INSULIN REGULATES CYTOKINES AND INTERCELLULAR ADHESION MOLECULE-1 GENE EXPRESSION THROUGH NUCLEAR FACTOR-kappa B ACTIVATION IN LPS-INDUCED ACUTE LUNG INJURY IN RATS

Diabetic patients have increased susceptibility to infection, which may be related to impaired inflammatory response observed in experimental models of diabetes, and restored by insulin treatment. The goal of this study was to investigate whether insulin regulates transcription of cytokines and intercellular adhesion molecule 1 (ICAM-1) via nuclear factor-kappa B (NF-kappa B) signaling pathway in Escherichia coli LIPS-induced lung inflammation. Diabetic male Wistar rats (alloxan, 42 mg/kg, iv., 10 days) and controls were instilled intratracheally with saline containing LPS (750 mu g/0.4 mL) or saline only. Some diabetic rats were given neutral protamine Hagedorn insulin (4 IU, s.c.) 2 h before LIPS. Analyses performed 6 h after LPS included: (a) lung and mesenteric lymph node IL-1 beta, TNF-alpha, IL-10, and ICAM-1 messenger RNA (mRNA) were quantified by real-time reverse transcriptase-polymerase chain reaction; (b) number of neutrophils in the bronchoalveolar lavage (BAL) fluid, and concentrations of IL-1 beta, TNF-alpha, and IL-10 in the BAL were determined by the enzyme-linked immunosorbent assay; and (c) activation of NF-kappa B p65 subunit and phosphorylation of I-kappa B alpha were quantified by Western blot analysis. Relative to controls, diabetic rats exhibited a reduction in lung and mesenteric lymph node IL-1 beta (40%), TNF-alpha (similar to 30%), and IL-10 (similar to 40%) mRNA levels and reduced concentrations of IL-1 beta (52%), TNF-alpha (62%), IL-10 (43%), and neutrophil counts (72%) in the BAL. Activation of NF-kappa B p65 subunit and phosphorylation of I-kappa B alpha were almost suppressed in diabetic rats. Treatment of diabetic rats with insulin completely restored mRNA and protein levels of these cytokines and potentiated lung ICAM-1 mRNA levels (30%) and number of neutrophils (72%) in the BAL. Activation of NF-kappa B p65 subunit and phosphorylation of I-kappa B alpha were partially restored by insulin treatment. In conclusion, data presented suggest that insulin regulates transcription of proinflammatory (IL-1 beta, TNF-alpha) and anti-inflammatory (IL-10) cytokines, and expression of ICAM-1 via the NF-kappa B signaling pathway.

Influence of experimental inflammatory response on hepatic hepcidin gene expression and plasma iron concentration in sheep

Hepcidin is a highly conserved disulfide-bonded peptide that plays a central role in iron homeostasis. During systemic inflammation, hepcidin up-regulation is responsible for hypoferremia. This study aimed to analyze the influence of the inflammatory process induced by complete Freund's adjuvant (CFA) or lipopolysaccharide (LPS) on the liver expression of hepcidin mRNA transcripts and plasma iron concentration of sheep. The expression levels of hepcidin transcripts were up-regulated after CFA or LPS. Hypoferremic response was observed at 12 h (15.46 +/- 6.05 mu mol/L) or 6 h (14.59 +/- 4.38 mu mol/L) and iron reached its lowest level at 96 h (3.08 +/- 1.18 mu mol/L) or 16 h (4.06 +/- 1.58 mu mol/L) after CFA administration or LPS infusion, respectively. This study demonstrated that the iron regulatory hormone hepcidin was up-regulated in sheep liver in response to systemic inflammation. These findings extend our knowledge on the relationship between the systemic inflammatory response, hepcidin and iron, and provide a starting point for additional studies on iron metabolism and the inflammatory process in sheep. (C) 2011 Elsevier B.V. All rights reserved.

