Software package for automatic microarray image analysis (MAIA)

MYB24 orchestrates terpene and flavonol metabolism as light responses to anthocyanin depletion in variegated grape berries

Variegation is a rare type of mosaicism not fully studied in plants, especially fruits. We examined red and white sections of grape (Vitis vinifera cv. 'Béquignol') variegated berries and found that accumulation of products from branches of the phenylpropanoid and isoprenoid pathways showed an opposite tendency. Light-responsive flavonol and monoterpene levels increased in anthocyanin-depleted areas in correlation with increasing MYB24 expression. Cistrome analysis suggested that MYB24 binds to the promoters of 22 terpene synthase (TPS) genes, as well as 32 photosynthesis/light-related genes, including carotenoid pathway members, the flavonol regulator HY5 HOMOLOGUE (HYH), and other radiation response genes. Indeed, TPS35, TPS09, the carotenoid isomerase gene CRTISO2, and HYH were activated in the presence of MYB24 and MYC2. We suggest that MYB24 modulates ultraviolet and high-intensity visible light stress responses that include terpene and flavonol synthesis and potentially affects carotenoids. The MYB24 regulatory network is developmentally triggered after the onset of berry ripening, while the absence of anthocyanin sunscreens accelerates its activation, likely in a dose-dependent manner due to increased radiation exposure. Anthocyanins and flavonols in variegated berry skins act as effective sunscreens but for different wavelength ranges. The expression patterns of stress marker genes in red and white sections of 'Béquignol' berries strongly suggest that MYB24 promotes light stress amelioration but only partly succeeds during late ripening.

Drawing Links from Transcriptome to Metabolites: The Evolution of Aroma in the Ripening Berry of Moscato Bianco (Vitis vinifera L.)

Monoterpenes confer typical floral notes to "Muscat" grapevine varieties and, to a lesser extent, to other aromatic non-Muscat varieties. Previous studies have led to the identification and functional characterization of some enzymes and genes in this pathway. However, the underlying genetic map is still far from being complete. For example, the specific steps of monoterpene metabolism and its regulation are largely unknown. With the aim of identifying new candidates for the missing links, we applied an integrative functional genomics approach based on the targeted metabolic and genome-wide transcript profiling of Moscato Bianco ripening berries. In particular, gas chromatography-mass spectrometry analysis of free and bound terpenoid compounds was combined with microarray analysis in the skins of berries collected at five developmental stages from pre-veraison to over-ripening. Differentially expressed metabolites and probes were identified in the pairwise comparison between time points by using the early stage as a reference. Metabolic and transcriptomic data were integrated through pairwise correlation and clustering approaches to discover genes linked with particular metabolites or groups of metabolites. These candidate transcripts were further checked for co-localization with quantitative trait loci (QTLs) affecting aromatic compounds. Our findings provide insights into the biological networks of grapevine secondary metabolism, both at the catalytic and regulatory levels. Examples include a nudix hydrolase as component of a terpene synthase-independent pathway for monoterpene biosynthesis, genes potentially involved in monoterpene metabolism (cytochrome P450 hydroxylases, epoxide hydrolases, glucosyltransferases), transport (vesicle-associated proteins, ABCG transporters, glutathione S-transferases, amino acid permeases), and transcriptional control (transcription factors of the ERF, MYB and NAC families, intermediates in light- and circadian cycle-mediated regulation with supporting evidence from the literature and additional regulatory genes with a previously unreported association to monoterpene accumulation).

OpenMSI Arrayed Analysis Toolkit: Analyzing Spatially Defined Samples Using Mass Spectrometry Imaging

Mass spectrometry imaging (MSI) has primarily been applied in localizing biomolecules within biological matrices. Although well-suited, the application of MSI for comparing thousands of spatially defined spotted samples has been limited. One reason for this is a lack of suitable and accessible data processing tools for the analysis of large arrayed MSI sample sets. The OpenMSI Arrayed Analysis Toolkit (OMAAT) is a software package that addresses the challenges of analyzing spatially defined samples in MSI data sets. OMAAT is written in Python and is integrated with OpenMSI ( http://openmsi.nersc.gov ), a platform for storing, sharing, and analyzing MSI data. By using a web-based python notebook (Jupyter), OMAAT is accessible to anyone without programming experience yet allows experienced users to leverage all features. OMAAT was evaluated by analyzing an MSI data set of a high-throughput glycoside hydrolase activity screen comprising 384 samples arrayed onto a NIMS surface at a 450 μm spacing, decreasing analysis time >100-fold while maintaining robust spot-finding. The utility of OMAAT was demonstrated for screening metabolic activities of different sized soil particles, including hydrolysis of sugars, revealing a pattern of size dependent activities. These results introduce OMAAT as an effective toolkit for analyzing spatially defined samples in MSI. OMAAT runs on all major operating systems, and the source code can be obtained from the following GitHub repository: https://github.com/biorack/omaat .

