FIRMA: a method for detection of alternative splicing from exon array data

Transcriptome Analysis in Mexican Adults with Acute Lymphoblastic Leukemia

Acute lymphoblastic leukemia (ALL) represents around 25% of adult acute leukemias. Despite the increasing improvement in the survival rate of ALL patients during the last decade, the heterogeneous clinical and molecular features of this malignancy still represent a major challenge for treatment and achieving better outcomes. To identify aberrantly expressed genes in bone marrow (BM) samples from adults with ALL, transcriptomic analysis was performed using Affymetrix Human Transcriptome Array 2.0 (HTA 2.0). Differentially expressed genes (DEGs) (±2-fold change, p-value < 0.05, and FDR < 0.05) were detected using the Transcriptome Analysis Console. Gene Ontology (GO), Database for Annotation, Visualization, and Integrated Discovery (DAVID), and Ingenuity Pathway Analysis (IPA) were employed to identify gene function and define the enriched pathways of DEGs. The protein-protein interactions (PPIs) of DEGs were constructed. A total of 871 genes were differentially expressed, and DNTT, MYB, EBF1, SOX4, and ERG were the top five up-regulated genes. Meanwhile, the top five down-regulated genes were PTGS2, PPBP, ADGRE3, LUCAT1, and VCAN. An association between ERG, CDK6, and SOX4 expression levels and the probability of relapse and death was observed. Regulation of the immune system, immune response, cellular response to stimulus, as well as apoptosis signaling, inflammation mediated by chemokines and cytokines, and T cell activation were among the most altered biological processes and pathways, respectively. Transcriptome analysis of ALL in adults reveals a group of genes consistently associated with hematological malignancies and underscores their relevance in the development of ALL in adults.

Transcriptomics for Clinical and Experimental Biology Research: Hang on a Seq

Sequencing the human genome empowers translational medicine, facilitating transcriptome-wide molecular diagnosis, pathway biology, and drug repositioning. Initially, microarrays are used to study the bulk transcriptome; but now short-read RNA sequencing (RNA-seq) predominates. Positioned as a superior technology, that makes the discovery of novel transcripts routine, most RNA-seq analyses are in fact modeled on the known transcriptome. Limitations of the RNA-seq methodology have emerged, while the design of, and the analysis strategies applied to, arrays have matured. An equitable comparison between these technologies is provided, highlighting advantages that modern arrays hold over RNA-seq. Array protocols more accurately quantify constitutively expressed protein coding genes across tissue replicates, and are more reliable for studying lower expressed genes. Arrays reveal long noncoding RNAs (lncRNA) are neither sparsely nor lower expressed than protein coding genes. Heterogeneous coverage of constitutively expressed genes observed with RNA-seq, undermines the validity and reproducibility of pathway analyses. The factors driving these observations, many of which are relevant to long-read or single-cell sequencing are discussed. As proposed herein, a reappreciation of bulk transcriptomic methods is required, including wider use of the modern high-density array data-to urgently revise existing anatomical RNA reference atlases and assist with more accurate study of lncRNAs.

Neuronal activity regulates alternative exon usage

Neuronal activity-regulated gene transcription underlies plasticity-dependent changes in the molecular composition and structure of neurons. A large number of genes regulated by different neuronal plasticity inducing pathways have been identified, but altered gene expression levels represent only part of the complexity of the activity-regulated transcriptional program. Alternative splicing, the differential inclusion and exclusion of exonic sequence in mRNA, is an additional mechanism that is thought to define the activity-dependent transcriptome. Here, we present a genome wide microarray-based survey to identify exons with increased expression levels at 1, 4 or 8 h following neuronal activity in the murine hippocampus provoked by generalized seizures. We used two different bioinformatics approaches to identify alternative activity-induced exon usage and to predict alternative splicing, ANOSVA (ANalysis Of Splicing VAriation) which we here adjusted to accommodate data from different time points and FIRMA (Finding Isoforms using Robust Multichip Analysis). RNA sequencing, in situ hybridization and reverse transcription PCR validate selected activity-dependent splicing events of previously described and so far undescribed activity-regulated transcripts, including Homer1a, Homer1d, Ania3, Errfi1, Inhba, Dclk1, Rcan1, Cda, Tpm1 and Krt75. Taken together, our survey significantly adds to the comprehensive understanding of the complex activity-dependent neuronal transcriptomic signature. In addition, we provide data sets that will serve as rich resources for future comparative expression analyses.

RNA Sequencing Data: Hitchhiker's Guide to Expression Analysis

Gene expression is the fundamental level at which the results of various genetic and regulatory programs are observable. The measurement of transcriptome-wide gene expression has convincingly switched from microarrays to sequencing in a matter of years. RNA sequencing (RNA-seq) provides a quantitative and open system for profiling transcriptional outcomes on a large scale and therefore facilitates a large diversity of applications, including basic science studies, but also agricultural or clinical situations. In the past 10 years or so, much has been learned about the characteristics of the RNA-seq data sets, as well as the performance of the myriad of methods developed. In this review, we give an overview of the developments in RNA-seq data analysis, including experimental design, with an explicit focus on the quantification of gene expression and statistical approachesfor differential expression. We also highlight emerging data types, such as single-cell RNA-seq and gene expression profiling using long-read technologies.

A Systems-Level Analysis Reveals Circadian Regulation of Splicing in Colorectal Cancer

Accumulating evidence points to a significant role of the circadian clock in the regulation of splicing in various organisms, including mammals. Both dysregulated circadian rhythms and aberrant pre-mRNA splicing are frequently implicated in human disease, in particular in cancer. To investigate the role of the circadian clock in the regulation of splicing in a cancer progression context at the systems-level, we conducted a genome-wide analysis and compared the rhythmic transcriptional profiles of colon carcinoma cell lines SW480 and SW620, derived from primary and metastatic sites of the same patient, respectively. We identified spliceosome components and splicing factors with cell-specific circadian expression patterns including SRSF1, HNRNPLL, ESRP1, and RBM 8A, as well as altered alternative splicing events and circadian alternative splicing patterns of output genes (e.g., VEGFA, NCAM1, FGFR2, CD44) in our cellular model. Our data reveals a remarkable interplay between the circadian clock and pre-mRNA splicing with putative consequences in tumor progression and metastasis.

Integrated genomics and functional validation identifies malignant cell specific dependencies in triple negative breast cancer

Triple negative breast cancers (TNBCs) lack recurrent targetable driver mutations but demonstrate frequent copy number aberrations (CNAs). Here, we describe an integrative genomic and RNAi-based approach that identifies and validates gene addictions in TNBCs. CNAs and gene expression alterations are integrated and genes scored for pre-specified target features revealing 130 candidate genes. We test functional dependence on each of these genes using RNAi in breast cancer and non-malignant cells, validating malignant cell selective dependence upon 37 of 130 genes. Further analysis reveals a cluster of 13 TNBC addiction genes frequently co-upregulated that includes genes regulating cell cycle checkpoints, DNA damage response, and malignant cell selective mitotic genes. We validate the mechanism of addiction to a potential drug target: the mitotic kinesin family member C1 (KIFC1/HSET), essential for successful bipolar division of centrosome-amplified malignant cells and develop a potential selection biomarker to identify patients with tumors exhibiting centrosome amplification.

Transcriptome and alternative splicing analysis of nucleus pulposus cells in response to high oxygen tension: Involvement of high oxygen tension in the pathogenesis of intervertebral disc degeneration

High oxygen tension caused by neovascularization in the microenvironment of intervertebral discs (IVDs) is associated with the pathogenesis of IVD degeneration (IDD). Pre-mRNAs undergo alternative splicing (AS) to produce structurally and functionally diverse mRNA and proteins. However, the precise role of high oxygen tension in IDD and the relationship between AS and high oxygen tension in disc cells remain unknown. To investigate the effect of high oxygen tension on disc cells, Affymetrix Rat Transcriptome Array 1.0 was used to determine differentially expressed genes (DEGs) and alternative splicing genes (ASGs) in rat nucleus pulposus (NP) cells treated with 20% O₂. NP cells at 1% O₂ served as the control. PCR was used for validation. GO and KEGG pathway analysis was performed. Furthermore, the reactive oxygen species (ROS) production, growth, cell cycle and matrix metabolism of NP cells were also investigated. In total, 2499 DEGs and 8451 ASGs were identified. Various GO terms and KEGG pathways were potently associated with IDD, including autophagy, mTOR signaling pathway and angiogenesis. Especially, high oxygen tension increased ROS production in NP cells. It also accelerated the matrix metabolism of NP cells and induced NP cell cycle arrest to retard cell growth. This study, for the first time, analyzes the transcriptome and AS of NP cells in response to high oxygen tension, indicating that high oxygen tension is involved in the establishment and progression of IDD through its wide effects on the viability and function of disc cells.

