The majority of newly diagnosed prostate cancers are slow growing, with a long natural life history. Yet a subset can metastasize with lethal consequences. We reconstructed the phylogenies of 293 ...localized prostate tumors linked to clinical outcome data. Multiple subclones were detected in 59% of patients, and specific subclonal architectures associate with adverse clinicopathological features. Early tumor development is characterized by point mutations and deletions followed by later subclonal amplifications and changes in trinucleotide mutational signatures. Specific genes are selectively mutated prior to or following subclonal diversification, including MTOR, NKX3-1, and RB1. Patients with low-risk monoclonal tumors rarely relapse after primary therapy (7%), while those with high-risk polyclonal tumors frequently do (61%). The presence of multiple subclones in an index biopsy may be necessary, but not sufficient, for relapse of localized prostate cancer, suggesting that evolution-aware biomarkers should be studied in prospective studies of low-risk tumors suitable for active surveillance.
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•The phylogenies of 293 localized prostate cancers were reconstructed•Multiple subclones were detected in 59% of patients•Specific genes are selectively mutated early or late in tumor evolution•Subclonal architecture adds prognostic ability to previously developed biomarkers
Tumors evolve during their natural life history. We studied the evolution of newly diagnosed prostate tumors and identified specific genes mutated early or late in a tumor’s life history. Considering subclonality improved predictions of disease aggressivity, identifying those patients who might be good candidates for receiving less treatment.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Global transcriptomic imbalance is a ubiquitous feature associated with cancer, including hepatocellular carcinoma (HCC). Analyses of 1,225 clinical HCC samples revealed that a large numbers of RNA ...binding proteins (RBPs) are dysregulated and that RBP dysregulation is associated with poor prognosis. We further identified that oncogenic activation of a top candidate RBP, negative elongation factor E (NELFE), via somatic copy-number alterations enhanced MYC signaling and promoted HCC progression. Interestingly, NELFE induces a unique tumor transcriptome by selectively regulating MYC-associated genes. Thus, our results revealed NELFE as an oncogenic protein that may contribute to transcriptome imbalance in HCC through the regulation of MYC signaling.
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•mRNA binding proteins (RBPs) are globally dysregulated in HCC•Tumor-associated RBPs are predictive of HCC patient survival•NELFE is oncogenic and enhances MYC signaling•NELFE regulates MYC signaling by binding directly to MYC or its targets
Dang et al. show that a large numbers of RNA binding proteins (RBPs) are dysregulated in hepatocellular carcinoma (HCC) and that NELFE, an RBP, enhances MYC-induced HCC development by regulating the binding of MYC to target promoters and the mRNA stability of several MYC-regulated genes.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Genomic analyses often involve scanning for potential transcription factor (TF) binding sites using models of the sequence specificity of DNA binding proteins. Many approaches have been developed to ...model and learn a protein's DNA-binding specificity, but these methods have not been systematically compared. Here we applied 26 such approaches to in vitro protein binding microarray data for 66 mouse TFs belonging to various families. For nine TFs, we also scored the resulting motif models on in vivo data, and found that the best in vitro-derived motifs performed similarly to motifs derived from the in vivo data. Our results indicate that simple models based on mononucleotide position weight matrices trained by the best methods perform similarly to more complex models for most TFs examined, but fall short in specific cases (<10% of the TFs examined here). In addition, the best-performing motifs typically have relatively low information content, consistent with widespread degeneracy in eukaryotic TF sequence preferences.
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DOBA, IJS, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Subclonal reconstruction from bulk tumor DNA sequencing has become a pillar of cancer evolution studies, providing insight into the clonality and relative ordering of mutations and mutational ...processes. We provide an outline of the complex computational approaches used for subclonal reconstruction from single and multiple tumor samples. We identify the underlying assumptions and uncertainties in each step and suggest best practices for analysis and quality assessment. This guide provides a pragmatic resource for the growing user community of subclonal reconstruction methods.
RNA-binding proteins are key regulators of gene expression, yet only a small fraction have been functionally characterized. Here we report a systematic analysis of the RNA motifs recognized by ...RNA-binding proteins, encompassing 205 distinct genes from 24 diverse eukaryotes. The sequence specificities of RNA-binding proteins display deep evolutionary conservation, and the recognition preferences for a large fraction of metazoan RNA-binding proteins can thus be inferred from their RNA-binding domain sequence. The motifs that we identify in vitro correlate well with in vivo RNA-binding data. Moreover, we can associate them with distinct functional roles in diverse types of post-transcriptional regulation, enabling new insights into the functions of RNA-binding proteins both in normal physiology and in human disease. These data provide an unprecedented overview of RNA-binding proteins and their targets, and constitute an invaluable resource for determining post-transcriptional regulatory mechanisms in eukaryotes.
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DOBA, IJS, IZUM, KILJ, KISLJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
We demonstrate that paired expression profiles of microRNAs (miRNAs) and mRNAs can be used to identify functional miRNA-target relationships with high precision. We used a Bayesian data analysis ...algorithm, GenMiR++, to identify a network of 1,597 high-confidence target predictions for 104 human miRNAs, which was supported by RNA expression data across 88 tissues and cell types, sequence complementarity and comparative genomics data. We experimentally verified our predictions by investigating the result of let-7b downregulation in retinoblastoma using quantitative reverse transcriptase (RT)-PCR and microarray profiling: some of our verified let-7b targets include CDC25A and BCL7A. Compared to sequence-based predictions, our high-scoring GenMiR++ predictions had much more consistent Gene Ontology annotations and were more accurate predictors of which mRNA levels respond to changes in let-7b levels.
