Picornaviridae. The Group B coxsackieviruses include six serotypes (B1 to B6) that cause a variety of human diseases, including myocarditis, meningitis, and diabetes. Among the group B, the B3 strain is mostly studied for its cardiovirulence and its ability to cause acute and persistent infections. Translation initiation of CVB3 RNA has been shown to be mediated by a highly ordered structure of the 5′-untranslated region (5′UTR), which harbors an internal ribosome entry site (IRES). Translation initiation is a complex process in which initiator tRNA, 40S and 60S ribosomal subunits are assembled by eukaryotic initiation factors (eIFs) into an 80S ribosome at the initiation codon of the mRNA. We have previously addressed the question of whether the attenuating mutations of domain V of the poliovirus IRES were specific for a given genomic context or whether they could be transposed and extrapolated to a genomic related virus, i.e., CVB3 wild-type strain. In this context, we have described that Sabin3-like mutation (U473→C) introduced in CVB3 genome led to a defective mutant with a serious reduction in translation efficiency. In this study, we analyzed the efficiency of formation of ribosomal initiation complexes 48S and 80S through 10%–30% and 10%–50% sucrose gradients using rabbit reticulocyte lysates (RRLs) and stage-specific translation inhibitors: 5′-Guanylyl-imidodiphosphate (GMP-PNP) and Cycloheximide (CHX), respectively. We demonstrated that the interaction of 48S and 80S ribosomal complexes within the mutant CVB3 RNA was abolished compared with the wild-type RNA by ribosome assembly analysis. Taken together, it is possible that the mutant RNA was unable to interact with some trans-acting factors critical for enhanced IRES function.Coxsackievirus B3 (CVB3) is an enterovirus of the family of  Picornaviridae, is an established human pathogen that causes heart disease [Coxsackievirus B3 (CVB3), a member of the genus enterovirus within the family  disease –3, pancr disease –7 and me disease ,9. CVB3  disease ,11. In a disease –14. Thes disease , several disease –20 and D disease ,22.The coxsackievirus genome has a long stretch of non-coding region (5′NCR) upstream of the authentic start codon. The 741 nt 5′NCR of the CVB3 RNA is highly structured containing multiple stem-loop elements ,23,24. ISince the discovery of IRES-mediated translation initiation in 1988 ,30, the Translation of mRNA into protein begins after assembly of initiator tRNA (Met-tRNAi), mRNA, and separated 40S and 60S ribosomal subunits into an 80S ribosome in which Met tRNAi is positioned in the ribosomal P site at the initiation codon . The comSabin3-like mutant of CVB3 was reported [Sabin3-like mutant was more pronounced in the CVB3 than the PV1 (M) context, despite the fact that the efficiency of translation of the wild-type CVB3 was similar to that of PV1 (M) [Sabin3-like CVB3 IRES mutant was more handicapped than the Sabin 3 in PV1 (M) and the attenuated Sabin 3 strain [Sabin3-like IRES mutant could be explicated by its inability to correctly bind some essential non-canonical translation factors during the initiation of translation. In order to test this hypothesis, formation of ribosomal complexes 48S and 80S was analyzed for both CVB3 IRESes (wild-type and Sabin3-like). The identity of ribosomal initiation complexes was confirmed by using stage-specific translation inhibitors.In a previous study, the limited efficiency of the translation of reported . This mureported ,45 or inreported . The def PV1 (M) . All of 3 strain . The pooSmall molecule inhibitors of bacterial protein synthesis have served as powerful tools in the elucidation of the function of the prokaryotic ribosome. Among the known inhibitors of eukaryotic translation is cycloheximide (CHX) . CHX hasSabin3-like IRESes, we developed an RNA-based affinity purification method sensitive to translation inhibitors and suitable for isolating IRES-associated ribosomal complexes. Thus, we have analyzed 48S and 80S complex assembly through 10%–30% and 10%–50% sucrose gradients in the presence of GMP-PNP and CHX, respectively. We demonstrated that neither the 48S nor the 80S complexes were assembled in the presence of the Sabin3-like RNA, thereby suggesting that the association of the small ribosomal subunit within the mutant RNA was abolished.Herein, in order to identify and compare ribosomal complexes formation within the wild-type and the Sabin3-like strain. The attenuation of the Sabin3-like strain was mainly conferred by a single point mutation in the IRES domain V sequence [In the present study, two CVB3 strains were analyzed: a wild-type and a mutant sequence   were cloned in a pUC19 vector between EcoRI/BamHI restriction sites. Transformed pUC19/IRES clones were confirmed by colony-PCR and sequencing as described in the “Experimental Section”.RT-PCR was performed using reverse-transcriptase MMLV and total RNA was isolated using the Trizol Reagent protocol. Both CVB3 wild-type and Sabin3-like RNAs were transcribed using T7 RNA polymerase from PCR products amplifying the IRES containing the AUG initiator codon and 15 nucleotides from the coding region (incorporated with an appropriate primer as described above) (IRES wild-type and d above) . RNAs wed above) , and thed above)  and quanWe analyzed the initiation complexes recruited on CVB3 wild-type and mutant IRESes by sedimentation on sucrose gradients. Fluorescent RNA was incubated in RRLs pretreated with a translation inhibitor, and formed initiation complexes were separated on sucrose gradients. RRLs were pretreated with either GMP-PNP or CHX. The total magnesium concentration in these binding reactions was in the physiological range and therefore allows dissociation of ribosomes to generate empty ribosomal subunits, which are, in turn, required for the association of ribosomal subunits driven by the RNA to be translated and the initiation factors involved. The ribosome assembly at the initiator AUG was blocked before or after the joining of the 60S ribosomal subunit using GMP-PNP or CHX, respectively.Sabin3-like RNAs are shown in Ribosomal complexes (48S and 80S) were unambiguously identified by (i) comparison with the profile obtained with a control gene, (ii) modification of the profiles depending on the inhibitor used, and (iii) analysis of the profile of the same gradients which detects 40S and 60S subunits. Once formed, ribosomal complexes were separated on sucrose gradients preserving initiation complexes which had been formed with the viral RNA. The efficiency of formation of these complexes was measured by the amount of incorporated fluorescence at 254 nm. The profiles of typical gradient runs for CVB3 wild-type and Sabin3-like IRES. 80S initiation complexes migrate at 6 mL from the top of the gradient , the rate-determining step is initiation ,54. InitSabin-like mutations in the equivalent positions  to the domain V of the CVB3 IRES resulted in a significant reduced viral titer of the Sabin3-like mutant but not on those of Sabin1- and Sabin2-like mutants [Sabin3-like RNA showed a reduced efficiency of translation compared to the CVB3 wild-strain.We have previously tried to find out if the results observed in the case of Sabin vaccine strains of poliovirus can be extrapolated to another virus belonging to the same genus of enteroviruses but with a different tropism. To test this hypothesis, we used coxsackievirus B3, known to be the most common causal agent of viral myocarditis. The introduction of the three PV  mutants . Additioin vitro system using purified ribosomal subunits, initiation factors, and possibly other cellular proteins required for the formation of ribosomal initiation complexes with the CVB3 IRES RNA, we developed an assay that allowed us to determine the efficiency of ribosome association with the mutated IRES elements. In this system, we demonstrated that the efficiency of ribosome assembly with the mutated IRES was considerably impaired compared with the wild-type RNA, correlating with the most severe decrease of translation efficiency with this construct [Sabin3-like RNA was abolished when compared with the wild-type, suggesting that the mutant prevented 48S ribosomal complex assembly on the CVB3 RNA. The 80S peak was also abolished in the presence of the mutant RNA compared with the wild-type RNA, demonstrating that neither the 48S nor the 80S complexes were assembled in the presence of Sabin3-like RNA. These results indicate that the serious reduction in translation efficiency observed previously with the Sabin3-like strain [In the absence of an onstruct . In facte strain ,17 is caSabin3-like point mutation in domain V of the CVB3 IRES seriously affects ribosome association with the IRES, coupled with the fact that domain V is the major determinant for the binding of eIF4G and eIF4B [Sabin3-like IRES can be due to its inability to correctly bind some essential non-canonical translation factors and/or to perturbations of the secondary and tertiary structure of domain V.The observation that the nd eIF4B , led us 473 by a cytosine  affects the formation of 48S and 80S ribosomal initiation complexes. This finding correlates with previous genetic results of impaired replication of a CVB3 U473→C mutant [Sabin3-like mutant of CVB3 is likely a general impairment, despite the fact that the same mutation was investigated.Taken together, these data demonstrate that the introduction of a single substitution of the nucleotide UC mutant . StudiesC mutant ,46. TherThe 40S ribosomal subunit has been shown to enter at a putative site 150 nucleotide upstream of the initiator AUG in the poliovirus 5′UTR ,68,69. Tetc. Also, several other cellular proteins have been shown to influence IRES function in general. DAP5, an 86-kDa protein, has been shown to be an ITAF for the IRES-mediated translation of its own mRNA [Several non-canonical cellular protein factors have been shown to specifically interact with picornavirus IRES elements and influence their function, which includes La (p52), PTB (p57), UNR (p97), PCBP2 (p39), PABP ,75–77. Sown mRNA .It was reported that MFOLD structure of CVB3 IRES and its secondary structure showed a high degree of similarity to that of poliovirus 5′UTR  and thatInvolvement of the local secondary structure in modulating IRES function has been reported in some viral RNAs ,81–83. IThe possible involvement of other trans-acting factors with this cis-acting element is not ruled out. Therefore, the possibility of involvement of this cis-acting RNA element with some other region of the RNA in long range RNA–RNA interactions cannot be ruled out.Sabin3-like mutant of CVB3, used as a “vaccine candidate” and obtained by direct mutagenesis [The coxsackievirus B3 (CVB3) Nancy prototype strain and the agenesis  were uselacZ) gene. The synthesis of lacZ fragment can be induced by IPTG . In the presence of IPTG, bacteria synthesize both fragments of the enzyme that can together hydrolyze the X-gal  and form blue colonies. Thus, bacteria carrying recombinant plasmids cannot hydrolyze the X-gal, giving rise to white colonies.The pUC19  (Invitrogen) was used for cloning the IRESes of both CVB3 strains. This plasmid has one ampicillin resistance gene, and an N-terminal fragment of β-galactosidase (Escherichia (E.) coli DH5α strain was transformed with recombinant plasmids, and was cultured in Luria–Bertani (LB) broth to select transformants. This strain has many mutations that make it useful for transformation, but the most useful of these mutations is the “lacZ Delta M15” mutation which allows blue/white screening for recombinant cells.Total RNA was extracted using the Acid Guanidinium Thiocyanate Phenol-Chloroform method . After e2, 1 mM dNTP (Promega), 10 mM DiThioThreitol (DTT), 1 mM antisense primer IRES-R (5′- TTT gCT gTA TTC AAC TTA ACA ATg-3′), 1U RNase inhibitor (Amersham), 50 U Moloney Murine Leukemia Virus Reverse Transcriptase (MMLV-RT) (Promega). The reaction was carried out at 42 °C for 30 min followed by a denaturation step during 5 min at 99 °C.cDNA was synthesized in 50 μL volume containing 2 μg RNA, 10 mM Tris–HCl (pH 8.3), 75 mM KCl, 2.5 mM MgClgAA TTCTAA TAC gAC TCA CTA Tag gTA ACT TAg AAg TAA CAC A -3′) and IRES-R′ (5′- TAT ggA