Enhanced Botrytis cinerea resistance of Arabidopsis plants grown in compost may be explained by increased expression of defense-related genes, as revealed by microarray analysis

Composts are the products obtained after the aerobic degradation of different types of organic matter waste and can be used as substrates or substrate/soil amendments for plant cultivation. There is a small but increasing number of reports that suggest that foliar diseases may be reduced when using compost, rather than standard substrates, as growing medium. The purpose of this study was to examine the gene expression alteration produced by the compost to gain knowledge of the mechanisms involved in compost-induced systemic resistance. A compost from olive marc and olive tree leaves was able to induce resistance against Botrytis cinerea in Arabidopsis, unlike the standard substrate, perlite. Microarray analyses revealed that 178 genes were differently expressed, with a fold change cut-off of 1, of which 155 were up-regulated and 23 were down-regulated in compost-grown, as against perlite-grown plants. A functional enrichment study of up-regulated genes revealed that 38 Gene Ontology terms were significantly enriched. Response to stress, biotic stimulus, other organism, bacterium, fungus, chemical and abiotic stimulus, SA and ABA stimulus, oxidative stress, water, temperature and cold were significantly enriched, as were immune and defense responses, systemic acquired resistance, secondary metabolic process and oxireductase activity. Interestingly, PR1 expression, which was equally enhanced by growing the plants in compost and by B. cinerea inoculation, was further boosted in compost-grown pathogen-inoculated plants. Compost triggered a plant response that shares similarities with both systemic acquired resistance and ABA-dependent/independent abiotic stress responses.

Dual activation of pathways regulated by steroid receptors and peptide growth factors in primary prostate cancer revealed by Factor Analysis of microarray data

Background: We use an approach based on Factor Analysis to analyze datasets generated for transcriptional profiling. The method groups samples into biologically relevant categories, and enables the identification of genes and pathways most significantly associated to each phenotypic group, while allowing for the participation of a given gene in more than one cluster. Genes assigned to each cluster are used for the detection of pathways predominantly activated in that cluster by finding statistically significant associated GO terms. We tested the approach with a published dataset of microarray experiments in yeast. Upon validation with the yeast dataset, we applied the technique to a prostate cancer dataset. Results: Two major pathways are shown to be activated in organ-confined, non-metastatic prostate cancer: those regulated by the androgen receptor and by receptor tyrosine kinases. A number of gene markers (HER3, IQGAP2 and POR1) highlighted by the software and related to the later pathway have been validated experimentally a posteriori on independent samples. Conclusion: Using a new microarray analysis tool followed by a posteriori experimental validation of the results, we have confirmed several putative markers of malignancy associated with peptide growth factor signalling in prostate cancer and revealed others, most notably ERRB3 (HER3). Our study suggest that, in primary prostate cancer, HER3, together or not with HER4, rather than in receptor complexes involving HER2, could play an important role in the biology of these tumors. These results provide new evidence for the role of receptor tyrosine kinases in the establishment and progression of prostate cancer.

Bioinformática: base de datos de matrices de expresión génica para su análisis vía web

El IBB ha desarrollado un servidor de aplicaciones: http://revolutionresearch.uab.es para el análisis de microarrays. Estas microarrays las obtienen y las suben a la base de datos local los usuarios de la aplicación. En la presente memoria se detalla el proceso realizado para automatizar la subida de microarrays públicas a la base de datos local. Estas microarrays se obtendrán del NCBI. El proceso de descarga de microarrays se realizará cada dos meses y estará sincronizado con un proceso de descarga de genes marcadores de microarrays del NCBI. En la memoria también se describen las fases realizadas para crear la interfaz web para gestionar estas microarrays públicas y las modificaciones realizadas sobre el aplicativo web para permitir realizar análisis con estas microarrays.

PGC-1α induces mitochondrial and myokine transcriptional programs and lipid droplet and glycogen accumulation in cultured human skeletal muscle cells

