Biomedical subjects

R Michael Raab

Publications and source records attributed to R Michael Raab.

6 recordsLinked to original sources

PubMed2006

Incorporating genome-scale tools for studying energy homeostasis.

Mammals have evolved complex regulatory systems that enable them to maintain energy homeostasis despite constant environmental challenges that limit the availability of energy inputs and their composition. Biological control relies upon intricate systems composed of multiple organs and specialized cell types that regulate energy up-take, storage, and expenditure. Because these systems simultaneously perform diverse functions and are highly integrated, they are extremely difficult to understand in terms of their individual component contributions to energy homeostasis. In order to provide improved treatments and clinical options, it is important to identify the principle genetic and molecular components, as well as the systemic features of regulation. To begin, many of these features can be discovered by integrating experimental technologies with advanced methods of analysis. This review focuses on the analysis of transcriptional data derived from microarrays and how it can complement other experimental techniques to study energy homeostasis.

Journal Article↗

PubMed2005

Regulation of mouse hepatic genes in response to diet induced obesity, insulin resistance and fasting induced weight reduction.

BACKGROUND: Obesity is associated with insulin resistance that can often be improved by caloric restriction and weight reduction. Although many physiological changes accompanying insulin resistance and its treatment have been characterized, the genetic mechanisms linking obesity to insulin resistance are largely unknown. We used DNA microarrays and RT-PCR to investigate significant changes in hepatic gene transcription in insulin resistant, diet-induced obese (DIO)-C57/BL/6J mice and DIO-C57/BL/6J mice fasted for 48 hours, whose weights returned to baseline levels during these conditions. RESULTS: Transcriptional profiling of hepatic mRNA revealed over 1900 genes that were significantly perturbed between control, DIO, and fasting/weight reduced DIO mice. From this set, our bioinformatics analysis identified 41 genes that rigorously discriminate these groups of mice. These genes are associated with molecular pathways involved in signal transduction, and protein metabolism and secretion. Of particular interest are genes that participate in pathways responsible for modulating insulin sensitivity. DIO altered expression of genes in directions that would be anticipated to antagonize insulin sensitivity, while fasting/weight reduction partially or completely normalized their levels. Among these discriminatory genes, Sh3kbp1 and RGS3, may have special significance. Sh3kbp1, an endogenous inhibitor of PI-3-kinase, was upregulated by high-fat feeding, but normalized to control levels by fasting/weight reduction. Because insulin signaling occurs partially through PI-3-kinase, increased expression of Sh3kbp1 by DIO mice may contribute to hepatic insulin resistance via inhibition of PI-3-kinase. RGS3, a suppressor of G-protein coupled receptor generation of cAMP, was repressed by high-fat feeding, but partially normalized by fasting/weight reduction. Decreased expression of RGS3 may augment levels of cAMP and thereby contribute to increased, cAMP-induced, hepatic glucose output via phosphoenolpyruvate carboxykinase (PCK1), whose mRNA levels were also elevated. CONCLUSION: These findings demonstrate that hepatocytes respond to DIO and weight reduction by controlling gene transcription in a variety of important molecular pathways. Future studies that characterize the physiological significance of the identified genes in modulating energy homeostasis could provide a better understanding of the mechanisms linking DIO with insulin resistance.

Journal Article↗

PubMed2005

Metabolic engineering.

Metabolic engineering is a powerful methodology aimed at intelligently designing new biological pathways, systems, and ultimately phenotypes through the use of recombinant DNA technology. Built largely on the theoretical and computational analysis of chemical systems, the field has evolved to incorporate a growing number of genome scale experimental tools. This combination of rigorous analysis and quantitative molecular biology methods has endowed metabolic engineering with an effective synergism that crosses traditional disciplinary bounds. As such, there are a growing number of applications for the effective employment of metabolic engineering, ranging from the initial industrial fermentation applications to more recent medical diagnosis applications. In this review we highlight many of the contributions metabolic engineering has provided through its history, as well as give an overview of new tools and applications that promise to have a large impact on the field's future.

