1
|
Srivastava M, Dukeshire MR, Mir Q, Omoru OB, Manzourolajdad A, Janga SC. Experimental and computational methods for studying the dynamics of RNA-RNA interactions in SARS-COV2 genomes. Brief Funct Genomics 2024; 23:46-54. [PMID: 36752040 PMCID: PMC10799312 DOI: 10.1093/bfgp/elac050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 10/24/2022] [Accepted: 11/11/2022] [Indexed: 02/09/2023] Open
Abstract
Long-range ribonucleic acid (RNA)-RNA interactions (RRI) are prevalent in positive-strand RNA viruses, including Beta-coronaviruses, and these take part in regulatory roles, including the regulation of sub-genomic RNA production rates. Crosslinking of interacting RNAs and short read-based deep sequencing of resulting RNA-RNA hybrids have shown that these long-range structures exist in severe acute respiratory syndrome coronavirus (SARS-CoV)-2 on both genomic and sub-genomic levels and in dynamic topologies. Furthermore, co-evolution of coronaviruses with their hosts is navigated by genetic variations made possible by its large genome, high recombination frequency and a high mutation rate. SARS-CoV-2's mutations are known to occur spontaneously during replication, and thousands of aggregate mutations have been reported since the emergence of the virus. Although many long-range RRIs have been experimentally identified using high-throughput methods for the wild-type SARS-CoV-2 strain, evolutionary trajectory of these RRIs across variants, impact of mutations on RRIs and interaction of SARS-CoV-2 RNAs with the host have been largely open questions in the field. In this review, we summarize recent computational tools and experimental methods that have been enabling the mapping of RRIs in viral genomes, with a specific focus on SARS-CoV-2. We also present available informatics resources to navigate the RRI maps and shed light on the impact of mutations on the RRI space in viral genomes. Investigating the evolution of long-range RNA interactions and that of virus-host interactions can contribute to the understanding of new and emerging variants as well as aid in developing improved RNA therapeutics critical for combating future outbreaks.
Collapse
Affiliation(s)
- Mansi Srivastava
- Department of BioHealth Informatics, School of Informatics and Computing, Indiana University Purdue University, 535 West Michigan Street, Indianapolis, Indiana 46202, USA
- Department of Biology, Indiana University, 1001 East 3 St, Bloomington, Indiana 47405, USA
| | - Matthew R Dukeshire
- Department of BioHealth Informatics, School of Informatics and Computing, Indiana University Purdue University, 535 West Michigan Street, Indianapolis, Indiana 46202, USA
| | - Quoseena Mir
- Department of BioHealth Informatics, School of Informatics and Computing, Indiana University Purdue University, 535 West Michigan Street, Indianapolis, Indiana 46202, USA
| | - Okiemute Beatrice Omoru
- Department of BioHealth Informatics, School of Informatics and Computing, Indiana University Purdue University, 535 West Michigan Street, Indianapolis, Indiana 46202, USA
| | - Amirhossein Manzourolajdad
- Department of BioHealth Informatics, School of Informatics and Computing, Indiana University Purdue University, 535 West Michigan Street, Indianapolis, Indiana 46202, USA
- Department of Computer Science, Colgate University, Hamilton, NY, USA
| | - Sarath Chandra Janga
- Department of BioHealth Informatics, School of Informatics and Computing, Indiana University Purdue University, 535 West Michigan Street, Indianapolis, Indiana 46202, USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Medical Research and Library Building, 975 West Walnut Street, Indianapolis, Indiana 46202, USA
- Centre for Computational Biology and Bioinformatics, Indiana University School of Medicine, 5021 Health Information and Translational Sciences (HITS), 410 West 10th Street, Indianapolis, Indiana 46202, USA
| |
Collapse
|
2
|
Heruye S, Myslinski J, Zeng C, Zollman A, Makino S, Nanamatsu A, Mir Q, Janga SC, Doud EH, Eadon MT, Maier B, Hamada M, Tran TM, Dagher PC, Hato T. Inflammation primes the kidney for recovery by activating AZIN1 A-to-I editing. bioRxiv 2023:2023.11.09.566426. [PMID: 37986799 PMCID: PMC10659426 DOI: 10.1101/2023.11.09.566426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
