Effects of PM2.5 exposure during gestation on maternal gut microbiota and pregnancy outcomes

Journal Pre-proof Effects of PM2.5 exposure during gestation on maternal gut microbiota and pregnancy outcomes Wei Liu, Yalin Zhou, Yong li, Yong Qin, Lanlan Yu, Ruijun Li, Yuhan Chen, Yajun Xu PII:

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DOI:

https://doi.org/10.1016/j.chemosphere.2020.125879

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CHEM 125879

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Received Date: 6 November 2019 Revised Date:

6 January 2020

Accepted Date: 8 January 2020

Please cite this article as: Liu, W., Zhou, Y., Yong li, , Qin, Y., Yu, L., Li, R., Chen, Y., Xu, Y., Effects of PM2.5 exposure during gestation on maternal gut microbiota and pregnancy outcomes, Chemosphere (2020), doi: https://doi.org/10.1016/j.chemosphere.2020.125879. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier Ltd.

1

Effects of PM2.5 Exposure during Gestation on Maternal Gut Microbiota and

2

Pregnancy Outcomes

3

Wei Liua, Yalin Zhou a, Yong lia, Yong Qina, Lanlan Yua, Ruijun Lia, Yuhan Chena, Yajun Xua,b*

4

a

Department of Nutrition and Food Hygiene, School of Public Health, Peking University, Beijing, 100083, China

5

b

Beijing Key Laboratory of Toxicological Research and Risk Assessment for Food Safety, Peking University, Beijing

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100083, China

7

* Correspondence: [email protected]. Department of Nutrition and Food Hygiene, School of Public Health, Peking

8

University, Beijing 100083, China

9 10

ABSTRACT A number of studies have reported that fine particulate matter (PM2.5) exposure is

11

associated with adverse pregnancy outcomes. Moreover, PM2.5 exposure contributes to changes of gut

12

microbiota. However, influences of PM2.5 exposure during gestation on maternal gut microbiota and

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pregnancy outcomes were not well understood. Here we performed a study using mice models. Dams

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were exposed to PM2.5 suspension by intratracheal instillation on gestational day (GD) 3, 6, 9, 12 and

15

15. Pregnancy outcomes, maternal gut microbiota and short chain fatty acids on GD 18 were all

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measured. The fetal body weight of PM2.5 group was significantly lower than that of control group (p

17

< 0.05). Meanwhile, the fetal body length of PM2.5 group was significantly shorter than that of control

18

group (p < 0.05). The Shannon or Simpson index of PM2.5 group were higher than that of control

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group (p < 0.05). At the phyla level, compared to dams in control group, mice in the PM2.5 group had

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higher ratio of phyla Proteobacteria, Candidatus Saccharibacteria and Fusobacteria and lower ratio

21

of phyla Acidobacteria, Gemmatimonadetes and Deferribacteres in the gut. Compared with control

22

group, the concentration of isobutyric acid was higher in PM2.5 group, but butyric acid concentration

23

was lower in PM2.5 group (p < 0.05). These findings suggested that prenatal exposure to PM2.5 had an

24

effect on birth weight of fetus. Meanwhile, PM2.5 tracheal exposure during gestation caused changes in

25

the distribution and structure of gut microbiota of dams.

26

KEY WORDS: PM2.5, Pregnancy outcomes, Gut microbiota, Short chain fatty acids

27 1

28

1. Introduction

29

Large numbers of studies have found that air pollution has the capacity to exert adverse influences

30

on human health (Yuan et al., 2019). Emerging data has implicated a link between maternal exposure

31

to air pollution and adverse birth outcomes, including higher infant mortality, lower birth weight, and

32

early alterations in immune development (Proietti et al., 2013). In particular, a number of studies have

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reported that fine particulate matter (PM2.5, particulate matter with aerodynamic diameter ≤ 2.5 µm)

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exposure is associated with numerous pregnancy and birth outcomes such as newborn size, birth

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weight, and small for gestational age (Hyder et al., 2014; Lavigne et al., 2016; Stieb et al., 2016).

