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RESEARCH PAPER
Evolution of E. coli in a mouse model of inammatory bowel disease leads to a
disease-specic bacterial genotype and trade-os with clinical relevance
Rahul Unni
a,b
#
, Nadia Andrea Andreani
a,b
#
, Marie Vallier
a,b
#
,
§
, Silke S. Heinzmann
c
, Jan Taubenheim
d
,
Martina A. Guggeis
e,f
, Florian Tran
e,f
, Olga Vogler
a
, Sven Künzel
a
, Jan-Bernd Hövener
g
,
Philip Rosenstiel
e
, Christoph Kaleta
d
, Astrid Dempe
h
, Daniel Unterweger
a,b
, and John F. Baines
a,b
a
Section Evolutionary Medicine, Max Planck Institute for Evolutionary Biology, Plön, Germany;
b
Section of Evolutionary Medicine, Institute for
Experimental Medicine, Kiel University, Kiel, Germany;
c
Research Unit Analytical BioGeoChemistry, Helmholtz Munich, Neuherberg, Germany;
d
Research Group Medical Systems Biology, Institute for Experimental Medicine, Kiel University, Kiel, Germany;
e
Institute of Clinical Molecular
Biology, Christian-Albrechts-University Kiel and University Medical Center Schleswig-Holstein, Kiel, Germany;
f
Department of Internal
Medicine I, University Medical Center Schleswig-Holstein, Kiel, Germany;
g
Section Biomedical Imaging, Molecular Imaging North Competence
Center (MOIN CC), Department of Radiology and Neuroradiology, University Medical Center Kiel, Kiel, Germany;
h
Institute of Medical
Informatics and Statistics, Kiel University, Kiel, Germany
ABSTRACT
Inammatory bowel disease (IBD) is a persistent inammatory condition that aects the gastro-
intestinal tract and presents signicant challenges in its management and treatment. Despite the
knowledge that within-host bacterial evolution occurs in the intestine, the disease has rarely been
studied from an evolutionary perspective. In this study, we aimed to investigate the evolution of
resident bacteria during intestinal inammation and whether- and how disease-related bacterial
genetic changes may present trade-os with potential therapeutic importance. Here, we perform
an in vivo evolution experiment of E. coli in a gnotobiotic mouse model of IBD, followed by
multiomic analyses to identify disease-specic genetic and phenotypic changes in bacteria that
evolved in an inamed versus a non-inamed control environment. Our results demonstrate
distinct evolutionary changes in E. coli specic to inammation, including a single nucleotide
variant that independently reached high frequency in all inamed mice. Using ex vivo tness
assays, we nd that these changes are associated with a higher tness in an inamed environment
compared to isolates derived from non-inamed mice. Further, using large-scale phenotypic
assays, we show that bacterial adaptation to inammation results in clinically relevant phenotypes,
which intriguingly include collateral sensitivity to antibiotics. Bacterial evolution in an inamed gut
yields specic genetic and phenotypic signatures. These results may serve as a basis for developing
novel evolution-informed treatment approaches for patients with intestinal inammation.
ARTICLE HISTORY
Received 4 September 2023
Revised 16 November 2023
Accepted 17 November 2023
KEYWORDS
Inflammatory bowel disease;
E. coli; experimental
evolution;
evolutionary trade-offs
Introduction
Dysbiosis of the intestinal microbial community,
characterized by alterations in bacterial composi-
tion and function, is a hallmark of inflammatory
bowel disease (IBD). The nature of these microbial
imbalances has been described extensively and at
numerous complementary levels, including diver-
sity parameters, taxonomic- and functional geno-
mic changes, and at the level of gene expression.
1–6
This important body of work accounts for changes
observed at the ecological level, while the latter also
considers the potential contribution of phenotypic
plasticity of gut microbes to disease susceptibility.
Moreover, numerous studies have highlighted the
association of dysbiotic microbial signatures with
distinct clinical IBD subtypes, including Crohn’s
disease (CD) and ulcerative colitis (UC), further
emphasizing the intricate relationship between the
gut microbiota and disease pathogenesis.
3,4,7
While research has focused extensively on the
altered ecology of the gut microbiota in IBD, its
potential for evolutionary change during disease
pathogenesis has received comparatively limited
attention. However, numerous recent studies have
CONTACT Daniel Unterweger d.unterweger@iem.uni-kiel.de Max Planck Institute for Evolutionary Biology, Plön, Germany; Section of Evolutionary
Medicine, Institute for Experimental Medicine, Kiel University, Kiel, Germany; John F. Baines baines@evolbio.mpg.de Max Planck Institute for
Evolutionary Biology, Plön, Germany; Section of Evolutionary Medicine, Institute for Experimental Medicine, Kiel University, Kiel, Germany.
#
These authors contributed equally.
§
Current affiliation: Valbiotis, R&D Center, Riom, France.
Supplemental data for this article can be accessed online at https://doi.org/10.1080/19490976.2023.2286675
GUT MICROBES
2023, VOL. 15, NO. 2, 2286675
https://doi.org/10.1080/19490976.2023.2286675
© 2023 The Author(s). Published with license by Taylor & Francis Group, LLC.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use,
distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted
Manuscript in a repository by the author(s) or with their consent.
revealed the capacity of bacteria to undergo adap-
tive evolution within the host environment, includ-
ing the gut [reviewed in
8
]. This phenomenon is
evident in both mouse models
9–11
and human
subjects,
12
even in the absence of overt disease.
Importantly, recent studies indicate that evolution-
ary changes also occur in the context of host
inflammation. Elhenawy et al. focused on CD-
related adherent-invasive E. coli (AIEC) using
a murine model of chronic colonization, and
revealed the evolution of lineages displaying
enhanced invasive and metabolic capabilities.
13
Notably, they found that the fitness benefits con-
ferred by increased motility were specific to the
host environment, suggesting an evolutionary
trade-off. In a second study exploring bacterial
evolution in aging mice, significant differences in
E. coli evolution between old and young mice were
revealed.
14
The aged mouse environment exhibited
increased inflammation, leading to the specific tar-
geting of stress-related functions in E. coli.
These findings emphasize the importance of
including evolutionary perspectives when studying
dysbiosis of the gut microbiome. In particular,
documenting bacterial evolution within the
inflamed gut has the potential to reveal the timing
of disease-specific signatures, i.e., it may help dis-
entangle the classical “chicken or egg” dilemma in
microbiome research,
15
as well as to shed light on
potential trade-offs resulting from adaptive
changes. Evolutionary trade-offs occur when an
increase in fitness in one environment is accompa-
nied by a decrease in fitness in another.
16,17
These
trade-offs can have significant therapeutic implica-
tions and are observed in various related fields,
including antibiotic resistance
18–21
and cancer
treatment.
22–25
Thus, elucidating trade-offs asso-
ciated with bacterial adaptations in the inflamed
gut may offer insight into potential collateral effects
on bacterial fitness and prove useful for developing
novel treatment strategies based on evolutionary
principles.
In this study, we investigated the evolutionary
dynamics of resident gut bacteria during intest-
inal inflammation using an established gnotobio-
tic mouse model of IBD.
