Paraoxonase 2 deficiency in mice alters motor behavior and causes region-specific transcript changes in the brain

Paraoxonase 2 (PON2) is an intracellular antioxidant enzyme shown to play an important role in mitigating oxidative stress in the brain. Oxidative stress is a common mechanism of toxicity for neurotoxicants and is increasingly implicated in the etiology of multiple neurological diseases. While PON2 deficiency increases oxidative stress in the brain in-vitro, little is known about its effects on behavior in-vivo and what global transcript changes occur from PON2 deficiency. We sought to characterize the effects of PON2 deficiency on behavior in mice, with an emphasis on locomotion, and evaluate transcriptional changes with RNA-Seq. Behavioral endpoints included home-cage behavior (Noldus PhenoTyper), motor coordination (Rotarod) and various gait metrics (Noldus CatWalk). Home-cage behavior analysis showed PON2 deficient mice had increased activity at night compared to wildtype controls and spent more time in the center of the cage, displaying a possible anxiolytic phenotype. PON2 deficient mice had significantly shorter latency to fall when tested on the rotarod, suggesting impaired motor coordination. Minimal gait alterations were observed, with decreased girdle support posture noted as the only significant change in gait with PON2 deficiency. Beyond one home-cage metric, no significant sex-based behavioral differences were found in this study. Finally, A subset of samples were utilized for RNA-Seq analysis, looking at three discrete brain regions: cerebral cortex, striatum, and cerebellum. Highly regional- and sex-specific changes in RNA expression were found when comparing PON2 deficient and wildtype mice, suggesting PON2 may play distinct regional roles in the brain in a sex-specific manner. Taken together, these findings demonstrates that PON2 deficiency significantly alters the brain on both a biochemical and phenotypic level, with a specific impact on motor function. These data have implications for future gene-environment toxicological studies and warrants further investigation of the role of PON2 in the brain

High-throughput processing and normalization of one-color microarrays for transcriptional meta-analyses

<p>Abstract</p> <p>Background</p> <p>Microarray experiments are becoming increasingly common in biomedical research, as is their deposition in publicly accessible repositories, such as Gene Expression Omnibus (GEO). As such, there has been a surge in interest to use this microarray data for meta-analytic approaches, whether to increase sample size for a more powerful analysis of a specific disease (e.g. lung cancer) or to re-examine experiments for reasons different than those examined in the initial, publishing study that generated them. For the average biomedical researcher, there are a number of practical barriers to conducting such meta-analyses such as manually aggregating, filtering and formatting the data. Methods to automatically process large repositories of microarray data into a standardized, directly comparable format will enable easier and more reliable access to microarray data to conduct meta-analyses.</p> <p>Methods</p> <p>We present a straightforward, simple but robust against potential outliers method for automatic quality control and pre-processing of tens of thousands of single-channel microarray data files. GEO GDS files are quality checked by comparing parametric distributions and quantile normalized to enable direct comparison of expression level for subsequent meta-analyses.</p> <p>Results</p> <p>13,000 human 1-color experiments were processed to create a single gene expression matrix that subsets can be extracted from to conduct meta-analyses. Interestingly, we found that when conducting a global meta-analysis of gene-gene co-expression patterns across all 13,000 experiments to predict gene function, normalization had minimal improvement over using the raw data.</p> <p>Conclusions</p> <p>Normalization of microarray data appears to be of minimal importance on analyses based on co-expression patterns when the sample size is on the order of thousands microarray datasets. Smaller subsets, however, are more prone to aberrations and artefacts, and effective means of automating normalization procedures not only empowers meta-analytic approaches, but aids in reproducibility by providing a standard way of approaching the problem.</p> <p>Data availability: matrix containing normalized expression of 20,813 genes across 13,000 experiments is available for download at . Source code for GDS files pre-processing is available from the authors upon request.</p

SS-31 and NMN: Two paths to improve metabolism and function in aged hearts

The effects of two different mitochondrial-targeted drugs, SS-31 and NMN, were tested on Old mouse hearts. After treatment with the drugs, individually or Combined, heart function was examined by echocardiography. SS-31 partially reversed an age-related decline in diastolic function while NMN fully reversed an age-related deficiency in systolic function at a higher workload. Metabolomic analysis revealed that both NMN and the Combined treatment increased nicotinamide and 1-methylnicotinamide levels, indicating greater NA

A Novel Statistical Algorithm for Gene Expression Analysis Helps Differentiate Pregnane X Receptor-Dependent and Independent Mechanisms of Toxicity

