Examining the Developmental Trajectory of an in Vitro Model of Mouse Primordial Germ Cells following Exposure to Environmentally Relevant Bisphenol A Levels
2021; National Institute of Environmental Health Sciences; Volume: 129; Issue: 9 Linguagem: Inglês
10.1289/ehp8196
ISSN1552-9924
AutoresSteen K.T. Ooi, Hui Jiang, Yanyuan Kang, Patrick Allard,
Tópico(s)Animal Genetics and Reproduction
ResumoVol. 129, No. 9 ResearchOpen AccessExamining the Developmental Trajectory of an in Vitro Model of Mouse Primordial Germ Cells following Exposure to Environmentally Relevant Bisphenol A Levelsis companion ofOff to a Rough Start: Environmental Exposures May Alter Germ Cell Development Steen K.T. Ooi, Hui Jiang, Yanyuan Kang, and Patrick Allard Steen K.T. Ooi UCLA Institute for Society & Genetics, University of California, Los Angeles, Los Angeles, California, USA , Hui Jiang UCLA Institute for Society & Genetics, University of California, Los Angeles, Los Angeles, California, USA , Yanyuan Kang UCLA Institute for Society & Genetics, University of California, Los Angeles, Los Angeles, California, USA , and Patrick Allard Address correspondence to Patrick Allard, UCLA Institute for Society & Genetics, 621 Charles E. Young Dr. South, Life Science Building 3360, University of California, Los Angeles, Los Angeles, CA 90095 USA. Email: E-mail Address: [email protected] https://orcid.org/0000-0001-7765-1547 UCLA Institute for Society & Genetics, University of California, Los Angeles, Los Angeles, California, USA Molecular Biology Institute, University of California, Los Angeles, Los Angeles, California, USA Published:29 September 2021CID: 097013https://doi.org/10.1289/EHP8196Cited by:1AboutSectionsPDF Supplemental Materials ToolsDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InRedditEmail AbstractBackground:Animal-based studies indicate that bisphenol A (BPA) exposure is detrimental to reproductive health, but its impact on the earliest stages of germ cell development remains poorly defined.Objectives:Using a murine in vitro model of early germ cell specification and differentiation, we sought to assess whether exposure to low levels of BPA prior to formation of primordial germ cells (PGCs) alters their differentiation trajectory and unique molecular program.Methods:We used an established method of in vitro differentiation of mouse embryonic stem cells (ESCs) into epiblast-like cells (EpiLCs) followed by PGC-like cells (PGCLCs), which together recapitulate defined stages of early germ cell development. Cellular consequences were determined using hemocytometer-based cell counting, fixation, and intracellular staining, followed by flow cytometry/fluorescence-activated cell sorting (FACS) of cells exposed to increasing concentrations (range: 1 nM–10 μM) of BPA. To interrogate and characterize gene expression differences resulting from BPA exposure, we also generated RNA-seq libraries from RNA extracted from FACS-purified PGCLCs and performed transcriptome analysis using bioinformatics-based approaches.Results:Exposure of EpiLCs to BPA resulted in higher numbers of cells that were associated with a higher proportion of cells in S-phase as well as a lower proportion undergoing apoptosis; this difference occurred in a concentration-dependent manner. Exposure also resulted in a greater fraction of EpiLCs showing signs of DNA damage. Remarkably, EpiLC exposure did not negatively affect PGC specification and resulted in a concentration-dependent effect on PGCLC proliferation in XX but not XY cells. PGCLC transcriptome analysis revealed an aberrant program with significant deregulation of X-linked genes and retrotransposon expression. Differential gene expression analysis also revealed the deregulation of genes associated with lipid metabolism as well as deregulated expression of genes associated with later stages of gametogenesis.Conclusions:To the best of our knowledge our findings represent the first characterization of the consequences of early BPA exposure on a model of mammalian PGC development, highlighting altered cell behavior, altered underlying pathways, and altered molecular processes. https://doi.org/10.1289/EHP8196IntroductionGerm cells represent the bridge between generations, essential for the formation of new individuals in sexually reproducing organisms. It