Human PDE4D isoform composition is deregulated in primary prostate cancer and indicative for disease progression and development of distant metastases

Phosphodiesterase 4D7 was recently shown to be specifically over-expressed in localized prostate cancer, raising the question as to which regulatory mechanisms are involved and whether other isoforms of this gene family (PDE4D) are affected under the same conditions.We investigated PDE4D isoform composition in prostatic tissues using a total of seven independent expression datasets and also included data on DNA methylation, copy number and AR and ERG binding in PDE4D promoters to gain insight into their effect on PDE4D transcription.We show that expression of PDE4D isoforms is consistently altered in primary human prostate cancer compared to benign tissue, with PDE4D7 being up-regulated while PDE4D5 and PDE4D9 are down-regulated. Disease progression is marked by an overall down-regulation of long PDE4D isoforms, while short isoforms (PDE4D1/2) appear to be relatively unaffected. While these alterations seem to be independent of copy number alterations in the PDE4D locus and driven by AR and ERG binding, we also observed increased DNA methylation in the promoter region of PDE4D5, indicating a long lasting alteration of the isoform composition in prostate cancer tissues.We propose two independent metrics that may serve as diagnostic and prognostic markers for prostate disease: (PDE4D7 - PDE4D5) provides an effective means for distinguishing PCa from normal adjacent prostate, whereas PDE4D1/2 - (PDE4D5 + PDE4D7 + PDE4D9) offers strong prognostic potential to detect aggressive forms of PCa and is associated with metastasis free survival. Overall, our findings highlight the relevance of PDE4D as prostate cancer biomarker and potential drug target.

Alternative transcription of a shorter, non-anti-angiogenic thrombospondin-2 variant in cancer-associated blood vessels

Thrombospondin-2 (TSP2) is an anti-angiogenic matricellular protein that inhibits tumor growth and angiogenesis. Tumor-associated blood vascular endothelial cells (BECs) were isolated from human invasive bladder cancers and from matched normal bladder tissue by immuno-laser capture microdissection. Exon expression profiling analyses revealed a particularly high expression of a short TSP2 transcript containing only the last 9 (3′) exons of the full-length TSP2 transcript. Using 5′ and 3′ RACE (rapid amplification of cDNA ends) and Sanger sequencing, we confirmed the existence of the shorter transcript of TSP2 (sTSP2) and determined its sequence which completely lacked the anti-angiogenic thrombospondin type 1 repeats domain. The largest open reading frame predicted within the transcript comprises 209 amino acids and matches almost completely the C-terminal lectin domain of full-length TSP2. We produced recombinant sTSP2 and found that unlike the full-length TSP2, sTSP2 did not inhibit vascular endothelial growth factor-A-induced proliferation of cultured human BECs, but in contrast when combined with TSP2 blocked the inhibitory effects of TSP2 on BEC proliferation. In vivo studies with stably transfected A431 squamous cell carcinoma cells revealed that full-length TSP2, but not sTSP2, inhibited tumor growth and angiogenesis. This study reveals that the transcriptional program of tumor stromal cells can change to transcribe a new version of an endogenous angiogenesis inhibitor that has lost its anti-angiogenic activity.

Microarray Analysis of Hypertension

Hypertension is a complex disorder in which multiple genes, pathways, and organ systems simultaneously interact to contribute to the final level of blood pressure. Fully elucidating these interactions is an important area of hypertension research and one in which high-throughput methods such as microarrays can play a key role. With recent advances in microarray technology, reliable and accurate quantification of all known mRNA transcripts in a sample is now routinely performed. In addition, with improved statistical methods and publicly available tools and resources, robust analysis of the large amount of data generated from microarray experiments is now achievable for all research laboratories as will be outlined in this review.

A long non-coding RNA inside the type 2 transglutaminase gene tightly correlates with the expression of its transcriptional variants

The long non-coding RNAs (lncRNAs) are matter of intense investigation as potential regulators of gene expression. In the case of the transglutaminase 2 gene (TGM2) the databases of genome sequence indicate location of a lncRNA (LOC107987281) within the first intron. This lncRNA is 1000 bp long, arises from 2 exons and starts few nucleotides 3' of the first splicing site of translated TGM2. We have analysed correlations between expression of LOC107987281 lncRNA and TGM2 mRNA by real-time PCR in K562 cell line untreated or treated with the anticancer drugs TPA (12-O-tetradecanoylphorbol-13-acetate), Docetaxel and Doxorubicin. In the treated cells the lncRNA increase follows the trend of TGM2 transcript. To validate this finding we used HumanExon1_0ST Affymetrix; chip data were background-adjusted, quantile-normalized and summarized using robust multi-array average analysis implemented in the R package. The probesets recognize sequences inside each exon, near intronic splicing sites and others located in the untranslated regions of TGM2 gene. The analysis of total RNA samples in GEO datasets from K562, HL-60, THP-1 and U937 cell lines, untreated or treated with TPA in replicated experiments confirmed our earlier results. These demonstrate correlation between LOC107987281 and TGM2 mRNA in the cell lines (K562, HL60 and THP-1) where increased levels of TGM2 mRNA are produced. Additional array study on 358 samples of several normal and paired tumor tissues leads to the same conclusions, indicating a correlation between full-length TGM2 mRNA and LOC107987281 lncRNA in relation to the development of several tumors.

Dietary Fat Quantity and Type Induce Transcriptome-Wide Effects on Alternative Splicing of Pre-mRNA in Rat Skeletal Muscle

Background: Fat-enriched diets produce metabolic changes in skeletal muscle, which in turn can mediate changes in gene regulation.Objective: We examined the high-fat-diet-induced changes in skeletal muscle gene expression by characterizing variations in pre-mRNA alternative splicing.Methods: Affymetrix Exon Array analysis was performed on the transcriptome of the gastrocnemius/plantaris complex of male obesity-prone Sprague-Dawley rats fed a 10% or 60% fat (lard) diet for 2 or 8 wk. The validation of exon array results was focused on troponin T (Tnnt3). Tnnt3 splice form analyses were extended in studies of rats fed 10% or 30% fat diets across 1- to 8-wk treatment periods and rats fed 10% or 45% fat diets with fat sources from lard or mono- or polyunsaturated fats for 2 wk. Nuclear magnetic resonance (NMR) was used to measure body composition.Results: Consumption of a 60% fat diet for 2 or 8 wk resulted in alternative splicing of 668 and 726 pre-mRNAs, respectively, compared with rats fed a 10% fat diet. Tnnt3 transcripts were alternatively spliced in rats fed a 60% fat diet for either 2 or 8 wk. The high-fat-diet-induced changes in Tnnt3 alternative splicing were observed in rats fed a 30% fat diet across 1- to 8-wk treatment periods. Moreover, this effect depended on fat type, because Tnnt3 alternative splicing occurred in response to 45% fat diets enriched with lard but not in response to diets enriched with mono- or polyunsaturated fatty acids. Fat mass (a proxy for obesity as measured by NMR) did not differ between groups in any study.Conclusions: Rat skeletal muscle responds to overconsumption of dietary fat by modifying gene expression through pre-mRNA alternative splicing. Variations in Tnnt3 alternative splicing occur independently of obesity and are dependent on dietary fat quantity and suggest a role for saturated fatty acids in the high-fat-diet-induced modifications in Tnnt3 alternative splicing.

