Entity

Time filter

Source Type

Huntsville, AL, United States

Fowler D.M.,University of Washington | Cooper S.J.,University of Washington | Cooper S.J.,HudsonAlpha Institute | Stephany J.J.,University of Washington | And 6 more authors.
Molecular BioSystems | Year: 2011

Lovastatin and other statins inhibit HMG-CoA reductase, which carries out an early step in the sterol biosynthesis pathway. Statins lower cholesterol and are widely prescribed to prevent heart disease, but like many drugs, they can interact with nutritionally acquired metabolites. To probe these interactions, we explored the effect of a diverse library of metabolites on statin effectiveness using a Saccharomyces cerevisiae model. In yeast, treatment with lovastatin results in reduced growth. We combined lovastatin with the library of metabolites, and found that copper and zinc ions impaired the ability of the statin to inhibit yeast growth. Using an integrated genomic and metabolomic approach, we found that lovastatin plus metal synergistically upregulated some sterol biosynthesis genes. This altered pattern of gene expression resulted in greater flux through the sterol biosynthesis pathway and an increase in ergosterol levels. Each sterol intermediate level was correlated with expression of the upstream gene. Thus, the ergosterol biosynthetic response induced by statin is enhanced by copper and zinc. In cultured mammalian cells, these metals also rescued statin growth inhibition. Because copper and zinc impair the ability of statin to reduce sterol biosynthesis, dietary intake of these metals could have clinical relevance for statin treatment in humans. © The Royal Society of Chemistry. Source


Moghaddam S.M.,North Dakota State University | Song Q.,U.S. Department of Agriculture | Mamidi S.,North Dakota State University | Schmutz J.,HudsonAlpha Institute | And 4 more authors.
Frontiers in Plant Science | Year: 2014

Next generation sequence data provides valuable information and tools for genetic and genomic research and offers new insights useful for marker development. This data is useful for the design of accurate and user-friendly molecular tools. Common bean (Phaseolus vulgaris L.) is a diverse crop in which separate domestication events happened in each gene pool followed by race and market class diversification that has resulted in different morphological characteristics in each commercial market class. This has led to essentially independent breeding programs within each market class which in turn has resulted in limited within market class sequence variation. Sequence data from selected genotypes of five bean market classes (pinto, black, navy, and light and dark red kidney) were used to develop InDel-based markers specific to each market class. Design of the InDel markers was conducted through a combination of assembly, alignment and primer design software using 1.6× to 5.1× coverage of Illumina GAII sequence data for each of the selected genotypes. The procedure we developed for primer design is fast, accurate, less error prone, and higher throughput than when they are designed manually. All InDel markers are easy to run and score with no need for PCR optimization. A total of 2687 InDel markers distributed across the genome were developed. To highlight their usefulness, they were employed to construct a phylogenetic tree and a genetic map, showing that InDel markers are reliable, simple, and accurate. © 2014 Moghaddam, Song, Mamidi, Schmutz, Lee, Cregan, Osorno and McClean. Source


Schmidt M.D.,Cornell University | Vallabhajosyula R.R.,CFD Research Corporation | Jenkins J.W.,HudsonAlpha Institute | Hood J.E.,CFD Research Corporation | And 3 more authors.
Physical Biology | Year: 2011

The reverse engineering of metabolic networks from experimental data is traditionally a labor-intensive task requiring a priori systems knowledge. Using a proven model as a test system, we demonstrate an automated method to simplify this process by modifying an existing or related model - suggesting nonlinear terms and structural modifications - or even constructing a new model that agrees with the system's time series observations. In certain cases, this method can identify the full dynamical model from scratch without prior knowledge or structural assumptions. The algorithm selects between multiple candidate models by designing experiments to make their predictions disagree. We performed computational experiments to analyze a nonlinear seven-dimensional model of yeast glycolytic oscillations. This approach corrected mistakes reliably in both approximated and overspecified models. The method performed well to high levels of noise for most states, could identify the correct model de novo, and make better predictions than ordinary parametric regression and neural network models. We identified an invariant quantity in the model, which accurately derived kinetics and the numerical sensitivity coefficients of the system. Finally, we compared the system to dynamic flux estimation and discussed the scaling and application of this methodology to automated experiment design and control in biological systems in real time. © 2011 IOP Publishing Ltd. Source


