Gene Expression Profiling of Cultured Cells From Brainstem of Newborn Spontaneously Hypertensive and Wistar Kyoto Rats

The spontaneously hypertensive rat (SHR) is a good model to study several diseases such as the attention-deficit hyperactivity disorder, cardiopulmonary impairment, nephropathy, as well as hypertension, which is a multifactor disease that possibly involves alterations in gene expression in hypertensive relative to normotensive subjects. In this study, we used high-density oligoarrays to compare gene expression profiles in cultured neurons and glia from brainstem of newborn normotensive Wistar Kyoto (WKY) and SHR rats. We found 376 genes differentially expressed between SHR and WKY brainstem cells that preferentially map to 17 metabolic/signaling pathways. Some of the pathways and regulated genes identified herein are obviously related to cardiovascular regulation; in addition there are several genes differentially expressed in SHR not yet associated to hypertension, which may be attributed to other differences between SHR and WKY strains. This constitute a rich resource for the identification and characterization of novel genes associated to phenotypic differences observed in SHR relative to WKY, including hypertension. In conclusion, this study describes for the first time the gene profiling pattern of brainstem cells from SHR and WKY rats, which opens up new possibilities and strategies of investigation and possible therapeutics to hypertension, as well as for the understanding of the brain contribution to phenotypic differences between SHR and WKY rats.

Chronic resistance training decreases MuRF-1 and Atrogin-1 gene expression but does not modify Akt, GSK-3 beta and p70S6K levels in rats

Long-term adaptation to resistance training is probably due to the cumulative molecular effects of each exercise session. Therefore, we studied in female Wistar rats the molecular effects of a chronic resistance training regimen (3 months) leading to skeletal muscle hypertrophy in the plantaris muscle. Our results demonstrated that muscle proteolytic genes MuRF-1 and Atrogin-1 were significantly decreased in the exercised group measured 24 h after the last resistance exercise session (41.64 and 61.19%, respectively; P < 0.05). Nonetheless, when measured at the same time point, 4EBP-1, GSK-3 beta and eIF2B epsilon mRNA levels and Akt, GSK-3 beta and p70S6K protein levels (regulators of translation initiation) were not modified. Such data suggests that if gene transcription constitutes a control point in the protein synthesis pathway this regulation probably occurs in early adaptation periods or during extreme situations leading to skeletal muscle remodeling. However, proteolytic gene expression is modified even after a prolonged resistance training regimen leading to moderate skeletal muscle hypertrophy.

The role of glucocorticoid in SIRP alpha and SHP-1 gene expression in AIHA patients

The aim of this work was to evaluate the regulation of SIRP alpha, an inhibitory phagocyte receptor, and the phosphatase SHP-1 in monocytes of patients with autoimmune hemolytic anemia, and the role of dexamethasone on SIRP alpha and SHP-1 gene expression and erythrophagocytosis in vitro. SIRP alpha and SHP-1 expression was higher in monocytes from AIHA patients compared with normal, returning to normal after glucocorticoid therapy. SIRP alpha and SHP-1 mRNA expression was upregulated in healthy monocytes treated with dexamethasone compared with basal; however, the erythrophagocytic ability was not altered. Our results point to a minor role of SIRP alpha and SHP-1 in determining AIHA.

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.

Gene expression profiling reveals molecular marker candidates of laryngeal squamous cell carcinoma

Laryngeal squamous cell carcinoma is very common in head and neck cancer, with high mortality rates and poor prognosis. In this study, we compared expression profiles of clinical samples from 13 larynx tumors and 10 non-neoplastic larynx tissues using a custom-built cDNA microarray containing 331 probes for 284 genes previously identified by informatics analysis of EST databases as markers of head and neck tumors. Thirty-five genes showed statistically significant differences (SNR >= 11.01, p <= 0.001) in the expression between tumor and non-tumor larynx tissue samples. Functional annotation indicated that these genes are involved in cellular processes relevant to the cancer phenotype, such as apoptosis, cell cycle, DNA repair, proteolysis, protease inhibition, signal transduction and transcriptional regulation. Six of the identified transcripts map to intronic regions of protein-coding genes and may comprise non-annotated exons or as yet uncharacterized long ncRNAs with a regulatory role in the gene expression program of larynx tissue. The differential expression of 10 of these genes (ADCY6, AES, AL2SCR3, CRR9, CSTB, DUSP1, MAP3K5, PLAT, UBL1 and ZNF706) was independently confirmed by quantitative real-time RT-PCR. Among these, the CSTB gene product has cysteine protease inhibitor activity that has been associated with an antimetastatic function. Interestingly, CSTB showed a low expression in the tumor samples analyzed (p<0.0001). The set of genes identified here contribute to a better understanding of the molecular basis of larynx cancer, and provide candidate markers for improving diagnosis, prognosis and treatment of this carcinoma.