Automatic Spot Identification for High Throughput Microarray Analysis

High throughput microarray analysis has great potential in scientific research, disease diagnosis, and drug discovery. A major hurdle toward high throughput microarray analysis is the time and effort needed to accurately locate gene spots in microarray images. An automatic microarray image processor will allow accurate and efficient determination of spot locations and sizes so that gene expression information can be reliably extracted in a high throughput manner. Current microarray image processing tools require intensive manual operations in addition to the input of grid parameters to correctly and accurately identify gene spots. This work developed a method, herein called auto-spot, to automate the spot identification process. Through a series of correlation and convolution operations, as well as pixel manipulations, this method makes spot identification an automatic and accurate process. Testing with real microarray images has demonstrated that this method is capable of automatically extracting subgrids from microarray images and determining spot locations and sizes within each subgrid, regardless of variations in array patterns and background noises. With this method, we are one step closer to the goal of high throughput microarray analysis.

Predictive integration of gene functional similarity and co-expression defines treatment response of endothelial progenitor cells

Background

Endothelial progenitor cells (EPCs) have been implicated in different processes crucial to vasculature repair, which may offer the basis for new therapeutic strategies in cardiovascular disease. Despite advances facilitated by functional genomics, there is a lack of systems-level understanding of treatment response mechanisms of EPCs. In this research we aimed to characterize the EPCs response to adenosine (Ado), a cardioprotective factor, based on the systems-level integration of gene expression data and prior functional knowledge. Specifically, we set out to identify novel biosignatures of Ado-treatment response in EPCs.

Results

The predictive integration of gene expression data and standardized functional similarity information enabled us to identify new treatment response biosignatures. Gene expression data originated from Ado-treated and -untreated EPCs samples, and functional similarity was estimated with Gene Ontology (GO)-based similarity information. These information sources enabled us to implement and evaluate an integrated prediction approach based on the concept of k-nearest neighbours learning (kNN). The method can be executed by expert- and data-driven input queries to guide the search for biologically meaningful biosignatures. The resulting integrated kNN system identified new candidate EPC biosignatures that can offer high classification performance (areas under the operating characteristic curve > 0.8). We also showed that the proposed models can outperform those discovered by standard gene expression analysis. Furthermore, we report an initial independent in vitro experimental follow-up, which provides additional evidence of the potential validity of the top biosignature.

Conclusion

Response to Ado treatment in EPCs can be accurately characterized with a new method based on the combination of gene co-expression data and GO-based similarity information. It also exploits the incorporation of human expert-driven queries as a strategy to guide the automated search for candidate biosignatures. The proposed biosignature improves the systems-level characterization of EPCs. The new integrative predictive modeling approach can also be applied to other phenotype characterization or biomarker discovery problems.

A transcriptomic study of grapevine (Vitis vinifera cv. Cabernet-Sauvignon) interaction with the vascular ascomycete fungus Eutypa lata

Eutypa dieback is a vascular disease that may severely affect vineyards throughout the world. In the present work, microarrays were made in order (i) to improve our knowledge of grapevine (Vitis vinifera cv. Cabernet-Sauvignon) responses to Eutypa lata, the causal agent of Eutypa dieback; and (ii) to identify genes that may prevent symptom development. Qiagen/Operon grapevine microarrays comprising 14,500 probes were used to compare, under three experimental conditions (in vitro, in the greenhouse, and in the vineyard), foliar material of infected symptomatic plants (S(+)R(+)), infected asymptomatic plants (S(-)R(+)), and healthy plants (S(-)R(-)). These plants were characterized by symptom notation after natural (vineyard) or experimental (in vitro and greenhouse) infection, re-isolation of the fungus located in the lignified parts, and the formal identification of E. lata mycelium by PCR. Semi-quantitative real-time PCR experiments were run to confirm the expression of some genes of interest in response to E. lata. Their expression profiles were also studied in response to other grapevine pathogens (Erysiphe necator, Plasmopara viticola, and Botrytis cinerea). (i) Five functional categories of genes, that is those involved in metabolism, defence reactions, interaction with the environment, transport, and transcription, were up-regulated in S(+)R(+) plants compared with S(-)R(-) plants. These genes, which cannot prevent infection and symptom development, are not specific since they were also up-regulated after infection by powdery mildew, downy mildew, and black rot. (ii) Most of the genes that may prevent symptom development are associated with the light phase of photosynthesis. This finding is discussed in the context of previous data on the mode of action of eutypin and the polypeptide fraction secreted by Eutypa.

