Comparative analyses between retained introns and constitutively spliced introns in Arabidopsis thaliana using random forest and support vector machine

Function of Cajal Bodies in Nuclear RNA Retention in A. thaliana Leaves Subjected to Hypoxia

Retention of RNA in the nucleus precisely regulates the time and rate of translation and controls transcriptional bursts that can generate profound variability in mRNA levels among identical cells in tissues. In this study, we investigated the function of Cajal bodies (CBs) in RNA retention in A. thaliana leaf nuclei during hypoxia stress was investigated. It was observed that in ncb-1 mutants with a complete absence of CBs, the accumulation of poly(A⁺) RNA in the leaf nuclei was lower than that in wt under stress. Moreover, unlike in root cells, CBs store less RNA, and RNA retention in the nuclei is much less intense. Our results reveal that the function of CBs in the accumulation of RNA in nuclei under stress depends on the plant organ. Additionally, in ncb-1, retention of introns of mRNA RPB1 (largest subunit of RNA polymerase II) mRNA was observed. However, this isoform is highly accumulated in the nucleus. It thus follows that intron retention in transcripts is more important than CBs for the accumulation of RNA in nuclei. Accumulated mRNAs with introns in the nucleus could escape transcript degradation by NMD (nonsense-mediated mRNA decay). From non-fully spliced mRNAs in ncb-1 nuclei, whose levels increase during hypoxia, introns are removed during reoxygenation. Then, the mRNA is transferred to the cytoplasm, and the RPB1 protein is translated. Despite the accumulation of isoforms in nuclei with retention of introns in reoxygenation, ncb-1 coped much worse with long hypoxia, and manifested faster yellowing and shrinkage of leaves.

A dynamic intron retention program regulates the expression of several hundred genes during pollen meiosis

Intron retention is a stage-specific mechanism of functional attenuation of a subset of co-regulated, functionally related genes during early stages of pollen development. To improve our understanding of the gene regulatory mechanisms that drive developmental processes, we performed a genome-wide study of alternative splicing and isoform switching during five key stages of pollen development in field mustard, Brassica rapa. Surprisingly, for several hundred genes (12.3% of the genes analysed), isoform switching results in stage-specific expression of intron-retaining transcripts at the meiotic stage of pollen development. In such cases, we report temporally regulated switching between expression of a canonical, translatable isoform and an intron-retaining transcript that is predicted to produce a truncated and presumably inactive protein. The results suggest a new pervasive mechanism underlying modulation of protein levels in a plant developmental program. The effect is not based on gene expression induction but on the type of transcript produced. We conclude that intron retention is a stage-specific mechanism of functional attenuation of a subset of co-regulated, functionally related genes during meiosis, especially genes related to ribosome biogenesis, mRNA transport and nuclear envelope architecture. We also propose that stage-specific expression of a non-functional isoform of Brassica rapa BrSDG8, a non-redundant member of histone methyltransferase gene family, linked to alternative splicing regulation, may contribute to the intron retention observed.

Intron Retention as a Mode for RNA-Seq Data Analysis

Intron retention (IR) is an alternative splicing mode whereby introns, rather than being spliced out as usual, are retained in mature mRNAs. It was previously considered a consequence of mis-splicing and received very limited attention. Only recently has IR become of interest for transcriptomic data analysis owing to its recognized roles in gene expression regulation and associations with complex diseases. In this article, we first review the function of IR in regulating gene expression in a number of biological processes, such as neuron differentiation and activation of CD4⁺ T cells. Next, we briefly review its association with diseases, such as Alzheimer's disease and cancers. Then, we describe state-of-the-art methods for IR detection, including RNA-seq analysis tools IRFinder and iREAD, highlighting their underlying principles and discussing their advantages and limitations. Finally, we discuss the challenges for IR detection and potential ways in which IR detection methods could be improved.

InMeRF: prediction of pathogenicity of missense variants by individual modeling for each amino acid substitution

In predicting the pathogenicity of a nonsynonymous single-nucleotide variant (nsSNV), a radical change in amino acid properties is prone to be classified as being pathogenic. However, not all such nsSNVs are associated with human diseases. We generated random forest (RF) models individually for each amino acid substitution to differentiate pathogenic nsSNVs in the Human Gene Mutation Database and common nsSNVs in dbSNP. We named a set of our models ‘Individual Meta RF’ (InMeRF). Ten-fold cross-validation of InMeRF showed that the areas under the curves (AUCs) of receiver operating characteristic (ROC) and precision–recall curves were on average 0.941 and 0.957, respectively. To compare InMeRF with seven other tools, the eight tools were generated using the same training dataset, and were compared using the same three testing datasets. ROC-AUCs of InMeRF were ranked first in the eight tools. We applied InMeRF to 155 pathogenic and 125 common nsSNVs in seven major genes causing congenital myasthenic syndromes, as well as in VANGL1 causing spina bifida, and found that the sensitivity and specificity of InMeRF were 0.942 and 0.848, respectively. We made the InMeRF web service, and also made genome-wide InMeRF scores available online (https://www.med.nagoya-u.ac.jp/neurogenetics/InMeRF/).

