An overview of omics approaches to characterize the effect of perfluoroalkyl substances in environmental health

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An overview of omics approaches to characterize the effect of perfluoroalkyl substances in environmental health Xinglei Yao a, b, c, Dong Cao a, Fengbang Wang b, c, Wenjuan Zhang a, Chunyan Ma b, Maoyong Song a, b, c, * a

Stake Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China Key Laboratory of Environmental Nanotechnology and Health Effects, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China c College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China b

a r t i c l e i n f o

a b s t r a c t

Article history: Available online xxx

The production and widespread use of perfluoroalkyl substances (PFASs) has led to their presence in the environment, wildlife, and the human body. PFAS exposure has been indicated that could adversely affect human health. PFASs trigger chemical signals to cells, which further emit new signals to tissues and even to organs, and finally cause diseases. Detection of these signals and understanding of the meaning behind them are great challenges, which may be tackled through high-throughput ‘omics’ technologies, including transcriptomics, epigenomics, proteomics, and metabolomics. This review summarizes the analytical methodologies, such as sequencing, microarray, mass spectrometry (MS), and nuclear magnetic resonance (NMR), used in these ‘omics’ technologies and therefore applied in the bioanalysis of PFAS-related health risks. We provide a summary of the important roles that analytic methodologies have played in the study of human health. We also focus on new analytical technologies that will certainly shed light on new health-related fields. © 2018 Elsevier B.V. All rights reserved.

Keywords: Perfluoroalkyl substances (PFASs) Perfluorooctanoate (PFOA) Perfluorooctane sulfonate (PFOS) Transcriptomics Epigenomics Proteomics Metabolomics RNA-Sequencing (RNA-seq) Mass spectrometer (MS) Nuclear magnetic resonance (NMR)

1. Introduction Perfluoroalkyl substances (PFASs) are a group of artificial compounds with strong C-F bonds. PFASs are extensively used in consumer products and industrial processes, including fire-fighting foams, lubricants, pesticides, surfactants, and coatings [1]. These compounds include perfluorooctanoate (PFOA), perfluorooctane sulphonate (PFOS), perfluorononanoic acid (PFNA), perfluorooctane sulphonamide (PFOSA), perfluorohexane sulphonic acid (PFHxS), perfluorododecanoic acid (PFDoA), and others. PFASs have been widely detected in ambient and indoor air, house dust, drinking water, and food, as well as human biomonitored samples such as blood, urine, breast milk, nails, and hair [2e5]. Food, drinking water, and airborne sources are the main routes for PFASs entering into the human body. Many studies have

* Corresponding author. Stake Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China. E-mail address: [email protected] (M. Song).

reported that PFASs may be associated with human diseases, including liver cancer, chronic kidney disease, thyroid diseases, asthmatic disorders, hyperuricemia, paediatric atopy, behavioural disorders, and immune toxicity [2]. Potential mechanisms of PFASmediated effects on human health include peroxisome proliferation, oxidative stress-dependent mitochondrial dysfunction, loss of gap junction intercellular communication, and interruption of thyroid and reproductive hormones [6]. The detection of both the health effects and the mechanism responsible deeply relies on the methods of analytical chemistry. This is because the lifecycle is essentially dependent on a chemical signal network at every level of biological organization, from single-cell metabolism to population-based epidemics. And so it is in PFAS-induced adverse health effects. PFASs trigger some chemical signals to single cell, which further emit new signals to tissue and even to organ, and finally cause diseases. The detection of these chemical signals and their translation into meaningful data fully rely on analytical chemistry, especially high-throughput screening methods. Technological advances in the past twenty years have enabled chemical signal analyses at several molecular levels, including gene