Pre-processing for noise detection in gene expression classification data

Due to the imprecise nature of biological experiments, biological data is often characterized by the presence of redundant and noisy data. This may be due to errors that occurred during data collection, such as contaminations in laboratorial samples. It is the case of gene expression data, where the equipments and tools currently used frequently produce noisy biological data. Machine Learning algorithms have been successfully used in gene expression data analysis. Although many Machine Learning algorithms can deal with noise, detecting and removing noisy instances from the training data set can help the induction of the target hypothesis. This paper evaluates the use of distance-based pre-processing techniques for noise detection in gene expression data classification problems. This evaluation analyzes the effectiveness of the techniques investigated in removing noisy data, measured by the accuracy obtained by different Machine Learning classifiers over the pre-processed data.

Xylella fastidiosa gene expression analysis by DNA microarrays

Xylella fastidiosa genome sequencing has generated valuable data by identifying genes acting either on metabolic pathways or in associated pathogenicity and virulence. Based on available information on these genes, new strategies for studying their expression patterns, such as microarray technology, were employed. A total of 2,600 primer pairs were synthesized and then used to generate fragments using the PCR technique. The arrays were hybridized against cDNAs labeled during reverse transcription reactions and which were obtained from bacteria grown under two different conditions (liquid XDM2 and liquid BCYE). All data were statistically analyzed to verify which genes were differentially expressed. In addition to exploring conditions for X. fastidiosa genome-wide transcriptome analysis, the present work observed the differential expression of several classes of genes (energy, protein, amino acid and nucleotide metabolism, transport, degradation of substances, toxins and hypothetical proteins, among others). The understanding of expressed genes in these two different media will be useful in comprehending the metabolic characteristics of X. fastidiosa, and in evaluating how important certain genes are for the functioning and survival of these bacteria in plants.

Yerba Maté (Ilex paraguariensis) aqueous extract decreases intestinal SGLT1 gene expression but does not affect other biochemical parameters in alloxan-diabetic wistar rats

Yerba mate´ (Ilex paraguariensis) is rich in polyphenols, especially chlorogenic acids. Evidence suggests that dietary polyphenols could play a role in glucose absorption and metabolism. The aim of this study was to evaluate the antidiabetic properties of yerba mate´ extract in alloxan-induced diabetic Wistar rats. Animals (n ) 41) were divided in four groups: nondiabetic control (NDC, n=11), and diabetic yerba maté (DY, n = 10). The intervention consisted in the administration of yerba mate´ extract in a 1 g extract/ kg body weight dose for 28 days; controls received saline solution only. There were no significant differences in serum glucose, insulin, and hepatic glucose-6-phosphatase activity between the groups that ingested yerba maté extract (NDY and DY) and the controls (NDC and DC). However, the intestinal SGLT1 gene expression was significantly lower in animals that received yerba maté both in upper (p = 0.007) and middle (p < 0.001) small intestine. These results indicate that bioactive compounds present in yerba maté might be capable of interfering in glucose absorption, by decreasing SGLT1 expression

The full Bayesian significance test for mixture models: results in gene expression clustering

Gene clustering is a useful exploratory technique to group together genes with similar expression levels under distinct cell cycle phases or distinct conditions. It helps the biologist to identify potentially meaningful relationships between genes. In this study, we propose a clustering method based on multivariate normal mixture models, where the number of clusters is predicted via sequential hypothesis tests: at each step, the method considers a mixture model of m components (m = 2 in the first step) and tests if in fact it should be m - 1. If the hypothesis is rejected, m is increased and a new test is carried out. The method continues (increasing m) until the hypothesis is accepted. The theoretical core of the method is the full Bayesian significance test, an intuitive Bayesian approach, which needs no model complexity penalization nor positive probabilities for sharp hypotheses. Numerical experiments were based on a cDNA microarray dataset consisting of expression levels of 205 genes belonging to four functional categories, for 10 distinct strains of Saccharomyces cerevisiae. To analyze the method's sensitivity to data dimension, we performed principal components analysis on the original dataset and predicted the number of classes using 2 to 10 principal components. Compared to Mclust (model-based clustering), our method shows more consistent results.

