RNA approach to equilibrium sequencing (RATE-seq), which uses in vivo metabolic labeling of RNA and approach to equilibrium kinetics, to determine absolute RNA degradation and synthesis rates. RATE-seq does not disturb cellular physiology, uses straightforward normalization with exogenous spike-ins, and can be readily adapted for studies in most organisms. We demonstrate the use of RATE-seq to estimate genome-wide kinetic parameters for coding and noncoding transcripts in Saccharomyces cerevisiae.The abundance of a transcript is determined by its rate of synthesis and its rate of degradation; however, global methods for quantifying RNA abundance cannot distinguish variation in these two processes. Here, we introduce  Remodeling of gene expression is critical for a broad range of biological processes from the cell division cycle and embryo development  to celluA variety of high-throughput methods have been introduced with the goal of estimating in vivo rates of either RNA synthesis or degradation. Genomic run on assays  provide Recently, methods using in vivo metabolic labeling of mRNAs  have beeHere, we report a general method for accurate measurement of absolute RNA kinetic parameters in vivo. We use approach to equilibrium labeling , which md[RNA]/dt) can be modeled as a function of a constant rate of synthesis (k) and a degradation rate proportional to RNA abundance (α[RNA]) using the relationship d[RNA]/dt = k-α[RNA]. If a labeled nucleotide is added to the culture the concentration of labeled transcript will increase with time to an equilibrium value at a rate solely determined by the transcript's degradation rate constant (αRNA) and the cells’ division rate constant (αgrowth) . Approach to equilibrium labeling, using radiolabeling, was developed over 40 yr ago to estimate the rate of total mRNA turnover (Saccharomyces cerevisiae (encoded by FUR1), which converts 4tU into 4-thiouridine monophosphate allowing its incorporation in RNA. For the purpose of normalizing RNA-seq libraries from different time points following labeling, we added a constant quantity of three different in vitro-transcribed thiolated transcripts  to isolated RNA prior to fractionation. As deadenylated transcripts can persist in the cytoplasm or be readenylated in some species  cannot grow when supplemented with 4tU alone , suggesting that highly thiolated transcripts are not tolerated by the cell. As we found comparable 4tU incorporation in a ura3− strain and prototrophic strain  we performed all subsequent experiments in a prototrophic strain. Over the timescale and concentrations of 4tU used for RATE-seq we detect no effect on cell growth , although prolonged exposure and higher concentrations appear to have slight effects . We confirmed that the concentration of 4tU used for RATE-seq does not affect global gene expression . Using a dot blot and colorimetric assay , we find that the pool of newly synthesized mRNA approaches equilibrium consistent with a model of constant synthesis and exponential degradation  of spike-ins are observed to decrease with time and concomitantly, the proportion of counts mapping to the transcriptome increases A. We fouUsing RATE-seq we determined degradation rate constants, and corresponding half-lives, with 95% confidence intervals (CI) for 5308 mRNAs . Most transcripts are rapidly degraded, with a mean and median half-life of 15 and 10 min, respectively C. Thus, In addition to variation in mRNA degradation rates we find evidence for variation in rates of noncoding transcripts including small nuclear RNA (snRNA) and long noncoding RNAs (lncRNA) B. The poSaccharomyces cerevisiae . As noted in previous reports  and transcript synthesis and degradation are related by the expression k = α[RNA]. Therefore, the rate of transcript production can be estimated using the degradation rate constant and the steady-state abundance of the transcript. As only a fraction of transcripts are labeled with 4tU  or FY3 (MATa ura3-52), which are isogenic to S288C. All RATE-seq analyses were performed using the prototrophic strain FY4 in which a single colony was inoculated into an overnight culture in synthetic complete medium containing 500 µM uracil. The saturated overnight culture was back-diluted 1:50 into fresh medium of the same composition. Log phase cells were treated with 4tU to a final concentration of 500 µM. Cells were collected at multiple time points over the course of 100 min by vacuum filtration onto nitrocellulose filters and immediately frozen in liquid nitrogen.Experiments were performed using either FY4  have a GC content of 0.42 and lengths of 700, 900, and 1200 bases, respectively. Three regions of the B. subtilis genome were PCR amplified and cloned into the pSP64 poly(A) in vitro transcription vector (Promega). Plasmids were linearized using EcoRI restriction and run off transcription performed as recommended by the manufacturer with the addition of thiolated UTP:UTP at a ratio of 2:1 in the reaction. Spike-in RNA was subsequently treated with DNAse and purified.To generate three RNA spike-ins with similar GC content to RNA was purified from cells using a hot acid phenol/chloroform extraction. Briefly, 750 μL of lysis buffer  was added to each sample and vortexed. An equal volume of acid phenol was then added to the sample and vortexed. Samples were incubated for 1 h at 65°C with occasional vortexing. Filters were removed and samples were placed on ice for 10 min. After centrifugation, the aqueous phase was transferred to Phase Lock Gel (PLG) tubes and an equal volume of chloroform added. The aqueous phase was collected and RNA was precipitated using two volumes of 95% ethanol and 0.1 volume of 3 M Sodium Acetate. RNA pellets were washed with 70% ethanol twice and dried at room temperature for half an hour and resuspended in RNAse free water.For biotinylation reactions 100 µg of total RNA was added to a solution of 10 mM Tris-HCl (pH 7.4), and 1 mM EDTA. Biotin-HPDP (1 mg/mL) was added to a final concentration of 2 µg for each 1 µg of RNA . In addition, the three spike-in RNAs were pooled and 12 ng of the mixture added to the reaction mixture containing 100 µg of RNA sample. The reaction was allowed to proceed for 3 h in the dark, after which reactants were removed using chloroform extraction. RNA pellets were precipitated with 1 volume of isopropanol and 1/10 volume of 5 M NaCl. RNA pellets were washed once with 75% ethanol and resuspended in RNase-free water.The biotinylated RNA was fractionated from unlabeled RNA using streptavidin magnetic beads (NEB) . Pull downs were performed essentially as previously described . Beads wFor isolation of poly(A) RNA from total RNA, Oligo d(T)25 magnetic beads (New England Biosciences) were used in combination with a 12-tube magnetic rack. Beads were washed once in a binding buffer/wash buffer  similar to manufacturer recommendations except that DTT was left out of the buffer, as this would cleave the RNA conjugated to biotin-HPDP. At least 40 µg of total RNA was added to 200 µL of beads. Samples were washed in 1x binding buffer, then 1x low-salt buffer, and eluted from beads in TE buffer following incubation for 3 min at 50°C.For each sample, 200 ng of mRNA was blotted onto a Zeta-Probe nylon membrane (BioRad) using a BioRad DotBlot. The RNA was cross-linked using a UV cross-linker. The blot was blocked using blocking buffer  for 20 min. Samples were then probed with Streptavidin Alkaline phosphatase in blocking solution (1:1000). The membrane was washed in PBS at decreasing concentrations of SDS  for 10 min each. Spots were visualized using ECF substrate , visualized on a Typhoon FLA 9500, and analyzed using ImageQuant software.Following fractionation of thiolated transcripts, 100 ng was depleted of 18S and 25S ribosomal transcripts. Two rounds of ribosomal depletion were performed using LNA probes provided in the Ribominus kit (Invitrogen). RNA was then precipitated using 2 volumes ethanol, 1/10 volume 3 M sodium acetate, and glycogen. Pellets were resuspended in 6 µL of RNAse free water.First strand synthesis of rRNA-depleted RNA was performed using the Super Script III kit (Life Technologies) and random priming using random hexamers. Second-strand synthesis was performed with dUTP in place of dTTP to enable strand-specific sequencing . SamplesfeatureCounts function of the Subread package in R, using the argument for strand specific counting. Each library had between 5 and 13 million reads mapping to non-rRNA transcripts.Ilumina sequencing reads were first filtered for rRNA sequences by aligning to the ribosomal DNA of the yeast genome using Bowtie with default settings. All remaining reads were then aligned to the rest of the yeast genome and the three spike-in sequences using Bowtie2 . After ck = 3, 5, 7, 11, 13, and 25 min following label addition for replicate 1, and time points k = 5, 7, 9, 13, 20, 25, 30, and 100 min following label addition for replicate 2. To normalize data within each time series we used the following normalization scheme:1)A, between the read count M, for each gene i in replicate j at time point k and the read count S for each spike-in n in replicate j at time point k:n = 1,2,3 for each of the three spike-ins and j = 1 or 2 depending on the replicateWe first computed a ratio, 2)K time points within a replicate j:We then computed a scaling factor, β, for each spike-in by calculating the average ratio between each spike-in and a reference spike-in across all 3)j we multiplied the ratio, A for each gene by the scaling factor:To normalize the data within a replicate, 4)K time points from both replicates:To return the data to the original scale we then multiplied the normalized ratio for each gene by the average spike-in count across all We performed RATE-seq using two biological replicates, with time points We excluded all data from a time-point if a spike-in was