Classifying Included and Excluded Exons in Exon Skipping Event Using Histone Modifications

Inferring causal relationships among histone modifications in exon skipping event

Alternative splicing is a crucial process of gene expression. Over 90% multi-exonic genes in human genome undergo alternative splicing. Although the splicing code has been proposed, it still couldn't satisfactorily explain the tissue-specific alternative splicing. Results of co-transcriptional RNA processing analysis demonstrated that, except for trans- and cis-acting elements, histone modifications also play a role in alternative splicing. In the present work, we analyzed the associations among 27 kinds of histone modifications in H1 human embryonic stem cell. In order to illustrate the casual relationships between histone modification and alternative splicing, we built the Bayesian network and validated its robustness by using cross validation test. In addition to the combinatorial patterns, distinct histone modification patterns were also observed in the alternative spliced exons and surrounding intron regions, indicating that histone modifications could substantially mark alternative splicing.

Adverse maternal environment affects hippocampal HTR2c variant expression and epigenetic characteristics in mouse offspring

BACKGROUND

An adverse maternal environment (AME) predisposes progeny towards cognitive impairment in humans and mice. Cognitive impairment associates with hippocampal dysfunction. An important regulator of hippocampal function is the hippocampal serotonergic system. Dysregulation of hippocampal serotonin receptor 2c (HTR2c) expression is linked with cognitive impairment. HTR2c contains multiple mRNA variants and isoforms that are epigenetically regulated including DNA methylation, histone modifications, and small nucleolar RNA MBII-52. We tested the hypotheses that AME increases HTR2c variant expression and alters epigenetic modifications along the HTR2c gene locus.

METHODS

We create an AME through maternal Western diet and prenatal environmental stress in the mouse. We analyzed hippocampal HTR2c and variants' expression, DNA methylation and histone modifications along the gene locus, and MBII-52 levels in postnatal day 21 offspring.

RESULTS

AME significantly increased the expressions of total HTR2c and full-length variants (V201 and V202) concurrently with an altered epigenetic profile along the HTR2c gene locus in male offspring hippocampi. Moreover, increased full-length variants' expression in AME males was in line with increased MBII-52 levels.

CONCLUSIONS

AME affects male offspring hippocampal expression of HTR2c and full-length variants via epigenetic mechanisms. Altered hippocampal HTR2c expression may contribute to cognitive impairment seen in adult males in this model.

IMPACT

The key message of our article is that an adverse maternal environment increases expression of total HTR2c mRNA and protein, alters proportions of HTR2c mRNA variants, and impacts HTR2c epigenetic modifications in male offspring hippocampi relative to controls. Our findings add to the literature by providing the first report of altered HTR2c mRNA variant expression in association with altered epigenetic modifications in the hippocampus of offspring mice exposed to an adverse maternal environment. Our findings suggest that an adverse maternal environment affects the expression of genes previously determined to regulate cognitive function through an epigenetic mechanism in a sex-specific manner.

Role of Epitranscriptomic and Epigenetic Modifications during the Lytic and Latent Phases of Herpesvirus Infections

Herpesviruses are double-stranded DNA viruses occurring at a high prevalence in the human population and are responsible for a wide array of clinical manifestations and diseases, from mild to severe. These viruses are classified in three subfamilies (Alpha-, Beta- and Gammaherpesvirinae), with eight members currently known to infect humans. Importantly, all herpesviruses can establish lifelong latent infections with symptomatic or asymptomatic lytic reactivations. Accumulating evidence suggest that chemical modifications of viral RNA and DNA during the lytic and latent phases of the infections caused by these viruses, are likely to play relevant roles in key aspects of the life cycle of these viruses by modulating and regulating their replication, establishment of latency and evasion of the host antiviral response. Here, we review and discuss current evidence regarding epitranscriptomic and epigenetic modifications of herpesviruses and how these can influence their life cycles. While epitranscriptomic modifications such as m⁶A are the most studied to date and relate to positive effects over the replication of herpesviruses, epigenetic modifications of the viral genome are generally associated with defense mechanisms of the host cells to suppress viral gene transcription. However, herpesviruses can modulate these modifications to their own benefit to persist in the host, undergo latency and sporadically reactivate.

