Fig 1.
Workflow developed for the metabolic network reconstruction of iACB23LX.
The created workflow consists of eight main steps: extraction of the annotated genome, draft model reconstruction, model refinement, gap-filling, investigation of energy-generating cycles, model annotation, quality control and quality assurance (QC/QA), and model validation using experimental data. Growth simulations include the examination of growth requirements and the definition of a minimal growth medium. The last six processes are continuously iterated until the model reaches a satisfied quality and can recapitulate known phenotypes. Figure created with BioRender.com.
Table 1.
Composition of the computationally defined minimal growth medium, iMinMed.
It consists of nine transition metals, a carbon source, a nitrogen source, a sulfur source, and a phosphorus source. Oxygen was used to represent aerobic conditions.
Fig 2.
Properties of all metabolic networks for A. baumannii.
Blue highlights the metabolic network for ATCC 17978 presented in this publication. The left ordinate shows the counts, while the right ordinate represents the MEMOTE scores. The abscissa labels are annotated with the respective strains, each accompanied by the count of open reading frames (ORFs) and the percentage of model gene coverage. The reconstruction process is divided into manual (M, nocomputational tool was used to reconstruct and refine the model) and semi-automated (S, draft obtained via an automated reconstruction tool, while further extension was done manually) and is written together with the publication year. The new model presented in this work exhibits the highest quality score and is more comprehensive and complete than the preceding reconstructions.
Fig 3.
Schematic representation of the SBO and ECO terms mapping.
It follows the graphs defined in the repository for biomedical ontologies Ontology Lookup Service (OLS) [42]. The SBO terms were added using the SBOannotator tool [43]. The ECO terms annotated metabolic reactions and were declared based on the presence of GPR along with KEGG and UniProt annotations. Providing UniProt identifiers, the Protein Existence Level guides the mapping to appropriate ECO terms. Figure created with yEd [44].
Table 2.
Simulated and empirical growth rates of ATCC 17978 in various growth media.
The tested media are the computationally-defined minimal medium (iMinMed), the LB, and the SNM. Computational growth rates are given in mmol/(gDW · h), while in vitro rates in h−1. Doubling times are calculated in minutes. The media formulations are available in S1 Table.
Fig 4.
Model predictions compared to the Biolog experimental measurements for various carbon and nitrogen sources.
From the Biolog data, only substances mappable to model metabolites were included, while the M9 medium was applied. (a) and (b) The model’s ability to catabolize various carbon and nitrogen sources was assessed using the strain-specific phenotypic data by Farrugia et al. [26]. Grey indicates no growth, and orange indicates growth. Totally, 80 and 48 compounds were tested as sole carbon and nitrogen sources, respectively. Out of these, 69 and 38 phenotypes were recapitulated successfully by iACB23LX. (c) Confusion matrices of model predictions and Biolog experimental measurements. The overall accuracy of iACB23LX is 86.3% for the carbon (left matrix) and 79.2% for the nitrogen (right matrix) testings. Orange represents correct predictions, and grey represents wrong predictions.
Fig 5.
Gene essentiality analysis using iACB23LX.
(a) and (b) Distribution of the FCgr values calculated for all genes included in iACB23LX. Red lines represent FBA predictions and grey are ratios derived with MOMA. Totally 1,164 knockouts were conducted using each method in LB and rich media. (c) Classification of gene essentialities in essential, inessential, and partially essential based on their FCgr values. (d) Accuracy of gene essentiality predictions based on empirical data. The in silico results were compared to the Wang et al. transposon library [57]. The LB medium was applied to mirror the experimental settings. The metabolic network exhibited 87% accuracy with FBA (left) and MOMA (right). Beige indicates correct predictions; grey indicates incorrect predictions. (e) Comparative analysis of essential genes predicted by iACB23LX versus those identified in multiple Tn-seq studies.
Fig 6.
Collection of strain-specific A. baumannii metabolic models.
(a) Debugging workflow to curate and evaluate already published models. Following the community standards, the existing A. baumannii models were curated and transformed into re-usable, simulatable, and translatable models. Quality controls and metabolic standardized tests were conducted using MEMOTE, while the validity of the file format and syntax were examined with the SBML Validator [46]. ModelPolisher enhanced the models with missing metadata. (b) In silico-derived growth rates in various media. The empirical and predicted growth rates of iACB23LX are listed in Table 2.
Table 3.
List of genome-scale metabolic models curated for A. baumannii, along with information relevant to the manual refinement.
Default growth rates (i.e., model simulated as downloaded), the cellular compartments (C: cytosol, E: extracellular space, P: periplasm, and ER: endoplasmic reticulum), and the reactions and metabolites identifiers are listed in the table. MEMOTE scores before and after manual curation are given in the last column. Dark red highlights our reconstruction for the strain ATCC 17978. After manual curation, our model developed following our workflow in Fig 1 has the highest quality score and comes along with a minimal medium defined.