A Data-Driven Transcriptional Taxonomy of Adipogenic Chemicals to Identify White and Brite Adipogens
2021; National Institute of Environmental Health Sciences; Volume: 129; Issue: 7 Linguagem: Inglês
10.1289/ehp6886
ISSN1552-9924
AutoresStephanie Kim, Eric Reed, Stefano Monti, Jennifer J. Schlezinger,
Tópico(s)Metabolomics and Mass Spectrometry Studies
ResumoVol. 129, No. 7 ResearchOpen AccessA Data-Driven Transcriptional Taxonomy of Adipogenic Chemicals to Identify White and Brite Adipogens Stephanie Kim, Eric Reed, Stefano Monti, and Jennifer J. Schlezinger Stephanie Kim Boston University Superfund Research Program, Boston University, Massachusetts, USA Department of Environmental Health, Boston University School of Public Health, Massachusetts, USA Search for more papers by this author , Eric Reed Boston University Superfund Research Program, Boston University, Massachusetts, USA Section of Computational Biomedicine, Boston University School of Medicine, Massachusetts, USA Boston University Bioinformatics Program, Boston University, Massachusetts, USA Search for more papers by this author , Stefano Monti Address correspondence to Jennifer J. Schlezinger, Boston University School of Public Health, Department of Environmental Health, 715 Albany St., R-405, Boston, MA 02118 USA. Telephone: (617) 358-1708. Email: E-mail Address: [email protected], or Stefano Monti, Boston University School of Medicine, Division of Computational BioMedicine, 72 E. Concord St., E-611, Boston, MA 02118 USA. Telephone: (617) 358-7087. Email: E-mail Address: [email protected] Boston University Superfund Research Program, Boston University, Massachusetts, USA Section of Computational Biomedicine, Boston University School of Medicine, Massachusetts, USA Boston University Bioinformatics Program, Boston University, Massachusetts, USA Search for more papers by this author , and Jennifer J. Schlezinger Address correspondence to Jennifer J. Schlezinger, Boston University School of Public Health, Department of Environmental Health, 715 Albany St., R-405, Boston, MA 02118 USA. Telephone: (617) 358-1708. Email: E-mail Address: [email protected], or Stefano Monti, Boston University School of Medicine, Division of Computational BioMedicine, 72 E. Concord St., E-611, Boston, MA 02118 USA. Telephone: (617) 358-7087. Email: E-mail Address: [email protected] Boston University Superfund Research Program, Boston University, Massachusetts, USA Department of Environmental Health, Boston University School of Public Health, Massachusetts, USA Search for more papers by this author Published:29 July 2021CID: 077006https://doi.org/10.1289/EHP6886AboutSectionsPDF Supplemental Materials ToolsDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InRedditEmail AbstractBackground:Chemicals in disparate structural classes activate specific subsets of the transcriptional programs of peroxisome proliferator-activated receptor-γ (PPARγ) to generate adipocytes with distinct phenotypes.Objectives:Our objectives were to a) establish a novel classification method to predict PPARγ ligands and modifying chemicals; and b) create a taxonomy to group chemicals on the basis of their effects on PPARγ’s transcriptome and downstream metabolic functions. We tested the hypothesis that environmental adipogens highly ranked by the taxonomy, but segregated from therapeutic PPARγ ligands, would induce white but not brite adipogenesis.Methods:3T3-L1 cells were differentiated in the presence of 76 chemicals (negative controls, nuclear receptor ligands known to influence adipocyte biology, potential environmental PPARγ ligands). Differentiation was assessed by measuring lipid accumulation. mRNA expression was determined by RNA-sequencing (RNA-Seq) and validated by reverse transcription–quantitative polymerase chain reaction. A novel classification model was developed using an amended random forest procedure. A subset of environmental contaminants identified as strong PPARγ agonists were analyzed by their effects on lipid handling, mitochondrial biogenesis, and cellular respiration in 3T3-L1 cells and human preadipocytes.Results:We used lipid accumulation and RNA-Seq data to develop a classification system that a) identified PPARγ agonists; and b) sorted chemicals into likely white or brite adipogens. Expression of Cidec was the most efficacious indicator of strong PPARγ activation. 