A metabolic modeling-based framework for predicting trophic dependencies in native rhizobiomes of crop plants

Reviewer #1 (Public Review):

The work by Ginatt et al. uses genome-scale metabolic modeling to identify and characterize trophic interactions between rhizosphere-associated bacteria. Beyond identifying microbial species associated with specific host and soil traits (e.g., disease tolerance), a detailed understanding of the interactions underlying these associations is necessary for developing targeted microbiome-centered interventions for plant health. It has nonetheless remained challenging to define the roles of specific organisms and metabolic species in natural rhizobiomes. Here, the authors combine microbial compositional data obtained through metagenomic sequencing with a new collection of genome-scale models to predict interactions in the native rhizosphere communities of apple rootstocks. To do this, they have established processes to integrate these sources of data and model specific trophic exchanges, which they use to obtain testable hypotheses for targeted modulation of microbiota members in situ.

The authors carry out a careful model curation process based on metagenomic sequencing data and existing model generation tools, which, together with basing the in silico medium composition on known root exudates, strengthens their predictions of interaction network features. Moreover, its reliance on genome-scale models provides a broader basis for linking sequence-based information to predictions of function on a multispecies level beyond rhizosphere microbiomes.

Having generated a set of predicted trophic interactions, the authors carried out a detailed analysis linking features of these interactions to organism taxonomy and broader ecosystem properties. Intriguingly, the organisms predicted to grow in the first iteration of their framework (i.e., on only root exudates) broadly correspond to taxonomic groups experimentally shown to benefit from these compounds. Additionally, the simulations predicted some patterns of vitamin and amino acid secretion that are known to form the basis for interactions in the rhizosphere. Together, these outcomes underscore the applicability of this method to help disentangle trophic interaction networks in complex microbiomes.

The methodology described in this paper represents a useful and promising framework to better understand the complexity of microbial interaction networks in situ. However, the degree to which the predictions can vary according to environmental composition remains difficult to quantify, and the work does not address the sensitivity of the modeling predictions beyond a simulated medium containing 33 root exudates. I find this especially important given that relatively few (84 of 243) species were predicted to grow even after cross-feeding, suggesting that a richer medium could lead to different interaction network structures. While the authors do state the importance of environmental composition and have carefully designed an in silico medium, I believe that simulating a broader set of resource pools would add necessary insight into both the predictive power of the models themselves and trophic interactions in the rhizosphere more generally.

Reviewer #2 (Public Review):

Summary:

The authors present a framework for exploiting shotgun metagenomics and metabolomics data along with constraint-based analysis (CBA) to study, in their case, the dynamics and interactions between the apple rootstock and rhizosphere's microbial community. This study should be considered as a follow-up of Berihu et al. (2022) where the shotgun data were first introduced. A set of 395 Metagenome-Assembled Genomes (MAGs) was derived from those reads and from the latter, using an automatic Genome-scale Metabolic Model (GSMM) reconstruction tool (CarveMe), 243 GSMMs. Metabolomics data from a set of studies were gathered to describe/represent root exudates. Flux Variability Analysis (FVA), a type of constraint-based analysis, was conducted iteratively. Three distinct in silico media were used (optimal, poor, and realistic, with the latter informed by metabolomics data) to examine the potential impact of root exudates on bacterial growth. Additionally, the study investigated the extent to which compounds secreted by bacteria could support the growth of other community members. Further, an exchange network representing all potential metabolic exchanges within the rhizosphere community was built and motifs on it were classified with healthy and/or symptomized soil.

Strengths:

The study provides a great starting point for how one can bring together shotgun metagenomics and other omics technologies such as metabolomics with metabolic modelling approaches. MAGs and the automatic reconstruction of corresponding GSMMs become more and more a common practice and frameworks for their analysis and interpretation are more than needed. The usage of FVA instead of the Flux Balance Analysis allows the authors to get all the range of potentially produced metabolites. The iterative approach can highlight what species are supported by the plant and which need the first to join the community while correlating microbial metabolic interactions with soil performance through differential abundance can bring up valid hypotheses to examine further. On top of that, avoiding modelling approaches that require community objective functions and optimization of that makes the simulation more realistic.

