Association between serum concentrations of perfluoroalkyl substances (PFAS) and expression of serum microRNAs in a cohort highly exposed to PFAS from drinking water

Environment International 136 (2020) 105446

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Association between serum concentrations of perfluoroalkyl substances (PFAS) and expression of serum microRNAs in a cohort highly exposed to PFAS from drinking water

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Yiyi Xua, Simona Jurkovic-Mlakarb, Ying Lia, Karin Wahlbergc, Kristin Scottc, Daniela Pinedac, ⁎ Christian H. Lindhc, Kristina Jakobssona,d, Karin Engströme, a

School of Public Health and Community Medicine, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden CANSEARCH Research Laboratory, Faculty of Medicine, University of Geneva, Geneva, Switzerland Occupational and Environmental Medicine, Department of Laboratory Medicine, Lund University, Lund, Sweden d Occupational and Environmental Medicine, Sahlgrenska University Hospital, Gothenburg, Sweden e EPI@LUND, Department of Laboratory Medicine, Lund University, Lund, Sweden b c

A R T I C LE I N FO

A B S T R A C T

Handling Editor: Lesa Aylward

Background: Perfluoroalkyl substances (PFAS) are widespread synthetic substances with various adverse health effects. Not much is known about the modes of action of PFAS toxicity, but one likely mechanism is alteration of microRNA expression. Objectives: To investigate whether PFAS exposure is associated with altered microRNA expression in serum. Methods: We selected women from the Ronneby cohort, with high exposure to perfluorooctane sulfonic acid (PFOS) and perfluorohexane sulfonic acid (PFHxS), emanating from drinking water contaminated by firefighting foam, and a control group of women from a neighbouring municipality without drinking water contamination. Serum levels of PFAS were analysed using LC/MS/MS. High coverage microRNA expression was analysed by next generation sequencing (NGS) in 53 individuals to screen for microRNAs associated with PFAS exposure. After verification by qPCR, associations between PFAS exposure and expression of 18 selected microRNAs were validated by qPCR in 232 individuals. In silico functional analyses were performed using Ingenuity pathway analysis (IPA). Results: Three microRNAs were consistently associated with PFAS exposure in the different steps of the study: miR-101-3p, miR-144-3p and miR-19a-3p (all downregulated with increasing exposure). In silico functional analyses suggested that these PFAS-associated microRNAs were annotated to e.g. cardiovascular function and disease, Alzheimer’s disease, growth of cancer cell lines and cancer. Seven predicted target genes for the downregulated microRNAs were annotated to PFAS in IPA knowledge database: DNA methyltransferase 3 alpha (DNMT3a), epidermal growth factor receptor (EGFR), 3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR), nuclear receptor subfamily 1, group H, member 3 (NR1H3), peroxisome proliferator-activated receptor alpha (PPARα), prostaglandin-endoperoxide synthase 2 (PTGS2), and tumour growth factor alpha (TGFα). Discussion: PFAS exposure was associated with downregulation of specific microRNAs. Further, in silico functional analyses suggest potential links between the specific PFAS-associated microRNAs, specific microRNA target genes and possibly also health effects.

Keywords: Epigenetics Environmental pollutants Perfluoroalkyl substances

1. Introduction Perfluoroalkyl substances (PFAS) comprise a group of man-made synthetic substances that have been produced and widely used for approximately 50 years. PFAS are persistent and ubiquitously distributed in the environment and has grown into a global contamination

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problem. The widespread presence of PFAS in the drinking water has been reported from many countries, including Sweden (Eriksson et al., 2013; Guelfo and Adamson, 2018; Hu et al., 2016; Li et al., 2018). The most studied forms of PFAS are perfluorooctane sulfonic acid (PFOS) and perfluorooctanoic acid (PFOA). Numbers of studies have linked PFAS exposure to various health

Corresponding author at: Tornbladsinstitutet, Biskopsgatan 7, 223 62 Lund, Sweden. E-mail address: [email protected] (K. Engström).

https://doi.org/10.1016/j.envint.2019.105446 Received 7 November 2019; Received in revised form 19 December 2019; Accepted 24 December 2019 0160-4120/ © 2020 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/BY/4.0/).

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higher, respectively, than the concentrations observed among the Karlshamn participants. The average PFOA concentrations among the Ronneby participants were lower, but still approximately six times higher than among the Karlshamn participants (Li et al., 2018). The PFAS levels in the Karlshamn population (control group) were similar to the PFAS levels observed in NHANES among a US population (ATSDR, 2018). For the present study we included in total 293 non-smoking women (aged 20–45 years) since it has previously been shown that smoking may have a potential impact on miRNA expression (Badrnya et al., 2014). Moreover, antidepressant medication may also influence miRNA expression (Bocchio-Chiavetto et al., 2013), thus women with self-reported anti-depressive medications were excluded when information was available. All participants gave informed oral and written consent to take part in the study. The study was approved by the Regional Ethical Review Board in Lund, Sweden (approved, date: 2014-04-22, approval number: 2014/4).

effects, for a summary see the review by the European Food Safety Authority (EFSA, 2018). However, there is still remarkable scientific uncertainty about the modes of action of PFAS toxicity. Studies in humans have shown associations between PFAS exposure and changes in gene expression levels (Caserta et al., 2013; Fletcher et al., 2013; Galloway et al., 2015) and studies in rodents and cell lines have shown associations between PFAS exposure and changes in protein levels (Hansmeier et al., 2015). These changes in gene expression or protein levels may be due to epigenetic mechanisms acting on the transcriptional and post-transcriptional levels, such as changes in DNA methylation (Miura et al., 2018) or in microRNA (miRNA) expression. MiRNA modulates gene expression by triggering degradation or blocking translation of its target (Bartel, 2004), e.g. in response to environmental exposures (Vrijens et al., 2015). Indeed, PFAS exposure has been associated with altered expression patterns of specific miRNAs experimentally in rodents (Cui et al., 2019; Wang et al., 2015; Yan et al., 2014), human cells (Dong et al., 2016; Guo et al., 2017; Li et al., 2015) and in a small human observational study evaluating the association with PFOA concentrations in serum (Wang et al., 2012). Epigenetic changes, such as altered miRNA expression, are sensitive biomarkers for detection of exposure and adverse effects, since epigenetic biomarkers often are preceding the development of measurable subclinical effects or pathological states (Maunakea et al., 2010). Thus, changes in miRNAs expression is an intermediate step that may aid the understanding of the mechanisms for PFAS toxicity. In this study, we aimed to investigate whether exposure to PFAS (based on serum concentrations of PFOS, which in the present study also was highly correlated with PFHxS and PFOA; Li et al., 2018) is associated with altered miRNA expression in serum. We performed in silico analyses to identify potential biological functions and target genes associated with the altered miRNAs. We used a cohort of adult women from the general population in Southern Sweden, which represented a wide range of exposure levels, from unexposed controls to individuals with very high exposure through drinking water, which had been contaminated by Aqueous Film-Forming Foam (AFFFs) used in a military airfield for decades.

