Dietary vitamin C down-regulates inflammatory gene expression in apoE4 smokers

The deleterious impact of cigarette smoking on cardiovascular health may be in part attributable to a free radical mediated proinflammatory response in circulating monocytes. In the current investigation, the impact of vitamin C supplementation on monocyte gene expression was determined in apoE4 smokers versus non-smokers. A total of 10 smokers and 11 non-smokers consumed 60 mg/day of vitamin C for four weeks and a fasting blood sample was taken at baseline and post-intervention for the determination of plasma vitamin C and monocyte gene expression profiles using cDNA array and real time PCR. In apoE4 smokers, supplementation resulted in a 43% increase in plasma vitamin C concentrations. Furthermore, a number of genes were differentially expressed more than 2-fold in response to treatment, including a downregulation of the proinflammatory mediators tumor necrosis factor (TNF) beta, TNF receptor, neurotrophin-3 growth factor receptor, and monocyte chemoattractant protein I receptor. The study has identified a number of molecular mechanisms underlying the benefit of vitamin C supplementation in smokers. (c) 2005 Elsevier Inc. All rights reserved.

Gene expression profiling of human hibernating myocardium: Increased expression of B-type natriuretic peptide and proenkephalin in hypocontractile vs normally-contracting regions of the heart

A greater understanding of the molecular basis of hibernating myocardium may assist in identifying those patients who would most benefit from revascularization. Paired heart biopsies were taken from hypocontractile and normally-contracting myocardium (identified by cardiovascular magnetic resonance) from 6 patients with chronic stable angina scheduled for bypass grafting. Gene expression profiles of hypocontractile and normally-contracting samples were compared using Affymetrix microarrays. The data for patients with confirmed hibernating myocardium were analysed separately and a different, though overlapping, set (up to 380) of genes was identified which may constitute a molecular fingerprint for hibernating myocardium. The expression of B-type natriuretic peptide (BNP) was increased in hypocontractile relative to normally-contracting myocardium. The expression of BNP correlated most closely with the expression of proenkephalin and follistatin 3, which may constitute additional heart failure markers. Our data illustrate differential gene expression in hypocontractile and/hibernating myocardium relative to normally-contracting myocardium within individual human hearts. Changes in expression of these genes, including increased relative expression of natriuretic and other factors, may constitute a molecular signature for hypocontractile and/or hibernating myocardium.

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.

Early Changes in Gene Expression Induced by Tobacco Smoke: Evidence for the Importance of Estrogen within Lung Tissue

Lung cancer is the leading cause of cancer deaths in the United States, surpassing breast cancer as the primary cause of cancer-related mortality in women. The goal of the present study was to identify early molecular changes in the lung induced by exposure to tobacco smoke and thus identify potential targets for chemoprevention. Female A/J mice were exposed to either tobacco smoke or HEPA-filtered air via a whole-body exposure chamber (6 h/d, 5 d/wk for 3, 8, and 20 weeks). Gene expression profiles of lung tissue from control and smoke-exposed animals were established using a 15K cDNA microarray. Cytochrome P450 1b1, a phase I enzyme involved in both the metabolism of xenobiotics and the 4-hydroxylation of 17 beta-estradiol (E(2)), was modulated to the greatest extent following smoke exposure. A panel of 10 genes were found to be differentially expressed in control and smoke-exposed lung tissues at 3, 8, and 20 weeks (P < 0.001). The interaction network of these differentially expressed genes revealed new pathways modulated by short-term smoke exposure, including estrogen metabolism. In addition, E(2) was detected within murine lung tissue by gas chromatography-coupled mass spectrometry and immunohistochemistry. Identification of the early molecular events that contribute to lung tumor formation is anticipated to lead to the development of promising targeted chemopreventive therapies. In conclusion, the presence of E2 within lung tissue when combined with the modulation of cytochrome P450 1b1 and other estrogen metabolism genes by tobacco smoke provides novel insight into a possible role for estrogens in lung cancer. Cancer Prev Res; 3(6); 707-17. (C) 2010 AACR.