deviant in its expected behavior. Thus, each time point has between two and six values depending on whether the time point was replicated and whether any data were removed.where Yunlabeled is the abundance of unlabeled transcript, Ylabeled is the abundance of labeled transcript, Yss is the total abundance of a transcript, and α is the transcript's degradation rate constant. The approach to equilibrium equation is then obtained by substitution of equation 1 for the value of Yunlabeled in equation 2 and solving for Ylabeled leading to The approach to equilibrium method assumes transcript decay follows first order kinetics and transcript synthesis follows zeroth order kinetics. This leads to the following two equations:Y(t) is the amount of the labeled transcript at time t, Yeq is the abundance of labeled transcript at steady state, αRNA is the transcript's degradation rate constant, αgrowth is the growth rate constant of the culture, t is the time after addition of label, and td is a time delay between the addition of label and the time at which labeled transcripts can be detected.Based on this equation, we modeled the abundance of labeled transcript for each mRNA as RNA and αgrowth) and Yeq. To account for biological variation, we combined the data from both replicates to generate a single parameter estimate for αRNA and Yeq. Because the variance of the RATE-seq data increases with increasing time we used weighted least squares regression with weights of 1/Y, which avoids undue influence of later time points on the model fit, using the gnls function in the nlme package in R. To minimize the parameters that we needed to estimate we set the time delay parameter, td equal to 2 min, since labeled transcripts were pulled down as early as 3 min after addition of 4tU. As the doubling time of the culture is 100 min, αgrowth is equal to 0.0069. Transcript half-lives were calculated as ln(2)/(α-0.0069).To calculate the degradation rate constant for each transcript, we performed nonlinear regression, estimating both α . Consistent with previous observations , there idRNA/dt) at steady-state is equal to zero and therefore used the relationship k =α[mRNA]. We also assumed 60,000 mRNA/cell (RNA.To estimate the synthesis rate for each transcript we assumed that the rate of change in mRNA (RNA/cell . ConfideGene enrichment analysis was performed as in . NonrandfeatureCounts of the Subread package and gnls of the nlme package were used for data analysis of nonlinear regression. The following functions and packages, in addition to base functions in R and custom written functions were used for presentation of figures: subplot of the TeachingDemos package, axis.break of Plotrix, and heatscatter of LSD.All analyses were performed using R  and seveSequencing data are available through the Sequence Read Archive under BioProject ID PRJNA236614.Supplemental material is available for this article.


Post-transcriptional regulation is a very important mechanism to control gene expression in changing environments. In the past decade, a lot of interest has been directed toward the role of small RNAs (sRNAs) in bacterial post-transcriptional regulation. However, sRNAs are not the only molecules controlling gene expression at this level, RNA-binding proteins (RBPs) play an important role as well. CsrA and Hfq are the two best studied bacterial proteins of this type, but recently, additional proteins involved in post-transcriptional control have been identified. This review focuses on the general working mechanisms of post-transcriptionally active RBPs, which include (i) adaptation of the susceptibility of mRNAs and sRNAs to RNases, (ii) modulating the accessibility of the ribosome binding site of mRNAs, (iii) recruiting and assisting in the interaction of mRNAs with other molecules and (iv) regulating transcription terminator/antiterminator formation, and gives an overview of both the well-studied and the newly identified proteins that are involved in post-transcriptional regulatory processes. Additionally, the post-transcriptional mechanisms by which the expression or the activity of these proteins is regulated, are described. For many of the newly identified proteins, however, mechanistic questions remain. Most likely, more post-transcriptionally active proteins will be identified in the future. Bacteria need to survive in constantly changing environments. Therefore, they must be able to alter their gene expression in response to environmental signals, causing protein levels to be adjusted according to the needs of the cell. This can be achieved by adjusting transcription initiation with sigma factors and proteins that activate or repress transcription. However, gene expression regulation also occurs after transcription is initiated . The impEscherichia coli was already reported almost 20 years ago  and RNA-binding proteins (RBPs). sRNAs are typically defined as non-coding RNA molecules that bind with limited complementarity near the ribosome binding site (RBS) of their target mRNA, causing competition with the ribosome for binding to this region. However, the number of sRNAs that deviate from this general definition is increasing . The newears ago . Lately,Bacterial post-transcriptionally active regulatory proteins typically bind RNA molecules and regulate translation initiation, stability, and transcript elongation of their RNA targets, using different regulatory mechanisms. These mechanisms include (i) adaptation of the susceptibility of the target RNAs to RNases, (ii) modulation of the accessibility of the RBS of mRNA targets for ribosome binding, (iii) acting as a chaperone for the interaction of the RNA target with other effector molecules, and (iv) modulation of transcription terminator/antiterminator structure formation, and will be described hereafter.E. coli, single-stranded RNA-specific endoribonucleases  or double-stranded RNA-specific endoribonucleases  generally initiate mRNA decay by making endoribonucleolytic cleavages. This yields smaller products that are further degraded by a combination of endo- and exonucleases, like PNPase (polynucleotide phosphorylase), RNaseII, or occasionally RNaseR .Regulation of RNA stability is an important mechanism to post-transcriptionally control gene expression, as it affects the number of mRNAs that can be translated or the number of sRNAs that can execute their regulatory function. RNA stability is determined by intrinsic RNA elements, such as primary sequence and secondary structure, but can be affected by sRNAs or proteins that bind to the RNA molecule. These proteins are mainly ribonucleases (RNases). In  E. coli . Other rE. coli regulatory RBPs can interfere with the poly(A)-assisted decay of mRNA molecules by binding to the poly(A) tail and protecting the mRNA to which it is bound from degradation  and addition of a single stranded poly(A) extension at the 3′ end of the mRNA by PAPI [Poly(A) polymerase I] both promote mRNA degradation. Additionally, the exonucleolytic decay of highly structured mRNAs can be facilitated by RhlB, which unwinds RNA structures in an ATP-dependent way . The mechanism of action of these RBPs is described here as different compared to the previous two, because they typically bind simultaneously to an RNA target and an effector molecule. The effector molecule bound by the regulatory protein can be an sRNA or a protein. As previously mentioned, sRNAs typically regulate translation efficiency and RNA stability by binding near the RBS of their mRNA targets. Intermolecular basepairing between the sRNAs and mRNAs is facilitated by regulatory RBPs that function as a chaperone . In general, the activity of this type of RBPs is controlled by their phosphorylation state or by a bound ligand, which induces major conformational changes in the proteins (rut) sequence that is normally inaccessible for the rho-factor. Protein binding enables access to this region and rho-dependent transcription termination takes place  binding riboswitch. It is unclear whether MOCO and CsrA can bind simultaneously or whether MOCO prevents CsrA from binding  stretches  at or near the RBS, thereby inhibiting translation initiation stabilizing factor for RNA molecules and as an interaction platform for RNA/RNA an RNA/protein interactions, Hfq carries out other functions. One of these functions is competing with initiating ribosomes for access to the RBS by binding AU-rich regions close to the RBS which are acting as translational enhancers . These tWhile CsrA and Hfq use different mechanisms to globally regulate gene expression, there are proteins that have only been shown to use a confined number of these mechanisms or regulate a more limited number of genes. They will be described hereafter.In addition to its role in facilitating ribosome interactions with the mRNA, protein S1 also has a function in post-transcriptional regulation. The protein can stabilize RNA molecules by directly shielding RNase recognition sites . AlthougS1 directly competes with RNases for binding to specific sequences. However, other proteins affect mRNA stability by promoting a change in the secondary structure of their mRNA targets. Consequently, RNase recognition sites become more or less available for RNases. An example of proteins that use this mechanism are cold shock proteins. These proteins are induced when bacteria encounter a temperature downshift. One of the physiological effects of cold is the stabilization of secondary structures that make RNase recognition sites inaccessible, which likewise impairs RNA degradation. Binding of the cold shock protein, CspA, to these mRNAs causes, together with the induced helicases, cold-induced secondary structures in the mRNA to be melted. Adversely, cold shock proteins can also prevent RNA degradation. CspE can bind poly(A) sites and can consequently interfere with either binding of PNPase or with