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Soil Biology and Biochemistry

journal homepage: www.elsevier.com/locate/soilbio

Growth explains microbial carbon use efficiency across soils differing in land
use and geology
Qing Zheng, Yuntao Hu, Shasha Zhang, Lisa Noll,

heresa Böckle, Andreas Richter,
Wolfgang Wanek∗

Department of Microbiology and Ecosystem Science, Research Network “Chemistry Meets Microbiology”, University of Vienna, Althanstraße 14, 1090 Vienna, Austria

A R T I C L E I N F O

Keywords:
Carbon use efficiency
Microbial biomass turnover time
Temperature
Moisture
Short-term environmental effects

A B S T R A C T

The ratio of carbon (C) that is invested into microbial growth to organic C taken up is known as microbial carbon
use efficiency (CUE), which is influenced by environmental factors such as soil temperature and soil moisture.
How microbes will physiologically react to short-term environmental changes is not well understood, primarily
due to methodological restrictions. Here we report on two independent laboratory experiments to explore short-
term temperature and soil moisture effects on soil microbial physiology (i.e. respiration, growth, CUE, and
microbial biomass turnover): (i) a temperature experiment with 1-day pre-incubation at 5, 15 and 25 °C at 60%
water holding capacity (WHC), and (ii) a soil moisture/oxygen (O2) experiment with 7-day pre-incubation at
20 °C at 30%, 60% WHC (both at 21% O2) and 90% WHC at 1% O2. Experiments were conducted with soils from
arable, pasture and forest sites derived from both silicate and limestone bedrocks. We found that microbial CUE
responded heterogeneously though overall positively to short-term temperature changes, and decreased sig-
nificantly under high moisture level (90% WHC)/suboxic conditions due to strong decreases in microbial
growth. Microbial biomass turnover time decreased dramatically with increasing temperature, and increased
significantly at high moisture level (90% WHC)/suboxic conditions. Our findings reveal that the responses of
microbial CUE and microbial biomass turnover to short-term temperature and moisture/O2 changes depended
mainly on microbial growth responses and less on respiration responses to the environmental cues, which were
consistent across soils differing in land use and geology.

1. Introduction

As the major organic carbon (C) reservoir in terrestrial ecosystems,
soils comprise approximately 1

0–2400 Pg C (IPCC, 2013). This large
reservoir of organic C is mainly decomposed and transformed by soil
microorganisms (Schimel and Schaeffer, 2012; Xu et al., 2013). Het-
erotrophic microbes consume organic C and eventually mineralize a
part of it to CO2, thereby contributing significantly to global CO2 fluxes.
A part of the C taken up is, however, transformed into microbial bio-
mass and eventually necromass which becomes stabilized in soils (Liang
et al., 2017; Six et al., 2006; Tucker et al., 2013). This partitioning of C
between growth and respiration can be described by microbial carbon
use efficiency (CUE), a critical synthetic representation of microbial
community C metabolism in models. Low CUE implies a decreased
potential for long-term C sequestration in soils (Sinsabaugh et al.,
2013). Besides CUE, microbial growth and biomass turnover also de-
termine C sequestration in soils, with faster growth and microbial ne-
cromass production (i.e. lower turnover times) promoting soil C

accumulation (Hagerty et al., 2014). Microbial biomass C (MBC) only
accounts for about 1.2% of total soil organic C (Xu et al., 2013), but is
the most active portion in driving SOM decomposition and storage
(Kaschuk et al., 2010). Additionally, MBC is an important indicator of
soil quality since it is highly sensitive to environmental variation
(Kaschuk et al., 2010). Thus, a sound understanding of the controls of
microbial CUE, growth, and biomass turnover will greatly improve our
knowledge of soil microbial C cycling.

Environmental factors such as soil temperature and moisture have
been widely recognized as primary controls on microbial physiology
(e.g. respiration and growth). In recent years, extensive studies have
focused on investigating the responses of microbial metabolism to en-
vironmental changes (Dijkstra et al., 2011; Suseela et al., 2012; Frey
et al., 2013; Hagerty et al., 2014). For example, previous studies gen-
erally reported decreasing microbial CUE in soils with increasing tem-
peratures (Devêvre and Horwáth, 2000; Schindlbacher et al., 2015).
Moreover, higher temperatures could lead to acclimation of microbial
activity, depletion of readily accessible substrates and nutrient

https://doi.org/10.1016/j.soilbio.2018.10.006
Received 23 February 2018; Received in revised form 9 October 2018; Accepted 14 October 2018

∗ Corresponding author.
E-mail address: wolfgang.wanek@univie.ac.at (W. Wanek).

Soil Biology and Biochemistry 128 (2019) 45–

Available online 15 October 2018
0038-0717/ © 2018 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/BY-NC-ND/4.0/).

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limitation (Kirschbaum, 2004), which can adversely affect microbial
growth and CUE. Temperature could also influence microbial biomass
turnover, for instance, microbial turnover might accelerate with soil
warming (Hagerty et al., 2014). Changing soil moisture could shift
microbial CUE (Manzoni et al., 2012) and affect microbial biomass
turnover, e.g. declining soil moisture could trigger microbial water-
stress responses (e.g. dormancy or the synthesis of stress metabolites)
and consequently affect microbial respiration and growth. Bacterial
growth was shown to increase with soil moisture, reaching a broad
plateau between 30 and 70% WHC in soils while respiration peaked
only at 60% WHC (Iovieno and Bååth, 2008) decreasing towards water
saturation (Singh et al., 2017; Wang et al., 2010). High soil moisture
could also decrease O2 diffusivity and availability, which might con-
sequently inhibit microbial activity and respiration (Picek et al., 2000),
as well as shift the balance of aerobic towards anaerobic respiration and
fermentation in soils (Rubol et al., 2013).

As naturally occurring environmental fluctuations directly result in
rapid changes of soil temperature and soil moisture, a thorough ex-
ploration of microbial physiological responses to short-term fluctuating
environmental conditions is essential for better understanding soil mi-
crobial C metabolism. However, this understanding is impeded by
methodological restrictions (Sinsabaugh et al., 2013) since in most
studies soil microbial CUE was estimated with 13C-labeled labile sub-
strates such as glucose (Brant et al., 2006; Dijkstra et al., 2015) that
tends to overestimate CUE (Sinsabaugh et al., 2013). Additionally, it is
difficult to disentangle microbial physiological responses to environ-
mental changes from those of microbial community adaptations (e.g.
shifts in microbial community composition) over longer incubation
periods (Bárcenas-Moreno et al., 2009; Bradford et al., 2008; Pettersson
and Bååth, 2003). Here, we aimed to explore the effects of short-term
(i.e., < 7 days) temperature and soil moisture changes on microbial physiology, particularly on growth, respiration, CUE and microbial biomass turnover time. We applied short incubation periods to avoid the confounding effects of changes in microbial community composi- tion or acclimation of microbial physiology, which may occur after weeks of incubation (Bradford, 2013). We employed an 18O technique that quantifies the incorporation of 18O into newly formed microbial dsDNA to estimate microbial CUE as well as microbial biomass turnover time. We hypothesized (1) that short-term increases in soil temperature would decrease microbial CUE since the response of microbial re- spiration might outpace the response of microbial growth (Allison et al., 2010; Buesing and Gessner, 2006). We also hypothesized (2) that mi- crobial CUE will increase at high soil moisture and suboxic conditions as we assumed that microbial respiration will be inhibited more strongly than growth when O2 diffusivity becomes limiting (Bastviken et al., 2001; Iovieno and Bååth, 2008; Picek et al., 2000). We tested for the generality of these patterns by comparing soils from two bedrocks (limestone, silicate) under three management regimes (arable field, pasture and forest).

2. Materials and methods

2.1. Site characteristics and soil sampling

In June 2016, soils were sampled in the central Enns valley, Styria,
Austria at two locations differing in geology: the LFZ Raumberg-
Gumpenstein (

° 29′ N, 14° 6′ E, 690m above sea level) with silicate
bedrock (Gneiss) and the Moarhof in Trautenfels-Pürgg (47° 30′ N, 14°
4′ E, 708m above sea level) with calcareous bedrock (limestone, do-
lomite). These two locations exhibit similar climate as they lie on op-
posing sides of the Enns valley, with 7.2 °C mean annual temperature
and 980mm mean annual precipitation. From both geologies, soils
from three land uses were collected, including arable land (A), pasture
(P) and forest (F). Soils were classified according to the World
Reference Base (WRB) as Luvisols (limestone, L) and Cambisols (sili-
cate, S). Details on site locations, land use, and vegetation can be found

in Supplementary Table S1. From each site, quadruplicates of fresh
mineral soils were sampled to a soil depth of 15 cm using a root corer
(Eijkelkamp, Netherlands), after removal of litter and organic layers.
Soil replicates were stored and treated separately for further analysis.

2.2. Sample preparation

After transferring the samples back to the laboratory, all soils were
sieved (< 2mm). Subsequently, 250 g of sieved soils were adjusted to 60% WHC and stored at 15 °C in individual polyethylene Zip-Lock bags before the start of the incubation experiments. The bags were aerated every two days and water replenished when necessary. To investigate short-term temperature impacts on soil microbial C cycling processes, duplicates of each soil sample (0.4 g) were weighed into 2mL screw cap vials to measure 18O-incorporation and respiration, and meanwhile duplicates of 2 g of each soil sample were weighed into 20mL scintil- lation vials to determine soil extractable organic C and MBC. All weighed soils were pre-incubated in parallel for 1 day at three different temperatures: 5 °C, 15 °C and 25 °C, at 60% WHC. Four weeks later, to study short-term effects of soil moisture/O2 content on soil microbial C processes, duplicates of each soil sample were prepared as described above and pre-incubated at 20 °C for 7 days at three different levels of moisture (WHC %) at two O2 contents: low (30% WHC, 21% O2), in- termediate (60% WHC, 21% O2) and high moisture level (90% WHC, 1% O2 achieved by incubation in a suboxic chamber) before start of measurements.

2.3. Soil properties

Soil water contents (SWC) were determined by weighing soils before
and after drying in a ventilated drying oven at 80 °C for three days. Soil
water holding capacity (WHC) was measured by repeatedly saturating
soils (10 g fresh weight) with deionized water and draining in between
for 2.5 h in a funnel with an ash-free cellulose filter paper. Soil pH was
determined in Milli-Q water (Millipore, Germany) at a fresh soil to
solution ratio of 1:2.5 (w: v) with an ISFET electrode (Sentron,
Netherlands). Aliquots of oven-dried soils were ball milled (MM200,
Retsch, Germany) for total organic C and N analyses by an Elemental
Analyzer (Carlo Erba 1110, CE Instruments) coupled to a DeltaPlus

Isotope Ratio Mass Spectrometer (Finnigan MAT, Germany) via a
Conflo III interface (Thermo Fisher, Austria). Soil carbonates were re-
moved by treatment with 2 M HCl and drying before elemental analysis.
Ammonium (NH4+) and nitrate (NO3−) concentrations were de-
termined photometrically in 1M KCl extracts (Hood-Nowotny et al.,
2010). Parallel to the start of the incubation experiments, dissolved
organic C (DOC) and total dissolved N (TDN) were extracted from non-
fumigated fresh soils in 1M KCl (1:7.5 (w: v)) for 1 h and filtered
through ash-free cellulose filters and then measured by a TOC/TN
analyzer (TOC-VCPH/TNM-1, Shimadzu, Austria). In parallel, soils
were fumigated for

h with chloroform to estimate microbial biomass
C (MBC) and microbial biomass N (MBN) by the chloroform fumigation
extraction (CFE) method (Vance et al., 1987), and afterwards were
extracted in 1M KCl as described for non-fumigated samples above.
MBC and MBN were both calculated as the difference in DOC and TDN
between fumigated and non-fumigated soils, corrected with an extrac-
tion efficiency factor of 0.45. Soil total P (TP) and total inorganic P
(TIP) were measured in 0.5M H2SO4 extracts of ignited (450 °C, 4 h)
and control soils (Kuo, 1996) by malachite green measurements of re-
active phosphate (Robertson et al., 1999). Soil total organic P (TOP)
was calculated as the difference between TP and TIP. Dissolved in-
organic P (DIP) was determined using the malachite green method in
0.5M NaHCO3 (pH 8.5; 1:7.5 (w: v)) extracts. Acid persulfate digestion
(Robertson et al., 1999) was applied to measure total dissolved P (TDP)
and allowed calculating dissolved organic P (DOP). Microbial biomass P
(MBP) was calculated as the difference of fumigated and non-fumigated
soil TDP, corrected with an extraction efficiency factor of 0.4. Soil

Q. Zheng et al. Soil Biology and Biochemistry 128 (2019) 45–55

texture, effective cation-exchange capacity (CECeff), and base saturation
were determined by the Soil Analysis Laboratory of the Federal Office
for Food Safety (AGES, Vienna, Austria) according to standard protocols
(ÖNORM). Phospholipid fatty acids (PLFAs) were analyzed to de-
termine soil microbial community composition (Hu et al., 2018).

