Quantifying microbiota impact on plant traits for the guidance of breeding programs

Understanding the drivers of plant phenotypic variation is central to crop improvement. Root-associated microbiota are increasingly recognized as important drivers of plant growth and disease resistance, yet their quantitative contribution to phenotypic variation remains unclear. Here, we extend the classical genotype–environment framework by explicitly incorporating soil microbiota as a distinct factor. By combining contrasting genotypes, soil matrices and microbiota in a full-factorial experiment, we show that microbiota contribute substantially to plant trait variation, either directly or through interactions with genotype and environment, depending on the trait. We conclude by discussing the implications of these direct and interaction effects for the design of plant breeding programs. Understanding the sources of phenotypic variation in plants is central to both fundamental biology and agricultural innovation. Classically, plant phenotype (P) is modeled as the additive and interactive effects of plant genotype (G) and the environment (E), formalized as P = G + E + G × E (Falconer, 1989). This framework underlies most quantitative genetic approaches, allowing identification of loci associated with important traits. However, a substantial fraction of organism phenotypic variance often remains unexplained by genetic information alone – a gap referred to as ‘missing heritability’ (Maher, 2008). A convincing explanation could be that, besides plant's genes, additional information sources can be inherited across generations; the so-called ‘inclusive heritability’ (Danchin et al., 2011). In that context, one increasingly acknowledged, yet rarely mobilized, contributor to phenotypic variance is plant-associated microbiota (Lemanceau et al., 2017). Specifically, soil microbial communities are known to influence plant physiology, growth, and health, hence being now proposed by microbial ecologists as a promising way to breed plants (Wei & Jousset, 2017; Trivedi et al., 2022). However, plant geneticists are reluctant to incorporate microbiota into breeding programs, given the already extensive experimental designs involved and the limited knowledge of how strongly microbiota influence plant phenotype compared to plant genotype. Soil microbiota is often considered to be entirely part of the environment (E), due to its strong correlation with soil physicochemical parameters (pH, organic matter, etc.) (Bahram et al., 2018; Delgado-Baquerizo et al., 2018). However, rhizosphere and endosphere microbiota were found to be strongly influenced by host genotype, consistently across different soils, giving rise to the concept of a ‘core microbiota’ (Turnbaugh et al., 2009; Lundberg et al., 2012; Lemanceau et al., 2017). Other studies have highlighted that both plant species and soil parameters contribute to shaping the rhizosphere microbiota (Berg & Smalla, 2009), indicating that it cannot be attributed solely to either the environment or the plant genotype. Moreover, microbial inoculations can alter microbial community structure and biomass (Li et al., 2024), as well as plant growth (Schütz et al., 2018), particularly when consortia rather than single strains are used (Liu et al., 2023). These findings indicate that microbiota are influenced by factors beyond soil properties or host genotype, such as agricultural practices and microbial interactions, justifying their consideration as a factor capable of shaping plant phenotype independently of soil properties and host genotype. In this perspective, quantifying the proportion of plant phenotypic variance attributable to microbiota (M), beyond the traditional effects of genotype (G) and environment (E), is a central challenge. The proportion of host trait variance explained by the microbiota has been conceptualized as ‘microbiability’, referring specifically to the additive main effect of microbial communities on host phenotype (Difford et al., 2018) and proved successful for quantifying microbiota effects in animal breeding (Camarinha-Silva et al., 2017; Buitenhuis et al., 2019). Hereafter, we explicitly distinguish additive microbiability (M) from microbiota-driven interaction effects (G × M, E × M and G × E × M), which capture complementary, non-additive pathways by which microbiota modulate host phenotype in a genotype- and environment-dependent manner. Here, the classical model was enhanced to include the microbiota (M), yielding the extended formula: P = G + E + M + G × E + G × M + E × M + G × E × M (Oyserman et al., 2021). This captures the idea that microbiota can act as an independent factor (M), but also as a factor dependent on host genotype (G × M), environment (E × M), or both (G × E × M). This distinction has major implications for both plant (Nerva et al., 2022; Shen et al., 2024) and microbial breeding (Mueller & Linksvayer, 2022). If microbial effects are largely additive (M), they may be harnessed through broad-spectrum microbial inoculants, efficient across plant genotypes and environments. Conversely, significant G × M or E × M interactions imply that microbial efficacy depends on plant genotypes or environmental conditions, requiring more targeted strategies – like pairing specific