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\begin{document}

\title{Diesel exhaust particles alter gut microbiome and gene expression in the
bumblebee Bombus terrestris}



\author[1]{Dimitri Seidenath}%
\author[1]{Alfons Weig}%
\author[2]{Andreas Mittereder}%
\author[2]{Thomas Hillenbrand}%
\author[2]{Dieter  Brüggemann}%
\author[2]{Thorsten Opel}%
\author[2]{Nico Langhof}%
\author[1]{Marcel Riedl}%
\author[1]{Heike Feldhaar}%
\author[3]{Oliver Otti}%
\affil[1]{University of Bayreuth Bayreuth Center of Ecology and Environmental Research}%
\affil[2]{University of Bayreuth Faculty of Engineering Science}%
\affil[3]{TU Dresden}%


\vspace{-1em}



  
  \date{March 14, 2025}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Insect decline is a major threat for ecosystems around the world as they
provide many important functions, such as pollination or pest control.
Pollution is one of the main reasons for the decline, besides changes in
land use, global warming, and invasive species. While negative impacts
of pesticides are well studied, there is still a lack of knowledge about
the effects of other anthropogenic pollutants, such as airborne
particulate matter, on insects. To address this, we exposed workers of
the bumblebee Bombus terrestris to sublethal doses of diesel exhaust
particles (DEPs) and brake dust, orally or via air. After seven days, we
looked at the composition of the gut microbiome and tracked changes in
gene expression. While there were no changes in the other treatments,
oral DEP exposure significantly altered the structure of the gut
microbiome. In particular, the core bacterium Snodgrassella had a
decreased abundance in the DEP treatment. Similarly, transcriptome
analysis revealed changes in gene expression after oral DEP exposure,
but not in the other treatments. The changes are related to metabolism
and signal transduction which indicates a general stress response. Taken
together, our results suggest potential health effects of DEP exposure
on insects, here shown in bumblebees, as gut dysbiosis may increase the
susceptibility of bumblebees to pathogens, while a general stress
response may lower available energy resources. However, experiments with
multiple stressors and on colony level are needed to provide a more
comprehensive understanding of the impact of DEPs on insects.%
\end{abstract}%



\sloppy


Research article

Diesel exhaust particles alter gut microbiome and gene expression in the
bumblebee \emph{Bombus terrestris}

Dimitri Seidenath\textsuperscript{1}, Alfons R. Weig\textsuperscript{2},
Andreas Mittereder\textsuperscript{3}, Thomas
Hillenbrand\textsuperscript{3}, Dieter Br\selectlanguage{ngerman}üggemann\textsuperscript{3},
Thorsten Opel\textsuperscript{4}, Nico Langhof\textsuperscript{4},
Marcel Riedl\textsuperscript{1}, Heike Feldhaar\textsuperscript{1}*,
Oliver Otti\textsuperscript{1,5}

\textsuperscript{1}Animal Ecology I, Bayreuth Center of Ecology and
Environmental Research (BayCEER), University of Bayreuth,
Universitätsstrasse 30, 95440 Bayreuth, Germany

\textsuperscript{2}Keylab Genomics and Bioinformatics, Bayreuth Center
of Ecology and Environmental Research (BayCEER), University of Bayreuth,
Universitätsstrasse 30, 95440 Bayreuth, Germany

\textsuperscript{3}Department of Engineering Thermodynamics and
Transport Processes, University of Bayreuth, Germany,
Universitätsstrasse 30, 95440 Bayreuth, Germany

\textsuperscript{4}Department of Ceramic Materials Engineering,
University of Bayreuth, Prof.-Rüdiger-Bormann-Str. 1, 95447 Bayreuth,
Germany

\textsuperscript{5}Applied Zoology, TU Dresden, Zellescher Weg 20b,
01062 Dresden, Germany

ORCID ID Heike Feldhaar 0000-0001-6797-5126

ORCID ID Oliver Otti 0000-0002-2361-9661

ORCID ID Alfons R. Weig 0000-0001-8712-7060

\textbf{*Corresponding author} : Heike Feldhaar

Phone: +49921552645, e-mail: feldhaar@uni-bayreuth.de

\textbf{Keywords} : transcriptome, air pollution, particulate matter,
insect decline\textbf{,} brake dust, pollinator

Abstract

Insect decline is a major threat for ecosystems around the world as they
provide many important functions, such as pollination or pest control.
Pollution is one of the main reasons for the decline, besides changes in
land use, global warming, and invasive species. While negative impacts
of pesticides are well studied, there is still a lack of knowledge about
the effects of other anthropogenic pollutants, such as airborne
particulate matter, on insects. To address this, we exposed workers of
the bumblebee \emph{Bombus terrestris} to sublethal doses of diesel
exhaust particles (DEPs) and brake dust, orally or via air. After seven
days, we looked at the composition of the gut microbiome and tracked
changes in gene expression. While there were no changes in the other
treatments, oral DEP exposure significantly altered the structure of the
gut microbiome. In particular, the core bacterium
\emph{Snodgrassella}had a decreased abundance in the DEP treatment.
Similarly, transcriptome analysis revealed changes in gene expression
after oral DEP exposure, but not in the other treatments. The changes
are related to metabolism and signal transduction which indicates a
general stress response. Taken together, our results suggest potential
health effects of DEP exposure on insects, here shown in bumblebees, as
gut dysbiosis may increase the susceptibility of bumblebees to
pathogens, while a general stress response may lower available energy
resources. However, experiments with multiple stressors and on colony
level are needed to provide a more comprehensive understanding of the
impact of DEPs on insects.

Introduction

Global biodiversity loss is one of the major challenges humanity
currently faces (Diaz et al. 2006, Dirzo et al. 2014). Especially the
rapid decline in insects is cause for concern, as they provide or
contribute to many important ecosystem functions such as pollination,
nutrient cycling, pest control, and linking trophic levels (Cardoso et
al. 2020, Noriega et al. 2018). Pollution is one of the major reasons
for the decline besides intensification of land use, climate change, and
invasive species (Milicic et al. 2021, Sanchez-Bayo \& Wyckhuys 2019).

Pesticides harm insects on many different levels ranging from subtle
changes in the gut microbiome over behavioral changes to increased
mortality (Desneux et al. 2007, Motta et al. 2018, Ndakidemi et al.
2016). Other anthropogenic pollutants might also contribute to the
observed declines in insects, but their impacts are often less well
studied (Cameron \& Sadd 2020, Feldhaar \& Otti 2020, Sanchez-Bayo \&
Wyckhuys 2019). Airborne particulate matter deriving from traffic or
industrial processes has become ubiquitous in the environment (Gieré \&
Querol 2010, Zereini \& Wisemann 2010). While the harmful effects on
mammals, in particular humans, have been intensively studied, research
investigating the impact on insects remains scarce (Kim et al. 2015,
Valavanidis et al. 2008). Insects can encounter these pollutants in
various ways, e.g. by foraging in contaminated areas, consuming
contaminated food or direct deposition on the insect's cuticle (Feldhaar
\& Otti 2020, Lukowski et al. 2018, Negri et al. 2015). The airborne
particulate matter might enter an insect´s body via oral ingestion or
the tracheal system (Feldhaar \& Otti 2020, Negri et al. 2015). Social
insects might be at an increased risk, as pollutants are transferred to
and stored in their nests, which could lead to a higher exposure to
conspecifics and the brood (Feldhaar \& Otti 2020, Hladun et al. 2016).

