In vitro and transfection studies identified 5′-triphosphate containing blunt-ended double-strand RNAs as potent RIG-I inducers and these findings were confirmed by next-generation sequencing of RIG-I associated RNAs from virus-infected cells. The nature of RNA ligands of MDA5 is less clear. Several studies suggest that double-stranded RNAs are the preferred agonists for the protein. However, the exact nature of physiological MDA5 ligands from virus-infected cells needs to be elucidated. In this work, we combine a crosslinking technique with next-generation sequencing in order to shed light on MDA5-associated RNAs from human cells infected with measles virus. Our findings suggest that RIG-I and MDA5 associate with AU-rich RNA species originating from the mRNA of the measles virus L gene. Corresponding sequences are poorer activators of ATP-hydrolysis by MDA5 in vitro, suggesting that they result in more stable MDA5 filaments. These data provide a possible model of how AU-rich sequences could activate type I interferon signaling.RIG-I-like receptors  play a major role in the innate immune response against viral infections and detect patterns on viral RNA molecules that are typically absent from host RNA. Upon RNA binding, RLRs trigger a complex downstream signaling cascade resulting in the expression of type I interferons and proinflammatory cytokines. In the past decade extensive efforts were made to elucidate the nature of putative RLR ligands.  in vivo occurring ligand for MDA5 is poorly characterized. A major challenge in examining MDA5 agonists is the apparently transient interaction between the protein and its ligand. To improve the stability of interaction, we have used an approach to crosslink MDA5 to RNA in measles virus-infected cells. The virus-infected cells were treated with the photoactivatable nucleoside analog 4-thiouridine, which is incorporated in newly synthesized RNA. Upon 365 nm UV light exposure of living cells, a covalent linkage between the labeled RNA and the receptor protein is induced, resulting in a higher RNA recovery from RLR immunoprecipitates. Based on next generation sequencing, bioinformatics and in vitro approaches, we observed a correlation between the AU-composition of viral RNA and its ability to induce an MDA5-dependent immune response.RIG-I-like receptors (RLRs) are helicase-like molecules that detect cytosolic RNAs that are absent in the non-infected host. Upon binding to specific RNA patterns, RLRs elicit a signaling cascade that leads to host defense via the production of antiviral molecules. To understand how RLRs sense RNA, it is important to characterize the nature and origin of RLR-associated RNA from virus-infected cells. While it is well established that RIG-I binds 5′-triphosphate containing double-stranded RNA, the  In vitro studies have shown that RIG-I and MDA5 recognize the majority of viruses in a complementary manner. While many negative-strand RNA viruses like rabies and influenza viruses are predominantly sensed by RIG-I, picornaviruses are predominantly recognized by MDA5. The observed preferences are, however, unlikely to be exclusive and the exact role of LGP2 still needs to be investigated The retinoic acid inducible gene I (RIG-I)-like receptor (RLR) proteins are key players in innate immunity and act by recognizing viral RNA (vRNA) in the cytosol. The RLR family consists of the members retinoic acid inducible gene I (RIG-I), melanoma differentiation associated protein 5 (MDA5), and laboratory of genetics and physiology 2 (LGP2) The RLR proteins belong to the DExD/H-box ATPases sharing a central ATP-dependent helicase domain and a C-terminal regulatory domain (RD) that is responsible for initial RNA binding. In addition, RIG-I and MDA5 possess N-terminal tandem caspase activation and recruitment domains (CARDs) that are responsible for downstream signaling transduction Viruses have developed numerous strategies to evade the immune system. For instance, viruses of the paramyxovirus family  encode V inhibitor proteins that specifically bind to MDA5 and LGP2, but not always to RIG-I in vitro transfection experiments and it has so far not been possible to isolate a natural RNA ligand for MDA5 directly from virus-infected cells.One of the remaining key questions in this field is how RLR proteins are able to distinguish between self and non-self RNA in the cytosol. Recently, several studies showed that 5′-triphosphate RNA is not the only RNA ligand for RIG-I. Specific poly U/C-rich regions within certain viral genomes seem to contribute to efficient recognition by the protein in vivo RNA ligands for RLR proteins from virus-infected cells. Based on the crosslinking we were able to co-purify immunostimulatory RNA in a RIG-I and MDA5 dependent manner from measles virus (MeV)-infected cells. Deep sequencing and bioinformatics analysis revealed that RIG-I and MDA5 bind RNA of positive polarity originating from the L gene of the MeV genome. In addition, RIG-I binds to the 5′ ends of genomic and antigenomic RNAs, which probably represent 5′-triphosphate RNA, and are therefore not recognized by MDA5. Furthermore, we showed that RIG-I, but not MDA5, binds RNA of negative polarity, indicating that MDA5 does not efficiently recognize the MeV genome. Based on bioinformatics analysis, we observed a correlation between MDA5-enriched RNA sequences and the AU content and this was confirmed by in vitro transcription assays. In summary, we report the isolation of MDA5-associated RNA from virus-infected cells and the discovery of in vivo occurring activating viral RNA ligands for MDA5.In this study we combined different methods, including RNA-protein crosslinking and deep sequencing, to investigate in vitro studies showed that MDA5 preferably recognizes long dsRNA stretches Photoactivatable-Ribonucleoside-Enhanced Crosslinking and Immunoprecipitation) methodology in vivo occurring RNA ligands for RLR proteins.Several Figure S1). The reason for this may lie in the heterogeneity and the need for precise timing of viral replication cycles or in the efficiency of 4SU incorporation and crosslinking. Utilization of this technique for other viruses may require adjustment of parameters, such as the time points of 4SU addition, crosslinking and harvesting after infection.Next, we validated the crosslinking approach on cells that were infected with a variety of viruses, including negative-stranded (−) RNA viruses MeV  and posiParamyxoviridae. MeV has a single-stranded RNA genome of negative polarity consisting of 15,894 nucleotides. It comprises six non-overlapping genes, which are flanked by small terminal non-coding regions known as leader (le) and trailer (tr) sequences. These sequences serve as promoter regions during viral replication and transcription Figure S2), as previously published Based on the above-mentioned results, we focused our studies on MeV, which belongs to the order of In order to shed light on the exact nature of RIG-I and MDA5-associated RNAs derived from MeV-infected cells, we performed a deep sequencing analysis on isolated RNA species from co-immunopurifications with antibodies against endogenous RIG-I and MDA5. As a control, we used an antibody against GFP (GFP protein was not present). The MeV strain used for the studies presented here was a recombinant measles virus rescued from cDNA with the exact sequence of the Schwarz vaccine strain (Genbank AF266291.1) le or trRNA generated in the course of replication or transcription. Additionally, internal genomic and antigenomic sequences found in the pull-downs could potentially originate from MeV DI particles Figure S3). We did not find DIs in the GFP control pull-downs.Obtained sequences were mapped to the MeV antigenome and the relative abundances of these sequences between RIG-I pull-down, MDA5 pull-down, and GFP pull-down were compared. Analysis of the reads showed that RIG-I and MDA5 bind to similar regions within the L gene-derived RNAs. In addition, RIG-I, but not MDA5, binds to RNAs derived from the 3′ and the 5′ ends of the MeV genome  but also in distinct regions within the L gene. In contrast, sequences of negative polarity, representing the viral genome, are exclusively enriched in the 5′ end of the genome (trailer region) and in regions of the L gene  RNA from the RIG-I library  and (−) RNA that might represent replication intermediates, as previously suggested for a positive-strand RNA virus Figure S4). Enrichment (greater than 2× compared to the control library) of RIG-I-associated RNA of positive polarity can be found across the whole genome, whereas only few reads of negative polarity are enriched within the N and L segment. In contrast, enriched sequences of MDA5-associated RNA are exclusively present within the L segment of positive polarity, whereas no specific enrichment was observed for (−) RNA.To further validate the specificity of the accumulation of RIG-I and MDA5-associated RNA, we calculated specific read enrichments Based on the data, we observed a good correlation between the deep sequencing analysis and enrichment calculations, indicating that distinct regions within the MeV genome are indeed specifically enriched in a RIG-I- and MDA5-dependent manner in comparison to the control.To independently validate the relative amount of RLR-associated RNA, qPCR amplification was performed. The obtained copy read numbers were normalized to the GFP negative control in order to compare the genomic segments in the RIG-I and MDA5 samples .To elucidate the exact nature of sequences enriched by RIG-I and MDA5 immunoprecipitations, we conducted a bioinformatics analysis. For this, the complete genome was divided into fragments of size 201 nt with a shifting window of 5 nt. Each sequence was folded  RNAfold  and sevein vitro transcripts (IVTs) covering different regions of the MeV antigenome (Table S1). RNAs were double-dephosphorylated in order to ensure that 5′-triphosphate groups were removed. For immunoactivity experiments IVTs were transfected into 293T ISRE-FF reporter cells. The stimulatory effect revealed a correlation of high read numbers from deep sequencing analysis and high stimulatory activity of the IVT sequences   were tested for their immunostimulatory activity. The transcripts were generated according to the protocol for MeV RNA sequences. We again observed a correlation (cor = 0.583) of the AU content of the tested sequences and their immunostimulatory potential . These data are consistent with the in