Comparison of frozen and RNALater solid tissue storage methods for use in RNA expression microarrays

BACKGROUND: Primary human tissues are an invaluable widely used tool for discovery of gene expression patterns which characterize disease states. Tissue processing methods remain unstandardized, leading to unanswered concerns of how to best store collected tissues and maintain reproducibility between laboratories. We subdivided uterine myometrial tissue specimens and stored split aliquots using the most common tissue processing methods (fresh, frozen, RNALater) before comparing quantitative RNA expression profiles on the Affymetrix U133 human expression array. Split samples and inclusion of duplicates within each processing group allowed us to undertake a formal genome-wide analysis comparing the magnitude of result variation contributed by sample source (different patients), processing protocol (fresh vs. frozen vs. 24 or 72 hours RNALater), and random background (duplicates). The dataset was randomly permuted to define a baseline pattern of ANOVA test statistic values against which the observed results could be interpreted. RESULTS: 14,639 of 22,283 genes were expressed in at least one sample. Patient subjects provided the greatest sources of variation in the mixed model ANOVA, with replicates and processing method the least. The magnitude of variation conferred by processing method (24 hours RNALater vs 72 hours RNALater vs. fresh vs frozen) was similar to the variability seen within replicates. Subset analysis of the test statistic according to gene functional class showed that the frequency of "outlier" ANOVA results within each functional class is overall no greater than expected by chance. CONCLUSIONS: Ambient storage of tissues for 24 or 72 hours in RNALater did not contribute any systematic shift in quantitative RNA expression results relative to the alternatives of fresh or frozen tissue. This nontoxic preservative enables decentralized tissue collection for expression array analysis without a requirement for specialized equipment

The Reproducibility of Lists of Differentially Expressed Genes in Microarray Studies

Reproducibility is a fundamental requirement in scientific experiments and clinical contexts. Recent publications raise concerns about the reliability of microarray technology because of the apparent lack of agreement between lists of differentially expressed genes (DEGs). In this study we demonstrate that (1) such discordance may stem from ranking and selecting DEGs solely by statistical significance (P) derived from widely used simple t-tests; (2) when fold change (FC) is used as the ranking criterion, the lists become much more reproducible, especially when fewer genes are selected; and (3) the instability of short DEG lists based on P cutoffs is an expected mathematical consequence of the high variability of the t-values. We recommend the use of FC ranking plus a non-stringent P cutoff as a baseline practice in order to generate more reproducible DEG lists. The FC criterion enhances reproducibility while the P criterion balances sensitivity and specificity

A microbiota signature associated with experimental food allergy promotes allergic sensitization and anaphylaxis

Background: Commensal microbiota play a critical role in maintaining oral tolerance. The effect of food allergy on the gut microbial ecology remains unknown. Methods: Food allergy–prone mice with a gain-of-function mutation in the IL-4 receptor α chain (Il4raF709) and wild-type (WT) control animals were subjected to oral sensitization with chicken egg ovalbumin (OVA). Enforced tolerance was achieved by using allergen-specific regulatory T (Treg) cells. Community structure analysis of gut microbiota was performed by using a high-density 16S rDNA oligonucleotide microarrays (PhyloChip) and massively parallel pyrosequencing of 16S rDNA amplicons. Results: OVA-sensitized Il4raF709 mice exhibited a specific microbiota signature characterized by coordinate changes in the abundance of taxa of several bacterial families, including the Lachnospiraceae, Lactobacillaceae, Rikenellaceae, and Porphyromonadaceae. This signature was not shared by similarly sensitized WT mice, which did not exhibit an OVA-induced allergic response. Treatment of OVA-sensitized Il4raF709 mice with OVA-specific Treg cells led to a distinct tolerance-associated signature coincident with the suppression of the allergic response. The microbiota of allergen-sensitized Il4raF709 mice differentially promoted OVA-specific IgE responses and anaphylaxis when reconstituted in WT germ-free mice. Conclusion: Mice with food allergy exhibit a specific gut microbiota signature capable of transmitting disease susceptibility and subject to reprogramming by enforced tolerance. Disease-associated microbiota may thus play a pathogenic role in food allergy