3T3-L1 cells treated with two known environmental PPARγ ligands, tetrabromobisphenol A and triphenyl phosphate, which sorted distinctly from therapeutic ligands, had higher expression of white adipocyte genes but no difference in Pgc1a and Ucp1 expression, and higher fatty acid uptake but not mitochondrial biogenesis. Moreover, cells treated with two chemicals identified as highly ranked PPARγ agonists, tonalide and quinoxyfen, induced white adipogenesis without the concomitant health-promoting characteristics of brite adipocytes in mouse and human preadipocytes.Discussion:A novel classification procedure accurately identified environmental chemicals as PPARγ ligands distinct from known PPARγ-activating therapeutics.Conclusion:The computational and experimental framework has general applicability to the classification of as-yet uncharacterized chemicals. https://doi.org/10.1289/EHP6886IntroductionSince 1980, the prevalence of obesity has been increasing in the United States (Flegal et al. 2016). Further, in 2015, it was estimated that a total of 108 million children and 604 million adults were obese worldwide (GBD 2015 Obesity Collaborators et al. 2017). This poses a major public health threat given that overweight and obesity increase the risk of metabolic syndrome, which, in turn, sets the stage for metabolic diseases, such as type 2 diabetes, cardiovascular disease, nonalcoholic fatty liver disease, and stroke (Park et al. 2003). The Endocrine Society’s latest scientific statement on the obesity pathogenesis states that obesity is a disorder of the energy homeostasis system, rather than just a passive accumulation of adipose, and that environmental factors, including chemicals, confer obesity risk (Schwartz et al. 2017). The rapid increases in obesity and metabolic diseases correlate with substantial increases in environmental chemical production and exposures over the last few decades, and experimental evidence in animal models demonstrates the ability of a broad spectrum of various environmental metabolism-disrupting chemicals to induce adiposity and metabolic disruption (Heindel et al. 2017).Adipocytes are essential for maintaining metabolic homeostasis because they are the storage depot of free fatty acids and release hormones that can modulate body fat mass (Rosen and Spiegelman 2006). Adipogenesis is a highly regulated process that involves a network of transcription factors acting at different time points during differentiation (Farmer 2006). Peroxisome proliferator-activated receptor-γ (PPARγ) is a ligand-activated nuclear receptor that is required for adipocyte formation and function (Tontonoz et al. 1994) as well as for metabolic homeostasis. In both PPARγ-haploinsufficient (Gumbilai et al. 2016) and knockout (He et al. 2003; Jiang et al. 2014; O’Donnell et al. 2016; Zhang et al. 2004) rodent models, there was a lack of adipocyte formation and metabolic disruption.PPARγ regulates energy homeostasis by activating the expression of genes involved in both the storage of excess energy as lipids in white adipocytes and energy utilization by triggering mitochondrial biogenesis, fatty acid oxidation, and thermogenesis in brite (brown-in-white) and brown adipocytes. The white adipogenic, brite/brown adipogenic, and insulin-sensitizing activities of PPARγ are regulated distinctly through ligand-specific posttranslational modifications (Banks et al. 2015; Choi et al. 2010, 2011; Qiang et al. 2012) and coregulator recruitment (Burgermeister et al. 2006; Feige et al. 2007; Ohno et al. 2012; Villanueva et al. 2013). Although adult humans have long been thought to not have brown adipose tissue, people with minimal brite adipocyte populations are at higher risk for obesity and type 2 diabetes (Claussnitzer et al. 2015; Sidossis and Kajimura 2015; Timmons and Pedersen 2009).Growing evidence supports the hypothesis that environmental PPARγ ligands induce phenotypically distinct adipocytes. Tributyltin (TBT) induces the formation of an adipocyte with lower adiponectin expression and altered glucose homeostasis (Regnier et al. 2015). Furthermore, TBT failed to induce expression of genes associated with browning of adipocytes [e.g., Ppara, Pparγ coactivator (PGC)-1-alpha (Pgc1a), cell death-inducing DNA fragmentation