Analysis Of Gene Array Data Research Articles

A novel analysis of gene array data: yeast cell cycle.

Many gene array studies of the yeast cell cycle have been performed (Cho RJ, Campbell MJ, Winzeler EA et al. A genome-wide transcriptional analysis of the mitotic cell cycle. Mol Cell 1998;2:65–73; Orlando DA, Lin CY, Bernard A et al. Global control of cell-cycle transcription by coupled CDK and network oscillators. Nature 2008;453:944–7; Pramila T, Wu W, Miles S et al. The Forkhead transcription factor Hcm1 regulates chromosome segregation genes and fills the S-phase gap in the transcriptional circuitry of the cell cycle. Genes Dev 2006;20:2266–78; Spellman PT, Sherlock G, Zhang MQ et al. Comprehensive identification of cell cycle–regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. MBoC 1998;9:3273–97). Largely, these studies contain elements drawn from laboratory experiments. The present investigation determines cell division cycle (CDC) genes solely from the data (Orlando DA, Lin CY, Bernard A et al. Global control of cell-cycle transcription by coupled CDK and network oscillators. Nature 2008;453:944–7). It is shown by simple reasoning that the dynamics of the approximately 6000 yeast genes are described by an approximately six-dimensional space. This leads a precisely determined cell-cycle period, along with the quality and timing of the identified CDC genes. Convincing evidence for the role of the identified genes is obtained. While these show good agreement with standard CDC gene representatives (Orlando DA, Lin CY, Bernard A et al. Global control of cell-cycle transcription by coupled CDK and network oscillators. Nature 2008;453:944–7; Spellman PT, Sherlock G, Zhang MQ et al. Comprehensive identification of cell cycle–regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. MBoC 1998;9:3273–97; de Lichtenberg U, Jensen LJ, Fausbøll A et al. Comparison of computational methods for the identification of cell cycle-regulated genes. Bioinformatics 2005;21:1164–71) several hundred newly revealed CDC genes appear, which merit attention. The present approach employs an adaptation of a method introduced to study turbulent flows (Schmid PJ. Dynamic mode decomposition of numerical and experimental data. J Fluid Mech 2010;656:5–28), “dynamic mode decomposition” (DMD). From this, one can infer that singular value decomposition, analysis of the data entangles the underlying (gene) dynamics implicit in the data; and that DMD produces the disentangling transformation. It is the assertion of this study that a new tool now exists for the analysis of the gene array signals, and in particular for investigating the yeast cell cycle.

Thymidylate synthase is functionally associated with ZEB1 and contributes to the epithelial-to-mesenchymal transition of cancer cells.

Thymidylate synthase (TS) is a fundamental enzyme of nucleotide metabolism and one of the oldest anti-cancer targets. Beginning from the analysis of gene array data from the NCI-60 panel of cancer cell lines, we identified a significant correlation at both gene and protein level between TS and the markers of epithelial-to-mesenchymal transition (EMT), a developmental process that allows cancer cells to acquire features of aggressiveness, like motility and chemoresistance. TS levels were found to be significantly augmented in mesenchymal-like compared to epithelial-like cancer cells, to be regulated by EMT induction, and to negatively correlate with micro-RNAs (miRNAs) usually expressed in epithelial-like cells and known to actively suppress EMT. Transfection of EMT-suppressing miRNAs reduced TS levels, and a specific role for miR-375 in targeting the TS 3'-untranslated region was identified. A particularly relevant association was found between TS and the powerful EMT driver ZEB1, the shRNA-mediated knockdown of which up-regulated miR-375 and reduced TS cellular levels. The TS-ZEB1 association was confirmed in clinical specimens from lung tumours and in a genetic mouse model of pancreatic cancer with ZEB1 deletion. Interestingly, TS itself appeared to have a regulatory role in EMT in cancer cells, as TS knockdown could directly reduce the EMT phenotype, the migratory ability of cells, the expression of stem-like markers, and chemoresistance. Taken together, these data indicate that the TS enzyme is functionally linked with EMT and cancer differentiation, with several potential translational implications. Copyright © 2017 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

TNF-related apoptosis-inducing ligand (TRAIL) regulates midline-1, thymic stromal lymphopoietin, inflammation, and remodeling in experimental eosinophilic esophagitis

