Modeling trophic dependencies and exchanges among insects’ bacterial symbionts in a host-simulated environment

Background Individual organisms are linked to their communities and ecosystems via metabolic activities. Metabolic exchanges and co-dependencies have long been suggested to have a pivotal role in determining community structure. In phloem-feeding insects such metabolic interactions with bacteria ena...
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Autor*in:	Opatovsky, Itai [verfasserIn] Santos-Garcia, Diego Ruan, Zhepu Lahav, Tamar Ofaim, Shany Mouton, Laurence Barbe, Valérie Jiang, Jiandong Zchori-Fein, Einat Freilich, Shiri

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	Metabolic interactions Network analysis Symbionts

Anmerkung:	© The Author(s). 2018

Übergeordnetes Werk:	Enthalten in: BMC genomics - London : BioMed Central, 2000, 19(2018), 1 vom: 25. Mai
Übergeordnetes Werk:	volume:19 ; year:2018 ; number:1 ; day:25 ; month:05

Links:	Volltext

DOI / URN:	10.1186/s12864-018-4786-7

Katalog-ID:	SPR027143007

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520			\|a Background Individual organisms are linked to their communities and ecosystems via metabolic activities. Metabolic exchanges and co-dependencies have long been suggested to have a pivotal role in determining community structure. In phloem-feeding insects such metabolic interactions with bacteria enable complementation of their deprived nutrition. The phloem-feeding whitefly Bemisia tabaci (Hemiptera: Aleyrodidae) harbors an obligatory symbiotic bacterium, as well as varying combinations of facultative symbionts. This well-defined bacterial community in B. tabaci serves here as a case study for a comprehensive and systematic survey of metabolic interactions within the bacterial community and their associations with documented occurrences of bacterial combinations. We first reconstructed the metabolic networks of five common B. tabaci symbionts genera (Portiera, Rickettsia, Hamiltonella, Cardinium and Wolbachia), and then used network analysis approaches to predict: (1) species-specific metabolic capacities in a simulated bacteriocyte-like environment; (2) metabolic capacities of the corresponding species’ combinations, and (3) dependencies of each species on different media components. Results The predictions for metabolic capacities of the symbionts in the host environment were in general agreement with previously reported genome analyses, each focused on the single-species level. The analysis suggests several previously un-reported routes for complementary interactions and estimated the dependency of each symbiont in specific host metabolites. No clear association was detected between metabolic co-dependencies and co-occurrence patterns. Conclusions The analysis generated predictions for testable hypotheses of metabolic exchanges and co-dependencies in bacterial communities and by crossing them with co-occurrence profiles, contextualized interaction patterns into a wider ecological perspective.
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allfields	10.1186/s12864-018-4786-7 doi (DE-627)SPR027143007 (SPR)s12864-018-4786-7-e DE-627 ger DE-627 rakwb eng Opatovsky, Itai verfasserin aut Modeling trophic dependencies and exchanges among insects’ bacterial symbionts in a host-simulated environment 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2018 Background Individual organisms are linked to their communities and ecosystems via metabolic activities. Metabolic exchanges and co-dependencies have long been suggested to have a pivotal role in determining community structure. In phloem-feeding insects such metabolic interactions with bacteria enable complementation of their deprived nutrition. The phloem-feeding whitefly Bemisia tabaci (Hemiptera: Aleyrodidae) harbors an obligatory symbiotic bacterium, as well as varying combinations of facultative symbionts. This well-defined bacterial community in B. tabaci serves here as a case study for a comprehensive and systematic survey of metabolic interactions within the bacterial community and their associations with documented occurrences of bacterial combinations. We first reconstructed the metabolic networks of five common B. tabaci symbionts genera (Portiera, Rickettsia, Hamiltonella, Cardinium and Wolbachia), and then used network analysis approaches to predict: (1) species-specific metabolic capacities in a simulated bacteriocyte-like