factor-alpha–like effector A (Cidea), Elovl3, uncoupling protein 1 (Ucp1)] in differentiating 3T3-L1 adipocytes (Kim et al. 2018; Shoucri et al. 2018). As a result, TBT-induced adipocytes failed to up-regulate mitochondrial biogenesis and had low levels of cellular respiration (Kim et al. 2018; Shoucri et al. 2018). The structurally similar environmental PPARγ ligand, triphenyl phosphate (TPhP), also failed to induce brite adipogenesis, and this correlated with an inability to prevent PPARγ from being phosphorylated at serine 273 (Ser273) in vitro (Kim et al. 2020).The U.S. Environmental Protection Agency developed the Toxicity Forecaster (ToxCast) program to use high-throughput screening assays to prioritize chemicals and inform regulatory decisions regarding thousands of environmental chemicals (Kavlock et al. 2012). Several ToxCast assays can measure the ability of chemicals to bind to or activate PPARγ, and these assays have been used to generate a toxicological priority index (ToxPi) that was expected to predict the adipogenic potential of chemicals in cell culture models (Auerbach et al. 2016). Yet, it was shown that the results of ToxCast PPARγ assays do not always correlate well with activity measured in a laboratory setting and that the ToxPi designed for adipogenesis was prone to predicting false positives (Janesick et al. 2016). Furthermore, the ToxCast/ToxPi approach cannot distinguish between white and brite adipogens (Pereira-Fernandes et al. 2014).In the present study, we investigated differences in cellular response between adipogenic and nonadipogenic compounds, as well as the heterogeneity of response across adipogenic compounds. Our ultimate goal was to create a method for identification of novel adipogenic compounds using the taxonomic organization of known and predicted adipogenic compounds on the basis of their divergent transcriptional response. To this end, we generated phenotypic and transcriptomic data from adipocytes differentiated in the presence of 76 different chemicals. We combined the cost-effective generation of agonistic transcriptomic data by 3′ digital gene expression (3′ DGE)—a highly multiplexed RNA-sequencing (RNA-Seq) technology—with a new classification method to predict PPARγ-activating and modifying chemicals. Further, we investigated metabolism-related outcome pathways as effects of the chemical exposures. We created a data-driven taxonomy to specifically classify chemicals into distinct categories on the basis of their various interactions with and effects on adipogenesis, presumably through their interaction with PPARγ. Based on the taxonomy-based predictions, we tested the phenotype (white vs. brite adipocyte functions) of environmental adipogens predicted to fail to induce brite adipogenesis in 3T3-L1 cells and primary human adipocytes.MethodsChemicalsDimethyl sulfoxide (DMSO) was purchased from American Bioanalytical. Chemical Abstract Service numbers, sources, and catalog numbers of experimental chemicals are provided in Table S1. Human insulin, dexamethasone, 3-isobutyl-1-methylxanthine (IBMX), and all other chemicals were from Sigma-Aldrich unless noted otherwise.Cell Culture3T3-L1 [RRID:CVCL_0123, Lot # 63343749; American Type Culture Collection (ATCC)] cells were originally derived from a Swiss mouse embryonic fibroblast line (Green and Kehinde 1975). The cells were maintained in growth medium [high-glucose Dulbecco’s Modified Eagle Medium (DMEM; Corning; 10-013-CV) with 10% calf serum (Sigma), 100U/mL penicillin, 100μg/mL streptomycin, and 0.25μg/mL amphotericin B]. All experiments were conducted with cells between passages 3 and 9. Experimental conditions are outlined in Table 1 and Figure S1A. For experiments, cells were plated in growth medium and incubated for 4 d, at which time the cultures are confluent for 2 d. Naïve preadipocytes were cultured in every experiment and grown in growth medium for the duration of an experiment. On Day 0, differentiation was induced by replacing the medium with differentiation medium [DMEM,10% fetal bovine serum (FBS; Sigma-Aldrich), 100U/mL penicillin; 100μg/mL streptomycin; 250 nM dexamethasone (Figures 1–3), 1 nM dexamethasone (Figures 5–10), or no dexamethasone (Figure S3); 167 nM human insulin, and 0.5 mM IBMX]. Also on Day 0, single experimental wells were