Metabolic and genetic basis for auxotrophies in Gram-negative species

Significance Nutrient requirements play an important role in host–microbe interactions, and can heavily affect the composition of microbial communities by setting hard constraints on microbe–microbe interactions. The auxotrophic capabilities of strains and their underlying genetic and metabolic basis have rarely been studied on a large scale. This paper presents a computational approach using genome-scale models of metabolism and genomic sequences to predict auxotrophies in over 1,300 Gram-negative strains. Our network-based approach identifies auxotrophs, their nutrient requirements, and the corresponding causal genetic and metabolic basis, making it one of the most comprehensive efforts in large-scale strain-specific auxotrophy prediction.

L-cysteine to simulate growth. Gene essentiality was then determined using the cobra.flux_analysis.single_gene_deletion 48 command from the COBRApy package (10). A gene was deemed to be essential when its knockout resulted in a maximal 49 growth of less than 0.001 hr − 1 or when the solution status was not optimal. We proceeded to identify the conditionally 50 essential genes by allowing for the exchange of all modeled extracellular nutrients and predicting gene essentiality under the 51 simulated rich medium condition. The set of conditionally essential genes were extracted by subtracting the predicted set 52 of essential genes in rich medium from the set of predicted essential genes in minimal medium. A similar methodology was 53 followed to identify CEGs in anaerobic conditions in which case the uptake of glucose was blocked (by setting the lower bound 54 to the corresponding exchange reaction to 0).

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Comparative genomics to identify missing CEGs. A reference sequence for each genus was chosen based on the availability of 56 high quality GEMs; iML1515 models E.coli K12 strain MG1655 (PATRIC accession number: 511145.12), STM_v1.0 models S. 57 enterica ser. Typhimurium strain LT2, iPC815 models Y. pestis strain CO92, iJN1411 models P. putida strain KT2440, iYL1228 58 models K. pneumoniae strain MGH 78578 and iPAU1129 models P. aeruginosa strain PA14. In each strain, we searched for 59 homologous sequences which matched the modelled metabolic genes in the reference strain by running a bi-directional best 60 BLASTp hit (PID > 70 and e-val <0.0001) (11). Whenever a homologous sequence was found for a gene in a given strain's 61 genome, we assumed that it was functional (i.e., we did not account for pseudogenization events). We made this rigorous 62 assumption to decrease the false positive rate of gene absence calling. During the first iteration of this workflow, we manually 63 checked the locus of each of the missing essential genes across all relevant strains to adjust the best bi-directional BLAST hit 64 percent ID threshold (which was initially set at 80%). Care was taken to list out the cases where we found paralogs to a gene Prediction of auxotrophy (AuxoFind). We wrote a custom optimization algorithm (AuxoFind) by modifying an existing algorithm which was initially developed for the purpose of predicting missing reactions in a GEM that would reconcile in silico predictions 72 with in vitro observations (12). Instead of minimizing the number of reactions that need to be added to a network to achieve growth, we minimized the number of reactions allowing the free influx of extracellular metabolites that were not contained in 74 the minimal medium when a set of genes were removed. To support our results, we analyzed the shadow prices for extracellular 75 metabolites, which indicate the sensitivity of the predicted growth to their quantity (13). We found that both methods led to 76 similar predictions but that complex auxotrophic profiles (such as the simultaneous dependence on multiple nutrients) could 77 not be captured using shadow prices alone. In addition, AuxoFind can predict alternative solutions, and sub-optimal solutions.

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The absence of a CEG in a strain's genome may indicate a nutrient auxotrophy. In the previous step, we identified the 79 missing CEGs across all strains. For each CEG, we identified the metabolic reactions which cannot occur if it is absent by 80 we applying the command "cobra.manipulation.find_gene_knockout_reactions" available in the COBRApy package (10). 81 We subsequently blocked the identified reactions by setting the corresponding upper and lower bound to 0. To identify the 82 necessary supplementations needed to rescue growth, we changed the objective of the optimization problem to minimizing the 83 number of additional constraints on nutrient exchanges to relax (excluding those that allow the uptake of nutrients available in 84 M9+glucose minimal medium). In other words, we searched for the minimal number of extracellular nutrients needed to rescue 85 growth of the mutant GEM. 86 We set the lower bound for the biomass function to 50% of the simulated value for wild type growth and the mixed integer 87 linear programming solver configuration feasibility tolerance to 10 − 9. We subsequently extract the stored solutions from the 88 optimization Gurobi's "solver.problem.SolCount" and used the single search tree method to expose all possible epsilon-optimal 89 alternative solutions using Gurobi's PoolSearchMode (14). We lowered the solver configuration feasibility tolerance because 90 with the default settings, metabolite requirements for biomass precursors with low objective coefficient could not initially be 91 predicted. The resulting nutrient supplementations predicted by the modified optimization problem were added to in silico 92 medium for the mutant GEMs to verify that in silico growth could be achieved. We filtered the results in which the predicted 93 requirement was for membrane precursors (such as glycolipids) because the predicted requirement is likely caused by the fact 94 that the strain produces different biomass components (e.g. structural variation of the O-antigen across Gram-negatives).

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Algorithm (AuxoFind). Input: list of missing CEGs, list of extracellular nutrients to choose from for supplementation.

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Step 1. For each CEG, find and knock-out the relevant reactions.

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Step 2. Set the lower bound to all exchange reactions to -10 mMol/gDW/hr. 98 Step 3. Solve the MILP:    S3. Nutrient supplementation experiments for Y. ruckeri str. NVH_3758 and YRB. M9 +glucose minimal medium was supplemented with a total of 8 nutrients including L-valine, L-arginine, L-methionine, L-isoleucine, L-cysteine, L-phenylalanine, R-pantothenate and thiamine. Nine media were tested, including M9 + glucose, M9 + glucose + 8 supplements, and M9 + glucose + 7 supplements (iterating through each of the 8 nutrients). For example, valine auxotrophy (yellow) was tested by removing valine from the richest media type.      Note that all strains were tested for growth on M9 + glucose minimal medium (as a negative control) and Luria Bertani broth (as a positive control).
♠ For these experiments, we iteratively removed one of the nutrients from the complete medium (consisting of M9 + glucose + all supplements) and tested growth at each iteration.
Here, the aim was to assess the specificity of purine and pyrimidine requirements. We thus tested growth of on media in which either adenine or adenosine was included, and similarly either cytidine or uridine was included.