The developmental transcriptome landscape of receptive endometrium during embryo implantation in dairy goats

Under natural conditions, some embryos cannot implant successfully because of the dysfunction of receptive endometrium (RE). Thus, it is imperative for us to study the molecular mechanisms involved in the formation of the RE from pre-receptive endometrium (PE). In this study, the endometrium from gestational day 5 (D5, PE) and gestational day 15 (D15, RE) dairy goats were selected to systematically analyze the transcriptome using strand-specific Ribo-Zero RNA-Seq, >120 million high-quality paired-end reads were generated and 47,616 transcripts were identified in the endometrium of dairy goats. A total of 810 mRNAs were differentially expressed genes (DEGs) between the RE and PE meeting the criteria of P-values<0.05. Bioinformatics analysis of the DEGs revealed that a number of biological processes and pathways were potentially involved in the establishment of the RE, notably energy metabolism and amino acid metabolism. Furthermore, we speculated that CXCL14, IGFBP3, and LGALS15 potentially participated in the development of endometrium. What's more, putative SNPs, InDels and AS events were identified and analyzed in the endometrium. In a word, this resulting view of the transcriptome greatly enhances the comprehensive transcript catalog and uncovers the global trends in gene expression during the formation of receptive endometrium in dairy goats.

Gene Expression Profiling of Acute Lymphoblastic Leukemia in Children with Very Early Relapse

BACKGROUND AND AIMS

Acute lymphoblastic leukemia (ALL) is the most common childhood cancer worldwide. Mexican patients have high mortality rates, low frequency of good prognosis biomarkers (i.e., ETV6-RUNX1) and a high proportion is classified at the time of diagnosis with a high risk to relapse according to clinical features. In addition, very early relapses are more frequently observed than in other populations. The aim of the study was to identify new potential biomarkers associated with very early relapse in Mexican ALL children through transcriptome analysis.

METHODS

Microarray gene expression profiling on bone marrow samples of 54 pediatric ALL patients, collected at time of diagnosis and/or at relapse, was performed. Eleven patients presented relapse within the first 18 months after diagnosis. Affymetrix Human Transcriptome Array 2.0 (HTA 2.0) was used to perform gene expression analysis. Annotation and functional enrichment analyses were carried out using Gene Ontology, KEGG pathway analysis and Ingenuity Pathway Analysis tools.

RESULTS

BLVRB, ZCCHC7, PAX5, EBF1, TMOD1 and BLNK were differentially expressed (fold-change >2.0 and p value <0.01) between relapsed and non-relapsed patients. Functional analysis of abnormally expressed genes revealed their important role in cellular processes related to the development of hematological diseases, cancer, cell death and survival and in cell-to-cell signaling interaction.

CONCLUSIONS

Our data support previous findings showing the relevance of PAX5, EBF1 and ZCCHC7 as potential biomarkers to identify a subgroup of ALL children in high risk to relapse.

Validation of Cyclic Adenosine Monophosphate Phosphodiesterase-4D7 for its Independent Contribution to Risk Stratification in a Prostate Cancer Patient Cohort with Longitudinal Biological Outcomes

BACKGROUND

The clinical metrics used to date to assess the progression risk of newly diagnosed prostate cancer patients only partly represent the true biological aggressiveness of the underlying disease.

OBJECTIVE

Validation of the prognostic biomarker phosphodiesterase-4D7 (PDE4D7) in predicting longitudinal biological outcomes in a historical surgery cohort to improve postsurgical risk stratification.

DESIGN, PATIENTS, AND METHODS

RNA was extracted from biopsy punches of resected tumors from 550 patients. PDE4D7 was quantified using one-step quantitative reverse transcription-polymerase chain reaction. PDE4D7 scores were calculated by normalization of PDE4D7 to reference genes. Multivariate analyses were adjusted for clinical prognostic variables. Outcomes tested were: prostate-specific antigen relapse, start of salvage treatment, progression to metastases, overall mortality, and prostate cancer-specific mortality. The PDE4D7 score was combined with the clinical risk model Cancer of the Prostate Risk Assessment Postsurgical Score (CAPRA-S) using multivariate regression modeling; the combined score was tested in post-treatment progression free survival prediction.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Correlations with outcomes were analyzed using multivariate Cox regression and logistic regression statistics.

RESULTS AND LIMITATIONS

The PDE4D7 score was significantly associated with time-to-prostate specific antigen failure after prostatectomy (hazard ratio [HR]: 0.53, 95% confidence interval [CI]: 0.41-0.67 for each unit increase, p<0.0001). After adjustment for postsurgical prognostic variables the HR was 0.56 (95% CI: 0.43-0.73, p<0.0001). The PDE4D7 score remained significant after adjusting the multi-variate analysis for the CAPRA-S model categories (HR=0.54, 95% CI=0.42-0.69, p<0.0001). Combination of the PDE4D7 score with the CAPRA-S demonstrated a significant incremental value of 4-6% in 2-yr (p=0.004) or 5-yr (p=0.003) prediction of progression free survival after surgery. The combined model of PDE4D7 and CAPRA-S improves patient selection with very high risk of fast disease relapse after primary intervention.

CONCLUSIONS

The PDE4D7 score has the potential to provide independent risk information and to restratify patients with clinical intermediate- to high-risk characteristics to a very low-risk profile.

PATIENT SUMMARY

In this report, we studied the potential of a novel biomarker to predict outcomes of a cohort of prostate cancer patients who underwent surgery more than 10 yr ago. We found that a gene called phosphodiesterase-4D7 added extra information to the available clinical data. We conclude that the measurement of this gene in tumor tissue may contribute to more effective treatment decisions.

An indicator cell assay for blood-based diagnostics

We have established proof of principle for the Indicator Cell Assay Platform™ (iCAP™), a broadly applicable tool for blood-based diagnostics that uses specifically-selected, standardized cells as biosensors, relying on their innate ability to integrate and respond to diverse signals present in patients' blood. To develop an assay, indicator cells are exposed in vitro to serum from case or control subjects and their global differential response patterns are used to train reliable, disease classifiers based on a small number of features. In a feasibility study, the iCAP detected pre-symptomatic disease in a murine model of amyotrophic lateral sclerosis (ALS) with 94% accuracy (p-Value = 3.81E-6) and correctly identified samples from a murine Huntington's disease model as non-carriers of ALS. Beyond the mouse model, in a preliminary human disease study, the iCAP detected early stage Alzheimer's disease with 72% cross-validated accuracy (p-Value = 3.10E-3). For both assays, iCAP features were enriched for disease-related genes, supporting the assay's relevance for disease research.

Splice variants as novel targets in pancreatic ductal adenocarcinoma

Despite a wealth of genomic information, a comprehensive alternative splicing (AS) analysis of pancreatic ductal adenocarcinoma (PDAC) has not been performed yet. In the present study, we assessed whole exome-based transcriptome and AS profiles of 43 pancreas tissues using Affymetrix exon array. The AS analysis of PDAC indicated on average two AS probe-sets (ranging from 1-28) in 1,354 significantly identified protein-coding genes, with skipped exon and alternative first exon being the most frequently utilised. In addition to overrepresented extracellular matrix (ECM)-receptor interaction and focal adhesion that were also seen in transcriptome differential expression (DE) analysis, Fc gamma receptor-mediated phagocytosis and axon guidance AS genes were also highly represented. Of note, the highest numbers of AS probe-sets were found in collagen genes, which encode the characteristically abundant stroma seen in PDAC. We also describe a set of 37 'hypersensitive' genes which were frequently targeted by somatic mutations, copy number alterations, DE and AS, indicating their propensity for multidimensional regulation. We provide the most comprehensive overview of the AS landscape in PDAC with underlying changes in the spliceosomal machinery. We also collate a set of AS and DE genes encoding cell surface proteins, which present promising diagnostic and therapeutic targets in PDAC.

A random effects model for the identification of differential splicing (REIDS) using exon and HTA arrays

Background

Alternative gene splicing is a common phenomenon in which a single gene gives rise to multiple transcript isoforms. The process is strictly guided and involves a multitude of proteins and regulatory complexes. Unfortunately, aberrant splicing events do occur which have been linked to genetic disorders, such as several types of cancer and neurodegenerative diseases (Fan et al., Theor Biol Med Model 3:19, 2006). Therefore, understanding the mechanism of alternative splicing and identifying the difference in splicing events between diseased and healthy tissue is crucial in biomedical research with the potential of applications in personalized medicine as well as in drug development.

Results

We propose a linear mixed model, Random Effects for the Identification of Differential Splicing (REIDS), for the identification of alternative splicing events. Based on a set of scores, an exon score and an array score, a decision regarding alternative splicing can be made. The model enables the ability to distinguish a differential expressed gene from a differential spliced exon. The proposed model was applied to three case studies concerning both exon and HTA arrays.