50/50 Expressional Odds of Retention Signifies the Distinction between Retained Introns and Constitutively Spliced Introns in Arabidopsis thaliana

Intron retention, one of the most prevalent alternative splicing events in plants, can lead to introns retained in mature mRNAs. However, in comparison with constitutively spliced introns (CSIs), the relevantly distinguishable features for retained introns (RIs) are still poorly understood. This work proposes a computational pipeline to discover novel RIs from multiple next-generation RNA sequencing (RNA-Seq) datasets of Arabidopsis thaliana. Using this pipeline, we detected 3,472 novel RIs from 18 RNA-Seq datasets and re-confirmed 1,384 RIs which are currently annotated in the TAIR10 database. We also use the expression of intron-containing isoforms as a new feature in addition to the conventional features. Based on these features, RIs are highly distinguishable from CSIs by machine learning methods, especially when the expressional odds of retention (i.e., the expression ratio of the RI-containing isoforms relative to the isoforms without RIs for the same gene) reaches to or larger than 50/50. In this case, the RIs and CSIs can be clearly separated by the Random Forest with an outstanding performance of 0.95 on AUC (the area under a receiver operating characteristics curve). The closely related characteristics to the RIs include the low strength of splice sites, high similarity with the flanking exon sequences, low occurrence percentage of YTRAY near the acceptor site, existence of putative intronic splicing silencers (ISSs, i.e., AG/GA-rich motifs) and intronic splicing enhancers (ISEs, i.e., TTTT-containing motifs), and enrichment of Serine/Arginine-Rich (SR) proteins and heterogeneous nuclear ribonucleoparticle proteins (hnRNPs).

In silico analysis of the sequence features responsible for alternatively spliced introns in the model green alga Chlamydomonas reinhardtii

Alternatively spliced introns are the ones that are usually spliced but can be occasionally retained in a transcript isoform. They are the most frequently used alternative splice form in plants (~50% of alternative splicing events). Chlamydomonas reinhardtii, a unicellular alga, is a good model to understand alternative splicing (AS) in plants from an evolutionary perspective as it diverged from land plants a billion years ago. Using over 7 million cDNA sequences from both pyrosequencing and Sanger sequencing, we found that a much higher percentage of genes (~20% of multi-exon genes) undergo AS than previously reported (3-5%). We found a full component of SR and SR-like proteins possibly involved in AS. The most prevalent type of AS event (40%) was retention of introns, most of which were supported by multiple cDNA evidence (72%) while only 20% of them have coding capacity. By comparing retained and constitutive introns, we identified sequence features potentially responsible for the retention of introns, in the framework of an "intron definition" model for splicing. We find that retained introns tend to have a weaker 5' splice site, more Gs in their poly-pyrimidine tract and a lesser conservation of nucleotide 'C' at position -3 of the 3' splice site. In addition, the sequence motifs found in the potential branch-point region differed between retained and constitutive introns. Furthermore, the enrichment of G-triplets and C-triplets among the first and last 50 nt of the introns significantly differ between constitutive and retained introns. These could serve as intronic splicing enhancers. All the alternative splice forms can be accessed at http://bioinfolab.miamioh.edu/cgi-bin/PASA_r20140417/cgi-bin/status_report.cgi?db=Chre_AS .

Prediction and feature analysis of intron retention events in plant genome

Alternative splicing (AS) is a major contributor to increase the potential informational content of eukaryotic genomes by creating multiple mRNA species and proteins from a single gene. In plants, up to 60% genes are alternatively spliced and the most common type of AS is intron retention (IR). Genomic analyses of IR have illuminated its crucial role in shaping the evolution of genomes, in the control of developmental processes, and in the dynamic regulation of the transcriptome to influence phenotype. To explore the relationship between the sequence feature and the formation mechanism of IR, we statistically analyzed the retained introns and proposed an improved random forest-based hybrid method to predict intron retention events in plant genome. The results indicate that IR has significant relationship with individual introns which have weaker 5' splice sites, lower GC content and less termination codon occurrence. By the method we proposed, 93.48% retained introns can be correctly distinguished from constitutive introns. Strikingly, our study will facilitate a better understanding of underlying mechanisms involved in intron retention.

