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ABSTRACT: The poly(A)-binding protein nuclear 1 (PABPN1) is a ubiquitously expressed protein that is thought to function during mRNA poly(A) tail synthesis in the nucleus. Despite the predicted role of PABPN1 in mRNA polyadenylation, little is known about the impact of PABPN1 deficiency on human gene expression. Specifically, it remains unclear whether PABPN1 is required for general mRNA expression or for the regulation of specific transcripts. Using RNA sequencing (RNA-seq), we show here that the large majority of protein-coding genes express normal levels of mRNA in PABPN1-deficient cells, arguing that PABPN1 may not be required for the bulk of mRNA expression. Unexpectedly, and contrary to the view that PABPN1 functions exclusively at protein-coding genes, we identified a class of PABPN1-sensitive long noncoding RNAs (lncRNAs), the majority of which accumulated in conditions of PABPN1 deficiency. Using the spliced transcript produced from a snoRNA host gene as a model lncRNA, we show that PABPN1 promotes lncRNA turnover via a polyadenylation-dependent mechanism. PABPN1-sensitive lncRNAs are targeted by the exosome and the RNA helicase MTR4/SKIV2L2; yet, the polyadenylation activity of TRF4-2, a putative human TRAMP subunit, appears to be dispensable for PABPN1-dependent regulation. In addition to identifying a novel function for PABPN1 in lncRNA turnover, our results provide new insights into the post-transcriptional regulation of human lncRNAs.
PLoS Genetics 11/2012; 8(11):e1003078. · 8.69 Impact Factor
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ABSTRACT: RNA editing, the post-transcriptional recoding of RNA molecules, has broad potential implications for gene expression. Several recent studies of human transcriptomes reported a high number of differences between DNA and RNA, including events not explained by any known mammalian RNA-editing mechanism. However, RNA-editing estimates differ by orders of magnitude, since technical limitations of high-throughput sequencing have been sometimes overlooked and sequencing errors have been confounded with editing sites. Here, we developed a series of computational approaches to analyze the extent of this process in the human transcriptome, identifying and addressing the major sources of error of a large-scale approach. We apply the detection pipeline to deep sequencing data from lymphoblastoid cell lines expressing ADAR1 at high levels, and show that noncanonical editing is unlikely to occur, with at least 85%-98% of candidate sites being the result of sequencing and mapping artifacts. By implementing a method to detect intronless gene duplications, we show that most noncanonical sites previously validated originate in read mismapping within these regions. Canonical A-to-G editing, on the other hand, is widespread in noncoding Alu sequences and rare in exonic and coding regions, where the validation rate also dropped. The genomic distribution of editing sites we find, together with the lack of consistency across studies or biological replicates, suggest a minor quantitative impact of this process in the overall recoding of protein sequences. We propose instead a primary role of ADAR1 protein as a defense system against elements potentially damaging to the genome.
RNA 07/2012; 18(9):1586-96. · 5.09 Impact Factor
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ABSTRACT: Li et al. (Research Articles, 1 July 2011, p. 53; published online 19 May 2011) reported large numbers of differences between DNA and messenger RNA in human cells, indicating unprecedented levels of RNA editing, and including sequence changes not produced by any of the known RNA editing mechanisms. However, common sources of systematic errors in high-throughput sequencing technology, which were not properly accounted for in this study, explain most of the claimed differences.
Science 03/2012; 335(6074):1302; author reply 1302. · 31.20 Impact Factor
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ABSTRACT: Expression levels of many human genes are under the genetic control of expression quantitative trait loci (eQTLs). Despite technological advances, the precise molecular mechanisms underlying most eQTLs remain elusive. Here, we use deep mRNA sequencing of two CEU individuals to investigate those mechanisms, with particular focus on the role of splicing control loci (sQTLs). We identify a large number of genes that are differentially spliced between the two samples and associate many of those differences with nearby single nucleotide polymorphisms (SNPs). Subsequently, we investigate the potential effect of splicing SNPs on eQTL control in general. We find a significant enrichment of alternative splicing (AS) events within a set of highly confident eQTL targets discovered in previous studies, suggesting a role of AS in regulating overall gene expression levels. Next, we demonstrate high correlation between the levels of mature (exonic) and unprocessed (intronic) RNA, implying that ∼75% of eQTL target variance can be explained by control at the level of transcription, but that the remaining 25% may be regulated co- or post-transcriptionally. We focus on eQTL targets with discordant mRNA and pre-mRNA expression patterns and use four examples: USMG5, MMAB, MRPL43, and OAS1, to dissect the exact downstream effects of the associated genetic variants.
