Climate drives rhizosphere microbiome variation and divergent selection between geographically distant Arabidopsis populations

Disentangling the contribution of climatic and edaphic factors to microbiome variation and local adaptation in plants requires an experimental approach to uncouple their effects and test for causality. We used microbial inocula, soil matrices and plant genotypes derived from two natural Arabidopsis thaliana populations in northern and southern Europe in an experiment conducted in climatic chambers mimicking seasonal changes in temperature, day length and light intensity of the home sites of the two genotypes. The southern A. thaliana genotype outperformed the northern genotype in the southern climate chamber, whereas the opposite was true in the northern climate chamber. Recipient soil matrix, but not microbial composition, affected plant fitness, and effects did not differ between genotypes. Differences between chambers significantly affected rhizosphere microbiome assembly, although these effects were small in comparison with the shifts induced by physicochemical differences between soil matrices. The results suggest that differences in seasonal changes in temperature, day length and light intensity between northern and southern Europe have strongly influenced adaptive differentiation between the two A. thaliana populations, whereas effects of differences in soil factors have been weak. By contrast, below-ground differences in soil characteristics were more important than differences in climate for rhizosphere microbiome differentiation

Tryptophan metabolism and bacterial commensals prevent fungal dysbiosis in Arabidopsis roots

In nature, roots of healthy plants are colonized by multikingdom microbial communities that include bacteria, fungi, and oomycetes. A key question is how plants control the assembly of these diverse microbes in roots to maintain host–microbe homeostasis and health. Using microbiota reconstitution experiments with a set of immunocompromised Arabidopsis thaliana mutants and a multikingdom synthetic microbial community (SynCom) representative of the natural A. thaliana root microbiota, we observed that microbiota-mediated plant growth promotion was abolished in most of the tested immunocompromised mutants. Notably, more than 40% of between-genotype variation in these microbiota-induced growth differences was explained by fungal but not bacterial or oomycete load in roots. Extensive fungal overgrowth in roots and altered plant growth was evident at both vegetative and reproductive stages for a mutant impaired in the production of tryptophan-derived, specialized metabolites (cyp79b2/b3). Microbiota manipulation experiments with single- and multikingdom microbial SynComs further demonstrated that 1) the presence of fungi in the multikingdom SynCom was the direct cause of the dysbiotic phenotype in the cyp79b2/b3 mutant and 2) bacterial commensals and host tryptophan metabolism are both necessary to control fungal load, thereby promoting A. thaliana growth and survival. Our results indicate that protective activities of bacterial root commensals are as critical as the host tryptophan metabolic pathway in preventing fungal dysbiosis in the A. thaliana root endosphere

A robust sequential hypothesis testing method for brake squeal localisation

This contribution deals with the in situ detection and localisation of brake squeal in an automobile. As brake squeal is emitted from regions known a priori, i.e., near the wheels, the localisation is treated as a hypothesis testing problem. Distributed microphone arrays, situated under the automobile, are used to capture the directional properties of the sound field generated by a squealing brake. The spatial characteristics of the sampled sound field is then used to formulate the hypothesis tests. However, in contrast to standard hypothesis testing approaches of this kind, the propagation environment is complex and time-varying. Coupled with inaccuracies in the knowledge of the sensor and source positions as well as sensor gain mismatches, modelling the sound field is difficult and standard approaches fail in this case. A previously proposed approach implicitly tried to account for such incomplete system knowledge and was based on ad hoc likelihood formulations. The current paper builds upon this approach and proposes a second approach, based on more solid theoretical foundations, that can systematically account for the model uncertainties. Results from tests in a real setting show that the proposed approach is more consistent than the prior state-of-the-art. In both approaches, the tasks of detection and localisation are decoupled for complexity reasons. The localisation (hypothesis testing) is subject to a prior detection of brake squeal and identification of the squeal frequencies. The approaches used for the detection and identification of squeal frequencies are also presented. The paper, further, briefly addresses some practical issues related to array design and placement. (C) 2019 Author(s)

Application and comparative performance of network modularity algorithms to ecological communities classification

Network modularity is a well-studied large-scale connectivity pattern in networks. The detection of modules in real networks constitutes a crucial step towards a description of the network building blocks and their evolutionary dynamics. The performance of modularity detection algorithms is commonly quantified using simulated networks data. However, a comparison of the modularity algorithms utility for real biological data is scarce. Here we investigate the utility of network modularity algorithms for the classification of ecological plant communities. Plant community classification by the traditional approaches requires prior knowledge about the characteristic and differential species, which are derived from a manual inspection of vegetation tables. Using the raw species abundance data we constructed six different networks that vary in their edge definitions. Four network modularity algorithms were examined for their ability to detect the traditionally recognized plant communities. The use of more restrictive edge definitions significantly increased the accuracy of community detection, that is, the correspondence between network-based and traditional community classification. Random-walk based modularity methods yielded slightly better results than approaches based on the modularity function. For the whole network, the average agreement between the manual classification and the network-based modules is 76% with varying congruence levels for different communities ranging between 11% and 100%. The network-based approach recovered the known ecological gradient from riverside – sand and gravel bank vegetation – to dryer habitats like semidry grassland on dykes. Our results show that networks modularity algorithms offer new avenues of pursuit for the computational analysis of species communities