Metabolic adaptation of two in silico mutants of Mycobacterium tuberculosis during infection
ABSTRACT: Background: Up to date, Mycobacterium tuberculosis (Mtb) remains as the worst intracellular killer pathogen. To establish infection, inside the granuloma, Mtb reprograms its metabolism to support both growth and survival, keeping a balance between catabolism, anabolism and energy supply. M...
- Autores:
-
López Agudelo, Víctor Alonso
Baena García, Andrés
Ramírez Malule, Howard
Ochoa Cáceres, Silvia Mercedes
Barrera Robledo, Luis Fernando
Ríos Estepa, Rigoberto
- Tipo de recurso:
- Article of investigation
- Fecha de publicación:
- 2017
- Institución:
- Universidad de Antioquia
- Repositorio:
- Repositorio UdeA
- Idioma:
- eng
- OAI Identifier:
- oai:bibliotecadigital.udea.edu.co:10495/12825
- Acceso en línea:
- http://hdl.handle.net/10495/12825
- Palabra clave:
- Mycobacterium tuberculosis
Análisis del plano de fase fenotípica
Modelado metabólico a escala del genoma
Reprogramación metabólica
- Rights
- openAccess
- License
- Atribución 2.5 Colombia (CC BY 2.5 CO)
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|
dc.title.spa.fl_str_mv |
Metabolic adaptation of two in silico mutants of Mycobacterium tuberculosis during infection |
title |
Metabolic adaptation of two in silico mutants of Mycobacterium tuberculosis during infection |
spellingShingle |
Metabolic adaptation of two in silico mutants of Mycobacterium tuberculosis during infection Mycobacterium tuberculosis Análisis del plano de fase fenotípica Modelado metabólico a escala del genoma Reprogramación metabólica |
title_short |
Metabolic adaptation of two in silico mutants of Mycobacterium tuberculosis during infection |
title_full |
Metabolic adaptation of two in silico mutants of Mycobacterium tuberculosis during infection |
title_fullStr |
Metabolic adaptation of two in silico mutants of Mycobacterium tuberculosis during infection |
title_full_unstemmed |
Metabolic adaptation of two in silico mutants of Mycobacterium tuberculosis during infection |
title_sort |
Metabolic adaptation of two in silico mutants of Mycobacterium tuberculosis during infection |
dc.creator.fl_str_mv |
López Agudelo, Víctor Alonso Baena García, Andrés Ramírez Malule, Howard Ochoa Cáceres, Silvia Mercedes Barrera Robledo, Luis Fernando Ríos Estepa, Rigoberto |
dc.contributor.author.none.fl_str_mv |
López Agudelo, Víctor Alonso Baena García, Andrés Ramírez Malule, Howard Ochoa Cáceres, Silvia Mercedes Barrera Robledo, Luis Fernando Ríos Estepa, Rigoberto |
dc.subject.none.fl_str_mv |
Mycobacterium tuberculosis Análisis del plano de fase fenotípica Modelado metabólico a escala del genoma Reprogramación metabólica |
topic |
Mycobacterium tuberculosis Análisis del plano de fase fenotípica Modelado metabólico a escala del genoma Reprogramación metabólica |
description |
ABSTRACT: Background: Up to date, Mycobacterium tuberculosis (Mtb) remains as the worst intracellular killer pathogen. To establish infection, inside the granuloma, Mtb reprograms its metabolism to support both growth and survival, keeping a balance between catabolism, anabolism and energy supply. Mtb knockouts with the faculty of being essential on a wide range of nutritional conditions are deemed as target candidates for tuberculosis (TB) treatment. Constraint-based genome-scale modeling is considered as a promising tool for evaluating genetic and nutritional perturbations on Mtb metabolic reprogramming. Nonetheless, few in silico assessments of the effect of nutritional conditions on Mtb’s vulnerability and metabolic adaptation have been carried out. Results: A genome-scale model (GEM) of Mtb, modified from the H37Rv iOSDD890, was used to explore the metabolic reprogramming of two Mtb knockout mutants (pfkA- and icl-mutants), lacking key enzymes of central carbon metabolism, while exposed to changing nutritional conditions (oxygen, and carbon and nitrogen sources). A combination of shadow pricing, sensitivity analysis, and flux distributions patterns allowed us to identify metabolic behaviors that are in agreement with phenotypes reported in the literature. During hypoxia, at high glucose consumption, the Mtb pfkA-mutant showed a detrimental growth effect derived from the accumulation of toxic sugar phosphate intermediates (glucose-6-phosphate and fructose-6-phosphate) along with an increment of carbon fluxes towards the reductive direction of the tricarboxylic acid cycle (TCA). Furthermore, metabolic reprogramming of the