Experimental setups and considerations to study microbial interactions.
Within ecosystems microorganisms coexist and interact. Knowledge of these interactions is of great importance in the fields of ecology, food production, and medicine. Such interactions often involve the synthesis of antibiotic secondary metabolites. Different kinds of s molecules or direct contacts are other forms of microbial interactions. Recently, modern molecular methods such as microarrays and proteomics have been employed to investigate such interactions. In this chapter, the use of proteomics for studies of microbial interactions is discussed. The choice of experimental setup is dependent on the aims of the specific study. One aspect of competition between microbes can be simulated by treatment of one microbe with antibiotics produced by a competing microbe. A more complicated approach involves cocultivation of the competitors, but in order to reveal species-specific protein patterns it is advisable to keep the organisms separated. Alternative techniques are to monitor alterations in the proteomes between the wild-type and mutant strains. The mutant can be either natural or created using random or targeted mutagenesis. Generally, a proteomic study will reveal proteins with both expected and surprising changes in abundance upon competition, but also previously unknown proteins are likely to be identified. A proteomic approach is usually insufficient to obtain a complete data set describing microbial interactions. Therefore, it is essential to follow up identification of proteins with changed abundance by, e.g., the creation of knockout strains for phenotypic analyses. Despite the limitations, proteomics is a useful method, and an important complement to other approaches for studies of microbial interactions.
Melin
Petter
P
Department of Microbiology, Swedish University of Agricultural Sciences, Uppsala, Sweden.
D001419:Bacteria / Q000737:chemistry / Q000378:metabolism; D018929:Cell Culture Techniques; D004463:Ecology; D005658:Fungi / Q000737:chemistry / Q000378:metabolism; D020869:Gene Expression Profiling; D006801:Humans; D040901:Proteomics / Q000295:instrumentation / Q000379:methods