They also revealed surprisingly high degradation rates for newly synthesized RNAs, particularly RNA polymerase III transcripts.In eukaryotes, the exosome complex degrades RNA backbones and plays key roles in RNA processing and surveillance. It was predicted that RNA substrates are threaded through a central channel. This pathway is conserved between eukaryotic and archaeal complexes, even though nuclease activity was lost from the nine-subunit eukaryotic core (EXO-9) and transferred to associated proteins. The exosome cooperates with nuclear and cytoplasmic cofactors, including RNA helicases Mtr4 and Ski2, respectively. Structures of an RNA-bound exosome and both helicases revealed how substrates are channeled through EXO-9 to the associated nuclease Rrp44. Recent high-throughput analyses provided fresh insights relating exosome structure to its diverse  Almost the entire eukaryotic genome generates RNA transcripts, which participate in all aspects of gene expression and must be maintained at the correct level for any specific cellular state In eukaryotes and Archaea, a multi-protein complex called the exosome provides the major 3′ to 5′ exonuclease activity for a wide range of substrates drawn from all nuclear and cytoplasmic 3′ to 5′ RNA degradation and processing pathways. Known roles for the eukaryotic exosome include accurate 3′ end processing of highly expressed, stable functional RNAs such as ribosomal RNAs (rRNAs) and small nuclear and nucleolar RNAs [sn(o)RNAs], turnover of diverse non-coding RNAs (ncRNAs) in the nucleus and protein-coding mRNAs in the cytoplasm, and the surveillance and degradation of aberrant RNAs of many types in both the nucleus and cytoplasm in vitro. In addition, we discuss current knowledge about the target range and substrate acquisition mechanisms of the yeast exosome in vivo.Here we review recent structural and biochemical studies on the eukaryotic exosome and selected cofactors that have generated detailed information about their interplay and mechanistic properties in vivo is therefore unclear.The exosome complex is an important, evolutionarily conserved factor in RNA processing and surveillance. Exosome complexes from eukaryotes and Archaea have very similar barrel-like core structures that are related to the phosphorolytic RNase complex PNPase from Eubacteria. In each case the central channel is wide enough 8–10 Å) to accommodate single-stranded RNA, but not double-stranded RNA. This suggests that RNA substrates are unwound and then threaded through the complex towards (an) internal catalytic site(s) –10 Å to In contrast to the core phosphorolytic RNase activity of the archaeal exosome and the bacterial PNPase, the nuclease activity of eukaryotic complexes is hydrolytic and dependent on the association of enzymatic subunits with the core structure. Early structural studies on budding yeast and human exosome complexes revealed a two-layered, barrel-like core composed of nine catalytically inert subunits EXO-9,  [6], altin vitro and in vivo. Exonuclease active sites were localized to both Rrp44 and Rrp6, but subsequent studies identified an additional endonuclease activity within Rrp44. The 3′ to 5′ exonuclease activity of Rrp44 resides in an RNB domain (named after the gene locus encoding Escherichia coli RNase II), which is located towards the C terminus  that, together with the PIN domain, mediates the interaction between Rrp44 and EXO-9 in yeast and modulates its enzymatic activities When the yeast exosome was identified more than 15 years ago terminus A. By conArabidopsis thaliana. Two of the plant RRP6 variants exhibit specialized intranuclear localizations and functions, whereas the third variant is restricted to the cytoplasm There is only one copy each of Rrp6 and Rrp44 in fungi and invertebrates, but three RRP6-like proteins have been identified in The apparent lack of any DIS3 isoform in the human nucleolus, the key location for early steps of ribosome biogenesis, was unexpected given that yeast Rrp44 and Rrp6 have prominent, non-redundant roles in pre-rRNA processing. hRRP6 is found throughout the nucleus and, to a small extent, in the cytoplasm, but is enriched in the nucleolus. The apparent nucleolar exclusion of hDIS3 suggests that nucleolar pre-rRNA processing steps are performed only by RRP6 in humans. Moreover, it was recently shown that hRRP6 plays a key role in removal of the structured, pre-ribosomal internal transcribed spacer 1 (ITS1) region from the 3′ end of 18S rRNA. This function is not conserved in yeast, in which the equivalent steps are endonucleolytic, indicating that hRRP6 has acquired additional roles in pre-rRNA processing Consistent with their nuclear/cytoplasmic distributions, hDIS3 and hRRP6 participate in the nuclear degradation of unstable, promoter-associated transcripts (PROMPTs), whereas DIS3L1 degrades cytoplasmic rRNA that has undergone polyadenylation In budding yeast, two related DExH-box RNA helicases, Mtr4 and Ski2, are important activators of the exosome in the nucleus and cytoplasm, respectively. Mtr4 can function alone to assist substrate unwinding in the nucleus, but it also acts as a component of the Trf4/5–Air1/2–Mtr4 polyadenylation (TRAMP) complexes . In addiRecent crystal structures of yeast Mtr4 and Ski2 have revealed the presence of large, β-barrel insertion domains in both helicases In the human nucleus, exosome cofactors show localization to specific subnuclear regions that appear to correlate with the localization of exosome-associated nucleases . The putHuman homologs exist for the yeast Ski2/3/8 complex components and the hSKI complex localizes to both cytoplasm and nucleus NAB2 and CTH2) that are processed from 3′ extended precursors. This may ensure that incorrect transcripts are rapidly eliminated, and competition between surveillance involving Rrp6 and polyadenylation can regulate the expression of these genes NAB2 mRNA by Rrp6 In addition to their roles in surveillance and degradation, the exosome and TRAMP complexes function in specific RNA maturation steps. In yeast, these include 3′ end formation on snoRNAs and on some mRNAs  to nuclease components of the yeast exosome (Rrp44 and Rrp6), two structural subunits (Rrp41 and Csl4), and a cofactor (Trf4) The second approach was the application of in vitro RNase protection experiments, had previously suggested that substrates can reach the Rrp44 exonucleolytic active site following passage through the lumen of the exosome barrel structure, analogous to the RNA path described for the archaeal exosome Structural analyses of exosome subunits or subcomplexes, as well as in vivo crosslinking performed using cleavable Rrp44 proteins (split-CRAC) suggested that Rrp44 endonuclease and exonuclease activities cooperate on most substrates Why these structural features of Rrp41 should have been so strikingly conserved in the absence of a conserved nuclease function was a mystery, which has now been partly resolved by recent structural data. At the exit site from the central barrel of EXO-9, the exonuclease domain of Rrp44 adopts a closed conformation on RNA binding that captures the 3′ end of the RNA in close vicinity to the active site compare B and 1C.in vitro, and this has been confirmed in vivo for selected exosome substrates The overall importance of RNA threading through the channel is supported by the finding that mutations occluding the channel negatively affect all three exosome RNase activities in the context of EXO-10 and EXO-11 Taken together, these results suggest that although the chemistry of ribonucleolytic activities involved is different, the channeling mechanism for eukaryotic exosome complexes appears to be remarkably well preserved with respect to both the archaeal exosome and bacterial PNPase. Moreover, all three machineries mirror fundamental mechanisms used by the proteasome to prepare polypeptides for processive degradation in vitro, and Rrp6 itself is also inhibited by a mutation in the Rrp44 exoribonuclease active site in EXO-11 in vitro studies on model RNAs imply cooperation between all three catalytic activities in the nuclear exosome on threaded substrates, the number of in vivo targets affected by this is currently unclear The crystal structure of yeast EXO-11 bound to RNA includes only a small C-terminal portion of the nuclear exonuclease Rrp6 In vivo crosslinking revealed that structured RNA targets are preferentially associated with Rrp6 in vivo crosslinking studies revealed that highly structured Rrp44-dependent RNA substrates are often associated with both Rrp6 and Rrp44, with little or no contact to the remaining core exosome. This suggests the use of an alternative entry site to the Rrp44 catalytic center for some substrates, possibly aided by docking to Rrp6 and other exosome cofactors There are many reported links between Rrp6 and the TRAMP complex, which helps to unwind nuclear structured substrates in vitro, but it seems likely that ATP hydrolysis by RNA helicases greatly accelerates this process in vivo. It is perhaps notable that the widely conserved Ro protein has an apparently related role in threading RNA substrates through a central cavity RNA can be threaded through the narrow pore into the exosome core without cofactors in vitro analyses of these complexes, has provided invaluable insights into the mechanism by which active nucleases reach their target RNAs. In parallel, novel high-throughput approaches have been applied to systematically identify the complete set of exosome substrates in budding yeast. However, we are only starting to comprehend which catalytic activity is responsible for which substrate.In summary, the recent crystal structure of an RNA-bound yeast exosome and some of its cofactors, together with numerous in-depth in vivo.In the 15 years since its initial identification, many key features of exosome structure and function have been defined by biochemical analyses. We now know that the exosome harbors three distinct hydrolytic activities and that the relative importance of these varies significantly between different subcellular compartments and organisms, especially in metazoans. Exosome activity depends on cofactors, which also show specialization in localization and targeting. This organization provides the versatility needed for the exosome to cope with the huge variety of RNA substrates in the cell. The combination of structural and genome-wide analyses has further increased our understanding of targeting mechanisms and provided a comprehensive view of the exosome substrate range and specificities in vivo analyses remain technically challenging and many unresolved questions remain. In particular, how and why are specific substrates targeted to each of the active sites? What is the full target range for the exosome in multicellular organisms? How does the exosome decide to either accurately process or completely trash a particular substrate? Exosome components undergo post-translational modification; does this affect activity or substrate specificity? The importance of these questions is underlined by recent studies linking exosome function to several other cellular pathways, including an unexpected role in generating antibody diversity However, detailed


