The importance of RNA-protein interactions in controlling mRNA regulation and non-coding RNA function is increasingly appreciated. A variety of methods exist to comprehensively define RNA-protein interactions. We describe these methods and the considerations required for designing and interpreting these experiments. Over the past decade there has been an increasing appreciation of the importance of RNA-protein interactions in controlling many aspects of gene regulation ,2. The eThe central role of RNA-protein interactions in controlling mRNA processing ,2 and ncThe past decade has seen a strong interplay between method development, exploration and discovery about RNA biology. The methods for exploring RNA-protein interactions can be split into two general categories: ‘protein-centric’ and ‘RNA-centric’ methods. The protein-centric methods generally rely on the ability to purify a protein -40, or cProtein-centric approaches have been widely used to generate binding maps of different RBPs across the transcriptome and have provided important insights into how mRNA processing is controlled in the cell ,23,47,48In this review, we discuss approaches for identifying RNA-protein interactions and the challenges associated with interpreting these data. We describe the various protein-centric methods, including native and crosslinking-based methods, and explore the caveats and considerations required for designing, performing and interpreting the results of these experiments. We describe approaches that have been developed to account for analytical biases that can arise in these data. Furthermore, we describe the various RNA-centric methods for the identification of unknown RNA-binding proteins, including the various RNA tags used, purification schemes and detection methods. While conceptually simple, the RNA-centric methods are still not as common as the protein-centric methods because they require an extraordinary amount of starting material to purify enough protein required for detection . We descThe predominant methods for examining RNA-protein interactions are based on protein immunoprecipitation. These methods generally utilize antibodies to pull down the protein of interest and its associated RNA, which is reverse transcribed into cDNA, PCR amplified and sequenced ,54-59. BThere are several variants of these methods, which can be broken down into two main classes: native ,58,62-64in vivo[Native purification methods, often known simply as RIP (RNA immunoprecipitation), purify RNA-protein complexes under physiological conditions. The advantage to these methods is that they preserve the native complexes present in the cell. Yet, these methods also have several limitations. The first and perhaps best described is due to the non-physiological formation of RNA-protein interactions in solution. Indeed, it has been shown that purification of an RNA-binding protein can retrieve RNAs, even when the RNA and protein are not present in the same cell type and therefore could not be interacting in vivo. Furtherin vivo. As a coin vivo.Because of these issues, there has been some controversy about the nature of the interactions detected by these methods. For example, many lncRNA-protein interactions have been explored using native purifications of proteins such as those found in Polycomb repressive complex 2 (PRC2) ,58,62. Iin vivo interactions that are crosslinked in the cell from interactions that form subsequently in solution.To account for these concerns, denaturing methods were introduced. By crosslinking RNA-protein complexes in the cell and purifying the complex under denaturing conditions, one can distinguish The dominant method for crosslinking RNA-protein complexes is treatment of cells with short wavelength UV light to create a covalent linkage between physically interacting RNA and protein molecules in the cell, but not between interacting proteins . MethodsA significant concern with using UV crosslinking methods is that they may miss real RNA-protein interactions simply because they are not efficiently captured by UV crosslinking. Indeed, several RBP families that do not directly interact with nucleic acid bases but instead interact with other features, such as the sugar phosphate backbone, have been shown to have lower crosslinking efficiency with UV . BecauseOther crosslinking methods, such as formaldehyde, can eliminate the need to know the directly interacting protein, but alternative denaturing strategies are needed since purification from a denaturing SDS-PAGE gel would not resolve at the size of the protein. An alternative approach that leverages many of the conceptual features of the CLIP method is to utilize direct denaturing conditions rather than separation through an SDS-PAGE gel. These methods use affinity tags coupled to the protein of interest for capture by purification in denaturating conditions  -65. The There are two primary goals in the analysis of protein-centric experiments: defining which RNAs are bound by the specific protein and defining the specific protein-binding sites on these RNAs.It is important to compare the sample to a negative control since observing reads from a specific RNA alone may not be indicative of a real interaction. One control is to normalize the coverage level of an RNA observed after purification to its abundance in total lysate. Yet, this control only accounts for issues due to RNA abundance: interactions can occur due to association with the purification resin or other features of the system. To account for this, other proteins can be used as negative controls. However, the negative control should be selected with care, as a non-RNA-binding protein is likely to have lower non-specific RNA binding. Indeed, simply mutating the RNA-binding domain of a protein has been shown to remove both specific and non-specific interactions formed by a protein . The ideFurthermore, comparing the sample with a negative control requires proper statistical methods because the inherently low denominator for low abundance RNAs will lead to a higher variance in the enrichment measurement Figure b. This cThe second goal is to map protein-binding sites on RNA. A major consideration is the size of the RNA after digestion. While in theory the ideal size is that of the protein footprint itself, several considerations favor slightly larger sizes. One issue is the alignability of the sequencing reads, as very small fragments may not be able to be uniquely aligned to the transcriptome. Another concern is that overdigestion may lead to a loss of real binding sites by preferentially eliminating certain protein footprints .Because UV-crosslinking is irreversible, reverse transcription can halt at the site of crosslinking even after protein removal ,25. WhilProtein-centric methods are of limited utility for identifying novel RBPs that interact with a specific RNA or for the characterization of novel classes of ncRNAs for which the identities of the RNA-binding proteins are still unknown. An alternative approach is to use an RNA-centric protein identification strategy. The general idea is simple: rather than using an antibody to capture a protein of interest and sequencing the associated RNA, these methods purify an RNA of interest and identify the associated protein complexes, using methods such as MS. We will explore the different variants of these methods below, focusing on those designed to comprehensively identify novel RNA-protein interactions.One general approach to capture RNA is to exploit naturally occurring interactions between RNA and protein - such as the bacteriophage MS2 viral coat protein, which binds tightly to an RNA stem loop structure . In thisThe differences between these methods can be exploited when trying to elute their respective RNA-protein complexes. In general, protein complexes are eluted from a support resin by boiling in SDS . This apin vitro and in vivo purification methods . In oneThe choice of which MS approach to use for the identification of RBPs depends on the nature of the upstream purification. When utilizing a protocol where the resulting protein purification yields little background in the control sample, a non-quantitative approach may work well. The CRISPR-Csy4 system, for instance, was previously shown to enable very high stringency and specific elution, and because of this a non-quantitative approach provided reliable results . Similarin vivo crosslinked complexes [in vivo crosslinking followed by a denaturing purification and therefore is not applicable to all purification methods. In the absence of this, several different negative controls should be included to ensure robustness of the results identified.There are several analytical challenges for identifying proteins associated with an RNA by MS. Similar to the protein-centric methods, great care must be taken to select informative negative controls for the RNA-centric methods. Controls that are often used include a different cellular RNA , sequencomplexes . HoweverA significant challenge in the identification of unknown RBPs is the generation of sufficient material for MS, particularly for low abundance RNA-protein complexes. Unlike sequencing methods that enable nucleic acid amplification, the amount of protein purified in these experiments cannot be amplified. For this reason, RNA-centric methods have mostly been applied to highly abundant RNAs, such as 7SK , snRNPs While much work has been done to develop methods to identify and examine RNA-protein interactions, there are still significant challenges that need to be addressed. To date, we still do not know the protein complexes that interact with most RNAs in the cell - including mRNAs, classical ncRNAs and lncRNAs. For lncRNAs in particular, we know little about the diversity of proteins that they may interact with. Many of the protein complexes that have been identified to interact with lncRNAs do not fall into traditional RNA-binding protein classes, making it difficult to generate accurate predictions of what these complexes may look like. Understanding the protein complexes that interact with lncRNAs will be an important first step toward understanding their various biological functions and mechanisms. The major challenge with defining these proteins is that the RNA-centric methods are still not well suited for exploring low abundance transcripts. Future work will be needed to address this challenge and to define the protein complexes that interact with a given lncRNA or individual mRNA.Although the development of the protein-centric and RNA-centric approaches has mostly proceeded independently, we can now begin to combine the results of these complementary approaches to build a complete picture of the repertoire of RBPs in a cell and define their roles in binding and modulating the functions of various classes of RNA . SeveralCLIP: Crosslinking and immunoprecipitation; CRISPR: Clustered regularly interspaced short palindromic repeats; hnRNP: Heterogenous ribonucleoprotein; lncRNA: Large non-coding RNA; MS: Mass spectrometry; ncRNA: Non-coding RNA; PAR: Photoactivatable-ribonucleoside-enhanced; PCR2: Polycomb repressive complex 2; RBP: RNA-binding protein.


RNA‐sequencing (RNA‐seq) allows global gene expression analysis at the individual transcript level. Accurate quantification of transcript variants generated by alternative splicing (AS) remains a challenge. We have developed a comprehensive, nonredundant Arabidopsis reference transcript dataset (AtRTD) containing over 74 000 transcripts for use with algorithms to quantify AS transcript isoforms in RNA‐seq.RTD was formed by merging transcripts from TAIR10 and novel transcripts identified in an AS discovery project. We have estimated transcript abundance in RNA‐seq data using the transcriptome‐based alignment‐free programmes Sailfish and Salmon and have validated quantification of splicing ratios from RNA‐seq by high resolution reverse transcription polymerase chain reaction (HR RT‐PCR).The AtRNA‐seq and HR RT‐PCR were obtained demonstrating the accuracy of abundances calculated for individual transcripts in RNA‐seq.Good correlations between splicing ratios from RTD is a resource that will have immediate utility in analysing Arabidopsis RNA‐seq data to quantify differential transcript abundance and expression.The At AS generates multiple transcript isoforms from a gene through variable selection of different splice sites in the precursor mRNA  to Arabidopsis RNA‐seq data. These related programmes are based on lightweight models of sequence alignment and efficient, parallel statistical inference algorithms. Sailfish is a k‐mer based method that uses lightweight algorithms to assign k‐mers from RNA‐seq reads to transcripts defined in a reference transcriptome  units , and have generated quantitative TPM data from RNA‐seq. We have obtained high correlations between data from RNA‐seq and from the high resolution reverse transcription polymerase chain reaction (HR RT‐PCR) system . The GFF transcript annotation files from the two sources described earlier were then sorted, checked and intron coordinates were extracted using Genome Tools version 1.5.3  and Salmon programmes   has three different transcripts resulting from AS in a 3′UTR intron: fully spliced, use of alternative 3′ splice site which removes 8 nt from exon 3 and intron 2 retention  undergoes AS in intron 6 . As in some cases HR RT‐PCR detected relatively low abundance AS transcripts which were not identified in RNA‐seq, for this comparison we calculated a splicing ratio by comparing the abundance of the AS transcript to that of the fully spliced transcript  with SNew Phytologist Central Office.Please note: Wiley Blackwell are not responsible for the content or functionality of any supporting information supplied by the authors. Any queries  should be directed to the Table S1 Transcript complexity of AtRTD: distribution of the number of transcripts per geneClick here for additional data file.


