Local translation of mRNAs in dendrites is vital for synaptic plasticity and learning and memory. Tight regulation of this translation is key to preventing neurological disorders resulting from aberrant local translation. Here we find that CNOT7, the major deadenylase in eukaryotic cells, takes on the distinct role of regulating local translation in the hippocampus. Depletion of CNOT7 from cultured neurons affects the poly(A) state, localization, and translation of dendritic mRNAs while having little effect on the global neuronal mRNA population. Following synaptic activity, CNOT7 is rapidly degraded resulting in polyadenylation and a change in the localization of its target mRNAs. We find that this degradation of CNOT7 is essential for synaptic plasticity to occur as keeping CNOT7 levels high prevents these changes. This regulation of dendritic mRNAs by CNOT7 is necessary for normal neuronal function in vivo, as depletion of CNOT7 also disrupts learning and memory in mice. We utilized deep sequencing to identify the neuronal mRNAs whose poly(A) state is governed by CNOT7. Interestingly these mRNAs can be separated into two distinct populations: ones that gain a poly(A) tail following CNOT7 depletion and ones that surprisingly lose their poly(A) tail following CNOT7 depletion. These two populations are also distinct based on the lengths of their 3’ UTRs and their codon usage, suggesting that these key features may dictate how CNOT7 acts on its target mRNAs. This work reveals a central role for CNOT7 in the hippocampus where it governs local translation and higher cognitive function.

Huntington’s disease is a neurodegenerative disorder caused by expansion of the CAG repeat in huntingtin exon 1. Early studies demonstrated the huntingtin gene is transcribed into two 3′UTR isoforms in normal human tissue. Decades later, researchers identified a truncated huntingtin mRNA isoform in disease but not control human brain. We speculated the amount of huntingtin 3′UTR isoforms might also vary between control and Huntington’s disease brains. We provide evidence that the abundance of huntingtin 3′UTR isoforms, including a novel mid-3′UTR isoform, differs between patient and control neural stem cells, fibroblasts, motor cortex, and cerebellum. Both alleles of huntingtin contribute to isoform changes. We show huntingtin 3′UTR isoforms are metabolized differently. The long and mid isoforms have shorter half-lives, shorter polyA tails, and more microRNA and RNA binding protein sites than the short isoform. 3′UTR Isoform changes are not limited to huntingtin. Isoforms from 11% of genes change abundance in Huntington’s motor cortex. Only 17% of genes with isoform alterations are differentially expressed in disease tissue. However, gene ontology analysis suggests they share common pathways with differentially expressed genes. We demonstrate knockdown of the RNA binding protein CNOT6 in control fibroblasts results in huntingtin isoform changes similar to those in disease fibroblasts. This study further characterizes Huntington’s disease molecular pathology and suggests RNA binding protein expression may influence mRNA isoform expression in the Huntington’s disease brain.

Machine-learning (ML) techniques have been widely applied to solve different problems in biology. However, biological data are large and complex, which often result in extremely intricate ML models. Frequently, these models may have a poor performance or may be computationally unfeasible. This study presents a set of novel computational methods and focuses on the application of genetic algorithms (GAs) for the simplification and optimization of ML models and their applications to biological problems. The dissertation addresses the following three challenges. The first is to develop a generalizable classification methodology able to systematically derive competitive models despite the complexity and nature of the data. Although several algorithms for the induction of classification models have been proposed, the algorithms are data dependent. Consequently, we developed OmniGA, a novel and generalizable framework that uses different classification models in a treeXlike decision structure, along with a parallel GA for the optimization of the OmniGA structure. Results show that OmniGA consistently outperformed existing commonly used classification models. The second challenge is the prediction of translation initiation sites (TIS) in plants genomic DNA. We performed a statistical analysis of the genomic DNA and proposed a new set of discriminant features for this problem. We developed a wrapper method based on GAs for selecting an optimal feature subset, which, in conjunction with a classification model, produced the most accurate framework for the recognition of TIS in plants. Finally, results demonstrate that despite the evolutionary distance between different plants, our approach successfully identified conserved genomic elements that may serve as the starting point for the development of a generic model for prediction of TIS in eukaryotic organisms. Finally, the third challenge is the accurate prediction of polyadenylation signals in human genomic DNA. To achieve this, we analyzed genomic DNA sequences for the 12 most frequent polyadenylation signal variants and proposed a new set of features that may contribute to the understanding of the polyadenylation process. We derived Omni-PolyA, a model, and tool based on OmniGA for the prediction of the polyadenylation signals. Results show that Omni-PolyA significantly reduced the average classification error rate compared to the state-of-the-art results.

No description available.

No description available.

No description available.

No description available.

No description available.

京都大学 / 新制・課程博士 / 博士(理学) / 甲第24082号 / 理博第4849号 / 新制||理||1694(附属図書館) / 京都大学大学院理学研究科生物科学専攻 / (主査)准教授 柘植 知彦, 教授 森 和俊, 教授 川口 真也 / 学位規則第4条第1項該当 / Doctor of Science / Kyoto University / DGAM

High-throughput genomic datasets obtained from microarray or sequencing studies have revolutionized the field of molecular biology over the last decade. The complexity of these new technologies also poses new challenges to statisticians to separate biological relevant information from technical noise. Two methods are introduced that address important issues with normalization of array comparative genomic hybridization (aCGH) microarrays and the analysis of RNA sequencing (RNA-Seq) studies. Many studies investigating copy number aberrations at the DNA level for cancer and genetic studies use comparative genomic hybridization (CGH) on oligo arrays. However, aCGH data often suffer from low signal to noise ratios resulting in poor resolution of fine features. Bilke et al. showed that the commonly used running average noise reduction strategy performs poorly when errors are dominated by systematic components. A method called pcaCGH is proposed that significantly reduces noise using a non-parametric regression on technical covariates of probes to estimate systematic bias. Then a robust principal components analysis (PCA) estimates any remaining systematic bias not explained by technical covariates used in the preceding regression. The proposed algorithm is demonstrated on two CGH datasets measuring the NCI-60 cell lines utilizing NimbleGen and Agilent microarrays. The method achieves a nominal error variance reduction of 60%-65% as well as an 2-fold increase in signal to noise ratio on average, resulting in more detailed copy number estimates. Furthermore, correlations of signal intensity ratios of NimbleGen and Agilent arrays are increased by 40% on average, indicating a significant improvement in agreement between the technologies. A second algorithm called gamSeq is introduced to test for differential gene expression in RNA sequencing studies. Limitations of existing methods are outlined and the proposed algorithm is compared to these existing algorithms. Simulation studies and real data are used to show that gamSeq improves upon existing methods with regards to type I error control while maintaining similar or better power for a range of sample sizes for RNA-Seq studies. Furthermore, the proposed method is applied to detect differential 3' UTR usage.