Fold change and p-value cutoffs significantly alter microarray interpretations

<p>Abstract</p> <p>Background</p> <p>As context is important to gene expression, so is the preprocessing of microarray to transcriptomics. Microarray data suffers from several normalization and significance problems. Arbitrary fold change (FC) cut-offs of >2 and significance p-values of <0.02 lead data collection to look only at genes which vary wildly amongst other genes. Therefore, questions arise as to whether the biology or the statistical cutoff are more important within the interpretation. In this paper, we reanalyzed a zebrafish (<it>D. rerio</it>) microarray data set using GeneSpring and different differential gene expression cut-offs and found the data interpretation was drastically different. Furthermore, despite the advances in microarray technology, the array captures a large portion of genes known but yet still leaving large voids in the number of genes assayed, such as leptin a pleiotropic hormone directly related to hypoxia-induced angiogenesis.</p> <p>Results</p> <p>The data strongly suggests that the number of differentially expressed genes is more up-regulated than down-regulated, with many genes indicating conserved signalling to previously known functions. Recapitulated data from Marques et al. (2008) was similar but surprisingly different with some genes showing unexpected signalling which may be a product of tissue (heart) or that the intended response was transient.</p> <p>Conclusions</p> <p>Our analyses suggest that based on the chosen statistical or fold change cut-off; microarray analysis can provide essentially more than one answer, implying data interpretation as more of an art than a science, with follow up gene expression studies a must. Furthermore, gene chip annotation and development needs to maintain pace with not only new genomes being sequenced but also novel genes that are crucial to the overall gene chips interpretation.</p

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

Quality assessment and data handling methods for Affymetrix Gene 1.0 ST arrays with variable RNA integrity

BBackground: RNA and microarray quality assessment form an integral part of gene expression analysis and, although methods such as the RNA integrity number (RIN) algorithm reliably asses RNA integrity, the relevance of RNA integrity in gene expression analysis as well as analysis methods to accommodate the possible effects of degradation requires further investigation. We investigated the relationship between RNA integrity and array quality on the commonly used Affymetrix Gene 1.0 ST array platform using reliable within-array and between-array quality assessment measures. The possibility of a transcript specific bias in the apparent effect of RNA degradation on the measured gene expression signal was evaluated after either excluding quality-flagged arrays or compensation for RNA degradation at different steps in the analysis. Results: Using probe-level and inter-array quality metrics to assess 34 Gene 1.0 ST array datasets derived from historical, paired tumour and normal primary colorectal cancer samples, 7 arrays (20.6%), with a mean sample RIN of 3.2 (SD = 0.42), were flagged during array quality assessment while 10 arrays from samples with RINs 2), with longer and shorter than average transcripts under- and overrepresented in quality-flagged samples respectively. Applying compensatory measures for RNA degradation performed at least as well as excluding quality-flagged arrays, as judged by hierarchical clustering, gene expression analysis and Ingenuity Pathway Analysis; importantly, use of these compensatory measures had the significant benefit of enabling lower quality array data from irreplaceable clinical samples to be retained in downstream analyses. Conclusions: Here, we demonstrate an effective array-quality assessment strategy, which will allow the user to recognize lower quality arrays that can be included in the analysis once appropriate measures are applied to account for known or unknown sources of variation, such as array quality- and batch- effects, by implementing ComBat or Surrogate Variable Analysis. This approach of quality control and analysis will be especially useful for clinical samples with variable and low RNA qualities, with RIN scores ≥ 2

Identification and characterization of mouse otic sensory lineage genes

Vertebrate embryogenesis gives rise to all cell types of an organism through the development of many unique lineages derived from the three primordial germ layers. The otic sensory lineage arises from the otic vesicle, a structure formed through invagination of placodal non-neural ectoderm. This developmental lineage possesses unique differentiation potential, giving rise to otic sensory cell populations including hair cells, supporting cells, and ganglion neurons of the auditory and vestibular organs. Here we present a systematic approach to identify transcriptional features that distinguish the otic sensory lineage (from early otic progenitors to otic sensory populations) from other major lineages of vertebrate development. We used a microarray approach to analyze otic sensory lineage populations including microdissected otic vesicles (embryonic day 10.5) as well as isolated neonatal cochlear hair cells and supporting cells at postnatal day 3. Non-otic tissue samples including periotic tissues and whole embryos with otic regions removed were used as reference populations to evaluate otic specificity. Otic populations shared transcriptome-wide correlations in expression profiles that distinguish members of this lineage from non-otic populations. We further analyzed the microarray data using comparative and dimension reduction methods to identify individual genes that are specifically expressed in the otic sensory lineage. This analysis identified and ranked top otic sensory lineage-specific transcripts including Fbxo2, Col9a2, and Oc90, and additional novel otic lineage markers. To validate these results we performed expression analysis on select genes using immunohistochemistry and in situ hybridization. Fbxo2 showed the most striking pattern of specificity to the otic sensory lineage, including robust expression in the early otic vesicle and sustained expression in prosensory progenitors and auditory and vestibular hair cells and supporting cells

