Gene Isoform Usage Research Articles

Time-course RNA-Seq profiling reveals isoform-level gene expression dynamics of the cGAS-STING pathway

The cGAS-STING pathway, orchestrating complicated transcriptome-wide immune responses, is essential for host antiviral defense but can also drive immunopathology in severe COVID-19. Here, we performed time-course RNA-Seq experiments to dissect the transcriptome expression dynamics at the gene-isoform level after cGAS-STING pathway activation. The in-depth time-course transcriptome after cGAS-STING pathway activation within 12 h enabled quantification of 48,685 gene isoforms. By employing regression models, we obtained 13,232 gene isoforms with expression patterns significantly associated with the process of cGAS-STING pathway activation, which were named activation-associated isoforms. The combination of hierarchical and k-means clustering algorithms revealed four major expression patterns of activation-associated isoforms, including two clusters with increased expression patterns enriched in cell cycle, autophagy, antiviral innate-immune functions, and COVID-19 coronavirus disease pathway, and two clusters showing decreased expression pattern that mainly involved in ncRNA metabolism, translation process, and mRNA processing. Importantly, by merging four clusters of activation-associated isoforms, we identified three types of genes that underwent isoform usage alteration during the cGAS-STING pathway activation. We further found that genes exhibiting protein-coding and non-protein-coding gene isoform usage alteration were strongly enriched for the factors involved in innate immunity and RNA splicing. Notably, overexpression of an enriched splicing factor, EFTUD2, shifted transcriptome towards the cGAS-STING pathway activated status and promoted protein-coding isoform abundance of several key regulators of the cGAS-STING pathway. Taken together, our results revealed the isoform-level gene expression dynamics of the cGAS-STING pathway and uncovered novel roles of splicing factors in regulating cGAS-STING pathway mediated immune responses.

Transcriptional variation and divergence of host-finding behaviour in Steinernema carpocapsae infective juveniles

BackgroundSteinernema carpocapsae is an entomopathogenic nematode that employs nictation and jumping behaviours to find potential insect hosts. Here we aimed to investigate the transcriptional basis of variant host-finding behaviours in the infective juvenile (IJ) stage of three S. carpocapsae strains (ALL, Breton and UK1), with a focus on neuronal genes known to influence behaviour in other nematode species. Identifying gene expression changes that correlate with variant host-finding behaviours will further our understanding of nematode biology.ResultsRNA-seq analysis revealed that whilst up to 28% of the S. carpocapsae transcriptome was differentially expressed (P < 0.0001) between strains, remarkably few of the most highly differentially expressed genes (> 2 log2 fold change, P < 0.0001) were from neuronal gene families. S. carpocapsae Breton displays increased chemotaxis toward the laboratory host Galleria mellonella, relative to the other strains. This correlates with the up-regulation of four srsx chemosensory GPCR genes, and a sodium transporter gene, asic-2, relative to both ALL and UK1 strains. The UK1 strain exhibits a decreased nictation phenotype relative to ALL and Breton strains, which correlates with co-ordinate up-regulation of neuropeptide like protein 36 (nlp-36), and down-regulation of an srt family GPCR gene, and a distinct asic-2-like sodium channel paralogue. To further investigate the link between transcriptional regulation and behavioural variation, we sequenced microRNAs across IJs of each strain. We have identified 283 high confidence microRNA genes, yielding 321 predicted mature microRNAs in S. carpocapsae, and find that up to 36% of microRNAs are differentially expressed (P < 0.0001) between strains. Many of the most highly differentially expressed microRNAs (> 2 log2 fold, P < 0.0001) are predicted to regulate a variety of neuronal genes that may contribute to variant host-finding behaviours. We have also found evidence for differential gene isoform usage between strains, which alters predicted microRNA interactions, and could contribute to the diversification of behaviour.ConclusionsThese data provide insight to the transcriptional basis of behavioural variation in S. carpocapsae, supporting efforts to understand the molecular basis of complex behaviours in nematodes.

Splice Expression Variation Analysis (SEVA) for inter-tumor heterogeneity of gene isoform usage in cancer.

