Tumor vaccines largely depend on targetable tumor-specific neoantigens. However, in certain tumors, mutation-based neoantigens are exceptionally rare or even absent. To address this challenge, we developed an innovative...Tumor vaccines largely depend on targetable tumor-specific neoantigens. However, in certain tumors, mutation-based neoantigens are exceptionally rare or even absent. To address this challenge, we developed an innovative strategy beyond genomic mutation events by utilizing tumor-specific chimeric RNAs and their encoded chimeric proteins. In this study, we demonstrated through flow cytometry and cell counting kit-8 (CCK-8) assays that aberrantly spliced tumor-specific chimeric RNAs (e.g., the ASTN2-PAPPA antisense chimeric RNA, A-PaschiRNA) can serve as tumor neoantigens. Furthermore, utilizing mouse tumorigenesis models, we have developed a novel therapeutic strategy involving extracellular vesicle (EV)-based vaccines loaded with chimeric RNAs to treat esophageal squamous cell carcinoma.
Cancer vaccines inducing immunogenic responses to tumor-specific neoantigens are rapidly emerging into a new frontier of cancer therapy. Chimeric RNAs encoding fusion proteins are a rich source of novel neoantigens. Here...Cancer vaccines inducing immunogenic responses to tumor-specific neoantigens are rapidly emerging into a new frontier of cancer therapy. Chimeric RNAs encoding fusion proteins are a rich source of novel neoantigens. Here, we present a straightforward bioinformatic pipeline to identify immunogenic peptides produced from chimeric RNAs. We apply a bespoke script to identify fusion-specific peptide regions from chimeric transcript predictions and leverage the netMHCpan program to identify immunogenic peptides. In this paper, we provide a guide for installation and running of the program as well as discuss the rationale behind its design.
Knockdown experiments are commonly employed to examine the functions of a gene of interest. A group of well-known techniques known as RNA interference (RNAi) is used to lower the expression of a target gene by using shor...Knockdown experiments are commonly employed to examine the functions of a gene of interest. A group of well-known techniques known as RNA interference (RNAi) is used to lower the expression of a target gene by using short hairpin RNAs (shRNAs) or short interfering RNAs (siRNAs) to degrade the target gene's mRNA. The issues associated with chimeric RNA knockdown differ from those associated with traditional RNAi targeting of normal genes. Sequence homology, in particular, limits the targeting region to the chimeric junction and may have unintended consequences for the parental genes. This chapter includes instructions on how to study chimeric RNAs using RNA interference (RNAi), how to knock down chimeric RNAs, how to conduct downstream tests for chimeric RNA functional studies, and what controls are required for each set of experiments.
Knockdown assays aim to silence the expression of a target gene. By reducing expression of a gene, researchers can investigate the gene's function in biological processes. Common knockdown assays use RNA interference (RN...Knockdown assays aim to silence the expression of a target gene. By reducing expression of a gene, researchers can investigate the gene's function in biological processes. Common knockdown assays use RNA interference (RNAi) to reduce gene expression by degrading its mRNA. However, RNAi requires the use of double-stranded RNA to target mature mRNA transcripts. Antisense oligonucleotides (ASO) offer an alternative pathway to knocking down gene expression by allowing the use of single-stranded DNA or RNA to target pre-mRNA, mature mRNA, long non-coding RNAs or DNA. Here, we have provided guidelines and procedures for ASO knockdown, application to chimeric RNAs, and necessary controls for a successful experiment.
Constructing overexpression vectors for genes is a common method for studying gene function. Similarly, the open reading frame (ORF) of chimeric RNA can be cloned into eukaryotic expression vectors to explore the functio...Constructing overexpression vectors for genes is a common method for studying gene function. Similarly, the open reading frame (ORF) of chimeric RNA can be cloned into eukaryotic expression vectors to explore the functions of chimeric RNAs. Here, taking the chimeric RNA BCL2L2-PABPN1 as an example, we provide a detailed protocol for inserting the BCL2L2-PABPN1 fusion ORF into a mammalian expression vector. This approach enables the overexpression of the BCL2L2-PABPN1 fusion protein in a bladder cancer cell line through retroviral or lentiviral transduction.