Pre-mRNA splicing repression triggers abiotic stress signaling in plants

Alternative splicing (AS) of precursor RNAs enhances transcriptome plasticity and proteome diversity in response to diverse growth and stress cues. Recent work has shown that AS is pervasive across plant species, with more than 60% of intron-containing genes producing different isoforms. Mammalian cell-based assays have discovered various inhibitors of AS. Here, we show that the macrolide pladienolide B (PB) inhibits constitutive splicing and AS in plants. Also, our RNA sequencing (RNA-seq) data revealed that PB mimics abiotic stress signals including salt, drought and abscisic acid (ABA). PB activates the abiotic stress- and ABA-responsive reporters RD29A::LUC and MAPKKK18::uidA in Arabidopsis thaliana and mimics the effects of ABA on stomatal aperture. Genome-wide analysis of AS by RNA-seq revealed that PB perturbs the splicing machinery and leads to a striking increase in intron retention and a reduction in other forms of AS. Interestingly, PB treatment activates the ABA signaling pathway by inhibiting the splicing of clade A PP2C phosphatases while still maintaining to some extent the splicing of ABA-activated SnRK2 kinases. Taken together, our data establish PB as an inhibitor and modulator of splicing and a mimic of abiotic stress signals in plants. Thus, PB reveals the molecular underpinnings of the interplay between stress responses, ABA signaling and post-transcriptional regulation in plants.

Investigating genetic characteristics of hepatitis B virus-associated and -non-associated hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is a primary liver malignancy that mainly occurs in patients with chronic liver disease and cirrhosis. Risk factors for HCC include hepatitis B virus (HBV) infection. However, the specific role of HBV infection in HCC development is not yet completely understood. In order to reveal the effects of HBV on HCC, we compare the genes of HCC patients infected with HBV with those who are not infected.

METHODS

We encoded the genes of these two types of HCC in databases using enrichment scores of Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway terms. A random forest algorithm was employed in order to distinguish these two types in the classifier, and a series of feature selection approaches was used in order to select their optimal features. Novel HBV-associated and -non-associated HCC genes were predicted, respectively, based on their optimal features in the classifier. A shortest-path algorithm was also employed in order to find all of the shortest-paths genes connecting the known related genes.

RESULTS

A total of 54 different features between HBV-associated and -non-associated HCC genes were identified. In total, 1236 and 881 novel related genes were predicted for HBV-associated and -non-associated HCC, respectively. By integrating the predicted genes and shortest path genes in their gene interaction network, we identified 679 common genes involved in the two types of HCC.

CONCLUSION

We identified the significantly different genetic features between two types of HCC. We also predicted related genes for the two types based on their specific features. Finally, we determined the common genes and features that were involved in both of these two types of HCC.

A genetic features and gene interaction study for identifying the genes that cause hereditary spherocytosis

OBJECTIVE

Hereditary spherocytosis (HS) is a hemolytic disorder characterized by the presence of spherical-shaped red blood cells on the peripheral blood smear. Non-dominant HS cases are due to de novo mutations of the type associated with dominant inheritance or recessive genes. This study is aimed to identify HS-related biological mechanisms and predicting HS candidate genes.

METHODS

We searched the known HS-related genes from the public databases. By analyzing the gene ontology (GO) and biological pathway of these genes, we extracted the optimal features to encode HS genes. Based on them, we predicted the HS-related genes from genes of whole genomes using the Random Forest classification. We used the gene interaction networks analysis to further identify the core regulatory genes that were related to HS.

RESULTS

Forty-one known HS-related genes were found out and encoded. Three hundred and sixty-seven GO terms and ten biological pathway terms were identified as the optimal features for prediction. We subsequently predicted 150 novel HS-related genes and identified the core regulatory genes in the interaction network of predicted and known genes. These features and genes that we identified could complement the genetic features of HS.

Predicting novel salivary biomarkers for the detection of pancreatic cancer using biological feature-based classification

AIM

The use of saliva as a diagnostic fluid enables non-invasive sampling and thus is a prospective sample for disease tests. This study fully utilized the information from the salivary transcriptome to characterize pancreatic cancer related genes and predict novel salivary biomarkers.

METHODS

We calculated the enrichment scores of gene ontology (GO) and pathways annotated in Kyoto Encyclopedia of Genes and Genomes database (KEGG) for pancreatic cancer-related genes. Annotation of GO and KEGG pathway characterize the molecular features of genes. We employed Random Forest classification and incremental feature selection to identify the optimal features among them and predicted novel pancreatic cancer-related genes.

RESULTS

A total of 2175 gene ontology and 79 KEGG pathway terms were identified as the optimal features to identify pancreatic cancer-related genes. A total of 516 novel genes were predicted using these features. We discovered 29 novel biomarkers based on the expression of these 516 genes in saliva. Using our new biomarkers, we achieved a higher accuracy (92%) for the detection of pancreatic cancer. Another independent expression dataset confirmed that these novel biomarkers performed better than the previously described markers alone.

CONCLUSION

By analyzing the information of the salivary transcriptome, we predict pancreatic cancer-related genes and novel salivary gene markers for detection.

