Thesis (Ph. D.)--University of Washington, 2006. / Vita. Includes bibliographical references (leaves 159-176).

Hepatocellular carcinoma (HCC) is the most common form of primary liver cancer, ranking the sixth most common cancer and third most common cause of cancer mortality worldwide. Alpha-fetoprotein (AFP) is a plasma protein that is highly expressed in the fetal liver and shut off after birth. AFP expression is elevated in regenerating adult liver and HCC and has been used extensively as a diagnostic marker of liver cancer. We have been studying mouse liver gene regulation to better understand mechanisms by which changes in gene expression contribute to liver development, homeostasis and disease. Zinc Fingers and Homeoboxes 2 (Zhx2) has been identified as a repressor of AFP, but the mechanism of this regulation remains unknown. Interestingly, all targets of Zhx2 that have been identified to date, including H19, Glypican 3, Elovl3 and Cytochrome P450 (CYP) genes, are also known to be misregulated in HCC. Thus, a better understanding of the mechanism by which these genes are regulated by Zhx2 will likely lead to new insights into gene regulation during HCC progression. Antisense transcripts belong to a diverse class of long noncoding RNA molecules > 200 nucleotides in length that often structurally resemble mRNAs, but do not encode proteins. While studying AFP mRNA regulation by Zhx2 in the mouse, our lab identified novel antisense AFP (asAFP) RNA transcripts that partially overlap the 3’ half of the mouse AFP gene. ENCODE tracks of ChIP-seq data for histone modifications in mouse liver show that the genomic region around the 5’ end of asAFP RNA has peaks for marks associated with promoters and enhancers. To better understand asAFP regulation, I identified the asAFP RNA 5’ end and the promoter elements that drive transcription. asAFP RNAs are ~5kb alternatively spliced, mainly cytoplasmic transcripts containing 2-4 exons. These transcripts were also detected in adult mouse liver RNA-seq data. asAFP is likely a noncoding RNA because it contains several small open reading frames that are 98 aa or smaller with no known functional domains or homology to known proteins. There is no evidence for similar transcripts in human liver. The abundance of asAFP RNA inversely correlates with AFP mRNA levels during postnatal liver development. Normally, asAFP RNA levels are high and AFP mRNA levels are low in the adult mouse liver. However, in the absence of Zhx2, AFP mRNA levels are higher and asAFP RNA levels are reduced, suggesting asAFP may be involved in the developmental regulation of AFP. Antisense transcripts function through a variety of mechanisms to positively or negatively regulate the expression of target genes. To explore the role of asAFP RNA in AFP gene regulation, I expressed segments of asAFP RNA in a mouse liver cell line and measured endogenous AFP mRNA levels. My data revealed that all segments of asAFP repressed endogenous AFP mRNA in trans. To determine the mechanism by which asAFP RNA regulates AFP, I expressed asAFP segments that overlapped only with exons or introns of AFP. The asAFP segments that overlap with the exons showed greater repression of endogenous AFP mRNA levels than those overlapping with intronic sequences. Additionally, I considered whether asAFP RNA repression of AFP mRNA may involve RNA editing by Adenosine deaminase acting on RNA (ADAR). ADARs convert adenosine to inosine in double-stranded RNAs that results in RNA degradation. My data indicate that AFP and asAFP dsRNA is not extensively edited, suggesting ADAR mediated decay is not involved in the regulation of AFP mRNA expression. However, further studies are required to determine the mechanism of cytoplasmic AFP mRNA degradation. Together, my data characterizes the transcriptional regulation of novel mouse asAFP transcripts and provides a model system to investigate how these transcripts regulate AFP mRNA through RNA-RNA interaction.

京都大学 / 0048 / 新制・課程博士 / 博士(医学) / 甲第22348号 / 医博第4589号 / 新制||医||1042(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 小柳 義夫, 教授 中川 一路, 教授 伊藤 貴浩 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

Starting with the discovery of microRNAs and the advent of genome‐wide transcriptomics, non‐protein‐coding transcripts have moved from a fringe topic to a central field research in molecular biology. In this contribution we review the state of the art of “computational RNomics”, i.e., the bioinformatics approaches to genome‐wide RNA annotation. Instead of rehashing results from recently published surveys in detail, we focus here on the open problem in the field, namely (functional) annotation of the plethora of putative RNAs. A series of exploratory studies are used to provide non‐trivial examples for the discussion of some of the difficulties.

