Identification and visualization of differential isoform expression in RNA-seq time series

Temporal Analysis of Gene Expression and Isoform Switching in Brown Bears (Ursus arctos)

Hibernation in brown bears is an annual process involving multiple physiologically distinct seasons-hibernation, active, and hyperphagia. While recent studies have characterized broad patterns of differential gene regulation and isoform usage between hibernation and active seasons, patterns of gene and isoform expression during hyperphagia remain relatively poorly understood. The hyperphagia stage occurs between active and hibernation seasons and involves the accumulation of large fat reserves in preparation for hibernation. Here, we use time-series analyses of gene expression and isoform usage to interrogate transcriptomic regulation associated with all three seasons. We identify a large number of genes with significant differential isoform usage (DIU) across seasons and show that these patterns of isoform usage are largely tissue-specific. We also show that DIU and differential gene-level expression responses are generally non-overlapping, with only a small subset of multi-isoform genes showing evidence of both gene-level expression changes and changes in isoform usage across seasons. Additionally, we investigate nuanced regulation of candidate genes involved in the insulin signaling pathway and find evidence of hyperphagia-specific gene expression and isoform regulation that may enhance fat accumulation during hyperphagia. Our findings highlight the value of using temporal analyses of both gene- and isoform-level gene expression when interrogating complex physiological phenotypes and provide new insight into the mechanisms underlying seasonal changes in bear physiology.

Systematic analysis of alternative splicing in time course data using Spycone

MOTIVATION

During disease progression or organism development, alternative splicing may lead to isoform switches that demonstrate similar temporal patterns and reflect the alternative splicing co-regulation of such genes. Tools for dynamic process analysis usually neglect alternative splicing.

RESULTS

Here, we propose Spycone, a splicing-aware framework for time course data analysis. Spycone exploits a novel IS detection algorithm and offers downstream analysis such as network and gene set enrichment. We demonstrate the performance of Spycone using simulated and real-world data of SARS-CoV-2 infection.

AVAILABILITY AND IMPLEMENTATION

The Spycone package is available as a PyPI package. The source code of Spycone is available under the GPLv3 license at https://github.com/yollct/spycone and the documentation at https://spycone.readthedocs.io/en/latest/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Cardiac developmental toxicity and transcriptome analyses of zebrafish (Danio rerio) embryos exposed to Mancozeb

Mancozeb (MZ), an antibacterial pesticide, has been linked to reproductive toxicity, neurotoxicity, and endocrine disruption. However, whether MZ has cardiactoxicity is unclear. In this study, the cardiotoxic effects of exposure to environment-related MZ concentrations ranging from 1.88 μM to 7.52 μM were evaluated at the larval stage of zebrafish. Transcriptome sequencing predicted the mechanism of MZ-induced cardiac developmental toxicity in zebrafish by enrichment analysis of Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO). Consistent with morphological changes, the osm, pfkfb3, foxh1, stc1, and nrarpb genes may effect normal development of zebrafish heart by activating NOTCH signaling pathways, resulting in pericardial edema, myocardial fibrosis, and congestion in the heart area. Moreover, differential gene expression analysis indicated that cyp-related genes (cyp1c2 and cyp3c3) were significantly upregulated after MZ treatment, which may be related to apoptosis of myocardial cells. These results were verified by real-time quantitative RT-qPCR and acridine orange staining. Our findings suggest that MZ-mediated cardiotoxic development of zebrafish larvae may be related to the activation of Notch and apoptosis-related signaling pathways.

Time-series transcriptomic analysis of bronchoalveolar lavage cells from virulent and low virulent PRRSV-1-infected piglets

Porcine reproductive and respiratory syndrome virus (PRRSV) has evolved to escape the immune surveillance for a survival advantage leading to a strong modulation of host’s immune responses and favoring secondary bacterial infections. However, limited data are available on how the immunological and transcriptional responses elicited by virulent and low-virulent PRRSV-1 strains are comparable and how they are conserved during the infection. To explore the kinetic transcriptional signature associated with the modulation of host immune response at lung level, a time-series transcriptomic analysis was performed in bronchoalveolar lavage cells upon experimental in vivo infection with two PRRSV-1 strains of different virulence, virulent subtype 3 Lena strain or the low-virulent subtype 1 3249 strain. The time-series analysis revealed overlapping patterns of dysregulated genes enriched in T-cell signaling pathways among both virulent and low-virulent strains, highlighting an upregulation of co-stimulatory and co-inhibitory immune checkpoints that were disclosed as Hub genes. On the other hand, virulent Lena infection induced an early and more marked “negative regulation of immune system process” with an overexpression of co-inhibitory receptors genes related to T-cell and NK cell functions, in association with more severe lung lesion, lung viral load, and BAL cell kinetics. These results underline a complex network of molecular mechanisms governing PRRSV-1 immunopathogenesis at lung level, revealing a pivotal role of co-inhibitory and co-stimulatory immune checkpoints in the pulmonary disease, which may have an impact on T-cell activation and related pathways. These immune checkpoints, together with the regulation of cytokine-signaling pathways, modulated in a virulence-dependent fashion, orchestrate an interplay among pro- and anti-inflammatory responses.

