<p>  </p> <p>Shikonin is 1,4-naphthoquinones produced exclusively in Boraginaceae species. The compound and its derivatives are predominantly made in roots where they function in mediating plant-plant (allelopathic) and plant-microbe interactions. Moreover, this compound has been a target for drug development due to its strong anti-cancer properties. Our genome assembly and analysis of <em>Lithospermum erythrorhizon</em> uncovered metabolic innovation events that contributed to the evolution of the shikonin biosynthesis. This metabolic innovation also reveals the evolutionary link between shikonin biosynthesis and ubiquinone biosynthesis, one of the central metabolism functions in aerobic cellular respiration. To explore additional links between these two pathways, we used a transcriptome-based network analysis which uncovered a shikonin gene network model that predicts strong associations between primary metabolic pathway genes and known shikonin biosynthesis genes, as well as links with uncharacterized genes. <em>L. erythrorhizon</em> geranyldiphosphate (GPP) synthase (<em>LeGPPS</em>) is one of the candidates predicted by the network analysis, of which encodes a cytoplasmic enzyme shown in vitro to produce GPP. Knocking down of <em>LeGPPS</em> in <em>L. erythrorhizon </em>hairy roots (<em>LeGPPSi </em>lines) results in reduced shikonin content. This result provides functional evidence that cytoplasmic LeGPPS supplies GPP precursor to the shikonin biosynthesis. <em>LeGPPSi </em>lines also increased ubiquinone content, further supporting our hypothesis on the metabolic and evolutionary connection between shikonin and ubiquinone biosynthesis. Further RNA-seq analysis of the <em>LeGPPSi</em> line showed that downregulating <em>LeGPPS</em> significantly reduces the expression of benzenoid/phenylpropanoid genes, indicating the presence of factors that coordinately regulate the pathways providing the 4-hydroxybenzoic acid and GPP precursors to the shikonin pathway. In addition to <em>LeGPPS</em>, we also found<em> ubiquinone biosynthesis protein COQ4-like </em>gene (<em>LeCOQ4-L</em>) which provided another evolutionary link between shikonin and ubiquinone biosynthesis. The enzymatic activity of canonical COQ4 is unknown. In yeast, the protein is essential for ubiquinone biosynthesis and its metabolon formation. With the existing connections between shikonin and ubiquinone biosynthesis, if LeCOQ4 functions in the same manner as yeast COQ4, it is possible that <em>LeCOQ4-L </em>has an analogous function in shikonin biosynthesis as a structural protein for stabilizing biosynthesis metabolon. This leads us to the characterization of<em> COQ4</em> ortholog in Arabidopsis (<em>AtCOQ4</em>) to gain insight into its functional mechanism. Characterization of <em>atcoq4 </em>T-DNA mutant line showed that reduced <em>AtCOQ4</em> expression resulted in reduced ubiquinone. Further subcellular localization study revealed that AtCOQ4 and <em>LeCOQ4-L</em> localize in mitochondria without conventional transit peptide. We also performed pull-down assay to identify AtCOQ4 interactors which might be the missing enzymes that cannot be identified based on homology. 80 potential AtCOQ4 interactors were found including proteins like AtCHLM, GRIM-19, and AtSSLs. However, further study is needed to verify the protein interactions captured by pull-down assay. Taken all together, our study sheds light on the metabolic innovations that give rise to shikonin biosynthesis from ubiquinone biosynthesis and provide insight into the dynamics of the metabolic networks.</p>

Estrogens exert numerous actions throughout the human body, targeting healthy tissue while also enhancing the proliferative capacity of breast cancers. Estrogen signaling is mediated by the estrogen receptor (ER), which binds DNA and ultimately affects the expression of adjacent genes. Current understanding of ER-mediated transcriptional regulation is mostly limited to genes whose transcript levels increase following estrogen exposure, though recent studies demonstrate that direct down-regulation of estrogen-responsive genes is also a significant feature of ER action. We hypothesized that differences in cis-regulatory DNA was a factor in determining target gene expression and performed computational and experimental studies to test this hypothesis. From our in silico analyses, we show that the binding motifs for certain transcription factors are enriched in cis-regulatory sequences adjacent to repressed target genes compared to induced target genes, including the motif for RUNX1. In silico analyses were tested experimentally using dual luciferase reporter assays, which indicate that several ER binding sites are estrogen responsive. Mutagenesis of transcription factor motifs (for ER and RUNX1) reduced the response of reporter gene. Further experiments demonstrated that co-recruitment of ER and RUNX1 is necessary for repression of gene expression at some target genes. These findings highlight a novel interaction between ER and RUNX1 and their role in transcriptional repression in breast cancer.

No description available.