PKCθ show impaired NF-κB as well as NFAT/AP-1 activation resulting in strongly decreased IL-2 expression and proliferation. However, it is not yet entirely clear, how the function of PKCθ - upon T cell activation - is regulated on a molecular level.Protein kinase C-θ (PKCθ) plays an important role in signal transduction down-stream of the T cell receptor and T cells deficient of 2-terminal WD40 domains of Coro1A and the C2-like domain of PKCθ are sufficient for the interaction. Furthermore, we confirm a physical interaction by GST-Coro1A mediated pull-down of endogenous PKCθ protein. Functionally, wild-type but not Coro1A lacking its actin-binding domain negatively interferes with PKCθ-dependent NF-κB, Cyclin D1 and IL-2 transactivation when analysed with luciferase promoter activation assays in Jurkat T cells. This could be phenocopied by pharmacological inhibitors of actin polymerization and PKC, respectively. Mechanistically, Coro1A overexpression attenuates both lipid raft and plasma membrane recruitment of PKCθ in CD3/CD28-activated T cells.Employing a yeast two-hybrid screen and co-immunoprecipitation analyses, we here identify coronin 1A (Coro1A) as a novel PKCθ-interacting protein. We show that the NH+ T cells, we observed that (opposite to PKCθ) Coro1A does not localize preferentially to the immunological synapse. In addition, we show that CD3+ T cells isolated from Coro1A-deficient mice show impaired IKK/NF-κB transactivation.Using primary CD3Together, these findings both in Jurkat T cells as well as in primary T cells indicate a regulatory role of Coro1A on PKCθ recruitment and function downstream of the TCR leading to NF-κB transactivation.The online version of this article (doi:10.1186/s12964-015-0100-3) contains supplementary material, which is available to authorized users. An efficient adaptive immune response depends on the activation of T lymphocytes by antigen-presenting cells (APC) and the acquisition of appropriate effector T cell function. Activation of T lymphocytes occurs upon engagement of T cell receptors (TCR) and the corresponding antigen-MHC complexes together with the ligation of the co-receptor CD28 by B7 molecules – leading to the formation of the immunological synapse (IS) between T cell and APC. PKCθ, a member of the protein kinase C (PKC) family of serine/threonine kinases, is rapidly, within seconds after TCR engagement, recruited to the peripheral supra-molecular activation cluster (pSMAC) of the immunological synapse (IS) ,2. It ha2-terminus that resembles PKC substrates and forms an auto-inhibitory loop to keep the enzyme in an inactive conformation. PKCθ is released from the auto-inhibition after recruitment to the plasma membrane, where it binds to diacylglycerol  domain in the NHiewed in ). Notewoiewed in . However2) fused to the DNA-binding domain as “bait”. With this approach we identify a group of clones that interact strongly with PKCθ. A detailed description of all methods used is provided in the Additional file 2-terminal domain of human Coro1A  screen using the regulatory domain of PKCθ tyrosine). Of note, while as already published, activation-induced PKCθ recruitment to the IS was consistently observed by confocal microscopy , Coro1A et al. described a physical interaction between Coro1A and PLC- γ1 promoting calcium mobilization from intracellular stores upon activation of naive T cells [et al. did not observe any impairment of T cell activation at all when analysing T cell function using their knockout strain [Coro1a knockout mice [Coro1a-deficient mice upon stimulation with anti-CD3 and anti-CD28   Figure D.Using a combination of phorbol ester and ionomycin, which bypass early activation events downstream of the TCR by directly activating PKC isotypes and inducing calcium influx, respectively, only partially restored I-κBα phosphorylation and NF-κB:DNA binding, pointing to an important role of Coro1A for PKC activation processes.+ T cells.Taken together, the present results provide evidence that Coro1A is a functional interaction partner of PKCθ in the established PKCθ/IKK/NF-κB/IL-2 transactivation pathway in CD3


We highlight the existing biotechnological applications of archaeal DNA ligases and RNA ligases. Finally, we draw attention to recent experiments in which protein engineering was used to modify the activities of the DNA ligase from Pyrococcus furiosus and the RNA ligase from Methanothermobacter thermautotrophicus, thus demonstrating the potential for further work in this area.With their ability to catalyse the formation of phosphodiester linkages, DNA ligases and RNA ligases are essential tools for many protocols in molecular biology and biotechnology. Currently, the nucleic acid ligases from bacteriophage T4 are used extensively in these protocols. In this review, we argue that the nucleic acid ligases from Archaea represent a largely untapped pool of enzymes with diverse and potentially favourable properties for new and emerging biotechnological applications. We summarise the current state of knowledge on archaeal DNA and RNA ligases, which makes apparent the relative scarcity of information on  Their+ as a high-energy cofactor [α-phosphate of the cofactor by an active site lysine yields a ligase-AMP intermediate. Secondly, the AMP is transferred to the 5′-phosphate of one polynucleotide strand, resulting in an adenylated nucleic acid intermediate (with the active site lysine as the leaving group). Finally, the 3′-hydroxyl group of the second polynucleotide strand attacks the 5′-phosphate of the opposing strand, joining the two strands with a new phosphodiester bond and liberating AMP.Together with the RNA capping enzymes and tRNA ligases, DNA and RNA ligases constitute the nucleotidyl transferase superfamily . All of cofactor –6. In th in vitro conditions. A familiar example is the widespread use of the Pyrococcus furiosus DNA polymerase in the Polymerase Chain Reaction (PCR) [Species from the domain Archaea not only survive, but thrive, under conditions of extreme temperature, salinity, pH, and pressure. Evolution in these extreme environments has resulted in archaeal proteins that have properties of value to biotechnologists, including stability and activity under a range of comparatively harshon (PCR) , in whicon (PCR) , 9. In tIn vivo, DNA ligases catalyse the formation of phosphodiester bonds at single-stranded nicks in double-stranded DNA. This activity is critical for maintaining genomic integrity during DNA replication, DNA recombination, and DNA excision repair [+-dependent DNA ligases (EC 6.5.1.2) are typically found in bacteria and some eukaryotic viruses. There are, however, exceptions to this rule. Most notably, the archaeal species Haloferax volcanii possesses two active DNA ligases: one ATP-dependent (LigA) and the other NAD+-dependent (LigN) [n repair , 6. Theyt (LigN) . Thermus aquaticus (Taq), has become important for Gibson assembly. This is an isothermal, one-pot method for assembling overlapping DNA molecules without the use of restriction enzymes [DNA ligases are essential for numerous applications in molecular biology and biotechnology. For decades, DNA ligases have been used to construct recombinant DNA molecules  and for genetic disease detection using the ligation chain reaction . More re enzymes . A numbe enzymes , 14, eit Taq ligase for Gibson assembly.With its ability to ligate both cohesive- and blunt-ended, double-stranded DNA molecules , the mosTo date, fewer than 25 archaeal DNA ligases have been characterised to any extent at all, and in general data on them is limited compared with DNA ligases from other domains of life , 18.  Ta+. Interestingly, a number of archaeal DNA ligases can utilise multiple cofactors. Sequence homology suggested that the DNA ligases from Thermococcus kodakaraensis, T. fumicolans, and T. onnurineus belong to the ATP-dependent family (EC 6.5.1.1); however, in vitro characterisation of each has shown that they are able to use either ATP or NAD+ as their cofactor [ Sulfophobococcus zilligii also shows relative activity of 63% when the cofactor is switched from ATP to GTP [ S. zilligii DNA ligase, activity with GTP has only been described for RNA capping enzymes [ S. zilligii enzyme, are also able to use ADP , but not fragments with shorter (4-nucleotide) cohesive ends or with blunt ends [ Thermococcus sp. 1519, may drive the development of new methods, with the promise that ligation at higher temperatures (60–70°C) would decrease the number of incorrect ligation events that arise from misannealing of fragments with short overhangs.The most immediate applications for archaeal DNA ligases are likely to be those that exploit their high temperature optima  outperformed the wild type at two temperatures. At 60°C, maximum amplification of ligated DNA product was achieved after only 3 cycles with the mutant but took 10 cycles with the wild type enzyme. At 30°C, the engineered enzyme gave maximum product yield after 5 cycles of ligation-amplification, whereas the product yield with the wild type ligase was only ~30% as great, even after 10 cycles . Further P. furiosus DNA ligase carrying either three point mutations (D540R/K554A/K558A), or D540R plus deletion of the final four amino acids of the C-terminal helix, had further enhanced nick-joining activities. While it is yet to be tested with other archaeal DNA ligases, this design approach, releasing the interactions of the C-terminal helix with the AdD and OBD domains, appears as though it could be a generally applicable one for enhancing activity.In a follow-up study, the same group has recently used further mutagenesis to eliminate the ionic interactions between the AdD and the OBD entirely . P. furiRNA ligases (EC 6.5.1.3) are RNA end-joining enzymes involved in RNA repair, splicing, and editing pathways. In contrast to the ubiquitous DNA ligases, RNA ligases have a narrower phylogenetic distribution. Sequence similarity searches have found RNA