treated with vehicle (DMSO; 0.2% final concentration), rosiglitazone (positive control, 200 nM), or test chemicals. On Days 3 and 5 of differentiation, the medium was replaced with maintenance medium (DMEM, 10% FBS, 167 nM human insulin, 100U/mL penicillin, and 100μg/mL streptomycin), and the cultures were re-dosed. On Day 7 of differentiation, the medium was replaced with adipocyte medium (DMEM, 10% FBS, 100U/mL penicillin, and 100μg/mL streptomycin), and the cultures were re-dosed. On Day 10, cytotoxicity was assessed by microscopic inspection, with cultures containing more than 10% rounded cells excluded from consideration. For most chemicals, only a single concentration was tested; however, for those chemicals inducing toxicity, the concentration was reduced until no toxicity was observed (see Table S1 for information on maximum tested and maximum nontoxic concentrations). Wells with healthy cells were harvested for analysis of gene expression, lipid accumulation, fatty acid uptake, mitochondrial biogenesis, mitochondrial membrane potential, and cellular respiration.Table 1 Summary of experimental conditions.Table 1, in three columns, lists Categories, 3T3-L1 cells, and Human Preadipocytes.Conditions3T3-L1 cellsHuman preadipocytesExposure period (d)1014Times dosed (n)46Positive controlRosiglitazoneRosiglitazoneNegative control (vehicle)DMSODMSONote: DMSO, dimethyl sulfoxide.OP9 cells (RRID:CVCL_4398, Lot # 63544739; ATCC) are a bone marrow stromal cell line derived from newborn calvaria of the (C57BL/6×C3H)F2-op/op mouse (Nakano et al. 1994). The cells were maintained in growth medium [alpha minimum essential medium (αMEM; Gibco; 12-561-056) with 20% FBS, 26 mM sodium bicarbonate (NaHCO3), 100U/mL penicillin, 100μg/mL streptomycin, and 0.25μg/mL amphotericin B]. The cells were plated in 24-well plates at 50,000 cells per well in 500μL medium and incubated for 4 d. Induction and maintenance of adipogenesis and treatment were as described for 3T3-L1 cells, except that the dexamethasone concentration was 125 nM. Following 10 days of differentiation, cells were analyzed for lipid accumulation.Primary human subcutaneous preadipocytes from five individual female patients were obtained from the Boston Nutrition Obesity Research Center (Boston, MA). The patients were 32–51 years of age and had body mass indexes (BMIs) ranging from 26.0–30.7 mg/cm2. One patient was prediabetic, and four were nondiabetic. Adipocytes were differentiated as previously described (Lee and Fried 2014). Experimental conditions are outlined in Table 1 and Figure S1B. The preadipocytes were maintained in growth medium (αMEM with 10% FBS, 100U/mL penicillin, 100μg/mL streptomycin, and 0.25μg/mL amphotericin B). For experiments, human preadipocytes were plated in growth medium and grown to confluence (3–5 days). Naïve preadipocytes were cultured in every experiment and grown in growth medium for the duration of an experiment. On Day 0, differentiation was induced by replacing the growth medium with differentiation medium (DMEM/F12, 25 mMNaHCO3, 100U/mL penicillin, 100μg/mL streptomycin, 33μMd-biotin, 17μM pantothenate, 100 nM dexamethasone, 100 nM human insulin, 0.5 mM IBMX, 2 nM thyroxine, and 10μg/mL transferrin). Also on Day 0, single experimental wells were treated with vehicle (DMSO, 0.1% final concentration), rosiglitazone (positive control, 4μM) or test chemicals. On Day 3 of differentiation, the medium was replaced with fresh differentiation medium, and the cultures were re-dosed. On Days 5, 7, 10, and 12 of differentiation, the medium was replaced with maintenance medium (DMEM/F12, 25 mMNaHCO3, 100U/mL penicillin, 100μg/mL streptomycin, 3% FBS, 33μMd-biotin, 17μM pantothenate, 10 nM dexamethasone, and 10 nM insulin), and the cultures were re-dosed. Following 14 d of differentiation and dosing, cells were harvested for analysis of gene expression, lipid accumulation, fatty acid uptake, mitochondrial biogenesis, and cellular respiration.Lipid Accumulation3T3-L1 cells or human preadipocytes were plated in 24-well plates at 50,000–100,000 cells per well in 0.5mL maintenance medium at the initiation of the experiment. Dosing is outlined in Table 1. The medium was removed from the differentiated cells, and they were rinsed with phosphate-buffered saline (PBS). The cells were then incubated