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Given the need to predict the outcomes of (co)evolution in host-associated microbiomes, whether microbial and host fitnesses tend to trade-off, generating conflict, remains a pressing question. Examining the relationships between host and microbe fitness proxies at both the phenotypic and genomic levels can illuminate the mechanisms underlying interspecies cooperation and conflict. We examined naturally occurring genetic variation in 191 strains of the model microbial symbiont Sinorhizobium meliloti , paired with each of two host Medicago truncatula genotypes in single- or multi-strain experiments to determine how multiple proxies of microbial and host fitness were related to one another and test key predictions about mutualism evolution at the genomic scale, while also addressing the challenge of measuring microbial fitness. We found little evidence for interspecies fitness conflict; loci tended to have concordant effects on both microbe and host fitnesses, even in environments with multiple co-occurring strains. Our results emphasize the importance of quantifying microbial relative fitness for understanding microbiome evolution and thus harnessing microbiomes to improve host fitness. Additionally, we find that mutualistic coevolution between hosts and microbes acts to maintain, rather than erode, genetic diversity, potentially explaining why variation in mutualism traits persists in nature. 

ABSTRACT The environmental context of the nitrogen-fixing mutualism between leguminous plants and rhizobial bacteria varies over space and time. Variation in resource availability, population density, and composition likely affect the ecology and evolution of rhizobia and their symbiotic interactions with hosts. We examined how host genotype, nitrogen addition, rhizobial density, and community complexity affected selection on 68 rhizobial strains in the Sinorhizobium meliloti – Medicago truncatula mutualism. As expected, host genotype had a substantial effect on the size, number, and strain composition of root nodules (the symbiotic organ). The understudied environmental variable of rhizobial density had a stronger effect on nodule strain frequency than the addition of low nitrogen levels. Higher inoculum density resulted in a nodule community that was less diverse and more beneficial but only in the context of the more selective host genotype. Higher density resulted in more diverse and less beneficial nodule communities with the less selective host. Density effects on strain composition deserve additional scrutiny as they can create feedback between ecological and evolutionary processes. Finally, we found that relative strain rankings were stable across increasing community complexity (2, 3, 8, or 68 strains). This unexpected result suggests that higher-order interactions between strains are rare in the context of nodule formation and development. Our work highlights the importance of examining mechanisms of density-dependent strain fitness and developing theoretical predictions that incorporate density dependence. Furthermore, our results have translational relevance for overcoming establishment barriers in bioinoculants and motivating breeding programs that maintain beneficial plant-microbe interactions across diverse agroecological contexts. IMPORTANCE Legume crops establish beneficial associations with rhizobial bacteria that perform biological nitrogen fixation, providing nitrogen to plants without the economic and greenhouse gas emission costs of chemical nitrogen inputs. Here, we examine the influence of three environmental factors that vary in agricultural fields on strain relative fitness in nodules. In addition to manipulating nitrogen, we also use two biotic variables that have rarely been examined: the rhizobial community's density and complexity. Taken together, our results suggest that (i) breeding legume varieties that select beneficial strains despite environmental variation is possible, (ii) changes in rhizobial population densities that occur routinely in agricultural fields could drive evolutionary changes in rhizobial populations, and (iii) the lack of higher-order interactions between strains will allow the high-throughput assessments of rhizobia winners and losers during plant interactions. 

In Mediterranean climates, the timing of seasonal rains determines germination, flowering phenology and fitness. As climate change alters seasonal precipitation patterns, it is important to ask how these changes will affect the phenology and fitness of plant populations. We addressed this question experimentally with the annual plant speciesArabidopsis thaliana.

In a first experiment, we manipulated the date of rainfall onset and recorded germination phenology on sand and soil substrates. In a second experiment, we manipulated germination date, growing season length and mid‐season drought to measure their effects on flowering time and fitness. Within each experiment, we manipulated seed dormancy and flowering time using multilocus near‐isogenic lines segregating strong and weak alleles of the seed dormancy geneDOG1and the flowering time geneFRI. We synthesized germination phenology data from the first experiment with fitness functions from the second experiment to project population fitness under different seasonal rainfall scenarios.