environment; (2) metabolic capacities of the corresponding species’ combinations, and (3) dependencies of each species on different media components. Results The predictions for metabolic capacities of the symbionts in the host environment were in general agreement with previously reported genome analyses, each focused on the single-species level. The analysis suggests several previously un-reported routes for complementary interactions and estimated the dependency of each symbiont in specific host metabolites. No clear association was detected between metabolic co-dependencies and co-occurrence patterns. Conclusions The analysis generated predictions for testable hypotheses of metabolic exchanges and co-dependencies in bacterial communities and by crossing them with co-occurrence profiles, contextualized interaction patterns into a wider ecological perspective. Metabolic interactions (dpeaa)DE-He213 Network analysis (dpeaa)DE-He213 Symbionts (dpeaa)DE-He213 Santos-Garcia, Diego aut Ruan, Zhepu aut Lahav, Tamar aut Ofaim, Shany aut Mouton, Laurence aut Barbe, Valérie aut Jiang, Jiandong aut Zchori-Fein, Einat aut Freilich, Shiri aut Enthalten in BMC genomics London : BioMed Central, 2000 19(2018), 1 vom: 25. Mai (DE-627)326644954 (DE-600)2041499-7 1471-2164 nnns volume:19 year:2018 number:1 day:25 month:05 https://dx.doi.org/10.1186/s12864-018-4786-7 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 19 2018 1 25 05
spelling	10.1186/s12864-018-4786-7 doi (DE-627)SPR027143007 (SPR)s12864-018-4786-7-e DE-627 ger DE-627 rakwb eng Opatovsky, Itai verfasserin aut Modeling trophic dependencies and exchanges among insects’ bacterial symbionts in a host-simulated environment 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2018 Background Individual organisms are linked to their communities and ecosystems via metabolic activities. Metabolic exchanges and co-dependencies have long been suggested to have a pivotal role in determining community structure. In phloem-feeding insects such metabolic interactions with bacteria enable complementation of their deprived nutrition. The phloem-feeding whitefly Bemisia tabaci (Hemiptera: Aleyrodidae) harbors an obligatory symbiotic bacterium, as well as varying combinations of facultative symbionts. This well-defined bacterial community in B. tabaci serves here as a case study for a comprehensive and systematic survey of metabolic interactions within the bacterial community and their associations with documented occurrences of bacterial combinations. We first reconstructed the metabolic networks of five common B. tabaci symbionts genera (Portiera, Rickettsia, Hamiltonella, Cardinium and Wolbachia), and then used network analysis approaches to predict: (1) species-specific metabolic capacities in a simulated bacteriocyte-like environment; (2) metabolic capacities of the corresponding species’ combinations, and (3) dependencies of each species on different media components. Results The predictions for metabolic capacities of the symbionts in the host environment were in general agreement with previously reported genome analyses, each focused on the single-species level. The analysis suggests several previously un-reported routes for complementary interactions and estimated the dependency of each symbiont in specific host metabolites. No clear association was detected between metabolic co-dependencies and co-occurrence patterns. Conclusions The analysis generated predictions for testable hypotheses of metabolic exchanges and co-dependencies in bacterial communities and by crossing them with co-occurrence profiles, contextualized interaction patterns into a wider ecological perspective. Metabolic interactions (dpeaa)DE-He213 Network analysis (dpeaa)DE-He213 Symbionts (dpeaa)DE-He213 Santos-Garcia, Diego aut Ruan, Zhepu aut Lahav, Tamar aut Ofaim, Shany aut Mouton, Laurence aut Barbe, Valérie aut Jiang, Jiandong aut Zchori-Fein, Einat aut Freilich, Shiri aut Enthalten in BMC genomics London : BioMed Central, 2000 19(2018), 1 vom: 25. Mai (DE-627)326644954 (DE-600)2041499-7 1471-2164 nnns volume:19 year:2018 number:1 day:25 month:05 https://dx.doi.org/10.1186/s12864-018-4786-7 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 19 2018 1 25 05
