Genetic engineering of the polyamine biosynthetic pathway and somatic embryogenesis in carrot (Daucus carota L)

Ornithine decarboxylase (ODC), arginine decarboxylase (ADC), and S-adenosylmethionine decarboxylase (SAMDC) are three of the key regulatory enzymes involved in the biosynthesis of polyamines (putrescine, spermidine and spermine). To modulate the biosynthesis of putrescine, carrot (Daucus carota L.) cells were transformed with Agrobacterium tumefaciens strains containing 3\sp\prime-truncated mouse ornithine decarboxylase (ODC) cDNA under the control of a CaMV 35S promoter. Neomycin phosphotransferase gene linked with nopaline synthase promoter was used to select transformed cell lines on kanamycin. While the non-transformed cells contained no ODC, high levels of ODC activity were observed in the transformed cells. A significant increase in the cellular levels of putrescine in transgenic cells as compared to control cells was observed. Spermidine levels, however, remained unaffected. Not only did the transformed cells exhibit improved somatic embryogenesis in the auxin-free medium, they also regenerated embryos in the presence of inhibitory levels of 2,4-D. These cells acquired tolerance to $\alpha$-difluoromethylarginine (a potent inhibitor of arginine decarboxylase) at concentrations that inhibit growth as well as embryogenesis in non-transformed carrot cells. Transformation of carrot cells with a human SAMDC cDNA lead to increased production of SAMDC enzyme. This increase in the biosynthesis of SAMDC translated to an increase in the cellular levels of spermidine and a decrease of putrescine. The transgenic cells were highly embryogenic and also tolerant to low levels of methylglyoxal bis(guanylhydrazone)

Investigation of Microbe and Host Tissue Interactions Contributing to the Pathogenesis of Colorectal Cancer

Colorectal cancer (CRC) is one of the leading causes of cancer-related death worldwide. The pathogenesis of this disease can fall under broad categories; however, the specific precursory mechanism of CRC pathogenesis is still unknown. Dysregulations of the gut microbiome have been identified in the CRC tissue environment. Additionally, CRC tissue gene expression has been observed to differ from that of healthy tissue. Despite these noticeable changes, few studies have directly compared the microorganism composition to the gene expression of CRC tissue. Doing so may identify whether the differentially abundant microorganisms influence the changes in gene expression. The goal of this study is to utilize bioinformatics to identify enriched microorganism populations as well as differentially expressed genes within CRC tissue samples to explore the potential interactions leading to disease pathogenesis. The results of this study found that populations of toxin-producing bacteria, as well as oral bacteria, are present within the CRC tissue environment. Additionally, changes in gene expression suggest that these bacteria may be causing an inflammatory immune response, which could catalyze tumorigenesis

Cycles of Electronic Health Records Adaptation by Physicians: How Do the Positive and Negative Experiences with the EHR System Affect Physicians’ EHR Adaptation Process?

The integration of EHR in IT infrastructures supporting organizations enable improved access and recording of patient data, enhanced ability to make improved decisions, improved quality and reduced errors in patient care. Despite these benefits, there are mixed results as to the use of EHR. The literature suggests that the reasons for the limited use relate to policy, financial and usability considerations, but it does not provide an understanding of reasons for physicians’ limited interaction and adaptation of EHR. Following an analysis of qualitative data, collected in a case study at a hospital using interviews, this research explains how physicians interact with EHR. The key contribution of this research is in explaining how physicians interact with EHR in terms of concepts that are grounded in the real world experiences of physicians. The model of positive and negative physician work cycles is introduced and discussed. Contributions to research and practice are presented

Cycles of Electronic Health Records Adaptation by Physicians: How Do The Positive and Negative Experience with the EHR System Affect Physicians\u27 EHR Adaptation Process?

The integration of EHR in IT infrastructures supporting organizations enable improved access and recording of patient data, enhanced ability to make improved decisions, improved quality and reduced errors in patient care. Despite these benefits, there are mixed results as to the use of EHR. The literature suggests that the reasons for the limited use relate to policy, financial and usability considerations, but it does not provide an understanding of reasons for physicians’ limited interaction and adaptation of EHR. Following an analysis of qualitative data, collected in a case study at a hospital using interviews, this research explains how physicians interact with EHR. The key contribution of this research is in explaining how physicians interact with EHR in terms of concepts that are grounded in the real world experiences of physicians. The model of positive and negative physician work cycles is introduced and discussed. Contributions to research and practice are presented

Coexpression Network Analysis of miRNA-142 Overexpression in Neuronal Cells

MicroRNAs are small noncoding RNA molecules, which are differentially expressed in diverse biological processes and are also involved in the regulation of multiple genes. A number of sites in the 3′ untranslated regions (UTRs) of different mRNAs allow complimentary binding for a microRNA, leading to their posttranscriptional regulation. The miRNA-142 is one of the microRNAs overexpressed in neurons that is found to regulate SIRT1 and MAOA genes. Differential analysis of gene expression data, which is focused on identifying up- or downregulated genes, ignores many relationships between genes affected by miRNA-142 overexpression in a cell. Thus, we applied a correlation network model to identify the coexpressed genes and to study the impact of miRNA-142 overexpression on this network. Combining multiple sources of knowledge is useful to infer meaningful relationships in systems biology. We applied coexpression model on the data obtained from wild type and miR-142 overexpression neuronal cells and integrated miRNA seed sequence mapping information to identify genes greatly affected by this overexpression. Larger differences in the enriched networks revealed that the nervous system development related genes such as TEAD2, PLEKHA6, and POGLUT1 were greatly impacted due to miRNA-142 overexpression

A Parallel Non-Alignment Based Approach to Efficient Sequence Comparison using Longest Common Subsequences