The SWI/SNF subunit BRG1 affects alternative splicing by changing RNA binding factor interactions with nascent RNA

BRG1 and BRM are ATPase core subunits of the human SWI/SNF chromatin remodelling complexes mainly associated with transcriptional initiation. They also have a role in alternative splicing, which has been shown for BRM-containing SWI/SNF complexes at a few genes. Here, we have identified a subset of genes which harbour alternative exons that are affected by SWI/SNF ATPases by expressing the ATPases BRG1 and BRM in C33A cells, a BRG1- and BRM-deficient cell line, and analysed the effect on splicing by RNA sequencing. BRG1- and BRM-affected sub-sets of genes favouring both exon inclusion and exon skipping, with only a minor overlap between the ATPase. Some of the changes in alternative splicing induced by BRG1 and BRM expression did not require the ATPase activity. The BRG1-ATPase independent included exons displayed an exon signature of a high GC content. By investigating three genes with exons affected by the BRG-ATPase-deficient variant, we show that these exons accumulated phosphorylated RNA pol II CTD, both serine 2 and serine 5 phosphorylation, without an enrichment of the RNA polymerase II. The ATPases were recruited to the alternative exons, together with both core and signature subunits of SWI/SNF complexes, and promoted the binding of RNA binding factors to chromatin and RNA at the alternative exons. The interaction with the nascent RNP, however, did not reflect the association to chromatin. The hnRNPL, hnRNPU and SAM68 proteins associated with chromatin in cells expressing BRG1 and BRM wild type, but the binding of hnRNPU to the nascent RNP was excluded. This suggests that SWI/SNF can regulate alternative splicing by interacting with splicing-RNA binding factor and influence their binding to the nascent pre-mRNA particle.

iBLP: An XGBoost-Based Predictor for Identifying Bioluminescent Proteins

Bioluminescent proteins (BLPs) are a class of proteins that widely distributed in many living organisms with various mechanisms of light emission including bioluminescence and chemiluminescence from luminous organisms. Bioluminescence has been commonly used in various analytical research methods of cellular processes, such as gene expression analysis, drug discovery, cellular imaging, and toxicity determination. However, the identification of bioluminescent proteins is challenging as they share poor sequence similarities among them. In this paper, we briefly reviewed the development of the computational identification of BLPs and subsequently proposed a novel predicting framework for identifying BLPs based on eXtreme gradient boosting algorithm (XGBoost) and using sequence-derived features. To train the models, we collected BLP data from bacteria, eukaryote, and archaea. Then, for getting more effective prediction models, we examined the performances of different feature extraction methods and their combinations as well as classification algorithms. Finally, based on the optimal model, a novel predictor named iBLP was constructed to identify BLPs. The robustness of iBLP has been proved by experiments on training and independent datasets. Comparison with other published method further demonstrated that the proposed method is powerful and could provide good performance for BLP identification. The webserver and software package for BLP identification are freely available at http://lin-group.cn/server/iBLP.

Recent Advances of Computational Methods for Identifying Bacteriophage Virion Proteins

Phage Virion Proteins (PVP) are essential materials of bacteriophage, which participate in a series of biological processes. Accurate identification of phage virion proteins is helpful to understand the mechanism of interaction between the phage and its host bacteria. Since experimental method is labor intensive and time-consuming, in the past few years, many computational approaches have been proposed to identify phage virion proteins. In order to facilitate researchers to select appropriate methods, it is necessary to give a comprehensive review and comparison on existing computational methods on identifying phage virion proteins. In this review, we summarized the existing computational methods for identifying phage virion proteins and also assessed their performances on an independent dataset. Finally, challenges and future perspectives for identifying phage virion proteins were presented. Taken together, we hope that this review could provide clues to researches on the study of phage virion proteins.

Recent Development of Computational Predicting Bioluminescent Proteins

Bioluminescent Proteins (BLPs) are widely distributed in many living organisms that act as a key role of light emission in bioluminescence. Bioluminescence serves various functions in finding food and protecting the organisms from predators. With the routine biotechnological application of bioluminescence, it is recognized to be essential for many medical, commercial and other general technological advances. Therefore, the prediction and characterization of BLPs are significant and can help to explore more secrets about bioluminescence and promote the development of application of bioluminescence. Since the experimental methods are money and time-consuming for BLPs identification, bioinformatics tools have played important role in fast and accurate prediction of BLPs by combining their sequences information with machine learning methods. In this review, we summarized and compared the application of machine learning methods in the prediction of BLPs from different aspects. We wish that this review will provide insights and inspirations for researches on BLPs.

