Gene expression analysis in developmentally arrested root nodules formed by Sinorhizobium meliloti and Medicago truncatula

The bacterium, Sinorhizobium meliloti, interacts symbiotically with leguminous plants such as Medicago truncatula to form nitrogen-fixing root nodules. During symbiosis, plant and bacterial cells differentiate in a coordinated manner, resulting in specialized plant cells that contain nitrogen-fixing bacteroids. Medicago nodules are organized in structurally distinct tissue zones, representing different stages of bacterial and plant differentiation. We used laser-capture microdissection (LCM) to analyze bacterial and plant gene expression in four root nodule regions. In parallel, we analyzed gene expression in nodules formed by wild type bacteria on six plant mutants with nitrogen fixation deficiencies (dnf). We found that bacteroid metabolism is drastically remodeled during bacteroid differentiation. Many processes required for bacterial growth are down-regulated in the nitrogen fixation zone. The overall transcriptional changes are similar to those occurring during nutrient limitation by the stringent response. We also observed differential expression of bacterial genes involved in nitrogen fixation, cell envelope homeostasis, cell division, stress response and polyamine biosynthesis at distinct stages of nodule development. In M. truncatula we observed the differential regulation of several host processes that may trigger bacteroid differentiation and control bacterial infection. We analyzed plant and bacterial gene expression simultaneously, which allowed us to correlate processes in both organisms. We harvested root nodules from nitrogen fixation deficient Medicago truncatula mutants dnf1, dnf2, dnf3, dnf4, dnf5, dnf7 and two wild type lines M. truncatula [Gaertn.] cv Jemalong and A-17 21 days after inoculation with Sinorhizobium meliloti CL150. For each of three replicates we harvested root nodules from 9-17 plants. In order to identify symbiotically regulated genes we carried out pairwise comparisons between the samples and used hierarchical clustering to group genes with similar expression profiles.