2.4. Determination of soil microbial C metabolism and soil enzymes

After pre-incubation, MBC, basal respiration and 18O incorporation
into dsDNA were measured (Spohn et al., 2016) to determine microbial
growth, respiration, growth normalized to MBC (qGrowth), the meta-
bolic quotient (qCO2, microbial respiration normalized to MBC), as well
as C uptake normalized to MBC (qCuptake), microbial CUE and microbial
biomass turnover time. Double-stranded DNA (dsDNA) is only newly
formed during replication. 18O incorporation into dsDNA from H218O
therefore only happens during cell division and the method hence
provides estimates of gross growth rates. The method tends to slightly
underestimate gross growth rates due to concurrent mortality of
growing microbes which decreases the 18O enrichment in dsDNA over
24 h.

The pre-weighed and pre-incubated soil aliquots (0.4 g in 2mL
screw cap vials) were each transferred to 50mL glass serum bottles
(Supelco, Sigma-Aldrich Chemie GmbH, Germany). In half of the soil
replicates, the 18O content of soil water was adjusted to 20.0 at% 18O by
addition of H218O (97.0 at%, Campro Scientific, Germany; diluted to
lower 18O enrichments where needed), limiting increases in soil
moisture (WHC < 5%). Milli-Q water was added to the other half of the soil replicates in order to serve as natural 18O abundance (NA) controls. Directly after adding the water, all serum bottles were sealed with a crimp cap and butyl rubber stoppers (Supelco, Sigma-Aldrich Chemie GmbH, USA) and 5mL headspace gas were sampled im- mediately by a gas-tight syringe (time 0 h, t0) into 3mL glass exetainer vials previously flushed with He and evacuated. Blank serum bottles containing no soil were treated accordingly and processed through all steps to serve as negative controls for DNA analysis. Subsequently, 5 mL of air with known CO2 concentration from a tank with compressed air was injected back into each incubation vial in order to keep a balanced atmospheric pressure inside. Afterwards, all 18O labeled and NA sam- ples were incubated for 24 h at the respective conditions as during the pre-incubation period. Gas samples were then collected from each in- cubation vial at the end of the incubation period (time 24 h, t24) and CO2 concentrations were determined in all gas samples with a Trace Gas Chromatograph (TRACE Ultra Gas Chromatograph, Thermo Scientific, Germany) equipped with a methanizer-FID. After stopping the incubation experiments, the soil aliquots (in the 2mL plastic vials) were retrieved and closed with screw caps, immediately frozen in liquid nitrogen, and stored at −80 °C until DNA extraction. Total soil DNA was extracted with a DNA extraction kit (FastDNA™ SPIN Kit for Soil, MP Biomedicals, Germany) following the modified manufacturer's re- commendations (Spohn et al., 2016). DNA concentrations were then quantified by the Picogreen fluorescence assay (Quant-iT™ PicoGreen®

dsDNA Reagent, Thermo Fisher, Germany) using a Microplate spec-
trophotometer (Infinite® M200, Tecan, Austria). Afterwards, aliquots
(50 μL) of the DNA extracts were pipetted into silver capsules (70 μL
nominal volume; IVA Analysentechnik, Germany), and dried in a ven-
tilated drying oven at 60 °C for two days. Finally, the silver capsules
were folded and analyzed for oxygen isotope composition (18O: 16O) by
a Thermochemical Elemental Analyzer (glassy carbon reactor tem-
perature at 1350 °C) coupled to an Isotope Ratio Mass Spectrometer
(TC/EA-IRMS, Delta V Advantage, Thermo Fisher, Germany).

The potential extracellular oxidative (phenoloxidase (POX)) and
hydrolytic enzyme (β-glucosidase (BG)) activities that are involved in
soil microbial C metabolism were measured photometrically using a
modified microplate assay (Kaiser et al., 2010) at the respective tem-
peratures. The POX activities were measured by a modified method
(Floch et al., 2007), using 0.4 mM ABTS (2,2′-azinobis-(-3

ethylbenzothiazoline-6-sulfonic acid)) as the substrate for photometric
analysis at an absorbance wavelength of 420 nm. A modified method
(Robertson et al., 1999) using 5mM p-nitrophenyl (pNP)-linked β-
glucopyranoside as substrate was used to measure BG activities at an
absorbance wavelength of 410 nm. POX and BG activities were then
calculated as the increase in color during the incubation period.

2.5. Calculations and statistical analyses

The enrichment of 18O in the final soil solution (at%label) was cal-
culated by the following equation:

= +
+

at at A W
W A

% % 0.2label added

where at%added and A are the at% and amount of the labeled water (mL)
added to the soils, respectively. W is the soil water content (SWC, mL),
and 0.2 is the natural 18O abundance (NA).

Total dsDNA produced (μg) during the 24 h incubation period was
calculated according to differences in 18O measurements between la-
beled and unlabeled NA samples.

=DNA O at
at

%
100

100
%

100
31.21produced total

excess

label

where Ototal is the total O content (μg) of the dried DNA extract, at%excess
is the at% excess 18O of the labeled sample compared to the mean at%
18O of NA samples. The average weight% of O in DNA is 31.21 ac-
cording to the average formula (C39H44O24N15P4). Here it was assumed
that all newly incorporated O in DNA derived from soil water.
Moreover, the mortality of newly produced 18O-labeled microbial cells
is negligible in the experiments due to the short incubation time and
slow turnover rates.

A conversion factor (fDNA) was calculated to represent the ratio of
soil MBC (μg g−1 DW) to soil DNA content (μg g−1 DW), which was
measured and calculated for each individual soil sample, and is re-
presentative for the entire soil microbial community. The specific fDNA
values were then applied to each replicate individually that, multiplied
by the DNA production rate, allows calculating microbial growth rates
based on dry soil mass (Cgrowth, ng C g−1 h−1).

=C
f DNA

DW t
1000

growth

DNA

produced

where DW is the dry mass of soil in gram and t is the incubation time in
hours. Additionally, microbial basal respiration rate (Crespiration, ng C
g−1 h−1) was calculated by the following equation:

=C D
DW t

p n
R T

V 1000respiration hsCO

where t (h) is the incubation time, p is the atmosphere pressure (kPa), n
is the molecular mass of the element C (12.01 gmol−1), R is the ideal
gas constant (8.314 Jmol−1 K−1), and T is the absolute temperature of
the gas (295.15 K). Vhs is the volume (L) of the head space vials. DCO2
(ppm) is the increase in CO2 concentration during the 24 h incubation
period that can be calculated as:

= +D V C C
V

C( 0.005) 0.005hs t k
hs

tCO2
0

where Ct0 and Ct24 are the CO2 concentration (ppm) measured at the
start (t0) and end (t24) of the incubation period, respectively. Ck and
0.005 are the CO2 concentration (ppm) and air volume (L) that was
injected back into the incubation vial at the start time (t0), respectively.

Microbial CUE and microbial Cuptake (ng C g−1 h−1) then could be
calculated by the following equations (Sinsabaugh et al., 2013):

=
+

=CUE
C

C C
C
C

growth

growth respiration

growth

uptake

When dividing Cgrowth, Crespiration and Cuptake by the respective MBC

Q. Zheng et al. Soil Biology and Biochemistry 128 (2019) 45–55
47

content (μg g−1 DW), microbial biomass based growth rates (qGrowth;
ng C (μg MBC)−1 h−1), metabolic quotients (qCO2, ng C (μg MBC)−1

h−1) and microbial biomass based C uptake rates (qCuptake, ng C (μg
MBC)−1 h−1) can be estimated as follows:

=qGrowth
C
MBC

growth

=qCO
C

MBC
respiration

=qC
C
MBCuptake

uptake

Finally, microbial biomass turnover time (d) was calculated as fol-
lows:

=t DNA
DNA t/24turnover

amount

produced

where DNAamount (μg) is the DNA content in each soil quantified by
Picogreen assay, and t is incubation time in hours.

The temperature sensitivity of qCO2, qGrowth, microbial CUE,
Cuptake and soil enzyme activities was determined based on a log-linear
regression and calculated by the following modified equation (Janssens
and Pilegard, 2003):

= +Ln R Ln Q T b( ) ( )

where R is the measured process rate (qCO2, qGrowth, microbial CUE,
Cuptake) or potential enzyme activity (POX, BG), Q10 is the temperature
sensitivity of the measured process or enzyme, T is incubation tem-
perature (°C) and b is a fitted coefficient. Q10 is the factor by which the
measured process rate increases when the temperature increases by 10
°C.

We also – for the first time – used a polynomial multiple regression
model to estimate maintenance respiration rates of soil microbial
communities, which partitioned total heterotrophic respiration in
maintenance respiration and respiration linked to combined growth,
uptake, and transport. The regression model is based on the strong co-
variation of qCO2 and qGrowth within and across sites (bedrock x
management) and was used to extrapolate qCO2 at qGrowth=0 which
represents maintenance respiration. The model is based on the fol-
lowing equation:

= + + + +qCO a b qGrowth c T d T e2 2

in which qCO2 is the modeled microbial respiration rate normalized to
MBC content (mean and standard error) measured at 5, 15 and 25 °C in
the temperature experiment. qGrowth is the measured microbial growth
rate normalized to MBC content, and T is the incubation temperature.
Afterwards, a polynomial regression was fitted and the fitted coeffi-
cients a, b, c, d, e were obtained, where a is the intercept of the equation

and e is the effect of bedrock and management. When qGrowth is zero,
we can calculate microbial maintenance respiration (qCO2 maintenance)
for a specific temperature as:

= + + +qCO a c T d T emaintenance2 2

Here in this study, we calculated qCO maintenance2 at 5, 15 and 25 °C.
Error propagation was performed throughout all models by using

online tools (http://julianibus.de/physik/propagation-of-uncertainty),
to estimate the variance around the mean of qCO2 maintenance for each soil
type at the three set temperatures.

All statistical analyses were performed in R software version 3.4.2
(R Core Team, 2017). The data were transformed when necessary to
improve normality and homogeneity. Two-way ANOVA and Tukey-HSD
tests were applied to test for the effects of bedrock and land manage-
ment on soil physicochemical and biological properties as well as on the
microbial C processes, temperature sensitivities of processes and po-
tential enzyme activities. Student’s t-tests were applied to compare the
temperature sensitivity of microbial growth and of microbial respira-
tion. Regression analysis between soil physicochemical and biological
parameters, process rates and potential enzyme activities as well as
their temperature sensitivities were applied and expressed as Pearson
coefficient (R). Three-way ANOVA and Tukey-HSD tests were applied to
test for effects of temperature treatment, land use and bedrock in the
temperature incubation experiment, and for effects of soil moisture/O2
treatment, land use and bedrock in the soil moisture incubation ex-
periment. Variance partitioning was performed based on three-way
ANOVA results and was calculated as the fraction of the total sum of
squares (SS) explained by each specific factor and factor combination.
P < 0.05 was set as the threshold value for significance.