Small RNAs are functional molecules that modulate mRNA transcripts and have been implicated in the aetiology of several common diseases. However, little is known about the extent of their variability within the human population. Here, we characterise the extent, causes, and effects of naturally occurring variation in expression and sequence of small RNAs from adipose tissue in relation to genotype, gene expression, and metabolic traits in the MuTHER reference cohort. We profiled the expression of 15 to 30 base pair RNA molecules in subcutaneous adipose tissue from 131 individuals using high-throughput sequencing, and quantified levels of 591 microRNAs and small nucleolar RNAs. We identified three genetic variants and three RNA editing events. Highly expressed small RNAs are more conserved within mammals than average, as are those with highly variable expression. We identified 14 genetic loci significantly associated with nearby small RNA expression levels, seven of which also regulate an mRNA transcript level in the same region. In addition, these loci are enriched for variants significant in genome-wide association studies for body mass index. Contrary to expectation, we found no evidence for negative correlation between expression level of a microRNA and its target mRNAs. Trunk fat mass, body mass index, and fasting insulin were associated with more than twenty small RNA expression levels each, while fasting glucose had no significant associations. This study highlights the similar genetic complexity and shared genetic control of small RNA and mRNA transcripts, and gives a quantitative picture of small RNA expression variation in the human population. Genetic information is transmitted to the cell only through RNA molecules. A special class of RNAs is comprised of the small (up to 30 nucleotide) ones, known to be potent regulators of various cellular processes. At the same time, they have not been as widely studied as messenger RNAs—we do not know how much variation in their sequence and expression level occurs naturally in human populations or how this variability influences other traits. We measured small RNA levels and genetic variability in fat tissue from 131 individuals by high-throughput sequencing. We could associate the expression levels with genetic background of the individuals, as well as changes in metabolic traits. Surprisingly, we found no large scale influence of small RNA variation on mRNA levels, their main regulatory target. Overall, our study is the first to give a quantitative picture of the naturally occurring variation in these important regulatory molecules in human fat tissue. A world of noncoding RNA molecules has been uncovered in the last decades, expanding our understanding of functional elements in the genome MicroRNA genes (miRNAs) were the first animal small RNA genes to be discovered The recent ability to quantify levels of small RNA expression invites questions about the extent and causes of their variability in the human population. Importantly, the quantity and quality of transcripts are the only way genetic variation can influence phenotype. Thus, the genetic contribution to small RNA expression trait variability has to be assessed for accurate understanding of transmission of heritable information. Such questions have already been successfully addressed for mRNA expression levels, where variability between tissues Small RNA expression can be viewed as a primary genetic trait to be mapped in isolation, but also as a quantitative trait with downstream influences on gene expression and other phenotypes. Recent studies have been successful in combining information about genotype and intermediate phenotypes (such as mRNA levels The MuTHER (Multi-Tissue Heritability Resource) cohort was established with the aim of analysing the genetics of gene expression in multiple human tissues in over 800 individuals −10, −10) and introns , with 233 regions arising from intergenic sequence.We sequenced subcutaneous adipose tissue small RNAs of 131 females from the UK TWINS cohort We quantified expression levels of 418 known miRNA gene products, 239 tRNAs, 173 snoRNAs, 111 lincRNAs and 107 other RNAs that had at least 1000 total sequencing reads . For fur−5 of the 14,005 mature miRNA and star sequence sites that could pass our filters, consistent with previous reports of strong purifying selection in the functional small RNA regions th, 8th, and 9th bases of the mature product, and edited in 25, 18, and 11 percent of the reads, indicating that additional variability is tolerated in the functionally important seed region. We also observed bases at the ends of mapped reads not matching the genome in line with previous reports , suggesting that selection acts on not just average expression, but also expression variation.As mature miRNA sequences and analogous snoRNA products function via base pair complementarity, there is selective pressure against accumulating variants in their regions. Previous reports from DNA sequence data have confirmed increased conservation of miRNA sequence compared to intronic and intergenic background, but also a more pronounced effect for more highly expressed genes. We also observed a lack of miRNAs with at least 1000 reads on average and UCSC primate conservation score of less than 0  for eight of 418 miRNAs and six of 173 snoRNAs  miRNA cis-eQTLs reported in human fibroblasts We validated our eQTL findings in an independent cohort of 70 human samples with array-based miRNA expression data from abdominal adipose tissue The full MuTHER cohort of 776 individuals was profiled for mRNA levels from adipose tissue using the same RNA sample for the individuals in our study, as well as skin and lymphoblastoid cell lines from a separate RNA sample. Thus, we could directly assess any overlap in genetic control of transcripts of different type and across multiple tissues. We found seven of our small RNA eQTL SNPs to also be significantly associated with a nearby mRNA probe . The mRNAs our cohort has been phenotyped for DEXA-derived measurements of percentage trunk fat mass (PTFM), BMI, fasting insulin, and fasting glucose  expression required for appropriate insulin secretion Four of the phenotype-associated small RNAs have previously been associated with metabolic phenotypes and/or adipogenesis. In a recent study, mir-1179 was found to be significantly associated (FDR<5%) with metabolic syndrome case control status, with lower expression levels in cases cis-eQTLs (cis-SNPs), with SNPs that are directly associated with obesity-related phenotypes in published genome wide association study (GWAS) data cis-SNPs were associated  with body mass index (BMI) We overlapped the SNPs for our 14 significant cis-SNPs were genome-wide significant in the GWAS data, they were significantly enriched for nominally significant (p<0.05) SNPs in the BMI GWAS results , as well as BMI  and PTFM , suggesting a mechanism for the rs2440129 association. Rs6658641 has a significant  cis association with mir-197-3p in our data . On the other hand, we found little evidence for relation between miRNA expression level and expression of its nearest mRNA probe. The distribution of correlation coefficients was centered on zero, without a heavy tail of positive correlation , they indicate no strong enrichment of extreme negative correlations compared to random miRNA-mRNA pairs . For mir-186 and mir-29a, high abundance of the alternate arm sequence was indicative of low mature sequence levels, suggesting mutually exclusive selection of the arms. As the arm choice is suggested to be influenced by the nearby RNA context −4, FDR<10%).The mature miRNA is processed from a double-stranded RNA hairpin by the Dicer RNAse We have presented the largest small RNA sequencing dataset in a human reference cohort to date, and demonstrated the extent, causes, and consequences of the variability in small RNA expression.In spite of the medium complexity of the small RNA transcriptome, we quantified close to 1,000 different small RNA species. The highly expressed small RNAs fell into two categories in terms of inter-tissue variability - adipose-specific, and ubiquitously expressed microRNAs, corroborating previous observations Unexpectedly, some of the largest sources of variability in our data were due to the experimental protocol. The barcoding method used in this study, whereby the indexing tag and the unknown RNA are sequenced in the same read, caused a bias in terms of the profile of small RNAs that were captured. This could be addressed by using a generic 5′ adaptor and one that incorporates the indexing tag via PCR, such that the RNA sequence and the indexing tag are determined in separate reads, or performing the reverse transcription step directly on the flow cell Differences in sample preparation and sequencing platform introduce technical variation that biases and reduces the power of direct comparisons between small RNA sequencing studies Another important issue for comparing RNA levels between samples and finding genetic associations was mapping bias due to sequence variants. Previously uncharacterised polymorphisms resulted in fewer reads mapped to samples with derived alleles, which also created a significant eQTL at a known linked SNP. We recommend projects using small RNA sequencing to employ our technique of including known genetic variation in the reference sequence, and to use an ambiguity aware aligner, such as NovoAlign, to avoid such pitfalls.cis, or proximal signal does not have to be contained to the near vicinity of the transcript, and that distal regulatory sites are shared between multiple genes.Correcting for these technical issues, we were able to explore the biological causes of small RNA expression variation. We found genetic associations at a rate comparable with mRNA transcripts, and replicated them in an independent cohort. Unexpectedly, we found eight cases of a locus genotype influencing expression levels of a nearby mRNA and a nearby small RNA, where in four of these cases the two were unlikely to share a transcript as they were separated by at least one additional transcribed region. This highlights that We also looked for coordinated transcription by direct correlation of nearby transcripts. Small RNAs are known to be expressed in clusters from a shared promoter, as well as cleaved from intronic RNA sequence Finally, phenotypic and environmental differences can and do elicit changes in the transcriptome. To this end, we found 51 small RNA genes whose expression level is significantly associated with metabolic phenotypes available for our cohort. Given the strength of the observed signal, it is not possible without additional information to distinguish between causal, reactive, and common cause models for the relationship between the expression and phenotype traits. Studies in mouse models and human cohorts have shown that environmental factors, such as diet, can influence the expression of both mRNA The MuTHER cohort was set up with the aim to assess heritability of gene expression in different tissues using twins. However, as using highly related subjects reduces the power to map eQTLs using association, we focused our resources on unrelated individuals in the clinically relevant adipose tissue for which related phenotype data and an eQTL replication cohort were available. Analysing multiple tissues, or employing a co-twin design to provide heritability estimates and immediate replication of the results could be followed up from this pilot study.A major goal of this study was to assess the effect of naturally occurring variation in miRNA expression levels on the mRNA levels. However, we found no evidence for miRNA expression variation to be correlated with target mRNA variation. This negative result cannot be due to the amount of noise in our data alone, as we could successfully detect genetic effects and phenotype correlations. Thus, the strength of association between natural variation of miRNA expression and variation in their target mRNA expression is limited to a smaller scale than that of genetic control or downstream effects of global metabolic phenotypes. This lack of tight target regulation supports the growing body of evidence The small effect size of drastic miRNA level perturbation via knockdown, transfection, or overexpression of a single miRNA on its target mRNA expression levels has already been shown in several recent studies in human cell lines. For example, the median log2 expression level change of the top 150 TargetScan conserved targets was 0.096 (6.9%) for mir-29 knockdown in fetal lung fibroblasts For the first time, we were able to assess the expression variation of both microRNA arms. We found that while the alternative arms (star sequences) are not highly expressed in general, there are several of them