Isoform level expression profiles provide better cancer signatures than gene level expression profiles

Isoform-Level Transcriptome Analysis of Peripheral Blood Mononuclear Cells from Breast Cancer Patients Identifies a Disease-Associated RASGEF1A Isoform

Background: Breast cancer (BC) comprises multiple subtypes with distinct molecular features, which differ in their interplay with host immunity, prognosis, and treatment. Non-invasive blood analyses can provide valuable insights into systemic immunity during cancer. The aim of this study was to analyze the expression of transcriptional isoforms in peripheral blood mononuclear cells (PBMCs) from BC patients and healthy women to identify potential BC immune biomarkers. Methods: RNA sequencing and isoform-level bioinformatics were performed on PBMCs from 12 triple-negative and 13 luminal A patients. Isoform expression validation by qRT-PCR and clinicopathological correlations were performed in a larger cohort (156 BC patients and 32 healthy women). Results: Transcriptional analyses showed a significant (p < 0.001) decrease in the ENST00000374459 RASGEF1A isoform in PBMCs of BC compared to healthy subjects, indicating disease-related expression changes. The decrease was associated with higher ctDNA and Ki-67 values. Conclusions: The levels of the RASGEF1A transcriptional isoform ENST00000374459 may have the potential to distinguish between BC and healthy subjects. The downregulation of ENST00000374459 in breast cancer is associated with higher proliferation and ctDNA shedding. Specialized bioinformatics analyses such as isoform analyses hold significant promise in the detection of biomarkers, since standard RNA sequencing analyses may overlook specific transcriptional changes that may be disease-associated and biologically important.

Unraveling the Peroxidase Activity in Peroxiredoxins: A Comprehensive Review of Mechanisms, Functions, and Biological Significance

Peroxiredoxins (Prxs) are members of the antioxidant enzymes necessary for every living object in the three domains of life and play critical roles in controlling peroxide levels in cells. This comprehensive literature review aims to elucidate the peroxidase activity of Prxs, examining their roles and significance for organisms across various taxa. Ironically, the primary role of the Prxs is the peroxidase activity, which comprises the reduction of hydrogen peroxide and other organic hydroperoxides and decreases the risk of oxidative damage in the cells. The above enzymatic activity occurs through the reversible oxidation-reduction catalyzed by cysteine residues in the active site by forming sulfenic acid and reduction by intracellular reductants. Structurally and functionally, Prxs function as dimers or decamers and show different catalytic patterns according to their subfamilies or cellular compartments. Compared to the mechanisms of the other two subgroups of Prxs, including 2-Cys Prxs and atypical Prxs, the 1-Cys Prxs have monomer-dimer switch folding coupled with catalytic activity. In addition to their peroxidase activity, which is widely known, Prxs are becoming acknowledged to be involved in other signaling processes, including redox signaling and apoptosis. This aversion to oxidative stress and regulation by the cellular redox state places them at the heart of adaptive cellular responses to changes in the environment or manifestations of diseases. In conclusion, based on the data obtained and on furthering the knowledge of Prxs' structure and function, these enzymes may be classified as a diverse yet essential family of proteins that can effectively protect cells from the adverse effects of oxidative stress due to peroxidase activity. This indicates secondary interactions, summarized as peroxide detoxification or regulatory signaling, and identifies their applicability in multiple biological pathways. Such knowledge is valuable for enhancing the general comprehension of essential cellular functions and disclosing further therapeutic approaches to the diseases caused by the increased production of reactive oxygen species.

Geneapp: A web application for visualizing alternative splicing for biomedicine

Alternative Splicing (AS) is an essential mechanism for eukaryotes. However, the consequences of deleting a single exon can be dramatic for the organism and can lead to cancer in humans. Additionally, alternative 5' and 3' splice sites, which define the boundaries of exons, also play key roles to human disorders. Therefore, Investigating AS events is crucial for understanding the molecular basis of human diseases and developing therapeutic strategies. Workflow for AS event analysis can be sampling followed by data analysis with bioinformatics to identify the different AS events in the control and case samples, data visualization for curation, and selection of relevant targets for experimental validation. The raw output of the analysis software does not favor the inspection of events by bioinformaticians requiring custom scripts for data visualization. In this work, we propose the Geneapp application with three modules: GeneappScript, GeneappServer, and GeneappExplorer. GeneappScript is a wrapper that assists in identifying AS in samples compared in two different approaches, while GeneappServer integrates data from AS analysis already performed by the user. In GeneappExplorer, the user visualizes the previous dataset by exploring AS events in genes with functional annotation. This targeted screens that Geneapp allows to perform helps in the identification of targets for experimental validation to confirm the hypotheses under study. The Geneapp is freely available for non-commercial use at https://geneapp.net to advance research on AS for bioinformatics.

Pan-Cancer Single-Nucleus Total RNA Sequencing Using snHH-Seq

Tumor heterogeneity and its drivers impair tumor progression and cancer therapy. Single-cell RNA sequencing is used to investigate the heterogeneity of tumor ecosystems. However, most methods of scRNA-seq amplify the termini of polyadenylated transcripts, making it challenging to perform total RNA analysis and somatic mutation analysis.Therefore, a high-throughput and high-sensitivity method called snHH-seq is developed, which combines random primers and a preindex strategy in the droplet microfluidic platform. This innovative method allows for the detection of total RNA in single nuclei from clinically frozen samples. A robust pipeline to facilitate the analysis of full-length RNA-seq data is also established. snHH-seq is applied to more than 730 000 single nuclei from 32 patients with various tumor types. The pan-cancer study enables it to comprehensively profile data on the tumor transcriptome, including expression levels, mutations, splicing patterns, clone dynamics, etc. New malignant cell subclusters and exploring their specific function across cancers are identified. Furthermore, the malignant status of epithelial cells is investigated among different cancer types with respect to mutation and splicing patterns. The ability to detect full-length RNA at the single-nucleus level provides a powerful tool for studying complex biological systems and has broad implications for understanding tumor pathology.

