DOI: 10.5061/dryad.x95x69pj5
title: Competitiveness prediction for nodule colonization in Sinorhizobium meliloti through combined in vitro tagged strain characterization and genome-wide association analysis

description: 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 (&gt; 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.
description: Methods description is available at DOI:
 10.1128/mSystems.00550-21
description: <b>File S1.
 </b>List of top <i>k</i>-mers (raw data
 <i>k</i>-mers) for a) single occupied nodules and b) the sum
 of single and mixed occupied nodules. <b>File S2.</b> Genes
 hits identified by best <i>k</i>-mers (raw data
 <i>k</i>-mers - gene position) for a) single occupied nodules
 (51 <i>k</i>-mers) and b) the sum of single and mixed occupied
 nodules (50 <i>k</i>-mers). <b>File S3.
 </b>Regulatory region hits identified by best
 <i>k</i>-mers for a) single occupied nodules (10
 <i>k</i>-mers) and b) the sum of single and mixed occupied
 nodules (14 <i>k</i>-mers). <b>File S4</b>.
 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. <b>File S5. Predicted and
 actual competition patterns. </b>Single<b> </b>nodule
 occupancy of <i>S. meliloti</i> strains in competition
 experiments <i>vs</i> <i>S. meliloti</i> strains
 A) Rm1021, B), AK83 and C) BL225C. Single<b> </b>and
 mixed<b> </b>nodule occupancy of <i>S.
 meliloti</i> strains in competition experiments
 <i>vs</i> <i>S. meliloti</i> 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. <b>File S6. Genome annotation of the
 <i>S. meliloti </i>strains used. </b>Genome annotation
 with Prokka.