of 35
5
10
15
T
owards measuring gr
owth rates of pathogens during infections by D
2
O­labeling lipidomics
Cajetan
Neubauer
1
,
2
,
*
,
Alex
L.
Sessions
2
,
Ian
R.
Booth
4
,
Benjamin
P
.
Bowen
5
,
Sebastian
H.
Kopf
6
, Dianne K. Newman
1
,
2
, Nathan F
. Dalleska
3
1.
Division
of
Biology
and
Biological
Engineering,
California
Institute
of
T
echnology
,
Pasadena,
CA 91
125, USA
2. Division of Geological and Planetary Sciences, California Institute of T
echnology
3. Environmental Analysis Center
, California Institute of T
echnology
4. Institute of Medical Sciences, University of Aberdeen, Aberdeen AB25 2ZD, UK
5.
DOE Joint Genome Institute, W
alnut Creek, CA 94598, USA
6.
Department of Geological Sciences, University of Colorado, Boulder
, CO 80309, USA
§
IRB
was
a
CEMI­funded
visiting
research
fellow
at
Caltech
and
Leverhulme
Emeritus
Research
Fellow during this study
.
*
T
o whom correspondence should be addressed:
E­mail:
caj@caltech.edu
Keywords:
Lipidomics,
Isotope­ratio
mass
spectrometry
,
Microbial
growth
rate,
Stable­isotope
probing, Heavy water
1
.
CC-BY 4.0 International license
It is made available under a
(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint
.
http://dx.doi.org/10.1101/330464
doi:
bioRxiv preprint first posted online May. 25, 2018;
20
25
30
35
40
ABSTRACT
RA
TIONALE:
Microbial
growth
rate
is
an
important
physiological
parameter
that
is
challenging
to
measure
in
situ
,
partly
because
microbes
grow
slowly
in
many
environments.
Recently
,
it
has
been
demonstrated
that
generation
times
of
S. aur
eus
in
cystic
fibrosis
(CF)
infections
can
be
determined
by
D
2
O­labeling
of
actively
synthesized
fatty
acids.
T
o
improve
species
specificity
and
allow
growth
rate
monitoring
for
a
greater
range
of
pathogens
during
the
treatment
of
infections,
it
is
desirable to accurately quantify trace incorporation of deuterium into phospholipids.
METHODS:
Lipid
extracts
of
D
2
O­treated
E.
coli
cultures
were
measured
on
LC­ESI­MS
instruments
equipped
with
T
OF
and
Orbitrap
mass
analyzers,
and
used
for
comparison
with
the
analysis
of
fatty
acids
by
isotope­ratio
GC­MS.
W
e
then
develop
an
approach
to
enable
tracking
of
lipid
labeling,
by
following
the
transition
from
stationary
into
exponential
growth
in
pure
cultures.
Lastly
,
we
apply
D
2
O­labeling
lipidomics
to
clinical
samples
from
CF
patients
with
chronic
lung
infections.
RESUL
TS:
Lipidomics
facilitates
deuterium
quantification
in
lipids
at
levels
that
are
useful
for
many
labeling
applications
(>0.03
at%
D).
In
the
E. coli
cultures,
labeling
dynamics
of
phospholipids
depend
lar
gely
on
their
acyl
chains
and
between
phospholipids
we
notice
dif
ferences
that
are
not
obvious
from
absolute
concentrations
alone.
For
example,
cyclopropyl­containing
lipids
reflect
the
regulation
of
cyclopropane
fatty
acid
synthase,
which
is
predominantly
expressed
at
the
beginning
of
stationary
phase.
The
deuterium
incorporation
into
a
lipid
that
is
specific
for
S. aur
eus
in
CF
sputum,
indicates
an
average
generation
time
of
the
pathogen
on
the
order
of
one
cell
doubling per day
.
CONCLUSIONS:
This
study
demonstrates
how
trace
level
measurement
of
stable
isotopes
in
intact
lipids
can
be
used
to
quantify
lipid
metabolism
in
pure
cultures
and
provides
guidelines
that
enable
growth
rate
measurements
in
microbiome
samples
after
incubation
with
a
low
percentage
of
D
2
O.
2
.
CC-BY 4.0 International license
It is made available under a
(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint
.
http://dx.doi.org/10.1101/330464
doi:
bioRxiv preprint first posted online May. 25, 2018;
45
50
55
60
65
INTRODUCTION
Bacteria
continually
react
to
diverse
stimuli,
such
as
the
availability
of
nutrients
and
electron
acceptors,
exposure
to
antimicrobial
drugs
or
attack
by
the
immune
system.
However
,
measuring
microbial
metabolites
and
growth
rates
within
a
complex
environment
still
poses
many
technical
challenges.
T
wo
recent
advances
in
microbial
ecology
are
beginning
to
make
measuring
average
growth
rates
in
environmental
samples
possible.
The
first
advance
is
based
on
metagenomic
DNA
sequencing
and
takes
advantage
of
the
observation
that
growing
cells
yield
more
sequencing
reads
at
genomic
regions
near
the
origin
of
replication
[
1
,
2
]
.
This
method
is
applicable
to
any
microbial
species
in
a
microbiome
as
long
as
its
assembled
genome
has
a
high
sequence
coverage.
The
second
advance
uses
isotopic
labeling
to
determine
the
biosynthesis
rates
of
microbial
lipid
metabolites
by
mass
spectrometry
[
3
,
4
]
.