Exploring lncRNAs associated with human pancreatic islet cell death induced by transfer of adoptive lymphocytes in a humanized mouse model

Background

Long noncoding RNA (lncRNA)-mediated posttranscriptional and epigenetic landscapes of gene regulation are associated with numerous human diseases. However, the regulatory mechanisms governing human β-cell function and survival remain unknown. Owing to technical and ethical constraints, studying the direct role of lncRNAs in β-cell function and survival in humans in vivo is difficult. Therefore, we utilized humanized mice with human islets to investigate lncRNA expression using whole transcriptome shotgun sequencing. Our study aimed to characterize lncRNAs that may be crucial for human islet cell function and survival.

Methods

Human β-cell death was induced in humanized mice engrafted with functional human islets. Using these humanized mice harboring human islets with induced β-cell death, we investigated lncRNA expression through whole transcriptome shotgun sequencing. Additionally, we systematically identified, characterized, and explored the regulatory functions of lncRNAs that are potentially important for human pancreatic islet cell function and survival.

Results

Human islet cell death was induced in humanized mice engrafted with functional human islets. RNA sequencing analysis of isolated human islets, islet grafts from humanized mice with and without induced cell death, revealed aberrant expression of a distinct set of lncRNAs that are associated with the deregulated mRNAs important for cellular processes and molecular pathways related to β-cell function and survival. A total of 10 lncRNA isoforms (SCYL1-1:22, POLG2-1:1, CTRB1-1:1, SRPK1-1:1, GTF3C5-1:1, PPY-1:1, CTRB1-1:5, CPA5-1:1, BCAR1-2:1, and CTRB1-1:4) were identified as highly enriched and specific to human islets. These lncRNAs were deregulated in human islets from donors with different BMIs and with type 2 diabetes (T2D), as well as in cultured human islets with glucose stimulation and induced cell death induced by cytokines. Aberrant expression of these lncRNAs was detected in the exosomes from the medium used to culture islets with cytokines.

Conclusion

Islet-enriched and specific human lncRNAs are deregulated in human islet grafts and cultured human islets with induced cell death. These lncRNAs may be crucial for human β-cell function and survival and could have an impact on identifying biomarkers for β-cell loss and discovering novel therapeutic targets to enhance β-cell function and survival.

Supervised Relevance-Redundancy assessments for feature selection in omics-based classification scenarios

BACKGROUND AND OBJECTIVE

Many classification tasks in translational bioinformatics and genomics are characterized by the high dimensionality of potential features and unbalanced sample distribution among classes. This can affect classifier robustness and increase the risk of overfitting, curse of dimensionality and generalization leaks; furthermore and most importantly, this can prevent obtaining adequate patient stratification required for precision medicine in facing complex diseases, like cancer. Setting up a feature selection strategy able to extract only proper predictive features by removing irrelevant, redundant, and noisy ones is crucial to achieving valuable results on the desired task.

METHODS

We propose a new feature selection approach, called ReRa, based on supervised Relevance-Redundancy assessments. ReRa consists of a customized step of relevance-based filtering, to identify a reduced subset of meaningful features, followed by a supervised similarity-based procedure to minimize redundancy. This latter step innovatively uses a combination of global and class-specific similarity assessments to remove redundant features while preserving those differentiated across classes, even when these classes are strongly unbalanced.

RESULTS

We compared ReRa with several existing feature selection methods to obtain feature spaces on which performing breast cancer patient subtyping using several classifiers: we considered two use cases based on gene or transcript isoform expression. In the vast majority of the assessed scenarios, when using ReRa-selected feature spaces, the performances were significantly increased compared to simple feature filtering, LASSO regularization, or even MRmr - another Relevance-Redundancy method. The two use cases represent an insightful example of translational application, taking advantage of ReRa capabilities to investigate and enhance a clinically-relevant patient stratification task, which could be easily applied also to other cancer types and diseases.

CONCLUSIONS

ReRa approach has the potential to improve the performance of machine learning models used in an unbalanced classification scenario. Compared to another Relevance-Redundancy approach like MRmr, ReRa does not require tuning the number of preserved features, ensures efficiency and scalability over huge initial dimensionalities and allows re-evaluation of all previously selected features at each iteration of the redundancy assessment, to ultimately preserve only the most relevant and class-differentiated features.

Characterization of the Testis-specific LINC01016 Gene Reveals Isoform-specific Roles in Controlling Biological Processes

Long noncoding RNAs (lncRNAs) have emerged as critical regulators of biological processes. However, the aberrant expression of an isoform from the same lncRNA gene could lead to RNA with altered functions due to changes in their conformations, leading to diseases. Here, we describe a detailed characterization of the gene that encodes long intergenic non-protein-coding RNA 01016 (LINC01016, also known as LncRNA1195) with a focus on its structure, exon usage, and expression in human and macaque tissues. In this study we show that it is among the highly expressed lncRNAs in the testis, exclusively conserved among nonhuman primates, suggesting its recent evolution and is processed into 12 distinct RNAs in testis, cervix, and uterus tissues. Further, we integrate de novo annotation of expressed LINC01016 transcripts and isoform-dependent gene expression analyses to show that human LINC01016 is a multiexon gene, processed through differential exon usage with isoform-specific roles. Furthermore, in cervical, testicular, and uterine cancers, LINC01016 isoforms are differentially expressed, and their expression is predictive of survival in these cancers. This study has revealed an essential aspect of lncRNA biology, rarely associated with coding RNAs, that lncRNA genes are precisely processed to generate isoforms with distinct biological roles in specific tissues.