2.2. PFAS analysis in serum Venous blood samples were collected in 5 ml red Becton Dickinson (BD, Belliver Industrial Estate, Plymouth, UK) vacutainer blood collection tubes without gel, and were left in room temperature for at least 30 min before being centrifuged. Serum was immediately transferred to cryotubes and stored at −80 °C at biobanks in Lund. Serum concentrations of PFHxS, PFOS and PFOA were analysed at the Department of Occupational and Environmental Medicine in Lund, Sweden, using liquid chromatography-tandem mass spectrometry (LC/ MS/MS). Total, non-isomer-specific PFAS were determined. A detailed description of the analyses of PFAS in serum has been reported (Li et al., 2018). The analyses of PFOS and PFOA are part of a quality control program between analytical laboratories coordinated by Professor Hans Drexler, Institute and Outpatient Clinic for Occupational, Social and Environmental Medicine, University of Erlangen-Nuremberg, Germany. 2.3. Study design

2. Materials and methods The selection of individuals for this study was based on serum PFOS concentrations. PFOS was here used as a proxy for total PFAS exposure since serum PFOS was strongly correlated with serum PFOA and PFHxS (Spearman’s r > 0.89, p < 0.001 for all combinations) and therefore, effects of specific PFAS cannot be distinguished. Individuals were then divided into three groups according to their serum PFOS concentrations, i.e. low, medium and high exposure group (matched for age and BMI). We checked the serum levels of PFHxS, PFOS and PFOA across exposure groups to ensure the correctness of our assumption that PFOS level is a proxy for total PFAS exposure. Indeed, there was a clear difference in levels of each of the three types of PFAS (PFOS, PFHxS, and PFOA) between the exposure groups (see Table 1), although the contrast in serum PFOA, which had a more narrow exposure range, was rather small when comparing the low to medium exposure groups. We evaluated the association between PFAS exposure and miRNA expression in serum in three different analysis steps using two different analytical methods: next-generation sequencing (NGS) and quantitative polymerase chain reaction (qPCR): Step (1) High coverage miRNA expression was analysed by NGS in 53 individuals in order to screen for miRNAs associated with PFAS exposure; Step (2) Verification by qPCR for selected miRNAs (based on their association with PFAS exposure group in Step 1) in the same 53 individuals; Step (3) Validation by qPCR for selected miRNAs (based on their association with PFAS exposure group in Step (2) in 232 individuals; followed by Step (4) In silico functional analyses (using Ingenuity Pathway analysis, IPA) for selected PFAS-associated miRNAs aiming to identify potential biological functions and miRNA target genes. The flow chart of the whole study design is illustrated in Fig. 1. The

2.1. Study setting The study population was sampled from the Ronneby Cohort (N > 3000 individuals), which comprises individuals from Ronneby, Sweden, a municipality with very high PFAS exposure through drinking water from AFFFs used in a nearby military airfield to 1/3 of the households since the mid-1980s (Li et al., 2018). Clean water was provided from December 2013 when the exposure was discovered, and all inhabitants were offered serum sampling for exposure assessment between May 2014 and the end of 2015. In order to ensure a broad range of serum PFAS levels for research purposes, considerable efforts were made to recruit municipality inhabitants without PFAS exposure through drinking water. Moreover, a control group (N ~ 250) from a nearby municipality, Karlshamn was further sampled in 2016, including individuals between 20 and 60 years old who had never lived in the contaminated area in Ronneby between 1985 and 2013. Information and invitations about the open samplings in Ronneby and Karlshamn were disseminated through the municipal webpages and advertisements in the main local newspaper, and other local information channels. The biomonitoring revealed that people from Ronneby had very high average concentrations of PFOS and PFHxS. Even those who lived outside the contaminated area in Ronneby showed higher serum PFAS than the Karlshamn controls (representing a Swedish general population). The range of serum PFAS levels in the exposed Ronneby participants was wide, but in average, serum PFOS and PFHxS concentrations among the Ronneby participants were about 42 and 180 times 2

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Table 1 Descriptive statistics of the participants in the study (non-smoking women), stratified according to PFAS exposure group. Exploratory group: NGS & qPCR verification (N = 53)a

Validation group: qPCR validation (N = 232)b

Median (range)

Mean (SD)

Median (range)

Mean (SD)

BMI Low exposure group Medium exposure group High exposure group

24 (20–31) 23 (20–30) 24 (21–37)

25 (3.1) 24 (3.0) 27 (5.5)

24 (18–36) 25 (18–37) 25 (17–38)

25 (4.6) 25 (4.2) 26 (5.0)

Age at enrolment (years) Low exposure group Medium exposure group High exposure group

36 (22–45) 37 (19–44) 38 (19–44)

35 (5) 36 (6) 35 (8)

34 (19–45) 33 (19–45) 35 (19–45)

34 (7) 33 (7) 33 (8)

PFOS (ng/ml) Low exposure group Medium exposure group High exposure group

3 (0–8) 59 (16–112) 216 (113–315)

4 (1.8) 58 (27.2) 205 (60.0)

9 (1–17) 45 (25–81) 219 (142–685)

9 (4.8) 48 (16.7) 250 (102.6)

PFOA (ng/ml) Low exposure group Medium exposure group High exposure group

2 (1–4) 2 (1–7) 8 (2–12)

2 (0.9) 3 (1.9) 8 (2.8)

1 (0–4) 3 (1–10) 13 (5–36)

1 (0.8) 3 (1.6) 14 (6.7)

PFHxS (ng/ml) Low exposure group Medium exposure group High exposure group

1 (0–2) 48 (11–115) 168 (93–366)

1 (0.4) 49 (29.6) 176 (67.3)

5 (0–19) 37 (13–120) 180 (87–547)

5 (1.5) 38 (3.0) 211 (13.7)

Abbreviations: SD = Standard deviation, BMI = Body mass index, PFOS = perfluorooctane sulfonic acid, PFOA = perfluorooctanoic acid, PFHxS = perfluorohexane sulfonic acid. a 53 subjects were included (18 in low, 18 in medium and 17 in high exposure group). Originally, 18 individuals in each group were selected based on their serum PFOS levels. Then one individual (in the high exposure group) was found to have duplicate samples with different serial numbers, therefore, one of these two samples was excluded in the statistical analysis. b 232 subjects were included (70 in low, 79 in medium and 83 in the high exposure group). Originally, 239 individuals were chosen but seven individuals were excluded after failed quality control (i.e. reduced extraction efficiency or reverse transcriptase inhibition of PCR).