Low triioothyronine (T-3) or reverse triioothyronine (rT(3)) syndrome modifies gene expression in rats with congestive heart failure

Heart failure (NF) is frequently associated with euthyroid sicksyndrome (low T-3 and elevated rT(3)). We investigated if altered thyroid hormone in HF could affect expression of the TH receptor (TR alpha 1), and alpha and beta myosin heavy chains (alpha-MHC beta-MHC). HF was provoked in rats by aortic stenosis. We showed that rT(3) generated front liver and kidney deiodination significantly increased and T-3 decreased in HE; there was significantly higher TR alpha 1 expression, no alpha-MHC expression, but beta-MHC expression. Changes in TR alpha 1 could be compensating for low T-3 from HF.

Gene expression in placentation of farm animals: An overview of gene function during development

Eutherian mammals share a common ancestor that evolved into two main placental types, i.e., hemotrophic (e.g., human and mouse) and histiotrophic (e.g., farm animals), which differ in invasiveness. Pregnancies initiated with assisted reproductive techniques (ART) in farm animals are at increased risk of failure; these losses were associated with placental defects, perhaps due to altered gene expression. Developmentally regulated genes in the placenta seem highly phylogenetically conserved, whereas those expressed later in pregnancy are more species-specific. To elucidate differences between hemotrophic and epitheliochorial placentae, gene expression data were compiled from microarray studies of bovine placental tissues at various stages of pregnancy. Moreover, an in silico subtractive library was constructed based on homology of bovine genes to the database of zebrafish - a nonplacental vertebrate. In addition, the list of placental preferentially expressed genes for the human and mouse were collected using bioinformatics tools (Tissue-specific Gene Expression and Regulation [TiGER] - for humans, and tissue-specific genes database (TiSGeD) - for mice and humans). Humans, mice, and cattle shared 93 genes expressed in their placentae. Most of these were related to immune function (based on analysis of gene ontology). Cattle and women shared expression of 23 genes, mostly related to hormonal activity, whereas mice and women shared 16 genes (primarily sexual differentiation and glycoprotein biology). Because the number of genes expressed by the placentae of both cattle and mice were similar (based on cluster analysis), we concluded that both cattle and mice were suitable models to study the biology of the human placenta. (C) 2011 Elsevier B.V. All rights reserved.

Differential gene expression in Acidithiobacillus ferrooxidans LR planktonic and attached cells in the presence of chalcopyrite

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Impact of hypoxia on IGF-I, IGF-II, IGFBP-3, ALS and IGFBP-1 regulation and on IGF1R gene expression in children

Hypoxia is one of many factors involved in the regulation of the IGF system. However, no information is available regarding the regulation of the IGF system by acute hypoxia in humans. Objective: The aim of this study was to evaluate the effect of acute hypoxia on the IGF system of children. Design: Twenty-seven previously health children (14 boys and 13 girls) aged 15 days to 9.5 years were studied in two different situations: during a hypoxemic state (HS) due to acute respiratory distress and after full recovery to a normoxemic state (NS). In these two situations oxygen saturation was assessed with a pulse-oximeter and blood samples were collected for serum IGF-I, IGF-II, IGFBP-1, IGFBP-3, ALS and insulin determination by ELISA; fluoroimmunometric assay determination for GH and also for IGF1R gene expression analysis in peripheral lymphocytes by quantitative real-time PCR. Data were paired and analyzed by the Wilcoxon non-parametric test. Results: Oxygen saturation was significantly lower during HS than in NS (P<0.0001). IGF-I and IGF-II levels were lower during HS than in NS (P<0.0001 and P=0.0004. respectively). IGFBP-3 levels were also lower in HS than in NS (P=0.0002) while ALS and basal GH levels were higher during HS (P=0.0015 and P=0.014, respectively). Moreover, IGFBP-1 levels were higher during HS than in NS (P=0.004). No difference was found regarding insulin levels. The expression of IGF1R mRNA as 2(-Delta Delta CT) was higher during HS than in NS (P=0.03). Conclusion: The above results confirm a role of hypoxia in the regulation of the IGF system also in humans. This effect could be direct on the liver and/or mediated by GH and it is not restricted to the hepatocytes but involves other cell lines. During acute hypoxia a combination of alterations usually associated with reduced IGF action was observed. The higher expression of IGF1R mRNA may reflect an up-regulation of the transcriptional process. (C) 2012 Elsevier Ltd. All rights reserved.

