Browsing by Author "Janga, Sarath"

Now showing 1 - 7 of 7
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    CRISPR Cas13 sg A Designer
    Krohannon, Alexander; Janga, Sarath
    Recent discovery of the gene editing system - CRISPR (Clustered Regularly Interspersed Short Palindromic Repeats) associated proteins (cas), has resulted in its widespread use for improved understanding of a variety of biological systems, by enabling large-scale perturbation of the genomes and transcriptomes. Cas13, a lesser studied cas protein, has been repurposed to allow for efficient and precise editing of RNA molecules. The cas13 system utilizes base complementarity between a crRNA (crispr RNA) and a target RNA transcript, to preferentially bind to only the target transcript. Unlike targeting the upstream regulatory regions of protein coding genes on the genome, the transcriptome is significantly more redundant, leading to many transcripts having wide stretches of identical nucleotide sequences. Additionally, transcripts exhibit complex three-dimensional structures and interact with multiple RBPs (RNA binding proteins), both of which further limit the scope of effective target sequences. As a result, there currently exists no method to predict whether a crRNA will be effective or not. This project aims to create a novel machine learning model to predict the efficacy of a crRNA; using publicly available RNA knockdown data from cas13 characterization experiments1 for 555 sgRNAs targeting the transcriptome in HEK293 cells. Numerous types of machine learning models were tested during development including ARD (Automatic Relevance Determination), Bayesian Ridge, Elastic Net, Huber, K-Nearest Neighbors, Linear, and SVM (Support Vector Machines). K-Nearest Neighbors showed the greatest accuracy, predicting knockdown value within 10% of the mean value in 39.1% of the instances. Despite their differences in accuracy, Elastic Net had the lowest precision error (0.0638) and SVM had the lowest recall error (0.0094). Implementation of this model will allow for rapid deployment of new types of screening the transcriptomes and enable potential treatments for diseases linked with aberrations in RNA regulatory processes.
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    Human Stem Cell Differentiated Retinal Ganglion Cells for Developing Glaucoma Neuroprotection and Cell Replacement Strategies
    (2024-07) Anbarasu, Kavitha; Das, Arupratan; Corson, Timothy; Meyer, Jason; Graham, Brett; Janga, Sarath
    Progressive loss of retinal ganglion cells (RGCs) leads to glaucoma. Early diagnosis offers an opportunity to protect existing RGCs. In advanced glaucoma, most RGCs are lost causing blindness and cell replacement therapy the only option. We used a human stem cell-based RGC differentiation model to develop neuroprotection by restoring mitochondrial homeostasis and enhancing RGC differentiation efficiency to increase the success of cell replacement therapy. Unmyelinated axons in RGCs require high levels of ATP, making disrupted mitochondria a risk factor in glaucoma. Our goal was to restore mitochondrial homeostasis through mitophagy (mitochondrial autophagy) and mitobiogenesis (mitochondrial biogenesis). Mutations in the mitophagy protein Optineurin (OPTNE50K) are found in patients with normal tension glaucoma and hence, we also used RGCs with the E50K mutation. We discovered that hRGCE50Ks suffer from mitobiogenesis issues, Parkin/Pink mediated mitophagy defects, and have OPTNE50K-Tank binding kinase-1 (TBK1) aggregates. hRGCE50Ks have lower mitochondrial mass and a higher mitochondrial load. We inhibited TBK1 to induce mitochondrial biogenesis and dissolve OPTNE50K-TBK1 aggregates. Our results show TBK1 inhibition triggered mitobiogenesis, dissolved aggregates, decreased mitochondrial ATP production load, and increased spare respiratory capacity, leading to neuroprotection. With complete RGC loss, enhancing differentiation to progenitor cells with lower cell division capacity can improve the success of cell replacement therapy and reduce teratoma formation and poor tissue integration. We observed that stem cells use proteasomes for mitochondrial degradation, while hRGCs use the lysosomal mitophagy pathway. Our results indicate that proteasomal activity declines during differentiation to hRGCs. Inhibition of proteasomal activity during early differentiation resulted in higher and faster RGC differentiation, with similar effects seen in motor neuron differentiation. We did not observe metabolic reprogramming in differentiating cells upon proteasomal activity inhibition but saw changes in cell cycle distribution, specifically an increase in the number of cells in the G1 phase. Proteomics analysis post-inhibitory treatment showed elevated neuronal differentiation proteins. Our results can be translated to minimize injection cell numbers and other risks of cell replacement therapy. In summary, my research identifies novel mechanisms for restoring mitochondrial homeostasis for neuroprotection in glaucomatous RGCs and develops an enhanced differentiation strategy to aid the success of cell replacement therapy.