bioinformatics.babraham.ac.uk/projects/fastqc/). Cutadapt (1.11) was used to extract information of adapter and UMI in raw reads. Bowtie2 (2.2.2) was used for mapping the reads. The mapping criteria for aligning reads to spike-ins, abundant sequence and miRBase are the reads must have perfect match to the reference sequences. Mirbase_20 was used. MiRNA expression levels were measured as Tags per Million (TPM), and on average 2.6 million reads were obtained for each sample and the average genome mapping rate was 69.1%. After mapping the data and counting to relevant entries in mirbase_20 the number of known microRNAs was 476, 263 of which had reads over 10 TPM.

description of the study groups is shown in Table 1. 2.3.1. Step 1 – miRNA analysis using next generation sequencing (NGS) Firstly, in a smaller exploratory subgroup, we originally selected 18 individuals in each exposure group (matched for age and BMI), e.g. in total 54 individuals with information about smoking and antidepressant medication. However, it was later on found that two samples (both in the high exposure group) were from same individual who had attended the blood sampling twice at different occasions and these samples thus had different serial numbers. Therefore, we randomly removed one of these two samples for the statistical analysis, and thus 17 individuals were included in the high exposure group and in total 53 individuals were included in the exploratory subgroup. MiRNA expression levels were analysed by NGS to screen for miRNAs associated with serum PFAS using a large coverage approach. NGS were performed at Exiqon, Vedbeck, Denmark. RNA was isolated from 500 μl serum with proprietary RNA isolation protocol optimized for serum/plasma (no carrier added). The library preparation was done using the QIAseq miRNA Library Kit (QIAGEN, Hilden, Germany). A total of 5 µl total RNA was converted into miRNA NGS libraries. Library preparation quality control (QC) was performed using either Bioanalyzer 2100 (Agilent Technologies, Santa Clara, US) or TapeStation 4200 (Agilent Technologies, Santa Clara, US). Samples showed no signs of inhibition of the reverse transcriptase (RT) reaction (UniSp6 not fluctuating) and the spike-ins UniSp100 and UniSp101 showed little variation indicating equal RNA extraction efficiency for the sample set. Based on quality of the inserts and the concentration measurements the libraries were pooled in equimolar ratios. The library pool(s) were quantified using the qPCR ExiSEQ LNA™ Quant kit (QIAGEN, Hilden, Germany). The library pool was then sequenced on a NextSeq500 sequencing instrument according to the manufacturer instructions. Raw data was demultiplexed and FASTQ files for each sample were generated using the bcl2fastq software (Illumina inc.). FASTQ data were checked using the FastQC tool (http://www.

2.3.2. Step 2 and 3 – miRNA analysis using qPCR verification and validation In order to make a technical verification of the NGS expression data, we analysed the expression of selected miRNAs by qPCR (gold-standard and targeted approach) in the same 53 women as in the NGS analysis. Thereafter, we further validated selected miRNAs by qPCR in a larger validation group of 239 women. The number of subjects in the validation group was based on power calculations. For the power calculation R statistical programming language was used, employing the command power.t.test with a significance level of 0.05 and power of test of 0.95. The power analysis reports how many samples will be needed, per group, to reject the null hypothesis at 0.05 confidence intervals given the observed group averages and standard deviations. There was no overlap of individuals between the validation group of 239 women and the 53 women in the exploratory group. After QC for the validation group, seven subjects were removed. Samples from six subjects were removed due to reduced extraction efficiency (based on the levels of UniSp2 and UniSp4 assays) and the sample of one subject was removed due to reverse transcriptase qPCR inhibition (based on the expression of the included UniSp6 spike-in). Samples from the remaining 232 individuals were included in the qPCR. Information on usage of antidepressant medication was not 3

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The amplification curves were analysed using the Roche LC software, both for determination of Cq (by the 2nd derivative method) and for melting curve analysis. The amplification efficiency was calculated using algorithms similar to the LinReg software. All assays were inspected for distinct melting curves and the Tm was checked to be within known specifications for the assay. Furthermore, assays must be detected with 5 Cqs less than the negative control, and with Cq < 37 to be included in the data analysis. Data that did not pass these criteria were omitted from any further analysis. NormFinder (Andersen et al., 2004) was used to find the most suitable normalizers based on which miRNAs were most stably expressed across the different groups and within the groups. Normfinder was used to select possible normalizers for the qPCR studies and calculate a stability value to evaluate the used normalizers. Three miRNAs (let-7i5p, miR-30e-5p and miR-425-5p) were used as normalizers for the verification, and four miRNAs (let7i5p, miR-22-3p, miR-484 and miR-425-5p) were used as normalizers for the validation. 2.3.3. Step 4 – In silico functional analyses for selected miRNAs A theoretical investigation of the possible functions of the PFASassociated miRNAs was done by performing in silico functional analyses using Ingenuity Pathway analysis (IPA®, version 49309495, Qiagen, Ingenuity System, Redwood City, CA). The selection criteria for being considered to be a PFAS-associated miRNA were: (1) miRNAs should be differentially expressed, of statistical significance or of borderline significance (i.e. p = 0.05–0.1), across at least one comparison between exposure groups in all three analysis steps [in the NGS and verification analyses after false discovery rate (FDR) adjustment, and in the validation analyses before FDR adjustment], and (2) miRNAs should be consistently in the same direction (up or downregulated) in all three analysis steps. 2.4. Statistical analysis 2.4.1. Differential miRNAs expression For step 1 (NGS), differential expression analysis was performed using the EdgeR statistical software package (Bioconductor, http:// bioconductor.org/). For normalisation, the trimmed mean of M-values method based on log-fold and absolute gene-wise changes in expression levels between samples (TMM normalization) was used. MiRNAs with a low expression (mean TPM below 10) were removed. Pairwise t-tests were performed comparing the differences in miRNA expression between exposure groups (medium vs low exposure, high vs medium exposure, high vs low exposure, group denoted last is reference). FDR correction was performed to get adjusted p-values for NGS. MiRNAs with FDR adjusted p-values below 0.05 in any comparison were chosen for further analysis in the qPCR verification step. Some extra miRNAs of borderline significance (p = 0.05–0.10) were also included in order to fill up the chip (N = 48 assays, including QC, haemolysis markers, and normalizers). We also selected some miRNAs that have been associated with PFOA in a previous study of occupational exposure as well as in human cell studies (Li et al., 2015; Wang et al., 2012) to include in the chip. For step 2 (qPCR verification), delta Cq was calculated as the difference between each individual Cq and the normalizer. Differential expression was calculated using the 2−ΔΔCq method, and pairwise ttests were performed comparing the differential expression in miRNAs between exposure groups. FDR correction was performed. MiRNAs with FDR adjusted p-values below 0.1 were chosen for step 3 (qPCR validation). Sample size calculation for step 3 was performed based on the results from the t-tests in the verification, and indicated that 239 individuals would be enough in the validation step in order to reach a pvalue of 0.05 for all analyses except for one miRNA that demanded 258 individuals to reach a p-value of 0.05. For step 3 (qPCR validation), differential expression calculation and pairwise t-tests between exposure groups were evaluated as described

