Comparative effect of dairy fatty acids on cell adhesion molecules, nitric oxide and relative gene expression in healthy and diabetic human aortic endothelial cells

Dairy intake, despite its high saturated fatty acid (SFA) content, is associated with a lower risk of cardiovascular disease and diabetes. This in vitro study determined the effect of individual fatty acids (FA) found in dairy, and FA mixtures representative of a high SFA and a low SFA dairy lipid on markers of endothelial function in healthy and type II diabetic aortic endothelial cells.

Ethanol exposure alters monoamine oxidase gene-expression in neuronal SH-SY5Y cells

Abstract: Monoamine Oxidase (MAO) enzymes catabolise, and thus modulate abundance of, neurotransmitters in the brain. Variation in MAO enzyme activity has been linked to alcohol abuse behaviour, although the molecular mechanisms underlying this association are not understood. The present study evaluated relative gene-transcript abundance of MAO-A and MAO-B in the SH-SY5Y human neuroblastoma cell-line in response to ethanol exposure and following ethanol withdrawal. We found that each isoform of MAO was significantly transcriptionally up-regulated 55-80% in response to 100mM ethanol exposure. This trend was maintained following prolonged exposures (24 h-72 h) and with short exposures (24 h) followed by a period of ethanol withdrawal, suggesting that the transcriptional regulation is the result of a cellular change occurring within the first 24 hours of ethanol exposure. These results suggest a role for MAO transcriptional regulation in the complex neurobiochemical changes underlying alcohol addiction.

Gene Expression Profiling of the Cephalothorax and Eyestalk in Penaeus Monodon during Ovarian Maturation.

In crustaceans, a range of physiological processes involved in ovarian maturation occurs in organs of the cephalothorax including the hepatopancrease, mandibular and Y-organ. Additionally, reproduction is regulated by neuropeptide hormones and other proteins released from secretory sites within the eyestalk. Reproductive dysfunction in captive-reared prawns, Penaeus monodon, is believed to be due to deficiencies in these factors. In this study, we investigated the expression of gene transcripts in the cephalothorax and eyestalk from wild-caught and captive-reared animals throughout ovarian maturation using custom oligonucleotide microarray screening. We have isolated numerous transcripts that appear to be differentially expressed throughout ovarian maturation and between wild-caught and captive-reared animals. In the cephalothorax, differentially expressed genes included the 1,3-beta-D-glucan-binding high-density lipoprotein, 2/3-oxoacyl-CoA thiolase and vitellogenin. In the eyestalk, these include gene transcripts that encode a protein that modulates G-protein coupled receptor activity and another that encodes an architectural transcription factor. Each may regulate the expression of reproductive neuropeptides, such as the crustacean hyperglycaemic hormone and molt-inhibiting hormone. We could not identify differentially expressed transcripts encoding known reproductive neuropeptides in the eyestalk of either wild-caught or captive-reared prawns at any ovarian maturation stage, however, this result may be attributed to low relative expression levels of these transcripts. In summary, this study provides a foundation for the study of target genes involved in regulating penaeid reproduction.