vitro analysis of MeV RNA sequences indicating that the AU composition of RNA might play a general role in activating RLR signaling.In order to get a more general conclusion about the contribution of the AU content to the immunostimulatory potential of RNAs,  and Figure S8) and observed a negative correlation between the maximum number of sequencing reads in the MDA5 library and the ATP hydrolysis rate . Furthermore, the ATP hydrolysis rate negatively correlates with the immunostimulatory potential of RNA transcripts (cor = −0.426)  sense RNA within the trailer region as well as in the adjacent L gene. We propose that enriched RNA of positive polarity most likely represents mRNA species, since antigenomic RNA is only generated during replication and is immediately packed into nucleocapsids in vitro transcription experiments revealed a correlation between AU content and read coverage of the obtained sequences or IVTs, respectively. As shown before Bioinformatics analysis and E373Q and MDA5E444Q, which are mutated in the “Walker B” ATP hydrolysis motif Figure S9). Furthermore, pull-down studies with the RIG-I Walker B mutant revealed an increase in the amount of recovered RNA while their immunostimulatory potential decreased (data not shown). The increased immunostimulation of ATPase deficient RLRs is consistent with the model that RNAs that lead to a reduced ATP-hydrolysis rate are more proficient in immunostimulation, possibly by stabilizing RLR∶RNA complexes. The negative correlation between AU-rich sequences and the ATP hydrolysis rate suggests that MDA5 binds AU-rich RNA in preference to GC-rich RNA. This would lead to a stronger interaction between RNA and MDA5 and result in a higher immunostimulatory signal. In order to test this hypothesis, we performed binding assays with MDA5 and IVTs but we were not able to demonstrate differences in the binding affinities between the different transcripts that might support this theory (data not shown). Finally, we speculate that RNA ligands for RLR proteins could be divided into two classes. The first class would comprise RNA molecules originating from the 5′-triphosphate ends of the genome or antigenome. These molecules could be generated in the course of read-through transcription and abortive replication Interestingly, ATP hydrolysis assays performed with recombinant MDA5 and RNA transcripts indicate that the AU content of RNA negatively correlates with the ATP hydrolysis rate of the protein. This inverse correlation between the immunostimulatory potential of RNAs and their capability to stimulate ATP hydrolysis by MDA5 lets us speculate that the ATPase activity might not be necessary for, or even interfere with, the immunoactivity of RNA ligands. Although this observation disagrees with recent findings about the role of ATP hydrolysis in RIG-I oligomerization on 5′-triphosphate dsRNA in vitro transcribed RNA from the Mengo virus genome.It will be interesting to see what types of RNA associate with RIG-I and MDA5 during infections with different viruses and to what extent the AU composition and DI generation contributes to RNA recognition in these types of viruses. In particular, the finding that both RIG-I and MDA5 localize to AU rich regions suggests partially overlapping roles in detection of different viruses. The specificity of RIG-I and MDA5 for certain viruses may lie not only in the detection of 5′-triphosphate by RIG-I, but also in the heterogeneity of viral evasion strategies le-N read-through transcripts or abortive replication products containing 5′-triphosphate ends, leading to the activation of the signaling cascade. In a second round of recognition, RIG-I and MDA5 then recognize viral transcripts that are rich in AU. To further test this hypothesis, time dependent experiments need to be carried out.In general, the data support previous experiments indicating that MeV is mainly recognized by RIG-I, while MDA5 seems to play a minor role One feature of the applied crosslinking technique is the introduction of specific T to C transitions at the interaction sites of 4SU-labeled RNA and the protein upon UV light exposure In vitro, these RNA molecules appear to be a poorer stimulator of the ATPase activity of MDA5, and result in more stable MDA5 filaments and support better downstream signaling.In summary, our approach provides a first insight into the molecular basis of vRNA derived from MeV interaction with MDA5 in living cells and reveals a preference for binding of AU-rich regions originated from (+)-sense RNA of the L gene. Infection experiments were carried out in A459 human lung carcinoma cells. HEK 293T ISRE-FF reporter cells (stable expression of firefly luciferase under the control of an interferon stimulated response element) were used for interferon stimulation luciferase reporter gene assays. All cells were maintained in Dulbecco's Modified Eagle Medium supplemented with 2 mM L-glutamine, 1% Penicillin-Streptomycin and 10% FBS . Viruses used for infections were recombinant measles virus with a sequence identical to the vaccine strain Schwarz (AF266291.1.), Sendai virus, Sendai virus defective interfering particles H4 , Mengo virus strain pMC0  and EMCV. Primary antibodies to human MDA5 (AT113) and RIG-I (Alme-1) were purchased from Enzo Life Science . Antibody to GFP (ab1218) was obtained from Abcam . Secondary antibodies were supplied by GE Healthcare .2 365 nm UV light using a photocrosslinker (Vilbert Lourmat). Cells were harvested and incubated in Nonidet P-40 lysis buffer  for 10 min on ice. The lysate was cleared by centrifugation and endogenous proteins were immunoprecipitated for 4 h with the respective antibodies (1 µg/mL) bound to protein G Dynabeads (Life Technologies). The beads were washed five times with high-salt wash buffer  and incubated with proteinase K (Thermo Scientific) for 30 min at 55°C. The RNA was isolated by phenol/chloroform/isoamylalcohol extraction and subjected to further analysis.A549 cells were infected with virus with an MOI of 1.0 in the presence of 400 µM 4SU. Infection was allowed to proceed for 24 h and living cells were washed with PBS  and exposed to 1 J/cmA549 cells were infected with MeV with an MOI of 1. Cells were harvested 24 hpi. Total RNA was isolated according to manufacturer's protocol of the RNeasy Protect Mini Kit (Qiagen) and subjected to Illumina deep sequencing.5 HEK 293T ISRE-FF reporter cells were transfected with 250 ng of recovered RNA, 500 ng in vitro transcripts or 500 ng plasmid DNA using Lipofectamine 2000 (Invitrogen) according to manufacturer's protocol. After 24 h incubation, cells were subjected to immunoactivity experiments using the Dual-Glo luciferase assay system (Promega) according to manufacturer's instructions. The luciferase activity was determined in a 96-well plate reader. Significance of differences in luciferase activity between samples were determined via an unpaired t-test.Immunoactivity experiments were carried out in 24-well plates. 2.5×10http://hannonlab.cshl.edu/fastx_toolkit/) in order to remove adapter sequences and reads with PHRED scores below 30. Remaining sequences were mapped to human and viral genomes by utilization of the Bowtie algorithm 2 ratios above +1 were defined as significantly enriched in the RLR library.Isolated RNA was prepared for Illumina sequencing using the mRNA-Seq library preparation kit (Epicentre) according to manufacturer's protocol. To remove ribosomal RNA species from the sequencing libraries a Ribo-Zero rRNA removal kit (Epicentre) was used. Quality of RNA-Seq libraries was validated on a DNA1500 chip for the Bioanalyzer 2100 (Agilent). Sequencing was performed on the Illumina Genome Analyzer in the Gene Center sequencing facility (LAFUGA). Obtained sequences were processed with the FASTX toolkit (in vitro transcripts was performed by utilization of RNAfold from the ViennaRNA package in silico and the linear relationship between different data sets was quantified with the Pearson correlation coefficient.RNA secondary structure prediction from measles virus genome or DNase treatment of the immunoprecipitated RNAs and qPCR was performed as previously described Specific primers for reverse transcription (Roche transcriptor transcriptase) and the subsequent PCR (Biozym Phusion Polymerase) were adapted from Calain et al in vitro transcription in a PCR adding the T7 promoter sequence (TAATACGACTCACTATA GGG) to the 5′ end of the desired MeV or Mengo virus genomic fragment, respectively (for oligonucleotides see S4 respectively). PCR products were subsequently purified on agarose gels. RNA was transcribed using the Ambion Megashortscript T7 Kit according to the manufacturer's protocols. The reaction was incubated overnight at 37°C and RNA was precipitated using LiCl at −20°C for 30 minutes. Afterwards, RNA was subjected to triphosphate digestion using FastAP (Fermentas) according to the manufacturer's instructions and purified on denaturing 8 M urea/10% polyacrylamide gels at 25 mA constant current. Gel slices containing RNA were incubated overnight with 450 µL probe elution buffer . Eluted RNA was isolated by phenol/chloroform/isoamylalcohol extraction and precipitated with ethanol.Templates were generated for 32] ATP. Mouse MDA5 was purified as described previously in vitro transcribed RNA for 10 min at room temperature. The reaction was initiated by addition of ATPase hydrolysis buffer  containing 2 mM ATP and 0.2 µCi [γ-P32] ATP. The hydrolysis rate was monitored over 1 h and analyzed by thin layer chromatography (TLC).ATPase hydrolysis activity was determined using  ATP to the reaction mixture.(TIF)Click here for additional data file.Figure S9E373Q and MDA5E444Q compared to wildtype proteins in 293T ISRE-FF reporter cellsImmunostimulatory activity of overexpressed Walker B mutants RIG-I .(TIF)Click here for additional data file.Table S1Sequences of in vitro transcribed MeV RNAs. The gene annotation with the exact nucleotide position on the MeV genome is shown in brackets.(DOCX)Click here for additional data file.Table S2Oligonucleotides used for generation of in vitro transcribed MeV sequences.(DOCX)Click here for additional data file.Table S3Sequences of in vitro transcribed Mengo RNAs. The gene annotation with the exact nucleotide position on the Mengo virus genome is shown in brackets.(DOCX)Click here for additional data file.Table S4Oligonucleotides used for generation of in vitro transcribed Mengo sequences.(DOCX)Click here for additional data file.


