Shaffer et al. Metabolite-microbe profiles are shaped by the environment
1
Multi-omics profiling of Earth’s biomes reveals that microbial and metabolite composition are shaped by
the environment
Justin P. Shaffer
1,#
, Louis-Félix Nothias
2,3,#
, Luke R. Thompson
4,5,#
, Jon G. Sanders
6
, Rodolfo A. Salido
7
,
Sneha P. Couvillion
8
, Asker D. Brejnrod
3
, Shi Huang
1,9
, Franck Lejzerowicz
1,9
, Holly L. Lutz
1,10
, Qiyun
Zhu
11,12
, Cameron Martino
9,13
, James T. Morton
14
, Smruthi Karthikeyan
1
, Mélissa Nothias-Esposito
2,3
, Kai
Dührkop
15
, Sebastian Böcker
15
, Hyunwoo Kim
10
, Alexander A. Aksenov
2,3
, Wout Bittremieux
2,3,16
,
Jeremiah J. Minich
10
, Clarisse Marotz
1
, MacKenzie M. Bryant
1
, Karenina Sanders
1
, Tara Schwartz
1
, Greg
Humphrey
1
, Yoshiki Vásquez-Baeza
9
, Anupriya Tripathi
1,3
, Laxmi Parida
17
, Anna Paola Carrieri
18
, Niina
Haiminen
17
, Kristen L. Beck
19
, Promi Das
1,10
, Antonio González
1
, Daniel McDonald
1
, Søren M. Karst
20
,
Mads Albertsen
21
, Gail Ackermann
1
, Jeff DeReus
1
, Torsten Thomas
22
, Daniel Petras
2,10,23
, Ashley Shade
24
,
James Stegen
8
, Se Jin Song
9
, Thomas O. Metz
8
, Austin D. Swafford
9
, Pieter C. Dorrestein
2,3
, Janet K.
Jansson
8
, Jack A. Gilbert
1,10
, Rob Knight
1,7,9,25,
*, and the Earth Microbiome Project 500 (EMP500)
Consortium
1
Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, California,
USA.
2
Collaborative Mass Spectrometry Innovation Center; University of California San Diego; La Jolla, CA
92093; USA
3
Skaggs School of Pharmacy and Pharmaceutical Sciences; University of California San Diego; La Jolla
CA 92093; USA
4
Northern Gulf Institute, Mississippi State University, Mississippi State, Mississippi, USA
5
Ocean Chemistry and Ecosystems Division, Atlantic Oceanographic and Meteorological Laboratory,
National Oceanic and Atmospheric Administration, Miami, Florida, USA
6
Department of Ecology and Evolutionary Biology, Cornell University, Ithaca, New York, USA.
7
Department of Bioengineering, University of California San Diego, La Jolla, California, USA.
8
Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA
was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (whichthis version posted June 6, 2021. ; https://doi.org/10.1101/2021.06.04.446988doi: bioRxiv preprint
Shaffer et al. Metabolite-microbe profiles are shaped by the environment
2
9
Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego,
La Jolla, California, USA.
10
Scripps Institution of Oceanography, University of California San Diego, La Jolla, California, USA.
11
School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA
12
Biodesign Center for Fundamental and Applied Microbiomics, Arizona State University, Tempe, AZ
85281, USA
13
Bioinformatics and Systems Biology Program, Jacobs School of Engineering, University of California
San Diego, La Jolla, California, USA.
14
Center for Computational Biology, Flatiron Institute, Simons Foundation
15
Chair for Bioinformatics, Friedrich Schiller University, Jena, Germany
16
Department of Computer Science, University of Antwerp, Antwerp, Belgium
17
IBM Research, T.J. Watson Research Center, Yorktown Heights, NY, USA
18
IBM Research Europe - Daresbury, UK
19
IBM Research, Almaden Research Center, San Jose, CA, USA
20
Department of Virus and Microbiological Special Diagnostics, Statens Serum Institute, Copenhagen,
Denmark
21
Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark
22
Centre for Marine Science and Innovation, School of Biological, Earth and Environmental Science, The
University of New South Wales, Sydney, 2052, Australia
23
Interfaculty Institute of Microbiology and Infection Medicine, University of Tübingen, Tübingen,
Baden-Württemberg 72076, Germany
24
Department of Microbiology and Molecular Genetics, Michigan State University East Lansing MI USA
25
Department of Computer Science and Engineering, Jacobs School of Engineering, University of
California San Diego, La Jolla, California, USA.
#
Co-first author
was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (whichthis version posted June 6, 2021. ; https://doi.org/10.1101/2021.06.04.446988doi: bioRxiv preprint
Shaffer et al. Metabolite-microbe profiles are shaped by the environment
3
Abstract: Microbes produce an array of secondary metabolites that perform diverse functions from
communication to defense
1
. These metabolites have been used to benefit human health and sustainability
2
.