is therefore crucial that the information they contain is both correctly established, maintained, and executed over the course of development. Errors in these crucial developmental steps manifest as developmental defects, disease, and an overall reduction in reproductive fitness (Larose 2019).In mammals, gametes are specified initially as primordial germ cells (PGCs), which in mice arise in embryonic day 6.5 (E6.5) postimplantation embryos, from cells derived in the epiblast layer (Gardner and Rossant 1979; Snow 1981). Over the course of their development, PGCs undergo a unique and complex reprogramming in both transcriptional and epigenetic signatures, necessary for the correct formation of germ cells and resulting gametes in the adult animal. Errors in reprogramming and proper germ cell function lie at the root of a number of adverse health outcomes, including chromosome anomalies, birth defects (Hassold et al. 1993; Hassold and Hunt 2001; Hunt and Hassold 2008), infertility (Matzuk and Lamb 2008), and cancer (Oosterhuis and Looijenga 2005) Perturbations in normal germ cell development are therefore something that must be minimized or, ideally, altogether avoided.Germ cells at late stages of their development are extremely sensitive to environmental exposures, as highlighted by the breadth of chemicals exhibiting germ cell toxicity in both humans and rodents. These environmental agents include compounds as diverse as components of plastics (Hunt et al. 2003), pesticides (Harkonen 2005), cigarette smoke (Mailhes et al. 2000), antianxiety medication (Tanrikut et al. 2010), chemotherapeutic agents (Hales et al. 2005), and many more (Pacchierotti and Ranaldi 2006). However, there is a remarkable paucity of information concerning the impact of environmental agents on the earlier stages of germ cell development described above. Existing studies on the impact of toxicants on the window of germ cell development when they are potentially most vulnerable (i.e., when PGCs are present) are obfuscated by the difficulties intrinsic to their study: PGCs exist for a short developmental window between E6.5 and E11.5 in the developing embryo (Ginsburg 1990; Molyneaux 2001), and toxicant exposure typically covers multiple stages of germ cell development and differentiation; consequences of exposure can only be analyzed well after PGCs have differentiated into later developmental stages (e.g., mature oocytes or spermatozoa) or in resultant offspring derived from such gametes (reviewed in Matuszczak 2019).In this context, the development of in vitro methods to differentiate embryonic stem cells (ESCs) into PGC-like cells (PGCLCs), such as those pioneered by Saitou et al., provide an invaluable system to study environmental impacts on early germ cells (Hayashi et al. 2011). Transcriptome and methylome analyses indicate that PGCLCs closely mirror the expression program and epigenetic reprogramming observed in in vivo PGCs and have the ability to give rise to live offspring (Hayashi et al. 2011; Shirane et al. 2016), indicating the recapitulation of all essential steps for gamete generation. Furthermore, because differentiation is ordered and controlled by the provision of specified growth factors in the culture media, the system is highly tractable, allowing spatial and temporal control of environmental exposure.At moderate to high exposures, the plastic-manufacturing chemical bisphenol A (BPA) is a well-described reproductive toxicant in a wide variety of species, including nematodes (Allard and Colaiacovo 2010), fish (Chen et al. 2017), mouse (Hunt et al. 2003; Susiarjo et al. 2007), and humans (Brieño-Enríquez et al. 2011). Studies focused on the mammalian germline indicate detrimental consequences of BPA exposure on both male and female gametogenesis (Prins et al. 2019). However, owing to the complexity of gametogenesis, the delineation of critical phases for exposure, especially at the earlier stages of germ cell development, the capture of more subtle effects resulting from exposure to low environmentally relevant BPA levels, and the potential mechanisms involved all remain poorly defined.Here, we leveraged the stepwise generation of PGCLCs to perform developmental stage-specific exposure. Specifically, we aimed to test whether exposure to low, environmentally