Microb Ecol DOI 10.1007/s00248-017-1068-x

HUMAN MICROBIOME

Development of a Stable Lung Microbiome in Healthy Neonatal Mice Matea Kostric 1 & Katrin Milger 2,3 & Susanne Krauss-Etschmann 4,5 & Marion Engel 6 & Gisle Vestergaard 1 & Michael Schloter 1,7 & Anne Schöler 1

Received: 26 May 2017 / Accepted: 5 September 2017 # Springer Science+Business Media, LLC 2017

Abstract The lower respiratory tract has been previously considered sterile in a healthy state, but advances in cultureindependent techniques for microbial identification and characterization have revealed that the lung harbors a diverse microbiome. Although research on the lung microbiome is increasing and important questions were already addressed, longitudinal studies aiming to describe developmental stages of the microbial communities from the early neonatal period to adulthood are lacking. Thus, little is known about the earlylife development of the lung microbiome and the impact of external factors during these stages. In this study, we applied a barcoding approach based on high-throughput sequencing of 16S ribosomal RNA gene amplicon libraries to determine agedependent differences in the bacterial fraction of the murine lung microbiome and to assess potential influences of differing Benvironmental microbiomes^ (simulated by the application of used litter material to the cages). We could clearly show

that the diversity of the bacterial community harbored in the murine lung increases with age. Interestingly, bacteria belonging to the genera Delftia and Rhodococcus formed an ageindependent core microbiome. The addition of the used litter material influenced the lung microbiota of young mice but did not significantly alter the community composition of adult animals. Our findings elucidate the dynamic nature of the early-life lung microbiota and its stabilization with age. Further, this study indicates that even slight environmental changes modulate the bacterial community composition of the lung microbiome in early life, whereas the lung microbes of adults demonstrate higher resilience towards environmental variations.

Keywords Murine lung microbiome . 16S rRNA-based barcoding . Core microbiome . Delftia . Rhodococcus

Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00248-017-1068-x) contains supplementary material, which is available to authorized users. * Michael Schloter [email protected]

1

2

3

4

Division of Experimental Asthma Research, Research Center Borstel, Leibniz-Center for Medicine and Biosciences, Member of the German Center for Lung Research (DZL), Parkallee 1-40, 23845 Borstel, Germany

Research Unit Comparative Microbiome Analysis, Helmholtz Zentrum München, Ingolstädter Landstr. 1, 85716 Neuherberg, Germany

5

Institute for Experimental Medicine, Christian-Albrechts-Universität zu Kiel, Niemannsweg 11, 24105 Kiel, Germany

Department of Internal Medicine V, University of Munich, Comprehensive Pneumology Center, Member of the German Center for Lung Research (DZL), Munich, Germany

6

Research Unit Scientific Computing, Helmholtz Zentrum München, Ingolstädter Landstr. 1, 85716 Neuherberg, Germany

7

ZIEL Institute for Food and Health, Technische Universität München, Weihenstephaner Berg 1, 85354 Freising, Germany

Institute of Lung Biology and Disease (ILBD), Helmholtz Center Munich, Comprehensive Pneumology Center (CPC-M), Munich, Germany

Kostric M. et al.

Introduction The characterization of the human microbiome has received major interest in recent years as it became obvious that the diversity of microbes colonizing our body is a strong driver for health and disease [1]. Whereas in the early days of human microbiome research, mostly the gut microbiota had been studied, today there is increasing evidence for the importance of microbes colonizing other organs like the skin or the upper respiratory tract (URT) [2, 3]. Interestingly, despite all the enthusiasm about the human microbiome, there is much less data available on microbes colonizing the lower respiratory tract (LRT), including the lung. Previous studies investigating the LRT mostly focused on the analysis of shifts within the bacterial community structure in response to diseases such as asthma [4], chronic obstructive pulmonary disease [5], cystic fibrosis [6], bacterial pneumonia [7], HIV [8], or lung transplantation [9]. Research on the healthy LRT is rare but gives indication of a distinct lung-specific microbiome [10]. Major bacterial phyla include Bacteroidetes, Firmicutes, Proteobacteria, and Actinobacteria [11], but their main functional traits and their impact on lung health remain unclear. Besides missing functional aspects, little is known about factors driving the development of the lung microbiome in healthy individuals. Previous research that described developmental changes within the LRT community structure mainly focused on the link to lung diseases and immune development in early childhood [12]. It has been postulated that the primary source community for the LRT derives from the oropharynx [11, 13]. Nonetheless, the oral and lung microbiota vary distinctly within an individual [14], which might result from differences in nutrient availability and redox conditions between both environments [15]. This assumption is supported by a study from Dickson and colleagues [16] that demonstrated a decrease in the bacterial community richness of the LRT with increasing distance from the source community of the URT. Other processes that might trigger the formation of a specific lung microbiome include microbial immigration from the URT into the airways (e.g., inhalation or microaspiration of bacteria) and elimination of microbes from the airways (e.g., cough or host immune defense) [15]. In contrast to the gut or the URT [17, 18], no comprehensive longitudinal study on the temporal development of the healthy early-life LRT microbiome has been performed so far. In this study, we applied a molecular barcoding approach to determine age-dependent differences in the lung microbiota of healthy individuals and potential impacts of environmental changes on the bacterial community structure using mice as model organisms. To this end, we characterized the temporal development of the murine lung microbiota from birth to adult age and investigated the presence of a core microbiome as well as the assumed stabilization process with time. To simulate differing environmental conditions, we exposed the mice

to different amounts of excrements (as a source for microbes, which may enter the LRT via bioaerosols). We hypothesize that in healthy individuals, (i) the microbial diversity is relatively low in the lungs of neonatal mice but increases over time, (ii) the dynamics of shifts in bacterial community composition is higher in early life but stabilizes with age, and (iii) changes in environmental microbiome have a clear impact on the LRT microbial community structure in mice, mainly at younger development stages.

