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      Gene expression profiling in the intestinal mucosa of obese rats administered probiotic bacteria

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          Abstract

          We investigated whether the administration of Lactobacillus paracasei CNCM I-4034, Bifidobacterium breve CNCM I-4035 and Lactobacillus rhamnosus CNCM I-4036 modulate the expression of genes in the intestinal mucosa of obese Zucker rats. Forty-eight Zucker-Lepr fa/fa and 16 Zucker lean Lepr +/ fa rats were used. Eight Zucker lean Lepr +/ fa and 8 Zucker-Lepr fa/fa rats were euthanized as a reference. The remaining 40 Zucker-Lepr fa/fa rats were then assigned to receive 10 10 colony forming units (CFU) of one of the three probiotic strains, a mixture of L. paracasei CNCM I-4034 and B. breve CNCM I-4035, or a placebo by oral administration for 30 days. An additional group of 8 Zucker lean Lepr +/ fa rats received the placebo for 30 days. Over 27,000 rat genes were studied using a DNA array. Four animals per group were used. Total RNA was extracted from intestinal mucosa and cDNA was synthesized, fragmented and labeled. Labeled cDNA was hybridized using GeneChip kits, and the latter were scanned. Intensity values of each probe were processed and normalized to obtain an individual value for each set of probes.

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          Most cited references 13

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          Banting lecture 1988. Role of insulin resistance in human disease.

           G M Reaven (1988)
          Resistance to insulin-stimulated glucose uptake is present in the majority of patients with impaired glucose tolerance (IGT) or non-insulin-dependent diabetes mellitus (NIDDM) and in approximately 25% of nonobese individuals with normal oral glucose tolerance. In these conditions, deterioration of glucose tolerance can only be prevented if the beta-cell is able to increase its insulin secretory response and maintain a state of chronic hyperinsulinemia. When this goal cannot be achieved, gross decompensation of glucose homeostasis occurs. The relationship between insulin resistance, plasma insulin level, and glucose intolerance is mediated to a significant degree by changes in ambient plasma free-fatty acid (FFA) concentration. Patients with NIDDM are also resistant to insulin suppression of plasma FFA concentration, but plasma FFA concentrations can be reduced by relatively small increments in insulin concentration. Consequently, elevations of circulating plasma FFA concentration can be prevented if large amounts of insulin can be secreted. If hyperinsulinemia cannot be maintained, plasma FFA concentration will not be suppressed normally, and the resulting increase in plasma FFA concentration will lead to increased hepatic glucose production. Because these events take place in individuals who are quite resistant to insulin-stimulated glucose uptake, it is apparent that even small increases in hepatic glucose production are likely to lead to significant fasting hyperglycemia under these conditions. Although hyperinsulinemia may prevent frank decompensation of glucose homeostasis in insulin-resistant individuals, this compensatory response of the endocrine pancreas is not without its price. Patients with hypertension, treated or untreated, are insulin resistant, hyperglycemic, and hyperinsulinemic. In addition, a direct relationship between plasma insulin concentration and blood pressure has been noted. Hypertension can also be produced in normal rats when they are fed a fructose-enriched diet, an intervention that also leads to the development of insulin resistance and hyperinsulinemia. The development of hypertension in normal rats by an experimental manipulation known to induce insulin resistance and hyperinsulinemia provides further support for the view that the relationship between the three variables may be a causal one.(ABSTRACT TRUNCATED AT 400 WORDS)
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            Model-based analysis of oligonucleotide arrays: expression index computation and outlier detection.

            Recent advances in cDNA and oligonucleotide DNA arrays have made it possible to measure the abundance of mRNA transcripts for many genes simultaneously. The analysis of such experiments is nontrivial because of large data size and many levels of variation introduced at different stages of the experiments. The analysis is further complicated by the large differences that may exist among different probes used to interrogate the same gene. However, an attractive feature of high-density oligonucleotide arrays such as those produced by photolithography and inkjet technology is the standardization of chip manufacturing and hybridization process. As a result, probe-specific biases, although significant, are highly reproducible and predictable, and their adverse effect can be reduced by proper modeling and analysis methods. Here, we propose a statistical model for the probe-level data, and develop model-based estimates for gene expression indexes. We also present model-based methods for identifying and handling cross-hybridizing probes and contaminating array regions. Applications of these results will be presented elsewhere.
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              The metabolic syndrome: time for a critical appraisal: joint statement from the American Diabetes Association and the European Association for the Study of Diabetes.

              The term "metabolic syndrome" refers to a clustering of specific cardiovascular disease (CVD) risk factors whose underlying pathophysiology is thought to be related to insulin resistance. Since the term is widely used in research and clinical practice, we undertook an extensive review of the literature in relation to the syndrome's definition, underlying pathogenesis, and association with CVD and to the goals and impact of treatment. While there is no question that certain CVD risk factors are prone to cluster, we found that the metabolic syndrome has been imprecisely defined, there is a lack of certainty regarding its pathogenesis, and there is considerable doubt regarding its value as a CVD risk marker. Our analysis indicates that too much critically important information is missing to warrant its designation as a "syndrome." Until much needed research is completed, clinicians should evaluate and treat all CVD risk factors without regard to whether a patient meets the criteria for diagnosis of the "metabolic syndrome."
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                Author and article information

                Journal
                Sci Data
                Sci Data
                Scientific Data
                Nature Publishing Group
                2052-4463
                12 December 2017
                2017
                : 4
                Affiliations
                [1 ]Department of Biochemistry and Molecular Biology II, School of Pharmacy, University of Granada , Granada 18071, Spain
                [2 ]Institute of Nutrition and Food Technology ‘José Mataix’, Biomedical Research Center, Parque Tecnológico Ciencias de la Salud, University of Granada , Granada 18016, Spain
                [3 ]Instituto de Investigación Biosanitaria ibs.GRANADA , Spain
                [4 ]Department of Cell Biology, School of Sciences, University of Granada , Granada 18071, Spain
                [5 ]Department of Biochemistry and Molecular Biology I, School of Sciences, University of Granada , Granada 18071, Spain
                [6 ]CIBEROBN, Instituto de Salud Carlos III , Madrid 28029, Spain
                Author notes
                [a ] J.P.-D. (email: jrplaza@ 123456ugr.es )
                [b ] L.F. (email: fontana@ 123456ugr.es ).
                []

                A.G., C.G.L. and L.F. contributed to the study concept and design. J.P.D., C.R.S., V.M.C., F.A.M., and M.J.S.L. participated in the acquisition of data. J.P.D. and A.G. did the statistical analysis. All authors took part in the analysis and interpretation of data, the drafting of the manuscript and the critical revision of the manuscript. A.G., C.G.L. and L.F. obtained funding. J.P.D. and L.F. wrote the manuscript.

                Article
                sdata2017186
                10.1038/sdata.2017.186
                5726311
                29231922
                Copyright © 2017, The Author(s)

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files made available in this article.

                Categories
                Data Descriptor

                metabolic disorders, applied microbiology

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