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Browsing by Author "Hashemikhabir, Seyedsasan"
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Item Accurate Identification of RNA Editing Events Using Matched RNA and DNA Sequenced Samples Uncovers the Contribution of the Editing Landscape to Disease Progression in Glioblastoma PatientsHashemikhabir, Seyedsasan; Hundley, Heather A.; Janga, Sarath ChandraRNA editing event is increasingly appreciated as an important posttranscriptional regulatory mechanism in mammals. Adenosine deaminases that act on RNA (ADARs) are the enzymes that catalyze adenosine (A) to inosine (I) editing events. Human brain RNA is reported to have highest number of editing events. Many neurotransmitter receptors and ion channels undergo editing within exonic regions which generates a different protein than that encoded by the genome. ALU repeats in introns and untranslated regions of brain mRNAs are often targeted by editing events and result in altered splicing and post-transcriptional gene regulation.Item Dissecting Protein-RNA Interaction Network in Human Genome(2020-08) Hashemikhabir, Seyedsasan; Liu, Xiaowen; Janga, Sarath Chandra; Liu, Yunlong; Wu, HuanmeiIn eukaryotes, gene regulation is a complex multilevel process comprising of transcriptional, post-transcriptional, and post-translational control. Although the regulation at transcriptional and post-translational levels is gradually being understood, protein machinery and the mechanisms underlying the post-transcriptional regulation remain to be elucidated. In the first study of this dissertation, I designed and implemented a database of RNA Binding Protein (RBP) Expression and Disease Dynamics (READ-DB: darwin.soic.iupui.edu), a non-redundant, curated database of human RBPs. This RBP knowledge base includes data from different experimental studies providing a one stop portal for understanding the expression, evolutionary trajectories, and disease dynamics of RBPs in the context of post-transcriptional regulatory networks. Despite the existence of several experimental procedures to understand the function of RBPs, a lack of a proper computational method to profile differential occupancy limits the scope of research. In the second study, I built a scalable framework for comparing genome-wide protein occupancy profiles among cell-types data, to uncover alterations in protein-RNA interactomes. diffHunter (github.com/Sasanh/diffHunter), is a window based peak calling and profile comparison method that can efficiently store the base-pair level read information of every given sample in a NoSQL (Not Only SQL) database. It identifies and quantitates the genome-wide binding differences between a pair of samples in two stages: Peak Calling and Differential Binding Identification. Identifying such regions enables us to compare the biologically important regions that differ between two conditions. Finally, I studied A-to- I RNA editing as one of the special functions of an RBPs’ family. ADAR family RBPs are the primary driver in the conversion of adenosine to inosine (A-to-I) within mRNA. I developed a Cancer-specific RNA-editing Identification using Somatic variation Pipeline (CRISP: github.com/Sasanh/CRISP) a computational framework for accurate identification of A-to-I editing events contributing to the prognosis and stratification of glioblastoma subtypes as well as the editing events that can serve as molecular classifiers for therapeutic approaches. I proposed two models that explains the cis-regulatory role of A-to-I editing events in noncoding regions in modulating the post-transcriptional regulation of target transcripts in glioblastoma.Item ExSurv: A Web Resource for Prognostic Analyses of Exons Across Human Cancers Using Clinical Transcriptomes(SAGE, 2016-08-07) Hashemikhabir, Seyedsasan; Budak, Gungor; Janga, Sarath Chandra; Department of BioHealth Informatics, IU School of Informatics and ComputingSurvival analysis in biomedical sciences is generally performed by correlating the levels of cellular components with patients' clinical features as a common practice in prognostic biomarker discovery. While the common and primary focus of such analysis in cancer genomics so far has been to identify the potential prognostic genes, alternative splicing - a posttranscriptional regulatory mechanism that affects the functional form of a protein