Gene Expression : From Microarrays to Functional Genomics

The time of the large sequencing projects has enabled unprecedented possibilities of investigating more complex aspects of living organisms. Among the high-throughput technologies based on the genomic sequences, the DNA microarrays are widely used for many purposes, including the measurement of the relative quantity of the messenger RNAs. However, the reliability of microarrays has been strongly doubted as robust analysis of the complex microarray output data has been developed only after the technology had already been spread in the community. An objective of this study consisted of increasing the performance of microarrays, and was measured by the successful validation of the results by independent techniques. To this end, emphasis has been given to the possibility of selecting candidate genes with remarkable biological significance within specific experimental design. Along with literature evidence, the re-annotation of the probes and model-based normalization algorithms were found to be beneficial when analyzing Affymetrix GeneChip data. Typically, the analysis of microarrays aims at selecting genes whose expression is significantly different in different conditions followed by grouping them in functional categories, enabling a biological interpretation of the results. Another approach investigates the global differences in the expression of functionally related groups of genes. Here, this technique has been effective in discovering patterns related to temporal changes during infection of human cells. Another aspect explored in this thesis is related to the possibility of combining independent gene expression data for creating a catalog of genes that are selectively expressed in healthy human tissues. Not all the genes present in human cells are active; some involved in basic activities (named housekeeping genes) are expressed ubiquitously. Other genes (named tissue-selective genes) provide more specific functions and they are expressed preferably in certain cell types or tissues. Defining the tissue-selective genes is also important as these genes can cause disease with phenotype in the tissues where they are expressed. The hypothesis that gene expression could be used as a measure of the relatedness of the tissues has been also proved. Microarray experiments provide long lists of candidate genes that are often difficult to interpret and prioritize. Extending the power of microarray results is possible by inferring the relationships of genes under certain conditions. Gene transcription is constantly regulated by the coordinated binding of proteins, named transcription factors, to specific portions of the its promoter sequence. In this study, the analysis of promoters from groups of candidate genes has been utilized for predicting gene networks and highlighting modules of transcription factors playing a central role in the regulation of their transcription. Specific modules have been found regulating the expression of genes selectively expressed in the hippocampus, an area of the brain having a central role in the Major Depression Disorder. Similarly, gene networks derived from microarray results have elucidated aspects of the development of the mesencephalon, another region of the brain involved in Parkinson Disease.

NETGEM: Network Embedded Temporal GEnerative Model for gene expression data

Background: Temporal analysis of gene expression data has been limited to identifying genes whose expression varies with time and/or correlation between genes that have similar temporal profiles. Often, the methods do not consider the underlying network constraints that connect the genes. It is becoming increasingly evident that interactions change substantially with time. Thus far, there is no systematic method to relate the temporal changes in gene expression to the dynamics of interactions between them. Information on interaction dynamics would open up possibilities for discovering new mechanisms of regulation by providing valuable insight into identifying time-sensitive interactions as well as permit studies on the effect of a genetic perturbation. Results: We present NETGEM, a tractable model rooted in Markov dynamics, for analyzing the dynamics of the interactions between proteins based on the dynamics of the expression changes of the genes that encode them. The model treats the interaction strengths as random variables which are modulated by suitable priors. This approach is necessitated by the extremely small sample size of the datasets, relative to the number of interactions. The model is amenable to a linear time algorithm for efficient inference. Using temporal gene expression data, NETGEM was successful in identifying (i) temporal interactions and determining their strength, (ii) functional categories of the actively interacting partners and (iii) dynamics of interactions in perturbed networks. Conclusions: NETGEM represents an optimal trade-off between model complexity and data requirement. It was able to deduce actively interacting genes and functional categories from temporal gene expression data. It permits inference by incorporating the information available in perturbed networks. Given that the inputs to NETGEM are only the network and the temporal variation of the nodes, this algorithm promises to have widespread applications, beyond biological systems. The source code for NETGEM is available from https://github.com/vjethava/NETGEM

Inhibition of no tail (ntl) gene expression in zebrafish by external guide sequence (EGS) technique

External guide sequence (EGS) technique, a branch of ribozyme strategy, can be enticed to cleave the target mRNA by forming a tRNA-like structure. In the present study, no tail gene (ntl), a key gene participating in the formation of normal tail, was used as a target for ribonuclease (RNase) P-mediated gene disruption in zebrafish in vivo. Transient expression of pH1-m3/4 ntl-EGS or pH1-3/4 ntl-EGS produced the full no tail phenotype at long-pec stage in proportion as 24 or 35%, respectively. As is expected that the full-length ntl mRNA of embryos at 50% epiboly stage decreased relative to control when injected the embryos with 3/4 EGS or m3/4 EGS RNA. Interestingly, ntl RNA transcripts, including the cleaved by EGS and the untouched, increased. Taken together, these results indicate that EGS strategy can work in zebrafish in vivo and becomes a potential tool for degradation of targeted mRNAs.

Bayesian inference of the number of factors in gene-expression analysis: application to human virus challenge studies.