Apis mellifera) and hive products contain many pesticides derived from various sources. The most abundant pesticides are acaricides applied by beekeepers to control Varroa destructor. Beekeepers also apply antimicrobial drugs to control bacterial and microsporidial diseases. Fungicides may enter the hive when applied to nearby flowering crops. Acaricides, antimicrobial drugs and fungicides are not highly toxic to bees alone, but in combination there is potential for heightened toxicity due to interactive effects.Chemical analysis shows that honey bees  while amitraz toxicity was mostly unchanged (1 of 15). The sterol biosynthesis inhibiting (SBI) fungicide prochloraz elevated the toxicity of the acaricides tau-fluvalinate, coumaphos and fenpyroximate, likely through inhibition of detoxicative cytochrome P450 monooxygenase activity. Four other SBI fungicides increased the toxicity of tau-fluvalinate in a dose-dependent manner, although possible evidence of P450 induction was observed at the lowest fungicide doses. Non-transitive interactions between some acaricides were observed. Sublethal amitraz pre-treatment increased the toxicity of the three P450-detoxified acaricides, but amitraz toxicity was not changed by sublethal treatment with the same three acaricides. A two-fold change in the toxicity of tau-fluvalinate was observed between years, suggesting a possible change in the genetic composition of the bees tested.Interactions with acaricides in honey bees are similar to drug interactions in other animals in that P450-mediated detoxication appears to play an important role. Evidence of non-transivity, year-to-year variation and induction of detoxication enzymes indicates that pesticide interactions in bees may be as complex as drug interactions in mammals. Apis mellifera) and hive products show that most managed bee colonies in North America and Europe are repositories of a suite of chemical contaminants, including an assortment of insecticides, acaricides, herbicides and fungicides Varroa destructor. Varroa is the most serious pest of managed honey bee colonies in Europe and North America and clearly plays a role in the recent colony losses associated with colony collapse disorder Varroa weakens colonies in two ways: directly, by consuming the hemolymph of adult and pupal bees and, indirectly, by vectoring honey bee viruses and causing immunosuppression in parasitized bees Chemical analysis of honey bees  and the organic acids, oxalic acid (Oxivar) and formic acid (MiteAway Quick Strips). There is little doubt that bees can benefit from reduced Varroa populations through the effective use of acaricides in combination with other management techniques In the face of the serious challenges presented by Varroa populations have developed resistance to these acaricides. However, tau-fluvalinate and coumaphos remain common contaminants in the hive environment, partially as a result of continued application by beekeepers, and partially due to their lipophilic properties which lead to accumulation and persistence in beeswax Varroa control. While thymol and other monoterpenoids may be naturally present in floral sources at low concentration, the high concentrations needed for acaricidal activity may noticeably contaminate honey and wax The effectiveness of tau-fluvalinate Nosema apis and Nosema ceranae. Oxytetracycline (Terramycin™) and tylosin (Tylan™) are applied in powdered sugar or syrup to control American foulbrood (Paenibacillus larvae) and other bacterial infections. To protect harvested honey from contamination many antimicrobial drugs and acaricides are subject to a withholding period during which these therapeutics cannot be applied. As such, beekeepers are left with a relatively narrow window during which antimicrobial or acaricide applications are possible, potentially leading to a situation where multiple treatments are applied simultaneously.In addition to the acaricides, beekeepers may also apply antimicrobial drugs to control bacterial and microsporidial pathogens. Fumagillin (Fumadil-B) is fed in sucrose syrup to control infection by the microsporidian gut pathogens In addition to compounds applied by the beekeeper, bees may also be exposed to plant protection products applied to flowers and flowering crops. Fungicides are the most abundant and common of the plant protection products found in bees and bee products because fungicides can be applied during bloom when bees are present 50s). Acaricides were also combined with model enzyme inhibitors to characterize the classes of detoxicative enzymes that may be the basis for interactive effects.With the potential for managed honey bees to experience simultaneous exposure to acaricides, antimicrobials and fungicides it is important to consider the potential for harmful interactions between these compounds. Any interactions observed could provide an insight into honey bee physiology and will shed light on bees’ mechanisms of tolerance for both natural and synthetic xenobiotics. The present study aims to test for interactions between these most abundant contaminants of the hive environment using pair-wise lethal dose bioassays in which a sublethal pre-treatment with one acaricide, fungicide or antimicrobial is followed by a series of lethal doses of an acaricide. Mortality counts were then used to fit log-probit regression lines and determine lethal dose values ; eight fungicides of which three were non-SBI  and five were SBI fungicides ; three antimicrobials ; and three enzyme inhibitors . Lethal doses of five acaricides  were applied as a second treatment. We tested 25 of the 6×6 possible acaricide-acaricide combinations, 24 of the 6×5 fungicide-acaricide combinations, 15 of the 6×3 antimicrobial-acaricide combinations and 9 of the 6×3 enzyme inhibitor-acaricide combinations. The acaricide oxalic acid was never used as a lethal agent in pairwise combinations because it was not possible to deliver a lethal dose of this compound in the standardized volume of solvent. Enzyme inhibitors were not tested with tau-fluvalinate or coumaphos as these combinations have been tested previously Technical grade chemicals were used for all trials, with the exception of the antimicrobial drugs. The acaricides coumaphos, fenpyroximate and tau-fluvalinate were obtained from Chem Services Inc. , thymol from Aldrich Chemical Co.  and oxalic acid from Fisher Scientific . The model enzyme inhibitors piperonyl butoxide (PBO) and diethyl maleate (DEM) were obtained from Acros Organics  and S,S,S-tributylphosphorotrithioate (DEF) from Chem Services. The fungicides boscalid, pyraclostrobin, prochloraz, fenbuconazole, metconazole, myclobutanil and propiconazole were also purchased from Chem Services, and chlorothalonil from Fluka Analytical . All chemicals were serially diluted in HPLC-grade acetone for topical application.Nosema spp. infection was controlled using Fumagilin-B (fumagillin) fed in March 2009. Apiguard and oxalic acid were the only acaricides used in the apiary to control Varroa populations during the four years prior to conducting this study.Fourteen honey bee colonies maintained at the University of Nebraska – Lincoln East Campus provided bees for bioassays conducted from April – September 2009 and 2010. Colonies were requeened in 2010 with naturally mated Italian queens . Bacterial brood diseases were prevented with Terramycin (oxytetracycline) treatments in March of both years. Frames of late-stage brood were collected from these colonies and placed in a dark, humid incubator  at 34°C. Newly emerged adults were brushed from frames daily into screened wooden cages (21×14×12 cm) and weighed as a group to estimate the number of bees. Each cohort of 200–800 bees was provisioned with 1∶1 (w/w) granulated sugar dissolved in water and held for 3–4 days in the incubator.3; Solo S306, Highland Park, IL) covered with cotton cheesecloth and secured with two rubber bands. A maximum sublethal pre-treatment of an acaricide, fungicide, enzyme inhibitor, or solvent control, was then applied to bees in all subgroups. Model enzyme inhibitor pre-treatments were applied to bees at the following doses: 100 µg DEM, 10 µg DEF or 10 µg PBO 10 values determined in the current experiment. Acaricide pre-treatments applied to each bee were: 1 µg tau-fluvalinate, 3 µg coumaphos, 1 µg fenpyroximate, 1 µg amitraz, 10 µg thymol and 100 µg oxalic acid. Sublethal fungicide pre-treatment doses for each bee were: 10 µg chlorothalonil, 10 µg prochloraz, 10 µg pyraclostrobin, 20 µg boscalid, and 10 µg pyraclostrobin together with 20 µg boscalid to replicate the ratio of active ingredients in Pristine. All doses were delivered topically in 1 µl of acetone and applied to the thoracic notum using a 50 µl syringe fitted in a repeating dispenser . A control pre-treatment consisting of 1 µl of pure acetone was also included.Each treatment series was conducted using 3–4 day-old adult worker bees divided into 8–13 subgroups of 20 bees each. Bees were narcotized with carbon dioxide and each subgroup was placed in a separate wax-coated paper cup . Acetone was also applied as a pre-treatment control.Bees receiving formulated antimicrobial drug pre-treatments were fed Duramycin-10 , Tylan  and Fumagilin-B , dissolved in 50% sucrose water and fed to bees at a concentration of 1 mg/ml oxytetracycline active ingredient (a.i.), 1 mg/ml tylosin (a.i.) and 0.5 mg/ml fumagillin (a.i.). Drug feeding began 24 h prior to acaricide treatment and continued until mortality was scored. The sublethal concentrations used are similar to label recommendations for application of these drugs to whole colonies. The drugs in 50% sucrose water, and a sucrose water control, were fed to large groups of bees for the first 24 h of exposure through 20 ml glass scintillation vials covered with cotton cheesecloth, which were weighed before and after feeding to determine drug consumption. After acaricide treatment drugs in 50% sucrose water were provided in punctured 1.5 ml microcentrifuge tubes.Groups of pre-treated bees were allowed to recover for 1 h to minimize mortality due to extended carbon dioxide anaesthetization. Each subgroup of approximately 20 bees, of the 8–13 dose-groups in the bioassay, was then anaesthetized again and treated topically with either an acetone solvent control or one of a range of 7–12 acaricide doses, including doses eliciting 0% and 100% mortality, and at least three doses causing intermediate mortality. After treatment sucrose water was provided to groups of treated bees through a punctured 1.5 ml microcentrifuge tube following dosing, and bees were returned to the 34°C incubator. Mortality was scored at 24 h after treatment with all acaricides except fenpyroximate. Preliminary experiments showed that mortality was similar at both 24 and 48 h following treatment for acaricides except fenpyroximate. Immobile bees were scored as dead. Treatment series with greater than 5% mortality in the solvent control group were removed from analyses. At least three replicate treatment series were performed for each combination of pre-treatment and acaricide. Separately diluted acaricide dose series and bees taken from different colonies were used for each replicate treatment.50) values and accompanying 95% confidence intervals were calculated using Fieller’s method, with correction for heterogeneity where appropriate Lines were fitted to dose-mortality data on a log-probit scale for each pretreatment-acaricide combination using ‘glm’ in the R statistical package Statistical significance for the model comparisons was determined by comparing likelihood ratios, corrected for heterogeneity, against an F-distribution The relative toxicity of topical application of the five acaricides to bees receiving the control pre-treatment, from most to least toxic, was amitraz>fenpyroximate>tau-fluvalinate>coumaphos>thymol in 2009 . In 2010The different acaricides varied greatly in their propensity for interaction with other compounds. Tau-fluvalinate interacted with most other compounds tested, including all 5 acaricides, 8 of 9 fungicides or a fungicide combination, and 2 of 3 antimicrobial compounds. Fewer interactions were observed with coumaphos, fenpyroximate and thymol. Coumaphos interacted with 3 of 5 acaricides, 1 of 4 fungicides and none of the 3 antimicrobials. Fenpyroximate interacted with 3 of 5 acaricides, 2 of 5 fungicides and 2 of 3 antimicrobials. Thymol interacted with 3 of 5 acaricides, 2 of 5 fungicides and none of the 3 antimicrobials. Amitraz did not interact with any fungicides or antimicrobials, but did demonstrate an antagonistic interaction with a single acaricide, oxalic acid.50 level, but a significant increase in the slope of