Conclusion

The REIDS model provides a work flow for the identification of alternative splicing events relying on the established linear mixed model. The model can be applied to different types of arrays.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1687-8) contains supplementary material, which is available to authorized users.

Splicing imbalances in basal-like breast cancer underpin perturbation of cell surface and oncogenic pathways and are associated with patients' survival

Despite advancements in the use of transcriptional information to understand and classify breast cancers, the contribution of splicing to the establishment and progression of these tumours has only recently starting to emerge. Our work explores this lesser known landscape, with special focus on the basal-like breast cancer subtype where limited therapeutic opportunities and no prognostic biomarkers are currently available. Using ExonArray analysis of 176 breast cancers and 9 normal breast tissues we demonstrate that splicing levels significantly contribute to the diversity of breast cancer molecular subtypes and explain much of the differences compared with normal tissues. We identified pathways specifically affected by splicing imbalances whose perturbation would be hidden from a conventional gene-centric analysis of gene expression. We found that a large fraction of them involve cell-to-cell communication, extracellular matrix and transport, as well as oncogenic and immune-related pathways transduced by plasma membrane receptors. We identified 247 genes in which splicing imbalances are associated with clinical patients’ outcome, whilst no association was detectable at the gene expression level. These include the signaling gene TGFBR1, the proto-oncogene MYB as well as many immune-related genes such as CCR7 and FCRL3, reinforcing evidence for a role of immune components in influencing breast cancer patients’ prognosis.

Ubiquitination of hnRNPA1 by TRAF6 links chronic innate immune signaling with myelodysplasia

Toll-like receptor (TLR) activation contributes to premalignant hematologic conditions, such as myelodysplastic syndromes (MDS). TRAF6, a TLR-effector with ubiquitin (Ub) ligase activity, is overexpressed in MDS hematopoietic stem/progenitor cells (HSPC). Here we show that TRAF6 overexpression in mouse HSPC resulted in impaired hematopoiesis and bone marrow failure. Through the use of a global Ub screen, we identified hnRNPA1, an RNA-binding protein and auxiliary splicing factor, as a substrate of TRAF6. TRAF6 ubiquitination of hnRNPA1 regulated alternative splicing of Arhgap1, which resulted in Cdc42 activation and accounted for hematopoietic defects in TRAF6-expressing HSPC. These results implicate Ub signaling in coordinating RNA processing by TLR pathways during an immune response and in premalignant hematologic diseases, such as MDS.

Characterization of visceral and subcutaneous adipose tissue transcriptome in pregnant women with and without spontaneous labor at term: implication of alternative splicing in the metabolic adaptations of adipose tissue to parturition

OBJECTIVE

The aim of this study was to determine gene expression and splicing changes associated with parturition and regions (visceral vs. subcutaneous) of the adipose tissue of pregnant women.

STUDY DESIGN

The transcriptome of visceral and abdominal subcutaneous adipose tissue from pregnant women at term with (n=15) and without (n=25) spontaneous labor was profiled with the Affymetrix GeneChip Human Exon 1.0 ST array. Overall gene expression changes and the differential exon usage rate were compared between patient groups (unpaired analyses) and adipose tissue regions (paired analyses). Selected genes were tested by quantitative reverse transcription-polymerase chain reaction.

RESULTS

Four hundred and eighty-two genes were differentially expressed between visceral and subcutaneous fat of pregnant women with spontaneous labor at term (q-value <0.1; fold change >1.5). Biological processes enriched in this comparison included tissue and vasculature development as well as inflammatory and metabolic pathways. Differential splicing was found for 42 genes [q-value <0.1; differences in Finding Isoforms using Robust Multichip Analysis scores >2] between adipose tissue regions of women not in labor. Differential exon usage associated with parturition was found for three genes (LIMS1, HSPA5, and GSTK1) in subcutaneous tissues.

CONCLUSION

We show for the first time evidence of implication of mRNA splicing and processing machinery in the subcutaneous adipose tissue of women in labor compared to those without labor.

EventPointer: an effective identification of alternative splicing events using junction arrays

Background

Alternative splicing (AS) is a major source of variability in the transcriptome of eukaryotes. There is an increasing interest in its role in different pathologies. Before sequencing technology appeared, AS was measured with specific arrays. However, these arrays did not perform well in the detection of AS events and provided very large false discovery rates (FDR). Recently the Human Transcriptome Array 2.0 (HTA 2.0) has been deployed. It includes junction probes. However, the interpretation software provided by its vendor (TAC 3.0) does not fully exploit its potential (does not study jointly the exons and junctions involved in a splicing event) and can only be applied to case–control studies. New statistical algorithms and software must be developed in order to exploit the HTA 2.0 array for event detection.

Results

We have developed EventPointer, an R package (built under the aroma.affymetrix framework) to search and analyze Alternative Splicing events using HTA 2.0 arrays. This software uses a linear model that broadens its application from plain case–control studies to complex experimental designs. Given the CEL files and the design and contrast matrices, the software retrieves a list of all the detected events indicating: 1) the type of event (exon cassette, alternative 3′, etc.), 2) its fold change and its statistical significance, and 3) the potential protein domains affected by the AS events and the statistical significance of the possible enrichment.

Our tests have shown that EventPointer has an extremely low FDR value (only 1 false positive within the tested top-200 events). This software is publicly available and it has been uploaded to GitHub.

Conclusions

This software empowers the HTA 2.0 arrays for AS event detection as an alternative to RNA-seq: simplifying considerably the required analysis, speeding it up and reducing the required computational power.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-2816-x) contains supplementary material, which is available to authorized users.

A genome wide analysis of alternative splicing events during the osteogenic differentiation of human cartilage endplate-derived stem cells

Low back pain is a prevalent disease, which leads to suffering and disabilities in a vast number of individuals. Degenerative disc diseases are usually the underlying causes of low back pain. However, the pathogenesis of degenerative disc diseases is highly complex and difficult to determine. Current therapies for degenerative disc diseases are various. In particular, cell-based therapies have proven to be effective and promising. Our research group has previously isolated and identified the cartilage endplate‑derived stem cells. In addition, alternative splicing is a sophisticated regulatory mechanism, which greatly increases cellular complexity and phenotypic diversity of eukaryotic organisms. The present study continued to investigate alternative splicing events in osteogenic differentiation of cartilage endplate‑derived stem cells. An Affymetrix Human Transcriptome Array 2.0 was used to detect splicing changes between the control and differentiated samples. Additionally, molecular function and pathway analysis were also performed. Following rigorous bioinformatics analysis of the data, 3,802 alternatively spliced genes were identified, and 10 of these were selected for validation by reverse transcription‑polymerase chain reaction. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway analysis also revealed numerous enriched GO terms and signaling pathways. To the best of our knowledge, the present study is the first to investigate alternative splicing mechanisms in osteogenic differentiation of stem cells on a genome‑wide scale. The illumination of molecular mechanisms of stem cell osteogenic differentiation may assist the development novel bioengineered methods to treat degenerative disc diseases.

iGEMS: an integrated model for identification of alternative exon usage events

DNA microarrays and RNAseq are complementary methods for studying RNA molecules. Current computational methods to determine alternative exon usage (AEU) using such data require impractical visual inspection and still yield high false-positive rates. Integrated Gene and Exon Model of Splicing (iGEMS) adapts a gene-level residuals model with a gene size adjusted false discovery rate and exon-level analysis to circumvent these limitations. iGEMS was applied to two new DNA microarray datasets, including the high coverage Human Transcriptome Arrays 2.0 and performance was validated using RT-qPCR. First, AEU was studied in adipocytes treated with (n = 9) or without (n = 8) the anti-diabetes drug, rosiglitazone. iGEMS identified 555 genes with AEU, and robust verification by RT-qPCR (∼90%). Second, in a three-way human tissue comparison (muscle, adipose and blood, n = 41) iGEMS identified 4421 genes with at least one AEU event, with excellent RT-qPCR verification (95%, n = 22). Importantly, iGEMS identified a variety of AEU events, including 3′UTR extension, as well as exon inclusion/exclusion impacting on protein kinase and extracellular matrix domains. In conclusion, iGEMS is a robust method for identification of AEU while the variety of exon usage between human tissues is 5–10 times more prevalent than reported by the Genotype-Tissue Expression consortium using RNA sequencing.