BACKGROUND: Nurses have the opportunity to bring a unique perspective to genetic and genomic healthcare through their emphasis of health promotion, prevention, screening, caring, and patient, family, and community relationships. Nurses are expected to have genetic and genomic knowledge that can be integrated into clinical practice. However, researchers today are finding nursing students are not competent or comfortable in the clinical applications of genetics and genomics, even though these students will soon be working in healthcare as it advances in these fields. The purpose of this research was to evaluate the genetic and genomic knowledge of nursing undergraduate students and explore their attitudes about using this knowledge in practice. METHOD: A pre- and posttest design was used. Student knowledge was measured online using the Genomic Nursing Concept Inventory (GNCI©) in both tests. Demographic questions were included in the pretest and questions regarding attitudes toward comfort and readiness to apply that knowledge were included in the posttest. The pretest was administered at the beginning of the Spring 2014 semester. The posttest was administered at the end of the same semester, after the nursing students received the majority of genetic and genomic instruction from their program's curriculum. Descriptive statistics were used to examine all data. Total and subscale knowledge scores on the GNCI© were computed for each test. A paired t-test was used to compare pre- and post-GNCI© total and subscale scores. Correlations were calculated at both time points. A Spearman correlation was used to examine the relationship between prior experience with genetic education or exposure to people with a genetic condition and total pre-score knowledge on the pretest. For the posttest, a total attitude score was calculated to examine the relationship between attitude and post total knowledge scores using a Pearson's r correlation. FINDINGS: 109 undergraduate junior nursing students participated. Gains in total and subscale knowledge between the pre- and posttest were statistically significant (p < 0.05), except for the Mutations subscale. For the pretest GNCI©, the average mean score was 45%, which improved to 50% at the time of the posttest. Lowest scoring items were in the Genome Basics subscale, whereas highest scoring items were found within the Inheritance subscale for the posttest. Mean total attitude scores were 28.33 (SD = 5.17) indicating students had a relatively positive attitude towards using their knowledge base in practice. The majority of students (87.1%) agreed that it is important for the nurse to incorporate genetic and genomic knowledge into clinical practice although only 34.9% felt ready to do so. DISCUSSION: Genetics and genomic knowledge and preparedness were low among nursing students. This demonstrates a need for more integration of genetic and genomic content within nursing curriculum, including a review of basic concepts. Nurses are expected to perform comprehensive health assessments by incorporating knowledge of genetic, environmental, and genomic influences and risk factors. Lack of a basic understanding could lead to patient consequences related to inadequate risk assessment, referrals for genetic counseling, and patient education.