that are not degraded, and are expressed at appreciable levels. We also observed examples of high mature miRNA expression being correlated with low expression of the alternate arm, and a relatively strong genetic signal for arm choice of one miRNA.www.ebi.ac.uk/arrayexpress) under accession number E-TABM-1140. Metabolic phenotypes were measured at the same time point as the biopsies and were collected as previously described, including BMI The unrelated individuals included in this study are part of the MuTHER study of Caucasian females (median age 58) recruited from the UK Adult Twin Registry TwinsUK, . Punch bOnly samples with good quality total RNA  were selected for small RNA isolation. Low molecular weight RNA (<40 nucleotides) was size-selected from between 0.5 to 1.0 µg total RNA using a flashPAGE Fractionator . The recovered small RNAs were first ligated to the Illumina v1.5 small RNA 3′ adaptor  using T4 RNA ligase 2- truncated . This was followed by a second ligation, using T4 RNA ligase 1, to one of twelve modified Illumina SRA 5′ adaptors, each with a six-base index tag at the 3′ end . Both ligation steps were performed according to the Illumina v1.5 protocol. The 5′ and 3′ adaptor-ligated small RNAs were immediately reverse transcribed, amplified and size-selected as described in the Illumina v1.5 protocol. The completed cDNA libraries were pooled (12 libraries per pool) in equimolar amounts and were sequenced using 37 base reads on the Illumina GAII platform.Raw sequencing data was obtained in FASTQ format, and processed with R Accurate quantification of small RNA molecule counts from read data is challenging due to genetic variation in the sequence, ambiguities in read mapping, and frequent contamination by large numbers of adaptor dimers. To solve these problems, we used a multi-stage mapping approach to exclude contaminating molecules that could be due to the library preparation kit, prioritise alignments to known small RNAs, and take genetic variation into account.First, we aligned the known small RNA molecule sequences against the human reference genome (NCBI build 37), retrieved all known variants in the mapped regions from the UK10K sequencing data , and created an individual sequence of each small RNA, with variable bases denoted with the corresponding IUPAC ambiguity codes. We included all mapped regions for RNAs that mapped to more than one genomic location. We then created five synthetic reference genomes  corresponding to:Contaminating sequences of adaptors, linkers, adaptor-linker, and adaptor-tag combinations with all possible single nucleotide alterations .Human mature miRNA and miRNA* sequences from miRBase v17 All known and predicted human non-coding RNA sequences from Ensembl version 63 together with full-length miRBase hairpin sequences All known human non-coding RNA pseudogenes from Ensembl version 63 Human reference genome (NCBI Build 37)http://www.novocraft.com, parameters -h 60 60 -t30 -s -m -l 16 -R 0 -r A 30). The latter is aware of sequence ambiguities, but the former was more sensitive at detecting reads aligning to the contaminating sequences. We also tested Bowtie We mapped reads to these references using BWA For the mapping calls, we excluded reads mapping to contaminating sequence with either method. For both aligners, we then took all the alignments in the highest stratum of references (miRNA>ncRNA>pseudogenes>genome), and picked the ones with the smallest edit distance. Conservatively, we only retained alignments of a read if both aligners agreed on all the aligned locations. For reads mapping genome-wide, but not any known ncRNAs, we created the set of uncharacterised RNA loci covered by at least one read in at least one sample without gaps, and assigned reads to their corresponding uncharacterised loci.k alternative sequences or loci in one reference, we added 1/k to the count of each. We trimmed the data matrix to contain only RNAs that were observed at least 1000 times across all individuals, or at least 100 times in a single individual. We discarded individuals with less than 500,000 mapped reads. This retained 131 individuals, including 129 individuals with at least 800,000 reads, and 119 individuals with at least 1,500,000 reads.Finally, we quantified the expression level of each ncRNA and unannotated locus by counting the number of reads aligning to it. If a read mapped between This multi-stage approach excludes mapping contaminating sequences to the reference, avoids allelic imbalance due to ability to map by incorporating information on genetic variation, and resolves potential mapping ambiguities to reflect our belief of how small RNA molecules are generated.s for library j as the median inflation factor across all genes: sj = mediang (ngj/GMj(ngj)), where GM stands for the geometric mean, and ngj is the read count of gene g for individual j as recommended by Anders and Huber 2(ngj/sj) to account for heteroskedasticity in the data.To use the read counts quantitatively, we normalised the data to have a comparable total number of reads for each individual. We estimated a size factor We downloaded UCSC genome browser r between covariates and principal components of the raw read count data as well as log-transformed and normalised data by calculating a statistic t = r ((1−r2)/129)−0.5, and calculating the (two-tailed) probability of observing at least as extreme a value, and Bonferroni correcting for 131 tests (one for each PC). We called the correlation significant, if the corrected p-value was less than 0.05, corresponding to |r|>0.31.We tested for significance of the correlation coefficient For each sample, we created sorted BAM files from the alignment output, and called segregating sites using Samtools v.0.1.12 (samtools pileup –vcf ) s. We calculated the self-containment score c of hairpins with s>35 as described in s*c>35. We then mapped all reads to the filtered candidate hairpins using bowtie, allowing for zero mismatches, and manually assessed the structural characteristics, genomic context, and alignment pileup shape for each candidate hairpin.We applied the MapMi pipeline Mammalian conservation scores were downloaded from the UCSC genome browser We subtracted off the linear fit of sample covariates (library batch and multiplexing tag), from the log-transformed, normalised data, and calculated the Pearson correlation coefficient between the residual expression levels and other small RNAs, miRNA star expression levels, and mRNA probe expression levels from miRNA binding specificity is controlled through binding of its seed region (bases 1–8 of the mature miRNA) with seed complementary regions (SCRs) in the 3′ UTR of mRNAs. Binding is enhanced if a SCR matches the first seed nucleotide with an adenosine, irrespective of the seed nucleotide 8s ending in an adenosine, we considered its middle 6-mer 6s, the two constituent 7-mers 7,1s and 7,2s and the 8-mer 8s itself as the seeds. For each of these four seeds s and given n, we used Sylamer 08-185 n(s)p to assess the extent to which the number of their SCR incidences in the top n of the ordered 3′ UTRs deviated from the expected. Potential nucleotide composition biases were accounted for using third order Markov correction (flag -m 4). The seed enrichment score for 8s was calculated as maxn(−log10np(6s)−log10np−log10np−log10np(8s)) using a grid of values for n.For each miRNA, we ordered probes and their associated 3′ UTRs by correlation of probe expression values to the miRNA, with correlations calculated in four different ways as described above. For each possible 8-nucleotide sequence evd package. For the miRNA used to generate the list, the significance of its influence on the mRNA expression was evaluated by testing its seed enrichment score against the estimated null. q-values were calculated for the ordered list of miRNA p-values.For each miRNA that produced a ranked list of probes, the null distribution of observed scores was estimated by fitting an extreme value distribution for all calculated adenosine-ending 8-mer scores using the R function fgev from the We selected series GSE18651, GSE19737, GSE27718, GSE14507 from the Gene Expression Omnibus Cis-eQTL analysis was limited to SNPs located within 100 kB either side of the transcript. The linear model was adjusted for age, multiplex tag and library batch. The significance of the genotype or phenotype effect was calculated from the Chi-square distribution with 1 degree of freedom using −2log(likelihood ratio) as the test statistic. False discovery rate (FDR) was calculated using the qvalue package implemented in R 2.11 cis-located SNPs for each small RNA, were considered.Associations between snoRNA and miRNA expression and mean genotypes  or phenotypes were tested using a linear model implemented in R To test for difference in small RNA expression between obese (BMI>30) and lean (BMI<25) individuals we treated BMI, for subjects falling into one of the two BMI groups, as a binary categorical variable. Linear models where fitted with small RNA expression level as response variable and the lean/obese categorical variable as the predictor while adjusting for relevant covariates . Significance of the effect size estimates of the lean/obese predictor was determined by a likelihood ratio test, and FDR was calculated using the qvalue package.Dataset S1TTTTGTGTGTCAGGGTGCAGG of length 21 . The hairpin has minimum free energy of −45.7, MapMi score of 64.03, and self-containment score of 0.77. The rest of the lines give: 1) observed short-reads mapping to the candidate hairpin without mismatches, and the lane/tag combinations they were observed in 2) a text-based histogram of reads mapped to the hairpin to demonstrate the characteristic camel shape 3) the minimum free energy RNA structure.Novel miRNA gene calls. The fields in the first line of the file are: [Query#] [Sequence] [Reference] [window extension side ] [number of mature mismatches] [chromosome] [strand] [mature start] [mature end] [mature length] [hairpin start] [hairpin end] [hairpin length] [stem matches ] [Minimal free energy] [MapMi score] [Self-containment score]. E.g, the line #Query453204 TTTTGTGTGTCAGGGTGCAGG Homo_sapiens left 0 14+94580022 94580042 21 94579957 94580072 115 49−45.7000007629395 64.0318185632879 0.772222222222 corresponds to a new microRNA candidate with sequence (ZIP)Click here for additional data file.Figure S1Data analysis pipeline. Red boxes indicate new data and results produced in this study, blue boxes are existing data, and text labels describe tools used to arrive at the data.(PDF)Click here for additional data file.Figure S2Validation of eQTL p-values. Log10 p-value of a miRNA eQTL in our study (x-axis) is plotted against the eQTL p-value for the same gene in the replication cohort as reported in (PDF)Click here for additional data file.Figure S3Summaries of small RNA expression levels most strongly associated with metabolic traits stratified by BMI. Each plot contains smoothed densities of expression levels of a single small RNA for lean (BMI<25), obese (BMI>30), and remaining individuals (blue areas). A box plot is given by a black line (25th and 75th percentiles), and the median (white dot).(PDF)Click here for additional data file.Figure S4Summaries of small RNA expression levels most strongly associated with metabolic traits stratified by fasting insulin. Each plot contains smoothed densities of expression levels of a single small RNA for individuals with low (<60) and high (>60) fasting insulin (blue areas). A box plot is given by a black line (25th and 75th percentiles), and the median (white dot).(PDF)Click here for additional data file.Figure S5Densities of Pearson correlation coefficients of log-transformed, normalised miRNA expression levels. Top row - all pairwise correlations. Bottom row - correlations within clusters defined by Saini et al. (PDF)Click here for additional data file.Figure S6Densities of Pearson correlation coefficients between log-transformed, normalised miRNA expression levels and mRNA levels from the same RNA samples. Top - set of correlations between miRNA genes and their five nearest probes. Bottom - histogram of a random subset of correlations between miRNA genes and mRNA genes.