3. Results

3.1. The effects of soil physicochemical and biological properties on
microbial C metabolism

Soils pre-incubated at 15 °C were measured at 60% WHC. Silicate
soils exhibited lower pH values compared to calcareous soils, and
arable soils showed the highest pH (Table 1). Silicate soils showed re-
latively lower SOC but land use showed no effect on SOC. Higher DOC
contents were found in silicate soils and in forest soils. MBC was also
lower in silicate soils compared to calcareous soils, and highest MBC
was found in pasture soils. The potential activities of β-glucosidase and
phenoloxidase normalized to MBC (qBG and qPOX) were higher in si-
licate soils. Microbial CUE ranged between 0.26 and 0.66, and the
highest CUE was found in silicate and in pasture soils. Microbial CUE
decreased with increasing soil pH, base saturation, CECeff, and silt
content, and was positively affected by sand, DOC content, Cgrowth and
qGrowth (Table S3). In contrast, microbial CUE was not correlated with
respiration rates, PLFA-based microbial community metrics, MBC,

Table 1
Selected initial soil physicochemical and biological properties (0–15 cm soil depth, means ± 1SE, n= 4) measured at 15 °C, 60% WHC.

Soil SA SP SF LA LP LF P value

Bedrock Silicate Silicate Silicate Limestone Limestone Limestone M B M x B

Management Arable Pasture Forest Arable Pasture Forest

pH (water) 5.90 ± 0.37 5.38 ± 0.18 4.05 ± 0.10 8.15 ± 0.22 6.43 ± 0.19 6.13 ± 0.08 *** *** *
SOC (mg g−1) 21.8 ± 1.1 26.7 ± 0.9

.9 ± 7.6 47.0 ± 0.9 47.9 ± 7.6 36.8 ± 2.4 ns ** ***
DOC (μg g−1) 64.6 ± 3.6 85.6 ± 4.3 160.9 ± 13.1

.5 ± 1.8

.8 ± 12.6 53.4 ± 6.9 *** *** ***
MBC (μg g−1) 292 ± 19 647 ± 42 581 ± 80 1441 ±

1809 ± 336 890 ± 16 ** *** *
DNA (μg g−1) 17.2 ± 1.8 14.9 ± 3.4 22.8 ± 2.1 40.4 ± 2.0

.8 ± 8.4 40.3 ± 2.8 ns *** ns
fDNA 17.3 ± 1.3 60.2 ± 25.1 26.4 ± 4.8 35.7 ± 0.6 39.4 ± 12.5 22.4 ± 1.3 ns *** *
qβ-glucosidase (nmol (μg MBC)−1h−1) 0.94 ± 0.06 0.62 ± 0.04 0.33 ± 0.03 0.29 ± 0.02 0.34 ± 0.07 0.38 ± 0.05 ** *** ***
qPhenoloxidase (nmol (μg MBC)−1h−1) 8.37 ± 1.28 7.12 ± 0.39 16.72 ± 4.21 4.25 ± 0.47 6.42 ± 1.33 9.92 ± 0.59 ** * ns

M: management; B: bedrock; M x B: interaction of management and bedrock. Significance levels: ***, P < 0.001; **, P < 0.01; *, P < 0.05; ns, not significant.

Q. Zheng et al. Soil Biology and Biochemistry 128 (2019) 45–55
48

http://julianibus.de/physik/propagation-of-uncertainty

Fig. 1. Temperature effects on microbial respiration per unit MBC (metabolic quotient, qCO2; A), microbial growth rate per unit MBC (qGrowth; B), microbial CUE
(C) and microbial organic C uptake per unit MBC (qCuptake; D) in six soils from three land uses (A: arable; P: pasture and F: forest) on two bedrocks (S: silicate; L:
limestone) measured in the temperature incubation experiment. Error bars represent standard errors. The three different colors represent process rates obtained at
60%WHC at three temperatures: red, 5 °C; green, 15 °C; and blue, 25 °C. Main effects of single factors and their interactive effects are displayed for T: temperature; B:
bedrock; M: management. Significance levels: ***, P < 0.001; **, P < 0.01; *, P < 0.05; ns, not significant. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Table 2
Temperature sensitivities (Q10 values, 5–25 °C) of microbial C processes and extracellular enzyme activities (means ± 1SE, n=4).

Soil SA SP SF LA LP LF P value
Bedrock Silicate Silicate Silicate Limestone Limestone Limestone M B M x B
Management Arable Pasture Forest Arable Pasture Forest

Q10R 1.57 ± 0.14 2.39 ± 0.17 2.74 ± 0.35 1.41 ± 0.15 2.23 ± 0.33 2.27 ± 0.30 ** ns ns
Q10G 2.88 ± 0.28 3.14 ± 0.57 3.15 ± 0.29 3.05 ± 0.27 2.62 ± 0.19 2.05 ± 0.51 ns ns ns
Q10 CUE 1.69 ± 0.20 1.09 ± 0.08 1.19 ± 0.14 1.83 ± 0.11 1.24 ± 0.19 0.85 ± 0.18 *** ns ns
Q10 qCuptake 1.73 ± 0.13 2.83 ± 0.30 2.69 ± 0.25 1.70 ± 0.24 2.21 ± 0.23 2.34 ± 0.19 ** ns ns
Q10 qBG 1.81 ± 0.16 1.90 ± 0.04 2.13 ± 0.12 2.04 ± 0.02 2.10 ± 0.08 2.16 ± 0.21 ns ns ns
Q10 qPOX 1.10 ± 0.13 1.04 ± 0.04 1.15 ± 0.21 0.34 ± 0.08 0.97 ± 0.04 0.96 ± 0.08 * ** *

M: management; B: bedrock; M x B: interaction of management and bedrock. Significance levels: ***, P < 0.001; **, P < 0.01; *, P < 0.05; ns, not significant. Q. Zheng et al. Soil Biology and Biochemistry 128 (2019) 45–55 49

extractable nutrient contents and soil C: N: P stoichiometry. qCO2 de-
clined with MBC and Gram-positive bacteria, and was lowest in pasture
soils. Microbial growth normalized to MBC (qGrowth) increased with
SOC, soil C: P, DOC, and fungal: bacterial PLFA ratios, and decreased
with soil pH, base saturation, and was significantly affected by bedrock
and its interaction with management. Microbial biomass turnover times
were lower in silicate soils, decreased with sand and DOC content and
increased with soil pH, base saturation, CECeff, silt and nitrate content.
However, microbial communities with low turnover times i.e. fast
turnover and qGrowth had higher CUE, pointing to fast-growing and
active microbial communities having higher CUE than slow-growing
communities. Detailed information and statistical analyses of soil phy-
sicochemical and biological properties can be found in Tables S1–S3.

3.2. Short-term temperature effects on microbial C metabolism

For the temperature incubation experiment, the metabolic quotients
(qCO2) increased exponentially with increasing temperature in all soils
and differed significantly between soils at different temperatures
(Fig. 1A). The temperature sensitivity (Q10R) of qCO2 varied from 1.41
to 2.74 and was significantly different between land uses (Table 2),
with arable soils exhibiting the lowest temperature sensitivities (Q10R)
followed by pasture and forest soils (Table S2). Q10R decreased with soil
pH, base saturation, silt and TOP content (Table S3). Microbial growth
rates per unit MBC (qGrowth) also increased with temperature across
all tested soils (Fig. 1B). The temperature sensitivity of qGrowth (Q10G)
varied from 2.05 to 3.15 (Table 2), and was overall significantly higher
than Q10R (p < 0.01, Student's t-test) but was not affected by bedrock and management and not related to soil physicochemistry and micro- bial community composition. Temperature had a significant positive effect on microbial CUE, but the response of CUE to temperature changes was heterogeneous depending on land use (Fig. 1C). Arable soils consistently showed an increase in CUE with temperature (Q10 CUE= 1.69–1.83), pasture soils behaved neutral (Q10 CUE= 1.09–1.24) while forest soils showed divergent trends (Q10 CUE of 0.85 and 1.19). The temperature sensitivity of CUE (Q10 CUE) was strongly negatively related to Q10R (Table S3) but not related to soil physicochemical and microbial community parameters. qCuptake showed a strong increase

with temperature (Fig. 1D), with Q10 qCuptake values ranging between
1.70 and 2.83, with lowest values in arable soils (Table S2). We also
found a strong positive correlation between CUE and qGrowth
(Fig. 2A), but found no correlation between CUE and qCO2 (Fig. 2B). To
test for the effect of autocorrelation underlying the above relationship
we generated 100 random numbers for qGrowth (range from 0.2 to 4)
and qCO2 (range from 0.2 to 4), and calculated CUE as qGrowth/
(qGrowth + qCO2). In this case, CUE was negatively correlated to qCO2
and positively correlated to qGrowth. Here, in our study, CUE was not
correlated to qCO2 but positively correlated to qGrowth, highlighting
that the relationship was not caused by autocorrelation. Based on three-
way ANOVA analysis, approximately 54% of the variation in qCO2,
57% of the variation in qGrowth and 63% of the variation in qCuptake,
but only 8% of the variation in CUE could be directly accounted for by
temperature (Fig. 3A). Additionally, microbial biomass turnover time
decreased from an average of 325 days to 41 days when temperature
increased from 5 to 25 °C (Fig. S1A). The β-glucosidase activities nor-
malized to MBC (qBG) increased from 5 to 25 °C (Fig. S1B) with average
Q10 qBG values of 1.81–2.16 (Table 2). Phenoloxidase activities nor-
malized to MBC (qPOX) did not respond to temperature, with Q10 qPOX
values ranging between 0.96 and 1.15 (0.34 in arable soils on lime-
stone). Moreover, Q10 qPOX was strongly influenced by land use and
bedrock. The modeled maintenance respiration rates (qCO2 maintenance)
were found to be significantly higher at 25 °C compared to 5 °C, and
were significantly higher in arable soils compared to forest and pasture
soils (Fig. S2).

3.3. Short-term moisture/O2 effects on microbial C metabolism

In terms of the soil moisture/O2 incubation experiment, approxi-
mately 52% of the variation in qGrowth and in microbial biomass
turnover time, 39% of the variation in CUE and 35% variation in
qCuptake could be directly explained by soil moisture/O2 (Fig. 3B) based
on a three-way ANOVA analysis. The metabolic quotient (qCO2) varied
between soils and soil moisture treatments, but was not directly af-
fected by treatment at 30, 60 and 90% WHC (Fig. 4A). Microbial
growth rates per unit MBC (qGrowth) did not change between soils pre-
incubated at low and intermediate moisture level (30 and 60% WHC),

Fig. 2. Correlation between microbial CUE and microbial growth rate per unit MBC (qGrowth, A), and between CUE and microbial respiration per unit MBC
(metabolic quotient, qCO2, B) in the temperature experiment.

Q. Zheng et al. Soil Biology and Biochemistry 128 (2019) 45–55
50

but strongly decreased at high soil moisture level (90% WHC) under
suboxic (1% O2) conditions compared to ambient O2 conditions (21%
O2) (Fig. 4B). Microbial CUE and qCuptake followed the pattern of
qGrowth in that a significant decrease was observed at suboxic condi-
tions at high soil moisture level (Fig. 4C and D). Microbial biomass
turnover times across all tested soils increased from an average of 15
days at low and intermediate moisture level to 66 days at high moisture
level (Fig. S4A).

4. Discussion

Our study revealed that the response of microbial CUE to short-term
environmental changes more strongly depended on changes in micro-
bial growth than on changes in microbial respiration.