Biotechnology↗

PubMed2004

Dynamics of gene silencing by RNA interference.

The large number of candidate genes identified by modern high-throughput technologies require efficient methods for generating knockout phenotypes or gene silencing in order to study gene function. RNA interference (RNAi) is an efficient method that can be used for this purpose. Effective gene silencing by RNAi depends on a number of important parameters, including the dynamics of gene expression and the RNA dose. Using mouse hepatoma cells, we detail some of the principal characteristics of RNAi as a tool for gene silencing, such as the RNA dose level, RNA complex exposure time, and the time of transfection relative to gene induction, in the context of silencing a green fluorescent protein reporter gene. Our experiments demonstrate that different levels of silencing can be attained by modulating the dose level of RNA and the time of transfection and illustrate the importance of a dynamic analysis in designing robust silencing protocols. By quantifying the kinetics of RNAi-based gene silencing, we present a model that may be used to help determine key parameters in more complex silencing experiments and explore alternative gene silencing protocols.

Animals↗

PubMed2004

Metabolic and transcriptional patterns accompanying glutamine depletion and repletion in mouse hepatoma cells: a model for physiological regulatory networks.

An important objective in postgenomic biology is to link gene expression to function by developing physiological networks that include data from the genomic and functional levels. Here, we develop a model for the analysis of time-dependent changes in metabolites, fluxes, and gene expression in a hepatic model system. The experimental framework chosen was modulation of extracellular glutamine in confluent cultures of mouse Hepa1-6 cells. The importance of glutamine has been demonstrated previously in mammalian cell culture by precipitating metabolic shifts with glutamine depletion and repletion. Our protocol removed glutamine from the medium for 24 h and returned it for a second 24 h. Flux assays of glycolysis, the tricarboxylic acid (TCA) cycle, and lipogenesis were used at specified intervals. All of these fluxes declined in the absence of glutamine and were restored when glutamine was repleted. Isotopomer spectral analysis identified glucose and glutamine as equal sources of lipogenic carbon. Metabolite measurements of organic acids and amino acids indicated that most metabolites changed in parallel with the fluxes. Experiments with actinomycin D indicated that de novo mRNA synthesis was required for observed flux changes during the depletion/repletion of glutamine. Analysis of gene expression data from DNA microarrays revealed that many more genes were anticorrelated with the glycolytic flux and glutamine level than were correlated with these indicators. In conclusion, this model may be useful as a prototype physiological regulatory network where gene expression profiles are analyzed in concert with changes in cell function.

Animals↗

PubMed2004

Elucidation of gene interaction networks through time-lagged correlation analysis of transcriptional data.

The photosynthetic cyanobacterium Synechocystis sp. strain PCC 6803 uses a complex genetic program to control its physiological response to alternating light conditions. To study this regulatory program time-series experiments were conducted by exposing Synechocystis sp. to serial perturbations in light intensity. In each experiment whole-genome DNA microarrays were used to monitor gene transcription in 20-min intervals over 8- and 16-h periods. The data was analyzed using time-lagged correlation analysis, which identifies genetic interaction networks by constructing correlations between time-shifted transcription profiles with different levels of statistical confidence. These networks allow inference of putative cause-effect relationships among the organism's genes. Using light intensity as our initial input signal, we identified six groups of genes whose time-lagged profiles possessed significant correlation, or anti-correlation, with the light intensity. We expanded this network by using the average profile from each group of genes as a seed, and searching for other genes whose time-lagged profiles possessed significant correlation, or anti-correlation, with the group's average profile. The final network comprised 50 different groups containing 259 genes. Several of these gene groups possess known light-stimulated gene clusters, such as Synechocystis sp. photosystems I and II and carbon dioxide fixation pathways, while others represent novel findings in this work.

Cyanobacteria↗