Eosinophilic esophagitis (EoE) is an inflammatory disorder of the esophagus defined by eosinophil infiltration and tissue remodeling with resulting symptoms of esophageal dysfunction. TNF-related apoptosis-inducing ligand (TRAIL) promotes inflammation through upregulation of the E3 ubiquitin-ligase midline-1 (MID1), which binds to and deactivates the catalytic subunit of protein phosphatase 2Ac, resulting in increased nuclear factor κB activation. We sought to elucidate the role of TRAIL in EoE. We used Aspergillus fumigatus to induce EoE in TRAIL-sufficient (wild-type) and TRAIL-deficient (TRAIL(-/-)) mice and targeted MID1 in the esophagus with small interfering RNA. We also treated mice with recombinant thymic stromal lymphopoietin (TSLP) and TRAIL. TRAIL deficiency and MID1 silencing with small interfering RNA reduced esophageal eosinophil and mast cell numbers and protected against esophageal circumference enlargement, muscularis externa thickening, and collagen deposition. MID1 expression and nuclear factor κB activation were reduced in TRAIL(-/-) mice, whereas protein phosphatase 2Ac levels were increased compared with those seen in wild-type control mice. This was associated with reduced expression of CCL24, CCL11, CCL20, IL-5, IL-13, IL-25, TGFB, and TSLP. Treatment with TSLP reconstituted hallmark features of EoE in TRAIL(-/-) mice and recombinant TRAIL induced esophageal TSLP expression in vivo in the absence of allergen. Post hoc analysis of gene array data demonstrated significant upregulation of TRAIL and MID1 in a cohort of children with EoE compared with that seen in controls. TRAIL regulates MID1 and TSLP, inflammation, fibrosis, smooth muscle hypertrophy, and expression of inflammatory effector chemokines and cytokines in experimental EoE.

In vivo anti‐myeloma activity and modulation of gene expression profile induced by valproic acid, a histone deacetylase inhibitor

Valproic acid (VPA) is a well-tolerated anticonvulsant that exerts anti-tumour activity as a histone deacetylase inhibitor. This study investigated the in vitro and in vivo activity of VPA against multiple myeloma (MM) cells. In vitro exposure of interleukin-6-dependent or -independent MM cells to VPA inhibited cell proliferation in a time- and dose-dependent manner and induced apoptosis. In a cohort of severe combined immunodeficiency mice bearing human MM xenografts, VPA induced tumour growth inhibition and survival advantage in treated animals versus controls. Flow cytometric analysis performed on MM cells from excised tumours showed increase of G(0)-G(1) and a decreased G(2)/M- and S-phase following VPA treatment, indicating in vivo effects of VPA on cell cycle regulation. Gene expression profiling of MM cells exposed to VPA showed downregulation of genes involved in cell cycle progression, DNA replication and transcription, as well as upregulation of genes implicated in apoptosis and chemokine pathways. Pathfinder analysis of gene array data identified cell growth, cell cycle, cell death, as well as DNA replication and repair as the most important signalling networks modulated by VPA. Taken together, our data provide the preclinical rationale for VPA clinical evaluation as a single agent or in combination, to improve patient outcome in MM.

Are flavonoids agonists or antagonists of the natural hormone 17β‐estradiol?

Are flavonoids agonists or antagonists of the natural hormone 17β‐estradiol?

Pyrroloquinoline quinone deficiency and alterations in PPAR related pathways and oxidative metabolism

Pyrroloquinoline quinone (PQQ) deficiency influences oxidative metabolism in mice fed chemically defined diets. Changes in gene expression as assessed from analysis of gene array data (CodelinkTM microarray system) suggest that genes altered during PQQ deficiency may be related to peroxisome proliferator-activated receptor activity. Therefore, experiments were designed to verify a possible link between PQQ and particularly, PPARα relationships. Virgin Sprague Dawley dams were mated, and during the last 10 days of gestation, 2/3rds were fed a diet devoid of PQQ while the remaining rats were fed a PQQ-supplemented diet (2 mg PQQ/Kg diet). At weaning, offspring were continued on the same diets as their corresponding dams for seven additional weeks. Next, CodelinkTM microarray was performed using liver mRNA and validated in both liver and heart by QRT-PCR. In addition, plasma lipid profiles were analyzed (all major classes and their fatty acids). These profiles reflected altered oxidative metabolism due to reduced PPAR and/or PPAR related genes. In liver, PQQ deficiency resulted in decreased fatty acid binding protein (FABP), and in heart, PQQ deficiency resulted in decreased PPARα, FABP, peroxisomal membrane protein, and acyl CoA oxidase gene expression. Results from QRT-PCR of over 20 additional genes were also consistent with PPAR-related regulation. Further, HepG2 cells were transiently transfected with PPARα and treated with a known agonist or 0–150 μM PQQ. PPAR transactivation was observed in cells supplemented with PQQ. Although PQQ has been recently suggested to have vitamin-like activities, collectively the results herein suggest that PQQ most likely serves as a PPAR-related ligand.

