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A Comprehensive Assessment of Associations between Prenatal Phthalate Exposure and the Placental Transcriptomic Landscape

2021; National Institute of Environmental Health Sciences; Volume: 129; Issue: 9 Linguagem: Inglês

10.1289/ehp8973

1552-9924

Alison G. Paquette, James W. MacDonald, Samantha Lapehn, Theo K. Bammler, Laken Kruger, Drew B. Day, Nathan D. Price, Christine T. Loftus, Kurunthachalam Kannan, Carmen J. Marsit, W. Alex Mason, Nicole R. Bush, Kaja Z. LeWinn, Daniel A. Enquobahrie, Bhagwat Prasad, Catherine J. Karr, Sheela Sathyanarayana,

Birth, Development, and Health

Vol. 129, No. 9 ResearchOpen AccessA Comprehensive Assessment of Associations between Prenatal Phthalate Exposure and the Placental Transcriptomic Landscape Alison G. Paquette, James MacDonald, Samantha Lapehn, Theo Bammler, Laken Kruger, Drew B. Day, Nathan D. Price, Christine Loftus, Kurunthachalam Kannan, Carmen Marsit, W. Alex Mason, Nicole R. Bush, Kaja Z. LeWinn, Daniel A. Enquobahrie, Bhagwat Prasad, Catherine J. Karr, and Sheela Sathyanarayana, on behalf of program collaborators for Environmental influences on Child Health Outcomes Alison G. Paquette Seattle Children's Research Institute, Seattle, Washington, USA University of Washington, Seattle, Washington, USA , James MacDonald University of Washington, Seattle, Washington, USA , Samantha Lapehn Seattle Children's Research Institute, Seattle, Washington, USA , Theo Bammler University of Washington, Seattle, Washington, USA , Laken Kruger Washington State University, Spokane, Washington, USA , Drew B. Day Seattle Children's Research Institute, Seattle, Washington, USA , Nathan D. Price Institute For Systems Biology, Seattle, Washington, USA Onegevity Health, New York City, New York, USA , Christine Loftus University of Washington, Seattle, Washington, USA , Kurunthachalam Kannan NYU Grossman School of Medicine, New York City, New York, USA , Carmen Marsit Emory University, Atlanta, Georgia, USA , W. Alex Mason University of Tennessee Health Sciences Center, Memphis, Tennessee, USA , Nicole R. Bush University of California San Francisco, San Francisco California, USA , Kaja Z. LeWinn University of California San Francisco, San Francisco California, USA , Daniel A. Enquobahrie University of Washington, Seattle, Washington, USA , Bhagwat Prasad Washington State University, Spokane, Washington, USA , Catherine J. Karr University of Washington, Seattle, Washington, USA , and Sheela Sathyanarayana Address correspondence to Sheela Sathyanarayana, Center for Child Health, Behavior, and Development at Seattle Children Research Institute and Department of Pediatrics, University of Washington, 1920 Terry Ave, Seattle WA, USA, 98101. Email: E-mail Address: [email protected] Seattle Children's Research Institute, Seattle, Washington, USA University of Washington, Seattle, Washington, USA , on behalf of program collaborators for Environmental influences on Child Health Outcomes Published:3 September 2021CID: 097003https://doi.org/10.1289/EHP8973AboutSectionsPDF Supplemental Materials ToolsDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InRedditEmail AbstractBackground:Phthalates are commonly used endocrine-disrupting chemicals that are ubiquitous in the general population. Prenatal phthalate exposure may alter placental physiology and fetal development, leading to adverse perinatal and childhood health outcomes.Objective:We examined associations between prenatal phthalate exposure in the second and third trimesters and the placental transcriptome at birth, including genes and long noncoding RNAs (lncRNAs), to gain insight into potential mechanisms of action during fetal development.Methods:The ECHO PATHWAYs consortium quantified 21 urinary phthalate metabolites from 760 women enrolled in the CANDLE study (Shelby County, TN) using high-performance liquid chromatography–tandem mass spectrometry. Placental transcriptomic data were obtained using paired-end RNA sequencing. Linear models were fitted to estimate separate associations between maternal urinary phthalate metabolite concentration during the second and third trimester and placental gene expression at birth, adjusted for confounding variables. Genes were considered differentially expressed at a Benjamini-Hochberg false discovery rate (FDR) p<0.05. Associations between phthalate metabolites and biological pathways were identified using self-contained gene set testing and considered significantly altered with an FDR-adjusted p 70% of samples were above the LOD. Final analyses included 16 metabolites in the second trimester and 14 metabolites in the third trimester, as well as DEHP concentration. DEHP was calculated as the molar sum of five metabolites: MEHP, mono-2-ethyl-5-oxohexyl phthalate (MEOHP), mono-2-ethyl-5-hydroxyhexyl phthalate (MEHHP), mono-2-ethyl-5-carboxypentyl phthalate (MECPP), and mono 2-(carboxymethyl) hexyl phthalate (MCMHP). A subset of 155 participants (21% of the third-trimester samples) had two urine measurements within the third trimester (range of visits: 2.5–12.6 wk; median visit difference: 5.2 wk). For these individuals, we calculated the mean value for each phthalate metabolite from both measurements. We identified one