Finding gastric cancer related genes and clinical biomarkers for detection based on gene-gene interaction network

BACKGROUND/OBJECTIVE

Gastric cancer (GC) is the second leading cause of death resulted from cancer globally. The most common cause of GC is the infection of Helicobacter pylori, approximately 11% of cases are caused by genetic factors. The objective of this study was to develop an effective computational method to meaningfully interpret these GC-related genes and to predict potential prognostic genes for clinical detection.

METHODS

We employed the shortest path algorithm and permutation test to probe the genes that have relationship with known GC genes in gene-gene interaction network. We calculated the enrichment scores of gene ontology and pathways of gastric cancer related genes to characterize these genes in terms of molecular features. The optimal features that primly representing the gastric cancer related genes were selected using Random Forest classification and incremental feature selection. Random Forest classification was also used for the prediction of the novel gastric cancer related genes based on the selected features and the identification of novel prognostic genes based on the expression of genes.

RESULTS

Based on the shortest path analysis of 36 known GC genes, 39 genes occurring in shortest path were identified as GC-related genes. In subsequent classification, 4153 gene ontology terms and 157 pathway terms were identified as the optimal features to depict these gastric cancer related genes. Based on them, a total of 886 genes were predicted as related genes. These 886 genes could serve as expression biomarkers for clinical detection and they achieved a 100% accuracy for distinguishing gastric cancer from a case-control dataset, better than any of 886 random selected genes did.

CONCLUSION

By analyzing the features of known GC-related genes, we employed a systematic method to predict gastric cancer related genes and novel prognostic genes for accurate clinical detection.

Interpreting the distinct and shared genetic characteristics between Epstein-Barr virus associated and non-associated gastric carcinoma

Gastric carcinoma is one of the major causes of cancer mortality worldwide. There is a better prognosis for patients with Epstein-Barr virus (EBV)-associated gastric carcinoma (EBVaGC) compared with those with EBV negative gastric carcinoma (EBVnGC). It is partly due to the fact that EBV infection recruits lymphocytes infiltrating the tumor. It has been reported that this infection indeed resulted in the changes in immune response genes and thus preventing the development of tumor. It is worthwhile to do a systematic study of EBVaGC and EBVnGC based on genetic characteristics and pathways. In this study, we investigated the information of gene ontology (GO) and KEGG pathway annotations to characterize EBVaGC and EBVnGC-related genes. By applying minimum redundancy maximum relevance (mRMR) algorithm, we provided an optimal set of features for identifying the EBVaGC and EBVnGC. We also employed the shortest path algorithm to probe the novel EBVaGC- and EBVnGC-related genes based on the interaction network of genes that differently expressed in them respectively. We obtained 1039 and 1003 features to identify these two types of gastric carcinoma respectively. Based on the optimal features of classification, we predicted 1881 and 2475 novel genes as additional candidates to support clinical research respectively for these two types of gastric cancers. We compared the differences and similarities of molecular traits between EBVaGC and EBVnGC, which would facilitate the understanding of gastric cancer and its therapy and was thus clinically relevant.

Alternative splicing in plants: directing traffic at the crossroads of adaptation and environmental stress

In recent years, high-throughput sequencing-based analysis of plant transcriptomes has suggested that up to ∼60% of plant gene loci encode alternatively spliced mature transcripts. These studies have also revealed that alternative splicing in plants can be regulated by cell type, developmental stage, the environment, and the circadian clock. Alternative splicing is coupled to RNA surveillance and processing mechanisms, including nonsense mediated decay. Recently, non-protein-coding transcripts have also been shown to undergo alternative splicing. These discoveries collectively describe a robust system of post-transcriptional regulatory feedback loops which influence RNA abundance. In this review, we summarize recent studies describing the specific roles alternative splicing and RNA surveillance play in plant adaptation to environmental stresses and the regulation of the circadian clock.

Predicting A-to-I RNA editing by feature selection and random forest

RNA editing is a post-transcriptional RNA process that provides RNA and protein complexity for regulating gene expression in eukaryotes. It is challenging to predict RNA editing by computational methods. In this study, we developed a novel method to predict RNA editing based on a random forest method. A careful feature selection procedure was performed based on the Maximum Relevance Minimum Redundancy (mRMR) and Incremental Feature Selection (IFS) algorithms. Eighteen optimal features were selected from the 77 features in our dataset and used to construct a final predictor. The accuracy and MCC (Matthews correlation coefficient) values for the training dataset were 0.866 and 0.742, respectively; for the testing dataset, the accuracy and MCC were 0.876 and 0.576, respectively. The performance was higher using 18 features than all 77, suggesting that a small feature set was sufficient to achieve accurate prediction. Analysis of the 18 features was performed and may shed light on the mechanism and dominant factors of RNA editing, providing a basis for future experimental validation.