Human phosphodiesterase 4D7 (PDE4D7) expression is increased in TMPRSS2-ERG-positive primary prostate cancer and independently adds to a reduced risk of post-surgical disease progression

Background:

There is an acute need to uncover biomarkers that reflect the molecular pathologies, underpinning prostate cancer progression and poor patient outcome. We have previously demonstrated that in prostate cancer cell lines PDE4D7 is downregulated in advanced cases of the disease. To investigate further the prognostic power of PDE4D7 expression during prostate cancer progression and assess how downregulation of this PDE isoform may affect disease outcome, we have examined PDE4D7 expression in physiologically relevant primary human samples.

Methods:

About 1405 patient samples across 8 publically available qPCR, Affymetrix Exon 1.0 ST arrays and RNA sequencing data sets were screened for PDE4D7 expression. The TMPRSS2-ERG gene rearrangement status of patient samples was determined by transformation of the exon array and RNA seq expression data to robust z-scores followed by the application of a threshold >3 to define a positive TMPRSS2-ERG gene fusion event in a tumour sample.

Results:

We demonstrate that PDE4D7 expression positively correlates with primary tumour development. We also show a positive association with the highly prostate cancer-specific gene rearrangement between TMPRSS2 and the ETS transcription factor family member ERG. In addition, we find that in primary TMPRSS2-ERG-positive tumours PDE4D7 expression is significantly positively correlated with low-grade disease and a reduced likelihood of progression after primary treatment. Conversely, PDE4D7 transcript levels become significantly decreased in castration resistant prostate cancer (CRPC).

Conclusions:

We further characterise and add physiological relevance to PDE4D7 as a novel marker that is associated with the development and progression of prostate tumours. We propose that the assessment of PDE4D7 levels may provide a novel, independent predictor of post-surgical disease progression.

Novel long non-coding RNAs are specific diagnostic and prognostic markers for prostate cancer

Current prostate cancer (PCa) biomarkers such as PSA are not optimal in distinguishing cancer from benign prostate diseases and predicting disease outcome. To discover additional biomarkers, we investigated PCa-specific expression of novel unannotated transcripts. Using the unique probe design of Affymetrix Human Exon Arrays, we identified 334 candidates (EPCATs), of which 15 were validated by RT-PCR. Combined into a diagnostic panel, 11 EPCATs classified 80% of PCa samples correctly, while maintaining 100% specificity. High specificity was confirmed by in situ hybridization for EPCAT4R966 and EPCAT2F176 (SChLAP1) on extensive tissue microarrays. Besides being diagnostic, EPCAT2F176 and EPCAT4R966 showed significant association with pT-stage and were present in PIN lesions. We also found EPCAT2F176 and EPCAT2R709 to be associated with development of metastases and PCa-related death, and EPCAT2F176 to be enriched in lymph node metastases. Functional significance of expression of 9 EPCATs was investigated by siRNA transfection, revealing that knockdown of 5 different EPCATs impaired growth of LNCaP and 22RV1 PCa cells. Only the minority of EPCATs appear to be controlled by androgen receptor or ERG. Although the underlying transcriptional regulation is not fully understood, the novel PCa-associated transcripts are new diagnostic and prognostic markers with functional relevance to prostate cancer growth.

Transcriptome-wide targets of alternative splicing by RBM4 and possible role in cancer

This study determined transcriptome-wide targets of the splicing factor RBM4 using Affymetrix GeneChip(®) Human Exon 1.0 ST Arrays and HeLa cells treated with RBM4-specific siRNA. This revealed 238 transcripts that were targeted for alternative splicing. Cross-linking and immunoprecipitation experiments identified 945 RBM4 targets in mouse HEK293 cells, 39% of which were ascribed to "alternative splicing" by in silico pathway analysis. Mouse embryonic stem cells transfected with Rbm4 siRNA hairpins exhibited reduced colony numbers and size consistent with involvement of RBM4 in cell proliferation. RBM4 cDNA probing of a cancer cDNA array involving 18 different tumor types from 13 different tissues and matching normal tissue found overexpression of RBM4 mRNA (p<0.01) in cervical, breast, lung, colon, ovarian and rectal cancers. Many RBM4 targets we identified have been implicated in these cancers. In conclusion, our findings reveal transcriptome-wide targets of RBM4 and point to potential cancer-related targets and mechanisms that may involve RBM4.

Radiation-induced alternative transcription and splicing events and their applicability to practical biodosimetry

Accurate assessment of the individual exposure dose based on easily accessible samples (e.g. blood) immediately following a radiological accident is crucial. We aimed at developing a robust transcription-based signature for biodosimetry from human peripheral blood mononuclear cells irradiated with different doses of X-rays (0.1 and 1.0 Gy) at a dose rate of 0.26 Gy/min. Genome-wide radiation-induced changes in mRNA expression were evaluated at both gene and exon level. Using exon-specific qRT-PCR, we confirmed that several biomarker genes are alternatively spliced or transcribed after irradiation and that different exons of these genes exhibit significantly different levels of induction. Moreover, a significant number of radiation-responsive genes were found to be genomic neighbors. Using three different classification models we found that gene and exon signatures performed equally well on dose prediction, as long as more than 10 features are included. Together, our results highlight the necessity of evaluating gene expression at the level of single exons for radiation biodosimetry in particular and transcriptional biomarker research in general. This approach is especially advisable for practical gene expression-based biodosimetry, for which primer- or probe-based techniques would be the method of choice.

Characterization of Visceral and Subcutaneous Adipose Tissue Transcriptome and Biological Pathways in Pregnant and Non-Pregnant Women: Evidence for Pregnancy-Related Regional-Specific Differences in Adipose Tissue

Objective

The purpose of this study was to compare the transcriptome of visceral and subcutaneous adipose tissues between pregnant and non-pregnant women.

Study Design

The transcriptome of paired visceral and abdominal subcutaneous adipose tissues from pregnant women at term and matched non-pregnant women (n = 11) was profiled with the Affymetrix Human Exon 1.0 ST array. Differential expression of selected genes was validated with the use of quantitative reverse transcription–polymerase chain reaction.

Results

Six hundred forty-four transcripts from 633 known genes were differentially expressed (false discovery rate (FDR) <0.1; fold-change >1.5), while 42 exons from 36 genes showed differential usage (difference in FIRMA scores >2 and FDR<0.1) between the visceral and subcutaneous fat of pregnant women. Fifty-six known genes were differentially expressed between pregnant and non-pregnant subcutaneous fat and three genes in the visceral fat. Enriched biological processes in the subcutaneous adipose tissue of pregnant women were mostly related to inflammation.

Conclusion

The transcriptome of visceral and subcutaneous fat depots reveals pregnancy-related gene expression and splicing differences in both visceral and subcutaneous adipose tissue. Furthermore, for the first time, alternative splicing in adipose tissue has been associated with regional differences and human parturition.

Global Gene Expression Profiling and Alternative Splicing Events during the Chondrogenic Differentiation of Human Cartilage Endplate-Derived Stem Cells

Low back pain (LBP) is a very prevalent disease and degenerative disc diseases (DDDs) usually account for the LBP. However, the pathogenesis of DDDs is complicated and difficult to elucidate. Alternative splicing is a sophisticated regulatory process which greatly increases cellular complexity and phenotypic diversity of eukaryotic organisms. In addition, the cartilage endplate-derived stem cells have been discovered and identified by our research group. In this paper, we continue to investigate gene expression profiling and alternative splicing events during chondrogenic differentiation of cartilage endplate-derived stem cells. We adopted Affymetrix Human Transcriptome Array 2.0 (HTA 2.0) to compare the transcriptional and splicing changes between the control and differentiated samples. RT-PCR and quantitative PCR are used to validate the microarray results. The GO and KEGG pathway analysis was also performed. After bioinformatics analysis of the data, we detected 1953 differentially expressed genes. In terms of alternative splicing, the Splicing Index algorithm was used to select alternatively spliced genes. We detected 4411 alternatively spliced genes. GO and KEGG pathway analysis also revealed several functionally involved biological processes and signaling pathways. To our knowledge, this is the first study to investigate the alternative splicing mechanisms in chondrogenic differentiation of stem cells on a genome-wide scale.