the fitted dose-response line was observed. Bees fed antimicrobial drugs as a pre-treatment consumed 5.44±0.67 µl sugar water/bee/day, regardless of drug content .Approximately half of the acaricide-acaricide (15 of 25) and acaricide-fungicide (13 of 28) combinations tested showed evidence of interactions, nearly all of which were agonistic and resulted in increased acaricide toxicity  and 4. FPiperonyl butoxide, a model P450 inhibitor, and DEF, a model carboxylesterase inhibitor, both increased the toxicity of fenpyroximate, but not amitraz or thymol .There was no single pre-treatment that interacted with all acaricides, although oxalic acid pre-treatment caused an interactive effect with all acaricides except coumaphos. Three compounds did not interact with any acaricide tested: the antimicrobial drug tylosin, the enzyme inhibitor DEM, and the fungicide boscalid.The most potent agonistic interaction observed in the first year of the study was between prochloraz and tau-fluvalinate . To deteFor each major group of toxicants applied in combination with acaricides – model enzyme inhibitors, acaricides, fungicides, and antimicrobials – we first consider the physiological implications of our results, especially as they relate to the involvement of detoxification enzyme systems, as well as non-transitivity that was observed in some of the interactions. Finally, we address the environmental relevance of our findings for assessing risks of the toxicants to bee health.in vitroThe agonistic interactions observed between PBO and tau-fluvalinate Thymol and amitraz did not interact with any of the inhibitors, which indicates that detoxication is not important for bees’ tolerance of these compounds. However, despite their unchanged toxicity in the presence of enzyme inhibitors, both thymol and amitraz have the potential to interact with detoxicative enzymes The cluster of interactions observed between tau-fluvalinate, coumaphos and fenpyroximate, all of which appear to be detoxified by P450s and carboxylesterases, suggest that interactions between these compounds may be occurring because these acaricides interact with detoxicative enzymes The toxicity of thymol increased following pre-treatment with either tau-fluvalinate or coumaphos. Since it is not known how bees tolerate thymol, it is difficult to speculate about the basis for these interactions. Coumaphos toxicity was unchanged by thymol, but tau-fluvalinate toxicity was transitively increased when thymol was applied as a pre-treatment. Monoterpenoids, like thymol, have been shown to inhibit P450 activity in human liver microsomes Amitraz participated in three non-transitive acaricide-acaricide interactions. The toxicity of amitraz was unchanged when bees were pre-treated with tau-fluvalinate. However, amitraz pre-treatment increased the toxicity of tau-fluvalinate 5-fold. Interactions between formamidines and pyrethroids are known in other insects and may be due to synergism at the target site through cooperative binding Amitraz is bioactivated through a hydrolysis reaction to DMPF which is an octopamine receptor agonist In one of the few antagonistic interactions observed, oxalic acid pre-treatment reduced the toxicity of amitraz nearly four-fold. However, this interaction may be an artifact of the methods used. Amitraz was applied to the same location on the thoracic notum as the oxalic acid pre-treatment, which may have caused degradation of amitraz, which is known to undergo hydrolytic breakdown under acidic conditions 10) by the estimated daily dose. The LD10 can be determined from the fitted dose response lines  were significantly more tolerant of the pyrethroid cyfluthrin than African honey bees (A. mellifera scutellata) and suggested that this difference may be the product of inadvertent selection for pyrethroid tolerance resulting from the widespread use of tau-fluvalinate as an acaricide in managed European honey bee colonies. In the same manner, the observed difference in tau-fluvalinate toxicity between populations of European honey bees may be due to inadvertent selection for tolerance through queen breeders’ choice of acaricides.Honey bees treated in 2009 were more tolerant of tau-fluvalinate than bees in both previous studies Detoxication by P450s appears to be the basis for the tolerance bees show toward tau-fluvalinate, coumaphos and fenpyroximate, but not amitraz or thymol. Any SBI fungicide that inhibits P450-mediated detoxification has the potential to interact with tau-fluvalinate, and likely coumaphos and fenpyroximate as well. Given the large number of pesticides to which bees are potentially exposed, and the practical impossibility of testing every possible combination, examining pesticide interaction with P450s holds promise as a method to simplify pesticide interaction testing in a rational way. Testing for P450 interactions could serve as the first “tool” in a lab-based “toolbox” testing for potential pesticide interactions in bees.While these laboratory bioassays point to potential problems associated with the various acaricide treatments, any management recommendations must be based on additional information gained from field experiments using whole colonies. Lethal-dose bioassays, by definition, require the use of doses that are high enough to cause acute mortality in bees – doses that are often much higher than bees are likely to encounter under field conditions. However, documentation of these interactions provides a foundation for future experiments using field-relevant doses and helps to focus the limited resources available for field experiments on those pesticide combinations with the greatest potential to cause harm. The routes of exposure to acaricides, fungicides and antimicrobials in beehives may be different from the topical and oral applications used in these bioassays. The actual exposure bees receive to formulated acaricides, sequestered acaricides in beeswax, and fungicide applications in agriculture need to be quantified to accurately assess the risk posed by interactions. Additionally, bees may experience sublethal effects that are, by definition, not quantifiable in lethal dose bioassays, but may have a substantial effect on colony health Table S1Dose-response line parameters and pairwise comparisons for topical application of five acaricides following sublethal topical pre-treatment with another acaricide, fungicide or detoxicative enzyme inhibitor.(DOCX)Click here for additional data file.Table S2Dose-response line parameters and pairwise comparisons for topical application of five acaricides following 24 h oral treatment with antimicrobial drugs.(DOCX)Click here for additional data file.Table S3Dose-response line parameters and pairwise comparisons for tau-fluvalinate following treatment with sterol biosynthesis inhibiting fungicides.(DOCX)Click here for additional data file.Methods S1Sample R script for lethal dose determination and pairwise comparison of two dose-response curves.(DOC)Click here for additional data file.


Giardia lamblia is controlled post-transcriptionally by an RNA-interference (RNAi) pathway that includes a Dicer-like bidentate RNase III (gDicer). This enzyme, however, lacks the RNA helicase domain present in Dicer enzymes from higher eukaryotes. The participation of several RNA helicases in practically all organisms in which RNAi was studied suggests that RNA helicases are potentially involved in antigenic variation, as well as during Giardia differentiation into cysts.Regulation of surface antigenic variation in in silico analysis of the Giardia genome identified 32 putative Super Family 2 RNA helicases that contain almost all the conserved RNA helicase motifs. Phylogenetic studies and sequence analysis separated them into 22 DEAD-box, 6 DEAH-box and 4 Ski2p-box RNA helicases, some of which are homologs of well-characterized helicases from higher organisms. No Giardia putative helicase was found to have significant homology to the RNA helicase domain of Dicer enzymes. Additionally a series of up- and down-regulated putative RNA helicases were found during encystation and antigenic variation by qPCR experiments. Finally, we were able to recognize 14 additional putative helicases from three different families  that could be considered DNA helicases.An extensive G. lamblia. The relative and variable expression of particular RNA helicases during both antigenic variation and encystation agrees with the proposed participation of these enzymes during both adaptive processes. The putatives RNA and DNA helicases identified in this early-branching eukaryote provide initial information regarding the biological role of these enzymes in cell adaptation and differentiation.This is the first comprehensive analysis of the Super Family 2 helicases from the human intestinal parasite  Helicases are encoded by a large fraction of prokaryotic and eukaryotic genomes and are found in all organisms –from bacteria to humans– and in many viruses. These nucleic acid-dependent NTPases  have the ability to unwind DNA or RNA duplex substrates; to unwind/separate the helical structure of double-stranded nucleic acids and, in some cases, to disrupt protein-nucleic acid interactions ,2.DNA and RNA helicases are grouped into six superfamilies (SF). SF1 and SF2 do not form rings, whereas SF3 to SF6 comprise the ring-forming helicases  and bactThe helicases from SF1 and SF2 are further divided into families, based on their sequence, structural, and mechanistic features ,7. AccorIn the SF1 and SF2 helicases, the conserved motifs are clustered in a “central” core region that spans about 350 to 400 amino acids (named “Helicase Core Domain” - HCD). By contrast, the N- and C-terminal extensions of helicases are highly variable in size and composition. These regions are supposed to confer substrate specificity, comprising protein- and/or RNA-binding motifs that provide helicases with their capacity to be involved in multiple processes, and/or direct the helicases to their subcellular localization ,10. WithRNA is involved in virtually all aspects of gene expression, playing important regulatory roles in biological reactions and making RNAs biologically important molecules required by all living organisms. RNA helicases may act as a temporary clamp to prevent RNAs from re-associating, thereby allowing other RNA-RNA or RNA-protein interactions to occur. A recent report suggests that these proteins can also function as RNPases , which aGiardia lamblia is a single-celled eukaryotic microorganism that inhabits in the upper small intestine of humans and several other vertebrates. Phylogenetic studies have placed Giardia as one of the most early-branching eukaryotic cells . After three washes with TBS-Tween20, the membrane was incubated with goat anti-mouse immunoglobulin serum conjugated with alkaline phosphatase [1:2000] (Southern Biotechnology) and revealed with alkaline phosphatase substrate .Total protein extraction was performed from the same Trizol extraction procedure, as indicated by the manufacturer. Total protein content was determined with the BCA™ Protein Assay kit (Pierce). Fifty micrograms of total protein was loaded onto a 10% polyacrylamide gel (SDS-PAGE) and after running, it was transferred to a PVDF membrane . The membrane was blocked with 5% milk in TBS-Tween20 for 1 hour and then incubated with a monoclonal antibody (mAbs 7D6) specific against See Additional file SF: Superfamilies; HCD: Helicase Core Domain; VSP: Variant-specific Surface Protein; RNAi: RNA interference; RHA: RNA helicase A; Bel: Belle; Armi: Armitage; eIF4A: Eukaryotic Initiation Factor 4A; ORF: Open Reading Frame; HA2: Helicase-Associated Domain; SMART: Simple Modular Architecture Research Tool; DUF1605: Domain of Unknown Function; CHROMO: CHRromatin Organization Modifier; BLM: Bloom syndrome; RIBOc: Ribonuclease III; DSRM: Double-stranded RNA binding motif; DCL1: Dicer-like 1; CWP2: Cyst Wall Protein 2; qPCR: Quantitative PCR; SAGE: Serial Analysis of Gene Expression; ELM: Eukaryotic Linear Motif; gdh: Glutamate dehydrogenase.The authors declare that they have no competing interests.G. lamblia cultures. AT performed the induction of encystation and antigenic variation. HDL coordinated the project, writing process and analyses. All the authors read and approved the final manuscript. HDL is Guggenheim Fellow; PRG and HDL are Members of the Scientific Investigator’s Career of the National Research Council of Argentina (CONICET). All authors read and approved the final manuscript.PRG performed bioinformatics and sequence searching and comparison analysis, including motif and phylogenetic analyses, and assisted with manuscript writing. MCS performed the qPCR experiments, including