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expression, epigenetic changes, protein diversity, and metabolomic perturbations. These advances have also deepened our understanding of health effects at multiple levels [7]. High-throughput ‘omics’ technologies, including transcriptomics, epigenomics, proteomics, and metabolomics, are considered as optimal methods to detect and analyse these signals. This review describes the most recent analytical developments using ‘omics’ technologies and provides recent examples of their application in characterizing PFAS-related health effects. 2. Methodologies, technologies, and application of omics in PFAS studies Chemical signals are represented by a broad range of molecules and exhibit a great variety in their physical and chemical properties. These molecules include large saturated hydrocarbons, proteins, organic compounds, and small water-soluble molecules [8]. Scientific advances in different ‘omics’ technologies have facilitated the high-throughput identification and characterization of several of these molecules in complex biological matrices [7,8]. These advances have also allowed the evaluation of health effects that are mediated by environmental pollution. Findings derived from these studies profoundly alter our conception of PFAS studies; at the same time, they offer deep challenges for biologists, chemists, and scientists from all disciplines interested in the study of chemical signals. 2.1. Transcriptomics 2.1.1. Methodologies and technologies Because the transcriptome content can drastically vary in response to environmental factors such as PFAS exposure, high throughput technologies are needed to estimate the expression of constitutive genes and to compare them with genes that are differentially expressed [9,10]. There are now two techniques widely used in transcriptomics: microarrays and RNA-sequencing (RNA-seq) that utilize next-generation sequencing methodology (Fig. 1). The technique details are listed as follows. (1) Sample preparation. RNA is extracted from the selected tissue or cells and then reverse transcribed into complementary DNA (cDNA) in both microarrays and RNA-seq. Both of these two methods involve detecting fluorescence signals, rather than directly detecting the levels of the genes. In the microarray method, cDNA is fluorescently labelled and then hybridized to the microarray chip [9,10]. RNA-seq, by contrast, first requires the construction of a cDNA library. The most traditional procedure for RNA-seq includes enrichment of mRNA using magnetic oligo dT beads, RNA fragmentation, and reversed transcription to cDNA [11]. (2) Signal detection. The microarray is fixed with thousands of short, synthetic, single-stranded DNA sequences (called probes). After hybridization, the chip is then scanned with lasers to detect fluorescence labels. The intensity of fluorescence is determined and thereby allows quantification of the RNA transcripts in the sample [9,10]. In contrast to microarrays that use known probe sequences, sequencing techniques can read each base pair step-by-step, thus making the detection of unknown sequences possible. After construction of the cDNA library, it is sequenced on a high-throughput platform, such as Illumina, which analyses millions of short DNA fragments (or reads) of 25e450 base pairs during one sequencing run. The reads are then aligned to a reference genome/transcriptome or assembled de novo when genomes are not available. After alignment or assembling, the gene

expression level is estimated by mapping the reads to a specific gene [11]. (3) Data analysis. Both of these two methods require normalization to avoid bias in quantification and then to determine differential gene expression using various statistical tests, such as hierarchical cluster analysis and principal components analysis [10,12]. (4) Validation. Validation of microarray and RNA-seq data is important to avoid false-positive conclusions. This can be achieved through determination of gene expression by an alternative method, such as real-time quantitative reverse transcription PCR (RT-qPCR) or in situ hybridization. Alternatively, the results can be validated by measuring downstream effects resulting from changes in gene expression, which can be shown by western blotting, immunohistochemistry, or protein function assays [9,13]. Next-generation sequencing methods have some advantages over microarray, such as the ability to detect novel transcripts and alternative splicing, wider dynamic range, higher specificity and sensitivity, and simple detection of rare and low-abundance transcripts. Thus, RNA-seq has become more popular and may replace microarray [14]. Moreover, by using third-generation sequencing technologies, such as Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT), transcriptome assembly can most likely be replaced in the future [15]. Both PacBio and ONT are singlemolecule sequencing platforms that allow longer sequencing read lengths than that offered by Illumina. 2.1.2. Application Transcriptomics is the most widely used omics technique in PFAS studies. First, we will summarize the studies that used microarrays to evaluate gene expressions from different samples (e.g., cellular, animal, and human samples). In cell models, as an example, it was reported that after exposure of human follicular thyroid carcinoma cells to PFOS (5 mM, 48 h), 362 classifiers predicted the effect of the toxicants on recombinant thyroid peroxidase activity with about 70% accuracy. These classifiers are potential markers for predicting the effects of chemicals on thyroid hormone production [16]. In animal models, this technique was applied to a variety of species, including rodents (mice [17,18] and rats [19]), fish (zebrafish [20] and carp [21]), and birds (chickens [22] and cormorants [23]). Pregnant CD-1 mice were dosed with PFOS (5 or 10 mg/kg/day) or PFOA (1, 3, 5, or 10 mg/kg/day) from gestation days (GD) 1e17, and the expression of genes related to fatty acid catabolism was found to be altered in both the foetal liver and lungs [17,18]. Exposure of pregnant Sprague-Dawley rats to PFOS (0.1, 0.6 and 2.0 mg/kg/day, from GD 2e21) resulted in a significant difference in the global gene expression profile of offspring rats, especially in genes associated with mitochondrial function. Combined with other results, their findings indicated that PFOS prenatal exposure can induce cardiac mitochondrial injury and gene transcript change, which may be a significant mechanism underlying the developmental toxicity of rats towards PFOS [19]. In addition, zebrafish (Danio rerio) embryos exposed to PFOS (16 mM, during 48e96 h post-fertilization (hpf)) show disrupted larval morphology. A PFOS-induced network of perturbed transcripts related to the swim bladder and gut development revealed that misexpression of genes were involved in organogenesis [20]. In another study, common carp (Cyprinus carpio) were exposed to PFOS (0.1, 0.5 and 1 mg/L, 14 days) through the water, and their livers were shown to be specifically affected. Microarray data revealed that the expression of several genes in the liver was influenced, particularly those involved in energy metabolism, reproduction, and stress response [21]. The effects of PFOS