Stable­isotope
probing
has
a
lar
ger
dynamic
range
than
sequencing
and
can
be
used
to
quantify
the
slow
growth
rates
that
microbes
have
under
environmental
conditions.
A
limitation
of
stable­isotope
probing,
however
,
is
the
identification
of
metabolites
that
are
diagnostic
for
a
specific
microor
ganism.
It
is
therefore
desirable
to
combine
isotopic
labeling
with
a
method
such as lipidomics, which can detect a lar
ge number of microbial metabolites.
Lipids
have
been
used
for
decades
in
ecology
as
markers
of
microbial
metabolism,
where
they
reveal
information
about
viable
biomass,
nutritional
status
or
changes
of
the
microbial
community
structure
[
5
,
6
]
.
Also,
lipids
can
still
be
analyzed
long
after
nucleic
acids
and
peptides
are
degraded
[
7
]
.
In
order
to
estimate
the
growth
rates
of
microbes,
the
active
production
of
strain­
and
genus­specific
lipid
metabolites
can
be
measured
with
stable
isotope
labeling
[
3
]
.
W
ith
advances
in
soft­ionization
mass
spectrometry
we
can
now
attempt
to
combine
isotope
quantification
and
lipidomics for the study of microbes
in situ
.
Soft­ionization
mass
spectrometry
detects
thousands
of
lipids
in
environmental
extracts
and
would
in
principle
be
well­suited
to
quantify
the
biosynthesis
of
lipid
biomarkers
by
itself
[
8
1
1
]
.
However
,
extraction
yields
and
ionization
ef
ficiency
vary
widely
between
samples
[
1
2
1
6
]
.
This
3
.
CC-BY 4.0 International license
It is made available under a
(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint
.
http://dx.doi.org/10.1101/330464
doi:
bioRxiv preprint first posted online May. 25, 2018;
70
75
80
85
90
currently
poses
severe
constraints
on
measuring
lipid
production
rates
and
ef
fectively
limits
estimating bacterial growth rates in natural environments.
Ratios
of
isotopes
can
be
measured
with
high
accuracy
by
mass
spectrometry
,
partly
because
ratiometric
readouts
vary
less
than
absolute
ion
intensities
[
1
7
]
.
For
lipid
biosynthesis,
the
incorporation
of
an
isotope
tracer
such
as
1
3
C­labeled
substrates
hence
can
provide
a
robust
way
to
quantify
anabolic
activity
and
lipid
turnover
[
1
8
,
1
9
]
.
In
microbiome
samples
bacteria
dif
fer
widely
in
their
ability
to
take
up
carbon
sources
and
gases
such
as
CO
2
,
depending
on
their
genetic
capabilities
and
metabolic
states.
D
2
O
is
a
non­discriminating
tracer
of
de
novo
lipid
biosynthesis
and
thus
often
better
suited
for
microbiome
studies.
D
2
O­labeling
has
recently
been
used
to
estimate
in
situ
growth
rates
of
Staphylococcus
aur
eus
in
chronically
infected
lungs.
After
labeling
of
expectorated
sputum
with
D
2
O,
the
deuterium
enrichment
of
anteiso
fatty
acids
was
quantified
using
GC
pyrolysis
isotope­ratio
MS
(GC/P/IRMS)
in
order
to
estimate
the
growth
rate
of
the
pathogen
[
3
]
.
Previous
studies
have
been
used
to
study
lipid
biosynthesis
with
D
2
O
in
vivo
[
2
0
2
5
]
.
Environmental
samples,
however
,
provide
particular
challenges.
For
example,
microbes
often
grow
slowly
in
situ
and
one
can
expect
low
rates
of
deuterium
incorporation
into
lipids
[
3
]
.
Whether
trace
levels
of
incorporation
can
reliably
be
detected
remains
to
be
studied
before
deuterium
incorporation can be used to determine lipid biosynthesis rates
in situ
by lipidomics.
In
this
study
we
apply
stable
isotope
probing
with
D
2
O
and
measure
deuterium
incorporation
by
MS­based
lipidomics.
This
approach
can
be
used
to
obtain
labeling
rates
for
individual
intact
lipids
in
environmental
samples,
where
microor
ganisms
typically
grow
slowly
.
W
e
begin
by
characterising
several
technical
aspects
that
have
to
do
with
quantifying
low
levels
of
deuterium
labeling.
W
e
then
refine
our
application
of
D
2
O­labeling
lipidomics
by
tracking
of
lipid
labeling
in
E.
coli
during
the
stationary­to­log
phase
transition.
This
reveals
lipids
that
have
distinct
labeling
dynamics
that
are
not
obvious
from
measuring
absolute
analyte
concentrations
alone.
Lastly
,
we
test
D
2
O­labeling
lipidomics
in
a
clinical
context
with
the
aim
to
measure
the
growth
of
4
.
CC-BY 4.0 International license
It is made available under a
(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint
.
http://dx.doi.org/10.1101/330464
doi:
bioRxiv preprint first posted online May. 25, 2018;
95
100
105
1
10
1
15
S. aur
eus
in
cystic
fibrosis
lung
infections.
In
sum
this
study
establishes
principles
for
how
growth
rates of microbes
in situ
can be estimated by stable isotope probing lipidomics.
RESUL
TS
When
cells
grow
after
addition
of
heavy
water
,
newly
synthesized
biomass
will
contain
more
D.
This
also
means
that
each
lipid
pool
will
be
a
mixture
of
molecules
that
vary
more
in
their
D
abundance.