4.1. Effects of short-term temperature changes on soil microbial C
metabolism

Here by comparing microbial CUE of soils from three land uses and
two bedrocks, we found a general increase, though a heterogeneous
pattern, in microbial CUE in response to elevated temperature (Fig. 1C),
which was in contrast to our expectation that soil microbial CUE would
decrease with increasing temperature. Temperature was reported to
exert negative or negligible effects on microbial growth and CUE
(Dijkstra et al., 2011; Tucker et al., 2013; Hagerty et al., 2014), but
these studies did not directly measure actual microbial growth, and it
was already previously shown that methods based on 13C-substrate
addition can only estimate microbial substrate use efficiency (SUE)
rather than microbial CUE (Sinsabaugh et al., 2013), which might be
one of the reasons that we obtained contrasting results compared to
these previous studies. 13C-glucose taken up by microbes is not ne-
cessarily allocated to growth or respiration, but can be intermittently
stored without being metabolized (Hill et al., 2008). We also found that
microbial CUE increased in both arable soils, while pasture soils be-
haved neutral and microbial CUE behaved divergent in forest soils

when temperature increased. Therefore, the response of microbial CUE
to temperature (Q10 CUE) varied depending on land management, but
was not affected by bedrock and its interaction with land use (Table 2).
The overall effect of temperature on microbial CUE both via direct and
indirect pathways therefore was relatively weak.

After 1 day pre-incubation, we found that the metabolic quotient
(qCO2) of all tested soils both increased exponentially with tempera-
ture, in accordance with another study showing the same trend in qCO2
in the temperature range from 5 up to 35 °C (Xu et al., 2006). Increased
qCO2 at higher temperatures could partly be attributed to the increased
maintenance respiration rate per biomass, since elevated temperatures
could induce higher maintenance energy costs in microbes (Alvarez
et al., 1995), as shown for the first time in soils in this study (Fig. S2)
using our polynomial multiple regression model. Previous studies have
evaluated microbial maintenance energy by adding specific substrates
(e.g. glucose) and quantifying the amount of C needed to prevent mi-
crobial C loss during incubation (Anderson and Domsch, 1985a,
1985b). By using the substrate addition approach, the maintenance
energy demand of dormant soil microbial communities ranged between
0.03 and 0.3mg glucose C (g MBC)−1 h−1 (Anderson and Domsch,
1985b; Cheng, 2009), but between 2.8 and 14.4mg glucose C (g
MBC)−1 h−1 in pure cultures (Tännler et al., 2008; Vos et al., 2016).
Here by using the 18O method, without adding organic substrates to
soils, the mean maintenance qCO2 was 1.2mg C (g MBC)−1 h−1, which
is higher than found for dormant soil microbial communities but much
lower than for the pure microbial cultures that were assessed by the
substrate addition approach. The metabolic quotient (qCO2) is con-
sidered as a proxy of soil microbial community CUE in response to
short-term environmental change (Anderson and Domsch, 1993). Spe-
cifically, high qCO2 values have been used as a proxy for low microbial
CUE, since more energy is demanded for microbial biomass main-
tenance (Cheng et al., 2013), which would result in less substrate C
available for growth at a higher qCO2 and result in a trade-off between
microbial growth and CUE (Lipson, 2015; Lipson et al., 2009; Pfeiffer
et al., 2001). However, the obvious problem with this approach is that

Fig. 3. Total variation of microbial C metabolism i.e. microbial respiration per unit MBC (metabolic quotient, qCO2), microbial growth rate per unit MBC (qGrowth),
microbial CUE and microbial organic C uptake per unit MBC (qCuptake) explained in three-way ANOVAs by different environmental factors and their interactive
effects in the temperature incubation experiment (A) and in the moisture/O2 incubation experiment (B). T: temperature; B: bedrock; M: management; W: moisture.
Significance levels: ***, P < 0.001; **, P < 0.01; *, P < 0.05; ns, not significant.

Q. Zheng et al. Soil Biology and Biochemistry 128 (2019) 45–55
51

it can only be applied, if constant microbial C uptake is assumed, which
is unlikely at higher temperatures. We here clearly show that microbial
CUE was more strongly positively related to qGrowth (Fig. 2A) across
all temperatures, which means that we did not find a trade-off i.e. a
negative relationship between growth and yield (CUE) in this study.
This shows that slow-growing microbial communities had low CUE due
to high maintenance energy demand while fast-growing communities
had conversely high CUE (Lipson, 2015). Energy spilling or overflow
metabolism is typically high when organic C is in excess or nutrients are
strongly limiting (Russell and Cook, 1995), which did not happen here,
but – if present – might cause a trade-off between microbial growth,
respiration and CUE (Lipson, 2015). Therefore, here anabolism and
catabolism were not decoupled, and microbial growth increased while
contributions of maintenance respiration to C uptake decreased,
causing increases in microbial CUE. We also showed that microbial CUE
was not related to qCO2 (Fig. 2B), therefore qCO2 does not represent a
valid surrogate of the partitioning between microbial anabolic and

catabolic processes and for microbial CUE.
Microbial growth rates were reported to increase exponentially or

following a quadratic term with temperature in the studies available
(Díaz-Raviña et al., 1994; Pietikäinen et al., 2005; Sinsabaugh et al.,
2016). We also found that microbial growth rates per unit biomass
(qGrowth) increased with incubation temperature within the range
from 5 to 25 °C (Fig. 1B), which agrees with increasing soil fungal and/
or bacterial growth rates from 0 to around 25 °C (Díaz-Raviña et al.,
1994; Pietikäinen et al., 2005; Bárcenas-Moreno et al., 2009; Rinnan
et al., 2009). Elevated temperature could influence microbial growth by
affecting substrate availability (Shiah et al., 2000). For example, labile
substrates were shown to deplete under long-term warming conditions,
whereas it did not happen in our short-term incubation experiment, and
we did not find DOC concentrations to decrease during the pre-in-
cubation period (Fig. S5). As qGrowth increased with temperature, the
average turnover time of microbial biomass (which is inversely pro-
portional to qGrowth) across all tested soils decreased from a mean of

Fig. 4. Soil moisture/O2 effects on microbial respiration per unit MBC (metabolic quotient, qCO2; A), microbial growth rate per unit MBC (qGrowth; B), microbial
CUE (C) and microbial C uptake per unit MBC (qCuptake; D) in six soils from three land uses (A: arable; P: pasture and F: forest) on two bedrocks (S: silicate; L:
limestone) measured in the moisture/O2 incubation experiment. Error bars represent standard errors. The three different colors represent process rates obtained at
20 °C, at three pre-incubation moisture levels: red, low moisture level (30% WHC, 21% O2); green, intermediate moisture level (60% WHC, 21% O2); and blue, high
moisture level (90% WHC, 1% O2). Main effects of single factors and their interactive effects are displayed for W: moisture; B: bedrock; M: management. Significance
levels: ***, P < 0.001; **, P < 0.01; *, P < 0.05; ns, not significant. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Q. Zheng et al. Soil Biology and Biochemistry 128 (2019) 45–55
52

325 days to 41 days (Fig. S1A), and turnover times were in a similar
range as reported in pasture (197–322 days) and in forest soils (33–140
days) measured at 15 °C (Spohn et al., 2016). These declines in mi-
crobial biomass turnover time with temperature were due to faster
microbial growth rates under steady-state conditions where microbial
biomass did not change.

To what extent microbial respiration and growth influenced mi-
crobial CUE in the short-term depended on their respective responses to
elevated temperature, as expressed by their Q10 values. The tempera-
ture sensitivity of soil microbial respiration (Q10R) is regarded as an
important biogeochemical model parameter that reflects the feedback
between soil C cycling and global warming (Luo, 2007). The average
values of Q10R for the temperature range of 5–25 °C measured here
ranged from 1.41 to 2.74 (Table 2), which were in a similar range as
reported for global patterns of Q10R between 1.3 and 3.3 (Raich and
Schlesinger, 1992). In this study, we observed significantly lower Q10R
in arable soils than in pasture and forest soils (Table S2). This might be
attributed to lower substrate availabilities that are typically found in
arable soils, which can depress the temperature sensitivity of microbial
respiration due to lower Q10 of diffusion-limited than of enzyme-limited
processes (Blagodatskaya et al., 2016; Steinweg et al., 2012). In con-
trast, here we found higher average Q10 values of potential β-glucosi-
dase activities normalized to MBC (Q10 qBG) than of Q10R in both arable
soils (Table 2). The data therefore indicate that respiration (and
growth) of soil microbial communities was rather substrate- or diffu-
sion-limited than enzyme-limited. The temperature sensitivity of mi-
crobial growth or growth per unit biomass (Q10G) has rarely been tested
in soils, the studies typically showing greater Q10G values at lower
temperatures (0–10, 5–15 °C) than at higher temperatures (10–20,
15–25 °C) (Rinnan et al., 2009; Rousk and Bååth, 2011). Here, the
average values of Q10G ranged between 2.05 and 3.15 (Table 2), which
agrees with published Q10G values obtained at similar temperatures in
previous studies, ranging between 1.9 and 2.6 (Rinnan et al., 2009),
2.2–3.0 (Díaz-Raviña et al., 1994) and 2.1–2.3 (Birgander et al., 2013).
More importantly, we found that Q10G values were generally higher
than Q10R values across all tested soils (p < 0.01), except for the limestone forest soil that exhibiting similar Q10G and Q10R values (Table 2). Therefore, the pattern of Q10G > Q10R in most soils explains
the widespread increase in microbial CUE with increasing temperature
in the soils studied here. Therefore, in this study, microbial growth was
more sensitive to short-term temperature changes than respiration and
thus growth played a more important role in determining microbial
CUE responses to short-term temperature changes.

4.2. Effects of short-term soil moisture/O2 changes on soil microbial C
metabolism

In contrast to the less than 1-day pre-incubation period in the
warming experiment, in the moisture/O2 experiment soils were pre-
conditioned for seven days, which might involve changes in microbial
community structure accompanying changes in their physiology.
However, we here were only interested in the effects such changes have
on microbial community metabolism, independent of changes in their
structure and composition. Soil moisture and O2 are supposed to affect
microbial CUE as the change in water and O2 availability may alter the
balance between microbial growth and maintenance (Manzoni et al.,
2012). However, the impact of soil moisture/O2 on microbial CUE has
never been empirically tested in soils, and thus we have a limited un-
derstanding of how short-term changes in soil moisture/O2 affect mi-
crobial physiology. Here we found a decrease in microbial CUE at high
moisture level (90% WHC)/suboxic conditions that was accompanied
by almost constant qCO2 but strong declines in qGrowth (Fig. 4A and B
& C), which contradicted our expectation. The observed significant ef-
fects on microbial CUE across all soil types indicate that short-term
increases in soil moisture combined with decreases in O2 availability
have a profound impact on microbial CUE, especially when soil water

contents were close to saturation. Given that we did not find a con-
sistent positive effect of increasing soil moisture from low to inter-
mediate soil moisture level at 21% O2 but a dramatic decline in mi-
crobial CUE towards high moisture level under suboxic conditions, this
strongly points towards declining soil O2 having the greater effect than
increasing soil moisture in this experiment. Only in lake sediments
decreased microbial growth efficiency (=microbial CUE) was found
under anoxic compared to oxic conditions, i.e. 0.19 and 0.48 respec-
tively, triggered by stronger declines in bacterial growth (5.0-fold) than
in bacterial respiration (1.3-fold; Bastviken et al., 2003).

Soil water availability is suggested to influence microbial respira-
tion by limiting substrate diffusivity at low moisture content and the
diffusion of O2 at high moisture content (Davidson et al., 2006; Rubol
et al., 2013). However, in this study, we observed no significant in-
fluence of soil moisture on microbial respiration, even under suboxic
conditions (Fig. 4A). We expected microbial respiration under water-
saturated/suboxic conditions to be limited mainly by low O2 avail-
ability, as we expected that high water content would not limit sub-
strate diffusivity but rather enhance it. At low O2, however, we found
that qCO2 of soils from all sites did not exhibit significant differences
relative to the results obtained at 21% O2 concentration at low and
intermediate moisture level. Under O2 limitation soil microbes shift
from aerobic respiration (O2 as the terminal electron acceptor) to
anaerobic respiration (alternative electron acceptors, such as nitrate,
Mn4+, Fe3+, SO42− or CO2) and finally to fermentation, with strong
declines in the energy yield (32mol to about 2mol of ATP per mole of
glucose) of microbial catabolism (Boyd, 1995; Lipson et al., 2009;
Pfeiffer et al., 2001). Less energy gain at the same catabolic rate for
anaerobic respiration might cause an adjustment (upregulation) in
biochemical rates, keeping CO2 production constant, but at the expense
of microbial growth and eventually CUE, which both declined here
within the 7-day pre-incubation period. The shift from aerobic to
anaerobic respiration was further accompanied by the disappearance of
nitrate due to dissimilatory nitrate reduction processes coupled to SOC
decomposition (Fig. S6C) while methanogenic decomposition pathways
did not substantially contribute (data not shown).