Gene expression profile of the plant pathogen Fusarium graminearum under the antagonistic effect of Pantoea agglomerans

The pathogenic fungus Fusarium graminearum is an ongoing threat to agriculture, causing losses in grain yield and quality in diverse crops. Substantial progress has been made in the identification of genes involved in the suppression of phytopathogens by antagonistic microorganisms; however, limited information regarding responses of plant pathogens to these biocontrol agents is available. Gene expression analysis was used to identify differentially expressed transcripts of the fungal plant pathogen F. graminearum under antagonistic effect of the bacterium Pantoea agglomerans. A macroarray was constructed, using 1014 transcripts from an F. graminearum cDNA library. Probes consisted of the cDNA of F. graminearum grown in the presence and in the absence of P. agglomerans. Twenty-nine genes were either up (19) or down (10) regulated during interaction with the antagonist bacterium. Genes encoding proteins associated with fungal defense and/or virulence or with nutritional and oxidative stress responses were induced. The repressed genes coded for a zinc finger protein associated with cell division, proteins containing cellular signaling domains, respiratory chain proteins, and chaperone-type proteins. These data give molecular and biochemical evidence of response of F. graminearum to an antagonist and could help develop effective biocontrol procedures for pathogenic plant fungi.

Effects of trans-10, cis-12 conjugated linoleic acid on gene expression and lipid metabolism of adipose tissue of growing pigs

The objective of the present study was to determine the effects of trans-10, cis-12 conjugated linoleic acid (CLA) in adipose tissue explant cultures of growing pigs on the following responses: lipogenesis (measured as rate of C-14-labeled glucose incorporation over a subsequent 2-h incubation in the presence or absence of insulin), lipolysis (release of non-esterified fatty acid over a 2-h incubation in the presence or absence of isoproterenol), activities of lipogenic enzymes, and mRNA abundance of fatty acid synthase (FAS). Adipose tissue explants from nine growing pigs (78 +/- 3 kg) were cultured in 199 medium with insulin, dexamethasone and antibiotics for 4, 12, 24, and 48 h. The treatments were 1) control: 100 mu M polyvinyl alcohol (PVA); 2) pGH: 100 ng/mL porcine growth hormone (pGH) plus 100 mu M PVA; 3) CLA200: 200 mu M trans-10, cis-12 CLA; 4) CLA50: 50 mu M trans-10, cis-12 CLA, and 5) LA: 200 mu M linoleic acid. Fatty acids were added along with PVA (2: 1), respectively, for 24 h. Explants were collected after each culture period and assayed for lipogenesis. Transcripts of FAS mRNA were quantified by real-time RT-PCR after 24 and 48 h. Lipolysis and activities of FAS, glucose 6-phosphate dehydrogenase, 6-phosphogluconate dehydrogenase, and NADP-malate dehydrogenase were determined after 48 h. As expected, glucose incorporation was decreased (P < 0.05) in response to pGH treatment (positive control). LA had no effect on any parameter evaluated. Treatment with trans-10, cis-12 CLA decreased FAS activity (P < 0.05), but NADPH-generating enzymes were unaffected by treatments. Consistent with reduction in FAS activity, both lipid synthesis and FAS mRNA abundance were reduced with chronic CLA treatment, pGH increased baseline and stimulated lipolysis (P < 0.05) after 48 h of culture, while CLA treatment had no effect on non-esterified fatty acid release. Results of this study showed that trans-10, cis-12 CLA alters lipogenesis but has no effect on lipolysis in cultures of pig adipose tissue.

Validation of reference genes for gene expression studies in the honey bee, Apis mellifera, by quantitative real-time RT-PCR

For obtaining accurate and reliable gene expression results it is essential that quantitative real-time RT-PCR (qRT-PCR) data are normalized with appropriate reference genes. The current exponential increase in postgenomic studies on the honey bee, Apis mellifera, makes the standardization of qRT-PCR results an important task for ongoing community efforts. For this aim we selected four candidate reference genes (actin, ribosomal protein 49, elongation factor 1-alpha, tbp-association factor) and used three software-based approaches (geNorm, BestKeeper and NormFinder) to evaluate the suitability of these genes as endogenous controls. Their expression was examined during honey bee development, in different tissues, and after juvenile hormone exposure. Furthermore, the importance of choosing an appropriate reference gene was investigated for two developmentally regulated target genes. The results led us to consider all four candidate genes as suitable genes for normalization in A. mellifera. However, each condition evaluated in this study revealed a specific set of genes as the most appropriated ones.

