Browsing by Subject "splicing"

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    Interplay between estrogen receptor and AKT in Estradiol-induced alternative splicing
    (BMC, 2013-06-11) Bhat-Nakshatri, Poornima; Song, Eun-Kyung; Collins, Nikail R; Uversky, Vladimir N; Dunker, A Keith; O’Malley, Bert W; Geistlinger, Tim R; Carroll, Jason S; Brown, Myles; Nakshatri, Harikrishna
    Background Alternative splicing is critical for generating complex proteomes in response to extracellular signals. Nuclear receptors including estrogen receptor alpha (ERα) and their ligands promote alternative splicing. The endogenous targets of ERα:estradiol (E2)-mediated alternative splicing and the influence of extracellular kinases that phosphorylate ERα on E2-induced splicing are unknown. Methods MCF-7 and its anti-estrogen derivatives were used for the majority of the assays. CD44 mini gene was used to measure the effect of E2 and AKT on alternative splicing. ExonHit array analysis was performed to identify E2 and AKT-regulated endogenous alternatively spliced apoptosis-related genes. Quantitative reverse transcription polymerase chain reaction was performed to verify alternative splicing. ERα binding to alternatively spliced genes was verified by chromatin immunoprecipitation assay. Bromodeoxyuridine incorporation-ELISA and Annexin V labeling assays were done to measure cell proliferation and apoptosis, respectively. Results We identified the targets of E2-induced alternative splicing and deconstructed some of the mechanisms surrounding E2-induced splicing by combining splice array with ERα cistrome and gene expression array. E2-induced alternatively spliced genes fall into at least two subgroups: coupled to E2-regulated transcription and ERα binding to the gene without an effect on rate of transcription. Further, AKT, which phosphorylates both ERα and splicing factors, influenced ERα:E2 dependent splicing in a gene-specific manner. Genes that are alternatively spliced include FAS/CD95, FGFR2, and AXIN-1. E2 increased the expression of FGFR2 C1 isoform but reduced C3 isoform at mRNA level. E2-induced alternative splicing of FAS and FGFR2 in MCF-7 cells correlated with resistance to FAS activation-induced apoptosis and response to keratinocyte growth factor (KGF), respectively. Resistance of MCF-7 breast cancer cells to the anti-estrogen tamoxifen was associated with ERα-dependent overexpression of FGFR2, whereas resistance to fulvestrant was associated with ERα-dependent isoform switching, which correlated with altered response to KGF. Conclusion E2 may partly alter cellular proteome through alternative splicing uncoupled to its effects on transcription initiation and aberration in E2-induced alternative splicing events may influence response to anti-estrogens.
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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.