A Bioinformatics Tool for the Prediction of DNA N6-Methyladenine Modifications Based on Feature Fusion and Optimization Protocol

DNA N⁶-methyladenine (6mA) is closely involved with various biological processes. Identifying the distributions of 6mA modifications in genome-scale is of great significance to in-depth understand the functions. In recent years, various experimental and computational methods have been proposed for this purpose. Unfortunately, existing methods cannot provide accurate and fast 6mA prediction. In this study, we present 6mAPred-FO, a bioinformatics tool that enables researchers to make predictions based on sequences only. To sufficiently capture the characteristics of 6mA sites, we integrate the sequence-order information with nucleotide positional specificity information for feature encoding, and further improve the feature representation capacity by analysis of variance-based feature optimization protocol. The experimental results show that using this feature protocol, we can significantly improve the predictive performance. Via further feature analysis, we found that the sequence-order information and positional specificity information are complementary to each other, contributing to the performance improvement. On the other hand, the improvement is also due to the use of the feature optimization protocol, which is capable of effectively capturing the most informative features from the original feature space. Moreover, benchmarking comparison results demonstrate that our 6mAPred-FO outperforms several existing predictors. Finally, we establish a web-server that implements the proposed method for convenience of researchers' use, which is currently available at http://server.malab.cn/6mAPred-FO.

Disease modeling of core pre-mRNA splicing factor haploinsufficiency

The craniofacial disorder mandibulofacial dysostosis Guion-Almeida type is caused by haploinsufficiency of the U5 snRNP gene EFTUD2/SNU114. However, it is unclear how reduced expression of this core pre-mRNA splicing factor leads to craniofacial defects. Here we use a CRISPR-Cas9 nickase strategy to generate a human EFTUD2-knockdown cell line and show that reduced expression of EFTUD2 leads to diminished proliferative ability of these cells, increased sensitivity to endoplasmic reticulum (ER) stress and the mis-expression of several genes involved in the ER stress response. RNA-Seq analysis of the EFTUD2-knockdown cell line revealed transcriptome-wide changes in gene expression, with an enrichment for genes associated with processes involved in craniofacial development. Additionally, our RNA-Seq data identified widespread mis-splicing in EFTUD2-knockdown cells. Analysis of the functional and physical characteristics of mis-spliced pre-mRNAs highlighted conserved properties, including length and splice site strengths, of retained introns and skipped exons in our disease model. We also identified enriched processes associated with the affected genes, including cell death, cell and organ morphology and embryonic development. Together, these data support a model in which EFTUD2 haploinsufficiency leads to the mis-splicing of a distinct subset of pre-mRNAs with a widespread effect on gene expression, including altering the expression of ER stress response genes and genes involved in the development of the craniofacial region. The increased burden of unfolded proteins in the ER resulting from mis-splicing would exceed the capacity of the defective ER stress response, inducing apoptosis in cranial neural crest cells that would result in craniofacial abnormalities during development.

Computational Methods for Identifying Similar Diseases

Although our knowledge of human diseases has increased dramatically, the molecular basis, phenotypic traits, and therapeutic targets of most diseases still remain unclear. An increasing number of studies have observed that similar diseases often are caused by similar molecules, can be diagnosed by similar markers or phenotypes, or can be cured by similar drugs. Thus, the identification of diseases similar to known ones has attracted considerable attention worldwide. To this end, the associations between diseases at the molecular, phenotypic, and taxonomic levels were used to measure the pairwise similarity in diseases. The corresponding performance assessment strategies for these methods involving the terms “category-based,” “simulated-patient-based,” and “benchmark-data-based” were thus further emphasized. Then, frequently used methods were evaluated using a benchmark-data-based strategy. To facilitate the assessment of disease similarity scores, researchers have designed dozens of tools that implement these methods for calculating disease similarity. Currently, disease similarity has been advantageous in predicting noncoding RNA (ncRNA) function and therapeutic drugs for diseases. In this article, we review disease similarity methods, evaluation strategies, tools, and their applications in the biomedical community. We further evaluate the performance of these methods and discuss the current limitations and future trends for calculating disease similarity.

Evaluation of different computational methods on 5-methylcytosine sites identification

5-Methylcytosine (m5C) plays an extremely important role in the basic biochemical process. With the great increase of identified m5C sites in a wide variety of organisms, their epigenetic roles become largely unknown. Hence, accurate identification of m5C site is a key step in understanding its biological functions. Over the past several years, more attentions have been paid on the identification of m5C sites in multiple species. In this work, we firstly summarized the current progresses in computational prediction of m5C sites and then constructed a more powerful and reliable model for identifying m5C sites. To train the model, we collected experimentally confirmed m5C data from Homo sapiens, Mus musculus, Saccharomyces cerevisiae and Arabidopsis thaliana, and compared the performances of different feature extraction methods and classification algorithms for optimizing prediction model. Based on the optimal model, a novel predictor called iRNA-m5C was developed for the recognition of m5C sites. Finally, we critically evaluated the performance of iRNA-m5C and compared it with existing methods. The result showed that iRNA-m5C could produce the best prediction performance. We hope that this paper could provide a guide on the computational identification of m5C site and also anticipate that the proposed iRNA-m5C will become a powerful tool for large scale identification of m5C sites.

