Improved methods for RNAseq-based alternative splicing analysis

Integrative genomics important to understand host-pathogen interactions

Infectious diseases are the leading cause of morbidity and mortality worldwide. Causative pathogenic microbes readily mutate their genome and lead to outbreaks, challenging the healthcare and the medical support. Understanding how certain symptoms manifest clinically is integral for therapeutic decisions and vaccination efficacy/protection. Notably, the interaction between infecting pathogens, host response and co-presence of microbes influence the trajectories of disease progression and clinical outcome. The spectrum of observed symptomatic patients (mild, moderate and severe) and the asymptomatic infections highlight the challenges and the potential for understanding the factors driving protection/susceptibility. With the increasing repertoire of high-throughput tools, such as cutting-edge multi-omics profiling and next-generation sequencing, genetic drivers of factors linked to heterogeneous disease presentations can be investigated in tandem. However, such strategies are not without limits in terms of effectively integrating host-pathogen interactions. Nonetheless, an integrative genomics method (for example, RNA sequencing data) for exploring multiple layers of complexity in host-pathogen interactions could be another way to incorporate findings from high-throughput data. We further propose that a Holo-transcriptome-based technique to capture transcriptionally active microbial units can be used to elucidate functional microbiomes. Thus, we provide holistic perspective on investigative methodologies that can harness the same genomic data to investigate multiple seemingly independent but deeply interconnected functional domains of host-pathogen interaction that modulate disease severity and clinical outcomes.

Short-Read RNA-Seq

RNA sequencing (RNA-Seq) has emerged as a powerful and versatile tool for the comprehensive analysis of transcriptomes and has been widely used to investigate gene expression, copy number variation, alternative splicing, and novel transcript discovery. This chapter outlines the methodology for conducting short-read RNA-Seq, starting from RNA enrichment to library preparation and sequencing. Throughout the chapter, practical tips and best practices are provided to guide researchers in order to optimize each step of the RNA-Seq workflow. Multiple quality control steps throughout the workflow that are critical to obtain high-quality RNA-Seq data are also discussed.

A Method to Quantitatively Examine Heat Stress-Induced Alternative Splicing in Plants by RNA-Seq and RT-PCR

Alternative splicing (AS) of pre-mRNAs is a type of post-transcriptional regulation in eukaryotes that expands the number of mRNA isoforms. Intron retention is the primary form of AS in plants and occurs more frequently when plants are exposed to environmental stresses. Several wet-lab and bioinformatics techniques are used to detect AS events, but these techniques are technically challenging or unsuitable for studying AS in plants. Here, we report a method that combines RNA-sequencing and reverse transcription PCR for visualizing and validating heat stress-induced AS events in plants, using Arabidopsis thaliana and HEAT SHOCK PROTEIN21 (HSP21) as examples.

Comparison of Alternative Splicing Landscapes Revealed by Long-Read Sequencing in Hepatocyte-Derived HepG2 and Huh7 Cultured Cells and Human Liver Tissue

The long-read RNA sequencing developed by Oxford Nanopore Technologies provides a direct quantification of transcript isoforms, thereby making it possible to present alternative splicing (AS) profiles as arrays of single splice variants with different abundances. Additionally, AS profiles can be presented as arrays of genes characterized by the degree of alternative splicing (the DAS-the number of detected splice variants per gene). Here, we successfully utilized the DAS to reveal biological pathways influenced by the alterations in AS in human liver tissue and the hepatocyte-derived malignant cell lines HepG2 and Huh7, thus employing the mathematical algorithm of gene set enrichment analysis. Furthermore, analysis of the AS profiles as abundances of single splice variants by using the graded tissue specificity index τ provided the selection of the groups of genes expressing particular splice variants specifically in liver tissue, HepG2 cells, and Huh7 cells. The majority of these splice variants were translated into proteins products and appeal to be in focus regarding further insights into the mechanisms underlying cell malignization. The used metrics are intrinsically suitable for transcriptome-wide AS profiling using long-read sequencing.

Prediction of alternative pre-mRNA splicing outcomes

To understand the biological impact of alternative pre-mRNA splicing, it is vital to know which exons are involved, what protein domains they encode, and how the translated isoforms differ. Therefore, we developed a computational pipeline (RiboSplitter) focused on functional effect prediction. It builds on event-based alternative splicing detection with additional filtering steps leading to more efficient statistical testing, and with detection of isoform-specific protein changes. A key methodological advance is reading frame prediction by translating exonic DNA in all possible frames, then finding a single open reading frame, or a single frame with matches to known proteins of the gene. This allowed unambiguous translation in 93.9% of alternative splicing events when tested on RNA-sequencing data of B cells from Sjögren's syndrome patients. RiboSplitter does not depend on reference annotations and translates events even when one or both isoform(s) are novel (unannotated). RiboSplitter's visualizations illustrate each event with translation outcomes, show event location within the gene, and align exons to protein domains.