The
introduced
heterogeneity
causes
broadening
of
chromatographic
peaks,
which
could
skew
the
isotope
ratio
observed
by
LC­MS
as
ionization
ef
ficiency
varies
over
time
[
2
6
,
2
7
]
.
In
order
to
evaluate
how
this
af
fects
isotope
quantification
by
lipidomics,
we
grew
an
E. coli
culture
in
4 %
D
2
O
(fractional
D­abundance,
2
F
W
A
T
E
R
)
and
measured
lipids
after
chromatographic
separation
using an ESI­T
OF mass spectrometer
[
2
8
]
.
E. coli
has
a
comparatively
simple
lipid
composition
and
its
lipid
metabolism
has
been
studied
for
decades
[
2
9
]
.
The
bacterium
therefore
provides
a
solid
model
system
to
develop
and
test
methods
for
stable
isotope
labeling
lipidomics.
E.
coli
lipid
extracts
contains
mainly
phosphatidylethanolamines
(PE),
phosphatidylglycerol
(PG)
and
cardiolipins
(CL)
[
3
0
]
.
When
fully
labeled
in
4 %
D
2
O,
the
molecular
ions
from
PE
and
PG
lipids
extend
over
a
range
of
8
m/z
.
As
expected,
labeling
causes
a
shift
of
retention
time
(Figure 1A).
Strongly
deuterated
molecules
elute
earlier
than
lighter
ones
(Figure 1B).
The
maximum
shift
in
retention
time
is
about
half
of
the
chromatographic
peak
width,
which
indicates
that
all
molecular
species
have
overlapping
elution
profiles
(Figure 1C).
W
e
expect
that
this
degree
of
shifting
typically
does
not
alter
isotope
ratios,
as
long
as
a
moderate
amount
of
labeling
is
used
and
the
mass
spectrum
is
integrated
over
a
suf
ficiently lar
ge retention time window
.
The
quantification
of
D
in
intact
lipids
is
complicated
by
1
3
C,
which
is
naturally
present
in
lipids
at
about
1.1 %.
Mass
gained
by
1
3
C
or
D
cannot
be
distinguished
by
current
T
OF
mass
analyzers
(resolving
power ~30,000).
Resolving
the
minute
mass
dif
ference
(~3 mDa)
is
possible
5
.
CC-BY 4.0 International license
It is made available under a
(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint
.
http://dx.doi.org/10.1101/330464
doi:
bioRxiv preprint first posted online May. 25, 2018;
120
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130
135
140
for
small
lipids
(<600 Da)
by
Orbitrap
MS,
but
this
approach
is
currently
not
ideal
for
LC­MS
due
to
the
long
scan
times
(~1
second).
So
we
need
a
procedure
to
determine
the
gain
of
isotopic
label
indirectly
by
comparing
lipid
extracts
from
bacterial
cultures
grown
with
and
without
label.
The
average
molecular
weights
of
the
two
mass
distributions
are
calculated
and
their
dif
ference,
∆MW
,
interpreted
as
the
mass
gained
by
D
incorporation
(Figure 2).
The
fractional
abundance
of
D
in
a
lipid
(
2
F
L
I
P
I
D
)
is
then
calculated
by
dividing
∆MW
by
the
number
of
C­bound
hydrogens.
Using
this
method,
glycerophospholipids
produced
by
E. coli
grown
in
4 %
D
2
O
yield
a
2
F
L
I
P
I
D
of
about
2.5 %.
V
alues
lower
than
4 %
are
expected
because
hydrogen
atoms
from
the
unlabeled
carbon
source
are
incorporated
into
the
lipids
and
biosynthetic
enzymes
favor
1
H
over
D
due
to
kinetic
isotope
ef
fects.
Note
that
this
calculation
assumes
that
and
O­bound
hydrogen
atoms
equilibrate
fully
with
water
during
extraction
and
chromatography
[
3
1
]
.
Additionally
,
the
natural
level
of
D,
which
is
about
0.015 %, is neglected for the purpose of this study
.
In
order
to
evaluate
the
utility
of
D
2
O­labeling
lipidomics
for
estimating
bacterial
growth
rates,
we
grew
E. coli
cultures
in
glucose
minimal
medium
ranging
from
0.0156 %
(natural
abundance)
to
4 %
2
F
W
A
T
E
R
and
quantified
the
glycerophospholipids
PE
and
PG.
2
F
L
I
P
I
D
increases
linearly
(R
2
> 0.99)
with
2
F
W
A
T
E
R
(Figure 3A
and
B).
Analyzing
the
same
samples
on
a
Q
Exactive
Plus
Orbitrap
operated
at
R=35,000,
yields
nearly
identical
slopes.
Assuming
a
detection
limit
of
0.03 %
2
F
L
I
P
I
D
for
D
2
O­labeling
lipidomics,
we
suggest
that
incubating
cells
for
15 minutes
with
5 %
2
F
W
A
T
E
R
is
a
useful
range
to
quantify
lipid
biosynthesis
from
microbes
growing
at
one
doubling
per day
(Figure 3C).
These
boundary
conditions
indicate
that
D
2
O­labeling
lipidomics
can
be
developed
further
into
a
method
to
estimate
microbial
growth
rates
in
environmental
samples
[
3
]
.
An
important
consideration
for
microcosm
incubations
is
that
two
separate
populations
of
molecules
co­occur
after
labeling,
a
pool
that
contains
low
natural
D
abundance
and
a
new
pool
that
is
enriched
in
D.