IMPORTANCE Porcine reproductive and respiratory syndrome virus (PRRSV) is one of the major threats to swine health and global production, causing substantial economic losses. We explore the mechanisms involved in the modulation of host immune response at lung level performing a time-series transcriptomic analysis upon experimental infection with two PRRSV-1 strains of different virulence. A complex network of molecular mechanisms was revealed to control the immunopathogenesis of PRRSV-1 infection, highlighting an interplay among pro- and anti-inflammatory responses as a potential mechanism to restrict inflammation-induced lung injury. Moreover, a pivotal role of co-inhibitory and co-stimulatory immune checkpoints was evidenced, which may lead to progressive dysfunction of T cells, impairing viral clearance and leading to persistent infection, favoring as well secondary bacterial infections or viral rebound. However, further studies should be conducted to evaluate the functional role of immune checkpoints in advanced stages of PRRSV infection and explore a possible T-cell exhaustion state.

Disentangling the aging gene expression network of termite queens

Background

Most insects are relatively short-lived, with a maximum lifespan of a few weeks, like the aging model organism, the fruit-fly Drosophila melanogaster. By contrast, the queens of many social insects (termites, ants and some bees) can live from a few years to decades. This makes social insects promising models in aging research providing insights into how a long reproductive life can be achieved. Yet, aging studies on social insect reproductives are hampered by a lack of quantitative data on age-dependent survival and time series analyses that cover the whole lifespan of such long-lived individuals. We studied aging in queens of the drywood termite Cryptotermes secundus by determining survival probabilities over a period of 15 years and performed transcriptome analyses for queens of known age that covered their whole lifespan.

Results

The maximum lifespan of C. secundus queens was 13 years, with a median maximum longevity of 11.0 years. Time course and co-expression network analyses of gene expression patterns over time indicated a non-gradual aging pattern. It was characterized by networks of genes that became differentially expressed only late in life, namely after ten years, which associates well with the median maximum lifespan for queens. These old-age gene networks reflect processes of physiological upheaval. We detected strong signs of stress, decline, defense and repair at the transcriptional level of epigenetic control as well as at the post-transcriptional level with changes in transposable element activity and the proteostasis network. The latter depicts an upregulation of protein degradation, together with protein synthesis and protein folding, processes which are often down-regulated in old animals. The simultaneous upregulation of protein synthesis and autophagy is indicative of a stress-response mediated by the transcription factor cnc, a homolog of human nrf genes.

Conclusions

Our results show non-linear senescence with a rather sudden physiological upheaval at old-age. Most importantly, they point to a re-wiring in the proteostasis network and stress as part of the aging process of social insect queens, shortly before queens die.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07649-4.

Study on the differentially expressed genes and signaling pathways in dermatomyositis using integrated bioinformatics method