TCC TTg CTg TAT TCA ACT TAA CAA TgA ATT gTA ATg TTT TAA -3′). The amplification reaction was performed in a volume of 50 μL containing 100 ng cDNA, 25 mM Tris-HCl (pH 8), 50 mM KCl, 4 mM MgCl2, 200 μM dNTP, 40 pmoles from each primer and 1U Taq DNA polymerase (Promega). Distilled water was included as a negative control. Amplification was initiated by a denaturation step during 2 min at 95 °C, followed by 5 cycles of 30 s denaturation at 95 °C, 30 s annealing at 48 °C, and 30 s extension at 72 °C. Then, 25 cycles of 30 s at 94 °C, 30 s at 58 °C and 30 s at 72 °C were performed. Steps were followed by a final extension (10 min at 72 °C). All PCR products were electrophoresed on 1.5% agarose gel.IRESes of the two studied CVB3 strains were amplified using primers IRES-F (5′- TAT E. coli DH5α cells. Transformants were selected in LB agar supplemented with ampicillin (100 μg/mL), X-gal (64 μg/mL) and IPTG (0.2 mM). Blue–white selection was used to identify the recombinant clones.Amplicons encoding CVB3 IRESes were double-digested with EcoRI and BamHI (Roche Applied Science) (both restriction sites were added by PCR), then purified and inserted into the pUC19 plasmid (Invitrogen) to be digested with the same enzymes. Ligation products were then transformed in chemocompetent To analyze the cloned sequences, randomly chosen clones were tested by colony-PCR. A single colony isolated and added into 5 μL sterile water was used as template DNA in the PCR reaction containing 15 μL premix solution. The amplification program was a pre-denaturation at 94 °C for 4 min followed by 30 cycles of denaturating at 94 °C for 1 min, annealing at 50 °C for 1 min, extension at 72 °C for 1 min, and a final extension at 72 °C for 10 min. Then, a 1.5% agarose gel was stained and DNA bands were compared to the expected sizes to determine positive clones. Finally, these clones were sequenced using an ABI Prism BigDye Terminators Sequencing Kit (Applied Biosystems).CAT TTT gCT gTA TTC AAC TTA ACA ATg- 3′) as forward and reverse primers, respectively. Fifteen nucleotides from the coding region and the initiating codon AUG were incorporated in the reverse primer sequence. Reaction mixture components were: 50 ng linearized pUC19/IRES, 1 μM from each primer, 0.2 mM dNTPs, Taq Buffer (1X), 5U Taq polymerase and the final volume of reaction was adjusted by adding distilled sterile water. PCR was carried out following a thermal profile consisting in a pre-denaturation step during 3 min at 94 °C; 35 cycles denaturating for 1 min at 94 °C, annealing for 1 min at 50 °C, extension for 1 min at 72 °C, and a final extension step during 10 min at 72 °C.The DNA sequence corresponding to the CVB3 IRES cloned in pUC19 was amplified by PCR using T7 (5′- TAA TAC gAC TCA CTA TAg- 3′) and IRES-AUG , 5 mM DTT, 4 mM NTP’s, Transcription Buffer (1×)  and 0.04 U RNase inhibitor (Applied Biosystems/Ambion) and incubated 2 h at 37 °C. During this incubation, and when a precipitate of RNA appears, 1 U of Pyrophosphatase Alkaline (New England Biolabs) was added. Upon synthesis, RNAs were treated with RQ1 RNase-free DNAse (Promega) for 30 min at 37 °C.RNA was then produced by RNA was then precipitated with 2.5 M Lithium chloride (LiCl), incubated for 30 min at −20 °C, centrifuged at 13,000 rpm, 30 min at +4 °C, washed with 75% ethanol, resuspended in 50 μL of nuclease-free water and purified by size exclusion chromatography. RNA concentration was then determined spectrophotometrically (NanoDrop Technology) and RNA integrity was monitored by electrophoresis on an agarose gel.Sabin3-like RNAs was oxidized into dialdehyde by sodium periodate and then labeled with fluorescein-5-thiosemicarbazide. The detailed procedure was as follows: RNA was oxidized using sodium periodate (0.1 M) with sodium acetate  and incubated at 25 °C during 90 min. To stop the reaction, potassium chloride (250 mM) was added. The mix was incubated on ice for 10 min, then, centrifuged at 13,000 rpm for 5 min at +4 °C. The supernatant was purified using Sephadex G50 column  to remove the excess of oxidant. Coupling was performed by addition of 5 mM fluorescein-5-thiosemicarbazide in the presence of sodium acetate , followed by incubation at 25 °C for 4–17 h in the dark.Many single-molecule experimental techniques exploit fluorescence as a tool to investigate conformational dynamics and molecular interactions or track the movement of proteins in order to gain insight into their biological functions. In this context, to assess the efficacity of formation of ribosomal complexes during the initiation of translation of CVB3, the IRES RNA was labeled with fluorescein which is a synthetic organic compound available as a dark orange/red powder. Herein, the 3′terminus of IRES wild-type and Following a fluorescent labeling reaction, it is often necessary to remove any non-reacted fluorophore from the labeled target molecule. This is usually accomplished by size exclusion chromatography, taking advantage of the size difference between fluorophore and labeled RNA. Taking into account that fluorophores may interact with the separation matrix and reduce the efficiency of separation, specialized dye removal columns that account for the hydrophobic properties of fluorescent dyes are sometimes used. G50 Sephadex columns purification (Roche Applied Science) were used for this purpose.Then, fluorescent-labeled RNA was precipitated by adding LiCl (7.5 M) and 2.5 vol ethanol, standing at −20 °C for 30 min, and centrifuged  for 20 min. The precipitate was washed with 75% ethanol and RNA was resuspended in nuclease free water. The excess of fluorophore was removed by G50 Sephadex column purification (Roche Applied Science).in vitro translational system.Initiation complexes were analyzed on 10%–50% and 10%–30% sucrose gradients. Sucrose gradient analysis was used to distinguish between 48S and 80S complexes. This analysis was facilitated by inclusion of 5′-Guanylyl-imidodiphosphate (GMP-PNP)  that inhibits 60S subunit joining leading to a build up of 48S complexes and cycloheximide (CHX) that blocks the translational elongation process, leaving either the 48S or 80S complex stalled at the start codon, respectively. Rabbit reticulocyte lysates that contain cellular components necessary for protein synthesis  were used as an 2, 3M KCl; sucrose 60%; 1 M DTT and H2O) and sedimented by ultracentrifugation at 37,000 rpm in a SW40 rotor of a Beckman Coulter ultracentrifuge for 3 h at 4 °C. Fractions were then collected using a fraction collector FC 204 (Gilson). The amount of fluorescence in each fraction was determined by a 1420 multilabel counter, Wallac  using absorption at 254 nm to detect ribosomes. The obtained values were then extrapolated to draw a graph: % RNA-bound = f (fraction volume). Reported values are the average of three repetitions with standard errors.Ribosomal complexes were assembled on fluorescein-labeled IRES RNA. At first, 5 pmol of RNA were heated for 2 min at 80 °C, slowly cooled to room temperature in a mix containing: 25 mM magnesium acetate, 4 mM potassium acetate, 20 mM amino acid and 8 U RNAse inhibitor (Promega) for 10 min. RRLs (Promega) were preheated with either 2 mM GMP-PNP or 4 mM CHX for 10 min at 30 °C. Then, RNA was incubated with pretreated RRLs at 30 °C during 20 min. Reactions were stopped on ice, then were layered over 10%–30% (RNA/RRL+GMP-PNP) or 10%–50% (RNA/RRL+CHX) sucrose gradients (1 M Tris pH 7.6; 1M MgClSabin3-like strain can be mediated by the impaired binding of standard translation initiation factors to domain V of the CVB3 IRES. This, in turn, causes impaired association of ribosomes within the viral RNA, and thus may contribute to the attenuated phenotype of the CVB3 mutant strain. Hence, it will be interesting to identify the specific site of the inhibition of initiation factors binding to the mutant CVB3 RNA during the initiation of translation process.In summary, we have used in the present study a cell-free protein synthesis as the indicator of ribosome assembly initiation complexes in the presence of specific inhibitors of protein synthesis. We have demonstrated that the reduction in CVB3 translation efficiency caused by the single nucleotide exchange in the IRES of the


Carcharhinus perezi), the most common reef shark in the Western Atlantic, at Glover's Reef Marine Reserve (GRMR), Belize. Acoustically tagged sharks (N = 34) were detected throughout the year at this location and exhibited strong site-fidelity. Shark presence or absence on 200 BRUVs deployed at GRMR and three other sites (another reserve site and two fished reefs) showed that the factor “marine reserve” had a significant positive effect on reef shark presence. We rejected environmental factors or site-environment interactions as predominant drivers of this pattern. These results are consistent with the hypothesis that marine reserves can benefit reef shark populations and we suggest new hypotheses to determine the underlying mechanism(s) involved: reduced fishing mortality or enhanced prey availability.Carcharhinid sharks can make up a large fraction of the top predators inhabiting tropical marine ecosystems and have declined in many regions due to intense fishing pressure. There is some support for the hypothesis that carcharhinid species that complete their life-cycle within coral reef ecosystems, hereafter referred to as “reef sharks”, are more abundant inside no-take marine reserves due to a reduction in fishing pressure . Key predictions of this hypothesis are that (a) individual reef sharks exhibit high site-fidelity to these protected areas and (b) their relative abundance will generally be higher in these areas compared to fished reefs. To test this hypothesis for the first time in Caribbean coral reef ecosystems we combined acoustic monitoring and baited remote underwater video (BRUV) surveys to measure reef shark site-fidelity and relative abundance, respectively. We focused on the Caribbean reef shark ( Many tropical nations are turning to marine reserves to help maintain coral reef biodiversity, ecosystem function, ecotourism and fisheries Carcharhinus perezi) in Brazil were more abundant inside than immediately outside a marine reserve at an oceanic archipelago C. amblyrhynchos and whitetip reef, Triaenodon obesus) revealed higher relative abundance of sharks inside than outside parts of the reef that are zoned for no entry or no fishing There is a modest body of data supporting the hypothesis that marine reserves can benefit certain shark populations Marine reserves are increasingly being used for marine conservation in the Caribbean Here we combined acoustic monitoring with baited remote underwater video (BRUV) to examine site-fidelity and relative abundance of Caribbean reef sharks in a marine reserve in Belize (GRMR). Given the hypothesis that Caribbean reef shark populations can benefit from no-take marine reserves and increase in abundance in these areas, we predicted that (1) acoustically tagged Caribbean reef sharks at GRMR would exhibit site-fidelity to the reserve and (2) the relative abundance of Caribbean reef sharks would be higher at GRMR (and other reserve reefs) when compared to fished reefs.Carcharhinus perezi) is a large requiem shark  that is endemic to the Western Atlantic from Bermuda to southern Brazil The Caribbean reef shark (Glover's Reef Marine Reserve (GRMR) encompasses Glover's Reef Atoll , which lies approximately 25 km to the east of the Mesoamerican Barrier Reef and 45 km east of mainland Belize . The atoCaribbean reef sharks were collected using longlines and fitted with transmitters under permit from the Belize Department of Fisheries  were anchored to the substrate in various locations at GRMR from May 2004 to May 2008  to monitDetections from all receivers were sorted by transmitter, date and receiver to generate a complete monitoring record for each individual implanted with an acoustic tag. We only used strings of two or more consecutive detections for downstream analysis to avoid using spurious detections that arise from signal collisions or background noise. Caribbean reef shark movements were visualized by plotting presence/absence data, gathered from receivers in the array, over a map of GRMR. Three