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Fasting induces a biphasic adaptive metabolic response in murine small intestine

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      Abstract

      Background

      The gut is a major energy consumer, but a comprehensive overview of the adaptive response to fasting is lacking. Gene-expression profiling, pathway analysis, and immunohistochemistry were therefore carried out on mouse small intestine after 0, 12, 24, and 72 hours of fasting.

      Results

      Intestinal weight declined to 50% of control, but this loss of tissue mass was distributed proportionally among the gut's structural components, so that the microarrays' tissue base remained unaffected. Unsupervised hierarchical clustering of the microarrays revealed that the successive time points separated into distinct branches. Pathway analysis depicted a pronounced, but transient early response that peaked at 12 hours, and a late response that became progressively more pronounced with continued fasting. Early changes in gene expression were compatible with a cellular deficiency in glutamine, and metabolic adaptations directed at glutamine conservation, inhibition of pyruvate oxidation, stimulation of glutamate catabolism via aspartate and phosphoenolpyruvate to lactate, and enhanced fatty-acid oxidation and ketone-body synthesis. In addition, the expression of key genes involved in cell cycling and apoptosis was suppressed. At 24 hours of fasting, many of the early adaptive changes abated. Major changes upon continued fasting implied the production of glucose rather than lactate from carbohydrate backbones, a downregulation of fatty-acid oxidation and a very strong downregulation of the electron-transport chain. Cell cycling and apoptosis remained suppressed.

      Conclusion

      The changes in gene expression indicate that the small intestine rapidly looses mass during fasting to generate lactate or glucose and ketone bodies. Meanwhile, intestinal architecture is maintained by downregulation of cell turnover.

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      Most cited references 70

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      A comparison of normalization methods for high density oligonucleotide array data based on variance and bias.

      When running experiments that involve multiple high density oligonucleotide arrays, it is important to remove sources of variation between arrays of non-biological origin. Normalization is a process for reducing this variation. It is common to see non-linear relations between arrays and the standard normalization provided by Affymetrix does not perform well in these situations. We present three methods of performing normalization at the probe intensity level. These methods are called complete data methods because they make use of data from all arrays in an experiment to form the normalizing relation. These algorithms are compared to two methods that make use of a baseline array: a one number scaling based algorithm and a method that uses a non-linear normalizing relation by comparing the variability and bias of an expression measure. Two publicly available datasets are used to carry out the comparisons. The simplest and quickest complete data method is found to perform favorably. Software implementing all three of the complete data normalization methods is available as part of the R package Affy, which is a part of the Bioconductor project http://www.bioconductor.org. Additional figures may be found at http://www.stat.berkeley.edu/~bolstad/normalize/index.html
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        Cluster analysis and display of genome-wide expression patterns.

        A system of cluster analysis for genome-wide expression data from DNA microarray hybridization is described that uses standard statistical algorithms to arrange genes according to similarity in pattern of gene expression. The output is displayed graphically, conveying the clustering and the underlying expression data simultaneously in a form intuitive for biologists. We have found in the budding yeast Saccharomyces cerevisiae that clustering gene expression data groups together efficiently genes of known similar function, and we find a similar tendency in human data. Thus patterns seen in genome-wide expression experiments can be interpreted as indications of the status of cellular processes. Also, coexpression of genes of known function with poorly characterized or novel genes may provide a simple means of gaining leads to the functions of many genes for which information is not available currently.
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          Single-step method of RNA isolation by acid guanidinium thiocyanate-phenol-chloroform extraction.

          A new method of total RNA isolation by a single extraction with an acid guanidinium thiocyanate-phenol-chloroform mixture is described. The method provides a pure preparation of undegraded RNA in high yield and can be completed within 4 h. It is particularly useful for processing large numbers of samples and for isolation of RNA from minute quantities of cells or tissue samples.
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            Author and article information

            Affiliations
            [1 ]AMC Liver Centre, Academic Medical Centre, Amsterdam, The Netherlands
            [2 ]Bioinformatics Laboratory, Academic Medical Centre, Amsterdam, The Netherlands
            [3 ]BiGCaT Bioinformatics, University of Maastricht, Maastricht, The Netherlands
            [4 ]GeneGo, Inc., St. Joseph, MI, USA
            Contributors
            Journal
            BMC Genomics
            BMC Genomics
            BioMed Central
            1471-2164
            2007
            9 October 2007
            : 8
            : 361
            2148066
            1471-2164-8-361
            17925015
            10.1186/1471-2164-8-361
            Copyright © 2007 Sokolović et al; licensee BioMed Central Ltd.

            This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

            Categories
            Research Article

            Genetics

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