The RNA-binding protein (RBP) and Hu/ELAV family member HuD regulates mRNA metabolism of genes that encode proteins involved in neuronal differentiation, learning and memory, and certain neurological diseases. Given the important functions of HuD in a variety of processes, we set out to characterize the 5’ genomic region of the mammalian HuD gene and determine the mechanisms that regulate its mRNA expression in neurons using P19 cells and mouse brain as models. Bioinformatic and 5’RACE (rapid amplification of cDNA ends) analyses of the HuD 5’ genomic flanking region identified eight conserved leader exons (E1s), two of which are novel. Expression of all E1 variants was established in differentiating P19 cells, mouse embryonic (E14.5) and adult brains. Through several complementary approaches, we determined that the abundance of HuD mRNA is predominantly under transcriptional control in differentiating neurons. Sequential deletion of the 5’ regulatory region upstream of the predominantly expressed E1c variant revealed a well-conserved 400 bp DNA region that contains five E-boxes and is capable of directing expression of HuD specifically in neurons. Using electrophoretic mobility shift assays (EMSAs), chromatin immunoprecipitations (ChIPs), and E1c 5’ regulatory region (RR) deletion and mutation analysis, we found that two of these E-boxes are targeted by neurogenin 2 (NGN2/NEUROG2) and that this mechanism is important for induction of HuD mRNA in neurons. Additional deletion and mutation of the E1c 5’ RR revealed that putative cis-acting elements for Kruppel-like factors (KLFs) and nuclear DEAF-1-related (NuDR) transcription factors also positively regulate transcription of HuD. Together, our findings reveal that the intricate transcriptional regulation of mammalian HuD involves eight leader exons and potentially alternate promoters. We further demonstrate that transcription of HuD requires neuron-specific control by NGN2 and possibly KLF and NuDR transcription factors. To our knowledge, this is the first study to identify transcriptional events that positively regulate expression of HuD.

Dissertation (Ph.D.).--University of Texas Graduate School of Biomedical Sciences at San Antonio, 2007. / Vita. Includes bibliographical references.

Thesis (M.Sc.). / Written for the Dept. of Biochemistry. Title from title page of PDF (viewed 2008/01/15). Includes bibliographical references.

Thesis (Ph. D.)--University of Washington, 1998. / Vita. Includes bibliographical references (leaves [114]-131).

Thesis (Ph. D.)--University of Virginia, 1998. / Spine title: NMR structural studies of Rho130. Includes bibliographical references (p. 258-269). Also available online through Digital Dissertations.

Thesis (Ph. D.)--University of Washington, 2000. / Vita. Includes bibliographical references (leaves 103-112).

The RNA-binding protein (RBP) and Hu/ELAV family member HuD regulates mRNA metabolism of genes that encode proteins involved in neuronal differentiation, learning and memory, and certain neurological diseases. Given the important functions of HuD in a variety of processes, we set out to characterize the 5’ genomic region of the mammalian HuD gene and determine the mechanisms that regulate its mRNA expression in neurons using P19 cells and mouse brain as models. Bioinformatic and 5’RACE (rapid amplification of cDNA ends) analyses of the HuD 5’ genomic flanking region identified eight conserved leader exons (E1s), two of which are novel. Expression of all E1 variants was established in differentiating P19 cells, mouse embryonic (E14.5) and adult brains. Through several complementary approaches, we determined that the abundance of HuD mRNA is predominantly under transcriptional control in differentiating neurons. Sequential deletion of the 5’ regulatory region upstream of the predominantly expressed E1c variant revealed a well-conserved 400 bp DNA region that contains five E-boxes and is capable of directing expression of HuD specifically in neurons. Using electrophoretic mobility shift assays (EMSAs), chromatin immunoprecipitations (ChIPs), and E1c 5’ regulatory region (RR) deletion and mutation analysis, we found that two of these E-boxes are targeted by neurogenin 2 (NGN2/NEUROG2) and that this mechanism is important for induction of HuD mRNA in neurons. Additional deletion and mutation of the E1c 5’ RR revealed that putative cis-acting elements for Kruppel-like factors (KLFs) and nuclear DEAF-1-related (NuDR) transcription factors also positively regulate transcription of HuD. Together, our findings reveal that the intricate transcriptional regulation of mammalian HuD involves eight leader exons and potentially alternate promoters. We further demonstrate that transcription of HuD requires neuron-specific control by NGN2 and possibly KLF and NuDR transcription factors. To our knowledge, this is the first study to identify transcriptional events that positively regulate expression of HuD.

京都大学 / 新制・課程博士 / 博士(薬科学) / 甲第24206号 / 薬科博第159号 / 新制||薬科||17(附属図書館) / 京都大学大学院薬学研究科薬科学専攻 / (主査)教授 伊藤 貴浩, 教授 中山 和久, 教授 生田 宏一 / 学位規則第4条第1項該当 / Doctor of Pharmaceutical Sciences / Kyoto University / DFAM

Identification and annotation of RNA Binding Proteins (RBPs) and RNA Binding residues from sequence information alone is one of the most challenging problems in computational biology. RBPs play crucial roles in several fundamental biological functions including transcriptional regulation of RNAs and RNA metabolism splicing. Existing experimental techniques are time-consuming and costly. Thus, efficient computational identification of RBPs directly from the sequence can be useful to annotate RBP and assist the experimental design. Here, we introduce AIRBP, a computational sequence-based method, which utilizes features extracted from evolutionary information, physiochemical properties, and disordered properties to train a machine learning method designed using stacking, an advanced machine learning technique, for effective prediction of RBPs. Furthermore, it makes use of efficient machine learning algorithms like Support Vector Machine, Logistic Regression, K-Nearest Neighbor and XGBoost (Extreme Gradient Boosting Algorithm). In this research work, we also propose another predictor for efficient annotation of RBP residues. This RBP residue predictor also uses stacking and evolutionary algorithms for efficient annotation of RBPs and RNA Binding residue. The RNA-binding residue predictor also utilizes various evolutionary, physicochemical and disordered properties to train a robust model. This thesis presents a possible solution to the RBP and RNA binding residue prediction problem through two independent predictors, both of which outperform existing state-of-the-art approaches.

Thesis (Ph. D.)--University of Washington, 1999. / Vita. Includes bibliographical references (leaves 126-146).