Hepatocyte nuclear factors 1α/4α and forkhead box A2 regulate the solute carrier 2A2 (Slc2a2) gene expression in the liver and kidney of diabetic rats

AIMS: Solute carrier 2a2 (Slc2a2) gene codifies the glucose transporter GLUT2, a key protein for glucose flux in hepatocytes and renal epithelial cells of proximal tubule. In diabetes mellitus, hepatic and tubular glucose output has been related to Slc2a2/GLUT2 overexpression; and controlling the expression of this gene may be an important adjuvant way to improve glycemic homeostasis. Thus, the present study investigated transcriptional mechanisms involved in the diabetes-induced overexpression of the Slc2a2 gene. MAIN METHODS: Hepatocyte nuclear factors 1α and 4α (HNF-1α and HNF-4α), forkhead box A2 (FOXA2), sterol regulatory element binding protein-1c (SREBP-1c) and the CCAAT-enhancer-binding protein (C/EBPβ) mRNA expression (RT-PCR) and binding activity into the Slc2a2 promoter (electrophoretic mobility assay) were analyzed in the liver and kidney of diabetic and 6-day insulin-treated diabetic rats. KEY FINDINGS: Slc2a2/GLUT2 expression increased by more than 50% (P<0.001) in the liver and kidney of diabetic rats, and 6-day insulin treatment restores these values to those observed in non-diabetic animals. Similarly, the mRNA expression and the binding activity of HNF-1α, HNF-4α and FOXA2 increased by 50 to 100% (P<0.05 to P<0.001), also returning to values of non-diabetic rats after insulin treatment. Neither the Srebf1 and Cebpb mRNA expression, nor the SREBP-1c and C/EBP-β binding activity was altered in diabetic rats. SIGNIFICANCE: HNF-1α, HNF-4α and FOXA2 transcriptional factors are involved in diabetes-induced overexpression of Slc2a2 gene in the liver and kidney. These data point out that these transcriptional factors are important targets to control GLUT2 expression in these tissues, which can contribute to glycemic homeostasis in diabetes.

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.

CC chemokine ligand 3 and receptors 1 and 5 gene expression in recurrent aphthous stomatitis

Objective. The aim of this study was to investigate the local and systemic expression of CC-chemokine ligand 3 (CCL3) and its receptors (CCR1 and CCR5) in tissue samples and peripheral blood mononuclear cells of recurrent aphthous stomatitis (RAS) patients. Study Design. This case-control study enrolled 29 patients presenting severe RAS manifestations and 20 non-RAS patients proportionally matched by sex and age. Total RNA was extracted from biopsy specimens and peripheral blood mononuclear cells for quatitative reverse-transcription polymerase chain reaction. The data obtained by relative quantification were evaluated by the 2(-Delta Delta Ct) method, normalized by the expression of an endogenous control, and analyzed by Student t test. Results. The results demonstrated overexpression in RAS tissue samples of all of the chemokines evaluated compared with healthy oral mucosa, whereas the blood samples showed only CCR1 overexpression in RAS patients. Conclusions. These findings suggest that the increased expression of CCL3, CCR1, and CCR5 may influence the immune response in RAS by T(H)1 cytokine polarization. (Oral Surg Oral Med Oral Pathol Oral Radiol 2012;114:93-98)

Gene expression profiles displayed by peripheral blood mononuclear cells from patients with type 2 diabetes mellitus focusing on biological processes implicated on the pathogenesis of the disease

Patients with type 2 diabetes mellitus (T2DM) exhibit insulin resistance associated with obesity and inflammatory response, besides an increased level of oxidative DNA damage as a consequence of the hyperglycemic condition and the generation of reactive oxygen species (ROS). In order to provide information on the mechanisms involved in the pathophysiology of T2DM, we analyzed the transcriptional expression patterns exhibited by peripheral blood mononuclear cells (PBMCs) from patients with T2DM compared to non-diabetic subjects, by investigating several biological processes: inflammatory and immune responses, responses to oxidative stress and hypoxia, fatty acid processing, and DNA repair. PBMCs were obtained from 20 T2DM patients and eight non-diabetic subjects. Total RNA was hybridized to Agilent whole human genome 4x44K one-color oligo-microarray. Microarray data were analyzed using the GeneSpring GX 11.0 software (Agilent). We used BRB-ArrayTools software (gene set analysis - GSA) to investigate significant gene sets and the Genomica tool to study a possible influence of clinical features on gene expression profiles. We showed that PBMCs from T2DM patients presented significant changes in gene expression, exhibiting 1320 differentially expressed genes compared to the control group. A great number of genes were involved in biological processes implicated in the pathogenesis of T2DM. Among the genes with high fold-change values, the up-regulated ones were associated with fatty acid metabolism and protection against lipid-induced oxidative stress, while the down-regulated ones were implicated in the suppression of pro-inflammatory cytokines production and DNA repair. Moreover, we identified two significant signaling pathways: adipocytokine, related to insulin resistance; and ceramide, related to oxidative stress and induction of apoptosis. In addition, expression profiles were not influenced by patient features, such as age, gender, obesity, pre/post-menopause age, neuropathy, glycemia, and HbA(1c) percentage. Hence, by studying expression profiles of PBMCs, we provided quantitative and qualitative differences and similarities between T2DM patients and non-diabetic individuals, contributing with new perspectives for a better understanding of the disease. (C) 2012 Elsevier B.V. All rights reserved.