relevant levels of BPA prior to initiation of the PGCLC program could alter their induction and/or developmental trajectory.MethodsPGCLC Culture SystemMurine PGCLCs were derived according to the two-stage protocol [ESC to Epiblast-like cell (EpiLC) followed by induction into PGCLCs as previously described by Hayashi et al. (2011), using ESCs carrying B lymphocyte-induced maturation protein 1 Blimp1-mVenus and Stella-ECFP reporter transgenes (hereafter referred to as BVSC cells)] (Ohinata et al. 2008). Unless indicated otherwise, all experiments described herein were performed on genetically female (XX) Blimp1-mVenus Stella-ECFP (BVSC, clone H18) ESCs. All ESC lines used in this study, BVSC-H18, BVSC-R8, and v6.5, were obtained from M. Saitou (kind gift). All cells were cultured in a 37°C tissue culture incubator with 5% CO2. Briefly, BVSC ESCs were seeded onto Poly-L-ornithine (0.001%; A-004-C; Sigma-Aldrich) and laminin- (300 ng/mL; L2020; Sigma-Aldrich) coated wells and cultured in 2i+LIF [N2B27 Media, CHIR99021 (30μM; NC9785126; ThermoFisher Scientific), PD0325901 (10μM; NC9753132; ThermoFisher Scientific)], ESGRO® Leukemia Inhibitory Factor (LIF) (1,000U/mL, ESG1106; Sigma-Aldrich; 1,000U/mL) for 2 d. Typically, ESCs were cultured in either 24- or 12-well format, seeding 4×104 or 8×104 cells, respectively. EpiLCs were induced by seeding 4×104 or 8×104 cells onto human plasma fibronectin- (HPF) (16.7μg/mL; 33016015; ThermoFisher Scientific) coated wells of either a 24- or 12-well plate, respectively, in EpiLC Media (N2B27 medium containing activin A (20 ng/mL; 50-398-465; ThermoFisher Scientific), basic fibroblast growth factor (βFGF) (12 ng/mL; 3139FB025; R&D Systems), and KnockOut Serum Replacement (KSR, 1%; ThermoFisher Scientific). For ESC and EpiLC culture, cells were cultured on GenClone® tissue culture plates (tissue culture-treated polystyrene, Genesee Scientific). After 40 h, cells were collected by incubation with TrypLE™ Select (1X) (ThermoFisher Scientific). PGCLC induction was initiated by seeding 3×103 cells in a well of a low-cell-binding U-bottom 96-well virgin polystyrene suspension culture plate [either Genesee Scientific or Nunclon Sphera-Treated U-shaped 96-well plates (ThermoFisher Scientific)] in GK15 Media [GMEM (ThermoFisher Scientific)] supplemented with 15% knockout serum replacement (KSR), 0.1 mM minimal essential medium nonessential amino acids (MEM-NEAA), 1 mM sodium pyruvate, 0.1 mM 2-mercaptoethanol, 100U/mL penicillin, 0.1mg/mL streptomycin, and 2 mM L-glutamine in the presence of the cytokines bone morphogenetic protein 4 (BMP4; 500 ng/mL; 5020-BP-010/CF; R&D Systems), leukemia inhibitory factor (LIF; 1,000U/mL; ESG1106; Sigma-Aldrich), stem cell factor (SCF; 100 ng/mL; 50-399-595; R&D Systems), bone morphogenetic protein 8b (BMP8b; 500 ng/mL; 7540-BP-025; R&D Systems), and epidermal growth factor (EGF; 50 ng/mL; 2028EG200; R&D Systems). Cells were cultured for 5 d before collection and dissociation using TrypLE™ Select for further analysis.Differentiation Model and Exposure ParadigmES cells were maintained on 2i+LIF conditioned media for 2–3 d before being induced into EpiLCs by switching to EpiLC media (Figures 1A and 2A). Because the transition from ESCs to EpiLCs itself involves profound molecular reprogramming in DNA methylation (reviewed in Wu and Zhang 2014), exposures were performed after that first reprogramming phase during a 24-h window prior to PGCLC induction and washed out when cells were switched to PGCLC culture medium. Because even low quantities of dimethylsulfoxide (DMSO) can alter ESC differentiation (Adler et al. 2006) and BPA is soluble in water at the concentration range tested (Shareef et al. 2006; Plahuta et al. 2015), BPA (239658; Sigma-Aldrich) was dissolved in autoclaved, double-distilled water (ddH2O) at 55°C for several hours at a concentration of 1 mM (equivalent to 0.228mg/mL), before filtering and aliquoting, followed by storage at −20°C. The amount of BPA available in frozen stocks was verified by ELISA (BPA ELISA Kit; Detroit R & D, Inc.; see "BPA ELISA" section, Figure S1, and Excel Table S1). For each exposure experiment, aliquots of 1 mM BPA stocks were thawed and diluted in appropriate media (2i+LIF, EpiLC media, or PGCLC induction media). For water controls, the same volume of