Genome-Scale Modeling Specifies the Metabolic Capabilities of Rhizophagus irregularis

ABSTRACT Rhizophagus irregularis is one of the most extensively studied arbuscular mycorrhizal fungi (AMF) that forms symbioses with and improves the performance of many crops. Lack of transformation protocol for R. irregularis renders it challenging to investigate molecular mechanisms that shape the physiology and interactions of this AMF with plants. Here, we used all published genomics, transcriptomics, and metabolomics resources to gain insights into the metabolic functionalities of R. irregularis by reconstructing its high-quality genome-scale metabolic network that considers enzyme constraints. Extensive validation tests with the enzyme-constrained metabolic model demonstrated that it can be used to (i) accurately predict increased growth of R. irregularis on myristate with minimal medium; (ii) integrate enzyme abundances and carbon source concentrations that yield growth predictions with high and significant Spearman correlation (ρS = 0.74) to measured hyphal dry weight; and (iii) simulate growth rate increases with tighter association of this AMF with the host plant across three fungal structures. Based on the validated model and system-level analyses that integrate data from transcriptomics studies, we predicted that differences in flux distributions between intraradical mycelium and arbuscles are linked to changes in amino acid and cofactor biosynthesis. Therefore, our results demonstrated that the enzyme-constrained metabolic model can be employed to pinpoint mechanisms driving developmental and physiological responses of R. irregularis to different environmental cues. In conclusion, this model can serve as a template for other AMF and paves the way to identify metabolic engineering strategies to modulate fungal metabolic traits that directly affect plant performance. IMPORTANCE Mounting evidence points to the benefits of the symbiotic interactions between the arbuscular mycorrhiza fungus Rhizophagus irregularis and crops; however, the molecular mechanisms underlying the physiological responses of this fungus to different host plants and environments remain largely unknown. We present a manually curated, enzyme-constrained, genome-scale metabolic model of R. irregularis that can accurately predict experimentally observed phenotypes. We show that this high-quality model provides an entry point into better understanding the metabolic and physiological responses of this fungus to changing environments due to the availability of different nutrients. The model can be used to design metabolic engineering strategies to tailor R. irregularis metabolism toward improving the performance of host plants.


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I noted the experimental data of the fungal biomass was cited from other reports. This should be done by the authors by themselves, since the model was constructed on the assumption basis that was made under certain conditions. Therefore,experimental data are needed to verify the model.

Reviewer #2 (Comments for the Author):
This manuscript offers a new genome-scale model of R. irregularis. The model is validated with significant experimental data, which demonstrates the value of the model. The model is shown to be somewhat quantitatively predictive of experimental phenotypes. Overall, the manuscript is well written with a novel model and some novel methods which should be of general interest. I am left with only a few questions and concerns, although in full disclosure, I have reviewed this manuscript previously and the authors have already responded to my initial round of questions and concerns. Here I am reviewing the manuscript as it stands now in the context of submission to msystems.
Questions and comments: 1.) Any thoughts as to why the fluxes from iAL1006 and the iRi1574 had a narrower range compared to other models? The result is mentioned around line 218 but never really explained.
2.) Can the authors speak to the energy metabolism of their model (a critical detail in all models)? What ATP yield is produced from the input sugars?
3.) The myristate results described around line 265 are somewhat confusing. The implication seems to be that myristate can replace a portion of the palmitate for fatty acid production? Is this correct? 4.) Can the authors briefly explain in the manuscript why eMOMENT products strong correlations while FBA produces negative correlations with experimental data? 5.) In the study described around line 369, was transcriptomic data used instead of protein abundance data in the model, and can the authors justify that in these conditions the two datatypes are interchangeable?

Reviewer #1 (Comments for the Author):
I noted the experimental data of the fungal biomass was cited from other reports. This should be done by the authors by themselves, since the model was constructed on the assumption basis that was made under certain conditions. Therefore,experimental data are needed to verify the model.
We have used all available experimental data for R. irregularis published to date, and made sure to specify where the data come from and how they were generated, to ensure that their usage in modelling is well-justified.