Materials and Methods Sample Description Lung tissue samples were obtained from 40 female BALB/c mice (Charles River Laboratories, Sulzfeld, Germany) at the age of 5 days (neonate), 3 weeks (juvenile), 2 months (young adult), 4.5 months (mature adult), and 9 months (middle age), respectively. The five different time points were chosen in order to cover substantial stages of life, characterized by developmental, maturational, or senescent changes. Twenty mice (four animals per age group) were housed in individually ventilated cages in a specific pathogen-free facility with a 12-h day-night cycle and provided with standard rodent chow and water ad libitum. This included a complete exchange of the used in-cage litter with fresh litter material every 7 days (isolation group). The litter material consisted of sterilized standard laboratory bedding material suitable for mice and rats. Another 20 female mice (intervention group) did not receive fresh litter material but were weekly exposed to already-used litter to significantly increase the bacterial load of the BALB/c mouse cage environment. All animals received a standardized pellet diet and filtered drinking water until sacrifice. At each time point, lung tissue samples of four mice from both isolation and intervention groups were collected and stored at − 80 °C until further processing. During dissection, care was taken to minimize contamination and all animal procedures were realized in accordance with the Federal Guidelines for the Care and Use of Laboratory Animals. In addition, used litter samples were collected and stored as described above to analyze the Benvironmental microbiome.^ At the sampling time point, mice were approximately 5 months old and a total of four litter samples were obtained from the cages on day 7, prior to the exchange with fresh material. Sample Preparation and DNA Isolation Prior to DNA extraction, frozen lung tissue samples were thawed on ice. The samples were mechanically homogenized via bead beating (5.500 RPM for 2 × 30 s); DNA was isolated according to the manufacturer’s instructions, following the

Development of a stable lung microbiome in healthy neonatal mice

MasterPure DNA Purification protocol for tissue samples (Epicentre Biotechnologies, Madison, USA). Genomic DNA was precipitated using isopropanol and ethanol and resuspended in 35 μL of 10 mM Tris buffer (pH 8.0). All samples were extracted at the same time point using the same batch of reagents. A blank DNA extraction without any sample template served as a first negative control. DNA from litter samples was extracted following the same protocol and conditions, including a blank DNA extraction as a negative control.

step, two technical replicates were prepared for each sample and pooled during the purification using Agencourt AMPure beads. PCR products were quantified using the PicoGreen dsDNA assay (see above). The initial blank DNA extraction control (first negative control) was included in each PCR step, in parallel to the rest of the samples, and subsequent blank negative controls of each PCR were in parallel prepared for sequencing.

Bacterial 16S rRNA Gene Amplification

Library Preparation and Amplicon Sequencing

Due to high concentrations of eukaryotic DNA in relation to bacterial DNA in lung tissue samples, a nested PCR protocol was used to amplify specific 16S ribosomal RNA (rRNA) gene fragments. The two-PCR strategy minimized nonspecific primer binding and allowed amplification of the bacterial 16S rRNA gene despite a high background of mouse D NA [19]. We chose t he primer 27F (5′ -AGAG TTTGATCNTGGCTCAG-3′) [20] and 1401R (5′-CGGT GTGTACAAGACCC-3′) [21] for the amplification of long fragments of 1393 bp length, covering the hypervariable regions V1 to V8 of the 16S rRNA gene. NEBNext HighFidelity 2× PCR Master Mix (New England Biolabs, Ipswich, USA), 10 pmol of each primer, and 200 ng template DNA were used for the initial amplification. The thermal cycling profile was the following: initial denaturation at 98 °C for 1 min, followed by 30 cycles of denaturation at 98 °C for 10 s, annealing at 60.5 °C for 30 s, and elongation at 72 °C for 40 s; and a final elongation step at 72 °C for 5 min. For each sample, two technical replicates were prepared and pooled during purification. PCR products were purified using the Agencourt AMPure XP PCR purification system (Beckman Coulter, Brea, USA), and eluted in 40 μL of 10 mM Tris buffer (pH 8.0). Purified amplicons were quantified with the PicoGreen dsDNA assay (Thermo Fisher Scientific, Waltham, USA), according to the manufacturer’s instructions, and diluted to 10 ng/μL. The subsequent nested PCR reaction was performed using the primer pair S-D-Bact-0008-a-S-16 (5′-AGAG TTTGATCMTGGC-3′)/S-D-Bact-0343-a-A-15 (5′-CTGC TGCCTYCCGTA-3′) [22, 23], which was evaluated in silico by Klindworth et al. [24], suggesting a high overall coverage of 16S rRNA genes. This primer pair targets the V1 and V2 regions and produces short amplicons of 335-bp lengths. The forward and reverse primers contained overhanging sequences at their 5′ ends that were compatible with Nextera XT indices (Illumina, San Diego, USA). NEBNext HighFidelity 2× PCR Master Mix, 10 pmol of each primer, and 40 ng of the purified PCR product were used for the amplification. The thermal cycling profile of the nested PCR was equal to that of the first PCR reaction, except for the annealing temperature, which was adjusted to 62 °C. As in the first PCR