Genome Research 02/2011; 21(4):545-54. · 13.61 Impact Factor
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ABSTRACT: Assessing the influence of three-dimensional protein structure on sequence evolution is a difficult task, mainly because of the assumption of independence between sites required by probabilistic phylogenetic methods. Recently, models that include an explicit treatment of protein structure and site interdependencies have been developed: a statistical potential (an energy-like scoring system for sequence-structure compatibility) is used to evaluate the probability of fixation of a given mutation, assuming a coarse-grained protein structure that is constant through evolution. Yet, due to the novelty of these models and the small degree of overlap between the fields of structural and evolutionary biology, only simple representations of protein structure have been used so far. In this work, we present new forms of statistical potentials using a probabilistic framework recently developed for evolutionary studies. Terms related to pairwise distance interactions, torsion angles, solvent accessibility, and flexibility of the residues are included in the potentials, so as to study the effects of the main factors known to influence protein structure. The new potentials, with a more detailed representation of the protein structure, yield a better fit than the previously used scoring functions, with pairwise interactions contributing to more than half of this improvement. In a phylogenetic context, however, the structurally constrained models are still outperformed by some of the available site-independent models in terms of fit, possibly indicating that alternatives to coarse-grained statistical potentials should be explored in order to better model structural constraints.
Molecular Biology and Evolution 02/2010; 27(7):1546-60. · 5.55 Impact Factor
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ABSTRACT: In recent years, molecular evolutionary models formulated as site-interdependent Markovian codon substitution processes have been proposed as means of mechanistically accounting for selective features over long-range evolutionary scales. Under such models, site interdependencies are reflected in the use of a simplified protein tertiary structure representation and predefined statistical potential, which, along with mutational parameters, mediate nonsynonymous rates of substitution; rates of synonymous events are solely mediated by mutational parameters. Although theoretically attractive, the models are computationally challenging, and the methods used to manipulate them still do not allow for quantitative model evaluations in a multiple-sequence context. Here, we describe Markov chain Monte Carlo computational methodologies for sampling parameters from their posterior distribution under site-interdependent codon substitution models within a phylogenetic context and allowing for Bayesian model assessment and ranking. Specifically, the techniques we expound here can form the basis of posterior predictive checking under these models and can be embedded within thermodynamic integration algorithms for computing Bayes factors. We illustrate the methods using two data sets and find that although current forms of site-interdependent models of codon substitution provide an improved fit, they are outperformed by the extended site-independent versions. Altogether, the methodologies described here should enable a quantified contrasting of alternative ways of modeling structural constraints, or other site-interdependent criteria, and establish if such formulations can match (or supplant) site-independent model extensions.
Molecular Biology and Evolution 05/2009; 26(7):1663-76. · 5.55 Impact Factor
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ABSTRACT: The aim of protein design is to predict amino-acid sequences compatible with a given target structure. Traditionally envisioned as a purely thermodynamic question, this problem can also be understood in a wider context, where additional constraints are captured by learning the sequence patterns displayed by natural proteins of known conformation. In this latter perspective, however, we still need a theoretical formalization of the question, leading to general and efficient learning methods, and allowing for the selection of fast and accurate objective functions quantifying sequence/structure compatibility.
We propose a formulation of the protein design problem in terms of model-based statistical inference. Our framework uses the maximum likelihood principle to optimize the unknown parameters of a statistical potential, which we call an inverse potential to contrast with classical potentials used for structure prediction. We propose an implementation based on Markov chain Monte Carlo, in which the likelihood is maximized by gradient descent and is numerically estimated by thermodynamic integration. The fit of the models is evaluated by cross-validation. We apply this to a simple pairwise contact potential, supplemented with a solvent-accessibility term, and show that the resulting models have a better predictive power than currently available pairwise potentials. Furthermore, the model comparison method presented here allows one to measure the relative contribution of each component of the potential, and to choose the optimal number of accessibility classes, which turns out to be much higher than classically considered.
Altogether, this reformulation makes it possible to test a wide diversity of models, using different forms of potentials, or accounting for other factors than just the constraint of thermodynamic stability. Ultimately, such model-based statistical analyses may help to understand the forces shaping protein sequences, and driving their evolution.
BMC Bioinformatics 02/2006; 7:326. · 2.75 Impact Factor