High
labeling
strength
would
create
molecules
that
occur
further
away
from
the
monoisotopic mass in the spectrum and become dif
ficult to quantify
.
6
.
CC-BY 4.0 International license
It is made available under a
(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint
.
http://dx.doi.org/10.1101/330464
doi:
bioRxiv preprint first posted online May. 25, 2018;
145
150
155
160
Ionization
conditions
can
af
fect
isotopologue
distributions
and
thus
alter
isotope
ratios
[
3
2
]
.
W
e
varied
the
injected
sample
amount,
ionization
mode,
capillary
voltage,
desolvation
temperature
and
desolvation
gas
flow
without
noticing
significant
changes.
For
instance,
PE(16:0/16:1)
had
a
2
F
L
I
P
I
D
of
2.549±0.003
(1σ)
in
positive
ionization
mode
and
2.567±0.019
in
negative
mode
(lower
precision
due
to
increased
background
noise).
Less
abundant
analytes
have
greater
standard
deviations.
PE(16:0/16:0),
which
was
10­times
less
abundant,
had
a
2
F
L
I
P
I
D
of
2.432±0.078
(positive
mode).
These
trials
show
that
the
D
abundance
of
lipids
can
be
measured
reproducibly
.
However
,
the
most
intense
signals
must
be
within
the
linear
range
of
the
mass
analyzer
and
the
detector
must
not
be
in
dead
time
on
a
T
OF
instrument.
Also,
isotopologue
patterns
that
are
af
fected
by
co­eluting
compounds
have
to
be
excluded.
In
our
UPLC
setup
this
was
the
case
for
some
cardiolipins
(m/z >
1,200).
For
the
calculation
of
2
F
L
I
P
I
D
we
assume
that
C­bound
hydrogens
do
not
exchange
with
solvent
water
during
sample
preparation
and
electrospray
ionization,
while
and
O­bound
hydrogens
fully
equilibrate.
If
this
is
not
the
case,
we
would
obtain
inaccurate
2
F
L
I
P
I
D
values
[
3
3
,
3
4
]
.
T
o
test
for
H/D
exchange
we
compare
UPLC­ESI­T
OF
with
GC/P/IRMS,
which
quantifies
near
­natural
isotopic
composition
of
fatty
acids
[
3
5
]
.
Albeit
the
two
methods
are
distinct
in
many
ways,
they
should
yield
a
similar
linear
relationship
between
2
F
W
A
T
E
R
and
2
F
L
I
P
I
D
[
3
6
,
3
7
]
.
For
lipidomics,
we
determine
an
average
slope
for
intact
lipids
produced
by
E.
coli
in
glucose
minimal
medium
of
0.577±0.003
(Figure S1;
2
F
W
A
T
E
R
between
0.125
and
4 %).
Slightly
higher
slopes
have
been
reported
previously
for
E.
coli
fatty
acids
using
GC/P/IRMS
(0.65±0.04
for
C16:0,
0.60±0.02
for
C16:1
and
0.63±0.03
for
C18:1)
[
3
7
]
.
Growth
on
acetate
raises
2
F
in
E. coli
fatty
acids
analyzed
by
GC/P/IRMS,
and
it
does
so
also
for
intact
lipids
measured
by
LC­MS
(Figure S1).
Overall,
we
obtain comparable slopes by lipidomics and GC/P/IRMS.
7
.
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165
170
175
180
185
In
order
to
further
constrain
H/D
exchange,
we
dissolved
PE(18:0/18:1)
in
acetonitrile,
added
H
2
O
or
D
2
O
(sold
as
99.9
at%
D),
and
recorded
mass
spectra
by
direct
infusion.
The
addition
of
D
2
O
shifts
the
mass
spectrum
by
four
units
in
positive
ionization
mode,
as
expected
for
an
analyte
that
has
four
non
C­bound
hydrogens
(Figure 4).
The
∆MW
of
3.86
suggests
that
the
four
exchangeable
hydrogens
in
PE(18:0/18:1)
[M+H]+
have
an
average
probability
of
97.4 %
to
contain
D.
A
theoretical
spectrum
that
assumes
four
positions
in
the
ion
to
have
this
probability
for
D
closely
matches
the
measured
spectrum.
Importantly
,
D
2
O
addition
does
not
yield
detectable
signal
beyond
a
shift
of
the
unlabeled
distribution
by
four
mass
units.
Such
isotopologues
would
occur
if
the
exchange
of
C­bound
hydrogens
occurs
at
high
rates
during
ESI.
Absence
of
these
signals
indicates
that
C­bound
hydrogens
exchanged
at
least
2000­fold
slower
than
non­C­bound
hydrogens,
which
is
in
line
with
prior
assessments
of
C­bound
hydrogen
exchange
[
3
8
]
.
T
ogether
these
tests
imply
that
for
lipids
labeled
well
above
natural
D­abundance,
no
relevant
artifacts
of
2
F
L
I
P
I
D
values due to exchange of C­bound hydrogen are likely in UPLC­ESI­T
OF
.
W
ith
the
addition
of
small
quantities
of
D
2
O
to
pure
cultures
we
have
an
opportunity
to
measure
lipid
isotope
ratios
and
absolute
concentrations
simultaneously
and
compare
the
two
quantifications
side
by
side.
Our
test
case
here
is
the
lipid
metabolism
of
E.
coli
during
the
transition
from
stationary
phase
into
exponential
growth.