Fig. 1. Study design.

available for the validation group. All experiments and qPCR lab analyses were performed at QIAGEN, Hilden, Germany. For the technical verification of NGS analysis, we used the same 53 RNA samples that were also used for the NGS analysis. For the 239 women included in the qPCR validation, total RNA was extracted from 200 μl serum using miRCURY RNA isolation Kit–Biofluids; high-throughput bead-based protocol v.1 (QIAGEN, Hilden, Germany) in an automated 96 well format. For the miRNA realtime qPCR, RNA was reverse transcribed using the miRCURY LNA™ Universal RT miRNA PCR, Polyadenylation and cDNA synthesis kit (QIAGEN, Hilden, Germany). The miRNAs were assayed by qPCR on the miRNA custom panel using ExiLENT SYBR® Green master mix. The amplification was performed in a LightCycler® 480 Real-Time PCR System (Roche Diagnostics, Risch-Rotkreuz, Switzerland) in 384 well plates. Three replicates were used in the technical verification qPCR, while two replicates were used in the validation qPCR. However, in the verification qPCR, the Cq levels of UniSp6 were elevated in one of the reverse transcriptase (RT) replicates in 24 of the samples, hinting at an inhibition in the RT reaction, and so were the Cqs of the endogenous miRNAs. Repeated RT reactions on those samples or using a different plate layout did not improve of the situation, and the affected RT replicates were removed from further analysis. Thus, 24 of the 53 individuals had 2 replicates instead of 3. Due to the high well-to-well reproducibility (coefficient of variation generally below 5%) of the qPCR, we prioritized 2 × cDNA reactions instead of more replicates in qPCR reactions in the validation, therefore we had two replicates in this step. The replicates of the same sample were run on different plates. 4

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in step 2. FDR correction was also performed. The statistical analyses for step 2 and 3 were performed in IBM SPSS (Version 25.0; IBM SPSS Statistics for Windows, NY, USA). By summarizing the results from all three analysis steps (NGS, verification qPCR and validation qPCR), we selected the miRNAs which were considered to be associated with PFAS exposure in this study. These miRNAs were then included in in silico functional analyses.

Table 2 Top five miRNAs associated with PFAS exposure in NGS, qPCR verification, and qPCR validation (t-test). NGS miRNA High vs. low exposure groupa miR-484 miR-144-3p miR-20a-5p miR-92b-3p miR-92a-3p High vs. Medium exposure groupa miR-184 miR-484 miR-20a-5p miR-1180-3p miR-22-3p Medium vs. low exposure groupa miR-199a-5p miR-122-5p miR-206 miR-1246 miR-151a-3p

2.4.2. In silico functional analyses IPA (Ingenuity systems, Redwood City, CA, USA) is a database software containing large databases with detailed and structured findings reviewed by experts, which was derived from thousands of biological, chemical and medical researches (Thomas and Bonchev, 2010). First, we used IPA to predict “Top Diseases and Bio Functions” and “Toxicological Functions” for miRNAs which were associated with PFAS exposure in all three analysis steps. A list of these miRNAs and their direction of association with PFAS exposure group was uploaded into IPA and matched with Ingenuity knowledge human database. The p-value associated with in silico functional analyses for a dataset is a measure of the likelihood that the association between a set of in silico functional analyses molecules in a study and a given process is due to random chance. The p-value is calculated using the right-tailed Fisher Exact Test. Secondly, we performed a miRNA Target Filter analysis for miRNAs associated with PFAS exposure. The IPA miRNA Target Filter analysis couples miRNAs of interest with their mRNA target genes by using TargetScan (MIT) for predicted targets (in silico prediction scores for miRNA-mRNA interactions) and directly from the literature, and TarBase and MiRecords for experimentally demonstrated target information (“miR Target Database”). The output of target genes from the miRNA target filter were crosschecked with IPAs’ list of genes annotated to be associated with PFOS, PFHXS or PFOA in the curated IPA knowledge human database. Additionally, we also added some genes to the list of genes annotated to PFOA. These additions were genes whose expression were significantly associated with PFOA concentration in the C8 study (Fletcher et al., 2013) but were missing annotations for PFOA in the curated IPA knowledge human database.

qPCR verification High vs. low exposure groupa miR-16-2-3p miR-574-3p miR-186-5p miR-486-5p miR-92a-3p High vs. medium exposure groupa miR-15a-5p miR-16-2-3p miR-342-3p miR-486-5p miR-574-3p Medium vs. low exposure groupa miR-186-5p miR-122-5p miR-199a-3p miR-199a-5p miR-92b-3p qPCR validation High vs. low exposure groupa miR-342-3p miR-101-3p miR-19b-3p miR-122-5p miR-19a-3p High vs. medium exposure groupa miR-20a-5p miR-144-3p miR-101-3p miR-19a-3p miR-19b-3p Medium vs. low exposure groupa miR-20a-5p miR-144-3p miR-92a-3p miR-486-5p miR-1180-3p

3. Results 3.1. Step 1 – NGS analysis Twenty-four miRNAs were differentially expressed (adjusted pvalue < 0.05) in the comparisons between high and low exposure groups. Three miRNAs were differentially expressed in the comparisons between high and medium exposure group while only one miRNA was differentially expressed in the comparisons between low and medium exposure groups. Two miRNAs were significantly differentially expressed in more than one comparison between groups. Thus, in total 26 miRNAs were significantly differentially expressed between exposure groups and 20 of them (77%) were downregulated with increasing exposure (Table 2). In total, including the additional miRNAs previously described, 42 miRNAs were selected for further analysis with qPCR. Results for the t-tests from NGS for all miRNAs that were chosen for further analysis in the qPCR verification step is shown in supplemental table S1.

Fold change

p

FDR-adjusted p

0.67 0.58 0.73 0.62 0.71

< 0.001 < 0.001 < 0.001 < 0.001 < 0.001

0.001 0.003 0.004 0.004 0.005

3.9 0.7 0.8 0.7 0.8

< 0.001 < 0.001 < 0.001 0.001 0.001

0.031 0.031 0.047 0.056 0.056

1.6 2.2 2.7 0.6 1.3

< 0.001 0.001 0.001 0.023 0.043

0.038 0.11 0.11 0.62 0.74

0.73 1.63 1.24 0.72 0.82

< 0.001 < 0.001 0.001 0.004 0.005

0.007 0.007 0.016 0.047 0.047

0.82 0.8 1.35 0.74 1.33

0.007 0.006 0.007 0.007 0.006

0.063 0.063 0.063 0.063 0.063

1.18 1.74 1.25 1.32 0.83

0.013 0.058 0.076 0.067 0.071

0.58 0.67 0.67 0.67 0.67

0.79 0.83 0.89 0.70 0.90

0.009 0.021 0.037 0.043 0.088

0.20 0.23 0.24 0.24 0.39

0.87 0.74 0.85 0.90 0.90

0.007 0.013 0.032 0.039 0.039

0.14 0.14 0.17 0.17 0.17

1.10 1.25 0.91 0.87 1.19

0.057 0.076 0.082 0.11 0.13

0.58 0.58 0.58 0.58 0.58

Abbreviations: NGS = Next generation sequencing; FDR = False Discovery Rate; PFOS = perfluorooctane sulfonic acid. a The exposure group denoted last is the reference category.

