MetaPhlAn 4 profiling of unknown species-level genome bins improves the characterization of diet-associated microbiome changes in mice

Paolo Manghi; Aitor Blanco-Míguez; Serena Manara; Amir NabiNejad; Fabio Cumbo; Francesco Beghini; Federica Armanini; Davide Golzato; Kun D. Huang; Andrew M. Thomas; Gianmarco Piccinno; Michal Punčochář; Moreno Zolfo; Till R. Lesker; Marius Bredon; Julien Planchais; Jeremy Glodt; Mireia Valles-Colomer; Omry Koren; Edoardo Pasolli; Francesco Asnicar; Till Strowig; Harry Sokol; Nicola Segata

doi:10.1016/j.celrep.2023.112464

MetaPhlAn 4 profiling of unknown species-level genome bins improves the characterization of diet-associated microbiome changes in mice

Paolo Manghi, Aitor Blanco-Míguez, Serena Manara, Amir NabiNejad, Fabio Cumbo, Francesco Beghini, Federica Armanini, Davide Golzato, Kun D. Huang, Andrew M. Thomas, Gianmarco Piccinno, Michal Punčochář, Moreno Zolfo, Till R. Lesker, Marius Bredon, Julien Planchais, Jeremy Glodt, Mireia Valles-Colomer, Omry Koren, Edoardo PasolliFrancesco Asnicar, Till Strowig, Harry Sokol, Nicola Segata

Bar-Ilan University - Azrieli Faculty of Medicine

Research output: Contribution to journal › Article › peer-review

Abstract

Mouse models are key tools for investigating host-microbiome interactions. However, shotgun metagenomics can only profile a limited fraction of the mouse gut microbiome. Here, we employ a metagenomic profiling method, MetaPhlAn 4, which exploits a large catalog of metagenome-assembled genomes (including 22,718 metagenome-assembled genomes from mice) to improve the profiling of the mouse gut microbiome. We combine 622 samples from eight public datasets and an additional cohort of 97 mouse microbiomes, and we assess the potential of MetaPhlAn 4 to better identify diet-related changes in the host microbiome using a meta-analysis approach. We find multiple, strong, and reproducible diet-related microbial biomarkers, largely increasing those identifiable by other available methods relying only on reference information. The strongest drivers of the diet-induced changes are uncharacterized and previously undetected taxa, confirming the importance of adopting metagenomic methods integrating metagenomic assemblies for comprehensive profiling.

Original language	English
Article number	112464
Journal	Cell Reports
Volume	42
Issue number	5
DOIs	https://doi.org/10.1016/j.celrep.2023.112464
State	Published - 30 May 2023

Bibliographical note

Funding Information:
The work was supported by the European Research Council (ERC-STG project MetaPG-716575 and ERC-CoG microTOUCH-101045015) to N.S. and ERC-CoG (project grant agreement no. 101001355) to O.K. by the European H2020 program (ONCOBIOME-825410 project and MASTER-818368 project) to N.S. by the National Cancer Institute of the National Institutes of Health (1U01CA230551) to N.S. by the Premio Internazionale Lombardia e Ricerca 2019 to N.S. by the European Research Council under the European Union's Horizon 2020 Research and Innovation Programme (ERC-2016-StG-71577) to H.S. by VolkswagenStiftung's initiative “Niedersächsisches Vorab” (grant no. 76251-99) to T.S, and by the European Research Council (CoG 865466) to T.S. P.M. and N.S. conceived the study. A.B.-M. F.C. F.B. and E.P. contributed to the development of the MetaPhlAn version used in the work. P.M. and A.B.-M. performed most of the analyses. A.N. S.M. D.G. K.D.H. A.M.T. G.P. M.P. M.V.-C. M.Z. T.R.L. and F. Asnicar supported the design and validation of the methodology and contributed to the analyses. H.S. M.B. J.P. and J.G. collected the new mice samples. F. Armanini generated metagenomic data. P.M. A.B.-M. F. Asnicar, T.S. O.K. H.S. and N.S. wrote the paper with contribution and editing from all the authors. F. Asnicar and N.S. supervised the work. All the authors read and approved the final version of the manuscript. The authors declare no competing interests. We support inclusive, diverse, and equitable conduct of research.

Funding Information:
The work was supported by the European Research Council ( ERC-STG project MetaPG-716575 and ERC-CoG microTOUCH-101045015 ) to N.S. and ERC-CoG (project grant agreement no. 101001355 ) to O.K., by the European H2020 program ( ONCOBIOME-825410 project and MASTER-818368 project) to N.S., by the National Cancer Institute of the National Institutes of Health ( 1U01CA230551 ) to N.S., by the Premio Internazionale Lombardia e Ricerca 2019 to N.S., by the European Research Council under the European Union’s Horizon 2020 Research and Innovation Programme ( ERC-2016-StG-71577 ) to H.S., by VolkswagenStiftung’s initiative “Niedersächsisches Vorab” (grant no. 76251-99 ) to T.S, and by the European Research Council (CoG 865466) to T.S.