ligases in all three domains of life but only in a subset of species . Trypanosoma and Leishmania. These enzymes are primarily associated with sealing nicks in RNA made duplex by the presence of a bridging complementary strand [RNA ligases are typically classified into two broad families. The Rnl1 family includes the eponymous RNA ligase 1 (Rnl1) from bacteriophage T4  and the y strand , 57, 58. Pyrococcus horikoshii have also been reported. The first is a putative 2′-5′ RNA ligase, the structure of which has been solved [Like DNA ligases, RNA ligases are also important in molecular biology. T4 RNA ligases 1 and 2 have become essential for a subset of rapid amplification of cDNA ends (RACE) protocols, 3′ RNA labelling, and most importantly, at present, the preparation of microRNA (miRNA) sequencing libraries. ATP-dependent RNA ligases capable of forming phosphodiester bonds between 5′-phosphate and 3′-hydroxyl termini are of most use in these protocols; therefore, they will be the focus of the following sections. For completeness, we also note that two noncanonical RNA ligases from the archaeal speciesn solved . The secn solved , 61. Pyrococcus abyssi, previously annotated as encoding a DNA ligase, was found to encode an RNA ligase instead [ P. abyssi RNA ligase was a marginally closer structural homologue of T4 Rnl1  than T4 Rnl2  [ P. abyssi RNA ligase was active with single-stranded RNA substrates, but not double-stranded RNA, similar to T4 Rnl1.The first detailed biochemical characterisation of an archaeal RNA ligase was reported in 2008, when an open reading frame from instead . Previou instead . Howeveresidues) . Further P. abyssi RNA ligase [α-helical, had no structural homologues, and was not ascribed a function. The dimerisation domain had structural similarity to the copper-binding domain of the amyloid precursor protein, although the metal binding residues are absent in the ligase [Unlike the monomeric mesophilic ligases, X-ray crystallography revealed a homodimeric structure for theA ligase . Each moe ligase . P. abyssi RNA ligase is not known. It has been proposed that it may be functionally important for facilitating two symmetric and simultaneous ligation events, such as splicing and intron circularisation [The role of dimerisation in therisation . More gerisation . This st Methanobacterium thermoautotrophicum [ Methanothermobacter thermautotrophicus [ P. abyssi and M. thermautotrophicus RNA ligases are summarised in To date, the only other archaeal RNA ligase to be characterised biochemically is the one from the speciesrophicum , which irophicus . The pro in vivo, RNA ligases have become important tools in molecular biology [In addition to their roles biology . Shortly biology , RNA lig biology , oligonu biology , and 5′  biology .More recently RNA ligases have become essential for constructing sequencing libraries of microRNAs (miRNAs) and other small RNAs. During library preparation, T4 RNA ligases are used to join 5′- and 3′-adaptors to the RNA substrates, so that the adaptor sequences can be used for priming during reverse transcription and PCR –73. The  in vivo is to seal nicks in RNA hairpin loops (such as those in cleaved tRNA molecules). In vitro, this results in a propensity to circularise the RNA substrates, preventing adaptor ligation. Another limitation with the T4 RNA ligases is that they are biased towards ligating particular RNA sequences [Unfortunately, it has become increasingly apparent that miRNA sequencing datasets are prone to severe biases  and thatequences , 78 whicequences . This liequences . Therefoequences . M. thermautotrophicus RNA ligase has the ability to adenylate both single-stranded RNA and single-stranded DNA ( M. thermautotrophicus RNA ligase accumulates high quantities of the adenylated intermediates (AppRNA and AppDNA) when an excess of ATP is used in the reaction, making it an ideal substitute. This enzyme is currently available commercially as a component of a 5′ DNA adenylation kit (from New England Biolabs).Archaeal RNA ligases have found some use in molecular biology protocols. Thended DNA  and it h M. thermautotrophicus RNA ligase is highly active as a 5′ adenylation enzyme, a single point mutation (Lys97→Ala) resulted in an enzyme that was unable to perform adenylation at all, but which retained the ability to form phosphodiester bonds [ M. thermautotrophicus RNA ligase (and the K97A mutant) to function at 65°C also helps to remove ligation bias associated with RNA secondary structures. In order to implement this protocol, the mutated enzyme is commercially available (as the Thermostable 5′ AppDNA/RNA Ligase from New England Biolabs).While theer bonds . This haer bonds . The abi Pyrococcus furiosus DNA ligase [ Methanothermobacter thermautotrophicus RNA ligase [In this review, we have summarised the current literature on archaeal nucleic acid ligases. We have highlighted the relative dearth of knowledge on these enzymes, while discussing characteristics that are likely to make them valuable additions to the biotechnologist's toolbox in future. In particular, archaeal enzymes are archetypically thermostable. DNA ligases that are stable and active at elevated temperatures are becoming critical for emerging technologies such as Gibson assembly (which underpins synthetic biology) , while tA ligase , 47 and A ligase  demonstrOverall, the pool of archaeal nucleic acid ligases is diverse but currently undersampled. We anticipate that its further exploration will lead to the discovery of new enzymes with favourable properties for molecular biology and biotechnology, which in turn will drive the development of new methodologies.


Kdapp values spanning a 10-fold range. Furthermore, some RNAs that do not contain any of these motifs bind FUS with similar affinity. FUS binds RNA in a length-dependent manner, consistent with a substantial non-specific component to binding. Finally, investigation of FUS binding to different nucleic acids shows that it binds single-stranded DNA with three-fold lower affinity than ssRNA of the same length and sequence, while binding to double-stranded nucleic acids is weaker. We conclude that FUS has quite general nucleic acid-binding activity, with the various proposed RNA motifs being neither necessary for FUS binding nor sufficient to explain its diverse binding partners.FUS, a nuclear RNA-binding protein, plays multiple roles in RNA processing. Five specific FUS-binding RNA sequence/structure motifs have been proposed, but their affinities for FUS have not been directly compared. Here we find that human FUS binds all these sequences with  FUS are detected in ∼5% of familial ALS  patients as well as in sporadic ALS , is an abundant nuclear protein that has been implicated in transcription, mRNA splicing and mRNA transport –3. Mutatadic ALS ,5. ALS iadic ALS . PatientFUS, together with EWS (Ewing's sarcoma) and TAF15 (TBP-associated factor 15) in vertebrates, belongs to the FUS/EWS/TAF15 (FET) or TLS/EWS/TAF15 (TET) family . The FUSin cis, and it leads to reduced transcription of the cyclin D1 gene. More generally, our previous work has shown that FUS binds the C-terminal domain (CTD) of RNA polymerase II (RNA Pol II) in an RNA-dependent manner and orchestrates phosphorylation at position Ser2 of the CTD hexapeptide motif  and p300 on the cyclin D1 promoter . This inde motif ,11.in vitro SELEX analysis to identify GGUG as a preferred FUS-binding motif  of the same length and sequence. We conclude that FUS has a wide range of nucleic-acid binding ability.Elucidating the nucleic acid targets of FUS is important for understanding its cellular roles. To characterize the features of RNA targets necessary for FUS binding, we have thoroughly evaluated the binding affinities of FUS with all five published RNA motifs and additional sequences, using electrophoretic mobility shift assays (EMSAs). We found that FUS is able to bind all published RNA sequences within a 10-fold range of binding affinities. In contrast to expectation, however, FUS bound other RNAs including fragments of an 6-MBP  tag at the N-terminus of FUS, generating the His6-MBP-FUS construct  and growth for an additional 3–5 h at 37°C. Bacterial cells were pelleted at 6000 rpm for 10 min and lysed in lysis buffer , followed by sonication (15 s on and 15 s off) for a total time of 1 min. Lysates were cleared by centrifugation at 17 500 g for 20 min at 4°C and supernatants were incubated for 1 h with Ni-sepharose beads at 4°C. Beads were pelleted at 2000 rpm for 2 min and washed four times in wash buffer , followed by one time in wash buffer supplemented with 25 mM imidazole. Protein  was eluted in wash buffer supplemented with 250 mM imidazole. Highly concentrated FUS tends to form aggregates, but the maltose-binding protein (MBP) tag keeps FUS soluble. MBP tag itself does not bind RNA (2 to ensure the complete elimination of nucleic acid and the nuclease was then inactivated by chelating the Ca2+ with 1.0 mM ethylene glycol tetraacetic acid (EGTA); we determined that the residual inactivated micrococcal nuclease did not affect the measurement of FUS–RNA binding (data not shown). Protein was aliquoted with 10% glycerol, snap frozen in liquid nitrogen and stored at −80°C.The initial FUS expression plasmid was acquired as a gift from the M. G. Rosenfeld lab (UCSD). We added sequences encoding a Hisonstruct . This exbind RNA . Thus, MKdapp values based on active protein assuming a 1:1 complex so that they are directly comparable to those presented in our previous publication  as the substrate. It typically required 1300 nM FUS to fully bind 200 nM RNA. In the case of a 1:1 complex, this would mean that the protein was only 15% active but on the basis of our previous estimate of four FUS molecules per 48 nt RNA , the FUSlication , understin vitro transcription. The templates were as follows:MBP 1–10 Forward, TAATACGACTCACTATAGGGAGACCAAAACTGMBP 1–10 Reverse, CAGTTTTGGTCTCCCTATAGTGAGTCGTATTAMBP 