autoclaved ddH2O as the highest concentration of BPA dissolved in water being used was added to the culture media. Of the 4-order magnitude range of BPA concentrations tested, the 1–100 nM range was chosen to encompass the reported human BPA environmentally relevant range detected in serum, urine, and reproductive fluids (Vandenberg 2007), whereas the 1μM and 10μM concentrations represent moderate exposure levels comparable to those found in rodent in vivo models (Allard 2014).Figure 1. Proliferation analysis of EpiLCs to BPA for 24 h. (A) Graphic illustrating BPA exposure and cell differentiation strategy. [Illustration in part created with ©BioRender ( biorender.com), per the Biorender terms and conditions.] (B) Scatter-bar plot indicating cell counts of EpiLCs exposed to the different BPA concentrations indicated: error bars represent mean±standard deviation. n=12 for each condition. One-way ANOVA p-value 0.05 (vs. 1 nM); <0.05 (vs. 10 nM); <0.05 (vs. 100 nM); <0.05 (vs. 1μM); and <0.01 (vs. 10μM). For numerical values of data, see Excel Table S2. (C) Representative FACS contour plots showing distribution of live-gated events. First row indicates cell staining profile following incubation with DAPI and proliferation, measured using anti-BrdU antibody following a 30-min pulse with the thymidine analogue BrdU. Second row indicates distribution of proliferating cells labeled with anti-γH2AX antibody, a marker of DNA damage. Third panel indicates distribution of cells labeled with γH2AX antibody and anti-cPARP antibody, a marker of apoptosis. Numbers indicate proportion of live-gated events within the regions indicated. (D–I) Scatter-bar plots indicating proportion of live-gated events in the different populations indicated. Data indicate mean±standard deviation. n=12–15. For complete set of one-way ANOVA and calculated one-way ANOVA with Šidák-adjusted p-values for each treatment condition compared with control, see Table 1. For numerical values of data, see Excel Table S7. Note: ANOVA, analysis of variance; βFGF, basic fibroblast growth factor; BPA, bisphenol A; DAPI, 4′,6-diamidino-2-phenylindole; ESC, embryonic stem cell; EpiLC, epiblast-like cell; FACS, fluorescence-activated cell sorting; LIF, leukemia inhibitory factor; KSR, knockout serum replacement. Number of asterisks on plots indicate level of statistical significance: *(p<0.05); **(p<0.01); ***(p<0.001). Figure 2. Proliferation analysis of different cell populations in day 5 aggregates derived from EpiLCs following 24-h BPA exposure. (A) Graphic illustrating BPA exposure and cell differentiation strategy. Uncolored cells represent Blimp1-; Stella-/DN/nongerm cells; yellow cells represent Blimp1+;Stella-/SP/presumed transitioning germ cells; green cells represent Blimp1+;Stella+/DP/BVSC/d5 PGCLCs. [Illustration in part created with ©BioRender ( biorender.com), per the Biorender terms and conditions.] (B) Scatter-bar plots showing absolute numbers of cells in the different gated subpopulations indicated following exposure of EpiLCs to different BPA concentrations indicated. Plots represent mean±standard deviation. n=8. For calculated one-way ANOVA with Šidák-adjusted p-values for comparison to control and cell numbers, see Table 2 and Excel Table S8, respectively. (C) FACS contour plots showing cell populations indicated from d5 aggregates. EpiLCs were treated with the conditions indicated for 24 h prior to aggregate formation. (D) Representative histograms showing CellTrace™ Yellow staining profile of the different cell populations indicated. The Y-axis represents the number of cells, whereas the X-axis represents the fluorescence intensity. Recurring cell divisions create the appearance of secondary peaks, which widens the original peak of undivided cells. (E–G) Scatter-bar plots showing proportion of cells in the different populations indicated that remain undivided or have undergone one, two, or three cell divisions in day 5 aggregates. Number of divisions calculated using FlowJo Proliferation tool (version 10; FlowJo, LLC). Scatter plots show mean±standard deviation. n=3–4. For calculated one-way ANOVA with Šidák adjusted p-values for comparison and cell proportions, see Table 3 and Excel Table S11, respectively. Note: ANOVA, analysis of variance; βFGF, basic fibroblast growth factor; BMP4, bone