Genome-wide gene expression profiling has become standard for assessing potential liabilities as well as for elucidating mechanisms of toxicity of drug candidates under development. Analysis of microarray data is often challenging due to the lack of a statistical model that is amenable to biological variation in a small number of samples. Here we present a novel non-parametric algorithm that requires minimal assumptions about the data distribution. Our method for determining differential expression consists of two steps: 1) We apply a nominal threshold on fold change and platform p-value to designate whether a gene is differentially expressed in each treated and control sample relative to the averaged control pool, and 2) We compared the number of samples satisfying criteria in step 1 between the treated and control groups to estimate the statistical significance based on a null distribution established by sample permutations. The method captures group effect without being too sensitive to anomalies as it allows tolerance for potential non-responders in the treatment group and outliers in the control group. Performance and results of this method were compared with the Significant Analysis of Microarrays (SAM) method. These two methods were applied to investigate hepatic transcriptional responses of wild-type (PXR+/+) and pregnane X receptor-knockout (PXR−/−) mice after 96 h exposure to CMP013, an inhibitor of β-secretase (β-site of amyloid precursor protein cleaving enzyme 1 or BACE1). Our results showed that CMP013 led to transcriptional changes in hallmark PXR-regulated genes and induced a cascade of gene expression changes that explained the hepatomegaly observed only in PXR+/+ animals. Comparison of concordant expression changes between PXR+/+ and PXR−/− mice also suggested a PXR-independent association between CMP013 and perturbations to cellular stress, lipid metabolism, and biliary transport

COLOMBOS: Access Port for Cross-Platform Bacterial Expression Compendia

Background: Microarrays are the main technology for large-scale transcriptional gene expression profiling, but the large bodies of data available in public databases are not useful due to the large heterogeneity. There are several initiatives that attempt to bundle these data into expression compendia, but such resources for bacterial organisms are scarce and limited to integration of experiments from the same platform or to indirect integration of per experiment analysis results. Methodology/Principal Findings: We have constructed comprehensive organism-specific cross-platform expression compendia for three bacterial model organisms (Escherichia coli, Bacillus subtilis, and Salmonella enterica serovar Typhimurium) together with an access portal, dubbed COLOMBOS, that not only provides easy access to the compendia, but also includes a suite of tools for exploring, analyzing, and visualizing the data within these compendia. It is freely available at http://bioi.biw.kuleuven.be/colombos. The compendia are unique in directly combining expression information from different microarray platforms and experiments, and we illustrate the potential benefits of this direct integration with a case study: extending the known regulon of the Fur transcription factor of E. coli. The compendia also incorporate extensive annotations for both genes and experimental conditions; these heterogeneous data are functionally integrated in the COLOMBOS analysis tools to interactively browse and query the compendia not only for specific genes or experiments, but also metabolic pathways, transcriptional regulation mechanisms, experimental conditions, biological processes, etc. Conclusions/Significance: We have created cross-platform expression compendia for several bacterial organisms and developed a complementary access port COLOMBOS, that also serves as a convenient expression analysis tool to extract useful biological information. This work is relevant to a large community of microbiologists by facilitating the use of publicly available microarray experiments to support their research

The effects of timing of fine needle aspiration biopsies on gene expression profiles in breast cancers

<p>Abstract</p> <p>Background</p> <p>DNA microarray analysis has great potential to become an important clinical tool to individualize prognostication and treatment for breast cancer patients. However, with any emerging technology, there are many variables one must consider before bringing the technology to the bedside. There are already concerted efforts to standardize protocols and to improve reproducibility of DNA microarray. Our study examines one variable that is often overlooked, the timing of tissue acquisition, which may have a significant impact on the outcomes of DNA microarray analyses especially in studies that compare microarray data based on biospecimens taken <it>in vivo </it>and <it>ex vivo</it>.</p> <p>Methods</p> <p>From 16 patients, we obtained paired fine needle aspiration biopsies (FNABs) of breast cancers taken before (PRE) and after (POST) their surgeries and compared the microarray data to determine the genes that were differentially expressed between the FNABs taken at the two time points. qRT-PCR was used to validate our findings. To examine effects of longer exposure to hypoxia on gene expression, we also compared the gene expression profiles of 10 breast cancers from clinical tissue bank.</p> <p>Results</p> <p>Using hierarchical clustering analysis, 12 genes were found to be differentially expressed between the FNABs taken before and after surgical removal. Remarkably, most of the genes were linked to FOS in an early hypoxia pathway. The gene expression of FOS also increased with longer exposure to hypoxia.</p> <p>Conclusion</p> <p>Our study demonstrated that the timing of fine needle aspiration biopsies can be a confounding factor in microarray data analyses in breast cancer. We have shown that FOS-related genes, which have been implicated in early hypoxia as well as the development of breast cancers, were differentially expressed before and after surgery. Therefore, it is important that future studies take timing of tissue acquisition into account.</p