due to inclusion or exclusion of individual exons giving rise to alternative protein products, has increasingly gained attention due to the prevalence of splicing aberrations in cancer transcriptomes. Hence, uncovering the potential prognostic exons can not only help in rationally designing exon-specific therapeutics but also increase specificity toward more personalized treatment options. To address this gap and to provide a platform for rational identification of prognostic exons from cancer transcriptomes, we developed ExSurv (https://exsurv.soic.iupui.edu), a web-based platform for predicting the survival contribution of all annotated exons in the human genome using RNA sequencing-based expression profiles for cancer samples from four cancer types available from The Cancer Genome Atlas. ExSurv enables users to search for a gene of interest and shows survival probabilities for all the exons associated with a gene and found to be significant at the chosen threshold. ExSurv also includes raw expression values across the cancer cohort as well as the survival plots for prognostic exons. Our analysis of the resulting prognostic exons across four cancer types revealed that most of the survival-associated exons are unique to a cancer type with few processes such as cell adhesion, carboxylic, fatty acid metabolism, and regulation of T-cell signaling common across cancer types, possibly suggesting significant differences in the posttranscriptional regulatory pathways contributing to prognosis.Item A framework for identifying genotypic information from clinical records: exploiting integrated ontology structures to transfer annotations between ICD codes and Gene Ontologies(IEEE, 2015-09) Hashemikhabir, Seyedsasan; Xia, Ran; Xiang, Yang; Janga, Sarath Chandra; Department of Biohealth Informatics, School of Informatics and ComputingAlthough some methods are proposed for automatic ontology generation, none of them address the issue of integrating large-scale heterogeneous biomedical ontologies. We propose a novel approach for integrating various types of ontologies efficiently and apply it to integrate International Classification of Diseases, Ninth Revision, Clinical Modification (ICD9CM) and Gene Ontologies (GO). This approach is one of the early attempts to quantify the associations among clinical terms (e.g. ICD9 codes) based on their corresponding genomic relationships. We reconstructed a merged tree for a partial set of GO and ICD9 codes and measured the performance of this tree in terms of associations’ relevance by comparing them with two well-known disease-gene datasets (i.e. MalaCards and Disease Ontology). Furthermore, we compared the genomic-based ICD9 associations to temporal relationships between them from electronic health records. Our analysis shows promising associations supported by both comparisons suggesting a high reliability. We also manually analyzed several significant associations and found promising support from literature.Item Health Care Needs of Underserved Populations in the City of Indianapolis(2016) Mohammadi, Iman; Hashemikhabir, Seyedsasan; Toscos, Tammy; Wu, HuanmeiMeeting the health care needs for underserved populations is crucial. We used EMR data to investigate the relationship between diagnoses and patient characteristics to help providers redesign healthcare systems that can meet the needs of underserved patients.Item The Human RBPome: From Genes and Proteins to Human Disease(2015) Neelamraju, Yaseswini; Hashemikhabir, Seyedsasan; Janga, Sarath ChandraRNA Binding Proteins (RBPs) play a central role in mediating post transcriptional regulation of genes. However, less is understood about them and their regulatory mechanisms. In this study, we construct a repertoire of 1344 genes encoding RBPs identified from several experimental studies and present a comprehensive analysis to understand their characteristics at a global scale. The domain architecture of RBPs enabled us to classify them into three groups - Classical (29%), Non-classical (19%) and Unclassified (52%). A higher percentage of proteins with unclassified domains reveals the presence of various uncharacterized motifs that can potentially bind RNA. In addition, enrichment of various unconventional superfamilies' suggest that RBPs could form an integral part of the cellular architecture. RBPs were found to be highly disordered compared