26
Through the
monocolonization of both Interleukin 10-
knockout and wildtype mice with a single E. coli
strain (NC101), this setup allowed us to track
genetic and phenotypic changes over the course
of intestinal inflammation and to evaluate their
potential clinical relevance. By employing mul-
tiomic analysis and high-throughput phenotypic
screening, we identify genetic changes in bacteria,
alterations in the metabolome, and differences in
numerous phenotypic traits among bacterial
populations that are specifically associated with
the evolution in inflamed Il10-knockout mice.
Remarkably, among the phenotypic changes
observed in inflammation-adapted bacteria are
sensitivities to antibiotics with a known thera-
peutic value in IBD. These results further con-
firm the importance of understanding bacterial
adaptation to inflammation and suggest its more
widespread study of patients as a means to
develop novel treatment approaches.
Results
Gnotobiotic model of intestinal inammation
In order to capture the evolutionary dynamics of
bacteria evolving in the context of intestinal
inflammation, we implemented a previously
established gnotobiotic model, for which inflam-
mation develops upon colonization with the
E. coli NC101 strain in IL10-deficient (Il10
−/−
;
herein “KO”), but not wild type (WT) mice.
26
We performed two independent in vivo experi-
ments (see Methods), in which WT (total N = 14)
and KO (total N = 11) mice were monocolonized
and monitored over a period of 12 weeks, with
longitudinal sampling of feces for downstream
multiomic analyses (Figure 1a; Table S1, see
Methods). Inflammation was monitored via levels
of lipocalin-2 in feces, which steadily and signifi-
cantly increased in KO mice, but not in WT
mice, already at one week post-inoculation
(Figure 1b, Tables S2 and S3, Wilcoxon signed-
rank test Benjamini-Hochberg-corrected P < .05).
Histopathological assessment of colonic tissue at
the endpoint reveals significantly higher pathol-
ogy scores in KO mice than in WT mice
(Figure 1c, Kruskal-Wallis H test Benjamini-
Hochberg-corrected P < .05), further confirming
that inflammation was specific to KO mice. The
bacterial load in the fecal samples was stable
throughout the experiment, as measured by
2R. UNNI ET AL.
CFU counts normalized by wet feces weight (Fig
S1, Tables S4 and S5).
Bacterial populations show genetic diversication
in healthy and inamed mice
To assess the genetic diversification of the bacter-
ial populations during the evolution experiment,
we performed shotgun sequencing on each single
inoculum and all bacterial populations at weeks 1,
4, 8, and 12 and identified de-novo mutations
(i.e., mutations that were not present in an inocu-
lum; see Methods) by comparison with the gen-
ome of the ancestral strain. No statistically
significant difference was observed in the number
of mutations in the bacteria from KO and WT
mice at any point in the experiment, suggesting
that differences in the intestinal environment in
which the populations evolved did not exert
a measurable effect on the number of mutations
(Figure S2, Table S6). One week after gavage, the
bacterial populations showed a median number
of 87 de-novo mutations, with a subsequent
reduction at the following timepoints, although
the differences are not significant (median at
week 4 = 34, at week 8 = 45 and at week 12 = 36,
Figure S2, Table S7 Wilcoxon signed rank test,
corrected P > .05). Comparison of the evolved
populations at week 12 with the ancestral strain
reveals a median number of 39 mutations per
mouse in WT and 26 in KO mice (Figure 1d).
This indicates that the evolved bacterial
Figure 1. Application of a mouse model of IBD to test the effect of gut inflammation on the evolution of NC101. a Schematic of the
experimental setup. Germ-free WT and Il10
−/−
mice were monocolonized with E. coli NC101. Fecal samples were collected from each
mouse at the indicated time points and analyzed as described. b Fecal lipocalin-2 levels from the WT and Il10-/- mice. Lipocalin-2 was
measured using the Mouse Lipocalin-2/NGAL DuoSet ELISA and normalized to feces weight. Each dot represents one mouse at each
sampling point. Results of Wilcoxon signed-rank test of lipocalin-2 concentrations at each time point are reported in Table S3.
Differences were considered statistically significant at Benjamini-Hochberg-corrected P < .05. c Boxplots of the histopathology scores
of the colon tissue of mice after sacrifice at the end of the experiment. Each dot represents a single mouse. Differences were
considered statistically significant using the Kruskal-Wallis H test at Benjamini-Hochberg-corrected P < .05. d Number of de-novo
mutations in the bacterial populations at week 12 in WT and KO mice compared with the reference genome. Each dot represents
a bacterial population from a single mouse. Differences were considered statistically significant using the Kruskal-Wallis H test at
Benjamini-Hochberg-corrected P < .05. e Partial least squares-discriminant analysis of the de-novo mutated genes in the evolved
populations of E. coli NC101 at week 12 in WT and KO mice compared to the reference genome. Differences were considered
statistically significant at a PERMANOVA P < .05.
GUT MICROBES 3
populations in both groups of mice genetically
differ from the ancestral strain. Although the
unphased shotgun data do not enable us to
directly analyze the role of genetic hitchhiking,
Table S8 indicates the number of mutations at
week 12 already present at each earlier time
point. Interestingly, there remains a substantial
(on average approx. 60% in each mouse geno-
type) proportion of de-novo mutations that
arose between week 8 and 12, which suggests
that a large proportion of the mutations cannot
be explained by hitchhiking from standing
genetic variation at earlier time points.
To test for overall differences in the de-novo
mutations that accumulated in the bacterial popu-
lations that evolved in the WT and KO mice, we
performed a partial least-squares discriminant ana-
lysis (PLS-DA) on all mutated loci (genes or inter-
genic regions differing in their nucleotide sequence
compared to the inocula). We find a clear and
significant distinction between the populations
that evolved in the two mouse genotypes
(Figure 1e, PERMANOVA P = .0022). The same
result was observed when comparing single de-
novo mutated genomic positions between evolved
bacterial populations from WT and KO mice
(Figure S3, PERMANOVA P = .0016).
Single nucleotide polymorphism dierentiates
evolved bacteria of all inamed mice from evolved
bacteria of healthy mice
To identify candidates for genetic changes that may
be specifically selected in an inflamed environment,
we focused on parallel mutations (i.e., genes or
intergenic regions mutated in at least two mice that
were gavaged with an independent inoculum; see
Methods) and their associated functions. Analysis
of the KEGG pathways encompassed by these loci
reveals seven pathways unique to the bacterial popu-
lations from inflamed mice (Figure 2a). These path-
ways include those involved in amino acid
metabolism, pyruvate metabolis, all of which have
been associated with IBD in clinical settings.
27,28
In
addition to the pathways affected only in bacterial
populations from the inflamed mice, 18 pathways
are affected only in populations from healthy mice,
and 18 other pathways are mutated in populations
from both healthy and inflamed mice (Figure 2a).