1–20 Forward, TAATACGACTCACTATAGGGAGACCAAAACTGAAGAAGGTAAMBP 1–20 Reverse, TTACCTTCTTCAGTTTTGGTCTCCCTATAGTGAGTCGTATTAFor MBP 1–10 and MBP 1–20, DNA templates were synthesized by Integrated DNA Technologies (IDT). Complementary strands were annealed and used for E. coli. The primers used were as follows:T7-Forward, TAATACGACTCACTATAGGGAGACCAAAACTGAAGAAGGTAAACTGGTAATCTGGMBP 1–50 Reverse, CCTTTATCGCCGTTAATCCAGATTACMBP 1–100 Reverse, TTCCGGTATCTTTCTCGAATTTCTTACCGMBP 1–200 Reverse, CGGTCGTGTGCCCAGAAGATAATGMBP 1–300 Reverse, GTAACGTACGGCATCCCAGGTAAACFor other longer MBP RNA constructs, DNA templates were amplified from plasmid pFastBac1 containing the MBP gene from MBP 1–100 MS2 Reverse: TTCCGGTATACATGGGTAATCCTCFor the MBP RNA bearing the MS2 motif, only MBP1–100 Reverse was changed as follows:in vitro RNA transcription reactions were set up as described  to digest DNA template. The digestions were stopped by addition of 50 mM EDTA. Unincorporated nucleotides were removed by a microspin G25 column . Then, the reactions were mixed with formamide dye, incubated 5 min at 95°C and loaded onto a 10% w/v 29:1 acrylamide:bis 7 M urea gel. The bands containing radiolabeled RNA were excised from the gel and the RNAs were eluted for 1 h at 4°C by 0.3 M sodium acetate, pH 5.2. The eluant was precipitated with glycogen and ethanol at −80°C overnight and the body-labeled RNAs were quantified by liquid scintillation counting.DNA templates for transcription were generated by polymerase chain reaction (PCR) with high-fidelity DNA polymerase . The predicted size of PCR amplicons was confirmed by agarose gel electrophoresis with appropriate DNA size markers. The escribed . Briefly32P-ATP using T4 polynucleotide kinase (NEB); incubation was at 37°C for 45 min, followed by inactivation with EDTA. Unincorporated nucleotides were removed and RNA was gel-purified as described for in vitro transcription.prD RNA, GGUG RNA and other RNA oligos were synthesized by IDT and end-radiolabeled with γ-32P-labeled RNA was incubated with MBP-FUS in binding buffer , 0.1 mg/ml yeast tRNA, 0.1 mg/ml bovine serum albumin and trace amount of orange dye) at room temperature for 30 min. A portion of each reaction was loaded onto a 4–20% Tris-borate-EDTA (TBE) (Invitrogen EC62252BOX) gel and run at room temperature at 150 V for 70 min. Gels were vacuum dried for 60 min at 80°C and the [32P] radioactive signal was detected by exposure to phosphorimager screens. The signals were acquired with a Typhoon Trio phosphorimager  and densitometry was quantified with ImageQuant software . Quantified data were fit to a sigmoidal binding curve with MATLAB (MathWorks), allowing calculation of both dissociation constants and Hill coefficients.In a 20 μl binding reaction, a trace amount of For competition assays, an appropriate concentration of unlabeled competitor RNA or DNA was mixed with 5000 cpm radiolabeled RNA of the same sequence in a 20 μl reaction. The binding reaction was performed as described above.E. coli-expressed FUS protein to eight RNAs including the five published motifs and three negative control sequences (Supplementary Table S1). We also tested prD RNA, one of many human ncRNAs that recruits FUS in vivo identified in our previous study . The two proteins both formed discrete RNA–protein complexes and the protein concentration necessary to shift half of the radioactively labeled RNA was similar. In both cases, the observation of discrete complexes suggests well-folded protein. Therefore, we used MBP-FUS purified from E. coli for all remaining experiments.Our MBP-FUS protein was purified from ct cells . To testE. coli, an organism that does not possess FUS. Surprisingly, the first 200 nt of the E. coli MBP mRNA (MBP 1–200) bound FUS with a reasonably high affinity , first 20 nt (MBP 1–20) and so on, and then measured their binding to FUS. If MBP 1–200 contained some sequence or structure necessary to bind FUS, then there should be a sudden increase in affinity at the length corresponding to the inclusion of the motif. If no such sequence or structure existed in MBP 1–200, FUS might bind all the truncated sequences.Even though MBP–RNA originates from Kdapp decreased with increasing RNA length. Plotting log (Kd) versus log (RNA length) revealed a linear relationship between dissociation constant and RNA length with a slope of −1   . The posin vitro transcribed two series of MBP mRNAs, each comprising the first 10 nt, the first 20 nt and so on from the 5′-end of the mRNA. One series had the 21 nt MS2 motif substituted for nucleotides 71–92 and the other did not. RNA sequences in the series lacking the MS2 motif bound MS2 coat protein weakly with a micromolar binding affinity  somewhat lower than those of the 5′prD mutants, further indicating FUS does not differentiate dramatically among RNA sequences or structures for binding. In conclusion, FUS binds RNAs with diverse sequences and structures, and when same-length RNAs are compared the binding affinities are within an order of magnitude.Next, we synthesized four additional RNAs by mutating six consecutive nucleotides of 5′prD into a stretch of adenines Figure . As expeKdapp compared to RNA. This is consistent with the observation of weaker affinities with Htelo DNA than TERRA RNA (Supplementary Table S1).Weaker binding to DNA suggests that the 2′ hydroxyl group may contribute to binding FUS. In addition, FUS bound dsDNA and DNA/RNA hybrid more weakly. Surprisingly, full-length FUS was capable of binding dsRNA, indicating FUS is not only a single-stranded nucleic acid binding protein.Previous literature has shown that FUS binds ssDNA and double-stranded DNA (dsDNA) ,23. To tKdapp as anti-sense prD RNA. In addition, each truncation bound dsDNA and DNA/RNA hybrid very weakly. The FUS truncations, however, bound dsRNA with three- to six-fold higher Kdapp compared to full-length FUS, suggesting dsRNA may require both RRM and RGG-Znf-RGG for optimal recognition.Previous work demonstrated that both the RRM and zinc fingers of FUS contribute to binding RNAs. We generated two truncations of full-length FUS, including LC-RRM  and RGG-Znf-RGG . Consistent with our previous work , both LCIf the same nucleic-acid binding site(s) on FUS bound both ssRNA and ssDNA, then ssDNA should compete for ssRNA binding. To test this, we performed competition assays with radioactively labeled RNA and increasing concentrations of unlabeled ssRNA or ssDNA using EMSA. The radiolabeled prD RNA formed a slow-migrating complex with FUS Figure . A 100-fInterestingly, new discrete FUS–RNA complexes were observed with addition of cold ssRNA or ssDNA Figure  and 4, wWe performed the same competition assays between radioactively labeled dsRNA and cold ssRNA. The shifted FUS–dsRNA complexes migrated slightly slower than the FUS–ssRNA complexes. Excess amounts of cold ssRNA competed away the FUS-bound labeled dsRNA Figure , lane 4,Kdapp for binding to FUS. We found that FUS is able to bind all of these published motifs, but it also binds to their respective negative controls that disrupt the motifs with only slightly reduced affinity. Furthermore, even E. coli MBP mRNA binds FUS and we provide evidence that this is not due to a hidden motif. We conclude that FUS has the ability to bind many RNAs in vitro with similar binding affinities and without requiring a well-defined sequence or structure, in agreement with our previous publication  with relative RNA abundance (RNA-seq) shows a definite trend toward FUS binding to abundant RNA , consistent with a promiscuous binding component. However, the correlation between FUS-binding and RNA abundance is weak; this is not unexpected, because our biochemical results in no way preclude specific binding of FUS to certain RNAs in vivo, e.g. through cooperation with site-specific RNA-binding proteins.Promiscuous binding of FUS to RNA is consistent with many observations  introns ,13–15,24In vivo conditions are different from in vitro conditions and FUS binding to the identified motifs in vivo may be influenced by other RNA-binding proteins selectively bound to certain RNAs, either precluding or cooperating with FUS binding. Lagier-Tourenne et al.  was determined based on a SELEX analysis . The trae et al.  concludeet al.  family transcription factors, which gain higher DNA sequence specificity and enhanced affinity when paired with an Exd protein partner (FUS has the intrinsic ability to bind many RNAs without substantial differences in binding affinity, so what determines the FUS interactome  partner . One can partner , may cha