Lineage relationship of prostate cancer cell types based on gene expression

Background: Prostate tumor heterogeneity is a major factor in disease management. Heterogeneity could be due to multiple cancer cell types with distinct gene expression. Of clinical importance is the so-called cancer stem cell type. Cell type-specific transcriptomes are used to examine lineage relationship among cancer cell types and their expression similarity to normal cell types including stem/progenitor cells. Methods: Transcriptomes were determined by Affymetrix DNA array analysis for the following cell types. Putative prostate progenitor cell populations were characterized and isolated by expression of the membrane transporter ABCG2. Stem cells were represented by embryonic stem and embryonal carcinoma cells. The cancer cell types were Gleason pattern 3 (glandular histomorphology) and pattern 4 (aglandular) sorted from primary tumors, cultured prostate cancer cell lines originally established from metastatic lesions, xenografts LuCaP 35 (adenocarcinoma phenotype) and LuCaP 49 (neuroendocrine/small cell carcinoma) grown in mice. No detectable gene expression differences were detected among serial passages of the LuCaP xenografts. Results: Based on transcriptomes, the different cancer cell types could be clustered into a luminal-like grouping and a non-luminal-like (also not basal-like) grouping. The non-luminal-like types showed expression more similar to that of stem/progenitor cells than the luminal-like types. However, none showed expression of stem cell genes known to maintain stemness. Conclusions: Non-luminal-like types are all representatives of aggressive disease, and this could be attributed to the similarity in overall gene expression to stem and progenitor cell types.

Gene expression relationship between prostate cancer cells of Gleason 3, 4 and normal epithelial cells as revealed by cell type-specific transcriptomes

Background: Prostate cancer cells in primary tumors have been typed CD10(-)/CD13(-)/CD24(hi)/CD26(+)/CD38(lo)/CD44(-)/CD104(-). This CD phenotype suggests a lineage relationship between cancer cells and luminal cells. The Gleason grade of tumors is a descriptive of tumor glandular differentiation. Higher Gleason scores are associated with treatment failure. Methods: CD26(+) cancer cells were isolated from Gleason 3+3 (G3) and Gleason 4+4 (G4) tumors by cell sorting, and their gene expression or transcriptome was determined by Affymetrix DNA array analysis. Dataset analysis was used to determine gene expression similarities and differences between G3 and G4 as well as to prostate cancer cell lines and histologically normal prostate luminal cells. Results: The G3 and G4 transcriptomes were compared to those of prostatic cell types of non-cancer, which included luminal, basal, stromal fibromuscular, and endothelial. A principal components analysis of the various transcriptome datasets indicated a closer relationship between luminal and G3 than luminal and G4. Dataset comparison also showed that the cancer transcriptomes differed substantially from those of prostate cancer cell lines. Conclusions: Genes differentially expressed in cancer are potential biomarkers for cancer detection, and those differentially expressed between G3 and G4 are potential biomarkers for disease stratification given that G4 cancer is associated with poor outcomes. Differentially expressed genes likely contribute to the prostate cancer phenotype and constitute the signatures of these particular cancer cell types.

A reliable measure of similarity based on dependency for short time series: an application to gene expression networks

Background: Microarray techniques have become an important tool to the investigation of genetic relationships and the assignment of different phenotypes. Since microarrays are still very expensive, most of the experiments are performed with small samples. This paper introduces a method to quantify dependency between data series composed of few sample points. The method is used to construct gene co-expression subnetworks of highly significant edges. Results: The results shown here are for an adapted subset of a Saccharomyces cerevisiae gene expression data set with low temporal resolution and poor statistics. The method reveals common transcription factors with a high confidence level and allows the construction of subnetworks with high biological relevance that reveals characteristic features of the processes driving the organism adaptations to specific environmental conditions. Conclusion: Our method allows a reliable and sophisticated analysis of microarray data even under severe constraints. The utilization of systems biology improves the biologists ability to elucidate the mechanisms underlying celular processes and to formulate new hypotheses.