Although resurrecting ancestral proteins is a powerful tool for understanding the molecular-functional evolution of gene families, nearly all studies have examined proteins functioning in relatively stable biological processes. The extent to which more dynamic systems obey the same ‘rules’ governing stable processes is unclear. Here we present the first detailed investigation of the functional evolution of the RIG-like receptors (RLRs), a family of innate immune receptors that detect viral RNA in the cytoplasm.Using kinetic binding assays and molecular dynamics simulations of ancestral proteins, we demonstrate how a small number of adaptive protein-coding changes repeatedly shifted the RNA preference of RLRs throughout animal evolution by reorganizing the shape and electrostatic distribution across the RNA binding pocket, altering the hydrogen bond network between the RLR and its RNA target. In contrast to observations of proteins involved in metabolism and development, we find that RLR-RNA preference ‘flip flopped’ between two functional states, and shifts in RNA preference were not always coupled to gene duplications or speciation events. We demonstrate at least one reversion of RLR-RNA preference from a derived to an ancestral function through a novel structural mechanism, indicating multiple structural implementations of similar functions.Our results suggest a model in which frequent shifts in selection pressures imposed by an evolutionary arms race preclude the long-term functional optimization observed in stable biological systems. As a result, the evolutionary dynamics of immune receptors may be less constrained by structural epistasis and historical contingency.The online version of this article (doi:10.1186/s12862-016-0818-6) contains supplementary material, which is available to authorized users. Resurrection and biochemical analysis of ancestral proteins is a powerful technique for understanding the molecular-structural basis of functional evolution , 2. ExamOne of the major limitations of current ancestral sequence resurrection (ASR) studies is that they have—almost without exception—examined molecular systems that function in basic cellular processes and organism development, systems that are expected to be relatively slow-evolving –24. In cAnother major limitation of existing ASR studies is that they have focused almost exclusively on proteins that function by binding small ligands such as sugars, hormones or other metabolites , 31, 32 The RIG-like receptors, RIG-I (DDx58) MDA5 IFIH1) and LGP2 (DHx58), form a complement of cytoplasmic RNA-binding proteins contributing to innate antiviral immunity in a wide variety of vertebrates, including all mammals –46. RIG- and LGP2In order to function as effective antiviral receptors, RLRs must collectively recognize a variety of viral-associated RNA types without binding the specific motifs associated with cytoplasmic host RNAs. Although the precise ligand complement of human and other vertebrate RLRs has not been determined, RLRs have been shown to recognize specific end structures of RNA molecules via a C-terminal RNA recognition domain (RD), providing a structural mechanism capable of differentiating viral-derived from host RNA –51. An uRIG-I—by far the best understood of the RLRs—recognizes single- and double-stranded RNA molecules with and without 5′ triphosphate (5′ppp) moieties but does not recognize the 7-methylguanylate cap typical of eukaryote mRNA –54 AdditWe have recently found that RLRs were present in the earliest multicellular animals and functionally diversified through a series of gene duplication events . Our anaIn our view, the RLRs provide an excellent model with which to begin extending current ASR results obtained from slow-evolving small-ligand-binding proteins to fast-evolving immune receptors interacting with larger macromolecules. Better understanding the evolutionary trajectories leading to RLR functional diversity is expected to shed light on how pathogen-driven arms races can shape the molecular functions of primary immune receptors and how structural features of the receptors affect potential evolutionary trajectories.The aim of this study is to characterize how RNA preference changed during early RIG-like receptor (RLR) evolution. We employed an approach beginning with phylogenetic analysis to establish the RLR protein family tree, followed by analysis of protein-coding adaptation to identify potential functional shifts in RNA binding. Functional shifts were confirmed using ancestral sequence reconstruction and kinetic analyses of ancestral proteins. We used molecular dynamics simulations of ancestral proteins to characterize the likely mechanisms driving observed shifts in RNA preference and confirmed these by measuring the RNA-binding kinetics of mutant ancestral proteins incorporating historical amino-acid substitutions. This strategy represents a general approach for examining the mechanistic basis for molecular-functional evolution in protein families  , suggesting that the earliest RLR gene duplication occurred before the protostome-deuterostome split. Vertebrate MDA5 and LGP2 sequences—along with Saccoglossus and Branchiostoma MDA5/LGP2—were monophyletic with maximal support, placing the MDA5-LGP2 duplication very early in the jawed vertebrate lineage, consistent with our previous findings.In order to establish a robust framework within which to examine RIG-like receptor (RLR) functional evolution, we reconstructed the phylogeny of full-length RLR protein sequences using maximum likelihood, assuming two different alignments in order to incorporate alignment uncertainty see . All bilp < 0.023; see also [The phylogeny reconstructed using sequence data implies a loss of MDA5/LGP2 in all protostomes and a second loss of RIG-I in arthropods . Althousee also . The resAlthough the evolution of a protein family’s molecular function can occur in the absence of adaptation or positive selection, reliable identification of protein-coding adaptation is a strong predictor of changes in molecular function, as adaptive processes can only occur when there is some phenotypic change visible to selection. We investigated the evolutionary forces driving RLR functional divergence using a branch-sites test to identify protein-coding adaptation in specific functional domains across specific branches on the phylogeny see . After cFollowing the earliest duplication of the ancestral RLR (ancRLR), protein-coding adaptation specific to the RD was detected along the branch leading to the first ancestral MDA5/LGP2 (ancMDA5/LGP2a) but not along that leading to ancRIG-I. We observed evidence for continued adaptation of the RD along the ancestral MDA5/LGP2 branch before the MDA5-LGP2 duplication and again in the LGP2 lineage after the MDA5-LGP2 split Fig. . We did Although the branch-sites test is generally considered robust –66, concConsistent with evidence from analyses of protein-coding adaptation, we found that the branch lengths—measuring the expected number of protein substitutions/site—were generally longer on the earliest branches of the RLR phylogeny when optimized using only RD sequences, whereas later branches had longer lengths when optimized using the helicase + pincer domains, compared to the RD  –73. Sequ−3, the median was 2.1e−5 and the largest inferred slope was 7.6e−2 in absolute value.We ran molecular dynamics simulations for 11 ns, excluding the first nanosecond as ‘burnin’ see . All simAlthough it is not possible to completely rule out potential errors in ancestral sequence reconstruction, structural modeling or dynamics simulations, we did not observe any compelling evidence to suggest high potential for errors in these inferences. As a whole, our results suggest that the RLRs functionally diversified via gene duplications in bilateria and jawed vertebrates, associated with protein-coding adaptation specifically targeting residues in the RD likely affecting RNA binding by altering hydrogen bond networks.To examine the evolution of RNA preference across the early RLR phylogeny experimentally, we measured the affinity with which each ancestral RLR RD bound double-stranded RNAs (dsRNAs) having either a blunt end or a 5′-triphosphate (5′ppp) moiety see . These pp = 0.09 for steady-state affinities; p = 0.33 for initial binding rates), whereas ancMDA5/LGP2a RD evolved ~20-fold reduced affinity for 5′ppp dsRNA . The ~3-4-fold increase in affinity for blunt-ended dsRNA we observed along this branch was not significant (p > 0.15). In contrast, the RNA binding affinities of ancRIG-I RD were much more similar to those of the ancestral RLR and human RIG-I RD  and the first ancestral MDA5/LGP2 after the bilaterian gene duplication (ancMDA5/LGP2a), followed by an immediate reversion back to the ancestral binding preference along the ancestral MDA5/LGP2 lineage . This was caused by an observed ~20-fold increase in ancMDA5/LGP2b RD’s affinity for 5′ppp dsRNA, compared to the RD of ancMDA5/LGP2a . Binding to both 5′ppp and blunt-ended RNA types by ancMDA5/LGP2b RD were equivalent to that of ancRLR RD, suggesting an evolutionary reversion to an ancestral function . Ancestral RDs resurrected immediately following the MDA5-LGP2 duplication also bound blunt-ended and 5′ppp dsRNAs with equivalent affinities (p > 0.7), arguing against sequence reconstruction errors in ancMDA5/LGP2b as a major explanation of our results , whereas human MDA5 RD appears to have re-evolved a preference for blunt-ended over 5′ppp dsRNA , similar to what we observed for the RD of ancMDA5/LGP2a . AncMDA5/LGP2b helicase + pincer + RD displayed equal preference for 5′ppp and blunt-ended dsRNA (p > 0.58), similar to ancRLR (p > 0.42).Previous studies have shown that—in addition to the C-terminal RD—RLR helicase + pincer domains contribute to RNA binding and may affect RNA preference , 61, 85.These results indicate that the binding shift we observed for ancestral RDs is not an artifact of measuring RD-RNA binding in the absence of other protein functional domains and suggest that changes in the helicase + pincer domain likely contributed little to the early evolution of RLR-RNA preference, at least for these two RNA types. However, later changes in the helicase + pincer may have impacted RLR-RNA