Repression of Ppargc1a Gene in Liver of Hyperglycemic Rats Induced with High Fat Diet Combined with Streptozotocin

Type 2 diabetes mellitus implies deregulation of multiple metabolic processes, being the maintenance of glycemia one of the most important. Many genes are involved in the deregulation of this particular process. Therefore, the aim of this study was to evaluate gene expression of genes related to type 2 diabetes mellitus, in the liver and pancreas of rats with hyperglycemia induced by high fat diet along with a low single dose of streptozotocin. Ahsg and Ppargc1a genes were studied in liver, whereas Kcnj11 and Slc2a2 genes were analyzed in pancreas. For this purpose, 210-240 g female rats were fed a high fat diet or a control diet for three weeks. At day 14, animals fed with high fat diet were injected with a single low dose of streptozotocin (35 mg/kg) and the control group rats were injected only with the vehicle. Plasmatic glucose, triglycerides and total cholesterol levels were measured at the beginning, day 14 and end of treatment. Body weight was also measured. Once the treatment was complete, rats were appropriately euthanized and then, pancreas and liver were surgically removed and frozen in liquid nitrogen. Total RNA was isolated using TRIzol reagent, treated with DNase land reversely transcribed to cDNA. Gene expression analysis was performed using SYBR Green - Real time PCR and comparative Cq method, using three reference genes. Rats fed with high fat diet and treated with streptozotocin showed higher values of plasmatic glucose (17.09 +/- 0.43 vs. 5.91 +/- 0.29 mmol/L, p < 0.01) and a minor expression of Ppargc1a versus the control group (2-fold less expressed, p < 0.05) in liver. We conclude that repression of Ppargc1a gene may be an important process in the establishment of chronic hyperglycemia, probably through deregulation of hepatic gluconeogenesis. However, further studies need to be performed in order to clarify the role of Ppargc1a deregulation in liver glucose homeostasis.

Vitamin E Alters Inflammatory Gene Expression in Alcoholic Chronic Pancreatitis

Objective: To evaluate the effect of vitamin E supplementation on pancreatic gene expression of inflammatory markers in rats with alcoholic chronic pancreatitis. Methods: Wistar rats were divided into 3 groups: control (1), alcoholic chronic pancreatitis without (2) and with (3) vitamin E supplementation. Pancreatitis was induced by a liquid diet containing ethanol, cyclosporin A and cerulein. a-tocopherol content in plasma and liver and pancreas histopathology were analyzed. Gene expression of inflammatory biomarkers was analyzed by the quantitative real-time PCR technique. Results: The animals that received vitamin E supplementation had higher alpha-tocopherol amounts in plasma and liver. The pancreas in Group 1 showed normal histology, whereas in Groups 2 and 3, mild to moderate tissue destruction foci and mononuclear cell infiltration were detected. Real-time PCR analysis showed an increased expression of all genes in Groups 2 and 3 compared to Group 1. Vitamin E supplementation decreased the transcript number of 5 genes (alpha-SMA, COX-2, IL-6, MIP-3 alpha and TNF-alpha) and increased the transcript number of 1 gene (Pap). Conclusion: Vitamin E supplementation had anti-inflammatory and beneficial effects on the pancreatic gene expression of some inflammatory biomarkers in rats with alcoholic chronic pancreatitis, confirming its participation in the inflammatory response mechanisms in the pancreas. Copyright (c) 2012 S. Karger AG, Basel

Bayesian model accounting for within-class biological variability in Serial Analysis of Gene Expression (SAGE)