3.2. Step 2 – qPCR verification

exposure groups (two upregulated, three downregulated with increasing exposure) (Table 2). No miRNAs were differentially expressed in the comparisons between high and medium exposure group or between medium and low exposure group. Results for the t-tests from all miRNAs in the qPCR verification step is shown in supplemental table S2.

Among the 42 miRNAs selected from NGS analysis, 37 miRNAs were identified in all samples in the qPCR, whereas for 5 miRNAs (miR-184, miR-206, miR-1294, miR-548a-5p, miR-548d-5p), expression data was missing in > 20% of the individuals and were therefore excluded in further analyses. These miRNAs had fairly low expression levels in the NGS (mean TPM around 30). Five miRNAs were differentially expressed (adjusted p-value < 0.05) in the comparisons between high to the low 5

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3.3. Step 3 - qPCR validation

4. Discussion

In the qPCR validation including 232 individuals (7 individuals were removed due to failed QC), 12 out of 18 target miRNAs were identified in all samples. Three of the six miRNAs that were not identified in all samples had large fractions of individuals with missing expression data (missing in > 20% of the individuals) and these three miRNAs were thus excluded (miR-1180-3p, miR-486-3p, miR-574-3p). These miRNAs had fairly low expression levels (data not shown). When using FDR-adjusted p-values, no comparisons reached statistical significance. When using unadjusted p-values, four miRNAs were differentially expressed (p-value < 0.05) in the comparisons between high and low exposure groups and five miRNAs were differentially expressed in the comparisons between high and medium exposure group (Table 2). No miRNA was differently expressed between medium and low exposure group. Two miRNAs were significantly differentially expressed in more than one comparison between groups, thus in total seven miRNAs were differentially expressed (all downregulated with increasing exposure). Results for the t-tests from all miRNAs in the qPCR validation step is shown in supplemental table S3.

There is still a knowledge gap about the modes of action for PFAS toxicity. Some evidence suggests epigenetic mechanisms to be involved. Epigenetic biomarkers, such as miRNA expression, are sensitive biomarkers for detection of environmental exposure and adverse effects. However, previous studies regarding PFAS and epigenetic effects have mostly been limited to animal models and human cell studies. Only one human study has reported associations between miRNA and PFAS exposure, in this case high PFOA exposure (Wang et al., 2012). The present study included women with a different PFAS exposure profile, dominated by PFOS and PFHxS from drinking water contaminated from AFFF. Here, we found that increased PFAS exposure was associated with downregulation of three miRNAs (miR-101-3p, miR-144-3p and miR-19a-3p). In silico functional analyses suggested that these PFAS-associated miRNAs were related to e.g. cardiovascular disease, Alzheimer’s, as well as some types of cancers and effects on growth for different types of cancer cell lines. We found that seven predicted target genes for the downregulated miRNAs were also annotated to PFAS in IPA knowledge database (including one gene manually added from the literature), which may thus be potential target genes for PFAS mediated via miRNA expression: DNA methyltransferase 3 alpha (DNMT3a), epidermal growth factor receptor (EGFR), 3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR), nuclear receptor subfamily 1, group H, member 3 (NR1H3), peroxisome proliferator-activated receptor alpha (PPARα), prostaglandin-endoperoxide synthase 2 (PTGS2), and tumour growth factor alpha (TGFα). A few previous studies have evaluated the association between specific PFAS and miRNA expression. Wang et al. (2012) assessed the association between PFOA in serum and miRNA expression in a small study of in total 51 individuals (20 individuals analysed by miRNA microarray analysis, with a validation by 31 individuals analysed by Taqman assay), mainly occupationally exposed men. The levels of serum PFOA in the study of Wang et al. (2012) were substantially higher than in our study (about 100 times higher levels for the occupational participants in Wang et al. compared to the high-exposed group in our study), while the levels of PFOS was substantially lower (the high-exposed group in our study had about 10 times higher levels compared to the occupational participants in Wang et al.). Wang et al. (2012) found that serum concentrations of PFOA were associated with expression of miR-26b and miR-199-3p. These miRNAs were evaluated in our study by NGS and qPCR but were not significantly associated with PFAS exposure. Neither could we verify associations between PFAS and miRNAs that have been observed in rodents (Cui et al., 2019; Wang et al., 2015; Dong et al., 2016; Yan et al., 2014) or human cells (Guo et al., 2017; Li et al., 2015). This can in part be explained by that animal models do not always reflect genomic responses that occur in humans (Marczylo et al., 2016; Pasterkamp et al., 2016; Seok et al., 2013). One reason for the lack of confirmation of associations between PFAS and miRNAs from the human cell studies may be that these human cell studies were performed on SH-SY5Y human neuroblastoma cells, a cell line widely used to determine the neurotoxicity of xenobiotics, while our study evaluated miRNA in serum. Expression of miRNA in serum may not be that well correlated to the expression in SH-SY5Y human neuroblastoma cells, since miRNAs in serum are those secreted or excreted from cells or tissues in the body and the exact cellular sources of the measured miRNAs remain unclear. We performed in silico functional analyses in order to identify potential diseases and biological functions associated with the PFAS-associated miRNAs in our study. Downregulation of miR-101-3p and miR19a-3p have been seen in the patients with cardiovascular diseases (Ikeda et al., 2007), and in the serum and brain samples taken from patients with Alzheimer’s (Geekiyanage et al., 2012; Hébert et al., 2008; Nunez-Iglesias et al., 2010). In addition, PFAS-associated miRNAs were associated with different types of cancers and effects on growth for some cancers cell lines. For example, miR-101-3p have been shown to