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    Integrating Data Science into T32 Training Programs at IUPUI
    (2019-06-30) Dixon, Brian E.; Stumpff, Julia C.; Kasthurirathne, Suranga N.; Lourens, Spencer; Janga, Sarath; Liu, Yunlong; Huang, Kun
    Data science is critically important to the biomedical research enterprise. Many research efforts currently and in the future will employ advanced computational techniques to analyze extremely large datasets in order to discover insights relevant to human health. Therefore the next generation of biomedical scientists requires knowledge of and proficiency in data science. With support from the U.S. National Library of Medicine, a team of faculty from Indiana University-Purdue University Indianapolis (IUPUI) facilitated curricula enhancement for National Institutes of Health (NIH) T32 research training programs with respect to data science. In collaboration with the existing NIH T32 Program Directors at IUPUI and the IU School of Medicine, the interdisciplinary team of faculty drawn from multiple schools and departments examined the existing landscape of data science offerings on campus in parallel with an assessment of the competencies that future biomedical and clinician scientists will require to be comfortable using data science methods to advance their research. The IUPUI campus possesses a rich tapestry of data science education programs across multiple schools and departments. Furthermore, the campus is home to more than a dozen world-class T32 programs funded by the NIH to train biomedical and clinician scientists. However, existing training programs do not currently emphasize data science or provide specific curriculum designed to ensure T32 graduates possess basic competencies in data science. To position the campus for the future, robust T32 programs need to connect with the rapidly growing data science programs. This report summarizes the rationale for the importance of connection and the competencies that future biomedical and clinical scientists will require to be successful. The report further describes the curriculum mapping efforts to link competencies with available degree programs, courses and workshops on campus. The report further recommends next steps for campus leadership, including but not limited to T32 Program Directors, the Office of the Vice Chancellor for Research, the Executive Associate Dean for Research Affairs at the IU School of Medicine, and the President and CEO of the Regenstrief Institute. Together we can strengthen the IUPUI campus and help ensure its T32 graduates are successful in their research careers.
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    Investigating Disease Mechanisms and Drug Response Differences in Transcriptomics Sequencing Data
    (2022-01) Simpson, Edward Ronald Jr.; Liu, Yunlong; Janga, Sarath; Wan, Jun; Wu, Huanmei; Yan, Jingwen
    In eukaryotes, genetic information is encoded by DNA, transcribed to precursor messenger RNA (pre-mRNA), processed into mature messenger RNA (mRNA), and translated into functional proteins. Splicing of pre-mRNA is an important epigenetic process that alters the function of proteins through modifying the exon structure of mature mRNA transcripts and is known to greatly contribute to diversity of the human proteome. The vast majority of human genes are expressed through multiple transcript isoforms. Expression of genes through splicing of pre-mRNA plays crucial roles in cellular development, identity, and processes. Both the identity of genes selected for transcription and the specific transcript isoforms that are expressed are essential for normal cellular function. Deviations in gene expression or isoform proportion can be an indication or the cause of disease. RNA sequencing (RNAseq) is a high-throughput next-generation sequencing technology that allows for the interrogation of gene expression on a massive scale. RNAseq generates short sequences that reflect pieces of mRNAs present in a sample. RNAseq can therefore be used to explore differences in gene expression, reveal transcript isoform identities and compare changes in isoform proportions. In this dissertation, I design and apply advanced analysis techniques to RNAseq, phenotypic and drug response data to investigate disease mechanisms and drug sensitivity. Research Goals: The work described in this dissertation accomplishes 4 aims. Aim 1) Evaluate the gene expression signature of concussion in collegiate athletes and identify potential biomarkers for response and recovery. Aim 2) Implement a machine-learning algorithm to determine if splicing can predict drug response in cancer cell lines. Aim 3) Design a fast, scalable method to identify differentially spliced events related to cancer drug response. Aim 4) Construct a drug-splicing network and use a systems biology approach to search for similarities in underlying splicing events.