Univariate analysis reveals that only one parti-
cular locus is mutated at significantly higher fre-
quencies in evolved bacterial populations from
inflamed than healthy mice (Wilcoxon signed-
rank test, corrected P = .001, Table S9), which is
a single nucleotide polymorphism (SNP) (C>T)
nine nucleotides upstream of the gene mprA
Figure 2. Parallel evolution of E. coli in inflamed mice revealed a disease-specific genetic signature. a Parallelly mutated genes were
classified based on KEGG pathways; b An intergenic C>T mutation 9-nt upstream of mprA was the unique parallel mutation
significantly more abundant in populations evolved in the inflamed gut (Wilcoxon signed-rank test Benjamini-Hochberg corrected
P < .005). The heatmap shows the frequency of the mutation in bacterial populations from each mouse. c Mutation frequency (left
axis) of the intergenic C>T mutation 9-nt upstream of mprA and mean of lipocalin-2 concentrations across KO mice at different
timepoints during the in vivo evolution experiment. Each line represents a population from a single mouse. Each bar represents the
mean value of the normalized concentration of lipocalin-2 in fecal samples from KO mice.
4R. UNNI ET AL.
(position 3,009,211) (Figure 2b). It is found in 11/
11 KO mice at the endpoint with a frequency
between 0.373 and 1, compared to 2/14 WT mice
with a frequency between 0.085 and 0.239. Two
other mutations are detected in the same intergenic
region, although in a few populations only. The
bacteria from one WT mouse harbors a C>A muta-
tion at the same position (position 3,009,211) with
a frequency of 0.052, whereas those from one
inflamed mouse harbors a G>T SNP at a nearby
position (position 3,009,194) with a frequency of
0.611.
To further investigate the most frequent C>T
mutation, we analyzed its frequency over the
course of the experiment (weeks 1, 4, 8, and
12). At week 1 the mutation is not present or
below the detection threshold (minimum fre-
quency of 0.05; see Methods), while at week 4,
it is observed in 7/11 KO mice with a maximum
frequency of 0.616, in comparison to 1/14 WT
mice with a frequency of 0.364 (Figure 2c).
After a steady increase in frequency at weeks 8
and 12, the mutation reaches fixation or near
fixation in 7/11 KO mice and is also present at
intermediate frequencies in the remaining 4/11
KO mice (Figure 2c). Importantly, the increase
in frequency of the C>T mutation strongly coin-
cides with the increase in inflammation as mea-
sured by lipocalin-2 levels, both in KO mice
alone and when including all mice (Figure 2c,
Figure S4; Spearman’s rank correlation rho P =
.00005 for KO mice alone and P = 3.005e-11 for
all mice).
Interestingly, closer inspection of the inter-
genic region between the hypothetical pro-
tein_02879 and the gene mprA reveals that this
genomic region is a mutational hotspot, with six
others positions in this region having mutated
over the course of the in vivo experiment
(Figure S5).
Taken together, we identify a C>T change at
position 3,009,211 in the E. coli genome, which is
a parallel mutation significantly associated with
intestinal inflammation. This mutation is located
upstream of mprA (also known as emrR),
a transcriptional repressor of several genes, includ-
ing those encoding the efflux pump EmrAB and
AcrAB, and a putative outer membrane porin
OmpC (also known as NmpC).
29–31
Bacterial populations show signicantly dierent
expression proles in healthy and inamed mice
To quantify the expression of genes that are part of the
mprA regulon (mprA, emrAB, acrAB, and ompC),
29–31
as well as gain an overall view on differences in gene
expression in WT- compared to KO-evolved bacterial
populations, we perform metatranscriptomic sequen-
cing of all week 12 bacterial populations. To test for
overall differences in the gene expression, we perform
a partial least-squares discriminant analysis (PLS-DA)
on all genes, using RPKM (Reads per Kilobase
per million Mapped Reads) as a proxy for gene
expression. We find a clear and significant difference
in overall gene expression between populations
according to mouse genotype (Figure S6A,
PERMANOVA P < .00005). Differential expression
analysis performed with Deseq2 reveals 2,219 genes
that are up- or down-regulated in one of the two
conditions (1,022 upregulated for the populations
from the healthy gut and 1,197 for the population
from the inflamed gut; Benjamini-Hochberg cor-
rected P < .05, Table S10). Analysis of the KEGG path-
ways encompassed by these upregulated genes reveals
25 pathways unique to the bacterial populations from
inflamed mice (Figure S6B). These pathways include
Ribosome, Aminoacyl-tRNA biosynthesis, Glutat
hione metabolism, RNA degradation, Folate bio-
synthesis, Sulfur relay system, Arginine biosynthesis,
Protein export, Pantothenate and CoA biosynthesis,
and Homologous recombination, among others
(Table S11). Interestingly, mprA and ompC, which
can be putatively regulated by mprA,
30
are signifi-
cantly more expressed in the populations that evolved
in the inflamed gut (Table S10; Figure S6C).
Increase in tness of evolved bacteria in an
inammatory environment
As parallel mutations are a strong indication of
adaptation by natural selection, we hypothesized
that the evolved bacterial populations may have
a fitness advantage in the environment in which
they evolved, and thus next focused on the pheno-
types present in the evolved bacterial populations.
To test whether evolution in the inflamed mouse
gut confers a fitness advantage to E. coli, we per-
formed “reciprocal transplant” experiments based
on ex vivo assays using filtered cecal content from
GUT MICROBES 5
inflamed versus non-inflamed mice, collected after
sacrifice (referred to subsequently as “ex vivo
media”). Populations of E. coli from fecal samples
collected at week 12 from a subset of mice (N = 5
KO mice and N = 5 WT mice) were cultured in ex
vivo media derived from both inflamed and non-
inflamed mice. While no significant difference is
observed between bacteria from WT and KO mice
when grown in non-inflamed ex vivo media
(Kruskal-Wallis test P = .9168; Figure 3a), bacteria
from KO mice grow significantly better than those
from WT mice in the inflamed ex vivo media
(Kruskal-Wallis test P = .009023). Notably, bacteria
from KO mice grow significantly better in the
inflamed ex vivo media than in the non-inflamed
ex vivo media (Kruskal-Wallis test P = .0472), while
bacteria from WT mice show no difference in
growth in the two media (Kruskal-Wallis test
P = .4647). Thus, bacteria that evolved in the
inflamed gut possess a fitness advantage in the
Figure 3. Phenotypic characterization of evolved E. coli and the inflammatory environment in which they were selected. a Evolved
populations from a subset of mice (N = 5 KO mice and N = 5 WT mice) were cultured in media derived from the cecal content collected
from healthy (WT) and inflamed (KO) mice after sacrifice. The area under the curve (AUC) calculated from growth curves measured
during the growth of evolved populations from each mouse in inflamed and healthy cecal contents is shown. Each dot represents the
mean of three independent replicates. *P < .05 (Kruskal-Wallis H test); ns, not significant. b Partial least squares discriminant analysis
(PLS-DA) of the fecal metabolite abundances at different time points during the evolution experiment. Differences were considered
statistically significant at PERMANOVA P < .05. c Bacterial metabolism was measured in vitro in a range of compounds. Measurements
were performed on bacterial populations derived from fecal samples collected at weeks 1 and 12 of the in vivo evolution experiments.
Partial least squares discriminant analysis (PLS-DA) was performed on the area under the curves (AUCs) of the measured metabolic
activity of the evolved bacterial populations from all mice in the range of compounds. Differences were considered statistically
significant at PERMANOVA P < .05. d Compounds from the fecal metabolome and the in vitro screen with the shortest path were
inspected. One of the pairs with the shortest path was glutamate-arginine. Glutamate was enriched in the fecal metabolome of KO
mice, whereas KO-adapted bacteria showed higher metabolic activity in the presence of arginine. Each dot represents the mean
metabolic activity of the bacterial population from a mouse in the presence of L-arginine from two independent measurements (left)
or the abundance of glutamate in the fecal sample from a mouse (right). *P < .05 (Kruskal-Wallis test at Benjamini-Hochberg-
corrected), ns, not significant.