Vehicle brake dust and diesel exhaust particles (DEPs) are major classes
of airborne particulate matter deriving from traffic released into the
environment (Hamilton \& Hartnet 2013, Harrison et al. 2012, Rönkkö \&
Timonen 2019). Brake dust particles contain various metals and phenolic
compounds, depending on the brake lining used (Iijima et al. 2007,
Thorpe \& Harrison 2008). Exposure of different invertebrate species to
such particles showed mixed effects. Particulate matter contamination in
soil did not affect colony founding in the ant \emph{Lasius
niger}(Seidenath et al. 2021)\emph{.} However, soil-feeding earthworms
(\emph{Eisenia fetida} ) showed a strongly increased mortality when
exposed to soil spiked with brake dust particles (Holzinger et al.
2022). DEPs have a different composition than brake dust. They are
composed of an elemental carbon core with adsorbed organic compounds,
such as polycyclic aromatic hydrocarbons (PAHs), and traces of metals
and other elements (Greim 2019, Wichmann 2007). Exposure to high doses
of diesel exhaust particles (up to a concentration of 2 g/L) in food
over a period of seven days reduced survival in \emph{Bombus
terrestris}workers compared to controls (Hüftlein et al. unpublished).

Many classical ecotoxicology approaches focus on the effect of a
substance on mortality, growth, or reproduction. However, pollutants can
also have more subtle sublethal effects on insects which may have severe
consequences in the long-term (Straub et al. 2020). Direct sublethal
effects include changes in physiology such as stress reactions or
detoxification processes. By interacting with microorganisms inside the
insect's body, oral exposure to pollutants may indirectly affect insect
health.

Most eukaryotic organisms and their associated microbes form an entity,
the so-called holobiont (Theis et al. 2016, Zilber-Rosenberg \&
Rosenberg 2008). In insects, microorganisms can be found in the
digestive tract, the exoskeleton, the hemocoel, or within cells (Douglas
2015). The insect gut microbiome has a range of functions that include
protection from pathogens, detoxification, digestion, and the production
of essential nutrients (Engel \& Moran 2013). Social bumblebees
(\emph{Bombus spp.)} and honeybees (\emph{Apis mellifera} ) are model
organisms to study gut microbiota as their gut microbiome is rather
simple and highly conserved (Engel et al. 2016, Kwong \& Moran 2016,
Zhang \& Zheng 2022). A few core bacterial taxa dominate the gut
microbiome of bumblebees: \emph{Snodgrassella} , \emph{Gilliamella}
,\emph{Schmidhempelia} , Bifidobacteriaceae (\emph{Bifidobacterium and
Bombiscardovia} ) and two clusters within Lactobacillaceae (Hammer et
al. 2021, Koch \& Schmid-Hempel 2011a, Martinson et al. 2011). While
many functions of the bacterial symbionts in bumblebees have been
proposed, only very few have been demonstrated in experiments (Hammer
2021, Zhang \& Zheng 2022). Resistance to the common trypanosomatid
parasite\emph{Crithidia bombi} is higher in bumblebees with an intact
microbiome compared to microbiota-free individuals (Koch \&
Schmid-Hempel 2011b). Moreover, infection outcomes of \emph{C. bombi}
vary with host microbiota composition rather than genotype (Koch \&
Schmid-Hempel 2012). The gut microbiome of bumblebees may be important
for detoxification as microbiota-free individuals had lower survival
when exposed to toxic concentrations of selenate (Rothman et al. 2019).

Examining the effects of anthropogenic pollutants, such as airborne
particulate matter, on the gut microbiome is an important tool for
assessing their risk for insect health (Duperron et al. 2020). Even with
a conserved gut microbiome, the relative abundance of core bacteria and
the presence of other microorganisms will vary with age, diet and
changing environmental parameters (Kwong \& Moran 2016, Koch et al.
2012). Different pollutants affect the microbial composition of bee
guts. In honeybee workers, pesticides or antibiotics change the relative
and absolute abundance of core gut microbiota species (DeGrandi-Hoffmann
et al. 2017, Motta et al. 2018, Raymann et al. 2017). An array of
environmental toxicants, such as cadmium, copper, selenate, and hydrogen
peroxide, alter the gut microbiome of \emph{Bombus impatiens} at
field-realistic concentrations (Rothman et al. 2020). These shifts in
the microbial community may affect bumblebee health. Intestinal
dysbiosis, compositional and functional alteration of the microbiome, is
associated with various diseases and health problems in humans and
vertebrates (De Gruttola et al. 2016, Levy et al. 2017, Shreiner et al.
2015). In insects, dysbiosis negatively affects reproductive fitness,
immunity, and resistance to pathogens (Ami et al. 2010, Daisley et al.
2020, Raymann et al. 2017).

Transcriptome analysis is a sensitive tool to characterize sublethal
effects of potentially harmful substances on a molecular and cellular
level (Prat \& Degli-Esposti 2019, Schirmer et al. 2010). Changes in
gene expression help to identify biological processes, such as stress
responses and detoxification processes, at an early stage. Exposure to
different pollutants have been shown to induce changes in gene
expression in several insect species. Mosquitos (\emph{Aedes aegypti} )
exposed to anthropogenic pollutants (insecticides, PAHs) increased the
expression of genes related to detoxification, respiration and cuticular
proteins (David et al. 2010). Fireflies (\emph{Luciola leii} ) showed a
similar response when exposed to benzo(a)pyrene, a widespread PAH (Zhang
et al. 2019). In different bee species, the neonicotinoids imidacloprid,
thiamethoxan, and clothianidin induce an upregulation of metabolic,
immune and stress response genes (Aufauvre et al. 2014, Bebane et al.
2019, Christen et al. 2018, Colgan et al. 2019, Gao et al. 2020, Shi et
al. 2017). The expression of genes related to detoxification was higher
in honeybees (\emph{A. mellifera} ) exposed to heavy metals than in
controls (Al Naggar et al. 2020, Gizaw et al. 2020, Zhang et al. 2018).

In contrast to pesticides, the effects of other environmental
pollutants, such as particulate matter, on gene expression in bees as
well as their gut microbiome are largely unclear. To address this
knowledge gap, we exposed workers of the buff-tailed
bumblebee\emph{Bombus terrestris} to airborne particulate matter
deriving from traffic and investigated changes in the gut microbiome and
gene expression. Bumblebees were fed sugar water spiked with sublethal
concentrations of brake dust or diesel exhaust particles (DEPs). Adding
to this oral exposure, one group of bumblebees was exposed to DEPs via
air to enable potential uptake in the tracheal system. We expect changes
in the composition of the gut microbial community, as previous research
showed changes due to different metals in a closely related\emph{Bombus}
species (Rothman et al. 2020). Moreover, we expect changes in the
expression of detoxification and metabolic genes, indicating an
increased stress level, as the toxic compounds in the particulate matter
may interfere with bumblebee physiology.