Motivation: Understanding how nucleic acids interact is essential for understanding their function. Controlling these interactions, for example, can allow us to detect diseases and create new therapeutics. During quantitative reverse-transcription polymerase chain reaction (qRT-PCR) testing, having nucleic acids interact as designed is essential for ensuring accurate test results. Accurate testing is an important consideration during the detection of COVID-19, the disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Results: I introduced the program DinoKnot (Duplex Interaction of Nucleic acids with pseudoKnots) that follows the hierarchical folding hypothesis to predict the secondary structure of two interacting nucleic acid strands (DNA/RNA) of similar or different type. DinoKnot is the first program that utilizes stable stems in both strands as a guide to find the structure of their interaction. Using DinoKnot, I predicted the interaction structure between the SARS-CoV-2 genome and nine reverse primers from qRT-PCR primer-probe sets. I compared these results to an existing tool RNAcofold and highlighted an example to showcase DinoKnot’s ability to predict pseudoknotted structures. I investigated how mutations to the SARS-CoV-2 genome may affect the primer interaction and predicted three mutations that may prevent primer binding, reducing the ability for SARS-CoV-2 detection. Interaction structure results pre- dicted by DinoKnot that showed disruption of primer binding were consistent with a clinical example showing detection issues due to mutations. DinoKnot has the potential to screen new SARS-CoV-2 variants for possible detection issues and support existing applications involving DNA/RNA interactions, such as microRNA (miRNA) target site prediction, by adding structural considerations to the interaction to elicit functional information. / Graduate

RNAs play essential roles in various biological processes. Mounting evidence has demonstrated that RNA subcellular localization and intercellular trafficking govern their functions in coordinating plant growth at the organismal level. Beyond that, plants constantly encounter foreign RNAs (i.e., RNAs from pathogens including viruses and viroids). The subcellular localizations of RNAs are crucial for their function. While numerous types of RNAs (i.e., mRNAs, small RNAs, rRNAs, tRNAs, and long noncoding RNAs) have been found to traffic in a non-cell-autonomous fashion within plants, the underlying regulatory mechanism remains unclear. Viroids are single-stranded circular noncoding RNAs, which entirely rely on their RNA motifs to exploit cellular machinery for organelle entry and exit, cell-to-cell movement through plasmodesmata, and systemic trafficking. Viroids represent an excellent model to dissect the role of RNA 3-dimensional (3D) structural motifs in regulating RNA movement. Using nuclear-replicating viroids as a model, we showed that cellular Importin alpha-4 is likely involved in viroid RNA nuclear import, empirically supporting the involvement of Importin-based cellular pathway in RNA nuclear import. We also confirmed the involvement of a cellular protein (Virp1) that binds both Importin alpha-4 and viroids. Moreover, a conserved C-loop in nuclear-replicating viroids serves as a key signal for nuclear import. Disrupting C-loop impairs Virp1 binding, viroid nuclear accumulation and infectivity. Further, C-loop exists in a subviral satellite noncoding RNA that relies on Virp1 for nuclear import. On the other hand, no viroid can systemically infect the model plant Arabidopsis thaliana, suggesting the existence of non-host resistance yet to be understood. Here, we attempted to test whether a gene involved in RNA silencing, RNA-dependent RNA polymerase 6 (RDR6), plays a role in non-host resistance in Arabidopsis. I will discuss the data below in detail.

Diss. (sammanfattning) Stockholm : Karolinska institutet, 2007. / Härtill 4 uppsatser.