with Nile red (1μg/mL in PBS) for 15 min in the dark. Fluorescence (λex=485 nm, λem=530 nm) was measured using a Synergy2 plate reader (BioTek Inc.). The fluorescence in all experimental wells was normalized by subtracting the fluorescence measured in naïve preadipocyte cultures within each experiment and reported as naïve corrected relative fluorescence units (RFUs).Transcriptome Profiling3T3-L1 cells were plated in 24-well plates at 50,000 cells per well in 0.5mL maintenance medium at the initiation of the experiment. Dosing is outlined in Table 1. Total RNA was extracted and genomic DNA was removed using the Direct-zol MagBead RNA Kit and following manufacturer’s protocol (Zymo Research). RNA concentrations and contamination were determined spectrophotometrically using a Nanodrop (ND-1000; ThermoFisher). A final concentration of 5 ng RNA/μL was used for each sample. For each chemical, three to five biological replicates were profiled and carefully randomized across six 96-well plates, including 26 DMSO-vehicle controls, and 16 naïve preadipocyte cultures. Sequencing and gene expression quantification was carried out by the Broad Institute lab of the Massachusetts Institute of Technology (Cambridge, MA). RNA libraries were prepared using a highly multiplexed 3′ DGE protocol developed by Xiong et al. (2017) and sequenced on an Illumina NextSeq 500, generating between 2.13×108 and 3.87×108 reads and a mean of 3.02×108 reads per lane across 96 samples. All reads containing bases with Phred quality scores<Q10 were removed. The remaining reads were aligned to mouse reference genome, GRCm38, and counted in 21,511 possible transcripts annotations. Only instances of uniquely aligned reads were quantified (i.e., reads that aligned to only one transcript). Furthermore, multiple reads with the same unique molecular identifier, aligning to the same gene were quantified as a single count.Reverse Transcription–Quantitative Polymerase Chain Reaction3T3-L1 cells or human preadipocytes were plated in 24-well plates at 100,000 cells per well in 0.5mL maintenance medium at the initiation of the experiment. Dosing is outlined in Table 1. Total RNA was extracted and genomic DNA was removed using the 96-well Direct-zol MagBead RNA Kit (Zymo Research). RNA concentrations and contamination were determined spectrophotometrically using a Nanodrop. Complementary DNA was synthesized from total RNA using the iScript Reverse Transcription System (BioRad). All reverse transcription–quantitative polymerase chain reactions (RT-qPCRs) were performed in duplicate using the PowerUp SYBR Green Master Mix (Thermo Fisher Scientific). The qPCR reactions were performed using a 7500 Fast Real-Time PCR System (Applied Biosystems): Uracil-DNA glycosylase activation (50°C for 2 min), polymerase activation (95°C for 2 min), 40 cycles of denaturation (95°C for 15 s) and annealing (various temperatures for 15 s) and extension (72°C for 60 s). The primer sequences and annealing temperatures are provided in Table S2. All primers were obtained from Integrated DNA Technologies. Relative gene expression was determined using the Pfaffl method to account for differential primer efficiencies (Pfaffl 2001), using the geometric mean of the Cq values for beta-2-microglobulin (B2m) and 18s ribosomal RNA (Rn18s) for mouse gene normalization and of ribosomal protein L27 (RPL27) and B2M for human gene normalization. The Cq value from naïve, preadipocyte cultures within each experiment was used as the reference point for the experiment. Data are reported as relative expression.Cell Number Analysis3T3-L1 cells or human preadipocytes were plated in 96-well, black-sided plates at 12,500 cells per well in 0.2mL maintenance medium at the initiation of the experiment. Dosing is outlined in Table 1. Cellular protein was measured by Janus Green staining and measuring absorbance (595 nM) using a Synergy2 plate reader, according the manufacturer’s protocol (ab111622; Abcam). The absorbance in experimental wells was normalized by dividing by the absorbance measured in naïve preadipocyte cultures within the experiment and reported as relative cell density.Fatty Acid Uptake3T3-L1 cells or human preadipocytes were plated in 96-well, black-sided plates at 12,500 cells per well in 0.2mL maintenance medium at the initiation of