Integrative analysis of many RNA-seq datasets to study alternative splicing

Quantitative Assessment of Intracellular Effectors and Cellular Response in RAGE Activation

The review delves into the methods for the quantitative assessment of intracellular effectors and cellular response of Receptor for Advanced Glycation End products (RAGE), a vital transmembrane receptor involved in a range of physiological and pathological processes. RAGE bind to Advanced Glycation End products (AGEs) and other ligands, which in turn activate diverse downstream signaling pathways that impact cellular responses such as inflammation, oxidative stress, and immune reactions. The review article discusses the intracellular signaling pathways activated by RAGE followed by differential activation of RAGE signaling across various diseases. This will ultimately guide researchers in developing targeted and effective interventions for diseases associated with RAGE activation. Further, we have discussed how PCR, western blotting, and microscopic examination of various molecules involved in downstream signaling can be leveraged to monitor, diagnose, and explore diseases involving proteins with unique post-translational modifications. This review article underscores the pressing need for advancements in molecular approaches for disease detection and management involving RAGE.

Characterization of unique PMEPA1 gene splice variants (isoforms d and e) from RNA Seq profiling provides novel insights into prognostic evaluation of prostate cancer

Prostate cancer is a disease with heterogeneity of multiple gene transcriptomes and biological signaling pathways involved in tumor development. The prostate transmembrane protein, androgen induced 1 (PMEPA1), a multifunctional protein played critical roles in prostate tumorigenesis. The pleiotropic nature of PMEPA1 in modulating androgen and TGF-β signaling as well as splice variants mechanisms for functional regulations of cancer-associated genes prompted us to investigate the biological roles of PMEPA1 isoforms in prostate cancer. In addition to 4 reported PMEPA1 isoforms (a, b, c and d), one novel isoform PMEPA1-e was identified with RNA Seq analysis of hormone responsive VCaP, LNCaP cells and human prostate cancer samples from The Cancer Genome Atlas (TCGA) dataset. We analyzed the structures, expressions, biological functions and clinical relevance of PMEPA1-e isoform and less characterized isoforms c and d in the context of prostate cancer and AR/TGF-β signaling. The expression of PMEPA1-e was induced by androgen and AR. In contrast, PMEPA1-d was responsive to TGF-β and inhibited TGF-β signaling. Both PMEPA1-d and PMPEA1-e promoted the growth of androgen independent prostate cancer cells. Although PMEPA1-c was responsive to TGF-β, it was found to have no impacts on cell growth and androgen/TGF-β signaling. The TCGA data analysis from 499 patients showed higher expression ratios of PMEAP1-b versus -d or -e strongly associated with enhanced Gleason score. Taken together, our findings first time defined the prostate tumorigenesis mediated by PMEPA1-d and -e isoforms, providing novel insights into the new strategies for prognostic evaluation and therapeutics of prostate tumor.

Bioinformatics challenges and perspectives when studying the effect of epigenetic modifications on alternative splicing

It is widely known that epigenetic modifications are important in regulating transcription, but several have also been reported in alternative splicing. The regulation of pre-mRNA splicing is important to explain proteomic diversity and the misregulation of splicing has been implicated in many diseases. Here, we give a brief overview of the role of epigenetics in alternative splicing and disease. We then discuss the bioinformatics methods that can be used to model interactions between epigenetic marks and regulators of splicing. These models can be used to identify alternative splicing and epigenetic changes across different phenotypes.

This article is part of a discussion meeting issue ‘Frontiers in epigenetic chemical biology’.

Assessing the Effectiveness of Direct Data Merging Strategy in Long-Term and Large-Scale Pharmacometabonomics

Because of the extended period of clinic data collection and huge size of analyzed samples, the long-term and large-scale pharmacometabonomics profiling is frequently encountered in the discovery of drug/target and the guidance of personalized medicine. So far, integration of the results (ReIn) from multiple experiments in a large-scale metabolomic profiling has become a widely used strategy for enhancing the reliability and robustness of analytical results, and the strategy of direct data merging (DiMe) among experiments is also proposed to increase statistical power, reduce experimental bias, enhance reproducibility and improve overall biological understanding. However, compared with the ReIn, the DiMe has not yet been widely adopted in current metabolomics studies, due to the difficulty in removing unwanted variations and the inexistence of prior knowledges on the performance of the available merging methods. It is therefore urgently needed to clarify whether DiMe can enhance the performance of metabolic profiling or not. Herein, the performance of DiMe on 4 pairs of benchmark datasets was comprehensively assessed by multiple criteria (classification capacity, robustness and false discovery rate). As a result, integration/merging-based strategies (ReIn and DiMe) were found to perform better under all criteria than those strategies based on single experiment. Moreover, DiMe was discovered to outperform ReIn in classification capacity and robustness, while the ReIn showed superior capacity in controlling false discovery rate. In conclusion, these findings provided valuable guidance to the selection of suitable analytical strategy for current metabolomics.