icl-mutant (icl1&icl2) showed the importance of the methylmalonyl pathway for the detoxification of propionyl-CoA, during growth at high fatty acid consumption rates and aerobic conditions. At elevated levels of fatty acid uptake and hypoxia, we found a drop in TCA cycle intermediate accumulation that might create redox imbalance. Finally, findings regarding Mtb-mutant metabolic adaptation associated with asparagine consumption and acetate, succinate and alanine production, were in agreement with literature reports. Conclusions: This study demonstrates the potential application of genome-scale modeling, flux balance analysis (FBA), phenotypic phase plane (PhPP) analysis and shadow pricing to generate valuable insights about Mtb metabolic reprogramming in the context of human granulomas. |
publishDate |
2017 |
dc.date.issued.none.fl_str_mv |
2017 |
dc.date.accessioned.none.fl_str_mv |
2020-01-04T22:19:27Z |
dc.date.available.none.fl_str_mv |
2020-01-04T22:19:27Z |
dc.type.spa.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.coarversion.fl_str_mv |
http://purl.org/coar/version/c_970fb48d4fbd8a86 http://purl.org/coar/version/c_970fb48d4fbd8a85 |
dc.type.coar.spa.fl_str_mv |
http://purl.org/coar/resource_type/c_2df8fbb1 |
dc.type.redcol.spa.fl_str_mv |
https://purl.org/redcol/resource_type/ART |
dc.type.local.spa.fl_str_mv |
Artículo de investigación |
format |
http://purl.org/coar/resource_type/c_2df8fbb1 |
dc.identifier.citation.spa.fl_str_mv |
López Agudelo, V. A., Baena García, A., Ramírez Malule, H., Ochoa Cáceres, S. M., Barrera Robledo, L. F., & Ríos Estepa, R. (2017). Metabolic adaptation of two in silico mutants of Mycobacterium tuberculosis during infection. BMC Systems. Biology, 11(107), 1-18. https://doi.org/10.1186/s12918-017-0496-z |
dc.identifier.uri.none.fl_str_mv |
http://hdl.handle.net/10495/12825 |
dc.identifier.doi.none.fl_str_mv |
10.1186/s12918-017-0496-z |
dc.identifier.eissn.none.fl_str_mv |
1752-0509 |
identifier_str_mv |
López Agudelo, V. A., Baena García, A., Ramírez Malule, H., Ochoa Cáceres, S. M., Barrera Robledo, L. F., & Ríos Estepa, R. (2017). Metabolic adaptation of two in silico mutants of Mycobacterium tuberculosis during infection. BMC Systems. Biology, 11(107), 1-18. https://doi.org/10.1186/s12918-017-0496-z 10.1186/s12918-017-0496-z 1752-0509 |
url |
http://hdl.handle.net/10495/12825 |
dc.language.iso.spa.fl_str_mv |
eng |
language |
eng |
dc.relation.ispartofjournalabbrev.spa.fl_str_mv |
BMC Syst. Biol. |
dc.rights.*.fl_str_mv |
Atribución 2.5 Colombia (CC BY 2.5 CO) |
dc.rights.spa.fl_str_mv |
info:eu-repo/semantics/openAccess |
dc.rights.uri.*.fl_str_mv |
https://creativecommons.org/licenses/by/2.5/co/ |
dc.rights.accessrights.spa.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
dc.rights.creativecommons.spa.fl_str_mv |
https://creativecommons.org/licenses/by/4.0/ |
rights_invalid_str_mv |
Atribución 2.5 Colombia (CC BY 2.5 CO) https://creativecommons.org/licenses/by/2.5/co/ http://purl.org/coar/access_right/c_abf2 https://creativecommons.org/licenses/by/4.0/ |
eu_rights_str_mv |
openAccess |
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17 |
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application/pdf |
dc.publisher.spa.fl_str_mv |
BMC |
dc.publisher.group.spa.fl_str_mv |
Bioprocesos Grupo de Inmunología Celular e Inmunogenética Simulación, Diseño, Control y Optimización de Procesos (SIDCOP) |
dc.publisher.place.spa.fl_str_mv |
Londres, Inglaterra |
institution |
Universidad de Antioquia |
bitstream.url.fl_str_mv |
http://bibliotecadigital.udea.edu.co/bitstream/10495/12825/1/LopezVictor_2017_Metabolicadaptationtwo.pdf http://bibliotecadigital.udea.edu.co/bitstream/10495/12825/2/license_url http://bibliotecadigital.udea.edu.co/bitstream/10495/12825/3/license_text http://bibliotecadigital.udea.edu.co/bitstream/10495/12825/4/license_rdf http://bibliotecadigital.udea.edu.co/bitstream/10495/12825/5/license.txt |