internal cleavage by RNaseE . AdditioE. coli, this protein is involved in the turnover of the sRNAs CsrB and CsrC. These sRNAs regulate the activity of CsrA, which is an RBP that was described earlier. Although CsrD destabilizes the sRNAs CsrB and CsrC in an RNaseE-dependent way, these sRNAs have no obvious RNaseE recognition sites and CsrD has no RNase activity itself. Therefore, it was suggested that CsrD might induce structural changes in the sRNA, making it more susceptible for RNaseE  NAG repeats, separated by non-conserved spacers. Because of the extended recognition sequence, TRAP can regulate a small subset of genes, all involved in tryptophan metabolism. TRAP can act as a post-transcriptional regulator by directly blocking ribosome access to the RBS (TRAP (trp RNA-binding attenuation protein), a protein involved in the regulation of tryptophan metabolism of  the RBS . Althoug the RBS . TRAP ca the RBS . AdditioBorrelia burgdorferi possibly acts by competing with ribosome binding as well, as the protein has been shown to bind to the 5′ region of its mRNA target, thereby blocking translation. However, the only identified RNA target of this protein this far is its own mRNA. Most likely, BpuR is a post-transcriptional regulator of other genes in the Borrelia genome  that partially overlaps with the terminator sequence . Similarmination . Membershydrates .B. subtilis is involved in modulating transcription terminator/antiterminator formation, although its mechanism of action is a little different compared to the examples described above. PyrR, activated in the presence of uridine, stabilizes an anti-antiterminator structure. This structure sequesters nucleotides of the antiterminator by basepairing with sequences that lie further upstream, inducing terminator formation and preventing gene expression. Only in the absence of uridine the antiterminator can form and expression of the RNA target is possible  of h a T3SS . It tranh a T3SS . The speh a T3SS . Howeverh a T3SS . It is rShigella sonnei, RodZ was identified as a membrane-localized cytoskeletal protein that retains the rod-shaped morphology of the bacterium. Later, this protein was also shown to be involved in post-transcriptional regulation, as the protein has RNA-binding capacity and its expression leads to repression of InvE protein synthesis by means of a decreased stability of the mRNA. However, the working mechanism still needs to be resolved. Possibly it forms a platform where mRNAs and other putative regulatory factors coincide  domain proteins is a last example of RBPs acting at the post-transcriptional level with an undefined regulatory mechanism. They are widely distributed among different species and are involved in transcription antitermination. These proteins recognize an RNA motif consisting of two tandem stem-loops. However the exact molecular mechanism of antitermination has not been determined . For Nastructure .To ensure that gene expression is adjusted according to the needs of the cell, it is crucial that the expression and the activity of the regulatory RBPs themselves are tightly controlled as well. Remarkably, protein expression or protein activity is regulated itself by post-transcriptional regulatory processes. Proteins that modulate RBS accessibility, like CsrA and BpuR, have their recognition site present in the 5′UTR of their own mRNA. Protein binding to the mRNA consequently competes with ribosome binding, reducing translation initiation efficiency and the expression of the protein . SimilarE. coli sRNA, McaS, was shown to bind CsrA. This sRNA has at least two CsrA binding sites  adaptation of the susceptibility of the target RNAs for RNases, (ii) modulation of the accessibility of the RBS for ribosome binding, (iii) acting as a chaperone for the interaction of the RNA target with other effector molecules, and (iv) modulation of transcription terminator/antiterminator formation. Different post-transcriptionally active RBPs have been identified in E. coli. Therefore, methods have been developed to identify new RBPs. One of these methods is the in vitro or in vivo assembly of RNA and RBPs, followed by mass spectrometry (Helicobacter pylori, E. coli, Salmonella Typhimurium and P. aeruginosa (A number of post-transcriptionally active RBPs have been identified. However, it is very likely that more RBPs are active as post-transcriptional regulators, given that some known regulatory RBPs have another function, e.g., as a transcription regulator or a structural protein. Moreover, proteins that specifically regulate sRNA stability have only recently been discovered. Therefore, most likely, more proteins of this class will be identified in the future. Additionally, relatively little is known about this type of regulatory proteins in other bacteria than trometry . This meruginosa .Further studies on well-known and newly identified post-transcriptionally active proteins will lead to a better understanding of how bacteria use this type of gene regulation to respond to changes in the environment and how different post-transcriptional networks interact with transcriptional regulons and with each other. This knowledge will create opportunities for new or improved biotechnological applications, e.g., in synthetic biology as a tool to control gene expression, complementing the current approaches of transcription control. Additionally, as many of these proteins play a central role in RNA metabolism, interfering with the expression or the function of these proteins can be interesting as an alternative antimicrobial strategy.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.


Studies of the evolution of collective behavior consider the payoffs of individual versus social learning. We have previously proposed that the relative magnitude of social versus individual learning could be compared against the transparency of payoff, also known as the “transparency” of the decision, through a heuristic, two-dimensional map. Moving from west to east, the estimated strength of social influence increases. As the decision maker proceeds from south to north, transparency of choice increases, and it becomes easier to identify the best choice itself and/or the best social role model from whom to learn (depending on position on east–west axis). Here we show how to parameterize the functions that underlie the map, how to estimate these functions, and thus how to describe estimated paths through the map. We develop estimation methods on artificial data sets and discuss real-world applications such as modeling changes in health decisions. In studies of decision-making and health, social influence is becoming increasingly recognized. Coordinated behavior has benefits for groups and the individuals within them. When successful behaviors of the community are socially learned, cooperation can evolve in social networks extending beyond the limits of Hamiltonian inclusive fitness among kin For this reason, there is the important factor of how well informed decision makers are — what we might call the “transparency” of payoffs in their decisions. A relevant question about online social media, for example, is whether their searchability makes decision makers more well informed, or whether the deluge of social influence and similar options makes decisions less transparent in terms of payoffs We see two major factors in decision making: social/individual learning and transparency of choice At the macro scale, the map reduces the complexity of social decision process analysis to the coarse-grained simplicity of two axes, analogous to a principal components analysis reduced to two dominant factors. The north–south axis, which we parameterize as The framework has broad applicability, and one particular application we envisage is toward fertility decisions for example, using an exceptional long-term dataset on about 250,000 people, collected in the Matlab region of Bangladesh since 1966 Work on peer effects in smoking behavior is vast, but we have not found any work that attempts to estimate the dynamics of the intensity of choice function, as we propose. Work that is most closely related to ours and transparency of choice would provide a novel insight into the decision dynamics at the population scale.Our approach could be applied to far different scenarios than health, including criminal records or consumer sales, where long-term choice data are available alongside individual covariates. We see the framework as especially appropriate to online choices in the big-data era, as the covariate data could be comprehensive, including vast records of previous choices. In all cases, the characterization on social influence We previously described the map in terms of generalized data patterns diagnostic of each of its four quadrants In parametarizing our two-dimensional map, we divide transparency of choice into separable components for intrinsic utility and social influence. Our model builds on previous work in discrete choice theory by parameterizing the transparency of choice as a function of observable covariates see . To begiAssume thatIn order to incorporate the “east-west” axis of social influence, one option is to add a term for frequency-dependent social learning The model is intended to allow the estimation  to east . We may The covariates for each agent include those that predict the propensity of the behavior, denoted by We tested to see how these estimates can be used to describe a path, We generated artificial data to yield four different paths through the map to test whether our suggested estimation procedure actually works see . We can The specification in We then used the modelling to explore how the parameters In these tests, we found that estimation is reasonable for a priori assumptions regarding the strength of individual vs social learning — from We note that the inability to correctly