metrics of shark presence and movement within the array were calculated to test the hypothesis that Caribbean reef sharks exhibit a high degree of site-fidelity to GRMR. The distance between the sharks original capture location and that of each receiver at which it was detected was measured using ArcGIS. This was used to calculate “minimum linear dispersal” (MLD) for each individual, defined as the distance between the two furthest receivers at which it was ever detected. “Monitoring duration” was defined as the number of days elapsed between the date of tagging and the date of the last detection string. Because there is some variation in how long transmitters continue to produce detectable signals after their battery expiry date, a standardized “residency index” (RI) was also calculated for all sharks. RI was defined as the total number of days the shark was detected within the array divided by the number of days it could possibly be detected assuming its transmitter worked only up until the expiry date. Any detections recorded for an individual shark that occurred after the estimated battery life expired were not used to calculate RI. Linear regression was used to test for the effect of increasing shark size (age) on RI and MLD.If sharks exhibit fine-scale site-fidelity to certain parts of GRMR, then the number of detections on a monitor should decrease with distance from the shark's tagging location. The fraction of days each shark was detected by each monitor was modeled using a delta-lognormal approach A logistic (logit-link) generalized linear model (GLM) was used to predict the presence or absence of each shark anywhere in the receiver array by calendar month. The potential explanatory variables were: (1) the month, counted from when the shark was tagged, as a numerical variable and (2) the transmitter type (12 month versus 18 month battery life). The AIC was used to find the best model. Although some sharks were detected after the end of the assumed battery life of their transmitter, only data from within the first 12 months for each individual was included in this analysis so that all sharks could be compared. The two externally tagged individuals  were omitted from this analysis in order to maintain continuity.Caribbean reef shark abundance was surveyed on the fore-reef at four sites .All but one of the tagged Caribbean reef sharks were detected after release. Single, isolated detections were excluded from the analysis due to the possibility of them being spurious detections. It was highly unusual for more than two individuals to be detected simultaneously on the same receiver. When three individuals were detected simultaneously at a receiver we attempted to verify that detections of the third individual were not an artifact of signal collisions between the other two, which we reasoned would consist of detection strings with an unusually long lag time between detections given the transmission rate of the transmitter. No detection strings met this criterion. Both the total number of days that each individual was detected and the monitoring duration within the array was related to the tag type. For sharks tagged with V9 transmitters (N = 14), the number of days detected ranged from 17–361 days  of sharks by calendar month from the AIC best model, showed that the probability of a shark being detected remains quite high throughout the first 12 months . AlthougA total of 200 BRUV deployments were made, divided equally across the 4 study sites . The deployments were made between June 11–19 2009  and May 6–12 2010 (n = 6) at GRMR, with Caribbean reef sharks observed on 16 BRUVs (32% of deployments at this site), 6 of which recorded 2 individuals. At TU deployments occurred between June 21–26 2009 (n = 50), with a shark observed on 6 BRUVs (12% of deployments). Deployments occurred at SWC between July 3–8 2009 (BRUV's n = 39) and May 23–29 2010 (n = 11), with a shark observed on 2 BRUVs, (4% of deployments). CCMR deployments occurred between 30 June–2 July 2009  and 18–20 May 2010  with Caribbean reef sharks being recorded on 13 BRUVs , with 6 of these recording 2 individuals in frame at once (3 in 2009 and 3 in 2010). Overall, 35 of the 200 BRUV deployments  recorded at least one Caribbean reef shark. All but 8 of these were in marine reserves, with at least 10 of the marine reserve deployments recording at least 2 different individuals . As a reThe two reserve sites (GRMR and CCMR) had smaller total areas when compared to that of the two fished sites (TU and SWC). To ensure that the observed increase in relative abundance of reef sharks at the reserve sites was not biased by a greater proportion of the available habitat being sampled, the number of samples was proportionately reduced by bootstrapping according to total area, to attain an equal sample density per site. The reduced numbers of BRUVs per site of TU (n = 50), SWC (n = 30), GRMR (n = 25) and CCMR (n = 25) were randomly subsampled from the complete data set, using the R software There were also no significant differences in flow, salinity, depth or visibility between locations . Temperature and DO were both significantly higher at SWC than at any of the other three locations . Because the vast majority of BRUV deployments in the non-reserve sites resulted in zero reef sharks being observed it was not possible to evaluate the impact of environmental variables and reserve versus non-reserve effects in the same model. For the subset of data from marine reserves, there were no significant effects of site, location, water temperature or flow velocity . HoweverWe tested the hypothesis that Caribbean reef sharks are to benefit from the local respite from fishing occurring within marine reserves by examining two of its key predictions. The first prediction is that Caribbean reef sharks exhibit high site-fidelity to reserve areas. Acoustic monitoring showed that most individuals exhibit a high degree of site-fidelity at GRMR. The mean residency index (RI) indicated that the average shark was detected nearly one out of every two days at GRMR. Notably, RI is a conservative metric considering that receiver array coverage was modest (∼6% of the reef platform). We found that sharks tagged on the fore reef typically had higher RI and were less likely to be lost from the array than lagoon-tagged sharks. These observations most likely reflect differences in receiver coverage and effectiveness between these two reef habitats. Not only were more receivers deployed on the fore-reef, this habitat is so narrow, usually <500 m, that a line of receivers deployed along the reef slope is likely to regularly detect passing sharks. Reef sharks in the lagoon can swim in most directions and may not necessarily swim close to an isolated receiver despite being close to it. Transmitters are also more likely to be detected in the fore-reef because seafloor relief is low relative to water depth, whereas receivers in the lagoon are partially blocked by emergent patch reefs. Notwithstanding the limitations of acoustic monitoring in the lagoon we found that the probability of detecting tagged sharks by calendar month was high throughout the year following transmitter application. This indicates that sharks were typically year-round residents of GRMR as opposed to being seasonal immigrants. Several large individuals tagged with 18 month  transmitters were also generally detected right up until or slightly beyond projected transmitter battery life, indicating use of the atoll across successive years. Despite reasonably high RI for many individuals it is important to highlight that these sharks are capable of long range movements over short time periods days . Many inIndividual Caribbean reef sharks were mainly detected on a localized subset of receivers within the GRMR array. GLMM analysis indicated that a receiver's distance from the shark's original capture location was an important factor in determining the probability of shark detection. It therefore appears that we typically captured Caribbean reef sharks in an area that they regularly used after release, suggesting that they are a home ranging species (i.e., they regularly use a fraction of the available habitat, rather than moving throughout Sharks often exhibit an ontogenetic expansion of home range size Is the high fidelity of Caribbean reef sharks to GRMR largely driven by the isolation of this reef platform? Large individuals of this species monitored and tracked at GRMR moved across pelagic habitat The second prediction of our main hypothesis is that the relative abundance of Caribbean reef sharks is higher in reserves than similar fished reefs. The factor “marine reserve” was the most important predictor of shark presence or absence on BRUVs in the GLM, which is consistent with this prediction. This analysis assumes that random sampling of each site conducted over a few days is representative of relative abundance throughout the year. We suggest that is a reasonable assumption given the high degree of site-fidelity we observed at GRMR using acoustic monitoring and results from other telemetry studies of this species The issue of a potential bias arising from the difference in the total area of the reserve sites versus the fished sites was addressed by performing bootstrap sampling techniques. The fished sites had a larger total area to be sampled which could have led to pockets of higher shark abundance being under-sampled due to a lower sample density, when compared to the reserve sites. However, by analyzing reduced sample sizes to correct for the difference in sample density the result was found to be the same, with marine reserve being the only significant predictor of reef shark presence.The data do not support the competing hypothesis that reef shark relative abundance was primarily driven by environmental variation between sites. Most environmental parameters were not significantly different between the sites and none were consistently different between the reserves and non-reserve sites. All BRUV deployments occurred on ocean-facing fore-reef habitat within 1 km of the reef slope, which is typical habitat for the species Our combined telemetry and survey results support the hypothesis that marine reserves can have a positive effect on the local abundance of reef sharks or at least significantly reduce rates of population decline relative to fished ones. Time series of shark abundance inside marine reserves (e.g., In conclusion, our telemetry and BRUV survey results support the hypothesis that Caribbean reef shark populations can benefit from the local respite from fishing pressure provided by marine reserves. Of course, reserve size, placement and compliance will influence whether or not these benefits materialize. We suggest “direct enhancement” and “increased prey” as potential enhancement mechanisms. Our study also underscores that Caribbean reef shark abundance on some fished parts of the Mesoamerican Barrier Reef is relatively low, which is concerning from the perspectives of fisheries sustainability and ecotourism. Although the ecological role of reef sharks is not well studied, it is possible that the local reduction in these upper level predators has significant effects on the coral reef ecosystem e.g. . Our stu