Publisher Copyright:
© 2023 The Authors

Keywords

CP: Microbiology
MetaPhlAn
diet
metagenomics
mouse microbiome
uncharacterized microbial species

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10.1016/j.celrep.2023.112464

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Manghi, P., Blanco-Míguez, A., Manara, S., NabiNejad, A., Cumbo, F., Beghini, F., Armanini, F., Golzato, D., Huang, K. D., Thomas, A. M., Piccinno, G., Punčochář, M., Zolfo, M., Lesker, T. R., Bredon, M., Planchais, J., Glodt, J., Valles-Colomer, M., Koren, O., ... Segata, N. (2023). MetaPhlAn 4 profiling of unknown species-level genome bins improves the characterization of diet-associated microbiome changes in mice. Cell Reports, 42(5), [112464]. https://doi.org/10.1016/j.celrep.2023.112464

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abstract = "Mouse models are key tools for investigating host-microbiome interactions. However, shotgun metagenomics can only profile a limited fraction of the mouse gut microbiome. Here, we employ a metagenomic profiling method, MetaPhlAn 4, which exploits a large catalog of metagenome-assembled genomes (including 22,718 metagenome-assembled genomes from mice) to improve the profiling of the mouse gut microbiome. We combine 622 samples from eight public datasets and an additional cohort of 97 mouse microbiomes, and we assess the potential of MetaPhlAn 4 to better identify diet-related changes in the host microbiome using a meta-analysis approach. We find multiple, strong, and reproducible diet-related microbial biomarkers, largely increasing those identifiable by other available methods relying only on reference information. The strongest drivers of the diet-induced changes are uncharacterized and previously undetected taxa, confirming the importance of adopting metagenomic methods integrating metagenomic assemblies for comprehensive profiling.",

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Manghi, P, Blanco-Míguez, A, Manara, S, NabiNejad, A, Cumbo, F, Beghini, F, Armanini, F, Golzato, D, Huang, KD, Thomas, AM, Piccinno, G, Punčochář, M, Zolfo, M, Lesker, TR, Bredon, M, Planchais, J, Glodt, J, Valles-Colomer, M, Koren, O, Pasolli, E, Asnicar, F, Strowig, T, Sokol, H & Segata, N 2023, 'MetaPhlAn 4 profiling of unknown species-level genome bins improves the characterization of diet-associated microbiome changes in mice', Cell Reports, vol. 42, no. 5, 112464. https://doi.org/10.1016/j.celrep.2023.112464

TY - JOUR

T1 - MetaPhlAn 4 profiling of unknown species-level genome bins improves the characterization of diet-associated microbiome changes in mice

AU - Manghi, Paolo

AU - Blanco-Míguez, Aitor

AU - Manara, Serena

AU - NabiNejad, Amir

AU - Cumbo, Fabio

AU - Beghini, Francesco

AU - Armanini, Federica

AU - Golzato, Davide

AU - Huang, Kun D.

AU - Thomas, Andrew M.

AU - Piccinno, Gianmarco

AU - Punčochář, Michal

AU - Zolfo, Moreno

AU - Lesker, Till R.

AU - Bredon, Marius

AU - Planchais, Julien

AU - Glodt, Jeremy

AU - Valles-Colomer, Mireia

AU - Koren, Omry

AU - Pasolli, Edoardo

AU - Asnicar, Francesco

AU - Strowig, Till

AU - Sokol, Harry

AU - Segata, Nicola

PY - 2023/5/30

Y1 - 2023/5/30

N2 - Mouse models are key tools for investigating host-microbiome interactions. However, shotgun metagenomics can only profile a limited fraction of the mouse gut microbiome. Here, we employ a metagenomic profiling method, MetaPhlAn 4, which exploits a large catalog of metagenome-assembled genomes (including 22,718 metagenome-assembled genomes from mice) to improve the profiling of the mouse gut microbiome. We combine 622 samples from eight public datasets and an additional cohort of 97 mouse microbiomes, and we assess the potential of MetaPhlAn 4 to better identify diet-related changes in the host microbiome using a meta-analysis approach. We find multiple, strong, and reproducible diet-related microbial biomarkers, largely increasing those identifiable by other available methods relying only on reference information. The strongest drivers of the diet-induced changes are uncharacterized and previously undetected taxa, confirming the importance of adopting metagenomic methods integrating metagenomic assemblies for comprehensive profiling.

AB - Mouse models are key tools for investigating host-microbiome interactions. However, shotgun metagenomics can only profile a limited fraction of the mouse gut microbiome. Here, we employ a metagenomic profiling method, MetaPhlAn 4, which exploits a large catalog of metagenome-assembled genomes (including 22,718 metagenome-assembled genomes from mice) to improve the profiling of the mouse gut microbiome. We combine 622 samples from eight public datasets and an additional cohort of 97 mouse microbiomes, and we assess the potential of MetaPhlAn 4 to better identify diet-related changes in the host microbiome using a meta-analysis approach. We find multiple, strong, and reproducible diet-related microbial biomarkers, largely increasing those identifiable by other available methods relying only on reference information. The strongest drivers of the diet-induced changes are uncharacterized and previously undetected taxa, confirming the importance of adopting metagenomic methods integrating metagenomic assemblies for comprehensive profiling.

KW - CP: Microbiology

KW - MetaPhlAn

KW - diet

KW - metagenomics

KW - mouse microbiome

KW - uncharacterized microbial species

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U2 - 10.1016/j.celrep.2023.112464

DO - 10.1016/j.celrep.2023.112464

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C2 - 37141097

AN - SCOPUS:85153945401

SN - 2211-1247

VL - 42

JO - Cell Reports

JF - Cell Reports

IS - 5

M1 - 112464

ER -

MetaPhlAn 4 profiling of unknown species-level genome bins improves the characterization of diet-associated microbiome changes in mice

Abstract

Bibliographical note

Keywords

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