Recent advances in biological research point out that many ribonucleic acids (RNAs) are transcribed from the genome to perform a variety of cellular functions, rather than merely acting as information carriers for protein synthesis. These RNAs are usually referred to as the non-coding RNAs (ncRNAs). The versatile regulation mechanisms and functionalities of the ncRNAs contribute to the amazing complexity of the biological system. The ncRNAs perform their biological functions by folding into specific structures. In this case, the comparative study of the ncRNA structures is key to the inference of their molecular and cellular functions. We are especially interested in two computational problems for the comparative analysis of ncRNA structures: the alignment of ncRNA structures and their classification. Specifically, we aim to develop algorithms to align and cluster RNA structural motifs (recurrent RNA 3D fragments), as well as RNA secondary structures. Thorough understanding of RNA structural motifs will help us to disassemble the huge RNA 3D structures into functional modules, which can significantly facilitate the analysis of the detailed molecular functions. On the other hand, efficient alignment and clustering of the RNA secondary structures will provide insights for the understanding of the ncRNA expression and interaction in a genomic scale. In this dissertation, we will present a suite of computational algorithms and software packages to solve the RNA structural motif alignment and clustering problem, as well as the RNA iii secondary structure alignment and clustering problem. The summary of the contributions of this dissertation is as follows. (1) We developed RNAMotifScan for comparing and searching RNA structural motifs. Recent studies have shown that RNA structural motifs play an essential role in RNA folding and interaction with other molecules. Computational identification and analysis of RNA structural motifs remain to be challenging tasks. Existing motif identification methods based on 3D structure may not properly compare motifs with high structural variations. We present a novel RNA structural alignment method for RNA structural motif identi- fication, RNAMotifScan, which takes into consideration the isosteric (both canonical and non-canonical) base-pairs and multi-pairings in RNA structural motifs. The utility and accuracy of RNAMotifScan are demonstrated by searching for Kink-turn, C-loop, Sarcin-ricin, Reverse Kink-turn and E-loop motifs against a 23s rRNA (PDBid: 1S72), which is well characterized for the occurrences of these motifs. (2) We improved upon RNAMotifScan by incorporating base-stacking information and devising a new branch-and-bound algorithm called RNAMotifScanX. Model-based search of RNA structural motif has been focused on finding instances with similar 3D geometry and base-pairing patterns. Although these methods have successfully identified many of the true motif instances, each of them has its own limitations and their accuracy and sensitivity can be further improved. We introduce a novel approach to model the RNA structural motifs, which incorporates both base-pairing and base-stacking information. We also develop a new algorithm to search for known motif instances with the consideration of both base-pairing and base-stacking information. Benchmarking of RNAMotifScanX on searching known RNA structural motifs including kink-turn, C-loop, sarcin-ricin, reverse kink-turn, and E-loop iv clearly show improved performances compared to its predecessor RNAMotifScan and other state-of-the-art RNA structural motif search tools. (3) We develop an RNA structural motif clustering and de novo identification pipeline called RNAMSC. RNA structural motifs are the building blocks of the complex RNA architecture. Identification of non-coding RNA structural motifs is a critical step towards understanding of their structures and functionalities. We present a clustering approach for de novo RNA structural motif identification. We applied our approach on a data set containing 5S, 16S and 23S rRNAs and rediscovered many known motifs including GNRA tetraloop, kink-turn, C-loop, sarcin-ricin, reverse kink-turn, hook-turn, E-loop and tandem-sheared motifs, with higher accuracy than the currently state-of-the-art clustering method. More importantly, several novel structural motif families have been revealed by our novel clustering analysis. (4) We propose an improved RNA structural clustering pipeline that takes into account the length-dependent distribution of the structural similarity measure. We also devise a more efficient and robust CLique finding CLustering algorithm (CLCL), to replace the traditional hierarchical clustering approach. Benchmark of the proposed pipeline on Rfam data clearly demonstrates over 10% performance gain, when compared to a traditional hierarchical clustering pipeline. We applied this new computational pipeline to cluster the posttranscriptional control elements in fly 3’-UTR. The ncRNA elements in the 3’ untranslated regions (3’-UTRs) are known to participate in the genes’ post-transcriptional regulation, such as their stability, translation efficiency, and subcellular localization. Inferring co-expression patterns of the genes by clustering their 3’-UTR ncRNA elements will provide invaluable knowledge for further studies of their functionalities and interactions under specific physiological processes. v (5) We develop an ultra-efficient RNA secondary structure alignment algorithm ERA by using a sparse dynamic programming technique. Current advances of the next-generation sequencing technology have revealed a large number of un-annotated RNA transcripts. Comparative study of the RNA structurome is an important approach to assess the biological functionalities of these RNA transcripts. Due to the large sizes and abundance of the RNA transcripts, an efficient and accurate RNA structure-structure alignment algorithm is in urgent need to facilitate the comparative study. By using the sparse dynamic programming technique, we devised a new alignment algorithm that is as efficient as the tree-based alignment algorithms, and as accurate as the general edit-distance alignment algorithms. We implemented the new algorithm into a program called ERA (Efficient RNA Alignment). Benchmark results indicate that ERA can significantly speedup RNA structure-structure alignments compared to other state-of-the-art RNA alignment tools, while maintaining high alignment accuracy. These novel algorithms have led to the discovery of many novel RNA structural motif instances, which have significantly deepened our understanding to the RNA molecular functions. The genome-wide clustering of ncRNA elements in fly 3’-UTR has predicted a cluster of genes that are responsible for the spermatogenesis process. More importantly, these genes are very likely to be co-regulated by their common 3’-UTR elements. We anticipate that these algorithms and the corresponding software tools will significantly promote the comparative ncRNA research in the future

Chronic lung diseases including Chronic Obstructive Pulmonary Disease (COPD), Idiopathic Pulmonary Fibrosis (IPF) and lung cancer are major causes of morbidity and mortality in the United States due to high incidence and limited therapeutic options. In order to address this critical issue, I have leveraged RNA sequencing and integrative genomics to define disease-associated transcriptomic changes which could be potentially targeted to lead to new therapeutics. We sequenced the lung transcriptome of subjects with IPF (n=19), emphysema (n=19, a subtype of COPD), or neither (n=20). The expression levels of 1770 genes differed between IPF and control lung, and 220 genes differed between emphysema and control lung (p<0.001). Upregulated genes in both emphysema and IPF were enriched for the p53/hypoxia pathway. These results were validated by immunohistochemistry of select p53/hypoxia proteins and by GSEA analysis of independent expression microarray experiments. To identify regulatory events, I constructed an integrative miRNA target prediction and anticorrelation miRNA-mRNA network, which highlighted several miRNA whose expression levels were the opposite of genes differentially expressed in both IPF and emphysema. MiR-96 was a highly connected hub in this network and was subsequently overexpressed in cell lines to validate several potential regulatory connections. Building upon these successful experiments, I next sought to define gene expression changes and the miRNA-mRNA regulatory network in never smoker lung cancer. Large and small RNA was sequenced from matched lung adenocarcinoma tumor and adjacent normal lung tissue obtained from 22 subjects (8 never, 14 current and former smokers). I identified 120 genes whose expression was modified uniquely in never smoker lung tumors. Using a repository of gene-expression profiles associated with small bioactive molecules, several compounds which counter the never smoker tumor signature were identified in silico. Leveraging differential expression information, I again constructed an mRNA-miRNA regulatory network, and subsequently identified a potential never smoker oncomir has-mir-424 and its transcription factor target FOXP2. In this thesis, I have identified genes, pathways and the miRNA-mRNA regulatory network that is altered in COPD, IPF, and lung adenocarcinoma among never smokers. My findings may ultimately lead to improved treatment options by identifying targetable pathways, regulators, and therapeutic drug candidates. / 2017-02-01T00:00:00Z

