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Competitiveness prediction for nodule colonization in Sinorhizobium meliloti through combined in vitro tagged strain characterization and genome-wide association analysis

Citation

Bellabarba, Agnese et al. (2021), Competitiveness prediction for nodule colonization in Sinorhizobium meliloti through combined in vitro tagged strain characterization and genome-wide association analysis, Dryad, Dataset, https://doi.org/10.5061/dryad.x95x69pj5

Abstract

Associations between leguminous plants and symbiotic nitrogen-fixing rhizobia are a classic example of mutualism between a eukaryotic host and a specific group of prokaryotic microbes. Although this symbiosis is in part species-specific, different rhizobial strains may colonise the same nodule. Some rhizobial strains are commonly known as better competitors than others, but detailed analyses that aim to predict rhizobial competitive abilities based on genomes are still scarce. Here, we performed a bacterial genome-wide association (GWAS) analysis to define the genomic determinants related to the competitive capabilities in the model rhizobial species Sinorhizobium meliloti. For this, 13 tester strains were GFP-tagged and assayed vs. 3 RFP-tagged reference competitor strains (Rm1021, AK83, and BL225C) in a Medicago sativa nodule occupancy test. Competition data and strain genomic sequences were employed to build a model for GWAS based on k-mers. Among the k-mers with the highest scores, 51 k-mers mapped on the genomes of four strains showing the highest competition phenotypes (> 60% single strain nodule occupancy; GR4, KH35c, KH46 and SM11) vs. BL225C. These k-mers were mainly located on the symbiosis-related megaplasmid pSymA, specifically on genes coding for transporters, proteins involved in the biosynthesis of cofactors and proteins related to metabolism (e.g., fatty acids). The same analysis was performed considering the sum of single and mixed nodules obtained in the competition assays vs. BL225C, retrieving k-mers mapped on the genes previously found and on vir genes. Therefore, the competition abilities seem to be linked to multiple genetic determinants and comprise several cellular components.

Methods

Methods description is available at DOI: 10.1128/mSystems.00550-21

Usage Notes

File S1. List of top k-mers (raw data k-mers) for a) single occupied nodules and b) the sum of single and mixed occupied nodules.

File S2. Genes hits identified by best k-mers (raw data k-mers - gene position) for a) single occupied nodules (51 k-mers) and b) the sum of single and mixed occupied nodules (50 k-mers).

File S3. Regulatory region hits identified by best k-mers for a) single occupied nodules (10 k-mers) and b) the sum of single and mixed occupied nodules (14 k-mers).

File S4. Linear regression models for competitive phenotype prediction, both considered as single nodule occupancy and the sum of single and mixed nodule occupancy. The statistical models performed by PhenotypeSeeker were built for the three competition experiments, with 3-fold train/test splits of samples. The averaged model evaluation metrics of both training and test set are reported.

File S5. Predicted and actual competition patterns. Single nodule occupancy of S. meliloti strains in competition experiments vs S. meliloti strains A) Rm1021, B), AK83 and C) BL225C. Single and mixed nodule occupancy of S. meliloti strains in competition experiments vs S. meliloti strains D) Rm1021, E), AK83 and F) BL225C. Green bars indicate the actual phenotypes, red bars the averaged of predicted phenotypes in 3-fold train/test splits of samples.

File S6. Genome annotation of the S. meliloti strains used. Genome annotation with Prokka.

Funding

Estonian Ministry of Education and Research and the EU ERDF, Award: 2014-2020.4.01.15-0012

Ministero delle Politiche Agricole Alimentari e Forestali

Estonian Ministry of Education and Research and the EU ERDF, Award: 2014-2020.4.01.15-0012