Bioinformatics Techniques for Studying Drug Resistance In HIV and Staphylococcus Aureus

The worldwide HIV/AIDS pandemic has been partly controlled and treated by antivirals targeting HIV protease, integrase and reverse transcriptase, however, drug resistance has become a serious problem. HIV-1 drug resistance to protease inhibitors evolves by mutations in the PR gene. The resistance mutations can alter protease catalytic activity, inhibitor binding, and stability. Different machine learning algorithms (restricted boltzmann machines, clustering, etc.) have been shown to be effective machine learning tools for classification of genomic and resistance data. Application of restricted boltzmann machine produced highly accurate and robust classification of HIV protease resistance. They can also be used to compare resistance profiles of different protease inhibitors. HIV drug resistance has also been studied by enzyme kinetics and X-ray crystallography. Triple mutant HIV-1 protease with resistance mutations V32I, I47V and V82I has been used as a model for the active site of HIV-2 protease. The effects of four investigational antiviral inhibitors was measured for Triple mutant. The tested compounds had significantly worse inhibition of triple mutant with Ki values of 17-40 nM compared to 2-10 pM for wild type protease. The crystal structure of triple mutant in complex with GRL01111 was solved and showed few changes in protease interactions with inhibitor. These new inhibitors are not expected to be effective for HIV-2 protease or HIV-1 protease with changes V32I, I47V and V82I. Methicillin-resistant Staphylococcus aureus (MRSA) is an opportunistic pathogen that causes hospital and community-acquired infections. Antibiotic resistance occurs because of newly acquired low-affinity penicillin-binding protein (PBP2a). Transcriptome analysis was performed to determine how MuM (mutated PBP2 gene) responds to spermine and how Mu50 (wild type) responds to spermine and spermine–β-lactam synergy. Exogenous spermine and oxacillin were found to alter some significant gene expression patterns with major biochemical pathways (iron, sigB regulon) in MRSA with mutant PBP2 protein

THE ESTRADIOL-INDUCED TRANSCRIPTOME OF THE FEMALE MOUSE ANTEROVENTRAL PERIVENTRICULAR NUCLEUS: MORE THAN JUST A KISS

Estradiol (E2) is critical in the reproductive mechanisms of mammals. In female rodents E2 acts through the neurons of the anteroventral periventricular nucleus (AVPV) to exert neuroendocrine control over ovulation, via synaptic activation of the gonadotropin releasing hormone (GnRH) neurons. The neurocircuitry of the AVPV is complex, receiving input from the suprachiasmatic nucleus and ventral premammillary nucleus and the as well as projecting to organum vasculosum of lamina terminalis and the arcuate. This suggests a broader role for the AVPV as a center of multisignal-integration in regards to ovulation. I used full genome expression microarrays to assess the E2-induced transcriptome in the female mouse AVPV and further investigated several targets using mouse neuronal cells. I discovered that within the AVPV, E2 regulates several genes important for energy balance. Additionally, I found that E2 regulates transcription factor v-ets avian erythroblastosis virus E26 oncogene homolog 2 (Ets2), which in turn regulates estrogen receptor α and is necessary in the E2-dependent regulation of kisspeptin. Together these findings support a broader role for AVPV function and identify a novel mechanism by which E2 mediates transcription

Machine Learning Approaches for Biomarker Discovery Using Gene Expression Data

Biomarkers are of great importance in many fields, such as cancer research, toxicology, diagnosis and treatment of diseases, and to better understand biological response mechanisms to internal or external intervention. High-throughput gene expression profiling technologies, such as DNA microarrays and RNA sequencing, provide large gene expression data sets which enable data-driven biomarker discovery. Traditional statistical tests have been the mainstream for identifying differentially expressed genes as biomarkers. In recent years, machine learning techniques such as feature selection have gained more popularity. Given many options, picking the most appropriate method for a particular data becomes essential. Different evaluation metrics have therefore been proposed. Being evaluated on different aspects, a method’s varied performance across different datasets leads to the idea of integrating multiple methods. Many integration strategies are proposed and have shown great potential. This chapter gives an overview of the current research advances and existing issues in biomarker discovery using machine learning approaches on gene expression data.publishedVersio

Transcriptomic comparison of the retina in two mouse models of diabetes

Mouse models of type I diabetes offer the potential to combine genetic approaches with other pharmacological or physiological manipulations to investigate the pathophysiology and treatment of diabetic retinopathy. Type I diabetes is induced in mice through chemical toxins or can arise spontaneously from genetic mutations. Both models are associated with retinal vascular and neuronal changes. Retinal transcriptomic responses in C57BL/6J mice treated with streptozotocin and Ins2Akita/+ were compared after 3 months of hyperglycemia. Specific gene expression changes suggest a neurovascular inflammatory response in diabetic retinopathy. Genes common to the two models may represent the response of the retina to hyperglycemia, while changes unique to each model may represent time-dependent disease progression differences in the various models. Further investigation of the commonalities and differences between mouse models of type I diabetes may define cause and effect events in early diabetic retinopathy disease progression