the production of Giardia lamblia. The table indicates the Family, the gene number from the Assemblage A isolate WB (the number that is given should be preceded by the prefix GL50803_), the current Supercontig or positions where it is located, the number of nucleotides in base pairs (bp) and molecular mass of the putative protein in kDa, for each putative helicase. (DOCX 18 kb)Putative SF2 Helicases from Click here for fileGiardia lamblia. The table indicates the average length (in number of amino acids) of each SF2 helicase family. The incompletes sequences were not considered in the computation.Average lengths (amino acid) of SF2 helicase families from Click here for fileGiardia lamblia. Phylogenetic tree derived from the alignment of the “Helicase Core Domain” amino acid sequences. Each helicase is named after its gene number, as in the GiardiaDB. The family groups are indicated as follows: DEAD-box (orange), DEAH-box (green), Ski2 (violet), RecQ (pink), Swi2/Snf2 (light orange) and Rad3 (light blue).Phylogenetic tree of the 46 putative SF2 helicase genes in Click here for fileGiardia lamblia SF2 helicases homologues in human and yeast. The table indicates each putative Giardia helicase with its Accession Number and ORF, the protein length in aminoacid, its putative helicase homologue form human with the identity and similarity percentage, and its putative helicase homologue from yeast with their known functions.Click here for filehttp://www.bioinformatics.org/sms/index.html). The residues conserved at 70% or more are highlighted in dark; other similar residues within each column are highlighted in grey.Alignment of conserved DEAD-box helicase motifs. The sequences were aligned using the “Multiple Align Show” software at “The Sequence Manipulation Suite” , blue (basic), red (acidic) and black (hydrophobic).Schematic diagram of the Swi2-Snf2 helicase family in Click here for fileG. lamblia. The representation is to scale. Inset: sequence LOGO view of the consensus amino acids. The height of each amino acid represents the degree of conservation. Colors mark properties of the amino acids as: green (polar); blue (basic); red (acidic) and black (hydrophobic).Schematic diagram of the RecQ helicase family in Click here for fileG. lamblia. The representation is to scale. Inset: sequence LOGO view of the consensus amino acids. The height of each amino acid represents the degree of conservation. Colors indicate properties of the amino acids, as follows: green (polar), blue (basic), red (acidic) and black (hydrophobic).Schematic diagram of the Rad3 helicase family in Click here for fileWestern blot of trophozoites grown under proliferating conditions and after induction to encyst. Total protein extracts from trophozoites grown under normal proliferating conditions  or after 16hs induction in encystation medium (Encyst) were separated using a 10% SDS-polyacrylamide gel and transferred to a PVDF membrane. The membrane was incubated with a monoclonal antibody against CWP2. The iqual loading of the samples is shown in the figure at the right with a Ponceau S staining. The numbers indicate the molecular weight of protein standards in kDa.Click here for fileGiardia trophozoites (white bar) and four different encystation times . Under each ORF it is indicated if these ORFs were up-regulated (green up arrow), down-regulated (red down arrow) or remained unmodified . A line graph is also provided for a better identification of the expression pattern. The colored boxes represent our RT-qPCR results (with the same color code), divided into families. The asterisk under each box stands for a correlation between the SAGE and the RT-qPCR data.SAGE  data. The graph represents the sense tag percentage from Click here for fileWestern blot during antigenic variation induction. Trophozoites were incubated for the indicated times with a 1:10.000 dilution of mAb 5C1directed against VSP-1267, mAb 7D2 against Cyst Wall Protein 2 or without antibody (Control). Total protein was electrophoresed, transferred to a PVDF membrane and incubated with a mAb against the VSP-1267. The molecular weights of standards are indicated in kDa.Click here for fileAccession numbers. The table indicates a complete list of proteins cited in the manuscript, the organism it is derived and the NCBI Reference Sequence Number.Click here for file


The pairwise comparison of RNA secondary structures is a fundamental problem, with direct application in mining databases for annotating putative noncoding RNA candidates in newly sequenced genomes. An increasing number of software tools are available for comparing RNA secondary structures, based on different models  and computational principles . We describe here the website BRASERO that offers tools for evaluating such software tools on real and synthetic datasets. The software tools available on BRASERO are flexible and can be used with alternative benchmarks data sets, for example designed by a user with some specific application in mind, with the purpose to assess which models/software tools/parameters are relevant for their own specific application. We describe below the main features of BRASERO and illustrate its use by presenting a short evaluation of several A BRASERO benchmark, either provided on BRASERO or designed by a user, aims at assessing the ability of several pairwise RNA secondary structures comparison software tools to properly classify the sequences into positive and negative sets with respect to a given reference set. This assessment is motivated by the practical problem of identifying similar structures  into a large RNA database see .reference, positive, and negative sets. For the BRASERO benchmarks currently available, the reference is a set of RNA secondary structures which are all assumed to be members of a same RNA family and for which reliable secondary structures are known; the notion of family or reliable structure could be relaxed in an ad hoc way for specific new benchmarks. A benchmark is composed of three sets of RNA (sequences and structures): the ℱ be an RNA family. The reference set is denoted by R. A set Ps of RNA gene sequences that belong to ℱ but not to R is folded into putative secondary structures using various programs such as mfold [RNAshapes [RNAsubopt [positive set and denoted by P. Finally, we consider a set Ns of sequences randomly picked from a noise source that is supposed to be free of RNA from ℱ and whose lengths have the same distribution as the RNA in R. Sequences of Ns are folded (using the same programs and parameters as for Ps) to form the negative set N.The positive set contains RNA secondary structures that are assumed to belong to the same family as the reference set. The negative set is a set of RNA secondary structures that do not belong to the reference family. More precisely, let as mfold , RNAshapNAshapes , or RNAsNAsubopt . For eacF, we use several sources: viral genomes  [GenRGenS, a generator of random structured sequences [BRASERO currently comes with data for 5 families: subunit 16S of ribosomal RNAs, microRNAs, small RNAs (sRNAs), Signal Recognition Particles (SRP), and transfer RNAs (tRNAs). The reference genes have been selected manually by the RNA biologists of our team to satisfy the following criteria: accuracy of the structures and inclusion of a large set of possible variations, both in terms of structure and length. To generate sequences of the negative set esource) , and Genequences . The BRAR with each structure of T and F using this method. Then for each sequence of T and F the best score obtained over the comparison of its putative secondary structures and the elements of R is kept. Finally, sequences of T and F are sorted according to these scores. A receiver operating characteristic (ROC) curve is plotted to represent the capability of separating true events (sequences known to be from the ℱ family) and false events (sequences not in ℱ). This curve shows the false-positive rate versus the true-positive rate. The ROC curve of a given benchmark is based on a single run. Indeed, the process of analyzing a benchmark is purely deterministic, the only random aspect lying in the design of the benchmark. For a given RNA family it is possible to design several benchmarks, with several sources (possibly random) of negative sequences. To assess a pairwise RNA secondary structure comparison method, we compare each structure of benchmarking engine is available on the BRASERO website. It consists of a Java program, that takes as input a benchmark, the considered comparison software tools, and, for every comparison software, a parameters file and a Java class to interface it with the engine. The Java interfaces for several of the classical RNA secondary structure comparison software tools are provided on the website, and a documentation on the format of such interface is also available. For each integrated tool, a Java class indicates if the best score is the smallest (distance approach) or the largest (similarity approach). This information is used to sort the results. Additional Python and Java programs are available to analyze results, to compute ROC curves or to build new benchmarks.To perform such experiment with several RNA comparison methods on the same benchmark, a We conclude this section with two important remarks. First the results of a benchmark depend on the method used to fold the positive and negative sets, so our approach can be seen as an evaluation of the combined folding + comparison process. Next, in order to perform a proper assessment of pairwise RNA secondary structure comparison method, the scripts available on the BRASERO website do assume that the RNA structure comparison methods are symmetric and thus do not depend on the order in which two structures are compared. It is up to the users to ensure that the methods they compare satisfy this assumption; classical approach to handle such methods will, for example, average or take the minimum of comparing the structures in both possible orders. Such approaches can easily be implemented in the short JAVA class that has to be written to assess a comparison method (see below).We illustrate here a typical use of the BRASERO website, by comparing several programs based on computing an edit distance or an alignment between pairs of RNA secondary structures, applied on a benchmark for the RNA family of Signal Recognition Particle (SRP). We compare six tools: RNAdistance , RNAforeRNAforester is an ordered trees local/global alignment algorithm. It uses a special tree encoding that allows to break nucleotide pairings under certain conditions. MiGaL uses a multilevel representation of the secondary structure composed by four layers coded by rooted ordered trees. The layers model different structural levels from multiloop network to the sequence of nucleotides composing the RNA. The algorithm successively applies edit distance computations to each layer. TreeMatching is based on a quotiented tree representation of the secondary structure which is an autosimilar structure composed of two rooted ordered trees on two different scales . The core of the method relies on the comparison of both scales simultaneously: it computes an edit distance between quotiented trees at the macroscopic scale using edit costs defined as edit distances between subtrees at the microscopic scale. Gardenia and NestdAlign use an arc-annotated-based representation, that allows for complex edit operations, such as arc breaking or arc altering. They allow local and global alignment features. Gardenia notably allows affine gap scores, while NestedAlign implements an original local alignment algorithm. RNAStrAT performs the comparison in two steps. First, it compares stems of the two structures using an alignment algorithm with complex edit operations. Then it finds an optimal mapping between the different stems. All tools were used with the default parameters (in particular their default scoring scheme). We applied all tools on a benchmark available on BRASERO for the SRP family benchmark, with noise obtained from viral genomes (details are available on the website). Results are illustrated in We can observe on BRASERO provides useful tools and benchmarks for comparing RNA secondary structures software tools. Application can be in helping researchers decide on which tool to use either for comparing new RNA secondary structures with a specific family, or in assessing good parameters for pairwise comparison software tools in mining large sets of RNA secondary structures.Further developments will consist in increasing the number of benchmarks and allowing users to provide their own benchmarks, and in developing additional analysis tools.