Multiplexed paired-end sequencing libraries were prepared using a Nextera XT v2 Index Kit (Illumina, San Diego, USA) by combining amplicons with sequencing adapters and dual indices. For each sample, 10 ng of purified 16S rRNA gene amplicons served as template for an indexing PCR reaction with a thermal profile as follows: initial denaturation at 98 °C for 30 s, followed by 8 cycles of denaturation at 98 °C for 10 s, annealing at 55 °C for 30 s, and elongation at 72 °C for 30 s; and a final elongation at 72 °C for 5 min. PCR products were loaded on a 0.8% agarose gel, and gel electrophoresis was performed at 100 V for 45 min. Bands of the correct size were cut out and purified with the NucleoSpin Gel and PCR Clean-Up Kit (Macherey-Nagel, Düren, Germany), according to the manufacturer’s instructions. The size distribution of sample libraries was validated on an Agilent Bioanalyzer 2100 DNA Chip 7500 (Agilent Technologies, Santa Clara, USA), and DNA was quantified using the PicoGreen dsDNA assay. Sample libraries were normalized to 4 nM and pooled in equimolar amounts for multiplexed sequencing. The run was performed on the MiSeq instrument with the MiSeq Reagent Kit v3 for 600 cycles (Illumina, San Diego, USA). The sequence data was submitted to NCBI via the Sequence Read Archive (SRA) and is available under accession number PRJNA338109.

Data Analysis Generated paired-end reads were analyzed using QIIME 1.9.1 [25]. Several pre-processing steps were applied prior to binning of reads into operational taxonomic units (OTUs). They are described in detail in the Online Supplement. It has been previously shown that the removal of sequence reads that occur only once (singletons) significantly increases the OTU overlap among technical replicates [26]. Therefore, singletons were discarded and OTUs were retained if present in at least two samples. In addition, OTUs present in negative control samples were removed prior to diversity analyses in order to avoid misleading conclusions [27]. Further information on the data analysis and visualization can be found in the Online Supplement.

Kostric M. et al.

Results Summary of Sequencing Results and Processing In order to assess developmental changes in the murine lung microbiome during aging, lung tissue samples were analyzed from mice of different age groups (neonatal up to 9-monthold), including animals from both isolation and intervention groups. This investigation period included different crucial developmental stages of mice like weaning, sexual and breeding maturity, or early senescence. The sequencing of 16S rRNA gene amplicons resulted in a total of 2,460,691 raw reads with an average of 61,517 ± 5138 (mean ± standard error) reads per sample for lung samples. For litter samples, the sequencing yielded a total of 242,927 raw reads with an average of 60,732 ± 3015 (mean ± standard error) sequences per sample. A detailed summary of the sequencing results and further processing can be found in the Online Supplement (Fig. S1, S3; Table S2, S3). Bacterial communities derived from either lung or litter samples clustered distinctively from those of the respective negative controls (Fig. S2).

treatment groups when the same age groups were compared (Fig. 1c). Between-class PCA was performed to investigate changes in lung microbiota composition in mice of different age and treatment (Fig. 2). In general, samples derived from the oldest mouse groups (4.5 and 9 months) clustered closer together, meaning that their bacterial communities were more similar to each other compared to those of mice aged 5 days to 2 months. The major differences between samples were explained by mouse age (Fig. 2a), which was confirmed as statistically significant for both isolation (age: p = 0.001, R2 = 0.417) and intervention groups (age: p = 0.001, R2 = 0.460). Moreover, treatment-induced alterations of the bacterial community composition were observed especially for 3 weeks and 9 months. Here, communities derived from intervention samples clustered separately from those of isolation samples (Fig. 2b, c). Thus, the treatment-dependent grouping of samples was significant (p = 0.012), even though it explained only 3.1% (R2 = 0.031) of the variation between samples.

Age and Treatment Responders Changes in Bacterial Diversity with Age and Treatment Alpha and beta diversity analyses were performed to describe age- and treatment-dependent differences in bacterial community composition within and between sample groups. In order to investigate the age impact, the bacterial community composition in samples derived from the isolation group was compared regarding alpha diversity measures. Shannon diversity was significantly (p = 0.03) increased mainly for communities of mice belonging to the oldest age group (9 months) compared to younger animals (5 days, 3 weeks, and 2 months; Fig. 1a). Apart from a significantly decreased OTU richness (p = 0.03) in samples obtained from animals 3 weeks after birth, a continuous and significant (p ≤ 0.03) increase in OTU richness was observed with age (Fig. 1b). Interestingly, samples obtained from 9-month-old mice showed the least intragroup variation regarding diversity and richness, whereas samples from the age groups 2 and 4.5 months revealed relatively high variation, especially in respect of OTU richness. In contrast, animals of the age group 3 weeks revealed comparatively high but non-significant OTU evenness (Simpson’s evenness) compared to 5 days and 2 months, whereas a trend towards increasing evenness was perceived with age (Fig. 1c). The investigation of treatment-induced alterations (isolation vs. intervention) manifested significant (p = 0.03) differences in diversity for 3-week- and 9-month-old animals (Fig. 1a). Regarding richness estimation, significant differences (p = 0.03) between the isolation and intervention group were apparent only for animals of the age group 3 weeks (Fig. 1b). Here, the lowest richness value was measured for the isolation group. Evenness was comparable for both