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    Sarath Janga: Open Access and the Center for Digital Scholarship
    (2015-10-15) Odell, Jere D.; Janga, Sarath; Center for Digital Scholarship
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    Transcriptional Regulation of IL-9-Secreting T-Helper Cells in Allergic Airway Diseases
    (2021-12) Kharwadkar, Rakshin Prashant; Kaplan, Mark H.; Harrington, Maureen; Mosley, Amber; Janga, Sarath; Zhou, Baohua
    CD4 T cells are critical regulators of inflammatory diseases and play an important role in allergic airway diseases (AAD) by producing type 2 cytokines including IL-4, IL- 13, IL-5 and IL-9. In chronic AAD models, IL-9 producing CD4 T-helper (TH9) cells lead to accumulation of eosinophils and mast cells in the airway, increase levels of type-2 cytokines, stimulate ILC2 cell proliferation, and induce mucus production from airway epithelium. However, the transcriptional network that governs the development of TH9 cells and their function during allergic responses is not clearly understood. Naïve CD4 T cells differentiate into TH9 cells in the presence of IL-2, IL-4 and TGFβ, activating a complex network of transcription factors that restricts their development to TH9 lineage. In this study a variety of approaches were utilized, including characterizing Il9 reporter mice, to identify an additional Ets-transcription factor termed ERG (Ets-related gene) that is expressed preferentially in the TH9 subset. Knock-down of Erg during TH9 polarization led to a decrease in IL-9 production in TH9 cells in vitro. Deletion of Erg at the later stage of TH9 induced pathogenesis resulted in reduced IL-9 production in the airways in chronic AAD. Chromatin immunoprecipitation assays revealed that ERG interaction at the Il9 promoter region is restricted to the TH9 lineage and is sustained during TH9 polarization. In the absence of PU.1 and ETV5, ERG regulated IL-9 production independent of other Ets-transcription factors and the deletion of Erg further lead to a decrease in IL-9 production by lung-derived CD4-T cells in chronic AAD model. Lastly, I also identified IL-9 secreting CD4 tissue resident memory cell population that play an instrumental role in allergic recall responses. In summary, in this study novel transcription factors were identified that can regulate TH9 function and the role of IL-9 in allergic airway recall responses.
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    Uncovering RNA-Binding Proteins Implicated in Human Cancers by Integrating Genomics with Network-Based Approaches
    Kechavarzi, Bobak; Janga, Sarath
    RNA binding proteins (RBPs) are key regulatory mechanism in the cell. Their functions are varied depending on their localization, but many have been identified as essentially in translational regulation and cell proliferation. Their dysfunction has been linked to the development of various disease phenotypes and cancers. Using Human BodyMap v2.0 data and data made available through The Cancer Genome Atlas (TCGA), we proposed to observe the patterns of expression of RBPs in sixteen healthy tissues and across nine cancers, and their altering profiles. Additionally, by incorporating BioGrid protein-protein interaction data and CORUM protein complex information, we explore how network properties of the RNA may infer their dysfunction in cancers. The prognostic effect of RBPs classified by expression and network properties in breast cancer were determined using KM-Plotter. We observed that RBPs as a class are more highly expressed than other factors in the 16 human tissues, and furthermore that they are generally upexpressed in cancers. A smaller subset of RBPs (30) is many-fold higher expressed across a large portion of the observed cancers. Network metrics showed no significant differences, except for shortest path distances between subsets (Wilcox, p < 2x10·16). Similarly, complex size and membership did not show any trends or significant differences. The negative prognostic effect seems to be associated with mean path lengths of RBPs and their interaction with a highly dysregulated subset of RBPs.