Single-cell transcriptomics of the ocular anterior segment: a comprehensive review

Elucidating the cellular and genetic composition of ocular tissues is essential for uncovering the pathophysiology of ocular diseases. Since the introduction of single-cell RNA sequencing (scRNA-seq) in 2009, vision researchers have performed extensive single-cell analyses to better understand transcriptome complexity and heterogeneity of ocular structures. This technology has revolutionized our ability to identify rare cell populations and to make cross-species comparisons of gene expression in both steady state and disease conditions. Importantly, single-cell transcriptomic analyses have enabled the identification of cell-type specific gene markers and signalling pathways between ocular cell populations. While most scRNA-seq studies have been conducted on retinal tissues, large-scale transcriptomic atlases pertaining to the ocular anterior segment have also been constructed in the past three years. This timely review provides vision researchers with an overview of scRNA-seq experimental design, technical limitations, and clinical applications in a variety of anterior segment-related ocular pathologies. We review open-access anterior segment-related scRNA-seq datasets and illustrate how scRNA-seq can be an indispensable tool for the development of targeted therapeutics.

The tip of the iceberg-The roles of long noncoding RNAs in acute myeloid leukemia

Long noncoding RNAs (lncRNAs) are traditionally defined as RNA transcripts longer than 200 nucleotides that have no protein coding potential. LncRNAs have been identified to be dysregulated in various types of cancer, including the deadly hematopoietic cancer-acute myeloid leukemia (AML). Currently, survival rates for AML have reached a plateau necessitating new therapeutic targets and biomarkers to improve treatment options and survival from the disease. Therefore, the identification of lncRNAs as novel biomarkers and therapeutic targets for AML has major benefits. In this review, we assess the key studies which have recently identified lncRNAs as important molecules in AML and summarize the current knowledge of lncRNAs in AML. We delve into examples of the specific roles of lncRNA action in AML such as driving proliferation, differentiation block and therapy resistance as well as their function as tumor suppressors and utility as biomarkers. This article is categorized under: RNA in Disease and Development > RNA in Disease.

Alternative RNA Splicing in the Retina: Insights and Perspectives

Alternative splicing is a fundamental and highly regulated post-transcriptional process that enhances transcriptome and proteome diversity. This process is particularly important in neuronal tissues, such as the retina, which exhibit some of the highest levels of differentially spliced genes in the body. Alternative splicing is regulated both temporally and spatially during neuronal development, can be cell-type-specific, and when altered can cause a number of pathologies, including retinal degeneration. Advancements in high-throughput sequencing technologies have facilitated investigations of the alternative splicing landscape of the retina in both healthy and disease states. Additionally, innovations in human stem cell engineering, specifically in the generation of 3D retinal organoids, which recapitulate many aspects of the in vivo retinal microenvironment, have aided studies of the role of alternative splicing in human retinal development and degeneration. Here we review these advances and discuss the ongoing development of strategies for the treatment of alternative splicing-related retinal disease.

Integrative omics approaches to advance rare disease diagnostics

Over the past decade high-throughput DNA sequencing approaches, namely whole exome and whole genome sequencing became a standard procedure in Mendelian disease diagnostics. Implementation of these technologies greatly facilitated diagnostics and shifted the analysis paradigm from variant identification to prioritisation and evaluation. The diagnostic rates vary widely depending on the cohort size, heterogeneity and disease and range from around 30% to 50% leaving the majority of patients undiagnosed. Advances in omics technologies and computational analysis provide an opportunity to increase these unfavourable rates by providing evidence for disease-causing variant validation and prioritisation. This review aims to provide an overview of the current application of several omics technologies including RNA-sequencing, proteomics, metabolomics and DNA-methylation profiling for diagnostics of rare genetic diseases in general and inborn errors of metabolism in particular.