Two-step mixed model approach to analyzing differential alternative RNA splicing

Changes in gene expression can correlate with poor disease outcomes in two ways: through changes in relative transcript levels or through alternative RNA splicing leading to changes in relative abundance of individual transcript isoforms. The objective of this research is to develop new statistical methods in detecting and analyzing both differentially expressed and spliced isoforms, which appropriately account for the dependence between isoforms and multiple testing corrections for the multi-dimensional structure of at both the gene- and isoform- level. We developed a linear mixed effects model-based approach for analyzing the complex alternative RNA splicing regulation patterns detected by whole-transcriptome RNA-sequencing technologies. This approach thoroughly characterizes and differentiates three types of genes related to alternative RNA splicing events with distinct differential expression/splicing patterns. We applied the concept of appropriately controlling for the gene-level overall false discovery rate (OFDR) in this multi-dimensional alternative RNA splicing analysis utilizing a two-step hierarchical hypothesis testing framework. In the initial screening test we identify genes that have differentially expressed or spliced isoforms; in the subsequent confirmatory testing stage we examine only the isoforms for genes that have passed the screening tests. Comparisons with other methods through application to a whole transcriptome RNA-Seq study of adenoid cystic carcinoma and extensive simulation studies have demonstrated the advantages and improved performances of our method. Our proposed method appropriately controls the gene-level OFDR, maintains statistical power, and is flexible to incorporate advanced experimental designs.

A supervised machine learning-based methodology for analyzing dysregulation in splicing machinery: An application in cancer diagnosis

Deregulated splicing machinery components have shown to be associated with the development of several types of cancer and, therefore, the determination of such alterations can help the development of tumor-specific molecular targets for early prognosis and therapy. Determining such splicing components, however, is not a straightforward task mainly due to the heterogeneity of tumors, the variability across samples, and the fat-short characteristic of genomic datasets. In this work, a supervised machine learning-based methodology is proposed, allowing the determination of subsets of relevant splicing components that best discriminate samples. The methodology comprises three main phases: first, a ranking of features is determined by means of applying feature weighting algorithms that compute the importance of each splicing component; second, the best subset of features that allows the induction of an accurate classifier is determined by means of conducting an effective heuristic search; then the confidence over the induced classifier is assessed by means of explaining the individual predictions and its global behavior. At the end, an extensive experimental study was conducted on a large collection of transcript-based datasets, illustrating the utility and benefit of the proposed methodology for analyzing dysregulation in splicing machinery.

Splice variants of RAS-translational significance

One of the mechanisms potentially explaining the discrepancy between the number of human genes and the functional complexity of organisms is generating alternative splice variants, an attribute of the vast majority of multi-exon genes. Members of the RAS family, such as NRAS, KRAS and HRAS, all of which are of significant importance in cancer biology, are no exception. The structural and functional differences of these splice variants, particularly if they contain the canonical (and therefore routinely targeted for diagnostic purposes) hot spot mutations, pose a significant challenge for targeted therapies. We must therefore consider whether these alternative splice variants constitute a minor component as originally thought and how therapies targeting the canonical isoforms affect these alternative splice variants and their overall functions.

Genomic sites hypersensitive to ultraviolet radiation

If the genome contains outlier sequences extraordinarily sensitive to environmental agents, these would be sentinels for monitoring personal carcinogen exposure and might drive direct changes in cell physiology rather than acting through rare mutations. New methods, adductSeq and freqSeq, provided statistical resolution to quantify rare lesions at single-base resolution across the genome. Primary human melanocytes, but not fibroblasts, carried spontaneous apurinic sites and TG sequence lesions more frequent than ultraviolet (UV)-induced cyclobutane pyrimidine dimers (CPDs). UV exposure revealed hyperhotspots acquiring CPDs up to 170-fold more frequently than the genomic average; these sites were more prevalent in melanocytes. Hyperhotspots were disproportionately located near genes, particularly for RNA-binding proteins, with the most-recurrent hyperhotspots at a fixed position within 2 motifs. One motif occurs at ETS family transcription factor binding sites, known to be UV targets and now shown to be among the most sensitive in the genome, and at sites of mTOR/5' terminal oligopyrimidine-tract translation regulation. The second occurs at A2-15TTCTY, which developed "dark CPDs" long after UV exposure, repaired CPDs slowly, and had accumulated CPDs prior to the experiment. Motif locations active as hyperhotspots differed between cell types. Melanocyte CPD hyperhotspots aligned precisely with recurrent UV signature mutations in individual gene promoters of melanomas and with known cancer drivers. At sunburn levels of UV exposure, every cell would have a hyperhotspot CPD in each of the ∼20 targeted cell pathways, letting hyperhotspots act as epigenetic marks that create phenome instability; high prevalence favors cooccurring mutations, which would allow tumor evolution to use weak drivers.

Transcriptome analysis reveals significant differences between primary plasma cell leukemia and multiple myeloma even when sharing a similar genetic background

Primary plasma cell leukemia (pPCL) is a highly aggressive plasma cell dyscrasia characterised by short remissions and very poor survival. Although the 17p deletion is associated with poor outcome and extramedullary disease in MM, its presence does not confer the degree of aggressiveness observed in pPCL. The comprehensive exploration of isoform expression and RNA splicing events may provide novel information about biological differences between the two diseases. Transcriptomic studies were carried out in nine newly diagnosed pPCL and ten MM samples, all of which harbored the 17p deletion. Unsupervised cluster analysis clearly distinguished pPCL from MM samples. In total 3584 genes and 20033 isoforms were found to be deregulated between pPCL and MM. There were 2727 significantly deregulated isoforms of non-differentially expressed genes. Strangely enough, significant differences were observed in the expression of spliceosomal machinery components between pPCL and MM, in respect of the gene, isoform and the alternative splicing events expression. In summary, transcriptome analysis revealed significant differences in the relative abundance of isoforms between pPCL and MM, even when they both had the 17p deletion. The mRNA processing pathway including RNA splicing machinery emerged as one of the most remarkable mechanisms underlying the biological differences between the two entities.

OMICfpp: a fuzzy approach for paired RNA-Seq counts

BACKGROUND

RNA sequencing is a widely used technology for differential expression analysis. However, the RNA-Seq do not provide accurate absolute measurements and the results can be different for each pipeline used. The major problem in statistical analysis of RNA-Seq and in the omics data in general, is the small sample size with respect to the large number of variables. In addition, experimental design must be taken into account and few tools consider it.