Dermatomyositis is a common connective tissue disease. The occurrence and development of dermatomyositis is a result of multiple factors, but its exact pathogenesis has not been fully elucidated. Here, we used biological information method to explore and predict the major disease related genes of dermatomyositis and to find the underlying pathogenic molecular mechanism.The gene expression data of GDS1956, GDS2153, GDS2855, and GDS3417 including 94 specimens, 66 cases of dermatomyositis specimens and 28 cases of normal specimens, were obtained from the Gene Expression Omnibus database. The 4 microarray gene data groups were combined to get differentially expressed genes (DEGs). The gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichments of DEGs were operated by the database for annotation, visualization and integrated discovery and KEGG orthology based annotation system databases, separately. The protein-protein interaction networks of the DEGs were built from the STRING website. A total of 4097 DEGs were extracted from the 4 Gene Expression Omnibus datasets, of which 2213 genes were upregulated, and 1884 genes were downregulated. Gene ontology analysis indicated that the biological functions of DEGs focused primarily on response to virus, type I interferon signaling pathway and negative regulation of viral genome replication. The main cellular components include extracellular space, cytoplasm, and blood microparticle. The molecular functions include protein binding, double-stranded RNA binding and MHC class I protein binding. KEGG pathway analysis showed that these DEGs were mainly involved in the toll-like receptor signaling pathway, cytosolic DNA-sensing pathway, RIG-I-like receptor signaling pathway, complement and coagulation cascades, arginine and proline metabolism, phagosome signaling pathway. The following 13 closely related genes, XAF1, NT5E, UGCG, GBP2, TLR3, DDX58, STAT1, GBP1, PLSCR1, OAS3, SP100, IGK, and RSAD2, were key nodes from the protein-protein interaction network.This research suggests that exploring for DEGs and pathways in dermatomyositis using integrated bioinformatics methods could help us realize the molecular mechanism underlying the development of dermatomyositis, be of actual implication for the early detection and prophylaxis of dermatomyositis and afford reliable goals for the curing of dermatomyositis.

tappAS: a comprehensive computational framework for the analysis of the functional impact of differential splicing

Recent advances in long-read sequencing solve inaccuracies in alternative transcript identification of full-length transcripts in short-read RNA-Seq data, which encourages the development of methods for isoform-centered functional analysis. Here, we present tappAS, the first framework to enable a comprehensive Functional Iso-Transcriptomics (FIT) analysis, which is effective at revealing the functional impact of context-specific post-transcriptional regulation. tappAS uses isoform-resolved annotation of coding and non-coding functional domains, motifs, and sites, in combination with novel analysis methods to interrogate different aspects of the functional readout of transcript variants and isoform regulation. tappAS software and documentation are available at https://app.tappas.org.

Explorative analysis of the gene expression profile during liver regeneration of mouse: a microarray-based study

The liver is an amazing organ due to its powerful regenerative capacity. Although many studies on liver regeneration have been documented, the detailed mechanisms remain unclear. Two-third partial hepatectomy (PH) in rodents plays a crucial role in the study of liver regeneration. In this study, the time series data of gene expression during liver regeneration in mouse were analyzed using the gene set numbered GSE6998 in GEO. A variety of bioinformatics methods, including masigPro, Weighted Gene Co-expression Network Analysis (WGCNA), spatial analysis of functional enrichment (SAFE) and ingenuity canonical pathway analysis (IPA) were used to identify and compare the significantly changed pathways, potential upstream regulators and key genes during liver regeneration. Our study showed that liver regeneration in the mouse is a coordinated process, which cell-cycle-related progress are at the centre of the interaction network involved in liver regeneration. Several candidate upstream regulators including PPARA, NFE2L2, MAD1 and CNR1 and some key genes such as Cdk1, Plk1, Cdc20, Aurka, Racgap1, Cenpa, Rrm1, Rrm2 were identified. In conclusion, these findings could contribute to revealing the molecular mechanism of liver regeneration after PH, which could provide new ideas and treatment methods for regenerative medicine, oncological drug development and oncological treatment.

IRIS-EDA: An integrated RNA-Seq interpretation system for gene expression data analysis

Next-Generation Sequencing has made available substantial amounts of large-scale Omics data, providing unprecedented opportunities to understand complex biological systems. Specifically, the value of RNA-Sequencing (RNA-Seq) data has been confirmed in inferring how gene regulatory systems will respond under various conditions (bulk data) or cell types (single-cell data). RNA-Seq can generate genome-scale gene expression profiles that can be further analyzed using correlation analysis, co-expression analysis, clustering, differential gene expression (DGE), among many other studies. While these analyses can provide invaluable information related to gene expression, integration and interpretation of the results can prove challenging. Here we present a tool called IRIS-EDA, which is a Shiny web server for expression data analysis. It provides a straightforward and user-friendly platform for performing numerous computational analyses on user-provided RNA-Seq or Single-cell RNA-Seq (scRNA-Seq) data. Specifically, three commonly used R packages (edgeR, DESeq2, and limma) are implemented in the DGE analysis with seven unique experimental design functionalities, including a user-specified design matrix option. Seven discovery-driven methods and tools (correlation analysis, heatmap, clustering, biclustering, Principal Component Analysis (PCA), Multidimensional Scaling (MDS), and t-distributed Stochastic Neighbor Embedding (t-SNE)) are provided for gene expression exploration which is useful for designing experimental hypotheses and determining key factors for comprehensive DGE analysis. Furthermore, this platform integrates seven visualization tools in a highly interactive manner, for improved interpretation of the analyses. It is noteworthy that, for the first time, IRIS-EDA provides a framework to expedite submission of data and results to NCBI's Gene Expression Omnibus following the FAIR (Findable, Accessible, Interoperable and Reusable) Data Principles. IRIS-EDA is freely available at http://bmbl.sdstate.edu/IRIS/.

