Development of a cDNA microarray for the measurement of gene expression in the sheep scab mite Psoroptes ovis

Background: Sheep scab is caused by the ectoparasitic mite Psoroptes ovis which initiates a profound cutaneous inflammatory response, leading to the development of the skin lesions which are characteristic of the disease. Existing control strategies rely upon injectable endectocides and acaricidal dips but concerns over residues, eco-toxicity and the development of acaricide resistance limit the sustainability of this approach. In order to identify alternative means of disease control, a deeper understanding of both the parasite and its interaction with the host are required. Methods: Herein we describe the development and utilisation of an annotated P. ovis cDNA microarray containing 3,456 elements for the measurement of gene expression in this economically important ectoparasite. The array consists of 981 P. ovis EST sequences printed in triplicate along with 513 control elements. Array performance was validated through the analysis of gene expression differences between fed and starved P. ovis mites. Results: Sequences represented on the array include homologues of major house dust mite allergens and tick salivary proteins, along with factors potentially involved in mite reproduction and xenobiotic metabolism. In order to validate the performance of this unique resource under biological conditions we used the array to analyse gene expression differences between fed and starved P. ovis mites. These analyses identified a number of house dust mite allergen homologues up-regulated in fed mites and P. ovis transcripts involved in stress responses, autophagy and chemosensory perception up-regulated in starved mites. Conclusion: The P. ovis cDNA microarray described here has been shown to be both robust and reproducible and will enable future studies to analyse gene expression in this important ectoparasite

Integration of Global Signaling Pathways, cAMP-PKA, MAPK and TOR in the Regulation of FLO11

The budding yeast, Saccharomyces cerevisiae, responds to various environmental cues by invoking specific adaptive mechanisms for their survival. Under nitrogen limitation, S. cerevisiae undergoes a dimorphic filamentous transition called pseudohyphae, which helps the cell to forage for nutrients and reach an environment conducive for growth. This transition is governed by a complex network of signaling pathways, namely cAMP-PKA, MAPK and TOR, which controls the transcriptional activation of FLO11, a flocculin gene that encodes a cell wall protein. However, little is known about how these pathways co-ordinate to govern the conversion of nutritional availability into gene expression. Here, we have analyzed an integrative network comprised of cAMP-PKA, MAPK and TOR pathways with respect to the availability of nitrogen source using experimental and steady state modeling approach. Our experiments demonstrate that the steady state expression of FLO11 was bistable over a range of inducing ammonium sulphate concentration based on the preculturing condition. We also show that yeast switched from FLO11 expression to accumulation of trehalose, a STRE response controlled by a transcriptional activator Msn2/4, with decrease in the inducing concentration to complete starvation. Steady state analysis of the integrative network revealed the relationship between the environment, signaling cascades and the expression of FLO11. We demonstrate that the double negative feedback loop in TOR pathway can elicit a bistable response, to differentiate between vegetative growth, filamentous growth and STRE response. Negative feedback on TOR pathway function to restrict the expression of FLO11 under nitrogen starved condition and also with re-addition of nitrogen to starved cells. In general, we show that these global signaling pathways respond with specific sensitivity to regulate the expression of FLO11 under nitrogen limitation. The holistic steady state modeling approach of the integrative network revealed how the global signaling pathways could differentiate between multiple phenotypes