BACKGROUND: Nonparametric Bayesian techniques have been developed recently to extend the sophistication of factor models, allowing one to infer the number of appropriate factors from the observed data. We consider such techniques for sparse factor analysis, with application to gene-expression data from three virus challenge studies. Particular attention is placed on employing the Beta Process (BP), the Indian Buffet Process (IBP), and related sparseness-promoting techniques to infer a proper number of factors. The posterior density function on the model parameters is computed using Gibbs sampling and variational Bayesian (VB) analysis. RESULTS: Time-evolving gene-expression data are considered for respiratory syncytial virus (RSV), Rhino virus, and influenza, using blood samples from healthy human subjects. These data were acquired in three challenge studies, each executed after receiving institutional review board (IRB) approval from Duke University. Comparisons are made between several alternative means of per-forming nonparametric factor analysis on these data, with comparisons as well to sparse-PCA and Penalized Matrix Decomposition (PMD), closely related non-Bayesian approaches. CONCLUSIONS: Applying the Beta Process to the factor scores, or to the singular values of a pseudo-SVD construction, the proposed algorithms infer the number of factors in gene-expression data. For real data the "true" number of factors is unknown; in our simulations we consider a range of noise variances, and the proposed Bayesian models inferred the number of factors accurately relative to other methods in the literature, such as sparse-PCA and PMD. We have also identified a "pan-viral" factor of importance for each of the three viruses considered in this study. We have identified a set of genes associated with this pan-viral factor, of interest for early detection of such viruses based upon the host response, as quantified via gene-expression data.

Rod photoreceptor loss in Rho(-/-) mice reduces retinal hypoxia and hypoxia-regulated gene expression

PURPOSE. This study was conducted to evaluate whether regions of the retinal neuropile become hypoxic during periods of high oxygen consumption and whether depletion of the outer retina reduces hypoxia and related changes in gene expression.

METHODS. Retinas from rhodopsin knockout (Rho(-/-)) mice were evaluated along with those of wild-type (WT) control animals. Retinas were also examined at the end of 12-hour dark or light periods, and a separate group was treated with L-cis-diltiazem at the beginning of a 12-hour dark period. Hypoxia was assessed by deposition of hypoxyprobe (HP) and HP-protein adducts were localized by immunohistochemistry and quantified using ELISA. Also, hypoxia-regulated gene expression and transcriptional activity were assessed alongside vascular density.

RESULTS. Hypoxia was observed in the inner nuclear and ganglion cell layers in WT retina and was significantly reduced in Rho (-/-) mice (P < 0.05). Retinal hypoxia was significantly increased during dark adaptation in WT mice (P < 0.05), whereas no change was observed in Rho(-/-) or with L-cis-diltiazem-treated WT mice. Hypoxia-inducible factor (HIF)-1 alpha DNA-binding and VEGF mRNA expression in Rho(-/-) retina was significantly reduced in unison with outer retinal depletion (P < 0.05). Retina from the Rho(-/-) mice displayed an extensive intraretinal vascular network after 6 months, although there was evidence that capillary density was depleted in comparison with that in WT retinas.

CONCLUSIONS. Relative hypoxia occurs in the inner retina especially during dark adaptation. Photoreceptor loss reduces retinal oxygen usage and hypoxia which corresponds with attenuation of the retinal microvasculature. These studies suggest that in normal physiological conditions and diurnal cycles the adult retina exists in a state of borderline hypoxia, making this tissue particularly susceptible to even subtle reductions in perfusion.

Meta-analysis of prostate cancer gene expression data identifies a novel discriminatory signature enriched for glycosylating enzymes

BACKGROUND: Tumorigenesis is characterised by changes in transcriptional control. Extensive transcript expression data have been acquired over the last decade and used to classify prostate cancers. Prostate cancer is, however, a heterogeneous multifocal cancer and this poses challenges in identifying robust transcript biomarkers.

METHODS: In this study, we have undertaken a meta-analysis of publicly available transcriptomic data spanning datasets and technologies from the last decade and encompassing laser capture microdissected and macrodissected sample sets.