Modeling and analysis of RNA-seq data: a review from a statistical perspective

BACKGROUND

Since the invention of next-generation RNA sequencing (RNA-seq) technologies, they have become a powerful tool to study the presence and quantity of RNA molecules in biological samples and have revolutionized transcriptomic studies. The analysis of RNA-seq data at four different levels (samples, genes, transcripts, and exons) involve multiple statistical and computational questions, some of which remain challenging up to date.

RESULTS

We review RNA-seq analysis tools at the sample, gene, transcript, and exon levels from a statistical perspective. We also highlight the biological and statistical questions of most practical considerations.

CONCLUSIONS

The development of statistical and computational methods for analyzing RNA-seq data has made significant advances in the past decade. However, methods developed to answer the same biological question often rely on diverse statistical models and exhibit different performance under different scenarios. This review discusses and compares multiple commonly used statistical models regarding their assumptions, in the hope of helping users select appropriate methods as needed, as well as assisting developers for future method development.

A New Machine Learning-Based Framework for Mapping Uncertainty Analysis in RNA-Seq Read Alignment and Gene Expression Estimation

One of the main benefits of using modern RNA-Sequencing (RNA-Seq) technology is the more accurate gene expression estimations compared with previous generations of expression data, such as the microarray. However, numerous issues can result in the possibility that an RNA-Seq read can be mapped to multiple locations on the reference genome with the same alignment scores, which occurs in plant, animal, and metagenome samples. Such a read is so-called a multiple-mapping read (MMR). The impact of these MMRs is reflected in gene expression estimation and all downstream analyses, including differential gene expression, functional enrichment, etc. Current analysis pipelines lack the tools to effectively test the reliability of gene expression estimations, thus are incapable of ensuring the validity of all downstream analyses. Our investigation into 95 RNA-Seq datasets from seven plant and animal species (totaling 1,951 GB) indicates an average of roughly 22% of all reads are MMRs. Here we present a machine learning-based tool called GeneQC (Gene expression Quality Control), which can accurately estimate the reliability of each gene's expression level derived from an RNA-Seq dataset. The underlying algorithm is designed based on extracted genomic and transcriptomic features, which are then combined using elastic-net regularization and mixture model fitting to provide a clearer picture of mapping uncertainty for each gene. GeneQC allows researchers to determine reliable expression estimations and conduct further analysis on the gene expression that is of sufficient quality. This tool also enables researchers to investigate continued re-alignment methods to determine more accurate gene expression estimates for those with low reliability. Application of GeneQC reveals high level of mapping uncertainty in plant samples and limited, severe mapping uncertainty in animal samples. GeneQC is freely available at http://bmbl.sdstate.edu/GeneQC/home.html.

Transcriptional Profiling of Host Cell Responses to Virulent Haemophilus parasuis: New Insights into Pathogenesis

Haemophilus parasuis is the causative agent of Glässer’s disease in pigs. H. parasuis can cause vascular damage, although the mechanism remains unclear. In this study, we investigated the host cell responses involved in the molecular pathway interactions in porcine aortic vascular endothelial cells (PAVECs) induced by H. parasuis using RNA-Seq. The transcriptome results showed that when PAVECs were infected with H. parasuis for 24 h, 281 differentially expressed genes (DEGs) were identified; of which, 236 were upregulated and 45 downregulated. The 281 DEGs were involved in 136 KEGG signaling pathways that were organismal systems, environmental information processing, metabolism, cellular processes, and genetic information processing. The main pathways were the Rap1, FoxO, and PI3K/Akt signaling pathways, and the overexpressed genes were determined and verified by quantitative reverse transcription polymerase chain reaction. In addition, 252 genes were clustered into biological processes, molecular processes, and cellular components. Our study provides new insights for understanding the interaction between bacterial and host cells, and analyzed, in detail, the possible mechanisms that lead to vascular damage induced by H. parasuis. This may lead to development of novel therapeutic targets to control H. parasuis infection.