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RNA extraction Reverse Transcription & Fluorescence Labeling & Hybridization

Reverse Transcription & RNA-seq library

Microarray Platforms

Sequencing

Alignment/Assembing and Mapping

Data Analysis

qRT-PCR or In situ Hybridization

Validation Fig. 1. Schematic of transcriptomics protocols.

(0.02 or 0.1 mg/mL, 4 weeks) and PFOA (0.1, 0.5, or 5 mg/mL, 4 weeks) on the hepatic genes of chickens (Gallus gallus) were also investigated, and the genes that were affected after subcutaneous implantation of PFOS or PFOA were mainly related to the transport of electrons and oxygen, the metabolism of lipids and fatty acids, and protein amino acid phosphorylation and proteolysis [22]. Common cormorants (Phalacrocorax carbo) from Lake Biwa, Japan also showed exposure to PFOS, PFNA, and PFOSA. For these birds, PFAS levels were correlated with the expression of 74 genes, suggesting the induction of antioxidant enzymes in response to oxidative stress caused by PFASs and the suppression of molecular chaperones, leading to reduction in protein stability [23]. In human samples, a study analysed whether the cord blood transcriptome showed early indications of alterations in metabolic processes in 195 human samples in relation to cord blood levels of PFOA and PFOS [24]. Transcriptional changes at birth suggested a role for specific metabolic targets as a link between prenatal PFAS exposure and metabolic disorders later in life. For application of microarray studies in the PFAS field, not only were mRNAs studied for gene expression differences but also microRNAs (miRNAs) were examined for translational repression. Differences in the circulating miRNAs in mice after PFOA exposure demonstrated that circulating miRNA profiles were altered after exposure to PFOA (1.25 or 5 mg/kg/day, 14 days). MiR-28-5p, miR32-5p, miR-122-5p, miR-192-5p, and miR-26b-5p in serum may be linked to effects of PFOA, especially in occupationally exposed people [25]. PFOS-induced changes in miRNA expression in the developing rat liver and a potential mechanism of PFOS-induced toxic action were also investigated. PFOS exposure (3.2 mg/kg/ day, from GD 1 to postnatal day 7) induced significant changes in miRNA expression profiles, and many aberrantly expressed miRNAs

were related to various cancers [26]. PFOS-induced (1 mg/L PFOS from 6 hpf to 24 or 120 hpf) changes in miRNAs and target gene expression in zebrafish embryos as well as a potential mechanism of PFOS-induced toxic action were studied. These altered miRNAs were involved in development, apoptosis and cell signal pathways, cell cycle progression and proliferation, oncogenesis, adipose metabolism, and hormone secretion [27]. In addition to these microarray experiments, RNA-seq-based transcriptomic analysis has also been applied to study PFASs. The transcriptome of marine medaka (Oryzias melastigma) was sequenced, and differentially expressed genes were related to neurobehavioral defects, mitochondrial dysfunction, and the metabolism of proteins and fats [28]. In one study, wild-type and transport protein particle complex 11 (trappc11) mutant zebrafish (Danio rerio) embryos were used as the experimental model, and researchers found 499 and 1414 differentially expressed genes in PFOS-exposed (0.5 mg/L, 6 days) wild-type and trappc11 mutant zebrafish by RNA sequencing and comparative transcriptomic analysis, respectively. The deregulated gene clusters were closely related to hepatitis, inflammation, and fibrosis and cirrhosis of liver cells, suggesting that PFOS can cause liver pathogenesis and nonalcoholic fatty liver disease in zebrafish [29]. 2.2. Epigenomics 2.2.1. Methodologies and technologies Epigenomics can determine differences from cell type to cell type, as well as investigate the regulation of gene expression in each cell; some of these mechanisms include restricting or facilitating transcription factor access to DNA, organizing the nuclear architecture of the chromosomes, and preserving a memory of past