IsoDOT Detects Differential RNA-isoform Expression/Usage with respect to a Categorical or Continuous Covariate with High Sensitivity and Specificity

We have developed a statistical method named IsoDOT to assess differential isoform expression (DIE) and differential isoform usage (DIU) using RNA-seq data. Here isoform usage refers to relative isoform expression given the total expression of the corresponding gene. IsoDOT performs two tasks that cannot be accomplished by existing methods: to test DIE/DIU with respect to a continuous covariate, and to test DIE/DIU for one case versus one control. The latter task is not an uncommon situation in practice, e.g., comparing the paternal and maternal alleles of one individual or comparing tumor and normal samples of one cancer patient. Simulation studies demonstrate the high sensitivity and specificity of IsoDOT. We apply IsoDOT to study the effects of haloperidol treatment on the mouse transcriptome and identify a group of genes whose isoform usages respond to haloperidol treatment.

High-throughput alternative splicing detection using dually constrained correspondence analysis (DCCA)

Alternative splicing is an important component of tumorigenesis. Recent advent of exon array technology enables the detection of alternative splicing at a genome-wide scale. The analysis of high-throughput alternative splicing is not yet standard and methodological developments are still needed. We propose a novel statistical approach-Dually Constrained Correspondence Analysis-for the detection of splicing changes in exon array data. Using this methodology, we investigated the genome-wide alteration of alternative splicing in patients with non-small cell lung cancer treated by bevacizumab/erlotinib. Splicing candidates reveal a series of genes related to carcinogenesis (SFTPB), cell adhesion (STAB2, PCDH15, HABP2), tumor aggressiveness (ARNTL2), apoptosis, proliferation and differentiation (PDE4D, FLT3, IL1R2), cell invasion (ETV1), as well as tumor growth (OLFM4, FGF14), tumor necrosis (AFF3) or tumor suppression (TUSC3, CSMD1, RHOBTB2, SERPINB5), with indication of known alternative splicing in a majority of genes. DCCA facilitates the identification of putative biologically relevant alternative splicing events in high-throughput exon array data.

TIN: An R Package for Transcriptome Instability Analysis

Alternative splicing is a key regulatory mechanism for gene expression, vital for the proper functioning of eukaryotic cells. Disruption of normal pre-mRNA splicing has the potential to cause and reinforce human disease. Owing to rapid advances in high-throughput technologies, it is now possible to identify novel mRNA isoforms and detect aberrant splicing patterns on a genome scale, across large data sets. Analogous to the genomic types of instability describing cancer genomes (eg, chromosomal instability and microsatellite instability), transcriptome instability (TIN) has recently been proposed as a splicing-related genome-wide characteristic of certain solid cancers. We present the R package TIN, available from Bioconductor, which implements a set of methods for TIN analysis based on exon-level microarray expression profiles. TIN provides tools for estimating aberrant exon usage across samples and for analyzing correlation patterns between TIN and splicing factor expression levels.

Analyzing alternative splicing data of splice junction arrays from Parkinson patients' leukocytes before and after deep brain stimulation as compared with control donors

Few studies so far examined alternative splicing alterations in blood cells of neurodegenerative disease patients, particularly Parkinson's disease (PD). Prototype junction microarrays interrogate known human genome junctions and enable characterization of alternative splicing events; however, the analysis is not straightforward and different methods can be used to estimate junction-specific alternative splicing events (some of which can also be applied for analyzing RNA sequencing junction-level data). In this study, we characterized alternative splicing changes in blood leukocyte samples from Parkinson's patients prior to, and following deep brain stimulation (DBS) treatment; both on stimulation and following 1 h off electrical stimulation. Here, we describe in detail analysis approaches for junction microarrays and provide suggestions for further analyses to delineate transcript level effects of the observed alterations as well as detection of microRNA binding sites and protein domains in the alternatively spliced target regions spanning across both untranslated and the coding regions of the targets. The raw expression data files are publically available in the Gene Expression Omnibus (GEO) database (accession number: GSE37591) and in Synapse, and can be re-analyzed. The results may be useful for designing of future experiments and cross correlations with other datasets from PD or patients having other neurodegenerative diseases.

Tissue-specific isoform switch and DNA hypomethylation of the pyruvate kinase PKM gene in human cancers

The M2 isoform of pyruvate kinase (PKM2) plays an important role in aerobic glycolysis and is a mediator of the Warburg effect in tumors. It was previously thought that tumor cells switch expression of PKM from normal tissue-expressed PKM1 to tumor-specific PKM2 via an alternative splicing mechanism. This view was challenged by a recent report demonstrating that PKM2 is already the major PKM isoform expressed in many differentiated normal tissues. Here, through analyses on sixteen tumor types using the cancer genome atlas RNA-Seq and exon array datasets, we confirmed that isoform switch from PKM1 to PKM2 occurred in glioblastomas but not in other tumor types examined. Despite lacking of isoform switches, PKM2 expression was found to be increased in all cancer types examined, and correlated strongly to poor prognosis in head and neck cancers. We further demonstrated that elevated PKM2 expression correlated well with the hypomethylation status of intron 1 of the PKM gene in multiple cancer types, suggesting epigenetic regulation by DNA methylation as a major mechanism in controlling PKM transcription in tumors. Our study suggests that isoform switch of PKM1 to PKM2 in cancers is tissue-specific and targeting PKM2 activity in tumors remains a promising approach for clinical intervention of multiple cancer types.

C9ORF72 GGGGCC Expanded Repeats Produce Splicing Dysregulation which Correlates with Disease Severity in Amyotrophic Lateral Sclerosis

OBJECTIVE

An intronic GGGGCC-repeat expansion of C9ORF72 is the most common genetic variant of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia. The mechanism of neurodegeneration is unknown, but a direct effect on RNA processing mediated by RNA foci transcribed from the repeat sequence has been proposed.

METHODS

Gene expression profiling utilised total RNA extracted from motor neurons and lymphoblastoid cell lines derived from human ALS patients, including those with an expansion of C9ORF72, and controls. In lymphoblastoid cell lines, expansion length and the frequency of sense and antisense RNA foci was also examined.

RESULTS

Gene level analysis revealed a number of differentially expressed networks and both cell types exhibited dysregulation of a network functionally enriched for genes encoding 'RNA splicing' proteins. There was a significant overlap of these genes with an independently generated list of GGGGCC-repeat protein binding partners. At the exon level, in lymphoblastoid cells derived from C9ORF72-ALS patients splicing consistency was lower than in lines derived from non-C9ORF72 ALS patients or controls; furthermore splicing consistency was lower in samples derived from patients with faster disease progression. Frequency of sense RNA foci showed a trend towards being higher in lymphoblastoid cells derived from patients with shorter survival, but there was no detectable correlation between disease severity and DNA expansion length.

SIGNIFICANCE

Up-regulation of genes encoding predicted binding partners of the C9ORF72 expansion is consistent with an attempted compensation for sequestration of these proteins. A number of studies have analysed changes in the transcriptome caused by C9ORF72 expansion, but to date findings have been inconsistent. As a potential explanation we suggest that dynamic sequestration of RNA processing proteins by RNA foci might lead to a loss of splicing consistency; indeed in our samples measurement of splicing consistency correlates with disease severity.

Algorithms for differential splicing detection using exon arrays: a comparative assessment

Background

The analysis of differential splicing (DS) is crucial for understanding physiological processes in cells and organs. In particular, aberrant transcripts are known to be involved in various diseases including cancer. A widely used technique for studying DS are exon arrays. Over the last decade a variety of algorithms for the detection of DS events from exon arrays has been developed. However, no comprehensive, comparative evaluation including sensitivity to the most important data features has been conducted so far. To this end, we created multiple data sets based on simulated data to assess strengths and weaknesses of seven published methods as well as a newly developed method, KLAS. Additionally, we evaluated all methods on two cancer data sets that comprised RT-PCR validated results.

Results

Our studies indicated ARH as the most robust methods when integrating the results over all scenarios and data sets. Nevertheless, special cases or requirements favor other methods. While FIRMA was highly sensitive according to experimental data, SplicingCompass, MIDAS and ANOSVA showed high specificity throughout the scenarios. On experimental data ARH, FIRMA, MIDAS, and KLAS performed best.

Conclusions

Each method shows different characteristics regarding sensitivity, specificity, interference to certain data settings and robustness over multiple data sets. While some methods can be considered as generally good choices over all data sets and scenarios, other methods show heterogeneous prediction quality on the different data sets. The adequate method has to be chosen carefully and with a defined study aim in mind.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1322-x) contains supplementary material, which is available to authorized users.