estimate The simple bi-axial map of behavior in A hypothetical real-world example for Social-influence studies that treat transparency of choice as a variable suggest that it has a complex interaction with social learning. Some of this interaction might be captured, for example, by a specification such as These parameters relate to debates on modelling fertility decisions, for example, as explanations range from an intrinsic individual utility decision Fertility research also generates the sort of long-term, time-stratified demographic datasets that are appropriate to our proposed estimation method for the map. More generally, the growth of so-called “big data” on collective decisions also seems suited to this map As new digital technologies filter and search social influences and information, transparency of choice may be increased, but conversely if agents are overwhelmed the online deluge of information, options, and social influences The dimensions of the map are also relevant to studies that compare technological complexity with population size Having presented a two dimensional map  as a schThe map can now be applied to real-world case studies, especially those that feature large, time-stratified demographic data sets on binary decisions, such as those regarding health decisions. The parametrization we have presented allows us to extract, from these sorts of datasets, locations on the map representing degree of social learning and transparency of choice. As we apply this method in the future, we may be surprised to find that standard, universal assumptions regarding certain decisions may be becoming less appropriate, as the nature of such decisions changes through time or in different cultural contexts.We generated data for In order to identify parameters of the model that describe movements across the map, we need to separate transparency of choice from social influence. To do this, we start with the north–south axis of the map (the transparency of choice) and then add, via the east–west axis, social influence on individual choices. We can start with the north–south axis. In discrete-choice theory, we assume we have a certain number of choices available and a certain amount of utility that is divided up among those choices. We then effectively toss the choices randomly into bins of a certain utility and find how many choices we expect in each bin. To start, consider a population of individuals making a binary choice  and Non-Linear Least Squares (NLLS), we consider a binary decision via Now, denote by In the standard versions of estimation of discrete-choice models, the transparency of choice, In the scalar case, formulas for the partial derivatives of the likelihood function with respect to If the social-influence function is zero, however, or restricted to be zero, we normalize the four equations by dividing the equations by Packages such as Matlab or R are good for general ML estimation, which works well when we have few observations per cell and enough observations per cell to allow for logistic regression Note that Consider the functional-form specifications for the transparency of choice function and the social-influence function,Substituting 22 into 19, NLLS proceeds by selecting the parameter vector, is the prediction error of the model. In other words, NLLS chooses the parameter vector to minimize the sum of prediction errors. Taking the four partial derivatives of SSE with respect to We can see that if for some reason the term


We demonstrate that overall abundance of polyadenylated protein coding mRNAs is not affected, but several targets of RNA surveillance predicted from work in yeast accumulate as adenylated RNAs in the mMtr4KD. microRNAs are an added layer of transcriptional complexity not found in yeast. After Drosha cleavage separates the pre-miRNA from the microRNA's primary transcript, the byproducts of that transcript are generally thought to be degraded. We have identified the 5′ leading segments of pri-miRNAs as novel targets of mMtr4 dependent RNA surveillance.RNA surveillance plays an important role in posttranscriptional regulation. Seminal work in this field has largely focused on yeast as a model system, whereas exploration of RNA surveillance in mammals is only recently begun. The increased transcriptional complexity of mammalian systems provides a wider array of targets for RNA surveillance, and, while many questions remain unanswered, emerging data suggest the nuclear RNA surveillance machinery exhibits increased complexity as well. We have used a small interfering RNA in mouse N2A cells to target the homolog of a yeast protein that functions in RNA surveillance (Mtr4p). We used high-throughput sequencing of polyadenylated RNAs (PA-seq) to quantify the effects of the  Transcriptional activity in mammalian genomes, once considered limited, is now being viewed differently as more sophisticated techniques are developed and used to interrogate transcriptomes. The sheer abundance of regulatory RNAs  fostered their early discovery and characterization Following transcription, RNAs are routinely subject to a series of processing steps ranging from internal cleavage, trimming of ends, RNA editing, and in some cases, the introduction of covalent modifications into the nascent transcript. The production or processing of an RNA occasionally results in the introduction of errors, whereby important features critical to RNA function are disrupted. These defective RNAs can interfere with normal cellular functions, and have been linked to a host of diseases, most prominently neurodegenerative diseases and cancer To prevent accumulation or deployment of defective RNAs, eukaryotes invoke RNA surveillance as a “quality control” step that can identify and destroy aberrant RNAs Trf4/Air2/Mtr4 Polyadenylation) Extensive work in yeast led to the discovery of two multi-subunit complexes that are central to nuclear RNA surveillance: the nuclear exosome The surveillance and degradation machinery appears to be well conserved. Exosome complexes are found from archaea to humans Mus musculus nuclear RNA surveillance, we used a small interfering RNA (siRNA) to deplete the RNA dependent ATPase component of the TRAMP and NEXT complexes, designated as Skiv2l2 (mMtr4) in mouse N2A cells grown in culture. We hypothesized that targets of mammalian TRAMP or NEXT complexes would accumulate in the mMtr4-knockdown (mMtr4KD) as depletion of mMTR4 would impede degradation of these targets by the exosome. However, such targets should be successfully adenylated by the mTrf4 homolog, PAPD5, such that they could be identified based upon their poly(A) tails.To identify RNA targets of mMtr4KD than in the mControlKD. Our data support conserved roles for mMtr4 in processing rRNA 5′ETS for degradation, and in snoRNA surveillance. We identify a novel role for mMtr4 in targeting the 5′ leader sequences of microRNAs for degradation.We used an RNA-seq strategy specifically designed to capture adenylated transcripts and precisely map the 3′ end of the genomic template PA-Seq; . Our ana5 cells per 60 mm tissue culture dish and incubated 24 hours prior to transfection with siRNA. Independent culture dishes were transfected in triplicate with a 1 ml suspension of siRNA in Opti MEM medium and Lipofectamine 2000 (Invitrogen) according the manufacturer's recommendations to a final concentration of 67 nM. Pre-designed siRNAs were obtained from Ambion: skiv2l2 (Cat#177475) targeted to mouse Mtr4, and “Negative control #1” (Cat #AM4611). After 48 hr incubation with the siRNA, medium was removed, and cells from individual plates were harvested. For western blot analyses, cell pellets were resuspended in lysis buffer and quantified using a Bradford assay. For RNA experiments, cells were directly resuspended with TRIzol Reagent, total RNA was isolated according to manufacturer's recommendations, and quantified using a Nanodrop ND-1000 instrument.N2A cells  generated for each RNA sample to further demonstrate amplicons were specifically derived from an RNA template. Table S4 reports all primers used in this study, and includes information regarding amplicon size and intron(s) spanned.Efficiency of the siRNA knockdown was confirmed at both RNA and protein levels. Reverse transcription reactions were completed using SuperScript II (Invitrogen) according to manufacturer's recommendations with an oligo-dTMtr4 mRNA relative to CyclophilinB in the control versus knockdown cell lines, thereby determining the relative extent to which the target mRNA had been knocked down. Upon confirmation of efficient knockdown of the target mRNA, these total RNAs were further purified for deep sequencing using an RNeasy kit (QIAGEN) with on-column DNaseI digestion.Real-Time PCR reactions were performed in triplicate using iQ SYBR green master mix (BioRad) and equivalent aliquots of cDNA, with cycling reactions carried out in a My iQ thermocycler (BioRad). Real-Time PCR was used to quantify the level of Mtr4 protein levels were successfully knocked down. Anti-Mtr4 (Skiv2l2) was obtained from LSBio and anti-β actin from Thermo Scientific was used as a loading control.Western blot analyses were performed using three independent siRNA treated cell cultures to demonstrate that (16)/deoxyU/TTTVN-3′ primer to selectively identify adenylated RNAs. cDNAs were selected using Invitrogen's MyOne Streptavidin Dynabeads, dephosphorylated using APex Heat-Labile Alkaline Phosphatase (Epicentre), and digested with the USER enzyme (NEB) to selectively cut the RT primer at the Uracil, which released the dephosphorylated cDNAs from the Dynabeads, leaving a three T tag immediately adjacent to the complementary nucleotide representing the 3′ terminal residue of the adenylated RNA. Following this step, and between each of the subsequent modification steps, the released cDNAs were purified using the DNA clean and