The strong decrease in qGrowth under suboxic conditions relative to
the oxic treatments (Fig. 4B) was not due to a decrease in substrate
availability, since the amount of available DOC did not decline during
the 7-day pre-incubation period (Fig. S6A) and high water content
should also lead to increased substrate diffusivity and access. The data
therefore highlight that C mineralization is uncoupled from energy
yield while declines in free energy yield in anaerobic metabolism affects
energy (and C) allocation in microbial metabolism, causing the strong
declines in microbial growth as found here as well as in lake sediments
(Bastviken et al., 2003). With the strong decrease in qGrowth, we also
found that microbial biomass turnover times increased from an average
value of 15 days at 21% O2 to 66 days at 1% O2 (Fig. S4A), implying a
general slow-down of microbial C metabolism and of SOC turnover.
Given the negligible moisture/O2 effect on microbial respiration but the
significant negative impact on microbial growth, this translated to
marked declines in microbial C uptake and microbial CUE under sub-
oxic conditions. Under anoxia exudation of fermentation end products
such as short chain fatty acids would need to be accounted for in the
calculation of C uptake (growth, respiration plus exudation) (Manzoni
et al., 2012) alongside decreases in energy yield of anoxic metabolism
(Lipson et al., 2009; Pfeiffer et al., 2001). This would further decrease
CUE relative to the current estimates; however, we did not find evi-
dence for high rates of fermentation in the suboxic treatments (low CH4
and organic acid release, data not shown). Therefore, similar to the
short-term temperature effects on microbial CUE, the influence of short-
term soil moisture/O2 fluctuations on microbial CUE mainly depended
on the responses of microbial growth rather than of microbial respira-
tion. However, we need to highlight that in this experiment we could
not separate moisture and oxygen effects but studied their interactive
effect on microbial physiology.

Q. Zheng et al. Soil Biology and Biochemistry 128 (2019) 45–55
53

4.3. Other controls on microbial C metabolism

While there is a dearth of information on controls of soil hetero-
trophic respiration and qCO2, including temperature, moisture/O2 ef-
fects, microbial community structure and soil physicochemistry
(Graham et al., 2016; Xu et al., 2017; Zhou et al., 2016), much less is
known on controls of microbial growth (Rousk and Bååth, 2011) and
microbial CUE in soils (Manzoni et al., 2012). Beyond soil temperature
and moisture/O2 controls on soil microbial metabolism as demon-
strated in this study, microbial growth and microbial CUE are also
predicted to be influenced by factors such as substrate concentration
and quality, soil pH, nutrient limitation, or microbial community
composition (Manzoni et al., 2012). Bedrock explained 33% of the
variation in microbial CUE (Table S2), and calcareous soils exhibited
lower microbial CUE than silicate soils (Fig. 1C), causing a negative
relationship between soil pH and microbial CUE. Soil pH was also a
major regulator of bacterial and fungal growth in soils, where fungal
growth decreased and bacterial growth strongly increased above pH 5
(Rousk et al., 2011). This might be due to pH effects on substrate and
nutrient availability (Aciego Pietri and Brookes, 2008; Filep et al.,
2003) or on microbial community composition (Lauber et al., 2009).
Here, we observed a strong positive correlation between DOC and CUE
(Table S3), caused by strong increases in qGrowth when DOC increased
while respiration was non-responsive. Land management explained
another 18% of the variation in CUE, likely caused by enhanced nu-
trient inputs in the intensively managed land. However, extractable as
well as total nutrient contents did not explain a significant fraction of
the variability in microbial growth and CUE. Substrate availability and
elemental stoichiometry may affect microbial CUE by impacting mi-
crobial growth and respiration, as soil microbial biomass stoichiometry
may force microbial communities to adapt their foraging strategies to
accessible substrates (Sinsabaugh et al., 2013). Again we did not find
strong controls of soil C: N: P stoichiometry on microbial CUE and only
C: P was positively related to microbial growth. Additionally, it has
been suggested that fungi have a higher CUE than bacteria (Keiblinger
et al., 2010), which suggests soil microbial community composition
may impact microbial community CUE. Neither fungal or bacterial
PLFA biomarkers, nor microbial biomass were related to microbial CUE
but the fungal: bacterial PLFA ratio was positively related to microbial
growth, partially corroborating the findings cited above. Finally, soil
texture influenced microbial CUE but not growth and respiration. For
example, silt content was strongly negatively and sand strongly posi-
tively correlated to microbial CUE (Table S3), which might be due to
stronger sorption of organic matter on finer soil particle size fractions,
further enhanced by high base saturation in calcareous soils (Rowley
et al., 2018).

5. Conclusions

We here, for the first time, show that the response of microbial CUE
(and microbial biomass turnover time) to short-term environmental
changes and to soil physicochemical properties, are mainly determined
by microbial growth, and less by respiration. Therefore, our data in-
dicate that anabolic processes (microbial growth) play a more im-
portant role than catabolic processes (respiration) in promoting soil C
storage, as recently suggested (Liang et al., 2017). Promoting soil C
sequestration depends on an increase in microbial CUE (Sinsabaugh
et al., 2017) and/or a decrease in microbial biomass turnover time
(Hagerty et al., 2014). Here we found a strong positive relationship
between microbial growth and microbial CUE and a negative relation to
microbial turnover time. Therefore, factors enhancing microbial growth
are likely to decrease microbial turnover time, fostering microbial ne-
cromass production and soil C sequestration, and at the same time come
along with increases in microbial CUE which may also stimulate soil C
sequestration. However, such microbial physiological changes in the
short-term can be modified in the long-term and at the ecosystem level,

due to changes in labile plant C inputs relative to decomposition, due to
changes in labile C availability or due to adjustment in microbial
community structure and physiology to the external driver.

Conflicts of interest

The authors declare no conflict of interest.

Acknowledgments

This work was supported by the Austrian Science Fund (FWF; pro-
ject P-28037-B22).

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://
doi.org/10.1016/j.soilbio.2018.10.006.

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Growth explains microbial carbon use efficiency across soils differing in land use and geology

Introduction
Materials and methods
Site characteristics and soil sampling
Sample preparation
Soil properties
Determination of soil microbial C metabolism and soil enzymes
Calculations and statistical analyses
Results
The effects of soil physicochemical and biological properties on microbial C metabolism
Short-term temperature effects on microbial C metabolism
Short-term moisture/O2 effects on microbial C metabolism
Discussion
Effects of short-term temperature changes on soil microbial C metabolism
Effects of short-term soil moisture/O2 changes on soil microbial C metabolism
Other controls on microbial C metabolism
Conclusions
Conflicts of interest
Acknowledgments
Supplementary data
References

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Arch Bronconeumol. 2020;xxx(xx):xxx–xxx

www.archbronconeumol .org

riginal Article

he Roles of Bacteria and Viruses in Bronchiectasis Exacerbation:
Prospective Study

hun-Lan Chena,b, Yan Huanga, Jing-Jing Yuana, Hui-Min Lia, Xiao-Rong Hana,
iguel Angel Martinez-Garciac, David de la Rosa-Carrillod, Rong-chang Chene,
ei-Jie Guana,∗, Nan-Shan Zhonga,∗

State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital
f Guangzhou Medical University, Guangzhou, China
Guangdong General Hospital (Guangdong Academy of Medical Sciences), Guangzhou, China
Pneumology Department, University and Politechnic La Fe Hospital, Valencia, Spain
Pulmonology Service, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
Shenzhen People’s Hospital, Shenzhen, China

r t i c l e i n f o

rticle history:
eceived 12 September 2019
ccepted 9 December 2019
vailable online xxx

eywords:
ronchiectasis
cute exacerbation
acteria
irus

a b s t r a c

Background: Exacerbations are crucial events during bronchiectasis progression.
Objectives: To explore the associations between bacterial, viral, and bacterial plus viral isolations and
bronchiectasis exacerbations.
Methods: In this prospective study, we enrolled 108 patients who were followed up every 3–6 months and
at onset of exacerbations between March 2017 and November 2018. Spontaneous sputum was split for
detection of bacteria (routine culture) and viruses (quantitative polymerase chain reaction). Symptoms
and lung function were assessed during exacerbations.
Results: The median exacerbation rate was 2.0 (interquartile range: 1.0–2.5) per patient-year. At any visit,
viral isolations (V+) occurred more frequently during onset of exacerbations [odds ratio (OR): 3.28, 95%
confidence interval (95%CI): 1.76–6.12], as did isolation of new bacteria (NB+) (OR: 2.52, 95%CI: 1.35–4.71)
and bacterial plus viral isolations (OR: 2.24, 95%CI: 1.11–4.55). Whilst coryza appeared more common
in exacerbations with V+ than in exacerbations with no pathogen isolations and those with NB+, lower
airway symptoms were more severe in exacerbations with NB+ (P < .05). Sputum interleukin-1� levels were higher in exacerbations with NB+ than in exacerbations with no pathogen isolations and those with V+ (both P < .05). Significantly more coryza symptoms correlated with bacterial plus viral isolations at exacerbations (P = .019). Compared with V+ alone, bacterial with and without viral isolations tended to yield more severe lower airway symptoms, but not sputum cytokine levels at exacerbations. Conclusions: Viral isolations, isolation of new bacteria and bacterial plus viral isolation are associated with bronchiectasis exacerbations. Symptoms at exacerbations might inform clinicians the possible culprit pathogens.

El papel de las bacterias y los virus en la exacerbación de bronquiectasia: un
estudio prospectivo

r e s u m e n

Please cite this article in press as: Chen C-L, et al.

The Roles of Bacteria and Viruses in Bronchiectasis Exacerbation: A Prospective Study

.
Arch Bronconeumol. 2020. https://doi.org/10.1016/j.arbres.2019.12.010

alabras clave:
ronquiectasias
xacerbación aguda
acterias
irus

Contexto: Las exacerbaciones son eventos cruciales durante la progresión de la bronquiectasia.
Objetivos: Analizar las asociaciones entre el aislamiento de bacterias, virus y virus y bacterias juntas y las
exacerbaciones de las bronquiectasias.
Métodos: En este estudio prospectivo se incluyó a 108 pacientes a los que se siguió cada 3-6 meses y
al comienzo de las exacerbaciones entre marzo de 2017 y noviembre de 2018. La muestra de esputo
espontáneo se dividió para la detección de bacterias (cultivo de rutina) y virus (reacción en cadena de la
polimerasa cuantitativa). Se evaluaron los síntomas y la función pulmonar durante las exacerbaciones.∗ Corresponding author.