The preservation of microRNAs in formalin-fixed and paraffin-embedded (FFPE) tissue makes them particularly useful for biomarker studies. The utility of small RNA sequencing for microRNA expression profiling of FFPE samples has yet to be determined.Total RNA was extracted from de-paraffinized and proteinase K-treated FFPE specimens (15–20 years old) of 8 human lung adenocarcinoma tumors by affinity chromatography on silica columns. MicroRNAs in the RNA preparations were quantified by the Illumina HiSeq 2000 sequencing platform with sequencing libraries prepared with the TruSeq Small RNA Sample Preparation Kit (version 2.0) to obtain unpaired reads of 50 b for small RNA fragments. MicroRNAs were also quantified using Agilent Human miRNA (release 16.0) microarrays that can detect 1,205 mature microRNAs and by quantitative reverse transcription (RT)-PCR assays.Between 9.1–16.9 million reads were obtained by small RNA sequencing of extracted RNA samples. Of these, only 0.6–2.3% (mean = 1.5%) represented microRNAs. The sequencing method detected 454–625 microRNAs/sample (mean = 550) compared with 200–349 (mean = 286) microRNAs detected by microarray. In Spearman correlation analyses, the average correlation coefficient for the 126 microRNAs detected in all samples by both methods was 0.37, and >0.5 for 63 microRNAs. In correlation analyses of the sequencing- and RT-PCR-based measurements, the coefficients were 0.19–0.95 (mean = 0.73) and >0.7, respectively, for 7 of 9 examined microRNAs. The average inter-replicate Spearman correlation coefficient for the sequencing method was 0.81.Small RNA sequencing can be used to obtain microRNA profiles of FFPE tissue specimens with performance characteristics similar to those of microarrays, in spite of the fragmentation of ribosomal and messenger RNAs that reduces the method's informative capacity. The accuracy of the method can conceivably be improved by increasing sequencing depth and/or depleting FFPE tissue RNAs of ribosomal RNA fragments. MicroRNAs are single-stranded, non-coding RNAs of 18–25 nucleotides in length that function as epigenetic regulators by inhibiting protein translation from or inducing degradation of messenger RNA (mRNA) transcripts that they target , and theTissue specimens that have been preserved via fixation in formalin are a valuable resource for retrospective studies. Because of its simplicity and low cost, vast amounts of clinical specimens are routinely archived after formalin fixation. Unlike mRNAs, microRNAs preserve well in formalin-fixed tissue because of their ultra-short length , 14; thiHybridization microarrays are routinely used for global microRNA expression profiling of FFPE samples. Small RNA sequencing has emerged as a new technology for microRNA expression profiling. It offers the advantages of high sensitivity and specificity, detection of both novel and known microRNAs, identification of microRNA sequence polymorphisms and editing, and simpler data normalization strategies for comparing samples . With thThis study was conducted with implied written and informed consent of participants and approved by the Institutional Review Board of Memorial Sloan Kettering Cancer Center (MSK) in New York, NY (IRB number WA0269–08).FFPE specimens of 8 therapy-naïve solitary pathologic stage I lung adenocarcinoma tumors resected at MSK between January 1995 and December 1999 were used. The 8 tumors were from a set of stage I lung adenocarcinoma cases that have been previously described , 27. Rol3 of each tumor, using the affinity spin column-based High Pure FFPE RNA Isolation Kit . RNA was quantified via absorbance spectrophotometry on a Nanodrop 2000 instrument  and via RiboGreen dye fluorescence on a Qubit fluorometer . Integrity of RNA was assessed using electrophoresis on Eukaryote Total RNA Pico chip on Bioanalyzer 2100 . RNA aliquots were stored at -80°C.Total RNA was isolated from 4 sections, or approximately 3 mmEight RNA samples were assayed in duplicate and in separate microarray experiments for the two sets of replicates. The Agilent SurePrint G3 Human miRNA 8x60k (release 16.0) microarray platform was usedOne μg RNA was used to generate a small RNA sequencing library using reagents and methods provided with TruSeq Small RNA Sample Prep Kit version 2 . Briefly, T4 RNA ligase was used to ligate RA5 and RA3 RNA oligonucleotides to 5' and 3' ends of RNA, respectively. Adapter-ligated RNA was reverse-transcribed using a RTP primer and the resulting cDNA was amplified in an 11-cycle PCR that used RP1 and indexed RP1 primers. PCR products of 140–160 bp were isolated following electrophoresis through a 6% Novex Tris-borate polyacrylamide gel (Life Technologies). Quality of the generated small RNA sequencing library was confirmed using Agilent High Sensitivity DNA Analysis Kit on Bioanalyzer 2100 instrument. Quadruplexed sequencing of libraries to generate single-end reads of 50 b was performed on Illumina HiSeq 2000 instrument using HiSeq clustering and sequencing reagents (version 2.0). Illumina HCS (version 1.4.8), RTA (version 1.12.4.2), and CASAVA (version 1.8.2) software were used for base-calling and the generation of raw, de-multiplexed sequencing data in FASTQ format. Raw sequencing data was deposited in the NCBI Sequence Read Archive  (accessiq) values with automatic baseline and threshold detection. DNA oligonucleotides provided with TaqMan MicroRNA RT-PCR assays [miR-21–5p, -205–5p, -146b-5p, -22–3p, -223–3p, and-423–5p, respectively. Mature microRNAs miR-16–5p, -210–3p, and-486–5p were measured using custom assays that were designed based on the principle of TaqMan microRNA RT-PCR assays and were subsequently validated . The cDNA 1.33 μl) was used as template in real time PCR of 40 cycles and 15 μl that was performed in triplicate on a 7900HT machine (Life Technologies) with denaturation for 15 s at 95°C and combined annealing and extension for 60 s at 60°C. SDS software  was used to calculate quantification cycle  using a sliding window of 4 bases, remove the 5' terminal base if the average Phred33 score of the 4 bases was <15; and (4) completely discard trimmed reads with <16 remaining bases. For determining mature microRNA expression, miRExpress [Trimmomatic  (versionRExpress  (versionRExpress  (versionRExpress  (versionRExpress  of Subre2-transformed (with offset = 0.1) sequencing data were additionally processed with the parametric ComBat function of the SVA Bioconductor package [2-transformed and RT-PCR-based normalized Cq measurements for microRNAs were negative-transformed. Prism software  was used for plotting data. A P-value <0.05 was associated with statistical significance. RT-PCR assays and data analyses were performed at Roswell Park Cancer Institute, Buffalo, USA; all other work was completed at MSK.For analyses that involved comparison of replicates, normalized microarray, and normalized and log package  (version3 of FFPE tissue (n = 8) after overnight digestion with proteinase K. RNA yields ranged from 8.0–59.7 μg, as quantified by absorbance at 260 nm, but 6.1–34.6 μg, as quantified with RiboGreen dye. This has more specificity for RNA than it does for DNA  among the 8 samples, with 200–349 detected per sample  and 175 detected in all samples. Of these 175 microRNAs, 126 were also detected in all 8 samples by the small RNA sequencing method. In comparisons with the RNA sequencing- and microarray-based microRNA measurements, the average The Spearman correlation coefficient for the 126 microRNAs was 0.37 and for the 63 microRNAs was >0.5. Correlation between FFPE tissue microRNA measurements obtained by small RNA sequencing and microarray platforms has been confirmed by other groups .Microarray-based microRNA assays were replicated for 7 of the 8 RNA samples. Measured against an average inter-replicate Spearman correlation coefficient of 0.81, seen with the small RNA sequencing method, the inter-replicate correlation coefficients for the microarray platform were in the 0.98–0.99 range . However, in the Spearman correlation analyses of the microRNA measurements obtained by RT-PCR and microarray assays, the correlation coefficients were -0.36–0.84  and were smaller than those observed in the RNA sequencing-based measurements . Thus, wOverall, our findings demonstrate the feasibility of small RNA sequencing for microRNA profiling of FFPE tissue RNA in spite of its compromised quality and integrity. Even though only 1.54% of 12.1 million small RNA sequencing reads of FFPE tissues were identified as representing 550 known microRNAs in this study, the number of microRNAs that were detected and quantified was more than that by the microarray platform Tables . The SpeOnly one study  had repoS1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.


Actinobacillus pleuropneumoniae is the pathogen of porcine contagious pleuropneumoniae, a highly contagious respiratory disease of swine. Although the genome of A. pleuropneumoniae was sequenced several years ago, limited information is available on the genome-wide transcriptional analysis to accurately annotate the gene structures and regulatory elements. High-throughput RNA sequencing (RNA-seq) has been applied to study the transcriptional landscape of bacteria, which can efficiently and accurately identify gene expression regions and unknown transcriptional units, especially small non-coding RNAs (sRNAs), UTRs and regulatory regions. The aim of this study is to comprehensively analyze the transcriptome of A. pleuropneumoniae by RNA-seq in order to improve the existing genome annotation and promote our understanding of A. pleuropneumoniae gene structures and RNA-based regulation. In this study, we utilized RNA-seq to construct a single nucleotide resolution transcriptome map of A. pleuropneumoniae. More than 3.8 million high-quality reads (average length ~90 bp) from a cDNA library were generated and aligned to the reference genome. We identified 32 open reading frames encoding novel proteins that were mis-annotated in the previous genome annotations. The start sites for 35 genes based on the current genome annotation were corrected. Furthermore, 51 sRNAs in the A. pleuropneumoniae genome were discovered, of which 40 sRNAs were never reported in previous studies. The transcriptome map also enabled visualization of 5'- and 3'-UTR regions, in which contained 11 sRNAs. In addition, 351 operons covering 1230 genes throughout the whole genome were identified. The RNA-Seq based transcriptome map validated annotated genes and corrected annotations of open reading frames in the genome, and led to the identification of many functional elements . The transcriptional units described in this study provide a foundation for future studies concerning the gene functions and the transcriptional regulatory architectures of this pathogen. During the past decade, a large number of bacterial complete genomes have been finished, providing the sequence basis for transcriptomics and proteomics. The genome sequence contains all the information of the functional elements including genes, non-coding RNAs, and UTRs, which are required for a system-level understanding of the survival mechanisms and pathogenesis of microorganism . HoweverS. Typhi has identified 40 novel non-coding RNAs and re-annotated a number of hypothetical genes  . The 2.2th phase . After rdatabase  Fig 1)..A. pleurA. pleuropneumoniae