The pulmonary microbial communities were composed mainly of bacteria of the phylum Firmicutes (median = 42.5%; range = 19.0–84.8%), Proteobacteria (median = 22.3%; range = 4.3–47.4%), and Actinobacteria (median = 18.1%; range = 2.5–65.0%; Fig. S4). Rhodococcus (19.8%), Streptococcus (7.4%), Staphylococcus (4.6%), Delftia (3.3%), Bacillus (3.2%), Lactobacillus (2.5%), Ochrobactrum (2.3%), Bradyrhizobium (2.3%), Pseudomonas (1.8%), and Ruminococcus (1.7%) represented the top 10 genera (Table S1). Although no OTU was shared across all lung samples, Delftia and Rhodococcus OTUs were observed in 75% of all samples, and one Rhodococcus OTU (OTU37) even in 97% of lung samples. Eleven OTUs belonging to Delftia, Lactobacillus, Propionibacterium, Rhodococcus, and Streptococcus were shared across the majority (≥ 60%) of samples, irrespective of age and treatment (Fig. 3; Table S4). A summary of significantly differentially abundant taxa, responding to either age, treatment, or both age and treatment, is given in Table 1. Thus, the lung microbiota of newborn mice (5 days) was characterized mainly by the genera Staphylococcus and Streptococcus (p < 0.0001 and p = 0.0277, respectively). Bacillus was significantly (p = 0.0002) associated with 3 weeks and 2 months, and the genus Delftia was particularly high abundant at 2 months of age (p = 0.0498). The genus Kocuria was significantly (p = 0.0080) associated with the age groups of 2 and 4.5 months, whereas Granulicatella was mainly observed at 4.5 months (p = 0.0234). Haemophilus, Nocardioides, and Propionibacterium were discriminating genera at 4.5 and 9 months (p = 0.0058, p = 0.0456, and p = 0.0002,

Development of a stable lung microbiome in healthy neonatal mice

Age

a 5 2

3

4

p=0.03

12

Shannon diversity index

Diversity

Treatment

p=0.03

p=0.03 p=0.03

p=0.03

b

60

80

p=0.03

40

Richness

Count of unique otus

100

p=0.02

20

p=0.03

p=0.03

p=0.03

0.20 0.15 0.10 0.05

Evenness

Simpson’s evenness measure

0.25

c

5 days

3 weeks

2 months

4.5 months

9 months

Isolation

Intervention

Fig. 1 Alpha diversity of bacterial communities. Estimation of bacterial a diversity, b richness, and c evenness is visualized using boxplots for age and treatment. Isolation groups are representing age-dependent differences in microbiota, whereas differences between isolation and

intervention group of the same age are considered as treatment-dependent. The Wilcoxon rank-sum test was performed to determine statistical significance of alpha diversity analyses

respectively), and thus, the most overlapping taxa were observed between the two oldest age groups. As main responders to both age and treatment, Aggregatibacter and Rothia were almost exclusively

represented within the 5-day intervention group (p < 0.0001), whereas the genus Bradyrhizobium showed the opposite trend. At 3 weeks, however, Bradyrhizobium was distinctively present within the intervention group (p = 0.0006). Remarkably, the

Kostric M. et al.

a

b

c

Fig. 2 Changes in bacterial community composition with age and treatment. Scatter plot showing the first three principal components (PC1, PC2, and PC3) of a between-class analysis (BCA) for the factor age and treatment. a Sample clusters visualized on PC1 and PC2. b Sample clusters visualized on PC1 and PC3. c Sample clusters visualized on PC2 and PC3. Each sample is indicated by a dot and corresponds to a

particular cluster with an inertia ellipse coefficient of 1.5. Adonis was performed on Bray-Curtis distances using 999 Monte Carlo permutations to estimate the p value for age and treatment (age isolation groups: p = 0.001, R2 = 0.417; age intervention groups: p = 0.001, R2 = 0.460; treatment: p = 0.012; R2 = 0.031). Iso isolation group; Int intervention group

highest number of differentiating genera was observed between the 3-week isolation and intervention groups. Here, the genera Dorea, Marvinbryantia, and Ruminococcus were particularly strongly represented in 3-week intervention samples, also regarding other age groups (p = 0.0028, p = 0.0014, and p = 0.0002, respectively). Prevotella was identified as discriminating genus for the age group of 5 days but also the isolation groups of 2, 4.5, and 9 months (p = 0.0096). Further, the genus Oribacterium was distinctive for the 2-month isolation samples (p = 0.0168), and the genus Diaphorobacter was almost exclusively represented within the 4.5-month isolation group (p = 0.004). Lung microbiota of 9-month-old mice harbored the most differentiating taxa, including the genera Defluvibacter and Ochrobactrum, both higher abundant in intervention (p = 0.0038 and p = 0.0004, respectively; see also Fig. 3). In addition, the genera Mucispirillum and Oscillospira showed a significantly (p = 0.0058 and p = 0.0233, respectively) higher abundance in isolation, compared to the 9-month intervention samples.