Reviewer #2
1.) Any thoughts as to why the fluxes from iAL1006 and the iRi1574 had a narrower range compared to other models? The result is mentioned around line 218 but never really explained.
To find an explanation for this observation, we investigate the coupling of the reactions in each of the models to the respective objective, i.e., the biomass reaction. We then classified the flux-carrying reactions into: (1) hard-coupled to the objective if the range of a reaction was equal to the range of the biomass reaction with a tolerance of 10 −3 , (2) soft-coupled to the objective if the range was between zero and the range of the biomass reaction, (3) partially-coupled to the objective if the range was between the range of the biomass reaction and the maximum (i.e. 2000 / /ℎ ), and (4) uncoupled of the reaction range is equal to the maximum.
As a result, we found the both the iAL1006 and the iRi1574 model have the highest number of reactions the are hard-and soft-coupled to the biomass reaction, which provides an explanation for the narrow flux ranges at optimal biomass ( Fig S3).
2.) Can the authors speak to the energy metabolism of their model (a critical detail in all models)?
What ATP yield is produced from the input sugars?
We determined the ATP yield from all carbon sources (as single carbon sources) shown in Figure S3 by maximizing the flux through a sink reaction for cytosolic ATP while guaranteeing a growth rate of 50% or 0% of the predicted optimum ( Fig S5). As a result, we observed that none of the carbon sources allowed for unlimited ATP production, which can happen when the models contain stoichiometrically-balanced cycles. The highest ATP production can be achieved from utilizing trehalose, while the highest ATP yield can be achieved from myristate. The high ATP production from trehalose can be explained by the fact that trehalose is a substrate of the biomass reaction. With trehalose not being growth-limiting, the excess trehalose that is not used for optimal growth can therefore be used for ATP production. 3.) The myristate results described around line 265 are somewhat confusing. The implication seems to be that myristate can replace a portion of the palmitate for fatty acid production? Is this correct?
Myristate cannot be elongated to produce palmitate as the required enzyme machinery is missing.
However, it can restore biomass production via energy generation through beta-oxidation. We The addition to enzyme costs has been shown to increase the performance of constraint-based metabolic models. This is why we see a strong correlation with the eMOMENT approach while the results from FBA are rather unrealistic. Please note that the points for FBA and eMOMENT shown in Figure 2 are not completely mirrored, i.e., the relative increase or decrease in growth in comparison with the calculated growth rate are not exactly the opposite. The negative correlation may arise from the re-scaling of the biomass reaction with respect to the total protein content as well as from the usage of the enzyme constraints.
5.) In the study described around line 369, was transcriptomic data used instead of protein abundance data in the model, and can the authors justify that in these conditions the two datatypes are interchangeable?
We are not aware of a study that investigated absolute protein abundances of the different fungal structures. Therefore, we used transcript abundances instead as a proxy for protein abundance. We would like to point out that transcript abundances have been successfully applied to add constraints on reaction fluxes in constraint-based modelling in the past (10.1371/journal.pcbi.1000489, 10.1111/tpj.12763), despite evident low correlations between transcript and protein abundances. We did not employ these approaches because we wanted to use a similar method as we did for prediction of growth with different carbon sources (i.e., eMOMENT) for comparability. Thank you for submitting your revised article and for carefully addressing the revewiers' comments. Your manuscript has been accepted, and I am forwarding it to the ASM Journals Department for publication. For your reference, ASM Journals' address is given below. Before it can be scheduled for publication, your manuscript will be checked by the mSystems senior production editor, Ellie Ghatineh, to make sure that all elements meet the technical requirements for publication. She will contact you if anything needs to be revised before copyediting and production can begin. Otherwise, you will be notified when your proofs are ready to be viewed.
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