6R. UNNI ET AL.
inflamed gut environment, suggesting that evolu-
tion in the inflamed gut results in specific adapta-
tion to that environment.
Inamed gut environment displays an altered
metabolomic prole
To gain a better understanding of the environment
to which these bacteria are adapted, we performed
metabolomic analysis of fecal samples spanning the
experimental time course (weeks 1, 2, 5, 6, 10, and
12) using 1 H-NMR in mice from one of the two
experiments. While WT and KO mice initially dis-
play very similar fecal metabolomes (PERM
ANOVA P = .754), with time, the fecal metabolic
composition begins to vary concurrently with the
onset of inflammation and the frequency of the
C>T SNP upstream of mprA (Figures 3b, 1b, 2c).
By week 12 of the evolution experiment, WT and
KO mice exhibit notable differences in their fecal
metabolomes, as revealed by PLS-DA
(PERMANOVA P = .001; Figure 3b, Figure S7).
Among the 77 metabolite features initially found
in the samples from week 1, none show signifi-
cantly different abundances between the WT and
KO mice (Table S12). In contrast, in the week 12
samples, 59 of the 77 detected features exhibit sig-
nificant differences in abundance between WT and
KO mice (Benjamini-Hochberg corrected P < .05;
Table S13). Notably, 42 are significantly more
abundant in KO mice than in WT mice, including
all detected amino acids that were significantly
enriched in KO mice based on KEGG pathway
analysis of parallelly mutated loci. Among the 17
enriched features in the WT mice, most are puta-
tively annotated as (poly-)saccharides (Figure S8).
Notably, variations in fecal metabolic composition
at week 12 could arise from changes in both the
host, linked to inflammation-related changes, and
the bacterial population, associated with bacterial
adaptation to inflammation.
Phenotypic proling of evolved bacteria compared
to ancestor
Having established that the environment in which
the bacteria evolved significantly differs according
to the mouse genotype, we next focused on specific
bacterial traits that may have changed as
a consequence of adaptation. Accordingly, we
tested the metabolic activity of the E. coli popula-
tions using the Biolog GENIII test panel, which
comprises 94 unique biochemical tests, including
a range of compounds (e.g., sugars, amino acids,
and short-chain fatty acids), conditions (e.g., dif-
ferent pH and salt concentrations), and chemical
sensitivities (e.g., antibiotics; see Methods). Similar
to the pattern observed for the fecal metabolome,
the overall bacterial metabolic activity across all
tested conditions is similar among populations
derived from the WT and KO mice at week 1 of
the experiment (PERMANOVA, P = .519; Figure
S9), as also evidenced by the overlapping of the
groups in the PLS-DA based on the overall meta-
bolic activity observed in the BIOLOG GENIII tests
(Figure 3c). However, by week 12, the overall pat-
tern of metabolic activity across all tested condi-
tions significantly differs between the bacteria from
KO and WT mice (PERMANOVA, P = .005, Figure
S9). These results suggest that adaptation to the
inflamed intestine results in significantly altered
bacterial metabolism and the ability to grow in
the presence of different inhibitors. Among the
compounds included in this screen, bacteria from
WT and KO mice from week 1 showed signifi-
cantly different metabolic activity in only one com-
pound (Table S14). In contrast, bacteria from WT
and KO mice at week 12 display significantly dif-
ferent metabolic activities in the presence of 31
compounds (Wilcoxon signed-rank test, corrected
P < .05; Table S15, Figure S10).
To determine whether the differences in growth
observed among the 31 compounds at week 12 may
be related to the altered metabolomic environment
of inflamed mice, we compared our BIOLOG
GENIII results to the compounds found to be
enriched in the fecal metabolome of the KO mice.
Interestingly, metabolomics analysis revealed
a significant enrichment of the amino acid histi-
dine in the feces of KO mice compared to that of
WT mice (Fig S8). In our in vitro metabolic assay,
we found that bacteria adapted to the inflamed
intestine had a significantly higher ability to meta-
bolize histidine than bacteria adapted to the
healthy intestine (Fig S10). Thus, adaptation to an
environment enriched in histidine may have con-
ferred a higher metabolic activity in the presence of
this amino acid.
GUT MICROBES 7
To further explore how the results of our in vitro
phenotypic analyses may relate to the fecal meta-
bolomic profile observed in vivo, we performed
a shortest path analysis on the metabolic network
of the genome-scale metabolic model of E. coli
NC101
32
using metabolomic data and data from
the in vitro metabolic activity screen (BIOLOG
GENIII plates; see Methods). Pairs of compounds
from the two datasets that are converted easily to
each other by means of pathway length include
glutamate and L-arginine (Figure 3d, Table S16).
Glutamate is significantly more abundant in the
feces of the inflamed mice (Figure S8), whereas
bacteria adapted to the inflamed intestine are cap-
able of significantly higher metabolism in the pre-
sence of L-arginine (Figure S10). This result
suggests that the bacteria may be converting argi-
nine to glutamate, resulting in the enrichment of
glutamate in KO mice (Figure 3d, Table S16).
A second pair of compounds showing the same
pattern are taurine and L-arginine. However, ana-
lysis of the potential pathways between these com-
pounds through flux variability analysis revealed
that taurine cannot be produced by E. coli NC101
under any of the tested conditions (Figure S11).
Additionally, taurine abundance increased steadily
over time only in KO mice (Figure S12). Thus,
taurine is most likely to be produced by the host.
Finally, we examined 31 significant differences
in metabolic activity observed between bacteria
from WT and KO mice at week 12, in light of
their potential clinical value. A first compound of
interest is N-acetyl beta-D mannosamine
(NADM), which is a precursor to sialic acid and
has been shown to promote E. coli colonization of
inflamed gut.
33
Our E. coli populations show
higher levels of metabolic activity in the presence
of NADM after adaptation to the inflamed mouse
gut. In contrast, E. coli evolved in the non-inflamed
mouse gut show no such change (Figure 4). These
findings suggest that increased virulence may occur
in the context of adaptation to an inflamed
environment.
Another interesting candidate is lithium chlor-
ide, which is known to have anti-inflammatory
effects via the inhibition of a key host regulator of
inflammation, glycogen synthase kinase-3 beta
34
.
In our experiment, bacterial populations evolved
in WT mice display improved metabolic capacity
in the presence of lithium chloride compared to the
ancestor, while no such change is observed among
those evolved in the KO mice. Thus, our results
Figure 4. E. coli adapted to the inflamed gut show phenotypes with clinical relevance. Metabolic activity of bacteria isolated at weeks 1
and 12 of the evolution experiment in the presence of three selected compounds. Area under the curve (AUC) was calculated from
growth curves measured during the growth of evolved populations in the presence of each compound. Each dot represents the mean
of the metabolic activities of the E. coli population from a mouse measured in two independent experiments.