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transcriptional activities. Thus, epigenomics brings a new dimension to the genomic sequence and is crucial for studying cell-typespecific gene expression [30e32]. DNA methylation is widely investigated and well established in epigenomics; further, it has been generally applied to study PFASs. For a more extensive overview, please see Refs. [32,33]. Cytosine is by far the most dynamic of the four DNA-composing nucleotides as it can be methylated at its 5th carbon (5mC), and 60%e80% of the 28 million CpG dinucleotides are methylated in the human genome. Global DNA methylation technologies can measure DNA methylation abundance at all cytosines at base resolution in the human genome [34]. DNA methylation techniques can be classified into three methods based on molecular biology: digestion of genomic DNA with methyl-sensitive restriction endonuclease, affinity-based enrichment of methylated DNA fragments, and chemical conversion. The results of both endonuclease digestion-based assays (such as MRE-seq) and affinity-based enrichment assays (such as capturing methylated fragments from sonicated DNA with an antibody [MeDIP-seq] or a methyl-binding domain [MBD-seq]) are qualitative, although the sequencing costs involved are moderate (Table 1) [35]. Compared with the former two qualitative methods, the chemical conversion method based on bisulphite PCR allows direct detection of the methylation state of each cytosine (Fig. 2) and is widely accepted as a gold standard for mapping DNA methylation [32]. Sodium bisulphite can chemically convert unmethylated cytosines in genomic DNA to uracil. After PCR, all unmethylated cytosines become thymidines, so the remaining cytosines correspond to 5mC. After this conversion, a number of analytic chemistry methods can be used for high-throughput detection of methylation signals, such as sequencing, microarray, and matrix-assisted laser desorption/ionization time-of-flight mass spectrometer (MALDI-TOF-MS).

(1) Sequencing. Initially, Sanger sequencing was used to assay individual loci from bisulphite-treated genomic DNA with locus-specific PCR [36]. Pyrosequencing is considered to be a highly versatile method that offers significant advantages (Table 1) [33]. (2) Microarray. Sequencing costs can be limited by targeted DNA methylation mapping as accomplished by microarray technology using bisulphite padlock probes and Illumina's Infinium 450K BeadChIP (Table 1) [37]. (3) MALDI-TOF-MS. After bisulphite PCR treatment, in vitro transcription, and base-specific cleavage using RNase A, the products can be measured using MALDI-TOF-MS (Table 1) [33]. There are also other types of DNA methylation modifications, such as 5-hydroxymethylcytosine (5hmC), 5-formylcytosine (5fC), 5-carboxylcytosine (5caC), and N6-methyladenine (6mA) [32,38]. Besides DNA methylation, chromatin modification states are also an important part of epigenomics. They were well documented in other reviews, such as [32]. 2.2.2. Application Using the technique of quantitative bisulphite PCR pyrosequencing combined with other molecular assays, such as flow cytometric immunodetection of 5mC, has revealed weak but statistically significant associations of different PFASs (including PFOS, PFOA, PFHxS, and PFNA) with sperm DNA hypo- and hypermethylation in Arctic and European human populations [39]. A significant association of serum PFOS with LINE-1 methylation, a potential marker of cardiovascular risk, was found in adult residents in the mid-Ohio River Valley who were exposed to high levels of PFCs via contaminated drinking water [40]. Bisulphite PCR sequencing was also used to detect the methylation status of the brain-derived neurotrophic factor (BDNF) promoter in PFOS-

Table 1 Advantages and disadvantages of the techniques. Techniques Transcriptomics Microarray

RNA-seq

Epigenomics MRE-seq, MeDIP-seq or MBD-seq Bisulphite PCR pyrosequencing

Bisulphite PCR pyrosequencing Bisulphite PCR MALDI-TOF-MS

Proteomics Top-down proteomics Bottom-up proteomics Metabolomics MS-based Metabolomics NMR-based Metabolomics

Advantages

Disadvantages

Relatively cheap. Probes are known so avoid doing complex analysis such as alignment or assembling.

Require transcript-specific probes, so cannot detect novel transcripts; Gene expression measurement is limited by background at the low end and signal saturation at the high end. High price. Only can read less than around 450 base pairs during one sequencing run.

Ability to detect novel transcripts and alternative splicing. Wider dynamic range. Higher specificity and sensitivity. Simple detection of rare and low-abundance transcripts. Costs are moderate Gold standard for mapping DNA methylation. Ability to quantitatively interrogate multiple CpGs. Inclusion of multiple bisulphite controls. Quantitative. Sequencing costs can be limited. Quantitative. Minimal assay variance. Enabling consistent downstream reaction conditions amenable to automation

Qualitative Most expensive. Low temperature (28 C) of the reaction may cause the formation of many secondary structures. Limited with known bisulphite padlock probes

Analyses intact proteins. High-throughput ability. High resolution. Mature and widespread approach.

Difficulties with protein fractionation, protein ionization, and fragmentation in the gas phase Need to digest proteins into peptide fragments

Most widely used detection technique in metabolomics. High sensitive Detects all organic molecules with signal intensities proportional to metabolite concentration. Provides more extensive structural information.