RESULTS

We propose OMICfpp, a method for the statistical analysis of RNA-Seq paired design data. First, we obtain a p-value for each case-control pair using a binomial test. These p-values are aggregated using an ordered weighted average (OWA) with a given orness previously chosen. The aggregated p-value from the original data is compared with the aggregated p-value obtained using the same method applied to random pairs. These new pairs are generated using between-pairs and complete randomization distributions. This randomization p-value is used as a raw p-value to test the differential expression of each gene. The OMICfpp method is evaluated using public data sets of 68 sample pairs from patients with colorectal cancer. We validate our results through bibliographic search of the reported genes and using simulated data set. Furthermore, we compared our results with those obtained by the methods edgeR and DESeq2 for paired samples. Finally, we propose new target genes to validate these as gene expression signatures in colorectal cancer. OMICfpp is available at http://www.uv.es/ayala/software/OMICfpp_0.2.tar.gz .

CONCLUSIONS

Our study shows that OMICfpp is an accurate method for differential expression analysis in RNA-Seq data with paired design. In addition, we propose the use of randomized p-values pattern graphic as a powerful and robust method to select the target genes for experimental validation.

Network-based approach to identify principal isoforms among four cancer types

Protein isoforms are structurally similar proteins produced by alternative splicing of a single gene or genes from the same family. Isoforms of a protein can perform the same, similar, or even opposite biological functions. A previous study identified principal isoforms of proteins based on the extent of interactions per isoform in a functional relationship network, focusing on data from normal tissues. Additionally, the expression levels of specific isoforms of various genes associated with tumorigenesis and prognosis are frequently altered in tumors compared with those in normal tissues. In this study, we aimed to identify higher degree isoforms (HDIs) of multi-isoform genes (MIGs) in cancer by applying a meta-analytical framework to calculate co-expression between each pair of isoforms in two large datasets of RNA-seq profiles from breast cancer, lung cancer, leukemia, and colon cancer cell lines. Then, we compared HDIs with isoforms identified by proteomic data and prognostic and predictive evidence in various cancers. In addition, we separately analyzed the associations between HDIs and non-HDIs (nHDIs) of the same genes according to transcript expression and drug responses in various cancer type cell lines. Collectively, these results indicated the complex properties of HDIs per gene identified by cancer type-based isoform-isoform co-expression networks and showed the potential of HDIs as novel therapeutic targets for cancer treatment.

Biological classification with RNA-seq data: Can alternatively spliced transcript expression enhance machine learning classifiers?

RNA sequencing (RNA-seq) is becoming a prevalent approach to quantify gene expression and is expected to gain better insights into a number of biological and biomedical questions compared to DNA microarrays. Most importantly, RNA-seq allows us to quantify expression at the gene or transcript levels. However, leveraging the RNA-seq data requires development of new data mining and analytics methods. Supervised learning methods are commonly used approaches for biological data analysis that have recently gained attention for their applications to RNA-seq data. Here, we assess the utility of supervised learning methods trained on RNA-seq data for a diverse range of biological classification tasks. We hypothesize that the transcript-level expression data are more informative for biological classification tasks than the gene-level expression data. Our large-scale assessment utilizes multiple data sets, organisms, lab groups, and RNA-seq analysis pipelines. Overall, we performed and assessed 61 biological classification problems that leverage three independent RNA-seq data sets and include over 2000 samples that come from multiple organisms, lab groups, and RNA-seq analyses. These 61 problems include predictions of the tissue type, sex, or age of the sample, healthy or cancerous phenotypes, and pathological tumor stages for the samples from the cancerous tissue. For each problem, the performance of three normalization techniques and six machine learning classifiers was explored. We find that for every single classification problem, the transcript-based classifiers outperform or are comparable with gene expression-based methods. The top-performing techniques reached a near perfect classification accuracy, demonstrating the utility of supervised learning for RNA-seq based data analysis.

Influence of transcriptional variants on metastasis

Cancer metastasis is defined as the dissemination of malignant cells from the primary tumor site, leading to colonization of distant organs and the establishment of a secondary tumor. Metastasis is frequently associated with chemoresistance and is the major cause of cancer-related mortality. Metastatic cells need to acquire the ability to resist to stresses provided by different environments, such as reactive oxygen species, shear stress, hemodynamic forces, stromal composition, and immune responses, to colonize other tissues. Hence, only a small population of cells has a metastasis-initiating potential. Several studies have revealed the misregulation of transcriptional variants during cancer progression, and many splice events can be used to distinguish between normal and tumoral tissue. These variants, which are abnormally expressed in malignant cells, contribute to an adaptive response of tumor cells and the success of the metastatic cascade, promoting an anomalous cell cycle, cellular adhesion, resistance to death, cell survival, migration and invasion. Understanding the different aspects of splicing regulation and the influence of transcriptional variants that control metastatic cells is critical for the development of therapeutic strategies. In this review, we describe how transcriptional variants contribute to metastatic competence and discuss how targeting specific isoforms may be a promising therapeutic strategy.

Variant 2 of KIAA0101, antagonizing its oncogenic variant 1, might be a potential therapeutic strategy in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most lethal malignant tumors worldwide and effective therapies, including molecular therapy, remain elusive. Our previous work demonstrates that oncogenic KIAA0101 transcript variant (tv) 1 promotes HCC development and might be a HCC therapeutic target. However, the function of another KIAA0101 variant, KIAA0101 tv2, remains unknown. In this study, we reported that KIAA0101 tv2 was highly expressed in adjacent non-tumorous liver tissues (NTs) compared to HCC tissues. In vivo and in vitro results showed that KIAA0101 tv2 decreased cell survival, colony formation, tumor xenografts, migration, and invasion, as well as induced cell cycle arrest and apoptosis. Interestingly, it could inhibit the function of KIAA0101 tv1 by partially down-regulating KIAA0101 tv1, acting similar to KIAA0101 tv1 short hairpin RNA (shRNA). Further studies illustrated that KIAA0101 tv2 could increase the activity of p53 by competing with KIAA0101 tv1 for P53 binding. In conclusion, KIAA0101 tv2 exerts anti-tumor activity in HCC and acts as an endogenous competitor of tumor-associated KIAA0101 tv1. KIAA0101 tv2 has a potential to work as a therapeutic drug targeting the KIAA0101 tv1 in HCC.

