Human Synaptic Plasticity Gene Expression Profile and Dendritic Spine Density Changes in HIV-Infected Human CNS Cells: Role in HIV-Associated Neurocognitive Disorders (HAND)

HIV-associated neurocognitive disorders (HAND) is characterized by development of cognitive, behavioral and motor abnormalities, and occur in approximately 50% of HIV infected individuals. Our current understanding of HAND emanates mainly from HIV-1 subtype B (clade B), which is prevalent in USA and Western countries. However very little information is available on neuropathogenesis of HIV-1 subtype C (clade C) that exists in Sub-Saharan Africa and Asia. Therefore, studies to identify specific neuropathogenic mechanisms associated with HAND are worth pursuing to dissect the mechanisms underlying this modulation and to prevent HAND particularly in clade B infection. In this study, we have investigated 84 key human synaptic plasticity genes differential expression profile in clade B and clade C infected primary human astrocytes by using RT2 Profile PCR Array human Synaptic Plasticity kit. Among these, 31 and 21 synaptic genes were significantly (≥3 fold) down-regulated and 5 genes were significantly (≥3 fold) up-regulated in clade B and clade C infected cells, respectively compared to the uninfected control astrocytes. In flow-cytometry analysis, down-regulation of postsynaptic density and dendrite spine morphology regulatory proteins (ARC, NMDAR1 and GRM1) was confirmed in both clade B and C infected primary human astrocytes and SK-N-MC neuroblastoma cells. Further, spine density and dendrite morphology changes by confocal microscopic analysis indicates significantly decreased spine density, loss of spines and decreased dendrite diameter, total dendrite and spine area in clade B infected SK-N-MC neuroblastoma cells compared to uninfected and clade C infected cells. We have also observed that, in clade B infected astrocytes, induction of apoptosis was significantly higher than in the clade C infected astrocytes. In conclusion, this study suggests that down-regulation of synaptic plasticity genes, decreased dendritic spine density and induction of apoptosis in astrocytes may contribute to the severe neuropathogenesis in clade B infection.

Seawater carbonate chemistry, mass, length and gene expression of Sepia officinalis during experiments, 2011

The specific transporters involved in maintenance of blood pH homeostasis in cephalopod molluscs have not been identified to date. Using in situ hybridization and immuno histochemical methods, we demonstrate that Na+/K+-ATPase (soNKA), a V-type H+-ATPase (soV-HA), and Na+/HCO3- cotransporter (soNBC) are co-localized in NKA-rich cells in the gills of Sepia officinalis. mRNA expression patterns of these transporters and selected metabolic genes were examined in response to moderately elevated seawater pCO2 (0.16 and 0.35 kPa) over a time-course of six weeks in different ontogenetic stages. The applied CO2 concentrations are relevant for ocean acidification scenarios projected for the coming decades. We determined strong expression changes in late stage embryos and hatchlings, with one to three log2-fold reductions in soNKA, soNBCe, socCAII and COX. In contrast, no hypercapnia induced changes in mRNA expression were observed in juveniles during both short- and long-term exposure. However a transiently increased demand of ion regulatory demand was evident during the initial acclimation reaction to elevated seawater pCO2. Gill Na+/K+-ATPase activity and protein concentration were increased by approximately 15% in during short (2-11 day), but not long term (42 day) exposure. Our findings support the hypothesis that the energy budget of adult cephalopods is not significantly compromised during long-term exposure to moderate environmental hypercapnia. However, the down regulation of ion-regulatory and metabolic genes in late stage embryos, taken together with a significant reduction in somatic growth, indicates that cephalopod early life stages are challenged by elevated seawater pCO2.

Effects of Different Fatty Acids and Dietary Lipids on Adiponectin Gene Expression in 3T3-L1 Cells and C57BL/6J Mice Adipose Tissue

Obesity is positively correlated to dietary lipid intake, and the type of lipid may play a causal role in the development of obesity-related pathologies. A major protein secreted by adipose tissue is adiponectin, which has antiatherogenic and antidiabetic properties. The aim of this study was to evaluate the effects of four different high-fat diets (enriched with soybean oil, fish oil, coconut oil, or lard) on adiponectin gene expression and secretion by the white adipose tissue (WAT) of mice fed on a selected diet for either 2 (acute treatment) or 60 days (chronic treatment). Additionally, 3T3-L1 adipocytes were treated for 48 h with six different fatty acids: palmitic, linoleic, eicosapentaenoic (EPA), docosahexaenoic (DHA), lauric, or oleic acid. Serum adiponectin concentration was reduced in the soybean-, coconut-, and lard-enriched diets in both groups. Adiponectin gene expression was lower in retroperitoneal WAT after acute treatment with all diets. The same reduction in levels of adiponectin gene expression was observed in epididymal adipose tissue of animals chronically fed soybean and coconut diets and in 3T3-L1 cells treated with palmitic, linoleic, EPA, and DHA acids. These results indicate that the intake of certain fatty acids may affect serum adiponectin levels in mice and adiponectin gene expression in mouse WAT and 3T3-L1 adipocytes. The effects appear to be time dependent and depot specific. It is postulated that the downregulation of adiponectin expression by dietary enrichment with soybean oil or coconut oil may contribute to the development of insulin resistance and atherosclerosis.