Limited in quantitation capabilities without appropriate labelled standards Limited in terms of sensitivity

Quality is dependent on both the assay and the input material

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Fig. 2. Workflow of bisulphite-PCR-based DNA methylation protocols in epigenomics.

exposed (50 or 150 mM, 48 h) human neuroblastoma cells. The results suggested that methylation regulation of the BDNF gene promoter might underlie the mechanisms of PFOS-induced neurotoxicity [41]. Using microarray chips, differential DNA methylation at specific CpG sites in cord blood from Faroe islanders was demonstrated to be associated with different concentrations of PFOS, and a significant enrichment of specific X-chromosome sites in males implies potential sex-specific epigenome responses to prenatal chemical exposures [42]. 2.3. Proteomics 2.3.1. Methodologies and technologies The proteome is highly dynamic and spatiotemporally diverse in its response to different physiological and environmental stimuli. The proteins outnumber the genes via different posttranscriptional mechanisms. Altogether, these characteristics make the proteome one of the largest information libraries of chemical signals among cells, tissues, organs, and even individuals [7]. Luckily, high-throughput mass spectrometry (MS)-based analytical methodologies provide the possibility for detection and identification of large-scale proteins and have thus turned proteomics into a key element in modern life science studies. Details are in the following four steps (Fig. 3): (1) Protein enrichment and purification. This step reduces the biochemical complexity of the sample, concentrates proteins to a certain amount, and commonly depletes highly abundant background proteins [43]. (2) Protein Separation. Two different strategies, top-down proteomics and bottom-up proteomics, have been developed to approach this goal (Table 1) [44]. Currently, the bottom-up method is the most mature and widespread approach. A protein mixture was proteolytically digested by proteases (such as trypsin) into peptides, and then, the peptide

mixtures can be fractioned by liquid chromatography (LC) before injection in MS. Or the proteins mixture was isolated first (usually by 2D gel electrophoresis) before digested into peptides, and then the peptides can apply to MS. (3) Protein identification. Techniques that utilize MS can also be applied for this step. For quantification of proteins, most studies have used one of three main methodologies: (1) label-free quantification, (2) metabolic stable-isotope labelling, such as stable isotope labelling by amino acids in cell culture (SILAC), and (3) isobaric tags for relative and absolute quantification (iTRAQ). More details about these techniques have been extensively reviewed elsewhere [7,43]. (4) Data analysis. This step relies largely on available sequence information (DNA, RNA, proteins), and hence proteomics in many non-model organisms depends on other bioinformatics tools to de novo predict peptide sequences (e.g., PEAKS, PepNovo, pNovo) followed by homology driven protein identification (e.g., msBLAST, FASTS) [45].

2.3.2. Application Proteomics provided insight into the molecular mechanism and biomarkers for PFAS-induced effects. The applications below are divided according to different analytic methods. After exposure of zebrafish embryos to PFOS (0.5 mg/L) until 192 hpf, proteomic analysis with 2D gel electrophoresis and MALDITOF-MS was performed. From these results, proteins could be categorized into diverse functional classes that included detoxification, energy metabolism, lipid transport/steroid metabolic process, cell structure, signal transduction, and apoptosis [46]. Using a combination of 2D gel electrophoresis and nano-LC-MS/ MS methods, the differentially expressed proteins from short-term PFOS exposure (0.1 or 1 mg/L, 96 h) in the gills of the European bullhead fish were identified and provide clues to the cellular pathways and components affected by PFOS [47]. Proteins identified from European eel peripheral blood mononuclear cells (PBMC)

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Protein Digenstion & Separation

Proteolytic Digestion

Protein Enrichment & Purification

Peptide Identification

Data Analysis

LC

MS/MS

Proteolytic Digestion 2D Gel

Fig. 3. Overview of bottom-up proteomics protocols.