to non-RBPs (p<2.2e-16, Fisher's exact test), indicating a dynamic regulatory role of RBPs in cellular functioning. Evolutionary analysis in 62 different species showed that RBPs are highly conserved compared to non-RBPs (p<2.2e-16, Wilcox-test), reflecting a conservation of various biological processes like mRNA splicing, ribosome biogenesis. Expression patterns of RBPs from human proteome map revealed that majority (~60%) of the RBPs are tissue-specific. Additionally, non-classical proteins were found to be highly expressed than the classical proteins (p<0.05, Wilcox test) in ~50% of the tissues. RBPs were also seen to be highly associated with several neurological disorders, cancer and inflammatory diseases. Further, anatomical context like B cells, T-cells, Fetal Liver and Fetal Brain were found to be enriched, implying a prominent role of RBPs in mediating immune responses and different developmental stages. These analyses are made accessible to researchers in the form of a database called RNA Binding protein expression and disease dynamics database (READ DB).Item The human RBPome: From genes and proteins to human disease(Elsevier, 2015-09) Neelamraju, Yaseswini; Hashemikhabir, Seyedsasan; Janga, Sarath Chandra; Department of BioHealth Informatics, School of Informatics and ComputingRNA binding proteins (RBPs) play a central role in mediating post transcriptional regulation of genes. However less is understood about them and their regulatory mechanisms. In this study, we construct a catalogue of 1344 experimentally confirmed RBPs. The domain architecture of RBPs enabled us to classify them into three groups — Classical (29%), Non-classical (19%) and unclassified (52%). A higher percentage of proteins with unclassified domains reveals the presence of various uncharacterised motifs that can potentially bind RNA. RBPs were found to be highly disordered compared to Non-RBPs (p < 2.2e-16, Fisher's exact test), suggestive of a dynamic regulatory role of RBPs in cellular signalling and homeostasis. Evolutionary analysis in 62 different species showed that RBPs are highly conserved compared to Non-RBPs (p < 2.2e-16, Wilcox-test), reflecting the conservation of various biological processes like mRNA splicing and ribosome biogenesis. The expression patterns of RBPs from human proteome map revealed that ~ 40% of them are ubiquitously expressed and ~ 60% are tissue-specific. RBPs were also seen to be highly associated with several neurological disorders, cancer and inflammatory diseases. Anatomical contexts like B cells, T-cells, foetal liver and foetal brain were found to be strongly enriched for RBPs, implying a prominent role of RBPs in immune responses and different developmental stages. The catalogue and meta-analysis presented here should form a foundation for furthering our understanding of RBPs and the cellular networks they control, in years to come. This article is part of a Special Issue entitled: Proteomics in India.Item Kidney Specific Regulatory Network in Mouse Uncovers Functional, Evolutionary and Disease DynamicsHashemikhabir, Seyedsasan; Srivastava, Rajneesh; Janga, Sarath ChandraTranscription factors (TFs) operate in a combinatorial fashion to regulate the expression of a gene or a group of genes; however, their tissue-specific regulatory interactions are not fully characterized. In this study, we construct and investigate kindey-specific regulatory (KSR) network for mouse. We obtained upstream regions of genes in the mouse genome from ENSEMBL and extracted DNase 1 Hypersensitive sites (DHS) for 8-week mouse kidney from ENCODE project. Similarly, the position weight matrices (PWMs) for TF binding motifs (BMo) were extracted from JASPAR. Jolma, TRANSFAC and mapped in the mouse genome using FIMO. These BMo were integrated with obtained DHS signals (narrow peak) in 5 KBs upstream regions. The resulting TFs and their targeted genes were modeled as directed interaction network comprising of 619 TFs and their corresponding 13500 target genes. We trimmed the resulting network by only keeping the genes that function as TFs. Resulting TF-TF network (of 619 nodes) was analyzed to provide a holistic picture of TF-TF interactions in mouse kidney tissue while the global network was studied for conservation across 61 species and relevance in kidney associated diseases. We observed that genes related to diseases were