binding of more recently-derived receptors. We did not examine the impact of ancestral RLR CARD domains on RNA binding, as RLR CARD sequences were too divergent to reliably reconstruct ancestral CARDs on these deep nodes of the phylogeny, and CARDs are not considered to strongly impact RNA binding in extant receptors , 86, 87.Our examination of RNA preference across the early RLR phylogeny paints a general picture in which RLRs repeatedly gain and lose affinity for 5′ppp dsRNA, first by losing ancestral high-affinity after the first RLR duplication in deuterostomes, then by immediately re-gaining 5′ppp dsRNA affinity along the MDA5/LGP2 lineage and finally losing it again sometime after MDA5 diverged from LGP2 in jawed vertebrates. In contrast to what has been observed for proteins involved in metabolic and developmental processes, in which major changes in ligand preference generally occur following gene duplication or speciation events and are fairly conserved across large evolutionary distances , 88, 89,∆EK47TEE,K88S), binding to 5′ppp dsRNA was reduced, similar to what we observed in ancMDA5/LGP2a . These results indicate that the combined ΔEK47TEE and K88S substitutions are sufficient to recapitulate the observed functional shift between ancRLR and ancMDA5/LGP2a. Introducing other historical substitutions around the RNA-binding pocket into the ancRLR background did not shift ancRLR’s RNA preference  and to the 5′ppp moiety in particular (p = 0.02). We observed no differences in hydrogen bonding to 5′ppp dsRNA between the ancRLR∆EK47TEE,K88S mutant and ancMDA5/LGP2a (p > 0.13), and no overall differences in hydrogen bonding to blunt-ended dsRNA were observed among any of the ancestral or mutant RDs (p > 0.23). These results suggest that the ancRLR∆EK47TEE,K88S mutant is sufficient to recapitulate not only the observed functional shift in RNA preference but also the changes in hydrogen bond networks expected to be responsible for this functional shift.Hydrogen bond networks are expected to be important for stabilizing protein-RNA interactions. Consistent with this expectation, we found that all ionizable residues potentially contacting RNA ligands were heavily protonated during molecular dynamics simulations , we observed a complete reversion to the ancestral ancRLR function, in which blunt-ended and 5′ppp dsRNAs are bound with equal affinity  and to the 5′ppp moiety in particular (p = 0.13). Additionally, the 5′ppp dsRNA adopted the ancestral RLR conformation, with the 5′ppp moiety engaged in the RNA-binding loop . We found that this single substitution was sufficient to revert ancMDA5/LGP2a’s function to the ancestral-like pattern of equal binding to blunt-ended and 5′ppp dsRNA observed in ancMDA5/LGP2b , while affinity for blunt-ended dsRNA remained unchanged (p > 0.06). RNA binding by ancMDA5/LGP2aH63S was indistinguishable from that of ancMDA5/LGP2b (p > 0.28), suggesting that the single substitution was sufficient to recapitulate the observed functional shift from ancMDA5/LGP2a to ancMDA5/LGP2b.Between the origin of ancMDA5/LGP2a and the MDA5-LGP2 duplication in early vertebrates, we observed a functional reversion back to ancRLR’s equal preference for blunt-ended and 5′ppp dsRNAs, even though the ‘RNA binding loop’ remained largely acidic  as well as to the 5′ppp moiety in particular , suggesting that the single H63S substitution is sufficient to shift the overall hydrogen bond network from the ancestral ancMDA5/LGP2a to the derived ancMDA5/LGP2b conformation.Indroducing the H63S mutation into the ancMDA5/LGP2a background produced a modest increase in the average number of hydrogen bonds observed over molecular dynamics simulations between the protein and the 5′ppp dsRNA ligand as a whole  has allowed researchers to experimentally evaluate hypotheses about how the structures and functions of biological molecules evolve, deepening our understanding of important biological systems and informing molecular-evolutionary theory , 37, 93.Our study is the first to examine the structural mechanisms underlying long-term functional evolution in a fast-evolving primary immune receptor. In contrast to previous studies of slow-evolving proteins, we found no evidence for strong epistasis among substitutions, historical contingency or long-term ‘functional optimization.’ Rather, the repeated functional ‘flip flopping’ we observed was primarily caused by small numbers of substitutions that clustered in and around the ligand-binding pocket and exerted functional effects by reorganizing local structural dynamics. We also observed little evidence for strong structural constraints on RLR evolution, with similar functions evolving and re-evolving through novel structural mechanisms.This study focused on two model RNA ligands that have been associated with viral infections , 74–79 aUnlike studies of ligand-binding proteins involved in more stable processes, the exact ligands recognized by modern human RLRs are still unclear , 104. ThWhile our study has focused on the evolution of receptor-ligand binding affinity, factors other than ligand affinity affect how receptors ultimately function in immune signaling, including how receptors interact with themselves –117 withWhile speculative, we feel our results may reflect general differences in the evolutionary dynamics of immune receptors, compared to those of proteins involved in more stable biological processes. Because the functional requirements of pathogen recognition are likely to change rapidly, immune receptors may exist in constant non-equilibrium, unable to optimize a specific functional repertoire. In contrast, proteins evolving toward a more stable evolutionary goal have more time to optimize specific functions. This general model would predict that immune receptors should exhibit less epistasis among substitutions overall, and extant receptors should be less optimally tuned to their functions, compared to slow-evolving counterparts. Determining the validity of this general model will require characterizing the molecular-functional evolution of RLRs across a broader range of ligands and over a larger slice of evolutionary history. Testing generalizability will also require mechanistic investigations of the evolution of other immune receptors. Characterizing a large number of receptor-ligand systems is expected to provide important clues about the specific aspects of pathogen-receptor interactions of primary importance in evolutionary history, advancing our understanding of host-pathogen interactions in particular as well as molecular-evolutionary theory in general.−5. We used the RNA recognition domains (RDs) from human RIG-I, MDA5, LGP2 and our previously reconstructed ancestral RLR RD [RIG-like receptor (RLR) protein sequences were retrieved from the NCBI Reference Sequence database  using DEl RLR RD  as BLASTl RLR RD , using aSequences were aligned using PROBALIGN v1.4 (default parameters ) and MAFP values were calculated using the χ2 distribution [Protein-coding adaptation was assessed using the branch-sites model implemented in PAML v4.7 , 133, whribution , and we We constructed structural models of RLR RDs using MODELLER v9.13 . The strStructural models were processed for electrostatic surface visualization using PROPKA v3.1 and PDB2PQR v1.7 to determine residue side-chain pKas, optimize the structure for favorable hydrogen bonding and calculate charge and radius parameters from amber electrostatic force fields , 141. ThFor each RLR structure bound to each RNA type, we ran 4 replicate molecular dynamics simulations using GROMACS v4.6.5 . DynamicFrom each dynamics simulation, we inferred the central structure by calculating pairwise root mean square deviations (RMSDs) between every pair of simulation samples and identifying the sampled structure most equidistant to the others, using the g_cluster function in GROMACS. We measured the root mean square fluctuation (RMSF) of each residue’s side-chain and backbone over the dynamics simulation. We additionally calculated a number of biochemical properties from each sample taken from each molecular dynamics simulation. Secondary structure was calculated using DSSP v2.2.1 , and we t-test, assuming unequal variances [Significance of differences in each biochemical property inferred by structural dynamics was assessed using the 2-tailed, 2-sample independent ariances . We corrariances .de novo by IDT . The 3′ end of one RNA strand was biotinylated to facilitate kinetics assays using the Pierce™ 3′ End RNA Biotinylation Kit (Thermo).We generated GC-rich 29-base-pair RNA molecules in vitro using T7 RNA reverse transcriptase and synthetic dsDNA as template (3′-GAAAGAGGUGCGGAAAGAGGUAGAGGAGG-5′). Complementary purified single-stranded RNAs were annealed to produce double-stranded RNA by combining at 1:1 ratio, heating to 95 °C for 5 min and then cooling to 25 °C. Blunt-ended dsRNA was produced from 5′-triphosphate RNA by exposure to alkaline phosphatase or synthesized E. coli Rosetta™ 2(DE3)pLysS cells using pET-22b(+) constructs, which were verified by Sanger sequencing. Proteins were purified by His-affinity purification and visualized by SDS-page stained with 1 % coomassie. Protein concentrations were measured using a linear-transformed Bradford assay [RLR RDs were expressed in rd assay .We measured RD-RNA binding using a label-free in vitro kinetics assay at pH = 7 . Biotinyt-test, assuming unequal variances [For each replicate experiment, we estimated the RD concentration at which ½-maximal steady-state RNA binding was achieved (Kd) by fitting a one-site binding curve to the steady-state laser wavelengths measured across RD concentrations at saturation (60 min), using nonlinear regression. We additionally fit 1-site association/dissociation curves to the full time-course data in order to estimate the initial rates of RNA binding across RD concentrations and used these rates to calculate the RD concentration at which the ½-maximal RNA-binding rate was achieved (Km). Kds and Kms were negative-log transformed to facilitate visualization, and standard errors across 3 experimental replicates were calculated. We calculated the statistical significance of differences between Kds and Kms using the 2-tailed, 2-sample independent ariances .