E-mail addresses: battery203@163.com (W.-J. Guan), nanshan@vip.163.com (N.-
. Zhong).

https://doi.org/10.1016/j.arbres.2019.12.010
300-2896/© 2019 Published by Elsevier España, S.L.U. on behalf of SEPAR.

https://doi.org/10.1016/j.arbres.2019.12.010

http://www.archbronconeumol.org

mailto:battery203@163.com

mailto:nanshan@vip.163.com

https://doi.org/10.1016/j.arbres.2019.12.010

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2 C.-L. Chen et al. / Arch Bronconeumol. 2020;xxx(xx):xxx–xxx

Resultados: La mediana de la tasa de exacerbación fue de 2,0 (rango intercuartil: 1,0-2,5) por paciente/año.
En cualquier visita, los aislamientos de virus (V+) tuvieron lugar con mayor frecuencia durante el inicio de
las exacerbaciones (odds ratio [OR]: 3,28; intervalo de confianza del 95% [IC 95%]: 1,76-6,12), al igual que
el aislamiento de nuevas bacterias (NB+) (OR: 2,52; IC 95%: 1,35-4,71) y los aislamientos de bacterias y
virus juntos (OR: 2,24; IC 95%: 1,11-4,55). Mientras que la coriza parecía más común en las exacerbaciones
con V+ que en las exacerbaciones sin aislamientos de patógenos y en aquellas con NB+, los síntomas de
las vías respiratorias inferiores fueron más graves en las exacerbaciones con NB+ (p < 0,05). Los niveles de interleucina-1� en el esputo fueron más altos en las exacerbaciones con NB+ que en las exacerbaciones sin aislamiento de patógenos, y aquellas con V+ (ambos p < 0,05). De manera significativa, más síntomas de coriza se correlacionaron con aislamientos de bacterias y virus juntos durante las exacerbaciones (p = 0,019). Comparados con los V+ en solitario, los aislamientos de bacterias con y sin virus tienden a producir síntomas más graves en las vías respiratorias inferiores, pero no alteran los niveles de citocinas en el esputo durante las exacerbaciones. Conclusiones: Los aislamientos de virus, el aislamiento de nuevas bacterias y el aislamiento de bacterias y virus juntos están asociados a las exacerbaciones de las bronquiectasias. Los síntomas de las exac- erbaciones pueden proporcionar información a los médicos sobre los posibles patógenos responsables.

d
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ntroduction

Bronchiectasis is a debilitating chronic airway inflammatory
isease aggravated by bacterial and/or viral infections.1–4 Acute
xacerbations (AEs) are critical events associated with a con-
iderable morbidity and mortality,5 contributing to significantly
mpaired quality-of-life.6–8 Understanding the roles of pathogens

ay help diagnose and identify targets for interventions.
Infections are frequently associated with AEs. The dilated

ronchi become the niche for bacteria, viruses and fungi.9–1

iruses have frequently been isolated during AEs in bronchiectasis
detection rate: 30%–50%).11,12 No significant changes in total bac-
erial density and microbial compositions were observed during
Es.13 Nevertheless, antibiotics remain the principal effective man-
gement for AEs, suggesting that bacterial isolations might have
ggravated the inflammatory responses.14,15

Accumulating evidence has demonstrated the interactions
etween pathogenic bacteria, viruses, and host-defense in chronic
irway inflammatory diseases.16 Bacterial plus viruses (e.g. non-
ypeable Haemophilus influenzae with rhinovirus isolation) were

ore frequently detected during chronic obstructive pulmonary
isease (COPD) exacerbations than stable state, and correlated
ith more severe COPD exacerbations.17–19 The roles of bacteria

nd viruses in bronchiectasis have been reported separately. No
rospective study has investigated the impacts of bacterial plus
iral isolations in adults with bronchiectasis. Moreover, symptoms
hat could differentiate bacterial from viral or bacterial plus viral
solation during exacerbations are not entirely clear.

We aimed to explore the associations between bacterial and
iral isolations and AEs, and further investigate the clinical char-
cteristics which could indicate the possible pathogen isolations
uring AEs.

ethods

tudy Population

In this observational single-center prospective study, we
ecruited bronchiectasis patients aged 18–75 years from out-
atient clinics of The First Affiliated Hospital of Guangzhou Medical

Please cite this article in press as: Chen C-L, et al. The Roles of Bacteria
Arch Bronconeumol. 2020. https://doi.org/10.1016/j.arbres.2019.12.0

niversity between March 2017 and November 2018. Bronchiec-
asis was diagnosed according to chest high-resolution computed
omography (reviewed by an experienced radiologist) with com-
atible clinical symptoms (e.g. chronic cough, sputum production).

Eligible patients remained clinically stable (respiratory symptoms
not exceeding normal daily variations), and had no use of antibiotics
(except for low-dose macrolides) for four weeks. Active tubercu-
losis, malignancy, acute respiratory tract infections within four
weeks and asthma or COPD as the primary diagnosis were excluded.
The study protocol was approved by The Ethics Committee of The
First Affiliated Hospital of Guangzhou Medical University (Medical
Ethics 2012, the 29th). All patients signed informed consent.

Study Design and Clinical Assessment

At initial visits, clinical evaluations included demography, clini-
cal history, spirometry and exacerbation rate within the preceding
12 months. Blood and sputum were collected. Spirometry was per-
formed according to international guidelines.20 Radiologic severity
was assessed with modified Reiff score.21 Disease severity was
calculated with bronchiectasis severity index (BSI)8 and E-FACE

score.22 Patients were followed up at 3–6-month intervals until
November 2018 (multiple visits), and were requested to contact
investigators upon significant worsening of symptoms for an addi-
tional visit, scheduled within 48 h (antibiotic use, if any, did not
exceed 24 h). The upper limit of duration from symptom onset was
7 days (5 days after confirming symptom onset) for AE visits. Symp-
tom questionnaire (see Online Supplement) which queried upper
and lower airway symptoms [rating the severity with visual analog
scale (VAS, range: 0–10)], spirometry, sputum and blood specimens
were obtained during each follow-up, including stable visits and AE
visits.

AEs were defined as significant deterioration (>48 h) of
≥3 symptoms, including cough frequency, sputum volume and/or
consistency, sputum purulence, breathlessness and/or exercise
tolerance, fatigue and/or malaise, hemoptysis, which required
immediate changes in treatment.23 Treatment decisions were
made before all testing results became available.

Sputum Collection and Processing

Details are shown in Online Supplement. Briefly, patients thor-
oughly rinsed their mouth, followed by deep cough for collecting
spontaneous sputum. Sputum plugs (the most purulent portion)

and Viruses in Bronchiectasis Exacerbation: A Prospective Study.
10

were selected from eligible samples (leukocytes/epithelial cells
>2.5:1).10,11 No uniform techniques of chest physiotherapy was
employed. Sputum was immediately split for bacterial culture,
viral detection with multiplex quantitative polymerase chain reac-

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of pat

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Fig. 1. Flow chart

ion (qPCR), and ultracentrifugation (20,000 × g) for 2 h at 4 ◦C
or sputum sol preparation and storage at −80 ◦C for inflamma-
ory biomarkers (interleukin-1�, CXC motif chemokine-8, tumor
ecrosis factor-� and interferon-�) multiplex assays as described
reviously.10,11

acterial and Viral Detection

We did bacterial culture by homogenizing fresh sputum with
PUTASOL (Oxoid SR089A, UK), followed by inoculation in blood
nd chocolate agar plates (Biomeurix Inc., France) for overnight
ncubation.11 Pathogenic bacteria included, but not limited to,
seudomonas aeruginosa, Haemophilus influenzae, Haemophilus
arainfluenzae, Klebsiella pneumoniae, Streptococcus pneumoniae,
treptococcus aureus and Escherichia coli.10 Isolation of new bac-
eria denoted sputum culture findings switching from negative to
ositive, or from any pathogenic bacterium to another pathogenic
acterium.

We extracted viral nucleic acids using extraction kit (TaKaRa
iniBEST Viral RNA/DNA Extraction Kit Ver. 5.0). We conducted

PCR based on TaqManTM probes to identify sixteen common respi-
atory viruses: rhinovirus, influenza virus A/B, parainfluenza virus
–4, human coronavirus (HCoV-229E, OC43, NL63 and HKU1), res-
iratory syncytial virus, adenovirus, enterovirus, bocavirus and
uman metapneumovirus. Validated viral detection kits were pur-
hased from Guangzhou HuYanSuo Medical Technology Co., Ltd.,
uangzhou, China.11,24 The cycle threshold (Ct) of <40 was consid- red positive. Lower Ct indicated higher viral loads.

tatistical Analysis

No data exist regarding the proportion of patients with bacterial
lus viral isolations in bronchiectasis. Assuming an equivalent pro-
ortion of patients with bacterial isolation during stable-states and
Es, and the difference of 20% in virus detection rate between AEs
nd stable-states,11 107 bronchiectasis patients would be needed
ased on the two-sided significance of 0.05 and power of 80%, tak-

ng into account a 25% drop-out rate.
Data were expressed as mean ± standard deviation or median

Please cite this article in press as: Chen C-L, et al. The Roles of Bacteria
Arch Bronconeumol. 2020. https://doi.org/10.1016/j.arbres.2019.12.0

interquartile range, IQR) for continuous variables, and count
percentage) for categorical variables. Generalized estimating
quations with logit link were used to explore the association
etween pathogen isolation and the odds of AEs compared with

ient recruitment.

stable visits, taking into account repeated observations in indi-
vidual participants. Continuous variables were analyzed with
t-test, analysis-of-variance, Mann–Whitney or Kruskal–Wallis test
depending on the variable distribution. Categorical variables were
compared with Chi-square or Fisher’s exact test. Missing values
were not imputed. Statistical analysis was performed using SPSS
18.0 (SPSS Inc., Chicago, USA) and Graphpad Prism version 5.0
(Graphpad Inc., USA).

Results

Recruitment and Clinical Characteristics

Of 130 patients screened, 108 patients were enrolled and 98
were followed-up (Fig. 1). The median follow-up duration was
13.0 months. The 108 patients provided 375 sputum samples
(299 for stable-visits; 76 for AEs), with a median (IQR) of 3.0
(2.0–4.0) sputum specimens per patient. Seventy-three patients
(74.5%) experienced at least one AE, and reported 169 AEs during
follow-up (76 AEs sampled because 63.2% contacted too late, 21.1%
declined due to no availability, 10.5% administered antibiotics for
>2 days, and 5.2% yielded no sputum). Sputum was mostly sampled
before antibiotic administration during AEs except that 2 samples
were sampled within 24 h of antibiotic administration. The clinical
characteristics did not differ between patients who did and did not
provide sputum during AEs (Table E1).

Patient characteristics of the full and AE cohort are shown in
Table 1. The mean age was 46.8 years. The median BSI was 7.0 (IQR:
4–9) and the E-FACED score was 2.5 (IQR: 1.0–4.0). The most com-
mon etiologies were post-infective and tuberculosis. Asthma was
the primary etiology in eight (7.4%) patients.

Bacterial and Viral Compositions

The percentage of no pathogen detection, bacterial isolation,
viral detection and bacterial plus viral detection was 35.8%,
52.8%, 4.4% and 7.0%, respectively during stable-visits, while the
corresponding percentage was 23.7%, 47.3%, 14.5% and 14.5%,
respectively during AEs (P = .001, Fig. 2A). 59.8% of stable-visits

and Viruses in Bronchiectasis Exacerbation: A Prospective Study.
10

samples and 61.9% of AEs samples tested positive to bacteria
(P = .753). The three prevalent species isolated in stable-visits
and AEs samples were Pseudomonas aeruginosa (44.4% vs. 32.1%),
Haemophilus influenzae (9.8% vs. 15.4%) and Escherichia coli (2.0% vs.

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Table 1
Demographic and Clinical Characteristics of the Study Cohort.

