The rhizosphere supports the development and activity of a huge and diversified microbial community, including microorganisms capable to promote plant growth. Among the latter, plant growth-promoting rhizobacteria (PGPR) colonize roots of monocots and dicots, and enhance plant growth by direct and indirect mechanisms. Modification of root system architecture by PGPR implicates the production of phytohormones and other signals that lead, mostly, to enhanced lateral root branching and development of root hairs. PGPR also modify root functioning, improve plant nutrition and influence the physiology of the whole plant. Recent results provided first clues as to how PGPR signals could trigger these plant responses. Whether local and/or systemic, the plant molecular pathways involved remain often unknown. From an ecological point of view, it emerged that PGPR form coherent functional groups, whose rhizosphere ecology is influenced by a myriad of abiotic and biotic factors in natural and agricultural soils, and these factors can in turn modulate PGPR effects on roots. In this paper, we address novel knowledge and gaps on PGPR modes of action and signals, and highlight recent progress on the links between plant morphological and physiological effects induced by PGPR. We also show the importance of taking into account the size, diversity, and gene expression patterns of PGPR assemblages in the rhizosphere to better understand their impact on plant growth and functioning. Integrating mechanistic and ecological knowledge on PGPR populations in soil will be a prerequisite to develop novel management strategies for sustainable agriculture.

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Plant growth-promoting rhizobacteria and root system functioning.

Indole-3-acetic acid (IAA) is an important phytohormone with the capacity to control plant development in both beneficial and deleterious ways. The ability to synthesize IAA is an attribute that many bacteria including both plant growth-promoters and phytopathogens possess. There are three main pathways through which IAA is synthesized; the indole-3-pyruvic acid, indole-3-acetamide and indole-3-acetonitrile pathways. This chapter reviews the factors that effect the production of this phytohormone, the role of IAA in bacterial physiology and in plant–microbe interactions including phytostimulation and phytopathogenesis.

Indole-3-acetic acid in plant–microbe interactions

Plants do not grow as axenic organisms in nature, but host a diverse community of microorganisms, termed the plant microbiota. There is an increasing awareness that the plant microbiota plays a role in plant growth and can provide protection from invading pathogens. Apart from intense research on crop plants, Arabidopsis is emerging as a valuable model system to investigate the drivers shaping stable bacterial communities on leaves and roots and as a tool to decipher the intricate relationship among the host and its colonizing microorganisms. Gnotobiotic experimental systems help establish causal relationships between plant and microbiota genotypes and phenotypes and test hypotheses on biotic and abiotic perturbations in a systematic way. We highlight major recent findings in plant microbiota research using comparative community profiling and omics analyses, and discuss these approaches in light of community establishment and beneficial traits like nutrient acquisition and plant health.

The Plant Microbiota: Systems-Level Insights and Perspectives

In addition to its role in water and nutrient uptake, the root system is fundamentally important because it anchors a plant to its substrate. Although a wide variety of root systems exist across different species, all plants have a primary root (derived from an embryonic radicle) and different types of lateral roots. Adventitious roots, by comparison, display the same functions as lateral roots but develop from aerial tissues. In addition, they not only develop as an adaptive response to various stresses, such as wounding or flooding, but also are a key limiting component of vegetative propagation. Lateral and adventitious roots share key elements of the genetic and hormonal regulatory networks but are subject to different regulatory mechanisms. In this review, we discuss the developmental processes that give rise to lateral and adventitious roots and highlight knowledge acquired over the past few years about the mechanisms that regulate adventitious root formation.

Adventitious Roots and Lateral Roots: Similarities and Differences

Plants are rife with bacteria and fungi that colonize roots and shoots both externally and internally. By providing novel nutritional and defense pathways and influencing plant biochemical pathways, microbes can fundamentally alter plant phenotypes. Here we review the widespread nature of microbially mediated plant functional traits. We highlight that there is likely fitness conflict between hosts and symbionts and that fitness outcomes can depend on partner genotypes and ecological factors. Microbes may influence ecosystems through their effects on the functional trait values and population dynamics of their plant hosts. These effects may feed back on symbiont evolution by altering transmission rates of symbionts and scale up to ecosystem processes and services. We end by proposing new avenues of research in this emerging field.