Five different Hall Effect sensors were modeled and their performance evaluated using a three dimensional simulator. The physical structure of the implemented sensors reproduces a certain technological fabrication process. Hall voltage, absolute, current-related, voltage-related and power-related sensitivities were obtained for each sensor. The effect of artificial offset was also investigated for cross-like structures. The simulation procedure guides the designer in choosing the Hall cell optimum shape, dimensions and device polarization conditions that would allow the highest performance. These sensors are primarily employed as current sensors and serve many low-power applications like position sensing and contactless switching within automotive and industrial electronics ,2.etc. [Regarding the Hall Effect device performance investigation, one would need to look, among others, for the highest sensitivity and the lowest offset. The geometry plays an important role in the Hall Effect sensors performance and was studied by the authors in –5. A reaetc. .The offset and sensitivity are important figures of merit in Hall sensors performance evaluation . Within In the microelectronics recent development, the solving of fundamental semiconductor device equations by numerical methods is a productive investigation tool to predict the behavior and assess the performance of various devices.The present paper analyzes the influence of the shape, dimensions, position of contacts and offset on the Hall Effect sensors performance, including Hall voltage and sensitivity, with the aid of three dimensional physical simulations. In this sense, the study also proposes an analysis of artificially induced offset. In order to ensure Hall Effect sensors optimal design, we use three-dimensional numerical solutions to the system of partial differential equations governing galvanomagnetic carrier transport in magnetic-field-sensitive semiconductors.Section II presents the motivation behind the simulation approach, the basic physical model of the carrier transport in semiconductors and the methodology used for 3D structures simulation, presenting the design parameters for all analyzed Hall Effect devices. Section III is dedicated to accurate estimation of Hall voltage, different types of sensitivities and influence of geometrical mismatch on the structures offset by performing a comparative study on five different Hall devices. The purpose of this section is to finally reveal which of the simulated magnetic sensors displayed the best performance.2.2.1.G is the geometrical correction factor, Hr is the scattering factor of Silicon, usually 1.15, n is the carrier density, t is the thickness of the active region, biasI is the biasing current and B is the magnetic field induction [In general, the Hall voltage is defined by the relation:AS of a Hall sensor is given by the relation:The absolute sensitivity IS and voltage-related VS sensitivities are introduced as follows:biasV is the bias voltage and R is the input resistance of the Hall device.Relative sensitivities can also be defined. Therefore, the current-related Since the Hall voltage and therefore sensitivity are inversely proportional to the n-well doping concentration, a lightly doped n-well is normally used in the fabrication process of the Hall Effect sensors.etc. More precisely eight different Hall cells were integrated and subsequently tested. A part of the experimental results in conjunction with the geometry influence analysis on the considered devices performance were presented in paper [Different Hall Effect devices were integrated in a 0.35 μm CMOS technology and evaluated for Hall voltage, sensitivity, offset, The offset analysis was of particular interest because in reality, Hall Effect sensors have offset. In this sense several samples, each one containing 64 cells (eight different geometries times eight locations), were tested. The experimental data obtained for the offset at room temperature for different biasing currents is presented in . There iAmong the eight different integrated and tested Hall cells, the minimum offset was obtained for the XL, which is basically a classical Greek-cross but with the dimensions scaled up by a certain factor with respect to a basic shape. This particular geometry will be reproduced by simulation later within the present work, where additional details can be found including design parameters information.biasvs. I and the absolute sensitivity biasvs. I for the XL cell, tested 8 times using an automated measurement setup previously developed and presented by the authors in [thors in .biasI2 in the residual offset, cannot be completely compensated for biasI > 0.5 mA.In particular, the residual offset mathematical function of the biasing current has a quadratic dependence, for the 2-phases spinning current . The samThe aim of the present study is to employ three dimensional simulations for designing and selecting the best Hall device shape to be used in a certain integration process. The performance assessment is conducted by investigating the sensors Hall voltage, sensitivity and offset.2.2.V denotes the electrostatic potential, ε is the material electric permittivity, q is the electron charge and N = DN – AN is the fully ionized net impurity distribution. In turn, n and p, themselves functions of Fermi energy, temperature and electrostatic potential [V.In semiconductor materials, the classical carrier transport model –13 relie−∇⋅(ε∇V)=otential , represeIt is to be mentioned that the magnetic induction effect only manifests in the mathematical relations which define the current density. Equivalently speaking, in the absence of the magnetic field, the continuity equations and the Poisson's By using the Synopsys Sentaurus TCAD tool , which sV, n, p. Further on, we would need three equations and the corresponding boundary conditions to solve the nonlinear system of partial differential equations. In order to have a correct solution, the discretization of the Poisson's equation, electron and holes continuity equations will be needed and a coupled method, which is a generalization of the Newton method, will be used to compute the initial proposed system by numerical iterative procedure.At each point of the grid, three unknowns will be considered, namely α = n, p, αμ* is the Hall mobility [The magnetic field acting on the semiconductor structure for Hall voltage generation was handled by the galvanic transport model. The analysis of magnetic field effects in semiconductor devices is done by solving the transport equation of electrons and holes inside the device. The usual drift-diffusion model of the carrier densities The physical section of the simulation included a doping dependence mobility model together with Shockley-Read-Hall and Auger recombination processes as was considered in a paper . In the For a good tradeoff between accuracy and simulation run time, the three dimensional meshed structures of the Hall effect devices should contain a sufficient number of points. Smaller meshing dimensions and higher number of points increase the accuracy of the simulation results, but would require more CPU time and longer execution. With respect to the previous work the meshing strategy was improved.The meshes of the simulated geometries contained between 40,000–70,000 points with refinement functions included to ensure maximum convergence and minimum numerical offset. Additional refinement windows have been placed on contacts in order to improve the simulation convergence and to decrease the numerical offset. A mesh step between 0.1 and 1 μm on the three axes was used for the mesh refinement window. In this way, for all structures the numerical offset does not exceed two milivolts for maximum biasing current. To address any further convergence issues, the magnetic field was ramped up to the required value of the magnetic induction.2.3.To assess the Hall Effect sensors performance, FEM lumped circuit models were already developed by the authors . The int+15 cm−3 boron concentration and an active n-well region doped with 1.5 × 10+17 cm−3 arsenic concentration in the form of a Gaussian profile implantation were used. This doping profile allows an average mobility of 0.0630 m2·V−1·s−1. The thickness is 5 μm for the p-substrate and 1 μm for the implantation of the n-doped profile active region, respectively.Therefore, a p-substrate with 10Attention should be given to doping profile smoothing in order to ensure a good simulation convergence. Therefore the abrupt edges were avoided by imposing a decay length of a hundred of nanometers at the p-n junction. For testing purposes, each structure was endowed with four electrical contacts, amongst which two are for biasing the device and the other two opposite ones for the measurement of the voltage drop difference.L and W stand for the cell length and width, respectively, while s represents the contact length. The width of the contacts is in general provided by the technology used in the Hall Effect devices fabrication process. In our simulations it was considered to be 0.7 μm. The distance from the contacts to the n-well borders is 0.35 μm for basic, L and XL cells, 13.85 μm for borderless cell, 5.5 μm for optimum cell. These values were omitted on the drawings for aesthetic purpose. The position of contacts with respect to borders is important in the offset analysis as contour errors might increase it.The geometrical design parameters of all the five simulated Hall Effect devices are given in a-d) used for biasing and measurement purposes are depicted as well.The three-dimensional representations of the five simulated geometries are illustrated in a will force a current to flow between contacts a and c. The Hall voltage is actually recorded as the voltage difference between the other two opposite contacts, b and d respectively. It is also to be mentioned that for the cross-like devices the current flows vertically from a to c, while for the borderless and optimum cell the circulation of current has a diagonal path, between a and c contacts.Regarding the polarization of Hall cells, imposing a certain voltage on electrode 3.For the analysis of the Hall Effect devices behavior, all the structures were simulated using current biasing, without and with magnetic field. In the present study, the biasing current was ramped from 0 to 1 mA.The effects of the dimensions (input data), respectively the geometrical correction factor, on the Hall Effect sensors technical performance (output data) were analyzed by authors in a recent paper . In factThe classical cross structures dispose of contacts at the extremities of the four arms. The biasing and sensing will ensure a maximum sensitivity, but the structures can be more affected by any mismatch at the p-n junction. The idea is to increase the dimensions of this classical cross in order to be less prone to border asymmetries. The fourth analyzed shape, the borderless cell, is equipped with very small electrical contacts and they are located closer towards the center of the structure and farther away from the p-n junction. This specific structure could minimize the influence of any border errors but will also affect the sensitivity. The fifth shape, the optimum cell, is a combination of scaled up dimensions and contacts situated half way through with respect to the contacts of XL and borderless cells respectively.3.1.R for certain biasing currents is included in The Hall device I-V characteristic is obtained by simulations for each cell. Its representation for B = 0.5 T is incorporated in For the Hall Effect sensors, any nonlinearity that might be seen in the I-V characteristic is explained  by three3.2.The total output voltage of a Hall Effect device is given by the following relation:Even though the shapes are symmetric we obtain a non-zero offset, which is the numerical offset from the simulator. Therefore, the meshing strategy was adapted to minimize it as much as possible.We were interested to investigate the offset in order to have accurate information for Hall voltage and sensitivity, as the offset is a parasitic voltage adding to the total output voltage. The offset measurements were performed in the absence of magnetic field while for Hall voltage and sensitivity estimation the magnetic induction was considered B = 0.5 T. This particular value for the magnetic field induction was used to closely reproduce by simulation the magnetic field of B = 0.497 T used for the integrated Hall devices measurements.When applying a magnetic field of certain intensity, the carriers deviate under the influence of Lorentz force and thus the Hall voltage is forming between the opposite contacts. In vs. biasing current in L/W ratio and they only differ by the scaling factor. Therefore, according to the definition of the Hall voltage in HallV. By investigating the obtained graph, the following numerical values are extracted. At maximum biasing currentHall, V = 42 mV for basic cell, HallV = 44 mV for the L and XL cells, HallV = 17.7 mV for the borderless cell and HallV = 38 mV for the optimum cell, respectively.The Hall voltage of all the simulated structures is