after in vitro (10 mg/L and 1 mg/L, 48 h) and in vivo (1 or 10 mg/L PFOS, 28 days) exposure to PFOS can be categorized into diverse functional classes. Further, two proteins (plastin-2 and alphaenolase) were found in common and might be used as biomarkers for indicating early warning signals [48,49]. With the methods of SILAC labelling, gel separation, and LC-MS/ MS, an embryonic stem cell test procedure was used as a tool to assess the developmental cardiotoxicity of PFOS (40 mM, 10 days). Most differential proteins were identified to be mainly involved in catalytic activity, nucleus localization, or cellular component organization. Pathway analysis revealed that 32 signalling pathways were affected, particularly those involved in metabolism [50]. iTRAQ labelling quantitative proteomic technology was also applied for global characterization of the proteome. For example, the differential proteins from liver in PFOS-exposed (1.0 mg/kg/day, 2.5 mg/kg/day or 5.0 mg/kg, 7 days) mice were significantly enriched and mainly involved in lipid metabolism, transport, biosynthetic processes, and response to stimulus [51]. In nontumour human hepatic cells (L-02) exposed to PFOS (25 and 50 mg/L, 72 h), the proteomic results indicated that the inhibition of HNRNPC, HUWE1, and UBQLN1, as well as the induction of PAF1 is involved in the activation of the p53 and c-myc signalling pathways, which then trigger the apoptotic process [52]. In another human hepatic cell line (HL-7702) exposed to PFOS (50 mM, 48 h and 96 h) differentially expressed proteins were associated with cell proliferation, including hepatoma-derived growth factor (Hdgf) and proliferation biomarkers Mk167 (Ki67) and Top2a [53]. 2.4. Metabolomics 2.4.1. Methodologies and technologies Recent progress in technologies and equipment for analytical chemistry have allowed metabolomics to be applied to life science investigations [54]. Exploration of metabolic composition through metabolomics has led to new insights in diverse biological processes, allowing the screening of relevant mediator metabolites evolved in organism interaction triggering the elucidation of regulative principles and pathways [55]. Details are in the following four steps (Fig. 4): (1) Sample collection and preparation. Because this step is crucial for metabolomics, it is necessary to define the types of chemical classes carefully. Water-soluble versus insoluble

compounds must be extracted in different solvents; therefore, solubility plays a major role in real-world metabolomics studies. It is important to keep in mind that there is no universal solvent for such a great diversity of metabolites; thus, a combination of different strategies is highly recommended to examine a wide range of metabolites [7]. (2) Sample separation. Many analytical tools have been developed due to the great diversity of chemicals. LC and gas chromatography (GC) are the most widely used separation techniques in metabolomics. Most of the time, LC is employed because of its flexibility in coupling to many detection techniques. For further improvement, several separation approaches have been recently developed. Ultrahigh-pressure LC (UHPLC) can increase chromatographic resolution owing to the smaller particle size of the column stationary phase. Another important advance is microfluidic device. The interest in microfluidics has recently grown because the multifunction and miniaturization of the system helps with sample preparation, separation, and detection of small quantities of sample [56]. (3) Metabolite detection. After separation by chromatographic techniques, the consequent choice of detection method is extremely important. MS and Nuclear Magnetic Resonance (NMR) are two common detection technique used in metabolomics studies (Table 1). More details about these techniques have been extensively reviewed elsewhere [57,58]. (4) Data analysis and visualization. Metabolomic approaches generate vast data sets, which mean big challenges for analysis and visualization. This step requires computational pre-processing and statistical modelling methods, such as principal components analysis, hierarchical cluster analysis, partial least squares, and orthogonal variant [7].

2.4.2. Application Metabolomics provides tools for analysing a complete set of non-genetically encoded products of metabolic pathways associated with PFAS-exposed biological systems, from a cellular to individual level. A number of studies have employed MS techniques. After exposure of PFOA (0.5 or 2.5 mg/kg/day, 28 days) to male Balb/c mice, metabolomic analysis involving flow injection analysis-MS/

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Sample Collection and Preparation LC/GC

Sample l Separation

Metabolomics

Data Analysis and Visualization

Metabolite Detection

MS

NMR

Fig. 4. Summary of generalized metabolomic protocols.