Gene Expression in Bovine Oocytes and Cumulus Cells After Meiotic Inhibition with the Cyclin-Dependent Kinase Inhibitor Butyrolactone I

Contents The aim of this study was to determine the effect of temporary inhibition of meiosis using the cyclin-dependent kinase inhibitor butyrolactone I (BLI) on gene expression in bovine oocytes and cumulus cells. Immature bovine cumulusoocyte complexes (COCs) were assigned to groups: (i) Control COCs collected immediately after recovery from the ovary or (ii) after in vitro maturation (IVM) for 24 h, (iii) Inhibited COCs collected 24 h after incubation with 100 mu m BLI or (iv) after meiotic inhibition for 24 h followed by IVM for a further 22 h. For mRNA relative abundance analysis, pools of 10 denuded oocytes and respective cumulus cells were collected. Transcripts related to cell cycle regulation and oocyte competence were evaluated in oocytes and cumulus cells by quantitative real-time PCR (qPCR). Most of the examined transcripts were downregulated (p < 0.05) after IVM in control and inhibited oocytes (19 of 35). Nine transcripts remained stable (p > 0.05) after IVM in control oocytes; only INHBA did not show this pattern in inhibited oocytes. Seven genes were upregulated after IVM in control oocytes (p < 0.05), and only PLAT, RBP1 and INHBB were not upregulated in inhibited oocytes after IVM. In cumulus cells, six genes were upregulated (p < 0.05) after IVM and eight were downregulated (p < 0.05). Cells from inhibited oocytes showed the same pattern of expression regarding maturation profile, but were affected by the temporary meiosis inhibition of the oocyte when the same maturation stages were compared between inhibited and control groups. In conclusion, changes in transcript abundance in oocytes and cumulus cells during maturation in vitro were mostly mirrored after meiotic inhibition followed by maturation.

Gene copy number and differential gene expression in haploid and diploid males of the stingless bee, Melipona quadrifasciata

Complementary sex determination in Hymenoptera implies that heterozygosity at the sex locus leads to the development of diploid females, whereas hemizygosity results in haploid males. Diploid males can arise through inbreeding. In social species, these pose a double burden on colony fitness, from significant reduction in its worker force and through being less viable and fertile than haploid males. Apart from being "misfits", diploid males are of interest to assess molecular correlates for possibly ploidy-related bionomic differences. Herein, we generated suppression subtractive cDNA libraries from newly emerged haploid and diploid males of the stingless bee Melipona quadrifasciata to enrich for differentially expressed genes. Gene Ontology classification revealed that in haploid males more DEGs were related to stress responsiveness, biosynthetic processes, reproductive processes and spermatogenesis, whereas in diploid ones differentially expressed genes were associated with cellular organization, nervous system development and amino acid transport were prevalent. Furthermore, both libraries contained over 40 % ESTs representing possibly novel transcripts. Quantitative RT-PCR analyses confirmed the differential expression of a representative DEG set in newly emerged males. Several muscle formation and energy metabolism-related genes were under-expressed in diploid males. On including 5-day-old males in the analysis, changes in transcript abundance during sexual maturation were revealed.

Homeobox gene expression profile indicates HOXA5 as a candidate prognostic marker in oral squamous cell carcinoma

The search for molecular markers to improve diagnosis, individualize treatment and predict behavior of tumors has been the focus of several studies. This study aimed to analyze homeobox gene expression profile in oral squamous cell carcinoma (OSCC) as well as to investigate whether some of these genes are relevant molecular markers of prognosis and/or tumor aggressiveness. Homeobox gene expression levels were assessed by microarrays and qRT-PCR in OSCC tissues and adjacent non-cancerous matched tissues (margin), as well as in OSCC cell lines. Analysis of microarray data revealed the expression of 147 homeobox genes, including one set of six at least 2-fold up-regulated, and another set of 34 at least 2-fold down-regulated homeobox genes in OSCC. After qRT-PCR assays, the three most up-regulated homeobox genes (HOXA5, HOXD10 and HOXD11) revealed higher and statistically significant expression levels in OSCC samples when compared to margins. Patients presenting lower expression of HOXA5 had poorer prognosis compared to those with higher expression (P=0.03). Additionally, the status of HOXA5, HOXD10 and HOXD11 expression levels in OSCC cell lines also showed a significant up-regulation when compared to normal oral keratinocytes. Results confirm the presence of three significantly upregulated (>4-fold) homeobox genes (HOXA5, HOXD10 and HOXD11) in OSCC that may play a significant role in the pathogenesis of these tumors. Moreover, since lower levels of HOXA5 predict poor prognosis, this gene may be a novel candidate for development of therapeutic strategies in OSCC.