Identification of Splice Variants and Isoforms in Transcriptomics and Proteomics

Alternative splicing is pivotal to the regulation of gene expression and protein diversity in eukaryotic cells. The detection of alternative splicing events requires specific omics technologies. Although short-read RNA sequencing has successfully supported a plethora of investigations on alternative splicing, the emerging technologies of long-read RNA sequencing and top-down mass spectrometry open new opportunities to identify alternative splicing and protein isoforms with less ambiguity. Here, we summarize improvements in short-read RNA sequencing for alternative splicing analysis, including percent splicing index estimation and differential analysis. We also review the computational methods used in top-down proteomics analysis regarding proteoform identification, including the construction of databases of protein isoforms and statistical analyses of search results. While many improvements in sequencing and computational methods will result from emerging technologies, there should be future endeavors to increase the effectiveness, integration, and proteome coverage of alternative splicing events.

Tutorial: integrative computational analysis of bulk RNA-sequencing data to characterize tumor immunity using RIMA

RNA-sequencing (RNA-seq) has become an increasingly cost-effective technique for molecular profiling and immune characterization of tumors. In the past decade, many computational tools have been developed to characterize tumor immunity from gene expression data. However, the analysis of large-scale RNA-seq data requires bioinformatics proficiency, large computational resources and cancer genomics and immunology knowledge. In this tutorial, we provide an overview of computational analysis of bulk RNA-seq data for immune characterization of tumors and introduce commonly used computational tools with relevance to cancer immunology and immunotherapy. These tools have diverse functions such as evaluation of expression signatures, estimation of immune infiltration, inference of the immune repertoire, prediction of immunotherapy response, neoantigen detection and microbiome quantification. We describe the RNA-seq IMmune Analysis (RIMA) pipeline integrating many of these tools to streamline RNA-seq analysis. We also developed a comprehensive and user-friendly guide in the form of a GitBook with text and video demos to assist users in analyzing bulk RNA-seq data for immune characterization at both individual sample and cohort levels by using RIMA.

Using the ACMG/AMP framework to capture evidence related to predicted and observed impact on splicing: Recommendations from the ClinGen SVI Splicing Subgroup

The American College of Medical Genetics and Genomics (ACMG)/Association for Molecular Pathology (AMP) framework for classifying variants uses six evidence categories related to the splicing potential of variants: PVS1, PS3, PP3, BS3, BP4, and BP7. However, the lack of guidance on how to apply such codes has contributed to variation in the specifications developed by different Clinical Genome Resource (ClinGen) Variant Curation Expert Panels. The ClinGen Sequence Variant Interpretation Splicing Subgroup was established to refine recommendations for applying ACMG/AMP codes relating to splicing data and computational predictions. We utilized empirically derived splicing evidence to (1) determine the evidence weighting of splicing-related data and appropriate criteria code selection for general use, (2) outline a process for integrating splicing-related considerations when developing a gene-specific PVS1 decision tree, and (3) exemplify methodology to calibrate splice prediction tools. We propose repurposing the PVS1_Strength code to capture splicing assay data that provide experimental evidence for variants resulting in RNA transcript(s) with loss of function. Conversely, BP7 may be used to capture RNA results demonstrating no splicing impact for intronic and synonymous variants. We propose that the PS3/BS3 codes are applied only for well-established assays that measure functional impact not directly captured by RNA-splicing assays. We recommend the application of PS1 based on similarity of predicted RNA-splicing effects for a variant under assessment in comparison with a known pathogenic variant. The recommendations and approaches for consideration and evaluation of RNA-assay evidence described aim to help standardize variant pathogenicity classification processes when interpreting splicing-based evidence.

Introns: the "dark matter" of the eukaryotic genome

The emergence of introns was a significant evolutionary leap that is a major distinguishing feature between prokaryotic and eukaryotic genomes. While historically introns were regarded merely as the sequences that are removed to produce spliced transcripts encoding functional products, increasingly data suggests that introns play important roles in the regulation of gene expression. Here, we use an intron-centric lens to review the role of introns in eukaryotic gene expression. First, we focus on intron architecture and how it may influence mechanisms of splicing. Second, we focus on the implications of spliceosomal snRNAs and their variants on intron splicing. Finally, we discuss how the presence of introns and the need to splice them influences transcription regulation. Despite the abundance of introns in the eukaryotic genome and their emerging role regulating gene expression, a lot remains unexplored. Therefore, here we refer to introns as the "dark matter" of the eukaryotic genome and discuss some of the outstanding questions in the field.

