Computer-guided design of optimal microbial consortia for immune system modulation

Manipulation of the gut microbiota holds great promise for the treatment of diseases. However, a major challenge is the identification of therapeutically potent microbial consortia that colonize the host effectively while maximizing immunologic outcome. Here, we propose a novel workflow to select op...
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Autor*in:	Richard R Stein [verfasserIn] Takeshi Tanoue [verfasserIn] Rose L Szabady [verfasserIn] Shakti K Bhattarai [verfasserIn] Bernat Olle [verfasserIn] Jason M Norman [verfasserIn] Wataru Suda [verfasserIn] Kenshiro Oshima [verfasserIn] Masahira Hattori [verfasserIn] Georg K Gerber [verfasserIn] Chris Sander [verfasserIn] Kenya Honda [verfasserIn] Vanni Bucci [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	host–microbe interaction regulatory T-cells immune system modulation microbiome modeling

Übergeordnetes Werk:	In: eLife - eLife Sciences Publications Ltd, 2013, 7(2018)
Übergeordnetes Werk:	volume:7 ; year:2018

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.7554/eLife.30916

Katalog-ID:	DOAJ022131299

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spelling	10.7554/eLife.30916 doi (DE-627)DOAJ022131299 (DE-599)DOAJf7c2a82e817f466bb137c0d58c064ec5 DE-627 ger DE-627 rakwb eng QH301-705.5 Richard R Stein verfasserin aut Computer-guided design of optimal microbial consortia for immune system modulation 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Manipulation of the gut microbiota holds great promise for the treatment of diseases. However, a major challenge is the identification of therapeutically potent microbial consortia that colonize the host effectively while maximizing immunologic outcome. Here, we propose a novel workflow to select optimal immune-inducing consortia from microbiome compositicon and immune effectors measurements. Using published and newly generated microbial and regulatory T-cell (Treg) data from germ-free mice, we estimate the contributions of twelve Clostridia strains with known immune-modulating effect to Treg induction. Combining this with a longitudinal data-constrained ecological model, we predict the ability of every attainable and ecologically stable subconsortium in promoting Treg activation and rank them by the Treg Induction Score (TrIS). Experimental validation of selected consortia indicates a strong and statistically significant correlation between predicted TrIS and measured Treg. We argue that computational indexes, such as the TrIS, are valuable tools for the systematic selection of immune-modulating bacteriotherapeutics. host–microbe interaction regulatory T-cells immune system modulation microbiome modeling Medicine R Science Q Biology (General) Takeshi Tanoue verfasserin aut Rose L Szabady verfasserin aut Shakti K Bhattarai verfasserin aut Bernat Olle verfasserin aut Jason M Norman verfasserin aut Wataru Suda verfasserin aut Kenshiro Oshima verfasserin aut Masahira Hattori verfasserin aut Georg K Gerber verfasserin aut Chris Sander verfasserin aut Kenya Honda verfasserin aut Vanni Bucci verfasserin aut In eLife eLife Sciences Publications Ltd, 2013 7(2018) (DE-627)728518384 (DE-600)2687154-3 2050084X nnns volume:7 year:2018 https://doi.org/10.7554/eLife.30916 kostenfrei https://doaj.org/article/f7c2a82e817f466bb137c0d58c064ec5 kostenfrei https://elifesciences.org/articles/30916 kostenfrei https://doaj.org/toc/2050-084X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2018
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allfieldsSound	10.7554/eLife.30916 doi (DE-627)DOAJ022131299 (DE-599)DOAJf7c2a82e817f466bb137c0d58c064ec5 DE-627 ger DE-627 rakwb eng QH301-705.5 Richard R Stein verfasserin aut Computer-guided design of optimal microbial consortia for immune system modulation 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Manipulation of the gut microbiota holds great promise for the treatment of diseases. However, a major challenge is the identification of therapeutically potent microbial consortia that colonize the host effectively while maximizing immunologic outcome. Here, we propose a novel workflow to select optimal immune-inducing consortia from microbiome compositicon and immune effectors measurements. Using published and newly generated microbial and regulatory T-cell (Treg) data from germ-free mice, we estimate the contributions of twelve Clostridia strains with known immune-modulating effect to Treg induction. Combining this with a longitudinal data-constrained ecological model, we predict the ability of every attainable and ecologically stable subconsortium in promoting Treg activation and rank them by the Treg Induction Score (TrIS). Experimental validation of selected consortia indicates a strong and statistically significant correlation between predicted TrIS and measured Treg. We argue that computational indexes, such as the TrIS, are valuable tools for the systematic selection of immune-modulating bacteriotherapeutics. host–microbe interaction regulatory T-cells immune system modulation microbiome modeling Medicine R Science Q Biology (General) Takeshi Tanoue verfasserin aut Rose L Szabady verfasserin aut Shakti K Bhattarai verfasserin aut Bernat Olle verfasserin aut Jason M Norman verfasserin aut Wataru Suda verfasserin aut Kenshiro Oshima verfasserin aut Masahira Hattori verfasserin aut Georg K Gerber verfasserin aut Chris Sander verfasserin aut Kenya Honda verfasserin aut Vanni Bucci verfasserin aut In eLife eLife Sciences Publications Ltd, 2013 7(2018) (DE-627)728518384 (DE-600)2687154-3 2050084X nnns volume:7 year:2018 https://doi.org/10.7554/eLife.30916 kostenfrei https://doaj.org/article/f7c2a82e817f466bb137c0d58c064ec5 kostenfrei https://elifesciences.org/articles/30916 kostenfrei https://doaj.org/toc/2050-084X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2018
language	English
source	In eLife 7(2018) volume:7 year:2018
sourceStr	In eLife 7(2018) volume:7 year:2018
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topic_facet	host–microbe interaction regulatory T-cells immune system modulation microbiome modeling Medicine R Science Q Biology (General)
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container_title	eLife
