scholarly journals Systematic sequencing of chloroplast transcript termini fromArabidopsis thalianareveals >200 transcription initiation sites and the extensive imprints of RNA-binding proteins and secondary structures

2019 ◽  
Author(s):  
Benoît Castandet ◽  
Arnaud Germain ◽  
Amber M. Hotto ◽  
David B. Stern
Keyword(s):  
Quality Control ◽  
Transcription Initiation ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Dominant Role ◽  
Stem Loop ◽  
Transcription Start Sites ◽  
Intergenic Regions ◽  
The Many ◽  
Rna Protection

ABSTRACTChloroplast transcription requires numerous quality control steps to generate the complex but selective mixture of accumulating RNAs. To gain insight into how this RNA diversity is achieved and regulated, we systematically mapped transcript ends by developing a protocol called Terminome-Seq. UsingArabidopsis thalianaas a model, we catalogued >215 primary 5’ ends corresponding to transcription start sites (TSS), as well as 1,628 processed 5’ ends and 1,299 3’ ends. While most termini were found in intergenic regions, numerous abundant termini were also found within coding regions and introns, including several major TSS at unexpected locations. A consistent feature was the clustering of both 5’ and 3’ ends, contrasting with the prevailing description of discrete 5’ termini, suggesting an imprecision of the transcription and/or RNA processing machinery. Numerous termini correlated with the extremities of small RNA footprints or predicted stem-loop structures, in agreement with the model of passive RNA protection. Terminome-Seq was also implemented forpnp1-1, a mutant lacking the processing enzyme polynucleotide phosphorylase. Nearly 2,000 termini were altered inpnp1-1, revealing a dominant role in shaping the transcriptome. In summary, Terminome-Seq permits precise delineation of the roles and regulation of the many factors involved in organellar transcriptome quality control.

scholarly journals Systematic sequencing of chloroplast transcript termini from Arabidopsis thaliana reveals >200 transcription initiation sites and the extensive imprints of RNA-binding proteins and secondary structures

2019 ◽  
Author(s):  
Benoît Castandet ◽  
Arnaud Germain ◽  
Amber M Hotto ◽  
David B Stern
Keyword(s):  
Quality Control ◽  
Arabidopsis Thaliana ◽  
Transcription Initiation ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Dominant Role ◽  
Stem Loop ◽  
Transcription Start Sites ◽  
Intergenic Regions ◽  
Rna Protection

Abstract Chloroplast transcription requires numerous quality control steps to generate the complex but selective mixture of accumulating RNAs. To gain insight into how this RNA diversity is achieved and regulated, we systematically mapped transcript ends by developing a protocol called Terminome-seq. Using Arabidopsis thaliana as a model, we catalogued >215 primary 5′ ends corresponding to transcription start sites (TSS), as well as 1628 processed 5′ ends and 1299 3′ ends. While most termini were found in intergenic regions, numerous abundant termini were also found within coding regions and introns, including several major TSS at unexpected locations. A consistent feature was the clustering of both 5′ and 3′ ends, contrasting with the prevailing description of discrete 5′ termini, suggesting an imprecision of the transcription and/or RNA processing machinery. Numerous termini correlated with the extremities of small RNA footprints or predicted stem-loop structures, in agreement with the model of passive RNA protection. Terminome-seq was also implemented for pnp1–1, a mutant lacking the processing enzyme polynucleotide phosphorylase. Nearly 2000 termini were altered in pnp1–1, revealing a dominant role in shaping the transcriptome. In summary, Terminome-seq permits precise delineation of the roles and regulation of the many factors involved in organellar transcriptome quality control.