significantly enriched in second and third layers in network hierarchy. Conservation analysis of Mouse KSR revealed >50% conservation in close relatives such as rat, human, dog, squirrel and less conserved in invertebrates and yeast, thus elucidating network complexity increases with increase in kidney functionality from lower to higher species. In addition, mouse KSR was examined in its closest relative, rat for segments of nephron - TAL (Thick ascending limb), PT (Proximal tubules), IMCD (Inner medullary collecting duct), which revealed a significant enrichment of TFs for their corresponding original group in mouse KSR. Further, this network was investigated in diverse model kidney diseases such as hypertension, diabetes and kidney renal clear cell carcinoma (KIRC). The compendium of the network reported in this study can form a roadmap for increasing our understanding of the variations in regulatory wiring in kidney diseases.Item PSIP1/p75 promotes tumorigenicity in breast cancer cells by promoting the transcription of cell cycle genes(Oxford University Press, 2017-10-01) Singh, Deepak K.; Gholamalamdari, Omid; Jadaliha, Mahdieh; Li, Xiao Ling; Lin, Yo-Chuen; Zhang, Yang; Guang, Shuomeng; Hashemikhabir, Seyedsasan; Tiwari, Saumya; Zhu, Yuelin J.; Khan, Abid; Thomas, Anu; Chakraborty, Arindam; Macias, Virgilia; Balla, Andre K.; Bhargava, Rohit; Janga, Sarath Chandra; Ma, Jian; Prasanth, Supriya G.; Lal, Ashish; Prasanth, Kannanganattu V.; BioHealth Informatics, School of Informatics and ComputingBreast cancer (BC) is a highly heterogeneous disease, both at the pathological and molecular level, and several chromatin-associated proteins play crucial roles in BC initiation and progression. Here, we demonstrate the role of PSIP1 (PC4 and SF2 interacting protein)/p75 (LEDGF) in BC progression. PSIP1/p75, previously identified as a chromatin-adaptor protein, is found to be upregulated in basal-like/triple negative breast cancer (TNBC) patient samples and cell lines. Immunohistochemistry in tissue arrays showed elevated levels of PSIP1 in metastatic invasive ductal carcinoma. Survival data analyses revealed that the levels of PSIP1 showed a negative association with TNBC patient survival. Depletion of PSIP1/p75 significantly reduced the tumorigenicity and metastatic properties of TNBC cell lines while its over-expression promoted tumorigenicity. Further, gene expression studies revealed that PSIP1 regulates the expression of genes controlling cell-cycle progression, cell migration and invasion. Finally, by interacting with RNA polymerase II, PSIP1/p75 facilitates the association of RNA pol II to the promoter of cell cycle genes and thereby regulates their transcription. Our findings demonstrate an important role of PSIP1/p75 in TNBC tumorigenicity by promoting the expression of genes that control the cell cycle and tumor metastasis.Item RNA Editing in Pathogenesis of Cancer(AACR, 2017-07) Baysal, Bora E.; Sharma, Shraddha; Hashemikhabir, Seyedsasan; Janga, Sarath Chandra; BioHealth Informatics, School of Informatics and ComputingSeveral adenosine or cytidine deaminase enzymes deaminate transcript sequences in a cell type or environment-dependent manner by a programmed process called RNA editing. RNA editing enzymes catalyze A>I or C>U transcript alterations and have the potential to change protein coding sequences. In this brief review, we highlight some recent work that shows aberrant patterns of RNA editing in cancer. Transcriptome sequencing studies reveal increased or decreased global RNA editing levels depending on the tumor type. Altered RNA editing in cancer cells may provide a selective advantage for tumor growth and resistance to apoptosis. RNA editing may promote cancer by dynamically recoding oncogenic genes, regulating oncogenic gene expression by noncoding RNA and miRNA editing, or by transcriptome scale changes in RNA editing levels that may affect innate immune signaling. Although RNA editing markedly increases complexity of the cancer cell transcriptomes, cancer-specific recoding RNA editing events have yet to be discovered. Epitranscriptomic changes by RNA editing in cancer represent a novel mechanism contributing to sequence diversity independently of DNA mutations. Therefore, RNA editing studies should complement genome sequence data to understand the full impact of nucleic acid sequence alterations in cancer.