presented etc. In a and c) and sensing contacts (b and d) respectively.The same type of graphs is also investigated for the optimum and borderless cell respectively. The peak of the electrostatic potential is 1.8 V for the borderless cell and 2.3 V for the optimum cell. From these graphs we can also deduct the length of the electric contacts, for optimum and borderless cells as the electrostatic potential is constant in that region. On the Ox cuts of L, XL, basic cells, the descent from the peak is not always a straight line due to the non-homogeneity in material mobility, conductivity, sheet resistance, 3.3.vs. the biasing current, for a magnetic field intensity B = 0.5 T. Among the analyzed cells, for a current polarization, the XL cell displayed the highest absolute sensitivity and therefore current-related sensitivity. Nevertheless, for a voltage polarization of the devices, the optimum cell appears to be the best candidate as it has the maximum voltage-related sensitivity. In vs. the biasing current.Using the definitions for the absolute, current-related and voltage-related sensitivities in V can be rewritten as the ratio of the current-related sensitivity to the input resistance R and the denominator R increases more rapidly with the current than the numerator AS.The current-related sensitivity is increasing with the biasing current, while the voltage-related sensitivity is decreasing with the biasing current. The explanation of the latter mechanism is the fact that SThe selection process of the best Hall Effect device is based on analyzing several parameters behavior such as sensitivity, offset, dissipated power and Silicon surface. By consequence, it is advised to inspect a more complex cost function. There are circuit methods such as the current spinning technique to reduce the offset and the Silicon surface can be traded by the designer for a high sensitivity. Therefore, obtaining the highest sensitivity with a good tradeoff with offset and power dissipated seems to be prevalent.The dissipated power was calculated within each structure. The ratio of the absolute sensitivity to the power dissipated within the device was also investigated. Even though the relation (6) for power-related sensitivity is not a standalone equation and can be deducted from Pvs.S the dissipated power is presented in biasI = 1 mA, the maximum dissipated power is 2.18 mW for the basic cell, 2.3 mV for L and XL cells, 1.87 mW for the optimum cell and 1.42 mW for the borderless cell respectively. Nevertheless, even if the lowest dissipated power is obtained for low current, we cannot work in that region because the noise is prevalent and the signal to noise ratio (SNR) is too low.The variation of power-related sensitivity We can note that the Greek-like cells and the optimum cell have almost the same power-related sensitivity, with a slightly higher value for the optimum cell. We can observe that for this particular figure of merit, the geometry has less importance, with the best performance belonging to the optimum cell. There is an improvement of almost 10% of the optimum cell with respect to XL structure for constant current of 1 mA. From this perspective, the optimum cell seems like a good candidate.The above discussion on different types of sensitivities leads to the conclusion that the Hall device polarization is important and dictates which shape should be chosen in order to guarantee the best performance.3.4.ΔR of the branch resistance would produce an asymmetry of the bridge and the result will be offsetV.Offset voltage can be generated by imperfections in the fabrication process, misalignment of contacts, non-uniformity of material resistivity and thickness, mechanical stress in combination with the piezoresistance effect . AccordiinV is the input voltage and R is the branch resistance.Therefore, the offset voltage caused by an asymmetry of the bridge is given by:Previous measurements performed on the Hall Effect devices for offset evaluation released information about how this quantity changes with the shape. Numerical values of the offset for the cross-like integrated cells are displayed in a and c) and two sensing contacts (b and d). The following figure presents how the asymmetry was induced, for cross-like Hall cells, in the first instance on the biasing contact a by removing 0.5 μm (asymmetry 1), 0.25 μm (asymmetry 2), 0.15 μm (asymmetry 3), respectively (An analysis of the artificial offset is now intended to see how an induced asymmetry can influence the offset and finally find the shape to guarantee the lowest value of this quantity. At this point, we mainly focus on the offset that might be created by a misalignment in the sensors shape. The Hall devices are equipped with two biasing contacts (ectively .a. However, other asymmetries can be induced on the rest of the contacts, as desired.As the offset is a random process, a possible location was presumed on the contact a have been analyzed. The following effect of a 0.5 μm displacement on the cross-like Hall cells offset is presented in In the present work, simulated artificial offsets induced by small Ox-axis displacements of 0.5, 0.25 or 0.15 μm on the biasing contact For the XL cell, the influence of the mismatch on the offset was the least. It seems that by increasing the dimensions of a cell we will be diminishing any errors that might appear on the borders and therefore minimizing the offset. This fact was also analyzed in  by the a3.5.The performances of all the analyzed Hall Effect sensors, including Hall voltage, current-related sensitivity, power-related sensitivity and dissipated power are summarized in the 4.The influence of the shape, dimensions, offset on the Hall Effect sensors performance was analyzed using 3D physical simulations capable of considering the magnetic field influence on semiconductors.To this purpose, five different Hall Effect sensors of a certain technological CMOS fabrication process were modeled. The Hall Effect sensor configurations were simulated and evaluated for Hall voltage, absolute, current-related, voltage-related, power-related sensitivities and offset.The estimations of these important parameters finally allow choosing the best shape depending on the device polarization used in the circuit and the figures of merit priority. In particular, the simulation and experimental results are in good agreement.The simulation procedure guides the designer in accurately modeling and characterizing specific magnetic sensor shapes of a certain fabrication technology. Estimating their Hall voltage, sensitivity will aid in choosing the Hall cell optimum fabrication process, shape, dimensions and polarization in terms of the performances envisaged to be achieved.Future investigations on the causes of the offset and a systematic approach that would correlate the causes with quantification in the offset value are envisaged to be performed in future articles.


In 2004, we presented a web resource for stimulating the search for novel RNAs, RNA-As-Graphs (RAG), which classified, catalogued, and predicted RNA secondary structure motifs using clustering and build-up approaches. With the increased availability of secondary structures in recent years, we update the RAG resource and provide various improvements for analyzing RNA structures.Our RAG update includes a new supervised clustering algorithm that can suggest RNA motifs that may be "RNA-like". We use this utility to describe RNA motifs as three classes: existing, RNA-like, and non-RNA-like. This produces 126 tree and 16,658 dual graphs as candidate RNA-like topologies using the supervised clustering algorithm with existing RNAs serving as the training data. A comparison of this clustering approach to an earlier method shows considerable improvements. Additional RAG features include greatly expanded search capabilities, an interface to better utilize the benefits of relational database, and improvements to several of the utilities such as directed/labeled graphs and a subgraph search program.The RAG updates presented here augment the database's intended function - stimulating the search for novel RNA functionality - by classifying available motifs, suggesting new motifs for design, and allowing for more specific searches for specific topologies. The updated RAG web resource offers users a graph-based tool for exploring available RNA motifs and suggesting new RNAs for design. RAG'su.edu/rna-8. Many u.edu/rna,10, Rfamu.edu/rna,12, SCORu.edu/rna,14, RNA u.edu/rna, and Pseu.edu/rna,17.To aid these classification efforts, RAG was designed to enumerate and classify all possible RNA topologies including both existing and missing motifs Tables  and 2 11,2. SpecIn 2004, we expanded this work on RNA graphs to predict sequences that fold into 10 candidate topologies based on a modular approach using functional submotifs taken from existing structures . That woSince our 2004 work, RNA databases have grown significantly. For example, the RNA family database (Rfam), which displays consensus secondary structures for different families of RNA, had 367 families in 2004, and now contains 1,372 families  ,12. The This vast increase in the amount of RNA structural data provides an opportunity to update RAG and to compare our earlier "RNA-like" and "non-RNA-like" classifications to newly discovered RNA. Further, we propose an improved classification of RNA-like and non-RNA-like topologies using a supervised clustering algorithm based on existing RNAs. In addition, we implement various improvements to our RAG web resource such as expanded search tools and a user-friendly interface.The original RAG database was designed with the following elements: graphical representations of RNA secondary topologies; Laplacian eigenvalues for quantitative description of RNA graphs; prediction of candidate RNA topologies using a clustering algorithm; and a program for converting secondary structures into RNA tree graphs. In the updated version, we improve upon the database's functionality, apply a supervised clustering algorithm to suggest candidate topologies, and compile the new RNA structures into a user-friendly interface.We utilize both tree and dual graphs to represent RNA structure. Tree graphs offer a general description of RNA structures, while dual graphs are more specific and allow us to represent pseudoknot structures as well  The 3' and 5' ends of a helical stem constitute a single vertex (•).2) An RNA stem with more than one complementary base pair is considered an edge (-); complementary base pairs are considered to be AU, GC, and the special case of the GU wobble.3) A bulge, hairpin, or internal loop is a vertex (•) if there is more than one unpaired nucleotide or non-complementary base pair.4) An RNA junction is considered to be a vertex (•).The rules for transforming RNA 2D topologies into dual graphs are the opposite of those for tree graphs, namely:1) The 3' and 5' ends do not have any representation.2) An RNA stem with more than one complementary base pair is represented as a vertex (•).3) An edge (-) represents any single strand that has more than one unpaired nucleotide and occurs in segments connecting secondary elements of the 2D structure such as bulges, internal loops, junctions, and stems.The main advantage of such coarse-grained representations is their reduced space compared to the atomic-level structural space or RNA sequence space. This facilitates addressing many problems in RNA structure and allows cataloging of a finite set of graphs to represent all existing and hypothetical RNA structures.2) is a measure of the complexity of the graph . Thus, two graphs with differing spectrum are dissimilar, though the converse is not true [2) of the Laplacian matrix, the ordering of λ2 values for topologies within each vertex number (V) is used in RAG to derive a motif identification number (ID) to distinguish the topological complexities among topologies of a certain vertex number. In RAG, all possible tree graphs up to 10 vertices and dual graphs up to 9 vertices have been enumerated and classified through this system of .The graph connectivity is described by a Laplacian matrix constructed from the adjacency and degree matrices of each graph; the