3.4. Step 4 – selection of miRNAs associated with PFAS in all three analysis steps and further in silico functional analyses We then evaluated the results from the t-tests from all three analysis steps (NGS, qPCR verification, qPCR validation) in order to select miRNAs which were considered to be consistently associated with PFAS exposure in this study, according to the selection criteria (see Study design, step 4). Table 3 summarizes the results for all three analysis steps. Three miRNAs (miR-101-3p, miR-144-3p and miR-19a-3p) fitted the criteria and were chosen for further in silico functional analyses. Statistically significant “Top Diseases and Bio Functions” and “Toxicological Functions” from IPA are shown in Table 4 (both) and Fig. 2 (Top Diseases and Bio Functions only). The top categories were related to cardiovascular disease and cardiovascular system development and function (p = 0.00007–0.0004). The other statistically significant categories were related to e.g. cellular growth and proliferation, cellular development, organismal injury and abnormalities, and cancer, with diseases and functions annotations related to different types of cancers (e.g. lymphoma, liposarcoma, cervical squamous carcinoma), as well as effects on growth for different types of cancer cell lines for (e.g. breast, colorectal, thyroid, lymphoma). Another disease annotation to be statistically significant was Alzheimer’s disease (p = 0.005). The results of “Toxicological Functions” from IPA showed that the statistically significant toxicological functions of the three miRNAs (miR-101-3p, miR-144-3p and miR-19a-3p) were cardiac dilation and enlargement (p = 0.0004). We predicted miRNA targets in silico for the miRNAs associated with PFAS exposure in this study. These three miRNAs were predicted to target in total 2222 unique genes. Six genes were annotated to PFOA in the knowledge database as well as predicted to be target for miRNAs associated with PFAS exposure: DNA methyltransferase 3 alpha (DNMT3a, target for all three miRNAs), prostaglandin-endoperoxide synthase 2 (PTGS2, miR-101-3p and miR-144-3p), 3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR, miR-101-3p and miR-144-3p), tumour growth factor alpha (TGFα, miR-101-3p and miR-144-3p, peroxisome proliferator-activated receptor alpha (PPARα, miR-101-3p and miR-19a-3p) and epidermal growth factor receptor (EGFR, miR-1443p). Additionally, we also cross-checked miRNA targets with genes whose expression were significantly associated with PFOA concentration in the C8 study (Fletcher et al., 2013), and two of these genes were considered miRNA targets in the current study. These were PPARα as described above, as well as nuclear receptor subfamily 1, group H, member 3 (NR1H3, targeted by miR-19a-3p). No miRNA target genes were annotated to PFOS or PFHxS in the IPA knowledge database. 6

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Table 3 T-tests from NGS, qPCR verification, and qPCR validation for miRNAs included and successfully analyzed in all these three steps. NGSa

qPCR verificationa

qPCR validationb

miRNAs

Group comparisonc

Fold change

p

FDR-adjusted P

Fold change

p

FDR-adjusted P

Fold change

p

FDR-adjusted P

miR-101-3p

high-low high-medium medium-low high-low high-medium medium-low high-low high-medium medium-low high-low high-medium medium-low high-low high-medium medium-low high-low high-medium medium-low high-low high-medium medium-low high-low high-medium medium-low high-low high-medium medium-low high-low high-medium medium-low high-low high-medium medium-low high-low high-medium medium-low high-low high-medium medium-low high-low high-medium medium-low high-low high-medium medium-low high-low high-medium medium-low

0.73 0.83 0.89 0.74 0.76 0.98 2.49 1.15 2.18 0.58 0.67 0.87 0.65 0.68 0.96 0.90 0.85 1.07 0.73 0.79 0.94 0.81 0.85 0.97 0.73 0.75 0.99 0.81 0.96 0.85 0.76 0.77 1.00 1.41 1.4 1 0.62 0.69 0.91 0.67 0.67 1.00 0.62 0.65 0.96 0.71 0.80 0.90

0.00 0.028 0.18 0.002 0.005 0.85 0.00 0.63 0.001 0.00 0.003 0.31 0.00 0.004 0.74 0.11 0.03 0.34 0.00 0.006 0.40 0.016 0.069 0.68 0.00 0.00 0.86 0.027 0.71 0.10 0.004 0.001 0.96 0.002 0.002 0.89 0.001 0.013 0.53 0.00 0.00 0.97 0.00 0.001 0.76 0.00 0.011 0.23

0.012 0.23 0.82 0.027 0.12 0.99 0.006 0.85 0.11 0.003 0.099 0.86 0.010 0.11 0.95 0.34 0.24 0.86 0.012 0.12 0.86 0.12 0.34 0.95 0.004 0.047 0.99 0.15 0.90 0.81 0.052 0.056 0.99 0.029 0.067 0.99 0.022 0.15 0.90 0.001 0.031 0.99 0.007 0.056 0.95 0.005 0.14 0.82

0.82 0.85 0.97 1.06 1.05 1.01 1.77 1.02 1.74 0.74 0.84 0.88 0.73 0.8 0.91 1.24 1.05 1.18 0.87 0.9 0.97 0.87 0.9 0.97 0.9 0.87 1.03 0.98 1.04 0.95 1.02 1.01 1.01 1.28 1.35 0.95 0.73 0.75 0.98 1.02 0.96 1.07 0.73 0.74 0.99 0.82 0.87 0.94

0.011 0.022 0.65 0.45 0.55 0.91 0.035 0.95 0.058 0.021 0.23 0.38 0.00 0.006 0.32 0.001 0.43 0.013 0.01 0.084 0.63 0.015 0.092 0.58 0.078 0.031 0.60 0.79 0.69 0.49 0.76 0.95 0.87 0.027 0.007 0.66 0.009 0.023 0.85 0.64 0.38 0.20 0.008 0.009 0.95 0.005 0.038 0.37

0.057 0.11 0.93 0.55 0.76 0.97 0.097 0.95 0.67 0.079 0.41 0.79 0.007 0.063 0.79 0.016 0.68 0.58 0.057 0.23 0.93 0.066 0.24 0.93 0.17 0.14 0.93 0.81 0.90 0.87 0.80 0.95 0.97 0.081 0.063 0.93 0.056 0.11 0.97 0.70 0.65 0.75 0.056 0.068 0.97 0.047 0.15 0.79

0.83 0.85 0.98 1.08 1.01 1.07 0.70 0.86 0.82 0.92 0.74 1.25 0.96 0.97 0.99 0.91 0.87 1.04 0.90 0.90 1.01 0.89 0.90 0.98 0.96 0.87 1.10 0.93 0.94 0.99 1.01 0.99 1.01 0.79 0.89 0.89 1.04 1.02 1.02 0.99 1.00 0.99 0.87 0.96 0.91 0.95 1.03 0.91

0.021 0.032 0.78 0.16 0.84 0.23 0.043 0.39 0.26 0.54 0.013 0.076 0.50 0.57 0.88 0.25 0.052 0.64 0.088 0.039 0.85 0.037 0.039 0.73 0.48 0.007 0.057 0.14 0.16 0.80 0.92 0.84 0.78 0.009 0.15 0.22 0.54 0.74 0.78 0.74 0.98 0.73 0.18 0.64 0.34 0.28 0.42 0.082

0.23 0.17 0.97 0.41 0.88 0.63 0.24 0.83 0.63 0.65 0.14 0.58 0.65 0.88 0.97 0.51 0.19 0.97 0.39 0.17 0.97 0.24 0.17 0.97 0.65 0.14 0.58 0.41 0.39 0.97 0.92 0.88 0.97 0.20 0.39 0.63 0.65 0.88 0.97 0.77 0.98 0.97 0.41 0.88 0.75 0.51 0.83 0.58

miR-107

miR-122-5p

miR-144-3p

miR-16-2-3p

miR-186-5p

miR-19a-3p

miR-19b-3p

miR-20a-5p

miR-21-5p

miR-22-3pd

miR-342-3p

miR-451a

miR-484d

miR-486-3p

miR-92a-3p

Abbreviations: NGS = Next generation sequencing; qPCR = quantitative polymerase chain reaction; FDR = False Discovery Rate. a 53 subjects (18 in low, 18 in medium and 17 in high exposure group) were included in the NGS and verification analyses. b 232 subjects (70 in low, 79 in medium and 83 in high exposure group) were included in the validation analyses. c The exposure group denoted last is the reference category. d These microRNAs were not associated with PFAS in the qPCR verification analyses and were chosen as normalizers in the validation. The other two normalizers in the validation, which were not associated with PFAS in any analyses, are not shown here.