allfields_unstemmed	10.1186/s12864-018-4786-7 doi (DE-627)SPR027143007 (SPR)s12864-018-4786-7-e DE-627 ger DE-627 rakwb eng Opatovsky, Itai verfasserin aut Modeling trophic dependencies and exchanges among insects’ bacterial symbionts in a host-simulated environment 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2018 Background Individual organisms are linked to their communities and ecosystems via metabolic activities. Metabolic exchanges and co-dependencies have long been suggested to have a pivotal role in determining community structure. In phloem-feeding insects such metabolic interactions with bacteria enable complementation of their deprived nutrition. The phloem-feeding whitefly Bemisia tabaci (Hemiptera: Aleyrodidae) harbors an obligatory symbiotic bacterium, as well as varying combinations of facultative symbionts. This well-defined bacterial community in B. tabaci serves here as a case study for a comprehensive and systematic survey of metabolic interactions within the bacterial community and their associations with documented occurrences of bacterial combinations. We first reconstructed the metabolic networks of five common B. tabaci symbionts genera (Portiera, Rickettsia, Hamiltonella, Cardinium and Wolbachia), and then used network analysis approaches to predict: (1) species-specific metabolic capacities in a simulated bacteriocyte-like environment; (2) metabolic capacities of the corresponding species’ combinations, and (3) dependencies of each species on different media components. Results The predictions for metabolic capacities of the symbionts in the host environment were in general agreement with previously reported genome analyses, each focused on the single-species level. The analysis suggests several previously un-reported routes for complementary interactions and estimated the dependency of each symbiont in specific host metabolites. No clear association was detected between metabolic co-dependencies and co-occurrence patterns. Conclusions The analysis generated predictions for testable hypotheses of metabolic exchanges and co-dependencies in bacterial communities and by crossing them with co-occurrence profiles, contextualized interaction patterns into a wider ecological perspective. Metabolic interactions (dpeaa)DE-He213 Network analysis (dpeaa)DE-He213 Symbionts (dpeaa)DE-He213 Santos-Garcia, Diego aut Ruan, Zhepu aut Lahav, Tamar aut Ofaim, Shany aut Mouton, Laurence aut Barbe, Valérie aut Jiang, Jiandong aut Zchori-Fein, Einat aut Freilich, Shiri aut Enthalten in BMC genomics London : BioMed Central, 2000 19(2018), 1 vom: 25. Mai (DE-627)326644954 (DE-600)2041499-7 1471-2164 nnns volume:19 year:2018 number:1 day:25 month:05 https://dx.doi.org/10.1186/s12864-018-4786-7 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 19 2018 1 25 05
allfieldsGer	10.1186/s12864-018-4786-7 doi (DE-627)SPR027143007 (SPR)s12864-018-4786-7-e DE-627 ger DE-627 rakwb eng Opatovsky, Itai verfasserin aut Modeling trophic dependencies and exchanges among insects’ bacterial symbionts in a host-simulated environment 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2018 Background Individual organisms are linked to their communities and ecosystems via metabolic activities. Metabolic exchanges and co-dependencies have long been suggested to have a pivotal role in determining community structure. In phloem-feeding insects such metabolic interactions with bacteria enable complementation of their deprived nutrition. The phloem-feeding whitefly Bemisia tabaci (Hemiptera: Aleyrodidae) harbors an obligatory symbiotic bacterium, as well as varying combinations of facultative symbionts. This well-defined bacterial community in B. tabaci serves here as a case study for a comprehensive and systematic survey of metabolic interactions within the bacterial community and their associations with documented occurrences of bacterial combinations. We first reconstructed the metabolic networks of five common B. tabaci symbionts genera (Portiera, Rickettsia, Hamiltonella, Cardinium and Wolbachia), and then used network analysis approaches to predict: (1) species-specific metabolic capacities in a simulated bacteriocyte-like environment; (2) metabolic capacities of the corresponding species’ combinations, and (3) dependencies of each species on different media components. Results The predictions for metabolic capacities of the symbionts in the host environment were in general agreement with previously reported genome analyses, each focused on the single-species level. The analysis suggests several previously un-reported routes for complementary interactions and estimated the dependency of each