(PDF)Click here for additional data file.Figure S7Densities of Pearson correlation coefficients of log-transformed, normalised miRNA expression levels and their target mRNA expression levels. Top row - all pairwise correlations of miRNAs and their targets. Bottom row - random subset of all pairwise correlations between miRNAs and mRNAs. First column - TargetScan prediced targets. Second column - Biolead known targets.(PDF)Click here for additional data file.Figure S8Scatter plot of mature miRNA and alternative arm (star sequence) expression. Each blue data point corresponds to one miRNA that had expression of both arms quantified. Average log-transformed normalised read counts are plotted for the mature sequence (x-axis) and the alternate arm . Line y = x is plotted in red for comparison.(PDF)Click here for additional data file.Table S1Raw read counts for known RNAs. Every row corresponds to one quantified RNA molecule. The first seven columns describe the RNA - its common name (column 1), accession number , internal ID (column 3), chromosome (column 4), start coordinate (column 5), end coordinate (column 6), and small RNA type (column 7). The rest of the columns each correspond to one individual in the cohort. The entries in the columns are the raw read counts from mapping, before normalisation, obtained as described in (XLS)Click here for additional data file.Table S2Raw read counts for unannotated RNAs. Every row corresponds to one quantified RNA molecule previously unannotated. The first two columns describe the RNA - its location , and type (column 2). The rest of the columns each correspond to one individual in the cohort. The entries in the columns are the raw read counts from mapping, before normalisation, obtained as described in (XLS)Click here for additional data file.Table S3Small RNA level comparisons between tissues. Every row corresponds to one quantified small RNA molecule. The first columns is the RNA ID. The next two columns are the mean and variance of the log-transformed normalised RNA expression level in the adipose tissue measured in this study. Next, for each compared tissue, five statistics are given—average expression, variance of expression, Z score for difference from the mean adipose expression, p-value of the Z score using a two-tailed normal distribution, FDR for the Z-score, and q-value for the Z-score. The final five columns give the average T statistic of comparing the means of other tissues to the adipose tissue, number of tissues the p-value was significant in, median p-value, maximum q-value, and sum of q-values.(XLS)Click here for additional data file.Table S4List of identified polymorphisms and RNA edits. Each row corresponds to one variant. The columns contain RNA_name (column 1), miRBase_ID  SNP_position in the RNA (3), reference_allele (4), derived_allele (5), variant type (6), number of total observed alleles (7), total number of observed_reads covering the site (8), number of observed derived_alleles (9), number of observed reads with the derived allele (10), derived_allele_frequency (11), and frequency of reads with the derived allele (12).(XLS)Click here for additional data file.Table S5List of identified discrepancies between genomic sequence and RNA end sequence. Each row corresponds to one discrepancy. The columns contain RNA_name (column 1), miRBase_ID , internal RNA ID (3), position of the modification in the RNA (4), location of the modification , RNA base adjacent to the modification (6), modification sequence (7), number of libraries the modification was observed in (8), median frequency of modification across libraries (9), median raw RNA read count (10).(XLS)Click here for additional data file.Table S6Log-transformed, normalised read counts for known RNA loci. Same as (XLS)Click here for additional data file.Table S7Log-transformed, normalised read counts for unannotated RNA loci. Same as (XLS)Click here for additional data file.Table S8miRNA eQTL replication p-values. Each row corresponds to one miRNA eQTL determined in our study, for which we give miRNA accession (column 1), name (2), eQTL SNP ID (3), and eQTL p-value (6). For miRNAs also assayed by Rantalainen et al. (XLS)Click here for additional data file.Table S9Anthropometric characterisation of the cohort. Summaries of mean, SD, and range are given for each of the measured traits. * 21 individuals did not have the PTFM measurements. § Insulin values <13 (the detection limit of the assay) were set to 12 in analyses(XLS)Click here for additional data file.Table S10Targets of miRNAs associated with metabolic traits. For each mirna, the BioLead and TargetScan targets are listed. For each of the lists, we applied g:Profiler (XLS)Click here for additional data file.Table S11Small RNA expression difference between lean and obese individuals. Each row corresponds to one significant  association. The columns give the name (column 1), miRBase ID , p-value (3), q-value (4) and log likelihood ratio of the linear model fit.(XLS)Click here for additional data file.


Eukaryotic initiation factor 4A (eIF4A) plays a key role in the process of protein translation initiation by facilitating the melting of the 5′ proximal secondary structure of eukaryotic mRNA for ribosomal subunit attachment. It was experimentally postulated that the closed conformation of the eIF4A protein bound by the ATP and RNA substrates is coupled to RNA duplex unwinding to promote protein translation initiation, rather than an open conformation in the absence of ATP and RNA substrates. However, the allosteric process of eIF4A from the open to closed state induced by the ATP and RNA substrates are not yet fully understood.In the present work, we constructed a series of diplex and ternary models of the eIF4A protein bound by the ATP and RNA substrates to carry out molecular dynamics simulations, free energy calculations and conformation analysis and explore the allosteric properties of eIF4A.The results showed that the eIF4A protein completes the conformational transition from the open to closed state via two allosteric processes of ATP binding followed by RNA and vice versa. Based on cooperative allosteric network analysis, the ATP binding to the eIF4A protein mainly caused the relative rotation of two domains, while the RNA binding caused the proximity of two domains via the migration of RNA bases in the presence of ATP. The cooperative binding of ATP and RNA for the eIF4A protein plays a key role in the allosteric transition. Initiation of translation in eukaryotes is regarded as rate-limiting for protein synthesis. It requires eukaryotic translation initiation factors (eIFs) for recruiting ribosomal subunits to messenger ribonucleic acids (mRNAs) The eIF4A is the prototypic member of the DEAD-box protein family and represents the minimal helicase core. The first X-ray structure of amino-terminal domain (assigned as N-domain including the residues 1–222) of yeast eIF4A protein was determined respectively by Benz et al. (PDB: 1QVA) and Johnson et al. (PDB: 1QDE) in 1999 Since the eIF4A protein was a typical member of the DEAD-box protein family http://spdbv.vital-it.ch/). Based on the X-ray crystal structure of the human eIF4AIII+ADPNP+RNA complex with a closed conformation (PDB entry 2J0S (A chain)), we build the closed structure of the yeast eIF4A protein bound by ATP and RNA substrates by using the homology modeling technologies in the “Build Mutants protocol” of the Discovery Studio v.2.1  (assigned as ATP+RNA+C-eIF4A model) + and 16 Cl− counterions are added to the ATP+RNA+C-eIF4A model to achieve electroneutrality and to satisfy the experimental ionic strength of 100 mM Based on the previous experimental studies, the initial structure of the open eIF4A protein (assigned as O-eIF4A model) used in the MD simulations was employed from the X-ray crystal structure (PDB entry 1FUU (B chain)). The O-eIF4A model contains a N-terminal domain of 1–222 residues consisting of 9α helices and 8β strands, a C-terminal domain of 234–394 residues consisting of 6α helices and 7β strands and a linker of two domains consisting of 223–233 residues All CMD and TMD simulations were carried out using the AMBER9 package G) of each state is estimated from molecular mechanical energy EMM, solvation free energy GSOLV and vibrational, rotational, and translational entropies S, respectively.Eint is internal energy, i.e. the sum of bond, angle, and dihedral energies; Evdw is van der Waals energy; Eele is electrostatic energy; GSOLV is the sum of electrostatic solvation free energy, Gpb/solv, and the non-polar salvation free energy, Gnp/solv. The entropy S is estimated by a normal mode analysis of the harmonic vibrational frequencies, calculated using the Nmode module in AMBER9 package −4 kcal·mol−1·Å−1. Then, the entropy was calculated based on standard statistical mechanics expressions The molecular mechanics Poisson–Boltzmann surface area (MM-PBSA) method http://www.cmp.uea.ac.uk/dyndom/).DynDom is able to determine dynamic domains, hinge axes, and hinge-bending residues from two protein structures that have different conformations. DynDom generates short segments of the amino-acid chains of these proteins by use of a sliding window and the calculation of the rotation vector associated with the rotation of these segments between the two structures. By treating the components of these rotation vectors as coordinates in a ‘rotation space’, segments that rotate together will have rotation points collocated, indicating possible rigid domains within the structure. Moreover, in creating a domain decomposition, DynDom measures the ratio of interdomain displacement to intradomain displacement as defined by Hayward & Berendsen www.nmr.uhnres.utoronto.ca/ikura/interhlx/) was used to calculate the angle between two helices, including the sign in a structure or a family of structures. The program calculates the sign of the angle between two helices by following this convenient role: The two helices are taken to be positioned by helix I being in front of helix II. Helix I (from N to C) is used to define first vertical vector. A second vertical vector is defined with its tail at the C-terminus of helix II. The angle between helices I and II is the rotation required to align the head of the second vector with the N-terminus of helix II. The vector is rotated in the direction that produces an angle of less than 180 degrees with the clockwise or counterclockwise rotation represented by positive or negative sign. This program can also provide other geometry-based parameters such as interhelical distances To analyze conformational changes in the relative orientations of any two helices, the program interhlx , the position of the Cα atom of residue i at time t, and 〈ir〉, the time-averaged position of the Cα atom of residue i.The root-mean-square fluctuations (RMSF) values of residues are a measure of fluctuations and flexibility of backbone Cα of protein over the trajectory broken down by residues in comparison to the average structures C given as follows:ir and Δjr are the displacement vectors for atoms i and j, respectively, and the angle brackets denotes the ensemble average The dynamic feature of a protein and the extent of correlation of the motions of the different regions in a protein were assessed via the calculation of cross-correlation coefficients, The root-mean-square deviation (RMSD) values of all the backbone atoms relative to the corresponding starting structures over four trajectories for O-eIF4A, ATP+RNA+C-eIF4A, ATP+eIF4A and RNA+eIF4A models were examined to determine the system equilibrium. It is often considered that small RMSD values of one simulation indicate a stable state of the system and also suggest that the newly constructed models satisfactorily reproduced the experimental structures. However, the large