Methods

Bumblebee keeping

Four queenright colonies of \emph{B. terrestris} were ordered from
Biobest (Westerlo, Belgium) in March 2021. Colonies were kept in a
climate chamber at 26° C and 70 \% humidity under a constant, inverted
12:12 h light:dark cycle. Colonies were provided with sugar water (50\%
Apiinvert, Südzucker AG, Mannheim, Germany) and pollen (Imkerpur,
Osnabrück, Germany) \emph{ad libitum} .

Experimental procedure

At the beginning of the experiment, adult workers from the four colonies
were randomly assigned to one of six treatments. \textbf{Control} : fed
with sugar water only (50\% Apiinvert) (n=56); \textbf{Solvent control}
: fed with sugar water spiked with 0.02\% (v/v) of the emulsifier
Tween20 (n=56); \textbf{Brake dust} : fed with sugar water spiked with
0.02\% (v/v) of the emulsifier Tween20 and 0.4 g/l brake dust particles
(n=56);\textbf{DEP} : fed with sugar water spiked with 0.02\% (v/v) of
the emulsifier Tween20 and 0.4 g/l diesel exhaust particles (n=56);
\textbf{Flight control} : fed with sugar water (50\% Apiinvert) and
allowed to fly once per day in a plastic box (7 x 7 x 5 cm, EMSA,
Emsdetten, Germany) for 3 minutes (n=24); \textbf{DEP flight} : fed with
sugar water (50\% Apiinvert) and allowed to fly once per day for 3
minutes in a plastic box (7 x 7 x 5 cm, EMSA, Emsdetten, Germany) that
contained 1.5 (+-0.1) mg of diesel exhaust particles (n=24).

The experiment was conducted in a climate chamber at 26° C and 70 \%
humidity under a constant 12:12 h light:dark cycle. Bumblebees were kept
in Nicot cages (Nicotplast SAS, Maisod, France) connected to a 12 ml
syringe (B. Braun SE, Melsungen, Germany) with the tip cut off, that
contained 2 ml of the respective feeding solution (\emph{ad libitum} ).
Every day the syringes were replaced with fresh ones to prevent molding
or bacterial growth in the food. The exposure lasted for seven days. At
the end of the experiment, the animals were frozen at -20° C.

Within a week after the end of the experiment, we randomly selected
twelve (three workers per colony) bumblebees per treatment for
transcriptome analysis (N=72). Additionally, for the control, solvent
control, brake dust and DEP treatment, we randomly selected 20
bumblebees (five workers per colony) for microbiome analysis (N=80),
respectively.

Generation and collection of diesel exhaust particles (DEPs)

DEPs were collected from a four-cylinder diesel engine (OM 651, Daimler
AG, Stuttgart, Germany) during a repeating cycle of transient and
stationary operating points, resembling an inner-city driving scenario
with stop-and-go intervals. The engine was operated on a test bench with
a water-cooled eddy-current brake as previously described in Zöllner
(2019). DEP samples were collected by an electrostatic precipitator
(OekoTube Inside, Mels-Plons, Switzerland). A fast response differential
mobility particulate spectrometer DMS500 (Combustion, Cambridge,
England) was applied to measure sub-micron particle size distributions
of raw exhaust samples. Depending on engine load and speed during the
inner-city cycle, solid particles showed a median diameter between 52.1
± 1.8 nm and 101.9 ± 1.7 nm. DEP composition was characterized by
thermogravimetric analysis (TGA, STA 449 F5 Jupiter, Netzsch-Gerätebau
GmbH, Selb, Germany). A fraction of 72.2 ± 1.1~\% of the DEP mass was
attributed to elemental carbon, 23.2~±~0.9~\% w/w to organic fractions
and 4.6~±~0.7~\% w/w to inorganic matter. Quantification of PAHs
revealed concentrations of 444 ppm for pyrene, 220 ppm for phenanthrene,
and 107 ppm for fluoranthene.

The elemental composition of the DEP samples was analyzed by
Inductively-Coupled Plasma Optical Emission Spectrometry (ICP-OES,
Optima 7300 DV, PerkinElmer Inc., Waltham, United States of America) and
interpreted according to Zöllner (2019). It showed fractions of calcium
(1.63~\% w/w), zinc (0.53~\% w/w) and phosphorus (0.50~\% w/w) that can
be traced back to diesel fuel and lubrication oil. Copper (1.03~\% w/w),
aluminum (0.02 \% w/w) and iron (0.02~\% w/w) can be attributed to
abrasion of piston rings, cylinder head and engine block material,
respectively. In addition, small amounts of boron (0.13~\% w/w),
magnesium (0.10~\% w/w), molybdenum (0.03~\% w/w), natrium (0.02~\% w/w)
and sulphur (0.17~\% w/w) were found.

Generation of brake dust particles

The brake dust particles provided by the Chair of Ceramic Materials
Engineering of the University of Bayreuth are derived by LowMet brake
pads (provided by TMD Friction Holdings GmbH, Leverkusen, Germany) that
were milled for three minutes in a vibrating cup mill with a tungsten
carbide grinding set (Pulverisette 9, Fritsch GmbH, Idar-Oberstein,
Germany). LowMet brake pads are common and representative for passenger
cars in Europe and consist of non-ferrous metals (25 \% (w/w)), steel
wool (15 \% (w/w)), petrol coke (12 \% (w/w)), sulphides (10 \% (w/w)),
aluminum oxide (5 \% (w/w)), resin (5 \% (w/w)), graphite (4 \% (w/w)),
mica (4 \% (w/w)), silicon carbide (3 \% (w/w)), barite (2 \% (w/w)),
fibers (2 \% (w/w)), and rubber (1 \% (w/w)) (Wiaterek 2012). The
particle size distribution of the milled, fine-grained powder was
measured with a laser diffraction particle size analyzer (PSA 1190 LD,
Anton Paar GmbH, Ostfildern-Scharnhausen, Germany). The mean particle
size found was 10.19 ± 4.37 \selectlanguage{greek}µ\selectlanguage{english}m (D10 = 0.68 \selectlanguage{greek}µ\selectlanguage{english}m (10\% of all particles
being smaller in diameter than this size), D50 = 5.76 \selectlanguage{greek}µ\selectlanguage{english}m (median
particle size), D90 = 25.87 \selectlanguage{greek}µ\selectlanguage{english}m (90\% of particles being smaller in
diameter than this size)).

Bumblebee gut microbiome analysis

Prior to dissection bumblebees were defrosted and rinsed in 70\%
ethanol, 90\% ethanol and twice in ultrapure water. We placed each
bumblebee on an autoclaved square of aluminum foil (5 x 5 cm) and opened
the abdomen with sterilized tweezers and scissor. After carefully
separating the gut from the crop and transferring it to an Eppendorf
tube, we snap-froze the gut in liquid nitrogen. All samples were stored
at -80\selectlanguage{ngerman}° C until further processing.