community compositions were highly similar to each other (Fig. S5); thus, only minor differences were observed between the analyzed litter samples. To estimate shifts in the murine lung microbiota induced by the litter material, overlaps between lung and litter-derived OTUs were evaluated including all age groups. Overall, 90 litter-associated OTUs were detected across isolation samples, whereas intervention samples shared 146 OTUs with litter (Table 2). The majority of these OTUs belonged to the genera Lactobacillus, Oscillospira, and Ruminococcus as well as genera of the families Lachnospiraceae and S24–7 that could not be further classified. Comparing lung samples from animals which were kept under isolation and intervention revealed that OTUs belonging to genera of the family Clostridiales, as well as OTUs linked to Dorea, Marvinbryantia, and Ruminococcus, were more represented in intervention samples (Table 2). Remarkably, the litter addition revealed distinct temporal dynamics for isolation and intervention treatments (Fig. 4a). For lung samples derived from the isolation group, the number of litter-related OTUs increased during early life and reached with an average of 17.7% (± 0.004; standard error) a constant level of litter-associated OTUs after 3 weeks. For lung samples originating from the intervention groups, however, the number of shared OTUs was already high at 5 days (isolation 8.8%; intervention 14.5%), and reached a maximum at 3 weeks, where 31.2% of the OTUs obtained by the lung samples were shared with litter-derived OTUs. Here, intervention samples showed a considerably higher OTU overlap, compared to the 3-week isolation group (isolation 18.2%; intervention 31.2%). After 3 weeks, the overall level of litterrelated OTUs in the analyzed lung samples was similar but

Impact of Induced Environmental Changes by Litter Addition In the used litter samples, bacteria affiliated to Firmicutes (56.3%), Bacteroidetes (38.0%), and Actinobacteria (3.4%) were dominating; less abundant phyla were related to Proteobacteria, Tenericutes, Cyanobacteria, TM7, and Deferribacteres. The distribution of genera is given in Fig. S5, with Oscillospira (9.6%), Ruminococcus (5.4%), Bacteroides (3.1%), Adlercreutzia (2.6%), Lactobacillus (2.3%), and Marvinbryantia (1.4%) as predominant genera. The bacterial

Development of a stable lung microbiome in healthy neonatal mice

Fig. 3 Age and treatment responders. Heatmap displaying relative abundances of the top 77 core OTUs (i.e., present in 100% of samples/ group) across lung and litter samples. Samples are grouped according to age and treatment, and represented by a color legend. Low abundant (≤ 0.1%) OTUs are colored dark blue, whereas high abundant (≥ 0.9%)

OTUs are colored dark red. Shades emphasize abundance values between 0.1 and 0.9%. Framed rows denote the 11 core OTUs present in ≥ 60% of all samples. For simplification purposes, OTUs were numbered consecutively and an assignment to the respective OTU IDs is given in Table S4. OTU operational taxonomic unit

more fluctuating (15.8% ± 0.038; mean ± standard error) in lung samples from the intervention group, compared to the isolation group. Figure 4b illustrates these distinct temporal dynamics based on two representative litter-associated taxa. For the isolation group, the genus Oscillospira was not

observed in early life (5 days–3 weeks) but revealed a constant increase in relative abundance (group means) with age. Similarly to Oscillospira, a continuous increase in abundance was observed for the genus Ruminococcus with age. In contrast to isolation, Oscillospira and Ruminococcus were

Significantly discriminative bacterial taxa associated with age and treatment

0 0.18 0.36 0.41 1.43 11.33 0 13.65 1.73 0.47 0 13.28 0.97 0 0.36 0 0 0.24 0 0.74 7.72 0.35 0 0 0 1.00 0 1.35

0.0166 <0.0001 0.0096 0.0058 0.0028 0.0014 0.0168 0.0002 0.0233 0.0448 0.0006 0.0004 0.0038 0.0040 <0.0001 0.0251 0.0168 0.0360

0.35 0 0.10

1.48 4.41 2.26 0 0.05 0 0 1.32 1.86 1.83 0.31 0 0.05 0 6.84

0.17 0.13 0.84 1.13 1.19 17.30 0 12.12 2.49 0.47

0.06 0 0

0 48.71 0 0 0.13 0 0 0.30 0 0 0 0 0 0 0

0 0 0 0 12.06 0 0 1.47 7.10 0

Iso

Iso

Int

3 weeks

5 days

0.02 0.16 0

0 15.71 0.13 0.11 6.90 2.02 0 6.31 1.44 0 6.68 0 0 0.02 0.07

0 0 0.06 0 5.63 0.54 0 1.17 0.17 0

Int

% relative abundance of group means

0.0080 0.0456 0.0002 0.0250 0.0002 <0.0001 0.0234 0.0277 0.0498 0.0058

p value

0.72 0.06 0

0 30.09 1.67 0.75 0.38 0 1.75 0.40 1.83 0.56 0.92 0.64 0.03 0 0

0.19 0.67 1.13 0 4.84 1.63 0 5.12 5.81 0.23

Iso

2 months

0.22 1.62 0

0 47.71 0.05 0 0 0 0 0 0.50 0.05 0 0.71 0 0 0

0.18 0 0.52 0.12 4.42 1.62 0 4.87 12.98 0.12

Int

2.13 0 0.98

0.40 7.72 3.91 0.86 0.54 0 0 1.29 2.75 0.52 0.72 0.45 0 7.92 0

0.72 1.10 3.62 0 1.95 3.58 0.37 11.18 0.63 1.64

Iso

0 0.41 0.58

0 9.72 0 1.70 0.89 0.40 0.05 0.59 1.95 0.18 3.26 0.88 0.26 0.27 0

2.26 1.07 2.61 0 0.34 2.55 0.11 5.73 0.45 1.00

Int

4.5 months

0.93 0 1.18

0.47 7.03 1.57 1.49 0 0.27 0 2.25 2.98 0 1.34 5.91 1.47 0.06 0

0 1.37 3.16 0 1.26 4.22 0 3.70 0.32 1.35

Iso

0.84 0.15 0

0 5.10 0.03 0.28 0 0 0 0.44 0.19 2.13 0.41 14.69 3.75 0.86 0

0 0.55 0.65 0.34 1.86 1.71 0 13.34 0.88 0.39

Int

9 months

Iso isolation group, Int intervention group

Two-way ANOVA was used to assess statistically significant (α ≤ 0.05) differences between sample groups based on species abundances. p values were corrected by Fisher’s least significant difference (LSD) test. Phylogeny level of each taxon is given by its maximum degree of annotation