Predicting Ion Channels Genes and Their Types With Machine Learning Techniques

Motivation: The number of ion channels is increasing rapidly. As many of them are associated with diseases, they are the targets of more than 700 drugs. The discovery of new ion channels is facilitated by computational methods that predict ion channels and their types from protein sequences. Methods: We used the SVMProt and the k-skip-n-gram methods to extract the feature vectors of ion channels, and obtained 188- and 400-dimensional features, respectively. The 188- and 400-dimensional features were combined to obtain 588-dimensional features. We then employed the maximum-relevance-maximum-distance method to reduce the dimensions of the 588-dimensional features. Finally, the support vector machine and random forest methods were used to build the prediction models to evaluate the classification effect. Results: Different methods were employed to extract various feature vectors, and after effective dimensionality reduction, different classifiers were used to classify the ion channels. We extracted the ion channel data from the Universal Protein Resource (UniProt, http://www.uniprot.org/) and Ligand-Gated Ion Channel databases (http://www.ebi.ac.uk/compneur-srv/LGICdb/LGICdb.php), and then verified the performance of the classifiers after screening. The findings of this study could inform the research and development of drugs.

Identification of Phage Viral Proteins With Hybrid Sequence Features

The uniqueness of bacteriophages plays an important role in bioinformatics research. In real applications, the function of the bacteriophage virion proteins is the main area of interest. Therefore, it is very important to classify bacteriophage virion proteins and non-phage virion proteins accurately. Extracting comprehensive and effective sequence features from proteins plays a vital role in protein classification. In order to more fully represent protein information, this paper is more comprehensive and effective by combining the features extracted by the feature information representation algorithm based on sequence information (CCPA) and the feature representation algorithm based on sequence and structure information. After extracting features, the Max-Relevance-Max-Distance (MRMD) algorithm is used to select the optimal feature set with the strongest correlation between class labels and low redundancy between features. Given the randomness of the samples selected by the random forest classification algorithm and the randomness features for producing each node variable, a random forest method is employed to perform 10-fold cross-validation on the bacteriophage protein classification. The accuracy of this model is as high as 93.5% in the classification of phage proteins in this study. This study also found that, among the eight physicochemical properties considered, the charge property has the greatest impact on the classification of bacteriophage proteins These results indicate that the model discussed in this paper is an important tool in bacteriophage protein research.

Combinatorial Pattern of Histone Modifications in Exon Skipping Event

Histone modifications are associated with alternative splicing. It has been suggested that histone modifications act in combinational patterns in gene expression regulation. However, how they interact with each other and what is their casual relationships in the process of RNA splicing remain unclear. In this study, the combinatorial patterns of 38 kinds of histone modifications in the exon skipping event of the CD4⁺ T cell were analyzed by constructing Bayesian networks. Distinct combinatorial patterns of histone modifications that illustrating their casual relationships were observed in excluded/included exons and the surrounding intronic regions. The Bayesian networks also indicate that some histone modifications directly correlate with RNA splicing. We anticipate that this work could provide novel insights into the effects of histone modifications on RNA splicing regulation.

i6mA-Pred: identifying DNA N6-methyladenine sites in the rice genome

Motivation

DNA N6-methyladenine (6mA) is associated with a wide range of biological processes. Since the distribution of 6mA site in the genome is non-random, accurate identification of 6mA sites is crucial for understanding its biological functions. Although experimental methods have been proposed for this regard, they are still cost-ineffective for detecting 6mA site in genome-wide scope. Therefore, it is desirable to develop computational methods to facilitate the identification of 6mA site.

Results

In this study, a computational method called i6mA-Pred was developed to identify 6mA sites in the rice genome, in which the optimal nucleotide chemical properties obtained by the using feature selection technique were used to encode the DNA sequences. It was observed that the i6mA-Pred yielded an accuracy of 83.13% in the jackknife test. Meanwhile, the performance of i6mA-Pred was also superior to other methods.

Availability and implementation

A user-friendly web-server, i6mA-Pred is freely accessible at http://lin-group.cn/server/i6mA-Pred.