Genomic DNA microarray comparison of gene expression patterns in Paracoccidioides brasiliensis mycelia and yeasts in vitro

Paracoccidioides brasiliensis is a thermally dimorphic fungus, and causes the most prevalent systemic mycosis in Latin America. Infection is initiated by inhalation of conidia or mycelial fragments by the host, followed by further differentiation into the yeast form. Information regarding gene expression by either form has rarely been addressed with respect to multiple time points of growth in culture. Here, we report on the construction of a genomic DNA microarray, covering approximately 25% of the genome of the organism, and its utilization in identifying genes and gene expression patterns during growth in vitro. Cloned, amplified inserts from randomly sheared genomic DNA (gDNA) and known control genes were printed onto glass slides to generate a microarray of over 12 000 elements. To examine gene expression, mRNA was extracted and amplified from mycelial or yeast cultures grown in semi-defined medium for 5, 8 and 14 days. Principal components analysis and hierarchical clustering indicated that yeast gene expression profiles differed greatly from those of mycelia, especially at earlier time points, and that mycelial gene expression changed less than gene expression in yeasts over time. Genes upregulated in yeasts were found to encode proteins shown to be involved in methionine/cysteine metabolism, respiratory and metabolic processes (of sugars, amino acids, proteins and lipids), transporters (small peptides, sugars, ions and toxins), regulatory proteins and transcription factors. Mycelial genes involved in processes such as cell division, protein catabolism, nucleotide biosynthesis and toxin and sugar transport showed differential expression. Sequenced clones were compared with Histoplasma capsulatum and Coccidioides posadasii genome sequences to assess potentially common pathways across species, such as sulfur and lipid metabolism, amino acid transporters, transcription factors and genes possibly related to virulence. We also analysed gene expression with time in culture and found that while transposable elements and components of respiratory pathways tended to increase in expression with time, genes encoding ribosomal structural proteins and protein catabolism tended to sharply decrease in expression over time, particularly in yeast. These findings expand our knowledge of the different morphological forms of P. brasiliensis during growth in culture.

Differential gene expression in drought-tolerant sugarcane roots

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

A comparative transcriptome analysis reveals expression profiles conserved across three Eimeria spp. of domestic fowl and associated with multiple developmental stages

Coccidiosis of the domestic fowl is a worldwide disease caused by seven species of protozoan parasites of the genus Eimeria. The genome of the model species, Eimeria tenella, presents a complexity of 55-60 MB distributed in 14 chromosomes. Relatively few studies have been undertaken to unravel the complexity of the transcriptome of Eimeria parasites. We report here the generation of more than 45,000 open reading frame expressed sequence tag (ORESTES) cDNA reads of E. tenella, Eimeria maxima and Eimeria acervulina, covering several developmental stages: unsporulated oocysts, sporoblastic oocysts, sporulated oocysts, sporozoites and second generation merozoites. All reads were assembled to constitute gene indices and submitted to a comprehensive functional annotation pipeline. In the case of E. tenella, we also incorporated publicly available ESTs to generate an integrated body of information. Orthology analyses have identified genes conserved across different apicomplexan parasites, as well as genes restricted to the genus Eimeria. Digital expression profiles obtained from ORESTES/EST countings, submitted to clustering analyses, revealed a high conservation pattern across the three Eimeria spp. Distance trees showed that unsporulated and sporoblastic oocysts constitute a distinct clade in all species, with sporulated oocysts forming a more external branch. This latter stage also shows a close relationship with sporozoites, whereas first and second generation merozoites are more closely related to each other than to sporozoites. The profiles were unambiguously associated with the distinct developmental stages and strongly correlated with the order of the stages in the parasite life cycle. Finally, we present The Eimeria Transcript Database (http://www.coccidia.icb.usp.br/eimeriatdb), a website that provides open access to all sequencing data, annotation and comparative analysis. We expect this repository to represent a useful resource to the Eimeria scientific community, helping to define potential candidates for the development of new strategies to control coccidiosis of the domestic fowl. (C) 2011 Australian Society for Parasitology Inc. Published by Elsevier Ltd. All rights reserved.

Whole blood genome-wide gene expression profile in males after prolonged wakefulness and sleep recovery