PFAS, miRNAs, and potential miRNA target genes could be connected by cross-checking predicted and experimental microRNA targets for the PFAS-associated miRNAs with IPAs knowledge database for PFOA, PFOS and PFHxS. Thus, these are suggestive biological pathways in which PFAS exposure may influence the expression of PTGS2, HMGCR, NR1H3, PPARα, DNMT3a, EGFR or TGFα, with the PFAS-associated miRNAs as potential mediators. However, this is highly speculative and needs to be verified experimentally. Among these seven genes, only one gene has previously been associated with serum PFAS concentrations in a human population: NR1H3, the expression of which was negatively associated with PFOA in the C8 study (Fletcher et al., 2013). When also considering animal and cell studies, more of these genes have been linked to PFAS. One

be significantly downregulated in breast cancer tissues and cell lines (Wang et al., 2017) as well as in patients with diffuse large B cell lymphoma (Huang et al., 2019). There is still limited knowledge about the association between PFAS exposure and the diseases and biological functions annotated to the PFAS-associated miRNAs. Winquist and Steenland (2014) have reported associations between PFOA and cardiovascular diseases (dominated by ischemic cardiovascular disease) in a US population, and one Italian ecological study reported a higher mortality from Alzheimer disease in a PFAS contaminated area compared to uncontaminated area (Mastrantonio et al., 2018). PFOA was assigned to group 2B as being possibly carcinogenic to humans by IARC (IARC 2016), but the evidence for PFOS carcinogenicity to human was too limited to support a quantitative cancer assessment (US EPA, 2016). 7

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Table 4 Statistically significant “Top Diseases and Bio Functions” and “Toxicological Functions” from the Ingenuity pathway analyses for the miRNAs considered to be associated with PFAS exposure in this study. Categories Diseases and disorders Cardiovascular Disease, Organismal Injury and Abnormalities Cardiovascular Disease, Cardiovascular System Development and Function, Organ Morphology, Organismal Development, Organismal Injury and Abnormalities, Skeletal and Muscular Disorders Cell-To-Cell Signalling and Interaction, Cellular Growth and Proliferation Cellular Response to Therapeutics Cellular Development, Cellular Growth and Proliferation Cellular Development, Cellular Growth and Proliferation Developmental Disorder, Hereditary Disorder, Organismal Injury and Abnormalities, Skeletal and Muscular Disorders Metabolic Disease, Neurological Disease, Organismal Injury and Abnormalities, Psychological Disorders Cancer, Hematological Disease, Immunological Disease, Organismal Injury and Abnormalities Cellular Development, Cellular Growth and Proliferation Cancer, Connective Tissue Disorders, Organismal Injury and Abnormalities Cancer, Organismal Injury and Abnormalities, Reproductive System Disease Inflammatory Disease, Neurological Disease, Skeletal and Muscular Disorders Cancer, Hematological Disease, Immunological Disease, Neurological Disease, Organismal Injury and Abnormalities Cancer, Hematological Disease, Immunological Disease, Organismal Injury and Abnormalities Cellular Development, Cellular Growth and Proliferation Connective Tissue Disorders, Inflammatory Disease, Inflammatory Response, Organismal Injury and Abnormalities, Respiratory Disease Cancer, Hematological Disease, Immunological Disease, Organismal Injury and Abnormalities Cancer, Neurological Disease, Organismal Injury and Abnormalities Cancer, Organismal Injury and Abnormalities, Reproductive System Disease Cancer, Hematological Disease, Organismal Injury and Abnormalities Cancer, Endocrine System Disorders, Organismal Injury and Abnormalities, Respiratory Disease Organismal Injury and Abnormalities, Reproductive System Disease Cancer, Organismal Injury and Abnormalities, Reproductive System Disease, Skeletal and Muscular Disorders Toxicological functions Cardiac Dilation, Cardiac Enlargement

Diseases or Functions Annotation

p-value

MiRNAs

Stenosis of aorta

0.00007

Dilated cardiomyopathy

0.0004

miR-101-3p, miR-19b-3p miR-101-3p, miR-19b-3p

Suppression of colorectal cancer cell lines Radiosensitivity of lung cancer cell lines Cell proliferation of breast cancer cell lines

0.0004 0.0009 0.003

Arrest in growth of breast cancer cell lines LMNA-related congenital muscular dystrophy

0.004 0.005

Alzheimer disease

0.005

ALK positive anaplastic large cell lymphoma Proliferation of thyroid tumor cell lines

0.006 0.007

miR-101-3p, miR-19b-3p miR-101-3p miR-19b-3p

Dedifferentiated liposarcoma

0.009

miR-144-3p

Early stage invasive cervical squamous cell carcinoma

0.012

miR-19b-3p

Relapsing-remitting multiple sclerosis

0.017

miR-19b-3p

Primary central nervous system lymphoma

0.019

miR-19b-3p

Lymphoma

0.021

Cell proliferation of lymphoma cell lines

0.022

miR-101-3p, miR-19b-3p miR-101-3p

Idiopathic pulmonary fibrosis

0.023

miR-19b-3p

Nasal type extranodal NK-/T-cell lymphoma

0.023

miR-101-3p

Medulloblastoma Invasive ductal breast carcinoma

0.023 0.029

miR-19b-3p miR-19b-3p

Mature lymphocytic neoplasm

0.031

Small cell lung carcinoma

0.034

miR-101-3p, miR-19b-3p miR-19b-3p

Nonobstructive azoospermia Uterine leiomyoma

0.036 0.048

miR-19b-3p miR-144-3p

0.0004

miR-101-3p, miR-19b-3p

miR-19b-3p miR-101-3p miR-101-3p, miR-19b-3p miR-19b-3p miR-19b-3p

Fig. 2. Statistically significant ‘Top disease and Bio Functions’ from the Ingenuity pathway analysis (IPA) for the miRNAs considered to be associated with PFAS exposure in this study (i.e. miR-101-3p, miR-144-3p and miR-19a-3p). 8