genomes.Overall, 32 novel protein coding regions were identified according to the transcriptome map . The aveYrhG”   are short (30–500 bp) untranslated transcripts and often act as regulators of gene expression through a variety of mechanisms –33. The 3 genome . The avecle RNA) . The othA. pleuropneumoniae, the sequences of all predicted sRNA were searched against the non-redundant, nucleotide database at NCBI. Thirty of the sRNAs were highly conserved (>95% identity with 100% coverage) only in A. pleuropneumoniae. A set of 20 sRNAs were conserved in the related Pasteurellaceae family including M. varigena, M. haemolytica, A. suis, B. trehalosi and A. lignieresii. Only 1 sRNA was also present in the species far from A. pleuropneumoniae, such as in Mycoplasma pulmonis and Vibrio.To study the conservation of identified sRNAs of A. pleuropneumoniae JL03  contain regulatory elements to control gene expression , 38. Theiae JL03 . One 5’UA. pleuropneumoniae JL03 . With the accumulation of sequence information and the improvements in bioinformatic techniques, original genome annotations need to be revised to make them more accurate and useful to explore gene functions and regulations. To acquire complete and well described genome annotation, plenty of studies have worked on genome re-annotation with various methods in eukaryotes and prokaryotes –43. Bothca PHL213.A. pleuropneumoniae JL03 genome was sequenced and published in 2008 which provided the foundation for all subsequent analysis of the A. pleuropneumoniae genome and gene functions [A. pleuropneumoniae JL03 genome at the transcriptome level. A single resolution transcription map of the A. pleuropneumoniae JL03 genome was generated which enabled us to re-annotate the existing genome. Ninety-four percent of the RNA-seq reads mapped to the A. pleuropneumoniae JL03 genome, which is comparable with the other RNA-seq studies. Ninety-four percent cDNA reads were assignable to the genome of Bacillus anthracis [Burkholderia cenocepacia [Histophilus somni [The unctions –58. But nthracis , 95% in ocepacia  and 94% us somni , which iA. pleuropneumoniae. Because of the small size and low expression level, sRNAs are hard to detect using traditional screening strategy. On account of lack of defined sequence features, computational approach for sRNA prediction is not powerful enough. Previous studies have shown the power of RNA-seq in bacterial sRNA identification [A. pleuropneumoniae. We found that 10 of the 51 sRNAs were homologous to characterized sRNAs. For instance, APP-S17 was classified as T44 RNA, which has been identified in several species of enteric bacteria [E. coli, 6S RNA alters the transcription of most genes in stationary phase. During this phase, 6S RNA accumulates in cells; loss of 6S RNA leads to decreased survival compared to wild-type cells [E. coli suggest 6S RNA may contribute significantly to transcriptional regulation even when levels are not maximal, such as in exponential phase when 6S RNA is only one-tenth as abundant as in stationary phase [S-dependent genes in Gram-negative bacteria [A. pleuropneumoniae. We also identified APP-S9 which was classified as GcvB RNA. The GcvB RNA has been found in a range of bacteria such as E. coli [Y. pestis [H. somni [Salmonella [E. coli and Salmonella species, the GcvB RNA has been shown to regulate a large number of genes which are involved in the amino acid transport systems and amino acid biosynthesis [E. coli, transcription of the GcvB RNA is activated by the adjacent gcvA gene and regulates genes by acting as an antisense binding partner of the mRNAs for each regulated gene [gcvA (APJL_0132) which was annotated as DNA-binding transcriptional activator. Taken together, we hypothesize that APP-S9 may be activated by gcvA (APJL_0132) and have regulatory activity in the amino acid transport systems and amino acid biosynthetic genes in A. pleuropneumoniae. The other 41 novel identified sRNAs may play regulatory roles in basic life and/or virulence as described in other bacteria [A. pleuropneumoniae implying these sRNA are A. pleuropneumoniae-specific. Only 1 sRNA is present in the species far from Pasteurellceae family. This sRNA may be responsible for the basic survival of many species. The sRNAs present in different species may be functional in adaptation to different environmental conditions.In bacteria, small regulatory RNAs (sRNAs) play a pivotal role in regulation of the gene expression, controlling a variety of adaptation processes in response to changes in the environment. Although the genome has been sequenced several years ago, no information about sRNAs is available in fication , 60. In bacteria . The funpe cells . Howeverpe cells . Recent ry phase . In addibacteria . Taken t E. coli , Y. pest. pestis , H. somnlmonella . In E. cynthesis , 65, 66.ted gene . APP-S9 bacteria , 68. AmoA. pleuropneumoniae which can discover regulatory elements in these regions such as riboswitches, RNA thermometers or small leader peptides. We identified 5’-UTR of 715 annotated genes and 3’-UTR of 384 annotated genes in A. pleuropneumoniae JL03. Most of the identified 5’-UTRs (72%) were very short with the size less than 100 bp, which is similar to other bacterial, such as 75% of the 5’-UTRs for annotated genes were shorter than 100 bp in C. glutamicum ATCC 13032 [E. coli, the riboswitches upstream of moaA gene regulate translation of the moaABCDE operon by blocking ribosome-binding sites [moaA (APJL_0688) which was annotated as molybdenum cofactor biosynthesis protein A, implying its regulatory role in molybdenum cofactor biosynthesis. Additionally, one RNA thermometer (APP-S8) was observed within the 5’-UTR of cspC (APJL_0119). The gene cspC encodes a cold shock-like protein. Cold shock proteins are stress induced regulators which is functional in response to low temperature and also in normal conditions [cspC was repressed after acute infection of A. pleuropneumoniae [A. pleuropneumoniae. In E. coli, the cspA thermosensor acts as an RNA thermometer, regulating the expression of cspA in response to temperature [cspC. A recent study has documented that Gram-negative bacteria contain a conserved motif in their 5’-leader sequences [A. pleuropneumoniae, we found a small leader peptide (APP-S51) within 5’-UTRs of the operon involved in histidine synthesis . These leader peptides are possibly involved in transcription attenuation of the target genes dependent on the amino acid level [A. pleuropneumoniae.RNA-Seq data provides information to identify the UTRs of annotated genes. Until now, the mapping of UTRs has not been performed on CC 13032 , 69. WitCC 13032 . In E. cng sites . APP-S20 5’-UTRs % were veeumoniae  and afteeumoniae , suggestequences . In A. pid level . In concA. pleuropneumoniae. The majority of the identified operons contain 2–9 genes, which is a general trend in other previously reported bacteria [pgaA, pgaB, pgaC, pgaD) has been characterized as the operon involved in biofilm formation of A. pleuropneumoniae [A. pleuropneumoniae were also identified, such as cps3ABCD (APJL1611-1614) encoding CPS biosynthetic enzymes, cpxDCBA (APJL1615-1618) encoding proteins involved in the export of CPS and apxIIICABD (APJL1344-1347) encoding the ApxIII structure, activation and secretion proteins [apxII operon which has been reported to consist of apxIICA and a partial apxIIB’ without apxIID [putA and putP, annotated as bifunctional proline dehydrogenase/pyrroline-5-carboxylate dehydrogenase and proline symporter/permease was not predicted by DOOR. The functions of putAP gene products are quite conserved among different bacteria [E. coli and S. typhimurium, the orthologs of these genes are well known to form the proline utilization operon [A. pleuropneumoniae and also demonstrated RNA-Seq can improve operon identification in bacterial genomes.For prokaryotic organisms, identification of operon structures at the genome scale is significant in studying the gene function and the regulatory networks. Based on the RNA-seq data, we identified 351 expressed operons covering 1,230 genes of bacteria , 54. Fureumoniae . Operonsproteins . Remarkat apxIID . The probacteria , 81. In n operon , 83. OurA. pleuropneumoniae at single nucleotide resolution using deep RNA-seq was presented in this work. This study confirmed previously annotated open reading frames and provided a more accurate map of gene boundaries. The analysis also identified 32 novel protein-coding genes and 51 sRNAs, defined UTRs regions and generated complete operon structures throughout the genome. The analysis results indicated high complexity of the A. pleuropneumoniae transcriptome. The functional elements on a whole genome level identified in this study provide a useful reference transcriptome to comprehensively understand the A. pleuropneumoniae survival and infection process.In summary, a global transcriptome profiling of S1 Fig(TIF)Click here for additional data file.S2 Fig(A) The time of cell harvest for RNA sequencing is at mid-log phase . (B) Total RNA was extracted from cells and verified for integrity on a 1% agarose gel.(TIF)Click here for additional data file.S1 TableList of the UTRs and their genome location identified in this study.(XLS)Click here for additional data file.S2 TableLine A has a list of the co-expressed gene pairs predicted by DOOR program. Line B has a list of the co-expressed gene pairs predicted by RNA-seq. Line C has a list of the co-expressed gene pairs common to both DOOR and RNA-Seq. Line D has a list of the co-expressed gene pairs unique in RNA-Seq experiment.(XLS)Click here for additional data file.S3 TableOperons identified by RNA-seq were compared with the operons predicted by DOOR database. The operons along with their ID and genes are listed. Operon type: A- operons common to both DOOR and RNA-Seq; B- operons based on RNA-seq data and not predicted by DOOR; C-operon having different compositions.(XLS)Click here for additional data file.


