Browsing by Author "Janga, Sarath C."

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    Characterizing alternative splicing and long non-coding RNA with high-throughput sequencing technology
    (2018-10) Zhou, Ao; Wu, Huanmei; Liu, Yunlong; Janga, Sarath C.; Liu, Xiaowen
    Several experimental methods has been developed for the study of the central dogma since late 20th century. Protein mass spectrometry and next generation sequencing (including DNA-Seq and RNA-Seq) forms a triangle of experimental methods, corresponding to the three vertices of the central dogma, i.e., DNA, RNA and protein. Numerous RNA sequencing and protein mass spectrometry experiments has been carried out in attempt to understand how the expression change of known genes affect biological functions in various of organisms, however, it has been once overlooked that the result data of these experiments are in fact holograms which also reveals other delicate biological mechanisms, such as RNA splicing and the expression of long non-coding RNAs. In this dissertation, we carried out five studies based on high-throughput sequencing data, in an attempt to understand how RNA splicing and differential expression of long non-coding RNAs is associated biological functions. In the first two studies, we identified and characterized 197 stimulant induced and 477 developmentally regulated alternative splicing events from RNA sequencing data. In the third study, we introduced a method for identifying novel alternative splicing events that were never documented. In the fourth study, we introduced a method for identifying known and novel RNA splicing junctions from protein mass spectrometry data. In the fifth study, we introduced a method for identifying long non-coding RNAs from poly-A selected RNA sequencing data. Taking advantage of these methods, we turned RNA sequencing and protein mass spectrometry data into an information gold mine of splicing and long non-coding RNA activities.
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    Computational biology approaches in drug repurposing and gene essentiality screening
    (2016-06-20) Philips, Santosh; Li, Lang; Liu, Yunlong; Liu, Xiaowen; Skaar, Todd C.; Janga, Sarath C.
    The rapid innovations in biotechnology have led to an exponential growth of data and electronically accessible scientific literature. In this enormous scientific data, knowledge can be exploited, and novel discoveries can be made. In my dissertation, I have focused on the novel molecular mechanism and therapeutic discoveries from big data for complex diseases. It is very evident today that complex diseases have many factors including genetics and environmental effects. The discovery of these factors is challenging and critical in personalized medicine. The increasing cost and time to develop new drugs poses a new challenge in effectively treating complex diseases. In this dissertation, we want to demonstrate that the use of existing data and literature as a potential resource for discovering novel therapies and in repositioning existing drugs. The key to identifying novel knowledge is in integrating information from decades of research across the different scientific disciplines to uncover interactions that are not explicitly stated. This puts critical information at the fingertips of researchers and clinicians who can take advantage of this newly acquired knowledge to make informed decisions. This dissertation utilizes computational biology methods to identify and integrate existing scientific data and literature resources in the discovery of novel molecular targets and drugs that can be repurposed. In chapters 1 of my dissertation, I extensively sifted through scientific literature and identified a novel interaction between Vitamin A and CYP19A1 that could lead to a potential increase in the production of estrogens. Further in chapter 2 by exploring a microarray dataset from an estradiol gene sensitivity study I was able to identify a potential novel anti-estrogenic indication for the commonly used urinary analgesic, phenazopyridine. Both discoveries were experimentally validated in the laboratory. In chapter 3 of my dissertation, through the use of a manually curated corpus and machine learning algorithms, I identified and extracted genes that are essential for cell survival. These results brighten the reality that novel knowledge with potential clinical applications can be discovered from existing data and literature by integrating information across various scientific disciplines.
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    Embryonic ethanol exposure alters expression of sox2 and other early transcripts in zebrafish, producing gastrulation defects
    (Springer Nature, 2020-03-03) Sarmah, Swapnalee; Srivastava, Rajneesh; McClintick, Jeanette N.; Janga, Sarath C.; Edenberg, Howard J.; Marrs, James A.; Biology, School of Science
    Ethanol exposure during prenatal development causes fetal alcohol spectrum disorder (FASD), the most frequent preventable birth defect and neurodevelopmental disability syndrome. The molecular targets of ethanol toxicity during development are poorly understood. Developmental stages surrounding gastrulation are very sensitive to ethanol exposure. To understand the effects of ethanol on early transcripts during embryogenesis, we treated zebrafish embryos with ethanol during pre-gastrulation period and examined the transcripts by Affymetrix GeneChip microarray before gastrulation. We identified 521 significantly dysregulated genes, including 61 transcription factors in ethanol-exposed embryos. Sox2, the key regulator of pluripotency and early development was significantly reduced. Functional annotation analysis showed enrichment in transcription regulation, embryonic axes patterning, and signaling pathways, including Wnt, Notch and retinoic acid. We identified all potential genomic targets of 25 dysregulated transcription factors and compared their interactions with the ethanol-dysregulated genes. This analysis predicted that Sox2 targeted a large number of ethanol-dysregulated genes. A gene regulatory network analysis showed that many of the dysregulated genes are targeted by multiple transcription factors. Injection of sox2 mRNA partially rescued ethanol-induced gene expression, epiboly and gastrulation defects. Additional studies of this ethanol dysregulated network may identify therapeutic targets that coordinately regulate early development.