RMSD values suggest large conformational changes of the investigated system. Plots of RMSDs for the two equilibrium systems, O-eIF4A and ATP+RNA+C-eIF4A models, and two allosteric systems, ATP+eIF4A and RNA+eIF4A models, over simulation times are shown in 1-5-β1-8-2-7-6-3-5-4-α6-9) and the C-domain formed by a 2α-7β-4α fold (α1-2-β3-4-2-5-6-1-7-α3-6)  see . The RMS3-6) see , respect3-6) see . (2) The3-6) see . The int−1, which indicates that the initial binding of ATP substrate or the RNA molecule to eIF4A protein was energetically favorable and allosterically regulated. It is also apparent that the binding free energies of ATP substrate and RNA molecule to the eIF4A protein in the ATP+RNA+C-eIF4A model, respectively, were −30.32 and −64.85 kcal·mol−1. It indicates that the binding free energies of ATP substrate and RNA molecule to the eIF4A protein in the ternary systems increased respectively by −21.70 and −52.28 kcal·mol−1 compared with the corresponding binary systems, leading to positive cooperative binding with each other for the two substrates. The calculated data support the experimental observation that the affinity of the eIF4A protein for RNA or ATP was enhanced by the coupling binding of ATP or RNA To determine the favorable binding order and cooperativity of ATP and RNA substrate binding to the eIF4A protein, we performed binding free energy calculations of the ATP+eIF4A, RNA+eIF4A and ATP+RNA+C-eIF4A models by using the MM-PBSA methodology based on the MD simulations. All energy terms and the total binding free energies of these systems are displayed in To address the positive cooperativity of RNA for ATP binding with the eIF4A protein, the percentages of occurrences of hydrogen bonds in the ATP+eIF4A and ATP+RNA+C-eIF4A models were analyzed and respectively shown in Based on the structural differences of eIF4A protein in the O-eIF4A and ATP+RNA+C-eIF4A models, the conformational changes of eIF4A protein induced by the ATP and RNA sequential binding were analyzed by using the allosteric simulation of the O-eIF4A model to the ATP+eIF4A model, as the first step, and four RNA mediating models, I, II, III, IV, initiating from the TMD simulation of (ATP+eIF4A)+RNA→(ATP+RNA+C-eIF4A), as the second step see . These fIn the second step involving RNA sequential binding, the RNA binding sites at the N-domain close to the C-domain showed a decrease in their mass center distance from 32.5 Å in the ATP+eIF4A model to 13.5 Å in the ATP+RNA+C-eIF4A model see . Such prTo further investigate the interaction between the eIF4A protein and the ATP or RNA molecule and the conformational changes of eIF4A protein induced by the ATP and RNA binding via the residue position changes, the dynamics of every residue was determined and interpreted by residue fluctuations and correlations. The RMSF values of the eIF4A protein in the O-eIF4A, ATP+eIF4A and ATP+RNA+C-eIF4A models were analyzed and shown in To explore the allosteric communication of positional changes in the ATP/RNA binding regions and the N-domain – C-domain interface in the transition from open to closed conformations, we constructed and analyzed the motion correlations and their differences in all Cα atoms of the eIF4A proteins from the trajectories. They are respectively displayed in To address the allosteric mechanism involving the sequential binding of RNA and ATP, the RNA+eIF4A model of the RNA initial binding with the eIF4A protein and four I', II', III', IV' models +ATP→(ATP+RNA+C-eIF4A)) were simulated. The corresponding results are shown in Interestingly, ATP binding to the eIF4A protein caused the rotation of two domains and the RNA base migration, regardless of the order of binding. However, RNA mediating the proximity of two domains via the migration of RNA bases depends partially on ATP binding. Therefore, the ATP and RNA cooperative binding completes the conformational transition of the eIF4A protein from the open to closed state. Furthermore, to compare the intermediate structures of the eIF4A protein in the present allosteric processes with the X-ray structure of the eIF4G-eIF4A complex as “half-open” conformation, the mass center distances and the relative angles of rotation were analyzed with respect to the open state between two domains as shown in We explored the relationships between the motion correlations and their structural changes in order to understand fully the allosteric communication network of the eIF4A protein from the ATP binding region to the RNA binding region and the N-domain – C-domain interface for the two allosteric processes mediated by the ATP substrate. Binding with ATP initially followed by RNA showed that the motions of ATP-binding α2-α3/β2-P-loop-α4 region , correlate and anticorrelate with the motions of the RNA-binding β3-α5/β4-β5-α6 region of N-domain  and RNA-binding α2/β3-α3/β4-α4 region of C-domain , respectively see . As expeIn case of the initial RNA binding followed by ATP, it was apparent , correlated with the motions of the α5-β4/β3/6/7 interface segments of the N-domain. Such motions of the interface segments correlated with the motions of the α3/4/5 interface helices of the C-domain. Similar correlation occurred involving RNA-binding α2/β3-α3/β4-α4 region, C-domain and N-domain interfaces see . As expeIn summary, the RNA allosteric network analysis demonstrates that RNA binding with eIF4A protein mediated the proximity of the two protein domains via the migration of RNA bases cooperatively assisted by ATP binding. The migration of RNA bases may facilitate the unwinding of RNA duplex in the cytoplasm.−1 and −52.28 kcal·mol−1 induced by RNA and ATP binding, respectively. The two allosteric processes indicate that ATP binding to the eIF4A protein led to the relative rotation of two domains with a rotation angle of ∼60°–99°, while RNA binding caused the proximity of N- and C-domains with decreased distance of ∼12 Å via the migration of RNA bases. Initial binding by ATP with eIF4A induced the rearrangement of the RNA binding sites at the edge of N- and C-domains of eIF4A protein, which enhanced the subsequent RNA binding with the protein. The migration of RNA bases depends on the ATP binding to eIF4A protein, which favors the unwinding of the RNA duplex in the cytoplasm. It suggested that the cooperative mediation of ATP and RNA completes the conformational transition of eIF4A protein. The hydrogen bond analysis supported the conformational changes of eIF4A protein during the allosteric process. The allosteric correlation and network analysis demonstrates that the structural changes in ATP-binding region of α2-α3/β2-P-loop-α4 at N-domain communicated to allosteric changes at RNA-binding regions of β3-α5/β4-β5-α6 at N-domain and α2/β3-α3/β4-α4 at C-domain, and the N-domain – C-domain interface involved in initial ATP binding. Such network analysis of initial RNA binding suggested that the structural variation of RNA binding only communicates to the allostery of N-domain – C-domain interface. The present investigations provide useful insights into the cooperative mediation of ATP and RNA in the allosteric modulation of eIF4A protein.Molecular dynamics simulations and free energy calculations for a series of constructed O-eIF4A, ATP+RNA+C-eIF4A, ATP+eIF4A, RNA+eIF4A and intermediate models have been performed to explore the possible allosteric mechanisms from an open eIF4A to closed state due to ATP and RNA binding. The results for two stable open and closed eIF4A states show that the mass center distance and the relative angles of N- and C-domains in the eIF4A protein changed from 48.7 Å and 0° in the open state to 29.5 Å and 103.6° in the closed state, respectively. Based on the free energy calculations, the eIF4A protein completed the conformational transition from the open eIF4A to closed state via two allosteric mechanisms: ATP binding initially followed by RNA and vice versa. The ATP and RNA binding caused positive cooperativity by increasing the affinity of ATP and RNA of −21.70 kcal·molFigure S1The sequence alignment for the yeast eIF4A and human eIF4AIII proteins. Structure-based sequence alignment for the yeast eIF4A and human eIF4AIII. Conserved residues are colored in deep blue and nine conserved motifs  are shown in the sub-regions squared in magenta.(TIF)Click here for additional data file.Figure S2RMSD values of the intermediate models. RMSD values of all backbone atoms with respect to the corresponding starting structures for the CMD simulations of (A) I, (B) II, (C) III, (D) IV taken from TMD simulation of the (ATP+eIF4A)+RNA→(ATP+RNA+C-eIF4A) transition and (E) I', (F) II', (G) III', (H) IV' taken from the (RNA+eIF4A)+ATP→(ATP+RNA+C-eIF4A) transition.(TIF)Click here for additional data file.Figure S3The structures of the allosteric process. The average structures are extracted from the trajectories of the O-eIF4A, RNA+eIF4A, I', II', III', IV' and ATP+RNA+C-eIF4A models involved in the allosteric process of the RNA binding followed by ATP, i.e., the RNA+eIF4A model for the RNA first binding to the O-eIF4A model; the I'–IV' models for the ATP second binding to the equilibrium structure of the RNA+eIF4A model; the ATP+RNA+C-eIF4A model for the closed state of the eIF4A protein; the average mass center distance of two domains labeled in black lines.(TIF)Click here for additional data file.Figure S4Domain rotation angel between the RNA+eIF4A and ATP+RNA+C-eIF4A models. The relative rotation angel (magenta lines) of the two domains in the eIF4A protein between the RNA+eIF4A (yellow orange) and ATP+RNA+C-eIF4A  models.(TIF)Click here for additional data file.Figure S5The fluctuations of residues. The fluctuations of residues in O-eIF4A (green), RNA+eIF4A (magenta) and ATP+RNA+C-eIF4A (blue) models with the labeled α helices and β strands at the ATP/RNA binding region and the N-domain – C-domain interface in the shaded regions.(TIF)Click here for additional data file.Figure S6Correlation maps and the differences of correlations. Dynamical cross-correlation maps for (A) allosteric RNA+eIF4A simulation and (B) the differences of motion correlations between the ATP+RNA+C-eIF4A and RNA+eIF4A equilibrium simulations, with specific sub-regions squared in blue for the correlations of the N-domain RNA binding region and the N-domain – C-domain interface, in green for the correlations of the C-domain RNA binding region and the N-domain – C-domain interface, in black for the correlations of the ATP binding and RNA binding bases, and in magenta for the correlations of the ATP binding region and the N-domain – C-domain interface.(TIF)Click here for additional data file.Figure S7The distance and the rotation angel of two domains. The mass center distance (black line) of two domains of the eIF4A protein in the eIF4A-eIF4G complex; the rotation angel (magenta lines) of the eIF4A protein between the O-eIF4A state (slate) and the eIF4A-eIF4G complex (cyan). The eIF4A protein shown in cartoon form and the scaffold eIF4G protein colored in cyan semi-transparent surface.(TIF)Click here for additional data file.Table S1The occupancies (%) of hydrogen bonds between the RNA and the N-domain (ND) of the eIF4A protein, for the RNA+eIF4A (RNA+4A), I', II', III', IV' and ATP+RNA+C-eIF4A (C) models.(PDF)Click here for additional data file.Table S2The occupancies (%) of hydrogen bonds between the RNA and the C-domain (CD) of the eIF4A for the RNA+eIF4A (RNA+4A), I', II', III', IV' and ATP+RNA+C-eIF4A (C) models.(PDF)Click here for additional data file.Table S3The occupancies (%) of hydrogen bonds of the interface between the N-terminal domain and C-terminal domain in the RNA+eIF4A (RNA+4A), I', II', III', IV' and ATP+RNA+C-eIF4A (C) model.(PDF)Click here for additional data file.Text S1Molecular dynamics simulation protocols used in this work.(PDF)Click here for additional data file.Text S2Targeted molecular dynamics simulation protocols used in this work.(PDF)Click here for additional data file.Text S3MM-PBSA calculation for free energy.(PDF)Click here for additional data file.