Differentially expressed alternatively spliced genes in skeletal muscle from cancer patients with cachexia

BACKGROUND

Alternative splicing (AS) is a post-transcriptional gene regulatory mechanism that contributes to proteome diversity. Aberrant splicing mechanisms contribute to various cancers and muscle-related conditions such as Duchenne muscular dystrophy. However, dysregulation of AS in cancer cachexia (CC) remains unexplored. Our objectives were (i) to profile alternatively spliced genes (ASGs) on a genome-wide scale and (ii) to identify differentially expressed alternatively spliced genes (DASGs) associated with CC.

METHODS

Rectus abdominis muscle biopsies obtained from cancer patients were stratified into cachectic cases (n = 21, classified based on International consensus diagnostic framework for CC) and non-cachectic controls (n = 19, weight stable cancer patients). Human transcriptome array 2.0 was used for profiling ASGs using the total RNA isolated from muscle biopsies. Representative DASG signatures were validated using semi-quantitative RT-PCR.

RESULTS

We identified 8960 ASGs, of which 922 DASGs (772 up-regulated and 150 down-regulated) were identified at ≥1.4 fold-change and P < 0.05. Representative DASGs validated by semi-quantitative RT-PCR confirmed the primary findings from the human transcriptome arrays. Identified DASGs were associated with myogenesis, adipogenesis, protein ubiquitination, and inflammation. Up to 10% of the DASGs exhibited cassette exon (exon included or skipped) as a predominant form of AS event. We also observed other forms of AS events such as intron retention, alternate promoters.

CONCLUSIONS

Overall, we have, for the first time, conducted global profiling of muscle tissue to identify DASGs associated with CC. The mechanistic roles of the identified DASGs in CC pathophysiology using model systems is warranted, as well as replication of findings in independent cohorts.

Splice variants of the endonucleases XPF and XPG contain residual DNA repair capabilities and could be a valuable tool for personalized medicine

The two endonucleases XPF and XPG are essentially involved in nucleotide excision repair (NER) and interstrand crosslink (ICL) repair. Defects in these two proteins result in severe diseases like xeroderma pigmentosum (XP). We applied our newly CRISPR/Cas9 generated human XPF knockout cell line with complete loss of XPF and primary fibroblasts from an XP-G patient (XP20BE) to analyze until now uncharacterized spontaneous mRNA splice variants of these two endonucleases. Functional analyses of these variants were performed using luciferase-based reporter gene assays. Two XPF and XPG splice variants with residual repair capabilities in NER, as well as ICL repair could be identified. Almost all variants are severely C-terminally truncated and lack important protein-protein interaction domains. Interestingly, XPF-202, differing to XPF-003 in the first 12 amino acids only, had no repair capability at all, suggesting an important role of this region during DNA repair, potentially concerning protein-protein interaction. We also identified splice variants of XPF and XPG exerting inhibitory effects on NER. Moreover, we showed that the XPF and XPG splice variants presented with different inter-individual expression patterns in healthy donors, as well as in various tissues. With regard to their residual repair capability and dominant-negative effects, functionally relevant spontaneous XPF and XPG splice variants present promising prognostic marker candidates for individual cancer risk, disease outcome, or therapeutic success. This merits further investigations, large association studies, and translational research within clinical trials in the future.

Alternative splicing discriminates molecular subtypes and has prognostic impact in diffuse large B-cell lymphoma

Effect of alternative splicing (AS) on diffuse large B-cell lymphoma (DLBCL) pathogenesis and survival has not been systematically addressed. Here, we compared differentially expressed genes and exons in association with survival after chemoimmunotherapy, and between germinal center B-cell like (GCB) and activated B-cell like (ABC) DLBCLs. Genome-wide exon array-based screen was performed from samples of 38 clinically high-risk patients who were treated in a Nordic phase II study with dose-dense chemoimmunotherapy and central nervous system prophylaxis. The exon expression profile separated the patients according to molecular subgroups and survival better than the gene expression profile. Pathway analyses revealed enrichment of AS genes in inflammation and adhesion-related processes, and in signal transduction, such as phosphatidylinositol signaling system and adenosine triphosphate binding cassette transporters. Altogether, 49% of AS-related exons were protein coding, and domain prediction showed 28% of such exons to include a functional domain, such as transmembrane helix domain or phosphorylation sites. Validation in an independent cohort of 92 DLBCL samples subjected to RNA-sequencing confirmed differential exon usage of selected genes and association of AS with molecular subtypes and survival. The results indicate that AS events are able to discriminate GCB and ABC DLBCLs and have prognostic impact in DLBCL.

A computational integrative approach based on alternative splicing analysis to compare immortalized and primary cancer cells

Immortalized cell lines are widely used to study the effectiveness and toxicity of anti cancer drugs as well as to assess the phenotypic characteristics of cancer cells, such as proliferation and migration ability. Unfortunately, cell lines often show extremely different properties than tumor tissues. Also the primary cells, that are deprived of the in vivo environment, might adapt to artificial conditions, and differ from the tissue they should represent. Despite these considerations, cell lines are still one of the most used cancer models due to their availability and capability to expand without limitation, but the clinical relevance of their use is still a big issue in cancer research. Many studies tried to overcome this task, comparing cell lines and tumor samples through the definition of the genomic and transcriptomic differences. To this aim, most of them used nucleotide variation or gene expression data. Here we introduce a different strategy based on alternative splicing detection and integration of DNA and RNA sequencing data, to explore the differences between immortalized and tissue-derived cells at isoforms level. Furthermore, in order to better investigate the heterogeneity of both cell populations, we took advantage of a public available dataset obtained with a new simultaneous omics single cell sequencing methodology. The proposed pipeline allowed us to identify, through a computational and prediction approach, putative mutated and alternative spliced transcripts responsible for the dissimilarity between immortalized and primary hepato carcinoma cells.