Evaluation of two main RNA-seq approaches for gene quantification in clinical RNA sequencing: polyA+ selection versus rRNA depletion

To allow efficient transcript/gene detection, highly abundant ribosomal RNAs (rRNA) are generally removed from total RNA either by positive polyA+ selection or by rRNA depletion (negative selection) before sequencing. Comparisons between the two methods have been carried out by various groups, but the assessments have relied largely on non-clinical samples. In this study, we evaluated these two RNA sequencing approaches using human blood and colon tissue samples. Our analyses showed that rRNA depletion captured more unique transcriptome features, whereas polyA+ selection outperformed rRNA depletion with higher exonic coverage and better accuracy of gene quantification. For blood- and colon-derived RNAs, we found that 220% and 50% more reads, respectively, would have to be sequenced to achieve the same level of exonic coverage in the rRNA depletion method compared with the polyA+ selection method. Therefore, in most cases we strongly recommend polyA+ selection over rRNA depletion for gene quantification in clinical RNA sequencing. Our evaluation revealed that a small number of lncRNAs and small RNAs made up a large fraction of the reads in the rRNA depletion RNA sequencing data. Thus, we recommend that these RNAs are specifically depleted to improve the sequencing depth of the remaining RNAs.

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).

The determinants of alternative RNA splicing in human cells

Alternative splicing represents an important level of the regulation of gene function in eukaryotic organisms. It plays a critical role in virtually every biological process within an organism, including regulation of cell division and cell death, differentiation of tissues in the embryo and the adult organism, as well as in cellular response to diverse environmental factors. In turn, studies of the last decade have shown that alternative splicing itself is controlled by different mechanisms. Unfortunately, there is no clear understanding of how these diverse mechanisms, or determinants, regulate and constrain the set of alternative RNA species produced from any particular gene in every cell of the human body. Here, we provide a consolidated overview of alternative splicing determinants including RNA-protein interactions, epigenetic regulation via chromatin remodeling, coupling of transcription-to-alternative splicing, effect of secondary structures in pre-RNA, and function of the RNA quality control systems. We also extensively and critically discuss some mechanistic insights on coordinated inclusion/exclusion of exons during the formation of mature RNA molecules. We conclude that the final structure of RNA is pre-determined by a complex interplay between cis- and trans-acting factors. Altogether, currently available empirical data significantly expand our understanding of the functioning of the alternative splicing machinery of cells in normal and pathological conditions. On the other hand, there are still many blind spots that require further deep investigations.

RNA-Sequencing data supports the existence of novel VEGFA splicing events but not of VEGFAxxxb isoforms

Vascular endothelial growth factor (VEGFA), a pivotal regulator of angiogenesis and valuable therapeutic target, is characterised by alternative splicing which generates three principal isoforms, VEGFA₁₂₁, VEGFA₁₆₅ and VEGFA₁₈₉. A second set of anti-angiogenic isoforms termed VEGFA_xxxb that utilise an alternative splice site in the final exon have been widely reported, with mRNA detection based principally upon RT-PCR assays. We sought confirmation of the existence of the VEGFA_xxxb isoforms within the abundant RNA sequencing data available publicly. Whilst sequences derived specifically from each of the canonical VEGFA isoforms were present in many tissues, there were no sequences derived from VEGFA_xxxb isoforms. Sequencing of approximately 50,000 RT-PCR products spanning the exon 7–8 junction in 10 tissues did not identify any VEGFA_xxxb transcripts. The absence or extremely low expression of these transcripts in vivo indicates that VEGFA_xxxb isoforms are unlikely to play a role in normal physiology. Our analyses also revealed multiple novel splicing events supported by more reads than previously reported for VEGFA₁₄₅ and VEGFA₁₄₈ isoforms, including three from novel first exons consistent with existing transcription start site data. These novel VEGFA isoforms may play significant roles in specific cell types.

Undiscovered Physiology of Transcript and Protein Networks

The past two decades have witnessed a rapid evolution in our ability to measure RNA and protein from biological systems. As a result, new principles have arisen regarding how information is processed in cells, how decisions are made, and the role of networks in biology. This essay examines this technological evolution, reviewing (and critiquing) the conceptual framework that has emerged to explain how RNA and protein networks control cellular function. We identify how future investigations into transcriptomes, proteomes, and other cellular networks will enable development of more robust, quantitative models of cellular behavior whilst also providing new avenues to use knowledge of biological networks to improve human health. © 2016 American Physiological Society. Compr Physiol 6:1851-1872, 2016.

Next generation sequencing technology and genomewide data analysis: Perspectives for retinal research

The advent of high throughput next generation sequencing (NGS) has accelerated the pace of discovery of disease-associated genetic variants and genomewide profiling of expressed sequences and epigenetic marks, thereby permitting systems-based analyses of ocular development and disease. Rapid evolution of NGS and associated methodologies presents significant challenges in acquisition, management, and analysis of large data sets and for extracting biologically or clinically relevant information. Here we illustrate the basic design of commonly used NGS-based methods, specifically whole exome sequencing, transcriptome, and epigenome profiling, and provide recommendations for data analyses. We briefly discuss systems biology approaches for integrating multiple data sets to elucidate gene regulatory or disease networks. While we provide examples from the retina, the NGS guidelines reviewed here are applicable to other tissues/cell types as well.