Long Chain Saturated Fatty Acids Increase Haptoglobin Gene Expression in C57BL/6J Mice Adipose Tissue and 3T3-L1 Cells

Background Dietary lipids are directly related to the composition of adipose tissue, aetiology of obesity and arousal of obesity-related pathologies, like chronic inflammation states. Haptoglobin is an acute phase protein secreted by the liver and white adipose tissue, and its blood levels vary according to the volume of fat in the body. Aim of the study To investigate the effect of diets enriched with large amounts of dietary fats, which differ in their fatty acid composition, on the haptoglobin gene expression by visceral and subcutaneous adipose tissue of mice fed for 2 days or 8 weeks. 3T3-L1 cells were treated with fatty acids that are found in those types of dietary fats. Methods Mice were treated acutely (for 2 days) or chronically (for 8 weeks) with diets enriched with soybean oil, fish oil, coconut oil or lard. 3T3-L1 cells were treated with six different fatty acids. Haptoglobin gene expression was quantified by northern blotting. Results Both chronic and acute treatment with lard, which is rich in long chain saturated fatty acids, increased the haptoglobin mRNA expression in the retroperitoneal and epidydimal white adipose tissues. Chronic treatment with coconut oil, which is rich in medium chain saturated fatty acids, increased the haptoglobin expression in the epidydimal and subcutaneous depots. In 3T3-L1, palmitic acid increased the haptoglobin gene expression. Conclusion The type of lipids in the diet can differently modulate the white adipose tissue gene expression of haptoglobin, and saturated fatty acids play a major role in promoting a pro-inflammatory environment. This response is fat pad specific and dependant on the duration of treatment.

Whole Genome Analysis of Gene Expression Reveals Coordinated Activation of Signaling and Metabolic Pathways during Pollen-Pistil Interactions in Arabidopsis

Plant reproduction depends on the concerted activation of many genes to ensure correct communication between pollen and pistil. Here, we queried the whole transcriptome of Arabidopsis (Arabidopsis thaliana) in order to identify genes with specific reproductive functions. We used the Affymetrix ATH1 whole genome array to profile wild-type unpollinated pistils and unfertilized ovules. By comparing the expression profile of pistils at 0.5, 3.5, and 8.0 h after pollination and applying a number of statistical and bioinformatics criteria, we found 1,373 genes differentially regulated during pollen-pistil interactions. Robust clustering analysis grouped these genes in 16 time-course clusters representing distinct patterns of regulation. Coregulation within each cluster suggests the presence of distinct genetic pathways, which might be under the control of specific transcriptional regulators. A total of 78% of the regulated genes were expressed initially in unpollinated pistil and/or ovules, 15% were initially detected in the pollen data sets as enriched or preferentially expressed, and 7% were induced upon pollination. Among those, we found a particular enrichment for unknown transcripts predicted to encode secreted proteins or representing signaling and cell wall-related proteins, which may function by remodeling the extracellular matrix or as extracellular signaling molecules. A strict regulatory control in various metabolic pathways suggests that fine-tuning of the biochemical and physiological cellular environment is crucial for reproductive success. Our study provides a unique and detailed temporal and spatial gene expression profile of in vivo pollen-pistil interactions, providing a framework to better understand the basis of the molecular mechanisms operating during the reproductive process in higher plants.

Development of gene expression-based biomarkers of exposure to metals and pesticides in the freshwater amphipod Hyalella azteca