morphogenetic protein 4; BMP8b, bone morphogenetic protein 8b; BPA, bisphenol A; BVSC, B lymphocyte-induced maturation protein 1 Blimp1-mVenus and Stella-ECFP reporter transgenes; DAPI, 4′,6-diamidino-2-phenylindole; DN, double negative; DP, double positive; EGF, epidermal growth factor; ESC, embryonic stem cell; EpiLC, epiblast-like cell; FACS, fluorescence-activated cell sorting; LIF, leukemia inhibitory factor; KSR, knockout serum replacement; PGC, primordial germ cell; PGCLC, PGC-like cell; SP, single positive.BPA ELISAELISA was performed according to the manufacturer's protocol. Briefly, a six-point standard curve was set up, with BPA ranging in concentration between 1×106pg/mL and 1×101pg/mL, generated by 10-fold serial dilution using kit-provided BPA or dissolved BPA stock and sample dilution buffer. Samples were mixed with an equal volume (100μL) of diluted BPA-HRP conjugate and incubated at room temperature for 2 h. Plates were then washed three times in 400μL1×wash buffer before incubation with 200μL TMB substrate at room temperature for 30 min. Development was stopped by addition of 50μL of 2N sulfuric acid, and absorbance at 450 nM was read on a Tecan Infinite® M1000 plate reader (Tecan). Corrected absorbance readings were then used to generate standard curves, according to the manufacturer's instructions.Cell Proliferation AnalysisCells were exposed to a range of BPA concentrations: 10 nM, 100 nM, 1μM and 10μM. For ESCs, cells were exposed for 48 h; for EpiLCs, cells were exposed for 24 h. Proliferation was assessed using several methods: numbers of viable cells were determined by Trypan blue (0.4%, 3–5 min incubation) exclusion using a Countess II FL Automated Cell Counter (ThermoFisher Scientific); by dual staining with anti-BrdU antibody (see "Intracellular Staining" section) and DAPI-incorporation in cells pulsed with BrdU (10μM) 30–60 min prior to cell harvesting; and by proliferation tracing using cells loaded with CellTrace™ Yellow (5μM, loaded for 20 min at 37°C, protected from light) (ThermoFisher Scientific). This dye passively diffuses through the cell membrane, where it remains covalently bound to intracellular amines, resulting in a stable, well-retained fluorescent signal with no/low cellular toxicity (Tempany 2018). Cell division results in a progressive diminution of signal intensity, which can be used to estimate the number of cell divisions a cell has undergone. To calculate numbers of cell division, the Proliferation Platform on FlowJo™ software was used (version 10; FlowJo, LLC).Flow CytometryFor purification of PGCLCs from day-5 aggregates, TrypLE™ Select–dissociated cells [incubated for 8 min at 37°C with agitation (950 rpm) on a Thermomixer (Eppendorf)] were resuspended in cell sort buffer [1×Dulbecco's phosphate buffered saline (DPBS), 1% BSA, 1 mM EDTA, 25 mM HEPES], passed through a cell strainer (70μm), and sorted on a BD FACSAria III (BD Biosciences), gating and collecting cell populations of interest in Eppendorf tubes containing GK15 media. Purified cells were used for the extraction of total RNA and subsequent library construction (see below). For analysis, cells were resuspended in fluorescence-activated cell sorting (FACS) buffer (1×DPBS, 2% BSA, 1 mM EDTA, 25 mM HEPES, 100U/mL penicillin, 0.1mg/mL streptomycin, 2 mM L-glutamine), and analyzed on an LSR II (BD Biosciences). Cells were initially identified by forward- and side-scatter gating, with back-gating used to verify the accuracy by which target cell populations were identified. Cell populations of interest were identified by 2-D plots displaying the parameter of interest. Manually defined gates as well as quadrants were used, as indicated. For positive controls to show the presence of DNA damage and apoptosis, v6.5 ES cells were treated with etoposide (10μM; E1383; Sigma-Aldrich) and doxorubicin (500 nM; 44583; Sigma-Aldrich), both dissolved in DMSO, for 12 h prior to harvesting, fixing, and staining.Intracellular StainingCells were permeabilized, fixed, and stained using the Apoptosis, DNA Damage and Cell Proliferation Kit (BD Biosciences) according to the manufacturer's protocol. In summary, cells were harvested and pelleted by centrifugation [5 min, 300 relative centrifugal force (rcf)]. Typically, between 2×105 and 5×105 cells were collected