Bunyavirales, family Tospoviridae, genus Orthotospovirus. TSWV infects a broad range of plant species, causing serious economic losses. Despite its agronomic importance, molecular biological understanding of TSWV has been limited, partly due to the lack of a reverse genetics system, which would enable genetic manipulation of the virus. Here, we report that RNA synthesis by TSWV RNA polymerase occurs in the yeast Saccharomyces cerevisiae using a segment of the TSWV genome, S RNA expressed from cloned cDNA, as a template. Viral nucleocapsid protein was required for RNA synthesis. Replacement of the protein-coding and intergenic regions of TSWV S RNA by a yellow fluorescent protein (YFP)-coding sequence drastically increased the accumulation of both sense and antisense strands of the RNA, showing that this RNA was replicated. Using this system, we revealed that efficient RNA synthesis by TSWV RNA polymerase in yeast requires the 5′-terminal 17-nt and 3′-terminal ~50-nt regions of the TSWV S cRNA (complementary RNA to the genomic RNA) template.Tomato spotted wilt virus (TSWV) is a negative-strand RNA virus of the order  Reverse genetics systems have greatly advanced the field of virus molecular biology. In positive-strand RNA viruses, infectious RNA can be transcribed from cloned viral cDNA. However, the situation is more complicated for negative-strand RNA viruses, because their genomic RNA forms a ribonucleoprotein complex with viral RNA polymerase and nucleocapsid proteins, and naked RNA is not infectious. Negative-strand RNA viruses are classified into two groups, those with segmented genomes and those with non-segmented genomes. Schnell et al. (1994) described the recovery of recombinant rabies virus, a non-segmented negative-strand RNA virus, from host cells transformed with plasmids expressing viral RNA and proteins2. This was the first report of a reverse genetics system for a negative-strand RNA virus. Similar approaches have been successfully applied to many animal negative-strand RNA viruses, including segmented negative-strand RNA viruses3; however, the reverse genetics systems of plant-infecting negative-strand RNA viruses have not been reported until very recently. At present, the only plant negative-strand RNA virus for which reverse genetics system is available is Sonchus yellow net virus (SYNV), a non-segmented negative-strand RNA virus5. Wang et al.5 developed a method to rescue SYNV from Nicotiana benthamiana leaves expressing three viral proteins and antigenomic RNA by Agrobacterium infiltration, and used mutant viruses generated through this method to uncover viral protein function5.One of the most important achievements in RNA virus research is the development of reverse genetics systems which make possible the genetic manipulation of virusesTomato spotted wilt virus (TSWV) is a negative-strand RNA virus of the order Bunyavirales, family Tospoviridae, genus Orthotospovirus. TSWV infects more than 1,000 plant species and has spread worldwide, causing serious economic losses8. TSWV has three segmented genomic RNAs. L RNA is negative sense and encodes protein L (RNA polymerase). M and S RNAs are ambisense and encode protein NSm and glycoproteins G1 and G2, and proteins NSs and N (nucleocapsid protein), respectively. Functional analyses of these proteins using heterologous expression systems have revealed that NSs is an RNA silencing suppressor9 and NSm is a cell-to-cell movement protein10. NSs and NSm are also known as avirulence determinants for the resistance by pepper TSW gene and tomato SW-5b gene, respectively11. However, the lack of a reverse genetics system has been an obstacle to studying how viral genes or RNAs work in the infection process. Exceptionally, analyses of spontaneous knockout mutants and reassortants showed that NSs is involved in adult thrips infection and transmission12.Saccharomyces cerevisiae) is an excellent model organism for studying fundamental processes of eukaryotic cells. Yeast has also been used as a model host of many animal and plant RNA viruses, providing many insights into viral RNA replication13. In this study, we aimed to develop a TSWV replicon system in yeast, and to determine the sequence requirements for TSWV RNA replication.Budding yeast ] with precise terminal sequences was presumably generated after transcript self-cleavage. Yeast cells harboring the three plasmids were cultured for one day in the presence of CuSO4, and the accumulation of S cRNA and vRNA was examined by Northern blot hybridization. As negative controls, S cRNA was expressed alone, with either N or L protein, or with N protein and a mutant L protein whose SDD motif in the RNA polymerase catalytic site was changed to SAA. The mutant L(SAA) protein accumulated to a similar level as that of wild-type L protein in yeast  protein, larger amounts of S cRNA accumulated  corresponding to nucleotides 11–151 and 2917–3005 of TSWV S cRNA (3005 nucleotides), respectively, failed to accumulate vRNA Fig. , indicat17. We did not observe a significant increase in YFP fluorescence in cells harboring the YFP replicon and L and N proteins over non-replicating negative controls  were fused to a nonviral sequence complementary to the yellow fluorescent protein (YFP)-coding sequence. In the presence of L and N proteins, anti-expressed strand RNA was detected  for RNA synthesis Fig. . In contTo identify the minimal sequence required for TSWV RNA synthesis, we replaced each UTR of the YFP replicon by a series of short terminal sequences of TSWV S cRNA. YFPfw RNA was detectable when the 5′-terminal sequence was as short as 15 nt, and the accumulation level was comparable to the unmodified YFP replicon when the 5′-terminal sequence was 17 nt Fig. . The acc13. The power of yeast genetics has greatly facilitated our understanding of viral replication. In this report, we demonstrated for the first time that a segment of plant negative-strand RNA virus genome expressed from cloned cDNA can be a template for viral RNA polymerase in yeast.Yeast has been used as a model host for many eukaryotic virusesBunyavirales23, thus, this would appear to be a conserved property of these viruses. The 5′- and 3′-regions of the genomic RNAs of negative-strand RNA viruses show imperfect complementarity, and the genomic RNAs are proposed to form a panhandle structure through terminal base-paring. TSWV S RNA was predicted to form a panhandle structure of approximately 65–70 nt in length24. However, our results indicate that a sequence as short as 15–17 nt in the 5′ UTR was sufficient for complementary RNA synthesis whereas a longer sequence was required for the 3′ UTR. Thus, factors other than the formation of the panhandle structure are important for the terminal sequence of TSWV RNA in RNA synthesis. The crystal structure of the La Crosse orthobunyavirus L protein revealed that each of the 5′ and 3′ ends of the template RNA bind directly to different positions on the L protein, and the ~10-nt sequence from the 5′ end is likely important for the activation of the polymerase25. It is conceivable that the 17-nt region in the 5′ end of TSWV RNA also acts as an activator of the L protein. In contrast, a longer sequence in the 3′ UTR functions as a strand-specific enhancer of RNA replication. A longer sequence in the 5′ UTR of S cRNA also plays a role in vRNA-templated cRNA synthesis. Thus, the internal regions of the 3′ UTRs of both strands may have similar functions in RNA synthesis, possibly at the initiation step since RNA synthesis starts from the 3′ end of the template. S cRNA may contain a stronger 3′ enhancer of RNA replication than S vRNA to fulfill asymmetric replication.Using this system, we revealed that the terminal noncoding regions of TSWV S RNA are necessary and sufficient for replication. The importance of the terminal sequences for RNA synthesis has been reported in many viruses in the in vitro whereas transcription occurs only when a cell extract is supplemented27. The transcription-supporting activity copurified with translation elongation factor 1A from a tobacco cell extract28. Yeast may have an incompatible form of the transcription-supporting host factor. Thus, the yeast system may also be useful for studying the mechanism of TSWV mRNA transcription. Moreover, this system would facilitate the identification of host factors required for TSWV RNA replication. Considering the recent progress in new breeding techniques, the discovery of host factors may lead to future breeding of TSWV-resistant plants by disrupting the corresponding genes.In this yeast system, TSWV transcription was not clearly detected. Previous studies have indicated that purified TSWV virion has intrinsic RNA synthesis activity 29 and hepatitis delta virus ribozyme30 sequences were added to the 5′- and 3′-end, respectively, by PCR. The nucleotide sequence of the cloned fragment is shown in Supplementary Fig. CUP1 promoter and ADH1 terminator-containing plasmid YCp22-CUP31 using SnaBI and SacI. The gene cassette of CUP1 promoter, cloning site, and ADH1 terminator from YCp22-CUP was transferred to YCplac111 and YEplac195 for cloning of the yeast-optimized cDNA for N protein and S RNA, respectively. The YFP-coding sequence from pTLPYFP32 was amplified and fused to the UTR sequences of S RNA to construct the YFP replicon . TSWV S cDNA was amplified by reverse transcription-polymerase chain reaction (RT-PCR) from the viral RNA. A hammerhead ribozymeMATa ura3–52 trp1 leu2D1 his3D200 pep4::HIS3 prb1D1.6R can1) was used throughout the study. Precultured yeast cells harboring the plasmids were cultured in the presence of 0.2 mM CuSO4 at 30 °C for 5 h  or 20 h . The cell pellet was homogenized by vortexing with glass beads and the extract was analyzed by Western blotting. RNA isolation from the cell pellet was performed using an RNeasy Mini Kit .A protease-deficient yeast strain BJ5465 . To detect the YFP replicon, we used digoxigenin-labeled oligodeoxyribonucleotides 5′-TTCGAAAGATCCCAACGAAAAGAGAGACCACATGGTCCTT-3′ or 5′-AAGGACCATGTGGTCTCTCTTTTCGTTGGGATCTTTCGAA-3′. Labeled probes on the hybridized Hybond-N + membranes  were detected with anti-digoxigenin-AP, Fab fragments and CDP-star (Roche).Total RNA from yeast cells was analyzed by Northern blot hybridization. Probe RNA was transcribed by T7 RNA polymerase in the presence of digoxigenin-11-UTP from the PCR fragments of TSWV S cDNA amplified using primers 5′-CCCGGGEscherichia coli-expressed fragment of the TSWV L protein .Anti-N protein antiserum was purchased from the Japan Plant Protection Association. Anti-L protein rabbit antiserum was raised against an 4 at 30 °C for one day. YFP fluorescence of the cultured yeast cells was measured by Tecan Infinite 200 Pro  with 485-nm excitation and 535-nm emission wavelength filters. Values were normalized by the absorbance at 595 nm.Yeast cells were cultured in the presence of 0.2 mM CuSOThe nucleotide sequences for codon-optimized L and N proteins were deposited to the DDBJ/EMBL/Genbank Database under accession numbers LC270910 and LC270911, respectively. All other datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.Supplementary Figure S1, S2, S3