Human SLFN5 is a transcriptional co-repressor of STAT1-mediated interferon responses and promotes the malignant phenotype in glioblastoma

We provide evidence that the IFN-regulated member of the Schlafen (SLFN) family of proteins, SLFN5, promotes the malignant phenotype in glioblastoma multiforme (GBM). Our studies indicate that SLFN5 expression promotes motility and invasiveness of GBM cells, and that high levels of SLFN5 expression correlate with high grade gliomas and shorter overall survival in patients suffering from GBM. In efforts to uncover the mechanism by which SLFN5 promotes GBM tumorigenesis, we found that this protein is a transcriptional co-repressor of STAT1. Type-I IFN treatment triggers the interaction of STAT1 with SLFN5, and the resulting complex negatively controls STAT1-mediated gene transcription via interferon stimulated response elements (ISRE). Thus, SLFN5 is both an IFN-stimulated response gene and a repressor of IFN-gene transcription, suggesting the existence of a negative-feedback regulatory loop that may account for suppression of antitumor immune responses in glioblastoma.

Integrative gene set enrichment analysis utilizing isoform-specific expression

Gene set enrichment analysis (GSEA) aims at identifying essential pathways, or more generally, sets of biologically related genes that are involved in complex human diseases. In the past, many studies have shown that GSEA is a very useful bioinformatics tool that plays critical roles in the innovation of disease prevention and intervention strategies. Despite its tremendous success, it is striking that conclusions of GSEA drawn from isolated studies are often sparse, and different studies may lead to inconsistent and sometimes contradictory results. Further, in the wake of next generation sequencing technologies, it has been made possible to measure genome-wide isoform-specific expression levels, calling for innovations that can utilize the unprecedented resolution. Currently, enormous amounts of data have been created from various RNA-seq experiments. All these give rise to a pressing need for developing integrative methods that allow for explicit utilization of isoform-specific expression, to combine multiple enrichment studies, in order to enhance the power, reproducibility, and interpretability of the analysis. We develop and evaluate integrative GSEA methods, based on two-stage procedures, which, for the first time, allow statistically efficient use of isoform-specific expression from multiple RNA-seq experiments. Through simulation and real data analysis, we show that our methods can greatly improve the performance in identifying essential gene sets compared to existing methods that can only use gene-level expression.

Distinct splicing signatures affect converged pathways in myelodysplastic syndrome patients carrying mutations in different splicing regulators

Myelodysplastic syndromes (MDS) are heterogeneous myeloid disorders with prevalent mutations in several splicing factors, but the splicing programs linked to specific mutations or MDS in general remain to be systematically defined. We applied RASL-seq, a sensitive and cost-effective platform, to interrogate 5502 annotated splicing events in 169 samples from MDS patients or healthy individuals. We found that splicing signatures associated with normal hematopoietic lineages are largely related to cell signaling and differentiation programs, whereas MDS-linked signatures are primarily involved in cell cycle control and DNA damage responses. Despite the shared roles of affected splicing factors in the 3' splice site definition, mutations in U2AF1, SRSF2, and SF3B1 affect divergent splicing programs, and interestingly, the affected genes fall into converging cancer-related pathways. A risk score derived from 11 splicing events appears to be independently associated with an MDS prognosis and AML transformation, suggesting potential clinical relevance of altered splicing patterns in MDS.

Transcriptome analysis in different rice cultivars provides novel insights into desiccation and salinity stress responses

Drought and salinity are the major environmental factors that affect rice productivity. Comparative transcriptome analysis between tolerant and sensitive rice cultivars can provide insights into the regulatory mechanisms involved in these stress responses. In this study, the comparison of transcriptomes of a drought-tolerant [Nagina 22 (N22)] and a salinity-tolerant (Pokkali) rice cultivar with IR64 (susceptible cultivar) revealed variable transcriptional responses under control and stress conditions. A total of 801 and 507 transcripts were exclusively differentially expressed in N22 and Pokkali rice cultivars, respectively, under stress conditions. Gene ontology analysis suggested the enrichment of transcripts involved in response to abiotic stress and regulation of gene expression in stress-tolerant rice cultivars. A larger number of transcripts encoding for members of NAC and DBP transcription factor (TF) families in N22 and members of bHLH and C₂H₂ TF families in Pokkali exhibited differential regulation under desiccation and salinity stresses, respectively. Transcripts encoding for thioredoxin and involved in phenylpropanoid metabolism were up-regulated in N22, whereas transcripts involved in wax and terpenoid metabolism were up-regulated in Pokkali. Overall, common and cultivar-specific stress-responsive transcripts identified in this study can serve as a helpful resource to explore novel candidate genes for abiotic stress tolerance in rice.

Investigation of Histone Lysine-Specific Demethylase 5D KDM5D) Isoform Expression in Prostate Cancer Cell Lines: a System Approach

Background:

It is now well-demonstrated that histone demethylases play an important role in developmental controls, cell-fate decisions, and a variety of diseases such as cancer. Lysine-specific demethylase 5D (KDM5D) is a male-specific histone demethylase that specifically demethylates di- and tri-methyl H3K4 at the start site of active gene. In this light, the aim of this study was to investigate isoform/transcript-specific expression profiles of KDM5D in three prostate cancer cell lines, Du-145, LNCaP, and PC3.

Methods:

Real-time PCR analysis was performed to determine the expression levels of different KDM5D transcripts in the prostate cell lines. A gene regulatory network was established to analyze the gene expression profile.

Results:

Significantly different expression levels of both isoforms were found among the three cell lines. Interestingly, isoform I was expressed in three cell lines while isoform III did only in DU-145. The expression levels of both isoforms were higher in DU-145 when compared to other cell lines (P<0.0001). The observed expression profile was determined by using regulatory network analyses.