We recorded the eye movements in 2 × 2 symmetric games including dominance‐solvable games like prisoner's dilemma and asymmetric coordination games like stag hunt and hawk–dove. The evidence was most consistent with the accumulation of payoff differences over time: we found longer duration choices with more fixations when payoffs differences were more finely balanced, an emerging bias to gaze more at the payoffs for the action ultimately chosen, and that a simple count of transitions between payoffs—whether or not the comparison is strategically informative—was strongly associated with the final choice. The accumulator models do account for these strategic choice process measures, but the level‐k and cognitive hierarchy models do not. © 2015 The Authors. Journal of Behavioral Decision Making published by John Wiley & Sons Ltd.In risky and other multiattribute choices, the process of choosing is well described by random walk or drift diffusion models in which evidence is accumulated over time to threshold. In strategic choices, level‐ In these models, people choose by best responding to their simulation of the reasoning of others. In parallel, in the literature on risky and multiattribute choices, drift diffusion models have been developed. In these models, evidence accumulates until it hits a threshold and a choice is made. In this paper, we consider this family of models as an alternative to the level‐k‐type models, using eye movement data recorded during strategic choices to help discriminate between these accounts. We find that while the level‐k and cognitive hierarchy models can account for the choice data well, they fail to accommodate many of the choice time and eye movement process measures. In contrast, the drift diffusion models account for the choice data, and many of their signature effects appear in the choice time and eye movement data.When we make decisions, the outcomes that we receive often depend not only on our own choices but also on the choices of others. The related cognitive hierarchy and level‐k theory is an account of why people should, and do, respond differently in different strategic settings. In the simplest level‐k model, each player best responds assuming that everyone else is one level of reasoning behind them . What sort of eye movements or lookups are predicted by a level‐k theory with a 2 × 2 symmetric game taken from our experiment . That is, if a model requires a comparison between a pair of payoffs, those payoffs should appear next to one another at some point in the ordered sequence of payoffs viewed. Devetag, Di Guida, and Polonio . We tracked participants' right eye movements using the combined pupil and corneal reflection setting at a sampling rate of 500 Hz. Head movements were tracked, although we used a chin rest to minimize head movements.Stimuli were presented on an LCD monitor viewed from approximately 60 cm with a 60‐Hz refresh rate and a resolution of 1280 × 1024. Eye movements were recorded with an Eyelink 1000 desk‐mounted eye tracker , which has a reported average accuracy between 0.25° and 0.50° of visual angle and root mean square resolution of 0.01° .Each participant completed the sixty‐four 2 × 2 symmetric games, listed in Table x columns indicate how we generated the 64 games, as follows. x1, x3, x5, and x7 define the player's payoffs, with the actual y payoffs generated from the x values by multiplying by £10 and adding £30 so that payoffs were in the range £0–£90. For 2 × 2 symmetric games, games can be mapped onto a set of strategically equivalent games in two‐dimensional space  pairs in a 4 × 4 grid. With x1 − x5 and x3 − x7 set, we need to fix one of x1 and x5 and one of x3 and x7 to define a game. For top 16 games in Table x1 and x3 or, equivalently, y1 and y3. The green highlight indicates the fixed payoffs. Subsequent sets of 16 games were generated using the same  pairs but with other xs fixed.So that we can explore how eye movements vary across games, we varied x1 − x5, x3 − x7) space is useful because the type of the game depends on the signs of these two differences. Dominance solvable–conflict games, when x1 − x5 < 0 and x3 − x7 < 0, are dominance solvable and have a conflict between cooperation and maximizing one's own payoff and include some prisoner's dilemma games. Symmetric coordination games, when x1 − x5 > 0 and x3 − x7 < 0, include some stag hunt (or assurance) games. Asymmetric coordination games, when x1 − x5 < 0 and x3 − x7 > 0, include hawk–dove (or chicken or snowdrift), battle‐of‐the‐sexes, and leader games. Dominance solvable–no conflict games, when x1 − x5 > 0 and x3 − x7 > 0, are dominance solvable but with no conflict between cooperation and maximizing one's own payoff.Defining games in the  , but large and about equal effects of x1 − x5, odds ratio = 2.3, 95% CI , and x3 − x7, odds ratio = 2.5, 95% CI . Thus, the differences x1 − x5 and x3 − x7, which capture the strategic differences between games, capture the differences in player's choices across games well.Figure x1 − x5 < 0 and x3 − x7 < 0), which includes some prisoner's dilemma games, people almost always select bottom. This level of defection is high, but compared with the other games, cooperation in these games is relatively unappealing  where top is the dominant strategy, offering the highest outcomes irrespective of the other player's choice, people almost always choose top. Choice proportions are intermediate for the other games. Table The variation in choice proportions is large. In the dominance solvable–conflict games  predict the same choices, and even numbered levels except 0  predict the same choices.For each game, the level‐k fit to the choice proportions is shown as a dashed line in Figure k model, we have estimated the predictions of a mixture of different k levels. The best fitting mixture proportions were 19.5% level 0, 54.8% level 1, and 25.7% level 2. The level‐k model captures the qualitative pattern in the choice data quite well. These proportions match those reviewed in the Introduction, with mostly level‐1, few level‐0, and few level‐2.The level‐x1 − x5) + (x3 − x7), as the drift rate. But this is just the form for the logistic regression used previously to model how choice proportions varied over games, and so with a straightforward assumption about the evidence accumulated, the accumulator models account for choice data quite naturally.Accumulator models fit the choice proportion effects well too. For example, in the drift diffusion model, the probability of a choice is a logit function of the drift rate , people are faster. Figure r=.98, 95% CI ) and thus that the number of fixations shows the same relationship with choice proportions.Figure k is to assume that choice time is proportional to the number of payoffs required for a decision. For example, Figure The most straightforward way to consider choice time predictions for level‐k. Higher levels require more lookups, and, when k = 0 or 1, all games require the same number of fixations, and, for k ≥ 2, more fixations are required when (x1 − x5) = − (x3 − x7) because, in these cases, simulated strategies for the lower levels involve the need to consider the best response to a mixed strategy. No matter what proportions that we assume for the different levels of k, the level‐k model fails to capture the pattern in the choice time data, as noted in Table Figure 5) = − x − x7 bex1 − x5) + (x3 − x7) to explain the choice data. Rearranged, the drift rate is (x1 + x3) − (x5 + x7), which is the top payoffs less the bottom payoffs. The absolute value of this difference is plotted in Figure x1 − x5 and x3 − x7 have the same sign or not. When they agree in sign, both are evidence in the same direction—either both point to a top choice or both point to a bottom choice. When they differ in sign, one comparison favors a top‐row choice, and the other favors a bottom‐row choice. People should be faster when signs match, and so matching has been plotted at the bottom of the plot. By collapsing together x1 − x5 and x3 − x7 differences of the same sign—for this is the difference between Figure In accumulator models, choices take longer when the evidence for each alternative is more finely balanced. In the previous discussions, we considered a drift rate of .We examined how fixation durations varied over games. A mixed effects model of fixation duration as a function of the Fixation durations are also constant over the time course of a trial. A mixed effects model of fixation duration as a function of fixation number, which necessarily included full random effects for fixation duration, shows that each successive fixation is only 2.6 milliseconds faster, 95% CI .Fixation durations are important in the analysis of reading, because variation in their duration indicates differences in processing , the player's action , or other player's action . Figure y1 → y8, where the payoff owner and both actions change). Common transitions make up 76% of all transitions; rare transitions are the remaining 24%. On average, any given common transition is 5.3 times more frequent than any given rare transition.The thickness and blackness of the arrows in Figure y1 → y2) could be useful if people have other‐regarding preferences. Transitions where only the player's action changes  could be useful for calculating the difference in payoffs for each row. Transitions where only the other player's action changes  contain information about how the player's payoff changes if the other player switches action. It is harder to tell a story about the use to which rare transitions could be put. Too many things are changing at once. But some proportion of rare transitions is to be expected as people switch between comparisons.Common transitions could be useful comparisons. For example, transitions within a cell where only the owner of the payoff changes  = 0.59. The coefficients for the six remaining dummies are all small, as demonstrated by the limits of the 95% CIs, making no more than a 3% change to fixation counts : players have no tendency to fixate any row or column more often than another (the left and top coefficients), and there is no interaction between the owner and location of the payoff.The next seven coefficients in the “Fixation Frequencies” section of Table y1 ↔ y2) or a between‐cells change in (only one) action . For example, the own coefficient in this section at 0.83 indicates that bias described previously for a player to fixate his or her own payoffs is reduced by a factor of 0.83 when the transition is between the payoffs within a cell  and increased by a factor of 1/0.83 when the transition is not within a cell . The eight “Row vs. Column” coefficients are adjustments to transition frequencies depending on whether the transition is within columns  or within rows . For example, the adjustment coefficient at 0.83 indicates that within‐column transitions are less frequent than within‐row transitions. The 95% CIs for the others among these 16 coefficients indicate that the other effects are all small. This confirms the pattern in Figure The remaining 16 coefficients in the first column, in the “Within vs. Between‐Cell” and “Row vs. Column” sections, describe how the foregoing fixation frequencies vary as a function of the location of the previous fixation. The eight “Within vs. Between‐Cell” coefficients are adjustments to the fixation frequencies depending on whether the transition is within a cell between payoffs . We did not find any notable patterns in the coefficients we have omitted.Table k. For the fixation frequency predictions, we assumed that each payoff required at each stage is looked up once. For the transition frequency predictions, we assumed that all transitions across rows or columns (but not both) within a stage, in any order, are possible. For example, for the k = 2 case in Figure y2 ↔ y4, y2 ↔ y6, y4 ↔ y8, and y6 ↔ y8), and then all transitions between the level‐1 choice column are possible in the stage “Respond to level‐1 choice” . Thus, we do not make assumptions about adjacency as strong as those of Devetag et al. . The within‐cell transitions suggest incorporating with other‐regarding preferences in the level‐k model. Second, the model does not predict the higher frequency of between‐column transitions between the player's own payoffs.Figure x1 − x5) + (x3 − x7). So we would expect the player's payoffs to be fixated equally often. It is less clear how a bias to fixate the player's own payoffs more than the other player's follows. Because the games are symmetric, the information in x1, x3, x5, and x7 is repeated in the other player's payoffs, and so any bias is consistent with our earlier assumptions. We log no clear prediction in Table In fitting choice and choice time data, we assumed a drift rate based on the difference in the payoffs in each row of (x5) + x − x7. Sx1 − x5 and x3 − x7 vary. We constructed a second mixed effects Poisson regression including (x1 − x5), (x3 − x7), (x1 − x5) × (x3 − x7), and their interactions with the original 24 dummy variables. The payoff differences were scaled so that coefficients represent the effect of payoff differences changing from minimum to maximum. The top Intercept row with coefficients 0.92, 0.97, and 0.58 shows how the number of fixations varied across games. Some games have nearly twice as many fixations as others. Because fixation counts are so highly correlated with choice time, as we