Age Bacteria|Actinobacteria|Actinobacteria|Actinomycetales|Micrococcaceae|Kocuria Bacteria|Actinobacteria|Actinobacteria|Actinomycetales|Nocardioidaceae|Nocardioides Bacteria|Actinobacteria|Actinobacteria|Actinomycetales|Propionibacteriaceae|Propionibacterium Bacteria|Bacteroidetes|Flavobacteriia|Flavobacteriales|Weeksellaceae|Riemerella Bacteria|Firmicutes|Bacilli|Bacillales|Bacillaceae|Bacillus Bacteria|Firmicutes|Bacilli|Bacillales|Staphylococcaceae|Staphylococcus Bacteria|Firmicutes|Bacilli|Lactobacillales|Carnobacteriaceae|Granulicatella Bacteria|Firmicutes|Bacilli|Lactobacillales|Streptococcaceae|Streptococcus Bacteria|Proteobacteria|Betaproteobacteria|Burkholderiales|Comamonadaceae|Delftia Bacteria|Proteobacteria|Gammaproteobacteria|Pasteurellales|Pasteurellaceae|Haemophilus Age and treatment Bacteria|Actinobacteria|Actinobacteria|Actinomycetales|Micrococcaceae|Rothia Bacteria|Actinobacteria|Actinobacteria|Actinomycetales|Nocardiaceae|Rhodococcus Bacteria|Bacteroidetes|Bacteroidia|Bacteroidales|Prevotellaceae|Prevotella Bacteria|Deferribacteres|Deferribacteres|Deferribacterales|Deferribacteraceae|Mucispirillum Bacteria|Firmicutes|Clostridia|Clostridiales|Lachnospiraceae|Dorea Bacteria|Firmicutes|Clostridia|Clostridiales|Lachnospiraceae|Marvinbryantia Bacteria|Firmicutes|Clostridia|Clostridiales|Lachnospiraceae|Oribacterium Bacteria|Firmicutes|Clostridia|Clostridiales|Lachnospiraceae|Ruminococcus Bacteria|Firmicutes|Clostridia|Clostridiales|Ruminococcaceae|Oscillospira Bacteria|Firmicutes|Clostridia|Clostridiales|Tissierellaceae|Anaerococcus Bacteria|Proteobacteria|Alphaproteobacteria|Rhizobiales|Bradyrhizobiaceae|Bradyrhizobium Bacteria|Proteobacteria|Alphaproteobacteria|Rhizobiales|Brucellaceae|Ochrobactrum Bacteria|Proteobacteria|Alphaproteobacteria|Rhizobiales|Phyllobacteriaceae|Defluvibacter Bacteria|Proteobacteria|Betaproteobacteria|Burkholderiales|Comamonadaceae|Diaphorobacter Bacteria|Proteobacteria|Gammaproteobacteria|Pasteurellales|Pasteurellaceae|Aggregatibacter Treatment Bacteria|Actinobacteria|Actinobacteria|Actinomycetales|Micrococcaceae|Micrococcus Bacteria|Firmicutes|Bacilli|Turicibacterales|Turicibacteraceae|Turicibacter ;Bacteria|Proteobacteria|Gammaproteobacteria|Pseudomonadales|Moraxellaceae|Psychrobacter

Taxon

Table 1

Kostric M. et al.

Development of a stable lung microbiome in healthy neonatal mice

Taxon

No. of shared OTUs Iso

Int

Genus Adlercreutzia

1

1

Bacteroides

2

3

Butyricicoccus Coprococcus

1 2

1 3

Dehalobacterium Dorea

1 1

1 3

Lactobacillus

8

10

Marvinbryantia Mucispirillum

1 1

5 1

Oscillospira Prevotella

9 1

12 1

Roseburia Ruminococcus

0 8

1 11

Streptococcus Family

1

1

Erysipelotrichaceae Lachnospiraceae

1 15

0 29

Mogibacteriaceae Rikenellaceae Ruminococcaceae S24–7 YS2

0 4 0 13 0

1 4 1 13 1

20 90

43 146

Order Clostridiales Total no. of shared OTUs

The taxonomic affiliation and number of OTUs shared with litter are given for isolation and intervention samples across all age groups OUT operational taxonomic unit, Iso isolation group, Int intervention group

comparatively highly abundant in the early-life intervention groups, with a considerable high abundance of Ruminococcus at 3 weeks. Thus, the observed increase in litter-associated OTUs for the intervention treatment (e.g., Oscillospira and Ruminococcus OTUs; Table 2) is mainly related to changes in the microbial community structure at 5 days and 3 weeks. The potential impact of litter addition on the microbial community structure was further analyzed using co-occurrence analysis (Fig. 5; Table S5). The modularity indices of both treatment groups were higher than 0.40, suggesting a modular network structure [28], and inferring biologically relevant connections between communities. Overall, intervention samples demonstrated a higher connectedness to litter (isolation 3795 edges, mean degree = 19.99; intervention 4112 edges, mean degree = 20.80) and more co-occurring OTUs were revealed for intervention than isolation (1298 and 1247 nodes,