Abstract Background An important challenge for transcript counting methods such as Serial Analysis of Gene Expression (SAGE), "Digital Northern" or Massively Parallel Signature Sequencing (MPSS), is to carry out statistical analyses that account for the within-class variability, i.e., variability due to the intrinsic biological differences among sampled individuals of the same class, and not only variability due to technical sampling error. Results We introduce a Bayesian model that accounts for the within-class variability by means of mixture distribution. We show that the previously available approaches of aggregation in pools ("pseudo-libraries") and the Beta-Binomial model, are particular cases of the mixture model. We illustrate our method with a brain tumor vs. normal comparison using SAGE data from public databases. We show examples of tags regarded as differentially expressed with high significance if the within-class variability is ignored, but clearly not so significant if one accounts for it. Conclusion Using available information about biological replicates, one can transform a list of candidate transcripts showing differential expression to a more reliable one. Our method is freely available, under GPL/GNU copyleft, through a user friendly web-based on-line tool or as R language scripts at supplemental web-site.

Simcluster: clustering enumeration gene expression data on the simplex space

Abstract Background Transcript enumeration methods such as SAGE, MPSS, and sequencing-by-synthesis EST "digital northern", are important high-throughput techniques for digital gene expression measurement. As other counting or voting processes, these measurements constitute compositional data exhibiting properties particular to the simplex space where the summation of the components is constrained. These properties are not present on regular Euclidean spaces, on which hybridization-based microarray data is often modeled. Therefore, pattern recognition methods commonly used for microarray data analysis may be non-informative for the data generated by transcript enumeration techniques since they ignore certain fundamental properties of this space. Results Here we present a software tool, Simcluster, designed to perform clustering analysis for data on the simplex space. We present Simcluster as a stand-alone command-line C package and as a user-friendly on-line tool. Both versions are available at: http://xerad.systemsbiology.net/simcluster. Conclusion Simcluster is designed in accordance with a well-established mathematical framework for compositional data analysis, which provides principled procedures for dealing with the simplex space, and is thus applicable in a number of contexts, including enumeration-based gene expression data.

GEDI: a user-friendly toolbox for analysis of large-scale gene expression data

Abstract Background Several mathematical and statistical methods have been proposed in the last few years to analyze microarray data. Most of those methods involve complicated formulas, and software implementations that require advanced computer programming skills. Researchers from other areas may experience difficulties when they attempting to use those methods in their research. Here we present an user-friendly toolbox which allows large-scale gene expression analysis to be carried out by biomedical researchers with limited programming skills. Results Here, we introduce an user-friendly toolbox called GEDI (Gene Expression Data Interpreter), an extensible, open-source, and freely-available tool that we believe will be useful to a wide range of laboratories, and to researchers with no background in Mathematics and Computer Science, allowing them to analyze their own data by applying both classical and advanced approaches developed and recently published by Fujita et al. Conclusion GEDI is an integrated user-friendly viewer that combines the state of the art SVR, DVAR and SVAR algorithms, previously developed by us. It facilitates the application of SVR, DVAR and SVAR, further than the mathematical formulas present in the corresponding publications, and allows one to better understand the results by means of available visualizations. Both running the statistical methods and visualizing the results are carried out within the graphical user interface, rendering these algorithms accessible to the broad community of researchers in Molecular Biology.

Modeling gene expression regulatory networks with the sparse vector autoregressive model