Parameters Full Cohort AE Cohort P Value
(n = 108) (n = 49)

Age (yr) 46.8 (14.0) 46.1 (14.5) .78

Body-mass index (kg/m2) 20.4 (3.3) 19.6 (3.3) .179
Sex (% female) 65 (60.2%) 34 (69.5%) .268

Smoking status
Never smoke (No., %) 100 (92.6%) 46 (93.9%)

>
.999

Ex-smoke (No., %) 8 (7.4%) 3 (6.1%)
Current smoke (No., %) 0 (0.0%) 0 (0.0%)

FEV1% predicted 52.9 52.5 .792
IQR (41.0–70.1) (40.0–69.2)
Number of exacerbations in the previous year 1.0 (1.0–2.0) 1.8 (1.0–3.0) .066

Bronchiectasis severity index 7 (4–9) 8 (4–10) .446
Mild (No., %) 32 (29.6%) 14 (28.6%)

.673Moderate (No., %) 50 (46.3%) 20 (40.8%)
Severe (No., %) 26 (24.1%) 15 (30.6%)

E-FACED score 2.5 (1.0–4.0) 2.0 (1.0–4.0) .929
Mild (No., %) 73 (67.6%) 33 (67.3%)

>
.999

Moderate (No., %) 34 (31.5%) 16 (32.7%)
Severe (No., %) 1 (0.9%) 0 (0%)

Etiology
Post-infective (No., %) 27 (25.0%) 15 (30.6%)

.966
Idiopathic (No., %) 26 (24.1%) 11 (22.4%)
Post-tuberculous (No., %) 17 (15.7%) 7 (14.3%)
Primary immunodeficiency (No., %) 11 (10.2%) 5 (10.2%)
Others (No., %)a 27 (25.0%) 11 (22.4%)

Medication
Inhaled corticosteroids (No., %) 28 (25.9%) 14 (28.6%) .729
Low-dose macrolides (No., %) 13 (12.0%) 8 (16.3%) .464

Vaccine
Influenza vaccination within 1 year 7 (6.5%) 5 (10.2%) .518
Pneumococcal vaccination within 5 years 4 (3.7%) 3 (6.1%) .678

Notes: yr = year; FEV1 = forced expiratory volume in 1 s.
Data are presented as mean (standard deviation) or median (interquartile range) or n (%).

cond
3 ance r
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a Other aetiologies included: Kartagener’s syndrome: 8 (7.4%), asthma-associated
(2.8%), connective tissue disease: 2 (1.9%), cystic fibrosis transmembrane conduct
one of the study participants was receiving inhaled antibiotics during the study. N

.8%). However, we noted a higher detection rate of Haemophilus
nfluenzae, Moraxella catarrhalis and Streptococcus pneumoniae dur-
ng AEs. The overall bacterial compositions that took into account
ll bacterial species did not differ remarkably between stable-visits
nd AEs (P = .070, Fig. 2B).

The proportion of patients tested positive to virus increased
rom 11.4% at stable-visits to 29.0% at AEs (P = .003). Prevalent
iruses included coronavirus (3.4%), herpes simplex virus (2.4%)
nd influenza A (2.0%) at stable-visits, and influenza B (7.7%), coron-
virus (7.7%) and rhinovirus (6.4%) at AEs. The viral spectrum alone
id not differ between stable-visits and AEs (P = .396). Dual viral
pecies were detected during 4 (5.3%) AE episodes (Table E2).

Of the 49 patients, 17 (34.7%) provided ≥2 AE sputum sam-
les, for which no identical virus was detected whereas an identical
colonized) bacteria was isolated in 8 patients.

solation of New Bacteria and Viral Isolation Occurred More
requently During AEs

Among 375 sputum samples, isolation of bacteria alone did not
orrelate with AEs (P > .05, Fig. 3). Nonetheless, isolation of new
acteria occurred more frequently during AEs than stable-visits
OR = 2.52, 95%CI: 1.35–4.71), in which culture switching from neg-

Please cite this article in press as: Chen C-L, et al. The Roles of Bacteria
Arch Bronconeumol. 2020. https://doi.org/10.1016/j.arbres.2019.12.0

tive to positive accounted for 66.7% (12/18) of episodes [from
ulture negative to Haemophilus influenzae (38.9%) and Moraxella
atarrhalis (11.1%)], whereas bacterial class-switch accounted
or 33.3% (6/18) of episodes [from Pseudomonas aeruginosa to

ition 8 (7.4%), gastro-oesophageal reflux disease: 3 (2.8%), diffuse panbronchiolitis:
egulator-related disease: 1 (0.9%), congenital lung maldevelopment: 1 (0.9%).
f the study participants had physician-diagnosed cystic fibrosis.

Haemophilus influenzae (16.7%) and other bacteria (11.1%); Pseu-
domonas aeruginosa was not isolated at most AEs].

Viral isolations occurred more frequently during AEs than
stable-visits (OR = 3.28, 95%CI: 1.76–6.12). The odds was highest
for rhinovirus (OR = 8.14, 95%CI: 1.90–34.81), followed by influenza
A/B (OR = 4.81, 95%CI: 1.64–14.13). However, isolation of coron-
avirus did not correlate with AEs (OR = 2.80, 95%CI: 0.96–8.21).
Moreover, bacterial plus viral isolations occurred more frequently
during AEs than stable-visits (OR = 2.24, 95%CI: 1.11–4.55).

At baseline, pathogen (including Haemophilus influenzae) isola-
tion status failed to predict future risks of AEs during follow-up
(Table E3). Neither bacterial nor viral isolations alone at baseline
predicted a shorter time to the next AEs during follow-up (Fig. E1).

We collected 52 and 24 samples from warmer (May-October)
and colder seasons (November-April), between which the detec-
tion rate of viruses differed significantly (36.5% vs. 12.5%, P = .032).
However, we noted no significant difference in the rate of bacterial
isolation (61.5% vs. 62.5%, P = .936), nor did the rate of isolating new
bacteria (19.2% vs. 33.3%, P = .179) (Fig. E2).

Clinical Characteristics Differentiating AEs With Different
Pathogens

and Viruses in Bronchiectasis Exacerbation: A Prospective Study.
10

Next, we stratified patients at AEs as: (1) New bacterial AE
(50.0%): isolation of new bacteria; (2) Viral AE (21.1%): detection of
any virus; (3) Unexplained AE (26.3%): AE without isolation of new
bacteria or detection of viruses. Two AEs were not analyzed because

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Fig. 2. Percentage and composition of pathogens in sputum samples at AEs and stable-visits. Percentage of pathogens in sputum samples at AEs and stable visits. Bacterial
and viral composition in sputum samples at AEs and stable visits. AE: acute exacerbation of bronchiectasis. Other bacteria consisted of Proteus mirabilis (n = 4), Acinetobacter
baumannii (n = 2), Moraxella catarrhalis (n = 2), Pseudomonas ozanae (n = 1), Staphylococcus aureus (n = 1), Haemophilus haemolyticus (n = 1), Haemophilus parahaemolyti-
cus (n = 1), Streptococcus pneumoniae (n = 1), Shewanella algae (n = 1), Actinomyces ureae (n = 1), Pasteurella multocida (n = 1), Enterobacter aerogen (n = 1) and Serratia
marcescens (n = 1). There were more patients isolated with two bacteria when clinically stable. Hence, the overall percentage of patients isolated with pathogenic bacteria
appeared higher when clinically stable compared with AE onset.

F s. Not
a tive fo
n terium

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ig. 3. Association between the detection of different pathogens and the risks of AE
cute exacerbations of bronchiectasis. Any bacteria denotes bacterial culture posi
egative to positive, or sputum culture positive switching from one pathogenic bac

f simultaneous detection of virus and new bacteria. Demographic
nd clinical characteristics did not differ among these subgroups
Table E4).

Symptom questionnaires were obtained from 58 (78.4%) AEs.
iral AEs tended to yield more coryza symptoms than unexplained
Es and new bacterial AEs (P = .053, Table 2). Neither the num-
er nor the VAS of lower airway symptoms differed among the

Please cite this article in press as: Chen C-L, et al. The Roles of Bacteria
Arch Bronconeumol. 2020. https://doi.org/10.1016/j.arbres.2019.12.0

hree groups during stable-visits (Fig. 4A). However, increased spu-
um purulence (mean difference: 2–4 for VAS) and breathlessness
mean difference: 1.6 for VAS) deteriorated most notably during
ew bacterial AE (Fig. 4B and C).

es: OR= Odds Ratio, PA= Pseudomonas aeruginosa, HI= Haemophilus influenzae; AEs:
r any bacteria; Isolation of new bacteria denotes sputum culture switching from

to other pathogenic bacterium.

Spirometry was assessed in 46 (62.2%) AEs. Lung func-
tion decline did not differ among viral, new bacterial and
unexplained AEs (Figure E3). Sputum cytokines and total
leukocyte count were detected in 53 (71.6%) and 39 (51.3%)
AEs, respectively. Median interleukin-1� levels increased sig-
nificantly in new bacterial AEs than in unexplained AEs
(P = .006) and viral AEs (P = .005), as did tumor necrosis

and Viruses in Bronchiectasis Exacerbation: A Prospective Study.
10

factor-� levels except for the comparison with viral AEs
(P = .138). New bacterial AEs trended toward higher blood neu-
trophil counts, while viral AEs yielded higher monocyte counts
(Fig. 5).

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Table 2
Symptoms of AEs With Different Pathogen Detection.

Symptoms Unexplained AE (n = 27) New Bacterial AE (n = 15) Viral AE (n = 16) P Value

Number of coryza symptoms, Mean (SD) 2.5 (2.0) 2.2 (1.6) 3.9 (2.7) .053
Fever and or shivery, n (%) 12 (44.4%) 11 (73.3%) 9 (55.2%) .196
Headache, n (%) 10 (37.0%) 1 (6.7%) 8 (50.0%)a .030
Ocular itching, n (%) 2 (7.4%) 0 (0.0%) 6 (37.5%)a,b .009
Other systemic pain, n (%) 5 (18.5%) 6 (40.0%) 7 (43.8%) .147
Runny nose, n (%) 12 (44.4%) 2 (13.3%)c 10 (62.5%)a .019
Blocked or stuffy nose, n (%) 10 (37.0%) 1 (6.7%) 8 (50.0%)a .030
Sneezing, n (%) 7 (25.9%) 3 (20.0%) 2 (12.5%) .649
Sore throat, n (%) 5 (18.5%) 6 (40.0%) 8 (50.0%) .082
Hoarseness, n (%) 4 (14.8%) 3 (20.0%) 5 (31.3%) .450
Lower airway symptoms, Median (IQR) 4 (4–5) 4 (4–5) 3.5 (3–5) .175
Increased cough frequency, n (%) 25 (92.6%) 13 (86.7%) 13 (81.3%) .522
Increased sputum volume, n (%) 25 (92.6%) 14 (93.3%) 13 (81.3%) .535
Increased sputum purulence, n (%) 18 (66.7%) 11 (73.3%) 9 (56.3%) .598
Aggravated breathlessness, n (%) 20 (74.1%) 11 (73.3%) 11 (68.8%) .931
Fatigue/malaise, n (%) 20 (74.1%) 12 (80.0%) 10 (62.5%) .560
Hemoptysis, n (%) 10 (37.0%) 2 (13.3%) 3 (18.8%) .230

Notes: AE: acute exacerbations of bronchiectasis; New bacterial AE: AE with isolation of new bacteria, including situations of sputum culture switching from negative to
positive, or sputum culture positive switching from one pathogenic bacterium to another pathogenic bacterium; Viral AE: AE with virus detection positive; Unexplained AE:
AE without new occurrence of bacteria or virus detected.
Data are presented as mean (SD) or median (IQR) or n (%).

a Symptoms of viral AE compared with those of new bacterial AE, P < .05. b Symptoms of viral AE compared with those of unexplained AE, P < .05. c Symptoms of new bacterial AE compared with those of unexplained AE, P < .05.

F ale. (A
b : visua

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ig. 4. The severity of lower airway symptoms assessed with the visual analog sc
etween AEs and stable visits. Notes: AE: acute exacerbation of bronchiectasis; VAS

haracteristics of AEs With Bacterial plus Viral Isolations

To further investigate the characteristics of bacterial plus viral
solations, AEs were divided into: no bacteria/viruses detected
B−V−, n = 18); Bacteria detected alone (B+V−, n = 36); Viruses
etected alone (B−V+, n = 11); both bacteria and viruses detected
B+V+, n = 11).