Germination phenology tracked rainfall onset but was slower and more variable on sand than on soil. Many seeds dispersed on sand in spring and summer delayed germination until the cooler temperatures of autumn. The high‐dormancyDOG1allele also prevented immediate germination in spring and summer. Germination timing strongly affected plant fitness. Fecundity was highest in the October germination cohort and declined in spring germinants. The late floweringFRIallele had lower fecundity, especially in early fall and spring cohorts. Projections of population fitness revealed that: (1) Later onset of autumn rains will negatively affect population fitness. (2) Slow, variable germination on sand buffers populations against fitness impacts of variable spring and summer rainfall. (3) Seasonal selection favours high dormancy and early flowering genotypes in a Mediterranean climate with hot dry summers. The high‐dormancyDOG1allele delayed germination of spring‐dispersed fresh seeds until more favourable early fall conditions, resulting in higher projected population fitness.

These findings suggest that Mediterranean annual plant populations are vulnerable to changes in seasonal precipitation, especially in California where rainfall onset is already occurring later. The fitness advantage of highly dormant, early flowering genotypes helps explain the prevalence of this strategy in Mediterranean populations.

Read the freePlain Language Summaryfor this article on the Journal blog.

 

Most plant–microbe interactions are facultative, with microbes experiencing temporally and spatially variable selection. How this variation affects microbial evolution is poorly understood. Given its tractability and ecological and agricultural importance, the legume–rhizobia nitrogen‐fixing symbiosis is a powerful model for identifying traits and genes underlying bacterial fitness. New technologies allow high‐throughput measurement of the relative fitness of bacterial mutants, strains and species in mixed inocula in the host, rhizosphere and soil environments. I consider how host genetic variation (G × G), other environmental factors (G × E), and host life‐cycle variation may contribute to the maintenance of genetic variation and adaptive trajectories of rhizobia – and, potentially, other facultative symbionts. Lastly, I place these findings in the context of developing beneficial inoculants in a changing climate.

 

ABSTRACT Genome-wide association studies (GWAS) can identify genetic variants responsible for naturally occurring and quantitative phenotypic variation. Association studies therefore provide a powerful complement to approaches that rely on de novo mutations for characterizing gene function. Although bacteria should be amenable to GWAS, few GWAS have been conducted on bacteria, and the extent to which nonindependence among genomic variants (e.g., linkage disequilibrium [LD]) and the genetic architecture of phenotypic traits will affect GWAS performance is unclear. We apply association analyses to identify candidate genes underlying variation in 20 biochemical, growth, and symbiotic phenotypes among 153 strains of Ensifer meliloti . For 11 traits, we find genotype-phenotype associations that are stronger than expected by chance, with the candidates in relatively small linkage groups, indicating that LD does not preclude resolving association candidates to relatively small genomic regions. The significant candidates show an enrichment for nucleotide polymorphisms (SNPs) over gene presence-absence variation (PAV), and for five traits, candidates are enriched in large linkage groups, a possible signature of epistasis. Many of the variants most strongly associated with symbiosis phenotypes were in genes previously identified as being involved in nitrogen fixation or nodulation. For other traits, apparently strong associations were not stronger than the range of associations detected in permuted data. In sum, our data show that GWAS in bacteria may be a powerful tool for characterizing genetic architecture and identifying genes responsible for phenotypic variation. However, careful evaluation of candidates is necessary to avoid false signals of association. IMPORTANCE Genome-wide association analyses are a powerful approach for identifying gene function. These analyses are becoming commonplace in studies of humans, domesticated animals, and crop plants but have rarely been conducted in bacteria. We applied association analyses to 20 traits measured in Ensifer meliloti , an agriculturally and ecologically important bacterium because it fixes nitrogen when in symbiosis with leguminous plants. We identified candidate alleles and gene presence-absence variants underlying variation in symbiosis traits, antibiotic resistance, and use of various carbon sources; some of these candidates are in genes previously known to affect these traits whereas others were in genes that have not been well characterized. Our results point to the potential power of association analyses in bacteria, but also to the need to carefully evaluate the potential for false associations.