PCR amplification and sequencing of 16S rDNA fragments

Metagenomic DNA of bumblebee gut samples was purified using the
NucleoMag DNA Bacteria kit (Macherey-Nagel, no. 744310, Düren, Germany)
after disruption of samples with 1.4 mm (diam.) ceramic beads (no.
P000912-LYSK0A, Bertin Instruments, Montigny-le-Bretonneux, France) in a
FastPrep-24 bead beating device (MPbio, Irvine, USA) following the
instructions of the manufacturer. The metagenomic DNA was diluted to a
concentration of 5 ng/\selectlanguage{greek}µ\selectlanguage{english}l, and 2.5 \selectlanguage{greek}µ\selectlanguage{english}l DNA was used to amplify 16S rDNA
fragments using primers 515F-Y (Turner et al. 1999) and 806RB (Apprill
et al. 2015) as described in the 16S Metagenomic Sequencing Library
Preparation protocol (Part \# 15044223 Rev. B, www.illumina.com). Sample
libraries were barcoded using the Nextera XT index kit (v2 set A,
www.illumina.com), combined in equimolar amounts, and sequenced on
Illumina's iSeq-100 platform using a 293 cycle single-end R1 mode.
Demultiplexing of reads was performed by the iSeq-100 local run manager
and sample-specific reads were saved in FastQ format.

Microbiome analysis

Statistical analyses of the microbial data were performed using QIIME2
(Bolyen et al. 2019) and R 4.2.1 (R Core Team 2022). Forward reads of
16S rDNA fragments (R1 reads) were analyzed using the QIIME2 microbiome
analysis package (ver. 2021.11; Bolyen et al. 2019). Unless indicated
otherwise, all analysis tools were used as plugins of the QIIME2
package. The respective parameters used along the analysis steps are
readily accessible by provenance information in the QIIME2 data files
(available as supplemental data). In brief, the following analysis steps
were performed: Demultiplexed reads were trimmed for 16S primer
sequences (plugin cutadapt; Martin 2011), denoised, dereplicated and
chimera-checked (plugin DADA2; Callahan et al. 2016) resulting in
amplified sequence variants (ASVs). Rare ASVs were filtered using the
median frequency of ASVs over all samples. Taxonomic classification of
ASVs was performed (plugin feature-classifier; Bokulich et al. 2018)
using the pre-fitted sklearn-based taxonomy classifiers based on the
SILVA reference database (ver. 138.1; (Quast et al. 2013; Yilmaz et al.
2014). ASVs that could not be taxonomically assigned at any taxonomic
level (`unassigned') as well as samples with less than 3,900 reads in
total were removed prior to subsequent analysis steps. Alpha diversity
metrics, such as Shannon diversity index, Faith's phylogenetic
diversity, Pielou's eveness, and observed ASVs, were obtained using the
QIIME2's `core-metrics-phylogenetic' workflow (plugin diversity),
rarefied to 3,900 reads per sample. To find significant differences in
\selectlanguage{greek}α-\selectlanguage{english}diversity we fitted generalized linear models (GLMs) with treatment as
factor. We checked model assumptions using model diagnostic test plots,
i.e., qqplot and residual vs. predicted plot from the
package\emph{DHARMa} (Hartig 2022). Depending on model assumptions, we
then used Kruskal-Wallis tests or produced F-statistics with the
function\emph{Anova} () from the package \emph{car} (Fox \& Weisberg
2019) to calculate p-values for differences between treatments. For
significant treatment effects, we ran pairwise comparisons. In the case
of a significant Kruskal-Wallis test, pairwise comparisons were done
using Dunn's test for multiple comparisons with Benjamini-Hochberg
correction (package \emph{dunn.test} (Dinno 2007)). In the case of a
significant ANOVA, pairwise comparisons were made using Tukey HSD
post-hoc test with Benjamini-Hochberg correction from the package
\emph{multcomp} (Hothorn et al. 2008). Differential abundance of the
rarefied data we analyzed using the package \emph{DESeq2} with a
negative binomial distribution, a significance level cutoff of FDR
\textless{} 0.01, replacement of outliers turned off, and cooksCutoff
turned off (Love et al. 2014). Compositional differential abundance
analysis was performed using Aldex2 (plugin aldex2; Fernandes et al.
2013). Beta diversity of the sparse, compositional microbiome data were
calculated using QIIME2's plugin DEICODE which performs a robust
Aitchison PCA (Martino et al. 2019). Significance was tested in a
PERMANOVA with 999 permutations followed by pairwise PERMANOVA with
Benjamini-Hochberg (BH) correction for multiple testing (Anderson 2008).
We used the packages \emph{qiime2R} (Bisanz 2018) and \emph{mia} (Ernst
et al. 2022a) to import and process the microbiome data in R. Data were
arranged using the package \emph{tidyr}(Wickham \& Girlich 2022) and
were plotted using the packages\emph{ggplot2} (Wickham 2016),
\emph{ggpubr} (Kassambra 2020), and\emph{miaViz} (Ernst et al. 2022b).

Transcriptome analysis of whole bumblebee abdomens

Bumblebees were defrosted and rinsed in 70\% ethanol, 90\% ethanol and
twice in ultrapure water prior to dissection. The abdomen was cut off
with sterile scissors, placed in an Eppendorf tube and snap-frozen in
liquid nitrogen. All samples were stored at -80\selectlanguage{ngerman}° C until further
processing.

RNA sequencing

Total RNA was prepared from abdomen samples using the RNeasy Lipid
Tissue kit (Qiagen, no. 74804, Hilden, Germany). RNA-Seq libraries were
constructed from 100 ng RNA using the NEBNext Ultra II Directional
Library Prep Kit for Illumina (New England Biolabs, no. E7760, Ipswich,
USA) in combination with the NEBNext Poly(A) mRNA Magnetic Isolation
Module (New England Biolabs, no. E7490, Ipswich, USA). The samples were
combined at equimolar amounts and sent out for sequencing on an Illumina
device in 150 bp paired-end mode (Genewiz, Leipzig, Germany). A total of
1.470 million reads, corresponding to an average of 19.5 million reads
per sample, were obtained.

Differential expression analysis

RNA-Seq reads were further analyzed using the OmicsBox bioinformatics
platform (v. 2.0.36, www.biobam.com). Unless indicated otherwise, all
tools used for differential expression analyses are accessible within
the OmicsBox platform. RNA-Seq reads were preprocessed by Trimmomatic
(Bolger et al. 2014) to remove sequencing adapters, low-quality
sequences, and short reads from the dataset. The quality-trimmed reads
were mapped to the \emph{B. terrestris} genome assembly (Bter\_1.0,
GCA\_000214255.1, downloaded from metazoa.ensembl.org) using STAR (Dobin
et al. 2013). A gene-specific count table was created from the mapping
files using HTseq (Anders et al. 2015) and differentially expressed
genes were identified by edgeR (Robinson et al 2010), respectively.
Functional annotation of the\emph{B. terrestris} genome was based on
annotation release v. 102 (available in gff3 format from
metazoa.ensembl.org). Since 4,975 of the 12,008 genes did not contain
any functional annotation, the functional annotation workflow of the
OmicsBox platform was used to update the published annotation with
additional information. In brief, the coding sequences of unannotated
genes were used to extract functional annotations from refseq\_protein
database (www.ncbi.nlm.nih.gov) and InterProScan (www.ebi.ac.uk). These
we then fed into the GO mapping and annotation tools of the pipeline and
finally merged to the existing functional annotations. Gene Set
Enrichment Analyses (GSEA; Subramanian et al. 2005) were performed using
ranked list of genes (rank = sign(logFC) * -log10(p-value); FC:
fold-change) and gene sets defined by Gene Ontology's functional
annotations. For the functional network analysis of enriched GO terms we
used ClueGo (v. 2.5.9; Bindea et al. 2009) and CluePedia (v. 1.5.9;
Bindea et al. 2013) plugins in Cytoscape (v. 3.9.1; Shannon et al.
2003). We used the packages \emph{ggplot2} (Wickham 2016), \emph{ggpubr}
(Kassambra 2020) and \emph{pheatmap} (Kolde 2019) to plot transcriptome
data in R 4.2.1 (R Core Team 2022).

