Exploring Xylella fastidiosa’s Metabolic Traits Using a GSM Model of the Phytopathogenic Bacterium

Oliveira, Alexandre; Cunha, Emanuel; Silva, Miguel; Faria, Cristiana; Dias, Óscar

doi:10.1007/978-3-031-17024-9_8

Cited by 1 publication

(3 citation statements)

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“…By assuming the maximisation of biomass production, the model predicts 88.6% and 8.6% of the glucose flux towards the Entner-Doudoroff and the non-oxidative branch of the pentose phosphate pathways, respectively (Figure 1), which is in the range of the experimental data available for other organisms: 81 − 93% and 7 − 19% [42]. A more detailed analysis of the glucose flux pattern from this model was already published previously [16].…”

Section: Glucose Flux Patternsupporting

confidence: 58%

“…pauca De Donno, named iMS509. This work was a second iteration of a previouly developed model named iMS508 [16] The model was reconstructed using merlin , using an automatic genome annotation approach. Biological databases were also considered during the process of retrieving relevant information.…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, an in-house developed GSM model of X. fastidiosa subsp. pauca De Donno was published in a conference proceedings in 2022 [16]. Considering this, this work aimed at providing a second iteration of latter the metabolic model and its respective validation [8].…”

Section: Introductionmentioning

confidence: 99%

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An updated genome-scale model forXylella fastidiosasubsp.paucaDe Donno

Oliveira

Cunha

Silva

et al. 2022

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Xylella fastidiosa is a gram-negative phytopathogenic bacterium that caused a significant economic impact around the world. In the last decade, genome-scale metabolic models have become important systems biology tools for studying the metabolic behaviour of different pathogens and driving the discovery of novel drug targets. In this work, a GSM model of X. fastidiosa subsp. pauca De Donno was developed. The model comprises 1164 reactions, 1379 metabolites, and 508 genes. in silico validation of the metabolic model was achieved through the comparison of simulations with available experimental data. Aerobic metabolism was simulated properly and fastidian gum production rates predicted accurately. The GSM model allowed identifying potential drug targets, using a pipeline based on the model's gene essentially analysis.

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Section: Glucose Flux Patternsupporting

confidence: 58%

Section: Discussionmentioning

confidence: 99%