Genome level analysis of rice mRNA 3′-end processing signals and alternative polyadenylation

The position of a poly(A) site of eukaryotic mRNA is determined by sequence signals in pre-mRNA and a group of polyadenylation factors. To reveal rice poly(A) signals at a genome level, we constructed a dataset of 55 742 authenticated poly(A) sites and characterized the poly(A) signals. This resulted in identifying the typical tripartite cis-elements, including FUE, NUE and CE, as previously observed in Arabidopsis. The average size of the 3′-UTR was 289 nucleotides. When mapped to the genome, however, 15% of these poly(A) sites were found to be located in the currently annotated intergenic regions. Moreover, an extensive alternative polyadenylation profile was evident where 50% of the genes analyzed had more than one unique poly(A) site (excluding microheterogeneity sites), and 13% had four or more poly(A) sites. About 4% of the analyzed genes possessed alternative poly(A) sites at their introns, 5′-UTRs, or protein coding regions. The authenticity of these alternative poly(A) sites was partially confirmed using MPSS data. Analysis of nucleotide profile and signal patterns indicated that there may be a different set of poly(A) signals for those poly(A) sites found in the coding regions. Based on the features of rice poly(A) signals, an updated algorithm termed PASS-Rice was designed to predict poly(A) sites

Predictive modeling of plant messenger RNA polyadenylation sites

BACKGROUND: One of the essential processing events during pre-mRNA maturation is the post-transcriptional addition of a polyadenine [poly(A)] tail. The 3'-end poly(A) track protects mRNA from unregulated degradation, and indicates the integrity of mRNA through recognition by mRNA export and translation machinery. The position of a poly(A) site is predetermined by signals in the pre-mRNA sequence that are recognized by a complex of polyadenylation factors. These signals are generally tri-part sequence patterns around the cleavage site that serves as the future poly(A) site. In plants, there is little sequence conservation among these signal elements, which makes it difficult to develop an accurate algorithm to predict the poly(A) site of a given gene. We attempted to solve this problem. RESULTS: Based on our current working model and the profile of nucleotide sequence distribution of the poly(A) signals and around poly(A) sites in Arabidopsis, we have devised a Generalized Hidden Markov Model based algorithm to predict potential poly(A) sites. The high specificity and sensitivity of the algorithm were demonstrated by testing several datasets, and at the best combinations, both reach 97%. The accuracy of the program, called poly(A) site sleuth or PASS, has been demonstrated by the prediction of many validated poly(A) sites. PASS also predicted the changes of poly(A) site efficiency in poly(A) signal mutants that were constructed and characterized by traditional genetic experiments. The efficacy of PASS was demonstrated by predicting poly(A) sites within long genomic sequences. CONCLUSION: Based on the features of plant poly(A) signals, a computational model was built to effectively predict the poly(A) sites in Arabidopsis genes. The algorithm will be useful in gene annotation because a poly(A) site signifies the end of the transcript. This algorithm can also be used to predict alternative poly(A) sites in known genes, and will be useful in the design of transgenes for crop genetic engineering by predicting and eliminating undesirable poly(A) sites

Ratio-Based Analysis of Differential mRNA Processing and Expression of a Polyadenylation Factor Mutant pcfs4 Using Arabidopsis Tiling Microarray

US National Institutes of Health [1R15GM07719201A1]; US National Science Foundation [IOS-0817818]; Ohio Plant Biotech Consortium; National Natural Science Foundation of China [60774033]; Specialized Research Fund for the Doctoral Program of Higher EducatiBackground: Alternative polyadenylation as a mechanism in gene expression regulation has been widely recognized in recent years. Arabidopsis polyadenylation factor PCFS4 was shown to function in leaf development and in flowering time control. The function of PCFS4 in controlling flowering time was correlated with the alternative polyadenylation of FCA, a flowering time regulator. However, genetic evidence suggested additional targets of PCFS4 that may mediate its function in both flowering time and leaf development. Methodology/Principal Findings: To identify further targets, we investigated the whole transcriptome of a PCFS4 mutant using Affymetrix Arabidopsis genomic tiling 1.0R array and developed a data analysis pipeline, termed RADPRE (Ratio-based Analysis of Differential mRNA Processing and Expression). In RADPRE, ratios of normalized probe intensities between wild type Columbia and a pcfs4 mutant were first generated. By doing so, one of the major problems of tiling array data-variations caused by differential probe affinity-was significantly alleviated. With the probe ratios as inputs, a hierarchy of statistical tests was carried out to identify differentially processed genes (DPG) and differentially expressed genes (DEG). The false discovery rate (FDR) of this analysis was estimated by using the balanced random combinations of Col/pcfs4 and pcfs4/Col ratios as inputs. Gene Ontology (GO) analysis of the DPGs and DEGs revealed potential new roles of PCFS4 in stress responses besides flowering time regulation. Conclusion/Significance: We identified 68 DPGs and 114 DEGs with FDR at 1% and 2%, respectively. Most of the 68 DPGs were subjected to alternative polyadenylation, splicing or transcription initiation. Quantitative PCR analysis of a set of DPGs confirmed that most of these genes were truly differentially processed in pcfs4 mutant plants. The enriched GO term "regulation of flower development'' among PCFS4 targets further indicated the efficacy of the RADPRE pipeline. This simple but effective program is available upon request