participant as an outlier whose urinary measurements of MECPP, MEHHP, MEOHP, and DEHP were six standard deviations (SDs) from the median value and four SDs from the second lowest value. This participant's second-trimester measurements were removed from the analysis. Urine collected from these two clinical visits was also used to quantify urinary cotinine, which was also adjusted by specific gravity, as previously described (Ni et al. 2021). Maternal cotinine >200 ng/dL at either urine collection time point was used as a marker of maternal smoking given that this cutoff is commonly used to define smokers (Schick et al. 2017).Placental Sample Processing and RNA SequencingWithin 15 min of delivery, a piece of placental villous tissue in the shape of a rectangular prism with approximate dimensions of 2×0.5×0.5cm was dissected from the placental parenchyma and cut into four ∼0.5-cm cubes. The tissue cubes were placed in a 50-mL tube with 20mL of RNAlater and refrigerated at 4°C overnight (≥8h but≤24h). Each tissue cube was transferred to an individual 1.8-mL cryovial containing fresh RNAlater. The cryovials were stored at −80°C, and the fetal villous tissue was manually dissected and cleared of maternal decidua. Following dissection, the fetal samples were placed into RNAlater and stored at −80°C. Approximately 30mg of fetal villous placental tissue was used for RNA isolation. The tissue was homogenized in tubes containing 600μL of Buffer RLT Plus with β-mercaptoethanol using a TissueLyser LT instrument (Qiagen). RNA was isolated using the AllPrep DNA/RNA/miRNA Universal Kit (Qiagen) according to the manufacturer's recommended protocol. RNA purity was assessed by measuring optical density ratios (OD260/230 and OD280/260) with a NanoDrop 8000 spectrophotometer (Thermo Fischer Scientific). RNA integrity was determined with a Bioanalyzer 2100 using RNA 6000 Nanochips (Agilent). Only RNA samples with an RNA integrity number (RIN) of >7 were sequenced.RNA sequencing was performed at the University of Washington Northwest Genomics Center (NWGC). Total RNA was poly A enriched and complementary DNA libraries were prepared using the TruSeq Stranded mRNA kit (Illumina). Each library was uniquely barcoded and subsequently amplified by 13 cycles of polymerase chain reactions. Library concentrations were quantified using Qubit Quant-it dsDNA high sensitivity assay fluorometric quantitation (Life Technologies). Average fragment size and overall quality were evaluated by the DNA1000 assay on an Agilent 2100 Bioanalyzer. Each library was sequenced to an approximate depth of 30 million reads on an Illumina HiSeq 4000 instrument. De-multiplexed BAM files were converted to FASTQ format using Samtools bam2fq. RNA sequencing quality control was performed using both the FASTX-toolkit (version 0.0.13; ILRI Research Computing) and FastQC (version 0.11.2; ILRI Research Computing) (Brown et al. 2017). Transcript abundances were estimated by aligning to the GRCh38 transcriptome (Gencode version 33) using Kallisto (Bray et al. 2016), then collapsed to the gene level using the Bioconductor tximport package, scaling to the average transcript length (Soneson et al. 2015). Only protein-coding genes, processed pseudogenes, and lncRNAs were included in this analysis.Identification of Differentially Expressed GenesDifferentially expressed mRNAs were identified using the limma-voom pipeline (Law et al. 2014). Gene counts were scaled to library size (normalized using a trimmed mean of M-values) (Robinson and Oshlack 2010) and converted to log counts/million (log CPM). After filtering to remove unreliably expressed genes (defined as average log-CPM <0), observation-level weights were computed based on the relationship between the mean and variance of the log-CPM values. Comparisons were then made using conventional weighted linear models. We adjusted for multiple comparisons using the Benjamini-Hochberg approach (Benjamini and Hochberg, 1995). Genes were considered statistically significant at a false discovery rate (FDR) adjusted p<0.05. We selected potential confounders a priori by reviewing covariate data that was associated with phthalates and placental transcriptomics. These models included the following confounding variables: a) RNA sequencing batch; b) birthing method/labor type (labor vs. no labor); c) fetal sex; d) maternal race (Black vs. other); e) maternal age (continuous); and f) maternal education (college or above vs. high school or less). Separate models were run for the second and third trimester. Maternal race was dichotomized because of the small sample size (<5%) of specific race groups (multiple race, Asian, or other). We performed this analysis in all infants with complete data at each trimester, and we also performed a stratified analysis in only female and only male infants. In our stratified analysis, we did not adjust for fetal sex, but we did adjust for all other confounders. A complete overview of the sample collection and a directed acyclic diagram are provided in Figure S1.Pathway Enrichment AnalysisTo identify pathways with significant associations between gene expression and each individual phthalate metabolite, we applied a self-contained gene set test. We specifically used the FRY method (Giner and Smyth 