Conclusion:

The present study, for the first time, not only showed the expression profiles of KDM5D isoforms in prostate cancer cell lines but also evaluated the effects of the gene regulatory network on the expression profile of this gene.

Quantitative evaluation of alternatively spliced mRNA isoforms by label-free real-time plasmonic sensing

Alternative splicing of mRNA precursors enables cells to generate different protein outputs from the same gene depending on their developmental or homeostatic status. Its deregulation is strongly linked to disease onset and progression. Current methodologies for monitoring alternative splicing demand elaborate procedures and often present difficulties in discerning between closely related isoforms, e.g. due to cross-hybridization during their detection. Herein, we report a general methodology using a Surface Plasmon Resonance (SPR) biosensor for label-free monitoring of alternative splicing events in real-time, without any cDNA synthesis or PCR amplification requirements. We applied this methodology to RNA isolated from HeLa cells for the quantification of alternatively spliced isoforms of the Fas gene, involved in cancer progression through regulation of programmed cell death. We demonstrate that our methodology is isoform-specific, with virtually no cross-hybridization, achieving limits of detection (LODs) in the picoMolar (pM) range. Similar results were obtained for the detection of the BCL-X gene mRNA isoforms. The results were independently validated by RT-qPCR, with excellent concordance in the determination of isoform ratios. The simplicity and robustness of this biosensor technology can greatly facilitate the exploration of alternative splicing biomarkers in disease diagnosis and therapy.

BCL2 gene polymorphisms and splicing variants in chronic myeloid leukemia

Recent data suggest that constitutional genetic variation in the antiapoptotic BCL2 gene could be associated with the susceptibility to develop chronic myeloid leukemia (CML) and the clinical outcome in several hematological malignancies. The present study examines whether BCL2 single nucleotide polymorphisms (SNPs) predispose to CML or may potentially influence the disease characteristics at diagnosis. Notably, no association was observed between the four candidate BCL2 SNPs and the risk of developing CML. Instead, the 4777C>A (rs2279115) and the 5735A>G (rs1801018) SNPs were significantly associated with the disease risk profile as determined by the Sokal score. We found that such polymorphisms correlated with the expression of BCL2 alternative splicing transcripts (BCL2-α, BCL2-β) in healthy donors, but not in CML patients, although the relative levels of BCL2 mRNA splicing variants were shown to change during the clinical course of CML. Our findings suggest that BCL2 polymorphisms could influence the clinical features of CML patients at diagnosis. However, the pathogenic mechanisms involved in such association remain to be ascertained.

Isoform-Level Gene Expression Profiles of Human Y Chromosome Azoospermia Factor Genes and Their X Chromosome Paralogs in the Testicular Tissue of Non-Obstructive Azoospermia Patients

The human Y chromosome has an inevitable role in male fertility because it contains many genes critical for spermatogenesis and the development of the male gonads. Any genetic variation or epigenetic modification affecting the expression pattern of Y chromosome genes may thus lead to male infertility. In this study, we performed isoform-level gene expression profiling of Y chromosome genes within the azoospermia factor (AZF) regions, their X chromosome counterparts, and few autosomal paralogues in testicular biopsies of 12 men with preserved spermatogenesis and 68 men with nonobstructive azoospermia (NOA) (40 Sertoli-cell-only syndrome (SCOS) and 28 premiotic maturation arrest (MA)). This was undertaken using quantitative real-time PCR (qPCR) at the transcript level and Western blotting (WB) and immunohistochemistry (IHC) at the protein level. We profiled the expression of 41 alternative transcripts encoded by 14 AZFa, AZFb, and AZFc region genes (USP9Y, DDX3Y, XKRY, HSFY1, CYORF15A, CYORF15B, KDM5D, EIF1AY, RPS4Y2, RBMY1A1, PRY, BPY2, DAZ1, and CDY1) as well as their X chromosome homologue transcripts and a few autosomal homologues. Of the 41 transcripts, 18 were significantly down-regulated in men with NOA when compared with those of men with complete spermatogenesis. In contrast, the expression of five transcripts increased significantly in NOA patients. Furthermore, to confirm the qPCR results at the protein level, we performed immunoblotting and IHC experiments (based on 24 commercial and homemade antibodies) that detected 10 AZF-encoded proteins. In addition, their localization in testis cell types and organelles was determined. Interestingly, the two missing proteins, XKRY and CYORF15A, were detected for the first time. Finally, we focused on the expression patterns of the significantly altered genes in 12 MA patients with successful sperm retrieval compared to those of 12 MA patients with failed sperm retrieval to predict the success of sperm retrieval in azoospermic men. We showed that HSFY1-1, HSFY1-3, BPY2-1, KDM5C2, RBMX2, and DAZL1 transcripts could be used as potential molecular markers to predict the presence of spermatozoa in MA patients. In this study, we have identified isoform level signature that can be used to discriminate effectively between MA, SCOS, and normal testicular tissues and suggests the possibility of diagnosing the presence of mature sperm cell in azoospermic men to prevent additional testicular sperm extraction (TESE) surgery.

The transcriptome of equine peripheral blood mononuclear cells

Complete transcriptomic data at high resolution are available only for a few model organisms with medical importance. The gene structures of non-model organisms are mostly computationally predicted based on comparative genomics with other species. As a result, more than half of the horse gene models are known only by projection. Experimental data supporting these gene models are scarce. Moreover, most of the annotated equine genes are single-transcript genes. Utilizing RNA sequencing (RNA-seq) the experimental validation of predicted transcriptomes has become accessible at reasonable costs. To improve the horse genome annotation we performed RNA-seq on 561 samples of peripheral blood mononuclear cells (PBMCs) derived from 85 Warmblood horses. The mapped sequencing reads were used to build a new transcriptome assembly. The new assembly revealed many alternative isoforms associated to known genes or to those predicted by the Ensembl and/or Gnomon pipelines. We also identified 7,531 transcripts not associated with any horse gene annotated in public databases. Of these, 3,280 transcripts did not have a homologous match to any sequence deposited in the NCBI EST database suggesting horse specificity. The unknown transcripts were categorized as coding and noncoding based on predicted coding potential scores. Among them 230 transcripts had high coding potential score, at least 2 exons, and an open reading frame of at least 300 nt. We experimentally validated 9 new equine coding transcripts using RT-PCR and Sanger sequencing. Our results provide valuable detailed information on many transcripts yet to be annotated in the horse genome.