In their analysis of the Genomes from Earth’s Microbiomes (GEM) catalog
3
, Nayfach and co-authors
observed that, whereas genes coding for certain classes of secondary metabolites are limited or enriched
in certain microbial taxa, “specific chemistry is not limited or amplified by the environment, and that most
classes of secondary metabolites can be found nearly anywhere”. Although metagenome mining is a
powerful way to annotate biosynthetic gene clusters (BCGs), chemical evidence is required to confirm the
presence of metabolites and comprehensively address this fundamental hypothesis, as metagenomic data
only identify metabolic potential. To describe the Earth's metabolome, we use an integrated omics
approach: the direct survey of metabolites associated with microbial communities spanning diverse
environments using untargeted metabolomics coupled with metagenome analysis. We show, in contrast to
Nayfach and co-authors, that the presence of certain classes of secondary metabolites can be limited or
amplified by the environment. Importantly, our data indicate that considering the relative abundances of
secondary metabolites (i.e., rather than only presence/absence) strengthens differences in metabolite
profiles across environments, and that their richness and composition in any given sample do not directly
reflect those of co-occurring microbial communities, but rather vary with the environment.
was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (whichthis version posted June 6, 2021. ; https://doi.org/10.1101/2021.06.04.446988doi: bioRxiv preprint
Shaffer et al. Metabolite-microbe profiles are shaped by the environment
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From a genomics perspective, information regarding metabolic potential is obtained through detection
and classification of biosynthetic gene clusters (BGCs), the genomic loci underlying the production of
secondary metabolites and their precursors
4
. The most sensitive approaches amplify BGC-specific marker
sequences by PCR
5
, but only metagenomic methods can link BGCs to their genomes (i.e., metagenome-
assembled genomes, or MAGs) of origin and detect BGCs in novel MAGs. Nayfach and co-authors
uncovered 104,211 putative BGC regions from 52,515 microbial MAGs. Surprisingly, their analysis
showed that, although the main classes of secondary metabolites are enriched in particular microbial taxa,
the relative distribution of secondary metabolite biosynthetic potential across environments was
conserved, implying that most classes of secondary metabolites are not “limited or amplified” by the
environment. The authors acknowledged that most of their annotated BGCs had incomplete sequences,
potentially impacting annotation and quantification, but that this was consistent with previous studies.
More importantly, gene-level data about BGCs inferred from MAGs cannot offer information about
actual synthesis (e.g., gene expression), creating uncertainty about the distribution of secondary
metabolites across environments
6–9
. Even with high-coverage gene expression data, currently lacking for
most environments, the complex structural and modular nature of many secondary metabolites prevents
their accurate association with the underlying genomic origins
10
. Furthermore, quantifying metabolite
diversity from such metatranscriptomic and/or metaproteomic data (also lacking for most environments)
is problematic due to a suite of post-translational processes that can dissociate the level of gene
transcription from the abundance of gene products
11
. Finally, shotgun metagenomics does not capture
BGCs from low-abundance MAGs efficiently, as shown from comparative studies of targeted sequencing
approaches
5
.
An approach to surmount these issues is to complement metagenomics with a direct survey of
secondary metabolites using untargeted metabolomics. Liquid chromatography with untargeted tandem
mass spectrometry (LC-MS/MS) is a versatile method that detects tens-of-thousands of metabolites in
biological samples
12
. Although LC-MS/MS metabolomics has generally suffered from a low metabolite
annotation rate when applied to non-model organisms, recent computational advances can systematically
was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (whichthis version posted June 6, 2021. ; https://doi.org/10.1101/2021.06.04.446988doi: bioRxiv preprint
Shaffer et al. Metabolite-microbe profiles are shaped by the environment
5
classify metabolites using their fragmentation spectra
13
. Untargeted metabolomics provides the relative
abundance (i.e., intensity) of each metabolite detected across samples rather than just counts of unique
structures (e.g., Fig. 1a vs. 1b), and thus provides a direct readout of the surveyed environment, a result
that is difficult to achieve with a purely genomics approach. While there is a clear need for the use of
untargeted metabolomics to quantify the metabolic activities of microbiota, this methodology has been
limited by the challenge of discriminating the secondary metabolites produced by microbes from tens-of-
thousands of metabolites detected in the environment. To resolve this bottleneck, we devised a
computational method for recognizing and annotating putative secondary metabolites of microbial origin
from fragmentation spectra. The annotations were first obtained from spectral library matching and in
silico annotation
14
using the GNPS web-platform
15
. These annotations were then queried against
microbial metabolite reference databases (i.e., Natural Products Atlas
16
and MIBiG
17
), and molecular
networking
18
was used to propagate the annotation to similar metabolites. Finally, a global chemical
classification of these metabolites was achieved using SIRIUS/CANOPUS
13
.
We used this methodology to quantify microbial secondary metabolites from diverse microbial
communities that span 20 major environments from the Earth Microbiome Project 500 (EMP500) dataset
(Extended Data Fig. 1, Table S1). With this dataset, we show that although the presence/absence (i.e.,
occurrence) of major classes of microbially-related metabolites is indeed relatively conserved across
habitats, their relative abundance reveals specific chemistry that is limited or amplified by the
environment, especially at more resolved chemical class ontology levels (Fig. 1). Importantly, when
considering differences in the relative abundances of all microbially-related metabolites, profiles among
environments were so distinct that we could identify particular metabolites whose abundances were
enriched in certain environments (Fig. 2a,c, Table S2, Table S3). For example, microbially-related
metabolites associated with the carbohydrate pathway were especially enriched in aquatic samples,
whereas those associated with the polyketide- and shikimate and phenylpropanoid pathways enriched in
sediment, soil, and fungal samples (Fig. 2a). Interestingly, distinct analytical approaches confirmed
specific metabolites as particularly important for distinguishing aquatic samples (C
28
H
58
O
15
, pathway:
was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (whichthis version posted June 6, 2021. ; https://doi.org/10.1101/2021.06.04.446988doi: bioRxiv preprint