A-to-I RNA editing is found in all phyla of animals and contributes to transcript diversity that may have profound impacts on behavior and physiology. Many transcripts of genes involved in axonal conductance, synaptic transmission and modulation are the targets of A-to-I RNA editing. There are a number of methods to measure the extent of A-to-I RNA editing, but they are generally costly and time consuming. One way to determine the frequency of A-to-I RNA editing is the peak height ratio method, which compares the size of peaks on electropherograms that represent unedited and edited sites.Dα6 nicotinic acetylcholine receptor subunit with an antisense primer  showed very accurate and precise measurements of A-to-I RNA editing. The accuracy and precision were excellent for all editing sites, including those edited with high or low frequencies. The frequency of A-to-I RNA editing was comparable to the editing frequency as measured by clone counting from the same sample. Sequencing these same sites with the sense primer (which uses A/G peaks) yielded inaccurate and imprecise measurements.Sequencing of 4 editing sites of the We have validated and improved the accuracy and precision of the peak height ratio method to measure the frequency of A-to-I RNA editing, and shown that results are primer specific. Thus, the correct sequencing primer must be utilized for the most dependable data. When compared to other methods used to measure the frequency of A-to-I RNA editing, the major benefits of the peak height ratio are that this method is inexpensive, fast, non-labor intensive and easily adaptable to many laboratory and field settings. ADAR genes in vertebrates. ADAR1 and ADAR2 both have catalytic activity, whereas ADAR3 lacks activity, although the functional domains of ADAR3 are conserved. ADAR3 is likely is a duplicate of ADAR2. A single ADAR gene exists in insects. Drosophila melanogaster dAdar is homologous to vertebrate ADAR2 [A-to-I RNA editing is catalyzed by adenosine deaminases that act on RNA (ADARs) that bind to double stranded pre-mRNAs and convert adenosine (A) to inosine (I) which is recognized by the ribosome as a guanosine . ADARs a2+. Unedited GluR-B transcripts lead to neuronal death that causes seizures and premature death in editing deficient mice [A-to-I RNA editing regulates behavior and life history traits in many phyla of animals. The widespread conservation of this pathway is thought to be a viral defense mechanism ,4. A-to-ent mice .D. melanogaster have shown wide-spread editing of these genes [+, Na+ and Ca2+ channels, residues involved in channel gating or inactivation are edited. Editing sites on nAChR or GABA receptor subunits occur in crucial areas in the ligand-binding domain and TM2 that forms the channel pore [The transcripts of many ligand-gated or voltage-sensitive ion channels and G-protein coupled receptors are targets of A-to-I RNA editing ,7-9. Gennel pore .dAdar transcripts produced and dAdar expression is important for a number of functions in the fruit fly. Four common transcripts of dAdar result from alternative splicing and these transcripts are themselves subject to RNA editing [D. melanogaster dAdar null mutants show significant deficiencies in motor control and mating that grow progressively worse with age. Nervous system morphology is greatly affected by dAdar null mutants [D. melanogaster that had reduced dAdar expression. These mutants did not fly, exhibited diurnal activity patterns and displayed temperature sensitive paralysis [dAdar took longer to engage in courtship behaviors and had a dramatically altered courtship song [There are a number of  editing . D. mela mutants . Behavioaralysis . Males dhip song .Dα6 nicotinic acetylcholine receptor subunit, we demonstrate a detailed and improved method for a highly accurate and precise estimate of the frequency of editing using peak height ratios from Sanger sequencing electropherograms and compare its costs and benefits to other methods for measuring RNA editing frequency.There are a variety of methods for measuring the extent of A-to-I RNA editing ,18 and tDα6 nicotinic acetylcholine receptor subunit cDNA from the Canton-S strain of Drosophila melanogaster. Dα6 clones that were unedited or edited at four sites were mixed in different proportions by weight to produce varying levels of A-to-I RNA editing. Two primers,  and the ratio of the peak heights was compared to the expected heights [We used the peak height ratio method to estimate the level of A-to-I RNA editing at 4 sites of the  heights . The relDα6 editing at four sites was very accurate and precise when sequenced with Dα6IR2  when sequenced with Dα6285F at all four editing sites  or high frequency of editing  be used to accurately estimate the frequency of A-to-I RNA editing.We have validated and verified a very accurate, precise, fast and cost-effective method for estimating editing rates. The accuracy and precision of the editing estimates were primer specific Figures  and 3. Re Figure . When sey Figure , Table 2The disparity in editing estimation between primers may be a consequence of the sequencing chemistry commonly used for dye-terminator cycle sequencing prior to analysis on automated, capillary DNA analyzers. Current nucleotide formulations substitute dITP for dGTP to prevent compression of poly-G tracts in the resulting electropherogram. However, the incorporation of dITP is less efficient than dGTP .Table Besides these savings of time and money, there are many other technical advantages of using peak height ratios over other methods. RNA editing may introduce or eliminate a restriction enzyme site. Poisoned primer extension utilizes ddGTP as a reaction terminator in edited transcripts. A larger band will result from unedited transcripts because dATP would be incorporated into the product. These methods utilize fluorescent or phosphoimaging systems for quantification.32P. These two protocols are cost effective only when larger numbers of replications can be performed in a short period of time. In the case of restriction enzyme digestion, the edited site may alter a restriction enzyme recognition sequence for which an inexpensive or widely used restriction enzyme may not be readily available. In the case of adjacent or nearby editing sites, multiple enzymes would be needed to be used to account for recognition site variation. Complications may arise in cases when a restriction enzyme is not available to recognize the change in recognition sequence.The major drawbacks to poisoned primer extension and restriction digests are that they only allow for the quantification of a single editing site, they use radiolabeled or fluorescently labeled primers, and are somewhat labor intensive. The high cost of the restriction digest with radioisotopes and poisoned primer extension is mainly due to the high cost of fluorescent dyes and Dα6 nAChR subunit used in this experiment. Two sense primers would have to be utilized to account for transcripts with edited and unedited versions at site 415 in order to accurately assess editing at site 416 as mismatches at the 3' end of primer with template can lead to reduced amplification efficiency. Conversely, an antisense primer could be used to assess editing at site 416 without regard for editing status at site 415. However, this would require the use of ddCTP as the reaction terminator. The cost to measure the same 4 editing sites of Dα6 by poisoned primer extension would be 15 to 17 fold higher than by the peak height method  is extremely cost prohibitive and better suited for experiments that may not require comparing many biological samples. Next generation UHTS is the most accurate method to measure the frequency of RNA editing. It can even detect rare transcripts that are missed by clone counting methods . The majClone counting can be performed to quantify the frequency of editing. While there are many advantages to this method, the major drawback is that a large number of clones need to be sequenced to ensure an accurate reflection of editing frequency. This process may take a few days to complete and the cost of sequencing a large number of clones may be substantial. Screening colonies for positive inserts usually requires screening many more colonies than will actually go for sequencing. The waste generated by growing colonies on plates and in liquid media needs to properly disposal by autoclaving or incinerating at an approved facility.The peak height ratio method with the antisense primer utilizes the different intensities in the T/C signal of eletropherograms. The disadvantage of this method is that it does not allow for the identification of which editing sites are edited on each transcript. However, the cost, labor, turnaround time is unequivocal. The peak height ratio method is very advantageous in that there are few steps required to complete an analysis from biological sample to data point.The technical efficiency of the peak height ratio method makes it an ideal method for demonstrating the extent of A-to-I RNA editing in high school and introductory biology classes, or in research laboratories as a significant cost reduction method. Additionally, a mobile lab unit could be assembled for rapid sample processing in the field and other remote areas. The RNA extraction, reverse transcription and PCR could be done in the field and processed samples could be mailed to a sequencing facility. Processing field collected animals would help overcome potential changes in allele frequency that may result from genetic drift if they were returned to a lab and reared for a second generation. This would also reduce the risk of sample degradation and shipping nucleic acids across international borders is typically less burdensome than shipping organisms or tissues.The peak height method could also be used to estimate allele frequencies within populations. Using pools of animals, it is possible to simultaneously evaluate allele frequencies from as many as 10 diploid or 20 haploid individuals based on the upper (0.95) and lower (0.05) detection rates that we used in this analysis. This would allow for the detection of a single allele out of 20 potential alleles. It is likely that this method could be validated for even lower detection rates. This method would be extremely valuable in our lab for evaluating the frequency of insecticide resistance alleles from field collected populations.Dα6 from the antisense Dα6IR2 primer is in very good agreement with expected values and is comparable to the quantitative clone counting method. It is also very cost effective and fast compared to other current methods, especially when evaluating editing at multiple sites. Because of these many advantages, it is likely that this method will prove to be a powerful and useful tool in evaluating the extent of A-to-I RNA editing in this rapidly growing field of study, and has other uses in other fields (such as population genetics) as well.The accuracy and precision of the estimate of A-to-I RNA editing using the peak height ratio method with sequences of The authors declare that they have no competing interests.FDR and JGS designed the experiments and wrote the manuscript. FDR performed all laboratory work and data analysis. PAS provided technical commentary and analysis of sequencing methodology. All authors approved of the final manuscript.Detailed Methods.Click here for file