High­-throughput sequencing has become pervasive in all facets of genomic analysis. I developed computational methods to analyze high­-throughput sequencing data and derive biological conclusions in two research areas -- transcriptional regulation in mammals and evolution of virus under immune pressure. To investigate transcriptional regulation, I integrated data from multiple experiments performed by the ENCODE consortium. First, my analysis revealed that Transcription Factors (TFs) prefer to bind GC-­rich, histone­-depleted regions. By comparing in vivo and in vitro nucleosome dynamics, I observed that while histones have an innate preference for binding GC-­rich DNA, TF binding overrides this preference and produces a negative correlation between GC content and histone enrichment. In the next project, I found that the binding events of multiple TFs co-­occur at genomic regions enriched in activating histone marks that are typically associated with gene enhancers and promoters, suggesting that these regions may be enhancers or have TSS-­distal transcription. Lastly, I used supervised machine ­learning techniques to train histone enrichment signals and sequence features to predict transcriptional enhancers to be validated in mouse-­transgenic assays. In a post­-clinical trial exploratory analysis of Hepatitis C Virus (HCV), I traced the evolutionary path of the envelope proteins E1 and E2 in HCV-infected liver transplant patients, in response to a novel antibody. I developed a systematic amino acid­-level analysis pipeline that quantifies differences in amino acid frequencies in each position between two time points. Upon applying this method across all positions in the E1/E2 region and comparing pre-­liver­-transplant and post­-viral­-rebound time points, mutations in two positions emerged as being key to antibody evasion. Both these mutations--N415K/D and N417S--were in the epitope targeted by the antibody, but surprisingly, did not co­-occur. In post­-rebound viral genomes that contain the N417S mutation but retain the wild-­type variant at 415, N-­linked glycosylation of 415 is another possible escape mechanism. Using the same analysis pipeline, I also identified additional candidate escape mutations outside the epitope, which could be potential therapeutic targets.

Mapping of strongly inherited classical traits have been immensely helpful in understanding many important traits including diseases, yield and immunity. But some of these traits are too complex and are difficult to map. Taking into consideration gene expression, which mediates the genetic effects, can be helpful in understanding such traits. Together with genetic variation data such data-set is collectively known as expression genetics data. Presence of discrete and continuous variables, observed and latent variables, availability of partial causal information, and under-specfied nature of the data make expression genetics data computationally challenging, but potentially of great biological importance. In this dissertation the underlying regulatory processes are modeled as Bayesian networks consisting of gene expression and genetic variation nodes. Due to the underspecified nature of the data, inferring the complete regulatory network is impractical. Instead, the following techniques are proposed to extract interesting subnetworks with high confidence. The network motif searching technique is used to recover instances of a known regulatory mechanism. The local network inference technique is used to identify immediate neighbors of a given transcript. Application of these two techniques often results in identification of hundreds of individual networks. The network aggregation technique extracts the most common subnetwork from those networks, and identifies its immediate neighbors by collapsing them into a common network. In all the above tasks, simulation studies were carried out to estimate the robustness of the proposed methods and the results suggest that these techniques are capable of recovering the correct substructure with high precision and moderate recall. Moreover, manual biological review shows that the recovered regulatory network substructures are typically biologically sensible.

No description available.

Screening the Utah State Training School (a resident institution for the mentally retarded) for kindreds having at least two institutionalized sibs generated 54 sib groups. Of these, 20-male only sibships had histories compatible with sex-linked mental retardation without physical stigmata (Martin-Bell or Renpenning type). Affected males had no characteristic physical stigmata (an appreciable number did have speech problems and/or seizure disorders) and exhibited IQs ranging from 5 to 74 with a mean of 34.2. Obligate carrier females had a mean IQ score of 91.9 (range, 79 to 106), which is as would be predicted due to random X-inactivation. Carrier females were tested with the MMPI, and showed elevated profiles. The results also indicated that the FAM scale would differentiate between groups of carrier females and normal females and between groups of carrier females and other females who had retarded children. It is suggested that the extension of psychometric methods may be useful in the identification of high risk females. Identification of these females would then allow for accurate genetic counseling, an objective which has not yet been achieved.