One of the most important scientific discoveries in the recent past concerns RNA interference (RNAi), which is a post-transcriptional gene-silencing mechanism induced by small interfering RNA (siRNA) and micro-RNA (miRNA). RNAi has3 and 2′-F, lead to significantly enhanced performance of siRNA with diverse target genes, provided that they are positioned in a site-specific manner.methyl (2′-O-TOM)-protected nucleoside phosphoramidites[1 or 2, was achieved by activation with 1-(mesitylene-2-sulfonyl)-3-nitro-1,2,4-triazole (MSNT) by using two syringes attached to the column for reagent delivery and mixing under argon.[syn-2-pyridine aldoxime/tetramethylguanidine in dioxane/water to cleave the 2-chlorophenyl phosphate protecting groups.[3NH2 in ethanol/water followed by treatment with 1 m tetra-n-butylammonium fluoride (TBAF) in THF. After purification by anion-exchange HPLC, the expected molecular weights were confirmed by liquid chromatography ESI (LC-ESI) mass spectrometry , the unmodified hairpin 5′-GAAGGGCAACCUUCG melted at 72.5±0.5 °C while the 2′-azido-modified counterparts, 5′-GAAGGGCAACCU*UCG and 5′-GAA*GGGCAACCUUCG, displayed Tm values of 71.7±0.5 °C and 71.6±0.5 °C, respectively. This demonstrates that the 2′-azido group is modestly destabilizing, which might arise from a slight steric interference of the moiety with the 3′-phosphate.[Next we investigated the influence of the 2′-azido group on the thermal stability of RNA double helices by using temperature-dependent UV spectroscopy . At 150 hosphate. Addition10  and the O-methyl imidate (12 a) linked DR1-RNA conjugates (85 %), and a minor amount (15 %) of the reduced (nonconjugated) 2′-NH2 RNA derivative of 11  that are excellently tolerated in the guide strand.[In order to evaluate the potential of 2′-azido-modified oligoribonucleotides in siRNA applications, we employed a model system for the knockdown of the brain acid soluble protein 1 (BASP1) gene by transient siRNA nucleofection in the chicken DF-1 cell line by using standard commercial siRNA duplexes as previously described. Expressiormation. We synthe strand., 6a, 22In conclusion, the solid-phase synthesis approach presented here efficiently yields RNA with site-specific 2′-azido groups, and provides a reliable foundation for a wide range of applications in modern bioconjugation strategies and siRNA technologies. Clearly, to evaluate the full potential of this type of modification, systematic studies of 2′-azido-modified siRNA on nuclease resistance, immune activation, off-target effects, toxicity, membrane permeability, and delivery will be required. We are confident that, in particular, the small size and the high polarity of the azido group will provide improvements for at least some of these effects. Moreover, the 2′-azido group in siRNA is available for the attachment of hydrophobic tags  to enhance delivery properties. Additionally, the attachment of fluorescent dyes to the azido anchor (as exemplified here in the context of MBs) facilitates the localization and tracking of siRNA in the cell. Another feature that has to be explored in future studies of 2′-azido-RNA (and in particular of 2′-azido-siRNA) is photoreactivity, and whether it can be exploited for crosslinking to peptide and protein targets. All these aspects represent the focus of ongoing studies in our laboratory together with the expansion of the concept towards 2′-azido-2′-deoxycytidine- and 2′-azido-2′-deoxyguanosine-containing RNA. At the current state of research, the 2′-azido modification might prove superior to the more than 35 other types of chemical modifications that have been tested for their effects on siRNA performance.


Y. pestis pathogenesis. However, information on how Yersinia pestis modulates the expression of sRNAs during infection is largely unknown.Small non-coding RNAs (sRNAs) facilitate host-microbe interactions. They have a central function in the post-transcriptional regulation during pathogenic lifestyles. Hfq, an RNA-binding protein that many sRNAs act in conjunction with, is required for Y. pestis grown invitro and in the infected lungs of mice. A total of 104 sRNAs were found, including 26 previously annotated sRNAs, by searching against the Rfam database with 78 novel sRNA candidates. Approximately 89% (93/104) of these sRNAs from Y. pestis are shared with its ancestor Y. pseudotuberculosis. Ninety-seven percent of these sRNAs (101/104) are shared among more than 80 sequenced genomes of 135 Y. pestis strains. These 78 novel sRNAs include 62 intergenic and 16 antisense sRNAs. Fourteen sRNAs were selected for verification by independent Northern blot analysis. Results showed that nine selected sRNA transcripts were Hfq-dependent. Interestingly, three novel sRNAs were identified as new members of the transcription factor CRP regulon. Semi-quantitative analysis revealed that Y. pestis from the infected lungs induced the expressions of six sRNAs including RyhB1, RyhB2, CyaR/RyeE, 6S RNA, RybB and sR039 and repressed the expressions of four sRNAs, including CsrB, CsrC, 4.5S RNA and sR027.We used RNA-seq technology to identify the sRNA candidates expressed from Y. pestis-infected samples to direct high-throughput sequencing. Many novel sRNAs were identified and the expression patterns of relevant sRNAs in Y. pestis during invitro growth and invivo infection were revealed. The annotated sRNAs accounted for the most abundant sRNAs either expressed in bacteria grown invitro or differentially expressed in the infected lungs. These findings suggested these sRNAs may have important functions in Y. pestis physiology or pathogenesis.This study is the first attempt to subject RNA from  Yersinia pestis is the causative agent of plague, which is a deadly fulminant disease. Y. pestis is primarily transmitted between fleas and mammals and spread to humans by the bite of an infected flea or by contact with afflicted animals [Y. pestis can be spread from person to person by infectious respiratory droplets; Without timely treatment, pneumonic plague is almost 100% fatal [Y. pestis likely makes appropriate adaptive responses, primarily reflected by the transcriptional changes of specific sets of genes. Transcriptional regulation of protein-coding genes in response to environmental change is extensively analyzed in Y. pestis by array hybridization [ animals . Pneumon0% fatal . To survdization ,4,5,6.Y. pestis pathogenesis [Y. pestis under multiple growth conditions in vitro [in vitro [Yersinia pseudotuberculosis, the closely related species of Y. pestis [Y. pestis sRNAs expressed under normal growth conditions in vitro by RNomics or RNA-seq and preliminarily determined different expression patterns under multiple growth conditions [Y. pestis modulates the expression of sRNAs during infection is largely unknown. The present study use RNA-seq technology to more comprehensively discover the novel sRNAs and to monitor sRNA expression in Y. pestis infected lungs of mice.Small non-coding RNAs (sRNAs) are identified as regulators of gene expression, which function mostly at the post-transcriptional level through sRNA-mRNA interactions ,8. They ogenesis ,13. In ain vitro . RNA-seqin vitro ,16,17,18in vitro ,20,21. R. pestis . Recent nditions ,24. HoweY. pestis in vivo, total RNA was separately isolated from bacteria grown in rich, defective nutrition media or infected organs. A chemically defined TMH medium was used as defective nutrition medium and BHI as rich medium. RNAs from bacteria grown in TMH media under five conditions aiming at simulating normal growth or stress processes were pooled together and used as one RNA sample for subsequent experimental procedures as previously described [Y. pestis genome. After removing all the mouse-derived sequences, tRNAs, and rRNAs of Y. pestis, the remaining 13,260,871 reads with more than one billion bases were aligned to the non-rRNA-tRNA regions of the Y. pestis 91001 chromosome (NC_005810) and plasmids . Among these reads, 8,712,083 and 4,051,853 account for the two samples from bacterial growth in vitro and 422,774 and 74,161 for RNA samples from mice lung and spleen, respectively. These fractions represented 27%, 29.5%, 4% and 0.7% of the TMH, BHI, lung and spleen library, respectively . We extracted intergenic and antisense RNA transcripts with sequential bases forming peaks of high-level expression distinct from those of its flanking regions. A total of 104 sRNAs with predicted promoter or Rho-independent terminator were finally identified . AgainstY. pseudotuberculosis and Y. pestis KIM [Y. pestis strain 201 (Pgm+ pCD1+ pMT1+ pPst+) was used in our study, whereas Y. pseudotuberculosis and pCD1 plasmid-cured Y. pestis KIM6+ were used by Koo et al. and Beauregard et al., respectively. Second, some RNAs are specifically expressed under certain specific conditions that differ among different studies. For example, RyhB1 and RyhB2 known to be induced by iron depletion were detected in vivo in our study but missed by two other studies. Third, some sRNAs were failed to be detected in our study, because of the criteria that used in our study, that is, only transcripts with similar ends within at least two samples were kept for further determination of the presence of sRNAs. The criteria are used to identify the stable RNA products under all tested conditions but neglect the occasionally processed products under certain conditions.Given that most studies focus on sRNAs encoded in the intergenic region in bacteria, we compared 88 intergenic sRNAs including 26 known sRNAs and 62 novel sRNAs with two other experimental data ,23 Figu. The comstis KIM ,23. Threstis KIM  could beYersinia genus  and two enterobacteria  (Yersinia species. Approximately 89% (93/104) were conserved in Y. pseudotuberculosis and Y. pestis genomes and only seven sRNAs  were specific to Y. pestis. Except for sR001 and sR102 on the chromosome, the five other sRNAs were located on the Y. pestis-specific plasmid pMT1 and pPCP1, indicating that acquisition of sRNA may have an important function during the evolution of Y. pestis from Y. pseudotuberculosis. We also assessed the conservation of the 104 Y. pestis sRNAs across the sequenced genomes of 135 Y. pestis strains. Approximately 97% of these sRNAs (101/104) share high sequence homology with >95% among more than 80 sequenced genomes of 135 Y. pestis strains  . Of the  strains .phnI and phnJ) in chromosome, while sR104 is antisense to the YP_PCP04 gene located in plasmid pPCP1  