full eigenvalue spectrum of the Laplacian matrix can then be computed ,32. The We employed a clustering analysis in our previous work ,32 to de2, λ3,..., λV) into two variables  by applying the least-squares method regarding the index . The slope β and the intercept α represent the average spacing between eigenvalues and the second eigenvalue (λ2) adjusted by β, respectively.1) The values from the Laplacian eigenvalue spectrum are transformed through a linear regression into two variables - the intercept (α) and the slope (β). Specifically, we transform the non-zero Laplacian eigenvalues for a V-vertex graph (λ2)  is normalized to , because β decreases with the vertex number (V), and thus, V*β can be considered graph-size independent.3) A distance matrix is created from the variables  corresponding to the pair-wise distance between all RNA graphs.4) Using the distance matrix in Step 3, PAM is applied to cluster all RNA graphs into two groups.Briefly, the PAM clustering algorithm for two clusters functions by selecting in turn two representatives  and assigning each member into the closest group among two groups based on the distance matrix. These steps are repeated until the resulting two groups of data points have both maximum dissimilarity between groups and maximum similarity within groups. This method allowed us to classify hypothetical topologies as "RNA-like" or "non-RNA-like"; the former group of topologies is considered more likely to be found in nature.k-nearest neighbor (k-NN) algorithm [k-NN is an instance-based supervised classification algorithm for classifying objects based on k closest training data in the description space: an object is classified by a majority vote of its neighbors, with the object being assigned to the class most common among its k nearest neighbors. Here, neighbors are defined by the Euclidian distance between graph descriptors - the transformation of Laplacian eigenvalues. Typically, the parameter k is a small number from 1 to 5. For our training sets, we use partial sets of existing and missing graphs with 3-4 vertices for dual graphs and 3-8 vertices for tree graphs. We found that k = 3 yields reasonable accuracy when the error rate is calculated by cross-validation analysis for k = 1 to 5  into its graphical representation. Essentially, our RNA Matrix program converts a tree secondary structure into an adjacency matrix through the following two steps: (1) the secondary structure file ('ct' file format) is used to define paired (P) and unpaired (U) regions of the RNA sequence. Each U region is associated with a vertex label . Regions with the same label belong to the same structural elements . Identifying the U and P regions involves applying the RNA graph rules and also requires careful consideration of possible secondary structure configurations ; (2) the adjacency graph vertices are assigned by following the connecting arrows (from left to right). For dual graphs, the roles of U and P regions are reversed. RNA Matrix calculates the RNA graph's topological characteristics . Such information directs the user to the corresponding existing  RNA motif in the database, with links to other RNA sequence, structure (2D and 3D) and function databases. Our RAG update introduces three significant improvements to the RNA Matrix program: first, we have now automated the classification of dual graph as well as tree graph topologies; second, we have extended the limit of 200 nt to 1000 nt and of 10 vertices to any number for tree graphs; third, we added a function to specify the direction for the graphs (5' to 3') and list common subgraphs between two RNA secondary structures. To label vertices, RNA Matrix compares the adjacency matrix of given structures to a set of standard adjacency matrices corresponding to our labeled graphs (visible on the RAG website). Subgraphs are determined by permuting all square submatrices of the structures' adjacency matrices to identify smaller graphs shared by each structure.We determine and classify tree and dual graphs of available secondary structures according to our topological descriptors Tables  and 2. Tin vitro. Still, in the RAG resource, we show a list of synthetic RNA structures determined by experimental methods , as listed in NDB, PDB or Pseudobase++.We consider natural RNAs whose structures are solved by experimental methods, such as NMR or X-ray crystallography or identified by comparative analysis to be existing RNAs. All comparative structures are currently derived from the Pseudobase++, Rfam and RNAStrand databases, while solved structures are from NDB, PDB, and Pseudobase++. The secondary structure prediction by homology modeling exploits multiple RNA sequences to infer accurate conserved secondary structures: the homologous sequences are aligned to determine conserved residues and the common secondary structure is detected by the co-variation of base pairs . In our We use the RNA Matrix program (available on our website for single-molecule use) to rapidly deduce tree graph topologies from the available .ct files of RNA Strand and dual graph topologies from the .bpseq secondary structure files of Pseudobase++. We manually determine the topologies for the RNA families of Rfam, if each topology can be represented by a tree graph of 10 vertices or less or a dual graph of 4 vertices or less, because the database does not currently offer secondary structure files. The tree and dual graph topologies are then classified by motif class  and the method used to determine the structure.In our 2004 work, 200 tree graph topologies were enumerated for motifs up to 10 vertices, and 53,810 dual graph topologies were enumerated for motifs up to 9 vertices  and 71 dual graphs (29 in 2004). The first column of Tables  in 2004 For both tree and dual graphs, the accuracy of our predictions of RNA-like motifs depends on the method of structure discovery. Figure In particular, for the experimental structures, the dual graph has a much higher prediction rate ), the Tobamovirus internal ribosome entry site ), the mammalian CPEB3 gene ), the Tymovirus tRNA-like 3' UTR element ), and the Tombusvirus 3' UTR region IV ). RNAs such as the SAM riboswitch ), the Togavirus 5' cis-regulatory element ), and the viral 3' UTR ) were misclassified as non-RNA-like topologies in 2004. Figures As discussed above, PAM clustering does not employ existing data to weigh the results: PAM selects two representatives and assigns each graph into the closest group among the two groups based on the Euclidian distance matrix of graph descriptors. These steps are repeated until the resulting two groups of graphs minimize the total distance between members in each group as well as maximize the distance between the groups. The members in the group containing more existing graphs are classified as RNA-like topologies.k-nearest neighbor (k-NN) algorithm in conjunction with our RAG update. In the k-NN classification, missing motifs are assigned to RNA-like or non-RNA-like topologies based on k closest training data points of existing RNAs; the distances are measured by graph descriptors. The k-NN approach classifies an object according to a majority vote that depends on the object's neighbors: the object is assigned to the class most common among its k nearest neighbors. Thus, no medoids per se are defined as in PAM, and the classification changes as the data set of known RNAs increases. Our reclassification thus yields revised predictions of topologies that are more strongly guided by existing data.We now employ instead a supervised k-NN (with k = 1 to 5) and PAM clustering based on different graph sets using the R statistical package [To compare the performance of PAM and supervised clustering in terms of predicting RNA-like topologies, we use a standard statistical procedure, 10-fold cross-validation, in which the data are partitioned into 10 subsets, one of which is used as a training set and the others are used for prediction. Such a procedure is repeated by shuffling the data in various ways, and all predictions are averaged over these rounds. We perform 10-fold cross-validation for  package .k-NN compared to 26-36% for PAM. We find that k = 3 is a good choice for the former method  to 81% (k = 5) compared to 62% for PAM. The trend is similar for tree graphs  to define other training sets and again compare the performance of k-NN clustering of 146 dual graphs up to 5 vertices. PAM clusters 146 topologies without prior information of existing RNAs and predicts 61 to be RNA-like , while k-NN reclassifies 48 topologies as RNA-like   Table  based ony) Table  and 6d tk-NN algorithm applied to all existing RNA data is shown in Tables Our analyses above suggest that the supervised clustering algorithm can better take advantage of newly-confirmed topologies to lower the error rate and increase the accuracy in suggesting candidate RNA-like topologies. Our classification using the supervised 2) of 0.3249, we originally found, in addition to a signal recognition complex, more than 20 other regulatory non-coding RNAs corresponding including OxyS (Rfam:RF00035), SraG (Rfam:RF00082), GadY (Rfam: RF00122), t44 (Rfam: RF00127), and SL2 (Rfam: 00199). Note that because some of the structures available in 2004 were not readily classified in databases of the time, our updated search reports more overall topologies from the years leading up to 2004 than were reported in the original RAG paper.The RAG update incorporates newly-confirmed topologies and new predictions. Figure All 11 dual graph topologies for up to 3 vertices have now been discovered. The complete repertoire of newly-confirmed topologies can be seen in Figure in vitro selection based on RAG [in vitro selection in conjunction with our RAGPOOLS web server http://rubin2.biomath.nyu.edu/ to design new RNA pools with desired topologies [We have applied our RNA graph classification and prediction to RNA structure analysis and design in multiple ways. For example, we have applied our graph classifications to reveal modular RNA architectures by computational analysis of existing pseudoknots and ribosomal RNAs using dual graph isomorphism  and discd on RAG . Such copologies -45.Other groups have extended RAG to labeled dual graphs and directed tree graphs, including graph applications to non-coding RNA classification -22. For http://www.biomath.nyu.edu/rna/analysis/rna_matrix.php. For example, a set of long viral RNAs (~1000 nt) can be translated into tree and dual graphs. Second, neighboring topologies are related to existing ones by size, topology or function and can be explored, which will help to search a family of topologies. Third, new candidate RNA-like topologies can be used for searching genomes. Fourth, RNA graphs can help design new classes of structural RNAs by combining multiple graphs.Our RAG update includes various features such as expanded search tools, directed/labeled graph graphs, a subgraph search program, and newly suggested RNA-like topologies. We hope that these improved features will allow users to perform complex queries and apply our resource to RNA design and related problems. In particular, users can use our updated RAG in four significant ways. First, researchers can translate RNA secondary structure into tree and dual graphs without length limitations on our web server The updated version of RAG includes a program that converts a given secondary structure (in either 'ct' or 'bpseq' format) into a dual graph. Like the version of RNA Matrix for tree graphs released in 2004, users can submit a single secondary structure file for analysis. Using this file, we compute the necessary Laplacian eigenvalues and provide the corresponding dual graph topology, along with labeled vertices and directional information. The adjacency, degree, and Laplacian matrices for the structure are displayed and the common subgraphs between two structures can be evaluated. In addition, a set of secondary structures (up to 1000 ct files) can be submitted to the updated RNA Matrix program for batch processing and the resulting adjacency matrices and graph IDs can be downloaded.We have changed our back-end database from plain text files into a MYSQL relational database implementation; the