Microbially Mediated Plant Functional Traits

Association studies have been widely used to search for associations between common genetic variants observations and a given phenotype However, it is now generally accepted that genes and environment must be examined jointly when estimating phenotypic variance In this work we consider two types of biological markers: genotypic markers, which characterize an observation in terms of inherited genetic information, and metagenomic marker which are related to the environment Both types of markers are available in their millions and can be used to characterize any observation uniquely Our focus is on detecting interactions between groups of genetic and metagenomic markers in order to gain a better understanding of the complex relationship between environment and genome in the expression of a given phenotype We propose a novel approach for efficiently detecting interactions between complementary datasets in a high-dimensional setting with a reduced computational cost The method, named SICOMORE, reduces the dimension of the search space by selecting a subset of supervariables in the two complementary datasets These supervariables are given by a weighted group structure defined on sets of variables at different scales A Lasso selection is then applied on each type of supervariable to obtain a subset of potential interactions that will be explored via linear model testing We compare SICOMORE with other approaches in simulations, with varying sample sizes, noise, and numbers of true interactions SICOMORE exhibits convincing results in terms of recall, as well as competitive performances with respect to running time The method is also used to detect interaction between genomic markers in Medicago truncatula and metagenomic markers in its rhizosphere bacterial community An R package is available [4], along with its documentation and associated scripts, allowing the reader to reproduce the results presented in the paper

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Fast computation of genome-metagenome interaction effects

Transmission of Cauliflower mosaic virus (CaMV) by aphids depends on the presence of viral inclusions, the Transmission Bodies (TB), in infected plant cells. TB contain the aphid transmission factor, the viral protein P2, and the viral protein P3. When TB do not form, no transmission occurs even when infected cells contain functional P2 (Khelifa et al. 2007). Thus, TB are structures specialised for transmission, hence our interest to study their formation and function (Martiniere et al. 2009). We detected that stress induces import of apparently soluble tubulin into TB. FRAP experiments indicated a high turnover rate of TB-contained tubulin. In aphid transmission experiments, we found that aphids fed on stressed infected leaves transmitted CaMV better than aphids fed on control leaves, that there was a positive correlation between tubulin entry in TB and transmission efficiency, and that aphid punctures themselves might induce rapid (within seconds) tubulin influx into TB. The Ca2+ ionophor A23187 induced tubulin influx into TB; the Ca2+ channel blocker La3+ completely inhibited transmission. The microtubule depolymeriser oryzalin inhibited transmission indicating involvement of microtubules in CaMV transmission. Finally, incubation of infected protoplasts with NaN3 induced disintegration of TB and relocalisation of P2 and virions on microtubules, concomitant with drastically increased CaMV transmission. Preliminary data indicate that also ROS might induce TB disintegration. Taken together, our results indicate that a Ca2+ signalling cascade, which might be triggered as an early plant defence response to exploratory intracellular stylet punctures of the aphid vector, "activates" the otherwise "dormant" TB for transmission by causing massive entry of tubulin in TB, possibly followed in a second step by redistribution of P2 and virions on microtubules all over the cell. Thus it seems that CaMV deflects host perception and signalling pathways to perceive the presence of the aphid vector and to actively prepare its own acquisition. (Resume d'auteur)

Cauliflower mosaic virus uses the plant host cell to sense the aphid vector and optimise its own transmission

Motivation. Association studies have been widely used to search for associations between common genetic variants observations and a given phenotype. However, it is now generally accepted that genes and environment must be examined jointly when estimating phenotypic variance. In this work we consider two types of biological markers: genotypic markers, which characterize an observation in terms of inherited genetic information, and metagenomic marker which are related to the environment. Both types of markers are available in their millions and can be used to characterize any observation uniquely. Objective. Our focus is on detecting interactions between groups of genetic and metagenomic markers in order to gain a better understanding of the complex relationship between environment and genome in the expression of a given phenotype. Contributions. We propose a novel approach for efficiently detecting interactions between complementary datasets in a high-dimensional setting with a reduced computational cost. The method, named SICOMORE, reduces the dimension of the search space by selecting a subset of supervariables in the two complementary datasets. These supervariables are given by a weighted group structure defined on sets of variables at different scales. A Lasso selection is then applied on each type of supervariable to obtain a subset of potential interactions that will be explored via linear model testing. Results. We compare SICOMORE with other approaches in simulations, with varying sample sizes, noise, and numbers of true interactions. SICOMORE exhibits convincing results in terms of recall, as well as competitive performances with respect to running time. The method is also used to detect interaction between genomic markers in Medicago truncatula and metagenomic markers in its rhizosphere bacterial community. Software availability. A R package is available, along with its documentation and associated scripts, allowing the reader to reproduce the results presented in the paper.

Anouk Zancarini

Papers

Fast computation of genome-metagenome interaction effects

Cauliflower mosaic virus uses the plant host cell to sense the aphid vector and optimise its own transmission

Fast Computation of Genome-Metagenome Interaction Effects