RNA-binding proteins play crucial roles in directing RNA translation to neuronal synapses. Staufen2 (Stau2) has been implicated in both dendritic RNA localization and synaptic plasticity in mammalian ...neurons. Here, we report the identification of functionally relevant Stau2 target mRNAs in neurons. The majority of Stau2-copurifying mRNAs expressed in the hippocampus are present in neuronal processes, further implicating Stau2 in dendritic mRNA regulation. Stau2 targets are enriched for secondary structures similar to those identified in the 3′ UTRs of Drosophila Staufen targets. Next, we show that Stau2 regulates steady-state levels of many neuronal RNAs and that its targets are predominantly downregulated in Stau2-deficient neurons. Detailed analysis confirms that Stau2 stabilizes the expression of one synaptic signaling component, the regulator of G protein signaling 4 (Rgs4) mRNA, via its 3′ UTR. This study defines the global impact of Stau2 on mRNAs in neurons, revealing a role in stabilization of the levels of synaptic targets.
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•Stau2 binds to localized messages in embryonic rat brain•Stau2 predominantly stabilizes target mRNAs in primary neurons•The targets are enriched for RNA structures recognized by Staufen•Stau2 stabilizes neuronal expression of Rgs4 mRNA via its 3′ UTR
Staufen2 has been implicated in dendritic RNA localization and synaptic plasticity in mammalian neurons. Here, Doyle, Kiebler, and colleagues identify Staufen2 target mRNAs in rodent brain and demonstrate that the majority of hippocampus-expressed targets localize in neuronal processes. Computational analyses reveal these mRNAs are enriched in secondary structures similar to those identified in 3′ UTRs of Drosophila Staufen targets. Furthermore, they show that the majority of target mRNAs are downregulated in Staufen2-deficient neurons, indicating a role of Staufen2 in mRNA stabilization.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Most homeodomains are unique within a genome, yet many are highly conserved across vast evolutionary distances, implying strong selection on their precise DNA-binding specificities. We determined the ...binding preferences of the majority (168) of mouse homeodomains to all possible 8-base sequences, revealing rich and complex patterns of sequence specificity and showing that there are at least 65 distinct homeodomain DNA-binding activities. We developed a computational system that successfully predicts binding sites for homeodomain proteins as distant from mouse as
Drosophila and
C. elegans, and we infer full 8-mer binding profiles for the majority of known animal homeodomains. Our results provide an unprecedented level of resolution in the analysis of this simple domain structure and suggest that variation in sequence recognition may be a factor in its functional diversity and evolutionary success.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Finding the target sites of RNA-binding proteins Li, Xiao; Kazan, Hilal; Lipshitz, Howard D. ...
Wiley interdisciplinary reviews. RNA,
January/February 2014, Volume:
5, Issue:
1
Journal Article
Peer reviewed
Open access
RNA–protein interactions differ from DNA–protein interactions because of the central role of RNA secondary structure. Some RNA‐binding domains (RBDs) recognize their target sites mainly by their ...shape and geometry and others are sequence‐specific but are sensitive to secondary structure context. A number of small‐ and large‐scale experimental approaches have been developed to measure RNAs associated in vitro and in vivo with RNA‐binding proteins (RBPs). Generalizing outside of the experimental conditions tested by these assays requires computational motif finding. Often RBP motif finding is done by adapting DNA motif finding methods; but modeling secondary structure context leads to better recovery of RBP‐binding preferences. Genome‐wide assessment of mRNA secondary structure has recently become possible, but these data must be combined with computational predictions of secondary structure before they add value in predicting in vivo binding. There are two main approaches to incorporating structural information into motif models: supplementing primary sequence motif models with preferred secondary structure contexts (e.g., MEMERIS and RNAcontext) and directly modeling secondary structure recognized by the RBP using stochastic context‐free grammars (e.g., CMfinder and RNApromo). The former better reconstruct known binding preferences for sequence‐specific RBPs but are not suitable for modeling RBPs that recognize shape and geometry of RNAs. Future work in RBP motif finding should incorporate interactions between multiple RBDs and multiple RBPs in binding to RNA. WIREs RNA 2014, 5:111–130. doi: 10.1002/wrna.1201
This article is categorized under:
RNA Evolution and Genomics > Computational Analyses of RNA
RNA Interactions with Proteins and Other Molecules > Protein–RNA Recognition
RNA Methods > RNA Analyses In Vitro and In Silico
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Whole-genome sequencing can be used to estimate subclonal populations in tumours and this intra-tumoural heterogeneity is linked to clinical outcomes. Many algorithms have been developed for ...subclonal reconstruction, but their variabilities and consistencies are largely unknown. We evaluate sixteen pipelines for reconstructing the evolutionary histories of 293 localized prostate cancers from single samples, and eighteen pipelines for the reconstruction of 10 tumours with multi-region sampling. We show that predictions of subclonal architecture and timing of somatic mutations vary extensively across pipelines. Pipelines show consistent types of biases, with those incorporating SomaticSniper and Battenberg preferentially predicting homogenous cancer cell populations and those using MuTect tending to predict multiple populations of cancer cells. Subclonal reconstructions using multi-region sampling confirm that single-sample reconstructions systematically underestimate intra-tumoural heterogeneity, predicting on average fewer than half of the cancer cell populations identified by multi-region sequencing. Overall, these biases suggest caution in interpreting specific architectures and subclonal variants.
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