The expanding world of metabolic enzymes moonlighting as RNA binding proteins

2021 ◽  
Author(s):  
Nicole J. Curtis ◽  
Constance J. Jeffery
Keyword(s):  
Gene Expression ◽  
Regulatory Networks ◽  
Binding Proteins ◽  
Molecular Mechanisms ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Molecular Structures ◽  
Intermediary Metabolism ◽  
The Many ◽  
And Function

RNA binding proteins play key roles in many aspects of RNA metabolism and function, including splicing, transport, translation, localization, stability and degradation. Within the past few years, proteomics studies have identified dozens of enzymes in intermediary metabolism that bind to RNA. The wide occurrence and conservation of RNA binding ability across distant branches of the evolutionary tree suggest that these moonlighting enzymes are involved in connections between intermediary metabolism and gene expression that comprise far more extensive regulatory networks than previously thought. There are many outstanding questions about the molecular structures and mechanisms involved, the effects of these interactions on enzyme and RNA functions, and the factors that regulate the interactions. The effects on RNA function are likely to be wider than regulation of translation, and some enzyme–RNA interactions have been found to regulate the enzyme's catalytic activity. Several enzyme–RNA interactions have been shown to be affected by cellular factors that change under different intracellular and environmental conditions, including concentrations of substrates and cofactors. Understanding the molecular mechanisms involved in the interactions between the enzymes and RNA, the factors involved in regulation, and the effects of the enzyme–RNA interactions on both the enzyme and RNA functions will lead to a better understanding of the role of the many newly identified enzyme–RNA interactions in connecting intermediary metabolism and gene expression.

scholarly journals Deep-RBPPred: Predicting RNA binding proteins in the proteome scale based on deep learning

2017 ◽  
Author(s):  
Jinfang Zheng ◽  
Xiaoli Zhang ◽  
Xunyi Zhao ◽  
Xiaoxue Tong ◽  
Xu Hong ◽  
...  
Keyword(s):  
Deep Learning ◽  
Physicochemical Properties ◽  
High Performance ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Rna Binding Protein ◽  
Cell Processes ◽  
The Many ◽  
Deep Learning Model ◽  
Higher Sensitivity

AbstractRNA binding protein (RBP) plays an important role in cell processes. Identifying RBPs by computation and experiment are both essential. Recently, RBPPred is proposed in our group to predict RBP with a high performance. However, RBPPred is too slow for that it will generate PSSM matrix as its feature. Herein, we develop a deep learning model called Deep-RBPPred. The model has three advantages comparing to previous models. 1. Deep-RBPPred only needs few physicochemical properties. 2. Deep-RBPPred runs much faster. 3. Deep-RBPPred has a good generalization ability. In the meantime, the performance is still as good as the stats-of-the-art method. In the testing in A. thaliana, S. cerevisiae and H. sapiens proteomics, MCC (AUC) are 0.6077 (0.9421), 0.573 (0.9034) and 0.8141(0.9515) respectively when the score cutoff is set to 0.5. In the verifying in Gerstberger-1538, the SN of our model is 90.38%. The running times are 9s, 7s, 8s and 10s, respectively, for H.sapiens, A.thaliana, S.cerevisiae and Gerstberger-1538 when it is tested in GPU. Deep-RBPPred forecasts 94.65% of 299 new RBP and about 8% higher sensitivity than RBPPred. We also apply deep-RBPPred in 19 eukaryotes proteomics and 11 bacteria proteomics downloaded from Uniprot. The result shows that rate of RBPs in eukaryotes proteome are much higher than bacteria proteome. Testing in 6 proteomics shows the many RBPs may be still undiscovered so far.

scholarly journals PSDX: A Comprehensive Multi-Omics Association Database of Populus trichocarpa With a Focus on the Secondary Growth in Response to Stresses

2021 ◽  
Vol 12 ◽  
Author(s):  
Huiyuan Wang ◽  
Sheng Liu ◽  
Xiufang Dai ◽  
Yongkang Yang ◽  
Yunjun Luo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcriptional Regulation ◽  
Transcription Initiation ◽  
High Throughput Sequencing ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Populus Trichocarpa ◽  
Secondary Growth ◽  
A Genome ◽  
Post Transcriptional Regulation