Cells
from
two
stationary
phase
precultures
(
u
:‘unlabeled’
and
l:
‘labeled’
in
4 %
2
F
W
A
T
E
R
)
were
used
to
inoculate
two
cultures
each
of
unlabeled
(
U
)
or
labeled
(
L
)
medium
(Figure 5).
Four
growth
cultures
(
uU
,
lU
,
uL,
lL
)
were
sampled
to
determine
optical
density
(OD
6
0
0
),
as
well
as
bulk
protein
and
lipid
concentrations.
During
the
160
minutes of labeling, cells divided three to four times (Figure S2).
In
these
tests,
the
stationary
phase
E. coli
cultures
contain
a
high
proportion
of
cyclopropane
fatty
acids
(CF
A).
Greater
than
25
mol%
of
PE
and
PG
phospholipids
contain
at
least
one
acyl
chain
with
cyclopropyl
ring.
When
cells
resume
growth,
CF
A
abundance
decreases
to
about
12
8
.
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190
195
200
205
210
mol%
(Figure
5B
and
C).
The
formation
of
CF
As
in
E.
coli
is
a
post­synthetic
modification
of
the
unsaturated
phospholipids
that
occurs
predominantly
as
cultures
enter
the
stationary
phase.
CF
A
synthase
has
an
unusual
regulation
that
involves
enzyme
instability
as
well
as
transcription
of
the
cfa
gene
from
two
distinct
promoters
[
3
9
,
4
0
]
.
This
means
that,
although
CF
A
synthase
is
synthesized
at
basal
levels
throughout
the
growth
curve,
a
transient
spike
in
activity
occurs
during
the
log­to­stationary
phase
transition.
In
agreement
with
this
regulation,
CF
As
lar
gely
dilute
out
during
stationary­to­log
phase
transition.
Using
D
2
O­labeling
lipidomics
we
detect
small
levels
of
production
of
CF
A
lipids
as
well
as
D
incorporation,
which
shows
that
CF
A
lipids
were
actively
made during stationary phase exit (Figure 5D).
Untar
geted
labeling
reveals
striking
dif
ferences
between
phospholipids.
Here
we
describe
the
uL
scenario
in
detail.
Some
D
2
O­labeling
patterns
fit
an
exponential
growth
model
(Figure 6).
Other
lipids,
in
particular
CF
A­containing
lipids,
were
inconsistent
with
simple
exponential
de
novo
production.
For
them
the
growth
model
needs
to
be
extended.
W
e
include
a
parameter
that
accounts
for
lipid
biomass
in
the
inoculum
that
is
inactive,
i.e.
not
exponentially
reproduced
during
the
stationary­to­log phase transition (see Methods for details).
The
isotopic
labeling
patterns
of
E. coli
phospholipids
are
dominated
by
their
two
fatty
acyl
chains,
as
they
contain
most
of
the
C­bound
hydrogens.
A
major
trend
we
notice
is
that
lipids
that
contain
unsaturated
fatty
acids
label
rapidly
,
while
fully
saturated
lipids
incorporate
label
more
steadily
(Figure
also
Figure
S3
and
S4).
In
E.
coli
,
unsaturated
fatty
acids
are
made
during
de
novo
fatty
acid
biosynthesis
and
not
generated
by
modification
of
saturated
fatty
acids
or
phospholipids
[
2
9
]
.
The
faster
labeling
of
unsaturated
lipids
we
observe
thus
likely
reflects
that
the
unlabeled
inoculum
contained
little
unsaturated
phospholipids,
because
most
got
converted
into
CF
A
during
stationary
phase.
A
second
common
trend
is
that
most
CF
A­containing
lipids
show
slow
initial
increase
of
2
F
L
I
P
I
D
and
often
do
not
reach
full
saturation
levels.
This
reflects
that
CF
A
lipids
are
only
produced
in
small
quantities
after
inoculation
and
hence
a
lar
ge
proportion
of
9
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The copyright holder for this preprint
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215
220
225
230
235
unlabeled
material
is
carried
over
from
stationary
phase.
As
CF
A
formation
is
a
post­synthetic
modification,
labeling
of
CF
A
lipids
additionally
depends
on
the
prior
labeling
of
the
precursor
pool.
Interestingly
,
the
two
common
trends
we
observe,
namely
slower
labeling
of
saturated
lipids
compared
to
unsaturated
lipids
and
slow
and
incomplete
labeling
of
CF
A­containing
lipids,
do
not
apply
to
all
phospholipids.
For
example,
PE(16:0/18:1)
and
PG(16:0/18:1)
have
distinct
labeling
patterns
(Figure S3).
Generally
,
the
labeling
of
PE
lipids
that
have
one
unsaturated
and
one
saturated
straight
chain
fatty
acyl
reveal
a
significantly
lar
ger
proportion
of
unlabeled
lipid
compared
to
their
PG
analogs.
Distinct
labeling
dynamics
also
occur
for
some
CF
A
lipids.
While
most
CF
A
lipid
pools
label
slowly
and
do
not
reach
high
labeling,
production
of
PG(14:0/16:0(Cp))
is
stimulated
so
that
it
gains
label
rapidly
and
to
high
levels
(Figure
6).
This
lipid
occurs
only
in
trace
amounts
in
stationary
phase,
as
does
its
precursor
PG(14:0/16:1).
Therefore,
the
material
produced
during
outgrowth
of
the
cultures
is
highly
labeled
and
dominates
the
PG(14:0/16:0(Cp))
pool.