Current bioinformatics methods to detect changes in gene isoform usage in distinct phenotypes compare the relative expected isoform usage in phenotypes. These statistics model differences in isoform usage in normal tissues, which have stable regulation of gene splicing. Pathological conditions, such as cancer, can have broken regulation of splicing that increases the heterogeneity of the expression of splice variants. Inferring events with such differential heterogeneity in gene isoform usage requires new statistical approaches. We introduce Splice Expression Variability Analysis (SEVA) to model increased heterogeneity of splice variant usage between conditions (e.g. tumor and normal samples). SEVA uses a rank-based multivariate statistic that compares the variability of junction expression profiles within one condition to the variability within another. Simulated data show that SEVA is unique in modeling heterogeneity of gene isoform usage, and benchmark SEVA's performance against EBSeq, DiffSplice and rMATS that model differential isoform usage instead of heterogeneity. We confirm the accuracy of SEVA in identifying known splice variants in head and neck cancer and perform cross-study validation of novel splice variants. A novel comparison of splice variant heterogeneity between subtypes of head and neck cancer demonstrated unanticipated similarity between the heterogeneity of gene isoform usage in HPV-positive and HPV-negative subtypes and anticipated increased heterogeneity among HPV-negative samples with mutations in genes that regulate the splice variant machinery. These results show that SEVA accurately models differential heterogeneity of gene isoform usage from RNA-seq data. SEVA is implemented in the R/Bioconductor package GSReg. bahman@jhu.edu or favorov@sensi.org or ejfertig@jhmi.edu. Supplementary data are available at Bioinformatics online.

Abstract 3577: Splice expression variation analysis (SEVA) for differential gene isoform usage in cancer

Abstract Alternative splicing events (ASE) are a significant component of expression alterations in cancer, and have been demonstrated to be critically important in the development of malignant phenotypes in a variety of tumors. These alternative gene isoforms alter cell-signaling networks and serve as a hidden source of tumor-driving alterations not identified in multi-omics analyses. Recent studies have demonstrated that reads from RNA-seq data can infer gene isoforms expressed in a single sample. Therefore, RNA-seq data of tumors offers the opportunity to systematically evaluate expressed gene isoforms and identify splicing events in cancer samples. To characterize a cancer specific ASEs landscape, it is essential to perform differential splice variant expression analysis to identify isoform variants that are unique to tumor samples compared to normal tissue. In spite of the breadth of ASE algorithms, few have been validated in primary tumor samples. Current methods for differential splice variant analysis compare mean expression of gene isoforms in sample groups. Because these variants are tumor-specific, ASEs are expected to have more variable exon junction expression than normal samples. Therefore, current differential ASE analysis algorithms from RNA-seq may not account for heterogeneous gene isoform usage in tumors. To address this, we introduce Splice Expression Variability Analysis (SEVA) to detect differential splice variation usage in tumor and normal samples and accounts for tumor heterogeneity. This algorithm compares the degree of variability of junction expression profiles within a population of normal samples relative to that in tumor samples. The performance of SEVA was compared with two existing algorithms, EBSeq and DiffSplice, in simulated and real RNA-seq data. Simulated data suggest that SEVA is robust and computationally efficient relative to EBSeq and DiffSplice. In contrast to EBSeq and DiffSplice, SEVA was able to identify alternative splicing events independent of overall gene expression differences. Finally, additional validation was performed using RNA-seq data for primary tumor data from HPV-positive oropharynx squamous cell carcinoma (OPSCC) tumors and normal samples from both TCGA and an independent tumor cohort of 46 OPSCC tumors and 25 normal samples. In these tumor samples, SEVA finds cancer-specific ASEs in genes that are independent of their differential expression status. Moreover, SEVA finds approximately hundreds of splice variant candidates, manageable for experimental validation in contrast to the thousands of candidates found with EBSeq or DiffSplice. These candidates include experimentally validated splice variants in HNSCC from a previous microarray study. Based on performance in both simulated and real data, SEVA represents a robust algorithm that is well suited for differential ASE analysis, particularly in RNA-sequencing data from heterogeneous primary tumor samples. Citation Format: Bahman Afsari, Theresa Guo, Michael Considine, Dylan Kelley, Emily Flam, Liliana Florea, Patrick Ha, Donald Geman, Michael F. Ochs, Joseph A. Califano, Daria A. Gaykalova, Alexander V. Favorov, Elana J. Fertig. Splice expression variation analysis (SEVA) for differential gene isoform usage in cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3577. doi:10.1158/1538-7445.AM2017-3577