*, P < .05 (Kruskal-Wallis test at Benjamini-Hochberg-corrected). The clinical relevance of the three compounds is indicated.
8R. UNNI ET AL.
suggest that an inflamed intestinal environment
may prevent the acquisition of this phenotype in
E. coli.
A final promising category of candidates are anti-
biotics, including fusidic acid, vancomycin, and lin-
comycin. When considering the loading plot of the
PLS-DA of our Biolog GENIII data (Figure S13;
reporting which compounds contribute the most
to the separation between groups), all three of
these antibiotics contributed to the overall signifi-
cant difference in the metabolic activity of the bac-
teria from WT and KO mice (Figure 3c, Figure S13).
Furthermore, fusidic acid is also significantly differ-
ent between bacteria from the two mouse genotypes
in the univariate analysis (Wilcoxon signed-rank
test, corrected P < .05). Fusidic acid is an antibiotic
that targets gram-positive bacteria, and is commonly
used to treat skin infections.
35
Interestingly, it was
also shown to be effective in reducing disease activ-
ity in a small number of patients with Crohn’s dis-
ease, which is thought to be due to its
immunosuppressive properties.
36,37
We observe
E. coli to display decreased metabolic activity in
the presence of fusidic acid after adaptation to the
inflamed intestine but increased metabolic activity
after adaptation to the healthy intestine. These
results suggest a potential trade-off between adapta-
tion to inflammation and resistance to fusidic acid,
where E. coli adapted to inflammation also show
lower resistance to fusidic acid.
In summary, we observe widespread phenotypic
differences among the bacterial populations that
evolved in the inflamed intestines of KO mice
compared to non-inflamed WT mice. These results
relate to differences in the metabolome between
these two intestinal environments and include phe-
notypes that carry the potential for exploitation in
a clinical setting.
Discussion
In this study, we describe the findings of an in vivo
bacterial evolution experiment using a gnotobiotic
mouse model of IBD. We find that E. coli evolved
in the inflamed mouse gut accumulated specific
genetic changes and that these changes confer
a fitness advantage in the inflamed intestinal envir-
onment, which significantly differs in its metabo-
lome. Furthermore, we show that E. coli
populations in the gut of healthy and inflamed
mice have a distinct metatranscriptomic profile.
Finally, we showed that adaptation to the inflamed
intestine resulted in several phenotypic differences
that may be clinically relevant, such as differential
tolerance to antibiotics.
Recent studies have highlighted how strain-level
changes in members of the gut microbiome can
play a crucial role in the adaptation of the gut
microbiome to novel conditions.
14,38,39
However,
the effects of inflammation on the evolution of gut
commensals remain largely unexplored. Barreto
et al. (2020) followed the adaptation of E. coli in
mice of different ages in vivo.
14
They showed that
the aged mouse gut, which also showed high levels
of inflammation, was a more stressful environment
for E. coli, resulting in a higher number of muta-
tions and more severe selective pressure on com-
mensals, particularly in bacterial loci associated
with stress-related functions. Notably, we find no
difference in the number of mutations acquired by
E. coli in inflamed mice, suggesting that other fac-
tors associated with aging may contribute to the
increased bacterial mutation rate observed by
Barreto et al. (2020).
14
More recently, Tawk et al. (2023) conducted
a study of mice monocolonized with Bacteroides
thetaiotaomicron that were subsequently infected
with Citrobacter rodentium and developed inflam-
mation. In this setting, a single-nucleotide variant
of B. thetaiotaomicron, which had a higher toler-
ance to oxidative stress than the ancestral variant,
underwent selective sweeps and dominated the
intestinal community.
39
Although the experimen-
tal setup and duration of inflammation differ
between this study and the current study, our can-
didate mutation is also known to be associated with
oxidative stress.
40
This is consistent with the obser-
vations of Barreto et al. (2020) and Tawk et al.
(2023)
14,39
suggesting that oxidative stress is a key
selective pressure in an inflamed environment.
Moreover, oxidative stress plays a major role in
the pathophysiology of IBD.
41
Interestingly, this is
also confirmed by our metatranscriptomic analysis.
In particular, 14 of the genes upregulated in KO
mice belong to the pathway of glutathione metabo-
lism (eco00480), and previous studies demon-
strated that glutathione metabolism is involved in
protection against oxidative stress.
42,43
In addition,
GUT MICROBES 9
Sakamoto et al. reported mprA to be involved in
resistance to oxidative stress, together with the
activity of gshA (glutathione synthase), which is
also upregulated in our populations
40
. Addit
ionally, our metatranscriptomic data confirm the
findings of Tawk et al., suggesting a role for vita-
min B6 metabolism.
39
We observe not only the
upregulation of nine genes belonging to the vita-
min B6 metabolism (eco00750), but also
a significantly higher concentration of aspartate,
a substrate of vitamin B6-dependent enzymes, in
the feces of the inflamed mice.
39
Importantly, many of our findings are consistent
with the observations made in clinical IBD settings.
First, the KEGG pathways affected by parallel
mutations specific to the inflamed mice
(Figure 2a) include D-amino acid-, pyruvate-, and
thiamine metabolism. This is in line with previous
reports that the basic metabolism is reduced in
IBD.
27,44
Second, the metabolomic profiling of the fecal
samples reveals that KO mice have significantly
higher levels of many amino acids, which was also
reported in IBD patients.
45
Furthermore, the
results of our shortest path analysis suggest that
bacterial production of glutamate from arginine
may underlie the enrichment of glutamate in the
inflamed gut. Although taurine-arginine was also
one of the shortest paths we found, taurine could
not be produced by E. coli according to the flux
variability analysis. Thus, taurine is most likely to
be produced by the host. Taurine is known to be
a promoter of colonization resistance, and infec-
tion has been shown to prime the microbiota
against subsequent infections by inducing host
production of taurine.
46
Indeed, taurine was also
reported to ameliorate inflammation in rat models
of inflammation.
47
Furthermore, taurine is
a substrate for the microbiota-driven production
of hydrogen sulfide
48
and arginine is a precursor to
polyamines that can protect against reactive species
such as hydrogen sulfide.
49
Thus, bacterial adapta-
tion for improved metabolic activity in the pre-
sence of arginine may be a response to the host-
induced inflammatory changes in the intestine.
Third, E. coli adapted to the inflamed mouse gut
also showed some clinically relevant phenotypes
(Figure 4). N-acetyl beta-D mannosamine
(NADM) is a precursor of polysialic acid,
a pathogenic determinant, and studies have
shown that it can promote colonization of the
inflamed intestine by pathogenic E. coli.
33,50
As
NADM is a crucial player in mediating pathogenic
activity in the inflamed intestine, the improved
metabolic activity of inflammation-adapted E. coli
observed in the presence of NADM may be
a phenotype associated with virulence. This is con-
sistent with reports of virulence-associated pheno-
types, such as hypermotility, selected during the
evolution of an adherent-invasive E. coli in the
inflamed mouse gut.
13
Furthermore, lithium chlor-
ide is known to inhibit the activity of glycogen
synthase kinase 3-β, a master regulator of host
chronic intestinal inflammation mediated by toll-
like receptors, and has been used as a treatment in
a mouse model of IBD.