symbiont in specific host metabolites. No clear association was detected between metabolic co-dependencies and co-occurrence patterns. Conclusions The analysis generated predictions for testable hypotheses of metabolic exchanges and co-dependencies in bacterial communities and by crossing them with co-occurrence profiles, contextualized interaction patterns into a wider ecological perspective. Metabolic interactions (dpeaa)DE-He213 Network analysis (dpeaa)DE-He213 Symbionts (dpeaa)DE-He213 Santos-Garcia, Diego aut Ruan, Zhepu aut Lahav, Tamar aut Ofaim, Shany aut Mouton, Laurence aut Barbe, Valérie aut Jiang, Jiandong aut Zchori-Fein, Einat aut Freilich, Shiri aut Enthalten in BMC genomics London : BioMed Central, 2000 19(2018), 1 vom: 25. Mai (DE-627)326644954 (DE-600)2041499-7 1471-2164 nnns volume:19 year:2018 number:1 day:25 month:05 https://dx.doi.org/10.1186/s12864-018-4786-7 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 19 2018 1 25 05
allfieldsSound	10.1186/s12864-018-4786-7 doi (DE-627)SPR027143007 (SPR)s12864-018-4786-7-e DE-627 ger DE-627 rakwb eng Opatovsky, Itai verfasserin aut Modeling trophic dependencies and exchanges among insects’ bacterial symbionts in a host-simulated environment 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2018 Background Individual organisms are linked to their communities and ecosystems via metabolic activities. Metabolic exchanges and co-dependencies have long been suggested to have a pivotal role in determining community structure. In phloem-feeding insects such metabolic interactions with bacteria enable complementation of their deprived nutrition. The phloem-feeding whitefly Bemisia tabaci (Hemiptera: Aleyrodidae) harbors an obligatory symbiotic bacterium, as well as varying combinations of facultative symbionts. This well-defined bacterial community in B. tabaci serves here as a case study for a comprehensive and systematic survey of metabolic interactions within the bacterial community and their associations with documented occurrences of bacterial combinations. We first reconstructed the metabolic networks of five common B. tabaci symbionts genera (Portiera, Rickettsia, Hamiltonella, Cardinium and Wolbachia), and then used network analysis approaches to predict: (1) species-specific metabolic capacities in a simulated bacteriocyte-like environment; (2) metabolic capacities of the corresponding species’ combinations, and (3) dependencies of each species on different media components. Results The predictions for metabolic capacities of the symbionts in the host environment were in general agreement with previously reported genome analyses, each focused on the single-species level. The analysis suggests several previously un-reported routes for complementary interactions and estimated the dependency of each symbiont in specific host metabolites. No clear association was detected between metabolic co-dependencies and co-occurrence patterns. Conclusions The analysis generated predictions for testable hypotheses of metabolic exchanges and co-dependencies in bacterial communities and by crossing them with co-occurrence profiles, contextualized interaction patterns into a wider ecological perspective. Metabolic interactions (dpeaa)DE-He213 Network analysis (dpeaa)DE-He213 Symbionts (dpeaa)DE-He213 Santos-Garcia, Diego aut Ruan, Zhepu aut Lahav, Tamar aut Ofaim, Shany aut Mouton, Laurence aut Barbe, Valérie aut Jiang, Jiandong aut Zchori-Fein, Einat aut Freilich, Shiri aut Enthalten in BMC genomics London : BioMed Central, 2000 19(2018), 1 vom: 25. Mai (DE-627)326644954 (DE-600)2041499-7 1471-2164 nnns volume:19 year:2018 number:1 day:25 month:05 https://dx.doi.org/10.1186/s12864-018-4786-7 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 19 2018 1 25 05
language	English
source	Enthalten in BMC genomics 19(2018), 1 vom: 25. Mai volume:19 year:2018 number:1 day:25 month:05
sourceStr	Enthalten in BMC genomics 19(2018), 1 vom: 25. Mai volume:19 year:2018 number:1 day:25 month:05
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Metabolic interactions Network analysis Symbionts
isfreeaccess_bool	true
container_title	BMC genomics
authorswithroles_txt_mv	Opatovsky, Itai @@aut@@ Santos-Garcia, Diego @@aut@@ Ruan, Zhepu @@aut@@ Lahav, Tamar @@aut@@ Ofaim, Shany @@aut@@ Mouton, Laurence @@aut@@ Barbe, Valérie @@aut@@ Jiang, Jiandong @@aut@@ Zchori-Fein, Einat @@aut@@ Freilich, Shiri @@aut@@
publishDateDaySort_date	2018-05-25T00:00:00Z