the experiment. Dosing is outlined in Table 1, with duplicate wells dosed with vehicle or chemical. Fatty acid uptake was measured by treating differentiated cells with 100μL of Fatty Acid Dye Loading Solution (MAK156; Sigma-Aldrich). Fluorescence intensity (λex=485 nm, λem=530 nm) was measured at time zero and after a 10-min incubation using a Synergy2 plate reader. The fluorescence at time zero was subtracted from the fluorescence at the end of the incubation and reported as RFU.Mitochondrial Biogenesis3T3-L1 cells or human preadipocytes were plated in 24-well plates at 100,000 cells per well in 0.5mL maintenance medium at the initiation of the experiment. Dosing is outlined in Table 1. Mitochondrial biogenesis was measured in differentiated cells using the MitoBiogenesis In-Cell Enzyme-Linked Immunosorbent Assay (ELISA) Colorimetric Kit, following the manufacturer’s protocol (Abcam). The expression of the mitochondrial protein succinate dehydrogenase complex flavoprotein subunit A (SDH) was measured and normalized to total protein content via Janus Green staining. Absorbance (405 nm for SDH, and OD 595 nm for Janus Green) was measured using a Synergy2 plate reader. The absorbance ratios of SDH/Janus Green are reported as relative SDH protein expression.Oxygen Consumption3T3-L1 cells were plated in 24-well plates at 100,000 cells per well in 0.5mL maintenance medium at the initiation of the experiment. Dosing is outlined in Table 1, with duplicate wells dosed with vehicle or chemical. After 10 d of differentiation, cells were gently trypsinized, diluted in 400μL adipocyte medium, and 80μL per well was transferred to duplicate wells of a 96-well Agilent Seahorse plate. After 24 h of incubation, the medium was changed to Seahorse XF assay medium without glucose (1 mM sodium pyruvate, 1 mM GlutaMax; pH 7.4), and the cultures were incubated at 37°C in a non-carbon dioxide incubator for 1 h. To measure mitochondrial respiration, the Agilent Seahorse XF96 Cell Mito Stress Test Analyzer (available at the Boston University Medical Campus Analytical Instrumentation Core) was used, following the manufacturer’s standard protocol. The compounds and their concentrations used to determine oxygen consumption rate (OCR) included 0.5μM oligomycin, 1.0μM carbonyl cyanide-p-trifluoromethoxyphenylhydrazone (FCCP) and 2μM rotenone/2μM antimycin. After the Seahorse analysis, cells were fixed in 4% paraformaldehyde for 30 min and stained with Janus Green. Respiration rates were normalized by dividing by the Janus Green absorbance and reported as relative OCR. Basal respiration was determined by subtracting nonmitochondrial respiration from the last rate measurement before the injection of oligomycin. Maximum respiration was determined by subtracting nonmitochondrial respiration from the maximum rate measurement after the injection of FCCP. Spare capacity was determined by subtracting the basal respiration from the maximum respiration.Adiponectin Secretion3T3-L1 cells were plated in 24-well plates at 100,000 cells per well in 0.5mL maintenance medium at the initiation of the experiment. Dosing is outlined in Table 1. After 9 d of differentiation, the medium was replaced with washout medium (DMEM+0.1% bovine serum albumin), and cultures were incubated for 24 h. The washout medium was replaced, the cells re-dosed and the incubation continued for 24 h. Samples of the medium were collected, diluted 1:200, and analyzed for adiponectin, in duplicate, by ELISA, following the manufacturer’s protocol (Mouse Adiponectin/Acrp30 Quantikine ELISA Kit, MRP300; R&D Systems). Absorbance (450 nm) was measured using a Synergy2 plate reader and concentrations calculated relative to a standard curve.Statistical AnalysesAll statistical analyses were performed in R (version 3.4.3; R Development Core Team) and Prism 7 (GraphPad Software, Inc.). All R code used for processing and analysis of transcriptome profiles is publicly available on GitHub ( https://github.com/montilab/Adipogen2020) and were carried out using several R packages. Normalization and differential gene expression analysis was performed using limma (version 3.34.9) (Ritchie et al. 2015). Batch correction was performed using ComBat (version 3.26.0) (Leek et al. 2012). Gene set projection was performed using GSVA (version 1.30.0) (Hänzelmann