Detection of recurrent alternative splicing switches in tumor samples reveals novel signatures of cancer

The determination of the alternative splicing isoforms expressed in cancer is fundamental for the development of tumor-specific molecular targets for prognosis and therapy, but it is hindered by the heterogeneity of tumors and the variability across patients. We developed a new computational method, robust to biological and technical variability, which identifies significant transcript isoform changes across multiple samples. We applied this method to more than 4000 samples from the The Cancer Genome Atlas project to obtain novel splicing signatures that are predictive for nine different cancer types, and find a specific signature for basal-like breast tumors involving the tumor-driver CTNND1. Additionally, our method identifies 244 isoform switches, for which the change occurs in the most abundant transcript. Some of these switches occur in known tumor drivers, including PPARG, CCND3, RALGDS, MITF, PRDM1, ABI1 and MYH11, for which the switch implies a change in the protein product. Moreover, some of the switches cannot be described with simple splicing events. Surprisingly, isoform switches are independent of somatic mutations, except for the tumor-suppressor FBLN2 and the oncogene MYH11. Our method reveals novel signatures of cancer in terms of transcript isoforms specifically expressed in tumors, providing novel potential molecular targets for prognosis and therapy. Data and software are available at: http://dx.doi.org/10.6084/m9.figshare.1061917 and https://bitbucket.org/regulatorygenomicsupf/iso-ktsp.

Genomic profiling of collaborative cross founder mice infected with respiratory viruses reveals novel transcripts and infection-related strain-specific gene and isoform expression

Genetic variation between diverse mouse species is well-characterized, yet existing knowledge of the mouse transcriptome comes largely from one mouse strain (C57BL/6J). As such, it is unlikely to reflect the transcriptional complexity of the mouse species. Gene transcription is dynamic and condition-specific; therefore, to better understand the mouse transcriptional response to respiratory virus infection, we infected the eight founder strains of the Collaborative Cross with either influenza A virus or severe acute respiratory syndrome coronavirus and sequenced lung RNA samples at 2 and 4 days after infection. We found numerous instances of transcripts that were not present in the C57BL/6J reference annotation, indicating that a nontrivial proportion of the mouse genome is transcribed but poorly annotated. Of these novel transcripts, 2150 could be aligned to human or rat genomes, but not to existing mouse genomes, suggesting functionally conserved sequences not yet recorded in mouse genomes. We also found that respiratory virus infection induced differential expression of 4287 splicing junctions, resulting in strain-specific isoform expression. Of these, 59 were influenced by strain-specific mutations within 2 base pairs of key intron–exon boundaries, suggesting cis-regulated expression. Our results reveal the complexity of the transcriptional response to viral infection, previously undocumented genomic elements, and extensive diversity in the response across mouse strains. These findings identify hitherto unexplored transcriptional patterns and undocumented transcripts in genetically diverse mice. Host genetic variation drives the complexity and diversity of the host response by eliciting starkly different transcriptional profiles in response to a viral infection.

Identification and validation of novel candidate protein biomarkers for the detection of human gastric cancer

The timely detection of gastric cancer will contribute significantly towards effective treatment and is aided by the availability and reliability of appropriate biomarkers. A combination of several biomarkers can improve the sensitivity and specificity of cancer detection and this work reports results from a panel of 4 proteins. By combining a validated preclinical mouse model with a proteomic approach we have recently discovered novel biomarkers for the detection of gastric cancer. Here, we investigate the specificity of four of those biomarkers (afamin, clusterin, VDBP and haptoglobin) for the detection of gastric cancer using two independent methods of validation: ELISA, and a non antibody based method: Multiple Reaction Monitoring with High Resolution Mass Spectrometry (MRM-HR). All four biomarkers reliably differentiated GC from benign patient serum, and also in a small cohort of 11 early stage cases. We also present a novel isoform specific biomarker alpha-1-antitrypsin (A1AT) that was identified using a mouse model for gastric cancer. This isoform is distinct in charge and mobility in a pH gradient and was validated using human samples by isoelectric focussing and Western-blot (IEF-WB). This article is part of a Special Issue entitled: Biomarkers: A Proteomic Challenge.

Isoform-level gene signature improves prognostic stratification and accurately classifies glioblastoma subtypes

Molecular stratification of tumors is essential for developing personalized therapies. Although patient stratification strategies have been successful; computational methods to accurately translate the gene-signature from high-throughput platform to a clinically adaptable low-dimensional platform are currently lacking. Here, we describe PIGExClass (platform-independent isoform-level gene-expression based classification-system), a novel computational approach to derive and then transfer gene-signatures from one analytical platform to another. We applied PIGExClass to design a reverse transcriptase-quantitative polymerase chain reaction (RT-qPCR) based molecular-subtyping assay for glioblastoma multiforme (GBM), the most aggressive primary brain tumors. Unsupervised clustering of TCGA (the Cancer Genome Altas Consortium) GBM samples, based on isoform-level gene-expression profiles, recaptured the four known molecular subgroups but switched the subtype for 19% of the samples, resulting in significant (P = 0.0103) survival differences among the refined subgroups. PIGExClass derived four-class classifier, which requires only 121 transcript-variants, assigns GBM patients’ molecular subtype with 92% accuracy. This classifier was translated to an RT-qPCR assay and validated in an independent cohort of 206 GBM samples. Our results demonstrate the efficacy of PIGExClass in the design of clinically adaptable molecular subtyping assay and have implications for developing robust diagnostic assays for cancer patient stratification.