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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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    Odyssey: a semi-automated pipeline for phasing, imputation, and analysis of genome-wide genetic data
    (Biomed Central, 2019-06-28) Eller, Ryan J.; Janga, Sarath C.; Walsh, Susan; Biology, School of Science
    BACKGROUND: Genome imputation, admixture resolution and genome-wide association analyses are timely and computationally intensive processes with many composite and requisite steps. Analysis time increases further when building and installing the run programs required for these analyses. For scientists that may not be as versed in programing language, but want to perform these operations hands on, there is a lengthy learning curve to utilize the vast number of programs available for these analyses. RESULTS: In an effort to streamline the entire process with easy-to-use steps for scientists working with big data, the Odyssey pipeline was developed. Odyssey is a simplified, efficient, semi-automated genome-wide imputation and analysis pipeline, which prepares raw genetic data, performs pre-imputation quality control, phasing, imputation, post-imputation quality control, population stratification analysis, and genome-wide association with statistical data analysis, including result visualization. Odyssey is a pipeline that integrates programs such as PLINK, SHAPEIT, Eagle, IMPUTE, Minimac, and several R packages, to create a seamless, easy-to-use, and modular workflow controlled via a single user-friendly configuration file. Odyssey was built with compatibility in mind, and thus utilizes the Singularity container solution, which can be run on Linux, MacOS, and Windows platforms. It is also easily scalable from a simple desktop to a High-Performance System (HPS). CONCLUSION: Odyssey facilitates efficient and fast genome-wide association analysis automation and can go from raw genetic data to genome: phenome association visualization and analyses results in 3-8 h on average, depending on the input data, choice of programs within the pipeline and available computer resources. Odyssey was built to be flexible, portable, compatible, scalable, and easy to setup. Biologists less familiar with programing can now work hands on with their own big data using this easy-to-use pipeline.
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    The RavA-ViaA Chaperone-Like System Interacts with and Modulates the Activity of the Fumarate Reductase Respiratory Complex
    (Elsevier, 2017-01) Wong, Keith S.; Bhandari, Vaibhav; Janga, Sarath C.; Houry, Walid A.; Department of Biohealth Informatics, School of Informatics and Computing
    Regulatory ATPase variant A (RavA) is a MoxR AAA + protein that functions together with a partner protein that we termed VWA interacting with AAA + ATPase (ViaA) containing a von Willebrand Factor A domain. However, the functional role of RavA-ViaA in the cell is not yet well established. Here, we show that RavA-ViaA are functionally associated with anaerobic respiration in Escherichia coli through interactions with the fumarate reductase (Frd) electron transport complex. Expression analysis of ravA and viaA genes showed that both proteins are co-expressed with multiple anaerobic respiratory genes, many of which are regulated by the anaerobic transcriptional regulator Fnr. Consistently, the expression of both ravA and viaA was found to be dependent on Fnr in cells grown under oxygen-limiting condition. ViaA was found to physically interact with FrdA, the flavin-containing subunit of the Frd complex. Both RavA and the Fe–S-containing subunit of the Frd complex, FrdB, regulate this interaction. Importantly, Frd activity was observed to increase in the absence of RavA and ViaA. This indicates that RavA and ViaA modulate the activity of the Frd complex, signifying a potential regulatory chaperone-like function for RavA-ViaA during bacterial anaerobic respiration with fumarate as the terminal electron acceptor.
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    Splicing factor ESRP1 controls ER-positive breast cancer by altering metabolic pathways
    (EMBO Press, 2019-02) Gökmen‐Polar, Yesim; Neelamraju, Yaseswini; Goswami, Chirayu P.; Gu, Yuan; Gu, Xiaoping; Nallamothu, Gouthami; Vieth, Edyta; Janga, Sarath C.; Ryan, Michael; Badve, Sunil S.; Pathology and Laboratory Medicine, School of Medicine
    The epithelial splicing regulatory proteins 1 and 2 (ESRP1 and ESRP2) control the epithelial-to-mesenchymal transition (EMT) splicing program in cancer. However, their role in breast cancer recurrence is unclear. In this study, we report that high levels of ESRP1, but not ESRP2, are associated with poor prognosis in estrogen receptor positive (ER+) breast tumors. Knockdown of ESRP1 in endocrine-resistant breast cancer models decreases growth significantly and alters the EMT splicing signature, which we confirm using TCGA SpliceSeq data of ER+ BRCA tumors. However, these changes are not accompanied by the development of a mesenchymal phenotype or a change in key EMT-transcription factors. In tamoxifen-resistant cells, knockdown of ESRP1 affects lipid metabolism and oxidoreductase processes, resulting in the decreased expression of fatty acid synthase (FASN), stearoyl-CoA desaturase 1 (SCD1), and phosphoglycerate dehydrogenase (PHGDH) at both the mRNA and protein levels. Furthermore, ESRP1 knockdown increases the basal respiration and spare respiration capacity. This study reports a novel role for ESRP1 that could form the basis for the prevention of tamoxifen resistance in ER+ breast cancer.