Abstract Background To understand the molecular mechanisms underlying important biological processes, a detailed description of the gene products networks involved is required. In order to define and understand such molecular networks, some statistical methods are proposed in the literature to estimate gene regulatory networks from time-series microarray data. However, several problems still need to be overcome. Firstly, information flow need to be inferred, in addition to the correlation between genes. Secondly, we usually try to identify large networks from a large number of genes (parameters) originating from a smaller number of microarray experiments (samples). Due to this situation, which is rather frequent in Bioinformatics, it is difficult to perform statistical tests using methods that model large gene-gene networks. In addition, most of the models are based on dimension reduction using clustering techniques, therefore, the resulting network is not a gene-gene network but a module-module network. Here, we present the Sparse Vector Autoregressive model as a solution to these problems. Results We have applied the Sparse Vector Autoregressive model to estimate gene regulatory networks based on gene expression profiles obtained from time-series microarray experiments. Through extensive simulations, by applying the SVAR method to artificial regulatory networks, we show that SVAR can infer true positive edges even under conditions in which the number of samples is smaller than the number of genes. Moreover, it is possible to control for false positives, a significant advantage when compared to other methods described in the literature, which are based on ranks or score functions. By applying SVAR to actual HeLa cell cycle gene expression data, we were able to identify well known transcription factor targets. Conclusion The proposed SVAR method is able to model gene regulatory networks in frequent situations in which the number of samples is lower than the number of genes, making it possible to naturally infer partial Granger causalities without any a priori information. In addition, we present a statistical test to control the false discovery rate, which was not previously possible using other gene regulatory network models.

Physical training prevents body weight gain but does not modify adipose tissue gene expression

The relationship of body weight (BW) with white adipose tissue (WAT) mass and WAT gene expression pattern was investigated in mice submitted to physical training (PT). Adult male C57BL/6 mice were submitted to two 1.5-h daily swimming sessions (T, N = 18), 5 days/week for 4 weeks or maintained sedentary (S, N = 15). Citrate synthase activity increased significantly in the T group (P < 0.05). S mice had a substantial weight gain compared to T mice (4.06 ± 0.43 vs 0.38 ± 0.28 g, P < 0.01). WAT mass, adipocyte size, and the weights of gastrocnemius and soleus muscles, lung, kidney, and adrenal gland were not different. Liver and heart were larger and the spleen was smaller in T compared to S mice (P < 0.05). Food intake was higher in T than S mice (4.7 ± 0.2 vs 4.0 ± 0.3 g/animal, P < 0.05) but oxygen consumption at rest did not differ between groups. T animals showed higher serum leptin concentration compared to S animals (6.37 ± 0.5 vs 3.11 ± 0.12 ng/mL). WAT gene expression pattern obtained by transcription factor adipocyte determination and differentiation-dependent factor 1, fatty acid synthase, malic enzyme, hormone-sensitive lipase, adipocyte lipid binding protein, leptin, and adiponectin did not differ significantly between groups. Collectively, our results showed that PT prevents BW gain and maintains WAT mass due to an increase in food intake and unchanged resting metabolic rate. These responses are closely related to unchanged WAT gene expression patterns.

Semantic integration of gene expression analysis tools and data sources using software connectors

Abstract Background The study and analysis of gene expression measurements is the primary focus of functional genomics. Once expression data is available, biologists are faced with the task of extracting (new) knowledge associated to the underlying biological phenomenon. Most often, in order to perform this task, biologists execute a number of analysis activities on the available gene expression dataset rather than a single analysis activity. The integration of heteregeneous tools and data sources to create an integrated analysis environment represents a challenging and error-prone task. Semantic integration enables the assignment of unambiguous meanings to data shared among different applications in an integrated environment, allowing the exchange of data in a semantically consistent and meaningful way. This work aims at developing an ontology-based methodology for the semantic integration of gene expression analysis tools and data sources. The proposed methodology relies on software connectors to support not only the access to heterogeneous data sources but also the definition of transformation rules on exchanged data. Results We have studied the different challenges involved in the integration of computer systems and the role software connectors play in this task. We have also studied a number of gene expression technologies, analysis tools and related ontologies in order to devise basic integration scenarios and propose a reference ontology for the gene expression domain. Then, we have defined a number of activities and associated guidelines to prescribe how the development of connectors should be carried out. Finally, we have applied the proposed methodology in the construction of three different integration scenarios involving the use of different tools for the analysis of different types of gene expression data. Conclusions The proposed methodology facilitates the development of connectors capable of semantically integrating different gene expression analysis tools and data sources. The methodology can be used in the development of connectors supporting both simple and nontrivial processing requirements, thus assuring accurate data exchange and information interpretation from exchanged data.