a 35% Mean percentage of shared OTUs

Number of litter-associated OTUs detected in the lung

Isolation

Intervention

25%

15%

5%

5 days

b Relative abundance of group means

Table 2

3 weeks

Isolation

2 months

Intervention

4.5 months

Ruminococcus

9 months

Oscillospira

9%

7%

5%

3%

1%

5 days

3 weeks

2 months

4.5 months

9 months

Fig. 4 Litter-associated influences of lung microbiota. Line charts showing a the percentage of shared OTUs between litter and either isolation or intervention samples. b The relative abundance (%) for Oscillospira and Ruminococcus OTUs. Lines are colored according to a the treatment (isolation or intervention) and b the genus. Data points represent a the mean percentage of shared OTUs per age group and b the relative abundance of group means for isolation (solid line) and intervention (dashed line)

respectively). Samples from the 9-month isolation group showed the highest average node degree of 113.25 ± 9.55 (mean ± standard error), which reflects the highest number of connections between lung samples and litter via shared OTUs (Fig. 5a). Lower average node degrees were reached for the remaining age groups—from 86.00 ± 13.15 for 2 months to 41.50 ± 1.85 for 3 weeks. Regarding the intervention treatment, the lung samples from the age group 3 weeks showed the highest sample correlation with litter (Fig. 5b). A considerably high average node degree of 163.50 ± 43.38 was observed for 3 weeks, compared to the other age groups (5 days 47.25 ± 12.65; 2 months 71.25 ± 5.92; 4.5 months 79.00 ± 7.82; 9 months 83.00 ± 12.25).

Discussion In this study, we analyzed lung tissue collected from BALB/c mice that were kept under non-specific pathogen-free (SPF) conditions. Bronchoalveolar lavage (BAL) fluid was extracted beforehand to exclude inhaled bacteria as contamination source [29]; furthermore, lungs were dissected under sterile conditions. Focusing on female mice, we could exclude possible gender

Kostric M. et al.

a

b

5 days

3 weeks

2 months

4.5 months

9 months

litter

Fig. 5 Influences of litter on lung microbiota at different age. Networks of co-occurring OTUs depict interactions between litter and lung bacteria for the a isolation and b intervention treatment. Samples are represented by colored nodes reflecting their affiliation to a particular age and cluster according to the amount of shared OTUs. Black nodes represent co-

occurring OTUs, and each edge represents a connection between a sample and an OTU. Node size is proportional to its number of connections (i.e., degree), and edge size is proportional to OTU abundance (i.e., weight). A connection stands for a significant relationship (G-test; p < 0.001). OTU operational taxonomic unit

effects as well. To assess the impact of environmental factors, we added already used litter material to cages of the intervention group. In contrast, an isolation group was set up in parallel, which was only exposed to internal litter. Thus, we were able to explore the development of the lung microbiota over time and to determine influences on the microbial community, with respect to the long-term exposure of external litter material.

developmental period [32] and further evidence regarding an early developmental window and the stabilization of lung microbiota within the first month of life [33]. The most dominant phyla found in the lung were Actinobacteria, Firmicutes, and Proteobacteria, which is consistent with previous studies [34, 35]. While low abundance values of Bacteroidetes were detected in the lung, they accounted for up to 37% of the total microbial community in the litter samples and represented together with Firmicutes the main bacterial phyla in the litter. Remarkably, Actinobacteria prevailed at 3 weeks and 2 months, where they made up to 65% of the total bacterial community (Fig. S4). This transition of the microbial community structure, already visible at the phylum level, indicates age-dependent changes of the lung microbiota from a more dynamic state in young mice to a more stable state in adult mice. Two major developmental changes occurred consecutively between the 3week and 2-month sampling in this study: (i) the weaning process and the transition from milk suckling to solid foods, between 21 and 28 days, and (ii) sexual maturity, between 4 and 6 weeks. Both can be considered as major drivers for the microbial community structure in the lungs, as already described for the gut microbiota [36]. Indeed, changes in diet, which have been shown to alter the gut microbiota within a few days [37], can impact the respiratory microbiota indirectly via the gastrointestinal (GI) microbiota’s modulation of the host immunity, suggesting a cross talk between the GI and the respiratory tract

Changes in the Healthy Lung Microbiota with Age Our results clearly indicate age-dependent differences in the lung microbiota of mice, which was previously observed for other organs like the gut [30]. Overall, we perceived a temporal development towards a less dissimilar and more complex microbiome, which is also in agreement with previous research [31]. A developmental trend towards higher diversity was observed, which is consistent with the assumption of a continuous immigration of bacteria into the lung with time. Here, the increased diversity and similarity of lung communities within the oldest age group (i.e., 9 months) was apparent. Also, less dissimilar microbial communities were observed for the age group 4.5 and 9 months, in contrast to higher intragroup variations between samples of younger mice (5 days–2 months; Fig. 2). This developmental trend towards an increased similarity of the community composition is supported by the finding that the human gut microbiota remains largely stable after a