for each staining experiment. Pelleted cells were fixed in 100μL BD Cytofix™/Cytoperm™ fixation/permeabilization solution before incubating on ice for 30 min. Cells were then washed with 100μL1× BD Perm/Wash™ buffer (BD Biosciences) and pelleted by centrifugation. Following aspiration of supernatant, cells were resuspended in 100μL BD Cytofix™/Cytoperm™ Plus permeabilization buffer and incubated on ice for 10 min. Cells were then washed with 100μL1× BD Perm/Wash™ buffer (BD Biosciences) and pelleted by centrifugation. Following aspiration of supernatant, cells were resuspended in 100μL BD Cytofix™/Cytoperm™ fixation/permeabilization solution before incubating on ice for 5 min. Cells were then washed with 100μL1× BD Perm/Wash™ buffer and pelleted by centrifugation. Following aspiration of supernatant, cells were resuspended in 50μL1× BD Perm/Wash™ buffer supplemented with 150μg/mL DNase I and incubated at 37°C for 1 h. Following incubation, cells were washed in 150μL1× BD Perm/Wash™ buffer and pelleted by centrifugation. Following aspiration of supernatant, cells were resuspended in 30μL of 1× BD Perm/Wash™ buffer containing antibodies [2μL of each of anti-BrdU PerCP-Cy5.5 (clone 3D4); anti-ϒ-H2AX AF 647 (clone N1-431); and anti-cleaved poly(ADP-ribose) polymerase (cPARP) (Asp214) PE (clone F21-852), for a final antibody dilution of 1:15]. All antibodies were derived from BD Biosciences as part of the Apoptosis, DNA Damage and Cell Proliferation Kit. Cells were incubated in the dark for 20 min, before washing with 180μL1× BD Perm/Wash™ buffer and pelleting by centrifugation. Following aspiration of supernatant, cells were resuspended in 300μL of 1× BD Perm/Wash™ buffer with DAPI (final concentration 1μg/mL DAPI). Cells were kept at 4°C and shielded from light until ready for analysis.Total RNA ExtractionRNA was extracted from cell pellets (fresh or thawed snap-frozen) by using either an AllPrep DNA/RNA Mini or Micro Kit (Qiagen), according to the manufacturer's protocol. RNA concentration was measured using a NanoDrop™ 2000 UV spectrophotometer (ThermoFisher Scientific).RNA-Seq Library ConstructionStrand-specific RNA-seq libraries were prepared with Universal Plus mRNA-Seq kit (Nugen), according to the manufacturer's protocol. Briefly, this process consisted of poly(A) RNA selection, RNA fragmentation, and double-stranded cDNA generation using a mixture of random and oligo(dT) priming, followed by end repair to generate blunt ends, adaptor ligation, strand selection, and polymerase chain reaction (PCR) amplification to produce the final library. Different index adaptors were used for multiplexing samples in one sequencing lane. Sequencing was performed on Illumina HiSeq 3000 and NovaSeq 6000 sequencers for paired end (PE), 2×150 base pair (bp) runs. Data quality check was performed using Illumina Sequencing Analysis Viewer (SAV) software. Demultiplexing was performed with Illumina Bcl2fastq2 program (version 2.19.1.403; Illumina Inc.).Expression Analysis of RNA-Seq DataFastq reads were checked for overall quality by FastQC ( https://www.bioinformatics.babraham.ac.uk/projects/fastqc/). For differential gene expression analysis, reads were aligned using STAR (Dobin et al. 2013) with the following commands: STAR –runThreadN 20 –readFilesCommand zcat –runMode alignReads –outFilterMultimapNmax 1 –outSAMtype BAM Unsorted –quantMode GeneCounts –twopassMode Basic. After sorting, indexing and converting into SAM file format using SAMtools (Li et al. 2009), read counts were obtained using HTSeq ( https://htseq.readthedocs.io/en/master/) with the following commands: htseq-count −−mode=union−−stranded=no−−idattr=gene_id -r pos -f sam. Output files were filtered to remove genes with nine or fewer read counts. These were then used for differential gene expression analysis using the edgeR Bioconductor package ( https://bioconductor.org/packages/release/bioc/html/edgeR.html). Gene counts were normalized using the trimmed mean of M-values normalization (TMM) method (Robinson and Oshlack, 2010) before determining counts per million (cpm) values. For a gene to be classified as showing differential expression between treated and untreated germ cells, a threshold fold-change of ≥2 and Benjamini-Hochberg adjusted p≤0.05 had to be met.For analysis of X-linked gene expression