XL kit (TaKaRa). Synthesized cDNAs were 10-fold diluted and used for PCR by the incorporation of the fluorescent DNA dye SYBR green using the QuantiTect™ SYBR® Green PCR kit (Qiagen). Gene-specific primers were derived from the upstream  or downstream  sequence of the 25S rDNA region. Each reaction was performed in duplicate. Levels of the rice ubiquitin gene were used as the internal control Quantitative real-time PCR (RT-qPCR) was performed with a Light Cycler II system (Roche) using a previously described protocol


Reports of infections in travelers, clinical research, and epidemiologic studies have shown that TOSV affects the central nervous system and is a major cause of meningitis and encephalitis in Mediterranean countries in which the virus circulates  were adapted to sandflies with an ultra-fine mesh. Traps were hung 1–2 m above the ground. They were placed in the late afternoon inside or near animal housing facilities  in the suburbs of Marseille for 4 successive nights. In these areas, large numbers of geckos were noticed. Each morning, sandflies were collected, identified morphologically, and placed in 1.5-mL Eppendorf tubes. S. minuta flies were identified by appearance, and genus was confirmed by sequence analysis, as previously reported  with one 3-mm tungsten bead and clarified by low-speed centrifugation. We used 200 μL supernatant for total RNA purification onto the MagNAPure platform with the MagNA Pure LC RNA High Performance Kit . We used 10 μL RNA suspension for reverse transcription PCR, with primers targeting either a consensus sequence for the phlebovirus polymerase gene (L RNA segment) or Toscana virus (Only 1 TOSV-positive pool was observed with primers specific to TOSV polymerase and N genes, respectively. A positive result was observed with primers NPhlebo2+ and ATos2, previously found to target polymerase genes of a range of phleboviruses . CompariTo our knowledge, this is the first time that TOSV has been detected in phlebotomine flies other than P. perniciosus and P. perfiliewi. S. minuta was identified with morphologic keys and confirmed by sequencing a portion of the 28S gene (