RNA quality and quantity are important factors for ensuring the accuracy of gene expression analysis and other RNA-based downstream applications. Extraction of high quality nucleic acids is difficult from neuronal cells and brain tissues as they are particularly rich in lipids. In addition, most common RNA extraction methods are phenol-based, resulting in RNA that may be incompatible with downstream applications such as gene expression.® Mini, EasySpin and Ilustra RNAspin Mini RNA Isolation, all performed well and resulted in the isolation of high quality RNA, as evaluated by A260/A280. The RNA extracted with AxyPrep Multisource Total RNA Miniprep, RNeasy® Mini and EasySpin provided the highest RNA yields. In particular, the RNA isolated by AxyPrep Multisource Total RNA Miniprep Kit did not show any detectable genomic DNA contamination even without previous DNase treatment or after RNA direct PCR amplification using universal 18S primers.In this work, a comparative analysis of the RNA quality obtained from SK-N-MC cells was performed using six commonly used RNA isolation kits: two phenol-based kits and four non-phenol based kits. The non-phenol based kits tested AxyPrep Multisource Total RNA Miniprep, RNeasyThe RNA extracted from SK-N-MC cells with AxyPrep Multisource Total RNA Miniprep Kit was superior with respect to the RNA quality and concentration. This kit does not use aggressive organic solvents and RNA free of genomic DNA was isolated without the need for DNase treatment. The accuracy of gene expression evaluation is influenced by the concentration and quality of input RNA. The purity and integrity of RNA are critical elements for the overall success of RNA-based analyses . Startin2+, high genomic DNA concentrations, DNA binding proteins or other cell constituents are critical contaminants [To ensure acceptable total RNA quality, the RNA extraction procedure must fulfill a number of requirements: including, the final preparation must be free from protein, genomic DNA or enzyme inhibitors and must not include any phenol or alcohol carryover which may compromise downstream reactions . Also, taminants ,6.There are three major techniques extensively used for RNA extraction: organic extraction, such as phenol-Guanidine Isothiocyanate (GITC)-based solutions, silica-membrane based spin column technology, and paramagnetic particle technology. One of the most commonly used methods is the phenol-GITC-based organic extraction. However, RNA samples isolated by this method are frequently contaminated with proteins and other cellular materials, organic solvents such as phenol-chloroform, salts and ethanol. Additionally, these methods require safety precautions  which lengthen the procedure and employ liquid-liquid extraction leading to incomplete phase separation and increased carryover contamination with genomic DNA. Silica column and paramagnetic particle based RNA isolation systems do not require the use of toxic organic solvents, are relatively simple, efficient, low cost, and yield total intact RNA with low levels of contamination from proteins and other cellular materials . HoweverDigestion with DNase removes traces of DNA and is compulsory if the RNA samples are destined for use in RT-qPCR. DNase digestion after the final RNA precipitation step involves adding extra salts and proteins to the sample and since this can affect the efficiency of the cDNA synthesis, additional purification steps are required.In this work, a comparative analysis of the RNA quality achieved from a neuroblastoma cell line (SK-N-MC) by six commonly used RNA isolation kits is presented; two phenol-based kits and four kits utilizing non-aggressive solvents. For the SK-N-MC cell line in particular, both types of extraction methods have previously been described, but RNA has been isolated mainly using phenol-GITC-based methods -13.® Plus RNA Purification System (Invitrogen) and E.Z.N.A.™ Total RNA kit II (Omega Bio-Tek)) involve a more aggressive methodology which includes a mono-phasic solution of phenol and guanidine isothiocyanate. E.Z.N.A.™ Total RNA Kit II was selected for this analysis because it is mainly designed for fatty tissues by combining the advantage of one step RNA isolation technology and silica-membrane technology.RNA isolation methods such as acid phenol extraction, glass fibre filter purification, and single-step reagents can provide RNA with acceptable quality. However, all RNA isolation methods do not have the ability to completely remove genomic DNA contamination from RNA samples. To evaluate the differential efficiency in obtaining RNA with minimal DNA contamination, six commercial kits for RNA extraction were tested using SK-N-MC cells. Among these kits, two of them . An A260/A280 ratio greater than 1.8 is usually considered an acceptable indicator of good quality RNA with a low level of protein contamination [260/A230 ratio higher than 1.8 is used as an indicator of extracted RNA with a low level of polysaccharides contamination. Quantitatively, the highest RNA concentration and yield was obtained by the AxyPrep Multisource Total RNA Miniprep kit while RNeasy® Mini kit, EasySpin kit and TRIzol® Plus RNA Purification System presented intermediate values, and Illustra RNAspin Mini RNA Isolation kit and E.Z.N.A.™ Total RNA kit II provided the lowest recovery values (Table ® Plus RNA Purification System -(GITC based procedure) demonstrates low RNA recovery reproducibility. In terms of yield, among Axygen, Qiagen and Citomed kits, clearly the highest and most reproducible is the AxyPrep Multisource Total RNA Miniprep Kit, where RNA yield is at least four fold higher than the other methods. E.Z.N.A.™ Total RNA Kit II, a GITC based kit, and Illustra RNAspin Mini RNA Isolation Kit revealed rather poor efficiencies of RNA extraction from this neuroblastoma cell line  isolated by Axygen and Qiagen kits were directly amplified by PCR using universal 18S primers. The corresponding reaction products where visualized by agarose gel electrophoresis . In regards to quality and yield the AxyPrep Multisource Total RNA Miniprep kit was determined to be the best kit tested for the isolation of RNA from SK-N-MC cells. This kit uses no aggressive organic solvents and delivers RNA devoid of genomic DNA, without the need for DNase treatment. This is an important finding, especially in large scale gene-expression studies, since DNase treatment is time consuming and adds a substantial cost to the overall cost for such experiments. Furthermore, DNase treatment may lead to a loss in both RNA amount and mRNA integrity due to the exposure of the RNA samples to high temperatures during the heat inactivation step required for many commercial DNases. This effect extends further to include any downstream applications demanding compulsory genomic DNA removal. AxyPrep Multisource Total RNA Miniprep Kit allows the use of mild treatments minimizing the introduction of further contaminants to the extracted RNA.2. For routine culture, cells were grown until reaching approximately 90% confluence. For RNA isolation, cells were harvested using trypsin and stored frozen at -80°C.Human neuroblastoma SK-N-MC cells were obtained from the European Collection of Cell Cultures (ECACC) and were cultured in EMEM supplemented with 2 mM glutamine, 10% (v/v) heat-inactivated fetal bovine serum (Gibco), 1% (v/v) of non-essential amino acids (Sigma) and 1 mM sodium pyruvate. Cells were cultivated at 37°C in a humidified atmosphere containing 5% (v/v) CO® Mini (Qiagen), EasySpin (Citomed), Illustra RNAspin Mini RNA Isolation (GE), TRIzol® Plus RNA Purification System (Invitrogen) and E.Z.N.A.™ Total RNA kit II (Omega Bio-Tek). The same amount of cells (6.92E6) and the manufacturer protocols were followed for each kit.For SK-N-MC cell RNA isolation, six commercially available kits were tested: AxyPrep Multisource Total RNA Miniprep (Axygen), RNeasyWhen appropriate, the isolated RNA was treated with Turbo™ DNase I (Ambion), accordingly to the manufacturer's instructions.260/A280 and A260/A230 ratios for RNA preparation samples were analysed with a Nano-Drop® ND-1000 spectrophotometer (NanoDrop Technologies). RNA integrity and DNA contamination were determined by 28S/18S rRNA visualization in agarose gel, stained with ethidium bromide.For assessing RNA quality and yield, A2, 0.2 mM of each dNTP (Invitrogen), 75 mM Tris-HCl (pH 8.8 at 25°C), 20 mM (NH4)2SO4 and 0.01% (v/v) Tween 20. The following program was applied: 1 cycle of 3 min. at 95°C for denaturation, followed by 30 cycles  and a final 5 min extension at 72°C.PCR was used to detect potential DNA contamination, using primers specific for 18S rRNA as Quantum RNA Universal 18S Internal Standards primers (Ambion). PCR reaction contained 1 μL of input (20-200 ng RNA), 0.6 U Taq DNA Polymerase (Fermentas), 2.5 μL of 18S PCR Primer Pair (Ambion), 2 mM MgClPCR products were visualized in agarose gels, stained with ethidium bromide.The authors declare that they have no competing interests.LT performed the experiments. All authors contributed to the conception, design, analysis, and interpretation of the data, drafting of the manuscript and revision for important intellectual content. All authors read and approved the final version of the manuscript.