Results

Effect of pollutants on the bumblebee gut microbiome

Amplicon sequencing of the bacterial 16S rDNA fragments yielded a total
of 2,425,928 raw reads. After quality filtering and removal of
unassigned sequences, we also removed samples with a sampling depth
below 3900 reads (n=7), all from DEP treatment, to ensure adequate
sampling depth (13 DEP replicate samples remained in the analysis). In
the remaining samples we obtained 1,856,025 16S rDNA gene sequences with
a mean of 25,425 reads per sample (n=73), corresponding to 468 amplicon
sequence variants (ASVs). Sample-based rarefaction curves suggest a
sufficient sequencing depth for a representative coverage of the
microbiome as most of the samples reach a plateau (\textbf{Figure A1} ).

Taxa abundance

On the genus level, the most common bacterial taxa (\textgreater{} 1 \%
in at least one treatment) were: \emph{Gilliamella, Snodgrassella,
Lactobacillus, Asaia, Bombiscardovia, Methylorubrum}
and\emph{Bombilactobacillus.} The relative abundance of the most common
genera for each sample shows a different microbial composition in the
DEP treatment compared to the other treatment groups (\textbf{Figure 1}
).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

Figure 1: Relative abundance of the most common bacterial genera for
each sample. Samples are arranged according to treatment.

While the relative abundance of ASVs did not differ between control,
solvent control, and brake dust, DEP treatment had 16 differentially
abundant ASVs compared to the control, according to DESeq2
(\textbf{Figure 2} , \textbf{Table A1} ). Eleven ASVs had a higher
abundance in the DEP treatment than control. Five ASVs had reduced
abundance in comparison to the control treatment. A more conservative
approach to identify differential abundance is ALDEx2, which revealed
five ASVs with significantly altered abundance in the DEP treatment
compared to the control: \emph{Snodgrassella} 1 + 2,
Neisseriacae,\emph{Lactobacillus bombicola} , and \emph{Bombiscardovia}
(\textbf{Table A2} ).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

Figure 2: Log2 fold change in relative abundance of ASVs in the DEP
treatment in comparison to the control. Cutoff for inclusion of ASVs in
this plot was FDR (=P\textsubscript{adj}) \textless{} 0.01. Colors
represent most specific taxonomic label.

?-diversity of the gut microbiome

The number of observed ASVs did not differ between treatments (GLM with
gamma distribution: F\textsubscript{3,69} = 0.3008, \emph{P} =
0.825;\textbf{Figure 3A} ). Pielou's evenness differed between
treatments (Kruskal-Wallis rank sum test: \emph{X} \textsuperscript{2} =
23.296, df = 3, P \textless{} 0.001; \textbf{Figure 3B} ). The DEP
treatment had a significantly lower evenness than the other treatments
(Dunn's comparisons with Benjamini-Hochberg (BH) adjusted p-values: DEP
vs. control \emph{P} \textless{} 0.001, DEP vs. solvent control
\emph{P}\textless{} 0.001, DEP vs. brake dust \emph{P} \textless{}
0.001; Figure\textbf{3B} ). Shannon diversity differed between
treatments (Kruskal-Wallis rank sum test: \emph{X} \textsuperscript{2} =
14.642, df = 3, P = 0.002; \textbf{Figure 3C} ). The DEP treatment had a
significantly lower diversity than the other treatments (Dunn's
comparisons with BH adjusted p-values: DEP vs. control \emph{P} = 0.003,
DEP vs. solvent control \emph{P} = 0.001, DEP vs. brake dust \emph{P} =
0.010; \textbf{Figure 3C} ). Faith's PD differed between treatments
(Kruskal-Wallis rank sum test: \emph{X} \textsuperscript{2} = 10.777, df
= 3, P = 0.013; \textbf{Figure 3D} ). Faith's PD in the DEP treatment
was significantly higher than in the other treatments (Dunn's
comparisons with BH adjusted p-values: DEP vs. control \emph{P} = 0.009,
DEP vs. solvent control \emph{P} = 0.009, DEP vs. brake dust \emph{P} =
0.007;\textbf{Figure 3D} ).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

Figure 3: ?-diversity of the bumblebee gut microbiomes for the different
treatments. A) Observed ASVs, B) Pielou's Evenness, C) Shannon
Diversity, D) Faith's PD. Asterisks indicate significant differences
compared to the other treatments (P \textless{} 0.05). Boxplots show
median, first, and third quartile. Dots represent individual data
points.

?-diversity of the gut microbiome

The community composition of the bumblebee gut microbiome differed
between treatments indicated by significant differences between the
robust Aitchison distances (Overall PERMANOVA pseudo-F\textsubscript{4,
73} = 16.844, \emph{P} = 0.001). Microbial community composition of the
DEP treatment differed from all other treatments (Pairwise-PERMANOVA
with BH adjusted p-values; DEP vs. control: pseudo-F = 32.247, \emph{P}=
0.002; DEP vs. solvent control: pseudo-F = 30.651, \emph{P} = 0.002; DEP
vs. brake dust: pseudo-F = 25.699, \emph{P} = 0.002). We found no
differences between the other treatments (Pairwise-PERMANOVA with BH
adjusted p-values: \emph{P} \textgreater{} 0.05) (\textbf{Figure 4} ).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

Figure 4: DEICODE distances based on Robust Aitchison Principal
Components Analysis. Points represent single samples colored according
to treatment. Arrows represent Euclidian distances from the origin and
indicate ASVs with strong influence on the principal component axis.
Ellipses show 95\% confidence interval for multivariate t-distribution
of each treatment. The ASV of the eukaryotic organism Bombus rupestris
can explained by a remaining non-specificity of the used primers (as
analyzed by TestPrime, www.arb-silva.de).

Effect of pollutants on bumblebee gene expression

In the transcriptome analysis, we focused only on biologically relevant
comparisons of treatments to prevent unnecessary inflation of reported
results. We compared control vs. solvent control, control vs. DEP,
control vs. brake dust, and flight control vs. DEP flight. The analysis
for differently expresses genes (DEGs) revealed differences between our
treatments. In total, 324 genes were differentially expressed in the DEP
treatment compared to the control (low-count gene filter settings: CPM
Filter=1, samples reaching CPM Filter=2). 165 genes were upregulated
(LogFC \textgreater{} 1) and 159 genes downregulated (LogFC \textless{}
-1), respectively (\textbf{Figure A2} ). In the brake dust treatment
only one gene was differentially expressed (upregulated) in comparison
to the control. In the solvent control, there were no differentially
expressed genes compared to the control. In the DEP flight treatment, we
found no differentially expressed genes in comparison to the flight
control.

