Computational Methods and Correlation of Exon-skipping Events with Splicing, Transcription, and Epigenetic Factors

Deciphering the role of alternative splicing in neoplastic diseases for immune-oncological therapies

Alternative splicing (AS) is an important molecular biological mechanism regulated by complex mechanisms involving a plethora of cis and trans-acting elements. Furthermore, AS is tissue specific and altered in various pathologies, including infectious, inflammatory, and neoplastic diseases. Recently developed immuno-oncological therapies include monoclonal antibodies (mAbs) and chimeric antigen receptor (CAR) T cells targeting, among others, immune checkpoint (ICP) molecules. Despite therapeutic successes have been demonstrated, only a limited number of patients showed long-term benefit from these therapies with tumor entity-related differential response rates were observed. Interestingly, splice variants of common immunotherapeutic targets generated by AS are able to completely escape and/or reduce the efficacy of mAb- and/or CAR-based tumor immunotherapies. Therefore, the analyses of splicing patterns of targeted molecules in tumor specimens prior to therapy might help correct stratification, thereby increasing therapy success by antibody panel selection and antibody dosages. In addition, the expression of certain splicing factors has been linked with the patients' outcome, thereby highlighting their putative prognostic potential. Outstanding questions are addressed to translate the findings into clinical application. This review article provides an overview of the role of AS in (tumor) diseases, its molecular mechanisms, clinical relevance, and therapy response.

Characterizing alternative splicing effects on protein interaction networks with LINDA

MOTIVATION

Alternative RNA splicing plays a crucial role in defining protein function. However, despite its relevance, there is a lack of tools that characterize effects of splicing on protein interaction networks in a mechanistic manner (i.e. presence or absence of protein-protein interactions due to RNA splicing). To fill this gap, we present Linear Integer programming for Network reconstruction using transcriptomics and Differential splicing data Analysis (LINDA) as a method that integrates resources of protein-protein and domain-domain interactions, transcription factor targets, and differential splicing/transcript analysis to infer splicing-dependent effects on cellular pathways and regulatory networks.

RESULTS

We have applied LINDA to a panel of 54 shRNA depletion experiments in HepG2 and K562 cells from the ENCORE initiative. Through computational benchmarking, we could show that the integration of splicing effects with LINDA can identify pathway mechanisms contributing to known bioprocesses better than other state of the art methods, which do not account for splicing. Additionally, we have experimentally validated some of the predicted splicing effects that the depletion of HNRNPK in K562 cells has on signalling.

Derivation of splice junction-specific antibodies using a unique hapten targeting strategy and directed evolution

Alternative splicing of RNA occurs frequently in eukaryotic cells and can result in multiple protein isoforms that are nearly identical in amino acid sequence, but have unique biological roles. Moreover, the relative abundance of these unique isoforms can be correlative with diseased states and potentially used as biomarkers or therapeutic targets. However, due to high sequence similarities among isoforms, current proteomic methods are incapable of differentiating native protein isoforms derived from most alternative splicing events. Herein, a strategy employing a nonsynonymous, non-native amino acid (nnAA) pseudo-hapten (i.e. an amino acid or amino acid derivative that is different from the native amino acid at a particular position) as a targeting epitope in splice junction-spanning peptides was successful in directed antibody derivation. After isolating nnAA-specific antibodies, directed evolution reduced the antibody's binding dependence on the nnAA pseudo-hapten and improved binding to the native splice junction epitope. The resulting antibodies demonstrated codependent binding affinity to each exon of the splice junction and thus are splice junction- and isoform-specific. Furthermore, epitope scanning demonstrated that positioning of the nnAA pseudo-hapten within a peptide antigen can be exploited to predetermine the isolated antibody's specificity at, or near, amino acid resolution. Thus, this nnAA targeting strategy has the potential to robustly derive splice junction- and site-specific antibodies that can be used in a wide variety of research endeavors to unambiguously differentiate native protein isoforms.