relational database tables in RAG have been "normalized" to minimize the redundancy and define relationships between them. By requiring the existence of a related row in another table, we improve the database integrity and make sure that the data entered into the database are valid and consistent. The updated database using a relational database thus allows us to provide different views and more advanced queries for users . We plan to update RAG by adding new structures reported from publications or structural databases regularly, so that new information will become accessible on each topology's sub-page. We also invite users to submit their structures to us directly for inclusion in RAG.We have also modified the front-end code to produce a better user interface; we have changed the format from static pages written in HTML and PHP server-side scripting languages into a rich-Internet compatible interface utilizing Web 2.0 features such as Asynchronous JavaScript and XML (AJAX) techniques.An improved and updated RAG database has been designed to allow experimentalists and theoreticians to explore currently existing motifs and help suggest novel RNA motifs. The number of available structures has increased, the searching capabilities have been improved, and the web server has a more user-friendly interface and dynamic content.Because of the translation to a combined PHP and MYSQL interface, future updates of RAG will be accessible via the web resource in real time; each tree and dual graph topology has its own sub-page on the web resource that accesses the database and displays all structures corresponding to that particular topology. Thus, each addition to RAG will be displayed instantaneously on its respective online topology page.k-NN clustering approach , correction of the 2004 existing data (Table S2), cross-validation results for tree graphs (Table S3), and tree graphs classified by partial sets (Table S4).Click here for file


Streptococcus pyogenes (Group A Streptococcus or GAS) is a Gram-positive bacterial pathogen that has shown complex modes of regulation of its virulence factors to cause diverse diseases. Bacterial small RNAs are regarded as novel widespread regulators of gene expression in response to environmental signals. Recent studies have revealed that several small RNAs (sRNAs) have an important role in S. pyogenes physiology and pathogenesis by regulating gene expression at the translational level. To search for new sRNAs in S. pyogenes, we performed a genomewide analysis through computational prediction followed by experimental verification. To overcome the limitation of low accuracy in computational prediction, we employed a combination of three different computational algorithms . A total of 45 candidates were chosen based on the computational analysis, and their transcription was analyzed by reverse-transcriptase PCR and Northern blot. Through this process, we discovered 7 putative novel trans-acting sRNAs. Their abundance varied between different growth phases, suggesting that their expression is influenced by environmental or internal signals. Further, to screen target mRNAs of an sRNA, we employed differential RNA sequencing analysis. This study provides a significant resource for future study of small RNAs and their roles in physiology and pathogenesis of S. pyogenes. Small non-coding RNAs exist in all life forms and are now regarded as novel widespread regulators of gene expression. In bacteria, these RNAs are collectively referred to as ‘small RNAs’ or ‘sRNAs’. Bacterial small regulatory RNAs recently have received tremendous attention because of their abundance and important role in a variety of cellular processes including response to environmental stress and involvement in pathogenecity  supplemented with 0.2% yeast extract (Difco) at 37°C in sealed tubes without agitation.ftp://ftp.ncbi.nih.gov/genomes/Bacteria/): S. pyogenes MGAS315, S. equi subsp. zooepidemicus MGCS10565, S. mutans UA159, S. suis 05ZYH33, S. sanguinis SK36, S. gordonii str. Challis substr. CH1, S. pneumoniae CGSP14, S. agalactiae NEM316.In this study, we employed three computational algorithms to increase prediction accuracy: eQRNA, RNAz, and sRNAPredict. The computational approaches are illustrated in S. pyogenes MGAS315 were marked and then 1587 IGRs were extracted by Intergenic Sequence Inspector (ISI). To predict the location of Rho-independent terminators of S. pyogenes MGAS315, the RNAmotif program and the TransTerm database were used. RNAMotif searches RNA sequences that match a “motif” describing the interactions of secondary structures, which are defined via a pattern language whose symbols represent helices and single stranded stretches. Matches can be ranked by applying scoring rules that may provide finer distinctions than just matching to a profile. Rho-independent terminators predicted by TransTerm were obtained from the TransTerm website at University of Maryland. Putative terminators whose probabilities (confidence) were greater than 90% were chosen for sRNAPredict analysis. Parameters applied to sRNAPredict analysis are as follows; the minimum distance of predicted terminator from the end of an upstream ORF: 30 nts, the maximum length of gap between a putative terminator and a region of sequence conservation: 20 nts, the values for the minimum and maximum length of putative sRNAs: 30 nts and 550 nts.To run sRNAPredict, the coordinates and the orientations of 1865 ORFs, 67 tRNAs, 18 rRNAs and 1tmRNA in the genome of S. pyogenes MGAS315 was generated using ISI. The number of the extracted IGRs from S. pyogenes MGAS315 genome by ISI was 3166 sequences from both DNA strands, totaling about 0.681 MB which represents 17.9% of the full genome. The average sequence length was 138 nts, with the longest one being 1501 nts and the shortest one being 1 nt. Then, the WU-BLAST 2.0 program was used for genome wide sequence homology analysis between IGRs of S. pyogenes MGAS315 and the genomes of the other seven streptococci. The IGRs with a length >12 nts were used as queries. The output data of the BLAST analysis gave pairwise alignments between the query sequences, that are the IGRs of S. pyogenes MGAS315, and the subject sequences, that are the genomic segments of the other seven streptococci. These pairwise alignments of 2118 comparisons were filtered with the parameters of E-values <0.00001 and length >30 nts (S. pyogenes tRNAs and tmRNA were identified (95.6% sensitivity)  of  >30 nts . These pitivity) . To testitivity) . RNAz anitivity) .www.cs.siu.edu/~nyu/research.htm.The computer algorithms used in this study and the result of each process can be downloaded from S. pyogenes RNA was extracted using the combination of the miRNeasy kit (Qiagen) and the FastPrep beadbeater . An S. pyogenes cell pellet from 10 ml culture was resuspended in 700 μl of the Qiazol lysis reagent provided in the miRNeasy kit and transferred to a Lyse Matrix B blue cap tube . Cells were then lysed by the beadbeater, FastPrep 24  at the speed of 6.0 for 40 seconds twice. The remaining procedure for RNA extraction followed the manufacturer's protocol of the miRNeasy kit. The A260/A280 ratio of the extracted RNA was measured with NanoDrop (Thermo Scientific) to determine the RNA concentration and purity (accepted if >1.8). The extracted RNA was mixed with 1 μl of RNasin , and treated with RNase-Free DNase  according to the manufacturer's protocol.Total S. pyogenes MGAS315 was grown in THY medium at 37°C and harvested with centrifugation  at the exponential phase , the early stationary phase (OD600  = ∼1.2) or the late stationary phase . Then, Total RNA was extracted as described above. The extracted RNA (20 µg) was mixed with loading dye containing 50% (v/v) formaldehyde, loaded onto denaturing polyacrylamide gel (6% polyacrylamide (acrylamide: bis-acrylamide  = 29∶1) with 7% urea) pre-run at 400 V for an hour, and electrophoresed at 300 V. For an RNA size marker, 5 ng of Low Range SSRCNA Ladder  was loaded in a well. The separated RNA was transferred onto a nylon membrane  with a semi-dry electroblotter (Bio-Rad Trans-Blot SD Transfer Cell) at 400 mA for two hours at 4°C. The RNA on the nylon membrane was cross-linked to the membrane with 1-ethyl-3-(3-dimethylaminopropyl)-carbodiimide (EDC) 32P-labeled at 5′-end with γ-32P ATP  by T4 polynucleotide kinase (Epicentre Technologies). The probes were designed to bind at the center of putative small RNAs predicted by the computational algorithms. Because the list of sRNA candidates from the computational analysis did not provide information on which DNA strand encodes each sRNA candidate, we designed and tested two probes annealing to each strand. The sequence of each probe is shown in S. pyogenes MGAS315 was grown in THY medium at 37°C until it reached an OD600 of 0.5. The cells were harvested with centrifugation and total RNA was extracted as described in “RNA Extraction from S. pyogenes MGAS315”. The extracted RNA was converted to cDNA using reverse transcriptase  according to the manufacturer's protocol. Briefly, RNA (2 µg) was mixed with 500 ng of random primers (Promega) and adjusted to 5 μl with RNase free water. The RNA mixture was incubated at 65°C for five minutes and then chilled at 4°C for five minutes. A mix containing 2.4 μl of MgCl2 (25 nM), 4 μl of ImProm-II 5× reaction buffer, 1 μl of ImProm-II Reverse Transcriptase, 1 μl of dNTPs (10 nM) and 6.6 μl of RNase Free water was added to the RNA mix and incubated at 25°C for five minutes, followed by 42°C for one hour, and then heat inactivated at 70°C for fifteen minutes. Regular PCR was performed with the cDNA as a template. RNA (without reverse transcriptase reaction) was used as a control to confirm that a PCR product was not from chromosomal DNA contamination (RNA was rejected before producing cDNA if PCR amplification performed with the RNA template indicated the presence of contaminating DNA). PCR products and a DNA ladder  were electrophoresed on a 2% agarose gel.Primers for RT-PCR were designed with the parameters of 1 bp GC clamp at 3′ end, 20 nts size, 100–200 nts product size, and 60°C melting temperature .S. pyogenes cultures in the exponential phase (OD600 of 0.5) as described above. The RNA was treated with Tobacco Acid Pyrophosphatase (TAP) (Epicentre) to remove pyrophosphate from the 5′ end. The 5′ end was then ligated to the 3′ end with T4 RNA ligase (Epicentre) to make circular transcripts. The circular transcripts were reverse transcribed using gene specific primers to make first strand cDNA. The first strand cDNAs were amplified with PCR. The PCR products were cloned into pGEM-T (Promega) and sequenced to determine transcriptional start and stop sites. Primers for the circular RACE used in this study are listed in The transcriptional start and stop sites of sRNA candidates were determined using circular RACE (Rapid Amplification of cDNA ends) as described elsewhere cat) PstI were used to amplify 0.69 kbps DNA fragment downstream of SSRC21  that was PCR-amplified from pABG5 XmaI and 3catXmaI , and then used to transform MGAS315 (wild type) by electroporation. The mutant showing chloramphenicol resistance was selected and the chromosomal structure was confirmed by PCR.To create a deletion mutant of SSRC21, ΔSSRC21cat, 144 bps of the internal part of SSRC21 was deleted and replaced with a chloramphenicol acetyltransferase gene  as described above, and submitted to Otogenetics Corporation  for RNA-Seq assays. Briefly, the integrity and purity of total RNA were assessed using Agilent Bioanalyzer and OD260/280. Up to 5 μg of total RNA was subjected to rRNA depletion using the RiboZero Meta-Bacteria kit  and cDNA was generated from the depleted RNA using the NEBNext mRNA Sample Prep kit . cDNA was profiled using Agilent Bioanalyzer, and subjected to Illumina library preparation using NEBNext reagents . The quality and quantity and the size distribution of the Illumina libraries were determined using an Agilent Bioanalyzer 2100. The libraries were then submitted for Illumina HiSeq2000 sequencing according to the standard operation. Paired-end 90 or 100 nucleotide (nt) reads were generated and subjected to data analysis using the platform provided by Center for