The variation in gene expression of bumblebee workers is clearly
distinct between the control and the diesel exhaust particle treatment
(\textbf{Figure 5} ). The clear separation between the treatments across
all samples indicates substantial differences in gene expression of
bumblebees when exposed to DEP orally. The reliability of this
difference in gene expression is confirmed by a cluster analysis which
shows a definite clustering by treatment rather than by colony
(\textbf{Figure 6} ). The other treatments are not clearly distinct in a
nMDS plot and indicate no differences in gene expression
(\textbf{Figures A3-A5} ), thus we do not conduct further analyses on
these comparisons.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

Figure 5: Non-metric multidimensional scaling plot based on the log2
fold changes (FC) between control and DEP treatment. The axes of the
nMDS plot represent dimensional reductions of gene expression
visualizing the variability of the transcriptional changes for each
treatment. Each point represents one sample, colored according to
treatment.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image6/image6}

\end{center}
\end{figure}

Figure 6: Heatmap showing hierarchical clustering of samples (x-axis)
and differentially expressed genes (y-axis) for the control and DEP
treatment. Cluster color represents the expression level of genes in
log\textsubscript{2}CPM (Counts per million reads).

The 324 differentially expressed genes in the DEP treatment were
annotated to gene ontology (GO) terms, which describe gene properties
and group each into one of three categories: Cellular component,
molecular function, and biological process. We used GO enrichment
analysis to find the most over- and underrepresented term. The 30 most
significantly upregulated GO terms in the DEP treatment include
protein-binding functions, enzyme complexes and metabolic, especially
catabolic, processes (\textbf{Figure 7A} ). The 30 most significantly
downregulated GO terms in the DEP treatment include transferase
activity, mitochondrial and organelle membranes, as well as metabolic,
especially biosynthetic, processes (\textbf{Figure 7B} ).

The functional network analysis based on ?-Score [?] 0.4 for
differentially expressed GO terms with FDR [?] 0.05 in the DEP treatment
shows clustering to specific functional groups (\textbf{Figure A6A} ).
Upregulated functions are related to phosphorylation, regulation of
metabolic process, guanyl nucleotide binding, and signal transduction
(\textbf{Figure A6B} ). Downregulated functions are related to
mitochondria, lipid metabolic processes, the endoplasmic reticulum, and
phospholipid biosynthetic processes (\textbf{Figure A6C} ).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image7/image7}

\end{center}
\end{figure}

Figure 7: Gene ontology terms of A) the 30 most significantly
upregulated and B) downregulated genes in the DEP treatment colored by
category and sorted by -log10FDR.

Discussion

In this study, we found that oral exposure to diesel exhaust particles
(DEPs) changes the gut microbiome and gene expression of bumblebee
workers, while DEP exposure via air did not. Brake dust, the second
pollutant we tested via oral exposure, did not induce changes in the gut
microbiome or gene expression in the bumblebee workers.

While the composition of the microbial gut community in control, solvent
control, and brake dust exposure treatment was similar, we detected
major shifts in the DEP treatment. This raises several interesting
questions: 1) How do DEPs affect the bacteria to induce changes in the
gut microbiome composition? 2) Which components in diesel exhaust are
responsible for the observed changes? Our hypothesis is that PAHs could
be the component of DEP affecting bacteria directly. DEPs contain
different PAHs, a class of organic compounds well-known to be toxic,
mutagenic, and genotoxic to various life forms (Patel et al. 2020, Sun
et al. 2021). Also, shifts in the microbial gut community due to PAH
exposure have been reported in different animals, such as fish, sea
cucumbers, or potworms (Enchytraeidae) (DeBofsky et al. 2020, DeBofsky
2021, Ding et al. 2020, Quintanilla-Mena et al. 2021, Zhao et al. 2019).
Therefore, we suspect PAHs to be the leading cause of changes in the
bumblebee gut microbiome in our study. However, the large amount of
elemental carbon in DEPs, may itself provide another explanation. The
DEPs may function like activated carbon with its large
surface-area-to-volume ratio and may adsorb microbes that are then
discharged by excretion (Naka et al. 2001, Rivera-Utrilla et al. 2001,
Wichmann 2007). Even though activated carbon has no direct negative
impact, constant adsorption and discharge might disrupt the bacterial
community resulting in the compositional and quantitative changes
similar to those observed in our study.

The bacterium \emph{Snodgrassella} , one of the dominant core bacteria
in undisturbed gut microbiomes of bumblebees (Hammer et al. 2021), is
nearly absent after the DEP exposure. \emph{Snodgrassella} , together
with \emph{Gilliamella} , forms a biofilm coating the inner wall of the
ileum (Hammer et al. 2021, Martinson et al. 2012). Both, host and
symbionts could profit from this biofilm formation as it prevents
bacteria from washout and enables the formation of a syntrophic network
(Kwong et al. 2014, Powell et al. 2016, Zhang et al. 2022).
Additionally, the biofilm could protect the host against gut parasites,
such as \emph{C. bombi,} who need to attach to the gut wall to persist
(Koch et al. 2019, N\selectlanguage{ngerman}äpflin \& Schmid-Hempel 2018). However, the
mutualistic relationship between the microbes seems to be disrupted by
DEP exposition, as \emph{Snodgrassella} abundance is extremely
diminished. In contrast, \emph{Gilliamella} increases in relative
abundance after DEP exposure. This indicates that \emph{Gilliamella} may
be able to form a biofilm independently from \emph{Snodgrassella} . A
relatively simple explanation for the higher relative abundance
of\emph{Gilliamella} might be that the reduction of
\emph{Snodgrassella}leaves \emph{Gilliamella} as the only dominant
bacterium in the gut and therefore \emph{Gilliamella} might thrive
better or fill the void.\emph{Snodgrassella} seems especially prone to
pollutants, as Rothman et al. (2020) already reported a decrease in its
relative abundance after exposure of bees to copper, selenate, or
glyphosate. Additionally, we found an unknown bacterium from the family
Neisseriaceae, the same family to which also \emph{Snodgrassella}
belongs, having a lower relative abundance after DEP exposure. If this
is a consistent result, it might indicate a general susceptibility of
this family to DEPs.

The higher abundance of \emph{Asaia} in the DEP treatment was driven by
two samples, in which \emph{Asaia} dominates the bacterial community
with relative abundances of 99 \% and 67 \%, respectively.
\emph{Asaia}is a flower-associated acetic acid bacterium, which is
commonly found in the gut of members of different insect orders, such as
Hemiptera, Diptera, and Hymenoptera (Bassene et al. 2020, Crotti et al.
2009, Kautz et al. 2013). It can dominate the gut microbiome of
\emph{Anopheles}mosquitos, which is why it is considered a potential
tool in malaria control (Capone et al. 2013, Favia et al. 2008). While
there have been reports of \emph{Asaia} in bumblebees, the dominance of
\emph{Asaia} in some of the DEP samples is rather uncommon (Bosmans
2018). DEPs might disrupt the natural microbiome community opening the
door for opportunistic bacteria such as \emph{Asaia} (Favia et al.
2007). Even though we kept the bumblebees in this experiment indoors
throughout their lives, \emph{Asaia} bacteria may derive from pollen fed
to the bumblebees before the start of the experiment.

