Feb 2014
Scientists fighting to reduce dependence on chemical fungicides
Scientists at theInstitution of Plant Protection Biologywithin the Swedish University of Agricultural Sciences, have been researching into how to reduce the dependence on chemical fungicides in farming.
The goal of the researchers has been to understand plant defense mechanisms and so produce plants that are more resistant to disease, which will lessen pesticide use, and ultimately benefit the environment, and have been usingQlucore Omics Explorerto aid their research.
The research is focused on how plants defend themselves against oomycetes and fungi.Oomycetes, also known as "water molds", are a group of several hundred organisms that include some of the most devastating plant pathogens. The diseases they cause include seedling blights, damping-off, root rots, foliar blights and downy mildews. Some notable diseases are the late blight of potato, downy mildew of grape vine, sudden oak death, and root and stem rot of soybean. The team of 16 at theInstitution of Plant Protection Biologyhave been studying biochemical components of plant defense and the interactions with pathogens and trying to identify resistance factors that can be used in future breeding for disease resistance crops, and in developing methods for induced resistance by applying non-toxic inducing agents. The goal is to reduce the dependence on chemical fungicides.
The simple potato comes under particular scrutiny. Potato late blight, caused by Phytophthora infestans, is one of the most devastating pathogens worldwide. In Swedish agriculture almost half of all fungicides are used against this pathogen. To reduce the dependence on fungicides there is a great need for novel resistance, and several projects involve work with resistance against this disease, and also breeding of potatoes.The resistance mechanisms are ilucidated by identification of proteins involved in the interaction between this oomycete and the plant (proteomics and phosphoproteomics) and by studies of the infection process with microscopical and molecular methods.
In the past, one of the major problems facing the scientists was how to handle the increasingly vast amounts of data that was being produced from their research.
"Seeing structures in the data and finding meaningful biology in them has been a problem," commentedDr Erik Alexandersson, Assistant professor at SLU Alnarp, Institution of Plant Protection Biology. "We started using Qlucore Omics Explorer in 2009 for studies in, Proteomics, Evolutionary biology, Phylogenetics and Microarray analysis. Using Qlucore Omics Explorer has overcome these failings to a large extent through its dynamic visualization tool".
By the active use of Visualisation techniques important structures and patterns can be identified quickly, with the user getting instant feedback. Qlucore Omics Explorer allows 3D modeling and the ability to change parameters quickly and easily which has speeded up the whole process of analysis, and can be done by biologists and researchers with no specialist knowledge of mathematics.
"We have used Qlucore both for gene expression and quantitative proteomics data," continuedDr Erik Alexandersson. "Some of the sampling is done in the field in order to obtain molecular data in a realistic setting as it would be out in the farm avoiding laboratory artifacts. We see clear differences in the mechanisms at play in these two settings. Qlucore has turned out to be very powerful in handling noisy data and quickly assessing underlying structures not relevant to the research question. We were recently able to "save" a noisy dataset by taking the set-up time into account and use the function "eliminate factor" in Qlucore."
In other studies Qlucore has helped the researchers to identify transcript and proteins associated to resistance againstPhytopthoraby comparing the expression status under various conditions of potato lines with varying levels of resistance. These candidates are currently being confirmed by genetic transformations in the laboratory as part of the SSF grant "Resistance to late blight in potato".
The discoveries being made are now being tried out in wet lab studies.
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About Qlucore
Qlucore started as a collaborative research project at Lund University, Sweden, supported by researchers at the Departments of Mathematics and Clinical Genetics, in order to address the vast amount of high-dimensional data generated with microarray gene expression analysis. As a result, it was recognised that an interactive scientific software tool was needed to conceptualise the ideas evolving from the research collaboration.
The basic concept behind the software is to provide a tool that can take full advantage of the most powerful pattern recogniser that exists - the human brain. The result is a core software engine that visualises the data in 3D and will aid the user in identifying hidden structures and patterns. Over the last few years, major efforts have been made to optimise the early ideas and to develop a core software engine that is extremely fast, allowing the user to interactively and in real time instantly explore and analyse high-dimensional data sets with the use of a normal PC.
Qlucore was founded in early 2007 and the first product released was the "Qlucore Gene Expression Explorer 1.0". The latest version of this software, now called "Qlucore Omics Explorer 2.0", was released in May 2009, and represents a major step forward with the added support for hierarchical clustering, scatter plots and powerful log function. The combination of instant visualisation and advanced statistics support gives the user new opportunities. All user action is at most two mouse clicks away. The Company's early customers are mainly from the Life-science and Biotech industries, but solutions for other industries are currently under development.
One of the early key methods used by Qlucore Gene Expression Explorer to visualise data is dynamic principal component analysis (PCA), an innovative way of combining PCA analysis with immediate user interaction. Dynamic PCA is PCA analysis combined with instant user response, a combination which provides an optimal way for users to visualise and analyse a large dataset by presenting a comprehensive view of the data set at the same time, since the user is given full freedom to explore all possible versions of the presented view. Later versions combine PCA analysis with other analysis methods such as hierarchical clustering.