Ecological risk assessment (ERA) is a framework for monitoring risks of exposure and adverse effects of environmental stressors to populations or communities of interest. One tool of ERA is the biomarker, which is a characteristic of an organism that reliably indicates exposure to or effects of a stressor like chemical pollution. Traditional biomarkers which rely on characteristics at the tissue level and higher often detect only acute exposures to stressors. Sensitive molecular biomarkers may detect lower stressor levels than traditional biomarkers, which helps inform risk mitigation and restoration efforts before populations and communities are irreversibly affected. In this study I developed gene expression-based molecular biomarkers of exposure to metals and insecticides in the model toxicological freshwater amphipod Hyalella azteca. My goals were to not only create sensitive molecular biomarkers for these chemicals, but also to show the utility and versatility of H. azteca in molecular studies for toxicology and risk assessment. I sequenced and assembled the H. azteca transcriptome to identify reference and stress-response gene transcripts suitable for expression monitoring. I exposed H. azteca to sub-lethal concentrations of metals (cadmium and copper) and insecticides (DDT, permethrin, and imidacloprid). Reference genes used to create normalization factors were determined for each exposure using the programs BestKeeper, GeNorm, and NormFinder. Both metals increased expression of a nuclear transcription factor (Cnc), an ABC transporter (Mrp4), and a heat shock protein (Hsp90), giving evidence of general metal exposure signature. Cadmium uniquely increased expression of a DNA repair protein (Rad51) and increased Mrp4 expression more than copper (7-fold increase compared to 2-fold increase). Together these may be unique biomarkers distinguishing cadmium and copper exposures. DDT increased expression of Hsp90, Mrp4, and the immune response gene Lgbp. Permethrin increased expression of a cytochrome P450 (Cyp2j2) and decreased expression of the immune response gene Lectin-1. Imidacloprid did not affect gene expression. Unique biomarkers were seen for DDT and permethrin, but the genes studied were not sensitive enough to detect imidacloprid at the levels used here. I demonstrated that gene expression in H. azteca detects specific chemical exposures at sub-lethal concentrations, making expression monitoring using this amphipod a useful and sensitive biomarker for risk assessment of chemical exposure.

A study of computational methods to analyze gene expression data

The recent advent of new technologies has led to huge amounts of genomic data. With these data come new opportunities to understand biological cellular processes underlying hidden regulation mechanisms and to identify disease related biomarkers for informative diagnostics. However, extracting biological insights from the immense amounts of genomic data is a challenging task. Therefore, effective and efficient computational techniques are needed to analyze and interpret genomic data. In this thesis, novel computational methods are proposed to address such challenges: a Bayesian mixture model, an extended Bayesian mixture model, and an Eigen-brain approach. The Bayesian mixture framework involves integration of the Bayesian network and the Gaussian mixture model. Based on the proposed framework and its conjunction with K-means clustering and principal component analysis (PCA), biological insights are derived such as context specific/dependent relationships and nested structures within microarray where biological replicates are encapsulated. The Bayesian mixture framework is then extended to explore posterior distributions of network space by incorporating a Markov chain Monte Carlo (MCMC) model. The extended Bayesian mixture model summarizes the sampled network structures by extracting biologically meaningful features. Finally, an Eigen-brain approach is proposed to analyze in situ hybridization data for the identification of the cell-type specific genes, which can be useful for informative blood diagnostics. Computational results with region-based clustering reveals the critical evidence for the consistency with brain anatomical structure.

MediPlEx - a tool to combine in silico & experimental gene expression profiles of the model legume Medicago truncatula

Background: Expressed Sequence Tags (ESTs) are in general used to gain a first insight into gene activities from a species of interest. Subsequently, and typically based on a combination of EST and genome sequences, microarray-based expression analyses are performed for a variety of conditions. In some cases, a multitude of EST and microarray experiments are conducted for one species, covering different tissues, cell states, and cell types. Under these circumstances, the challenge arises to combine results derived from the different expression profiling strategies, with the goal to uncover novel information on the basis of the integrated datasets. Findings: Using our new analysis tool, MediPlEx (MEDIcago truncatula multiPLe EXpression analysis), expression data from EST experiments, oligonucleotide microarrays and Affymetrix GeneChips® can be combined and analyzed, leading to a novel approach to integrated transcriptome analysis. We have validated our tool via the identification of a set of well-characterized AM-specific and AM-induced marker genes, identified by MediPlEx on the basis of in silico and experimental gene expression profiles from roots colonized with AM fungi. Conclusions: MediPlEx offers an integrated analysis pipeline for different sets of expression data generated for the model legume Medicago truncatula. As expected, in silico and experimental gene expression data that cover the same biological condition correlate well. The collection of differentially expressed genes identified via MediPlEx provides a starting point for functional studies in plant mutants.

Synthetic regulation of eukaryotic gene expression by noncoding RNA

Synthetic biological systems promise to combine the spectacular diversity of biological functionality with engineering principles to design new life to address many pressing needs. As these engineered systems advance in sophistication, there is ever-greater need for customizable, situation-specific expression of desired genes. However, existing gene control platforms are generally not modular, or do not display performance requirements required for robust phenotypic responses to input signals. This work expands the capabilities of eukaryotic gene control in two important directions.

For development of greater modularity, we extend the use of synthetic self-cleaving ribozyme switches to detect changes in input protein levels and convey that information into programmed gene expression in eukaryotic cells. We demonstrate both up- and down-regulation of levels of an output transgene by more than 4-fold in response to rising input protein levels, with maximal output gene expression approaching the highest levels observed in yeast. In vitro experiments demonstrate protein-dependent ribozyme activity modulation. We further demonstrate the platform in mammalian cells. Our switch devices do not depend on special input protein activity, and can be tailored to respond to any input protein to which a suitable RNA aptamer can be developed. This platform can potentially be employed to regulate the expression of any transgene or any endogenous gene by 3’ UTR replacement, allowing for more complex cell state-specific reprogramming.