MS, LC-MS, or LC-MS/MS methods suggested that PFOA affected the metabolism of amino acids, lipids, carbohydrates, and energetics, which provides novel insights into the mechanisms of PFOAinduced hepatotoxicity and neurotoxicity [59]. LC-MS/MS-based targeted lipidomic analyses were conducted to identify the effects of in utero PFOS exposure (0.3 and 3 mg/g/day throughout gestation) on neonatal testes and its relationship to testicular dysfunction in adult offspring. Perturbations of lipid mediators suggested that PFOS has potential negative impacts on testicular functions [60]. Metabolomic perturbations in zebrafish embryo/larvae were measured following 24 h exposure to 13 individual chemicals (including 1.2 mM PFOS); next, metabolites (208 in total) were measured, including amino acids, biogenic amines, fatty acids, bile acids, sugars, and lipids [61]. An untargeted LC-HRMS metabolomic approach was used to study the effects of sub-lethal doses of PFOS (0.06, 0.2, 0.6, or 2 mM, from 48 to 120 hpf) on the metabolic profiles of zebrafish embryos. The analysis of the corresponding metabolic changes suggested that PFOS affected the metabolism of glycerophospholipids, amino acids, purines, and 2-oxocarboxylic acids [62]. NMR techniques have also been used to examine the effects of PFAS exposure. 1H NMR-based metabolomics was utilized to elucidate sub-lethal toxic mechanisms of Eisenia fetida earthworms after exposure to PFOA (6.25e50 mg/cm2, 2 days) or PFOS (3.125e25 mg/cm2, 2 days; 5, 10, 25, 50, 100 or 150 mg/kg, 2, 7 and 14 days.). The results indicated that PFOA and PFOS exposure may increase fatty acid oxidation and interrupt ATP synthesis due to a disruption in the inner mitochondrial membrane structure [63,64]. The response of a small crustacean Daphnia magna after sub-lethal exposure to PFOS (15, 30, 45, and 60 mg/L, 48 h) was also studied, and 1H NMR-based metabolomics demonstrated that PFOS exposure disrupts various energy metabolic pathways and also enhances protein degradation [65]. The same group was also found to have significant ontogenetic changes in metabolite levels in both neonate and adult D. magna exposed to sublethal exposures of PFOS (36 mg/L, 48 h) [66]. The metabolomic response of male rats to PFDoA exposure (0.02, 0.05, 0.2, or 0.5 mg/kg, 110 days) was also investigated; results from the analysis of both liver tissues and serum demonstrated that PFDoA exposure led to hepatic lipidosis, which was characterized by a severe elevation in hepatic triglycerides and a decline in serum lipoprotein levels [67].

3. Limitation and future outlook Omics studies have greatly accelerated our understanding of the health effects induced by PFAS-exposure. However, there still remain many limitations. First, many advanced analytical methods have already been used in omic studies for medical or biological research; however, the most advanced methods have rarely been used in toxicological studies, such as those examining PFAS-related health effects. For example, in transcriptomics, the RNA-seq technique is much more popular than microarray; however, in PFAS studies, microarray has been overwhelmingly more common than RNA-seq. The possible reasons are relative lower cost and easier analysis in microarray than these in RNA-seq. Even the few studies that employed RNAseq did not fully take advantage of RNA-seq, such as detection of novel transcripts or alternative splicing [28,29]. Therefore, environmental toxicological studies, represented by PFAS-related health effects, still have a large space to catch up with advanced omics techniques. Second, the amount of studies using epigenomics in the PFAS field is obviously less than those using transcriptomics. This phenomenon is associated with the relatively newer theory and methods of epigenomics. However, there is a growing body of literature suggesting a role for epigenetic factors in the complex interplay between genes and the environment. We believe that the application of epigenomics in environmental health studies, including those on PFASs, in the next ten years will enter a phase of explosive growth. Third, transcriptomics, epigenomics, and proteomics are potent enough to generate massive amount of data, but the bottleneck resides in the metabolomics, because of the wide diversity of metabolites, limitations in knowledge, and the lack of commercial standards for metabolite identification. Fourth, each omics technology has contributed to understanding the underlying mechanisms involved; however, each technology alone cannot capture the entire biological complexity of most human health risks. The integration of all levels of information to interpret the data generated by these technologies is still absent. Finally, these omics studies evaluate multicellular systems, providing an average at the cellular or molecular level. However, there is a growing interest from different disciplines in the development of single-cell omics, which allow the detection of complete

Please cite this article as: X. Yao et al., An overview of omics approaches to characterize the effect of perfluoroalkyl substances in environmental health, Trends in Analytical Chemistry, https://doi.org/10.1016/j.trac.2018.12.021