Polyaniline/Ag nanocomposite synthesized by using aniline as dispersant and stabilizer of nanosilver sol

Polyaniline/silver (PANI/Ag) nanocomposite was successfully prepared by in-situ polymerization from nanosilver sol using aniline (An) as both dispersant and stabilizer and characterized by FT-IR, XRD SEM, TEM and electrochemical methods, respectively. The results showed that core-shelled composite particles of less than 100 nm were synthesized with PANI as shell formed around a core of silver nanoparticles at higher aniline levels. Compared to pure PANI, PANI/Ag nanocomposites have higher electrical conductivity (65.98 S/cm) and current response and capacitance as well. Potentio dynamic polarization showed the anodic shifting of zero current potential and a lower exchange current density for the PANI/Ag composite. Compared with PANI, the PANI/Ag nanocomposite is a promising candidate for coatings with improved anti-corrosion performance. © 2012 Wiley Periodicals, Inc. J. Appl. Polym. Sci., 201

Polyaniline/Ag nanocomposite synthesized by using aniline as dispersant and stabilizer of nanosilver sol

Polyaniline/silver (PANI/Ag) nanocomposite was successfully prepared by in-situ polymerization from nanosilver sol using aniline (An) as both dispersant and stabilizer and characterized by FT-IR, XRD SEM, TEM and electrochemical methods, respectively. The results showed that core-shelled composite particles of less than 100 nm were synthesized with PANI as shell formed around a core of silver nanoparticles at higher aniline levels. Compared to pure PANI, PANI/Ag nanocomposites have higher electrical conductivity (65.98 S/cm) and current response and capacitance as well. Potentio dynamic polarization showed the anodic shifting of zero current potential and a lower exchange current density for the PANI/Ag composite. Compared with PANI, the PANI/Ag nanocomposite is a promising candidate for coatings with improved anti-corrosion performance. © 2012 Wiley Periodicals, Inc. J. Appl. Polym. Sci., 201

Messenger RNA Polyadenylation Site Recognition in Green Alga Chlamydomonas Reinhardtii

Recognition of polyadenylation [poly(A)] sites or messenger RNA is important in genome annotation and gene expression regulation analysis. In the paper, poly(A) sites of Chlamydomonas reinhardtii were identified using an updated version of poly(A) site recognition software PASS_VAR based on,generalized hidden Markov model. First, we analyzed the characteristics of the poly(A) sites and their surrounding sequence patterns, and used an entropy-based feature selection method to select important poly(A) signal patterns in conservative signal states. Then we improved the existing poly(A) sites recognition software PASS that was initially designed only for Arabidopsis to make it suitable for different species. Next, Chlamydomonas sequences were grouped according to their signal patterns and used to train the model parameters through mathematical statistics methods. Finally, poly(A) sites were identified using PASS_VAR. The efficacy of our model is showed up to 93% confidence with strong signals

Recognition of Plant mRNA Polyadenylation Sites Based on High-Dimensional Space Points' Covering Method

To accurately predict messenger RNA polyadenylation sites of the model plant Arabidopsis, we developed a new program using the method of High-Dimensional Space Points' Covering, based on Biomimetic Pattern Recognition Theory. Upon analysis of experimental data and comparing with the results produced by Support Vector Machine, it is demonstrated that our program is reliable and effective judged by its specificity and sensitivity of the prediction. The program performed especially well with a reduced training sample size