Intertwining threshold settings, biological data and database knowledge to optimize the selection of differentially expressed genes from microarray

Toxoplasma gondii could have a possible role in the cancer mechanism by modulating the host's cell response

Toxoplasma gondii, which manipulates many signaling pathways to achieve persistence in host cells, is intimately linked to immune and inflammation responses. However, there is still lack of information about the impact of T. gondii on cellular and immune responses. This study was designed to seek the impact of T. gondii infection causing life-long inflammation in brain, on cancer mechanism. To identify molecular effects of the T. gondii and understand the association between the functional perturbations occurring during infection and cancer development, the transcriptomic datasets obtained mice infected with T. gondii were downloaded from GEO. The differentially expressed genes (DEGs) were identified and functional enrichment analysis was performed using IPA platform, then all results were evaluated with comparison analyses. Subsequently, a T. gondii infection model with human neuroepithelioma cell culture was performed in order to validate top DEGs participated in common networks/pathways in cancer mechanism. Transcriptomic analyses of infected mice and in vitro cell culture model revealed a strong immune response and inflammation occurred by parasite-induced damage and parasite-associated immunopathology in host cell and tissue. T. gondii infection could modulate certain signaling pathways of host, which were also common to those perturbed in carcinogenesis. Interestingly, the network analysis of the data sets predicted an activation in development of solid cancer vice versa inhibition in hematological cancer during T. gondii infection. Parasite might also control the tumor growth due to its potent immune-stimulant effects. As result, T. gondii infection generating a continual inflammation in tissues might potentially contribute to cancer development by regulating critical host signaling pathways or reveal an anti-tumoral activity.

p16INK4a and its regulator miR-24 link senescence and chondrocyte terminal differentiation-associated matrix remodeling in osteoarthritis

INTRODUCTION

Recent evidence suggests that tissue accumulation of senescent p16INK4a-positive cells during the life span would be deleterious for tissue functions and could be the consequence of inherent age-associated disorders. Osteoarthritis (OA) is characterized by the accumulation of chondrocytes expressing p16INK4a and markers of the senescence-associated secretory phenotype (SASP), including the matrix remodeling metalloproteases MMP1/MMP13 and pro-inflammatory cytokines interleukin-8 (IL-8) and IL-6. Here, we evaluated the role of p16INK4a in the OA-induced SASP and its regulation by microRNAs (miRs).

METHODS

We used IL-1-beta-treated primary OA chondrocytes cultured in three-dimensional setting or mesenchymal stem cells differentiated into chondrocyte to follow p16INK4a expression. By transient transfection experiments and the use of knockout mice, we validate p16INK4a function in chondrocytes and its regulation by one miR identified by means of a genome-wide miR-array analysis.

RESULTS

p16INK4a is induced upon IL-1-beta treatment and also during in vitro chondrogenesis. In the mouse model, Ink4a locus favors in vivo the proportion of terminally differentiated chondrocytes. When overexpressed in chondrocytes, p16INK4a is sufficient to induce the production of the two matrix remodeling enzymes, MMP1 and MMP13, thus linking senescence with OA pathogenesis and bone development. We identified miR-24 as a negative regulator of p16INK4a. Accordingly, p16INK4a expression increased while miR-24 level was repressed upon IL-1-beta addition, in OA cartilage and during in vitro terminal chondrogenesis.

CONCLUSIONS

We disclosed herein a new role of the senescence marker p16INK4a and its regulation by miR-24 during OA and terminal chondrogenesis.

Promyelocytic leukemia zinc-finger induction signs mesenchymal stem cell commitment: identification of a key marker for stemness maintenance?

Introduction

Mesenchymal stem cells (MSCs) are an attractive cell source for cartilage and bone tissue engineering given their ability to differentiate into chondrocytes and osteoblasts. However, the common origin of these two specialized cell types raised the question about the identification of regulatory pathways determining the differentiation fate of MSCs into chondrocyte or osteoblast.

Methods

Chondrogenesis, osteoblastogenesis, and adipogenesis of human and mouse MSC were induced by using specific inductive culture conditions. Expression of promyelocytic leukemia zinc-finger (PLZF) or differentiation markers in MSCs was determined by RT-qPCR. PLZF-expressing MSC were implanted in a mouse osteochondral defect model and the neotissue was analyzed by routine histology and microcomputed tomography.

Results

We found out that PLZF is not expressed in MSCs and its expression at early stages of MSC differentiation is the mark of their commitment toward the three main lineages. PLZF acts as an upstream regulator of both Sox9 and Runx2, and its overexpression in MSC enhances chondrogenesis and osteogenesis while it inhibits adipogenesis. In vivo, implantation of PLZF-expressing MSC in mice with full-thickness osteochondral defects resulted in the formation of a reparative tissue resembling cartilage and bone.

Conclusions

Our findings demonstrate that absence of PLZF is required for stemness maintenance and its expression is an early event at the onset of MSC commitment during the differentiation processes of the three main lineages.

Involvement of angiopoietin-like 4 in matrix remodeling during chondrogenic differentiation of mesenchymal stem cells

Mesenchymal stem cells (MSCs) are considered for cartilage engineering given their ability to differentiate into chondrocytes. Chondrogenic differentiation of MSCs is currently triggered by micromass culture in the presence of a member of the TGF-β superfamily. However, the main constituents of the cartilaginous matrix, aggrecan and type II collagen, are degraded at the end of the differentiation process through induction of matrix metallopeptidase (MMP)13. We hypothesized that MSCs undergoing chondrogenic differentiation produce an intermediate cytokine that triggers this matrix remodeling. Analysis of transcriptomic data identified angiopoietin-like 4 (ANGPTL4) as one of the most strongly up-regulated gene encoding a secreted factor during TGF-β-induced chondrogenesis. To gain insight into the role of ANGPTL4 during chondrogenesis, we used recombinant ANGPTL4 as well as a RNA interference approach. Addition of exogenous ANGPTL4 during the course of TGF-β-induced differentiation reduced the mRNA levels of aggrecan and type II collagen, although it increased those of MMP1 and MMP13. Accordingly, deposition of aggrecan and total collagens was diminished, whereas release of MMP1 and MMP13 was increased. Conversely, transfection of MSCs with an siRNA targeting ANGPTL4 prior to induction of chondrogenesis increased expression of type II collagen and aggrecan, whereas it repressed that of MMP1, MMP3, and MMP13. A neutralizing antibody against integrin αVβ5, a known receptor for ANGPTL4, mimicked some of the effects observed after siRNA-mediated ANGPTL4 silencing. Our data provide evidence that ANGPTL4 promotes cartilage matrix remodeling by inhibiting expression of its two key components and by up-regulating the level of certain MMPs.

Literature aided determination of data quality and statistical significance threshold for gene expression studies

Background

Gene expression data are noisy due to technical and biological variability. Consequently, analysis of gene expression data is complex. Different statistical methods produce distinct sets of genes. In addition, selection of expression p-value (EPv) threshold is somewhat arbitrary. In this study, we aimed to develop novel literature based approaches to integrate functional information in analysis of gene expression data.

Methods

Functional relationships between genes were derived by Latent Semantic Indexing (LSI) of Medline abstracts and used to calculate the function cohesion of gene sets. In this study, literature cohesion was applied in two ways. First, Literature-Based Functional Significance (LBFS) method was developed to calculate a p-value for the cohesion of differentially expressed genes (DEGs) in order to objectively evaluate the overall biological significance of the gene expression experiments. Second, Literature Aided Statistical Significance Threshold (LASST) was developed to determine the appropriate expression p-value threshold for a given experiment.

Results

We tested our methods on three different publicly available datasets. LBFS analysis demonstrated that only two experiments were significantly cohesive. For each experiment, we also compared the LBFS values of DEGs generated by four different statistical methods. We found that some statistical tests produced more functionally cohesive gene sets than others. However, no statistical test was consistently better for all experiments. This reemphasizes that a statistical test must be carefully selected for each expression study. Moreover, LASST analysis demonstrated that the expression p-value thresholds for some experiments were considerably lower (p < 0.02 and 0.01), suggesting that the arbitrary p-values and false discovery rate thresholds that are commonly used in expression studies may not be biologically sound.

Conclusions

We have developed robust and objective literature-based methods to evaluate the biological support for gene expression experiments and to determine the appropriate statistical significance threshold. These methods will assist investigators to more efficiently extract biologically meaningful insights from high throughput gene expression experiments.

Intra- and inter-individual variance of gene expression in clinical studies

Background

Variance in microarray studies has been widely discussed as a critical topic on the identification of differentially expressed genes; however, few studies have addressed the influence of estimating variance.

Methodology/Principal Findings

To break intra- and inter-individual variance in clinical studies down to three levels–technical, anatomic, and individual–we designed experiments and algorithms to investigate three forms of variances. As a case study, a group of “inter-individual variable genes” were identified to exemplify the influence of underestimated variance on the statistical and biological aspects in identification of differentially expressed genes. Our results showed that inadequate estimation of variance inevitably led to the inclusion of non-statistically significant genes into those listed as significant, thereby interfering with the correct prediction of biological functions. Applying a higher cutoff value of fold changes in the selection of significant genes reduces/eliminates the effects of underestimated variance.

Conclusions/Significance

Our data demonstrated that correct variance evaluation is critical in selecting significant genes. If the degree of variance is underestimated, “noisy” genes are falsely identified as differentially expressed genes. These genes are the noise associated with biological interpretation, reducing the biological significance of the gene set. Our results also indicate that applying a higher number of fold change as the selection criteria reduces/eliminates the differences between distinct estimations of variance.