Alternative splicing in bladder cancer: potential strategies for cancer diagnosis, prognosis, and treatment

Bladder cancer is the most common malignancy of the urinary tract worldwide. The therapeutic options to tackle this disease comprise surgery, intravesical or systemic chemotherapy, and immunotherapy. Unfortunately, a wide number of patients ultimately become resistant to these treatments and develop aggressive metastatic disease, presenting a poor prognosis. Therefore, the identification of novel therapeutic approaches to tackle this devastating pathology is urgently needed. However, a significant limitation is that the progression and drug response of bladder cancer is strongly associated with its intrinsic molecular heterogeneity. In this sense, RNA splicing is recently gaining importance as a critical hallmark of cancer since can have a significant clinical value. In fact, a profound dysregulation of the splicing process has been reported in bladder cancer, especially in the expression of certain key splicing variants and circular RNAs with a potential clinical value as diagnostic/prognostic biomarkers or therapeutic targets in this pathology. Indeed, some authors have already evidenced a profound antitumor effect by targeting some splicing factors (e.g., PTBP1), mRNA splicing variants (e.g., PKM2, HYAL4-v1), and circular RNAs (e.g., circITCH, circMYLK), which illustrates new possibilities to significantly improve the management of this pathology. This review represents the first detailed overview of the splicing process and its alterations in bladder cancer, and highlights opportunities for the development of novel diagnostic/prognostic biomarkers and their clinical potential for the treatment of this devastating cancer type. This article is categorized under: RNA Processing > Splicing Regulation/Alternative Splicing RNA in Disease and Development > RNA in Disease.

The peptide woods are lovely, dark and deep: Hunting for novel cancer antigens

Harnessing the patient's immune system to control a tumor is a proven avenue for cancer therapy. T cell therapies as well as therapeutic vaccines, which target specific antigens of interest, are being explored as treatments in conjunction with immune checkpoint blockade. For these therapies, selecting the best suited antigens is crucial. Most of the focus has thus far been on neoantigens that arise from tumor-specific somatic mutations. Although there is clear evidence that T-cell responses against mutated neoantigens are protective, the large majority of these mutations are not immunogenic. In addition, most somatic mutations are unique to each individual patient and their targeting requires the development of individualized approaches. Therefore, novel antigen types are needed to broaden the scope of such treatments. We review high throughput approaches for discovering novel tumor antigens and some of the key challenges associated with their detection, and discuss considerations when selecting tumor antigens to target in the clinic.

APPLICATION OF THE ACMG/AMP FRAMEWORK TO CAPTURE EVIDENCE RELEVANT TO PREDICTED AND OBSERVED IMPACT ON SPLICING: RECOMMENDATIONS FROM THE CLINGEN SVI SPLICING SUBGROUP

The American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) framework for classifying variants uses six evidence categories related to the splicing potential of variants: PVS1 (null variant in a gene where loss-of-function is the mechanism of disease), PS3 (functional assays show damaging effect on splicing), PP3 (computational evidence supports a splicing effect), BS3 (functional assays show no damaging effect on splicing), BP4 (computational evidence suggests no splicing impact), and BP7 (silent change with no predicted impact on splicing). However, the lack of guidance on how to apply such codes has contributed to variation in the specifications developed by different Clinical Genome Resource (ClinGen) Variant Curation Expert Panels. The ClinGen Sequence Variant Interpretation (SVI) Splicing Subgroup was established to refine recommendations for applying ACMG/AMP codes relating to splicing data and computational predictions. Our study utilised empirically derived splicing evidence to: 1) determine the evidence weighting of splicing-related data and appropriate criteria code selection for general use, 2) outline a process for integrating splicing-related considerations when developing a gene-specific PVS1 decision tree, and 3) exemplify methodology to calibrate bioinformatic splice prediction tools. We propose repurposing of the PVS1_Strength code to capture splicing assay data that provide experimental evidence for variants resulting in RNA transcript(s) with loss of function. Conversely BP7 may be used to capture RNA results demonstrating no impact on splicing for both intronic and synonymous variants, and for missense variants if protein functional impact has been excluded. Furthermore, we propose that the PS3 and BS3 codes are applied only for well-established assays that measure functional impact that is not directly captured by RNA splicing assays. We recommend the application of PS1 based on similarity of predicted RNA splicing effects for a variant under assessment in comparison to a known Pathogenic variant. The recommendations and approaches for consideration and evaluation of RNA assay evidence described aim to help standardise variant pathogenicity classification processes and result in greater consistency when interpreting splicing-based evidence.

