(497d) Quantitative Proteomics As A Tool For Systems Biology: Assessment Of Metal Stresses In Burkholderia Cepacia

Proteomics is a powerful tool for experimental systems biology, relating global protein expression to phenotype. In this project, at strain of Burkholderia cepacia was isolated from a soil microbial community due to its high ability to tolerate cadmium. In previous studies, we used proteomics to determine the effect that cadmium shocks had on the whole bacterial community and to evaluate the effect of different cadmium concentrations on the isolate.

In the present study, B. cepacia was grown with and without 100 mg/L Cd²⁺. Cadmium-grown cells presented a very distinct blue phenotype compared to the control. Both types of cells were transferred to new plates containing Co²⁺, Cr³⁺, Cu²⁺, Fe³⁺, Ni²⁺, or Zn²⁺. The objective was to determine whether cells adapted to cadmium would grow faster on the different metals. After approximately 10 days of incubation, no significant difference in growth rates was noted , but new phenotypes were observed.

The overall goal of the experiment was to elucidate mechanisms involved in the protection against high concentrations of metals. Due to the different chemical properties of the metals chosen, different cellular machineries should be involved in metal resistance. Using a proteomic approach to implement a systems view of metal resistance, we had the ability to further describe specific mechanisms used by B. cepacia. In order to obtain quantitative information, we coupled iTRAQ labeling with two-dimensional chromatography and quadrupole time-of-flight mass spectrometry. The results reveal remarkable differences in protein expression among the samples, consistent with the previous observation of different phenotypes. Among the proteins identified as differentially expressed were a number of central carbon metabolism enzymes. As a consequence, we tried to gain further insight into the central cellular metabolism by measuring substrate consumption and ATP levels, and calculating cellular yields. The combination of metabolite and protein data offers unique information for future modeling efforts. This study illustrates the ability of proteomics to provide systems information on unknown cellular states.