Computational extraction of a neural molecular network through alternative splicing

BACKGROUND

Generally, the results of high throughput analyses contain information about gene expressions, and about exon expressions. Approximately 90% of primary protein-coding transcripts undergo alternative splicing in mammals. However, changes induced by alternative exons have not been properly analyzed for their impact on important molecular networks or their biological events. Even when alternative exons are identified, they are usually subjected to bioinformatics analysis in the same way as the gene ignoring the possibility of functionality change because of the alteration of domain caused by alternative exon. Here, we reveal an effective computational approach to explore an important molecular network based on potential changes of functionality induced by alternative exons obtained from our comprehensive analysis of neuronal cell differentiation.

RESULTS

From our previously identified 262 differentially alternatively spliced exons during neuronal cell differentiations, we extracted 241 sets that changed the amino acid sequences between the alternatively spliced sequences. Conserved domain searches indicated that annotated domain(s) were changed in 128 sets. We obtained 49 genes whose terms overlapped between domain description and gene annotation. Thus, these 49 genes have alternatively differentially spliced in exons that affect their main functions. We performed pathway analysis using these 49 genes and identified the EGFR (epidermal growth factor receptor) and mTOR (mammalian target of rapamycin) signaling pathway as being involved frequently. Recent studies reported that the mTOR pathway is associated with neuronal cell differentiation, vindicating that our approach extracted an important molecular network successfully.

CONCLUSIONS

Effective informatics approaches for exons should be more complex than those for genes, because changes in alternative exons affect protein functions via alterations of amino acid sequences and functional domains. Our method extracted alterations of functional domains and identified key alternative splicing events. We identified the EGFR and mTOR signaling pathway as the most affected pathway. The mTOR pathway is important for neuronal differentiation, suggesting that this in silico extraction of alternative splicing networks is useful. This preliminary analysis indicated that automated analysis of the effects of alternative splicing would provide a rich source of biologically relevant information.

P, Anderson KC, Li C, Avet-Loiseau H, Munshi NC. Gene expression profile alone is inadequate in predicting complete response in multiple myeloma

With advent of several treatment options in multiple myeloma (MM), a selection of effective regimen has become an important issue. Use of gene expression profile (GEP) is considered an important tool in predicting outcome; however, it is unclear whether such genomic analysis alone can adequately predict therapeutic response. We evaluated the ability of GEP to predict complete response (CR) in MM. GEP from pretreatment MM cells from 136 uniformly treated MM patients with response data on an IFM, France led study were analyzed. To evaluate variability in predictive power due to microarray platform or treatment types, additional data sets from three different studies (n=511) were analyzed using same methods. We used several machine learning methods to derive a prediction model using training and test subsets of the original four data sets. Among all methods employed for GEP-based CR predictive capability, we got accuracy range of 56-78% in test data sets and no significant difference with regard to GEP platforms, treatment regimens or in newly diagnosed or relapsed patients. Importantly, permuted P-value showed no statistically significant CR predictive information in GEP data. This analysis suggests that GEP-based signature has limited power to predict CR in MM, highlighting the need to develop comprehensive predictive model using integrated genomics approach.

Incorporating alternative splicing and mRNA editing into the genetic analysis of complex traits

The nomination of candidate genes underlying complex traits is often focused on genetic variations that alter mRNA abundance or result in non-conservative changes in amino acids. Although inconspicuous in complex trait analysis, genetic variants that affect splicing or RNA editing can also generate proteomic diversity and impact genetic traits. Indeed, it is known that splicing and RNA editing modulate several traits in humans and model organisms. Using high-throughput RNA sequencing (RNA-seq) analysis, it is now possible to integrate the genetics of transcript abundance, alternative splicing (AS) and editing with the analysis of complex traits. We recently demonstrated that both AS and mRNA editing are modulated by genetic and environmental factors, and potentially engender phenotypic diversity in a genetically segregating mouse population. Therefore, the analysis of splicing and RNA editing can expand not only the regulatory landscape of transcriptome and proteome complexity, but also the repertoire of candidate genes for complex traits.

A robust estimation of exon expression to identify alternative spliced genes applied to human tissues and cancer samples

BACKGROUND

Accurate analysis of whole-gene expression and individual-exon expression is essential to characterize different transcript isoforms and identify alternative splicing events in human genes. One of the omic technologies widely used in many studies on human samples are the exon-specific expression microarray platforms.

RESULTS

Since there are not many validated comparative analyses to identify specific splicing events using data derived from these types of platforms, we have developed an algorithm (called ESLiM) to detect significant changes in exon use, and applied it to a reference dataset of 270 human genes that show alternative expression in different tissues. We compared the results with three other methodological approaches and provided the R source code to be applied elsewhere. The genes positively detected by these analyses also provide a verified subset of human genes that present tissue-regulated isoforms. Furthermore, we performed a validation analysis on human patient samples comparing two different subtypes of acute myeloid leukemia (AML) and we experimentally validated the splicing in several selected genes that showed exons with highly significant signal change.

CONCLUSIONS

The comparative analyses with other methods using a fair set of human genes that show alternative splicing and the validation on clinical samples demonstrate that the proposed novel algorithm is a reliable tool for detecting differential splicing in exon-level expression data.

The functional landscape of Hsp27 reveals new cellular processes such as DNA repair and alternative splicing and proposes novel anticancer targets

Previously, we identified the stress-induced chaperone, Hsp27, as highly overexpressed in castration-resistant prostate cancer and developed an Hsp27 inhibitor (OGX-427) currently tested in phase I/II clinical trials as a chemosensitizing agent in different cancers. To better understand the Hsp27 poorly-defined cytoprotective functions in cancers and increase the OGX-427 pharmacological safety, we established the Hsp27-protein interaction network using a yeast two-hybrid approach and identified 226 interaction partners. As an example, we showed that targeting Hsp27 interaction with TCTP, a partner protein identified in our screen increases therapy sensitivity, opening a new promising field of research for therapeutic approaches that could decrease or abolish toxicity for normal cells. Results of an in-depth bioinformatics network analysis allying the Hsp27 interaction map into the human interactome underlined the multifunctional character of this protein. We identified interactions of Hsp27 with proteins involved in eight well known functions previously related to Hsp27 and uncovered 17 potential new ones, such as DNA repair and RNA splicing. Validation of Hsp27 involvement in both processes in human prostate cancer cells supports our system biology-predicted functions and provides new insights into Hsp27 roles in cancer cells.

The ErbB4 CYT2 variant protects EGFR from ligand-induced degradation to enhance cancer cell motility

The epidermal growth factor receptor (EGFR) is a member of the ErbB family that can promote the migration and proliferation of breast cancer cells. Therapies that target EGFR can promote the dimerization of EGFR with other ErbB receptors, which is associated with the development of drug resistance. Understanding how interactions among ErbB receptors alter EGFR biology could provide avenues for improving cancer therapy. We found that EGFR interacted directly with the CYT1 and CYT2 variants of ErbB4 and the membrane-anchored intracellular domain (mICD). The CYT2 variant, but not the CYT1 variant, protected EGFR from ligand-induced degradation by competing with EGFR for binding to a complex containing the E3 ubiquitin ligase c-Cbl and the adaptor Grb2. Cultured breast cancer cells overexpressing both EGFR and ErbB4 CYT2 mICD exhibited increased migration. With molecular modeling, we identified residues involved in stabilizing the EGFR dimer. Mutation of these residues in the dimer interface destabilized the complex in cells and abrogated growth factor-stimulated cell migration. An exon array analysis of 155 breast tumors revealed that the relative mRNA abundance of the ErbB4 CYT2 variant was increased in ER+ HER2- breast cancer patients, suggesting that our findings could be clinically relevant. We propose a mechanism whereby competition for binding to c-Cbl in an ErbB signaling heterodimer promotes migration in response to a growth factor gradient.

Transcriptome instability as a molecular pan-cancer characteristic of carcinomas

BACKGROUND

We have previously proposed transcriptome instability as a genome-wide, pre-mRNA splicing-related characteristic of colorectal cancer. Here, we explore the hypothesis of transcriptome instability being a general characteristic of cancer.