plant varieties with tailored microbial consortia. Finally, strong G × E × M interactions considerably hamper broad-spectrum microbial solutions, requiring the screening of a wide range of inoculants across numerous ‘host genotype × environment’ combinations. Here, we assessed the microbiability and microbiota-driven interaction effects of two key traits of Arabidopsis thaliana – shoot biomass and pathogen resistance (Botrytis cinerea), measured through necrosis size. In a full-factorial glasshouse experiment, we manipulated the three factors – G (three accessions: Can for Canary Islands; Col for Columbia; Cvi for Cape Verdi Islands), E (three autoclaved soils from Burgundy: Auxonne, Breteniere, Champdôtre) and M (three entire microbial communities extracted from the three soils before autoclaving, used as inoculants) — to evaluate if microbiota's effect on plant was dependent (or not) on the plant genotype and/or the abiotic environment (Supporting Information Fig. S1). At the end of the experiment, we checked that inoculated microbiota changed the structure of the bacterial and fungal rhizosphere communities. We also verified that our design had enough replicates to detect significant effects thanks to a power analysis and quantified the effects of G, E, M and their interactions on shoot dry biomass and leaf necrosis size. The bacterial and fungal rhizosphere communities at the end of the experiment were primarily shaped by M and E, with G playing a minor but significant role (Fig. 1a,c). For bacteria, E had the strongest influence on explained variance (40.8%, P < 0.001), followed by M (17.7%, P < 0.001) and the E × M interaction (14.7%, P < 0.001), for a total of 73.2% with only these two factors and their interaction (Fig. 1a; Table S1). For fungi, M had the strongest effect (25.6%, P < 0.001), followed by E (17.2%, P < 0.001) and the E × M interaction (11.8%, P < 0.001), for a total of 54.6% with only these two factors and their interaction (Fig. 1c; Table S1). Specifically, for the effect of E, EChampdôtre strongly influenced bacterial communities (Fig. 1b, circles apart from triangles and squares for EBretenière and EAuxonne), and fungal communities to a lesser extent (Fig. 1d). This finding aligns with recent work underlying the major role of soil abiotic environment in reshaping transplanted microbial communities. For instance, Bamba et al. (2024) observed that E (e.g. salt-treated vs control media) significantly shaped root (and not rhizosphere) microbiota variance by 22%. Regarding the effect of M, bacterial communities after inoculation by MChampdôtre were strongly segregating from those inoculated with MBretenière and MAuxonne (Fig. 1b). Fungal communities that received MChampdôtre or MBretenière were more stable across environments than those inoculated with MAuxonne (Fig. 1d). This also echoes previous findings (Bamba et al., 2024) that M (microbial inoculants from adjacent salt-treated or untreated fields) was responsible for 22% of root microbiota variance. G effects on rhizosphere microbiota were significant but minor (bacteria: 0.76%, fungi: 1.10%, Fig. 1a,c; Table S1), close to previous findings showing 4% of root microbiota variation due to G (Bamba et al., 2024). Taken together, these results indicate that despite the well-documented influence of soil physicochemical parameters (Bahram et al., 2018; Delgado-Baquerizo et al., 2018), host genotype (Yadav et al., 2023), and their interaction (Berg & Smalla, 2009), inoculated microbiota can effectively alter rhizosphere community structures. Although M and E are not strictly independent – since the microbial inoculants were derived from the same soils defining the abiotic environments – these results about bacterial and fungal rhizosphere communities at the end of the experiment support the relevance of this specific setup for estimating plant trait microbiability. The power simulation showed that our design allows us to detect potential effects of most of the terms of the G × E × M model (Fig. S2), since the simulated number of replicates to reach a power of 80% was below that of our experimental design, except for three interaction terms of the biomass data that needed more replications than what we actually had (G × M, E × M and G × E × M). Nevertheless, we found by chance a significant effect for G × M and G × E × M interactions (Fig. 2). These significant interactions, as well as the corresponding estimates of explained variance, should therefore be interpreted cautiously and require confirmation with a higher level of replication. In future studies of this kind, performing a power analysis would help determine which (interaction) effects can realistically be detected and guide the design of an optimal experimental setup. This includes choosing the appropriate number of genotypes, microbiota, environments, and biological replicates required for robust and reliable detection of effects. Shoot biomass was well explained by the three factors and their interactions (80.5% of variance explained), mainly shaped by the effect of environment (53.4%, Figs 2a, S3), due to differences in soil properties (Table S2). The following more influential effects were M (8.1%, Figs 2a, S4) and G (6.1%, Fig. 2a). When summing all its significant contributions (M + G × E × M + G × M), M was involved in 14.1% of the variance, and G in 18.2%. The simple M effect was thus higher than previous estimates for shoot and root lengths of Lotus japonicus (0.8% and 4%, respectively). But when summed with all significant interaction effects, it was comparable (M + G × E × M + G × M, 7.0% and 17.5% for shoot and root lengths, respectively) (Bamba et al., 2024). MAuxonne and MBretenière had similar effects on biomass, whereas MChampdôtre was associated with significantly lower biomass (MAuxonne: +144.6% and MBretenière: +144.7% compared to MChampdôtre; Fig. S4). The dominant influence of E over M and G likely reflects differential soil fertility: the lowest shoot biomass was observed in the soil of Champdôtre, with the lowest N, P, K, and C content, and the highest shoot biomass was observed in the Auxonne soil which had the highest K content (however, the Bretenière soil had the highest N and P content). Other properties like texture, pH, CaCO3, and CEC could also interfere. M effect may be due to the involvement of microbiota in nutrient cycling and G effect due to differences in nutrient uptake efficiency. The G × E × M interaction explained only 4.0% of the variance (Fig. 2b), with notable genotype-specific responses depending on the soil: in EAuxonne, only GCan was affected by M (MAuxonne: +249.2%, MBretenière: +207.3% vs MChampdôtre); in EBretenière and EChampdôtre, only GCol responded to M (MAuxonne: +197.3% and MBretenière: +306.1% vs MChampdôtre). Hidden by the G × E × M interaction, there was a small significant G × M interaction (2.0%), confirming that genotypes were not responding in the same way to M across all soils (Fig. S5). Taken together, these findings suggest that microbiota effects on shoot biomass were of the same importance as G effects, primarily genotype-independent and mediated through resource availability and soil context. Pathogen resistance was strongly modulated by host genotype, with microbiota acting through genotype-specific interactions. In total, 63.5% of the variance in leaf necrosis size was explained by the model. The most influential factor was G (28.9%), followed by G × E × M (14.8%), E (6.3%), and G × M (5.6%, Fig. Table S1). M had significant effect However, when summing all its significant interactions, M for of necrosis size variance. This is higher than G × M and G × E × M contributions observed for Lotus japonicus traits and for shoot and and for root respectively) (Bamba et al., 2024). GCan showed the necrosis size and compared to GCol and Fig. consistently with its higher than genotypes et al., The strong influence likely reflects of host in interactions. depending on the microbiota GCol can as as more (Fig. indicating that M can for a of genetic was by M of necrosis size of GCan was mainly influenced by E (e.g. effect of MChampdôtre EChampdôtre and and GCol was the most to M, with from to depending on E (Fig. The G × M interaction that, with genotypes in the in terms of size of GCan GCol et al., whereas MChampdôtre the of GCol GCol and MBretenière its to the level of the most genotype GCol = (Fig. M with pathogen through genotype- and environment-dependent the idea that microbiota effects on disease resistance were mediated by the plant strongly contribute to plant phenotypic variance and may part of the in plant traits. We found that microbiability was to for M to 14.1% microbiota-driven interaction effects for shoot biomass, comparable with G effects. M interaction effects explained to of variance for leaf necrosis a effect of This is close to results in animal like in which the microbiota explained of variance, and host genotype both effects being largely independent (Difford et al., 2018). results the potential of soil microbial communities to contribute to the unexplained phenotypic variance – often referred to as ‘missing heritability’ (Maher, et al., 2011). studies experimental and microbiota et al., are needed to determine this variance is host genotype effects on microbiota are well (Yadav et al., 2023), our findings for into the of plant traits mediated by microbial communities (Mueller & Linksvayer, 2022). important M effects observed in our experimental likely to an of microbiability and microbiota-driven interaction effects. were for their contrasting physicochemical their (Table S2), and genotypes were on known differences in pathogen resistance et al., but microbiota were not on they were from influence have been had the communities from soils with effects on plant growth or disease et al., et al., rhizosphere microbiota, rather than soil microbiota, have the effects on plants since rhizosphere soils are with due to (Lemanceau et al., 2017; et al., 2019). The of autoclaved not soils could also modulate microbial effects, their due to In inoculants effects but could community In soils, may the of a in estimating M effects in autoclaved studies at M effects in the would require an and such as the of species in to community structure (Liu & 2024) and microbiota et al., findings that microbiota influenced plant depending on the targeted plant trait biomass or pathogen which has important implications for Specifically, the trait in should guide how microbiota are into programs. 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