Populus trichocarpa (P. trichocarpa) is a model tree for the investigation of wood formation. In recent years, researchers have generated a large number of high-throughput sequencing data in P. trichocarpa. However, no comprehensive database that provides multi-omics associations for the investigation of secondary growth in response to diverse stresses has been reported. Therefore, we developed a public repository that presents comprehensive measurements of gene expression and post-transcriptional regulation by integrating 144 RNA-Seq, 33 ChIP-seq, and six single-molecule real-time (SMRT) isoform sequencing (Iso-seq) libraries prepared from tissues subjected to different stresses. All the samples from different studies were analyzed to obtain gene expression, co-expression network, and differentially expressed genes (DEG) using unified parameters, which allowed comparison of results from different studies and treatments. In addition to gene expression, we also identified and deposited pre-processed data about alternative splicing (AS), alternative polyadenylation (APA) and alternative transcription initiation (ATI). The post-transcriptional regulation, differential expression, and co-expression network datasets were integrated into a new P. trichocarpa Stem Differentiating Xylem (PSDX) database, which further highlights gene families of RNA-binding proteins and stress-related genes. The PSDX also provides tools for data query, visualization, a genome browser, and the BLAST option for sequence-based query. Much of the data is also available for bulk download. The availability of PSDX contributes to the research related to the secondary growth in response to stresses in P. trichocarpa, which will provide new insights that can be useful for the improvement of stress tolerance in woody plants.

Modulation of IRP Binding Activity by Oxygen in Primary Erythroid Cells.

Blood ◽  
2007 ◽  
Vol 110 (11) ◽  
pp. 2662-2662
Author(s):  
Matthias Schranzhofer ◽  
Manfred Schifrer ◽  
Prem Ponka ◽  
Ernst W. Muellner
Keyword(s):  
Rna Binding ◽  
Rna Binding Proteins ◽  
Binding Activity ◽  
Ammonium Citrate ◽  
Ferric Ammonium Citrate ◽  
Erythroid Cells ◽  
Stem Loop ◽  
Iron Regulatory Proteins ◽  
Protein Levels ◽  
Irp1 And Irp2

Abstract Iron regulatory proteins 1 and 2 (IRP1 and IRP2) are cytoplasmic RNA-binding proteins that target specific stem-loop RNA structures known as iron responsive elements (IRE). Binding of IRPs to IREs inhibits translation of ferritin mRNA and stabilizes transferrin receptor (TfR) mRNA. Various factors have been reported to regulate binding activity of IRPs, such as iron, phosphorylation, nitric oxide and hypoxia. While there is a consistent agreement on the negative effect of iron on the interaction between IRPs and IREs, reports regarding the influence of hypoxia on the IRE-binding activity of IRPs vary in a species and cell specific manner. It was the aim of this work to study the effect of hypoxic (3% oxygen) and normoxic (20% oxygen) conditions on IRP binding activity in primary erythroid cells. The cells were induced for differentiation and incubated under physiological, low (Desferrioxamine) and high (ferric ammonium citrate) iron conditions. Binding activity of IRPs and protein levels of ferritin and TfR as well as cell proliferation and differentiation parameters were determined to analyze the regulation of iron metabolism during terminal differentiation. The data show, that in developing red blood cells binding activities of IRP1 and IRP2 are reduced at 3% oxygen. This reduction correlates with increased ferritin protein levels and decreased TfR protein levels. Moreover, incubation under hypoxia strongly decreased cell expansion and reduces hemoglobinization. These results suggest that terminal erythroid differentiation in the bone marrow might occur under normoxic rather than hypoxic conditions.

scholarly journals Scanning mutagenesis of RNA-binding protein ProQ reveals a quality control role for the Lon protease

RNA ◽  
2021 ◽  
pp. rna.078954.121
Author(s):  
Youssef El Mouali ◽  
Falk Ponath ◽  
Vincent Scharrer ◽  
Nicolas Wenner ◽  
Jay CD Hinton ◽  
...  
Keyword(s):  
Quality Control ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Enteric Bacteria ◽  
Loss Of Function ◽  
Rna Targets ◽  
Bacterial Chromosomes ◽  
Function Selection ◽  
Quality Control Mechanism