Pre-processing Agilent microarray data

<p>Abstract</p> <p>Background</p> <p>Pre-processing methods for two-sample long oligonucleotide arrays, specifically the Agilent technology, have not been extensively studied. The goal of this study is to quantify some of the sources of error that affect measurement of expression using Agilent arrays and to compare Agilent's Feature Extraction software with pre-processing methods that have become the standard for normalization of cDNA arrays. These include log transformation followed by loess normalization with or without background subtraction and often a between array scale normalization procedure. The larger goal is to define best study design and pre-processing practices for Agilent arrays, and we offer some suggestions.</p> <p>Results</p> <p>Simple loess normalization without background subtraction produced the lowest variability. However, without background subtraction, fold changes were biased towards zero, particularly at low intensities. ROC analysis of a spike-in experiment showed that differentially expressed genes are most reliably detected when background is not subtracted. Loess normalization and no background subtraction yielded an AUC of 99.7% compared with 88.8% for Agilent processed fold changes. All methods performed well when error was taken into account by t- or z-statistics, AUCs ≥ 99.8%. A substantial proportion of genes showed dye effects, 43% (99%<it>CI </it>: 39%, 47%). However, these effects were generally small regardless of the pre-processing method.</p> <p>Conclusion</p> <p>Simple loess normalization without background subtraction resulted in low variance fold changes that more reliably ranked gene expression than the other methods. While t-statistics and other measures that take variation into account, including Agilent's z-statistic, can also be used to reliably select differentially expressed genes, fold changes are a standard measure of differential expression for exploratory work, cross platform comparison, and biological interpretation and can not be entirely replaced. Although dye effects are small for most genes, many array features are affected. Therefore, an experimental design that incorporates dye swaps or a common reference could be valuable.</p

Technical Analysis of cDNA Microarrays

Background: There is extensive variation in gene expression among individuals within and between populations. Accurate measures of the variation in mRNA expression using microarrays can be confounded by technical variation, which includes variation in RNA isolation procedures, day of hybridization and methods used to amplify and dye label RNA for hybridization. Methodology/Principal Findings: In this manuscript we analyze the relationship between the amount of mRNA and the fluorescent signal from the microarray hybridizations demonstrating that for a wide-range of mRNA concentrations the fluorescent signal is a linear function of the amount of mRNA. Additionally, the separate isolation, labeling or hybridization of RNA does not add significant amounts of variation in microarray measures of gene expression. However, single or double rounds of amplification for labeling do have small but significant affects on 10 % of genes, but this source of technical variation is easy to avoid. To examine both technical and stochastic biological variation, mRNA expression was measured from the same five individuals over a six-week time course. Conclusion: There were few, if any, meaningful differences in gene expression among time points. Thus, microarray measures using standard laboratory procedures can be precise and quantitative and are not subject to significant rando

Meta-analysis of several gene lists for distinct types of cancer: A simple way to reveal common prognostic markers

BACKGROUND: Although prognostic biomarkers specific for particular cancers have been discovered, microarray analysis of gene expression profiles, supported by integrative analysis algorithms, helps to identify common factors in molecular oncology. Similarities of Ordered Gene Lists (SOGL) is a recently proposed approach to meta-analysis suitable for identifying features shared by two data sets. Here we extend the idea of SOGL to the detection of significant prognostic marker genes from microarrays of multiple data sets. Three data sets for leukemia and the other six for different solid tumors are used to demonstrate our method, using established statistical techniques. RESULTS: We describe a set of significantly similar ordered gene lists, representing outcome comparisons for distinct types of cancer. This kind of similarity could improve the diagnostic accuracies of individual studies when SOGL is incorporated into the support vector machine algorithm. In particular, we investigate the similarities among three ordered gene lists pertaining to mesothelioma survival, prostate recurrence and glioma survival. The similarity-driving genes are related to the outcomes of patients with lung cancer with a hazard ratio of 4.47 (p = 0.035). Many of these genes are involved in breakdown of EMC proteins regulating angiogenesis, and may be used for further research on prognostic markers and molecular targets of gene therapy for cancers. CONCLUSION: The proposed method and its application show the potential of such meta-analyses in clinical studies of gene expression profiles