Barnes S.,University of Alabama at Birmingham | Benton H.P.,Scripps Research Institute | Casazza K.,University of Alabama at Birmingham | Cooper S.J.,HudsonAlpha Institute | And 9 more authors.
Journal of Mass Spectrometry | Year: 2016

The study of metabolism has had a long history. Metabolomics, a systems biology discipline representing analysis of known and unknown pathways of metabolism, has grown tremendously over the past 20 years. Because of its comprehensive nature, metabolomics requires careful consideration of the question(s) being asked, the scale needed to answer the question(s), collection and storage of the sample specimens, methods for extraction of the metabolites from biological matrices, the analytical method(s) to be employed and the quality control of the analyses, how collected data are correlated, the statistical methods to determine metabolites undergoing significant change, putative identification of metabolites and the use of stable isotopes to aid in verifying metabolite identity and establishing pathway connections and fluxes. The National Institutes of Health Common Fund Metabolomics Program was established in 2012 to stimulate interest in the approaches and technologies of metabolomics. To deliver one of the program's goals, the University of Alabama at Birmingham has hosted an annual 4-day short course in metabolomics for faculty, postdoctoral fellows and graduate students from national and international institutions. This paper is the first part of a summary of the training materials presented in the course to be used as a resource for all those embarking on metabolomics research. The complete set of training materials including slide sets and videos can be viewed at http://www.uab.edu/proteomics/metabolomics/workshop/workshop_june_2015.php. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd. Source


Beane J.,Boston University | Vick J.,Boston University | Schembri F.,Boston University | Anderlind C.,Boston University | And 11 more authors.
Cancer Prevention Research | Year: 2011

Cigarette smoke creates a molecular field of injury in epithelial cells that line the respiratory tract. We hypothesized that transcriptome sequencing (RNA-Seq) will enhance our understanding of the field of molecular injury in response to tobacco smoke exposure and lung cancer pathogenesis by identifying gene expression differences not interrogated or accurately measured by microarrays. We sequenced the highmolecular-weight fraction of total RNA (>200 nt) from pooled bronchial airway epithelial cell brushings (n = 3 patients per pool) obtained during bronchoscopy from healthy never smoker (NS) and current smoker (S) volunteers and smokers with (C) and without (NC) lung cancer undergoing lung nodule resection surgery. RNA-Seq libraries were prepared using 2 distinct approaches, one capable of capturing non-polyadenylated RNA (the prototype NuGEN Ovation RNA-Seq protocol) and the other designed to measure only polyadenylated RNA (the standard Illumina mRNA-Seq protocol) followed by sequencing generating approximately 29 million 36 nt reads per pool and approximately 22 million 75 nt paired-end reads per pool, respectively. The NuGEN protocol captured additional transcripts not detected by the Illumina protocol at the expense of reduced coverage of polyadenylated transcripts, while longer read lengths and a paired-end sequencing strategy significantly improved the number of reads that could be aligned to the genome. The aligned reads derived from the two complementary protocols were used to define the compendium of genes expressed in the airway epithelium (n = 20,573 genes). Pathways related to the metabolism of xenobiotics by cytochrome P450, retinol metabolism, and oxidoreductase activity were enriched among genes differentially expressed in smokers, whereas chemokine signaling pathways, cytokine-cytokine receptor interactions, and cell adhesion molecules were enriched among genes differentially expressed in smokers with lung cancer. There was a significant correlation between the RNA-Seq gene expression data and Affymetrix microarray data generated from the same samples (P < 0.001); however, the RNA-Seq data detected additional smoking- and cancer-related transcripts whose expression was were either not interrogated by or was not found to be significantly altered when using microarrays, including smokingrelated changes in the inflammatory genes S100A8 and S100A9 and cancer-related changes in MUC5AC and secretoglobin (SCGB3A1). Quantitative real-time PCR confirmed differential expression of select genes and non-coding RNAs within individual samples. These results demonstrate that transcriptome sequencing has the potential to provide new insights into the biology of the airway field of injury associated with smoking and lung cancer. The measurement of both coding and non-coding transcripts by RNA-Seq has the potential to help elucidate mechanisms of response to tobacco smoke and to identify additional biomarkers of lung cancer risk and novel targets for chemoprevention. ©2011 AACR. Source

Discover hidden collaborations