concentrator-5 kit (ZYMO Research). Modifications included removal/blunt ending of the 3′ poly(A) overhang remaining after USER digestion using T4 DNA polymerase (NEB), A-tailing using an exo- Klenow fragment (Epicentre), and ligation of the Barcoded Y-linker (Illumina) using T4 DNA ligase (NEB). Resulting cDNAs were separated on an agarose gel , and products in the 250–450 bp size range were excised and purified using a gel purification kit (ZYMO Research). Products were amplified for 16 cycles of PCR  using Phusion DNA polymerase (Finnzymes), and HPLC purified primers from IDT . Products for sequencing were size selected (∼250–400 bp) from an agarose gel.Deep sequencing of the control vs. Mtr4 knockdown RNAs was performed largely as described Paired-end sequencing was completed on an Illumina HiSeq2000 instrument. Raw sequencing reads from the Illumina platform were sorted based upon perfect match to a sample specific barcode (at the first 5 bases of read 1), trimmed, and finally split into separate fastq files for the 3′ and 5′ paired-end reads. Project data have been deposited to the NIH short read archive (SRP041394).Illumina sequencing data was sorted relative to barcode, and trimmed accordingly. The 3′ end of the sequence was evident based upon the presence of the paired end read that initiated with three T's. Paired end mapping to the mouse genome (mm9) was completed largely as described Clusters (a.k.a. peaks of adenylation) were identified largely as previously described All data were normalized to account for the sometimes significant difference in sequencing depth between sequencing runs for each of the knockdowns. The total number of PA ‘hits’ at a given genomic location were normalized to reads per million for that knockdown sequencing run. Rather than pooling the sequencing results from biological replicates, most data are reported from one sequencing run  or SuperScript II (Invitrogen) reverse transcriptase according to manufacturer's recommendations. Equivalent aliquots of each RT reaction were used in triplicate qPCR reactions assaying the levels of adenylated U3 snRNAs, and of the 5′ leader vs. the pri-miRNAs of several miRNAs. In addition to melt-curve analysis to confirm single amplicon yield, real-time amplification data were also analyzed using the DART  platform CycB as a housekeeping control gene. Oligonucleoltide primers used for PA-Seq data validation are also included in Table S4.Abundance of adenylated transcripts in the Abundance of the mature mir322 miRNA was quantified relative to U6 snRNA using their respective TaqMan assays (Applied Biosystems) performed according to manufacturer's recommendations.mMtr4 were assayed relative to cells transfected with a control siRNA. RNAs targeted for exosome degradation by fully functional TRAMP and NEXT complexes should be rare and largely undetectable in control knockdown cells, whereas these same RNAs should accumulate in cells depleted for mMtr4. In contrast, we hypothesized that canonical polyadenylation of messenger RNA would be unaffected by the mMtr4KD. Thus, adenylated RNAs that are substantially more abundant in the mMtr4KD are presumed to be substrates of mMTR4, and possible targets of the mammalian NEXT or TRAMP complexes. The ideal concentration and corresponding knockdown efficiency for individual siRNAs were empirically determined using qRT-PCR assays of the target mRNA .RNA samples from two replicate knockdowns were used for high-throughput paired-end sequencing designed to specifically capture the 3′ ends of adenylated RNAs. The paired-end reads were mapped to the mouse genome assembly using BWA . Roughly 75% of raw sequence reads were successfully mapped to unique sites in the genome Table 1.mMtr4KD should not affect the abundance of protein coding mRNAs, normalized polyadenylated sequencing reads terminating within +50 bases of the annotated 3′ end of RefSeq protein coding genes were identified for each KD. For each of the KDs, roughly 8500 RefSeq genes are represented in the PA-seq data with a minimum of five sequencing reads at the modal position. Data from the modal position were used to test the experimental reproducibility between sequencing experiments. A dot plot of the two mControlKDs (mControl-1 vs. mControl-2), with RNA isolation completed in parallel but library construction completed independently, demonstrated reproducibility of the PA-Seq data (R2 = 0.87). The mMtr4KD and mControlKD sequencing reads of 8,935 RefSeq modal PA-sites are shown as a dot plot (mMtr4KD versus mControlKD reads are presented as a histogram (mMtr4KD vs mControlKD indicated little variability in polyadenylation of the RefSeq protein coding mRNAs under conditions where mMtr4 has been depleted (2 = 0.95). The histogram provides an alternative view of the data for mMtr4KD vs mControlKD, revealing a normal distribution, with ∼94% of the RefSeq genes used in the analysis exhibiting less than a two-fold difference (|log2|<1) between mMtr4KD and mControlKD. The ∼6% of RefSeq genes falling outside the two-fold difference are largely observed at the lowest expression levels , comparable difference (>2-fold difference), or an inverse correlation (>2-fold difference in the opposite direction). The data were also examined for evidence of differential polyadenylation position usage between the mMtr4KD and mControlKD. Although RNAs corresponding to alternative positions exist within the data, no significant differences in the relative distribution of site usage were apparent.To test the hypothesis that dot plot Fig. 2A,mMtr4KD and mControlKD at a given locus would be the result of differences in RNA abundance, we focused on one of the known targets of the yeast TRAMP complex; the 5′ ETS of pre-rRNA. The 5′ ETS has also been observed to accumulate in human tissue culture cells depleted of hMtr4mMtr4KD relative to mControlKD. Processing intermediates of the mouse pre-rRNA 5′ ETS include the region from the transcription start to the A′ processing site, from A′ to A0, and from A0 to the 18S cleavage junction (0 terminating fragments (+2 bp) is 8-fold higher in the mMtr4KD than the mControlKD . Thus, accumulation of 5′ ETS adenylated RNAs in the mMtr4KD is specific to the A0 processing site. We conclude from this observation that degradation of the 5′ ETS at the A0 site is affected by depletion of mMtr4.Convinced that changes in amplitude of PA-seq reads between nd intron of Tex14 mRNA (NM_031386) on mouse chromosome 11 bona fide U3B processing rather than read through transcription. We report the mapped PA-seq data pooled from all four U3B loci . Slightly longer U3 snoRNAs are also observed, and are generally more abundant in the mMtr4KD. A comparable distribution of adenylated transcripts from the U3A locus on chromosome 10 are observed (data not shown). Given that normal U3 processing involves 3′→5′ exosome-mediated trimming Many of the snoRNAs function as guides directing accurate processing or modification of precursor rRNA transcripts to release 18S, 5.8S and 28S rRNAs, and facilitate their maturation and function through nucleotide modification U3B loci Fig. 4. mMtr4KD relative to mControlKD. Total RNA from two replicate knockdowns was reverse transcribed using oligo-dT, and subjected to qPCR to measure the level of poly(A)+U3 snoRNA in the mMtr4KD and mControlKD using U3 specific primers. Given that oligo-dT primed reverse transcription (RT) would not distinguish between full-length and shortened adenylated U3 snoRNAs, our qPCR results reflect the sum of all adenylation sites reported in mMtr4KD and mControlKD by qPCR is consistent with the PA-seq data (Table S1).Quantitative RT-PCR was used to confirm differential accumulation of adenylated U3 snoRNA in mControlKD to identify RNAs that exhibit differential accumulation. Those exhibiting the largest ratios are shown in mMtr4KD map to a microRNA or microRNA host gene.To identify additional targets of mMTR4 mediated degradation, the PA-seq data was analyzed using the F-Seq feature density estimator mMtr4KD peaks reveal three prominent areas of adenylation in this region of chromosome X: 1) at the 3′ annotated end of cDNA C43009B03RIK, 2) a loose clustering near the 3′ annotated end of a shorter RIKEN cDNA AK021262 that encodes a hypothetical 92aa protein but does not include the miRNA cluster, and 3) at mir322  containing a miRNA cluster (mir322-mir503-mir351). To gain a better appreciation for the distribution of adenylation across this cDNA, we examined all F-Seq peaks across the 4 kb span of C43009B03RIK. Distribution of the t mir322 Fig. 5. mControlKD  and a 3′ fragment . Although we cannot completely rule out that the mir322-mir503-mir351 cluster is more actively transcribed upon mMTR4 depletion, even a 2-fold increase would seem too small to explain the large, 67-fold increase in adenylated mir322 5′ leader by a mechanism of increased transcription alone. In a related experiment, PA-seq detected a modest increase in the mir322 5′leader and annotated 3′ end of RIKEN-C43009B03RIK cDNA in N2A cells depleted for the exonuclease mRrp6 (data not shown/unpublished). Taken together, these data specifically support the idea that differences between mMtr4KD and mControlKD are from impaired degradation of those transcripts, rather than increased transcription.The vast increase in abundance of adenylated mir322 5′ leader sequences in mMtr4KD (mMtr4KD sequencing data sets (mMtr4-2) identified a total of six miRNA peaks, expanding the list to include let7c-2, mir106b and mir26b  include reads mapping consistent with the predicted 5′ leader terminating at the Drosha cleavage site. Although a majority of the 105 additional miRNAs are represented by fewer than 5 total PA-seq reads, it is difficult to dismiss the correlation with the position of the 5′ leader as coincidental. All miRNA 5′ leader PA-seq data are available in Table S3.Two additional