Gene expression profile of primary human CD34 +CD38 lo cells differentiating along the megakaryocyte lineage

Objective To identify genes involved in megakaryopoiesis, high-density oligonucleotide microarrays were used to compare transcript profiles from undifferentiated CD34 +CD38 lo cells and culture-derived megakaryocytes (MKs). Materials and methods Megakaryocyte differentiation was achieved in vitro by inducing primary human CD34 +CD38 lo cells in serum-deprived media supplemented with the cytokine combination of interleukin-3, interleukin-6, stem cell factor, and thrombopoietin for 10 days. Three replicate microarray experiments were performed using hematopoietic cells isolated from three different organ donors and high-density oligonucleotide microarrays. Results Analysis of gene array data resulted in 304 differentially expressed genes ( p ≤ 0.001, fold change ≥3). A third of the 25 most highly up-regulated genes were known to participate in hemostasis (z = 6.75), and no genes known to be associated with MKs were among the down-regulated genes. We also found a large proportion of up-regulated transcripts in gene ontology categories of adhesion and receptor activity (85%) and signal transduction activity (68%). At the same time, 70% of genes within transcription factor functions were down-regulated. Confirmatory studies indicated that the array results correlated with mRNA and protein expression levels in primary MKs. Conclusion This study provides a global expression profile of human MKs and a list of novel and previously uncharacterized candidate genes that are important components of megakaryopoiesis.

Gene Interaction Network Suggests Dioxin Induces a Significant Linkage between Aryl Hydrocarbon Receptor and Retinoic Acid Receptor Beta

Gene expression arrays (gene chips) have enabled researchers to roughly quantify the level of mRNA expression for a large number of genes in a single sample. Several methods have been developed for the analysis of gene array data including clustering, outlier detection, and correlation studies. Most of these analyses are aimed at a qualitative identification of what is different between two samples and/or the relationship between two genes. We propose a quantitative, statistically sound methodology for the analysis of gene regulatory networks using gene expression data sets. The method is based on Bayesian networks for direct quantification of gene expression networks. Using the gene expression changes in HPL1A lung airway epithelial cells after exposure to 2,3,7,8-tetrachlorodibenzo-p-dioxin at levels of 0.1, 1.0, and 10.0 nM for 24 hr, a gene expression network was hypothesized and analyzed. The method clearly demonstrates support for the assumed network and the hypothesis linking the usual dioxin expression changes to the retinoic acid receptor system. Simulation studies demonstrated the method works well, even for small samples.

An analysis of gene array data related to cell adhesion and prostate cancer.

We embarked on the investigation presented in this report with three goals in mind. First, to discover a gene, or a group of genes, related to the attachment of prostate cells to their neighboring environment. The process of cell attachment is a very important process in cancer in general, and in prostate cancer in particular. Changes in cell attachment allow cells to move around the body, or metastasize, thereby causing the spread of cancer through the body, causing death. Second, we wanted to examine “statistical variability” of gene expression in a single cell line, with time as the only factor possibly related to cell event of interest: cell adhesion. Understanding gene variability is important with regards to future studies, which seek to measure changes in gene expression in human subjects, and is a prerequisite for making certain predictions about “effect size”, when planning to affect gene activity at some future time. We were especially interested in whether and how standardization of numerical values (e.g. division by a specific measure of abundance) obtained from microarrays may affect statistical parameters such as coefficient of correlation or coefficient of variation in measurements of gene expression. Finally, we wanted to take into consideration the notion of false positives, an issue of interest to researchers working with microarray data; this matter is addressed in the final section.

Some aspects of analysis of gene array data.

The past century brought along tremendous development in statistical methods, accompanying similar advances in the biomedical field and genetics (Elston and Thompson, 2000). In the later half of 1990’s another significant leap forward has occurred in the biomedical field with the advent of gene arrays (Duggan et al., 1999). Gene arrays provide the ability to measure the presence of tens of thousands of genes at one time, and to compare this set of genes between two or more systems. The emergence of this technology, along with attempts to integrate data output with underlying biology, has created one of most lively areas of applied multivariate statistics to recently emerge.