Interrogating the Human Diplome: Computational Methods, Emerging Applications, and Challenges

Human DNA sequencing protocols have revolutionized human biology, biomedical science, and clinical practice, but still have very important limitations. One limitation is that most protocols do not separate or assemble (i.e., "phase") the nucleotide content of each of the maternally and paternally derived chromosomal homologs making up the 22 autosomal pairs and the chromosomal pair making up the pseudo-autosomal region of the sex chromosomes. This has led to a dearth of studies and a consequent underappreciation of many phenomena of fundamental importance to basic and clinical genomic science. We discuss a few protocols for obtaining phase information as well as their limitations, including those that could be used in tumor phasing settings. We then describe a number of biological and clinical phenomena that require phase information. These include phenomena that require precise knowledge of the nucleotide sequence in a chromosomal segment from germline or somatic cells, such as DNA binding events, and insight into unique cis vs. trans-acting functionally impactful variant combinations-for example, variants implicated in a phenotype governed by compound heterozygosity. In addition, we also comment on the need for reliable and consensus-based diploid-context computational workflows for variant identification as well as the need for laboratory-based functional verification strategies for validating cis vs. trans effects of variant combinations. We also briefly describe available resources, example studies, as well as areas of further research, and ultimately argue that the science behind the study of human diploidy, referred to as "diplomics," which will be enabled by nucleotide-level resolution of phased genomes, is a logical next step in the analysis of human genome biology.

Splicing factor deficits render hematopoietic stem and progenitor cells sensitive to STAT3 inhibition

Hematopoietic stem and progenitor cells (HSPCs) sustain lifelong hematopoiesis. Mutations of pre-mRNA splicing machinery, especially splicing factor 3b, subunit 1 (SF3B1), are early lesions found in malignancies arising from HSPC dysfunction. However, why splicing factor deficits contribute to HSPC defects remains incompletely understood. Using zebrafish, we show that HSPC formation in sf3b1 homozygous mutants is dependent on STAT3 activation. Clinically, mutations in SF3B1 are heterozygous; thus, we explored if targeting STAT3 could be a vulnerability in these cells. We show that SF3B1 heterozygosity confers heightened sensitivity to STAT3 inhibition in zebrafish, mouse, and human HSPCs. Cells carrying mutations in other splicing factors or treated with splicing modulators are also more sensitive to STAT3 inhibition. Mechanistically, we illustrate that STAT3 inhibition exacerbates aberrant splicing in SF3B1 mutant cells. Our findings reveal a conserved vulnerability of splicing factor mutant HSPCs that could allow for their selective targeting in hematologic malignancies.

Transcriptome analysis provides critical answers to the "variants of uncertain significance" conundrum

While whole-genome and exome sequencing have transformed our collective understanding of genetics' role in disease pathogenesis, there are certain conditions and populations for whom DNA-level data fails to identify the underlying genetic etiology. Specifically, patients of non-White race and non-European ancestry are disproportionately affected by "variants of unknown/uncertain significance" (VUS), limiting the scope of precision medicine for minority patients and perpetuating health disparities. VUS often include deep intronic and splicing variants which are difficult to interpret from DNA data alone. RNA analysis can illuminate the consequences of VUS, thereby allowing for their reclassification as pathogenic versus benign. Here we review the critical role transcriptome analysis plays in clarifying VUS in both neoplastic and non-neoplastic diseases.

Comprehensive characterization of 11 prognostic alternative splicing events in ovarian cancer interacted with the immune microenvironment

Alternative splicing (AS) events play a crucial role in the tumorigenesis and progression of cancer. Transcriptome data and Percent Spliced In (PSI) values of ovarian cancer patients were downloaded from TCGA database and TCGA SpliceSeq. Totally we identified 1472 AS events that were associated with survival of ovarian serous cystadenocarcinoma (OC) and exon skipping (ES) was the most important type. Univariate and multivariate Cox regression analysis were performed to identify survival-associated AS events and developed the prognostic model based on 11-AS events. The immune cells and different response to cytotoxic T lymphocyte associated antigen 4 (CTLA-4) and programmed cell death protein 1 (PD-1) blockers in low-risk and high-risk group of OC patients were analyzed. Ten kinds of immune cells were found up-regulated in low-risk group. Activated B cell, natural killer T cell, natural killer cell and regulatory T cell were associated with survival of OC. The patients in low-risk group had good response to CTLA-4 and PD-1 blockers treatment. Moreover, a regulatory network was established according to the correlation between AS events and splicing factors (SFs). The present study provided valuable insights into the underlying mechanisms of OC. AS events that were correlated with the immune system might be potential therapeutic targets.