In molecular biology, one of the founding dogmas states that genetic information, stored in the form of DNA molecules in the majority of living organisms, is translated into proteins via transient intermediaries, the RNAs. However, over the past 50 years, an increasing number of studies have highlighted that RNAs have a more significant and broader role. Whereas all the genomes of living organisms, whether single-cell or multi-cellular, have a high proportion of loci that are transcribed into RNA, this transcript is not necessarily translated into protein but can perform functions within the cell as the form of RNA. As a consequence, RNAs translated into proteins were named messenger RNAs (mRNA) as opposed to those that do not, which are referred to as non-coding or regulatory RNAs (ncRNA), clearly discriminating RNAs according to their protein coding capacity. Until recently, the coding/non-coding distinction appeared to be obvious, although it became more blurred in recent years. The first hints emerged when it became evident that the repertoire of genome-encoded RNAs is far more extensive and complex than previously thought. Indeed, many RNAs, here referred to as bifunctional RNAs (bifRNA), have both regulatory/non-coding and coding functions. The balance between non-coding and coding RNAs levels is modulated depending on the stage of development or differentiation, environmental cues, disturbances caused by a pathogen, etc. After having re-defined this striking but expanding class of bifRNAs, we will propose an estimate of the fraction of the human transcriptome that they may represent.When we refer to bifRNAs, we assume that the same molecule is able to perform both functions, namely encode for a protein and possess its own regulatory function, but this term is often misused Figure . In factIn fact, for many bifRNAs that were described here or elsewhere  that have been described so far as being exclusively of intron origin, at least in humans, the host precursor RNA belongs to the bifunctional class as it can produce, after splicing, a mature mRNA and a mature snoRNA, with distinct functions; snoRNAs guide chemical modifications to other RNAs whereas mature mRNAs can then be translated into a protein product.The first non-coding RNA ever identified was the alanine (Ala) transfer RNA (tRNA), purified from yeast, whose structure was published in 1965  that are highly structured. Both UTRs are known to play key roles in post-transcriptional regulation, including the control of mRNAs transport, the translation efficiency, the subcellular localization and the overall stability of transcripts  indicate, among others, non-redundancy, data validation, distinct accession series, and ongoing curation by NCBI staff and collaborators. In this type of nomenclature, “NM_” corresponds to validated protein-coding transcripts, while the prefix “NR_” indicates non-protein-coding transcripts also validated and curated. Each transcript, and more specifically each isoform produced, is associated with a unique identifier, which is itself linked to a gene locus. For example, the homo sapiens SRA1 gene has four isoforms that are deposited and accessible in NCBI website, variants 1 and 2 that are coding mRNAs and referred to as NM_001253764.1 and NM_001035235.3, respectively, whereas variants 3 and 4 are non-coding RNAs and identified as NR_045586.1 and NR_045587.1, respectively , which may lead to substantial biased interpretations. In addition, expression levels of these isoforms are not very different whether the transcripts are coding or not  to predict genes that could produce both coding and non-coding RNA isoforms. The methodology is explained in the legend of Figure t Figure  using dat Figure .This result leads to at least three very important conclusions. The first is that the non-coding versions of bifRNAs are not subjected to Nonsense-Mediated RNA Decay (NMD). Indeed, since these transcripts are detected in some cells/tissues, they escape the RNA surveillance mechanisms that otherwise rapidly degrades RNA with premature stop-codons (Zhang et al., How to overcome this bias? An obvious but costly solution would be to develop new microarrays, taking into account all the annotated coding and non-coding isoforms. However, the constant discovery of new isoforms would rapidly overwhelm the process of probe design. The second solution may possibly come from the new technologies that are available today, such as high-throughput sequencing. However, at least in terms of isoform identification, this remains a challenge; the use of classical RNA-seq is still far from being accurate and the assembly of transcripts, although it is constantly being improved over time, still does not allow for true identification of the transcripts isoforms either. The latest generation of RNA-seq, the ultra-long reads method, seems to overcome most of these complications (Jain et al., Since the burst of studies on non-coding RNAs, i.e., for almost 20 years now, there is no longer any doubt about their existence and importance in cellular processes. Now that the scientific community has accepted the concept of coding and non-coding RNAs, we may have to take a step backward to reassess the possibility that these two categories of transcripts are more interdependent than thought. These new conclusions impose for a deeper examination into the functional significance of these dynamic bifRNAs and for increased efforts toward a more integrated view of transcriptome/proteome in a given cellular context.All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


PAI, is proposed to identify A-to-I editing site in D. melanogaster. In this model, RNA sequences are encoded by “pseudo dinucleotide composition” into which six RNA physiochemical properties were incorporated. PAI achieves promising performances in jackknife test and independent dataset test, indicating that it holds very high potential to become a useful tool for identifying A-to-I editing site. For the convenience of experimental scientists, a web-server was constructed for PAI and it is freely accessible at http://lin.uestc.edu.cn/server/PAI.The adenosine to inosine (A-to-I) editing is the most prevalent kind of RNA editing and involves in many biological processes. Accurate identification of A-to-I editing site is invaluable for better understanding its biological functions. Due to the limitations of experimental methods, in the present study, a support vector machine based-model, called  The adenosine to inosine (A-to-I) editing is the most prevalent kind of RNA editing, which has been found from fungi to human2The inosine yielded by A-to-I editing replaces the genomically encoded adenosine and is read by the cellular machinery as a guanosine (G)3478H. sapiens1011M. musculusD. melanogasterBy using RNA-Seq method, A-to-I editing sites have been detected in D. melanogasterBased on the RNA-Seq data, Laurent and his colleague constructed a high quality dataset and developed a computational model to detect A-to-I editing site in D. melanogaster. By using the pseudo dinucleotide composition as the input feature vector of support vector machine, the long-range sequence-order effects and RNA physicochemical properties were integrated together in the proposed model. It is encouraging that the proposed method obtained promising performances in jackknife test and independent dataset test. For the convenience of experimental scientists, a web-server for the proposed model is provided at http://lin.uestc.edu.cn/server/PAI.Keeping this in mind, in the present study, we proposed a support vector machine (SVM) based-method to identify the A-to-I editing site in w and λ . w is the weight factor usually within the range from 0 to 1, and λ is the global order effect. Generally speaking, the greater the λ is, the more global sequence-order information the model contains. However, if λ is too large, it would reduce the cluster-tolerant capacity so as to lower down the cross-validation accuracy due to over-fitting or “high dimension disaster” problem. Therefore, our searching for the optimal values of the two parameters is in the range of w∈ and λ∈ with the steps of 0.1 and 1, respectively.By using PseDNC, RNA samples in the benchmark dataset can be transferred to a discrete vector whose dimension and elements depend on the two parameters w = 0.3 and λ = 4, a peak of 78.86% was obtained for the Acc  at position 26 in the four query sequences can be edited to Inosine (I). These results are consistent with the experimental observations.RNA-seq analyses have demonstrated that A-to-I editing is associated with a number of key biological processes and plays important roles ranging from changing codon to regulating mRNA splicing. Therefore, genome-wide detection of A-to-I editing sites will facilitate our understanding of its biological functions.In the present study, we proposed a support vector machine based model for predicting A-to-I editing sites by using pseudo dinucleotide composition and found that the model is very promising as reflected by high success rates obtained from the rigorous jackknife test and independent dataset test.PAI, is provided. We hope that it will provide novel insights into the understanding of the distribution and function of A-to-I editing. As the current method is only applicable to D. melanogaster, future work will expand to other species once the high quality experimental data that can be used to train the model is available.For the convenience of researchers in the scientific community, a web-server for the proposed model, called et al.’s workD. melanogaster and RNAs of the ADAR-deficient D. melanogaster, and obtained a training dataset including 127 A-to-I editing site containing sequences and 127 non-A-to-I editing site containing sequences. After removing the redundant samples in their dataset, we obtained a benchmark dataset including 125 A-to-I editing site containing sequences and 119 non-A-to-I editing site containing sequences.The benchmark dataset used to train and test the proposed method was built based on Laurent http://lin.uestc.edu.cn/server/PAI.It was observed via preliminary trials that when the length of the sequences in the benchmark dataset is 51 nt with the A that can be edited to Inosine in the center, the corresponding predictive results were most promising. Accordingly, all the sequences in the training dataset are 51-nt long and are available at D. melanogaster from Yu and his colleagues’ workhttp://lin.uestc.edu.cn/server/PAI.To further verify the power of the proposed method, we also build an independent dataset by harvesting the A-to-I editing site containing sequences of In order to include the global sequence order information, the pseudo nucleotide composition was proposed to represent genomic sequences192021232425L nucleic acid residues, the pseudo nucleotide composition can be defined as,Suppose a RNA sequence with wherefu  is the normalized occurrence frequency of the non-overlapping k-tuple nucleotides in the RNA sequence. λ is the number of the total counted ranks of the correlations along a RNA sequence, and w is the weight factor. It is through the λ correlation factors that not only considerable global sequence-order effects can be incorporated but the RNA sequences in the benchmark dataset with extreme difference in length can also be converted into a set of feature vectors with a same dimension. The correlation factor θj represents the j-tier structural correlation factor between all the j-th most contiguous k-tuple nucleotide Ti = RiRi+1…Ri+k−1 and is defined as,In θ1 is called the first-tier correlation factor that reflects the sequence order correlation between all the most contiguous k-tuple nucleotide along a RNA sequence; θ2, the second-tier correlation factor between all the second most contiguous k-tuple nucleotide; θ3, the third-tier correlation factor between all the third most contiguous k-tuple nucleotide; and so forth. The correlation function Θ is given byFor example, v is the number of RNA physicochemical properties. Pu(Ti) is the numerical value of the u-th  property for the dinucleotide Tiat position i, and Pu(Tj) is the corresponding value for the dinucleotide Tj at position j.where Pu(Ti)  were subjected to a standard conversion as described by the following equation,Before substituting them into Eq. where the symbol <> means taking the average of the quantity therein over the 16 different dinucleotides, and SD means the corresponding standard deviation. The converted values obtained by k is equal to 2 meaning that the pseudo dinucleotide composition (PseDNC) was used, and v is equal to 6 reflecting the number of RNA physicochemical properties considered.It has been reported that A-to-I editing are correlated with RNA structures282930https://www.csie.ntu.edu.tw/~cjlin/libsvm/) was used to perform the predictions. The radial basis function (RBF) was chosen as the kernel of SVM, where the regularization parameter C and kernel parameter γ were optimized using a grid search approach as defined byAs a smart supervised machine learning algorithm, support vector machine (SVM) has been widely employed to build classifiers in the realm of computational genomics and proteomics32333435Sn), specificity (Sp), Accuracy (Acc) and the Mathew’s correlation coefficient (MCC), which are expressed asThe performance of the proposed method was evaluated by using the widely used four metrics, namely sensitivity .