The balance of reproducibility, sensitivity, and specificity of lists of differentially expressed genes in microarray studies

<p>Abstract</p> <p>Background</p> <p>Reproducibility is a fundamental requirement in scientific experiments. Some recent publications have claimed that microarrays are unreliable because lists of differentially expressed genes (DEGs) are not reproducible in similar experiments. Meanwhile, new statistical methods for identifying DEGs continue to appear in the scientific literature. The resultant variety of existing and emerging methods exacerbates confusion and continuing debate in the microarray community on the appropriate choice of methods for identifying reliable DEG lists.</p> <p>Results</p> <p>Using the data sets generated by the MicroArray Quality Control (MAQC) project, we investigated the impact on the reproducibility of DEG lists of a few widely used gene selection procedures. We present comprehensive results from inter-site comparisons using the same microarray platform, cross-platform comparisons using multiple microarray platforms, and comparisons between microarray results and those from TaqMan – the widely regarded "standard" gene expression platform. Our results demonstrate that (1) previously reported discordance between DEG lists could simply result from ranking and selecting DEGs solely by statistical significance (<it>P</it>) derived from widely used simple <it>t</it>-tests; (2) when fold change (FC) is used as the ranking criterion with a non-stringent <it>P</it>-value cutoff filtering, the DEG lists become much more reproducible, especially when fewer genes are selected as differentially expressed, as is the case in most microarray studies; and (3) the instability of short DEG lists solely based on <it>P</it>-value ranking is an expected mathematical consequence of the high variability of the <it>t</it>-values; the more stringent the <it>P</it>-value threshold, the less reproducible the DEG list is. These observations are also consistent with results from extensive simulation calculations.</p> <p>Conclusion</p> <p>We recommend the use of FC-ranking plus a non-stringent <it>P </it>cutoff as a straightforward and baseline practice in order to generate more reproducible DEG lists. Specifically, the <it>P</it>-value cutoff should not be stringent (too small) and FC should be as large as possible. Our results provide practical guidance to choose the appropriate FC and <it>P</it>-value cutoffs when selecting a given number of DEGs. The FC criterion enhances reproducibility, whereas the <it>P </it>criterion balances sensitivity and specificity.</p

New genetic loci link adipose and insulin biology to body fat distribution.

Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 × 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms

Combined analysis of oligonucleotide microarray data from transgenic and knockout mice identifies direct SREBP target genes

The synthesis of fatty acids and cholesterol, the building blocks of membranes, is regulated by three membrane-bound transcription factors: sterol regulatory element-binding proteins (SREBP)-1a, -1c, and -2. Their function in liver has been characterized in transgenic mice that overexpress each SREBP isoform and in mice that lack all three nuclear SREBPs as a result of gene knockout of SREBP cleavage-activating protein (SCAP), a protein required for nuclear localization of SREBPs. Here, we use oligonucleotide arrays hybridized with RNA from livers of three lines of mice (transgenic for SREBP-1a, transgenic for SREBP-2, and knockout for SCAP) to identify genes that are likely to be direct targets of SREBPs in liver. A total of 1,003 genes showed statistically significant increased expression in livers of transgenic SREBP-1a mice, 505 increased in livers of transgenic SREBP-2 mice, and 343 showed decreased expression in Scap(–/–) livers. A subset of 33 genes met the stringent combinatorial criteria of induction in both SREBP transgenics and decreased expression in SCAP-deficient mice. Of these 33 genes, 13 were previously identified as direct targets of SREBP action. Of the remaining 20 genes, 13 encode enzymes or carrier proteins involved in cholesterol metabolism, 3 participate in fatty acid metabolism, and 4 have no known connection to lipid metabolism. Through application of stringent combinatorial criteria, the transgenic/knockout approach allows identification of genes whose activities are likely to be controlled directly by one family of transcription factors, in this case the SREBPs