Transcriptional networks characterize ventricular dysfunction after myocardial infarction: a proof-of-concept investigation

There is currently no method powerful enough to identify patients at risk of developing ventricular dysfunction after myocardial infarction (MI). We aimed to identify major mechanisms related to ventricular dysfunction to predict outcome after MI. Based on the combination of domain knowledge, protein-protein interaction networks and gene expression data, a set of potential biomarkers of ventricular dysfunction after MI was identified. Here we propose a new strategy for the prediction of ventricular dysfunction after MI based on "network activity indices" (NAI), which encode gene network-based signatures and distinguishes between prognostic classes. These models outperformed prognostic models based on standard differential expression analysis. NAI-based models reported high classification accuracy, with a maximum area under the receiver operating characteristic curve (AUC) of 0.75. Furthermore, the classification capacity of these models was validated by performing evaluations on an independent patient cohort (maximum AUC=0.75). These results suggest that transcriptional network-based biosignatures can offer both powerful and biologically-meaningful prediction models of ventricular dysfunction after MI. This research reports a new integrative strategy for identifying transcriptional responses that characterize cardiac repair and for predicting clinical outcome after MI. It can be adapted to other clinical domains, such as those constrained by small molecular datasets and limited translational knowledge. Furthermore, it may reflect clinically-meaningful synergistic effects that cannot be identified by standard analyses.

Integrated protein network and microarray analysis to identify potential biomarkers after myocardial infarction

A significant proportion of patients develop left ventricular (LV) dysfunction and heart failure (HF) after acute myocardial infarction (MI). Existing biomarkers of HF provide limited information after MI. To identify new prognostic biomarkers in MI patients, we designed an approach combining protein interaction networks and microarray analysis of blood cells. Blood samples for RNA and protein analysis were taken from 127 acute MI patients. Echocardiography was performed at one month. Assuming that angiogenesis is related to cardiac repair after MI, a protein-protein interaction network of angiogenesis was constructed and analyzed. Among the 556 proteins and 686 interactions of this network, a cluster of 53 proteins highly specialized in regulation of cell growth was identified. Of these 53 proteins, 38 were found differentially expressed by microarrays between low (< or = 40%) and high (>40%) LV ejection fraction (EF) patients (n = 32). Among these 38 genes, prediction analysis identified a set of three genes able to predict significant LV dysfunction (EF < or = 40%) with an area under the receiver operating characteristic curve (AUC) of 0.82. These three genes-vascular endothelial growth factor B, thrombospondin-1 and placental growth factor-had a stronger predictive value than brain natriuretic peptide and troponin T (AUC of 0.63). Independent validations on protein expression and quantitative PCR datasets confirmed the results. In conclusion, a new strategy is described that allows identifying new potential biomarkers. The three specific biomarkers described here remain to be validated in a larger patient population.

Gene expression profiling in susceptible interaction of grapevine with its fungal pathogen Eutypa lata: extending MapMan ontology for grapevine

BACKGROUND

Whole genome transcriptomics analysis is a very powerful approach because it gives an overview of the activity of genes in certain cells or tissue types. However, biological interpretation of such results can be rather tedious. MapMan is a software tool that displays large datasets (e.g. gene expression data) onto diagrams of metabolic pathways or other processes and thus enables easier interpretation of results. The grapevine (Vitis vinifera) genome sequence has recently become available bringing a new dimension into associated research. Two microarray platforms were designed based on the TIGR Gene Index database and used in several physiological studies.

RESULTS

To enable easy and effective visualization of those and further experiments, annotation of Vitis vinifera Gene Index (VvGI version 5) to MapMan ontology was set up. Due to specificities of grape physiology, we have created new pictorial representations focusing on three selected pathways: carotenoid pathway, terpenoid pathway and phenylpropanoid pathway, the products of these pathways being important for wine aroma, flavour and colour, as well as plant defence against pathogens. This new tool was validated on Affymetrix microarrays data obtained during berry ripening and it allowed the discovery of new aspects in process regulation. We here also present results on transcriptional profiling of grape plantlets after exposal to the fungal pathogen Eutypa lata using Operon microarrays including visualization of results with MapMan. The data show that the genes induced in infected plants, encode pathogenesis related proteins and enzymes of the flavonoid metabolism, which are well known as being responsive to fungal infection.

CONCLUSION

The extension of MapMan ontology to grapevine together with the newly constructed pictorial representations for carotenoid, terpenoid and phenylpropanoid metabolism provide an alternative approach to the analysis of grapevine gene expression experiments performed with Affymetrix or Operon microarrays. MapMan was first validated on an already published dataset and later used to obtain an overview of transcriptional changes in a susceptible grapevine - Eutypa lata interaction at the time of symptoms development, where we showed that the responsive genes belong to families known to be involved in the plant defence towards fungal infection (PR-proteins, enzymes of the phenylpropanoid pathway).