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However, despite these findings, the mechanism of PM2.5 maternal exposure on adverse birth

37

outcomes is not clear. There are some possible mechanisms, such as oxidative stress, inflammation

38

and immune damage. Grevendonk et al. found that particulate air pollution exposure in early life plays

39

a role in increasing systemic oxidative stress both in mother and foetus (Grevendonk et al., 2016);

40

balance between pro-inflammatory and anti-inflammatory cytokines is paramount to successful

41

pregnancy (Kalagiri et al., 2016), but exposure to PM2.5 is associated with systemic inflammation

42

(Pope et al., 2016); Cew et al. reported PM2.5 in ambient air may influence fetal immune development

43

via shifting in cord blood lymphocytes distributions (Cew et al., 2010). In addition to the above

44

mechanisms, we suspected that poor birth outcomes due to maternal PM2.5 exposure may be related to

45

changes in the maternal gut microbiota.

46

The gastrointestinal tract is thought to house ~1014 microorganisms, nearly 1,000 distinct

47

bacterial species (Fujimura et al., 2010; Parekh et al., 2015). Recent efforts have demonstrated a

48

causal role for the microbiota in health and disease (Kroemer and Zitvogel, 2018; Round and Palm,

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2018; Zhao et al., 2018). Epidemiological studies have reported that short- and long-term exposure to

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PM2.5 contributes to gastrointestinal diseases (Kaplan et al., 2010; Ananthakrishnan et al., 2011). A

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study by Mutlu et al. demonstrated that PM2.5 exposure could significantly increase gut microbial

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diversity (Mutlu et al., 2018). During pregnancy maternal gut microbiota provide metabolites and

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substrates essential for fetal growth through metabolic provisioning, driving expansion and maturation

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of central and peripheral immune cells, and formation of neural circuits (Jasarevic and Bale, 2019). 2

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Therefore, changes in the maternal intestinal flora may affect the development of the fetus.

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As far as we know, to date, few studies have investigated the effects of maternal PM2.5 exposure

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during gestation on gut microbiota and the relation to pregnancy outcomes. To explore these effects,

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we exposed dams to PM2.5 during the whole pregnancy period, then maternal gut microbiota and

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pregnancy outcomes were measured. This study was conducive to further clarifying influences of

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PM2.5 on maternal conditions and offspring health.

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2. Material and methods

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2.1. Preparation of PM2.5 and chemicals

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The samples of PM2.5 were collected by a particulate sampler (TH-150C, Wuhan Tianhong

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Instruments Co. Ltd., Wuhan, China) in residential area of Beijing, China. The filter were agitated in

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ultrapure water. The solution was filtered and centrifuged at 12,000 rpm for 20 min. The sediment was

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collected by a vacuum freeze drier (FDU-1100, Tokyo Rikakikai Co. Ltd., Tokyo, Japan). The dry

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PM2.5 powder was diluted in sterile phosphate-buffered saline at a concentration of 15 mg/mL and kept

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at −20 °C before experiments. An extra control sample from unexposed filters was processed

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identically.

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2.2. Dose information

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The calculation method for PM2.5 dose was described in our article (Zhang et al., 2018). The

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respiratory volume for a normal adult is 6 L/min, which means that daily respiratory volume is 8.64

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m3. In the district where the samples of PM2.5 were collected, the daily highest concentration of PM2.5

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reached 0.43 mg/m3 (Huang et al., 2015). The dose of PM2.5 exposure for mice was estimated to 18.5

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mg/kg every 3 d considering extrapolation coefficient. In our previous animal experiments, we have

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found that PM2.5 could change postnatal open-field behaviors of offspring (Zhang et al., 2018), and

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could activate inflammatory reaction and oxidative stress level of pregnant mice at the concentration

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of 15 mg/kg (Liu et al., 2017). Herein, we took 15 mg/kg as the dose group.

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2.3. Animals and treatment

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Specific pathogen-free 8-week-old Institute of Cancer Research (ICR) mice were provided by the

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Department of Laboratory Animal Science of Peking University (Beijing, China, SCXK2016-0010). 3

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The animals were quarantined for 7 d after shipping and were maintained in a temperature- and

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humidity-controlled animal facility with a 12-h/12-h light/dark cycle (lights on 7:00 AM). Mice were

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provided with basic mouse chow and distilled water ad libitum until pregnancy was confirmed. After the

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quarantine period, female mice were mated with healthy male mice overnight and were checked for

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vaginal plugs the next morning at 7:00 AM. The presence of a vaginal plug signified gestational day

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(GD) 0. Body length and weight of female mice and male mice were measured.