Development of a stable lung microbiome in healthy neonatal mice

[38, 39]. Likewise, hormonal changes (such as variation in estrogen and progesterone at sexual maturity) regulate immune responses, for instance, the development of lung inflammation [40], and may consequently influence the microbial community structure of the lung. Gender might therefore be an important driver in structuring the microbial communities in the lung, as it was already shown to influence the intestinal microbiota [41]. In this study, we did not investigate gender-specific differences but it would be interesting to examine the impact of sex hormones on the lung microbiome development. Age and Treatment Responders While Ochrobactrum was identified as a common member of the lung microbiota at 9 months of age, the genus was significantly highly abundant in intervention samples (Fig. 3; Table 1). In addition, the genus Rhodococcus was present in almost all samples and particularly highly abundant at 3 weeks and 2 months (Fig. 3; Table S1), and thus the low microbial diversity observed at 3 weeks and 2 months of age was caused by the predominance of Rhodococcus. Previous studies associated opportunistic pathogen Rhodococcus and Ochrobactrum spp. to lung inflammation [42, 43]. At the age of 9 months, mice are considered as middle-aged but retired breeders [18] and are possibly affected by an aging-related decline in immunity [44]. Hence, the prevalence of Ochrobactrum spp. could be linked to altered immune responses. However, the genera Ochrobactrum and Rhodococcus also harbor non-pathogenic but pollutant-degrading species [45, 46]. Noteworthy, the increased abundance of bacteria as a result of the addition of used litter to the cages of the intervention group is in accordance with a particularly frequent appearance of Ochrobactrum (14.7%; relative abundance) within intervention samples of 9month-old mice. In this regard, bacteria such as Rhodococcus and Ochrobactrum that have the ability to degrade aromatic compounds like phenazines [47, 48] may be the main responding organisms to bioaerosol accumulation. Hence, those bacteria could provide beneficial functions within the lung ecosystems and may find a specific niche. These findings support the presumption of a consistent selection pressure in the lung [49], and thus the assumption that the lung microenvironment to a certain extent determines which bacteria survive [14]. However, the functional properties of these core microbes can only be assumed as our 16S rRNA-based survey only allowed taxonomic but no functional description of harbored bacterial communities. Impact of Long-Term Exposure to External Used Litter on the Community Structure The addition of already used litter material was chosen to investigate the impact of shifts in the abundance of bacteria in the environment for the lung microbiome development. As

expected, the murine lung microbiota was impacted by this alteration (Table 2, Figs. 4 and 5). In particular, the litter addition affected the lung microbiota of young mice more drastically compared to older mice (Fig. 4). Longitudinal studies, addressing microbial community dynamics and stability in the human intestine, previously described the presence of a stable microbiome in adults and Badult-like^ bacterial community structures [31, 50]. Further, these structures were shown to be marginally influenced by probiotic treatment (i.e., Lactobacillus rhamnosus GG intervention) [51], suggesting the existence of a stable personalized microbiome with core functions in healthy human adults [52]. In this context, our findings imply a stabilization of lung microbiota with a negligible impact of litter addition at adult age (Fig. 4a). A conceivable explanation for dynamics observed for the isolation treatment may be the constant immigration of microorganisms into the LRT, including those present in the surrounding environment of the mice, and an impaired elimination [53], which results in an increase of overlapping taxa with time (Fig. 4b). For the intervention treatment, the strong response at 3 weeks may be a result of the increased responsiveness towards the surrounding environment early in life [33, 54]. This result also goes in line with further studies elucidating the importance of microbial colonization within the early neonatal period for the development of an intact immune system [55, 56]. The lung microbiota of isolation and intervention mice differed the most at 3 weeks of life. Here, Dorea, Marvinbryantia, Oscillospira, and Ruminococcus were particular members of the 3-week intervention group’s microbial community (Table 1) as well as core genera within litter (Fig. S5), implying an increased sensitivity of the lung microbiota by external stimuli early in life. To our knowledge, no longitudinal study on the formation of the healthy lung microbiota has been performed yet. In addition, research describing the impact of minor environmental changes on these communities is lacking. With this study, we provide first insights into the developmental dynamics of the murine lung microbiota. Overall, our results indicate that even small environmental changes can impact the young mouse LRT harboring a highly dynamic microbiome. Further, the responsiveness to those changing conditions decreases in adulthood, which is in accordance with a stabilization of lung microbiota. This study paves the way for future research aiming to investigate the impact of severe environmental changes (e.g., antibiotic treatment or tobacco smoke). Hence, these findings can serve as a basis for future experimental frameworks. The understanding of what actually constitutes an undisturbed lung microbiota and characterizing its temporal development seems essential to elucidate factors that affect microbial community structures and microbiome functions. Moreover, further investigations will be required to comprehend the mechanisms underlying the dynamics of the lung microbiome, as well as its functional potential and core functions, with respect to the impact of external factors.

Kostric M. et al. Acknowledgements We gratefully thank Rabea Imker for performing all necessary animal experiments, Susanne Kublik for excellent assistance in sequencing, and Maria de Vries for great statistical support. We further thank the reviewers for their critical feedback and insightful suggestions. This work was supported by intramural funding for Environmental Health projects of Helmholtz Zentrum München.

10.

Compliance with Ethical Standards During dissection, care was taken to minimize contamination and all animal procedures were realized in accordance with the Federal Guidelines for the Care and Use of Laboratory Animals.

12.

11.

13.

Conflict of Interest The authors declare that they have no conflict of interest. 14.

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