We detected an interesting pattern in the genus \emph{Lactobacillus} ,
one of the core gut bacteria of bumblebees (Hammer et al. 2021). While
the species \emph{L. bombicola} , a bumblebee-associated bacterium, has
a lower abundance after DEP exposure, the abundance of the
honeybee-associated \emph{L. apis} increases. Again, the disruption of
the original microbiome caused by DEPs might explain that foreign
bacteria can establish themselves in the microbiome. As the pollen fed
to the bumblebees before the experiment was collected by honeybees, it
could be the source of \emph{L. apis} .

The DEP-induced changes in the gut microbiome may affect bumblebee
health, as core bacteria could prevent infections by parasites. The
abundance of \emph{Gilliamella} , \emph{Lactobacillus}
and\emph{Snodgrassella} is negatively correlated with the
parasites\emph{Crithidia} and \emph{Nosema} , while non-core bacteria
are more abundant in infected bumblebees (Cariveau et al. 2014, Koch et
al. 2012, Koch \& Schmid-Hempel 2012, Mockler et al. 2018). The biofilm
formation of \emph{Snodgrassella} and \emph{Gilliamella} may form a
physical barrier to the trypanosome \emph{C. bombi} which needs to
attach to the ileum wall to persist (Koch et al. 2019, Näpflin \&
Schmid-Hempel 2018). The disruption of this biofilm and the higher
abundance of non-core bacteria, such as \emph{Asaia} , may increase the
parasite susceptibility of bumblebees exposed to DEPs.

The transcriptome analysis revealed significant changes in gene
expression after oral exposure of bumblebees to a sublethal dose of
DEPs. In total, 165 genes were upregulated, and 159 genes were
downregulated. GO enrichment analysis and network analysis indicate that
these changes could be related to a general stress response against
pollutants. While upregulated GO terms involve many metabolic and
catabolic processes, downregulated GO terms include metabolic and
biosynthetic processes. DEP exposure might deplete stored reserves
causing the observed changes as a consequence of higher energetic costs.
Changes in metabolism seem to be a typical reaction to pollutants in
insects which seems reasonable as they often interfere with biochemical
processes. Transcriptional changes in bumblebees and honeybees exposed
to sublethal doses of neonicotinoids are mainly linked to metabolic
processes (Bebane 2019, Colgan 2019, Gao et al. 2020, Shi et al. 2017).
Exposure to heavy metals or PAHs induces similar changes in spiders,
mosquitos, moths, and fireflies (Chen et al. 2021, David et al. 2010, Li
et al. 2016, Zhang et al. 2019, Zhang et al. 2020). Even though the
changes differ in detail, certain processes seem commonly involved in
the response to pollutants. Consistent with our findings, exposure to
insecticides or PAHs affects mitochondrial functioning, an important
part of the insect energy metabolism (Colgan et al. 2019, Zhang et al.
2019, Zhang et al. 2020). This supports the idea of increased energy
demand caused by pollutants (Beyers et al. 1999, Calow 1991). We also
observed an upregulation of signal transduction in our study, similar to
observations in honeybees and fireflies exposed to Imidacloprid and the
PAH benzo(a)pyrene, respectively (Gao et al. 2020, Zhang et al. 2019,
2020). Typically, chemical stressors, such as PAHs, insecticides, and
heavy metals, affect genes associated with detoxification processes and
drug metabolism (Chen et al. 2021, David et al. 2010, Gizaw et al. 2020,
Zhang et al. 2019). However, in our study, we did not find any
differentially expressed detoxification-related genes. Possibly the
number of PAHs attached to the DEPs was not enough to trigger a reaction
that would lead to a measurable increase in detoxification. Overall, the
observed changes in gene expression after oral DEP exposure of
bumblebees resemble a general stress response to pollutants.

In contrast to oral exposure, we did not find any effect on gene
expression after exposure of bumblebees to DEPs via the air. To cause
changes, DEPs need to enter the tracheal system or attach to sensory
organs, such as the antennae. The exposure of bumblebees for three
minutes per day may not have been enough to affect them. Particles on
the antennae may have been removed quickly by cleaning behavior and the
spiracles seem to be an effective protective barrier against the uptake
of particles into the tracheae (Harrison 2009, Schönitzer 1986). Thus,
our results should be taken with care because probably only very few
particles entered the tracheal system of the bumblebees.

Unlike DEPs, oral exposure to brake dust particles did not affect the
gut microbial community nor the gene expression of the bumblebees.
However, some concerns remain about the experimental procedure. For one,
we did not use brake dust from a real braking scenario, but rather
artificially milled brake pads. Dust derived from them may have
different physicochemical properties. Milled brake dust particles have a
much higher mean particle size than DEPs (10 \selectlanguage{greek}µ\selectlanguage{english}m vs. 0.01 \selectlanguage{greek}µ\selectlanguage{english}m). As we
defined treatment concentration per weight, these different physical
properties lead to big differences in the particle counts of the
treatment solutions, i.e. solutions with brake dust contained far fewer
particles than those with DEPs. Moreover, large brake dust particles
tend to sink to the bottom of the feeding syringes which might have
reduced the particle uptake. While brake dust seems not to affect the
bumblebees, further studies are needed to address the indicated
limitations of the present study.

Taken together, the results from our microbiome and transcriptome
analysis indicate potential consequences for insect health, here shown
in bumblebees, after oral DEP exposure. Gut dysbiosis may increase the
susceptibility of bumblebees to pathogens, while a general stress
response may lower available energetic resources. To evaluate these
hypotheses further studies should investigate the combined effect of DEP
exposure and other stressors, such as parasites, limited food
availability, or abiotic factors. Bumblebees may be able to compensate
for facing one stressor but will eventually be overstrained by multiple
stressors. Additionally, whole colony experiments would add to the
evaluation of DEPs as a potential contributor to insect losses, as
effects may be small on the individual level but accumulate on the
colony level.

Data availability statement

The raw data supporting the conclusions of this article will be made
available by the authors, without undue reservation. The microbiome and
RNA-Seq sequencing data were deposited at NCBI's Sequence Read Archive
(SRA) under Bioproject numbers PRJNA907197 (16S microbiome sequencing)
and PRJNA907822 (transcriptome sequencing), respectively.

Author contributions

DS, AW, OO, and HF conceived the idea, designed the experiment, and
wrote the manuscript. AM, TH, TO, NL, and DB produced and analyzed the
particulate matter. DS, MR, and AW carried out the experiment. DS and AW
performed the data analysis. DS, AW, OO, and HF interpreted the results.
All authors read and approved of the final manuscript.

Funding

This project was funded by the Bavarian State Ministry of the
Environment and Consumer Protection as part of the project network
BayOekotox.

\par\null

Acknowledgements

We thank Sara P\selectlanguage{ngerman}ölloth, Simon Bitz, Frederic Hüftlein, and Helena
Hartmann for helping with the lab work, and Michaela Hochholzer and
Andrea Kirpal (Keylab Genomics and Bioinformatics) for preparing the NGS
libraries.