34,51
However, the effect of
lithium on the microbiome has not yet been eluci-
dated. E. coli tolerance to lithium ions is regulated
through antiporters, and proline has been shown to
induce the uptake of lithium ions by E. coli.
52,53
Our finding that adaptation to the healthy mouse
intestine confers improved metabolic capacity in
the presence of lithium chloride, while adaptation
to the inflamed intestine does not (Figure 4),
implies that lithium may affect both the host and
microbiome. This may also imply that adaptation
to the healthy intestine affects the antiporter-
dependent detoxification system of E. coli, resulting
in improved ion tolerance.
Lastly, we found that bacterial adaptation to the
inflamed gut decreases metabolic activity in the
presence of fusidic acid, while adaptation to the
healthy mouse gut improved metabolic activity in
the presence of fusidic acid (Figure 4). Fusidic acid
is an antibiotic with T cell-specific immunosup-
pressive effects that also stimulate gastric mucus
secretion. Furthermore, it was successfully applied
to alleviate inflammation in rats, as well as to treat
selected patients with Crohn’s disease for whom
conventional treatment was ineffective.
36,37
However, the effect of fusidic acid on the gut
microbiome in IBD has not yet been studied. Our
results imply that bacterial adaptation to the
inflamed gut can result in a trade-off for resistance
to fusidic acid. Importantly, such trade-offs are
a fundamental concept in evolutionary medicine,
where increased fitness in one context results in
a consequent decrease in fitness in another context,
10 R. UNNI ET AL.
and are proposed as possible potent therapeutic
strategies against antibiotic resistance and
cancer.
18,54
Thus, the therapeutic effect of fusidic
acid observed in patients with CD is due to trade-
offs in microbiome adaptation to the inflamed gut
and/or its immunosuppressive effects on the host.
Interestingly, it is known that lipocalin-2 plays
a role in iron-sequestration, which has been
reported to have a role in antibiotic resistance and
sensitivity.
55
It is thus worth noting that there is no
enrichment of genes involved in iron metabolism
among the mutated genes in the populations
evolved in the inflamed gut, as well as among the
upregulated genes in these populations.
Current treatment options for IBD are largely
limited to the treatment of inflammation with
a chronic risk of relapse. Corticosteroids, aminosa-
licylates, and immunosuppressive agents are the
conventional drugs of choice, but the safety and
efficacy of novel emerging strategies remain
unclear (reviewed in
56
). Given that disease-
specific aspects of the microbiome have been
shown to remain stable over long periods in IBD
patients despite treatment,
57
a persistent microbial
influence on the intestinal environment may be
a key risk factor for relapse. Thus, our results high-
light new avenues of research involving evolution-
informed therapeutic strategies that exploit trade-
offs to either prevent adaptation to inflammation
and/or help restore desirable ancestral traits in the
microbiome.
While our results provide valuable insights into
the potential role of evolution-informed therapeu-
tic strategies, it is important to acknowledge that it
remains unknown whether the same or similar
genotypic and phenotypic changes would be
observed in the context of a complex microbial
community as present in the human gut micro-
biome. Future work should therefore include simi-
lar in vivo evolution experiments using a complex
or synthetic microbial community.
Methods
Bacterial strains
E. coli NC101 strain was obtained from Balfour
Sartor, University of North Carolina, Chapel Hill,
NC, USA.
58
Escherichia coli NC101 is a mouse
strain isolated from the intestine of a WT 129S6/
SvEv mouse raised under specific-pathogen-free
(SPF) conditions.
58
The strain was cultured at
37°C in Luria Bertani (LB) medium with continu-
ous shaking. The ancestor strain for the in vivo
experiment was obtained by plating the overnight
culture on LB agar and picking single colonies to be
used as inocula for the in vivo evolution
experiment.
Mouse model
Two independent experiments were conducted to
study E. coli adaptation to the inflamed gut (Table
S1). In the first experiment, “Exp1”, a single inoculum
was used for all mice. In order to ensure that muta-
tions present in a common inoculum could not be
falsely identified as “parallel” mutations, the second
experiment, “Exp2” was performed with independent
inocula for each mouse. Accordingly, our definition
of a parallel mutated gene requires it to be mutated in
at least two mice that were gavaged with a different
inoculum. Germ-free (GF) C57BL/6NTac (WT) and
C57BL/6NTac-Il10em8Tac (KO) male mice were
purchased from Taconic Biosciences (Silkeborg,
Denmark) and housed in the Germ-Free Animal
Facility at the Max Planck Institute for Evolutionary
Biology (Ploen, Germany). GF mice were maintained
in sterile isolators (MB-10, Quip Laboratories,
Delaware, USA) and fed sterilized 50 kGy V1124–
927 Sniff (Soest, Deutschland). The animals were
allocated to independent cages with a maximum of
four mice per isolator until they reached an age of 12
weeks. Detailed information regarding the mice used
in this study is presented in Table S1. Initially, 19 WT
and 15 KO mice were mono-colonized with the E. coli
NC101 strain. Only mice that survived the duration
of the experiment (12 weeks; N = 14 WT mice and N
= 11 KO mice) were included in the study. The study
was performed in accordance with the approved ani-
mal protocols and institutional guidelines of the Max
Planck Institute for Evolutionary Biology, Plön. Mice
were maintained, and experiments were performed in
accordance with FELASA guidelines and German
animal welfare law (Tierschutzgesetz § 11; permits
from Veterinäramt Kreis Plön: PLÖ-0004697 and
Ministerium für Landwirtschaft, ländliche Räume,
Europa und Verbraucherschutz: 107-11/18).
GUT MICROBES 11
The strains used as inocula were diluted in sterile
PBS, and 200 μl (equivalent to 1 × 10^8 bacteria)
were gavaged using a sterile gavage needle
(Reusable Feeding Needles 18 G, Fine Science
Tools, Heidelberg, Deutschland). Fecal pellets
were collected in a sterile manner twice a week to
study the evolution of E. coli for a total of 24
samples collected per mouse and once a week for
metabolomic investigation. An overview of the
sampling plan is presented in Figure 1a.
Histopathological evaluation
Mice were sacrificed at week 12, and colon tissue
was collected and arranged to form a Swiss-roll.
59
Hematoxylin and eosin-stained sections of colonic
tissue Swiss rolls were scored by two independent
researchers in a blinded manner using the scoring
system described by Adolph et al. (2013).
60
The
score is composed of five sub-scores: mononuclear
cell infiltrate, crypt hyperplasia, epithelial injury or
erosion, polymorphonuclear cell infiltrates, and
transmural inflammation. Each of the first four
sub scores was awarded a score from 0 to 3,
whereas transmural inflammation was scored
from 0 to 4, with a higher score indicating a more
severe level of inflammatory activity. The sum of
the sub scores was then multiplied by a factor based
on the percentage of affected bowel length (1= <
10%; 2 = 10–25%; 3 = 25–50%; 4 = >50%).