Chimeric RNAs can exert biological functions in various ways, and their biological functions often depend on their RNA sequences or their potential protein-coding ability. Here, we introduce two relatively mature experim...Chimeric RNAs can exert biological functions in various ways, and their biological functions often depend on their RNA sequences or their potential protein-coding ability. Here, we introduce two relatively mature experimental methods. One method is to extract ribosomes followed by PCR to determine whether the chimeric RNA has the ability to bind to ribosomes, thereby preliminarily evaluating its potential to code for proteins. The other method is to verify whether the chimeric RNA is resistant to RNase R, thereby preliminarily evaluating whether it has a circular RNA structure.
Chimeric RNA molecules-formed from nucleotide sequences of multiple genes-can arise through chromosomal rearrangements, transcriptional read-through events, or trans-splicing between distinct transcripts. These chimeric...Chimeric RNA molecules-formed from nucleotide sequences of multiple genes-can arise through chromosomal rearrangements, transcriptional read-through events, or trans-splicing between distinct transcripts. These chimeric RNAs have been shown to play functional roles in both disease states and normal physiological processes, underscoring their biological relevance. Despite this, there are currently a limited number of tools available that aim to quantify chimeric RNA expression. Here, we introduce a metric called the Relative Index of Chimeric Expression (RICE), which assesses the expression of chimeric transcripts relative to their corresponding wild-type parental transcript, and we describe an easy-to-use bioinformatic tool called FusionBlaster for calculating RICE values from RNA sequencing data. After following this guide, users can apply the FusionBlaster pipeline to perform differential RICE analysis on their own RNA sequencing data by applying the appropriate statistical methods.
Fusion transcripts and their fused protein products are emerging as exciting entities in molecular biology, offering potential applications in diagnostics and therapeutics. These fusion proteins, derived from the transla...Fusion transcripts and their fused protein products are emerging as exciting entities in molecular biology, offering potential applications in diagnostics and therapeutics. These fusion proteins, derived from the translation of fusion transcripts, hold promise as unique biomarkers and targets for intervention. While numerous algorithms exist to identify fusion RNAs, the detection and validation of their protein counterparts through proteomics remains a growing area of research. This challenge is particularly intriguing in plant biology, where fusion events may affect stress responses, development, and adaptation. This chapter provides an accessible and practical workflow for validating plant fusion peptides using publicly available proteomics datasets.
Chimeric RNAs, formed by the fusion of exons from two or more distinct genes, represent a significant class of noncanonical transcripts with increasing implications in cancer biology, development, and other biological pr...Chimeric RNAs, formed by the fusion of exons from two or more distinct genes, represent a significant class of noncanonical transcripts with increasing implications in cancer biology, development, and other biological processes. Their inherent novelty and the potential for sequence similarity with parental transcripts pose significant challenges for accurate detection and validation. While next-generation sequencing (NGS) has become the primary tool for chimeric RNA discovery, orthogonal validation methods are crucial to confirm their existence, delineate their precise structure, and quantify their abundance. Mass spectrometry (MS)-based approaches offer a powerful and complementary strategy for the robust validation of chimeric RNAs. This chapter will delve into the principles and applications of MS-based techniques for the definitive characterization of these fusion transcripts, highlighting their strengths in providing direct evidence of the chimeric junction at the peptide level, confirming the reading frame, and offering quantitative insights. We will explore various MS workflows, including targeted and untargeted peptidomics, and discuss the critical considerations for sample preparation, data acquisition, and bioinformatic analysis to ensure reliable and high-confidence validation of chimeric RNAs.