Single-molecule long-read sequencing reveals the potential impact of posttranscriptional regulation on gene dosage effects on the avian Z chromosome

Background

Mammalian sex chromosomes provide dosage compensation, but avian lack a global mechanism of dose compensation. Herein, we employed nanopore sequencing to investigate the genetic basis of gene expression and gene dosage effects in avian Z chromosomes at the posttranscriptional level.

Results

In this study, the gonad and head skin of female and male duck samples (n = 4) were collected at 16 weeks of age for Oxford nanopore sequencing. Our results revealed a dosage effect and local regulation of duck Z chromosome gene expression. Additionally, AS and APA achieve tissue-specific gene expression, and male-biased lncRNA regulates its Z-linked target genes, with a positive regulatory role for gene dosage effects on the duck Z chromosome. In addition, GO enrichment and KEGG pathway analysis showed that the dosage effects of Z-linked genes were mainly associated with the cellular response to hormone stimulus, melanin biosynthetic, metabolic pathways, and melanogenesis, resulting in sex differences.

Conclusions

Our data suggested that post transcriptional regulation (AS, APA and lncRNA) has a potential impact on the gene expression effects of avian Z chromosomes. Our study provides a new view of gene regulation underlying the dose effects in avian Z chromosomes at the RNA post transcriptional level.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08360-8.

Multi-Omic Approaches to Identify Genetic Factors in Metabolic Syndrome

Metabolic syndrome (MetS) is a highly heritable disease and a major public health burden worldwide. MetS diagnosis criteria are met by the simultaneous presence of any three of the following: high triglycerides, low HDL/high LDL cholesterol, insulin resistance, hypertension, and central obesity. These diseases act synergistically in people suffering from MetS and dramatically increase risk of morbidity and mortality due to stroke and cardiovascular disease, as well as certain cancers. Each of these component features is itself a complex disease, as is MetS. As a genetically complex disease, genetic risk factors for MetS are numerous, but not very powerful individually, often requiring specific environmental stressors for the disease to manifest. When taken together, all sequence variants that contribute to MetS disease risk explain only a fraction of the heritable variance, suggesting additional, novel loci have yet to be discovered. In this article, we will give a brief overview on the genetic concepts needed to interpret genome-wide association studies (GWAS) and quantitative trait locus (QTL) data, summarize the state of the field of MetS physiological genomics, and to introduce tools and resources that can be used by the physiologist to integrate genomics into their own research on MetS and any of its component features. There is a wealth of phenotypic and molecular data in animal models and humans that can be leveraged as outlined in this article. Integrating these multi-omic QTL data for complex diseases such as MetS provides a means to unravel the pathways and mechanisms leading to complex disease and promise for novel treatments. © 2022 American Physiological Society. Compr Physiol 12:1-40, 2022.

ISOGO: Functional annotation of protein-coding splice variants

The advent of RNA-seq technologies has switched the paradigm of genetic analysis from a genome to a transcriptome-based perspective. Alternative splicing generates functional diversity in genes, but the precise functions of many individual isoforms are yet to be elucidated. Gene Ontology was developed to annotate gene products according to their biological processes, molecular functions and cellular components. Despite a single gene may have several gene products, most annotations are not isoform-specific and do not distinguish the functions of the different proteins originated from a single gene. Several approaches have tried to automatically annotate ontologies at the isoform level, but this has shown to be a daunting task. We have developed ISOGO (ISOform + GO function imputation), a novel algorithm to predict the function of coding isoforms based on their protein domains and their correlation of expression along 11,373 cancer patients. Combining these two sources of information outperforms previous approaches: it provides an area under precision-recall curve (AUPRC) five times larger than previous attempts and the median AUROC of assigned functions to genes is 0.82. We tested ISOGO predictions on some genes with isoform-specific functions (BRCA1, MADD,VAMP7 and ITSN1) and they were coherent with the literature. Besides, we examined whether the main isoform of each gene -as predicted by APPRIS- was the most likely to have the annotated gene functions and it occurs in 99.4% of the genes. We also evaluated the predictions for isoform-specific functions provided by the CAFA3 challenge and results were also convincing. To make these results available to the scientific community, we have deployed a web application to consult ISOGO predictions (https://biotecnun.unav.es/app/isogo). Initial data, website link, isoform-specific GO function predictions and R code is available at https://gitlab.com/icassol/isogo.