Extracellular vesicles (EVs), nanoparticles originated from different cell types, seem to be implicated in several cellular activities. In the Central Nervous System (CNS), glia and neurons secrete EVs and recent studies have demonstrated that the intercellular communication mediated by EVs has versatile functional impact in the cerebral homeostasis. This essential role may be due to their proteins and RNAs cargo that possibly modify the phenotypes of the targeted cells. Despite the increasing importance of EVs, little is known about their fluctuations in physiological as well as in pathological conditions. Furthermore, only few studies have investigated the contents of contemporary EVs subgroups  with the purpose of discriminating between their features and functional roles. In order to possibly shed light on these issues, we performed a pilot study in which MVs and EXOs extracted from serum samples of a little cohort of subjects  were submitted to deep small-RNA sequencing. Data were analysed by an in-home bioinformatics platform. In line with previous reports, distinct classes of non-coding RNAs have been detected in both the EVs subsets, offering interesting suggestions on their origins and functions. We also verified the feasibility of this extensive molecular approach, thus supporting its valuable use for the analysis of circulating biomarkers  in order to investigate and monitor specific diseases. Extracellular vesicles (EVs), especially microvesicles  and exosomes , are small membranous vesicles released by most cells that have been demonstrated to play a key role as mediators of several cellular functions. In the Central Nervous System (CNS), both glia and neurons secrete EVs; EVs-mediated intercellular communications seem to significantly influence the cerebral homeostasis, i.e., by impacting myelin formation, metabolic support and immune defence . This wi6 EXOs/µL) [The implication of EVs in the pathogenesis of neurological disorders, such as Alzheimer’s, Parkinson’s diseases and stroke, has been already reported ,7,8,9. IEXOs/µL) , the anaOn this ground, the main aim of this pilot study was to assess the feasibility and efficiency of EVs isolation followed by HT-sequencing of evRNAs extracted from serum samples of CIS subjects submitted to routinely clinical and instrumental follow-ups. To achieve this goal, we analysed a small cohort of CIS patients and HC subjects; the future purpose will be to apply the resulting experience to finalized scientific investigations on the role of EVs in the MS pathogenesis and its phenotypic characterization/monitoring.naïve for any disease-modifying therapy and three age-matched HC, were analysed. They were three females, two males (mean age 32.4 ± 12.1 years). The study was conducted in accordance with the Declaration of Helsinki: informed consent was obtained from all individual participants included in the study .Five subjects, two CIS patients g for 7 min to separate serum fraction from blood clot. Each sample was diluted 1:1 with phosphate buffer saline (PBS) and centrifuged at 2000 × g for 30 min at 4 °C to eliminate cell debris and other particles. The supernatant (SN) was carefully removed, filtered through a 0.8 µm filter and centrifuged at 12,000 × g for 45 min at 4 °C for collecting MVs. The SN was again collected and filtered through 0.2 µm filters; EXOs were pelleted from the filtered SN by ultracentrifugation at 120,000 × g for 70 min at 4 °C. All the EVs were re-suspended in 200 µL PBS. Ten mL/each of peripheral blood samples was obtained. MVs and EXOs were isolated by standardized differential centrifugations ,16: 10 mSize distribution of MVs and EXOs was measured by the dynamic light scattering (DLS) technique with Zetasizer Nano-ZS system . Ten µL of EVs were diluted in 990 µL of PBS (1:100) and then gently mixed to provide a homogenous solution. Three measurements were acquired per aliquot, with each acquisition taking 20 s. The data were acquired and analysed using Malvern Zetasizer Software (v7.11). DLS measurements allowed to discriminate between MVs and EXOs according to their different size.Total RNA was extracted using Total Exosome RNA and Protein Isolation Kit  following the manufacturer’s protocol. Ten picogramms of synthetic miRNA spike-in controls (cel-miR-39 and UniSp6) were added to the respective lysis/denaturant buffer. RNA samples were stored at −80 °C until used. RNA quantity and quality were assessed by capillary electrophoresis  with total RNA 6000 Nano Chip and Small RNA chip following the manufacturer’s instructions.Total RNA, including the small-RNA fraction, was converted into cDNA libraries using the SMARTer smRNA-Seq Kit for Illumina . Total RNA was polyadenylated in order to provide a priming sequence for an oligo(dT) primer. cDNA synthesis was primed by the 3′-smRNA-dT Primer, which incorporated an adapter sequence at the 5′-end of each first-strand cDNA molecule. PrimeScript™ Reverse Transcriptase was added at the 5′-end non-templated nucleotides, which were used as a template for the addition of a second adapter sequence to the 3′-end of each first-strand cDNA molecule. Full-length Illumina adapters were added during PCR amplification. All libraries were checked for quality , fluorimetrically quantified , then sequenced by an Illumina HiSeq2500 platform that generates 50 bp single-reads.http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) and trims adapter sequences and poly-A using cutadapt [http://ncRNAdb.ba.itb.cnr.it) that collects and integrates sequences and annotations of various ncRNA classes such as micro RNA (miRNA), piwi-interacting RNA (piRNA), tRNA, small nucleolar RNA (snoRNA), etc., from several public databases. In this step, two mapping tools such as Bowtie [The complete small (s)RNA-Seq analysis was performed by an in-house developed bioinformatics pipeline. Several steps compose the pipeline: raw reads filtering, reads mapping and classification, quantification . The fircutadapt . After ps Bowtie  and miRDs Bowtie  were appConsidering the explorative nature of this study and the small sample size, here we reported some preliminary results and comments about individual (MV/EXO) data without statistical comparisons between the subjects’ groups.clean reads, which means mapped and unmapped non-coding RNAs (ncRNAs) that resulted after removing: low-quality reads, adaptor sequences, poly-A fragments, reads smaller than 15 nucleotides, and those reads that matched mitochondrial RNAs and poorly represented ncRNAs classes . After mapping the clean reads to miRBase (Release v.21) in order to annotate known miRNAs, data showed that miRNAs fraction accounted for 17.62% in CIS-1 compared to a mean of 1.75% in HC1-2 (MVs fractions), whereas in EXOs they were 2.12% in HC-3 and 0.91% in CIS-2  and 608 (in CIS-2) different mature miRNAs in EXOs samples, compared to a mean of 569 (in HC-1-2) and 940 (in CIS-1) miRNAs in MVs samples. One-hundred and eighty-two mature miRNAs were shared in all EV samples; interestingly, MVs contained six unique mature miRNAs that were absent in EXOs  suggesting that they may be distinctive of MVs. Similarly, 34 unique mature miRNAs were exclusively present in EXO samples; five of them  have been previously found to be consistently enriched in EXOs compared to cellular profiles ,22.In Finally, small amounts of reads remained unmapped to ncRNA sequences in each of the two EVs categories; it was distinguished whether they mapped or failed to map the Human Genome sequences (GRCh38) , some of the NGS data of both EXOs and MVs were excluded from the analysis for technical reasons (mostly due to failure in the RNA extraction); we are now aware of this first crucial step, so we are quite confident of avoiding this inconvenience in the future (see take-home messages). Nevertheless, despite the individual differences that necessarily characterize the examined EVs  and the small sample size, to our view, the overall results confirmed that sRNA-deep sequencing represents a valuable and feasible approach for the molecular characterization of EVs.In line with previous reports ,14,21,26data available, not shown). Since, in our study, we reported the same difference (MVs versus EXOs) for several miRNAs, we believe that the extension of this study will also help to shed light on the functional meaning of these circulating biomarkers by looking at their expression levels in contemporary MVs and EXOs.Recently, an extensive analysis of the total circulating exosome transcriptome identified a distinct molecular profile in Relapsing-Remitting MS patients; the attention pointed towards four miRNAs  that were significantly decreased during clinical and MRI activity and compared to HC . AlthougOn the other hand, the identified nine common miRNAs out of the most abundant 20  showed a peculiar opposite trend between the subgroups. Preliminary pathways analysis revealed that they were mostly involved in immunological/inflammatory processes, as well as in neurodegenerative processes. Of interest, hsa-miR-24-3p has already been reported up-regulated in Primary Progressive MS compared to Secondary Progressive MS and HC , whereasIn conclusion, although with caution due to the small sample size, we believe that the data produced by this pilot observation do represent a start point for future in-depth analysis investigating the implications of EVs in MS pathogenesis. At the same time, it is plausible that circulating EVs may represent a specific target for novel treatments, leading to the identification of additional (and possibly more effective) therapeutic approaches.The analysis of EVs contents  may help to provide more information on their functional role(s) in several diseases; EVs contents may also be used for the isolation of circulating biomarkers .Few studies have investigated both the EV classes (Microvesicles and Exosomes) extracted simultaneously from the peripheral blood samples in the same patient, but this strategy should be suggested in order to fully understand the entire molecular picture of a given status.smallRNA deep sequencing is a valuable approach for studying the different classes of small non-coding RNA, although a solid bioinformatics support is recommended for the analysis and the interpretation of the obtained results.In order to obtain the right amount of evRNAs for the subsequent sRNA-seq analysis, we suggest/recommend to collect a higher quantity of blood samples (at least 20 mL/each) or to pool together samples belonging to different subjects with the same phenotype .