Non-coding RNAs are the most abundant class of RNAs found throughout genomes. These RNAs are key players of gene regulation and thus, the func- tion of whole organisms. Numerous methods have been developed so far for detecting novel classes of ncRNAs or finding homologs to the known ones. Because of their abundance, the sequence availability of these RNAs is rapidly increasing, as is the case for example for microRNAs. However, for classes of them, still only incomplete information is available, invertebrates 7SK snRNA for instance. Consequently, a lot of false positive outputs are produced in the former case, and more accurate annotation methods are needed for the latter cases to improve derivable knowledge. This makes the accuracy of gathering correct homologs a challenging task and it leads directly to a not less important problem, the curation of these data. Finding solutions for the aforementioned problems is more complex than one would expect as these RNAs are characterized not only by sequences informa- tion but also structure information, in addition to distinct biological features. In this work, data curation methods and sensitive homology search are shown as complementary methods to solve these problems. A careful curation and annotation method revealed new structural information in the invertebrates 7SK snRNA, which pushes the investigation in the area forward. This has been reflected by detecting new high potential 7SK RNA genes in different invertebrates groups. Moreover, the gaps between homology search and well- curated data on the one side, and between experimental and computational outputs on the other side, are closed. These gaps were bridged by a curation method applied to the microRNA data, which was then turned into a com- prehensive workflow implemented into an automated pipeline. MIRfix is a microRNA curation pipeline considering the detailed sequence and structure information of the metazoan microRNAs, together with biological features related to the microRNA biogenesis. Moreover, this pipeline can be integrated into existing methods and tools related to microRNA homology search and data curation. The application of this pipeline on the biggest open source microRNA database revealed its high capacity in detecting wrong annotated pre-miRNA, eventually improving alignment quality of the majority of the available data. Additionally, it was tested with artificial datasets highlighting the high accuracy in predicting the pre-miRNA components, miRNA and miRNA*.:Chapter 1: Introduction Chapter 2: Biological and Computational background 2.1 Biology 2.1.1 Non-coding RNAs 2.1.2 RNA secondary structure 2.1.3 Homology versus similarity 2.1.4 Evolution 2.2 The role of computational biology 2.2.1 Alignment 2.2.1.1 Pairwise alignment 2.2.1.2 Multiple sequence alignment (MSA) 2.2.2 Homology search 2.2.2.1 Sequence-based 2.2.2.2 Structure-based 2.2.3 RNA secondary structure prediction Chapter 3: Careful curation for snRNA 3.1 Biological background 3.2 Introduction to the problem 3.3 Methods 3.3.1 Initial seeds and models construction 3.3.2 Models anatomy then merging 3.4 Results 3.4.1 Refined model of arthropod 7SK RNA 3.4.1.1 5’ Stem 3.4.1.2 Extension of Stem A 3.4.1.3 Novel stem B in invertebrates 3.4.1.4 3’ Stem 3.4.2 Invertebrates model conserves the HEXIM1 binding site 3.4.3 Computationally high potential 7SK RNA candidate . 3.4.4 Sensitivity of the final proposed model 3.5 Conclusion Chapter 4: Behind the scenes of microRNA driven regulation 4.1 Biological background 4.2 Databases and problems 4.3 MicroRNA detection and curation approaches Chapter 5: Initial microRNA curation 5.1 Introduction 5.2 Methods 5.2.1 Data pre-processing 5.2.2 Initial seeds creation 5.2.3 Main course 5.3 Results and discussion 5.4 Conclusion Chapter 6: MIRfix pipeline 6.1 Introduction 6.2 Methods 6.2.1 Inputs and Outputs 6.2.2 Prediction of the mature sequences 6.2.3 The original precursor and its alternative 6.2.4 The validation of the precursor 6.2.5 Alignment processing 6.3 Results and statistics 6.4 Applications 6.4.1 Real life examples and artificial data tests 6.4.2 miRNA and miRNA* prediction 6.4.3 Covariance models 6.5 Conclusion Chapter 7: Discussion

In recent years it has become evident that functional RNAs in living organisms are not just curious remnants from a primoridal RNA world but an ubiquitous phenomenon complementing protein enzyme based activity. Functional RNAs, just like proteins, depend in many cases upon their well-defined and evolutionarily conserved three-dimensional structure. In contrast to protein folds, however, RNA molecules have a biophysically important coarse-grained representation: their secondary structure. At this level of resolution at least, RNA structures can be efficiently predicted given only the sequence information. As a consequence, computational studies of RNA routinely incorporate structural information explicitly. RNA secondary structure prediction has proven useful in diverse fields ranging from theoretical models of sequence evolution and biopolymer folding, to genome analysis and even the design biotechnologically or pharmaceutically useful molecules.

Even after their discovery more than 60 years ago, little is known about how messenger RNAs (mRNAs) are packaged inside the cells. To ensure efficient and accurate delivery of the intended message to its proper destination, it is important to package the informational molecule in a way that protects it from premature degradation but also proper decoding at the destination. However, very little is known about the this fundamentally important step of mRNA packaging inside eukaryotic cells. To this end, we developed a novel approach, RIPPLiT (RNA ImmunoPrecipitation and Proximity Ligation in Tandem), to capture the 3D architecture of the ribonucleoprotein particles (RNPs) of interest transcriptome-wide. To begin with, we applied RIPPLiT to the exon-junction complex (EJC), a set of proteins stably bound to a spliced RNA. EJCs have been shown to interact with other proteins like SR- and SR-like to form megadalton sized complexes and help protect large regions of mRNAs. Thus, we hypothesized that these RNPs would provide an ideal system to elucidate the higher order organization of mRNPs. Preliminary analysis of data obtained from RIPPLiT consisted of “chimeric reads”, reads with multiple RNA fragments ligated together, which could not be analyzed with any of the existing bioinformatics tools. Thus, we developed a new bioinformatics suite, ChimeraTie, to map, analyze and visualize chimeric reads. Performing polymer analysis on chimeric reads obtained for hundreds of mRNAs, we were able to predict that mRNPs are linearly and densely packed into flexible rod-like structures before they undergo translation. In this thesis, along with the detailed biological conclusion, I have also provided a step-wise manual to perform RIPPLiT experiment and analyze the ensuing data using ChimeraTie.