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24 pregnant ICR mice were randomly divided into 2 groups, including normal control (group A)

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group and PM2.5 group (group B), 12 dams in each subgroup. All dams were individually housed and

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provided with commercial pregnancy forage and sterile distill water until sacrificed. At 9:00–11:00 AM

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on GD 3, 6, 9, 12 and 15, dams were anesthetized with 3% isoflurane after body weight recording and

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received intratracheal instillation. Intratracheal instillation was conducted as reported in another paper

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(Hasegawa-Baba et al., 2014; Liu et al., 2017). Dams in group B were exposed to PM2.5 suspension

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(15.0 mg/kg) by intratracheal instillation. Dams in group A were administered with the same amount of

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suspension from extracts of “blank” filter at the same time points. All dams were sacrificed on GD18.

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2.4. Pregnancy outcomes

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Immediately after death, the uterine horns of dams were removed and opened on clean Petri

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dishes. All live and dead fetuses and reabsorptions were collected. Body weight and heart weight of

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fetuses were measured using electronic scales. Body length and tail length were measured. The

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placentas were also weighed.

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2.5. Colonic sample microbiota analysis

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Colon contents sample microbiota analysis was divided into the following sections: sample

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collection, DNA extraction and amplification, 16S rDNA sequencing and taxonomic classification.

104

Each colon contents sample of dams on GD18 was collected immediately and stored at −80 °C until

105

analyzed. Microbial DNA was extracted from colonic sample using the QIAamp DNA Stool Mini Kit

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(Qiagen, Venlo, Netherlands). The V3-V4 region of the bacteria 16S ribosomal RNA genes were

107

amplified

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5’-GGACTACVVGGGTATCTAATC-3’. Amplicons were extracted from 2% agarose gels and

by

PCR

using

primers

341F

5’-CCTACGGGRSGCAGCAG-3’

4

and

806R

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purified using the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA) and

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quantified using Qubit®2.0 (Invitrogen, USA). After preparation of library, these tags were sequenced

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on HiSeq platform (Illumina, Inc., CA, USA) for paired end reads of 250 bp. DNA extraction, library

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construction and sequencing were conducted at Realbio Genomics Institute (Shanghai, China). 16S

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tags were restricted between 220 bp and 500 bp. The copy number of tags was enumerated and

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redundancy of repeated tags was removed. Only the tags with frequency more than 1, which tend to be

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more reliable, were clustered into Operational Taxonomic Units (OTUs), each of which had a

116

representative tag. OTUs were clustered with 97% similarity using UPARSE（http://drive5.com/uparse

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/) and chimeric sequences were identified and removed using Userach (version 7.0). Each

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representative tags was assigned to a taxa by RDP Classifer (http://rdp.cme.msu.edu/) against the RDP

119

database (http://rdp.cme.msu.edu/) using confidence threshold of 0.8. OTU profling table and

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alpha/beta diversity analyses were also achieved by python scripts of Qiime.

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2.6. Determination of short chain fatty acids (SCFAs)

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Each colon contents sample of dams on GD18 was collected immediately and stored at −80 °C

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until analyzed. SCFAs in colon (acetic acid, propanoic acid, butyric acid, isobutyric acid, valeric acid

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and isovaleric acid) were quantified using gas chromatography (GC). Metaphosphoric acid (2.5000 ±

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0.0050 g) was added to 100 mL sterilized water, and was used as the extraction liquid. Colon contents

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samples (150 mg) were added to 1.5 mL SCFAs extracting liquid and were shocked for 1 min.

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Samples were centrifuged at 12,000 g for 10 min to remove the solid material. Supernatants were

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retained, then crotonic acid solution (7.5 mM) was added as internal standard, and the solution was

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filtered by through a 0.22 µm microporous membrane. Samples were analyzed by GC. 1 µL of sample

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was injected into GC, which was equipped with a DB‐FFAP column. Nitrogen was the carrier gas.

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The GC temperature program was as follows: begin at 70 °C, increase to 180 °C at 15 °C/min, hold at

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180 °C for 3 min, and then increase to 240 °C at 40 °C/min, hold at 240 °C for 5 min. Concentration

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of SCFAs were calculated using the internal standard method and expressed in mM.

