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   PID: hpt/lnx 1.9.0-cur 2019-01-08   
   TID: hpt/lnx 1.9.0-cur 2019-01-08   
    Building models to predict interactions in plant microbiomes    
      
     Date:   
         July 7, 2023   
     Source:   
         ETH Zurich   
     Summary:   
         Microbiomes play a key role for plant health and could   
         make agriculture more sustainable -- but the principles   
         behind the assembly of their communities have remained largely   
         unknown. Researchers have shown how bacteria can compete for food,   
         but also cooperate thanks to differences in metabolism -- resulting   
         in stably structured communities. Their models can accurately   
         predict these interactions and can help to design microbiomes for   
         specific applications in the future.   
      
      
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   Plants, animals, and humans are all home to numerous microorganisms such   
   as bacteria and fungi. These form complex communities that have a profound   
   impact on the health of their host. One notable microbiome is that of the   
   human gut, which helps digest our food and protect us against pathogens.   
      
   Plants are also host to microbial communities on their roots and   
   leaves. These communities can promote growth and keep off harmful   
   bacteria. Plant microbiomes therefore have the potential to make   
   agriculture more sustainable. However, we currently only have a   
   rudimentary understanding of the interspecies interactions that shape   
   these microbial communities.   
      
   Why is it that these communities tend to be populated only by certain   
   kinds of microbes and not others? "We already knew that leaf microbiomes   
   weren't just some random collections of microbes," says Julia Vorholt,   
   Professor of Microbiology at ETH Zurich. "But the rules that determine how   
   these communities form and what mechanisms shape their makeup remained to   
   be found."  Now, a team of researchers led by Vorholt has identified just   
   such an organising principle for the bacteria that live on the leaves   
   of the model plant Arabidopsis thaliana(thale cress). The researchers   
   have developed a set of models that use the nutrient preferences and   
   metabolic abilities of individual bacterial strains to predict how these   
   leaf surface microbes compete or cooperate with each other, thereby   
   helping us better understand the nature of the resulting microbiome.   
      
   The research team's study, which was carried out in collaboration   
   with colleagues at EPFL, has been published in the latest issue of the   
   journal Science.   
      
   Resource competition leads to distinct interactions As part of a previous   
   work, Vorholt's group had already shown that the microbial communities   
   found on plant leaves were remarkably similar. "The consistent composition   
   of these communities points to an underlying mechanism that controls   
   how the leaf microbiome is created," Vorholt says.   
      
   Martin Scha"fer, a postdoc in Vorholt's group and co-lead author on   
   the study, explains that "since all bacteria ultimately depend on   
   organic molecules as food, we asked whether we could predict the way   
   they interact by knowing which food molecules they can metabolise."   
   Alan Pacheco, also co-lead author, adds: "in a competitive environment,   
   food niches could lead to stable coexistence and collaboration, with   
   the microbes interacting for mutual advantage by exchanging resources."   
   The guiding question posed by Vorholt and her team is: Can the use the   
   metabolic capabilities of different bacteria to understand how the leaf   
   microbiome takes shape?  Carbon profiles reveal resource competition   
   To answer this question, the researchers began by testing the ability   
   of more than 200 representative strains of bacteria from Arabidopsis   
   thalianaleaves to grow using 45 different carbon sources. Using these   
   carbon profiles, they determined that there was extensive overlap between   
   the strains' food niches.   
      
   This indicates that there is fierce competition for resources.   
      
   The researchers then used these carbon profiles to build a set of reliable   
   metabolic models for all bacterial strains, and simulated interactions   
   between more than 17,500 pairs of bacteria. Consistent with the extensive   
   overlap in food niches, the simulations showed a marked dominance of   
   negative interactions: when competition causes the population of at   
   least one of the two strains to decrease.   
      
   Sidestepping competition through cooperation Despite this prevalence of   
   competition, the metabolic models also predicted positive interactions. A   
   closer analysis revealed that these cooperative interactions can be   
   traced back to the exchange of organic and amino acids. The study's   
   authors carried out plant experiments to test the models' predictions   
   and were able to confirm them to an accuracy of 89 percent.   
      
   The accuracy of the models came as a surprise even to the researchers   
   themselves: "The high degree of reliability suggests that our initial   
   assumptions about the importance of metabolic characteristics were   
   correct," Pacheco says.   
      
   Harnessing microbiomes for application "What's great about our models is   
   that they also work in reverse," Vorholt says, "in that they can be used   
   to identify mechanisms that trigger certain interaction patterns." This   
   paves the way for targeted microbiome design, which is a key prerequisite   
   for downstream applications in agriculture.   
      
   Currently, seed companies and agricultural chemical producers use   
   a process of trial and error to search for microbes that sustainably   
   support crop protection. The team's findings are therefore relevant not   
   only for fundamental research, but also for applications in microbiome   
   design for agriculture.   
      
   Vorholt is Co-Director of the Swiss National Centre of Competence   
   in Research (NCCR) Microbiomes. Her team's current study furthers the   
   research by a network of 20 groups, whose aim is to understand microbiomes   
   -- from plants to humans - - so that their vast potential for health,   
   agriculture, and environment can be realised.   
      
   This can be achieved by, for example, supplementing unbalanced communities   
   with the right microbe, removing certain species, or even treating   
   diseases with combinations of bacteria with special functions. Predictive   
   models will play a key role in this goal.   
      
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   Journal Reference:   
      1. Martin Scha"fer, Alan R. Pacheco, Rahel Ku"nzler, Miriam   
      Bortfeld-Miller,   
         Christopher M. Field, Evangelia Vayena, Vassily Hatzimanikatis,   
         Julia A.   
      
         Vorholt. Metabolic interaction models recapitulate leaf microbiota   
         ecology. Science, 2023; 381 (6653) DOI: 10.1126/science.adf5121   
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   Link to news story:   
   https://www.sciencedaily.com/releases/2023/07/230707111638.htm   
      
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