A Web-based and Grid-enabled dChip version for the analysis of large sets of gene expression data

<p>Abstract</p> <p>Background</p> <p>Microarray techniques are one of the main methods used to investigate thousands of gene expression profiles for enlightening complex biological processes responsible for serious diseases, with a great scientific impact and a wide application area. Several standalone applications had been developed in order to analyze microarray data. Two of the most known free analysis software packages are the R-based Bioconductor and dChip. The part of dChip software concerning the calculation and the analysis of gene expression has been modified to permit its execution on both cluster environments (supercomputers) and Grid infrastructures (distributed computing).</p> <p>This work is not aimed at replacing existing tools, but it provides researchers with a method to analyze large datasets without any hardware or software constraints.</p> <p>Results</p> <p>An application able to perform the computation and the analysis of gene expression on large datasets has been developed using algorithms provided by dChip. Different tests have been carried out in order to validate the results and to compare the performances obtained on different infrastructures. Validation tests have been performed using a small dataset related to the comparison of HUVEC (Human Umbilical Vein Endothelial Cells) and Fibroblasts, derived from same donors, treated with IFN-α.</p> <p>Moreover performance tests have been executed just to compare performances on different environments using a large dataset including about 1000 samples related to Breast Cancer patients.</p> <p>Conclusion</p> <p>A Grid-enabled software application for the analysis of large Microarray datasets has been proposed. DChip software has been ported on Linux platform and modified, using appropriate parallelization strategies, to permit its execution on both cluster environments and Grid infrastructures. The added value provided by the use of Grid technologies is the possibility to exploit both computational and data Grid infrastructures to analyze large datasets of distributed data. The software has been validated and performances on cluster and Grid environments have been compared obtaining quite good scalability results.</p

Modulation of ethylene- and heat-controlled hyponastic leaf movement in Arabidopsis thaliana by the plant defence hormones jasmonate and salicylate

Upward leaf movement (hyponastic growth) is adopted by several plant species including Arabidopsis thaliana, as a mechanism to escape adverse growth conditions. Among the signals that trigger hyponastic growth are, the gaseous hormone ethylene, low light intensities, and supra-optimal temperatures (heat). Recent studies indicated that the defence-related phytohormones jasmonic acid (JA) and salicylic acid (SA) synthesized by the plant upon biotic infestation repress low light-induced hyponastic growth. The hyponastic growth response induced by high temperature (heat) treatment and upon application of the gaseous hormone ethylene is highly similar to the response induced by low light. To test if these environmental signals induce hyponastic growth via parallel pathways or converge downstream, we studied here the roles of Methyl-JA (MeJA) and SA on ethylene- and heat-induced hyponastic growth. For this, we used a time-lapse camera setup. Our study includes pharmacological application of MeJA and SA and biological infestation using the JA-inducing caterpillar Pieris rapae as well as mutants lacking JA or SA signalling components. The data demonstrate that MeJA is a positive, and SA, a negative regulator of ethylene-induced hyponastic growth and that both hormones repress the response to heat. Taking previous studies into account, we conclude that SA is the first among many tested components which is repressing hyponastic growth under all tested inductive environmental stimuli. However, since MeJA is a positive regulator of ethylene-induced hyponastic growth and is inhibiting low light- and heat-induced leaf movement, we conclude that defence hormones control hyponastic growth by affecting stimulus-specific signalling pathways

A Revised Design for Microarray Experiments to Account for Experimental Noise and Uncertainty of Probe Response

Background Although microarrays are analysis tools in biomedical research, they are known to yield noisy output that usually requires experimental confirmation. To tackle this problem, many studies have developed rules for optimizing probe design and devised complex statistical tools to analyze the output. However, less emphasis has been placed on systematically identifying the noise component as part of the experimental procedure. One source of noise is the variance in probe binding, which can be assessed by replicating array probes. The second source is poor probe performance, which can be assessed by calibrating the array based on a dilution series of target molecules. Using model experiments for copy number variation and gene expression measurements, we investigate here a revised design for microarray experiments that addresses both of these sources of variance. Results Two custom arrays were used to evaluate the revised design: one based on 25 mer probes from an Affymetrix design and the other based on 60 mer probes from an Agilent design. To assess experimental variance in probe binding, all probes were replicated ten times. To assess probe performance, the probes were calibrated using a dilution series of target molecules and the signal response was fitted to an adsorption model. We found that significant variance of the signal could be controlled by averaging across probes and removing probes that are nonresponsive or poorly responsive in the calibration experiment. Taking this into account, one can obtain a more reliable signal with the added option of obtaining absolute rather than relative measurements. Conclusion The assessment of technical variance within the experiments, combined with the calibration of probes allows to remove poorly responding probes and yields more reliable signals for the remaining ones. Once an array is properly calibrated, absolute quantification of signals becomes straight forward, alleviating the need for normalization and reference hybridizations

Probe set algorithms: is there a rational best bet?