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López Agudelo, Víctor AlonsoBaena García, AndrésRamírez Malule, HowardOchoa Cáceres, Silvia MercedesBarrera Robledo, Luis FernandoRíos Estepa, Rigoberto2020-01-04T22:19:27Z2020-01-04T22:19:27Z2017López Agudelo, V. A., Baena García, A., Ramírez Malule, H., Ochoa Cáceres, S. M., Barrera Robledo, L. F., & Ríos Estepa, R. (2017). Metabolic adaptation of two in silico mutants of Mycobacterium tuberculosis during infection. BMC Systems. Biology, 11(107), 1-18. https://doi.org/10.1186/s12918-017-0496-zhttp://hdl.handle.net/10495/1282510.1186/s12918-017-0496-z1752-0509ABSTRACT: Background: Up to date, Mycobacterium tuberculosis (Mtb) remains as the worst intracellular killer pathogen. To establish infection, inside the granuloma, Mtb reprograms its metabolism to support both growth and survival, keeping a balance between catabolism, anabolism and energy supply. Mtb knockouts with the faculty of being essential on a wide range of nutritional conditions are deemed as target candidates for tuberculosis (TB) treatment. Constraint-based genome-scale modeling is considered as a promising tool for evaluating genetic and nutritional perturbations on Mtb metabolic reprogramming. Nonetheless, few in silico assessments of the effect of nutritional conditions on Mtb’s vulnerability and metabolic adaptation have been carried out. Results: A genome-scale model (GEM) of Mtb, modified from the H37Rv iOSDD890, was used to explore the metabolic reprogramming of two Mtb knockout mutants (pfkA- and icl-mutants), lacking key enzymes of central carbon metabolism, while exposed to changing nutritional conditions (oxygen, and carbon and nitrogen sources). A combination of shadow pricing, sensitivity analysis, and flux distributions patterns allowed us to identify metabolic behaviors that are in agreement with phenotypes reported in the literature. During hypoxia, at high glucose consumption, the Mtb pfkA-mutant showed a detrimental growth effect derived from the accumulation of toxic sugar phosphate intermediates (glucose-6-phosphate and fructose-6-phosphate) along with an increment of carbon fluxes towards the reductive direction of the tricarboxylic acid cycle (TCA). Furthermore, metabolic reprogramming of the icl-mutant (icl1&icl2) showed the importance of the methylmalonyl pathway for the detoxification of propionyl-CoA, during growth at high fatty acid consumption rates and aerobic conditions. At elevated levels of fatty acid uptake and hypoxia, we found a drop in TCA cycle intermediate accumulation that might create redox imbalance. Finally, findings regarding Mtb-mutant metabolic adaptation associated with asparagine consumption and acetate, succinate and alanine production, were in agreement with literature reports. Conclusions: This study demonstrates the potential application of genome-scale modeling, flux balance analysis (FBA), phenotypic phase plane (PhPP) analysis and shadow pricing to generate valuable insights about Mtb metabolic reprogramming in the context of human granulomas.COL0008639COL0023715COL005657417application/pdfengBMCBioprocesosGrupo de Inmunología Celular e InmunogenéticaSimulación, Diseño, Control y Optimización de Procesos (SIDCOP)Londres, Inglaterrainfo:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_2df8fbb1https://purl.org/redcol/resource_type/ARTArtículo de investigaciónhttp://purl.org/coar/version/c_970fb48d4fbd8a86http://purl.org/coar/version/c_970fb48d4fbd8a85Atribución 2.5 Colombia (CC BY 2.5 CO)info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/co/http://purl.org/coar/access_right/c_abf2https://creativecommons.org/licenses/by/4.0/Mycobacterium tuberculosisAnálisis del plano de fase fenotípicaModelado metabólico a escala del genomaReprogramación metabólicaMetabolic adaptation of two in silico mutants of Mycobacterium tuberculosis during infectionBMC Syst. Biol.BMC Systems Biology11811107ORIGINALLopezVictor_2017_Metabolicadaptationtwo.pdfLopezVictor_2017_Metabolicadaptationtwo.pdfArtículo de investigaciónapplication/pdf3319526http://bibliotecadigital.udea.edu.co/bitstream/10495/12825/1/LopezVictor_2017_Metabolicadaptationtwo.pdf4b86690803600340bcc0ba431b577bb1MD51CC-LICENSElicense_urllicense_urltext/plain; charset=utf-849http://bibliotecadigital.udea.edu.co/bitstream/10495/12825/2/license_url4afdbb8c545fd630ea7db775da747b2fMD52license_textlicense_texttext/html; charset=utf-80http://bibliotecadigital.udea.edu.co/bitstream/10495/12825/3/license_textd41d8cd98f00b204e9800998ecf8427eMD53license_rdflicense_rdfapplication/rdf+xml; charset=utf-80http://bibliotecadigital.udea.edu.co/bitstream/10495/12825/4/license_rdfd41d8cd98f00b204e9800998ecf8427eMD54LICENSElicense.txtlicense.txttext/plain; charset=utf-81748http://bibliotecadigital.udea.edu.co/bitstream/10495/12825/5/license.txt8a4605be74aa9ea9d79846c1fba20a33MD5510495/12825oai:bibliotecadigital.udea.edu.co:10495/128252022-02-07 09:35:31.631Repositorio Institucional Universidad de Antioquiaandres.perez@udea.edu.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 |