Pellegrino R, Sunaga DY, Guindalini C, Martins RC, Mazzotti DR, Wei Z, Daye ZJ, Andersen ML, Tufik S. Whole blood genome-wide gene expression profile in males after prolonged wakefulness and sleep recovery. Physiol Genomics 44: 1003-1012, 2012. First published September 4, 2012; doi: 10.1152/physiolgenomics.00058.2012.-Although the specific functions of sleep have not been completely elucidated, the literature has suggested that sleep is essential for proper homeostasis. Sleep loss is associated with changes in behavioral, neurochemical, cellular, and metabolic function as well as impaired immune response. Using high-resolution microarrays we evaluated the gene expression profiles of healthy male volunteers who underwent 60 h of prolonged wakefulness (PW) followed by 12 h of sleep recovery (SR). Peripheral whole blood was collected at 8 am in the morning before the initiation of PW (Baseline), after the second night of PW, and one night after SR. We identified over 500 genes that were differentially expressed. Notably, these genes were related to DNA damage and repair and stress response, as well as diverse immune system responses, such as natural killer pathways including killer cell lectin-like receptors family, as well as granzymes and T-cell receptors, which play important roles in host defense. These results support the idea that sleep loss can lead to alterations in molecular processes that result in perturbation of cellular immunity, induction of inflammatory responses, and homeostatic imbalance. Moreover, expression of multiple genes was downregulated following PW and upregulated after SR compared with PW, suggesting an attempt of the body to re-establish internal homeostasis. In silico validation of alterations in the expression of CETN3, DNAJC, and CEACAM genes confirmed previous findings related to the molecular effects of sleep deprivation. Thus, the present findings confirm that the effects of sleep loss are not restricted to the brain and can occur intensely in peripheral tissues.

Global gene expression under nitrogen starvation in Xylella fastidiosa: contribution of the σ54 regulon

Abstract Background Xylella fastidiosa, a Gram-negative fastidious bacterium, grows in the xylem of several plants causing diseases such as citrus variegated chlorosis. As the xylem sap contains low concentrations of amino acids and other compounds, X. fastidiosa needs to cope with nitrogen limitation in its natural habitat. Results In this work, we performed a whole-genome microarray analysis of the X. fastidiosa nitrogen starvation response. A time course experiment (2, 8 and 12 hours) of cultures grown in defined medium under nitrogen starvation revealed many differentially expressed genes, such as those related to transport, nitrogen assimilation, amino acid biosynthesis, transcriptional regulation, and many genes encoding hypothetical proteins. In addition, a decrease in the expression levels of many genes involved in carbon metabolism and energy generation pathways was also observed. Comparison of gene expression profiles between the wild type strain and the rpoN null mutant allowed the identification of genes directly or indirectly induced by nitrogen starvation in a σ54-dependent manner. A more complete picture of the σ54 regulon was achieved by combining the transcriptome data with an in silico search for potential σ54-dependent promoters, using a position weight matrix approach. One of these σ54-predicted binding sites, located upstream of the glnA gene (encoding glutamine synthetase), was validated by primer extension assays, confirming that this gene has a σ54-dependent promoter. Conclusions Together, these results show that nitrogen starvation causes intense changes in the X. fastidiosa transcriptome and some of these differentially expressed genes belong to the σ54 regulon.

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.

Identification of protein-coding and non-coding RNA expression profiles in CD34+ and in stromal cells in refractory anemia with ringed sideroblasts

Abstract Background Myelodysplastic syndromes (MDS) are a group of clonal hematological disorders characterized by ineffective hematopoiesis with morphological evidence of marrow cell dysplasia resulting in peripheral blood cytopenia. Microarray technology has permitted a refined high-throughput mapping of the transcriptional activity in the human genome. Non-coding RNAs (ncRNAs) transcribed from intronic regions of genes are involved in a number of processes related to post-transcriptional control of gene expression, and in the regulation of exon-skipping and intron retention. Characterization of ncRNAs in progenitor cells and stromal cells of MDS patients could be strategic for understanding gene expression regulation in this disease. Methods In this study, gene expression profiles of CD34+ cells of 4 patients with MDS of refractory anemia with ringed sideroblasts (RARS) subgroup and stromal cells of 3 patients with MDS-RARS were compared with healthy individuals using 44 k combined intron-exon oligoarrays, which included probes for exons of protein-coding genes, and for non-coding RNAs transcribed from intronic regions in either the sense or antisense strands. Real-time RT-PCR was performed to confirm the expression levels of selected transcripts. Results In CD34+ cells of MDS-RARS patients, 216 genes were significantly differentially expressed (q-value ≤ 0.01) in comparison to healthy individuals, of which 65 (30%) were non-coding transcripts. In stromal cells of MDS-RARS, 12 genes were significantly differentially expressed (q-value ≤ 0.05) in comparison to healthy individuals, of which 3 (25%) were non-coding transcripts. Conclusions These results demonstrated, for the first time, the differential ncRNA expression profile between MDS-RARS and healthy individuals, in CD34+ cells and stromal cells, suggesting that ncRNAs may play an important role during the development of myelodysplastic syndromes.