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Appendix\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image8/image8}

\end{center}
\end{figure}

Figure A1: Rarefaction curve of each sample, colored according to their
respective treatment. X-Axis is cut off at 10000 reads. Vertical dashed
line indicates sequencing depth of 3900.

Table A1: Differentially abundant ASVs comparing DEP to the control
treatment, according to DESeq2 (cutoff: FDR \textless{} 0.01). Positive
Log2 fold changes indicate higher abundance in the DEP treatment.\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
ASV & Log2 Fold Change & P\textsubscript{adj} (= FDR) & Feature
ID\tabularnewline
\midrule
\endhead
\emph{Lactobacillus bombicola} & -5.372 & \textless{}0.001 &
ac3366c90455cdc1a4ad414f21215a91\tabularnewline
\emph{Snodgrassella} 1 & -4.848 & \textless{}0.001 &
f9dff838e1ab76a58a54df65a2457d5a\tabularnewline
\emph{Snodgrassella} 2 & -4.256 & \textless{}0.001 &
8f7166172175c35bbfc8fa4dc5ef58b8\tabularnewline
Neisseriaceae & -3.108 & \textless{}0.001 &
f1ae3848b7e710b5da56f2a447ae0234\tabularnewline
\emph{Bombiscardovia} & -1.251 & 0.010 &
bf7591505d4138d52e3a9c537c958fa1\tabularnewline
\emph{Gilliamella} 1 & 2.146 & \textless{}0.001 &
36aed5b1dc9b5c1a2844e58f2d34b1f5\tabularnewline
\emph{Gilliamella 2} & 2.473 & \textless{}0.001 &
1e232cdf347e2b62b3b1d7347e891797\tabularnewline
Bacteria unspec. 1 & 3.162 & 0.001 &
6445d5095ad81f1b73aa974a171ebce6\tabularnewline
\emph{Bombus rupestris} & 3.645 & \textless{}0.001 &
6d53feb4ee4fac60aba11969e1e5fc01\tabularnewline
Bacteria unspec. 2 & 3.768 & 0.004 &
101de948d3a66ac329a31fd5f92c00d5\tabularnewline
Bacteria unspec. 3 & 4.008 & \textless{}0.001 &
7ebb40e08aa315a3ab9ae5fb0b47ae34\tabularnewline
\emph{Methylorubrum} & 4.025 & \textless{}0.001 &
92f1720367db58c68a96eceb9feb416a\tabularnewline
Bacteria unspec. 4 & 4.030 & \textless{}0.001 &
5c70c440562c05d292daf0c5b4694ef4\tabularnewline
Bacteria unspec. 5 & 4.201 & \textless{}0.001 &
a6ddcd6498df4ed3d6c3e05663f658fb\tabularnewline
\emph{Asaia sp.} & 10.960 & \textless{}0.001 &
49d46d00a93443b060707ab2db8ba82d\tabularnewline
\emph{Lactobacillus apis} & 14.158 & \textless{}0.001 &
96d14363f547715b65bf7d8ad1d31d17\tabularnewline
\bottomrule
\end{longtable}

Table A2: Differentially abundant ASVs comparing DEP to the control
treatment, according to ALDEx2. Negative effect indicates higher
abundance in the control. P\textsubscript{adj} = Expected
Benjamini-Hochberg corrected P value of Wilcoxon test. Effect = median
effect size (diff.btw/max(diff.win)).\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
ASV & Effect & P\textsubscript{adj} & Feature ID\tabularnewline
\midrule
\endhead
\emph{Snodgrassella} 2 & -5.516 & \textless{}0.001 &
8f7166172175c35bbfc8fa4dc5ef58b8\tabularnewline
Neisseriaceae & -2.659 & \textless{}0.001 &
f1ae3848b7e710b5da56f2a447ae0234\tabularnewline
\emph{Lactobacillus bombicola} & -2.393 & \textless{}0.001 &
ac3366c90455cdc1a4ad414f21215a91\tabularnewline
\emph{Snodgrassella} 1 & -2.356 & \textless{}0.001 &
f9dff838e1ab76a58a54df65a2457d5a\tabularnewline
\emph{Bombiscardovia} & -2.092 & \textless{}0.001 &
bf7591505d4138d52e3a9c537c958fa1\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image9/image9}

\end{center}
\end{figure}

Figure A2: Differential expression of genes in the DEP treatment in
comparison to the control. Blue dots represent significantly
downregulated genes, red dots represent significantly upregulated genes.
The horizontal red line marks a -log\textsubscript{10}(FDR=0.05). The
two vertical red lines mark a log\textsubscript{2}FC of -1 and 1,
respectively.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image10/image10}

\end{center}
\end{figure}

Figure A3: Non-metric multidimensional scaling plot based on the log2
fold changes (FC) between control and solvent control. The axes of the
nMDS plot represent dimensional reductions of genes expression
visualizing the variability of the transcriptional changes for each
treatment. Each point represents one sample, colored according to the
respective treatment.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image11/image11}

\end{center}
\end{figure}

Figure A4: Non-metric multidimensional scaling plot based on the log2
fold changes (FC) between control and brake dust treatment. The axes of
the nMDS plot represent dimensional reductions of genes expression
visualizing the variability of the transcriptional changes for each
treatment. Each point represents one sample, colored according to the
respective treatment.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image12/image12}

\end{center}
\end{figure}

Figure A5: Non-metric multidimensional scaling plot based on the log2
fold changes (FC) between flight control and DEP flight treatment. The
axes of the nMDS plot represent dimensional reductions of genes
expression visualizing the variability of the transcriptional changes
for each treatment. Each point represents one sample, colored according
to the respective treatment.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image13/image13}

\end{center}
\end{figure}

Figure A6: Network analysis of enriched gene terms and functional groups
in the DEP treatment based on Kappa-Score [?] 0.4 for GOs with FDR [?] 0.05
using the ClueGo and CluePedia plugins of Cytoscape. (A) Functionally
grouped network of upregulated (red) and downregulated (blue) gene
ontologies. (B) pie chart with functional groups, including specific
terms upregulated in the DEP treatment. (C) pie chart with functional
groups, including specific terms downregulated in the DEP treatment. The
area covered by each group represents the relative number of GO terms
within each group. The most significant term each group is labelled.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Seidenath-et-al/Seidenath-et-al}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-1-Taxa-Barplot/Figure-1-Taxa-Barplot}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-2-DESeq/Figure-2-DESeq}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-3-Diversity/Figure-3-Diversity}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-4-Deicode/Figure-4-Deicode}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-5-nMDS-DEP/Figure-5-nMDS-DEP}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-6-Heatmap/Figure-6-Heatmap}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-7-GO-terms/Figure-7-GO-terms}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-A1-Rarefaction/Figure-A1-Rarefaction}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-A2-Volcano/Figure-A2-Volcano}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-A3-nMDS-solvent-control/Figure-A3-nMDS-solvent-control}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-A4-nMDS-brake-dust/Figure-A4-nMDS-brake-dust}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-A5-nMDS-DEP-flight/Figure-A5-nMDS-DEP-flight}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-A6-Network/Figure-A6-Network}

\end{center}
\end{figure}

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