microRNAs, let7b and mir138-1, display clusters of adenylation for mMtr4KD Table 2.mMtr4KD relative to mControlKD. We used the same RNA samples used to generate the PA-seq data, and focused on mir322, mir503, let7b, let7c2 and mir138-1. qRT-PCR analysis of miRNA 5′ leader sequences was analyzed relative to qRT-PCR that spans the junction between the 5′ leader and the pre-miRNA . We used a PA-seq strategy to selectively amplify and sequence adenylated RNAs. Depletion of mMtr4 does not alter the abundance of adenylated protein coding mRNAs. In contrast, depletion of mMtr4 results in the accumulation of a number of adenylated non-coding RNA. Both the rRNA 5′ ETS and U3 snRNA have been previously reported as targets of TRAMP. The accumulation of adenylated 5′ leader transcripts from pri-miRNAs is novel, and suggests a role for mMtr4 in targeting these transcripts for degradation by the exosome. Together, these results are consistent with observations linking MTR4 homologs to RNA surveillance We have analyzed the effect of depleting Mtr4 is an essential gene, repressible or reduced function alleles of Mtr4 have been used to identify targets of TRAMP and characterize the role(s) played by Mtr4p as part of the TRAMP complex mtr4 resulted in time-course dependent accumulation of the 5′ ETS of yeast rRNA.RNA surveillance has been most extensively characterized in yeast, where the TRAMP complex functions to identify and target specific RNAs for degradation. The yeast TRAMP complex consists of the DExD/DExH box RNA helicase Mtr4p, as well as a poly(A) polymerase (either Trf4p or Trf5p), and a RNA-binding protein containing five Zn-knuckles (either Air1p or Air2p). Although 0 processing site in our mMtr4KD, consistent with this rRNA byproduct being a conserved target of the mammalian TRAMP complex. Nevertheless, given the relative abundance of rRNA, the frequency with which we observe these transcripts , accumulate in a similar fashion to those at A0. Among ∼31 million raw 3′ reads queried, including both the mMtr4KD and mControlKD, only 3 adenylated transcripts corresponding with the region 5′ of the A′ processing site and no reads corresponding to the 18S proximal segment were observed. Abundant data suggest that pre-rRNA processing should occur frequently at the A′ site mMtr4KD is consistent with the fact that the analogous portion of the yeast 5′ETS is not degraded by the exosome, but rather by 5′→3′ exonucleases Xrn1p and Rat1p mMtr4KD is specific to the A0 processing site.We observe significant accumulation of 5′ETS sequences adenylated at the AmControlKD is consistent with the idea that adenylation is part of the normal U3 life-cycle, from 3′ end maturation to normal turnover of snoRNAs. Derti and colleagues mMtr4KD, with a substantial increase in transcripts adenylated just 5′ of the annotated 3′ end, is consistent with the concept that mMTR4 functions not only in the normal life-cycle of U3 as observed in normal cells, but also in turnover of defective U3 snoRNAs, such as those that may have been over-trimmed by an exonuclease.snoRNAs have also been observed to be targets of Mtr4p in yeast It is commonly accepted that extraneous portions of miRNA primary transcripts released during miRNA biogenesis are ‘simply degraded’, but the cellular components responsible for targeting those RNAs for degradation have not been previously identified. Our PA-seq data suggest that mMTR4, possibly as part of the mammalian TRAMP complex, plays a role in directing some of these miRNA byproducts, particularly the 5′ leader, to the nuclear exosome for degradation., Table S3). Interestingly those that accumulate to the highest levels correspond to abundant miRNAs. Both mir322 and let7 are relatively ubiquitous across a wide range of tissues, and among the most abundant microRNAs detected While we were able to detect adenylated 5′ leader sequences corresponding to over 100 miRNAs in the mMtr4 knockdown, only a handful of these accumulate to highly significant levels . Similar to mir322, mir106b is the 5′ most microRNA within the mir106b-mir93-mir25 cluster, which is located within intron 13 of the Mcm7 gene. Finally, let-7c2 and let-7b are less than 1.0kb apart and presumed to be part of the same primary transcript. While the 5′ leader sequences for both of these microRNAs accumulate, we observed a greater amplitude of PA-seq accumulating in mMtr4KD for the more distal let-7b than for let-7c2, both being appreciably above that seen in the mControlKD. Overall, the reason we see preferential (sometimes exclusive) accumulation of the 5′ leader for one member of a miRNA cluster is unknown, but could reflect differences in processing order or efficiency, the possibility that only some of the processing byproducts are adenylated, or RNA structural determinants of the accumulating PA-seq.We explored the genomic context for several more of the most abundant microRNA 5′ leaders  in an effort to determine whether context might influence accumulation of the 5′ leader in the mMtr4KD, it is worth noting that we do not observe notable accumulation of a corresponding 3′ fragment that would simultaneously be liberated by Drosha cleavage of the pre-miRNA from the pri-miRNA. One factor that may influence miRNA 5′leader accumulation is the 5′ cap on these RNApolII derived transcripts preventing 5′→3′ exonuclease degradation, whereas the 3′ fragment would be susceptible to both 5′→3′ and 3′→5′ exonuclease degradation. Consistent with this distinction, 5′ proximal versus 3′ proximal RNAs resulting from cotranscriptional cleavage (CoTC) of the human beta-globin gene have alternate fates. The 3′ proximal RNAs are degraded by the Xrn2 5′→3′ exonuclease mMtr4KD cells, or the relative extent to which they will accumulate, but a select few are by far the most abundant adenylated RNAs we have thus far identified in the mMtr4KD.Although it remains unclear why specific miRNA 5′ leaders accumulate in the Although extensive research has uncovered a wealth of TRAMP substrates in yeast, the absence of microRNAs in yeast and thus our inability to predict this novel finding underscores the importance of expanding research into a wider array of organisms. This finding also reveals the extent to which nuclear RNA surveillance complexes may have evolved to target an ever increasing diversity of substrates as genomes evolve and increase in complexity. Finally, our observation suggests that depleting MTR4 proteins in a wide array of tissues or organisms may be a useful mechanism for characterizing the 5′ ends of pri-miRNAs, such as determining transcription start sites and identifying promoter elements.mMtr4 in adenylation mediated degradation of the 5′ ETS of rRNA and U3 snoRNAs, and suggest that some miRNA 5′ leader sequences utilize mMtr4 in a similar degradation pathway.Well conserved homologs of Mtr4, Trf4 and Air2 form a TRAMP-like complex in humans, but immunofluorescence localization suggests that the Air2 homolog ZCCHC7 exclusively localizes to the nucleolus Figure S1Density of PA-seq reads across the entire 5′ ETS. Graph depicts the level of adenylation at individual positions across the 4 kb 5′ ETS. Positions for which zero reads were detected in either the mMtr4KD or mControlKD were omitted for improved resolution of all remaining positions. Positions along the x-axis are not to scale, but several reference points are indicated.(TIF)Click here for additional data file.Figure S2Density of PA-seq reads encompassing U3B. Graph depicts the level of adenylation at individual positions across the full length of U3B snoRNA. Data are pooled for the four tandem copies of U3B found on chromosome 11 of the mouse genome.(TIF)Click here for additional data file.Table S1Quantitative RT-PCR results for U3 snoRNA.(XLSX)Click here for additional data file.Table S2Mature mir322 levels exhibit no consistent fluctuations in mMtr4 depleted cells.(XLSX)Click here for additional data file.Table S3All miR associated pA-seq reads.(XLSX)Click here for additional data file.Table S4All oligos used for qPCR.(XLSX)Click here for additional data file.


The principle cells of the brain- neurons- express more genes than any other cell type. As many rudimentary processes of the brain, such as long-lasting changes in synaptic efficacy, involve alterations to the expression of genes, the characterisation of proteins and processes that influence transcription is a high priority in neuroscience. An emerging system capable of transducing signals that likely influence transcription is synapse-to-nucleus macromolecular protein shuttling, in which synaptic proteins are relayed to the nucleus in a complex with importins  and molecular motors such as dynein , proteins larger than this require importins. Two related families of importins have been described: Importin-α and importin-β. In humans, there are 7 importin-α isoforms (1–7) and 19 importin-β isoforms  is novel and warrants validation but it isn't a giant leap, rather, it is an extension of the existing concept of cargo-specificity in nuclear import (Figure The classical importin pathway relies on the trimeric complex (importin-β-importin-α-Cargo, see Figure t Figure . Becauset Figure  found tht Figure . Assumint Figure . Our pret Figure . As the Whilst the hypothesis that importins function as adaptor proteins in the mediation of cargo-specific synapse-to-nucleus transport for key brain processes such as long-term forms of synaptic plasticity is informative and useful, it has several important caveats: Non-canonical importin functions are emerging and the role of importins in transport cargo specificity is likely being underestimated. The Importin Code best depicts the grand cargo specificity that importins provide to tightly regulate synaptic protein selection for nuclear import that ultimately governs cellular phenotypes.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.