Global gene expression profiling of oral cavity cancers suggests molecular heterogeneity within anatomic subsites

Abstract Background Oral squamous cell carcinoma (OSCC) is a frequent neoplasm, which is usually aggressive and has unpredictable biological behavior and unfavorable prognosis. The comprehension of the molecular basis of this variability should lead to the development of targeted therapies as well as to improvements in specificity and sensitivity of diagnosis. Results Samples of primary OSCCs and their corresponding surgical margins were obtained from male patients during surgery and their gene expression profiles were screened using whole-genome microarray technology. Hierarchical clustering and Principal Components Analysis were used for data visualization and One-way Analysis of Variance was used to identify differentially expressed genes. Samples clustered mostly according to disease subsite, suggesting molecular heterogeneity within tumor stages. In order to corroborate our results, two publicly available datasets of microarray experiments were assessed. We found significant molecular differences between OSCC anatomic subsites concerning groups of genes presently or potentially important for drug development, including mRNA processing, cytoskeleton organization and biogenesis, metabolic process, cell cycle and apoptosis. Conclusion Our results corroborate literature data on molecular heterogeneity of OSCCs. Differences between disease subsites and among samples belonging to the same TNM class highlight the importance of gene expression-based classification and challenge the development of targeted therapies.

Constitutive gene expression profile segregates toxicity in locally advanced breast cancer patients treated with high-dose hyperfractionated radical radiotherapy

[EN] Breast cancer patients show a wide variation in normal tissue reactions after radiotherapy. The individual sensitivity to x-rays limits the efficiency of the therapy. Prediction of individual sensitivity to radiotherapy could help to select the radiation protocol and to improve treatment results. The aim of this study was to assess the relationship between gene expression profiles of ex vivo un-irradiated and irradiated lymphocytes and the development of toxicity due to high-dose hyperfractionated radiotherapy in patients with locally advanced breast cancer. Raw data from microarray experiments were uploaded to the Gene Expression Omnibus Database http://www.ncbi.nlm.nih.gov/geo/ (GEO accession GSE15341). We obtained a small group of 81 genes significantly regulated by radiotherapy, lumped in 50 relevant pathways. Using ANOVA and t-test statistical tools we found 20 and 26 constitutive genes (0 Gy) that segregate patients with and without acute and late toxicity, respectively. Non-supervised hierarchical clustering was used for the visualization of results. Six and 9 pathways were significantly regulated respectively. Concerning to irradiated lymphocytes (2 Gy), we founded 29 genes that separate patients with acute toxicity and without it. Those genes were gathered in 4 significant pathways. We could not identify a set of genes that segregates patients with and without late toxicity. In conclusion, we have found an association between the constitutive gene expression profile of peripheral blood lymphocytes and the development of acute and late toxicity in consecutive, unselected patients. These observations suggest the possibility of predicting normal tissue response to irradiation in high-dose non-conventional radiation therapy regimens. Prospective studies with higher number of patients are needed to validate these preliminary results.

New approaches to open problems in gene expression microarray data

In the past decade, the advent of efficient genome sequencing tools and high-throughput experimental biotechnology has lead to enormous progress in the life science. Among the most important innovations is the microarray tecnology. It allows to quantify the expression for thousands of genes simultaneously by measurin the hybridization from a tissue of interest to probes on a small glass or plastic slide. The characteristics of these data include a fair amount of random noise, a predictor dimension in the thousand, and a sample noise in the dozens. One of the most exciting areas to which microarray technology has been applied is the challenge of deciphering complex disease such as cancer. In these studies, samples are taken from two or more groups of individuals with heterogeneous phenotypes, pathologies, or clinical outcomes. these samples are hybridized to microarrays in an effort to find a small number of genes which are strongly correlated with the group of individuals. Eventhough today methods to analyse the data are welle developed and close to reach a standard organization (through the effort of preposed International project like Microarray Gene Expression Data -MGED- Society [1]) it is not unfrequant to stumble in a clinician's question that do not have a compelling statistical method that could permit to answer it.The contribution of this dissertation in deciphering disease regards the development of new approaches aiming at handle open problems posed by clinicians in handle specific experimental designs. In Chapter 1 starting from a biological necessary introduction, we revise the microarray tecnologies and all the important steps that involve an experiment from the production of the array, to the quality controls ending with preprocessing steps that will be used into the data analysis in the rest of the dissertation. While in Chapter 2 a critical review of standard analysis methods are provided stressing most of problems that In Chapter 3 is introduced a method to adress the issue of unbalanced design of miacroarray experiments. In microarray experiments, experimental design is a crucial starting-point for obtaining reasonable results. In a two-class problem, an equal or similar number of samples it should be collected between the two classes. However in some cases, e.g. rare pathologies, the approach to be taken is less evident. We propose to address this issue by applying a modified version of SAM [2]. MultiSAM consists in a reiterated application of a SAM analysis, comparing the less populated class (LPC) with 1,000 random samplings of the same size from the more populated class (MPC) A list of the differentially expressed genes is generated for each SAM application. After 1,000 reiterations, each single probe given a "score" ranging from 0 to 1,000 based on its recurrence in the 1,000 lists as differentially expressed. The performance of MultiSAM was compared to the performance of SAM and LIMMA [3] over two simulated data sets via beta and exponential distribution. The results of all three algorithms over low- noise data sets seems acceptable However, on a real unbalanced two-channel data set reagardin Chronic Lymphocitic Leukemia, LIMMA finds no significant probe, SAM finds 23 significantly changed probes but cannot separate the two classes, while MultiSAM finds 122 probes with score >300 and separates the data into two clusters by hierarchical clustering. We also report extra-assay validation in terms of differentially expressed genes Although standard algorithms perform well over low-noise simulated data sets, multi-SAM seems to be the only one able to reveal subtle differences in gene expression profiles on real unbalanced data. In Chapter 4 a method to adress similarities evaluation in a three-class prblem by means of Relevance Vector Machine [4] is described. In fact, looking at microarray data in a prognostic and diagnostic clinical framework, not only differences could have a crucial role. In some cases similarities can give useful and, sometimes even more, important information. The goal, given three classes, could be to establish, with a certain level of confidence, if the third one is similar to the first or the second one. In this work we show that Relevance Vector Machine (RVM) [2] could be a possible solutions to the limitation of standard supervised classification. In fact, RVM offers many advantages compared, for example, with his well-known precursor (Support Vector Machine - SVM [3]). Among these advantages, the estimate of posterior probability of class membership represents a key feature to address the similarity issue. This is a highly important, but often overlooked, option of any practical pattern recognition system. We focused on Tumor-Grade-three-class problem, so we have 67 samples of grade I (G1), 54 samples of grade 3 (G3) and 100 samples of grade 2 (G2). The goal is to find a model able to separate G1 from G3, then evaluate the third class G2 as test-set to obtain the probability for samples of G2 to be member of class G1 or class G3. The analysis showed that breast cancer samples of grade II have a molecular profile more similar to breast cancer samples of grade I. Looking at the literature this result have been guessed, but no measure of significance was gived before.

Single-cell gene-expression profiling reveals qualitatively distinct CD8 T cells elicited by different gene-based vaccines

CD8 T cells play a key role in mediating protective immunity against selected pathogens after vaccination. Understanding the mechanism of this protection is dependent upon definition of the heterogeneity and complexity of cellular immune responses generated by different vaccines. Here, we identify previously unrecognized subsets of CD8 T cells based upon analysis of gene-expression patterns within single cells and show that they are differentially induced by different vaccines. Three prime-boost vector combinations encoding HIV Env stimulated antigen-specific CD8 T-cell populations of similar magnitude, phenotype, and functionality. Remarkably, however, analysis of single-cell gene-expression profiles enabled discrimination of a majority of central memory (CM) and effector memory (EM) CD8 T cells elicited by the three vaccines. Subsets of T cells could be defined based on their expression of Eomes, Cxcr3, and Ccr7, or Klrk1, Klrg1, and Ccr5 in CM and EM cells, respectively. Of CM cells elicited by DNA prime-recombinant adenoviral (rAd) boost vectors, 67% were Eomes(-) Ccr7(+) Cxcr3(-), in contrast to only 7% and 2% stimulated by rAd5-rAd5 or rAd-LCMV, respectively. Of EM cells elicited by DNA-rAd, 74% were Klrk1(-) Klrg1(-)Ccr5(-) compared with only 26% and 20% for rAd5-rAd5 or rAd5-LCMV. Definition by single-cell gene profiling of specific CM and EM CD8 T-cell subsets that are differentially induced by different gene-based vaccines will facilitate the design and evaluation of vaccines, as well as enable our understanding of mechanisms of protective immunity.