hierarchy_top_id	326644954
id	SPR027143007
language_de	englisch
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We first reconstructed the metabolic networks of five common B. tabaci symbionts genera (Portiera, Rickettsia, Hamiltonella, Cardinium and Wolbachia), and then used network analysis approaches to predict: (1) species-specific metabolic capacities in a simulated bacteriocyte-like environment; (2) metabolic capacities of the corresponding species’ combinations, and (3) dependencies of each species on different media components. Results The predictions for metabolic capacities of the symbionts in the host environment were in general agreement with previously reported genome analyses, each focused on the single-species level. The analysis suggests several previously un-reported routes for complementary interactions and estimated the dependency of each symbiont in specific host metabolites. No clear association was detected between metabolic co-dependencies and co-occurrence patterns. 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author	Opatovsky, Itai
spellingShingle	Opatovsky, Itai misc Metabolic interactions misc Network analysis misc Symbionts Modeling trophic dependencies and exchanges among insects’ bacterial symbionts in a host-simulated environment
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topic_title	Modeling trophic dependencies and exchanges among insects’ bacterial symbionts in a host-simulated environment Metabolic interactions (dpeaa)DE-He213 Network analysis (dpeaa)DE-He213 Symbionts (dpeaa)DE-He213
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title	Modeling trophic dependencies and exchanges among insects’ bacterial symbionts in a host-simulated environment
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title_full	Modeling trophic dependencies and exchanges among insects’ bacterial symbionts in a host-simulated environment
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author_browse	Opatovsky, Itai Santos-Garcia, Diego Ruan, Zhepu Lahav, Tamar Ofaim, Shany Mouton, Laurence Barbe, Valérie Jiang, Jiandong Zchori-Fein, Einat Freilich, Shiri
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title_sort	modeling trophic dependencies and exchanges among insects’ bacterial symbionts in a host-simulated environment
title_auth	Modeling trophic dependencies and exchanges among insects’ bacterial symbionts in a host-simulated environment
abstract	Background Individual organisms are linked to their communities and ecosystems via metabolic activities. Metabolic exchanges and co-dependencies have long been suggested to have a pivotal role in determining community structure. In phloem-feeding insects such metabolic interactions with bacteria enable complementation of their deprived nutrition. The phloem-feeding whitefly Bemisia tabaci (Hemiptera: Aleyrodidae) harbors an obligatory symbiotic bacterium, as well as varying combinations of facultative symbionts. This well-defined bacterial community in B. tabaci serves here as a case study for a comprehensive and systematic survey of metabolic interactions within the bacterial community and their associations with documented occurrences of bacterial combinations. We first reconstructed the metabolic networks of five common B. tabaci symbionts genera (Portiera, Rickettsia, Hamiltonella, Cardinium and Wolbachia), and then used network analysis approaches to predict: (1) species-specific metabolic capacities in a simulated bacteriocyte-like environment; (2) metabolic capacities of the corresponding species’ combinations, and (3) dependencies of each species on different media components. Results The predictions for metabolic capacities of the symbionts in the host environment were in general agreement with previously reported genome analyses, each focused on the single-species level. The analysis suggests several previously un-reported routes for complementary interactions and estimated the dependency of each symbiont in specific host metabolites. No clear association was detected between metabolic co-dependencies and co-occurrence patterns. Conclusions The analysis generated predictions for testable hypotheses of metabolic exchanges and co-dependencies in bacterial communities and by crossing them with co-occurrence profiles, contextualized interaction patterns into a wider ecological perspective. © The Author(s). 2018