Biotechnology and Computational Biology  as previously described http://www.ncbi.nlm.nih.gov/nuccore/21905618) with Bowtie2 (V2.0.0.5). Hits on regions defined by GenBank were then counted with bedtools. To determine the difference of gene expression between samples, EdgeR  was used. We deposited our RNA seq dataset to NIH Short Read Archive with the accession number SRP020234.Total RNA was extracted from S. pyogenes MGAS315, S. equi subsp. zooepidemicus MGCS10565, S. mutans UA159, S. suis 05ZYH33, S. sanguinis SK36, S. gordonii str. Challis substr. CH1, S. pneumoniae CGSP14, S. agalactiae NEM316.Along with experimental strategies based on shotgun cloning or microarray methods, computational predictions and validation with Northern blot have been a popular method used to identify many sRNAs  were removed from the candidates because they most likely correspond to putative riboswitches or other cis-regulatory elements. In addition, predicted candidates in prophages, all of which were located next to the integrase genes, were removed. Then, the intergenic genomic segments predicted by any two algorithms were considered as putative sRNA candidates. The final number of sRNA candidates left from these processes was 45 , SSRC13, SSRC21, SSRC27, SSRC29, SSRC30, SSRC31, SSRC32, SSRC34, SSRC38, and SSRC41 (http://www.ncbi.nlm.nih.gov/nuccore/21905618?report=fasta) and the others , SSRC8, SSRC10, SSRC12 .The size of each SSRC detected by Northern blot was calculated by measuring traveled distance of each band from a well of a polyacrylamide gel and compared to a standard curve obtained from the RNA ladder that was run with each sRNA side by side. The calculated approximate sizes of the SSRCs are shown in trans-acting regulatory sRNAs. SSRC13 and SSRC31 appear to be cis-elements in RNA-Seq analysis. The sequence of SSRC13 contains a T-box leader element, which is typically found upstream of aminoacyl-tRNA synthetase genes and some amino acid biosynthesis genes and involved in the regulation of those genes' expression by forming a transcription anti-terminator when uncharged tRNA binds the leader sequence Sequence analyses based on the predicted SSRC sequences  and nextWe performed circular RACE (Rapid Amplification of cDNA Ends) to determine the 5′ and 3′ ends of selected small RNA candidates. Based on the calculated sizes, Northern blot probe-binding sites, and/or putative transcriptional signatures (promoters and Rho-independent terminators), we designed primers for each SSRC. From this analysis, we could determine the sequences of SSRC8, SSRC10, SSRC21, SSRC29, SSRC34 and SSRC41 . All theS. pyogenes but also other streptococcal pathogens. Thus, we searched for the homologs of the SSRCs in the other streptococci whose genome sequences are available publically. Each streptococcal genome was blasted against the SSRC sequences determined through circular RACE. As expected, homologs of the SSRCs existed in many streptococcal bacteria, and more SSRC homologs were found in streptococci more closly related to S. pyogenes , early stationary (ES), and late stationary (LS) growth phases of cells through Northern blotting . Some transcripts encoding virulence factors also showed differential abundance: streptokinase A, FC 2.6; C5A peptidase, FC −2.1; M protein, FC −2.4; streptococcal phospholipase A2, FC −4.5).To screen putative target mRNAs of an sRNA detected in this study, we employed differential RNA sequencing analysis. SSRC21 was chosen for this analysis because its expression was changed between growth phases, and initial computational prediction suggested that SSRC21 might influence the expression of several virulence factors. The transcript abundance profile in an SSRC21 deletion mutant was determined through Next-Generation Sequencing, RNA-Seq, and compared to that of the wild type MGAS315. To construct the SSRC21 deletion mutant, ΔSSRC21cat, the SSRC21 gene in the chromosome was replaced with the chloramphenicol acetyl transferase gene, FDR, <5% . The tratrans-acting sRNA candidates in the human pathogen S. pyogenes using a combination of a bioinformatic prediction and verification with Northern blotting. To overcome the limitation of low accuracy of computational sRNA prediction, we employed three algorithms  that seek different kinds of conservations in sRNAs. Since only six were predicted as sRNA candidates by all the three algorithms, we also considered as sRNA candidates those that were predicted by any two algorithms. This resulted in 45 candidates, and the previously studied S. pyogenes sRNAs, Pel, FasX and RivX were included among them. Then, we examined their expression through reverse transcriptase PCR and Northern blotting and verified the expression of 14 candidates. In the end, this study added 7 new sRNAs, which are likely trans-acting, to the pool of streptococcal sRNAs, offering a significant source for future study of the role of sRNAs in S. pyogenes and related streptococcal pathogens.In this study, we discovered new putative S. pyogenes sRNA employing bioinformatics. However, previous studies were not as complete as ours: none of them verified their predictions through Northern blot. Livny et al. developed sRNAPredict and predicted sRNA-encoding genes in 10 bacterial pathogens including S. pyogenesS. pyogenes sRNAs, Pel, FasX and RivX, indicating low prediction accuracy. There were also other studies using a combination of algorithms to predict S. pyogenes small RNAs et al.cis-acting RNAs such as riboswitches from independently expressed trans-acting small RNAs.Previously, there have been attempts to predict et al. performed a genome wide search for S. pyogenes sRNA through a microarray-based analysis, and verified14 new sRNAs with Northern blot trans-acting sRNA candidates (SSRC21 and SSRC29). Thus, the two methods, microarray and computational prediction followed by Northern blot, appear to identify different sRNAs from each other. A possible explanation for this is that the microarray-based assay might not detect sRNAs with low expression. The expression of sRNAs is generally lower than that of the genes encoding house-keeping proteins, so the signals of sRNAs could be masked by higher signals of house-keeping gene transcripts. Another possibility is the expression difference of sRNAs between strains. The strain used in this study (MGAS315) was different from the strain used by Perez and coworkers . Perez and coworkers observed differences of sRNA expression between strains S. pyogenes M49 strain using intergenic tiling array et al.  overlapped with our sRNAs.Previously, Perez S. pyogenes have been studied previously. The Pel (pleiotropic effect locus) RNA, which comprises sagA, the structural gene of the hemolysin streptolysin S (SLS) S. pyogenes NAD(+) glycohydrolase) and SpeB  sagA mRNA, not Pel. This is probably because of the use of exponential phase cells in our study. Pel in MGAS315, the wild type strain we used for this study, is expressed at the stationary phase, not at the exponential phase S. pyogeneset al. discovered that the maturation of crRNAs  in S. pyogenes is performed by a trans-encoded small RNA, tracrRNA, with the assistance of RNase III and the CRISPR-associated Dsn1 protein S. pyogenes.Several sRNAs in S. pyogenes genes are differentially expressed during growth phases , RNApredator http://rna.tbi.univie.ac.at/RNApredator2/target_search.cgi) and IntaRNA http://rna.informatik.uni-freiburg.de:8080/v1/IntaRNA.jsp). Generally, these algorithms have been developed on the basis of the information of antisense-target RNA interactions previously identified experimentally. When we used these algorithms to find target mRNAs of the new sRNAs, there was not much overlap of target mRNA candidates between these algorithms. The general experimental approaches of high throughput screening for identification of target mRNAs are genomics- and proteomics-based approaches S. pyogenes, many important proteins in physiology and pathogenesis are tightly associated with the cell wall, which makes the use of proteomics-based approaches more difficult.The majority of ntp genes encoding V-type ATPase subunits were notably more abundant in the SSRC21 mutant. It seems that the ntp genes are in an operon, so SSRC21 might interact with the ntp genes transcript or the transcript of a transcriptional regulator. A transcript encoding a putative regulator upstream of the ntp genes was also more abundant in the SSRC21. All of the computational algorithms mentioned above predicted an interaction between the transcript of the regulator and SSRC21  from the cytosol to the organelles such as golgi and lysosome to acidify the inside of them S. pyogenes might be involved in pumping hydrogen ion from the cytosol to overcome acid stress during growth or infection. S. pyogenes performs only lactic acid fermentation for production of energy and lowers the pH to ∼5.4 in THY medium. In addition, S. pyogenes should survive the acidic condition inside the host's lysosome for successful infection. Another possible role of the V-type ATPase in S. pyogenes is to balance sodium ion concentration in the cytosol. Enterococcus hirae has a homologous V-type Na+ ATPase complex that pumps Na+ at high pH E. hirae the ability to grow at pH 9.5. However, S. pyogenes cannot grow at pH 9.5. Several transcripts encoding virulence factors showed differential abundance in the SSRC21 mutant over the wild type. Among them, the two virulence genes encoding M protein and C5A peptidase are in an operon. Since their expression levels were similar, SSRC21 might control the translation of Mga, the transcriptional regulator for M protein and C5A peptidase. However, computational prediction did not detect any significant interaction between SSRC21 and the mga transcript or the M protein transcript, so the influence of SSRC21 on the expression of M protein and C5A peptidase might be indirect.To screen target mRNAs of a trans-acting sRNA, we used a genomics-based approach employing the next generation sequencing, RNA-Seq. We chose SSRC21 for this analysis because i) it has been identified by previous studies {Patenge, 2012 #115;Perez, 2009 #71}, ii) its sequence analysis after circular RACE provided predictable promoter and Rho-independent transcription terminator, iii) its differential expression might imply differential regulation of transcripts linked to growth phases, and iv) The possibility that SSRC21 controls the expression of several virulence factors was suggested by computational prediction. The differential RNA sequencing showed that SSRC21 influences transcript abundance of 142 genes . Among td SSRC21 . The rolS. pyogenes and identified 7 novel streptococcal sRNAs. Since their abundance varied between growth phases, these new sRNAs may coordinate the expression of genes in response to stress conditions linked to growth phases. Differential RNA sequencing analysis to screen putative target mRNAs of an sRNA implied that SSRC21 might be involved in the tolerance to acid stress during growth and/or infection or in the homeostasis of sodium ion inside cells. The list and expression pattern of the novel sRNAs discovered in this study provide a significant resource for future study of small RNAs and their role in S. pyogenes.In summary, we searched for small regulatory RNAs in the human pathogen Table S1The number of S. pyogenes MGAS315 IGRs that have homologous sequences in the selected Streptococcus genome.(DOCX)Click here for additional data file.Table S2Calculation of sensitivity and specificity of RNAz and eQRNA independently and of the combination of the two analyses using known S. pyogenes RNAs.(DOCX)Click here for additional data file.Table S3Northern blot probe sequences.(DOCX)Click here for additional data file.Table S4RT-PCR and real time RT-PCR primer sequences.(DOCX)Click here for additional data file.Table S5Primers used in circular RACE.(DOCX)Click here for additional data file.Table S6The nucleotide coordinates of S. pyogenes Small RNA Candidates (SSRC) predicted by each computational algorithm.(DOCX)Click here for additional data file.Table S7Differential abundance of transcripts in the SSRC21 deletion mutant, compared to the wild type.(DOCX)Click here for additional data file.