Efficient nucleic acid extraction and purification are fundamental to cellular and molecular biology research but remain challenging for large-scale clinical RNA sequencing and PCR assays. This chapter introduces BLADE-R...Efficient nucleic acid extraction and purification are fundamental to cellular and molecular biology research but remain challenging for large-scale clinical RNA sequencing and PCR assays. This chapter introduces BLADE-R, a novel magnetic bead-based protocol that streamlines the RNA extraction process. BLADE-R integrates cell lysis and nucleic acid binding into a single step, followed by an innovative on-bead rinse to achieve nuclease-free separation of genomic DNA and RNA. The protocol's adaptability to a 96-well plate format enables simultaneous RNA purification from up to 96 human blood samples, significantly reducing time compared with single-sample methods. In this high-throughput setup, BLADE-R demonstrated no cross-contamination between wells during RNA purification, cDNA synthesis, and PCR. BLADE-R's versatility, efficiency, and suitability for low- and high-throughput applications make it an ideal method for RNA preparation in clinical and research settings, particularly for detecting and measuring chimeric RNAs using RT-PCR and sequencing assays. This protocol is especially advantageous in resource-limited environments, facilitating robust and scalable RNA extraction workflows.
Fusion transcripts are chimeric RNAs, produced by the joining of two different RNAs at the RNA level or as a product of gene fusion at the DNA level. In this era of high-throughput sequencing technologies, it is easy to...Fusion transcripts are chimeric RNAs, produced by the joining of two different RNAs at the RNA level or as a product of gene fusion at the DNA level. In this era of high-throughput sequencing technologies, it is easy to identify novel molecules like fusion transcripts in different systems. That's because, initially, supposed to be the well-known cancer biomarkers, fusion transcripts are also validated in normal human physiology. In Planta, discrete reports are available, indicating the presence of fusion transcripts but no dedicated web resource is available for the plant-specific fusion transcripts. This chapter describes the first plant-specific database of fusion transcripts, i.e., AtFusionDB ( http://www.nipgr.res.in/AtFusionDB ), which contains the information on fusion transcripts identified in the model plant Arabidopsis thaliana. This database can be exploited to get significant information about gene/transcript fusion in plants.
Chimeric RNAs are a class of understudied transcripts, characterized by their possession of sequence from two unique annotated parental transcripts. Definitionally, chimeric RNAs exist within gaps in annotation, and most...Chimeric RNAs are a class of understudied transcripts, characterized by their possession of sequence from two unique annotated parental transcripts. Definitionally, chimeric RNAs exist within gaps in annotation, and most efforts to catalog chimeric RNAs at scale have leveraged short-read paired-end RNAseq. While these have successfully established putative "chimeromes" in different tissue and disease contexts, chimeric RNAs predicted via short-read sequencing are defined by the chimeric exon-exon junction, and cannot provide information on the full-length isoforms which contain this junction. These gaps can be remedied by integration of these predictions with full-length, single-molecule, long-read sequencing. In this chapter, we provide instruction on how to integrate long-read sequencing with existing chimeric RNA predictions to establish full-length annotations of long-read transcripts.
Chimeric or fusion transcripts play critical roles in disease mechanisms and therapeutic targets, making their accurate identification crucial. The emerging field of long-read chimeric RNA prediction is transforming our...Chimeric or fusion transcripts play critical roles in disease mechanisms and therapeutic targets, making their accurate identification crucial. The emerging field of long-read chimeric RNA prediction is transforming our understanding of complex transcriptomic structures and their implications in various biological processes, particularly in cancer and genetic diseases. Long-read sequencing technologies offer significant advantages over traditional methods, enabling the identification and characterization of chimeric RNAs, which are often difficult to detect due to their complexity and the limitations of shorter reads. Long-read RNA sequencing has emerged as a powerful tool for detecting full-length fusion transcripts, overcoming limitations of short-read technologies. This chapter provides a comprehensive guide to computational prediction of fusion transcripts using CTAT-LR-fusion, a tool designed for analyzing long-read RNA-seq data. Key features, installation, usage, output interpretation, and challenges associated with its application are discussed, offering a practical framework for researchers exploring the fusion transcript landscape.
Chimeric RNAs (chiRNAs), generated via genomic rearrangements or splicing events, are increasingly recognized as biomarkers and therapeutic targets in cancer and neurodegenerative disorders. This chapter introduces an in...Chimeric RNAs (chiRNAs), generated via genomic rearrangements or splicing events, are increasingly recognized as biomarkers and therapeutic targets in cancer and neurodegenerative disorders. This chapter introduces an integrative framework for high-confidence chiRNA identification leveraging the ChiTaRS 8.0 database and the ChiTaH pipeline. ChiTaRS 8.0 encompasses 47,445 human chiRNAs, 1,055 Hi-C breakpoints, and 1,598 drug targets, while ChiTaH facilitates disease-specific analysis of RNA-seq data from 250 peripheral blood mononuclear cell (PBMC) samples-including glioblastoma and oral squamous cell carcinoma-and 199 healthy controls. Our approach combines reference-based fusion detection, BLAT validation against GRCh38, gene-pair compatibility checks, and protein domain conservation analysis. Functional annotation and protein-protein interaction modeling uncovered oncogenic chiRNAs absent from existing databases, exhibiting tissue-specific patterns. In Alzheimer's disease, liquid biopsy analyses identified unique chimeras-such as ENO1-MCUR1 and APOE-APOE-in cerebrospinal fluid, linked to neurotransmitter pathways and amyloid processing, and absent in healthy samples, highlighting their potential as early biomarkers. We describe a scalable digital hospital framework integrating AI-driven fusion detection, relational databases, and clinical metadata for real-time diagnostics and patient monitoring. This system supports fusion-targeted drug discovery and patient stratification, bridging translational gaps in oncology and neurodegeneration. By coupling computational pipelines with multiomics data, our approach advances personalized medicine while addressing challenges in artifact filtering and functional validation. Ultimately, the ChiTaRS-ChiTaH platform offers a versatile tool for chiRNA discovery and annotation across diverse disease contexts, providing insights into molecular mechanisms and clinical applications.
Chimeric RNAs could be originated from chromosome rearrangements at the DNA level or from posttranscriptional RNA fusion events, such as trans-spicing between distal genes and cis-splicing between adjacent genes. In addi...Chimeric RNAs could be originated from chromosome rearrangements at the DNA level or from posttranscriptional RNA fusion events, such as trans-spicing between distal genes and cis-splicing between adjacent genes. In addition to the mechanisms above, we have identified a new type of chimeric RNA, cross-strand chimeric RNA (cscRNA), which are fusion products of the transcripts encoded by the two opposite DNA strands. In this chapter, we present the workflow of cscMap, a specialized bioinformatics pipeline designed for de novo identification of the cscRNAs, directly from RNA deep sequencing data without prior annotations. cscMap employs a series of meticulous measurements to ensure high accuracy in detecting cross-strand junction events. This approach and the cscRNA species could serve as a valuable resource for further exploration of the origins and functions of cscRNAs.
Chimeric RNAs are composed of sequences from different genomic loci caused by various chromosomal rearrangements and splicing events. They are recognized as both biomarkers present in cancer as well as a source of transc...Chimeric RNAs are composed of sequences from different genomic loci caused by various chromosomal rearrangements and splicing events. They are recognized as both biomarkers present in cancer as well as a source of transcriptomic diversity in normal tissues. Numerous computational prediction tools have been developed and aim to analyze and predict chimeric RNAs. However, the performance of these tools vary in accuracy and depend on the sequencing context, necessitating a combination of multiple existing tools to produce the most comprehensive and accurate results. First, this study reviews several major chimeric RNA prediction tools: STAR-Fusion, Arriba, and FuSeq. It highlights the advantages of each program, as demonstrated by benchmarking studies. Second, it presents an integrated pipeline that combines all three top-ranking programs to produce a single output file including detailed annotations, such as chimeric RNA class, breakpoint types, and protein coding potential. The final computational product is a unified framework that supports results for high-confidence fusion transcript predictions for both research and clinical applications.
Therapeutic failure in glioblastoma (GBM) is increasingly attributed not only to tumor cell-intrinsic factors but also to the adaptive/supportive tumor microenvironment that nurtures glioma stem cells (GSCs) and drive th...Therapeutic failure in glioblastoma (GBM) is increasingly attributed not only to tumor cell-intrinsic factors but also to the adaptive/supportive tumor microenvironment that nurtures glioma stem cells (GSCs) and drive therapy resistance. GSCs reside within specialized niches shaped by extracellular matrix architecture, stromal interactions, metabolomic gradients, and immune-modulatory cues, enabling their survival, plasticity, and repopulation following conventional therapy. Effective targeting of GBM therefore requires strategies that disrupt both GSC-intrinsic niche and the supportive microenvironment context that limit drug penetration, retention, and therapeutic benefit.Traditional two-dimensional (2D) culture systems fail to capture these spatial and biological complexities, resulting in poor clinically actionable predictive power for successful outcomes. In contrast, three-dimensional (3D) models offer an opportunity to recapitulate relevance. Building on this context, this chapter highlights recent advances that integrate nanotechnology with stem cell-based 3D GBM organoid platforms to enable effective therapeutic delivery and resistance niche-level targeting.We discuss the design and functional evaluation of nanoparticle systems engineered for deep tumor penetration and selective delivery, including polymeric nanoparticles, mesoporous silica nanoparticles, and ultrasmall gold nanostructures. Emphasis is placed on mesenchymal and neural stem cell-mediated nanodelivery, biomimetic hydrogel- and nanofiber-based scaffolds for recreating GSC-associated niche, and advanced analytical readouts including electron microscopy, confocal Z-stack imaging, and ICP-MS. Collectively, this chapter presents a translational framework for leveraging 3D models and stem cell-directed nanotechnologies as preclinical tools to overcome therapy resistance and improve therapeutic outcomes in GBM.
Chromosome conformation capture (3C), when combined with high-throughput next-generation sequencing, known as Hi-C, is a powerful approach for visualizing three-dimensional genome architecture. This technique enables det...Chromosome conformation capture (3C), when combined with high-throughput next-generation sequencing, known as Hi-C, is a powerful approach for visualizing three-dimensional genome architecture. This technique enables detailed study of how chromosome topology influences gene regulation and other key cellular processes.Incorporating a sequence capture step into the Hi-C workflow enables targeted enrichment of predefined genomic regions of interest. Compared with conventional Hi-C, Capture Hi-C (CHi-C) increases the sequencing depth for the targeted regions, enabling high-resolution analysis of chromatin structure within a predefined locus at a lower cost.Here, we present a detailed protocol for CHi-C applied to a region of the Arabidopsis thaliana genome encompassing the thalianol biosynthetic gene cluster.
Streptomyces species are prolific producers of bioactive compounds, including antibiotics, but many of their biosynthetic gene clusters (BGCs) remain silent or are poorly expressed under laboratory conditions. Recent stu...Streptomyces species are prolific producers of bioactive compounds, including antibiotics, but many of their biosynthetic gene clusters (BGCs) remain silent or are poorly expressed under laboratory conditions. Recent studies have reported a close correlation between chromosomal organization and the expression activity of genes, including BGCs. Chromosome conformation capture with high-throughput sequencing (Hi-C) is a technique that allows for the profiling of three-dimensional genome organization by capturing physical interactions between chromosomal regions. In this chapter, we describe the application of Hi-C to Streptomyces coelicolor, focusing on the detailed protocol for constructing a Hi-C library and analyzing chromosomal interactions. By using this technique, researchers can explore the relationship between chromosomal architecture and the expression of BGCs, which may inform new strategies for improving natural product yields in Streptomyces.
Plants synthesise a diverse array of specialised metabolites with important functions in environmental adaptation and reproduction. Biosynthesis genes arranged in genomic clusters typically exhibit shared epigenomic feat...Plants synthesise a diverse array of specialised metabolites with important functions in environmental adaptation and reproduction. Biosynthesis genes arranged in genomic clusters typically exhibit shared epigenomic features that likely contribute to their transcriptional co-regulation. Here, we describe a protocol for the genome-wide profiling of histone modifications in Arabidopsis thaliana by CUT&Tag. In addition, we provide an overview about the bioinformatic analysis of tissue-specific enrichment patterns of epigenetic marks within biosynthetic gene clusters.