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sets of molecules in a unique cell, considering RNA, proteins, peptides, and small organic molecules. The analysis of an individual cell might highlight important information about molecular behaviour, which could be omitted in multicellular examinations. Although the single-cell approach has not yet been widely applied in PFAS studies, we believe that single-cell or single-molecular based methodology, such as microfluidics, digital PCR, single molecule sequencing, total internal reflection fluorescence (TIRF)-based heliscope, single-molecule fluorescence resonance energy transfer (FRET)-based methods, nanomanipulators like atomic force microscopy, may provide valuable information for the study of molecular interactions in the future. Acknowledgements This work was supported by the National Natural Science Foundation of China (Grant No. 21577167, 21677170, and 21876194) and the Strategic Priority Research Program of the Chinese Academy of Sciences (No. XDB14010300). References [1] T. Ruan, G. Jiang, Trac. Trends Anal. Chem. 95 (2017) 122. [2] J.-M. Jian, Y. Guo, L. Zeng, L. Liang-Ying, X. Lu, F. Wang, E.Y. Zeng, Environ. Int. 108 (2017) 51. [3] T. Ruan, Y. Lin, T. Wang, G. Jiang, N. Wang, Trac. Trends Anal. Chem. 67 (2015) 167. [4] J.M. Weiss, I. van der Veen, J. de Boer, S. van Leeuwen, W. Cofino, S. Crum, Trac. Trends Anal. Chem. 43 (2013) 204. €m, Trac. Trends Anal. Chem. 46 [5] S. Salihovic, H. Nilsson, J. Hagberg, G. Lindstro (2013) 129. [6] K.M. Rappazzo, E. Coffman, E.P. Hines, Int. J. Environ. Res. Publ. Health 14 (2017) 691. [7] A.E. Brunetti, C.F. Neto, M.C. Vera, C. Taboada, D.P. Pavarini, A. Bauermeister, N.P. Lopes, Chem. Soc. Rev. 47 (2017) 1574. [8] T.D. Wyatt, Pheromones and Animal Behavior: Chemical Signals and Signatures, second ed., Cambridge University Press, Cambridge, 2014. [9] J. Cooper-Knock, J. Kirby, L. Ferraiuolo, P.R. Heath, M. Rattray, P.J. Shaw, Nat. Rev. Neurol. 8 (2012) 518. [10] J.A. Martin, Z. Wang, Nat. Rev. Genet. 12 (2011) 671. [11] L. Ettwiller, J. Buswell, E. Yigit, I. Schildkraut, BMC Genomics 17 (2016) 199. [12] P.K. Tan, T.J. Downey, E.L. Spitznagel Jr., P. Xu, D. Fu, D.S. Dimitrov, R.A. Lempicki, B.M. Raaka, M.C. Cam, Nucleic Acids Res. 31 (2003) 5676. [13] A.K. White, M. VanInsberghe, O.I. Petriv, M. Hamidi, D. Sikorski, M.A. Marra, J. Piret, S. Aparicio, C.L. Hansen, Proc. Natl. Acad. Sci. U. S. A. 108 (2011) 13999. [14] Y.J. Xu, Trac. Trends Anal. Chem. 96 (2017) 14. [15] K.J. Karczewski, M.P. Snyder, Nat. Rev. Genet. 19 (2018) 299. [16] M. Song, Y.-J. Kim, M.-K. Song, H.-S. Choi, Y.-K. Park, J.-C. Ryu, Environ. Sci. Technol. 45 (2011) 7906. [17] M.B. Rosen, J.R. Thibodeaux, C.R. Wood, R.D. Zehr, J.E. Schmid, C. Lau, Toxicology 239 (2007) 15. [18] M.B. Rosen, J.E. Schmid, K.P. Das, C.R. Wood, R.D. Zehr, C. Lau, Reprod. Toxicol. 27 (2009) 278. [19] W. Xia, Y. Wan, Y.-y. Li, H. Zeng, Z. Lv, G. Li, Z. Wei, S.-q. Xu, Toxicology 282 (2011) 23. [20] J. Chen, R.L. Tanguay, T.L. Tal, Z. Gai, X. Ma, C. Bai, S.C. Tilton, D. Jin, D. Yang, C. Huang, Q. Dong, Aquat. Toxicol. 150 (2014) 124. [21] A. Hagenaars, D. Knapen, I.J. Meyer, K. van der Ven, P. Hoff, D.W. Coen, Aquat. Toxicol. 88 (2008) 155. [22] L. Yeung, K.S. Guruge, N. Yamanaka, S. Miyazaki, P. Lam, Toxicology 237 (2007) 111. [23] K. Nakayama, H. Iwata, L. Tao, K. Kannan, M. Imoto, E.Y. Kim, K. Tashiro, S. Tanabe, Environ. Toxicol. Chem. 27 (2008) 2378. [24] S. Remy, E. Govarts, B. Wens, P. Boever, E. Hond, K. Croes, I. Sioen, W. Baeyens, N. van Larebeke, J. Koppe, A. Covaci, T. Schettgen, V. Nelen, J. Legler, G. Schoeters, Reprod. Toxicol. 65 (2016) 307. [25] S. Yan, J. Wang, W. Zhang, J. Dai, Toxicol. Lett. 224 (2014) 24. [26] F. Wang, W. Liu, Y. Jin, F. Wang, J. Ma, Environ. Toxicol. 30 (2015) 712. [27] L. Zhang, Y.y. Li, H.c. Zeng, J. Wei, Y.j. Wan, J. Chen, S.q. Xu, J. Appl. Toxicol. 31 (2011) 210.

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Please cite this article as: X. Yao et al., An overview of omics approaches to characterize the effect of perfluoroalkyl substances in environmental health, Trends in Analytical Chemistry, https://doi.org/10.1016/j.trac.2018.12.021