et al. 2013). Statistical testing of partial correlation estimates was performed using ppcor (version 1.1) (Kim 2015).For 3T3-L1 experiments, the biological replicates correspond to independently plated experiments. For human primary preadipocyte experiments, the biological replicates correspond to distinct individuals’ preadipocytes (five individuals in all). For each experiment, naïve, undifferentiated 3T3-L1 cells were included for within-experiment normalization. The Nile red data and the qPCR data were not normally distributed; therefore, the data were log transformed before statistical analyses. One-factor analyses of variance (ANOVAs) (with Dunnett’s tests) were performed to analyze the qPCR and phenotypic data and determine differences from vehicle-treated cells. Data are presented as means±standard errors (SEs).Transcriptome Data ProcessingThe number of counted reads per sample transcriptome profile varied widely with a range of 7.90×101 to 2.27×106 (mean=2.25×105, standard deviation=2.94×105). To remove technical noise introduced by low overall expression quantification of individual samples, we performed an iterative clustering-based approach to determine sets of samples that segregate as a result of low total read counts. Each iteration included four steps: a) removal of low-count genes; b) normalization; c) plate-level batch correction; and d) hierarchical clustering. Low-count genes, defined as having mean counts <1 across all samples, were removed to reduce statistical noise introduced by inaccurate quantification of consistently lowly expressed transcripts. Normalization was performed using trimmed mean of M-values, the default method employed by limma (version 3.34.9) (Ritchie et al. 2015). Batch correction was performed by ComBat (version 3.26.0) (Leek et al. 2012). Hierarchical clustering was performed on the 3,000 genes with the largest median absolute deviation score, using Euclidean distance and 1−Pearson correlation as the distance metric for samples and genes, respectively, and Ward’s agglomerative method (Ward 1963). Clusters of samples clearly representative of low-expression quantification were removed. This process was repeated until no such low-expression outlier sample cluster was present (four iterations). For the remaining samples, low-count genes were removed, and samples were normalized and batch corrected by the same procedure.Following sample- and gene-level quality control filtering, the final processed data set included expression levels of 9,616 genes for each of 234 samples. These 234 samples included 2–4 remaining replicates of each compound, 25 DMSO-vehicle controls, and 15 naïve preadipocyte cultures. Sequencing data from 3′ DGE have been deposited to Gene Expression Omnibus (GEO; accession number GSE124564).PPARγ Activating/Modifier ClassificationA classification model was inferred from the training set consisting of 37 known PPARγ-modifying compounds and 23 known non-PPARγ modifying compounds, including vehicle, to predict the labeling of the test set of 17 potential PPARγ-modifying compounds (Table S1). Known compounds were selected on the basis of a literature search for experimental evidence of modification of PPARγ activity, including PPARγ binding assays, coactivator recruitment or computational modeling (definitive ligands), PPARγ-driven reporter assays (at least 25% of the rosiglitazone-induced maximum), expression of PPARγ target genes and differentiation of 3T3-L1 or multipotent stromal cells into adipocytes in the absence of a known PPARγ ligand (Table S1). Known non-PPARγ modifying compounds were selected on the basis of a literature search for chemicals that influence adipogenesis but that are ligands for other nuclear receptors (e.g., aryl hydrocarbon receptor, glucocorticoid receptor) or selected on the basis of their structural dissimilarity from known PPARγ ligands and lack of evidence of PPARγ activation (Table S1). The model inference was based on an amended random forest procedure developed to better account for the presence of biological replicates in the data. Specifically, for each classification tree, samples and genes were bagged using sampling techniques consistent with the techniques of Breiman (2001). In particular, samples were
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