Events of alternative splicing in head and neck cancer via RNA sequencing - an update

Background

Alternative splicing (AS) is a regulatory mechanism used to create many forms of mature messengers RNAs (mRNAs) from the same gene. Sequencing of RNA (RNA-Seq) is an advanced technology, which has been utilized by different studies to find AS mechanisms in head and neck cancer (HNC). Hitherto, there is no available review that could inform us of the major findings from these studies. Hence, we aim to perform a systematic literature search following PRISMA guidelines to study AS events in HNC identified through RNA-Seq studies.

Results

A total of five records were identified that utilized RNA-Seq data for identifying AS events in HNC. Five software was used in these records to identify AS events. Two genes influenced by AS i.e. MLL3 and RPS9 were found to be common in 4 out of 5 records. Likewise, 38 genes were identified to be similar in at least 3 records.

Conclusions

Alternative splicing in HNC is a multifaceted regulatory mechanism of gene expression. It can be studied via RNA-Seq using different bioinformatics tools. Genes MLL3, as well as RPS9, were repeatedly found to be associated with HNC, however needs further functional validation.

Electronic supplementary material

The online version of this article (10.1186/s12864-019-5794-y) contains supplementary material, which is available to authorized users.

Novel alternatively spliced isoforms of MEF2A and their mRNA expression patterns in pigs

The present study aimed to identify the alternatively spliced isoforms of pig MEF2A gene and to determine theirmRNA expression patterns. Four alternatively spliced isoforms of pig MEF2A gene (i.e. MEF2A1, MEF2A2, MEF2A3 and MEF2A4) were cloned according to the results of transcriptome sequencing. The fifth to eighth exons of MEF2A1 were normally spliced. In MEF2A2, the fifth exon was missing; the sixth exon had an extra 138 bp at its 5' end, and the seventh exon had an extra 102 bp at its 3' end. In MEF2A3, the fifth exon was missing, and the sixth exon had an additional 138 bp at its 5' end. In MEF2A4, the seventh exon had an extra 102 bp at its 3' end. Quantitative real-time polymerase chain reaction (qPCR) analysis indicated that the expression profiles of the four alternatively spliced transcripts in the longissimus dorsi differed between the Mashen and Large White pigs. MEF2A1 and MEF2A2 expression levels were the highest at 90 days of age and lowest at 180 days of age. MEF2A3 and MEF2A4 expression levels increased with age (in days). The four alternatively spliced isoforms of MEF2A were also expressed in the small intestine, cerebellum, pancreas, heart and lung. The discovery of new alternatively spliced transcripts of the MEF2A gene may be utilized in understanding its biological functions.

