AMDA 2.13: A major update for automated cross-platform microarray data analysis

Desirable cytolytic immune effector cell recruitment by interleukin-15 dendritic cells

Success of dendritic cell (DC) therapy in treating malignancies is depending on the DC capacity to attract immune effector cells, considering their reciprocal crosstalk is partially regulated by cell-contact-dependent mechanisms. Although critical for therapeutic efficacy, immune cell recruitment is a largely overlooked aspect regarding optimization of DC vaccination. In this paper we have made a head-to-head comparison of interleukin (IL)-15-cultured DCs and conventional IL-4-cultured DCs with regard to their proficiency in the recruitment of (innate) immune effector cells. Here, we demonstrate that IL-4 DCs are suboptimal in attracting effector lymphocytes, while IL15 DCs provide a favorable chemokine milieu for recruiting CD8⁺ T cells, natural killer (NK) cells and gamma delta (γδ) T cells. Gene expression analysis revealed that IL-15 DCs exhibit a high expression of chemokines involved in antitumor immune effector cell attraction, while IL-4 DCs display a more immunoregulatory profile characterized by the expression of Th2 and regulatory T cell-attracting chemokines. This is confirmed by functional data indicating an enhanced recruitment of granzyme B⁺ effector lymphocytes by IL-15 DCs, as compared to IL-4 DCs, and subsequent superior killing of tumor cells by the migrated lymphocytes. Elevated CCL4 gene expression in IL-15 DCs and lowered CCR5 expression on both migrated γδ T cells and NK cells, led to validation of increased CCL4 secretion by IL15 DCs. Moreover, neutralization of CCR5 prior to migration resulted in an important inhibition of γδ T cell and NK cell recruitment by IL-15 DCs. These findings further underscore the strong immunotherapeutic potential of IL-15 DCs.

Identification of a gene expression signature in peripheral blood of multiple sclerosis patients treated with disease-modifying therapies

Multiple Sclerosis (MS) is an inflammatory disease with neurodegenerative alterations, ultimately progressing to neurological handicap. Therapies are effective in counteracting inflammation but not neurodegeneration. Biomarkers predicting disease course or treatment response are lacking. We investigated whether altered gene and protein expression profiles were detectable in the peripheral blood of 78 relapsing remitting MS (RR-MS) patients treated by disease-modifying therapies. A discovery/validation study on RR-MS responsive to glatiramer acetate identified 8 differentially expressed genes: ITGA2B, ITGB3, CD177, IGJ, IL5RA, MMP8, P2RY12, and S100β. A longitudinal study on glatiramer acetate, Interferon-β, or Fingolimod treated RR-MS patients confirmed that 7 out of 8 genes were downregulated with reference to the different therapies, whereas S100β was always upregulated. Thus, we identified a peripheral gene signature associated with positive response in RR-MS which may also explain drug immunomodulatory effects. The usefulness of this signature as a biomarker needs confirmation on larger series of patients.

Microarray Data Processing Techniques for Genome-Scale Network Inference from Large Public Repositories

Pre-processing of microarray data is a well-studied problem. Furthermore, all popular platforms come with their own recommended best practices for differential analysis of genes. However, for genome-scale network inference using microarray data collected from large public repositories, these methods filter out a considerable number of genes. This is primarily due to the effects of aggregating a diverse array of experiments with different technical and biological scenarios. Here we introduce a pre-processing pipeline suitable for inferring genome-scale gene networks from large microarray datasets. We show that partitioning of the available microarray datasets according to biological relevance into tissue- and process-specific categories significantly extends the limits of downstream network construction. We demonstrate the effectiveness of our pre-processing pipeline by inferring genome-scale networks for the model plant Arabidopsis thaliana using two different construction methods and a collection of 11,760 Affymetrix ATH1 microarray chips. Our pre-processing pipeline and the datasets used in this paper are made available at http://alurulab.cc.gatech.edu/microarray-pp.

High Content Imaging (HCI) on Miniaturized Three-Dimensional (3D) Cell Cultures

High content imaging (HCI) is a multiplexed cell staining assay developed for better understanding of complex biological functions and mechanisms of drug action, and it has become an important tool for toxicity and efficacy screening of drug candidates. Conventional HCI assays have been carried out on two-dimensional (2D) cell monolayer cultures, which in turn limit predictability of drug toxicity/efficacy in vivo; thus, there has been an urgent need to perform HCI assays on three-dimensional (3D) cell cultures. Although 3D cell cultures better mimic in vivo microenvironments of human tissues and provide an in-depth understanding of the morphological and functional features of tissues, they are also limited by having relatively low throughput and thus are not amenable to high-throughput screening (HTS). One attempt of making 3D cell culture amenable for HTS is to utilize miniaturized cell culture platforms. This review aims to highlight miniaturized 3D cell culture platforms compatible with current HCI technology.

Cross-platform microarray meta-analysis for the mouse jejunum selects novel reference genes with highly uniform levels of expression

Reference genes (RGs) with uniform expression are used for normalization of reverse transcription quantitative PCR (RT-qPCR) data. Their optimization for a specific biological context, e.g. a specific tissue, has been increasingly considered. In this article, we compare RGs identified by expression data meta-analysis restricted to the context tissue, the jejunum of Mus musculus domesticus, i) to traditional RGs, ii) to expressed interspersed repeated DNA elements, and iii) to RGs identified by meta-analysis of expression data from diverse tissues and conditions. To select the set of candidate RGs, we developed a novel protocol for the cross-platform meta-analysis of microarray data. The expression stability of twenty-four putative RGs was analysed by RT-qPCR in at least 14 jejunum samples of the mouse strains C57Bl/6N, CD1, and OF1. Across strains, the levels of expression of the novel RGs Plekha7, Zfx, and Ube2v1 as well as of Oaz1 varied less than two-fold irrespective of genotype, sex or their combination. The gene set consisting of Plekha7 and Oaz1 showed superior expression stability analysed with the tool RefFinder. The novel RGs are functionally diverse. This facilitates expression studies over a wide range of conditions. The highly uniform expression of the optimized RGs in the jejunum points towards their involvement in tightly regulated pathways in this tissue. We also applied our novel protocol of cross-microarray platform meta-analysis to the identification of RGs in the duodenum, the ileum and the entire small intestine. The selection of RGs with improved expression stability in a specific biological context can reduce the number of RGs for the normalization step of RT-qPCR expression analysis, thus reducing the number of samples and experimental costs.