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In Silico Exploration of Mycobacterium tuberculosis Metabolic Networks Shows Host-Associated Convergent Fluxomic Phenotypes

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In Silico Exploration of Mycobacterium tuberculosis Metabolic Networks Shows Host-Associated Convergent Fluxomic Phenotypes

dc.contributor.author	Santamaria, Guillem
dc.contributor.author	Ruiz-Rodriguez, Paula
dc.contributor.author	Renau-Mínguez, Chantal
dc.contributor.author	Pinto, Francisco R.
dc.contributor.author	Coscollá, Mireia
dc.date.accessioned	2022-03-23T14:23:40Z
dc.date.available	2022-03-23T14:23:40Z
dc.date.issued	2022
dc.identifier.citation	Santamaria, Guillem Ruiz-Rodriguez, Paula Renau-Mínguez, Chantal Pinto, Francisco R. Coscollá, Mireia 2022 In Silico Exploration of Mycobacterium tuberculosis Metabolic Networks Shows Host-Associated Convergent Fluxomic Phenotypes Biomolecules 12 3 376
dc.identifier.uri	https://hdl.handle.net/10550/82008
dc.description.abstract	Mycobacterium tuberculosis, the causative agent of tuberculosis, is composed of several lineages characterized by a genome identity higher than 99%. Although the majority of the lineages are associated with humans, at least four lineages are adapted to other mammals, including different M. tuberculosis ecotypes. Host specificity is associated with higher virulence in its preferred host in ecotypes such as M. bovis. Deciphering what determines the preference of the host can reveal host-specific virulence patterns. However, it is not clear which genomic determinants might be influencing host specificity. In this study, we apply a combination of unsupervised and supervised classification methods on genomic data of ~27,000 M. tuberculosis clinical isolates to decipher host-specific genomic determinants. Host-specific genomic signatures are scarce beyond known lineage-specific mutations. Therefore, we integrated lineage-specific mutations into the iEK1011 2.0 genome-scale metabolic model to obtain lineage-specific versions of it. Flux distributions sampled from the solution spaces of these models can be accurately separated according to host association. This separation correlated with differences in cell wall processes, lipid, amino acid and carbon metabolic subsystems. These differences were observable when more than 95% of the samples had a specific growth rate significantly lower than the maximum achievable by the models. This suggests that these differences might manifest at low growth rate settings, such as the restrictive conditions M. tuberculosis suffers during macrophage infection.
dc.language.iso	eng
dc.relation.ispartof	Biomolecules, 2022, vol. 12, num. 3, p. 376
dc.subject	Tuberculosi
dc.subject	Biomolècules
dc.title	In Silico Exploration of Mycobacterium tuberculosis Metabolic Networks Shows Host-Associated Convergent Fluxomic Phenotypes
dc.type	journal article	es_ES
dc.date.updated	2022-03-23T14:23:41Z
dc.identifier.doi	10.3390/biom12030376
dc.identifier.idgrec	150810
dc.rights.accessRights	open access	es_ES