The progression of kidney disease varies among individuals, but a general methodology to quantify disease timelines is lacking. Particularly challenging is the task of determining the potential for recovery from acute kidney injury following various insults. Here, we report that quantitation of post-transcriptional adenosine-to-inosine (A-to-I) RNA editing offers a distinct genome-wide signature, enabling the delineation of disease trajectories in the kidney. A well-defined murine model of endotoxemia permitted the identification of the origin and extent of A-to-I editing, along with temporally discrete signatures of double-stranded RNA stress and Adenosine Deaminase isoform switching. We found that A-to-I editing of Antizyme Inhibitor 1 (AZIN1), a positive regulator of polyamine biosynthesis, serves as a particularly useful temporal landmark during endotoxemia. Our data indicate that AZIN1 A-to-I editing, triggered by preceding inflammation, primes the kidney and activates endogenous recovery mechanisms. By comparing genetically modified human cell lines and mice locked in either A-to-I edited or uneditable states, we uncovered that AZIN1 A-to-I editing not only enhances polyamine biosynthesis but also engages glycolysis and nicotinamide biosynthesis to drive the recovery phenotype. Our findings implicate that quantifying AZIN1 A-to-I editing could potentially identify individuals who have transitioned to an endogenous recovery phase. This phase would reflect their past inflammation and indicate their potential for future recovery.
Collapse
Affiliation(s)
- Segewkal Heruye
- Department of Medicine, Indiana University School of Medicine
| | - Jered Myslinski
- Department of Medicine, Indiana University School of Medicine
| | - Chao Zeng
- Faculty of Science and Engineering, Waseda University, Tokyo
| | - Amy Zollman
- Department of Medicine, Indiana University School of Medicine
| | - Shinichi Makino
- Department of Medicine, Indiana University School of Medicine
| | - Azuma Nanamatsu
- Department of Medicine, Indiana University School of Medicine
| | - Quoseena Mir
- Luddy School of Informatics, Computing, and Engineering, Indiana University
| | | | - Emma H Doud
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine
| | - Michael T Eadon
- Department of Medicine, Indiana University School of Medicine
| | - Bernhard Maier
- Department of Medicine, Indiana University School of Medicine
| | - Michiaki Hamada
- Faculty of Science and Engineering, Waseda University, Tokyo
- AIST-Waseda University Computational Bio Big-Data Open Innovation Laboratory, National Institute of Advanced Industrial Science and Technology, Tokyo
- Graduate School of Medicine, Nippon Medical School, Tokyo
| | - Tuan M Tran
- Department of Medicine, Indiana University School of Medicine
- Richard L. Roudebush Veterans Affairs Medical Center, Indianapolis
| | - Pierre C Dagher
- Department of Medicine, Indiana University School of Medicine
| | - Takashi Hato
- Department of Medicine, Indiana University School of Medicine
- Richard L. Roudebush Veterans Affairs Medical Center, Indianapolis
- Department of Medical and Molecular Genetics, Indiana University School of Medicine
| |
Collapse
|
3
|
Hassan D, Acevedo D, Daulatabad SV, Mir Q, Janga SC. Penguin: A Tool for Predicting Pseudouridine Sites in Direct RNA Nanopore Sequencing Data. Methods 2022; 203:478-487. [PMID: 35182749 DOI: 10.1016/j.ymeth.2022.02.005] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Revised: 02/03/2022] [Accepted: 02/14/2022] [Indexed: 01/04/2023] Open
Abstract
Pseudouridine is one of the most abundant RNA modifications, occurring when uridines are catalyzed by Pseudouridine synthase proteins. It plays an important role in many biological processes and has been reported to have application in drug development. Recently, the single-molecule sequencing techniques such as the direct RNA sequencing platform offered by Oxford Nanopore technologies have enabled direct detection of RNA modifications on the molecule being sequenced. In this study, we introduce a tool called Penguin that integrates several machine learning (ML) models to identify RNA Pseudouridine sites on Nanopore direct RNA sequencing reads. Pseudouridine sites were identified on single molecule sequencing data collected from direct RNA sequencing resulting in 723K reads in Hek293 and 500K reads in Hela cell lines. Penguin extracts a set of features from the raw signal measured by the Oxford Nanopore and the corresponding basecalled k-mer. Those features are used to train the predictors included in Penguin, which in turn, can predict whether the signal is modified by the presence of Pseudouridine sites in the testing phase. We have included various predictors in Penguin, including Support vector machines (SVM), Random Forest (RF), and Neural network (NN). The results on the two benchmark data sets for Hek293 and Hela cell lines show outstanding performance of Penguin either in random split testing or in independent validation testing. In random split testing, Penguin has been able to identify Pseudouridine sites with a high accuracy of 93.38% by applying SVM to Hek293 benchmark dataset. In independent validation testing, Penguin achieves an accuracy of 92.61% by training SVM with Hek293 benchmark dataset and testing it for identifying Pseudouridine sites on Hela benchmark dataset. Thus, Penguin outperforms the existing Pseudouridine predictors in the literature by 16 % higher accuracy than those predictors using independent validation testing. Employing penguin to predict Pseudouridine revealed a significant enrichment of "regulation of mRNA 3'-end processing" in Hek293 cell line and positive regulation of transcription from RNA polymerase II promoter involved in cellular response to chemical stimulus in Hela cell line. Penguin software and models are available on GitHub at https://github.com/Janga-Lab/Penguin and can be readily employed for predicting Ψ sites from Nanopore direct RNA-sequencing datasets.
Collapse
Affiliation(s)
- Doaa Hassan
- Department of BioHealth Informatics, School of Informatics and Computing, Indiana University Purdue University, 535 West Michigan Street, Indianapolis, Indiana 46202; Computers and Systems Department, National Telecommunication Institute, Cairo, Egypt
| | - Daniel Acevedo
- Department of BioHealth Informatics, School of Informatics and Computing, Indiana University Purdue University, 535 West Michigan Street, Indianapolis, Indiana 46202; Computer Science Department, University of Texas Rio Grande Valley
| | - Swapna Vidhur Daulatabad
- Department of BioHealth Informatics, School of Informatics and Computing, Indiana University Purdue University, 535 West Michigan Street, Indianapolis, Indiana 46202
| | - Quoseena Mir
- Department of BioHealth Informatics, School of Informatics and Computing, Indiana University Purdue University, 535 West Michigan Street, Indianapolis, Indiana 46202
| | - Sarath Chandra Janga
- Department of BioHealth Informatics, School of Informatics and Computing, Indiana University Purdue University, 535 West Michigan Street, Indianapolis, Indiana 46202; Department of Medical and Molecular Genetics, Indiana University School of Medicine, Medical Research and Library Building, 975 West Walnut Street, Indianapolis, Indiana, 46202; Centre for Computational Biology and Bioinformatics, Indiana University School of Medicine, 5021 Health Information and Translational Sciences (HITS), 410 West 10th Street, Indianapolis, Indiana, 46202.
| |
Collapse
|
4
|
Jha N, Hall D, Kanakan A, Mehta P, Maurya R, Mir Q, Gill HM, Janga SC, Pandey R. Geographical Landscape and Transmission Dynamics of SARS-CoV-2 Variants Across India: A Longitudinal Perspective. Front Genet 2022; 12:753648. [PMID: 34976008 PMCID: PMC8719586 DOI: 10.3389/fgene.2021.753648] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Accepted: 11/17/2021] [Indexed: 12/21/2022] Open
Abstract
Globally, SARS-CoV-2 has moved from one tide to another with ebbs in between. Genomic surveillance has greatly aided the detection and tracking of the virus and the identification of the variants of concern (VOC). The knowledge and understanding from genomic surveillance is important for a populous country like India for public health and healthcare officials for advance planning. An integrative analysis of the publicly available datasets in GISAID from India reveals the differential distribution of clades, lineages, gender, and age over a year (Apr 2020–Mar 2021). The significant insights include the early evidence towards B.1.617 and B.1.1.7 lineages in the specific states of India. Pan-India longitudinal data highlighted that B.1.36* was the predominant clade in India until January–February 2021 after which it has gradually been replaced by the B.1.617.1 lineage, from December 2020 onward. Regional analysis of the spread of SARS-CoV-2 indicated that B.1.617.3 was first seen in India in the month of October in the state of Maharashtra, while the now most prevalent strain B.1.617.2 was first seen in Bihar and subsequently spread to the states of Maharashtra, Gujarat, and West Bengal. To enable a real time understanding of the transmission and evolution of the SARS-CoV-2 genomes, we built a transmission map available on https://covid19-indiana.soic.iupui.edu/India/EmergingLineages/April2020/to/March2021. Based on our analysis, the rate estimate for divergence in our dataset was 9.48 e-4 substitutions per site/year for SARS-CoV-2. This would enable pandemic preparedness with the addition of future sequencing data from India available in the public repositories for tracking and monitoring the VOCs and variants of interest (VOI). This would help aid decision making from the public health perspective.
Collapse
Affiliation(s)
- Neha Jha
- Integrative Genomics of Host-Pathogen (INGEN-HOPE) Laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India
| | - Dwight Hall
- Department of Biohealth Informatics, School of Informatics and Computing, Indiana University Purdue University, Indianapolis, IN, United States
| | - Akshay Kanakan
- Integrative Genomics of Host-Pathogen (INGEN-HOPE) Laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India
| | - Priyanka Mehta
- Integrative Genomics of Host-Pathogen (INGEN-HOPE) Laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India
| | - Ranjeet Maurya
- Integrative Genomics of Host-Pathogen (INGEN-HOPE) Laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India.,Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
| | - Quoseena Mir
- Department of Biohealth Informatics, School of Informatics and Computing, Indiana University Purdue University, Indianapolis, IN, United States
| | - Hunter Mathias Gill
- Department of Biohealth Informatics, School of Informatics and Computing, Indiana University Purdue University, Indianapolis, IN, United States
| | - Sarath Chandra Janga
- Department of Biohealth Informatics, School of Informatics and Computing, Indiana University Purdue University, Indianapolis, IN, United States
| | - Rajesh Pandey
- Integrative Genomics of Host-Pathogen (INGEN-HOPE) Laboratory, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India.,Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
| |
Collapse
|
5
|
Mir Q, Lakshmipati DK, Ulrich BJ, Kaplan MH, Janga SC. Comparative Analysis of Alternative Splicing Profiles in Th Cell Subsets Reveals Extensive Cell Type-Specific Effects Modulated by a Network of Transcription Factors and RNA-Binding Proteins. Immunohorizons 2021; 5:760-771. [PMID: 34583937 DOI: 10.4049/immunohorizons.2100060] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Accepted: 08/30/2021] [Indexed: 11/19/2022] Open
Abstract
Alternative splicing (AS) plays an important role in the development of many cell types; however, its contribution to Th subsets has been clearly defined. In this study, we compare mice naive CD4+ Th cells with Th1, Th2, Th17, and T regulatory cells and observed that the majority of AS events were retained intron, followed by skipped-exon events, with at least 1200 genes across cell types affected by AS events. A significant fraction of the AS events, especially retained intron events from the 72-h time point, were no longer observed 2 wk postdifferentiation, suggesting a role for AS in early activation and differentiation via preferential expression of specific isoforms required during T cell activation, but not for differentiation or effector function. Examining the protein consequence of the exon-skipping events revealed an abundance of structural proteins encoding for intrinsically unstructured peptide regions, followed by transmembrane helices, β strands, and polypeptide turn motifs. Analyses of expression profiles of RNA-binding proteins (RBPs) and their cognate binding sites flanking the discovered AS events revealed an enrichment for specific RBP recognition sites in each of the Th subsets. Integration with publicly available chromatin immunoprecipitation sequencing datasets for transcription factors support a model wherein lineage-determining transcription factors impact the RBP profile within the differentiating cells, and this differential expression contributes to AS of the transcriptome via a cascade of cell type-specific posttranscriptional rewiring events.
Collapse
Affiliation(s)
- Quoseena Mir
- Department of BioHealth Informatics, School of Informatics and Computing, Indiana University-Purdue University, Indianapolis, IN
| | - Deepak K Lakshmipati
- Department of BioHealth Informatics, School of Informatics and Computing, Indiana University-Purdue University, Indianapolis, IN
| | - Benjamin J Ulrich
- Department of Pediatrics and Herman B Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN.,Department of Microbiology and Immunology, Indiana University School of Medicine, Indianapolis, IN
| | - Mark H Kaplan
- Department of Pediatrics and Herman B Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN.,Department of Microbiology and Immunology, Indiana University School of Medicine, Indianapolis, IN.,Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN
| | - Sarath Chandra Janga
- Department of BioHealth Informatics, School of Informatics and Computing, Indiana University-Purdue University, Indianapolis, IN; .,Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN; and.,Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN
| |
Collapse
|
6
|
Abstract
Modified nucleotides in mRNA are an essential addition to the standard genetic code of four nucleotides in animals, plants, and their viruses. The emerging field of epitranscriptomics examines nucleotide modifications in mRNA and their impact on gene expression. The low abundance of nucleotide modifications and technical limitations, however, have hampered systematic analysis of their occurrence and functions. Selective chemical and immunological identification of modified nucleotides has revealed global candidate topology maps for many modifications in mRNA, but further technical advances to increase confidence will be necessary. Single-molecule sequencing introduced by Oxford Nanopore now promises to overcome such limitations, and we summarize current progress with a particular focus on the bioinformatic challenges of this novel sequencing technology.
Collapse
Affiliation(s)
- Ina Anreiter
- Ontario Institute for Cancer Research, Toronto, ON M5G 0A3, Canada; Department of Computer Science, University of Toronto, Toronto, ON M5S 2E4, Canada
| | - Quoseena Mir
- Department of BioHealth Informatics, School of Informatics and Computing, Indiana University-Purdue University Indianapolis, Indianapolis, IN 46202, USA
| | - Jared T Simpson
- Ontario Institute for Cancer Research, Toronto, ON M5G 0A3, Canada; Department of Computer Science, University of Toronto, Toronto, ON M5S 2E4, Canada
| | - Sarath C Janga
- Department of BioHealth Informatics, School of Informatics and Computing, Indiana University-Purdue University Indianapolis, Indianapolis, IN 46202, USA; Department of Medical and Molecular Genetics, Medical Research and Library Building, Indiana University School of Medicine, Indianapolis, IN 46202, USA; Center for Computational Biology and Bioinformatics, 5021 Health Information and Translational Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Matthias Soller
- School of Biosciences, College of Life and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK.
| |
Collapse
|
7
|
Vemuri S, Srivastava R, Mir Q, Hashemikhabir S, Dong XC, Janga SC. SliceIt: A genome-wide resource and visualization tool to design CRISPR/Cas9 screens for editing protein-RNA interaction sites in the human genome. Methods 2020; 178:104-113. [PMID: 31494246 PMCID: PMC7056568 DOI: 10.1016/j.ymeth.2019.09.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2019] [Revised: 06/25/2019] [Accepted: 09/01/2019] [Indexed: 12/26/2022] Open
Abstract
Several protein-RNA cross linking protocols have been established in recent years to delineate the molecular interaction of an RNA Binding Protein (RBP) and its target RNAs. However, functional dissection of the role of the RBP binding sites in modulating the post-transcriptional fate of the target RNA remains challenging. CRISPR/Cas9 genome editing system is being commonly employed to perturb both coding and noncoding regions in the genome. With the advancements in genome-scale CRISPR/Cas9 screens, it is now possible to not only perturb specific binding sites but also probe the global impact of protein-RNA interaction sites across cell types. Here, we present SliceIt (http://sliceit.soic.iupui.edu/), a database of in silico sgRNA (single guide RNA) library to facilitate conducting such high throughput screens. SliceIt comprises of ~4.8 million unique sgRNAs with an estimated range of 2-8 sgRNAs designed per RBP binding site, for eCLIP experiments of >100 RBPs in HepG2 and K562 cell lines from the ENCODE project. SliceIt provides a user friendly environment, developed using advanced search engine framework, Elasticsearch. It is available in both table and genome browser views facilitating the easy navigation of RBP binding sites, designed sgRNAs, exon expression levels across 53 human tissues along with prevalence of SNPs and GWAS hits on binding sites. Exon expression profiles enable examination of locus specific changes proximal to the binding sites. Users can also upload custom tracks of various file formats directly onto genome browser, to navigate additional genomic features in the genome and compare with other types of omics profiles. All the binding site-centric information is dynamically accessible via "search by gene", "search by coordinates" and "search by RBP" options and readily available to download. Validation of the sgRNA library in SliceIt was performed by selecting RBP binding sites in Lipt1 gene and designing sgRNAs. Effect of CRISPR/Cas9 perturbations on the selected binding sites in HepG2 cell line, was confirmed based on altered proximal exon expression levels using qPCR, further supporting the utility of the resource to design experiments for perturbing protein-RNA interaction networks. Thus, SliceIt provides a one-stop repertoire of guide RNA library to perturb RBP binding sites, along with several layers of functional information to design both low and high throughput CRISPR/Cas9 screens, for studying the phenotypes and diseases associated with RBP binding sites.
Collapse
Affiliation(s)
- Sasank Vemuri
- Department of BioHealth Informatics, School of Informatics and Computing, Indiana University Purdue University, 719 Indiana Ave Ste 319, Walker Plaza Building, Indianapolis, IN 46202, United States
| | - Rajneesh Srivastava
- Department of BioHealth Informatics, School of Informatics and Computing, Indiana University Purdue University, 719 Indiana Ave Ste 319, Walker Plaza Building, Indianapolis, IN 46202, United States
| | - Quoseena Mir
- Department of BioHealth Informatics, School of Informatics and Computing, Indiana University Purdue University, 719 Indiana Ave Ste 319, Walker Plaza Building, Indianapolis, IN 46202, United States
| | - Seyedsasan Hashemikhabir
- Department of BioHealth Informatics, School of Informatics and Computing, Indiana University Purdue University, 719 Indiana Ave Ste 319, Walker Plaza Building, Indianapolis, IN 46202, United States
| | - X Charlie Dong
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, 635 Barnhill Drive, Indianapolis, IN 46202, United States
| | - Sarath Chandra Janga
- Department of BioHealth Informatics, School of Informatics and Computing, Indiana University Purdue University, 719 Indiana Ave Ste 319, Walker Plaza Building, Indianapolis, IN 46202, United States; Department of Medical and Molecular Genetics, Indiana University School of Medicine, Medical Research and Library Building, 975 West Walnut Street, Indianapolis, IN 46202, United States; Centre for Computational Biology and Bioinformatics, Indiana University School of Medicine, 5021 Health Information and Translational Sciences (HITS), 410 West 10th Street, Indianapolis, IN 46202, United States.
| |
Collapse
|
8
|
Mir Q, Srivastava R, Ulrich BJ, Kaplan MH, Janga SC. Identification of splice isoforms in T helper cells using Nanopore-based cDNA sequencing. The Journal of Immunology 2020. [DOI: 10.4049/jimmunol.204.supp.230.20] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Abstract
The T helper (Th) cell compartment of the adaptive immune response provides a central mechanism of balance between inflammation and immune tolerance. The differentiation of naïve Th cells into both regulatory and effector Th subsets is regulated through complex gene regulatory networks that lead to distinct transcriptomes. Alternative RNA splicing is a major component of cellular differentiation that allows protein heterogeneity and mechanistic diversity across cell types. However, our understanding of alternative splicing control is still limited. Here we show differential expression of RNA binding Proteins (RBP) genes among Th cell subsets suggesting a regulatory role of RBPs in RNA splicing and Th differentiation. In order to interrogate the regulatory gene network, we utilized innovative Nanopore-based sequencing to generate a single molecule transcriptome map of naïve CD4 T cells, and differentiated Treg and Th1 cells to characterize full-length cDNA transcripts and alternatively spliced isoforms. Gene expression was compared across four major gene categories; RBPs, Transcription Factors (TFs), Other Protein Coding (OPC) and LncRNA. Our study revealed that RBPs exhibited high splicing potential while LncRNA exhibited relatively less splicing potential in comparison to other gene categories in all cell types. Overall, we identify RBPs that are both differentially expressed in Th subsets and differentially spliced themselves. This might represent an important control mechanism of functional protein diversity in regulating immune responses.
Collapse
|