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2.7. Statistical analysis

5

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Indicators of alpha diversity are reported as the median ± interquartile range (IQR) and P values

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were calculated using the Mann Whitney U. Analysis of similarity (ANOSIM) was performed to

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determine the differences between groups. Principal co-ordinates analysis (PCoA) were also

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performed. Linear discriminant analysis (LDA) effect size (LEfSe) analyses were performed with the

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LEfSe tool (http://huttenhower.sph.harvard.edu/galaxy). The cladogram was generated using the

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online LEfSe project. For the LEfSe analysis, we used the Wilcoxon test to detect significantly

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different abundances and performed LDA scores to estimate the effect size (threshold: ≥ 2). The

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pregnancy outcomes and SCFAs were presented as mean ± SD, and the significant differences were

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examined using Student’s t-test. The results were statistically analyzed using SPSS 22.0 software

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(SPSS, Inc., Chicago, USA). P < 0.05 was considered statistically significant.

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3. Results

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3.1. Pregnancy outcomes

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There were no significant differences in body length and weight of female mice and male mice

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between the two groups (p > 0.05). Regarding the weight gain of the dams during pregnancy, a

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statistically significant difference was observed during the period from GD 10 to GD 18 between the

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two groups (p < 0.05, Table 1). No significant difference was observed during periods from GD 0 to

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GD 6 and GD 7 to GD 9 (p > 0.05). No statistically significant difference was observed between the

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two groups regarding the number of embryos, live fetuses, reabsorptions, and fetal death (p > 0.05).

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The fetal body weight of group B was significantly lower than that of group A (p < 0.05). Meanwhile,

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the fetal body length of group B was significantly shorter than that of group A (p < 0.05). There were

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no significant differences in fetal tail length, placental weight, and organ index of heart between the

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two groups (p > 0.05).

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Table 1. Effects of PM2.5 exposure during gestation on the pregnancy outcomes Parameters

Group A

Group B

P value

Body weight of female mice (g)

29.29 ± 1.57

29.70 ± 1.34

0.62

Body length of female mice (cm)

9.96 ± 0.30

10.05 ± 0.31

0.93

6

Body weight of male mice (g)

35.20 ± 1.34

34.91 ± 1.19

0.73

Body length of male mice (cm)

10.55 ± 0.34

10.53 ± 0.29

0.53

Weight gain from GD 0 to GD 6 (g)

3.13 ± 1.16

3.41 ± 1.13

0.59

Weight gain from GD 7 to GD 9 (g)

2.98 ± 0.94

2.52 ± 1.88

0.50

Weight gain from GD 10 to GD 18 (g)

25.04 ± 1.63

22.94 ± 1.78

0.02

Embryos

14.17 ± 0.72

13.83 ± 0.94

0.34

Live fetuses

13.58 ± 1.00

13.00 ± 1.04

0.18

Reabsorptions

0.42 ± 0.52

0.50 ± 0.52

0.70

Fetal death

0.17 ± 0.39

0.33 ± 0.49

0.37

Fetal body weight (g)

1.44 ± 0.07

1.28 ± 0.12

0.001

Fetal body length (cm)

2.62 ± 0.06

2.55 ± 0.06

0.008

Fetal tail length (cm)

1.42 ± 0.08

1.36 ± 0.07

0.07

Placental weight (g)

0.11 ± 0.01

0.11 ± 0.01

1.00

Organ index of heart (mg/g)

5.94 ± 0.55

5.65 ± 0.70

0.26

158 159

3.2. OTUs

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According to the sequence similarity (> 97%), high quality sequences were classified into

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multiple OTUs using QIIME to facilitate analysis. A total of 639 and 617 OTUs were found in the

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group A and group B, respectively. Dams in group B had 70 unique OTUs and shared 547 OTUs with

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the dams in group A (Figure 1).

7

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Figure 1. The venn diagram of group A and group B. The Venn diagram showed the numbers of OTUs (97% sequence

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identity) that were shared or not shared by two groups, respectively, depending of overlaps.