Extensive alternative splicing and polyadenylation of pre-mRNAs not only expands the protein coding potential of our genomes but also generates a wealth of mRNA isoforms with different 3′ untranslated regions (UTRs) ,2. SinceThe term NMD was initially coined to describe the accelerated degradation observed for mRNAs with nonsense mutations that prematurely truncate the open reading frame (ORF) . NMD serPLOS Genetics, now shed some unexpected new light on this question. During spermatogenesis, spermatogonia differentiate into spermatocytes, which undergo meiosis and postmeiotically develop into round and, further, elongated spermatids . CBs consist of RNA, many RNA-binding proteins, helicases, and several members of the TUDOR-domain protein family (TDRDs); furthermore, CBs are best known for their role in piRNA biogenesis ,14. A coIn summary, the two new studies ,12 corro


Atf4, but also several more mRNAs with hitherto unknown translational regulation. Additionally, our approach proved useful for identification of new transcript isoforms.Ribo-Seq maps the location of translating ribosomes on mature mRNA transcripts. While during normal translation, ribosome density is constant along the length of the mRNA coding region, this can be altered in response to translational regulatory events. In the present study, we developed a method to detect translational regulation of individual mRNAs from their ribosome profiles, utilizing changes in ribosome density. We used mathematical modeling to show that changes in ribosome density should occur along the mRNA at the point of regulation. We analyzed a Ribo-Seq data set obtained for mouse embryonic stem cells and showed that normalization by corresponding RNA-Seq can be used to improve the Ribo-Seq quality by removing bias introduced by deep-sequencing and alignment artifacts. After normalization, we applied a change point algorithm to detect changes in ribosome density present in individual mRNA ribosome profiles. Additional sequence and gene isoform information obtained from the UCSC Genome Browser allowed us to further categorize the detected changes into different mechanisms of regulation. In particular, we detected several mRNAs with known post-transcriptional regulation, e.g., premature termination for selenoprotein mRNAs and translational control of  The remtructure .cis-regulatory elements that control the speed of elongation (Ribosome profiling (Ribo-Seq)—a technique based on deep-sequencing of mRNA regions protected by ribosomes, which provides the positions of ribosomes on the entire transcriptome (ribosome profiles)—has enabled the study of translation regulation at the genome-wide level , 2012. Aongation , the seqongation , and newongation . The fulongation . A recenongation . HoweverThe aim of the present work was to develop a computational approach for detecting translational regulation, especially regulation during elongation, from ribosome profiles. We started from the assumption that most translational regulatory events  cause changes in ribosome density along the translated mRNAs. After determining by mathematical modeling that it is theoretically possible to discriminate between different forms of translational regulation based on the patterns of ribosome density, we used a change point (CP) algorithm  to find N codons and (an infinite pool of) ribosomes of size L codons. Free ribosomes bind to the available initiation codon of the mRNA at rate kI, before progressing along the mRNA at a codon-specific rate kEi  and leaving the mRNA at the last codon at rate kT. Each ribosome is allowed to progress only if there is no steric hindrance from the ribosome in front. Additional reactions, some of which have not been addressed previously with TASEP models, include scanning past the canonical TIS to an alternative TIS in the coding region of the mRNA (CDS) at rate kAlt and ribosome drop-off or alternative termination at rate kDi.To analyze the effects of translational regulation on protein synthesis and ribosome profiles, we built a family of totally asymmetric simple exclusion process (TASEP) models of protein translation . The modkT are due to a queue of ribosomes spanning the length of the mRNA  elongation rates, different regimes of elongation are possible, which result in significantly different protein synthesis rates and different ribosome profiles . If, at the mRNA C.If the elongation rate is codon dependent, there are several possible scenarios. Slow codons at 5′ positions have effects similar to slow initiation; slow codons at 3′ positions, effects similar to slow termination . Slow codons in the central region of the CDS, with elongation rates higher than the initiation rate, cause a peak in the ribosome profile at their respective positions; lower elongation rates cause a queue behind the stalled ribosome, with few ribosomes in front; and, finally, at very low elongation rates the queue reaches to the first codon and begins to limit initiation D.A special case of putative regulation of ribosome progression along the mRNA is the “ramp,” a stretch of the first 30 to 50 CDS codons that are more likely to include rare codons, which was discovered by ribosome profiling in yeast and further investigated in a computational study of codon bias . DecreasWe also found that all other simulated cases of translation regulation had significant effects on ribosome density, indicating that the analysis of changes in ribosome density could indeed lead to identification of translationally regulated mRNAs. Alternative initiation results in a step-like increase in density, while ribosome drop-off and alternative termination result in a step-like decrease E,F.After performing the simulations, we tested whether the CP algorithm could detect the changes in ribosome density in computationally generated ribosome profiles. In brief, the algorithm detects points along the length of the mRNA where the ribosome density is changed, while ignoring the effects of noise . If the change is abrupt, the CP position can be determined very exactly, while for slower changes, one or more CPs may be detected with larger positional uncertainty. Although the algorithm can detect very small changes, in this work we limited the search to only those changes that were >33%, and so, only CPs most likely to include strong translation regulation were included in the analysis.The CP algorithm provided a robust method for detecting changes in density for all the translational regulation modes that we tested. A single slow elongation codon, slow termination, alternative initiation, and ribosome drop-off/alternative termination all led to step-like changes in ribosome densities and were also detected as such. Slow termination and slow elongation that led to ribosome stalling, and queues were detected as oscillations in ribosome density . The minimum/maximum parameters at which CP detection was robust are presented in Supplemental Table S1, with example outputs from the CP analysis in Supplemental Figure S3.The next step was to use the CP algorithm to detect translation regulation events in a Ribo-Seq data set. We chose a recent mESC study  because From the combined Ribo-Seq/RNA-Seq data set, 8933 well-expressed mRNA transcripts were selected with high enough signals in both Ribo-Seq and RNA-Seq . The averaged ribosome and RNA-Seq profiles of the 8933 highly expressed mRNAs confirmed the presence of bias in the data sets. The ribosome density was highest at initiation and termination codons and then dropped toward the middle of the CDS. However, unexpectedly, the central region of the CDS exhibited higher ribosomal density than the 5′ and 3′ ends A. It shoAdditionally, analyzing individual ribosome (and RNA-Seq) profiles revealed that ribosome (and RNA-Seq) density along the transcripts was very rarely constant, as would be expected for translation without strong regulation. Instead, in addition to short peaks and valleys that can be accounted by slower/faster ribosome speeds at particular codons, many step-like changes were present in the profiles. Two likely reasons were alignment of RNA reads to multiple genomic regions and assignment of RNA reads aligned to single genomic regions to multiple gene isoforms that result from alternative splicing, which have both featured in the original study . The proBecause the effects of multiple alignments were estimated to be small in previous studies , we deciAlthough multiple assignments could not completely explain the Ribo-Seq bias that was seen from the average profiles, they caused undesirable step-like changes in ribosome profiles and needed to be removed before the CP analysis. Therefore, assuming that approximately the same number of multiple assignments and multiple alignments occurred in both Ribo-Seq and RNA-Seq, all individual ribosome profiles were normalized with the corresponding RNA-Seq profiles. In this way, CP analysis of ribosome and RNA-Seq profiles should detect alternative splicing and translational regulation, while the analysis of the derived ribo/RNA profiles should detect translational regulation only.Rrs1) and were all eliminated by the normalization, while for glutathione peroxidase 1 (Gpx1), two CPs were found in both the ribosome and RNA-Seq profiles and one CP remained after normalization (later identified as a true translation regulation event)  . Almost half of the CPs that were found in ribosome profiles, but were no longer present in ribo/RNA profiles, matched the positions of known exon junctions  . The necn event) . NeverthIn total, 34,844 CPs were found in all the different profiles. To find mRNA targets with a high probability of translation regulation, we applied several filters to this CP set. First, we categorized the CPs based on the profile  in which they were found : Translational regulation targets should have CPs in ribosome profiles but not in RNA-Seq profiles, and normalization of the ribosome profiles with RNA-Seq profiles should not eliminate the CP. As a second filter, we eliminated all those CPs that coincided with known exon junctions. We were left with 815 CPs belonging to 635 mRNAs. From these, 336 CPs that came in pairs  see , exon 4 By use of a decision tree, the CPs were then classified into the following groups: alternative