RESULTS: We identified a 33 gene signature that can discriminate between benign tissue controls and localised prostate cancers irrespective of detection platform or dissection status. These genes were significantly overexpressed in localised prostate cancer versus benign tissue in at least three datasets within the Oncomine Compendium of Expression Array Data. In addition, they were also overexpressed in a recent exon-array dataset as well a prostate cancer RNA-seq dataset generated as part of the The Cancer Genomics Atlas (TCGA) initiative. Biologically, glycosylation was the single enriched process associated with this 33 gene signature, encompassing four glycosylating enzymes. We went on to evaluate the performance of this signature against three individual markers of prostate cancer, v-ets avian erythroblastosis virus E26 oncogene homolog (ERG) expression, prostate specific antigen (PSA) expression and androgen receptor (AR) expression in an additional independent dataset. Our signature had greater discriminatory power than these markers both for localised cancer and metastatic disease relative to benign tissue, or in the case of metastasis, also localised prostate cancer.

CONCLUSION: In conclusion, robust transcript biomarkers are present within datasets assembled over many years and cohorts and our study provides both examples and a strategy for refining and comparing datasets to obtain additional markers as more data are generated.

IFN stimulated gene expression in the liver is a better predictor of treatment response in chronic hepatitis c than the IL28b (IFN lambda 3) genotype

Background: Therapy of chronic hepatitis C (CHC) with pegIFNa/ribavirin achieves sustained virologic response (SVR) in ~55%. Pre-activation of the endogenous interferon system in the liver is associated non-response (NR). Recently, genome-wide association studies described associations of allelic variants near the IL28B (IFNλ3) gene with treatment response and with spontaneous clearance of the virus. We investigated if the IL28B genotype determines the constitutive expression of IFN stimulated genes (ISGs) in the liver of patients with CHC. Methods: We genotyped 93 patients with CHC for 3 IL28B single nucleotide polymorphisms (SNPs, rs12979860, rs8099917, rs12980275), extracted RNA from their liver biopsies and quantified the expression of IL28B and of 8 previously identified classifier genes which discriminate between SVR and NR (IFI44L, RSAD2, ISG15, IFI22, LAMP3, OAS3, LGALS3BP and HTATIP2). Decision tree ensembles in the form of a random forest classifier were used to calculate the relative predictive power of these different variables in a multivariate analysis. Results: The minor IL28B allele (bad risk for treatment response) was significantly associated with increased expression of ISGs, and, unexpectedly, with decreased expression of IL28B. Stratification of the patients into SVR and NR revealed that ISG expression was conditionally independent from the IL28B genotype, i.e. there was an increased expression of ISGs in NR compared to SVR irrespective of the IL28B genotype. The random forest feature score (RFFS) identified IFI27 (RFFS = 2.93), RSAD2 (1.88) and HTATIP2 (1.50) expression and the HCV genotype (1.62) as the strongest predictors of treatment response. ROC curves of the IL28B SNPs showed an AUC of 0.66 with an error rate (ERR) of 0.38. A classifier with the 3 best classifying genes showed an excellent test performance with an AUC of 0.94 and ERR of 0.15. The addition of IL28B genotype information did not improve the predictive power of the 3-gene classifier. Conclusions: IL28B genotype and hepatic ISG expression are conditionally independent predictors of treatment response in CHC. There is no direct link between altered IFNλ3 expression and pre-activation of the endogenous system in the liver. Hepatic ISG expression is by far the better predictor for treatment response than IL28B genotype.