described earlier, we have already seen this effect as games with more finely balanced choice proportions taking longer and thus more fixations  or both small (−3), choices are fast because both differences point in the same direction.The “Game Interactions” columns in Table x1 − x5 and x3 − x7 made no more than a 10% difference in fixation counts. The Own, Top, and Left rows show that players looked a little more at larger payoffs. There is also a tendency for players to look more at the leading diagonal when x1 − x5 and x3 − x7 were either both large or both small. But to a first approximation, players fixated payoffs equally often across games.The remaining coefficients in the “Fixation Frequencies” rows of the “Game Interactions” columns indicate how the distribution of fixations varies across games. The coefficients are all small, and the limits of the 95% CIs mean that we can say that variations in Own row of the “Within‐ vs. Between Cell” section). The 0.87 value for the interaction of the Own dummy and (x1 − x5) × (x3 − x7), which appears in the “Within‐ vs. Between Cell” rows of the (x1 − x5) × (x3 − x7) column, indicates that this effect is even stronger when x1 − x5 and x3 − x7 agree in sign. But the overall pattern is for only small variation in transition frequencies across games. To sum up how eye movements vary across games, players make nearly twice as many eye movements on some games compared with others, but the type of eye movements they make changes very little across games—players just do more of the same eye movements on more balanced games.The coefficients in the “Within‐ vs. Between Cell” and “Row vs. Column” rows of Table Intercept row is uninteresting and just indicates that there are more fixations in the second half of a trial (because the middle fixation was arbitrarily assigned) and slightly more trials where the top action was selected. The only effect in the “Fixation Frequencies” section is that people make more fixations to the payoffs in the row that they ultimately choose and that this pattern develops over time. Figure The final columns headed “Gaze Bias Interactions” of Table The sections “Within‐ vs. Between Cell” and “Row vs. Column” show exactly which transitions change in frequency to create the overall gaze bias. Breaking the gaze cascade effect down into transitions, when top is ultimately chosen, the transitions that increase are the top‐row transitions, and the transitions that decrease are the bottom‐row transitions.k does not predict the gaze bias effect. Figure k, which were calculated by simulating out the process in Figure k, for all k > 0, the last pair of fixations is across rows. This means that level‐k is predicting no overall bias for the top or bottom row in the last two fixations, which is not consistent with the bias to fixate the ultimately chosen row being strongest at this point. We note this in Table Level‐Mullett and Stewart  demonstrWe have also explored the differences in the transitions made by row versus column players  by including a dummy variable for orientation and interactions with this dummy in the Poisson regression. The only notable difference is that, for between‐cell transitions, players like to make more horizontal than vertical eye movements, which means that row players make more transitions across the actions of the other player than column players do.Because we expect eye movements to be related to cognitive processing, we expect there to be an association between the choice that people ultimately make and their eye movements , effectively allowing free functional forms for people's sensitivity to these payoff differences and their interactions. This model uses a separate coefficient for each of the 64 games. No other choice model could do better. This model achieves an accuracy of 80.0%. But this flexibility is not warranted—a model with only single slope coefficients for x1 − x5 and x3 − x7 and no interactions  + (x3 − x7), where (x1 − x5) and (x3 − x7) entered as numerical and not factors)—achieves accuracy of 79.6%, and is preferred by Schwartz's Bayesian information criterion (BIC). Note that BIC values were corrected for the nesting of choices within subjects.The attributes model uses x1 − x5 and x3 − x7 and no interactions, is preferred over the much more complicated model means that the choice data do not support assuming anything more complicated than the difference in payoffs between rows is driving the evidence accumulation.We described earlier that the accumulator models predict choice probabilities as a logit function of drift rates. That the aforementioned simple logit model, with only choice ∼ 1 + I((F1 + F2 + F3 + F4) − (F5 + F6 + F7 + F8)), where the I identity function aggregates the difference in fixations so that a single slope is used in this model), achieves 66.9% accuracy, and is preferred by BIC. In essence, all that matters is how often participants looked at the top versus the bottom locations.The fixations model uses only the counts of fixations to each location to fit choice and achieves accuracy of 67.3%. Figure The attributes and fixations model fits better than either the attribute model or the fixations models and is preferred by BIC demonstrating that combining eye movement data with payoff data improves the ability to fit choices.The last fixation model uses only the location of the last fixation and fits choices with accuracy 70.0%. Recall from the discussion of the gaze bias effect that people have an emerging tendency to fixate the row that they ultimately choose. Note that the fixations model, which does not have any information about the ordering of fixations, is doing only slightly worse in accuracy.choice∼1+I((T51+T61+T71+T81+T52+T62+T72+T82+T53+T63+T73+T83+T54+T64+T74+T84)−T15+T25+T35+T45+T16+T26+T36+T46+T17+T27+T37+T47+T18+T28+T38+T48)),fits nearly as well with an accuracy of 62.9% and is preferred by BIC because of its increased simplicity. Note that the transitions that are important for fitting are the between‐row transitions, but the transitions that increased over time to give the gaze bias effect described previously were the within‐row transitions for the selected row.The transitions model uses the transition frequencies for each trial to fit the choice on that trial. The model achieves 71.1% accuracy and, despite its extra complexity, is preferred by BIC over the fixations model . Figure We have also taken into account the association between the difference in between‐row transition counts and the location of the last fixation. For example, if starting from the top row, the number of transitions to the top row from the bottom row minus the number of transitions to the bottom row from the top row must, necessarily, be 0 or −1. When the difference is 0, one is back where one started, and so the last fixation must be to the top row. When the difference is −1, the last fixation must be to the bottom row. By simply entering the last fixation into the regression before the transition frequencies, we have corrected the transition frequency coefficients for the last fixation. Although the coefficients are all a little smaller in magnitude, the pattern in Figure y1 − y4 are positive; coefficients for transitions to y5 − y8 are negative). An interesting feature of the corrected coefficients (and the uncorrected coefficients) is that many rare‐category transition frequencies are associated with the choice even though the comparison they are associated with is uninformative. For example, y1 ↔ y6 transitions involve a swap between payoffs for the player and the other player and simultaneously a swap of action by the player. Even though this direct comparison is not informative for strategy selection, it is associated with choice. Of the 15 transitions, which have a strong effect on choice, 11 are rare‐category transitions.Overall, transitions to a row increase the likelihood of a choice of that row  of the variance explained by transitions is also explained by attribute values. Or, equivalently, about one‐third (.17/.56) of the variance explained by attribute values is also explained by transitions. Using the fixations models instead, we can say that about one‐sixth of the variation in choice explained by attributes is explained by fixations, and two‐thirds of the variation in choices explained by fixations is also explained by attribute values. So some, but by no means all, of the processing of payoffs is picked up by eye movements. And some of the effect of eye movements has an effect on choice independent of the attribute values.It is useful to consider how much of the variance in choices is fitted by attribute values alone, transitions alone, and by both together. The Nagelkerke pseudo‐The last thing that we explored was whether the effect on an eye movement on choice varied depending on the magnitude of the payoff fixated. In risky choice, Stewart, Hermens, and Matthews  surprisik and accumulator models.Our participants played a set of 2 × 2 symmetric games where the payoffs were systematically varied to create dominance‐solvable games and symmetric‐ and asymmetric‐coordination games, including prisoner's dilemma, stag hunt, and hawk–dove games. We tracked participants' eye movements while they chose and explored whether and how eye movements varied within the time course of a choice and across the different types of games. In a second wave of modeling, we explored the relationship between eye movements and choice. We close with a summary of the core results and their implications for the level‐k model that assumed a mixture of levels 0, 1, and 2 participants (or strategies within a participant) captured much of the variation in choice proportions. But the accumulator model, which predicted choice proportions as a logistic function of the difference in payoffs across rows, also fitted well.Our players were very sensitive to the type of game presented, with choice proportions varying over a large range and systematically with the payoffs. Players clearly engaged with our games and differentiated between games in their choice behavior. A level‐k model is unable to predict this choice time pattern, but it is a natural consequence of the accumulator framework in which finely balanced evidence means lower drift rates and thus longer times to threshold.Choice times were very strongly related to choice proportions, such that for games where the difference in payoffs across rows was more finely balanced, choice proportions were nearer 0.5, and choice times were much longer. This pattern is ubiquitous  followed by a deliberative calculation phase (which would be associated with slower fixations). Instead, this stability is more consistent with a constant processing over time, as in accumulator models.k can predict this bias for odd levels of k. The accumulator account does not make a prediction as the framework does not make a clear prediction about how attention is distributed—although the pattern is not inconsistent with the model.Within a game, each payoff is fixated equally often, except for a small bias towards fixating one's own payoffs rather than the other players (also found by Devetag et al., k and accumulator models do not predict that larger payoffs will be fixated more.The pattern of eye movements varied only a little across games with a small bias to fixate the larger payoffs. Devetag et al.  also onlTransitions between payoffs—that is breaking the fixations down contingent upon the immediately preceding fixation—reveal that people are making the eye movements that have been associated with the meaningful comparison of payoffs (Arieli, Ben‐Ami, & Rubinstein, k model was unable to predict this and also fails to predict within‐cell transitions. The pattern is not inconsistent with the accumulator model driven by the net difference in payoffs between rows.Within the common transitions, transitions between the player's own payoffs under a change of action by their opponent were particularly frequent (see also Devetag et al., k model predicts no gaze bias effect, because the last two fixations are always a between‐row comparison of a pair of payoffs as the participant finally selects a row choice given their inference about the other player's column choice. But the gaze bias effect is a signature of an accumulator model with a difference‐based stopping rule (Mullett & Stewart, Over the time course of a choice, the pattern of eye movements is quite stable. Devetag et al.  report sk or rational choice models but is consistent with a simple accumulator model of choice where each visit to an alternative is associated with an increased chance of it being chosen.The small differences in eye movements across games are sufficient to fit choices reliably (see also Devetag et al., k or cognitive hierarchy models. First, choices were longer and took more fixations when the payoffs were finely balanced across rows. Second, as a choice unfolds a bias to gaze at the payoffs of the ultimately chosen row emerges. Third, transferring gaze to a row is associated with a higher likelihood of choosing that row. Thus, we argue that processing in strategic decisions, like processing in risky and other multiattribute decisions, is well described as the steady accumulation of evidence over time.For these strategic choices, the choice time and eye movement process data contain the signature effects of accumulator models but are not compatible with level‐