The FinO-domain protein ProQ belongs to a widespread family of RNA-binding proteins (RBPs) involved in gene regulation in bacterial chromosomes and mobile elements. Whilst the cellular RNA targets of ProQ have been established in diverse bacteria, the functionally crucial ProQ residues remain to be identified under physiological conditions. Following our discovery that ProQ deficiency alleviates growth suppression of Salmonella with succinate as the sole carbon source, an experimental evolution approach was devised to exploit this phenotype. By coupling mutational scanning with loss-of-function selection, we identified multiple ProQ residues in both the N-terminal FinO domain and the variable C-terminal region that are required for ProQ activity. Two C-terminal mutations abrogated ProQ function and mildly impaired binding of a model RNA target. By contrast, several mutations in the FinO domain rendered ProQ both functionally inactive and unable to interact with target RNA in vivo. Alteration of the FinO domain stimulated the rapid turnover of ProQ by Lon-mediated proteolysis, suggesting a quality control mechanism that prevents the accumulation of non-functional ProQ molecules. We extend this observation to Hfq, the other major sRNA chaperone of enteric bacteria. The Hfq Y55A mutant protein, defective in RNA-binding and oligomerization, proved to be labile and susceptible to degradation by Lon. Taken together, our findings connect the major AAA+ family protease Lon with RNA-dependent quality control of Hfq and ProQ, the two major sRNA chaperones of Gram-negative bacteria.

scholarly journals Comparative poly(A)+ RNA interactome capture of fission yeast RNA exosome mutants reveals insights into RNA biogenesis

2020 ◽  
Author(s):  
Adrien Birot ◽  
Cornelia Kilchert ◽  
Krzysztof Kus ◽  
Emily Priest ◽  
Ahmad Al Alwash ◽  
...  
Keyword(s):  
Quality Control ◽  
Rna Polymerase Ii ◽  
Rna Binding ◽  
Zinc Finger Protein ◽  
Rna Binding Proteins ◽  
Protein Coding ◽  
Multiple Protein ◽  
Nuclear Rna ◽  
Control Step ◽  
Rna Exosome

ABSTRACTThe nuclear RNA exosome plays a key role in quality control and processing of multiple protein-coding and non-coding transcripts made by RNA polymerase II. A mechanistic understanding of exosome function remains a challenge given it has multiple roles in RNA regulation. Here we have analysed changes in the poly(A)+ RNA transcriptome and interactome provoked by mutations in three distinct subunits of the nuclear RNA exosome. We have identified multiple proteins whose occupancy on RNA is altered in the exosome mutants. We demonstrate that the Zinc-finger protein Mub1 regulates exosome dependent transcripts that encode stress-responsive proteins. Furthermore, we assess impact of the exosome inactivation upon RNA binding of the components of the mRNA processing machineries such as spliceosome and mRNA cleavage polyadenylation complex. We show that mutations in the exosome lead to accumulation of the components of U1 and U2 snRNPs on poly(A)+ RNA and depletion of the components of the activated spliceosome from RNA suggesting that the early stages of spliceosome assembly might provide a critical quality control step. Collectively, our data provide a global view of how RNA metabolism is affected in the exosome-deficient cells and reveal RNA-binding proteins that may act as novel exosome cofactors.

scholarly journals StoatyDive: Evaluation and classification of peak profiles for sequencing data

GigaScience ◽  
2021 ◽  
Vol 10 (6) ◽  
Author(s):  
Florian Heyl ◽  
Rolf Backofen
Keyword(s):  
Quality Control ◽  
High Throughput ◽  
Binding Sites ◽  
Binding Proteins ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Typical Result ◽  
Sequence Motif ◽  
Peak Shape ◽  
Sequencing Data