termination, alternative initiation, ramp, drop-off, slow termination, stalling, and false positives . After automatic classification, each mRNA profile was visually inspected and compared with the genomic information available in UCSC Genome Browser . If the Gpx4, Tmem55b, Atf4, 2700094K13Rik , Sep15, and Gpx1  with a UGA codon in the central region of the CDS. The presence of a stop codon in the CDS is a characteristic feature of seleonoprotein mRNAs, where it codes for the amino acid selenocysteine  having the selenocysteine codon positioned immediately before the stop codon, making detection of alternative termination impossible. In addition, for the remaining two genes , CPs were found in ribosome profiles but then lost during normalization (Sepx1) or because the UGA was near an exon junction (Sepw1) .This analysis discovered six genes with potential for strong alternative termination: cysteine . There aAtf4 and Tmem55b) was frameshifted . For Atf4, we found two functional TISs, the canonical one and one in the 5′ UTR in frame −1, indicating high translation of an uORF. For Tmem55b, we discovered a functional TIS in the 5′ UTR in frame +1, while the canonical one was not functional . For both proteins, the translation was terminated in the middle of the annotated CDS, with potential production of functional peptides.In contrast to the mRNAs from selenoproteins that were translated in frame 0, the translation of the remaining two mRNAs  were also discovered by Psmd8 (and possibly Psmc2) was expressed as an unknown isoform  . Of these, most of the associated open reading frames (ORFs) were in the canonical frame and produced truncated proteins, while Mtf2, Isg20l1, and Ppp1ca were translated in frame +1 and produced a short peptide only, which can be seen by the decrease in ribosome density shortly after the alternative initiation/increase in density .We then tested whether CP analysis was useful for establishing the level of translation from alternative initiation sites predicted previously . We compPum2, Cinp, Spin1, Myst4, L3mbtl2, Uhrf1, Zmat3, and Arpp19) . In most cases, the drop in ribosome density was preceded by a large spike, indicating the drop could be a consequence of stalling at a slow codon. The drop-off group was enriched for methylated histone residue binding , indicating a possible role for particularly slow elongation in the correct folding of proteins that bind histone residues. However due to small sample size, this interpretation has to be taken with due caution. Interestingly, the genomic sequence of the eight genes around the predicted drop-off was enriched for G and A nucleotides, in particular the GAA codon .The analysis found eight genes with potential ribosome drop-off . When this group was compared with all well-expressed genes for translational efficiency, no difference was found, indicating that slow elongation at the start of the CDS and not fast initiation, which was a competing hypothesis brought forward by the modeling, is the main cause of the ramp . We also found no enrichment for rare codons or for any GO terms. Slow termination was predicted for 45 mRNAs . We found no enrichment for any particular stop codon, unusual termination context, rare codons preceding the stop codon, or any GO terms for this group. We also found no oscillation in the ribosome profiles, indicating that there is no significant ribosome queuing during translation.Despite efforts to eliminate false positives, ∼60% of CPs on the final list could not be attributed to the tested translational regulation mechanisms. Detailed manual analysis showed that many CPs were a consequence of alternative splicing; however because the CPs found around the known splice sites were eliminated, only CPs at unknown splice sites should have remained. By comparing the whole set of 462 candidates for translational regulation with UCSC isoform information, we found new isoforms for 31 genes  to search for frameshift sites upstream of the predicted alternative termination. The successful detection of both is an indirect verification of our approach .One of the other alternative termination targets, gulation , which wgulation . To our We are aware of three other studies that have used ribosome profiling to find potential TISs . In all We found a further eight mRNAs with potential ribosome drop-off, none of which were previously reported to be targets of translational regulation. The difference between these and the alternative termination mRNAs was that no stop codon was found at the positions of the CP for the latter. Analysis of the sequence surrounding the drop-off site showed enrichment for G and A nucleotides and, specifically, the GAA codon. A similar consensus was found for sites of ribosome pausing in the original Ribo-Seq study . It therWe also found several mRNAs with high ribosome density at the 5′ (ramp) or the 3′ (slow termination) of the CDS; however, sequence analysis did not reveal enrichment for any sequences. Instead of resulting from translation of slow codons, as in the current hypothesis, the TASEP simulations of translation have suggested that the ramp could be due to very fast initiation, but this has not been confirmed by analysis of translation efficiency. Therefore, slow codons and/or mRNA tertiary structure remain the most probable causes of the ramp .Although we tried to eliminate changes in density due to Ribo-Seq/RNA-Seq artifacts, a careful analysis showed that many CPs among the translation regulation candidates were a consequence of alternative splicing. Because all changes in density close to known exon junctions as annotated in the UCSC Genome Browser were eliminated from the final list, the remaining CPs were strong candidates for novel isoforms. By using this approach, we found 31 genes with hitherto unknown isoforms from UCSC or Ensembl  and a further eight for which some evidence already exists , suggesting that our method is useful not only for analyzing translational regulation but also for uncovering novel alternative isoforms, including alternative splicing. Indeed, in a very recent study, a strategy similar to ours has been developed specifically to estimate gene isoform expression with very encouraging results .In conclusion, the present study demonstrates that ribosome density patterns in the CDSs are a valuable source of information in the analysis of translational regulation. The CP approach taken in the study has proven useful in detecting translational regulation in the mRNA coding region but would easily be extendable to analysis of 5′ UTR and 3′ UTR regions and to detection of alternative splicing events.N = 500 codons is presented by a chain of 500 sites, while a ribosome attached to the mRNA covers L = 10 codons , the ribosome could either move to the next codon or detach from the mRNA.Ribosome drop-off and alternative termination were modeled as alternatives to the elongation step occurring at rate changepoint R package (http://cran.r-project.org/web/packages/changepoint/index.html) and a Bayesian CP algorithm from the bcp R package (http://cran.r-project.org/web/packages/bcp/index.html) (bcp returns the estimates of both the mean and the probability of a CP for each position in a sequence (whereas changepoint does not), we decided to use it for the rest of the analysis. The details of the algorithm are presented by In this study, we tested CP algorithms from the ex.html) . In a pryR = . The bcp algorithm assumes that there is an unknown partition, ρ, of each vector into contiguous block, such the means are equal in each block but different between neighboring blocks. A CP is defined as the position on the mRNA i ∈ {1,…,n − 1} that delimits two consecutive blocks. The algorithm begins with a zero partition ρ = ; Ui = 0; Un = 1 and then updates it in an MCMC scheme. In each step of the Markov chain, at each position i, a value of Ui is drawn from the conditional distribution of Ui given the data and the current partition. The transition probability, p, of a change at the position i + 1 is obtained from the following ratio presented by b is the number of blocks, p0 and w0 are parameters used in the definition of priors that control the sensitivity of the algorithm, and W0, B0, W1, and B1 are the within and between block sums of squares obtained when Ui = 0 and Ui= 1, respectively. In this study, different values of p0 and w0 were tested, with p0 = 0.001  and w0 = 0.2 (relevant changes were expected to be of reasonable size) producing reasonable results in detecting changes in the simulated data sets .The average ribosome profiles and RNA-Seq profiles were generated by first normalizing all individual profiles by length. This was performed by using function Based on the profile  in which a CP was found, we categorized the CPs into seven different groups . Group A contains the CPs found only in ribosome profiles—they are not found in RNA-Seq profiles—that are eliminated by normalization. These are most likely to be caused by noise, short segments of slow/fast codons, or weak multiple assignments detected in ribosome profiles, but not RNA-Seq profiles (vice versa for group B). Groups D and G contain CPs found in ribosome profiles , RNA-Seq profiles , and ribo/RNA profiles (G). These are most likely due to multiple alignments/assignments. Groups C and F are most likely false positives caused by normalization itself (C) or noise (F). We are thus left with the most likely targets for translation regulation, Group E: 1355 CPs (4%) found in ribosome and ribo/RNA profiles, but not in RNA-Seq profiles.This group was reduced further by eliminating all CPs that were fewer than three codons away from a known exon junction and CPs that came in pairs .http://lapti.ucc.ie/bicoding/Rscripts/PTS.R in September 2013), a measure of how the nucleotide triplet periodicity  changes in the ribosome profiles  and slow termination (3′ ends of the CDS). We scanned the genomic sequences surrounding the detected CPs for potential stop codons and strong initiation sequences (consensus sequence taken from profiles . If PTS profiles . If the Identified sets of genes were compared with the group of all well-expressed genes for GO enrichment with the web-based tool GOrilla, using the default settings .Supplemental material is available for this article.