could be predicted by BPROM within 20 nt upstream or downstream of those determined by RNA-seq in more than 70% of 104 sRNAs (74 sRNAs). To test whether the boundaries of novel sRNA transcripts were accurately defined by our methods, Northern blot and primer extension assays were performed to determine the RNA sizes and TSSs of some novel sRNAs, respectively. The expression of seven tested sRNAs was successfully validated and the transcript lengths detected by Northern blot analysis corresponded to the lengths observed by deep sequencing . We confid pPCP1 . The pretrans-acting sRNAs at a post-transcriptional level in enteric bacteria [The RNA chaperone Hfq is essential for the regulatory function of bacteria . In thisE. coli [Y. pestis, possibly by regulating certain virulence genes [crp mutant using Northern blot and seven RNA transcripts were stably detected. Two RNAs, namely, 5S rRNA and RyhB1, were detected in parallel as negative controls because of the absence of predicted CRP-binding sites  and a novel sRNA sR039. RyhB1 and RyhB2 were confirmed to be highly expressed in vivo by Northern Blot analysis [in vitro. However, CyaR and 4.5S RNA failed to be detected by PCR and Yp-sR039 was inconsistent with the results obtained with RNA-seq. We confirmed the induction or repression of seven sRNAs in vivo and in vitro using quantitative PCR. In spite of the similar tendency of differential expression ratios shown between qPCR and RNA-seq analysis, more than 10-fold reduction existed in their fold changes detected by qPCR compared with those by RNA-seq method  method. This method eliminates the influence of different sRNA lengths and sequencing discrepancies among different samples on gene expression calculation . Therefoanalysis . By contq method .in vitro, Northern blot analysis was used to measure the expression levels of six sRNAs in Y. pestis under various conditions, representing distinct growth phases or environmental cues  from Lathem’s lab . InteresY. pestis pathogenesis, five annotated sRNAs  were induced more than four fold in vivo compared with in vitro. In addition, six novel highly abundant sRNAs  were chosen to be subjected to subcutaneous and/or intranasal inoculation. The tmRNA/ssrA deletion mutant showed the obviously decreased virulence in mice compared with that in the WT strain after subcutaneous injection  were upregulated in vivo and three known sRNAs  were downregulated. RyhB and CyaR/RyeE are induced upon iron limitation and nutrition deficiency, respectively [Salmonella to restrain intracellular growth [Yersinia species [Streptococcus [ymoA, which encodes a thermo-sensitive regulator and sR084 with that of fur, ferric uptake regulator, which involves in intracellular iron homeostasis in bacteria. Further efforts should be carried out to determine whether direct interactions occur between RNA and its potential target.Identification of differentially expressed sRNAs derived from ectively ,33. Therr growth . 6S RNA/r growth ,35. CsrBr growth ,37,38. C species . 4.5S RNococcus . MoreoveY. pestis strain 201 is avirulent in humans but highly virulent in mice. This bacterium belongs to a newly established biovar, called Microtus [Y. pestis strain 91001 according to our previous DNA microarray-based comparative genomic analysis [in vitro including nutrient enrichment and deficiency were used in our study. For the nutrient enrichment condition, bacteria were grown at 26 °C in BHI medium with 2.5 mM CaCl2 to mid-exponential phase (OD620 = 1.0). For growth conditions of nutrient deficiency, bacteria were grown in a chemically defined TMH medium [icrotus . The genanalysis . Two groH medium  under fiY. pestis strain 201 was grown to mid-exponential phase in BHI medium aforementioned. The bacterial cultures were washed and resuspended in sterile PBS. Pathogen-free 8-week-old female BALB/c mice were lightly anesthetized and inoculated by the intranasal route (i.n.) with approximately 1×106 cells of Y. pestis in PBS. All infected mice were sacrificed after 48 hrs and the lungs and spleens were surgically removed and suspended in RNAlater Solution for subsequent RNA isolation.The intranasal mouse model of primary pneumonic plague was developed as previously described ,45. Y. pY. pestis grown under different growth conditions in vitro described above using the TRIzol Reagent according to the manufacturer’s protocol (Invitrogen). Y. pestis RNAs from the five different growth conditions in TMH medium were mixed in equimolar ratios. RNAlater solution was removed from the immersed lungs and spleens, followed by homogenization in the TRIzol Reagent. Lung or spleen-derived RNA samples were pooled from 40 infected mice. In total, four RNA samples from TMH, BHI, and infected lungs and spleens were obtained and used for the subsequent procedure. To eliminate DNA contamination, RNA samples were treated twice with DNase I (Fermentas). The total RNA samples were size-fractionated on a 6% TBE-Urea polyacrylamide gel. RNA fragments with length of 50 nt to 150 nt and 130 nt to 500 nt were individually isolated, quantified following gel filtration, and ethanol precipitated.Total RNA was extracted from DNA/DNA adaptor. RNA fragments with length of 130 nt to 500 nt were reversed transcribed to single-stranded cDNAs with S-hex primer. The 5' DNA/DNA adaptor was ligated to the 5′ end of the cDNAs, respectively. The adaptor-ligated cDNAs derived from two RNA fragments were amplified by PCR with adaptor-specific primers. A total of four cDNA libraries were constructed and nucleotide sequences were determined with Illumina/Solexa Genome Analyzer system  that generates 75-nt paired-end reads. The RNA-seq data was deposited in the Sequence Read Archive of NIH .The four fractionated RNA samples described above were subjected to cDNA library construction and deep sequencing performed by LC Sciences LLC, USA. Briefly, RNA fragments with length of 50 nt to 150 nt were dephosphorylated and ligated with 3′ RNA adaptor. The resulting ligated RNA fragments were reversed-transcribed and ligated with 5′ in vitro or one sample in vitro and two samples in vivo  were retained for further analysis. However, RyhB1 was maintained because it is highly abundant in two RNA samples in vivo. Blastx analysis was performed to identify the novel protein-coding sequences, which were further removed. Bacterial promoter regions were predicted by BPROM (http://linux1.softberry.com/) and Neural Network Promoter Prediction program (http://www.fruitfly.org/seq_tools/promoter.html). Rho-independent transcription terminators were identified using the TransTermHP program [Y. pestis expressed under four different conditions.The steps of RNA-seq data analysis is shown in  program . The seq program . Finally+ membranes (GE) by electroblotting. The membranes were UV cross-linked and pre-hybridized for 1 hr, and DIG-labeled RNA probes were added. The membranes were then hybridized overnight at 68 °C in a DIG Easy Hyb according to the manufacturer’s protocols. RNA probes were synthesized by in vitro transcription using T7 RNA polymerase. RNA was immunologically detected and scanned according to the instructions.Northern blot analysis was carried out using a DIG Northern Starter Kit (Roche) following the manufacturer’s protocol as described by Beckmann et al. . Total R32P-labelled primer. cDNA products were subjected to electrophoresis in a 6% polyacrylamide-8 M urea gel. The gel was analyzed by autoradiography (Kodak film). To serve as sequence ladders, sequencing reactions were also performed with the same primers used for primer extension, using an fmol DNA cycle sequencing system (Promega).To determine the TSSs of primary sRNA candidates, primer extension assay was performed using a primer extension system-AMV reverse transcriptase kit (Promega) previously described  with sliTM Green qPCR SuperMix UDG (TransGen Biotech) with an appropriate cDNA dilution as a template. Control reactions were carried out in parallel without a reverse transcriptase. 16S rRNA was used as the internal standard to normalize the expression levels of the tested sRNA candidates.cDNA was synthesized from RNA using the ThermoScrip RT-PCR System (Invitrogen) for gene-specific cDNA synthesis. qPCR was performed in duplicate for each RNA sample using the TransStartY. pestis sRNA deletion mutants were constructed by λ-Red or suicide plasmid homologous combination systems as previously described [kan cassette. For the suicide plasmid method, fused PCR products containing 500 nt to 600 nt upstream and downstream of sRNA were cloned into pGMB151 plasmid and transferred electroporately into Y. pestis. The markless deletion strain was obtained after incubation on LB plates with streptomycin, followed by those on LB plates with 5% sucrose.escribed ,49. For Y. pestis WT strain 201 and the sRNA mutants were grown in BHI medium at 26 °C overnight and transferred to 37 °C for 1 hr. The bacterial cultures were washed and diluted in 0.85% NaCl. A group of 10 6-week-old BALB/c mice were subcutaneously injected with ~100 cells of bacteria. Mortality was recorded daily for 14 days. All mouse experiments were carried out according to the Guidelines for the Welfare and Ethics of Laboratory Animals of Beijing. The approval number authorized by Committee of the Welfare and Ethics of Laboratory Animals, Academy of Military Medical Sciences, is scxk-(jun)-2007-004. BALB/c mice were lightly anesthetized and infected with Y. pestis. All infected mice were sacrificed BALB/c mice were finally anesthetized and sacrificed.Figure S1Validation of seven putative novel sRNAs by Northern Blot.(A) Northern blot results of RNAs isolated from Y. pestis strain 201 grown in BHI to exponential phase are shown. The sizes of the different transcripts inferred from RNA-seq are shown at the bottom. (B) The schematic diagrams of the corresponding candidate sRNAs (blue arrows) and their adjacent genes (black arrows) are shown. Gene length is not proportionally indicated.(TIF)Click here for additional data file.Figure S24 CFUs (B) of Y. pestis WT and sRNA deletion strains.Survival of mice subcutaneously infected with 100 CFUs (A) or intranasally infected with 5 × 10(TIF)Click here for additional data file.Table S1Y. pestis identified by RNA-seq analysis.The information on 104 sRNAs in (XLS)Click here for additional data file.Table S2Y. pestis strains.Conservation of 104 sRNAs in the sequenced genomes of 135 (XLS)Click here for additional data file.