Weaknesses:

There are two main drawback approaches like the one described here, both related only partially to the authors' work yet with great impact in the presented framework. First, the usage of automatic GSMM reconstruction requires great caution. It is indicative of how the semi-curated AGORA models are still considered reconstructions and expect the user to parameterize those in a model. In this study, CarveMe was used. CarveMe is a well-known tool with several pros [1]. Yet, several challenges need to be considered when using it [2]. For example, the biomass function used might lead to an overestimation of auxotrophies. Also, as its authors admit in their reply paper, CarveMe does gap fill in a way [3]; models are constructed to ensure no gaps and also secure a minimum growth. However, curation of such a high number of GSMMs is probably not an option. Further, even if FVA is way more useful than FBA for the authors' aim, it does not yet ensure that when a species secretes one compound (let's say metabolite A), the same flux vector, i.e. the same metabolic functioning profile, secretes another compound (metabolite B) at the same time, even if the FVA solution suggests that metabolite B could be secreted in general.

Besides those challenges, the suggested framework is promising and such approaches can work as the starting point for the next step in microbial ecology studies in general; from soil to marine and host ecosystems. The authors highlight perfectly this angle stating that this framework is currently conceptual and that it can be only used to formulate new hypotheses. Unbiased constraint-based approaches that focus on metabolite exchanges would benefit such approaches.

[1] Mendoza, Sebastián N., et al. "A systematic assessment of current genome-scale metabolic reconstruction tools." Genome biology 20.1 (2019): 1-20.
[2] Price, Morgan. "Erroneous predictions of auxotrophies by CarveMe." Nature Ecology & Evolution 7.2 (2023): 194-195.
[3] Machado, Daniel, and Kiran R. Patil. "Reply to: Erroneous predictions of auxotrophies by CarveMe." Nature Ecology & Evolution 7.2 (2023): 196-197.
[4] Ylva Katarina Wedmark, Jon Olav Vik, Ove Øyås bioRxiv 2023.09.05.556413; doi: https://doi.org/10.1101/2023.09.05.556413

Reviewer #3 (Public Review):

Summary:

This study presents a solid framework for the metabolic modeling of microbial species and resources in the rhizosphere environment. It is an ambitious effort to tackle the huge complexity of the rhizosphere and reveal the plant-microbiota interactions therein. Considering previously published data by Berihu et al., going through a series of steps, the framework then finds associations between an apple tree disease state and both microbes and metabolites. The framework is well explained and motivated. I think that further work should be done to validate the method, both using synthetic data, with a known ground truth and following up on key findings experimentally.

Strengths:

- The manuscript is well written with a good balance between detail and readability. The framework steps are well-motivated and explained.

- The authors faithfully acknowledge the limitations of their approach and do not try to "over-sell" their conclusions.

- The presented framework has the potential for significant discovery if the hypotheses generated are followed up with experimental validation.

Weaknesses:

- When presenting a computational framework, best practices include running it on artificial (synthetic) data where the ground truth is known and therefore the precision and accuracy of the method may be assessed. This is not an optional step, the same way that positive/negative controls in lab experiments are not optional. Without this validation step, the manuscript is severely limited. The authors should ask themselves: what have we done to convince the reader that the framework actually works, at least on our minimal synthetic data?

Justification of claims and conclusions:

The claims and conclusions are sufficiently well justified since the limitations of this approach are acknowledged by the authors.

Author Response

We are writing this response letter with regards to the insightful feedback you provided on our manuscript titled: "A metabolic modeling-based framework for predicting trophic dependencies in native rhizobiomes of crop plants" submitted for consideration in eLife.

We sincerely appreciate the thorough and constructive reviews, seeing and fitting the intentions behind our work. We intend to fully address all points raised by the reviewers in our revised manuscript. Specifically, we plan to incorporate targeted revisions to address concerns raised during the review process, with focus on process benchmarking and validation of our framework to enhance its reliability and accuracy.

We believe that the current revision would improve the consistency and quality of the framework, making it a suitable tool for the characterization of microbial trophic interactions in diverse biological landscapes.

Thank you once again for both your time and dedication in reviewing our manuscript, as well as the constructive review.