Overall,
these
tests
indicates
that
D
2
O
addition
allows
a
readout
of
how
much
of
the
material
has
been
newly
synthesized
even
for
minority
components,
whose
absolute
concentrations
can
be
challenging to quantify in complex lipid extracts.
The
results
so
far
indicate
that
lipidomics
can
be
used
to
measure
bacterial
lipid
biosynthesis
in
pure
cultures.
If
D
2
O­labeling
lipidomics
could
quantify
microbial
growth
reliably
in
situ
,
this
might
for
example
enable
the
use
of
microcosm
incubations
to
test
how
dif
ferent
drugs
impact
the
microbial
community
of
individual
patients.
A
disease
context
that
is
well­suited
to
assess
the
practicability
of
D
2
O­labeling
lipidomics
for
complex
samples
are
cystic
fibrosis
(CF).
These
chronic
lung
infections
contain
heterogeneous
mixtures
of
human
biomass
and
microor
ganisms.
Some
of
the
bacteria
in
CF
lungs
become
pathogenic
and
tend
to
develop
drug­resistant
phenotypes.
In
previous
1
0
.
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doi:
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240
245
250
255
260
work
on
D
2
O­labeled
expectorated
CF
sputum
we
have
examined
the
growth
of
S. aur
eus
via
D/H
ratios
of
anteiso
­fatty
acids,
an
abundant
fatty
acids
of
this
pathogen
(3).
It
is
important,
however
,
that
anteiso
­fatty
acids
are
produced
also
by
other
bacterial
species.
In
the
context
of
CF
sputum,
Pr
evotella
melaninogenica
and
Stenotr
ophomonas
maltophilia
are
relevant
sources
of
anteiso
­C15:0
and
anteiso
­C17:0
fatty
acids
in
some
CF
patients
[
3
,
4
1
,
4
2
]
.
Certain
phospholipids,
specifically
those
that
contain
anteiso
fatty
acyls,
may
therefore
be
more
specific
markers
of
S. aur
eus
in
CF
infections
and
could
be
used
to
assess
activity
of
the
pathogen
by
lipidomics.
T
o
evaluate
this
hypothesis,
we
analyzed
samples
that
had
been
collected
and
characterised
as
part
of
a
longitudinal study of CF patients under
going pulmonary exacerbations
[
4
2
]
.
A
lipid
that
is
appears
well­suited
to
monitor
the
growth
of
S. aur
eus
is
PG(
a
­C15:0/
a
­C17:0).
This
compound
was
detected
in
lipid
extracts
of
S. aur
eus
and
its
structure
assigned
based
on
the
m/z
of
the
molecular
ion
in
positive
and
negative
ionization
mode
as
well
as
MS/MS
fragmentation
spectra.
Subsequently
,
signals
from
this
lipid
were
also
detected
in
expectorated
sputum
from
several
CF
patients
with
S. aur
eus
infection
(Figure
7).
T
wo
patient
whose
lung
infections
did
not
contain
S. aur
eus
showed
no
signal
corresponding
to
PG(
a
­C15:0/
a
­C17:0).
Based
on
these
observations
PG(
a
­C15:0/
a
­C17:0)
in
CF
sputum
appears
to
be an specific marker for
S. aur
eus
in CF sputum.
Microbial
lipid
metabolites
make
up
only
a
minute
fraction
of
the
total
lipid
content
of
CF
sputum.
The
high
sensitivity
of
ESI
mass
spectrometry
allows
detection
of
trace
components,
however
,
we
had
to
use
concentrated
lipid
extracts
to
yield
suf
ficiently
high
signal
intensities
for
the
tar
get
analyte.
In
order
to
minimize
contamination
of
the
mass
spectrometer
,
we
only
collected
MS
data
at
retention
times
that
are
needed
to
detect
abundant
phospholipids
of
S. aur
eus
(5­8
min.).
Labeling
of
CF
sputum
with
4% D
2
O
for
1
hour
resulted
in
0.054±0.04
at%
D
enrichment
of
PG(
a
­C15:0/
a
­C17:0).
This
value
can
be
used
to
estimate
that
the
average
generation
time
of
S. aur
eus
was
approximately
one
cell
doubling
per
day
.
This
estimation
is
based
on
a
previously
1
1
.
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265
270
275
280
established
procedure
that
takes
into
account
dif
fusion
of
the
label,
cell
maintenance
and
other
factors
[
3
]
.
For
comparison,
the
generation
time
estimated
by
the
D/H
ratio
of
anteiso
­C15:0
fatty
acid
in
this
sample
was
3.3
days
[
4
2
]
.
The
slower
estimate
based
on
GC
isotope­ratio
MS
could
for
example
be
caused
by
contributions
of
anteiso
­C15:0
from
other
sources
or
variability
in
the
production
rates
of
anteiso
­C15:0
containing
phospholipids
in
S. aur
eus
.
In
summary
,
these
initial
tests
indicate
that
it
is
possible
to
measure
the
activity
of
microbial
pathogens
in situ
by
D
2
O­labeling
lipidomics.
Its
main
benefits
are
that
LC­MS
has
increased
species
specificity
,
requires
smaller
sample
amounts,
it
is
faster
than
alternative
MS
methods
[
4
3
]
.
Furthermore,
D
2
O­lipidomics
can
be
performed
on
instrumentation
that
is
available
in
many
biomedical
laboratories.
CONCLUSIONS
The
combination
of
D
2
O­labeling
and
lipidomics
allows
a
robust
isotope
ratio
measurement,
which
reveals
dynamic
aspects
of
biosynthesis
not
accessible
from
absolute
concentrations
alone.