Lassa virus causes hemorrhagic fever characterized by immunosuppression. The nucleoprotein of Lassa virus, termed NP, binds the viral genome. It also has an additional enzymatic activity as an exonuclease that specifically digests double-stranded RNA (dsRNA). dsRNA is a strong signal to the innate immune system of viral infection. Digestion of dsRNA by the NP exonuclease activity appears to cause suppression of innate immune signaling in the infected cell. Although the fold of the NP enzyme is conserved and the active site completely conserved with other exonucleases in its DEDDh family, NP is atypical among exonucleases in its preference for dsRNA and its strict specificity for one substrate. Here, we present the crystal structure of Lassa virus NP in complex with dsRNA. We find that unlike the exonuclease in Klenow fragment, the double-stranded nucleic acid in complex with Lassa NP remains base-paired instead of splitting, and that binding of the paired complementary strand is achieved by “relocation” of a basic loop motif from its typical exonuclease position. Further, we find that just one single glycine that contacts the substrate strand and one single tyrosine that stacks with a base of the complementary, non-substrate strand are responsible for the unique substrate specificity. This work thus provides templates for development of antiviral drugs that would be specific for viral, rather than host exonucleases of similar fold and active site, and illustrates how a very few amino acid changes confer alternate specificity and biological phenotype to an enzyme. The causative agent of Lassa Fever, an arenavirus called Lassa, is endemic in Western Africa, causes hundreds of thousands of infections per year, and is the viral hemorrhagic fever most frequently transported to the United States and Europe. The nucleoprotein NP of Lassa virus, and other members of the arenavirus family, suppresses host innate immune signaling pathways E. coli DNA polymerase IIIε (ε186) Recent structural analysis identified a previously unknown exonuclease function for NP, placing it within the DEDDh exonuclease superfamily dsRNA is a unique product of virus infection and a powerful signal for host immune sensors that mount innate immune signaling pathways when its presence is detected The Lassa (Josiah strain) NP-exonuclease domain  was produced as previously described for the unbound NPΔ340 crystal structureDeprotected, desalted, 8-base RNA oligonucleotides were purchased from Integrated DNA Technologies and delivered as lyophilized powder. One strand of the RNA was designed to include all purine residues (5′ GGAGGGAG 3′) and the other to include all pyrimidine residues (5′ CUCCCUCC 3′). Oligos were resuspended in nuclease-free water to a final concentration of 1 mM. Equal parts of sense and anti-sense oligos were combined and annealed at 65°C for 10 minutes before slow cooling to room temperature to yield dsRNA. Tris pH 8.0 and NaCl were added to the dsRNA to achieve final concentrations of 10 mM and 300 mM, respectively.2 hexahydrate and 25% PEG 3350. Crystals were cryoprotected with well solution containing 15% glycerol and flash-cooled in liquid nitrogen. Data were collected at the Advanced Photon Source, Beamline 19-ID.Crystals of the NPΔ340/E391A–8bp dsRNA complex were grown in 0.1 M Bis-Tris pH 6.5, 0.2 M MgCl1212 using HKL 2000 Rfree calculations. Simulated annealing was employed early in refinement to reduce model bias of the molecular replacement search model and simulating-annealing omit maps were used to confirm the RNA density. Custom hydrogen-bonding restraints between base-pairing nucleotides were generated using the ‘PDB to 3D restraints’ server Rfree of 19.70% and Rwork of 25.44% using all data to 2.9 Å . Standard reactions contained 50 nM wild-type or mutant NPΔ340 and 2 nM (labeled) and 10 nM (unlabeled) oligo(ribo)nucleotide. Sense and anti-sense RNA strands or, for hybrid experiments, sense RNA and anti-sense DNA were annealed at 65°C for 2 minutes and then cooled for 10 minutes at room temperature. Reactions were performed in 20 mM Tris pH 7.5, 150 mM NaCl, and 5 mM MgCl2. After incubation for 15 minutes, reactions were stopped with the addition of an equal volume of formamide loading buffer and boiled. The products were then analyzed in 18% polyacrylamide gels containing 8 M urea and buffered with 0.5x Tris-borate-EDTA. Gels were exposed overnight to a phosphorscreen and then imaged using a Typhoon phosphorimager.18 base DNA and RNA were purchased from IDT. Sequences of the oligonucleotides are as follows: RNA (sense) 5′ CAGGAUGUUGAUGCUGCU 3′; RNA (anti-sense) 5′ AGCAGCAUCAACAUCCUG 3′; DNA (anti-sense) 5′ DEDDh exonucleases all have five invariant residues in the active site  that are crucial for catalytic activity. In Lassa NP, these active site residues correspond to D389, E391, D466, D533 and H528. Wild-type Lassa NP rapidly digests dsRNA, and hence, it is not possible to stably crystallize wild-type NP with dsRNA for structural analysis. To increase the chances of obtaining a co-crystal structure of the exonuclease domain of Lassa NPΔ340 in complex with dsRNA, E391 was mutated to alanine. This mutation prevents the coordination of one of the two divalent cations required for the catalytic activity. As a result, NPΔ340E391A is able to bind dsRNA, but cannot catalyze the exonuclease reaction to digest it.Following molecular replacement using the unbound NPΔ340 structure (PDB code 3Q7B) as a search model, an Fo-Fc difference map showed positive electron density for a double-stranded substrate leading into the active site. By design, each individual strand of RNA is composed of either all-purine or all-pyrimidine bases. The single all-purine and all-pyrimidine strands were annealed to yield dsRNAs that each contain one all-purine and one all-pyrimidine strand. Digestion of dsRNA by Lassa NP is sequence-independent, and thus, there is equal likelihood for either the purine-containing or the pyrimidine-containing strand to be digested. Correspondingly, the electron density does not distinguish between the two. Hence, we have arbitrarily modeled the strand leading into the active site (the substrate strand) as the purine-containing strand and the complementary, non-substrate strand as the pyrimidine-containing strand .2 in the crystallization buffer, and is coordinated by D389 and by one of the phosphate oxygens of the 3′ terminal nucleotide of the digested RNA strand (G8) . This site B cation is modeled as a magnesium ion, because of the presence of 5 mM MgCland (G8) . Alignmeand (G8) . In ISG-and (G8) . The sitRNA base G8 is also observed to make several contacts to protein residues not included in the canonical DEDDh motif. In particular, the 2′OH of the ribose group and N2 of the nitrogenous base of G8 hydrogen bond to D426 of Lassa NP. Further, the 2′ and 3′ OH groups of G8 form hydrogen bonds to the main-chain nitrogen of G392 .5′GGAGG6A7G8G3′)  .5′1CUC4CCUCC3′) are bound by the side chains of Q425 and D465, respectively. In addition, the nucleotide base of C1 forms a π-π interaction with Y429 (The ribose 2′ oxygens of C1 and C4 of the complementary, non-substrate strand (in the sequence ith Y429 . All conIn order to assess the relative contribution of each amino acid contact to RNA, we made alanine point mutations and tested each mutant for exonuclease function. As previously determined, mutation of E391, G392 and R492 to alanine prevents digestion of dsRNA The retention of exonuclease activity when Y429 is mutated to phenyalanine suggests that the stacking of the aromatic ring with base C1 is important. To determine if this base stacking was critical for RNA binding, or if it was necessary for efficient procession of the exonuclease activity, we performed a time-course study with WT, Y429A and Y429L Lassa NPs. When dsRNA is incubated with WT NP, the labeled RNA is digested within five minutes. By contrast, Y429A and Y429L-containing NPs show incomplete digestion even after 60 minutes . It appeOur structure of NP bound to dsRNA demonstrates that contacts to the non-substrate strand are to the 2′OH of the ribose groups. However, based on our mutagenesis studies, these contacts appear to be non-essential. This observation led us to speculate that the non-substrate strand could be DNA as well as RNA. We thus generated a hybrid DNA:RNA double-stranded substrate in which either the DNA or RNA alone were 5′-labeled. When DNA was labeled and tracked, we saw no digestion. Conversely, when the RNA was labeled and tracked, there was digestion . These rThere is little structural information available for exonuclease enzymes in complex with more than a single mononucleotide. Within the DEDDh exonuclease family, only Trex1 and 2 have been crystallized with an oligo: four bases of single-stranded DNA KF, Trex 1 and 2 and ε186 are preferentially active towards unpaired 3′ termini Mutational analysis of Lassa NP suggests that Y429 is the only critical contact to the non-substrate strand. Processive exonucleases such as Lassa NP require the stepwise procession of the substrate into and the subsequent removal of the product out of the active site. It is possible that interaction between Y429 and the RNA may facilitate this action. This is supported by the fact that mutation of Y429 to alanine or leucine permits binding, but greatly inhibits the digestion of RNA. Interestingly, other DEDDh exonucleases such as ISG-20 Additionally, modeling of hybrid substrates suggests that Lassa NP can only efficiently bind to A-form duplex substrates. Modeling of B-form DNA demonstrates clashes would likely occur between the DNA and protein. However, there do not appear to be any clashes when A-form ssDNA is modeled as part of a hybrid RNA:DNA substrate . NotablyFurther, a hydrogen bond between the RNA-specific 2′OH and the G392 in the active site could explain why the exonuclease activity of Lassa NP is specific for RNA over DNA in the substrate strand. This additional hydrogen bond, which would only be present in RNA, may help anchor the nucleotide within the active site. Although the contact is to the main-chain nitrogen and G392 is not part of the DEDDh motif, previous work demonstrated that mutation of G392 to alanine disrupts both the exonuclease function and the immunosuppressive function of NP In summary, we have identified two residues that lie outside the DEDDh active site motif that are essential for binding to and processing RNA. G392 appears to provide specificity for RNA within the active site, and is completely conserved amongst all arenaviruses and also the coronaviruses. Y429 appears to be important for the processive motion of the RNA, and is conserved among close relatives of Lassa such as LCMV and Lujo virus. Other arenaviruses, such as Junin and Machupo have a histidine at this position, which could provide a similar interaction with the RNA as a tyrosine.The structure presented here suggests that the dsRNA-specific Lassa NP exonuclease enzyme binds to and processes double-stranded substrates using a mechanism distinct from other exonucleases. To our knowledge, Lassa NP is the only member of the DEDD families that has a basic loop to the left of the active site rather than to the right and is the only member thus far described that has strict specificity for double-stranded substrates. This work thus provides a template for design of anti-viral inhibitors of Lassa virus that could selectively target NP while leaving human DEDD exonucleases unaffected.