Alphacoronavirus, Betacoronavirus and Gammacoronavirus, with Betacoronavirus consisting of subgroups A, B, C and D, exist. A fourth genus, Deltacoronavirus, which includes bulbul coronavirus HKU11, thrush coronavirus HKU12 and munia coronavirus HKU13, is emerging. Molecular clock analysis using various gene loci revealed that the time of most recent common ancestor of human/civet SARS related coronavirus to be 1999–2002, with estimated substitution rate of 4×10−4 to 2×10−2 substitutions per site per year. Recombination in coronaviruses was most notable between different strains of murine hepatitis virus (MHV), between different strains of infectious bronchitis virus, between MHV and bovine coronavirus, between feline coronavirus (FCoV) type I and canine coronavirus generating FCoV type II, and between the three genotypes of human coronavirus HKU1 (HCoV-HKU1). Codon usage bias in coronaviruses were observed, with HCoV-HKU1 showing the most extreme bias, and cytosine deamination and selection of CpG suppressed clones are the two major independent biological forces that shape such codon usage bias in coronaviruses.The drastic increase in the number of coronaviruses discovered and coronavirus genomes being sequenced have given us an unprecedented opportunity to perform genomics and bioinformatics analysis on this family of viruses. Coronaviruses possess the largest genomes (26.4 to 31.7 kb) among all known RNA viruses, with G + C contents varying from 32% to 43%. Variable numbers of small ORFs are present between the various conserved genes  and downstream to nucleocapsid gene in different coronavirus lineages. Phylogenetically, three genera,  Based on their antigenic relationships, coronaviruses were classified into three groups. Group 1 and 2 are composed of mammalian coronaviruses and group 3 avian coronaviruses. The invention of and advances in nucleic acid amplification technologies, automated DNA sequencing and bioinformatics tools in the recent two decades have revolutionized the characterization and classification of all kinds of infectious disease agents. Using molecular methods, coronaviruses are classified as positive-sense, single-stranded RNA viruses. Furthermore, the results of using phylogenetic methods for classification also supported the group boundaries of the traditional antigenic classification. Phylogenetic methods have also enabled the classification of SARS-related coronavirus (SARSr-CoV) as a subgroup of group 2, group 2b, coronavirus; as well as the discovery of group 2c, 2d, 3b and 3c coronaviruses , and one avian coronavirus (IBV). The SARS epidemic that originated from southern China in 2003 has boosted interest in all areas of coronavirus research, most notably, coronavirus biodiversity and genomics . After tirus SW1 ,11–15; airus SW1 ,11–15. T2.Coronaviruses possess the largest genomes [26.4 kb (ThCoV HKU12) to 31.7 kb (SW1)] among all known RNA viruses  2,13,16,1613,16.2.1.pro) and 3C-like protease (3CLpro), proteins encoded by ORF1ab of the coronavirus genome, at consensus cleavage sites, into 15 to 16 non-structural proteins (nsps) named nsp1, nsp2, nsp3, etc (pro (nsp3), 3CLpro (nsp5), RNA-dependent RNA polymerase (Pol) (nsp12) and helicase (nsp13) (Alphacoronavirus and Betacoronavirus subgroup A possess two PLpro (PL1pro and PL2pro), while those of all known members of Betacoronavirus subgroup B, C and D and Gammacoronavirus possess only one PLpro (ORF1ab of coronaviruses occupy about two thirds of their genomes. It encodes the replicase polyprotein and is translated from ORF1a (11826 to 13425 nt) and ORF1b (7983 to 8157 nt). In all coronaviruses, a slippery sequence (UUUAAAC), followed by sequences that form a putative pseudoknot structure, are present at the junction between ORF1a and ORF1b. Translation occurs by a -1 RNA-mediated ribosomal frameshift at the end of the slippery sequence. Instead of reading the transcript as UUUAAACGGG, it will be read as UUUAAACCGGG. The replicase polyprotein is cleaved by papain-like protease(s)  . The genne PLpro . The genpro or PL1pro in nsp3 is the X domain which contains putative ADP-ribose 1″-phosphatase (ADRP) activity [Saccharomyces cerevisiae and other eukaryotes, ADRP and its functionally related enzyme cyclic nucleotide phosphodiesterase (CPDase), were important for tRNA processing [In addition to the nsps with essential functions, bioinformatics analysis of some other nsps revealed their putative functions. Downstream to PLocessing . ADP-rib2.2.Betacoronavirus subgroup A, a haemagglutinin esterase (HE) gene, which encodes a glycoprotein with neuraminate O-acetyl-esterase activity and the active site FGDS, is present downstream to ORF1ab and upstream to S gene  , with th2.4.The E and M proteins are small transmembrane proteins associated with the envelope of all coronaviruses. In some coronaviruses, such as MHV and SDAV and possibly HCoV-HKU1, the translation of the E protein is cap-independent, via an internal ribosomal entry site. Although these two genes are conserved among all coronaviruses, they are not good targets for phylogenetic studies because of their short sequences.2.5.Similar to the conserved proteins encoded by ORF1ab, the N gene is also another common target for phylogenetic analysis. Due to its immunogenicity, it is also a common target for cloning and generation of recombinant proteins for serological assays.2.6.Coronaviridae family  ×10−4 nucleotide substitutions per site per year [Coronaviridae family were estimated by various groups using different approaches [In 1992, Sanchez per year . 1n 2005per year . The estper year ,32. Subsproaches ,33–35. Aproaches . One groRecently, we used the uncorrelated exponentially distributed relaxed clock model (UCED) in BEAST version 1.4  to estim5.As a result of their unique random template switching during RNA replication, thought to be mediated by a “copy-choice” mechanism, coronaviruses have a high frequency of homologous RNA recombination ,42. RecoThe best documented example of generation of coronavirus species through homologous RNA recombination is the generation of FCoV type II by double recombination between FCoV type I and CCoV. It was first observed that the sequence of the S protein in FCoV type II was closely related to that of CCoV ,48 but tAs for the generation of different genotypes in a coronavirus species through homologous RNA recombination, the best documented example is HCoV-HKU1. The possibility of homologous RNA recombination was first suspected when a few strains of HCoV-HKU1 showed differential clustering when the Pol, S and N genes were used for phylogenetic tree construction . This ob6.Recently, using the complete genome sequences of the 19 coronavirus genomes, we analyzed the codon usage bias in coronaviruses as well as selection of CpG suppressed clones by the immune system and cytosine deamination being the two major independent biochemical and biological selective forces that has shaped such codon usage bias . In the In addition to CpG suppression, marked cytosine deamination is also observed in all the 19 coronavirus genomes . Using tInterestingly, among all the 19 coronaviruses, HCoV-HKU1 showed the most extreme codon usage bias. HCoV-HKU1 is the only coronavirus that had effective number of codons outside the mean ± 2 standard deviations range. In addition, HCoV-HKU1 also possessed the lowest G + C content, highest GC skew, lowest percentages of G and C and highest percentage of U among all coronavirus genomes. Furthermore, HCoV-HKU1 showed extremely high NNU/NNC ratio of 8.835. The underlying mechanism for the extreme codon usage bias, cytosine deamination and G + C content in HCoV-HKU1 is intriguing.7.Rapid and accurate batch sequence retrieval is always the cornerstone and bottleneck for all kinds of comparative genomics and bioinformatics analysis. During the process of batch sequence retrieval for comparative genomics and other bioinformatics analysis of the coronavirus genomes that we have sequenced, we encountered a number of major problems about the coronavirus sequences in GenBank and other coronavirus databases. First, in GenBank, the non-structural proteins encoded by ORF1ab are not annotated. Second, in all databases, the annotations for the non-structural proteins encoded by ORFs downstream to ORF1ab are often confusing because they are not annotated using a standardized system. Third, multiple accession numbers are often present for reference sequences. These problems will often lead to confusion during sequence retrieval. Fourth, coronaviruses, especially SARSr-CoV, amplified from different specimens may contain the same gene or genome sequences, which will lead to redundant work when they are analyzed. In view of these problems, we have developed a comprehensive database, CoVDB, of annotated coronavirus genes and genomes, which offers rapid, efficient and user-friendly batch sequence retrieval and analysis . In CoVD8.Coronaviridae family. Three genera, Alphacoronavirus, Betacoronavirus and Gammacoronavirus, have been used to replace the traditional group 1, 2 and 3 coronaviruses. A fourth genus, Deltacoronavirus, which includes BuCoV HKU11, ThCoV HKU12 and MunCoV HKU13, is likely to emerge. Under this new classification system, bat coronaviruses dominate the Alphacoronavirus and Betacoronavirus genera and bird coronaviruses dominate the Gammacoronavirus and Deltacoronavirus genera. This huge diversity of coronaviruses in bats and birds has made them excellent gene pools for coronaviruses in these four genera [After the SARS epidemic, there has been a marked increase in the number of coronaviruses discovered and coronavirus genomes being sequenced. This increase in the number of coronavirus species and genomes, a comprehensive and user-friendly database for efficient sequence retrieval, and the ever improving bioinformatics tools have enabled us to perform meaningful genomic, phylogenetic, evolutionary rate and divergence, recombination, and other bioinformatics analyses on the r genera .