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3.3. Alpha diversity analysis

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The Observed species can reflect the actual number of OTUs observed. Chao1 is an estimator of

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phylotype richness, and the Shannon or Simpson index of diversity reflects both the richness and

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community evenness. There was no significant difference found in the Observed species and Chao1

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index (p > 0.05). However, group B had higher evenness indexes (Shannon, 5.98; and Simpson, 0.97)

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than group A (Shannon, 5.68; and Simpson, 0.95). These results suggested that group B had higher

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evenness than group A (p < 0.05). Table 2. Estimation of alpha diversity

173 Group

Observed species

Chao1

Shannon

Simpson

Group A

296.50 (280.00, 344.25)

0.04 (0.03, 0.04)

5.68 (5.38, 5.93)

0.95 (0.95, 0.97)

Group B

277.00 (262.50, 295.75)

0.03 (0.03, 0.04)

5.98 (5.71, 6.07)

0.97 (0.96, 0.97)

P value

0.10

0.20

0.03

0.03

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Note: Indicators of alpha diversity were reported as the median ± IQR.

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3.4. Beta diversity analysis

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Beta diversity analysis represents the extent of similarity between different microbial

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communities. Two principal components were extracted by PCoA. Figure 2a showed a clear

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separation between the fecal samples from group A and group B. Percentage values at the axes

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indicated contribution of the principal components to the explanation of total variance in the dataset.

8

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The figure showed that the percentages of variation explained by PC1 and PC2 were 47.75% and

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17.79%, respectively.

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In addition, ANOSIM demonstrated the differences of the gut microbiota between the two groups.

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The inter-group differences of two groups were greater than the intra-group differences, showing that

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the gut microbiota composition in group B was significantly different with group A (R = 0.247, p =

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0.003).

(a)

(b)

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Figure 2. Effects on beta diversity. (a) PCoA of dams on GD18. (b) Weighted Unifrac Anosim of dams on GD18.

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R-value range (-1, 1).

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3.5. Classification abundance analysis

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Figures 3 showed microbial distributions at the phylum and genus level in the fecal samples from

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the two groups. Firmicutes and Bacteroidetes accounted for the largest proportion at the phylum level.

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At the genus level, Alistipes was the most proportional.

9

(a)

(b) 10

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Figure 3. Microbial distributions at the phylum level and the genus level in the fecal samples from the two groups. Each

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bar represents the microbiota composition of one sample.

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3.6. Axonomic composition

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To explore the specific bacterial taxa associated with PM2.5 exposure, a LEfSe comparison of the

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gut microbiota between the two groups was performed. The greatest difference in taxa from phylum to

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genus level was identified via LDA score (Figure 4a, 4b).

198

There were six significantly different phylums, with enrichment of Proteobacteria, Candidatus

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Saccharibacteria and Fusobacteria in group B and Acidobacteria, Gemmatimonadetes and

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Deferribacteres in group A. Bacterial genus including Oscillibacter, Desulfovibrio, Flavonifractor,

201

Saccharibacteria genera incertae sedis, Fusobacterium were found to be enhanced by maternal PM2.5

202

exposure, Gp4, Pseudomonas, Sphingomonas, Gemmatimonas, Delftia, Selenomonas, Streptococcus,

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Bacillus, Leucobacter, Mucispirillum and Odoribacter in group A were higher than that in group B.

204

11

205

(b)

(c)

(a)

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Figure 4. Effects on axonomic composition. (a) The most differentially abundant taxa between the two groups were

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identified through the LDA score which was generated from LEfSe analysis (phylum to genus: p, phylum; c, class; o,

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order; f, family; g, genus). (b) The enriched taxa in group A and group B of dams were represented in Cladogram. The

209

central point represents the root of the tree (Bacteria), and each ring represents the next lower taxonomic level (phylum

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to genus: p, phylum; c, class; o, order; f, family; g, genus). The diameter of each circle represents the relative

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abundance of the taxon. (c) Heat map of the 17 key genus in gut microbiota from dams. The color of each cell

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represents the expression level of each sample.

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3.7. SCFAs 12

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Compared with group A, the concentration of isobutyric acid was higher in group B, but butyric

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acid concentration was lower in group B (p < 0.05). There was no significant difference found in the

216

acetic acid, propanoic acid, valeric acid and isovaleric acid (p > 0.05).

217

Figure 5. SCFAs concentration of dams. The data was expressed as mean ± SD of each group. Compared with group A,

218

* indicates p < 0.05.

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4. Discussion

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A number of studies have proposed that PM2.5 may have an effect on birth outcomes such as

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preterm birth, birth weight (Nieuwenhuijsen et al., 2013; Zhu et al., 2015), while the mechanism of it

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has not been well described. In the current study, animal models were used to investigate the influence

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of PM2.5 exposure on birth outcomes and maternal gut microbiota. Our results indicated that maternal

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PM2.5 exposure led to fetuses significant lower birth weight and shorter body length. In addition, the

225

results from the 16s rDNA assay showed that gut microbiota in dams was effected by PM2.5 exposure.