The
technology
is
also
suf
ficiently
sensitive
to
be
adapted
for
environmental
samples.
Based
on
this
study
we
encourage
the
development
of
LC­MS
assays
for
the
analysis
of
microbial
growth
in
microbiome
samples.
Routine
methods
to
measure
bacterial
growth
in
clinical
samples
are
important to better understand microbial physiology in infections and improve diagnostics.
A
critical
parameter
for
D
2
O­labeling
lipidomics
is
labeling
strength.
V
ery
high
concentrations
of
D
2
O
(e.g.,
20
to
~100
%)
are
tolerated
by
microor
ganisms
and
can
be
used
to
monitor
biosynthesis
[
3
,
4
]
.
For
LC­MS
high
labeling
strengths
are
not
desirable
because
they
cause
broad
isotopic
distributions.
Quantification
would
become
especially
dif
ficult
when
only
a
small
proportion
of
the
lipid
is
newly
produced.
In
such
a
scenario,
the
labeled
lipid
would
have
a
broad
mass
distribution
far
from
the
monoisotopic
mass
and
potentially
even
overlap
with
other
compounds.
Interestingly
,
the
ratio
M1/M0
increases
approximately
linearly
with
2
F
L
I
P
I
D
1
2
.
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285
290
295
300
305
(Figure S5).
M1/M0
could
be
a
simple
readout
of
D
incorporation
in
environmental
samples.
When
we
assume
an
excess
of
unlabeled
over
labeled
lipid,
as
it
is
the
case
for
many
environmental
incubations,
we
anticipate
an
optimal
labeling
strength
that
causes
the
greatest
change
of
the
M1/M0
ratio.
This
is
achieved
when
the
∆MW
of
the
newly­made
lipid
is
about
+1.5
Da.
Overall,
a
concentration
of
2­3
%
2
F
W
A
T
E
R
seems
most
suited
for
environmental
microcosm
incubations.
The
optimal
value
will
depend
on
the
complexity
of
the
lipid
sample,
i.e.
whether
D
incorporation
can
be
assessed
from
isotopologue
distributions
or
M1/M0
ratio.
Another
consideration
is
that
the
fraction
of
D
that
enters
the
lipid
varies
with
microbial
metabolism
[
3
7
,
4
4
]
.
W
e
estimate
that
the
combination
of
D
2
O­labeling
and
lipidomics
as
used
here
can
roughly
quantify
growth
rates
greater
than one cell doubling per day after labeling for 15 minutes.
D
2
O­lipidomics
can
in
principle
track
lipid
biosynthesis
for
many
lipids
in
the
same
way
as
we
have
done
here
for
27
abundant
glycerophospholipids
in
E.
coli
.
As
lipid
extracts
from
tissues
or
environmental
samples
are
much
more
complex,
initial
chemical
fractionation
of
lipids
could
be
used
to
make
data
analysis
more
tractable.
Isotope
ratios
should
be
little
af
fected
by
chemical
separation
and
thus
D
2
O­labeling
lipidomics
can
be
optimized
to
a
specific
ecosystem.
The
readout
that
D
2
O­labeling
lipidomics
enables
can
be
applied
to
the
study
of
microbial
growth
rates
in
clinical
samples.
It
can,
for
example,
also
be
applied
to
dif
ferentiate
biologically
active
from
inactive biomass, necromass, and contaminants.
Currently
,
dif
ferences
in
the
lipid
composition
between
microbes
are
already
used
to
identify
strains
by
chemotaxonomy
[
4
5
,
4
6
]
.
By
combining
lar
ge­scale
lipid
detection
with
the
quantification
of
isotopic
labeling,
new
applications
might
become
possible.
These
include
identifying
microbial
adaptations
to
drugs,
determining
instantaneous
microbial
growth
rates
and
forecasting
composition
of
microbial
community
composition
after
exposure
to
a
stressor
.
Recording
isotope
labeling
dynamics
of
lipids
can
help
to
rationalize
microbial
lipid
function
and
metabolism.
These
ef
forts
will
benefit
from
related
lines
of
research
in
environmental
microbiology
1
3
.
CC-BY 4.0 International license
It is made available under a
(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint
.
http://dx.doi.org/10.1101/330464
doi:
bioRxiv preprint first posted online May. 25, 2018;
310
315
320
325
330
and
in
human
physiology
that
measure
the
synthesis
and
turnover
of
lipids
with
isotope
labeling
lipidomics, mass isotopomer distribution analysis or biomarkers analysis
[
8
,
2
1
,
4
7
]
.
MA
TERIALS AND METHODS
Deuterium­enriched gr
owth medium
M9
minimal
medium
was
prepared
with
3.8
μM
thiamine
pyrophosphate
and
glucose
(22.2 mM)
or
sodium
acetate
(15
mM)
[
4
8
]
.
All
media
were
sterilized
by
filtration
(0.2
μm).
D
content
was
adjusted
by
isotope
dilution
(measured
by
weight)
of
D
2
O
(D,
99.9
at%;
Cambridge
Isotope
Laboratories)
with
natural
abundance
water
(MilliQ,
EMD
Millipore)
of
known
isotopic
composition.
2
F
W
A
T
E
R
of
culture
medium
was
measured
on
a
DL
T
­100
liquid
water
isotope
analyzer
(Los
Gatos
Research).
Samples
were
analyzed
in
three
technical
replicates,
each
comprising
10­12
injections.