Globin Gene Expression: Role of Transcription Factors

La dérégulation de l'expression génétique est une base pathophysiologique de plusieurs maladies. On a utilisé le locus du gène β-globine humain comme modèle pour élucider le mécanisme de régulation de la transcription génétique et évaluer son expression génétique durant l'érythropoïèse. La famille des protéines 'E' est composée de facteurs de transcription qui possèdent plusieurs sites de liaison au sein de locus du gène β-globine, suggérant leur rôle potentiel dans la régulation de l’expression de ces gènes. Nous avons montré que les facteurs HEB, E2A et ETO2 interagissent d’une manière significative avec la région contrôle du Locus (LCR) et avec les promoteurs des gènes de la famille β-globine. Le recrutement de ces facteurs au locus est modifié lors de l'érythropoïèse dans les cellules souches hematopoitiques et les cellules erythroides de souris transgéniques pour le locus de la β-globine humain, ainsi que dans les cellules progénitrices hématopoïétiques humaines. De plus par cette étude, nous démontrons pour la première fois que le gène β-globine humain est dans une chromatine active et qu’il interagit avec des facteurs de transcriptions de type suppresseurs dans les cellules progénitrices lymphoïdes (voie de différentiation alternative). Cette étude a aussi été faite dans des souris ayant une génétique mutante caractérisée par l'absence des facteurs de transcription E2A ou HEB.

“Unmixing” Tissue Gene Expression Signatures from Tumor Biopsies

Emergent molecular measurement methods, such as DNA microarray, qRTPCR, and many others, offer tremendous promise for the personalized treatment of cancer. These technologies measure the amount of specific proteins, RNA, DNA or other molecular targets from tumor specimens with the goal of “fingerprinting” individual cancers. Tumor specimens are heterogeneous; an individual specimen typically contains unknown amounts of multiple tissues types. Thus, the measured molecular concentrations result from an unknown mixture of tissue types, and must be normalized to account for the composition of the mixture. For example, a breast tumor biopsy may contain normal, dysplastic and cancerous epithelial cells, as well as stromal components (fatty and connective tissue) and blood and lymphatic vessels. Our diagnostic interest focuses solely on the dysplastic and cancerous epithelial cells. The remaining tissue components serve to “contaminate” the signal of interest. The proportion of each of the tissue components changes as a function of patient characteristics (e.g., age), and varies spatially across the tumor region. Because each of the tissue components produces a different molecular signature, and the amount of each tissue type is specimen dependent, we must estimate the tissue composition of the specimen, and adjust the molecular signal for this composition. Using the idea of a chemical mass balance, we consider the total measured concentrations to be a weighted sum of the individual tissue signatures, where weights are determined by the relative amounts of the different tissue types. We develop a compositional source apportionment model to estimate the relative amounts of tissue components in a tumor specimen. We then use these estimates to infer the tissuespecific concentrations of key molecular targets for sub-typing individual tumors. We anticipate these specific measurements will greatly improve our ability to discriminate between different classes of tumors, and allow more precise matching of each patient to the appropriate treatment

Juvenile-mature wood transition in pine: correlation between wood properties and candidate gene expression profiles

There is a strong desire to exploit transcriptomics data from model species for the genetic improvement of non-model crops. Here, we use gene expression profiles from the commercial model Pinus taeda to identify candidate genes implicated in juvenile-mature wood transition in the non-model relative, P. sylvestris. Re-analysis of 'public domain' SAGE data from xylem tissues of P. taeda revealed 283 mature-abundant and 396 juvenile-abundant tags (P < 0.01), of which 70 and 137, respectively matched to genes with known function. Based on sequence similarity, we then isolated 16 putative homologues of genes that in P. taeda exhibited widest divergence in expression between juvenile and mature samples. Candidate expression levels in P. sylvestris were almost invariably differential between juvenile and mature woody tissue samples among two cohorts of five trees collected from the same seed source and selected for genetic uniformity by genetic distance analysis. However, the direction of differential expression was not always consistent with that described in the original P. taeda SAGE data. Correlation was observed between gene expression and juvenile-mature wood anatomical characteristics by OPLS analysis. Four candidates (alpha-tubulin, porin MIP1, lipid transfer protein and aquaporin like protein) apparently had greatest influence on the wood traits measured. Speculative function of these genes in relation to juvenile-mature wood transition is briefly explored. Thus, we demonstrate the feasibility of exploiting SAGE data from a model species to identify consistently differentially expressed candidates in a related non-model species.