RNA visualization software tools have traditionally presented a static visualization of RNA molecules with limited ability for users to interact with the resulting image once it is complete. Only a few tools allowed for dynamic structures. One such tool is jViz.RNA. Currently, jViz.RNA employs a unique method for the creation of the RNA molecule layout by mapping the RNA nucleotides into vertexes in a graph, which we call the detailed graph, and then utilizes a Newtonian mechanics inspired system of forces to calculate a layout for the RNA molecule. The work presented here focuses on improvements to jViz.RNA that allow the drawing of RNA secondary structures according to common drawing conventions, as well as dramatic run-time performance improvements. This is done first by presenting an alternative method for mapping the RNA molecule into a graph, which we call the compressed graph, and then employing advanced numerical integration methods for the compressed graph representation.Comparing the compressed graph and detailed graph implementations, we find that the compressed graph produces results more consistent with RNA drawing conventions. However, we also find that employing the compressed graph method requires a more sophisticated initial layout to produce visualizations that would require minimal user interference. Comparing the two numerical integration methods demonstrates the higher stability of the Backward Euler method, and its resulting ability to handle much larger time steps, a high priority feature for any software which entails user interaction.https://jviz.cs.sfu.ca/download/download.html.The work in this manuscript presents the preferred use of compressed graphs to detailed ones, as well as the advantages of employing the Backward Euler method over the Forward Euler method. These improvements produce more stable as well as visually aesthetic representations of the RNA secondary structures. The results presented demonstrate that both the compressed graph representation, as well as the Backward Euler integrator, greatly enhance the run-time performance and usability. The newest iteration of jViz.RNA is available at The online version of this article (doi:10.1186/s12859-017-1682-0) contains supplementary material, which is available to authorized users. Ribo-nucleic Acid (RNA) is a polymer of nitrogenous bases, composed mainly of Adenine, Cytosine, Guanine, and Uracil . RNA is very similar to Deoxyribo-nucleic Acid (DNA) in its basic composition, but while DNA is regularly found as two complimentary strands, RNA can be found as a single strand of nucleotides. This primary structure (the string of nucleotides) then folds over itself into a secondary structure when the bases in the RNA strands pair up via hydrogen bonding. The RNA molecule can then twist, fold, or otherwise change its conformation in 3D space, giving it a functional three dimensional form, known as the tertiary structure.Single stranded, functional RNA is an important agent in many biological processes. From humans to bacteria and viruses, there are many examples of RNA molecules that are important to understand, classify, and research. Some notable examples include RNA motifs that allow viruses to manipulate host replication machinery –5, bacteThere are many RNA visualization tools that have been developed, and two excellent reviews of them can be found in , 10, witHowever, the majority of RNA visualization software designed produce a static layout of the RNA molecule that may not be ideal for the user. While for small RNA molecules such as transfer RNA (tRNA), this problem almost never arises, for large RNA molecules, this layout can be such that sections of the RNA overlap each other, making annotation of certain regions problematic and uninformative. There are only three notable examples that create dynamic layouts which are responsive to user interactions: jViz.RNA, PseudoViewer, and VARNA.The designers of VARNA do not explicitly state how they construct the RNA molecule and how the algorithm responsible for user response behaves, but one can estimate how the algorithm operates from interacting with the software as a web applet . The RNAPseudoViewer is another application that allows its users to manipulate the RNA structure. Originally an application designed focusing on RNA pseudoknots, PseudoViewer puts a great deal of effort into creating the initial layout of the RNA molecule. Additionally, the user can manipulate the RNA structural motifs. However, certain manipulations of the RNA conformation destabilize the system and cause the RNA model to break apart. Furthermore, since PseudoViewer’s focus is primarily on pseudoknotted RNA structures, it tends to arrange the RNA structure layout focusing very heavily on clear display of pseudoknot types, at the expense of the aesthetics related to the rest of the structure.detailed graph.jViz.RNA, a third software designed for dynamic RNA visualization, employs a different approach to creating the RNA model. jViz.RNA translates the RNA molecule into a graph as well, but maps each nucleotide to a vertex and each bond between nucleotides  to an edge. jViz.RNA then uses repulsion and attraction forces between all nucleotides to calculate the position and movement. This process continues until the forces reach an equilibrium for all nucleotides. Users can still move the individual nucleotides, and by doing so interact with the RNA structure. Mapping each nucleotide into a vertex creates a graph with a high number of vertexes and edges. As such, we denote this method as constructing a A software similar to jViz.RNA, Forna, also uses a detailed graph representation in order to construct a dynamic RNA model. All nucleotides and chemical bonds are translated into vertexes and edges, and an automatic layout is constructed utilizing attraction and repulsion forces, as well as invisible "helper vertexes" which aid in the improvement of loop appearance. Like the previous three software tools, Forna allows for users to interact with the constructed dynamic model.Of the four software tools mentioned, the approach employed by jViz.RNA and Forna relies mostly on the program to produce the layout, rather than require user intervention. This approach makes the production of RNA images less involved for the user, due to very little, if any, overlap even for large molecules. However, both Forna and jViz.RNA require more computation time for structure layout than PseudoViewer or VARNA. For very large structures of over 900nt, waiting for the automatic layout can be very inconvenient, and interaction with the structure becomes problematic as the structure’s movement becomes delayed. In addition, current jViz.RNA output contains some inconsistencies with regards to current accepted RNA visualization guidelines. The following section discusses these shortcomings in more detail.While the current implementation for jViz.RNA produces dynamic RNA models that display secondary structure elements very well, the simple graph representation described presents some visual shortcomings. Most RNA images found in the literature follow several visualization norms, and two such norms that are very important are that stems are drawn such that the distance between base-pairs is consistent across the stem, and that loops are drawn as circular elements wherever they occur in the structure. Figure To create an RNA representation which was more consistent with existing RNA visual conventions, such as round loops and equidistant base pairs.To design an automatic layout algorithm which works with the new representation, while introducing noticeable speed-ups to that automatic layout algorithm, and reduce overlap of structural elements.The main motivation for the work presented in this manuscript was to address the visual shortcomings presented by the current algorithm employed in jViz.RNA, as well as improvements in the run-time performance. The objectives of this work were: Both objectives address important areas for improvement in jViz.RNA. While currently the resulting RNA molecule is laid out very clearly, personal feedback from RNA researchers, as well as visual comparison with other software, demonstrates that it ignores several visualization conventions such as the shape of loops and stems. This visualization pattern makes it more difficult to share informative images and diagrams about RNA, as the resulting visualization is not in the format most RNA scientists expect.ni is in O(1) since there are at most three nucleotides bonded to it, so the total calculation time for all the nucleotides’ attraction forces is in O(N) at each iteration (where N is the number of nucleotides in the RNA molecule). However, calculating the repulsion forces for each nucleotide is in O(N) since it must account for the repulsion of all other nucleotides, so the total calculation time for all the nucleotides’ repulsing forces is in O(N2). This run-time can make the automatic layout algorithm perform slowly for large RNA structures such as 16S ribosomal RNA. As such, the work presented in this manuscript was set to implement the improvements while reducing the run-time required for the RNA structures to stabilize. In order to achieve this two-pronged effect, the method by which jViz.RNA builds the graph to represent the RNA structure had to be modified; the simple graphs built were replaced by a more compressed representation of the RNA molecule, named compressed graphs.Another challenge the current setup faces is its time complexity. Calculating the attraction force for each nucleotide The work in this manuscript is divided into three main sections. First, we discuss how the RNA molecule can be mapped into a graph with a smaller number of vertexes, a compressed graph, similar to the one used by VARNA. Secondly, we profile the performance of the original version of jViz.RNA against the performance obtained by employing the reduced graph. Third and finally, a superior method for calculating movement is presented and profiled against the original movement calculation method.The code presented in this manuscript was implemented using Java 6.0. The Swing library was used for the graphical component of the code. Measurements were all taken on a PC with an Intel Core i7-4790 3.60 GHz processor, running Ubuntu 14.04.5 LTS.O(N2), decreasing the number of vertexes would theoretically have a tremendous effect on reducing the run-time. However, the mapping of the structure into vertexes and edges must be done in a way that still produces visually pleasing layouts and RNA diagrams. Inspired