We also address an important concern with ribozyme switches, and riboswitch performance in general, their dynamic range. While riboswitches have generally allowed for versatile and modular regulation, so far their dynamic ranges of output gene modulation have been modest, generally at most 10-fold. We address this shortcoming by developing a modular genetic amplifier for near-digital control of eukaryotic gene expression. We combine ribozyme switch-mediated regulation of a synthetic TF with TF-mediated regulation of an output gene. The amplifier platform allows for as much as 20-fold regulation of output gene expression in response to input signal, with maximal expression approaching the highest levels observed in yeast, yet being tunable to intermediate and lower expression levels. EC50 values are more than 4 times lower than in previously best-performing non-amplifier ribozyme switches. The system design retains the modular-input architecture of the ribozyme switch platform, and the near-digital dynamic ranges of TF-based gene control.

Together, these developments suggest great potential for the wide applicability of these platforms for better-performing eukaryotic gene regulation, and more sophisticated, customizable reprogramming of cellular activity.

Exposure To Bisphenol-a During Pregnancy Partially Mimics The Effects Of A High-fat Diet Altering Glucose Homeostasis And Gene Expression In Adult Male Mice.

Bisphenol-A (BPA) is one of the most widespread EDCs used as a base compound in the manufacture of polycarbonate plastics. The aim of our research has been to study how the exposure to BPA during pregnancy affects weight, glucose homeostasis, pancreatic β-cell function and gene expression in the major peripheral organs that control energy flux: white adipose tissue (WAT), the liver and skeletal muscle, in male offspring 17 and 28 weeks old. Pregnant mice were treated with a subcutaneous injection of 10 µg/kg/day of BPA or a vehicle from day 9 to 16 of pregnancy. One month old offspring were divided into four different groups: vehicle treated mice that ate a normal chow diet (Control group); BPA treated mice that also ate a normal chow diet (BPA); vehicle treated animals that had a high fat diet (HFD) and BPA treated animals that were fed HFD (HFD-BPA). The BPA group started to gain weight at 18 weeks old and caught up to the HFD group before week 28. The BPA group as well as the HFD and HFD-BPA ones presented fasting hyperglycemia, glucose intolerance and high levels of non-esterified fatty acids (NEFA) in plasma compared with the Control one. Glucose stimulated insulin release was disrupted, particularly in the HFD-BPA group. In WAT, the mRNA expression of the genes involved in fatty acid metabolism, Srebpc1, Pparα and Cpt1β was decreased by BPA to the same extent as with the HFD treatment. BPA treatment upregulated Pparγ and Prkaa1 genes in the liver; yet it diminished the expression of Cd36. Hepatic triglyceride levels were increased in all groups compared to control. In conclusion, male offspring from BPA-treated mothers presented symptoms of diabesity. This term refers to a form of diabetes which typically develops in later life and is associated with obesity.

Hypothyroidism decreases proinsulin gene expression and the attachment of its mRNA and eEF1A protein to the actin cytoskeleton of INS-1E cells

The actions of thyroid hormone (TH) on pancreatic beta cells have not been thoroughly explored, with current knowledge being limited to the modulation of insulin secretion in response to glucose, and beta cell viability by regulation of pro-mitotic and pro-apoptotic factors. Therefore, the effects of TH on proinsulin gene expression are not known. This led us to measure: a) proinsulin mRNA expression, b) proinsulin transcripts and eEF1A protein binding to the actin cytoskeleton, c) actin cytoskeleton arrangement, and d) proinsulin mRNA poly(A) tail length modulation in INS-1E cells cultured in different media containing: i) normal fetal bovine serum - FBS (control); ii) normal FBS plus 1 µM or 10 nM T3, for 12 h, and iii) FBS depleted of TH for 24 h (Tx). A decrease in proinsulin mRNA content and attachment to the cytoskeleton were observed in hypothyroid (Tx) beta cells. The amount of eEF1A protein anchored to the cytoskeleton was also reduced in hypothyroidism, and it is worth mentioning that eEF1A is essential to attach transcripts to the cytoskeleton, which might modulate their stability and rate of translation. Proinsulin poly(A) tail length and cytoskeleton arrangement remained unchanged in hypothyroidism. T3 treatment of control cells for 12 h did not induce any changes in the parameters studied. The data indicate that TH is important for proinsulin mRNA expression and translation, since its total amount and attachment to the cytoskeleton are decreased in hypothyroid beta cells, providing evidence that effects of TH on carbohydrate metabolism also include the control of proinsulin gene expression.

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.