Host-mediated microbiome engineering (HMME) of drought tolerance in the wheat rhizosphere

Abstract

Host-mediated microbiome engineering (HMME) is a strategy that utilizes the host phenotype to indirectly select microbiomes though cyclic differentiation and propagation. In this experiment, the host phenotype of delayed onset of seedling water deficit stress symptoms was used to infer beneficial microbiome-host interactions over multiple generations. By utilizing a host-centric selection approach, microbiota are selected at a community level, therein using artificial selection to alter microbiomes through both ecological and evolutionary processes. After six rounds of artificial selection using host-mediated microbiome engineering (HMME), a microbial community was selected that mediated a 5-day delay in the onset of drought symptoms in wheat seedlings. Seedlings grown in potting medium inoculated with the engineered rhizosphere from the 6th round of HMME produced significantly more biomass and root system length, dry weight, and surface area than plants grown in medium similarly mixed with autoclaved inoculum (negative control). The effect on plant water stress tolerance conferred by the inoculum was transferable at subsequent 10-fold and 100-fold dilutions in fresh non-autoclaved medium but was lost at 1000-fold dilution and was completely abolished by autoclaving, indicating the plant phenotype is mediated by microbial population dynamics. The results from 16S rRNA amplicon sequencing of the rhizosphere microbiomes at rounds 0, 3, and 6 revealed taxonomic increases in proteobacteria at the phylum level and betaproteobacteria at the class level. There were significant decreases in alpha diversity in round 6, divergence in speciation with beta diversity between round 0 and 6, and changes in overall community composition. This study demonstrates the potential of using the host as a selective marker to engineer microbiomes that mediate changes in the rhizosphere environment that improve plant adaptation to drought stress.

Natural Holobiome Engineering by Using Native Extreme Microbiome to Counteract the Climate Change Effects

“…Although this new theory is mainly related to biotic stresses, Liu et al (2015) showed that the respiratory metabolic capacity of mycorrhizal rice (Oryza sativa) under low-temperature stress accelerated the biosynthesis of strigolactone to recruit AMF (Glomus intraradices). On the other hand, in a study published by Jochum et al (2019b), it was possible to modify the phenotype of Triticum aestivum subsp. aestivum, using host-mediated microbiome engineering as a strategy to improve the resistance of this crop to drought stress.…”

“…In this context, microbiome engineering has recently emerged as an alternative to promote positive interactions between microorganisms and host plants (Mueller and Sachs, 2015;Sheth et al, 2016;Foo et al, 2017;Timmusk et al, 2017;Trivedi et al, 2017;Hussain, 2019). Recently, Jochum et al (2019b) have performed host-mediated microbiome engineering to attribute drought resistance in wheat plants, which demonstrates the potential for modification of native microbiomes to attribute crop resistance. Thus, the transfer of whole microbiomes to improve crop yields under different stress situations could represent an unparalleled advance to start a new revolution in the bioinoculants development (Santoyo et al, 2017;Woo and Pepe, 2018).…”

Artificially selecting bacterial communities using propagule strategies†

“…These challenges of engineering community functions have motivated a surge of interest in evolution-inspired approaches, which treat the community as a unit of selection and explore its ecological landscape in search of consortia with desirable traits (Swenson et al 2000b;Arias-Sánchez et al 2019). This approach has been found to work in silico (Xie et al 2019;Williams 2007;Williams and Lenton 2007;Penn 2003;Doulcier et al 2020) and it has been attempted several times in the laboratory to optimize functions such as toxin removal (Swenson et al 2000a), the manipulation of environmental pH (Swenson et al 2000b), or the modulation of various host traits (Panke-Buisse et al 2015Swenson et al 2000b;Mueller et al 2016;Mueller and Sachs 2015;Gopal and Gupta 2016;Jochum et al 2019). The results of these experimental studies have been mixed (Blouin et al 2015;Arora et al 2019), and it is becoming increasingly clear that the details of how exactly the "offspring" communities are generated from the "parental" communities can be critical for the success of this approach (Raynaud et al 2019;Mueller et al 2016).…”

Artificially selecting microbial communities using propagule strategies

“…The difficulty of engineering community functions from the bottom-up (Escalante et al 2015) has motivated a surge of interest in evolutionary design (Bentley 1999) approaches, which treat the community as a unit of selection and explore the ecological landscape in search of consortia with desirable traits (Arias-Sánchez et al 2019) . This approach has been found to work in silico (Penn 2003,Williams andLenton 2007a,b;Doulcier et al 2019;Xie et al 2019) and it has been attempted several times in the laboratory to optimize functions such as toxin removal (Swenson et al 2000a) , the manipulation of environmental pH (Swenson et al 2000b) , or the modulation of various host traits (Swenson et al 2000b;Mueller and Sachs 2015;Panke-Buisse et al 2015Gopal and Gupta 2016;Mueller et al 2016;Jochum et al 2019) . The results of these experimental studies have been mixed (Blouin et al 2015;Arora et al 2019) , and it is becoming increasingly clear that the details of how exactly the "offspring" communities are generated from the "parental" communities can be critical for the success of this approach (Mueller et al 2016;Raynaud et al 2019) .…”