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expression may be a mechanism of toxicity of PFAS. Further, our results from in silico functional analyses suggest potential links between PFAS concentrations in serum, miRNA expression, specific miRNA target genes and perhaps also health effects such as cardiovascular disease and function, Alzheimer disease, growth of cancer cell lines and cancer. However, these potential links are highly speculative and needs to be verified experimentally.

example is PPARα, since PFOA could act as a peroxisome proliferator or PPARα agonist in rodents and in human cells (Rosen et al., 2008; Vanden Heuvel et al., 2006). Moreover, PFOA was found to be associated with increased expression of PTGS2 in mice livers and mast cells (Singh et al., 2012; Yang et al., 2014; Zou et al., 2015), as well as increased expression of DNMT3A in liver cells (Tian et al., 2012). PFOS exposure have been shown to increase the cardiac mitochondrial gene expression of EGFR in rats, prenatally exposed to PFOS (Xia et al., 2011). However, extrapolation of data from animal and cell studies to human populations can be questionable and further population-based studies are needed to confirm the associations between these seven genes and biomarkers of PFAS. It is notable that there were very few differently expressed miRNAs between the low and medium exposure groups. Differently expressed miRNAs were mainly observed when contrasting the high exposure group with the low or medium exposure group, indicating that the threshold exposure for attaining an effect on miRNA expression may be fairly high. The exposure to PFOS and PFHxS in the Ronneby population is very high compared to those of most other investigated general populations worldwide (Brede et al., 2010; Worley et al., 2017). Other PFAS exposed cohorts, such as the C8 studies, have lower levels of PFOS in serum but higher levels of PFOA (Frisbee et al., 2009). Unfortunately, some assays failed in the qPCR lab analyses (e.g. had missing values for > 20% of the individuals) for miRNAs that were strongly associated with PFAS exposure in the preceding analysis step. The lab analysis of the top miRNA associated with PFAS exposure in the comparison between high vs. medium exposure in NGS (miR-184) failed in the following verification step, and the lab analyses of two of the five miRNAs that were associated with PFAS exposure in the verification step failed in the following validation step. Thus, some miRNAs that may have been associated with PFAS exposure could have been missed due to failed lab analyses. However, the miRNAs for which the qPCR lab analyses failed were all rather low expressed in NGS. Also, some miRNAs that were strongly associated with PFAS exposure in the NGS analyses (such as miR-484 and miR-199a-5p) showed no associations with PFAS exposure in qPCR in the same study group. This may be due to methodological differences, e.g. that sequencing platforms have been shown to be less accurate than qPCR platforms for the analysis of low copy number miRNAs in samples such as biofluids (Mestdagh et al., 2014), and thus miRNAs at the lower TPM range have been shown to have inferior correlation with qPCR, in terms of detection level, log fold change and direction of change (Blondal et al., 2017). Our study has several strengths: MiRNA expression was analysed using two different methods and two different study groups. We used a consistent experimental setup at a company specializing in miRNA analysis, having a uniform sample handling and data processing. There were large ranges of exposure and large contrasts in serum PFAS concentrations between the exposure groups, except for PFOA in the low vs. medium group. A limitation of the study is that the low exposed group have somewhat higher levels of PFAS than the general non-exposed population. Also, our study consisted of women only. The sex difference in epigenetic effect of PFAS is still unclear and unfortunately could thus not be reflected in our study. Additionally, since we measured circulating miRNAs, the exact cellular sources of the measured miRNAs remain unclear. Many organs are likely to contribute to the extracellular miRNA content in serum, and the miRNAs profile in serum may not be representative for the miRNA profile of the organs that are most affected by PFAS exposure. Also, it is not known if the changes in miRNA expression are merely symptoms of physiological processes after PFAS exposure, or whether miRNAs are the drivers responsible for these changes.

CRediT authorship contribution statement Yiyi Xu: Methodology, Formal analysis, Writing - original draft, Writing - review & editing. Simona Jurkovic-Mlakar: Formal analysis, Writing - review & editing. Ying Li: Methodology, Writing - review & editing. Karin Wahlberg: Conceptualization, Methodology, Writing review & editing. Kristin Scott: Resources, Investigation, Writing review & editing. Daniela Pineda: Investigation, Writing - review & editing. Christian H. Lindh: Resources, Writing - review & editing. Kristina Jakobsson: Funding acquisition, Supervision, Writing - review & editing. Karin Engström: Conceptualization, Conceptualization, Formal analysis, Writing - original draft, Project administration, Supervision, Funding acquisition, Writing - review & editing, Writing - review & editing. Acknowledgements We acknowledge the work of the field team during serum samplings, and the study participants. Thanks to Tony Fletcher for commenting on the manuscript. Thanks to Pia Tallving for collecting the serum samples and communicating with study participants. We also would like to thank Annarita Farina (UNIGE) for her generous help with IPA. Funding The study was supported by the Swedish Research Council FORMAS (grant reference numbers 216-2014-1709 and 942-2015-1280) and FORTE 2015-00732. Declaration of Competing Interest 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. Appendix A. Supplementary material Supplementary data to this article can be found online at https:// doi.org/10.1016/j.envint.2019.105446. References Andersen, C.L., Jensen, J.L., Ørntoft, T.F., 2004. Normalization of real-time quantitative reverse transcription-PCR data: a model-based variance estimation approach to identify genes suited for normalization, applied to bladder and colon cancer data sets. Cancer Res. 64, 5245–5250. Agency for Toxic Substances and Disease Registry (ATSDR). An Overview of Perfluoroalkyl and Polyfluoroalkyl Substances and Interim Guidance for Clinicians Responding to Patient Exposure Concerns. Available on line: https://www.atsdr.cdc. gov/pfas/docs/pfas_clinician_fact_sheet_508.pdf. Retrieved on 18th Dec, 2019. Badrnya, S., Baumgartner, R., Assinger, A., 2014. Smoking alters circulating plasma microvesicle pattern and microRNA signatures. Thromb. Haemost. 112, 128–136. https://doi.org/10.1160/TH13-11-0977. Epub 2014 Feb 27. Bartel, D.P., 2004. MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 116, 281–297. Blondal, T., Brunetto, M.R., Cavallone, D., Mikkelsen, M., Thorsen, M., Mang, Y., Pinheiro, H., Bonino, F., Mouritzen, P., 2017. Genome-wide comparison of nextgeneration sequencing and qPCR platforms for microRNA profiling in serum. Methods Mol. Biol. 1580, 21–44. https://doi.org/10.1007/978-1-4939-6866-4_3. Bocchio-Chiavetto, L., Maffioletti, E., Bettinsoli, P., Giovannini, C., Bignotti, S., Tardito, D., Corrada, D., Milanesi, L., Gennarelli, M., 2013. Blood microRNA changes in depressed patients during antidepressant treatment. Eur. Neuropsychopharmacol. 23

5. Conclusion In conclusion, PFAS exposure was associated with downregulation of specific miRNAs. These results indicate that modulation of miRNA 9

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