by VARNA, we have employed a representation which maps each RNA loop, as well as every stem base pair, to a vertex, and connects those via edges. ), decreasing the number of participating vertexes in the repulsion interaction should greatly reduce the run time of the algorithm. Constructing the system in such a way that only loops experience repulsion ensures that loops will be pushed away from each other, thus not intersecting each other. In theory, this should aid the structure in adopting a final layout that has minimal or no intersection of any structural elements.Furthermore, we have implemented the system in such a way that repulsion only occurs between loops, and not base pairs. Since the repulsion step is the main time consuming step of each iteration  is a vertex v∈V, and the edges are graph elements which connect the different structure elements , at time-step n (since the simulation operates in discrete time-steps), during the k-th Newton iteration (since a portion of the experiments in this paper use Newton’s method for converging on the position of vertex i at time-step n).For the purposes of the following sections, the notation j,vj) to point i,vi) as follows: The most basic computation done at each iteration is the unit vector function, vi experiences from vertex vj can be described as: In each iteration, each vertex moves based on two forces: repulsion and attraction. The repulsion forces each vertex vj to vertex vi, and G is a coefficient to control the size of the force experienced. The attraction forces each vertex vi experiences from vertex vj can be described as: where K is an attraction coefficient to control for the size of the force, and rideal is the ideal desired distance between the vertexes vi and vj.where vi|1≤i≤N} the following holds: The iterative process stops when the forces for all vertexes have reached equilibrium, or when for all vertexes {Ci is the set of all vertexes connected to vertex vi , and L is the set of all loops .Where ε. Setting ε=0 will force the simulation to continue to calculate until the forces are perfectly at odds, but setting a small value for ε allows the layout algorithm to stop sooner when achieving a stable structure. ε as such, controls the degree of stability required before simulation of the structure’s movement stops. In this work, we chose to explore two methods of implementing the physics based RNA model: The Forward Euler method, and the Backward Euler method. The two methods make it possible to evaluate the movement of the vertexes. However, the latter is more numerically stable than the former, and allows for greater time steps and faster visualizations, as well as a more stable user interaction experience.That is to say, the iterative process stops when the sum of the forces acting on the vertexes is smaller than The Euler method is a first-order integration method which belongs to a larger class called the Runge-Kutta methods (most famous being the fourth-order method ). The sitn is advanced by the time-step Δt and then the position of the vertex vi is updated based on the size of the time-step, and the current behaviour of the particle, f .which means the time When applied to the movement of the RNA vertexes, the Forward Euler method can be written as: vj∈L, where L is the set of all loops) and attraction : 1: 1D of and the matrix D is defined as: Given the function k+1, the value of both the function D−1, are evaluated at the point O(L2) where L is the number of loops in the simulation.where 1. The structure lengths are given in “nt,” which stands for “nucleotides.”For the purposes of these experiments, 17 RNA molecules were chosen from the RNA STRAND v2.0 database , and werDifferent time-steps were chosen for the different configurations Table . Configuε). The average run-time was calculated and plotted. If a structure’s stabilization process took more than 30 mins (1800 s) it was terminated and its stabilization time was taken as 1800 s.Each structure was run 20 times and the CPU time of the run was measured until the structure stabilized  [te (CRW) , the Prote (CRW) , the Sigte (CRW) , and thete (CRW) .


Mammalian cells harbor more than a thousand RNA-binding proteins (RBPs), with half of these employing unknown modes of RNA binding. We developed RBDmap to determine the RNA-binding sites of native RBPs on a proteome-wide scale. We identified 1,174 binding sites within 529 HeLa cell RBPs, discovering numerous RNA-binding domains (RBDs). Catalytic centers or protein-protein interaction domains are in close relationship with RNA-binding sites, invoking possible effector roles of RNA in the control of protein function. Nearly half of the RNA-binding sites map to intrinsically disordered regions, uncovering unstructured domains as prevalent partners in protein-RNA interactions. RNA-binding sites represent hot spots for defined posttranslational modifications such as lysine acetylation and tyrosine phosphorylation, suggesting metabolic and signal-dependent regulation of RBP function. RBDs display a high degree of evolutionary conservation and incidence of Mendelian mutations, suggestive of important functional roles. RBDmap thus yields profound insights into native protein-RNA interactions in living cells.  •Experimental generation of an atlas of RNA-binding sites (RBS) in human cells•RBS overlap with enzymatic cores and protein-protein interaction sites•About half of the total RBS map to disordered protein regions•RBS are enriched for phosphorylation, acetylation, and methylation sites Many recently discovered RNA-binding proteins (RBPs) do not show architectural similarities with classical RBPs, and their modes of interaction with RNA were unclear. We developed and employed RBDmap as a method for the comprehensive determination of the RNA-interacting sites of RBPs, identifying more than a thousand such sites. These data yield unprecedented insight into RNA-protein interactions in cells with implications for numerous biological contexts. RNA metabolism relies on the dynamic interplay of RNAs with RNA-binding proteins (RBPs) forming ribonucleoprotein complexes, which control RNA fate from synthesis to decay . Due to Many RBPs are modular, built from a limited pool of RNA-binding domains (RBDs), including the RNA recognition motif (RRM) and other canonical RBDs . These dSaccharomyces cerevisiae, and Caenorhabditis elegans  RNA are isolated using oligo(dT) magnetic beads and purified by stringent washes that include 500 mM LiCl and chaotropic detergents (0.5% LiDS), efficiently removing non-covalent binders . After eWe collected an input sample aliquot after UV irradiation, oligo(dT) selection, and protease digestion, which in principle should reflect the RNA interactome A. When cAnalysis of the RNA-bound and released fractions by quantitative proteomics shows high correlation of the resulting peptide intensity ratios between independent biological replicates. These ratios follow a bimodal distribution with one mode representing the released peptides (gray) and the other the RNA-bound ones red; E and S1FAbout half of the RBPs covered by RBDpeps harbor well-established RBDs and play known functions in RNA biology, reflected by a strong and significant enrichment of RNA-related protein domains and biological processes comparable to the HeLa RNA interactome I and S1JInterestingly, RNA-bound and released proteolytic fragments display distinct chemical properties. Released peptides are rich in negatively charged and aliphatic residues, which are generally underrepresented in RNA-binding protein surfaces A. ConverValidating the RBDmap data, classical RBDs such as RRM, KH, cold shock domain (CSD), and Zinc finger CCHC, are strongly enriched in the RNA-bound fraction C. This eUnexpected initially, helicase domains are underrepresented in the RNA-bound fraction C. Howeve3NNC)  repository. These were “digested” in silico with either LysC or ArgC, and the predicted proteolytic fragments were considered as “proximal” to RNA when the distance to the closest RNA molecule is 4.3 Å or less; otherwise, they were categorized as non-proximal A. About 3NNC) . At leas3NNC) .RBDmap also correctly assigns RNA-binding regions within large protein complexes such as the nuclear cap-binding complex. The small nuclear cap-binding protein (NCBP) 2 (or CBP20) directly contacts mRNA via the cap structure (m7GpppG), while the larger NCBP1 (CBP80) interacts with NCBP2 . In agreTo test whether RNA-binding assignments of RBDmap can reach near single-amino acid resolution, we collected the complete set of RBDpeps and released peptides mapping to a given RBD class  and assessed their relative position within the domain (from 0 to 1) as well as its adjacent upstream (from −1 to 0) and downstream regions (from 1 to 2) B. The MSThis analysis also successfully assigned correct RNA-binding sites to KH, DEAD-box helicase, and CSD, as shown in For more than half of the RBPs characterized by RBDmap, no functional or domain annotation related to RNA biology is currently available I and S1Jcis/trans isomerases are classified based on their domain architecture into two groups: PPI and FKBP. This protein superfamily has close links to RNA metabolism, and two members, PPIE and PPIL4, harbor classical RRMs  90 and 70 families D. HSPs aApparently, numerous enzymes of intermediary metabolism bind RNA through regions in close proximity to their substrate-binding pockets. Specifically, the di-nucleotide binding domain (or Rossmann fold) and mono-nucleotide binding folds emerge as bona fide RBDs with 12 proteins mapped by RBDmap , extendiRBDmap also uncovers RNA-binding activities within PDZ, 14-3-3, ERM, and the tubulin-binding domains, which are involved in protein-protein interactions and protein localization G–S4I. DuRBDmap also identifies RNA-binding sites within domains of unknown function such as NDR and DZF. N-myc downstream-regulated genes (NDRGs) represent a family of proteins with unknown function. NDRG1 is a metastasis suppressor relevant for cancer progression and prognosis , its exa32P]-ATP, followed by SDS-PAGE and autoradiography. We generated Tet-inducible HeLa cell lines expressing the PDZ domain of β-1-syntrophin (SNTB) 1 and SNTB2, as well as the DZF domains of Zinc finger RNA-binding protein (ZFR) and interleukin enhancer-binding factor (ILF) 2 and ILF3, all fused to a FLAG-HA tag. As positive controls, we used the full-length ILF3 (FL), its DSRM domain alone, and hnRNPC, while actin (ACTB) was used as a negative control. The PNK assay shows radioactive bands of the expected molecular weight for all tagged PDZ and DFZ domains and only when UV light was applied to the cultured cells  assay as an orthogonal approach. In brief, cells are irradiated with UV light and, after lysis, RNA is trimmed with RNase I. Proteins of interest are immunoprecipitated under stringent conditions and the presence of RNA revealed by 5′ end phosphorylation with PNK and [γ-ed cells J and 4K.Even if functional studies will have to define the physiological roles of these unconventional RBDs in the future, their biological relevance warrants consideration. It is possible that these RBDs may endow RBPs with “moonlighting” activities in posttranscriptional regulation, akin to cytosolic aconitase (IRP1) . AlternaA high proportion of the human RBPs lack native 3D structure , and theDetailed analysis identifies clusters of disordered RBDpeps that can be classified on the basis of sequence motifs. While a few R-rich, RGG, and SR repeats have previously been shown to bind RNA experimentally , RBDmap Aromatic residues are typically found in hydrophobic cores. However, histidines (H), phenylalanines (F), and especially tyrosines (Y) occur within the RNA-binding disordered regions D and S5CLysine (K) combines with negatively charged residues, G, P, or Q, to form distinctive RNA-binding motifs D and S5CTo validate the disordered regions identified by RBDmap as bona fide RNA-binding motifs, we fused the RGG-rich and the K-rich sequences from FUS and Methyl-CpG-binding protein 2 (MECP2), respectively, to eGFP and tested the fusion proteins with the same assay as in The biological function and mode of interaction of disordered regions with RNA should be further investigated.Previously unknown RNA-binding globular and disordered regions display similar mean isoelectric points as known RBDs A, while −08), including tyrosine phosphorylation, methylation, acetylation, and malonylation  . For exaRBDmap provides an unprecedented identification of RNA-binding regions of RBPs in living cells. It describes 1,174 high confidence (1% FDR) RNA-binding sites within 529 proteins. These sites have been validated as a whole by stringent statistical analyses  and crosOur data suggest that multifunctional globular domains, which combine RNA-binding with enzymatic functions or protein-protein interaction surfaces, are commonplace, not rare exceptions. These invoke additional functions for RNA, including the  control of catalytic activities and of protein-protein interactions. Moreover, disordered regions are found to play common roles in native protein-RNA interactions, comprising half of the total RNA-binding sites identified.The RNA-binding motifs identified here share physico-chemical features of well-established RBDs, are conserved across evolution, and represent hot spots for posttranslational modifications and disease-associated mutations. Individually and in combination, these features suggest important biological roles.S. cerevisiae, Caenorhabditis elegans, or Drosophila melanogaster to study the evolution of RBDs. It can also be applied to cells subjected to different experimental conditions to investigate the responses of RBPs to physiological cues such as e.g., stress, starvation, or differentiation.As a method, RBDmap can now be applied to other cell types and organisms such as http://www.bioconductor.org). RBDmap data can be accessed under http://www-huber.embl.de/users/befische/RBDmap.Initial UV crosslinking and oligo(dT) purification followed the mRNA interactome capture protocol . CompletProteins were processed following standard protocols, and the resulting peptides were labeled with stable isotopes in vitro, fractionated, and analyzed on a nano-HPLC system (Proxeon) or nano-Acquity UPLC system (Waters) coupled directly to an LTQ Orbitrap Velos (Thermo Fisher Scientific).A complete description of data analysis can be found in the 254 (Tet-on HeLa cells expressing eGFP fusion proteins were generated as described elsewhere . Upon in254 . After cA.C., B.F., and M.W.H. contributed to the conception and design of the project. A.C., R.H., and A.-M.A. carried out the experimental work. C.K.F., S.F., and J.K. performed the proteomic analyses. B.F., T.C., A.C., C.K.F., J.K., and M.W.H. performed the data analyses. A.C. and M.W.H. wrote the manuscript with input from all authors.


Over the past 2 decades, molecular techniques have established the critical role of both free-living and host-associated microbial partnerships in the environment. Advancing research to link microbial community dynamics simultaneously to host physiology and ecosystem biogeochemistry is required to broaden our understanding of the ecological roles of environmental microbes. ecosystem microbiology with an eye to providing managers with microbial indicators of reef ecosystem processes, coral health, and resilience to both local and global stressors.Over the past 2 decades, molecular techniques have established the critical role of both free-living and host-associated microbial partnerships in the environment. Advancing research to link microbial community dynamics simultaneously to host physiology and ecosystem biogeochemistry is required to broaden our understanding of the ecological roles of environmental microbes. Studies on coral reefs are actively integrating these data streams at multiple levels, from the symbiotic habitat of the coral holobiont to microbially mediated interactions between corals and algae to the effects of these interactions on the microbial community structure, metabolism, and organic geochemistry of the reef ecosystem. Coral reefs endure multiple anthropogenic impacts, including pollution, overfishing, and global change. In this context, we must develop  Symbiodinium) and a complex consortium of bacteria, archaea, fungi, and viruses, collectively referred to as the coral holobiont. These microbial symbionts mediate the stringent recycling of nutrients that establish corals as the dominant competitors in reef habitats. Threatened on all sides by the upheaval of the Anthropocene epoch, reefs worldwide are undergoing rapid change. This includes shifts to fleshy algal dominance as overfishing and nutrient enrichment foster algal growth and multiple stressors both local  and global  weaken corals. Because of the central role of microbial communities in reefs across water, sediment, corals, and a host of other benthic and pelagic denizens, it is apparent that microbial ecosystem science will be key to understanding how, when, and where reefs are most resilient to this onslaught.Coral reefs present a rich opportunity to develop a holistic microbial system biology integrating across scales from evolutionarily entrenched animal symbioses to the free-market synchrony of the oceanic plankton. As ecological systems, coral reefs maintain remarkably high rates of biomass production even as they emerge from some of the most nutrient-depleted oceans on Earth. Scleractinian corals, the keystone animals that define the shape and extent of reefs, maintain stable mutualisms with zooxanthellae (photosynthetic dinoflagellates in the genus Driven by the need to understand reef transitions on a global scale, we posit that research actively linking microbial community dynamics simultaneously to host physiology and biogeochemical transformations will unravel the mechanistic linkages underpinning whether coral reefs live or die. Most of the microbial taxa inhabiting marine systems are poorly characterized, with limited representation via culturable isolates to model adaptive physiology and functional attributes. As a result, the toolbox of methods we and others use to investigate these systems is dominated by modern molecular approaches, including high-throughput sequencing of phylogenetic marker genes to define community structure , 2 and c3–10–in situ communities on reefs varying in benthic composition (The oceanic interface of reefs, as a blossoming wall of macroorganismal diversity and biomass bathed in waters dominated by submicrometer bacteria and archaea, forms a striking gradient that defines the microbial system biology of the coral ecosystem. Reefs maintain their own unique planktonic microbiomes and geochemical milieu even when flushed continually by the surrounding ocean . Each of10–position , 6, 10. position , as do lposition . Given tposition  will facCoral holobionts exert a significant influence over reef metabolism and geochemistry through photosynthesis, nitrogen fixation, calcification, and dissolution; thus, the influence of holobiont microorganisms on these ecosystem processes is also of interest. The role of this microbiome in coral health and resistance to stressors remains a central question in coral reef biology. While past characterization of this complex coral microbiome has focused on marker gene surveys, whole-community shotgun sequencing is required to provide functional information about the metabolic capacity of coral microbiomes ; in the in situ on reefs demonstrate how benthic macroalgae can enhance microbial growth and organic matter remineralization relative to coral-dominated benthos (The changes in reef microbial community structure associated with algal phase shifts can have significant impacts on reef biogeochemistry. For example, mesocosms deployed  benthos . Microbi benthos  creates  benthos  that har benthos , as refl benthos , fundame benthos , 10. The benthos , 6, 12.  benthos , 5. Our in situ surveys. The potential for using microbial taxa and metabolites as pragmatic windows into the physiology, stress response, and resilience of corals and coral reefs is of great interest to coral biologists, reef ecologists, and those tasked with managing our coral reefs for the future. To facilitate the development of microbial and molecular biomarkers of reef health and resilience, we are establishing baselines of these properties across dozens of Pacific islands and atolls; combining broad regional surveys and detailed experimentation will enable the detection of shifts in ecosystem functioning and define the chemical drivers and microbially mediated mechanisms maintaining the health of reefs worldwide.Linking community genomics and transcriptomics to metabolomics within hosts, between holobionts, and with the surrounding planktonic microbiome will go a long way toward resolving central questions in coral reef ecology by defining how stresses to individual reef organisms translate through host and habitat microbiomes to wholesale shifts in reef geochemistry. However, we envision ecosystem microbiology moving toward a predictive understanding of microbial community function and dynamics , requiri