A Network of Splice Isoforms for the Mouse

The laboratory mouse is the primary mammalian species used for studying alternative splicing events. Recent studies have generated computational models to predict functions for splice isoforms in the mouse. However, the functional relationship network, describing the probability of splice isoforms participating in the same biological process or pathway, has not yet been studied in the mouse. Here we describe a rich genome-wide resource of mouse networks at the isoform level, which was generated using a unique framework that was originally developed to infer isoform functions. This network was built through integrating heterogeneous genomic and protein data, including RNA-seq, exon array, protein docking and pseudo-amino acid composition. Through simulation and cross-validation studies, we demonstrated the accuracy of the algorithm in predicting isoform-level functional relationships. We showed that this network enables the users to reveal functional differences of the isoforms of the same gene, as illustrated by literature evidence with Anxa6 (annexin a6) as an example. We expect this work will become a useful resource for the mouse genetics community to understand gene functions. The network is publicly available at: http://guanlab.ccmb.med.umich.edu/isoformnetwork.

Advanced Applications of RNA Sequencing and Challenges

Next-generation sequencing technologies have revolutionarily advanced sequence-based research with the advantages of high-throughput, high-sensitivity, and high-speed. RNA-seq is now being used widely for uncovering multiple facets of transcriptome to facilitate the biological applications. However, the large-scale data analyses associated with RNA-seq harbors challenges. In this study, we present a detailed overview of the applications of this technology and the challenges that need to be addressed, including data preprocessing, differential gene expression analysis, alternative splicing analysis, variants detection and allele-specific expression, pathway analysis, co-expression network analysis, and applications combining various experimental procedures beyond the achievements that have been made. Specifically, we discuss essential principles of computational methods that are required to meet the key challenges of the RNA-seq data analyses, development of various bioinformatics tools, challenges associated with the RNA-seq applications, and examples that represent the advances made so far in the characterization of the transcriptome.

RNA-Seq Uncovers SNPs and Alternative Splicing Events in Asian Lotus (Nelumbo nucifera)

RNA-Seq is an efficient way to comprehensively identify single nucleotide polymorphisms (SNPs) and alternative splicing (AS) events from the expressed genes. In this study, we conducted transcriptome sequencing of four Asian lotus (Nelumbo nucifera) cultivars using Illumina HiSeq2000 platform to identify SNPs and AS events in lotus. A total of 505 million pair-end RNA-Seq reads were generated from four cultivars, of which 86% were mapped to the lotus reference genome. Using the four sets of data together, a total of 357,689 putative SNPs were identified with an average density of one SNP per 2.2 kb. These SNPs were located in 1,253 scaffolds and 15,016 expressed genes. A/G and C/T were the two major types of SNPs in the Asian lotus transcriptome. In parallel, a total of 177,540 AS events were detected in the four cultivars and were distributed in 64% of the expressed genes of lotus. The predominant type of AS events was alternative 5’ first exon, which accounted for 41.2% of all the observed AS events, and exon skipping only accounted for 4.3% of all AS. Gene Ontology analysis was conducted to analyze the function of the genes containing SNPs and AS events. Validation of selected SNPs and AS events revealed that 74% of SNPs and 80% of AS events were reliable, which indicates that RNA-Seq is an efficient approach to uncover gene-associated SNPs and AS events. A large number of SNPs and AS events identified in our study will facilitate further genetic and functional genomics research in lotus.

The landscape of alternative splicing in cervical squamous cell carcinoma

BACKGROUND

Alternative splicing (AS) is a key regulatory mechanism in protein synthesis and proteome diversity. In this study, we identified alternative splicing events in four pairs of cervical squamous cell carcinoma (CSCC) and adjacent nontumor tissues using RNA sequencing.

METHODS

The transcripts of the four paired samples were thoroughly analyzed by RNA sequencing. SpliceMap software was used to detect the splicing junctions. Kyoto Encyclopedia of Genes and Genomes pathway analysis was conducted to detect the alternative spliced genes-related signal pathways. The alternative spliced genes were validated by reverse transcription-polymerase chain reaction (RT-PCR).

RESULTS

There were 35 common alternative spliced genes in the four CSCC samples; they were novel and CSCC specific. Sixteen pathways were significantly enriched (P<0.05). One novel 5'AS site in the KLHDC7B gene, encoding kelch domain-containing 7B, and an exon-skipping site in the SYCP2 gene, encoding synaptonemal complex 2, were validated by RT-PCR. The KLHDC7B gene with 5'AS was found in 67.5% (27/40) of CSCC samples and was significantly related with cellular differentiation and tumor size. The exon-skipping site of the SYCP2 gene was found in 35.0% (14/40) of CSCC samples and was significantly related with depth of cervical invasion.

CONCLUSION

The KLHDC7B and the SYCP2 genes with alternative spliced events might be involved in the development and progression of CSCC and could be used as biomarkers in the diagnosis and prognosis of CSCC.