Introduction: Cancer Gene Networks

Constructing, evaluating, and interpreting gene networks generally sits within the broader field of systems biology, which continues to emerge rapidly, particular with respect to its application to understanding the complexity of signaling in the context of cancer biology. For the purposes of this volume, we take a broad definition of systems biology. Considering an organism or disease within an organism as a system, systems biology is the study of the integrated and coordinated interactions of the network(s) of genes, their variants both natural and mutated (e.g., polymorphisms, rearrangements, alternate splicing, mutations), their proteins and isoforms, and the organic and inorganic molecules with which they interact, to execute the biochemical reactions (e.g., as enzymes, substrates, products) that reflect the function of that system. Central to systems biology, and perhaps the only approach that can effectively manage the complexity of such systems, is the building of quantitative multiscale predictive models. The predictions of the models can vary substantially depending on the nature of the model and its inputoutput relationships. For example, a model may predict the outcome of a specific molecular reaction(s), a cellular phenotype (e.g., alive, dead, growth arrest, proliferation, and motility), a change in the respective prevalence of cell or subpopulations, a patient or patient subgroup outcome(s). Such models necessarily require computers. Computational modeling can be thought of as using machine learning and related tools to integrate the very high dimensional data generated from modern, high throughput omics technologies including genomics (next generation sequencing), transcriptomics (gene expression microarrays; RNAseq), metabolomics and proteomics (ultra high performance liquid chromatography, mass spectrometry), and "subomic" technologies to study the kinome, methylome, and others. Mathematical modeling can be thought of as the use of ordinary differential equations and related tools to create dynamic, semi-mechanistic models of low dimensional data including gene/protein signaling as a function of time/dose. More recently, the integration of imaging technologies into predictive multiscale modeling has begun to extend further the scales across which data can be obtained and used to gain insight into system function.There are several goals for predictive multiscale modeling including the more academic pursuit of understanding how the system or local feature thereof is regulated or functions, to the more practical or translational goals of identifying predictive (selecting which patient should receive which drug/therapy) or prognostic (disease progress and outcome in an individual patient) biomarkers and/or identifying network vulnerabilities that represent potential targets for therapeutic benefit with existing drugs (including drug repurposing) or for the development of new drugs. These various goals are not necessarily mutually exclusive or inclusive. Within this volume, readers will find examples of many of the activities noted above. Each chapter contains practical and/or methodological insights to guide readers in the design and interpretation of their own and published work.

Computer Analysis of Glioma Transcriptome Profiling: Alternative Splicing Events

Here we present the analysis of alternative splicing events on an example of glioblastoma cell culture samples using a set of computer tools in combination with database integration. The gene expression profiles of glioblastoma were obtained from cell culture samples of primary glioblastoma which were isolated and processed for RNA extraction. Transcriptome profiling of normal brain samples and glioblastoma were done by Illumina sequencing. The significant differentially expressed exon-level probes and their corresponding genes were identified using a combination of the splicing index method. Previous studies indicated that tumor-specific alternative splicing is important in the regulation of gene expression and corresponding protein functions during cancer development. Multiple alternative splicing transcripts have been identified as progression markers, including generalized splicing abnormalities and tumor- and stage-specific events. We used a set of computer tools which were recently applied to analysis of gene expression in laboratory animals to study differential splicing events. We found 69 transcripts that are differentially alternatively spliced. Three cancer-associated genes were considered in detail, in particular: APP (amyloid beta precursor protein), CASC4 (cancer susceptibility candidate 4) and TP53. Such alternative splicing opens new perspectives for cancer research.

A Path to Implement Precision Child Health Cardiovascular Medicine

Congenital heart defects (CHDs) affect approximately 1% of live births and are a major source of childhood morbidity and mortality even in countries with advanced healthcare systems. Along with phenotypic heterogeneity, the underlying etiology of CHDs is multifactorial, involving genetic, epigenetic, and/or environmental contributors. Clear dissection of the underlying mechanism is a powerful step to establish individualized therapies. However, the majority of CHDs are yet to be clearly diagnosed for the underlying genetic and environmental factors, and even less with effective therapies. Although the survival rate for CHDs is steadily improving, there is still a significant unmet need for refining diagnostic precision and establishing targeted therapies to optimize life quality and to minimize future complications. In particular, proper identification of disease associated genetic variants in humans has been challenging, and this greatly impedes our ability to delineate gene–environment interactions that contribute to the pathogenesis of CHDs. Implementing a systematic multileveled approach can establish a continuum from phenotypic characterization in the clinic to molecular dissection using combined next-generation sequencing platforms and validation studies in suitable models at the bench. Key elements necessary to advance the field are: first, proper delineation of the phenotypic spectrum of CHDs; second, defining the molecular genotype/phenotype by combining whole-exome sequencing and transcriptome analysis; third, integration of phenotypic, genotypic, and molecular datasets to identify molecular network contributing to CHDs; fourth, generation of relevant disease models and multileveled experimental investigations. In order to achieve all these goals, access to high-quality biological specimens from well-defined patient cohorts is a crucial step. Therefore, establishing a CHD BioCore is an essential infrastructure and a critical step on the path toward precision child health cardiovascular medicine.