RESULTS

Exon-level microarray expression data from ten cancer datasets were analyzed, including breast cancer, cervical cancer, colorectal cancer, gastric cancer, lung cancer, neuroblastoma, and prostate cancer (555 samples), as well as paired normal tissue samples from the colon, lung, prostate, and stomach (93 samples). Based on alternative splicing scores across the genomes, we calculated sample-wise relative amounts of aberrant exon skipping and inclusion. Strong and non-random (P < 0.001) correlations between these estimates and the expression levels of splicing factor genes (n = 280) were found in most cancer types analyzed (breast-, cervical-, colorectal-, lung- and prostate cancer). This suggests a biological explanation for the splicing variation. Surprisingly, these associations prevailed in pan-cancer analyses. This is in contrast to the tissue and cancer specific patterns observed in comparisons across healthy tissue samples from the colon, lung, prostate, and stomach, and between paired cancer-normal samples from the same four tissue types.

CONCLUSION

Based on exon-level expression profiling and computational analyses of alternative splicing, we propose transcriptome instability as a molecular pan-cancer characteristic. The affected cancers show strong and non-random associations between low expression levels of splicing factor genes, and high amounts of aberrant exon skipping and inclusion, and vice versa, on a genome-wide scale.

Reproducible probe-level analysis of the Affymetrix Exon 1.0 ST array with R/Bioconductor

The presence of different transcripts of a gene across samples can be analysed by whole-transcriptome microarrays. Reproducing results from published microarray data represents a challenge owing to the vast amounts of data and the large variety of preprocessing and filtering steps used before the actual analysis is carried out. To guarantee a firm basis for methodological development where results with new methods are compared with previous results, it is crucial to ensure that all analyses are completely reproducible for other researchers. We here give a detailed workflow on how to perform reproducible analysis of the GeneChip®Human Exon 1.0 ST Array at probe and probeset level solely in R/Bioconductor, choosing packages based on their simplicity of use. To exemplify the use of the proposed workflow, we analyse differential splicing and differential gene expression in a publicly available dataset using various statistical methods. We believe this study will provide other researchers with an easy way of accessing gene expression data at different annotation levels and with the sufficient details needed for developing their own tools for reproducible analysis of the GeneChip®Human Exon 1.0 ST Array.

Long non-coding RNA and alternative splicing modulations in Parkinson's leukocytes identified by RNA sequencing

The continuously prolonged human lifespan is accompanied by increase in neurodegenerative diseases incidence, calling for the development of inexpensive blood-based diagnostics. Analyzing blood cell transcripts by RNA-Seq is a robust means to identify novel biomarkers that rapidly becomes a commonplace. However, there is lack of tools to discover novel exons, junctions and splicing events and to precisely and sensitively assess differential splicing through RNA-Seq data analysis and across RNA-Seq platforms. Here, we present a new and comprehensive computational workflow for whole-transcriptome RNA-Seq analysis, using an updated version of the software AltAnalyze, to identify both known and novel high-confidence alternative splicing events, and to integrate them with both protein-domains and microRNA binding annotations. We applied the novel workflow on RNA-Seq data from Parkinson's disease (PD) patients' leukocytes pre- and post- Deep Brain Stimulation (DBS) treatment and compared to healthy controls. Disease-mediated changes included decreased usage of alternative promoters and N-termini, 5′-end variations and mutually-exclusive exons. The PD regulated FUS and HNRNP A/B included prion-like domains regulated regions. We also present here a workflow to identify and analyze long non-coding RNAs (lncRNAs) via RNA-Seq data. We identified reduced lncRNA expression and selective PD-induced changes in 13 of over 6,000 detected leukocyte lncRNAs, four of which were inversely altered post-DBS. These included the U1 spliceosomal lncRNA and RP11-462G22.1, each entailing sequence complementarity to numerous microRNAs. Analysis of RNA-Seq from PD and unaffected controls brains revealed over 7,000 brain-expressed lncRNAs, of which 3,495 were co-expressed in the leukocytes including U1, which showed both leukocyte and brain increases. Furthermore, qRT-PCR validations confirmed these co-increases in PD leukocytes and two brain regions, the amygdala and substantia-nigra, compared to controls. This novel workflow allows deep multi-level inspection of RNA-Seq datasets and provides a comprehensive new resource for understanding disease transcriptome modifications in PD and other neurodegenerative diseases.

Long non-coding RNAs (lncRNAs) comprise a novel, fascinating class of RNAs with largely unknown biological functions. Parkinson's-disease (PD) is the most frequent motor disorder, and Deep-brain-stimulation (DBS) treatment alleviates the symptoms, but early disease biomarkers are still unknown and new future genetic interference targets are urgently needed. Using RNA-sequencing technology and a novel computational workflow for in-depth exploration of whole-transcriptome RNA-seq datasets, we detected and analyzed lncRNAs in sequenced libraries from PD patients' leukocytes pre and post-treatment and the brain, adding this full profile resource of over 7,000 lncRNAs to the few human tissues-derived lncRNA datasets that are currently available. Our study includes sample-specific database construction, detecting disease-derived changes in known and novel lncRNAs, exons and junctions and predicting corresponding changes in Polyadenylation choices, protein domains and miRNA binding sites. We report widespread transcript structure variations at the splice junction and exons levels, including novel exons and junctions and alteration of lncRNAs followed by experimental validation in PD leukocytes and two PD brain regions compared with controls. Our results suggest lncRNAs involvement in neurodegenerative diseases, and specifically PD. This comprehensive workflow will be of use to the increasing number of laboratories producing RNA-Seq data in a wide range of biomedical studies.

An integrative framework identifies alternative splicing events in colorectal cancer development

Alternative splicing (AS) is a common mechanism which creates diverse RNA isoforms from a single gene, potentially increasing protein variety. Growing evidence suggests that this mechanism is closely related to cancer progression. In this study, whole transcriptome analysis was performed with GeneChip Human exon 1.0 ST Array from 80 samples comprising 23 normal colon mucosa, 30 primary colorectal cancer and 27 liver metastatic specimens from 46 patients, to identify AS events in colorectal cancer progression. Differentially expressed genes and exons were estimated and AS events were reconstructed by combining exon-level analyses with AltAnalyze algorithms and transcript-level estimations (MMBGX probabilistic method). The number of AS genes in the transition from normal colon mucosa to primary tumor was the most abundant, but fell considerably in the next transition to liver metastasis. 206 genes with probable AS events in colon cancer development and progression were identified, that are involved in processes and pathways relevant to tumor biology, as cell-cell and cell-matrix interactions. Several AS events in VCL, CALD1, B3GNT6 and CTHRC1 genes, differentially expressed during tumor development were validated, at RNA and at protein level. Taken together, these results demonstrate that cancer-specific AS is common in early phases of colorectal cancer natural history.

The shaping and functional consequences of the dosage effect landscape in multiple myeloma

Background

Multiple myeloma (MM) is a malignant proliferation of plasma B cells. Based on recurrent aneuploidy such as copy number alterations (CNAs), myeloma is divided into two subtypes with different CNA patterns and patient survival outcomes. How aneuploidy events arise, and whether they contribute to cancer cell evolution are actively studied. The large amount of transcriptomic changes resultant of CNAs (dosage effect) pose big challenges for identifying functional consequences of CNAs in myeloma in terms of specific driver genes and pathways. In this study, we hypothesize that gene-wise dosage effect varies as a result from complex regulatory networks that translate the impact of CNAs to gene expression, and studying this variation can provide insights into functional effects of CNAs.

Results

We propose gene-wise dosage effect score and genome-wide karyotype plot as tools to measure and visualize concordant copy number and expression changes across cancer samples. We find that dosage effect in myeloma is widespread yet variable, and it is correlated with gene expression level and CNA frequencies in different chromosomes. Our analysis suggests that despite the enrichment of differentially expressed genes between hyperdiploid MM and non-hyperdiploid MM in the trisomy chromosomes, the chromosomal proportion of dosage sensitive genes is higher in the non-trisomy chromosomes. Dosage-sensitive genes are enriched by genes with protein translation and localization functions, and dosage resistant genes are enriched by apoptosis genes. These results point to future studies on differential dosage sensitivity and resistance of pro- and anti-proliferation pathways and their variation across patients as therapeutic targets and prognosis markers.

Conclusions

Our findings support the hypothesis that recurrent CNAs in myeloma are selected by their functional consequences. The novel dosage effect score defined in this work will facilitate integration of copy number and expression data for identifying driver genes in cancer genomics studies. The accompanying R code is available at http://www.canevolve.org/dosageEffect/.