2016), which is a technical improvement over the commonly used Roast method (Wu et al. 2010). The FRY method evaluates whether the average t-statistic for each gene set is larger than expected under the null hypothesis. We included all Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathways (Kanehisa et al. 2016) except disease pathways (KEGG release 98.1). Because this was an exploratory analysis, pathways were considered statistically significant at FDR-adjusted p<0.2. Individual pathways were visualized using the Bioconductor Pathview package (Luo and Brouwer 2013).ResultsMaternal urine samples were collected at clinic visits in the second trimester (13–26 wk, N=594 samples) and the third trimester (26–38 wk, N=735 samples) to quantify phthalate metabolites. A total of 570 placental samples collected at birth had matched urinary phthalate measurements at both time points, and 760 placental samples had urinary phthalate measurements at either time point. The time between the second and third visit ranged from 4 to 20 wk, with a median time between visits of 10 wk. Complete covariate data from individuals at each time point are shown in Table 1. The majority of individuals in this cohort identified as Black (58% of samples with second-trimester phthalate measurements and 56% of samples with third-trimester phthalate measurements). Most of the remaining participants ("other") identified as White (37.5% of participants with urine collected in the second trimester and 38.37% of participants with urine collected in the third trimester). The rest of the participants ( 200 ng/dL at either time point and were considered smokers based on this criterion. We identified a number of identification of differentially expressed genes (DEGs) associated with our a priori–selected confounding variables, including race, birthing method/labor type, fetal sex, maternal education, maternal age, maternal smoking status, and RNA sequencing batch, using a cutoff of FDR-adjusted p 200 ng/dL No54591.8%Ref67692.0%Ref Yes498.2%1598.0%18Continuous Variables Maternal Age (Years)26.7616–402182716–40464 Gestational Age at Birth (weeks)39.2826.85–41.851,49639.2931.7–41.91,378Note: This analysis included only participants with complete covariate data, so there are no missing data. Placental expression was quantified in placentas following delivery, but we constructed two data sets based on which samples had matched urine collected from the second or third trimester. The number reported here represents the number of genes that were considered statistically significant with an false discovery rate adjusted p 0.4 and p<0.05 (Table S2). The correlations of different phthalate concentrations with each other within each time point are shown in Figure S2. MEHP, MEHHP, MEOHP, MECPP, and MCMHP were strongly correlated given that these phthalates are derivatives of the parent compound, DEHP. We observed other correlations between phthalates that are derived from phthalic acid, including MCMHP. Mono (carboxyisooctyl) phthalate (MCIOP) and mono (carboxyisononyl) phthalate (MCINP) were strongly correlated, which is reflective of the fact that MCINP can be converted to MCIOP, although they are primarily derived from different parent compounds (Saravanabhavan and Murray 2012).Figure 1. Box plot depicting concentrations of phthalate metabolites in CANDLE participants detectable in urine in the second (N=594) and third trimester (N=735). In this box plot, the box represents the 25th to 27th percentile (i.e., 50% of the data), with the horizontal line in the box representing the median expression (50th percentile). The whiskers represent the minimum and maximum values that do not exceed 1.5 times the interquartile range, with the remaining values plotted as outlier dots. Note: CANDLE, Conditions Affecting Neurocognitive Development and Learning in Early Childhood; MBP, monobutyl phthalate; MBZP, monobenzyl phthalate; MCINP, monocarboxy isononyl phthalate; MCIOP, mono (carboxyisooctyl) phthalate; MCMHP, mono[2-(carboxymethyl) hexyl] phthalate; MCPP, mono-3- carboxypropyl phthalate; MECPP, mono-2-ethyl-5-carboxypentyl phthalate; MEHHP, mono-2-ethyl-5-hydroxyhexyl phthalate; MEHP, mono-2-ethylhexyl phthalate; MEOHP, mono-2-ethyl-5-oxohexyl phthalate; MEP, monoethyl phthalate; MHPP, mono(4-hydroxypentyl) phthalate; MHXP, mono-n-hexyl phthalate; MIBP, mono-isobutyl phthalate; MINP, mono-isononyl phthalate; MMP, mono-methyl phthalate; M.W., molecular weight.Table 2 Distribution of phthalate metabolite concentrations at each trimester (ng/mL) in CANDLE participants in the second (N=594) and third trimester (N=735), all with complete data.Table 2 has six main columns, namely, Name, Parent Compound(s), Primary or Secondary Metabolite, Molecular Weight, Second Trimester, and Third Trimester. The Second Trimester and Third Trimester columns each are subdivided into six columns, namely, Range (Minimum to Maximum), Twenty-fifth percentile, Fiftieth percentile, Mean, Seventy-fifth percentile, and Standard Deviation.NameParent compound(s)Primary or secondary metaboliteMol. wt.Second trimesterThird trimesterRange (min-max)25th percentile50th percentileMean75th percentileSDRange (min-max)25th percentile50th percentileMean75th percentileSDMono-methyl phth