Microbeads on microposts: an inverted architecture for bead microarrays

The rapid development of genomics and proteomics requires accelerated improvement of the microarrays density, multiplexing, readout capabilities and cost-effectiveness. The bead arrays are increasingly attractive because of their self-assembly-based fabrication, which alleviates many problems of top-down microfabrication. Here we present a simple, reliable, robust and modular technique for the fabrication of bead microarrays, which combines the directed assembling of beads in microstructures and PDMS-based replica molding. The beads are first self-assembled in pyramidal microwells fabricated by anisotropic etching of silicon substrates, then transferred on the apex of PDMS pyramids that replicate the silicon microstructures. The arrays are chemically and biochemically robust; they are spatially addressable and have the potential for being informationally addressable; and they appear to offer better readout capabilities than the classical microarrays.

Advanced spot quality analysis in two-colour microarray experiments

Background

Image analysis of microarrays and, in particular, spot quantification and spot quality control, is one of the most important steps in statistical analysis of microarray data. Recent methods of spot quality control are still in early age of development, often leading to underestimation of true positive microarray features and, consequently, to loss of important biological information. Therefore, improving and standardizing the statistical approaches of spot quality control are essential to facilitate the overall analysis of microarray data and subsequent extraction of biological information.

Findings

We evaluated the performance of two image analysis packages MAIA and GenePix (GP) using two complementary experimental approaches with a focus on the statistical analysis of spot quality factors. First, we developed control microarrays with a priori known fluorescence ratios to verify the accuracy and precision of the ratio estimation of signal intensities. Next, we developed advanced semi-automatic protocols of spot quality evaluation in MAIA and GP and compared their performance with available facilities of spot quantitative filtering in GP. We evaluated these algorithms for standardised spot quality analysis in a whole-genome microarray experiment assessing well-characterised transcriptional modifications induced by the transcription regulator SNAI1. Using a set of RT-PCR or qRT-PCR validated microarray data, we found that the semi-automatic protocol of spot quality control we developed with MAIA allowed recovering approximately 13% more spots and 38% more differentially expressed genes (at FDR = 5%) than GP with default spot filtering conditions.

Conclusion

Careful control of spot quality characteristics with advanced spot quality evaluation can significantly increase the amount of confident and accurate data resulting in more meaningful biological conclusions.

RReportGenerator: automatic reports from routine statistical analysis using R

With the establishment of high-throughput (HT) screening methods there is an increasing need for automatic analysis methods. Here we present RReportGenerator, a user-friendly portal for automatic routine analysis using the statistical platform R and Bioconductor. RReportGenerator is designed to analyze data using predefined analysis scenarios via a graphical user interface (GUI). A report in pdf format combining text, figures and tables is automatically generated and results may be exported. To demonstrate suitable analysis tasks we provide direct web access to a collection of analysis scenarios for summarizing data from transfected cell arrays (TCA), segmentation of CGH data, and microarray quality control and normalization.

AVAILABILITY

RReportGenerator, a user manual and a collection of analysis scenarios are available under a GNU public license on http://www-bio3d-igbmc.u-strasbg.fr/~wraff

Automatic analysis of microRNA microarray images using mathematical morphology

The micro array are an experimental technique for parallel determination of molecular concentration. The image analysis is an important, time consuming and error prone step of the process. We describe here an automatic procedure able to analyze the micro array data and to accurately provide the level of concentration for each microRNA (miRNA). The proposed method has the advantage, compared to commercial products, to minimize the user interaction, leading to a more reproducible data analysis.

An algorithm for automatic evaluation of the spot quality in two-color DNA microarray experiments

Background

Although DNA microarray technologies are very powerful for the simultaneous quantitative characterization of thousands of genes, the quality of the obtained experimental data is often far from ideal. The measured microarrays images represent a regular collection of spots, and the intensity of light at each spot is proportional to the DNA copy number or to the expression level of the gene whose DNA clone is spotted. Spot quality control is an essential part of microarray image analysis, which must be carried out at the level of individual spot identification. The problem is difficult to formalize due to the diversity of instrumental and biological factors that can influence the result.

Results

For each spot we estimate the ratio of measured fluorescence intensities revealing differential gene expression or change in DNA copy numbers between the test and control samples. We also define a set of quality characteristics and a model for combining these characteristics into an overall spot quality value. We have developed a training procedure to evaluate the contribution of each individual characteristic in the overall quality. This procedure uses information available from replicated spots, located in the same array or over a set of replicated arrays. It is assumed that unspoiled replicated spots must have very close ratios, whereas poor spots yield greater diversity in the obtained ratio estimates.

Conclusion

The developed procedure provides an automatic tool to quantify spot quality and to identify different types of spot deficiency occurring in DNA microarray technology. Quality values assigned to each spot can be used either to eliminate spots or to weight contribution of each ratio estimate in follow-up analysis procedures.