Samples
with
D
abundances
close
to
natural
abundance
were
calibrated
against
standards
ranging
from
0.0136 %
to
0.0200 %
2
F
W
A
T
E
R
(corresponding
to
δD
values
from
−124
to
+287
‰).
These
were
in
turn
calibrated
against
the
VSMOW
,
GISP
,
and
SLAP
international
standards
[
4
9
]
.
More
enriched
samples
were
measured
against
working
standards
made
in­house,
ranging
from
0.050 %
to
0.150 %
2
F
W
A
T
E
R
.
The
presence
of
doubly­substituted
species
(D­O­D)
was
not
taken
into
consideration
due
to
fast
equilibration
of
water
molecules
below
0.0150 %.
Samples
beyond
this
scale
were
no
longer
in
the
linear
response
range
of
the
instrument,
and
we
calculated
2
F
W
A
T
E
R
based
on
the
gravimetric
preparation
of
the
medium.
Their
2
F
values
were
confirmed
by
water
isotope analysis after dilution with natural abundance water of known isotopic composition.
E. coli
cultur
es
Escherichia
coli
K­12
(FRAG1)
was
streaked
on
LB
agar
plates
for
single
colonies
and
used
to
inoculate
6 mL
precultures
of
M9
minimal
medium
with
glucose
as
carbon
source
[
5
0
]
.
All
cultures
were
incubated
at
37 °C
and
shaking
at
250 rpm
(Innova
44
shaker
,
New
Brunswick
Scientific).
Cultures
were
checked
by
phase
contrast
microscopy
(Axio
Scope.A1,
Zeiss).
Optical
density
(OD)
1
4
.
CC-BY 4.0 International license
It is made available under a
(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint
.
http://dx.doi.org/10.1101/330464
doi:
bioRxiv preprint first posted online May. 25, 2018;
335
340
345
350
355
was measured at 600 nm wavelength (DU 800 spectrophotometer; Beckman Coulter).
T
o
investigate
the
detection
limits
of
2
F
L
I
P
I
D
and
the
comparison
of
LC­MS
with
GC/P/IRMS,
precultures
were
grown
for
20
hours
at
natural
D
abundance.
75
μL
were
used
to
inoculate
150
mL
medium
in
1
L
Erlenmeyer
flasks.
For
analysis
with
GC/P/IRMS
the
medium
had
a
2
F
W
A
T
E
R
in
the
range
from
0.0142 %
to
0.0202 %
(δD
−90
to
+300 ‰).
For
analysis
with
LC­MS
the
medium
had
a
D
content
of
0.0142 %
to
4 %
2
F
W
A
T
E
R
.
Cultures
were
harvested
at
OD
6
0
0
0.2­0.3
by
chilling
50
mL
culture
in
ice
and
centrifugation
at
4
°C
for
20
min
at
5,000
×
g.
Cell
pellets
were
frozen
in
liquid
nitrogen and stored at ­20 °C.
For
monitoring
change
of
2
F
L
I
P
I
D
during
stationary
phase
exit,
two
precultures
(0.0142 %
and
4 %
2
F
W
A
T
E
R
)
were
centrifuged
at
15
°C
for
10
minutes
at
15,000
×
g.
The
pellets
were
resuspended
in
prewarmed
medium
(0.0142 %
or
4
%
2
F
W
A
T
E
R
)
and
used
to
inoculate
200 mL
M9
glucose
medium
at
an
initial
OD
6
0
0
of
0.1.
This
yielded
four
combinations
(
uU
,
uL
,
lU
,
lL
)
of
unlabeled/labeled
inoculum
(
u,l
)
in
unlabeled/labeled
growth
medium
(
U,
L
).
Aliquots
were
incubated
and
sampled
as
described
above
(20
minutes:
40
mL,
50
minutes:
30 mL,
100
minutes:
30 mL
and
150
minutes:
30
mL).
At
each
time
point,
OD
6
0
0
was
recorded
and
protein
content
of
the
bacterial
culture
was
measured
via
BCA
protein
assay
(Thermo
Scientific)
from
a
cell
pellet
(2
mL
aliquot,
centrifuged
at
4 °C
for
2
minutes
at
16,900
×
g).
Maximum
OD
6
0
0
was
0.8
for
cultures
uU
,
uL
and
1.2­1.3
for
cultures
lU
,
lL.
Before
the
BCA
assay
cells
were
chemically
lysed
(BugBuster
,
EMD
Chemicals).
Absorbance was recorded at 562 nm on a plate reader (Syner
gy 4, BioT
ek).
Liquid chr
omatography mass spectr
ometry (LC­MS)
For
LC­MS
analysis
lipids
were
extracted
based
on
the
procedure
by
Matyash
et
al.
[
5
1
]
.
Cell
pellets
were
resuspended
in
0.1
%
ammonium
acetate
to
a
protein
concentration
of
200 mg/mL
(20
OD
6
0
0
/mL).
100
μL
of
this
suspension
were
added
to
1.5 mL
methanol,
then
5
mL
methyl
t­butyl
ether
(MTBE)
and
a
mix
of
standards
containing
PE(17:0/17:0),
PG(17:0/17:0)
and
14:1(3)­15:1
cardiolipin
was
added
(A
vanti
Polar
Lipids).
After
incubation
in
a
ultrasonic
bath
for
1
hour
,
lipids
1
5
.
CC-BY 4.0 International license
It is made available under a
(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint
.
http://dx.doi.org/10.1101/330464
doi:
bioRxiv preprint first posted online May. 25, 2018;