Affymetrix microarrays have become a standard experimental platform for studies of mRNA expression profiling. Their success is due, in part, to the multiple oligonucleotide features (probes) against each transcript (probe set). This multiple testing allows for more robust background assessments and gene expression measures, and has permitted the development of many computational methods to translate image data into a single normalized "signal" for mRNA transcript abundance. There are now many probe set algorithms that have been developed, with a gradual movement away from chip-by-chip methods (MAS5), to project-based model-fitting methods (dCHIP, RMA, others). Data interpretation is often profoundly changed by choice of algorithm, with disoriented biologists questioning what the "accurate" interpretation of their experiment is. Here, we summarize the debate concerning probe set algorithms. We provide examples of how changes in mismatch weight, normalizations, and construction of expression ratios each dramatically change data interpretation. All interpretations can be considered as computationally appropriate, but with varying biological credibility. We also illustrate the performance of two new hybrid algorithms (PLIER, GC-RMA) relative to more traditional algorithms (dCHIP, MAS5, Probe Profiler PCA, RMA) using an interactive power analysis tool. PLIER appears superior to other algorithms in avoiding false positives with poorly performing probe sets. Based on our interpretation of the literature, and examples presented here, we suggest that the variability in performance of probe set algorithms is more dependent upon assumptions regarding "background", than on calculations of "signal". We argue that "background" is an enormously complex variable that can only be vaguely quantified, and thus the "best" probe set algorithm will vary from project to project

Kinome Profiling Reveals an Interaction Between Jasmonate, Salicylate and Light Control of Hyponastic Petiole Growth in Arabidopsis thaliana

Plants defend themselves against infection by biotic attackers by producing distinct phytohormones. Especially jasmonic acid (JA) and salicylic acid (SA) are well known defense-inducing hormones. Here, the effects of MeJA and SA on the Arabidopsis thaliana kinome were monitored using PepChip arrays containing kinase substrate peptides to analyze posttranslational interactions in MeJA and SA signaling pathways and to test if kinome profiling can provide leads to predict posttranslational events in plant signaling. MeJA and SA mediate differential phosphorylation of substrates for many kinase families. Also some plant specific substrates were differentially phosphorylated, including peptides derived from Phytochrome A, and Photosystem II D protein. This indicates that MeJA and SA mediate cross-talk between defense signaling and light responses. We tested the predicted effects of MeJA and SA using light-mediated upward leaf movement (differential petiole growth also called hyponastic growth). We found that MeJA, infestation by the JA-inducing insect herbivore Pieris rapae, and SA suppressed low light-induced hyponastic growth. MeJA and SA acted in a synergistic fashion via two (partially) divergent signaling routes. This work demonstrates that kinome profiling using PepChip arrays can be a valuable complementary ∼omics tool to give directions towards predicting behavior of organisms after a given stimulus and can be used to obtain leads for physiological relevant phenomena in planta

A robust method for estimating gene expression states using Affymetrix microarray probe level data

<p>Abstract</p> <p>Background</p> <p>Microarray technology is a high-throughput method for measuring the expression levels of thousand of genes simultaneously. The observed intensities combine a non-specific binding, which is a major disadvantage with microarray data. The Affymetrix GeneChip assigned a mismatch (MM) probe with the intention of measuring non-specific binding, but various opinions exist regarding usefulness of MM measures. It should be noted that not all observed intensities are associated with expressed genes and many of those are associated with unexpressed genes, of which measured values express mere noise due to non-specific binding, cross-hybridization, or stray signals. The implicit assumption that all genes are expressed leads to poor performance of microarray data analyses. We assume two functional states of a gene - expressed or unexpressed - and propose a robust method to estimate gene expression states using an order relationship between PM and MM measures.</p> <p>Results</p> <p>An indicator 'probability of a gene being expressed' was obtained using the number of probe pairs within a probe set where the PM measure exceeds the MM measure. We examined the validity of the proposed indicator using Human Genome U95 data sets provided by Affymetrix. The usefulness of 'probability of a gene being expressed' is illustrated through an exploration of candidate genes involved in neuroblastoma prognosis. We identified the candidate genes for which expression states differed (un-expressed or expressed) when compared between two outcomes. The validity of this result was subsequently confirmed by quantitative RT-PCR.</p> <p>Conclusion</p> <p>The proposed qualitative evaluation, 'probability of a gene being expressed', is a useful indicator for improving microarray data analysis. It is useful to reduce the number of false discoveries. Expression states - expressed or unexpressed - correspond to the most fundamental gene function 'On' and 'Off', which can lead to biologically meaningful results.</p

Molecular and genetic control of plant thermomorphogenesis

Temperature is a major factor governing the distribution and seasonal behaviour of plants. Being sessile, plants are highly responsive to small differences in temperature and adjust their growth and development accordingly. The suite of morphological and architectural changes induced by high ambient temperatures, below the heat-stress range, is collectively called thermomorphogenesis. Understanding the molecular genetic circuitries underlying thermomorphogenesis is particularly relevant in the context of climate change, as this knowledge will be key to rational breeding for thermo-tolerant crop varieties. Until recently, the fundamental mechanisms of temperature perception and signalling remained unknown. Our understanding of temperature signalling is now progressing, mainly by exploiting the model plant Arabidopsis thaliana. The transcription factor PHYTOCHROME INTERACTING FACTOR 4 (PIF4) has emerged as a critical player in regulating phytohormone levels and their activity. To control thermomorphogenesis, multiple regulatory circuits are in place to modulate PIF4 levels, activity and downstream mechanisms. Thermomorphogenesis is integrally governed by various light signalling pathways, the circadian clock, epigenetic mechanisms and chromatin-level regulation. In this Review, we summarize recent progress in the field and discuss how the emerging knowledge in Arabidopsis may be transferred to relevant crop systems