There were more coryza symptoms in B+V+ group than in B−V−
roup (median: 4.6 vs. 1.6, P = .019). Ocular itching appeared more
requent in B+V+ group (42.9%) than in B−V− (0%) and B+V− group
7.1%, Table 3). The VAS for cough, sputum and sputum purulence
ended to be higher in B+V+ and B+V− groups (Ptrend < 0.05, Fig. 4). The greatest lung function decline (Ptrend = 0.043 for forced ital capacity) was noted in B−V− group (Fig. E5). However, there as no notable among-group difference in sputum inflammatory

iomarker levels (all P > .05) (Fig. E6).

iscussion

Please cite this article in press as: Chen C-L, et al. The Roles of Bacteria
Arch Bronconeumol. 2020. https://doi.org/10.1016/j.arbres.2019.12.0

This is the first study that evaluates bacterial plus viral infections
n adults with bronchiectasis. Isolation of new pathogenic bacteria
nd viral isolations were associated with AEs. Pathogen isolations
hen clinically stable did not predict future risks of AE. Bacterial

) The VAS during stable visits. (B) The VAS during AEs. (C) The difference in VAS
l analog scale.

plus viral isolations occurred more frequently during AE. Coryza
symptoms were more frequent in viral AEs. Bacterial plus viral iso-
lations did not correlate with greater respiratory symptom burden,
airway inflammation or lung function impairment compared with
bacterial or viral isolations alone.

Next-generation sequencing has been applied in
bronchiectasis.13,25,26 Here, we applied routine culture for
detection of bacteria and PCR assay for detecting virus because
these methods are simple, accurate and reliable, and has been
widely used in clinical practice. Furthermore, the bacteria detected
with culture indicate the viability and/or virulence. Hence, these
routine techniques could be applied as the point-of-care tests in
real-world practice.

Consistent with previous findings in COPD exacerbation,27,28

isolation of new bacterial strain (class-switch) was associated
with AEs (accounting for 20% of AEs), which warranted antibiotics
treatment. However, the total bacterial load and microbiota taxa,
analyzed by 16s rDNA sequencing, changed unremarkably before
and after antibiotic treatment for AEs.13 Intriguingly, isolation of

and Viruses in Bronchiectasis Exacerbation: A Prospective Study.
10

new bacterial strain (Haemophilus influenzae, Moraxella catarrhalis,
or Streptococcus pneumoniae) was associated with increased risks
of COPD exacerbations.28-30 Our study showed that isolation of
new bacteria (mainly Haemophilus influenzae) was associated with

https://doi.org/10.1016/j.arbres.2019.12.010

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Fig. 5. The airway and systemic inflammations in different groups. (A) The level of sputum cytokines during AEs. (B) White blood cell count during AEs. (C) The level of
C-reactive protein during AEs. (D) The difference in inflammatory cell count between AEs and stable visits. Notes: AE: acute exacerbation of bronchiectasis.

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Table 3
Symptoms of AEs With Bacterial and or Viral Infection.

Symptoms B−V− (n = 14) B+V− (n = 28) B−V+ (n = 9) B+V+ (n = 7) P Value
Number of coryza symptoms, Mean (SD) 1.6 (1.5) 2.8 (2.0) 3.4 (2.4) 4.6 (3.2)a .020
Fever, n (%) 7 (50.0%) 16 (57.1%) 5 (55.6%) 4 (57.1%) .978
Headache, n (%) 2 (14.3%) 9 (32.1%) 3 (33.3%) 5 (71.4%) .082
Ocular itching, n (%) 0 (0%) 2 (7.1%) 3 (33.3%) 3 (42.9%) .009
Other systemic pain, n (%) 1 (7.1%) 10 (35.7%) 3 (33.3%) 4 (57.1%) .082
Running nose, n (%) 5 (35.7%) 9 (32.1%) 6 (66.7%) 4 (57.1%) .245
Nasal congestion, n (%) 4 (28.6%) 7 (25.0%) 5 (55.6%) 3 (42.9%) .342
Sneezing, n (%) 1 (7.1%) 9 (32.1%) 1 (11.1%) 1 (20.7%) .262
Sore throat, n (%) 1 (7.1%) 10 (35.7%) 4 (44.4%) 4 (57.1%) .057
Hoarseness, n (%) 1 (7.1%) 6 (21.4%) 1 (11.1%) 4 (57.1%) .073
Lower airway symptoms, Median (IQR) 4.2 (1.2) 4.4 (0.9) 3.2 (1.6) 4.3 (0.8) .063
Increased cough frequency, n (%) 13 (92.9%) 25 (89.3%) 7 (77.8%) 6 (85.7%) .737
Increased sputum volume, n (%) 12 (85.7%) 27 (96.4%) 6 (66.7%) 7 (100.0%) .053
Increased sputum purulence, n (%) 9 (64.3%) 20 (71.4%) 7 (77.8%) 2 (28.6%) .181
Aggravated breathlessness, n (%) 9 (64.3%) 22 (78.6%) 4 (44.4%) 7 (100.0%) .069
Fatigue/malaise, n (%) 11 (78.6%) 21 (75.0%) 4 (44.4%) 6 (85.7%) .286
Hemoptysis, n (%) 5 (35.7%) 7 (25.0%) 1 (11.1%) 2 (28.6%) .658

Notes: AE: acute exacerbations of bronchiectasis; B−V−: no bacteria and viruses detected; B+V−: any pathogenic bacteria detected but no viruses detected; B−V+: viruses
detected but no pathogenic bacteria detected; B+V+: both bacteria and viruses detected.
Data are presented as mean (SD) or median (IQR) or n (%).
K

V− su

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ruskal–Wallis comparison with Bonferroni test was applied.

a The number of coryza symptoms in B+V+ subgroup compared with those in B−

Es in bronchiectasis, implicating that Pseudomonas aeruginosa
as not the culprit of AEs and that antibiotic therapy targeting

t the emerging bacterial species would be warranted. Hence,
apid microbiological assessments are important to clinical practice
ecause they may help avoid over-treatment or under-treatment.

Viral isolations occurred more frequently in AEs. The viral detec-
ion rate was lower (30%) than previously reported (∼48%).11,12
he sources of samples (sputum vs. nasopharyngeal aspirates
s. sputum plus nasopharyngeal swab) and the characteristics of
ronchiectasis patients might have contributed to the differences.
he most prevalent viruses were influenza A/B, coronavirus and rhi-
ovirus in our population, congruent with the findings from COPD
atients in Hong Kong.31 Influenza A/B and rhinovirus, but not
oronavirus, played crucial roles in AEs. However, the mechanisms
nderlying these observations are unclear and warrant further

nvestigations. Overall, our findings mirrored those reported in
dults and children.11,12 Compared with the study by Mitchell
t al.,32 the difference in viral spectrum could have resulted from
he differences in: (1) detection methods and assay kits; (2) the
ositive threshold of CT values; (3) the geographic regions.

We have confirmed that bacterial plus viral isolations occurred
ore frequently during AEs than stable-visits. Interestingly, bac-

erial plus viral isolations were not associated with greater lung
unction impairment or airway inflammation than bacterial or
iral isolations alone during AEs. By contrast, bacterial plus viral
solations yielded greater lung function impairment and height-
ned inflammatory responses during COPD exacerbations.17,18 This
ight be because bacterial plus viral isolations frequently com-

rised Pseudomonas aeruginosa and viral isolations in bronchiec-
asis as opposed to Haemophilus influenzae and viral isolations in
OPD. Indeed, viral isolation may induce secondary bacterial isola-
ion and further aggravated inflammation.33,34 However, the load
f Pseudomonas aeruginosa did not increase significantly regard-
ess of viral isolation status or disease status (exacerbation vs.
teady-state) in cystic fibrosis.35,36 Moreover, respiratory syncy-
ial virus isolation reportedly enhanced Pseudomonas aeruginosa
iofilm growth, rendering Pseudomonas aeruginosa infection more
ustainable.37 Collectively, Pseudomonas aeruginosa isolation was

Please cite this article in press as: Chen C-L, et al. The Roles of Bacteria
Arch Bronconeumol. 2020. https://doi.org/10.1016/j.arbres.2019.12.0

nlikely the trigger of AEs despite co-existing viral isolations in
ronchiectasis.

bgroup, P < .05.

This is the first study which analyzed the association between
symptoms and AEs in bronchiectasis because this would help
clinicians more rapidly identify the possible culprits. Coryza
symptoms were indicators of viral isolations. Fever was ubiquitous
in different types of AEs, hence viral AEs cannot be judged based on
fever alone. AE associated with new occurrence of bacteria yielded
more severe lower airway symptoms and heightened airway
inflammatory responses, therefore clinicians should be vigilant for
the identification and management with antibiotics if appropriate.

Some limitations should be considered. We did not recruit ‘dry’
bronchiectasis patients who still might have AEs attributable to
pathogen infections. Our sample size was insufficient to power
subgroup analyses. Some blood tests, spirometry were not avail-
able because some patients declined due to repeated assessments
and poor overall well-being during AEs. We’ve only captured half
AE episodes although the clinical characteristics of these patients
did not differ from those whose AEs were not captured. Further-
more, we did not measure bacterial loads which reportedly changed
insignificantly during AEs.11 The AEs were managed at out-patient
clinics, hence our findings might not be extrapolated to severe AEs
needing hospitalization. Some viruses detected during AEs might
not be pathogenic; however, the GEE model did reveal the associa-
tion between pathogen isolation and AEs. Finally, findings of the
symptoms associated with viral or bacterial isolations were not
specific to bronchiectasis. However, our study would still be infor-
mative because our findings help clinicians to infer from possible
culprit pathogens before further assays became available.

In summary, building on our previous publication,11 the current
study has further provided important clinical insights. Isolation of
new bacteria, viral isolations, and bacterial plus viral isolations are
associated with AEs in bronchiectasis. Further study determining
the causes of unexplained AEs are needed.

Funding

This work was supported by National Natural Science Founda-
tion No. 81870003, Pearl River S&T Nova Program of Guangzhou

and Viruses in Bronchiectasis Exacerbation: A Prospective Study.
10

No. 201710010097 and Guangdong Province Universities and Col-
leges Pearl River Scholar Funded Scheme 2017 (to Prof. Guan),
The Impact and Mechanisms of Physical, Chemical and Biologi-
cal Interventions on the Development and Outcome of Acute Lung

https://doi.org/10.1016/j.arbres.2019.12.010

ING ModelA
coneum

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3

ARTICLERBRES-2366; No. of Pages 9
C.-L. Chen et al. / Arch Bron

njury No. 81490534, National Key Technology R&D Program No.
018YFC1311902, Guangdong Science and Technology Foundation
o. 2019B030316028 (to Prof. Zhong).

uthorship

C. L. C., W. J. G. and N. S. Z. participated in study design; C. L. C.
erformed laboratory experiments and data analysis; C. L. C., H. M.
., J. J. Y., Y. H., W. J. G., X. R. H., R. C. C. and N. S. Z. recruited patients;
. L. C., H. M. L., J. J. Y., and Y. H. performed follow-up; C. L. C., W. J.
., D. R. C. and M. A. M. drafted the manuscript; W. J. G., N. S. Z., D. R.
. and M. A. M. were responsible for study conception and provided
ritical review of the manuscript. C. L. C., H. M. L., J. J. Y., Y. H., W. J.
., X. R. H., R. C. C., D. R. C., M. A. M. and N. S. Z. approved the final
raft for publication. W. J. G. and N. S. Z. were the guarantors of the
tudy.

cknowledgments

We thank Dan-Hong Su (Department of microbiology, The First
ffiliated Hospital of Guangzhou Medical University), for her assis-

ance in sputum bacterial culture, Wen-Kuan Liu, Shi-Guan Wu
nd Shu-Yan Qiu (Department of virology, State Key Laboratory
f Respiratory Diseases, Guangzhou Medical University) for their
ssistance in viral detection.

ppendix A. Supplementary data

Supplementary data associated with this article can be found, in
he online version, at doi:10.1016/j.arbres.2019.12.010.

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