abstractGer	Background Individual organisms are linked to their communities and ecosystems via metabolic activities. Metabolic exchanges and co-dependencies have long been suggested to have a pivotal role in determining community structure. In phloem-feeding insects such metabolic interactions with bacteria enable complementation of their deprived nutrition. The phloem-feeding whitefly Bemisia tabaci (Hemiptera: Aleyrodidae) harbors an obligatory symbiotic bacterium, as well as varying combinations of facultative symbionts. This well-defined bacterial community in B. tabaci serves here as a case study for a comprehensive and systematic survey of metabolic interactions within the bacterial community and their associations with documented occurrences of bacterial combinations. We first reconstructed the metabolic networks of five common B. tabaci symbionts genera (Portiera, Rickettsia, Hamiltonella, Cardinium and Wolbachia), and then used network analysis approaches to predict: (1) species-specific metabolic capacities in a simulated bacteriocyte-like environment; (2) metabolic capacities of the corresponding species’ combinations, and (3) dependencies of each species on different media components. Results The predictions for metabolic capacities of the symbionts in the host environment were in general agreement with previously reported genome analyses, each focused on the single-species level. The analysis suggests several previously un-reported routes for complementary interactions and estimated the dependency of each symbiont in specific host metabolites. No clear association was detected between metabolic co-dependencies and co-occurrence patterns. Conclusions The analysis generated predictions for testable hypotheses of metabolic exchanges and co-dependencies in bacterial communities and by crossing them with co-occurrence profiles, contextualized interaction patterns into a wider ecological perspective. © The Author(s). 2018
abstract_unstemmed	Background Individual organisms are linked to their communities and ecosystems via metabolic activities. Metabolic exchanges and co-dependencies have long been suggested to have a pivotal role in determining community structure. In phloem-feeding insects such metabolic interactions with bacteria enable complementation of their deprived nutrition. The phloem-feeding whitefly Bemisia tabaci (Hemiptera: Aleyrodidae) harbors an obligatory symbiotic bacterium, as well as varying combinations of facultative symbionts. This well-defined bacterial community in B. tabaci serves here as a case study for a comprehensive and systematic survey of metabolic interactions within the bacterial community and their associations with documented occurrences of bacterial combinations. We first reconstructed the metabolic networks of five common B. tabaci symbionts genera (Portiera, Rickettsia, Hamiltonella, Cardinium and Wolbachia), and then used network analysis approaches to predict: (1) species-specific metabolic capacities in a simulated bacteriocyte-like environment; (2) metabolic capacities of the corresponding species’ combinations, and (3) dependencies of each species on different media components. Results The predictions for metabolic capacities of the symbionts in the host environment were in general agreement with previously reported genome analyses, each focused on the single-species level. The analysis suggests several previously un-reported routes for complementary interactions and estimated the dependency of each symbiont in specific host metabolites. No clear association was detected between metabolic co-dependencies and co-occurrence patterns. Conclusions The analysis generated predictions for testable hypotheses of metabolic exchanges and co-dependencies in bacterial communities and by crossing them with co-occurrence profiles, contextualized interaction patterns into a wider ecological perspective. © The Author(s). 2018
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title_short	Modeling trophic dependencies and exchanges among insects’ bacterial symbionts in a host-simulated environment
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author2	Santos-Garcia, Diego Ruan, Zhepu Lahav, Tamar Ofaim, Shany Mouton, Laurence Barbe, Valérie Jiang, Jiandong Zchori-Fein, Einat Freilich, Shiri
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Modeling trophic dependencies and exchanges among insects’ bacterial symbionts in a host-simulated environment

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