Feces processing
Feces weight and consistency were recorded. Feces
were homogenized in 1.5 ml sterile PBS on
a horizontal vortexer (Vortex Mixer Model Vortex-
Genie® 2, Scientific Industries, Bohemia, NY, USA)
at maximum speed for 30 min, then separated in
three aliquots of 500 μl. Each aliquot was centri-
fuged at 10,000 rpm for 5 min. The supernatants
were transferred to a new tube and stored at −20°C
for subsequent lipocalin-2 concentration measure-
ment. One pellet aliquot was resuspended in 500 μl
of PBS and used to prepare a 12-point 1/10 dilution
series in a 96-well plate. Ten μl were plated onto an
LB agar plate. Plates were incubated overnight at
37°C and colonies were counted to estimate the
bacterial load. The bacterial load in the fecal
samples was calculated by normalizing CFU counts
by wet feces weight. The second pellet aliquot was
resuspended in 1 ml RNAlater and stored at + 4°C
for 24 h, after which the tube was centrifuged at
10,000 rpm for 5 min to remove the RNAlater, and
the pellet was stored at −20°C for subsequent
nucleic acid extraction and sequencing. The final
pellet aliquot was resuspended in 500 μl of LB con-
taining 20% glycerol and stored at −70°C for
further phenotypic investigation.
Lipocalin-2 quantication
Lipocalin-2 concentration in the supernatants was
measured using the commercial kit Mouse
Lipocalin-2/NGAL DuoSet ELISA (R&D Systems,
Minneapolis, MN, USA) for mouse Lipocalin-2
(DY1857). Testing was performed according to
the manufacturer’s instructions. The samples were
diluted 1:10 and added to the plate. The optical
density of each well was determined using a plate
reader (SPARK, Tecan, Tecan, Männedorf,
Switzerland) at 450 and 540 nm. Lipocalin-2 con-
centrations were normalized to the weight of feces
for each sample.
Shotgun sequencing
Total DNA and RNA were extracted using the
ZymoBIOMICS DNA/RNA Mini Kit (Zymo
Research, Freiburg, Germany), following the man-
ufacturer’s instructions from fecal pellets collected
at weeks 1, 4, 8, and 12, as well as from the E. coli
NC101 cultures that served as inocula. Shotgun
metagenomic sequencing was performed on four
Illumina Nextseq (HighOutput 300 cycles) sequen-
cing runs at the Max Planck Institute for
Evolutionary Biology (Plön, Germany).
Raw reads were filtered and trimmed to ensure
good quality using Cutadapt (version 3.2).
61
First,
any pair containing Ns, homopolymers (10 nucleo-
tides or more), or those longer than 151 bp were
discarded. Sequences were trimmed with a quality
cutoff of 25 at both ends for both reads, Illumina
adapters were removed, and sequences shorter
than 50 bp were discarded. Good quality sequences
were then filtered to exclude mouse sequences
(mouse_C57BL) using KneadData.
62
A final filter
was applied to remove any adapter leftovers using
12 R. UNNI ET AL.
Trimmomatic
63
and sequences shorter than 105 bp
were discarded. Mutations were identified using
Breseq
64
by comparing the obtained metagenomes
with the reference genome of E. coli NC101
(PRJNA596436) using default options (i.e. muta-
tions identified by a threshold frequency > 0.05)
and by subtracting the mutations that were
detected in the respective inoculum. Gdtools was
used to compare mutations in the samples.
Metatranscriptomics
Depletion of rRNA from extracted RNA was per-
formed using QIAseq FastSelect−5S/16S/23S
(Qiagen). Samples were incubated at 89°C for 8
minutes. Libraries for metatranscriptomics were
prepared using the Illumina TruSeq® Stranded
mRNA Library Prep Kit (Illumina) according to
the manufacturer’s instructions. Shotgun meta-
transcriptomic sequencing of week 12 fecal samples
was performed on an Illumina NextSeq 500/550
using the HighOutput Kit v2.5 (75 cycles).
Quality control and trimming were performed as
described above for shotgun sequencing. Gene
expression in the evolved population was calculated
in Geneious (v 2022.2.1; https://www.geneious.com)
by mapping reads to the reference genome of E. coli
NC101 (PRJNA596436). Differential expression was
calculated using DeSeq2
65
as implemented in
Geneious, using the parametric model and by
assigning the conditions to “WT-” or “KO-evolved”.
Measurement of the metabolic activity of bacteria
Biolog GENIII MicroPlates (Biolog, Hayward, CA,
USA) was used to investigate the metabolism of
evolved populations. Evolved populations stored
in glycerol were inoculated onto the Inoculation
Fluid-A (IF-A) provided by the manufacturer such
that the OD
600
was between 0.02–0.05. After
adjusting the OD
600
, the inoculated IF-A was sha-
ken well and distributed into a Biolog GENIII
MicroPlate (100 μl in each well). The plate was
then covered with a sterile Breathe-Easy membrane
(Sigma-Aldrich, St. Louis, MO) and placed in
a plate reader (SPARK, Tecan, Männedorf,
Switzerland) at a preset temperature of 37°C. The
plate reader was then run with the following para-
meters: temperature: 37°C, shaking: 250 rpm,
OD
590
measurement: every 15 min, total running
time: 36 h. From the OD
590
measures, area under
the curve (AUC) was used as a proxy for metabolic
activity. The Biolog assay was repeated for popula-
tions from fecal samples collected at weeks 1 and 12
from all 25 mice, with two replicates each.
Ex vivo assay
The cecal content was collected from each mouse
during dissection at the end of the second experi-
ment. The amount of cecal content from each
mouse was different based on the amount in the
cecum at the time of dissection. Sterile PBS was
added to the cecal content, and the volume was
adjusted based on the amount of cecal content
collected from each mouse (20 mg/ml). The proto-
col was adapted from Kitamoto et al. (2020).
66
The
PBS-cecal content mixture was centrifuged twice
(once at 500g for 5 min and again at 10,000g for 5
min) and then filter-sterilized through a 0.2 μm
filter. The ex vivo medium from each mouse was
mixed well and 100 μl was spread onto an LB agar
plate to ensure that they were sterile. Ex vivo media
from all healthy mice were pooled, as were the ex
vivo media from all inflamed mice, resulting in two
ex vivo media. Populations from fecal samples col-
lected at week 12 from a subset of mice (N = 5 KO
and N = 5 WT mice) were inoculated into the ex
vivo media and incubated in the TECAN Spark
plate reader at a preset temperature of 37°C. The
plate reader was then run with the following para-
meters: temperature: 37°C, shaking: 250 rpm,
OD
600
measurement: every 15 min, total running
time: 36 h. From the OD
600
measures, area under
the curve (AUC) was used as a proxy for growth.
Metabolomic evaluation of the fecal pellets
During the second experiment, samples for meta-
bolomic investigation were collected once a week
GUT MICROBES 13
and snap-frozen in liquid nitrogen. Samples were
stored at −70°C and delivered at Helmholtz Center
Munich for metabolomic investigation. A non-
targeted metabolomics approach of mouse fecal
samples was undertaken using NMR spectroscopy.
To extract the aqueous metabolites, we homoge-
nized 2–3 fecal pellets in 1 ml water using ceramic
beads (NucleoSpin, Macherey – Nagel, Dueren,
Germany) and a TissueLyser (Qiagen, Hilden,
Germany), mixing the sample for 3 × 30 s at
4,500 rpm with a 10 s cooling break (<0°C).
Subsequently, the homogenate was centrifuged
(13,000 rpm for 10 min at 4°C), the supernatant
evaporated using a SpeedVac, and the dried extract
reconstituted in 200 µl NMR buffer (10% D
2
O, 100
mM phosphate buffer with 0.1% trimethylsilyl-
tetradeuteropropionic acid (TSP), pH 7.4).
Samples were transferred to 3 mm NMR tubes,
and immediate NMR analysis was performed in
a randomized order with a Bruker 800 MHz spec-
trometer operating at 800.35 MHz equipped with
a Bruker SampleJet for sample cooling (283 K) and
a QCI-cryogenic probe. A standard one-
dimensional pulse sequence (noesygppr1d) pro-
vides an overview of all molecules. The acquisition
parameters were as follows: water suppression irra-
diation during recycle delay (2 s), mixing time of
200 ms, 90 °pulse of 12.5 µs. We collected 512 scans
of 64 K data points with a spectral width of 12 ppm.
The software TopSpin 3.6 (Bruker BioSpin,
Ettlingen, Germany) was used for processing, i.e.,
Fourier transformation, manual phasing, baseline
correction, and calibration to TSP (δ 0.00). Data
were imported into Matlab software R2011b
(Mathworks, Natick, MA, USA) and further pro-
cessed, i.e., the water region was removed, baseline
adjusted
67
and spectra normalized.
68
Relative
quantification of metabolites was performed using
the peak heights of selected peaks and compounds
identified as described in our published
workflow.
69
Shortest path analysis
For the shortest path analysis, we used the meta-
bolic model reconstructed using the AGORA2
resource for the human microbiome.
32
The model
was translated into a graph with metabolites as
nodes, and the reactions converting metabolites
into one another as edges. The shortest path ana-
lysis was performed using the Dijkstra algorithm
implemented in the igraph package (1.3.5) of
R (4.2.2).
70,71
To avoid shortcuts through the net-
work using cofactors as intermediate compounds,
the edges were weighted by the sum of degree of the
nodes it connected, as suggested by Faust et al.
72
For each compound from the in vitro screen, we
calculated the set of shortest paths to all other
metabolites in the model. Each value in the set
was the average shortest path of the five shortest
paths between two metabolites, which was per-
formed to account for uncertainties in pathway
calculation based only on the network properties.
For our analysis, we only considered pathways
between compounds from the two datasets, which
were among the shortest 5% of pathways in
a pathway set.
Prediction of possible conversions between
metabolites
We wanted to understand whether the altered
metabolic capabilities observed in the Biolog plates
or the changed environmental conditions observed
in the metabolomic data, together with the meta-
bolism of E. coli could contribute to the observed
changes in the metabolomic data between KO and
WT mice. To this end, we performed flux variable
analysis (FVA) on the metabolic model for E. coli
NC101 (retrieved from AGORA2).
32
We defined
every significantly detected metabolite in the
Biolog GENIII experiment or the metabolomic
data set as a possible source metabolites, while the
metabolites from the metabolomic data set were
defined as target metabolites. Metabolites found
in the metabolomics are those known to be present
in vivo. Compounds found to be significantly dif-
ferentially metabolized in the Biolog GENIII assay
are those that we know the evolved bacteria are
capable of metabolizing. By using the sum of
these as the source and the detected metabolites
as the target, we aimed to capture as many of the
possible conversions as possible to help explain the
composition of the in vivo environment and the
role of bacterial metabolism in shaping it. For the
simulation background, we employed a minimal
14 R. UNNI ET AL.
medium (Table S17), removed D-Glucose, and
adjusted the oxygen level according to the condi-
tions tested (anoxic = 0 mmol h
− 1
gDW
− 1
, micro-
aerobic = 1 mmol h
− 1
gDW
− 1
, aerobic
= 10 mmol h
− 1
gDW
− 1
). For the simulation, we
added each source metabolite individually to the
growth medium (100 mmol h
− 1
gDW
− 1
) and
calculated the maximum production rates of the
target metabolites, assuming that at least 50% of
maximum growth rates were achieved. We consid-
ered a maximum flux of > 1e
− 6
as the possible
production of the target metabolite from the source
metabolite. FVA was performed in cobrapy
73
and
analyses were performed using the data.table and
ggplot2 packages for R.
Statistical investigation
Statistical analyses were performed using packages
in RStudio 2023.03.0 + 386. For PERMANOVA
analyses, adonis was used
74
on Bray-Curtis dis-
tances of the data
75
and mixomics was used for PLS-
DA.
76
AUCs were calculated using desctools.
77
Acknowledgments
We are grateful to Balfour Sartor for providing the NC101
E. coli strain, the MPI-Plön mouse team for support with
animal experiments, Britt Marie Hermes for support in manu-
script preparation, and Wael Elhenawy and Isabel Gordo for
their helpful and constructive discussions. The authors
acknowledge the use of ChatGPT for the purpose of support-
ing the drafting of material for the introduction section.
Disclosure statement
No potential conflict of interest was reported by the
author(s).
Funding
This study was supported by the Deutsche Forschung
sgemeinschaft (DFG) Research Unit FOR5042 “miTarget –
The Microbiome as a Target in Inflammatory Bowel Disease”
(subprojects P3, P10, Z, and INF) and Cluster of Excellence
2167 “Precision Medicine in Chronic Inflammation” (grant
no. EXC2167). RU was funded by the International Max-
Planck Research School for Evolutionary Biology (IMPRS
EvolBio). Work in the Unterweger group is funded by the
German Federal Ministry for Education and Research (grant
01KI2020).
ORCID
Rahul Unni http://orcid.org/0000-0002-4656-7542
Nadia Andrea Andreani http://orcid.org/0000-0003-0330-
9874
Marie Vallier http://orcid.org/0000-0002-7188-7070
Silke S. Heinzmann http://orcid.org/0000-0003-0257-8837
Jan Taubenheim http://orcid.org/0000-0001-7283-1768
Martina A. Guggeis http://orcid.org/0000-0003-4505-3731
Florian Tran http://orcid.org/0000-0002-3735-9872
Olga Vogler http://orcid.org/0000-0001-8930-7671
Sven Künzel http://orcid.org/0000-0003-4992-5963
Jan-Bernd Hövener http://orcid.org/0000-0001-7255-7252
Philip Rosenstiel http://orcid.org/0000-0002-9692-8828
Christoph Kaleta http://orcid.org/0000-0001-8004-9514
Astrid Dempfle http://orcid.org/0000-0002-2618-3920
Daniel Unterweger http://orcid.org/0000-0001-7459-6386
John F. Baines http://orcid.org/0000-0002-8132-4909
Author contributions
MV, DU, and JB conceptualized the study; RU, NAA, MV,
SH, MAG, OV, SK and DU performed experiments; RU,
NAA, MV, SH, FT, JH, and PR analyzed the data; JT and
CK performed the metabolic modeling; RU, NAA, and AD
performed the statistical analyses.
RU, NAA, DU, and JB wrote the first draft of the manu-
script. All coauthors revised the manuscript and agreed to its
publication.
Data availability statement
Escherichia coli NC101 reference genome is available under
Bioproject number PRJNA596436. Shotgun metagenomic
sequencing of the inocula and evolved populations and meta-
transcriptomic of the populations at week 12 are available
with Bioproject number PRJNA1012288.
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