levels, genes were binned based on chromosomal location, before determining median expression levels for each chromosome. Median expression for all autosomal genes (all chromosomes except X) was determined and then used to normalize the median expression level for all chromosomes.For analysis of repeat expression, fastq files were aligned using HiSat2 (Kim et al. 2019) with the -k parameter set to allow up to 100 alignments. Resultant sam files were then used to generate read counts for repeats using TETools (Lerat et al. 2017) and a mouse-specific repeat reference file generated using RepeatMasker. For each biological repeat between conditions (untreated and exposed), total numbers of reads following alignment using TETools was determined before down sampling of aligned sam files to ensure that an equivalent number of reads was analyzed across all samples. For an element to be considered differentially expressed between untreated and exposed samples, there needed to be 10 or more reads aligning to the repeat in either or both conditions, as well as a two-fold or greater difference in read number. Fastq files and Excel table showing trimmed mean of M-values (TMM)-normalized cpm of genes ≥10 reads are accessible via the Gene Expression Omnibus (GEO), accession number GSE157570. Gene Ontology (GO) Analysis and CirGO Plot GenerationLists of differentially expressed genes as well as a list of all genes analyzed were generated from read counts using edgeR Bioconductor package (Robinson et al. 2010). Enrichment of GO terms in lists of up- and down-regulated genes was determined using Gene Ontology enRIchment anaLysis and visuaLizAtion tool (GOrilla, http://cbl-gorilla.cs.technion.ac.il) (Eden, 2009). Redundant GO terms were removed using reduce + visualize gene ontology (REVIGO, http://revigo.irb.hr) (Supek et al. 2011) to generate .csv files. These were then used to generate Circular Gene Ontology (CirGO) plots using the open source CirGO software (version 1.0; https://github.com/IrinaVKuznetsova/CirGO.git) (Kuznetsova 2019). Terms were included if the Benjamini-Hochberg-adjusted p-value was less than 0.05.Statistical MethodsFor analysis of proliferation and cell staining data, calculated one-way analysis of variance (ANOVA) was initially performed, followed by Holm-Šidák testing for pairwise comparison between exposed and water/untreated control samples. For differential gene expression analysis, adjusted p-values were calculated using the Benjamini-Hochberg method. For statistical analysis of gene expression data derived from differential expression of RNA-seq data, Wilcoxon-signed rank test was used. In all cases, significance was determined by p-values less than or equal to 0.05. For analysis of repeat representation, chi-square analysis was performed. For each experiment, unless otherwise noted, technical n=3.ResultsCell Proliferation and Viability Analysis of BPA-Exposed Murine EpiLCsBecause our exposure window corresponds to the stage of EpiLCs prior to PGCLC differentiation, we first examined the consequences of BPA exposure on EpiLCs. Exposure of these cells for 24 h resulted in a significantly higher numbers of cells recovered, which was in a concentration-dependent manner (Figure 1B; p=0.0086, one-way ANOVA, and Excel Table S2). By contrast, exposure of BVSC ESCs to BPA for up to 48 h had no significant effect on the number of cells recovered (Figure S2; p=0.62, one-way ANOVA, and Excel Table S3). To determine the generality of BPA's effect on cell number in EpiLCs, we also examined two additional male (XY) ESC clones, v6.5 and BVSC-R8 (Figure S3 and Excel Table S4). An interesting finding was that we did not detect any significant difference in cell number recovered when comparing unexposed to BPA-exposed cells over a range of different concentrations. This extended to the lack of perturbation in cell number in day 5 (d5) aggregates generated from BVSC-R8, (Figure S4A and Excel Table S5). Transcriptome analysis revealed an absence of differentially expressed genes between BVSC-R8 PGCLCs derived from untreated and 100-nM BPA-exposed EpiLCs (Figure S4B and Excel Table S6). We found it interesting that d5 PGCLCs derived from BVSC-H18 and BVSC-R8 displayed a marked difference in their transcrip
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