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In our study, numbers of embryos, live fetuses, reabsorptions, and fetal death were not

227

influenced by maternal PM2.5 exposure. It was worth noting that the fetal weight of PM2.5 group

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animals were significantly lower compared with control group. Additionally, a shorter fetal length

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showed in the PM2.5 group. A systematic review reported that PM2.5 exposure was associated with low

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birth weight (Shah and Balkhair, 2011), which was consistent with our finding.

13

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Some reports have suggested that inhaled PM2.5 may influence gut microbiota (Mutlu et al., 2018).

232

However, the influence of PM2.5 exposure during gestation on gut microbiota of dams have not been

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well described. The results of present research indicated that PM2.5 exposure during pregnancy had

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great impact on gut microbiota of dams. Bacterial diversity and composition of gut microbiota in PM2.5

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group were changed. Firmicutes and Bacteroidetes, two major phyla of gut microbiota, showed no

236

significant difference between the two groups. However, Shannon or Simpson index were increased by

237

maternal PM2.5 exposure, suggesting that PM2.5 exposure during pregnancy could make gut microbiota

238

show a higher evenness. This indicated that the abundance of some bacteria with low abundance

239

increased, and that of some bacteria with high abundance decreased. Genus level barplot also showed

240

the trend.

241

Reasons of PM2.5 impacting intestinal flora may be related to its components. The major

242

components of PM2.5 are sulfates, nitrates, organic carbon, mineral dust, polycyclic aromatic

243

hydrocarbons, metals, ions, and biological components (Salim et al., 2014). Human colon microbiota

244

can directly bioactivate polycyclic aromatic hydrocarbons. Breton et al. reported that non-absorbed

245

heavy metals have a direct impact on the gut microbiota (Breton et al., 2013). Dietary

246

tetrachlorodibenzofuran altered the gut microbiota by shifting the ratio of Firmicutes to Bacteroidetes

247

(Zhang et al., 2015). These results highlight that gut microbes are engaged in bioactivation of

248

inorganic compounds, which in turn may contribute to the composition of intestinal flora. In addition,

249

bacteria in PM2.5 could also affect the composition of gut microbiota in animals. Bacteria are

250

ubiquitous as an important component of atmospheric aerosols (Bowers et al., 2013). Streptomyces,

251

Clostridium, Bacillus were found by Metagenomic analysis on the same PM2.5 samples which were

252

collected in Beijing (Wang et al., 2015). Andrea et al. found Riemerella was the most abundant

253

bacterial community of Milan (Franzetti et al., 2011). After entering the gut these external bacteria

254

could interact with the gut microbiota and affect its composition.

255

Changes in intestinal permeability may be one of the reasons that PM2.5 could influence the flora.

256

Higher Oscillibacter abundance was observed in PM2.5 group. Until now little is known about the

257

physiological role of Oscillibacter, however, evidence showed that it may be related to intestinal 14

258

permeability. Lam et al. found that increased Oscillibacter abundance was also associated with a

259

reduction in the mRNA expression of ZO-1 (Lam et al., 2012), and lower ZO-1 was reported to be

260

related with increased colonic permeability (Poritz et al., 2007). It was possible that PM2.5 could

261

influence the maintenance of gut barrier integrity, then Oscillibacter abundance might be a secondary

262

effect consequent upon alterations in the gut permeability.

263

The metabolic process of PM2.5 components by bacteria might also affect the composition of gut

264

microbiota. In our study, dams given PM2.5 showed significant changes in the relative amounts

265

of Desulfovibrio. We suspected that the growth of Desulfovibrio was related to the metals in PM2.5.

266

According to a previous study, in which PM2.5 collected in the same area, the PM2.5 exhibited high

267

densities of O, Si, C, Fe, Ca, Mg, Al, K, and S (Shi et al., 2015). Desulfovibrio are able to reduce

268

heavy metals by a chemical reduction via the production of H2S and by a direct enzymatic process

269

involving hydrogenases and c3 cytochromes (Goulhen et al., 2006).

270

The inflammatory state caused by PM2.5 might also play a role in the change of gut microbiota.