The transcriptional coactivator peroxisome proliferator-activated receptor-gamma coactivator 1 alpha (PGC-1α) is a chief activator of mitochondrial and metabolic programs and protects against atrophy in skeletal muscle (skm). Here we tested whether PGC-1α overexpression could restructure the transcriptome and metabolism of primary cultured human skm cells, which display a phenotype that resembles the atrophic phenotype. An oligonucleotide microarray analysis was used to reveal the effects of PGC-1α on the whole transcriptome. Fifty-three different genes showed altered expression in response to PGC-1α: 42 upregulated and 11 downregulated. The main gene ontologies (GO) associated with the upregulated genes were mitochondrial components and processes and this was linked with an increase in COX activity, an indicator of mitochondrial content. Furthermore, PGC-1α enhanced mitochondrial oxidation of palmitate and lactate to CO2, but not glucose oxidation. The other most significantly associated GOs for the upregulated genes were chemotaxis and cytokine activity, and several cytokines, including IL-8/CXCL8, CXCL6, CCL5 and CCL8, were within the most highly induced genes. Indeed, PGC-1α highly increased IL-8 cell protein content. The most upregulated gene was PVALB, which is related to calcium signaling. Potential metabolic regulators of fatty acid and glucose storage were among mainly regulated genes. The mRNA and protein level of FITM1/FIT1, which enhances the formation of lipid droplets, was raised by PGC-1α, while in oleate-incubated cells PGC-1α increased the number of smaller lipid droplets and modestly triglyceride levels, compared to controls. CALM1, the calcium-modulated δ subunit of phosphorylase kinase, was downregulated by PGC-1α, while glycogen phosphorylase was inactivated and glycogen storage was increased by PGC-1α. In conclusion, of the metabolic transcriptome deficiencies of cultured skm cells, PGC-1α rescued the expression of genes encoding mitochondrial proteins and FITM1. Several myokine genes, including IL-8 and CCL5, which are known to be constitutively expressed in human skm cells, were induced by PGC-1α.

Bacterial lipopolysaccharide induces apoptosis in the trout ovary

BACKGROUND: In mammals it is well known that infections can lead to alterations in reproductive function. As part of the innate immune response, a number of cytokines and other immune factors is produced during bacterial infection or after treatment with lipopolysaccharide (LPS) and acts on the reproductive system. In fish, LPS can also induce an innate immune response but little is known about the activation of the immune system by LPS on reproduction in fish. Therefore, we conducted studies to examine the in vivo and in vitro effects of lipopolysaccharide (LPS) on the reproductive function of sexually mature female trout. METHODS: In saline- and LPS -injected brook trout, we measured the concentration of plasma steroids as well as the in vitro steroidogenic response (testosterone and 17alpha-hydroxyprogesterone) of ovarian follicles to luteinizing hormone (LH), the ability of 17alpha,20beta-dihydroxy-4-pregnen-3-one to induce germinal vesicle breakdown (GVBD) in vitro, and that of epinephrine to stimulate follicular contraction in vitro. We also examined the direct effects of LPS in vitro on steroid production, GVBD and contraction in brook trout ovarian follicles. The incidence of apoptosis was evaluated by TUNEL analysis. Furthermore, we examined the gene expression pattern in the ovary of saline- and LPS-injected rainbow trout by microarray analysis. RESULTS: LPS treatment in vivo did not affect plasma testosterone concentration or the basal in vitro production of steroids, although a small but significant potentiation of the effects of LH on testosterone production in vitro was observed in ovarian follicles from LPS-treated fish. In addition, LPS increased the plasma concentration of cortisol. LPS treatment in vitro did not affect the basal or LH-stimulated steroid production in brook trout ovarian follicles. In addition, we did not observe any effects of LPS in vivo or in vitro on GVBD or follicular contraction. Therefore, LPS did not appear to impair ovarian steroid production, oocyte final maturation or follicular contraction under the present experimental conditions. Interestingly, LPS administration in vivo induced apoptosis in follicular cells, an observation that correlated with changes in the expression of genes involved in apoptosis, as evidenced by microarray analysis. CONCLUSION: These results indicate that female trout are particularly resistant to an acute administration of LPS in terms of ovarian hormone responsiveness. However, LPS caused a marked increase in apoptosis in follicular cells, suggesting that the trout ovary could be sensitive to the pro-apoptotic effects of LPS-induced inflammatory cytokines.

GSVA: gene set variation analysis for microarray and RNA-seq data

Gene set enrichment (GSE) analysis is a popular framework for condensing information from gene expression profiles into a pathway or signature summary. The strengths of this approach over single gene analysis include noise and dimension reduction, as well as greater biological interpretability. As molecular profiling experiments move beyond simple case-control studies, robust and flexible GSE methodologies are needed that can model pathway activity within highly heterogeneous data sets. To address this challenge, we introduce Gene Set Variation Analysis (GSVA), a GSE method that estimates variation of pathway activity over a sample population in an unsupervised manner. We demonstrate the robustness of GSVA in a comparison with current state of the art sample-wise enrichment methods. Further, we provide examples of its utility in differential pathway activity and survival analysis. Lastly, we show how GSVA works analogously with data from both microarray and RNA-seq experiments. GSVA provides increased power to detect subtle pathway activity changes over a sample population in comparison to corresponding methods. While GSE methods are generally regarded as end points of a bioinformatic analysis, GSVA constitutes a starting point to build pathway-centric models of biology. Moreover, GSVA contributes to the current need of GSE methods for RNA-seq data. GSVA is an open source software package for R which forms part of the Bioconductor project and can be downloaded at http://www.bioconductor.org.