authorswithroles_txt_mv	Richard R Stein @@aut@@ Takeshi Tanoue @@aut@@ Rose L Szabady @@aut@@ Shakti K Bhattarai @@aut@@ Bernat Olle @@aut@@ Jason M Norman @@aut@@ Wataru Suda @@aut@@ Kenshiro Oshima @@aut@@ Masahira Hattori @@aut@@ Georg K Gerber @@aut@@ Chris Sander @@aut@@ Kenya Honda @@aut@@ Vanni Bucci @@aut@@
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callnumber-first	Q - Science
author	Richard R Stein
spellingShingle	Richard R Stein misc QH301-705.5 misc host–microbe interaction misc regulatory T-cells misc immune system modulation misc microbiome modeling misc Medicine misc R misc Science misc Q misc Biology (General) Computer-guided design of optimal microbial consortia for immune system modulation
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topic_title	QH301-705.5 Computer-guided design of optimal microbial consortia for immune system modulation host–microbe interaction regulatory T-cells immune system modulation microbiome modeling
topic	misc QH301-705.5 misc host–microbe interaction misc regulatory T-cells misc immune system modulation misc microbiome modeling misc Medicine misc R misc Science misc Q misc Biology (General)
topic_unstemmed	misc QH301-705.5 misc host–microbe interaction misc regulatory T-cells misc immune system modulation misc microbiome modeling misc Medicine misc R misc Science misc Q misc Biology (General)
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title	Computer-guided design of optimal microbial consortia for immune system modulation
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title_full	Computer-guided design of optimal microbial consortia for immune system modulation
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author_browse	Richard R Stein Takeshi Tanoue Rose L Szabady Shakti K Bhattarai Bernat Olle Jason M Norman Wataru Suda Kenshiro Oshima Masahira Hattori Georg K Gerber Chris Sander Kenya Honda Vanni Bucci
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title_sort	computer-guided design of optimal microbial consortia for immune system modulation
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title_auth	Computer-guided design of optimal microbial consortia for immune system modulation
abstract	Manipulation of the gut microbiota holds great promise for the treatment of diseases. However, a major challenge is the identification of therapeutically potent microbial consortia that colonize the host effectively while maximizing immunologic outcome. Here, we propose a novel workflow to select optimal immune-inducing consortia from microbiome compositicon and immune effectors measurements. Using published and newly generated microbial and regulatory T-cell (Treg) data from germ-free mice, we estimate the contributions of twelve Clostridia strains with known immune-modulating effect to Treg induction. Combining this with a longitudinal data-constrained ecological model, we predict the ability of every attainable and ecologically stable subconsortium in promoting Treg activation and rank them by the Treg Induction Score (TrIS). Experimental validation of selected consortia indicates a strong and statistically significant correlation between predicted TrIS and measured Treg. We argue that computational indexes, such as the TrIS, are valuable tools for the systematic selection of immune-modulating bacteriotherapeutics.
abstractGer	Manipulation of the gut microbiota holds great promise for the treatment of diseases. However, a major challenge is the identification of therapeutically potent microbial consortia that colonize the host effectively while maximizing immunologic outcome. Here, we propose a novel workflow to select optimal immune-inducing consortia from microbiome compositicon and immune effectors measurements. Using published and newly generated microbial and regulatory T-cell (Treg) data from germ-free mice, we estimate the contributions of twelve Clostridia strains with known immune-modulating effect to Treg induction. Combining this with a longitudinal data-constrained ecological model, we predict the ability of every attainable and ecologically stable subconsortium in promoting Treg activation and rank them by the Treg Induction Score (TrIS). Experimental validation of selected consortia indicates a strong and statistically significant correlation between predicted TrIS and measured Treg. We argue that computational indexes, such as the TrIS, are valuable tools for the systematic selection of immune-modulating bacteriotherapeutics.
abstract_unstemmed	Manipulation of the gut microbiota holds great promise for the treatment of diseases. However, a major challenge is the identification of therapeutically potent microbial consortia that colonize the host effectively while maximizing immunologic outcome. Here, we propose a novel workflow to select optimal immune-inducing consortia from microbiome compositicon and immune effectors measurements. Using published and newly generated microbial and regulatory T-cell (Treg) data from germ-free mice, we estimate the contributions of twelve Clostridia strains with known immune-modulating effect to Treg induction. Combining this with a longitudinal data-constrained ecological model, we predict the ability of every attainable and ecologically stable subconsortium in promoting Treg activation and rank them by the Treg Induction Score (TrIS). Experimental validation of selected consortia indicates a strong and statistically significant correlation between predicted TrIS and measured Treg. We argue that computational indexes, such as the TrIS, are valuable tools for the systematic selection of immune-modulating bacteriotherapeutics.
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title_short	Computer-guided design of optimal microbial consortia for immune system modulation
url	https://doi.org/10.7554/eLife.30916 https://doaj.org/article/f7c2a82e817f466bb137c0d58c064ec5 https://elifesciences.org/articles/30916 https://doaj.org/toc/2050-084X
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author2	Takeshi Tanoue Rose L Szabady Shakti K Bhattarai Bernat Olle Jason M Norman Wataru Suda Kenshiro Oshima Masahira Hattori Georg K Gerber Chris Sander Kenya Honda Vanni Bucci
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up_date	2024-07-04T00:19:43.951Z
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Computer-guided design of optimal microbial consortia for immune system modulation

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