Abstract Background The prediction of binding sites (peak-calling) is a common task in the data analysis of methods such as cross-linking immunoprecipitation in combination with high-throughput sequencing (CLIP-Seq). The predicted binding sites are often further analyzed to predict sequence motifs or structure patterns. When looking at a typical result of such high-throughput experiments, the obtained peak profiles differ largely on a genomic level. Thus, a tool is missing that evaluates and classifies the predicted peaks on the basis of their shapes. We hereby present StoatyDive, a tool that can be used to filter for specific peak profile shapes of sequencing data such as CLIP. Findings With StoatyDive we are able to classify peak profile shapes from CLIP-seq data of the histone stem-loop-binding protein (SLBP). We compare the results to existing tools and show that StoatyDive finds more distinct peak shape clusters for CLIP data. Furthermore, we present StoatyDive’s capabilities as a quality control tool and as a filter to pick different shapes based on biological or technical questions for other CLIP data from different RNA binding proteins with different biological functions and numbers of RNA recognition motifs. We finally show that proteins involved in splicing, such as RBM22 and U2AF1, have potentially sharper-shaped peaks than other RNA binding proteins. Conclusion StoatyDive finally fills the demand for a peak shape clustering tool for CLIP-Seq data that fine-tunes downstream analysis steps such as structure or sequence motif predictions and that acts as a quality control.

scholarly journals Primary and Secondary Sequence Structure Requirements for Recognition and Discrimination of Target RNAs by Pseudomonas aeruginosa RsmA and RsmF

2016 ◽  
Vol 198 (18) ◽  
pp. 2458-2469 ◽  
Author(s):  
Kayley H. Schulmeyer ◽  
Manisha R. Diaz ◽  
Thomas B. Bair ◽  
Wes Sanders ◽  
Cindy J. Gode ◽  
...  
Keyword(s):  
Pseudomonas Aeruginosa ◽  
Binding Sites ◽  
Posttranscriptional Regulation ◽  
Binding Proteins ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Consensus Sequence ◽  
Stem Loop ◽  
Content Type

ABSTRACTCsrA family RNA-binding proteins are widely distributed in bacteria and regulate gene expression at the posttranscriptional level.Pseudomonas aeruginosahas a canonical member of the CsrA family (RsmA) and a novel, structurally distinct variant (RsmF). To better understand RsmF binding properties, we performed parallel systematic evolution of ligands by exponential enrichment (SELEX) experiments for RsmA and RsmF. The initial target library consisted of 62-nucleotide (nt) RNA transcripts with central cores randomized at 15 sequential positions. Most targets selected by RsmA and RsmF were the expected size and shared a common consensus sequence (CANGGAYG) that was positioned in a hexaloop region of the stem-loop structure. RsmA and RsmF also selected for longer targets (≥96 nt) that were likely generated by rare PCR errors. Most of the long targets contained two consensus-binding sites. Representative short (single consensus site) and long (two consensus sites) targets were tested for RsmA and RsmF binding. Whereas RsmA bound the short targets with high affinity, RsmF was unable to bind the same targets. RsmA and RsmF both bound the long targets. Mutation of either consensus GGA site in the long targets reduced or eliminated RsmF binding, suggesting a requirement for two tandem binding sites. Conversely, RsmA bound long targets containing only a single GGA site with unaltered affinity. The RsmF requirement for two binding sites was confirmed withtssA1, anin vivoregulatory target of RsmA and RsmF. Our findings suggest that RsmF binding requires two GGA-containing sites, while RsmA binding requirements are less stringent.IMPORTANCEThe CsrA family of RNA-binding proteins is widely conserved in bacteria and plays important roles in the posttranscriptional regulation of protein synthesis.P. aeruginosahas two CsrA proteins, RsmA and RsmF. Although RsmA and RsmF share a few RNA targets, RsmF is unable to bind to other targets recognized by RsmA. The goal of the present study was to better understand the basis for differential binding by RsmF. Our data indicate that RsmF binding requires target RNAs with two consensus-binding sites, while RsmA recognizes targets with just a single binding site. This information should prove useful to future efforts to define the RsmF regulon and its contribution toP. aeruginosaphysiology and virulence.

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