Gene Expression Networks Esra Erdin CS 790g Fall 2010

Introduction Project Description Related Work Conclusion References

Introduction Plants have important role in the lives of organisms

Plant growth in the natural environment is affected by a number of factors. We call these factors stresses.

These include environmental factors such as low temperature, heat, drought, wind, ultraviolet light, anoxia and high salinity and biological factors such as pathogens (bacteria, viruses, fungi)

Every organism gives different responses to different stresses they are subjected to. However generally the plants they are affected adversely by these factors.

Adapted from

Human life will also be affected negatively as a result of adverse effects that plants encounters.

Crop losses due to these stresses are in the billions of dollars annually. It has been estimated that stress factors depress the yield of agronomically important crops in the United States by 78%, of which about 70% is due to unfavorable environmental conditions.

Because crops have importance in human life, it is a crucial issue to understand the behavior of crops under any stress factors.

When this behavior is analyzed and obtained it is important and now easy to develop and improve stress tolerance in crops. This has significant implications for people and farmers worldwide.

Project Description The analysis of stress tolerance of crops; rice, wheat and maize under drought, high salinity and low temperature.

Collect data from studies that are published Analyse sets of genes that give any response to these stresses separately. For every stress condition there will be an undirected graph whose vertices correspond to genes, and the vertices of two genes are connected by an edge if their expressions are correlated. Apply the network metrics on these graphs I constructed.

Related Work “Similarities and differences of gene expression in yeast stress conditions”, Rokhlenko O, Wexler Y, Yakhini Z They studied stress response mechanisms in Saccharomyces cerevisiae by identifying genes that, according to very stringent criteria, have persistent co-expression under a variety of stress conditions.

To study the relationship of different stress conditions they constructed co-co-expression graphs for each pair of conditions. They make a fast clique search method to the intersection of several co-expression graphs calculated over data.

A hierarchical tree representing the distances in terms of number of edges in co-co- expression graphs of 19 stress conditions. The tree was constructed using an average-linkage neighbor-joining method.

As a result of their method they detected cliques in the intersection graphs that are much larger than expected under a null model of changing gene identities for different stress conditions but keeping the co- expression topology within each one. They also showed the genes of many cliques in the intersection graphs are co-localized in the yeast genome.

Stress-induced changes in the Arabidopsis thaliana transcriptome analyzed using whole-genome tiling arrays, Zeller G, Henz SR, Widmer CK, Sachsenberg T, Ratsch G, Weigel D, Laubinger S.(2009)

They used whole-genome tiling arrays to analyze the effects of salt, osmotic, cold and heat stres to provide an exthaustive view of stres-induced changes.

They found many stress-responsive genes as a result of their study. They also discover several transcription factor genes as well as pseudegenes and transposons that have been missed in previous analyses with standard expression arrays.

Conclusion Developing stress tolerant crops by modifying the genes that are co-expressed under all stress conditions is a crucial topic. To be able to develop stress tolerant genes, their analysis should be done properly.

Gasch,A. et al. (2000) Genomic expression programs in the response of yeast cells to environmental changes. Mol. Biol. Cell, 11, 4241–4257. Zeller G, Henz SR, Widmer CK, Sachsenberg T, Ratsch G, Weigel D, Laubinger S.(2009) Stress-induced changes in the Arabidopsis thaliana transcriptome analyzed using whole- genome tiling arrays.