Biological sequence comparison programs have revolutionized the practice of biochemistry, and molecular and evolutionary biology. Pairwise comparison of genomic sequences is a popular method of choice for analyzing genetic sequence data. However the quality of results from most sequence comparison methods are significantly affected by small perturbations in the data and furthermore, there is a dearth of computational tools to compare sequences beyond a certain length. In this paper, we describe a parallel algorithm for comparing genetic sequences using an alignment free-method based on computing the Longest Common Subsequence (LCS) between genetic sequences. We validate the quality of our results by comparing the phylogenetic tress obtained from ClustalW and LCS. We also show through complexity analysis of the isoefficiency and by empirical measurement of the running time that our algorithm is very scalable

A base composition analysis of natural patterns for the preprocessing of metagenome sequences

Background: On the pretext that sequence reads and contigs often exhibit the same kinds of base usage that is also observed in the sequences from which they are derived, we offer a base composition analysis tool. Our tool uses these natural patterns to determine relatedness across sequence data. We introduce spectrum sets (sets of motifs) which are permutations of bacterial restriction sites and the base composition analysis framework to measure their proportional content in sequence data. We suggest that this framework will increase the efficiency during the pre-processing stages of metagenome sequencing and assembly projects. Results: Our method is able to differentiate organisms and their reads or contigs. The framework shows how to successfully determine the relatedness between these reads or contigs by comparison of base composition. In particular, we show that two types of organismal-sequence data are fundamentally different by analyzing their spectrum set motif proportions (coverage). By the application of one of the four possible spectrum sets, encompassing all known restriction sites, we provide the evidence to claim that each set has a different ability to differentiate sequence data. Furthermore, we show that the spectrum set selection having relevance to one organism, but not to the others of the data set, will greatly improve performance of sequence differentiation even if the fragment size of the read, contig or sequence is not lengthy. Conclusions: We show the proof of concept of our method by its application to ten trials of two or three freshly selected sequence fragments (reads and contigs) for each experiment across the six organisms of our set. Here we describe a novel and computationally effective pre-processing step for metagenome sequencing and assembly tasks. Furthermore, our base composition method has applications in phylogeny where it can be used to infer evolutionary distances between organisms based on the notion that related organisms often have much conserved code

MTAP: The Motif Tool Assessment Platform

Background: In recent years, substantial effort has been applied to de novo regulatory motif discovery. At this time, more than 150 software tools exist to detect regulatory binding sites given a set of genomic sequences. As the number of software packages increases, it becomes more important to identify the tools with the best performance characteristics for specific problem domains. Identifying the correct tool is difficult because of the great variability in motif detection software. Consequently, many labs spend considerable effort testing methods to find one that works well in their problem of interest. Results: In this work, we propose a method (MTAP) that substantially reduces the effort required to assess de novo regulatory motif discovery software. MTAP differs from previous attempts at regulatory motif assessment in that it automates motif discovery tool pipelines (something that traditionally required many manual steps), automatically constructs orthologous upstream sequences, and provides automated benchmarks for many popular tools. As a proof of concept, we have run benchmarks over human, mouse, fly, yeast, E. coli and B. subtilis. Conclusion: MTAP presents a new approach to the challenging problem of assessing regulatory motif discovery methods. The most current version of MTAP can be downloaded from http:// biobase.ist.unomaha.edu

A novel approach to identify shared fragments in drugs and natural products

Fragment-based approaches have now become an important component of the drug discovery process. At the same time, pharmaceutical chemists are more often turning to the natural world and its extremely large and diverse collection of natural compounds to discover new leads that can potentially be turned into drugs. In this study we introduce and discuss a computational pipeline to automatically extract statistically overrepresented chemical fragments in therapeutic classes, and search for similar fragments in a large database of natural products. By systematically identifying enriched fragments in therapeutic groups, we are able to extract and focus on few fragments that are likely to be active or structurally important as scaffolds. We show that several therapeutic classes (including antibacterial, antineoplastic, and drugs active on the cardiovascular system, among others) have enriched fragments that are also found in many natural compounds. Further, our method is able to detect fragments shared by a drug and a natural product even when the global similarity between the two molecules is generally low. A further development of this computational pipeline is to help predict putative therapeutic activities of natural compounds, and to help identify novel leads for drug discovery

Multiplatform biomarker identification using a data-driven approach enables single-sample classification

Background: High-throughput gene expression profiles have allowed discovery of potential biomarkers enabling early diagnosis, prognosis and developing individualized treatment. However, it remains a challenge to identify a set of reliable and reproducible biomarkers across various gene expression platforms and laboratories for single sample diagnosis and prognosis. We address this need with our Data-Driven Reference (DDR) approach, which employs stably expressed housekeeping genes as references to eliminate platform-specific biases and non-biological variabilities. Results: Our method identifies biomarkers with “built-in” features, and these features can be interpreted consistently regardless of profiling technology, which enable classification of single-sample independent of platforms. Validation with RNA-seq data of blood platelets shows that DDR achieves the superior performance in classification of six different tumor types as well as molecular target statuses (such as MET or HER2-positive, and mutant KRAS, EGFR or PIK3CA) with smaller sets of biomarkers. We demonstrate on the three microarray datasets that our method is capable of identifying robust biomarkers for subgrouping medulloblastoma samples with data perturbation due to different microarray platforms. In addition to identifying the majority of subgroup-specific biomarkers in CodeSet of nanoString, some potential new biomarkers for subgrouping medulloblastoma were detected by our method. Conclusions: In this study, we present a simple, yet powerful data-driven method which contributes significantly to identification of robust cross-platform gene signature for disease classification of single-patient to facilitate precision medicine. In addition, our method provides a new strategy for transcriptome analysis