271

Fusobacterium were found to be enhanced by maternal PM2.5 exposure in our study. Several studies

272

reported that Fusobacterium species might be associated with inflammatory bowel diseases (IBD),

273

including both ulcerative colitis and Crohn’s disease (Neut et al., 2002; Ohkusa et al., 2002; Strauss et

274

al., 2011; Kostic et al., 2012). Inflammation is widely believed to be central in the development of

275

adverse health effects due to PM2.5 exposure (Brook et al., 2010) , suggesting that inflammation may

276

play a role in the alteration of gut microbiota due to PM2.5 exposure (Wang et al., 2018).

277

These changes in microbial abundance correlated with SCFAs production. SCFAs are produced

278

at high levels through fermentation fibre by gut microbiota in the colon (Canfora et al., 2015). SCFAs

279

play an important role in supplying nutrients and energy to the host, meanwhile SCFAs were reported

280

to suppress production of pro-inflammatory cytokines, and activate Treg cells, leading to amelioration

281

of colitis (Smith et al., 2013). In our study, lower level of butyric acid was observed in PM2.5 group.

282

Butyric acid is essential for colonocytes and mucosal immune cells, and a depletion in butyric acid is

283

commonly associated with a decrease in barrier function and increased susceptibility to mucosal

284

inflammation. Meanwhile, higher level of isobutyric acid was showed in PM2.5 group. Isobutyric acid 15

285

originates from the degradation of amino acids valine, leucine or isoleucine. The finding of an

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increase in isobutyric acid indicated a shift from a carbohydrate to a protein fermentation

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environment, showing changes in microbial composition.

288

We suspected that changes in the maternal gut flora might be related to birth outcomes. Effects

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on the gut microbiota of dams would cause changes in the concentration of metabolites, therefore

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multiple systems including the immune system and digestive system of dams would be influenced.

291

These effects would indirectly affect the growth and development of the fetus. Priyadarshini et al.

292

found that maternal serum acetate and propionate were associated with newborn length and body

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weight (Priyadarshini et al., 2014). In addition, these metabolites can also enter the blood circulation

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of fetus with cord blood, which could affects the function and development of the fetus.

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Although this study could provide new insights to understand the effects of maternal PM2.5

296

exposure on gut microbiota and pregnancy outcomes, our study has multiple limitations. First, we only

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observed that maternal PM2.5 exposure caused changes in the maternal gut flora and low birth weight,

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but we did not explored associations between the two results and could not confirm whether the two

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results were related. Additionally, intratracheal instillation could lead to less homogeneous particle

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distribution compared to inhalation. Further studies are needed to explore key bacteria which could

301

play an important role in the birth outcomes. We will also track the health status of offspring when

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dams are exposed to PM2.5 during gestation.

303

Conclusions

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The results revealed that prenatal PM2.5 exposure had an effect on lower birth weight of fetus.

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Meanwhile, PM2.5 tracheal exposure during gestation caused changes in the distribution and structure

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of gut microbiota of dams.

307

Acknowledgments

308 309 310

We are grateful to Quanchao Li for his assistance in animal experiments. Role of the funding source This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit

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sectors.

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Author contributions statement 16

313

Wei Liu: Conceptualization, Methodology, Data analysis, Investigation, Writing - Original Draft. Yalin Zhou:

314

Conceptualization, Methodology, Data analysis. Yong li: Methodology, Investigation. Yong Qin: Investigation,

315

Resources. Lanlan Yu: Investigation. Ruijun Li: Resources. Yuhan Chen: Resources. Yajun Xu: Conceptualization,

316

Methodology, Writing - Original Draft, Supervision.

317

Conflicts of Interest

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The authors declare no conflict of interest.

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21

HIGHLIGHTS


Fetal body weight and body length were effected by maternal PM2.5 exposure.




Changes in the gut microbiota of dams in the PM2.5 group were observed.




Butyric acid and isobutyric acid were influenced by maternal PM2.5 exposure.

Author contributions statement Wei Liu: Conceptualization, Methodology, Data analysis, Investigation, Writing - Original Draft. Yalin Zhou: Conceptualization, Methodology, Data analysis. Yong li: Methodology, Investigation. Yong Qin: Investigation, Resources. Lanlan Yu: Investigation. Ruijun Li: Resources. Yuhan Chen: Resources. Yajun Xu: Conceptualization, Methodology, Writing - Original Draft, Supervision.

Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: