Browsing by Author "Mir, Quoseena"

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    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
    (American Association of Immunologists, 2021-09-28) Mir, Quoseena; Lakshmipati, Deepak K.; Ulrich, Benjamin J.; Kaplan, Mark H.; Janga, Sarath Chandra; Biomedical Engineering and Informatics, Luddy School of Informatics, Computing, and Engineering
    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.
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    Experimental and computational methods for studying the dynamics of RNA-RNA interactions in SARS-COV2 genomes
    (Oxford University Press, 2024) Srivastava, Mansi; Dukeshire, Matthew R.; Mir, Quoseena; Omoru, Okiemute Beatrice; Manzourolajdad, Amirhossein; Janga, Sarath Chandra; Biomedical Engineering and Informatics, Luddy School of Informatics, Computing, and Engineering
    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.
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    Geographical Landscape and Transmission Dynamics of SARS-CoV-2 Variants Across India: A Longitudinal Perspective
    (Frontiers Media, 2021-12-17) Jha, Neha; Hall, Dwight; Kanakan, Akshay; Mehta, Priyanka; Maurya, Ranjeet; Mir, Quoseena; Gill, Hunter Mathias; Janga, Sarath Chandra; Pandey, Rajesh; Biomedical Engineering and Informatics, Luddy School of Informatics, Computing, and Engineering
    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.
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    Identification of Splice Isoforms in T Helper (TH) Cells Using Nanopore-based cDNA sequencing
    (2020) Mir, Quoseena; Srivastava, Rajneesh; Ulrich, Benjamin; Kaplan, Mark H.; Janga, Sarath Chandra
    Naïve T cells are the precursors for the adaptive immune response against pathogens. They circulate the blood and secondary lymphoid organs until they are stimulated through a diverse pool of T cell receptors to become effector or memory cells. The differentiation of naïve T cells to effector subsets is regulated through a complex gene regulatory network resulting in transcriptomic alterations. Our understanding of how this process is controlled transcriptionally is limited. Nanopore-based RNA sequencing technology enables identification of the full length transcripts and alternatively spliced isoforms. We used this technique to identify the transcriptional signatures of differentiated T cell subtypes. Here, we present a single molecule transcriptome map of differentiated Treg cells obtained from mouse spleen to characterize full-length transcripts and alternatively spliced isoforms. We obtained 5,092,757 quality filtered sequence reads from cDNA sequencing of Treg cells with mean base quality of 9.2 and mean length of 876.4 bp. Preprocessing and filtering of the data set followed by alignment to the mouse genome (mmlO) resulted in 4,724,442 mappable reads (92.7% with 15% error allowance) corresponding to 29,716 transcripts and 15,645 annotated genes. Functional analysis of 212 transcript expressed at TPM > 0.25 resulted in the enrichment of ''Nonsense Mediated Decay (NMD)", "apoptotic signaling pathway", "Antigen processing" and other significant (<1% FDR) pathways. While splicing analysis revealed hundreds of genes exhibiting multiple splicing isoforms.
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    Inflammation primes the kidney for recovery by activating AZIN1 A-to-I editing
    (bioRxiv, 2023-11-09) Heruye, Segewkal; Myslinski, Jered; Zeng, Chao; Zollman, Amy; Makino, Shinichi; Nanamatsu, Azuma; Mir, Quoseena; Janga, Sarath Chandra; Doud, Emma H.; Eadon, Michael T.; Maier, Bernhard; Hamada, Michiaki; Tran, Tuan M.; Dagher, Pierre C.; Hato, Takashi; Biochemistry and Molecular Biology, School of Medicine
    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.
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    Inflammation primes the murine kidney for recovery by activating AZIN1 adenosine-to-inosine editing
    (American Society for Clinical Investigation, 2024-09-03) Heruye, Segewkal Hawaze; Myslinski, Jered; Zeng, Chao; Zollman, Amy; Makino, Shinichi; Nanamatsu, Azuma; Mir, Quoseena; Janga, Sarath Chandra; Doud, Emma H.; Eadon, Michael T.; Maier, Bernhard; Hamada, Michiaki; Tran, Tuan M.; Dagher, Pierre C.; Hato, Takashi; Medicine, School of Medicine
    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.
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    New Twists in Detecting mRNA Modification Dynamics
    (Elsevier, 2020-07-01) Anreiter, Ina; Mir, Quoseena; Simpson, Jared T.; Janga, Sarath C.; Soller, Matthias; BioHealth Informatics, School of Informatics and Computing
    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.
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    New Twists in Detecting mRNA Modification Dynamics
    (Elsevier, 2020-07-01) Anreiter, Ina; Mir, Quoseena; Simpson, Jared T.; Janga, Sarath C.; Soller, Matthias; Medical and Molecular Genetics, School of Medicine
    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.
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    Nm-Nano: a machine learning framework for transcriptome-wide single-molecule mapping of 2´-O-methylation (Nm) sites in nanopore direct RNA sequencing datasets
    (Taylor & Francis, 2024) Hassan, Doaa; Ariyur, Aditya; Daulatabad, Swapna Vidhur; Mir, Quoseena; Janga, Sarath Chandra; Biomedical Engineering and Informatics, Luddy School of Informatics, Computing, and Engineering
    2´-O-methylation (Nm) is one of the most abundant modifications found in both mRNAs and noncoding RNAs. It contributes to many biological processes, such as the normal functioning of tRNA, the protection of mRNA against degradation by the decapping and exoribonuclease (DXO) protein, and the biogenesis and specificity of rRNA. Recent advancements in single-molecule sequencing techniques for long read RNA sequencing data offered by Oxford Nanopore technologies have enabled the direct detection of RNA modifications from sequencing data. In this study, we propose a bio-computational framework, Nm-Nano, for predicting the presence of Nm sites in direct RNA sequencing data generated from two human cell lines. The Nm-Nano framework integrates two supervised machine learning (ML) models for predicting Nm sites: Extreme Gradient Boosting (XGBoost) and Random Forest (RF) with K-mer embedding. Evaluation on benchmark datasets from direct RNA sequecing of HeLa and HEK293 cell lines, demonstrates high accuracy (99% with XGBoost and 92% with RF) in identifying Nm sites. Deploying Nm-Nano on HeLa and HEK293 cell lines reveals genes that are frequently modified with Nm. In HeLa cell lines, 125 genes are identified as frequently Nm-modified, showing enrichment in 30 ontologies related to immune response and cellular processes. In HEK293 cell lines, 61 genes are identified as frequently Nm-modified, with enrichment in processes like glycolysis and protein localization. These findings underscore the diverse regulatory roles of Nm modifications in metabolic pathways, protein degradation, and cellular processes. The source code of Nm-Nano can be freely accessed at https://github.com/Janga-Lab/Nm-Nano.
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    Penguin: A Tool for Predicting Pseudouridine Sites in Direct RNA Nanopore Sequencing Data
    (Elsevier, 2022) Hassan, Doaa; Acevedo, Daniel; Daulatabad, Swapna Vidhur; Mir, Quoseena; Janga, Sarath Chandra; BioHealth Informatics, School of Informatics and Computing
    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.